\")')\n print(\"2. Launch the Rich Presence (Requires /tokens folder)\")\n print(\"3. Delete your Credentials\")\n print(\"4. Exit\")\n print(\"------------------------------------------------------------------------------------------------------------------------\")\n print(\"Current Build: 0.3.3\")\n print(\"------------------------------------------------------------------------------------------------------------------------\")\n selection = int(input(\"Select from list: \"))\n return selection\n\ndef splashScreen():\n \"\"\"Creates a Screen to show the devs and other program info.\n \"\"\"\n clear()\n print(\"------------------------------------------------------------------------------------------------------------------------\")\n print(\"Halo: MCC Rich Presence\")\n print(\"------------------------------------------------------------------------------------------------------------------------\")\n print(\"Created by kay-el-zed, Gurrman375D.\")\n print(\"Maintained by Gurrman375D.\")\n print(\"------------------------------------------------------------------------------------------------------------------------\")\n print(\"Current Build: 0.3.3\")\n print(\"------------------------------------------------------------------------------------------------------------------------\")\n sleep(5)\n clear()\n\n \nif __name__ == \"__main__\":\n system('color 2')\n try:\n ctypes.windll.kernel32.SetConsoleTitleW(\"Halo Master Chief Collection Rich Presence\")\n main()\n except KeyboardInterrupt as e:\n print(e)\n sleep(1)\n clear()\n print(\"Manually Close the App\")\n optionUI()","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":3694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"426582849","text":"# Copyright 2015 IBM Corp.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport os\n\nfrom oslo_log import log as logging\n\nfrom trove.guestagent.datastore.experimental.couchdb import service\nfrom trove.guestagent.datastore import manager\nfrom trove.guestagent import volume\n\n\nLOG = logging.getLogger(__name__)\n\n\nclass Manager(manager.Manager):\n \"\"\"\n This is CouchDB Manager class. It is dynamically loaded\n based off of the datastore of the Trove instance.\n \"\"\"\n\n def __init__(self):\n self.appStatus = service.CouchDBAppStatus()\n self.app = service.CouchDBApp(self.appStatus)\n super(Manager, self).__init__('couchdb')\n\n @property\n def status(self):\n return self.appStatus\n\n def do_prepare(self, context, packages, databases, memory_mb, users,\n device_path, mount_point, backup_info,\n config_contents, root_password, overrides,\n cluster_config, snapshot):\n \"\"\"This is called from prepare in the base class.\"\"\"\n self.app.install_if_needed(packages)\n if device_path:\n self.app.stop_db()\n device = volume.VolumeDevice(device_path)\n # unmount if device is already mounted\n device.unmount_device(device_path)\n device.format()\n if os.path.exists(mount_point):\n device.migrate_data(mount_point)\n device.mount(mount_point)\n LOG.debug('Mounted the volume (%s).' % device_path)\n self.app.start_db()\n self.app.change_permissions()\n self.app.make_host_reachable()\n\n def stop_db(self, context, do_not_start_on_reboot=False):\n \"\"\"\n Stop this CouchDB instance.\n This method is called when the guest agent\n gets a stop message from the taskmanager.\n \"\"\"\n LOG.debug(\"Stopping the CouchDB instance.\")\n self.app.stop_db(do_not_start_on_reboot=do_not_start_on_reboot)\n\n def restart(self, context):\n \"\"\"\n Restart this CouchDB instance.\n This method is called when the guest agent\n gets a restart message from the taskmanager.\n \"\"\"\n LOG.debug(\"Restarting the CouchDB instance.\")\n self.app.restart()\n\n def start_db_with_conf_changes(self, context, config_contents):\n LOG.debug(\"Starting CouchDB with configuration changes.\")\n self.app.start_db_with_conf_changes(config_contents)\n","sub_path":"trove/guestagent/datastore/experimental/couchdb/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":2981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"141985131","text":"# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport time\nfrom statistics import mean\nfrom typing import Iterator\n\nimport torch\nfrom torch.utils.data import DataLoader, IterableDataset\n\nfrom pytorch_lightning import LightningModule, Trainer\nfrom pytorch_lightning.utilities.types import STEP_OUTPUT\nfrom tests.helpers.runif import RunIf\n\n\ndef count_cycles_per_ms() -> float:\n \"\"\"\n Measure and return approximate number of cycles per millisecond for torch.cuda._sleep\n\n Copied from: github.com/pytorch/pytorch/blob/master/test/test_cuda.py\n \"\"\"\n\n def measure() -> float:\n start = torch.cuda.Event(enable_timing=True)\n end = torch.cuda.Event(enable_timing=True)\n start.record()\n torch.cuda._sleep(1000000)\n end.record()\n end.synchronize()\n cycles_per_ms = 1000000 / start.elapsed_time(end)\n return cycles_per_ms\n\n # Get 10 values and remove the 2 max and 2 min and return the avg.\n # This is to avoid system disturbance that skew the results, e.g.\n # the very first cuda call likely does a bunch of init, which takes\n # much longer than subsequent calls.\n #\n # Tested on both Tesla V100, Quadro GP100, Titan RTX, RTX 3090 GPUs\n # and seems to return stable values. Therefore, we enable caching\n # using lru_cache decorator above.\n num = 10\n vals = []\n for _ in range(num):\n vals.append(measure())\n vals = sorted(vals)\n return mean(vals[2 : num - 2])\n\n\n_CYCLES_PER_MS = int(count_cycles_per_ms()) if torch.cuda.is_available() else 0\n_BATCH_SIZE = 128\n_EMB_SZ = 100\n_EMB_DIM = 64\n\n\nclass RandomSparseDataset(IterableDataset):\n def __init__(self, emb_dim: int, batch_size: int, count: int) -> None:\n self.emb_dim = emb_dim\n self.batch_size = batch_size\n self.count = count\n\n def __iter__(self):\n for _ in range(self.count):\n yield torch.randint(self.emb_dim, [self.batch_size])\n\n\nclass ToyDLRMModel(LightningModule):\n \"\"\"\n A toy model for mimicking the communication overhead of sharded embedding\n modules in DLRM models.\n\n DLRM models can be trained in a DDP-like fashion, where each trainer\n receives different batches (embedding indices in this example). Since the\n embeddings are sharded across trainers, the lookup process involves (1)\n routing the indices to the trainer that possesses the corresponding\n embeddings (2) performing local lookup (3) routing the embedding lookup\n result back.\n\n The toy model doesn't actually performs index/result routing. It simply\n uses torch.cuda._sleep() to mimic the cost of the communication op (i.e.\n a2a).\n \"\"\"\n\n def __init__(self):\n super().__init__()\n self.automatic_optimization = False\n self.local_embedding = torch.nn.Embedding(_EMB_SZ, _EMB_DIM)\n\n def _route_indices(self, batch: torch.Tensor, non_blocking=False):\n \"\"\"\n This can be parallelized across different batches since it's model\n weight independent.\n\n Why not run this in dataloader/datamodule?\n - The routing logic depends on how model is sharded\n - Putting this in data preprocessor changes the semantic of the model\n \"\"\"\n torch.cuda._sleep(_CYCLES_PER_MS * 1_000)\n if not non_blocking:\n torch.cuda.synchronize()\n return batch\n\n def _route_result(self, result: torch.Tensor, non_blocking=False):\n torch.cuda._sleep(_CYCLES_PER_MS * 1_000)\n if not non_blocking:\n torch.cuda.synchronize()\n return result\n\n def forward(self, indices: torch.Tensor):\n local_indices = self._route_indices(indices)\n result = self.local_embedding(local_indices)\n return self._route_result(result)\n\n def training_step(self, batch: torch.Tensor, batch_idx: int) -> STEP_OUTPUT:\n return self.forward(batch)\n\n def configure_optimizers(self):\n return torch.optim.SGD(self.local_embedding.parameters(), lr=0.1)\n\n def train_dataloader(self):\n return DataLoader(RandomSparseDataset(_EMB_DIM, _BATCH_SIZE, 5))\n\n\nclass AsyncToyDLRMModel(ToyDLRMModel):\n def __init__(self):\n super().__init__()\n self.comm_stream = torch.cuda.Stream()\n self.batch_i = None\n self.batch_i_ready = torch.cuda.Event()\n\n def training_step(self, dataloader_iter: Iterator) -> STEP_OUTPUT:\n if self.batch_i is None:\n self.batch_i = next(dataloader_iter)\n with torch.cuda.stream(self.comm_stream):\n self._route_indices(self.batch_i, non_blocking=True)\n self.batch_i_ready.record()\n\n # Invariant: the routing for batch[i] has been kicked off\n is_last = False\n batch_ip1 = None\n batch_ip1_ready = torch.cuda.Event()\n try:\n batch_ip1 = next(dataloader_iter)\n with torch.cuda.stream(self.comm_stream):\n self._route_indices(batch_ip1, non_blocking=True)\n batch_ip1_ready.record()\n except StopIteration:\n is_last = True\n\n self.batch_i_ready.wait()\n\n result = self.local_embedding(self.batch_i)\n self._route_result(result)\n\n self.batch_i = batch_ip1\n self.batch_i_ready = batch_ip1_ready\n\n return {\"is_last\": is_last}\n\n\n@RunIf(min_gpus=1)\ndef test_inter_batch_parallelism(tmpdir):\n \"\"\"\n Verify the speedup of a simple inter-batch parallelization use case enabled\n by exposing `dataloader_iter` to `training_step`.\n \"\"\"\n begin_time = time.time()\n m = AsyncToyDLRMModel()\n trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)\n trainer.fit(m)\n async_duration = time.time() - begin_time\n\n begin_time = time.time()\n m = ToyDLRMModel()\n trainer = Trainer(max_epochs=1, default_root_dir=tmpdir)\n trainer.fit(m)\n sync_duration = time.time() - begin_time\n\n # We expect 2x speedup. However, we only assert that the async\n # training_step is faster in order to avoid flaky tests\n assert async_duration < sync_duration, \"Expect `AsyncToyDLRMModel` to train faster than `ToyDLRMModel`.\"\n","sub_path":"tests/loops/test_inter_batch_parallelism.py","file_name":"test_inter_batch_parallelism.py","file_ext":"py","file_size_in_byte":6647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"128638608","text":"import json\nimport os\nimport time\n\nfrom django.shortcuts import render\n\n# Create your views here.\nfrom andrewtfesta.settings import CONTENT_ROOT\n\n\ndef get_content_dict(page_name: str, section_name: str) -> dict:\n content_fname = os.path.join(CONTENT_ROOT, page_name, '{}.{}'.format(section_name, 'json'))\n if not os.path.isfile(content_fname):\n content_dict = ['Unable to locate {} {} information at: {}'.format(section_name, page_name, content_fname)]\n else:\n with open(content_fname, 'r+', encoding='utf8') as cfile:\n content_dict = json.load(cfile)\n return content_dict\n\n\ndef approach(request):\n time_stamp = time.strftime('%b %d, %Y, %H:%M:%S', time.gmtime())\n\n approach_sections = get_content_dict('thesis', 'approach')\n render_dict = {\n 'page_title': 'Approach',\n 'sections': approach_sections\n }\n return render(request, 'thesis/approach.html', render_dict)\n\n\ndef architecture(request):\n time_stamp = time.strftime('%b %d, %Y, %H:%M:%S', time.gmtime())\n\n architecture_sections = get_content_dict('thesis', 'architecture')\n render_dict = {\n 'page_title': 'Architecture',\n 'sections': architecture_sections\n }\n return render(request, 'thesis/architecture.html', render_dict)\n\n\ndef background(request):\n time_stamp = time.strftime('%b %d, %Y, %H:%M:%S', time.gmtime())\n\n background_sections = get_content_dict('thesis', 'background')\n render_dict = {\n 'page_title': 'Background',\n 'sections': background_sections\n }\n return render(request, 'thesis/background.html', render_dict)\n\n\ndef index(request):\n time_stamp = time.strftime('%b %d, %Y, %H:%M:%S', time.gmtime())\n\n index_sections = get_content_dict('thesis', 'index')\n render_dict = {\n 'page_title': 'Data Representation for Motor Imagery Classification',\n 'sections': index_sections\n }\n return render(request, 'thesis/index.html', render_dict)\n\n\ndef tasks(request):\n time_stamp = time.strftime('%b %d, %Y, %H:%M:%S', time.gmtime())\n\n task_sections = get_content_dict('thesis', 'tasks')\n render_dict = {\n 'page_title': 'Tasks',\n 'sections': task_sections\n }\n return render(request, 'thesis/tasks.html', render_dict)\n\n\ndef timeline(request):\n time_stamp = time.strftime('%b %d, %Y, %H:%M:%S', time.gmtime())\n\n timeline_sections = get_content_dict('thesis', 'timeline')\n render_dict = {\n 'page_title': 'Timeline',\n 'sections': timeline_sections\n }\n\n return render(request, 'thesis/timeline.html', render_dict)\n\n\ndef updates(request):\n time_stamp = time.strftime('%b %d, %Y, %H:%M:%S', time.gmtime())\n\n updates_sections = get_content_dict('thesis', 'updates')\n render_dict = {\n 'page_title': 'Updates',\n 'sections': updates_sections\n }\n\n return render(request, 'thesis/updates.html', render_dict)\n","sub_path":"thesis/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"478698040","text":"import threading\nfrom threading import Timer\nimport random\nimport time\n\nfrom datetime import datetime, timedelta\n\nfrom configparser import ConfigParser\n\nfrom trading.esssencial.logger import log\nfrom trading.esssencial.api import Poloniex\nfrom trading.model.order import Order\nfrom trading.model.trade_currency import TradeCurrency\nfrom trading.model.data_source import BacktestDataSource,LiveDataSource\nfrom trading import ITradeAlgorithm, ANN, SniperBacktest, MACD, MyTradeAlgorithm, SimpleStrategy\n\napi_key = ''\napi_secret = ''\n\nupdate_interval = 0\n\ntrade_currencies = []\n\nlock = threading.Lock()\n\nmain_percent = 'main_percent'\nalt_percent = 'alt_percent'\nmin_buy_profit = 'min_buy_profit'\nmin_sell_profit = 'min_sell_profit'\nnew_order_threshold = 'new_order_threshold'\nmin_main = 'min_main'\nmin_alt = 'min_alt'\ntrading_history = 'trading_history'\ninitial_buy_rate = 'initial_buy_rate'\ninitial_sell_rate = 'initial_sell_rate'\nann_order_size = 'ann_order_size'\nann_threshold = 'ann_threshold'\n\n\ndef load_defaults(cfg, currency):\n dft_tc = TradeCurrency(currency_pair='',\n main_percent=float(cfg[currency][main_percent]) / 100.0,\n alt_percent=float(cfg[currency][alt_percent]) / 100.0,\n min_buy_profit=float(cfg[currency][min_buy_profit]) / 100.0,\n min_sell_profit=float(cfg[currency][min_sell_profit]) / 100.0,\n new_order_threshold=float(cfg[currency][new_order_threshold]) / 100.0,\n min_main=float(cfg[currency][min_main]),\n min_alt=float(cfg[currency][min_alt]),\n trading_history_in_minutes=float(cfg[currency][trading_history]),\n initial_buy_rate=float(cfg[currency][initial_buy_rate]),\n initial_sell_rate=float(cfg[currency][initial_sell_rate]),\n ann_order_size=float(cfg[currency][ann_order_size]),\n ann_threshold=float(cfg[currency][ann_threshold]))\n return dft_tc\n\n\ndef load_custom(cfg, dft_tc, pair):\n # initialize with defaults\n tc = TradeCurrency.from_tc(dft_tc)\n tc.currency_pair = pair\n\n # custom values\n if pair in cfg:\n tc.main_percent = float(cfg[pair][main_percent]) / 100 if main_percent in cfg[pair] else tc.main_percent\n tc.alt_percent = float(cfg[pair][alt_percent]) / 100.0 if alt_percent in cfg[pair] else tc.alt_percent\n tc.min_buy_profit = float(cfg[pair][min_buy_profit]) / 100.0 if min_buy_profit in cfg[pair] else tc.min_buy_profit\n tc.min_sell_profit = float(cfg[pair][min_sell_profit]) / 100.0 if min_sell_profit in cfg[pair] else tc.min_sell_profit\n tc.new_order_threshold = float(cfg[pair][new_order_threshold]) / 100.0 if new_order_threshold in cfg[pair] else tc.new_order_threshold\n tc.min_main = float(cfg[pair][min_main] if min_main in cfg[pair] else tc.min_main)\n tc.min_alt = float(cfg[pair][min_alt] if min_alt in cfg[pair] else tc.min_alt)\n tc.trading_history_in_minutes = float(cfg[pair][trading_history] if trading_history in cfg[pair] else tc.trading_history_in_minutes)\n tc.initial_buy_rate = float(cfg[pair][initial_buy_rate] if initial_buy_rate in cfg[pair] else tc.initial_buy_rate)\n tc.initial_sell_rate = float(cfg[pair][initial_sell_rate] if initial_sell_rate in cfg[pair] else tc.initial_sell_rate)\n tc.ann_order_size = float(cfg[pair][ann_order_size] if ann_order_size in cfg[pair] else tc.ann_order_size)\n tc.ann_threshold = float(cfg[pair][ann_threshold] if ann_threshold in cfg[pair] else tc.ann_threshold)\n\n return tc\n\n\ndef load_config():\n global api_key, api_secret, update_interval, trade_currencies\n\n cfg = ConfigParser()\n cfg.read('config.cfg')\n\n api_key = cfg['API']['key']\n api_secret = cfg['API']['secret']\n\n update_interval = float(cfg['PROCESS']['update_interval']) * 60\n\n btc_pairs = cfg['CURRENCY']['btc_pairs'].split(',') if 'btc_pairs' in cfg['CURRENCY'] else []\n usdt_pairs = cfg['CURRENCY']['usdt_pairs'].split(',') if 'usdt_pairs' in cfg['CURRENCY'] else []\n\n dft_tc_btc = load_defaults(cfg, 'BTC')\n dft_tc_usdt = load_defaults(cfg, 'USDT')\n\n for pair in btc_pairs:\n trade_currencies.append(load_custom(cfg, dft_tc_btc, pair))\n\n for pair in usdt_pairs:\n trade_currencies.append(load_custom(cfg, dft_tc_usdt, pair))\n\n\ndef update_loop(algorithm):\n with lock:\n assert isinstance(algorithm, ITradeAlgorithm)\n try:\n algorithm.update()\n try_again = False\n except Exception as e:\n log('An error occurred: ' + str(e.args), True)\n try_again = True\n\n if try_again:\n loop = Timer(random.randint(1, 10), update_loop, [algorithm])\n loop.start()\n else:\n loop = Timer(update_interval + random.randint(1, 10), update_loop, [algorithm])\n loop.start()\n\n\ndef main():\n try:\n load_config()\n\n template = \"{0:20}{1:>15}\\t\\t\\t{2:33}\"\n print('initializing')\n poloniex = Poloniex(api_key, api_secret)\n\n offset = 60 * 24 * 2 #2 Days Offset\n start = datetime.now() - timedelta(days=31)\n\n #MODE: 'BACKTEST' 'LIVE'\n mode = 'BACKTEST'\n\n for currency in trade_currencies:\n total_profit = 0\n\n if mode == 'LIVE':\n for currency in trade_currencies:\n source = LiveDataSource(currency, poloniex, start, offset, update_interval / 60)\n algorithm = SimpleStrategy(source, offset)\n update_loop(algorithm)\n time.sleep(10)\n if mode == 'BACKTEST':\n print('\\n\\nBackTest Mode - Gathering Data for ' + currency.currency_pair)\n source = BacktestDataSource(currency, poloniex, start, offset, update_interval / 60)\n algorithm = SimpleStrategy(source, offset)\n\n #source.plot_result()\n\n print('Updating... ' + currency.currency_pair)\n print(template.format('Initial Balances:', '$' + \"{0:.2f}\".format(source.main_balance), str(source.alt_balance)))\n while algorithm.update():\n continue\n total_main = 0\n total_alt = 0\n total_fee = 0\n total_trades = algorithm.winning_trades + algorithm.losing_trades\n accuracy = (abs(algorithm.winning_trades - algorithm.losing_trades) / total_trades) * 100 if total_trades > 0 else 0\n for order in algorithm.data_source.orders:\n assert isinstance(order, Order)\n total_main += abs(order.total)\n total_alt += abs(order.amount)\n total_fee += order.fee\n\n main_profit = algorithm.data_source.main_balance - algorithm.data_source.main_balance_init\n alt_profit = algorithm.data_source.alt_balance - algorithm.data_source.alt_balance_init\n highest_bid = float(poloniex.returnTicker()[currency.currency_pair]['highestBid'])\n profit = (alt_profit * highest_bid) + main_profit\n total_profit += profit\n\n print(template.format('Final Balances:', '$' + \"{0:.2f}\".format(algorithm.data_source.main_balance), str(algorithm.data_source.alt_balance)))\n print(template.format('Difference:', '$' + \"{0:.2f}\".format(main_profit), str(alt_profit)))\n print(template.format('Total Moved:', '$' + \"{0:.2f}\".format(total_main), str(total_alt)))\n print(template.format('Accuracy: ' , '%' + \"{0:.2f}\".format(accuracy), str(total_alt)))\n print(template.format('Fees:' , '$' + \"{0:.2f}\".format(total_fee), str(total_alt)))\n print(template.format('Winning Trades:' , str(algorithm.winning_trades), str(total_alt)))\n print(template.format('Losing Trades:' , str(algorithm.losing_trades), str(total_alt)))\n print(template.format('Profit:' , '$' + \"{0:.2f}\".format(profit), str(total_alt)))\n print(template.format('Total Profit:', '$' + \"{0:.2f}\".format(profit), str(total_profit)))\n\n except KeyboardInterrupt:\n quit()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"tradingbot.py","file_name":"tradingbot.py","file_ext":"py","file_size_in_byte":8376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"491027302","text":"from vk_api.bot_longpoll import VkBotLongPoll, VkBotEventType\r\nimport vk_api\r\nimport random\r\nfrom datetime import datetime\r\n\r\nimport parser_daimond\r\nimport parser_advance\r\n\r\ntoken = ''\r\n\r\nvk_session = vk_api.VkApi(token = token)\r\n\r\nsession_api = vk_session.get_api()\r\n\r\nid_group = 185787449\r\n\r\nlongpoll = VkBotLongPoll(vk_session, id_group)\r\n\r\ndef send_message(session_api, peer_id, message):\r\n session_api.messages.send(peer_id = peer_id, message = message, random_id = random.randint(-99999,+99999))\r\n\r\ndef get_online(session_api, peer_id):\r\n return session_api.users.get(peer_id=peer_id, fields='online')[0]\r\n\r\nfor event in longpoll.listen():\r\n if event.type == VkBotEventType.MESSAGE_NEW:\r\n\r\n max_member = session_api.messages.getConversationMembers(peer_id = event.obj.peer_id)['count']-1\r\n print('Участников: ' + str(max_member))\r\n\r\n if event.obj.peer_id != event.obj.from_id:\r\n\r\n # members_list = session_api.messages.getConversationMembers(peer_id=event.obj.peer_id)\r\n #\r\n # sender_name = list(filter(lambda name: name['id'] == event.obj.from_id, [name for name in session_api.messages.getConversationMembers(peer_id=event.obj.peer_id, fields='profiles')['profiles']]))[0]\r\n #\r\n # last_and_ferst_name = str(sender_name['first_name']) + ' ' +str(sender_name['last_name'])\r\n #\r\n # send_message(session_api,peer_id=event.obj.peer_id, message='@.id{0}({1})'.format(event.obj.from_id,last_and_ferst_name) +'\\nУчастников беседы: ' + str(max_member))\r\n #\r\n # print(event.obj)\r\n\r\n if event.obj.text == '/help':\r\n text = 'Доступные команды:\\n\\n' \\\r\n '/help - Команды Бота\\n' \\\r\n '/online - Показывает онлайн на сервере Paradox RP\\n' \\\r\n '/ \\n' \\\r\n '/ \\n'\r\n send_message(session_api, peer_id=event.obj.peer_id, message=text)\r\n\r\n\r\n if event.obj.text == '/online':\r\n text = ''\r\n for i in range(max_member):\r\n print('i = ',i)\r\n\r\n id = session_api.messages.getConversationMembers(peer_id=event.obj.peer_id)['profiles'][i]['id']\r\n print('ID = ',id)\r\n\r\n name = session_api.users.get(user_ids = id)[0]\r\n print('Объект по ID: ',name)\r\n\r\n operation_name = name['first_name'] + ' ' +name['last_name']\r\n print('Преобразование в Имя: ',operation_name)\r\n\r\n text = text + '\\n' +str(operation_name)\r\n\r\n print('============================================================')\r\n\r\n print('Участники:' + text)\r\n\r\n send_message(session_api, peer_id=event.obj.peer_id, message='Участники:{0}'.format(text))\r\n\r\n if event.obj.text == '/test':\r\n send_message(session_api, peer_id=event.obj.peer_id, message=str(parser_daimond.result))\r\n send_message(session_api, peer_id=event.obj.peer_id, message=str(parser_advance.result))\r\n\r\n\r\n\r\n\r\n","sub_path":"VK_BOT.py","file_name":"VK_BOT.py","file_ext":"py","file_size_in_byte":3272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"70456777","text":"from django.db import models\nfrom django.utils.translation import ugettext as _\nfrom plus import utilplus, modelplus\n\n\nclass Arquivo(models.Model):\n\t'''\n\tGuarda arquivos .csv e .pdf\n\t'''\n\tnome = models.CharField(max_length=64, unique=True, verbose_name='Nome')\n\tarquivo = modelplus.FileField(\tupload_to='arquivos/',\n\t\t\t\t\t\t\t\t\tcontent_types=['application/vnd.ms-excel', 'text/csv', 'application/pdf'],\n\t\t\t\t\t\t\t\t\tmax_upload_size=33554432,\n\t\t\t\t\t\t\t\t\tverbose_name=_('Arquivo'),\n\t\t\t\t\t\t\t\t\thelp_text=_('Tipos de Arquivos: .pdf .csv. Tamanho Máximo: 32MB'))\n\n\tclass Meta:\n\t\tverbose_name = _('Arquivo')\n\t\tverbose_name_plural = _('Arquivos')\n\n\tdef __str__(self):\n\t\treturn '{nome:s}'.format(nome=self.nome)\n\n\tdef clean(self, *args, **kwargs):\n\t\t# Renomeia o arquivo\n\t\tif self.arquivo:\n\t\t\tif self.id is None:\n\t\t\t\text = utilplus.get_file_extension(self.arquivo)\n\t\t\t\tself.arquivo.name = utilplus.unique_name(extension=ext)\n\t\t\telse:\n\t\t\t\tarquivo = Arquivo.objects.get(id=self.id)\n\t\t\t\tif self.arquivo != arquivo.arquivo:\n\t\t\t\t\text = utilplus.get_file_extension(self.arquivo)\n\t\t\t\t\tself.arquivo.name = utilplus.unique_name(extension=ext)\n\n\t\treturn super(Arquivo, self).clean(*args, **kwargs)\n","sub_path":"transpsesisenai/apps/repo/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"247889175","text":"# \n\"\"\"\nВыгружает произвольный файл по FTP в двоичном режиме.\nИспользует анонимный доступ к ftp, если функции не был передан\nкортеж user=(имя, пароль) аргументов.\n\"\"\"\n\nimport ftplib\n\ndef putfile(file, site, dir, user=(), *, verbose=True):\n \"\"\"\n выгружает произвольный файл по FTP на сайт/каталог, используя анонимный\n доступ или дейст��ительную учетную запись, двоичный режим передачи\n \"\"\"\n if verbose: print('Uploading', file)\n local = open(file, 'rb') # локальный файл с тем же именем\n remote = ftplib.FTP(site) # соединиться с FTP-сайтом\n remote.login(*user) # анонимная или действительная учетная запись\n remote.cwd(dir)\n remote.storbinary('STOR ' + file, local, 1024)\n remote.quit()\n local.close()\n if verbose: print('Upload done.')\n","sub_path":"3/ftp/putfile_through_ftp.py","file_name":"putfile_through_ftp.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"215245740","text":"__author__ = 'Administrator'\n\nfrom collections import namedtuple,deque,defaultdict,OrderedDict,Counter\n\n#namedtuple是一个函数，它用来创建一个自定义的tuple对象，并且规定了tuple元素的个数，并可以用属性而不是索引来引用tuple的某个元素。\nPointValues = namedtuple('pointvalues',['x','y'])\npv = PointValues(1,2)\nprint('pv.x is : ',pv.x,' and pv.y is : ',pv.y)\n#print('type(PointValues) is : ',type(PointValues))\n\n#使用list存储数据时，按索引访问元素很快，但是插入和删除元素就很慢了，因为list是线性存储，数据量大的时候，插入和删除效率很低。\n#deque是为了高效实现插入和删除操作的双向列表，适合用于队列和栈：\nq = deque(['a', 'b', 'c'])\nq.append('x')\nq.appendleft('y')\nprint(q)\n\n#使用dict时，如果引用的Key不存在，就会抛出KeyError。如果希望key不存在时，返回一个默认值，就可以用defaultdict：\ndd = defaultdict(lambda: 'N/A')\ndd['key1'] = 'abc'\nprint(\"dd['key1'] is : \",dd['key1']) # key1存在\nprint(\"dd['key2'] is : \",dd['key2']) # key1存在\n\n#使用dict时，Key是无序的。在对dict做迭代时，我们无法确定Key的顺序。如果要保持Key的顺序，可以用OrderedDict：\nd = dict([('a', 1), ('b', 2), ('c', 3)])\nprint('non orderedDict is : ',d)\nod = OrderedDict([('a', 1), ('b', 2), ('c', 3)])\nprint('used orderdDict is : ',od)\n\n#Counter是一个简单的计数器，例如，统计字符出现的个数\ncounters = Counter()\nfor ch in 'programming':\n counters[ch] = counters[ch] + 1\nprint(counters)\n","sub_path":"src/fundamental/basic/handleCollections.py","file_name":"handleCollections.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"315192919","text":"from authzed.api.v1 import (\n Client,\n ObjectReference,\n Relationship,\n RelationshipUpdate,\n SubjectReference,\n WriteRelationshipsRequest,\n)\nfrom grpcutil import bearer_token_credentials\n\nclient = Client(\n \"grpc.authzed.com:443\",\n bearer_token_credentials(\"t_your_token_here_1234567deadbeef\"),\n)\n\nresp = client.WriteRelationships(\n WriteRelationshipsRequest(\n updates=[\n # Emilia is a Writer on Post 1\n RelationshipUpdate(\n operation=RelationshipUpdate.Operation.OPERATION_CREATE,\n relationship=Relationship(\n resource=ObjectReference(object_type=\"blog/post\", object_id=\"1\"),\n relation=\"writer\",\n subject=SubjectReference(\n object=ObjectReference(\n object_type=\"blog/user\",\n object_id=\"emilia\",\n )\n ),\n ),\n ),\n # Beatrice is a Reader on Post 1\n RelationshipUpdate(\n operation=RelationshipUpdate.Operation.OPERATION_CREATE,\n relationship=Relationship(\n resource=ObjectReference(object_type=\"blog/post\", object_id=\"1\"),\n relation=\"reader\",\n subject=SubjectReference(\n object=ObjectReference(\n object_type=\"blog/user\",\n object_id=\"beatrice\",\n )\n ),\n ),\n ),\n ]\n )\n)\n","sub_path":"examples/v1/write_relationships.py","file_name":"write_relationships.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"49051307","text":"#!/usr/bin/env python\nimport re\nimport cv2\nimport argparse\nimport numpy as np\nfrom pathlib import Path\n\n\nGRID_WIDTH = 29.8 # mm\nGRID_COLS = 13\nGRID_ROWS = 6\nGRID_NUM = GRID_COLS * GRID_ROWS\n\n\ndef execute(dn_rgb_img: Path, dn_rgb_txt: Path, dn_calib: Path):\n if not dn_rgb_txt.exists() or not dn_rgb_txt.is_dir():\n dn_rgb_txt.mkdir()\n\n # load images\n fp_rgb_img_list = [p for p in dn_rgb_img.glob(\"*\") if re.search(\".*(jpg|JPG|png|PNG)\", str(p))]\n fp_rgb_img_list = sorted(fp_rgb_img_list)\n\n # prepare 3D points\n obj_coords = np.zeros((1, GRID_COLS * GRID_ROWS, 3), np.float32)\n obj_coords[0, :, :2] = np.mgrid[0:GRID_COLS, 0:GRID_ROWS].T.reshape(-1, 2) * GRID_WIDTH\n\n objpoints = []\n imgpoints = []\n\n print(\"----- Circle Grid Detection -----\")\n for fp_rgb_img in fp_rgb_img_list:\n print(\"{}\".format(fp_rgb_img))\n rgb_img = cv2.imread(str(fp_rgb_img), cv2.IMREAD_GRAYSCALE)\n rgb_img = rgb_img.astype(np.uint8)\n\n # detect circle grid centers\n params = cv2.SimpleBlobDetector_Params()\n params.filterByArea = True\n params.minArea = 10 ** 2\n params.maxArea = 100 ** 2\n params.filterByColor = True\n params.minThreshold = 0\n params.maxThreshold = 192\n params.filterByCircularity = True\n params.filterByInertia = False\n params.filterByConvexity = True\n detector = cv2.SimpleBlobDetector_create(params)\n detect_flags = cv2.CALIB_CB_SYMMETRIC_GRID + cv2.CALIB_CB_CLUSTERING\n is_found, centers = cv2.findCirclesGrid(rgb_img, (GRID_COLS, GRID_ROWS), detect_flags, blobDetector=detector)\n\n # check if centers are valid or not\n if not is_found or centers is None or len(centers) != GRID_NUM:\n print(\"- FAILED\")\n continue\n\n # convert to color image format\n rgb_img = cv2.cvtColor(rgb_img, cv2.COLOR_GRAY2RGB)\n cv2.drawChessboardCorners(rgb_img, (GRID_COLS, GRID_ROWS), centers, True)\n center_coords = []\n for i, corner in enumerate(centers):\n cv2.putText(rgb_img, str(i), (int(corner[0][0]) + 2, int(corner[0][1]) - 2),\n cv2.FONT_HERSHEY_PLAIN, 2, (255, 255, 255), 2)\n center_coords.append((corner[0][0], corner[0][1]))\n\n objpoints.append(obj_coords)\n imgpoints.append(centers)\n\n cv2.imwrite(str(dn_rgb_txt / fp_rgb_img.name), rgb_img)\n np.savetxt(str(dn_rgb_txt / (fp_rgb_img.stem + \".txt\")), np.array(center_coords))\n\n # convert to grayscale image format\n calib_flags = cv2.CALIB_FIX_K2 + cv2.CALIB_FIX_K3 + cv2.CALIB_FIX_TANGENT_DIST\n criteria_flags = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS, 1000, 1e-12)\n\n # dummy input (these initial values won't be used because cv2.CALIB_USE_INTRINSIC_GUESS is not specified)\n camera_matrix = np.zeros((3, 3))\n dist_params = np.zeros((5, 1))\n rmse, camera_matrix, dist_params, rvecs, tvecs \\\n = cv2.calibrateCamera(objpoints, imgpoints, (rgb_img.shape[1], rgb_img.shape[0]),\n camera_matrix, dist_params, flags=calib_flags, criteria=criteria_flags)\n print(\"\")\n\n print(\"----- Calibration Results -----\")\n print(\"RMSE:\")\n print(rmse)\n print(\"K:\")\n print(camera_matrix)\n print(\"D:\")\n print(dist_params.T)\n print(\"\")\n\n print(\"----- Undistortion of Calibration Images -----\")\n\n if not dn_calib.exists() or not dn_calib.is_dir():\n dn_calib.mkdir()\n\n np.savetxt(dn_calib / \"camera_matrix.txt\", camera_matrix)\n np.savetxt(dn_calib / \"dist_params.txt\", dist_params)\n\n # create a new optimal camera matrix\n new_camera_matrix, _ = cv2.getOptimalNewCameraMatrix(camera_matrix, dist_params,\n (rgb_img.shape[1], rgb_img.shape[0]), 1.0)\n remap = cv2.initUndistortRectifyMap(camera_matrix, dist_params, np.eye(3), new_camera_matrix,\n (rgb_img.shape[1], rgb_img.shape[0]), cv2.CV_32FC1)\n for fp_rgb_img in fp_rgb_img_list:\n rgb_img = cv2.imread(str(fp_rgb_img), cv2.IMREAD_COLOR)\n rgb_img = rgb_img.astype(np.uint8)\n rgb_img = cv2.remap(rgb_img, remap[0], remap[1], cv2.INTER_AREA)\n fp_rgb_img_undist = dn_rgb_txt / (\"undist_\" + fp_rgb_img.name)\n print(f\"- {fp_rgb_img_undist}\")\n cv2.imwrite(str(fp_rgb_img_undist), rgb_img)\n print(\"\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Calibration script for a perspective camera\")\n parser.add_argument(\"dp_rgb_img\", type=Path, help=\"directory of RGB images\")\n parser.add_argument(\"dp_rgb_txt\", type=Path, help=\"directory of circle detection output\")\n parser.add_argument(\"dp_calib\", type=Path, help=\"directory of calibration output\")\n args = parser.parse_args()\n\n execute(args.dp_rgb_img, args.dp_rgb_txt, args.dp_calib)\n","sub_path":"calib_perspective.py","file_name":"calib_perspective.py","file_ext":"py","file_size_in_byte":4895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"331226854","text":"import psycopg2\nimport sys\nfrom gym.envs.postgres_idx_advisor.envs.Utils import Utils\nfrom gym.envs.postgres_idx_advisor.envs.Constants import Constants\nfrom pglast import Node, parse_sql\nimport re\nimport numpy as np\nimport json\n\nclass PostgresQueryHandler:\n # attribute which will hold the postgres connection\n connection = None\n connectionDefault = None\n hypo_indexes_dict = dict()\n\n @staticmethod\n def __get_connection():\n # create connection only if it is not done before\n if PostgresQueryHandler.connection is None:\n # connect to postgres\n try:\n # read database config from config file\n database_config = Utils.read_config_data(Constants.CONFIG_DATABASE)\n # connect to postgres\n PostgresQueryHandler.connection = psycopg2.connect(database=database_config[\"dbname\"],\n user=database_config[\"user\"],\n password=database_config[\"password\"],\n host=database_config[\"host\"],\n port=database_config[\"port\"])\n PostgresQueryHandler.connection.autocommit = True\n # capture connection exception\n except psycopg2.OperationalError as exception:\n print('Unable to connect to postgres \\n Reason: {0}').format(str(exception))\n # exit code\n sys.exit(1)\n else:\n cursor = PostgresQueryHandler.connection.cursor()\n # Print PostgreSQL version\n cursor.execute(\"SELECT version();\")\n record = cursor.fetchone()\n print(\"***You are connected to below Postgres database*** \\n \", record, \"\\n\")\n return PostgresQueryHandler.connection\n\n @staticmethod\n def __get_default_connection():\n if PostgresQueryHandler.connectionDefault is None:\n # connect to postgres\n try:\n # read database config from config file\n database_config = Utils.read_config_data(Constants.CONFIG_DATABASE)\n # connect to postgres\n PostgresQueryHandler.connectionDefault = psycopg2.connect(database=database_config[\"dbname\"],\n user=database_config[\"user\"],\n password=database_config[\"password\"],\n host=database_config[\"host\"],\n port=database_config[\"port\"])\n PostgresQueryHandler.connectionDefault.autocommit = True\n # capture connection exception\n except psycopg2.OperationalError as exception:\n print('Unable to connect to postgres \\n Reason: {0}').format(str(exception))\n # exit code\n sys.exit(1)\n else:\n cursor = PostgresQueryHandler.connectionDefault.cursor()\n # Print PostgreSQL version\n cursor.execute(\"SELECT version();\")\n record = cursor.fetchone()\n print(\"***You are connected to below Postgres database*** \\n \", record, \"\\n\")\n return PostgresQueryHandler.connectionDefault\n\n @staticmethod\n def execute_select_query(query: str, load_index_advisor: bool = False, get_explain_plan: bool = False):\n #cursor = PostgresQueryHandler.__get_connection().cursor()\n if load_index_advisor:\n print('$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$')\n cursor = PostgresQueryHandler.__get_connection().cursor()\n cursor.execute(Constants.CREATE_EXTENSION)\n cursor.execute(Constants.LOAD_PG_IDX_ADVISOR)\n cursor = PostgresQueryHandler.__get_connection().cursor()\n else:\n print('################################')\n cursor = PostgresQueryHandler.__get_default_connection().cursor()\n cursor.execute(Constants.DROP_PG_IDX_ADVISOR)\n if get_explain_plan:\n query = Constants.QUERY_EXPLAIN_PLAN.format(query)\n cursor.execute(query)\n returned_rows = cursor.fetchall()\n\n #PostgresQueryHandler.check_hypo_indexes()\n\n cursor.close()\n return returned_rows\n\n @staticmethod\n def get_table_row_count(table_name):\n cursor = PostgresQueryHandler.__get_connection().cursor()\n cursor.execute(Constants.QUERY_FIND_NUMBER_OF_ROWS.format(table_name))\n returned_count = cursor.fetchone()\n cursor.close()\n return returned_count[0]\n\n @staticmethod\n def execute_count_query(query):\n cursor = PostgresQueryHandler.__get_connection().cursor()\n cursor.execute(query)\n returned_count = cursor.fetchone()\n cursor.close()\n return returned_count[0]\n\n @staticmethod\n def execute_select_query_and_get_row_count(query):\n cursor = PostgresQueryHandler.__get_connection().cursor()\n # trim whitespaces and add wrapper query to get count of rows i.e select count(*) from () table1\n query_to_execute = ' '.join(query.strip().replace('\\n', ' ').lower().split())\n query_to_execute = \"Select count(*) from (\" + query_to_execute + \" ) table1\"\n cursor.execute(query_to_execute)\n returned_count = cursor.fetchone()\n cursor.close()\n return returned_count\n\n @staticmethod\n def create_hypo_index(table_name, col_name):\n key = table_name + Constants.MULTI_KEY_CONCATENATION_STRING + col_name\n # create hypo index if it is not already present\n if key not in PostgresQueryHandler.hypo_indexes_dict:\n cursor = PostgresQueryHandler.__get_default_connection().cursor()\n #print('setting index', table_name, col_name)\n # replace placeholders in the create hypo index query with table name and column name\n cursor.execute(Constants.QUERY_CREATE_HYPO_INDEX.format(table_name, col_name))\n returned_index_id = cursor.fetchone()\n cursor.close()\n PostgresQueryHandler.hypo_indexes_dict[key] = returned_index_id[0]\n\n @staticmethod\n def remove_hypo_index(table_name, col_name):\n key = table_name + Constants.MULTI_KEY_CONCATENATION_STRING + col_name\n # check whether index is already present\n if key in PostgresQueryHandler.hypo_indexes_dict:\n cursor = PostgresQueryHandler.__get_connection().cursor()\n # retrieve index id from dict and replace the place holder with index id\n cursor.execute(Constants.QUERY_REMOVE_HYPO_INDEX.format(PostgresQueryHandler.hypo_indexes_dict.get(key, 0)))\n cursor.close()\n PostgresQueryHandler.hypo_indexes_dict.pop(key, None)\n\n @staticmethod\n def remove_all_hypo_indexes():\n cursor = PostgresQueryHandler.__get_default_connection().cursor()\n cursor.execute(Constants.QUERY_REMOVE_ALL_HYPO_INDEXES)\n print(cursor.fetchall())\n cursor.close()\n\n @staticmethod\n def get_where_clause_list_for_query(query: str):\n query_tree = Node(parse_sql(query))\n for tre in query_tree:\n for node in tre.stmt.whereClause:\n print(str(node))\n\n @staticmethod\n def check_hypo_indexes():\n cursor = PostgresQueryHandler.__get_default_connection().cursor()\n cursor.execute(Constants.QUERY_CHECK_HYPO_INDEXES)\n print(cursor.fetchall())\n cursor.close()\n\n @staticmethod\n def add_query_cost_suggested_indexes(result):\n #PostgresQueryHandler.check_hypo_indexes()\n explain_plan = ' \\n'.join(map(str, result))\n # extract cost\n print(explain_plan)\n cost_pattern = \"cost=(.*)row\"\n cost_match = re.search(cost_pattern, explain_plan)\n if cost_match is not None:\n cost_query = cost_match.group(1).split('..')[-1]\n return float(cost_query)\n\n @staticmethod\n def get_observation_space(queries_list):\n observation_space = np.array(np.ones((8,61)))\n observation_space[0,:] = np.array((np.zeros((61,))))\n action_space = PostgresQueryHandler.read_json_action_space()\n for query_number in range(len(queries_list)):\n for key, value in queries_list[query_number].where_clause_columns_query.items():\n print(key + ' : ' + value + ' : ' + str(\n queries_list[query_number].selectivity_for_where_clause_columns[key]) + ' : ' + str(queries_list[query_number].query_cost_without_index))\n table_name, col_name = key.split(Constants.MULTI_KEY_CONCATENATION_STRING)\n print(key, table_name, col_name)\n selectivity_index = action_space[(table_name + \".\" + col_name).upper()]\n print(selectivity_index)\n observation_space[query_number+1, selectivity_index] = queries_list[query_number].selectivity_for_where_clause_columns[key]\n return observation_space\n\n @staticmethod\n def read_json_action_space():\n with open('../index_action_space.json') as json_file:\n action_space = json.load(json_file)\n return action_space","sub_path":"envs/PostgresQueryHandler.py","file_name":"PostgresQueryHandler.py","file_ext":"py","file_size_in_byte":9412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"269711279","text":"#encoding:utf-8\r\nimport re\r\nfrom nltk.stem.snowball import SnowballStemmer\r\nimport nltk\r\nnltk.download(\"stopwords\")\r\nfrom nltk.corpus import stopwords\r\n\r\nreplacement = {\r\n \"aren't\" : \"are not\",\r\n \"can't\" : \"cannot\",\r\n \"couldn't\" : \"could not\",\r\n \"didn't\" : \"did not\",\r\n \"doesn't\" : \"does not\",\r\n \"don't\" : \"do not\",\r\n \"hadn't\" : \"had not\",\r\n \"hasn't\" : \"has not\",\r\n \"haven't\" : \"have not\",\r\n \"he'd\" : \"he would\",\r\n \"he'll\" : \"he will\",\r\n \"he's\" : \"he is\",\r\n \"i'd\" : \"I would\",\r\n \"i'll\" : \"I will\",\r\n \"i'm\" : \"I am\",\r\n \"isn't\" : \"is not\",\r\n \"it's\" : \"it is\",\r\n \"it'll\":\"it will\",\r\n \"i've\" : \"I have\",\r\n \"let's\" : \"let us\",\r\n \"mightn't\" : \"might not\",\r\n \"mustn't\" : \"must not\",\r\n \"shan't\" : \"shall not\",\r\n \"she'd\" : \"she would\",\r\n \"she'll\" : \"she will\",\r\n \"she's\" : \"she is\",\r\n \"shouldn't\" : \"should not\",\r\n \"that's\" : \"that is\",\r\n \"there's\" : \"there is\",\r\n \"they'd\" : \"they would\",\r\n \"they'll\" : \"they will\",\r\n \"they're\" : \"they are\",\r\n \"they've\" : \"they have\",\r\n \"we'd\" : \"we would\",\r\n \"we're\" : \"we are\",\r\n \"weren't\" : \"were not\",\r\n \"we've\" : \"we have\",\r\n \"what'll\" : \"what will\",\r\n \"what're\" : \"what are\",\r\n \"what's\" : \"what is\",\r\n \"what've\" : \"what have\",\r\n \"where's\" : \"where is\",\r\n \"who'd\" : \"who would\",\r\n \"who'll\" : \"who will\",\r\n \"who're\" : \"who are\",\r\n \"who's\" : \"who is\",\r\n \"who've\" : \"who have\",\r\n \"won't\" : \"will not\",\r\n \"wouldn't\" : \"would not\",\r\n \"you'd\" : \"you would\",\r\n \"you'll\" : \"you will\",\r\n \"you're\" : \"you are\",\r\n \"you've\" : \"you have\",\r\n \"'re\": \" are\",\r\n \"wasn't\": \"was not\",\r\n \"we'll\":\" will\",\r\n \"tryin'\":\"trying\",\r\n}\r\n\r\nstemmer = SnowballStemmer(\"english\")\r\nstopwords_en = set(stopwords.words('english'))\r\n\r\n# now build a custom tokenizer based on these\r\n__tokenization_pattern = r'''(?x) # set flag to allow verbose regexps\r\n \\$?\\d+(?:\\.\\d+)?%? # currency and percentages, e.g. $12.40, 82%\r\n | (?:[A-Z]\\.)+ # abbreviations, e.g. U.S.A.\r\n | \\w+(?:-\\w+)* # words with optional internal hyphens\r\n | \\.\\.\\. # ellipsis\r\n | [][.,;\"'?():_`-] # these are separate tokens; includes ], [\r\n '''\r\n\r\ntokenizer = nltk.tokenize.regexp.RegexpTokenizer(__tokenization_pattern)\r\n\r\nclass EnglishPreProcessor(object):\r\n def __init__(self,min_len = 2,stopwords_path = None):\r\n self.min_len = min_len\r\n\r\n def preprocessor(self,sentence):\r\n '''\r\n turns text into tokens after tokenization, stemming, stop words removal\r\n imput:\r\n - text: document to process\r\n output: =>\r\n - tokens: list of tokens after tokenization, stemming, stop words removal\r\n '''\r\n stems = []\r\n tokens = tokenizer.tokenize(sentence.lower())\r\n\r\n for token in tokens:\r\n if token.isalpha() and token not in stopwords_en:\r\n stems.append(str(stemmer.stem(token)))\r\n if not stems:\r\n for token in tokens:\r\n if token not in stopwords_en:\r\n stems.append(str(stemmer.stem(token)))\r\n return stems\r\n\r\n\r\n def clean_length(self,sentence):\r\n '''\r\n 去除长度小于min_len的文本\r\n :param sentence:\r\n :return:\r\n '''\r\n if len([x for x in sentence]) >= self.min_len:\r\n return sentence\r\n\r\n def replace(self,sentence):\r\n '''\r\n 一些特殊缩写替换\r\n :param sentence:\r\n :return:\r\n '''\r\n # Replace words like gooood to good\r\n sentence = re.sub(r'(\\w)\\1{2,}', r'\\1\\1', sentence)\r\n # Normalize common abbreviations\r\n words = sentence.split(' ')\r\n words = [replacement[word] if word in replacement else word for word in words]\r\n sentence_repl = \" \".join(words)\r\n return sentence_repl\r\n\r\n def remove_website(self,sentence):\r\n '''\r\n 处理网址符号\r\n :param sentence:\r\n :return:\r\n '''\r\n sentence_repl = sentence.replace(r\"http\\S+\", \"\")\r\n sentence_repl = sentence_repl.replace(r\"https\\S+\", \"\")\r\n sentence_repl = sentence_repl.replace(r\"http\", \"\")\r\n sentence_repl = sentence_repl.replace(r\"https\", \"\")\r\n return sentence_repl\r\n\r\n def remove_name_tag(self,sentence):\r\n # Remove name tag\r\n sentence_repl = sentence.replace(r\"@\\S+\", \"\")\r\n return sentence_repl\r\n\r\n def remove_time(self,sentence):\r\n '''\r\n 特殊数据处理\r\n :param sentence:\r\n :return:\r\n '''\r\n # Remove time related text\r\n sentence_repl = sentence.replace(r'\\w{3}[+-][0-9]{1,2}\\:[0-9]{2}\\b', \"\") # e.g. UTC+09:00\r\n sentence_repl = sentence_repl.replace(r'\\d{1,2}\\:\\d{2}\\:\\d{2}', \"\") # e.g. 18:09:01\r\n sentence_repl = sentence_repl.replace(r'\\d{1,2}\\:\\d{2}', \"\") # e.g. 18:09\r\n # Remove date related text\r\n # e.g. 11/12/19, 11-1-19, 1.12.19, 11/12/2019\r\n sentence_repl = sentence_repl.replace(r'\\d{1,2}(?:\\/|\\-|\\.)\\d{1,2}(?:\\/|\\-|\\.)\\d{2,4}', \"\")\r\n # e.g. 11 dec, 2019 11 dec 2019 dec 11, 2019\r\n sentence_repl = sentence_repl.replace(\r\n r\"([\\d]{1,2}\\s(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)|(jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)\\s[\\d]{1,2})(\\s|\\,|\\,\\s|\\s\\,)[\\d]{2,4}\",\r\n \"\")\r\n # e.g. 11 december, 2019 11 december 2019 december 11, 2019\r\n sentence_repl = sentence_repl.replace(\r\n r\"[\\d]{1,2}\\s(january|february|march|april|may|june|july|august|september|october|november|december)(\\s|\\,|\\,\\s|\\s\\,)[\\d]{2,4}\",\r\n \"\")\r\n return sentence_repl\r\n\r\n def remove_breaks(self,sentence):\r\n # Remove line breaks\r\n sentence_repl = sentence.replace(\"\\r\", \"\")\r\n sentence_repl = sentence_repl.replace(\"\\n\", \"\")\r\n sentence_repl = re.sub(r\"\\\\n\\n\", \".\", sentence_repl)\r\n return sentence_repl\r\n\r\n def remove_ip(self,sentence):\r\n # Remove phone number and IP address\r\n sentence_repl = sentence.replace(r'\\d{8,}', \"\")\r\n sentence_repl = sentence_repl.replace(r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}', \"\")\r\n return sentence_repl\r\n\r\n def adjust_common(self,sentence):\r\n # Adjust common abbreviation\r\n sentence_repl = sentence.replace(r\" you re \", \" you are \")\r\n sentence_repl = sentence_repl.replace(r\" we re \", \" we are \")\r\n sentence_repl = sentence_repl.replace(r\" they re \", \" they are \")\r\n sentence_repl = sentence_repl.replace(r\"@\", \"at\")\r\n return sentence_repl\r\n\r\n\r\n def full2half(self,sentence):\r\n '''\r\n 全角转化为半角\r\n :param sentence:\r\n :return:\r\n '''\r\n ret_str = ''\r\n for i in sentence:\r\n if ord(i) >= 33 + 65248 and ord(i) <= 126 + 65248:\r\n ret_str += chr(ord(i) - 65248)\r\n else:\r\n ret_str += i\r\n return ret_str\r\n\r\n def remove_stopword(self,sentence):\r\n '''\r\n 去除停用词\r\n :param sentence:\r\n :return:\r\n '''\r\n words = sentence.split()\r\n x = [word for word in words if word not in self.stopwords]\r\n return \" \".join(x)\r\n\r\n # 主函数\r\n def __call__(self, sentence):\r\n x = sentence\r\n x = self.preprocessor(x)\r\n return x\r\n","sub_path":"pybert/preprocessing/preprocessor.py","file_name":"preprocessor.py","file_ext":"py","file_size_in_byte":7442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"460243950","text":"from pylgbst import *\nfrom pylgbst.hub import MoveHub\nfrom pylgbst.peripherals import *\nfrom robot import *\nfrom time import sleep\n\nBTN_FORWARD=19\nBTN_BACKWARD=26\n\nlogging.basicConfig(level = logging.WARN)\nlog = logging.getLogger(\"test\")\n\nmac=\"00:16:53:B2:30:37\"\nconn = get_connection_bluegiga(hub_mac=mac)\n\nprint(\"Connected.\")\n\nrobot = Robot(conn)\nrobot.addButton(BTN_BACKWARD)\nrobot.addButton(BTN_FORWARD)\nrobot.setInvert(False)\n\n#################################################################\nmoving = False\nisWhite = False\nisRed = False\n\ndef callback(color, distance):\n print(color)\n\n global isRed\n global isWhite\n if color == COLOR_RED:\n isRed=True\n isWhite = False\n elif color == COLOR_WHITE:\n isWhite = True\n isRed = False\n else:\n isRed = False\n isWhite = False\n\nrobot.setSensorCallback(callback)\n\ntry:\n while True:\n\n if robot.isButtonPressed(BTN_FORWARD):\n moving=True\n\n if robot.isButtonPressed(BTN_BACKWARD):\n moving = False\n\n\n step=0.2\n if moving:\n if isRed:\n robot.motor_AB.timed(step, 0.7, -0.3)\n robot.motor_AB.timed(step, 0.5) # FORWARD\n #robot.turn(LEFT)\n elif isWhite:\n robot.motor_AB.timed(step, -0.3, 0.7)\n robot.motor_AB.timed(step, 0.5) # FORWARD\n #robot.turn(RIGHT)\n else:\n robot.motor_AB.timed(step, 0.5)\n print(\"FORWARD\")\n\n sleep(0.1)\n\nfinally:\n robot.say(\"Goodbye\")\n GPIO.cleanup()\n\n","sub_path":"robot3_color_sensor.py","file_name":"robot3_color_sensor.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"124817005","text":"\"\"\"\n\nThe hashing and sha stuff\n\n\"\"\"\n\nfrom Crypto.Hash import SHA256\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Signature import PKCS1_v1_5\n\nfrom util import insertBytes\n\n\ndef createRRSetData(rr_set, rrsig_record, domain):\n \"\"\"\n Puts together data for an RRSet and computes hash\n https://tools.ietf.org/html/rfc4034#section-3.1.8.1\n :param rr_set: The RRset\n :param rrsig_record: The RRSIG record\n :param domain: The domain name of the RRSIG\n :return: The data ready for verification\n \"\"\"\n data = rrsig_record.type_covered.to_bytes(2, 'big') + \\\n rrsig_record.algorithm.to_bytes(1, 'big') + rrsig_record.labels.to_bytes(1, 'big') + \\\n rrsig_record.orig_ttl.to_bytes(4, 'big') + rrsig_record.expiration + \\\n rrsig_record.inception + rrsig_record.tag.to_bytes(2, 'big') + \\\n rrsig_record.signer_name\n\n for rr in rr_set:\n data += RRSignableData(rr, domain, rrsig_record.orig_ttl)\n\n return data\n\n\ndef createDSRecord(dnskey, domain):\n \"\"\"\n A DS record is just the hash of a public key. Uses SHA256\n :param dnskey: The DNSKEY record\n :param domain: The domain name\n :return: The SHA256 hash of the DNSKEY record\n \"\"\"\n data = formatName(domain)\n\n data += dnskey.rdata\n\n hasher = SHA256.new()\n hasher.update(data)\n return hasher.digest()\n\n\ndef formatName(name):\n \"\"\"\n Puts a domain name into that form DNS loves so much\n :param name: A domain name string\n :return: The name formatting for verification use\n \"\"\"\n name_bytes = bytearray(len(name) + 2)\n i = 0\n for domain in name.split('.'):\n name_bytes[i] = len(domain)\n i += 1\n insertBytes(name_bytes, domain.encode('utf-8', 'strict'), i)\n i += len(domain)\n name_bytes[i] = 0\n return name_bytes\n\n\ndef RRSignableData(rr, owner, orig_ttl):\n \"\"\"\n This will take a RR and return the bytes to be used in signing\n # RFC on signiture calculation: https://tools.ietf.org/html/rfc4034#section-3.1\n\n owner is the domain owner (EX: 'example.com', 'com'). This TECHNICALLY should\n be in the rr, but we never figured out the pointer name storage thing\n :param rr: The Resource record\n :param owner: The owner of the RR\n :param orig_ttl: original TTL from RRSIG record\n :return: The data set for verification\n \"\"\"\n formatted_owner = formatName(owner)\n return formatted_owner + rr.type.to_bytes(2, 'big') + rr.clazz.to_bytes(2, 'big') + \\\n orig_ttl.to_bytes(4, 'big') + rr.rdata_len.to_bytes(2, 'big') + rr.rdata\n\n\ndef verify_signature(signature, key, recordset):\n \"\"\"\n Verifies a signature\n :param signature: The signature\n :param key: The key\n :param recordset: The recordset to verify\n :return: True if verified, false otherwise\n \"\"\"\n expo, mod = get_expo_and_mod(key)\n constructed_key = RSA.construct((mod, expo))\n cipher = PKCS1_v1_5.new(constructed_key)\n return cipher.verify(SHA256.new(recordset), signature)\n\n\ndef get_expo_and_mod(dnskey):\n \"\"\"\n Gets the exponent and modulus from a DNSKEY record\n :param dnskey: The DNSKEY record\n :return: Tuples of (exponent, modulus)\n \"\"\"\n data = bytearray(dnskey.key)\n cursor = 1\n expo_len = int.from_bytes([data[0]], 'big')\n if expo_len == 0:\n expo_len = int.from_bytes(data[1:3], 'big')\n cursor = 3\n expo = int.from_bytes(data[cursor:cursor + expo_len], 'big')\n cursor += expo_len\n mod = int.from_bytes(data[cursor:], 'big')\n return expo, mod\n","sub_path":"crypto.py","file_name":"crypto.py","file_ext":"py","file_size_in_byte":3522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"292584173","text":"\nimport microfabrication_functions as mfc\nimport microfabrication_constant as mconst\nimport time as time\nimport sys\nimport os as os\nimport sdl2.ext \nimport sdl2.sdlimage\n#os.environ[\"PYSDL2_DLL_PATH\"] = \"/Users/vishwanathan/Desktop/UM-2/undergrad_research/PySDL2-0.9.5\"\nRESOURCES = sdl2.ext.Resources(__file__, mconst.IMAGE_PATH)\nimage_list = mfc\nimage_filelist = mfc.read_files(mconst.IMAGE_PATH)\nsdl2.ext.init()\nwindow = sdl2.ext.Window(\"Hello World!\", size=(1000, 1000))\nwindow.show()\n#sdl2.SDL_SetWindowFullscreen(window, 1, flags = sdl2.SDL_WINDOW_FULLSCREEN_DESKTOP)\n\n\nfactory = sdl2.ext.SpriteFactory(sdl2.ext.SOFTWARE)\n\n\nspritelist =[]\n\nfor filename in image_filelist :\n sprite = factory.from_image(RESOURCES.get_path(filename.original_filename))\n spritelist.append(sprite)\n\nspriterenderer = factory.create_sprite_render_system(window)\nprint(\"yoh\")\nprocessor = sdl2.ext.TestEventProcessor()\nprint(\"yeh\")\nprocessor.run(window)\nsdl2.ext.get_events()\nstart = time.time()\nprint(\"yuh\")\nfor sprite in spritelist:\n spriterenderer.render(sprite)\n sdl2.timer.SDL_Delay(50)\n\n \nend = time.time()\nprint(end-start)\n\n\nsdl2.ext.quit()\n\n\n\n\n\n\n\n\n\n\n","sub_path":"image_display.py","file_name":"image_display.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"335423714","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom scipy.optimize import leastsq\nimport weave\n\nimport sys\nimport scipy.io as sio\nimport neo\n\n#import GIF\n#from Experiment import *\n#from AEC_Badel import *\n\n###########################################################\n# Remove axis\n###########################################################\n\ndef removeAxis(ax, which_ax=['top', 'right']):\n\n for loc, spine in ax.spines.iteritems():\n if loc in which_ax:\n spine.set_color('none') # don't draw spine\n\n ax.xaxis.set_ticks_position('bottom')\n ax.yaxis.set_ticks_position('left')\n\n\n###########################################################\n# Reprint\n###########################################################\ndef reprint(str):\n sys.stdout.write('%s\\r' % (str))\n sys.stdout.flush()\n\n\n###########################################################\n# Generate Ornstein-Uhlenbeck process\n###########################################################\n\ndef generateOUprocess(T=10000.0, tau=3.0, mu=0.0, sigma=1.0, dt=0.1):\n\n \"\"\"\n Generate an Ornstein-Uhlenbeck (stationnary) process with:\n - mean mu\n - intensity parameter sigma\n - temporal correlation tau (ms)\n The duration of the signal is specified by the input parameter T (in ms).\n The process is generated in discrete time with temporal resolution dt (in ms)\n \"\"\"\n\n T_ind = int(T/dt)\n\n white_noise = np.random.randn(T_ind)\n white_noise = white_noise.astype(\"double\")\n\n OU_process = np.zeros(T_ind)\n OU_process = OU_process.astype(\"double\")\n\n '''OU_k1 = dt / tau\n OU_k2 = np.sqrt(dt / tau)\n for t in xrange(T_ind-1) :\n OU_process[t + 1] = OU_process[t] + (mu - OU_process[t]) * OU_k1 + sigma * OU_k2 * white_noise[t]\n '''\n\n code = \"\"\"\n\n #include \n\n int cT_ind = int(T_ind);\n float cdt = float(dt);\n float ctau = float(tau);\n float cmu = float(mu);\n float csigma = float(sigma);\n\n float OU_k1 = cdt / ctau ;\n float OU_k2 = sqrt(cdt/ctau) ;\n\n for (int t=0; t < cT_ind-1; t++) {\n OU_process[t+1] = OU_process[t] + (cmu - OU_process[t])*OU_k1 + csigma*OU_k2*white_noise[t] ;\n }\n\n \"\"\"\n\n vars = ['T_ind', 'dt', 'tau', 'sigma','mu', 'OU_process', 'white_noise']\n v = weave.inline(code,vars)\n\n return OU_process\n\n\ndef generateOUprocess_sinSigma(f=1.0, T=10000.0, tau=3.0, mu=0.0, sigma=1.0, delta_sigma=0.5, dt=0.1):\n\n \"\"\"\n Generate an Ornstein-Uhlenbeck process with time dependent standard deviation:\n - mean mu\n - sigma(t) = sigma*(1+delta_sigma*sin(2pift)), f in Hz\n - temporal correlation tau (ms)\n The duration of the signal is specified by the input parameter T (in ms).\n The process is generated in discrete time with temporal resolution dt (in ms)\n \"\"\"\n\n OU_process = generateOUprocess(T=T, tau=tau, mu=0.0, sigma=1.0, dt=dt)\n t = np.arange(len(OU_process))*dt\n\n sin_sigma = sigma*(1+delta_sigma*np.sin(2*np.pi*f*t*10**-3))\n\n I = OU_process*sin_sigma + mu\n\n return I\n\n\ndef generateOUprocess_sinMean(f=1.0, T=10000.0, tau=3.0, mu=0.2, delta_mu=0.5, sigma=1.0, dt=0.1):\n\n \"\"\"\n Generate an Ornstein-Uhlenbeck process with time dependent mean:\n - sigma\n - mu(t) = mu*(1+delta_mu*sin(2pift)), f in Hz\n - temporal correlation tau (ms)\n The duration of the signal is specified by the input parameter T (in ms).\n The process is generated in discrete time with temporal resolution dt (in ms)\n \"\"\"\n\n OU_process = generateOUprocess(T=T, tau=tau, mu=0.0, sigma=sigma, dt=dt)\n t = np.arange(len(OU_process))*dt\n\n sin_mu = mu*(1+delta_mu*np.sin(2*np.pi*f*t*10**-3))\n\n I = OU_process + sin_mu\n\n return I\n\n\n###########################################################\n# Functin to convert spike times in spike indices\n###########################################################\ndef timeToIndex(x_t, dt):\n\n x_t = np.array(x_t)\n x_i = np.array( [ int(np.round(s/dt)) for s in x_t ] )\n x_i = x_i.astype('int')\n\n return x_i\n\n\n###########################################################\n# Functions to perform exponential fit\n###########################################################\n\ndef multiExpEval(x, bs, taus):\n\n result = np.zeros(len(x))\n L = len(bs)\n\n for i in range(L) :\n result = result + bs[i] *np.exp(-x/taus[i])\n\n return result\n\n\ndef multiExpResiduals(p, x, y, d):\n bs = p[0:d]\n taus = p[d:2*d]\n\n return (y - multiExpEval(x, bs, taus))\n\n\n\ndef fitMultiExpResiduals(bs, taus, x, y) :\n x = np.array(x)\n y = np.array(y)\n d = len(bs)\n p0 = np.concatenate((bs,taus))\n plsq = leastsq(multiExpResiduals, p0, args=(x,y,d), maxfev=100000,ftol=0.00000001)\n p_opt = plsq[0]\n bs_opt = p_opt[0:d]\n taus_opt = p_opt[d:2*d]\n\n fitted_data = multiExpEval(x, bs_opt, taus_opt)\n\n ind = np.argsort(taus_opt)\n\n taus_opt = taus_opt[ind]\n bs_opt = bs_opt[ind]\n\n return (bs_opt, taus_opt, fitted_data)\n\n\n###########################################################\n# Get indices far from spikes\n###########################################################\n\ndef getIndicesFarFromSpikes(T, spikes_i, dt_before, dt_after, initial_cutoff, dt) :\n\n T_i = int(T/dt)\n flag = np.zeros(T_i)\n flag[:int(initial_cutoff/dt)] = 1\n flag[-1] = 1\n\n dt_before_i = int(dt_before/dt)\n dt_after_i = int(dt_after/dt)\n\n for s in spikes_i :\n flag[ max(s-dt_before_i,0) : min(s+dt_after_i, T_i) ] = 1\n\n selection = np.where(flag==0)[0]\n\n return selection\n\n\ndef getIndicesDuringSpikes(T, spikes_i, dt_after, initial_cutoff, dt) :\n\n T_i = int(T/dt)\n flag = np.zeros(T_i)\n flag[:int(initial_cutoff/dt)] = 1\n flag[-1] = 1\n\n dt_after_i = int(dt_after/dt)\n\n for s in spikes_i :\n flag[ max(s,0) : min(s+dt_after_i, T_i) ] = 1\n\n selection = np.where(flag>0.1)[0]\n\n return selection\n\n\n###########################################################\n# Load AEC data\n###########################################################\ndef load_AEC_data(filename):\n if filename.find('.mat') > 0:\n mat_contents = sio.loadmat(filename)\n analogSignalsAEC = mat_contents['analogSignals']\n timesAEC = mat_contents['times']\n timesAEC = timesAEC.reshape(timesAEC.size)\n lengthAEC = timesAEC[-1]\n voltage_traceAEC = analogSignalsAEC[0, 0, :]\n current_traceAEC = analogSignalsAEC[1, 0, :] - 5. # Correction for exp bias\n maxT = int((lengthAEC - 0.5) / (timesAEC[1] - timesAEC[0]))\n minT = int(0.5 / (timesAEC[1] - timesAEC[0]))\n current_traceAEC = current_traceAEC[minT:maxT]\n voltage_traceAEC = voltage_traceAEC[minT:maxT]\n timesAEC = timesAEC[minT:maxT] * 10. ** 3\n sampling_time = timesAEC[1] - timesAEC[0]\n elif filename.find('.abf') > 0:\n r = neo.io.AxonIO(filename=filename)\n bl = r.read_block()\n voltage_traceAEC = bl.segments[0].analogsignals[0].magnitude\n current_traceAEC = bl.segments[0].analogsignals[1].magnitude - 5.\n timesAEC = bl.segments[0].analogsignals[0].times.rescale('ms').magnitude\n lengthAEC = timesAEC[-1]\n sampling_time = timesAEC[1] - timesAEC[0]\n maxT = int((lengthAEC - 0.5) / sampling_time)\n minT = int(0.5 / sampling_time)\n current_traceAEC = current_traceAEC[minT:maxT]\n voltage_traceAEC = voltage_traceAEC[minT:maxT]\n return (sampling_time, voltage_traceAEC, current_traceAEC)\n\n\ndef load_training_data(filename):\n if filename.find('.mat') > 0:\n mat_contents = sio.loadmat(filename)\n analogSignals = mat_contents['analogSignals']\n voltage_trace = analogSignals[0,0,:]\n current_trace = analogSignals[1,0,:] - 5. #offset due to uncalibrated current\n times = mat_contents['times'];\n times = times.reshape(times.size)\n times = times*10.**3 #convert to ms\n sampling_time = times[1] - times[0]\n elif filename.find('.abf') > 0:\n r = neo.io.AxonIO(filename=filename)\n bl = r.read_block()\n voltage_trace = bl.segments[0].analogsignals[0].magnitude\n current_trace = bl.segments[0].analogsignals[1].magnitude - 5.\n times = bl.segments[0].analogsignals[0].times.rescale('ms').magnitude\n sampling_time = times[1] - times[0]\n return (sampling_time, voltage_trace, current_trace, times)\n\n\n##########################################################\n# Plotting training trace with forced spikes\n##########################################################\n'''\ndef plot_training_forced(filename_AEC, filename_training, filename_model):\n \"\"\"\n :param filename_training: training data file name (string)\n :param filename_model: name of the model (ending in .pck)\n :return: none\n \"\"\"\n # Load AEC data\n (sampling_timeAEC, voltage_traceAEC, current_traceAEC) = load_AEC_data(filename_AEC)\n\n experiment = Experiment('Experiment 1', sampling_timeAEC)\n experiment.setAECTrace(voltage_traceAEC, 10. ** -3, current_traceAEC, 10. ** -12,\n len(voltage_traceAEC) * sampling_timeAEC, FILETYPE='Array')\n\n # Load preprocessed training data set\n (sampling_time, voltage_trace, current_trace) = load_training_data(filename_training)\n experiment.addTrainingSetTrace(voltage_trace, 10 ** -3, current_trace, 10 ** -12,\n len(voltage_trace) * sampling_time, FILETYPE='Array')\n\n # Compensate traces\n myAEC = AEC_Badel(experiment.dt)\n\n # Define metaparametres\n myAEC.K_opt.setMetaParameters(length=150.0, binsize_lb=experiment.dt, binsize_ub=2.0, slope=30.0, clamp_period=1.0)\n myAEC.p_expFitRange = [3.0, 150.0]\n myAEC.p_nbRep = 15\n\n # Assign myAEC to experiment and compensate the voltage recordings\n experiment.setAEC(myAEC)\n experiment.performAEC()\n\n # Load model\n model = GIF.load(filename_model)\n\n # Simulate model\n (time, V_est, eta_sum_est) = model.simulateDeterministic_forceSpikes(current_trace, voltage_trace[0], experiment.trainingset_traces[0].spks)\n\n #Plot\n plt.plot(time, V_est, 'r-', label='V_$\\mathrm{est}$', lw=0.5)\n plt.plot(time, voltage_trace, 'k-', label='V__$\\mathrm{training}$', lw=0.5)\n plt.xlabel('Time (ms)')\n plt.ylabel('V (mV)')\n plt.xlim(4000, 4500)\n plt.savefig(filename_model[:-4] + '_training_forced.png')\n plt.close()\n'''\n","sub_path":"src/Tools.py","file_name":"Tools.py","file_ext":"py","file_size_in_byte":10470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"487052043","text":"from bs4 import BeautifulSoup\nimport requests\n\nwith open('simple.html') as html_file:\n soup = BeautifulSoup(html_file, 'lxml')\nprint(soup.prettify())\n\nmatch = soup.find('div', class_ = 'footer')\nmatch = match.find('p')\nprint(match.text)\n","sub_path":"example_1.py","file_name":"example_1.py","file_ext":"py","file_size_in_byte":240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"421768222","text":"from six.moves.urllib.request import urlopen\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom scrapeTools import get_first_table\n\ndef nba_team_names():\n\ttable = get_first_table(\"https://www.foxsports.com/nba/standings?season=2018&seasonType=1&grouping=3&advanced=0\")\n\tdf = pd.read_html(str(table))[0]\n\treturn df[\"NBA\"]\n\ndef get_east_west():\n\ttable = get_first_table(\"https://www.foxsports.com/nba/standings?season=2018&seasonType=1&grouping=1&advanced=0\")\n\tdf = pd.read_html(str(table))[0]\n\teast = list(df.iterrows())[0:15]\n\twest = list(df.iterrows())[17:]\n\treturn east, west\n\ndef east_vs_west():\n\teast, west = get_east_west()\n\tWL =[0,0]\n\tfor index, row in east:\n\t\tteam_conf_rec = row['Conf'].split('-')\n\t\ttotal_wins = int(row['W'])\n\t\ttotal_losses = int(row['L'])\n\t\tWL[0] += total_wins - int(team_conf_rec[0])\n\t\tWL[1] += total_losses - int(team_conf_rec[1])\n\n\treturn \"East vs. West\\nRecord: \" + \"-\".join([str(i) for i in WL]) + \"\\nWPCT: \" + str(round(WL[0] / sum(WL), 3))\n\t","sub_path":"nbateams.py","file_name":"nbateams.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"562243947","text":"# -*- coding: utf-8 -*-\n'''\nTroisième étape : Destruction de la seam déterminée et décalage de l'image\n\nA ce stade, on dispose de : \n - une image à traiter\n - (Anthony) sa carte d'énergie\n - (Antoine) une fonction calculate_cost_matrix qui détermine une matrice des coûts de l'image\n - (Antoine) une fonction detect_seam utilisant la matrice de coûts, et qui retourne la seam verticale\n à enlever sous forme de tuple de coordonnées\n\nCe fichier permet de supprimer la seam calculée en deux étapes : \n - Destruction de la seam en décalant les pixels par dessus\n - Redimensionnement de l'image\n'''\n\nfrom PIL import Image\n\ndef remove_seam(im,seam):\n '''\n Cette fonction décale tous les pixels à droite du seam par dessus, la\n redimensionne et la retourne \n '''\n image = im.load()\n for elm in seam: #pour chaque element du seam (de la forme (x,y))\n for x in range (elm[0],im.size[0]-1): #pour chaque x \n image[x,elm[1]] = image[x+1,elm[1]]\n #final = resize(im,(im.size[0]-1,im.size[1]))\n box = (0,0,im.size[0]-1,im.size[1])\n im2 = im.crop(box) \n return im2 #l'image modifiée est retournée\n","sub_path":"seam_treatment.py","file_name":"seam_treatment.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"444815683","text":"import redis, time, random\n\nr = redis.Redis('localhost', 6379, socket_timeout=10)\n\n#r.execute_command('TS.CREATE', 'test-ts', 'retention', '60', 'labels', 'name', 'test')\n \nstart = time.time()\nend = start + 10\n#while time.time() < end:\nwhile True:\n r.execute_command('TS.ADD', 'test-ts', '*', random.uniform(0, 10))\n time.sleep(0.5)\n","sub_path":"TimeSeries/LoadTS.py","file_name":"LoadTS.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"367726339","text":"# \n# NaverNewsCrawler.py\n# Web crawling program for Naver News\n# Author : Ji-yong219\n# Project Start:: 2021.07.24\n# Last Modified from Ji-yong 2021.07.27\n#\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException, ElementNotInteractableException, StaleElementReferenceException\nimport datetime\nimport json\n\nfrom tqdm import trange\n\nfrom utils.util import *\nfrom crawlers.BaseCrawler import Crawler\n\nclass NaverCrawler(Crawler):\n chrome_options = None\n driver = None\n url_page_num = 0\n url = ''\n news_dic = {}\n news_queue = []\n\n # 생성자\n def __init__(self, driver_url, chrome_options):\n self.chrome_options = chrome_options\n \n self.driver_url = driver_url\n\n # 기사 링크 수집 메소드\n def crawlLinks(self, search, start_date, end_date):\n self.news_queue = []\n self.driver = webdriver.Chrome(self.driver_url, chrome_options=self.chrome_options)\n self.url_page_num = 0\n \n start_date_ = datetime.date(int(start_date[:4]), int(start_date[4:6]), int(start_date[6:]))\n end_date_ = datetime.date(int(end_date[:4]), int(end_date[4:6]), int(end_date[6:])) + datetime.timedelta(days=1)\n for date_ in daterange(start_date_, end_date_):\n self.url_page_num = 1\n\n while True:\n date__ = str(date_).replace('-', '.')\n date___ = str(date_).replace('-', '')\n self.url = f'https://search.naver.com/search.naver?where=news&sm=tab_pge&query={search}&sort=2&photo=0&field=0&pd=3&ds={date__}&de={date__}&mynews=0&office_type=0&office_section_code=0&news_office_checked=&nso=so:r,p:from{date___}to{date___},a:all&start={self.url_page_num}'\n \n \n print(f\"크롤링시작 URL:{self.url}\")\n self.driver.get(self.url)\n\n try:\n element = WebDriverWait(self.driver, 1).until(\n EC.presence_of_element_located((By.XPATH, '//*[@id=\"main_pack\"]/div[2]'))\n \n # class = 'api_noresult_wrap'\n )\n\n except TimeoutException:\n print(\"타임아웃\")\n \n break\n\n div, news = None, None\n\n div = self.driver.find_element_by_xpath('//*[@id=\"main_pack\"]/div[2]')\n\n if div.get_attribute('class') == 'api_noresult_wrap':\n break\n\n div = self.driver.find_element_by_xpath('//*[@id=\"main_pack\"]/section/div/div[2]/ul')\n\n news = div.find_elements_by_css_selector('a[class=\"info\"]')\n\n news = list(set(news))\n\n for i in range(len(news)):\n link = None\n\n link = news[i].get_attribute('href')\n\n if link is None or link == \"\":\n continue\n\n link = link.replace('\\n', '')\n # if link.startswith(\"https://news.naver.com/main/\") and link not in self.news_queue:\n if news[i].text == '네이버뉴스' and link not in self.news_queue:\n print(f'link : {link}')\n try:\n pass\n\n except ValueError as e:\n print(\"value Error\", e, link)\n\n else:\n link = link.replace(\"?f=o\", \"\")\n self.news_queue.append(link)\n\n self.url_page_num += 10\n\n with open(f'result/naver_news/urls_{search}_naver_{start_date}_{end_date}.json.txt', 'w', encoding='utf8') as f:\n f.writelines('\\n'.join(self.news_queue))\n\n self.news_queue = []\n self.driver.close()\n\n def crawlNews(self, search, start_date, end_date):\n self.news_queue = []\n news_dic = {}\n\n with open(f'result/naver_news/urls_{search}_naver_{start_date}_{end_date}.json.txt', 'r', encoding='utf8', newline='\\n') as f:\n for row in f.readlines():\n row = row.replace('\\n', '').replace('\\r', '')\n self.news_queue.append(row)\n\n self.driver = webdriver.Chrome(self.driver_url, chrome_options=self.chrome_options)\n \n \n\n for url in self.news_queue:\n count = 0\n print(f\"네이버뉴스 댓글 크롤링 시작 :{self.url}\")\n\n reply_texts = []\n\n self.driver.get(url)\n\n try:\n element = WebDriverWait(self.driver, 1).until(\n EC.presence_of_element_located((By.XPATH, '//*[@id=\"cbox_module\"]/div[2]/div[2]/ul/li[1]/span')) \n )\n\n except TimeoutException:\n print(\"타임아웃\")\n \n break\n\n div = self.driver.find_element_by_xpath('//*[@id=\"cbox_module_wai_u_cbox_content_wrap_tabpanel\"]')\n\n comment_count = self.driver.find_element_by_xpath('//*[@id=\"cbox_module\"]/div[2]/div[2]/ul/li[1]/span')\n\n if comment_count == '0':\n continue\n\n\n # date = self.driver.find_element_by_xpath('//*[@id=\"main_content\"]/div[1]/div[3]/div/span').text[:10].strip().replace('.', '')\n date = self.driver.find_element_by_css_selector('#main_content span[class=\"t11\"]').text[:10].strip().replace('.', '')\n if date not in news_dic.keys():\n news_dic[date] = []\n\n \n # all_comments_mode = div.find_element_by_xpath('//*[@id=\"cbox_module_wai_u_cbox_sort_option_tab2\"]')\n all_comments_mode = WebDriverWait(div, 2).until(EC.presence_of_element_located((By.XPATH, '//*[@id=\"cbox_module_wai_u_cbox_sort_option_tab2')) )\n all_comments_mode.click()\n\n safe_bot_mode1 = div.find_element_by_xpath('//*[@id=\"cbox_module\"]/div[2]/div[7]/a')\n safe_bot_mode1.click()\n safe_bot_mode2 = div.find_element_by_xpath('//*[@id=\"cleanbot_dialog_checkbox_cbox_module\"]')\n safe_bot_mode2.click()\n safe_bot_mode3 = div.find_element_by_xpath('/html/body/div[3]/div/div[2]/div[4]/button')\n safe_bot_mode3.click()\n\n while True:\n try:\n element = WebDriverWait(self.driver, 2).until(\n EC.presence_of_element_located((By.XPATH, '//*[@id=\"cbox_module\"]/div[2]/div[9]/a')) \n )\n \n more_btn = self.driver.find_element_by_xpath('//*[@id=\"cbox_module\"]/div[2]/div[9]/a')\n print(\"댓글 더보기 클릭\")\n more_btn.click()\n\n except TimeoutException:\n print(\"more 버튼 없음 타임아웃\")\n \n break\n\n except ElementNotInteractableException:\n print(\"more 버튼 없음\")\n \n break\n\n \n\n\n\n try:\n element = WebDriverWait(self.driver, 1).until(\n EC.presence_of_element_located((By.XPATH, '//*[@id=\"cbox_module\"]/div[2]/div[2]/ul/li[1]/span')) \n )\n\n except TimeoutException:\n print(\"타임아웃\")\n \n break\n\n div = self.driver.find_element_by_xpath('//*[@id=\"cbox_module_wai_u_cbox_content_wrap_tabpanel\"]')\n \n # comments = div.find_elements_by_xpath('//li[**starts-with(id,\"comment\")**]')\n comments = div.find_elements_by_css_selector('ul>li')\n reply_count = 0\n\n for i in range(len(comments)):\n comment = comments[i]\n this_class = comment.get_attribute('class')\n\n if \"comment_\" not in this_class:\n continue\n\n this_class = this_class.split(' ')[1]\n # print(f'this_class : {this_class}')\n\n is_exists_reply = True\n\n\n try:\n element = WebDriverWait(comment, 1).until(\n EC.presence_of_element_located((By.CSS_SELECTOR, f'a[class=\"u_cbox_btn_reply\"]')) \n )\n reply_count = element.text\n if element.text == \"답글0\":\n is_exists_reply = False\n\n except TimeoutException:\n print(\"답글 버튼 없음 타임아웃\")\n is_exists_reply = False\n\n \n try:\n text = WebDriverWait(comment, 1).until(EC.presence_of_element_located((By.CSS_SELECTOR , f'div[class=\"u_cbox_text_wrap\"]'))).text\n if text != \"작성자에 의해 삭제된 댓글입니다.\":\n reply_texts.append( text )\n count += 1\n print(f\"수집한 댓글 : {count}개\\t{text}\")\n except:\n print(\"댓 못가져와서 패스\")\n continue\n\n if is_exists_reply:\n count2 = 0\n reply_btn = comment.find_element_by_css_selector(f'a[class=\"u_cbox_btn_reply\"]')\n # reply_btn.click()\n reply_btn.send_keys(Keys.ENTER)\n \n while True:\n try:\n element = WebDriverWait(comment, 1).until(\n EC.presence_of_element_located((By.CSS_SELECTOR, f'a[class=\"u_cbox_btn_more\"]')) \n )\n \n more_btn2 = comment.find_element_by_css_selector(f'a[class=\"u_cbox_btn_more\"]')\n # print(\"답글 더보기 클릭\")\n more_btn2.send_keys(Keys.ENTER)\n\n except:\n # print(\"답글 더보기 버튼 없음 타임아웃\")\n break\n\n\n replys = []\n try:\n replys = WebDriverWait(reply, 1).until(EC.presence_of_element_located((By.CSS_SELECTOR , f'li[class=\"u_cbox_comment\"]')))\n except:\n pass\n\n for reply in replys:\n try:\n text = WebDriverWait(reply, 1).until(EC.presence_of_element_located((By.CSS_SELECTOR , 'span[class=\"u_cbox_contents\"] > p'))).text\n if text != \"작성자에 의해 삭제된 댓글입니다.\":\n reply_texts.append( text )\n count+=1\n count2+=1\n print(f\"수집한 댓글 : {count}개\\t{reply_count}개 중 {count2}개 수집\")\n\n except:\n print(\"답글 못가져와서 패스\")\n continue\n\n \n # reply_btn.send_keys(Keys.ENTER)\n # for i in reply_texts:\n # print(i)\n print(f'수집한 댓글 : {len(reply_texts)}')\n \n news_dic[date[0:8]].append(\n {\n url: {\n 'comments': reply_texts,\n 'emotions': []\n }\n }\n )\n\n self.driver.close()\n \n with open(f'result/naver_news/news_{search}_naver_{start_date}_{end_date}.json.txt', 'w', encoding='utf8') as f:\n json.dump(news_dic, f, indent=4, sort_keys=True, ensure_ascii=False)","sub_path":"crawlers/NaverNewsCrawler.py","file_name":"NaverNewsCrawler.py","file_ext":"py","file_size_in_byte":11905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"521677565","text":"# coding:utf-8\nimport tornado.web\nimport tornado.ioloop\nimport tornado.httpserver\nimport tornado.options\n\nimport os.path\nimport sqlite3\nimport datetime\nimport time\n\nimport common\nfrom tornado.options import define, options\nfrom tornado.escape import json_encode\n\nconn = sqlite3.connect('chatroom.db', timeout=1)\ncur = conn.cursor()\n\n\n# 用户信息修改\nclass ModifyHandler(tornado.web.RequestHandler):\n def get(self):\n self.set_status(403)\n self.finish()\n return\n\n def post(self):\n username = self.get_secure_cookie(\"username\")\n if username is None:\n self.redirect('/login')\n password = self.get_argument('password')\n rep_password = self.get_argument('rep_password')\n email = self.get_argument('email')\n phone = self.get_argument('phone')\n if password != rep_password:\n # self.write(\"两次密码输入不一致\")\n self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n self.set_header('Content-Type', 'application/json; charset=UTF-8')\n self.write(json_encode({\"modify\": False, \"err\": 1}))\n self.finish()\n return\n conn.execute(\"UPDATE user SET password=?, email=?, phone=? WHERE username =? \",\n (password, email, phone, username))\n conn.commit()\n self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n self.set_header('Content-Type', 'application/json; charset=UTF-8')\n self.write(json_encode({\"modify\": True}))\n self.finish()\n return\n\n\n# 管理员特权操作\n\n\nclass AdminHandler(tornado.web.RequestHandler):\n def get(self):\n cookie_user = self.get_secure_cookie(\"username\")\n if cookie_user is None:\n # self.redirect('/login')\n # self.set_status(403)\n # self.finish()\n self.render('login.html')\n return\n else:\n usertype = common.get_usertype(cookie_user)\n self.render(\"admin.html\", usertype=usertype,\n roomlist=common.getRoomList())\n return\n\n def post(self):\n username = self.get_secure_cookie('username')\n setvip_username = self.get_argument(\"username1\", None)\n cancelvip_username = self.get_argument(\"username2\", None)\n delete_username = self.get_argument(\"username3\", None)\n delete_roomname = self.get_argument(\"deleteRoomname\", None)\n create_roomname = self.get_argument(\"createRoomname\", None)\n cookie_user = self.get_secure_cookie(\"username\")\n\n if 2 != common.get_usertype(cookie_user):\n self.set_status(403)\n if setvip_username:\n conn.execute(\"UPDATE user SET usertype = 1 WHERE username = ? \", (\n setvip_username,))\n conn.commit()\n self.write(\"设置成功\")\n if cancelvip_username:\n conn.execute(\"UPDATE user SET usertype = 0 WHERE username = ? \", (\n cancelvip_username,))\n conn.commit()\n self.write(\"取消成功\")\n if delete_username:\n conn.execute(\"DELETE FROM user WHERE username = ? \",\n (delete_username,))\n conn.commit()\n self.write(\"删除成功\")\n if delete_roomname:\n if not self.check_is_userd(delete_roomname):\n usertype = common.get_usertype(username)\n self.write('聊天室不存在')\n return\n else:\n conn.execute(\"DELETE FROM room WHERE roomname = ? \", (\n delete_roomname,))\n conn.commit()\n self.write('聊天室删除成功')\n return\n if create_roomname:\n username = self.get_secure_cookie('username')\n # roomname被使用过\n if self.check_is_userd(create_roomname):\n usertype = common.get_usertype(username)\n self.write('同名聊天室已经存在')\n return\n\n cursor = conn.execute(\n \"SELECT userid FROM user WHERE username = ? \", (username,))\n for row in cursor:\n userid = row[0]\n # 创建\n conn.execute(\n \"INSERT INTO room (roomname, created_time, owner_id) VALUES(?, datetime('now'), ?)\",\n (create_roomname, userid))\n conn.commit()\n # self.redirect(\"/chatroom\")\n self.write('聊天室添加成功')\n self.finish()\n return\n\n def check_is_userd(self, roomname):\n cur.execute(\n \"SELECT roomname FROM room WHERE roomname = ? \", (roomname,))\n if cur.fetchall():\n return True\n return False\n\n\nif __name__ == '__main__':\n define(\"port\", default=8000, help=\"run on given port\", type=int)\n tornado.options.parse_command_line()\n app = tornado.web.Application(\n handlers=[(r'/modify', ModifyHandler)],\n template_path=os.path.join(os.path.dirname(__file__), \"templates\")\n )\n http_server = tornado.httpserver.HTTPServer(app)\n http_server.listen(options.port)\n tornado.ioloop.IOLoop.instance().start()\n","sub_path":"src/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":5197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"639587877","text":"\"\"\"\nTest Vanilla PG on standard environment, where state is (ob_dim) and action is continuous/discrete\n\n\"\"\"\n\nimport pprint\n\nimport numpy as np\nimport torch.optim\n\nimport torchlib.deep_rl.algorithm as algo\nimport torchlib.utils.random\nfrom torchlib import deep_rl\n\nif __name__ == '__main__':\n parser = algo.a2c.make_default_parser()\n args = vars(parser.parse_args())\n pprint.pprint(args)\n\n env_name = args['env_name']\n\n assert args['discount'] < 1.0, 'discount must be smaller than 1.0'\n torchlib.utils.random.set_global_seeds(args['seed'])\n\n print('Env {}'.format(env_name))\n\n if deep_rl.envs.is_ple_game(env_name) or deep_rl.envs.is_atari_env(env_name):\n if args['recurrent']:\n args['frame_history_len'] = 1\n else:\n args['frame_history_len'] = 4\n\n env = deep_rl.envs.make_env(env_name, args)\n\n policy_net = algo.a2c.get_policy_net(env, args)\n\n policy_optimizer = torch.optim.Adam(policy_net.parameters(), args['learning_rate'])\n\n if args['recurrent']:\n init_hidden_unit = np.zeros(shape=(args['hidden_size']), dtype=np.float32)\n else:\n init_hidden_unit = None\n\n agent = algo.a2c.A2CAgent(policy_net, policy_optimizer,\n init_hidden_unit=init_hidden_unit,\n nn_baseline=args['nn_baseline'],\n lam=args['gae_lambda'],\n max_grad_norm=args['max_grad_norm'],\n value_coef=args['value_coef'],\n initial_state_mean=args['initial_state_mean'],\n initial_state_std=args['initial_state_std'])\n\n max_path_length = args['ep_len'] if args['ep_len'] > 0 else None\n\n agent.train(args['exp_name'], env, args['n_iter'], args['discount'], args['batch_size'],\n max_path_length, logdir=None, seed=args['seed'])\n","sub_path":"examples/deep_rl/policy_gradient/a2c.py","file_name":"a2c.py","file_ext":"py","file_size_in_byte":1902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"130814446","text":"from django.test import TestCase\nimport datetime\nfrom decimal import Decimal, InvalidOperation\nfrom django.core.exceptions import ValidationError\nfrom django.db import transaction\nfrom .. import models\nfrom .. import dbapi\nfrom ._check_model_count import check_model_count\n\ndef _all_in_category(cat):\n return models.Transaction.objects.filter(category=cat)\n\nclass TestCategoryDelete(TestCase):\n\n fixtures = ['test_categories']\n ex_list = (TypeError, ValueError, AssertionError, ValidationError,\n InvalidOperation, AttributeError)\n\n def setUp(self):\n dbapi.create_category('Replacement Category')\n dbapi.create_account(name='Test Account', is_budgeted=True,\n starting_balance=0)\n self.test_acct = models.Account.objects.get(name='Test Account')\n self.test_params = {'account': self.test_acct,\n 'date': datetime.date.today(),\n 'payee': 'Test Payee',\n 'memo': 'Test Memo',\n 'amount': Decimal('123.45')}\n self.target_cat = models.Category.objects.get(name='Test Category')\n self.replacement_cat = models.Category.objects.get(name='Replacement Category')\n\n def test_success_no_affected_transactions(self):\n dbapi.create_category('Unaffected Category')\n unaffected_cat = models.Category.objects.get(name='Unaffected Category')\n with check_model_count({models.Transaction: 3}):\n for _ in range(3):\n dbapi.create_transaction(category=unaffected_cat,\n **self.test_params)\n with check_model_count({models.Category: -1, models.Transaction: 0}):\n dbapi.delete_category(self.target_cat, self.replacement_cat)\n self.assertNotIn('Test Category', (cat.name for cat in\n models.Category.objects.all()))\n self.assertEqual(_all_in_category(unaffected_cat).count(), 3)\n self.assertEqual(_all_in_category(self.target_cat).count(), 0)\n self.assertEqual(_all_in_category(self.replacement_cat).count(), 0)\n\n def test_success_some_affected_transactions(self):\n dbapi.create_category('Unaffected Category')\n unaffected_cat = models.Category.objects.get(name='Unaffected Category')\n with check_model_count({models.Transaction: 6}):\n for _ in range(3):\n dbapi.create_transaction(category=unaffected_cat,\n **self.test_params)\n dbapi.create_transaction(category=self.target_cat,\n **self.test_params)\n with check_model_count({models.Category: -1, models.Transaction: 0}):\n dbapi.delete_category(self.target_cat, self.replacement_cat)\n self.assertEqual(_all_in_category(unaffected_cat).count(), 3)\n self.assertEqual(_all_in_category(self.target_cat).count(), 0)\n self.assertEqual(_all_in_category(self.replacement_cat).count(), 3)\n\n def test_fail_bad_target_category(self):\n with self.assertRaises(self.ex_list):\n dbapi.delete_category('BAD', self.replacement_cat)\n\n def test_fail_bad_replacement_category(self):\n with self.assertRaises(self.ex_list):\n dbapi.delete_category(self.target_cat, 'BAD')\n\n def test_fail_no_replacement_category(self):\n with self.assertRaises(self.ex_list):\n dbapi.delete_category(self.target_cat)\n\n def test_fail_special_replacement_category(self):\n # transaction.atomic() required here to avert a known Django bug\n # See https://code.djangoproject.com/ticket/21540\n with self.assertRaises(self.ex_list):\n with transaction.atomic():\n dbapi.delete_category(self.target_cat, dbapi.transfer_category())\n with self.assertRaises(self.ex_list):\n with transaction.atomic():\n dbapi.delete_category(self.target_cat, dbapi.starting_bal_category())\n\n def test_fail_special_category(self):\n # transaction.atomic() required here to avert a known Django bug\n # See https://code.djangoproject.com/ticket/21540\n with self.assertRaises(self.ex_list):\n with transaction.atomic():\n dbapi.delete_category(dbapi.transfer_category(), self.replacement_cat)\n with self.assertRaises(self.ex_list):\n with transaction.atomic():\n dbapi.delete_category(dbapi.starting_bal_category(), self.replacement_cat)\n","sub_path":"moneybin/ledger/tests/test_category_delete.py","file_name":"test_category_delete.py","file_ext":"py","file_size_in_byte":4567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"331524376","text":"import cv2 as cv\nimport numpy as np\n\nblank = np.zeros((500, 500, 3), dtype='uint8')\ncv.imshow('Blank', blank)\n\n#1. paint the image to a color\n# blank[:] = 0,255,0\n# cv.imshow('Green', blank) \n\n#2. paint a rectangle\ncv.rectangle(blank, (0,0), (250,250), (0,0,255), thickness=2)\ncv.imshow('Rectangle', blank)\n\n#3. draw a circle\ncv.circle(blank, (blank.shape[1]//2, blank.shape[0]//2), 40, (0, 255, 0), thickness=-1)\ncv.imshow('Circle', blank)\n\n#4. draw a line\ncv.line(blank, (0,0), (blank.shape[1]//2, blank.shape[0]//2), (255, 255, 0), thickness=3)\ncv.imshow('Line', blank)\n\n#5. Add text\ncv.putText(blank, 'Feedback', (250, 250), cv.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 1)\ncv.imshow('Text', blank)\n\ncv.waitKey(0)\n","sub_path":"draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"552113211","text":"def sequencia_fibonaci():\n \"\"\" sequencia fibonnaci com 47 termos \"\"\"\n anterior = 1\n sucessor = 2\n somaAS = 0\n fibonnaci = [0,1,1,2]\n\n n = 0\n while n <= 47:\n\n somaAS = anterior + sucessor\n anterior = sucessor\n sucessor = somaAS\n\n fibonnaci.append(somaAS)\n n += 1\n \n return fibonnaci\n\nentradas_usuario = []\ncount = 0\nwhile True:\n entradas_usuario.append(int(input()))\n if entradas_usuario[count] == 0:\n break\n count += 1\n\n\"\"\"\npara cada entrada do usuario, imprimir os valores da sequencia,\nde 0 ate o valor do elemento da lista de entrada.\n\"\"\"\nfor x in entradas_usuario:\n for y in range(0,x):\n if y == x-1:\n print(sequencia_fibonaci()[y],end='')\n else:\n print(sequencia_fibonaci()[y],end=' ')\n print()\n \n\n \n\n \n\n \n \n\n\n\n","sub_path":"fibonnaci.py","file_name":"fibonnaci.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"267742626","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 17 22:29:31 2016\n\n@author: youngwan\n\"\"\"\n\nimport os\nimport sys\nimport subprocess\nimport pandas as pd\n\n#matplotlib.use('Agg')\nimport matplotlib.pylab as plt\n\n#plt.style.use('ggplot')\ndef load_data(data_file, field_idx0, field_idx1):\n data = [[], []]\n with open(data_file, 'r') as f:\n for line in f:\n line = line.strip() \n if line[0] != '#':\n fields = line.split()\n data[0].append(float(fields[field_idx0].strip()))\n data[1].append(float(fields[field_idx1].strip()))\n return data\n\n\ncaffe_path = '/home/youngwan/caffe/'\n\n#model_log_path = caffe_path + sys.argv[1]\n#learning_curve_path = caffe_path + sys.argv[2]\n\n\n#model_log_path = caffe_path + 'jobs/New/KITTI/inception_v3/SSD_Inception_v3_4_preActRes_OriginASP_BN_ImageNet_pretrained_unFreezed300x300/KITTI_SSD_Inception_v3_4_preActRes_OriginASP_BN_ImageNet_pretrained_unFreezed300x300_2016-11-6-17:41:14.log'\n#model_log_path_2 = caffe_path + 'jobs/New/KITTI/inception_v3/SSD_Inception_v3_4_preActRes_basic_OriginASP_BN_No_pretrained_300x300/KITTI_SSD_Inception_v3_4_preActRes_basic_OriginASP_BN_No_pretrained_300x300_2016-11-14-23:26:29.log'\n\nmodel_log_path = caffe_path + 'jobs/New/KITTI/inception_v3/SSD_Inception_v3_4_preActRes_OriginASP_BN_ImageNet_pretrained_unFreezed_5th_300x300/KITTI_SSD_Inception_v3_4_preActRes_OriginASP_BN_ImageNet_pretrained_unFreezed_5th_300x300_2016-11-19-15:56:0.log'\nmodel_log_path_2 = caffe_path + 'jobs/New/KITTI/inception_v3/SSD_Inception_v3_4_preActRes_basic_OriginASP_BN_No_pretrained_4th300x300/KITTI_SSD_Inception_v3_4_preActRes_basic_OriginASP_BN_No_pretrained_4th300x300_2016-11-17-23:47:35.log'\nmodel_log_path_3 = caffe_path + 'jobs/New/KITTI/inception_v3/SSD_Inception_v3_4_preActRes_basic_OriginASP_BN_cifar_pretrained_3rd_300x300/KITTI_SSD_Inception_v3_4_preActRes_basic_OriginASP_BN_cifar_pretrained_3rd_300x300_2016-11-18-10:56:17.log'\nmodel_log_path_4 = caffe_path + 'jobs/New/KITTI/inception_v3/SSD_Inception_v3_4_preActRes_basic_OriginASP_BN_cifar100_pretrained_1st_300x300/KITTI_SSD_Inception_v3_4_preActRes_basic_OriginASP_BN_cifar100_pretrained_1st_300x300_2016-11-23-17:53:56.log'\n\nlearning_curve_path = caffe_path + 'jobs/figures/Total_dpi5000.png'\n\n\n#Get directory where the model logs is saved, and move to it\nmodel_log_dir_path = os.path.dirname(model_log_path)\nos.chdir(model_log_dir_path)\ncommand = caffe_path + 'tools/extra/parse_log.sh ' + model_log_path\nprocess = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\nprocess.wait()\n#Read training and test logs\ntrain_log_path = model_log_path + '.train'\ntest_log_path = model_log_path + '.test'\ntrain_log = pd.read_csv(train_log_path, delim_whitespace=True)\ntest_log = pd.read_csv(test_log_path, delim_whitespace=True)\n\n\n\n\n#Parsing\n#train_data = load_data(train_log_path,1,3)\n#test_data = load_data(test_log_path,1,3)\n\n\ntrain_loss = train_log['TrainingLoss']\ntrain_iter = train_log['#Iters']\ntrain_lr = train_log['LearningRate']\ntest_iter = test_log['#Iters']\ntest_loss = test_log['TestLoss']\ntest_acc = test_log['TestAccuracy']\n#test_error = 1- test_accuracy\n\n\n'''\nMaking learning curve\n'''\nfig, ax1 = plt.subplots()\n\n#Plotting training and test losses\n#train_loss, = ax1.plot(train_log['#Iters'], train_log['TrainingLoss'], color='red',linewidth=2, alpha=.5)\n#test_loss, = ax1.plot(test_log['#Iters'], test_log['TestLoss'], linewidth=2, color='green')\n\n\ntrain_loss = plt.plot(train_iter,train_loss, label='Loss', color='red',linewidth=1)\n\n\nplt.xlabel('Iterations', fontsize=15)\nplt.ylabel('Loss', fontsize=15)\nplt.yscale('log')\nplt.tick_params(labelsize=10)\nplt.legend(bbox_to_anchor=(0.5, 1), loc=2, borderaxespad=0.)\n\n#Plotting Accuracy\nax2 = plt.twinx()\n#test_accuracy, = plt.plot(test_log['#Iters'], test_acc, label='Acc. ImageNet',linewidth=3, color='red', linestyle=':')\ntrain_lr, = plt.plot(test_log['#Iters'], train_lr, label='learning rate',linewidth=3, color='blue', linestyle=':')\n\nax2.set_ylim(ymin=0.00001, ymax=0.1)\nax2.set_ylabel('Accuracy', fontsize=15)\nax2.tick_params(labelsize=10)\nplt.legend(bbox_to_anchor=(0.03, 1), loc=2, borderaxespad=0.)\nplt.title('Training Curve', fontsize=18)\n\n#Saving learning curve\n#plt.savefig(learning_curve_path,dpi=2000)\nplt.show()\n\n\n'''\nDeleting training and test logs\n'''\ncommand = 'rm ' + train_log_path\nprocess = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\nprocess.wait()\n\ncommand = command = 'rm ' + test_log_path\nprocess = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\nprocess.wait()\n\n\n\n\n\n","sub_path":"learning_rate_plot.py","file_name":"learning_rate_plot.py","file_ext":"py","file_size_in_byte":4585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"605733795","text":"'''\n7/27/2018\ncreate hind leg set up\n\nnote:\n don't line foot jnt, knuck joint and toe joint in straight line\n'''\nfrom maya import cmds\n\nimport joint_utility as ju\nreload(ju)\nimport control_utility as cu\nreload(cu)\nimport math_utility as mu\nreload(mu)\n\nfrom leg_rig import legRig\n\n\nclass hindLegRig(legRig):\n def __init__(self, side=\"LF\", index=1, alias=\"HindLeg\", aliasList=[\"Hip\", \"Knee\", \"Ankle\", \"Foot\", \"Knuckle\", \"Toe\"], par=\"MD_Torso_01_Hip_Ctrl\", parJnt=\"MD_Torso_01_01_Out_Jnt\", \n ik=True, fk=True, stretch=True, squeeze=True, localScale=False, elbowLock=True, softIK=True, autoSmooth=True, bendy=(5, 5, 5),\n ikSpace=[\"World\", \"Master_Sub_Ctrl\", \"COG_Sub_Ctrl\"], pvSpace=[\"World\", \"Master_Sub_Ctrl\", \"COG_Sub_Ctrl\"], shortFoot=False):\n '''\n short foot(boolean): whether treat the foot as a single joint chain or adding a dividing joint in the middle\n '''\n super(hindLegRig, self).__init__(side=side, index=index, alias=alias, aliasList=aliasList, par=par, parJnt=parJnt, ik=ik, fk=fk, stretch=stretch, squeeze=squeeze, localScale=localScale,\n elbowLock=elbowLock, softIK=softIK, autoSmooth=autoSmooth, bendy=bendy[:2], ikSpace=ikSpace, pvSpace=pvSpace)\n\n #the initial ik fk status\n self.ikFk = 1 #as ik\n self.ikMatchCoordinate = True #whether to have the ik ctrl match the world coordinate\n\n self.proxyCtrls.update({\"Rear\": \"{0}_Rear_Proxy_Ctrl\".format(self.longAlias), \n \"ToeIK2\": self.proxyJnts[-1].replace(\"Proxy_Jnt\", \"IK_Sub_Proxy_Ctrl\"),\n \"KnuckleFK\": self.proxyJnts[-2].replace(\"Proxy_Jnt\", \"FK_Proxy_Ctrl\"),\n \"KnuckleIK\": self.proxyJnts[-2].replace(\"Proxy_Jnt\", \"IK_Proxy_Ctrl\"),\n \"BotBendy\": \"{0}_Bot_Bendy_Proxy_Ctrl\".format(self.longAlias),\n \"BotBendy2\": \"{0}_Bot_Bendy_02_Proxy_Ctrl\".format(self.longAlias)})\n\n self.ctrlShape.update({\"BotBendy\": {\"shape\": \"circle\", \"ax\": (1, 0, 0), \"p\": (0, 0, 0), \"sd\": [1, 1, 8]},\n \"BotBendy2\": {\"shape\": \"nail3\", \"ax\": (0, 0, -self.negDic[self.side]), \"p\": (0, 0, 0), \"s\": (0.6, 0.6, 0.6)},\n \"ToeIK2\": {\"shape\": \"trapezoid3\", \"ax\": (0, 1, 0), \"p\": (0, 0.5, 0), \"s\": (0.5, 0.5, 0.5)},\n \"KnuckleFK\": {\"shape\": \"circle\", \"ax\": (1, 0, 0), \"p\": (0, 0, 0), \"sd\": [1, 0.5, 8]},\n \"KnuckleIK\": {\"shape\": \"trapezoid3\", \"ax\": (0, 1, 0), \"p\": (0, 0.5, 0), \"s\": (0.5, 0.5, 0.5)},\n \"Rear\": {\"shape\": \"circle\", \"ax\": (1, 0, 0), \"p\": (0, 0, 0), \"sd\": [1, 0.8, 8]}})\n\n self.attrValue.update({\"botBendy\": bendy[2], \"shortFoot\": self.boolDic[shortFoot]})\n\n\n def createProxy(self, repositionIK=True):\n botBendy = self.attrValue[\"botBendy\"]\n del self.attrValue[\"botBendy\"]\n super(hindLegRig, self).createProxy()\n\n #reset foot ik ctrl's aim constraint up vector\n cmds.setAttr(\"{0}_Grp_aimConstraint1.upVector\".format(self.proxyCtrls[\"FootIK\"]), 1, 0, 0, type=\"double3\")\n cmds.setAttr(\"{0}_Grp_aimConstraint1.worldUpVector\".format(self.proxyCtrls[\"FootIK\"]), 1, 0, 0, type=\"double3\")\n\n cu.createCtrl(n=self.proxyCtrls[\"KnuckleFK\"], colorNo=self.colorDic[self.side], ctrlShape=self.ctrlShape[\"KnuckleFK\"], depth=1, lockAttrs=[\"t\", \"r\", \"s\", \"v\"])\n cmds.parentConstraint(self.proxyJnts[-2], \"{0}_Grp\".format(self.proxyCtrls[\"KnuckleFK\"]), mo=False)\n cmds.parent(\"{0}_Grp\".format(self.proxyCtrls[\"KnuckleFK\"]), self.proxyGrp)\n\n cu.createCtrl(n=self.proxyCtrls[\"KnuckleIK\"], colorNo=self.colorDic2[self.side], ctrlShape=self.ctrlShape[\"KnuckleIK\"], depth=1, lockAttrs=[\"r\", \"s\", \"v\"])\n cmds.parent(\"{0}_Grp\".format(self.proxyCtrls[\"KnuckleIK\"]), self.proxyGrp)\n cmds.pointConstraint(self.proxyJnts[-2], \"{0}_Grp\".format(self.proxyCtrls[\"KnuckleIK\"]), mo=False)\n cmds.aimConstraint(self.proxyJnts[-3], \"{0}_Grp\".format(self.proxyCtrls[\"KnuckleIK\"]), aim=(0, 0, -1), u=(0, 1, 0), wu=(0, 1, 0), wut=\"objectrotation\", wuo=self.proxyGrp)\n cmds.setAttr(\"{0}.tz\".format(self.proxyCtrls[\"KnuckleIK\"]), -0.5)\n\n #create toe ik sub ctrl \n cu.createCtrl(n=self.proxyCtrls[\"ToeIK2\"], colorNo=self.colorDic2[self.side], ctrlShape=self.ctrlShape[\"ToeIK2\"], depth=1, lockAttrs=[\"r\", \"s\", \"v\"])\n cmds.parent(\"{0}_Grp\".format(self.proxyCtrls[\"ToeIK2\"]), self.proxyGrp)\n cmds.pointConstraint(self.proxyJnts[4], \"{0}_Grp\".format(self.proxyCtrls[\"ToeIK2\"]), mo=False)\n cmds.aimConstraint(self.proxyJnts[-1], \"{0}_Grp\".format(self.proxyCtrls[\"ToeIK2\"]), aim=(0, 0, 1), u=(0, 1, 0), wu=(0, 1, 0), wut=\"objectrotation\", wuo=self.proxyGrp)\n cmds.setAttr(\"{0}.tz\".format(self.proxyCtrls[\"ToeIK2\"]), 0.5)\n\n #reset initial pose\n cmds.move(self.negDic[self.side]*2.5, 12, -7.5, self.proxyJnts[0], a=True)\n cmds.move(self.negDic[self.side]*7, 0, 0, self.proxyJnts[1], a=True, os=True)\n cmds.move(self.negDic[self.side]*6, 0, 0, self.proxyJnts[2], a=True, os=True)\n cmds.move(self.negDic[self.side]*2.5, 0, 3.8, self.proxyJnts[3], a=True, ls=True)\n cmds.move(self.negDic[self.side]*1.25, 0, -0.3, self.proxyJnts[4], a=True, os=True)\n cmds.move(self.negDic[self.side]*1.25, 0, 0, self.proxyJnts[5], a=True, os=True)\n\n cmds.rotate(0, -45*self.negDic[self.side], -90*self.negDic[self.side], self.proxyJnts[0], a=True, os=True)\n cmds.rotate(0, 90*self.negDic[self.side], 0, self.proxyJnts[1], a=True, os=True)\n cmds.rotate(0, -45*self.negDic[self.side], 0, self.proxyJnts[2], a=True, os=True)\n cmds.rotate(0, -90*self.negDic[self.side], 0, self.proxyJnts[3], a=True, os=True)\n\n cmds.xform(self.proxyCtrls[\"IKTilt\"], t=(self.negDic[self.side]*2.8, 0, 6.8), ro=(0, 0, 0), a=True, os=True)\n cmds.xform(self.proxyCtrls[\"IKTilt2\"], t=(self.negDic[self.side]*2.8, 0, 2.5), ro=(0, 0, 0), a=True, os=True)\n\n cmds.setAttr(\"{0}.ty\".format(self.proxyCtrls[\"PV\"]), 8)\n cmds.setAttr(\"{0}.tz\".format(self.proxyCtrls[\"FootIK\"]), -1.5)\n #cmds.setAttr(\"{0}.tz\".format(self.proxyCtrls[\"ToeIK2\"]), 1.5)\n\n if repositionIK:\n cmds.delete(\"{0}_Grp_pointConstraint1\".format(self.proxyCtrls[\"IK\"]))\n cmds.pointConstraint(self.proxyJnts[3], \"{0}_Grp\".format(self.proxyCtrls[\"IK\"]), mo=False)\n\n #add bottom bendy \n cu.createCtrl(n=self.proxyCtrls[\"BotBendy\"], colorNo=self.colorDic2[self.side], ctrlShape=self.ctrlShape[\"BotBendy\"], depth=1, lockAttrs=[\"t\", \"r\", \"s\", \"v\"])\n cmds.aimConstraint(self.proxyJnts[2], \"{0}_Grp\".format(self.proxyCtrls[\"BotBendy\"]), aim=(-self.negDic[self.side], 0, 0), u=(0, 1, 0), wut=\"objectrotation\", wu=(0, 1, 0), wuo=self.proxyJnts[2])\n cmds.pointConstraint(self.proxyJnts[2], self.proxyJnts[3], \"{0}_Grp\".format(self.proxyCtrls[\"BotBendy\"]))\n cmds.parent(\"{0}_Grp\".format(self.proxyCtrls[\"BotBendy\"]), self.proxyGrp)\n #add secondary bendy ctrl\n cu.createCtrl(n=self.proxyCtrls[\"BotBendy2\"], colorNo=self.colorDic2[self.side], ctrlShape=self.ctrlShape[\"BotBendy2\"], depth=1, lockAttrs=[\"t\", \"r\", \"s\", \"v\"])\n cmds.parent(\"{0}_Grp\".format(self.proxyCtrls[\"BotBendy2\"]), self.proxyCtrls[\"BotBendy\"])\n cmds.xform(\"{0}_Grp\".format(self.proxyCtrls[\"BotBendy2\"]), t=(0, 0, 0), ro=(90*self.negDic[self.side], 0, 0), os=True, a=True)\n if self.side == self.right:\n cmds.xform(\"{0}_Grp\".format(self.proxyCtrls[\"BotBendy2\"]), ro=(0, 0, 180), os=True, r=True) \n\n #rear ctrl\n cu.createCtrl(n=self.proxyCtrls[\"Rear\"], colorNo=self.colorDic2[self.side], ctrlShape=self.ctrlShape[\"Rear\"], depth=1, lockAttrs=[\"t\", \"r\", \"s\", \"v\"])\n cmds.parentConstraint(self.proxyJnts[2], \"{0}_Grp\".format(self.proxyCtrls[\"Rear\"]))\n cmds.parent(\"{0}_Grp\".format(self.proxyCtrls[\"Rear\"]), self.proxyGrp)\n\n #save bot bendy ctrl number:\n cmds.addAttr(self.proxyJnts[0], ln=\"botBendy\", at=\"long\", k=True)\n cmds.setAttr(\"{0}.botBendy\".format(self.proxyJnts[0]), botBendy)\n\n self.attrValue.update({\"botBendy\": botBendy})\n return True\n\n def copyCleanJointsChain(self, alias=\"Jnt\"):\n '''\n ducplicate the proxy chain, auto orient it and freeze the tranfrom\n overwrite this part from limbRig object\n input:\n alias(string): the new name alisa for the duplicated joint chain\n '''\n #create a bind joint chain\n jnts = ju.copyJointChain(self.proxyJnts, oldAlias=\"Proxy_Jnt\", newAlias=alias, freeze=False)\n\n aimAxisDic = {self.left : \"x\", self.right: \"-x\"}\n sideAxisDic = {self.left : \"y\", self.right: \"-y\"}\n ju.orientJointChain(jnts, aimAxis=aimAxisDic[self.side], sideAxis=sideAxisDic[self.side])\n\n #set prefered angle\n cmds.setAttr(\"{0}.preferredAngleY\".format(jnts[1]), self.negDic[self.side]*10)\n\n [cmds.setAttr(\"{0}.segmentScaleCompensate\".format(jnt), 0) for jnt in jnts]\n return jnts\n\n def createRig(self):\n if not super(hindLegRig, self).createRig():\n return False\n\n self.bindJnts.remove(self.resultJnts[2])\n cmds.sets(self.resultJnts[2], rm=\"{0}_Bind_Jnt_Set\".format(self.longAlias))\n return True\n\n def createBind(self):\n super(hindLegRig, self).createBind()\n\n #add angle offset joint\n midJnt = self.resultJnts[1]\n ankleJnt = self.resultJnts[2]\n footJnt = self.resultJnts[3]\n midStartJnt = midJnt.replace(\"Jnt\", \"Start_Jnt\")\n midEndJnt = midJnt.replace(\"Jnt\", \"End_Jnt\")\n\n #create a midStartJnt\n ankleStartJnt = cmds.duplicate(ankleJnt, po=True, n=ankleJnt.replace(\"Jnt\", \"Start_Jnt\"))[0]\n cmds.parent(ankleStartJnt, ankleJnt)\n \n cmds.aimConstraint(footJnt, ankleStartJnt, mo=False, aim=(self.negDic[self.side], 0, 0), u=(0, 1, 0), wut=\"objectrotation\", wu=(0, 1, 0), wuo=ankleJnt) \n cmds.disconnectAttr(\"{0}.t\".format(ankleJnt), \"{0}.t\".format(midEndJnt))\n cmds.pointConstraint(ankleStartJnt, midEndJnt)\n #reset knee start joint's aim target\n oAttrs = [\"translate\", \"rotatePivotTranslate\", \"parentMatrix[0]\", \"rotatePivot\"]\n iattrs = [\"target[0].targetTranslate\", \"target[0].targetRotateTranslate\", \"target[0].targetParentMatrix\", \"target[0].targetRotatePivot\"]\n for oAttr, iattr in zip(oAttrs, iattrs):\n if self.attrValue[\"autoSmooth\"]:\n cmds.connectAttr(\"{0}.{1}\".format(ankleStartJnt, oAttr), \"{0}_aimConstraint1.{1}\".format(midStartJnt.replace(\"Jnt\", \"Null\"), iattr), f=True)\n else:\n cmds.connectAttr(\"{0}.{1}\".format(ankleStartJnt, oAttr), \"{0}_aimConstraint1.{1}\".format(midStartJnt, iattr), f=True)\n\n #create a midEndJnt\n ankleEndJnt = cmds.duplicate(footJnt, po=True, n=ankleJnt.replace(\"Jnt\", \"End_Jnt\"))[0]\n cmds.setAttr(\"{0}.jointOrient\".format(ankleEndJnt), 0, 0, 0, type=\"double3\")\n cmds.connectAttr(\"{0}.t\".format(footJnt), \"{0}.t\".format(ankleEndJnt))\n cmds.connectAttr(\"{0}.s\".format(footJnt), \"{0}.s\".format(ankleEndJnt))\n cmds.aimConstraint(ankleStartJnt, ankleEndJnt, mo=False, aim=(-self.negDic[self.side], 0, 0), u=(0, 1, 0), wut=\"objectrotation\", wu=(0, 1, 0), wuo=footJnt) \n\n #create mid tweak ctrl\n rearCtrl = \"{0}_Rear_Ctrl\".format(self.longAlias)\n lockAttrs = [\"r\", \"v\"] if self.attrValue[\"localScale\"] else [\"r\", \"s\", \"v\"]\n cu.createCtrlFromProxy(n=rearCtrl, colorNo=self.colorDic2[self.side], proxyShape=\"default\", depth=1, lockAttrs=lockAttrs, pSet=\"default\")\n cmds.parentConstraint(ankleJnt, \"{0}_Grp\".format(rearCtrl), mo=False)\n cmds.scaleConstraint(ankleJnt, \"{0}_Grp\".format(rearCtrl))\n cmds.connectAttr(\"{0}.t\".format(rearCtrl), \"{0}.t\".format(ankleStartJnt))\n cmds.connectAttr(\"{0}.s\".format(rearCtrl), \"{0}.s\".format(ankleStartJnt))\n\n cmds.disconnectAttr(\"{0}.s\".format(ankleJnt), \"{0}.s\".format(midEndJnt))\n cmds.scaleConstraint(rearCtrl, midEndJnt)\n self.ctrls[\"Bendy\"][\"Rear\"] = rearCtrl\n cmds.parent(\"{0}_Grp\".format(rearCtrl), self.mainGrp)\n\n #add bot bendy\n newBindJnts = []\n if self.attrValue[\"botBendy\"] > 2:\n botBendyResults = self.createBendy(\"{0}_Bot\".format(self.longAlias), ankleStartJnt, ankleEndJnt, noOfJnts=self.attrValue[\"botBendy\"])\n botBendyCtrl = botBendyResults[0]\n self.ctrls[\"Bendy\"][\"BotBendy\"] = botBendyCtrl\n self.ctrls[\"Bendy\"][\"Bot\"] = botBendyResults[2]\n\n cmds.sets(ankleStartJnt, rm=\"{0}_Bind_Jnt_Set\".format(self.longAlias))\n cmds.sets(ankleEndJnt, rm=\"{0}_Bind_Jnt_Set\".format(self.longAlias))\n\n for jnt in botBendyResults[1]:\n self.bindJnts.insert(-3, jnt)\n newBindJnts.append(jnt)\n \n else:\n self.bindJnts.insert(-3, ankleStartJnt)\n self.bindJnts.insert(-3, ankleEndJnt)\n newBindJnts.append(ankleStartJnt)\n newBindJnts.append(ankleEndJnt)\n\n for jnt in newBindJnts:\n ju.labelJoint(jnt, displayAxis=True)\n ju.setJoint(jnt)\n \n return True\n\n def createIK(self, repositionIK=True):\n super(legRig, self).createIK()\n\n #delete the ik tip handle and the old end end joint\n cmds.delete(\"{0}_Tip_IKHandle\".format(self.longAlias))\n cmds.delete(\"{0}_{1}_IK_End_Jnt\".format(self.longAlias, self.aliasList[2]))\n\n #create new single chain ik handle for the foot and toe\n ikFootHandle = cmds.ikHandle(n=\"{0}_{1}_IKHandle\".format(self.longAlias, self.aliasList[3]), \n startJoint=self.ikJnts[2], endEffector=self.ikJnts[3], solver=\"ikSCsolver\")[0]\n\n ikKnuckleHandle = cmds.ikHandle(n=\"{0}_{1}_IKHandle\".format(self.longAlias, self.aliasList[4]), \n startJoint=self.ikJnts[3], endEffector=self.ikJnts[4], solver=\"ikSCsolver\")[0]\n\n ikToeHandle = cmds.ikHandle(n=\"{0}_{1}_IKHandle\".format(self.longAlias, self.aliasList[5]), \n startJoint=self.ikJnts[4], endEffector=self.ikJnts[5], solver=\"ikSCsolver\")[0]\n\n cmds.setAttr(\"{0}.v\".format(ikFootHandle), 0)\n cmds.setAttr(\"{0}.v\".format(ikKnuckleHandle), 0)\n cmds.setAttr(\"{0}.v\".format(ikToeHandle), 0)\n\n #get some position data\n self.footPos = cmds.xform(self.ikJnts[3], q=True, t=True, ws=True)\n self.knucklePos = cmds.xform(self.ikJnts[4], q=True, t=True, ws=True)\n toePos = cmds.xform(self.ikJnts[5], q=True, t=True, ws=True)\n upVec = mu.vPlus(toePos, self.footPos, \"-\")\n tiltOri = mu.vectorToAngle((0, 1, 0), upVec=upVec, sideVec=None, aimAxis=\"y\", upAxis=\"z\", sideAxis=None)\n\n #reset IK tilt ctrl's orientation--------------------------------------\n cmds.xform(\"{0}_Grp\".format(self.ikCtrls[\"Tilt\"]), ro=tiltOri, a=True, ws=True)\n\n #create the second IK tilt ctrl\n self.ikCtrls[\"Tilt2\"] = \"{0}_IK_Tilt_02_Ctrl\".format(self.longAlias)\n cu.createCtrlFromProxy(n=self.ikCtrls[\"Tilt2\"], colorNo=self.colorDic[self.side], proxyShape=\"default\", posObj=self.proxyCtrls[\"IKTilt2\"], depth=1, lockAttrs=[\"s\", \"v\"])\n cmds.xform(\"{0}_Grp\".format(self.ikCtrls[\"Tilt2\"]), ro=(0, 0, 0), a=True, ws=True)\n cmds.parent(\"{0}_Grp\".format(self.ikCtrls[\"Tilt2\"]), self.ikTiltOutputGrp)\n self.ikTiltOutputGrp2 = cmds.duplicate(self.ikCtrls[\"Tilt2\"], po=True, n=\"{0}_Output_Grp\".format(self.ikCtrls[\"Tilt2\"]))[0]\n cmds.connectAttr(\"{0}.r\".format(self.ikCtrls[\"Tilt2\"]), \"{0}.r\".format(self.ikTiltOutputGrp2))\n cmds.connectAttr(\"{0}.t\".format(self.ikCtrls[\"Tilt2\"]), \"{0}.scalePivot\".format(self.ikTiltOutputGrp2))\n cmds.connectAttr(\"{0}.t\".format(self.ikCtrls[\"Tilt2\"]), \"{0}.rotatePivot\".format(self.ikTiltOutputGrp2))\n #reset tilt controls orientation\n cmds.xform(\"{0}_Grp\".format(self.ikCtrls[\"Tilt2\"]), ro=tiltOri, a=True, ws=True)\n\n #create ctrl footik toe ik------------------------------------------------------\n self.ikCtrls[\"FootIK\"] = \"{0}_{1}_IK_Ctrl\".format(self.longAlias, self.aliasList[3])\n ikFootCtrlGrp = \"{0}_Grp\".format(self.ikCtrls[\"FootIK\"])\n cu.createCtrlFromProxy(n=self.ikCtrls[\"FootIK\"], colorNo=self.colorDic[self.side], proxyShape=\"default\", offset=True, posObj=self.proxyCtrls[\"FootIK\"], depth=1, lockAttrs=[\"t\", \"s\", \"v\"])\n\n self.ikCtrls[\"ToeIK\"] = \"{0}_{1}_IK_Ctrl\".format(self.longAlias, self.aliasList[5])\n ikToeCtrlGrp = \"{0}_Grp\".format(self.ikCtrls[\"ToeIK\"])\n cu.createCtrlFromProxy(n=self.ikCtrls[\"ToeIK\"], colorNo=self.colorDic[self.side], proxyShape=\"default\", offset=True, posObj=self.proxyCtrls[\"ToeIK\"], depth=1, lockAttrs=[\"t\", \"s\", \"v\"])\n\n cmds.xform(ikFootCtrlGrp, t=self.footPos, a=True, ws=True)\n cmds.xform(ikToeCtrlGrp, t=self.footPos, a=True, ws=True)\n cmds.parent(self.ikHandleGrp, self.ikCtrls[\"FootIK\"])\n cmds.parent(ikFootHandle, self.ikCtrls[\"FootIK\"])\n\n #add toe tip ctrl\n self.ikCtrls[\"ToeTip\"] = \"{0}_{1}_Tip_Ctrl\".format(self.longAlias, self.aliasList[-1])\n cu.createCtrlFromProxy(n=self.ikCtrls[\"ToeTip\"], colorNo=self.colorDic[self.side], proxyShape=\"default\", posObj=self.proxyJnts[5], depth=1, lockAttrs=[\"v\"])\n \n self.toeOffsetJnt = cmds.duplicate(self.ikJnts[-1], n=self.ikJnts[-1].replace(\"Jnt\", \"Offset_Jnt\"), po=True)[0]\n toePMA = cmds.createNode(\"plusMinusAverage\", n=\"{0}_ToeTip_PMA\".format(self.longAlias))\n defaultT = cmds.getAttr(\"{0}.t\".format(self.toeOffsetJnt))[0]\n cmds.setAttr(\"{0}.input3D[0]\".format(toePMA), defaultT[0], defaultT[1], defaultT[2], type=\"double3\")\n cmds.connectAttr(\"{0}.t\".format(self.ikCtrls[\"ToeTip\"]), \"{0}.input3D[1]\".format(toePMA))\n cmds.connectAttr(\"{0}.output3D\".format(toePMA), \"{0}.t\".format(self.toeOffsetJnt))\n cmds.connectAttr(\"{0}.r\".format(self.ikCtrls[\"ToeTip\"]), \"{0}.r\".format(self.toeOffsetJnt))\n cmds.connectAttr(\"{0}.s\".format(self.ikCtrls[\"ToeTip\"]), \"{0}.s\".format(self.toeOffsetJnt))\n\n cu.setControl(self.ikCtrls[\"FootIK\"], key=\"IK\")\n cu.setControl(self.ikCtrls[\"ToeIK\"], key=\"IK\")\n cu.setControl(self.ikCtrls[\"Tilt2\"], key=\"IK\")\n cu.setControl(self.ikCtrls[\"ToeTip\"], key=\"IK\")\n\n if not self.attrValue[\"shortFoot\"]:\n self.ikCtrls[\"KnuckleIK\"] = \"{0}_{1}_IK_Ctrl\".format(self.longAlias, self.aliasList[4])\n ikKnuckleCtrlGrp = \"{0}_Grp\".format(self.ikCtrls[\"KnuckleIK\"])\n cu.createCtrlFromProxy(n=self.ikCtrls[\"KnuckleIK\"], colorNo=self.colorDic2[self.side], proxyShape=\"default\", offset=True, posObj=self.proxyCtrls[\"KnuckleIK\"], depth=2, lockAttrs=[\"t\", \"s\", \"v\"])\n\n self.ikCtrls[\"ToeIK2\"] = \"{0}_{1}_IK_Sub_Ctrl\".format(self.longAlias, self.aliasList[5])\n ikToeCtrl2Grp = \"{0}_Grp\".format(self.ikCtrls[\"ToeIK2\"])\n cu.createCtrlFromProxy(n=self.ikCtrls[\"ToeIK2\"], colorNo=self.colorDic2[self.side], proxyShape=\"default\", offset=True, posObj=self.proxyCtrls[\"ToeIK2\"], depth=1, lockAttrs=[\"t\", \"s\", \"v\"])\n \n cmds.xform(ikKnuckleCtrlGrp, t=self.knucklePos, a=True, ws=True)\n cmds.xform(ikToeCtrl2Grp, t=self.knucklePos, a=True, ws=True)\n \n cmds.parent(ikKnuckleHandle, self.ikCtrls[\"KnuckleIK\"])\n cmds.parent(ikToeHandle, self.ikCtrls[\"ToeIK2\"])\n\n cmds.parent(ikKnuckleCtrlGrp, self.ikCtrls[\"ToeIK\"])\n cmds.parent(ikToeCtrl2Grp, self.ikCtrls[\"ToeIK\"])\n\n KnuckleRollGrp = cmds.group(em=True, n=\"{0}_{1}_Roll_Grp\".format(self.longAlias, self.aliasList[4]), p=self.ikTiltOutputGrp2)\n cmds.xform(KnuckleRollGrp, t=self.knucklePos, a=True, ws=True)\n cmds.parent(ikFootCtrlGrp, KnuckleRollGrp)\n\n KnuckleRollPivotGrp = cmds.group(em=True, n=\"{0}_{1}_Roll_Pivot_Grp\".format(self.longAlias, self.aliasList[4]), p=KnuckleRollGrp)\n cmds.parent(KnuckleRollPivotGrp, self.ikTiltOutputGrp2)\n KnuckleRollPivot = cmds.group(em=True, n=\"{0}_{1}_Roll_Pivot_Null\".format(self.longAlias, self.aliasList[4]), p=KnuckleRollPivotGrp)\n cmds.pointConstraint(self.ikCtrls[\"KnuckleIK\"], KnuckleRollPivot, mo=True)\n cmds.connectAttr(\"{0}.t\".format(KnuckleRollPivot), \"{0}.rotatePivot\".format(KnuckleRollGrp))\n cmds.connectAttr(\"{0}.t\".format(KnuckleRollPivot), \"{0}.scalePivot\".format(KnuckleRollGrp))\n\n cu.setControl(self.ikCtrls[\"KnuckleIK\"], key=\"IK\")\n cu.setControl(self.ikCtrls[\"ToeIK2\"], key=\"IK\")\n\n cmds.parent(\"{0}_Grp\".format(self.ikCtrls[\"ToeTip\"]), self.ikCtrls[\"ToeIK2\"])\n else:\n cmds.parent(ikKnuckleHandle, self.ikCtrls[\"ToeIK\"])\n cmds.parent(ikToeHandle, self.ikCtrls[\"ToeIK\"])\n cmds.parent(\"{0}_Grp\".format(self.ikCtrls[\"ToeTip\"]), self.ikCtrls[\"ToeIK\"])\n\n\n #adding additional attribute on foot rolling system ---------------------------------------------------------------------------------- \n footAttrs = [\"ballRoll\", \"ballTwist\", \"toeRoll\", \"toeTwist\", \"toeBend\", \"heelRoll\", \"heelTwist\", \"footRock\"]\n if not self.attrValue[\"shortFoot\"]:\n footAttrs.insert(1, \"ballRoll2\")\n footAttrs.insert(6, \"toeBend2\")\n\n [cmds.addAttr(self.ikCtrls[\"IK\"], ln=attr, at=\"double\", dv=0, k=True) for attr in footAttrs]\n\n #---toe---\n toeBendGrp = cmds.group(self.ikCtrls[\"ToeIK\"], n=\"{0}_Toe_Bend_Grp\".format(self.longAlias))\n cmds.xform(toeBendGrp, rp=(0, 0, 0), sp=(0, 0, 0), a=True, os=True)\n cmds.connectAttr(\"{0}.toeBend\".format(self.ikCtrls[\"IK\"]), \"{0}.rx\".format(toeBendGrp))\n\n toeRollTwistGrp = cmds.group(self.ikCtrls[\"Tilt\"], self.ikTiltOutputGrp, n=\"{0}_Toe_Roll_Grp\".format(self.longAlias))\n cmds.connectAttr(\"{0}.t\".format(self.ikCtrls[\"Tilt\"]), \"{0}.scalePivot\".format(toeRollTwistGrp))\n cmds.connectAttr(\"{0}.t\".format(self.ikCtrls[\"Tilt\"]), \"{0}.rotatePivot\".format(toeRollTwistGrp))\n cmds.connectAttr(\"{0}.toeRoll\".format(self.ikCtrls[\"IK\"]), \"{0}.rx\".format(toeRollTwistGrp))\n cmds.connectAttr(\"{0}.toeTwist\".format(self.ikCtrls[\"IK\"]), \"{0}.ry\".format(toeRollTwistGrp))\n\n #---foot---\n footRollGrp = cmds.group(self.ikCtrls[\"FootIK\"], n=\"{0}_Foot_Roll_Grp\".format(self.longAlias))\n cmds.xform(footRollGrp, rp=(0, 0, 0), sp=(0, 0, 0), a=True, os=True)\n cmds.connectAttr(\"{0}.ballRoll\".format(self.ikCtrls[\"IK\"]), \"{0}.rx\".format(footRollGrp))\n\n #---heel---\n heelRollTwistGrp = cmds.group(self.ikCtrls[\"Tilt2\"], self.ikTiltOutputGrp2, n=\"{0}_Heel_Roll_Grp\".format(self.longAlias))\n cmds.connectAttr(\"{0}.t\".format(self.ikCtrls[\"Tilt2\"]), \"{0}.scalePivot\".format(heelRollTwistGrp))\n cmds.connectAttr(\"{0}.t\".format(self.ikCtrls[\"Tilt2\"]), \"{0}.rotatePivot\".format(heelRollTwistGrp))\n cmds.connectAttr(\"{0}.heelTwist\".format(self.ikCtrls[\"IK\"]), \"{0}.ry\".format(heelRollTwistGrp))\n cmds.connectAttr(\"{0}.heelRoll\".format(self.ikCtrls[\"IK\"]), \"{0}.rx\".format(heelRollTwistGrp))\n\n #---footRock---\n innerPos = cmds.xform(self.proxyLoc[\"Inner\"], q=True, ws=True, t=True)\n outerPos = cmds.xform(self.proxyLoc[\"Outer\"], q=True, ws=True, t=True)\n innerGrp = cmds.group(em=True, n=\"{0}_Foot_Inner_Grp\".format(self.longAlias), p=self.ikTiltOutputGrp2)\n outerGrp = cmds.group(em=True, n=\"{0}_Foot_Outer_Grp\".format(self.longAlias), p=innerGrp)\n cmds.xform(innerGrp, t=innerPos, ws=True, a=True)\n cmds.xform(outerGrp, t=outerPos, ws=True, a=True)\n\n #ball twist\n footTwistGrp = cmds.group(em=True, n=\"{0}_Foot_Twist_Grp\".format(self.longAlias), p=outerGrp)\n cmds.xform(footTwistGrp, t=self.footPos, a=True, ws=True)\n cmds.parent(ikToeCtrlGrp, footTwistGrp)\n cmds.connectAttr(\"{0}.ballTwist\".format(self.ikCtrls[\"IK\"]), \"{0}.ry\".format(footTwistGrp))\n\n rockCond = cmds.createNode(\"condition\", n=\"{0}_Foot_Rock_Cond\".format(self.longAlias))\n cmds.setAttr(\"{0}.operation\".format(rockCond), 2)\n cmds.setAttr(\"{0}.colorIfFalseG\".format(rockCond), 0)\n [cmds.connectAttr(\"{0}.footRock\".format(self.ikCtrls[\"IK\"]), \"{0}.{1}\".format(rockCond, attr)) for attr in [\"firstTerm\", \"colorIfTrueG\", \"colorIfFalseR\"]]\n \n if self.side == self.right:\n cmds.connectAttr(\"{0}.outColorR\".format(rockCond), \"{0}.rz\".format(innerGrp))\n cmds.connectAttr(\"{0}.outColorG\".format(rockCond), \"{0}.rz\".format(outerGrp))\n else:\n cmds.connectAttr(\"{0}.outColorG\".format(rockCond), \"{0}.rz\".format(innerGrp))\n cmds.connectAttr(\"{0}.outColorR\".format(rockCond), \"{0}.rz\".format(outerGrp))\n\n if not self.attrValue[\"shortFoot\"]:\n cmds.parent(KnuckleRollGrp, footTwistGrp)\n cmds.parent(KnuckleRollPivotGrp, footTwistGrp)\n\n toeBend2Grp = cmds.group(self.ikCtrls[\"ToeIK2\"], n=\"{0}_Toe_Sub_Bend_Grp\".format(self.longAlias))\n cmds.xform(toeBend2Grp, rp=(0, 0, 0), sp=(0, 0, 0), a=True, os=True)\n cmds.connectAttr(\"{0}.toeBend2\".format(self.ikCtrls[\"IK\"]), \"{0}.rx\".format(toeBend2Grp))\n\n rollADL = cmds.createNode(\"addDoubleLinear\", n=\"{0}_{1}_Roll_ADL\".format(self.longAlias, self.aliasList[4]))\n cmds.connectAttr(\"{0}.rx\".format(self.ikCtrls[\"KnuckleIK\"]), \"{0}.input1\".format(rollADL))\n cmds.connectAttr(\"{0}.ballRoll2\".format(self.ikCtrls[\"IK\"]), \"{0}.input2\".format(rollADL))\n cmds.connectAttr(\"{0}.output\".format(rollADL), \"{0}.rx\".format(KnuckleRollGrp))\n cmds.connectAttr(\"{0}.ballRoll2\".format(self.ikCtrls[\"IK\"]), \"{0}_02_Grp.rx\".format(self.ikCtrls[\"KnuckleIK\"]))\n else:\n cmds.parent(ikFootCtrlGrp, footTwistGrp)\n\n #--------------------------------------------------------------------------------------------------------------------------------------------\n #add bot length\n cmds.addAttr(self.ikCtrls[\"IK\"], ln=\"botLength\", at=\"double\", dv=1, min=0.001, k=True)\n lengthMDL = cmds.createNode(\"multDoubleLinear\", n=\"{0}_Bot_Length_MDL\".format(self.longAlias))\n\n footLength = cmds.getAttr(\"{0}.tx\".format(self.ikJnts[3]))\n cmds.setAttr(\"{0}.input2\".format(lengthMDL), footLength)\n cmds.connectAttr(\"{0}.botLength\".format(self.ikCtrls[\"IK\"]), \"{0}.input1\".format(lengthMDL))\n cmds.connectAttr(\"{0}.output\".format(lengthMDL), \"{0}.tx\".format(self.ikJnts[3]))\n\n if self.side == self.left:\n lengthMDL2 = cmds.createNode(\"multDoubleLinear\", n=\"{0}_Bot_Length_inverse_MDL\".format(self.longAlias))\n cmds.setAttr(\"{0}.input2\".format(lengthMDL2), -1)\n cmds.connectAttr(\"{0}.output\".format(lengthMDL), \"{0}.input1\".format(lengthMDL2))\n cmds.connectAttr(\"{0}.output\".format(lengthMDL2), \"{0}.tz\".format(self.ikHandleGrp))\n else:\n cmds.connectAttr(\"{0}.output\".format(lengthMDL), \"{0}.tz\".format(self.ikHandleGrp))\n\n #change IK ctrl's position to foot jnt\n if repositionIK:\n IKTiltPos = cmds.xform(self.ikCtrls[\"Tilt\"], q=True, t=True, ws=True)\n\n cmds.xform(\"{0}_02_Grp\".format(self.ikCtrls[\"IK\"]), t=self.footPos, ws=True, a=True)\n cmds.xform(\"{0}_Grp\".format(self.ikCtrls[\"Tilt\"]), t=IKTiltPos, ws=True, a=True)\n cmds.xform(self.ikTiltOutputGrp, t=IKTiltPos, ws=True, a=True)\n\n #reset the offset value between ik ctrl and foot joint\n offset = self.getOffset(self.ikCtrls[\"IK\"], self.resultJnts[3])\n [cmds.setAttr('{0}.ikOffset{1}'.format(self.ikCtrls[\"IK\"], attr), l=False) for attr in [\"X\", \"Y\", \"Z\"]]\n cmds.setAttr('{0}.ikOffset'.format(self.ikCtrls[\"IK\"]), offset[0], offset[1], offset[2], type='double3')\n [cmds.setAttr('{0}.ikOffset{1}'.format(self.ikCtrls[\"IK\"], attr), l=True) for attr in [\"X\", \"Y\", \"Z\"]]\n\n #add additonal anim constraint for the foot ik ctrl\n aimGrp = cmds.group(em=True, n=\"{0}_AutoAim_Grp\".format(self.longAlias), p=outerGrp)\n cmds.xform(aimGrp, t=self.footPos, a=True, ws=True)\n cmds.aimConstraint(self.mainGrp, aimGrp, aim=(0, 0, -1), u=(1, 0, 0), wut=\"objectrotation\", wu=(1, 0, 0), wuo=footTwistGrp)\n\n if not self.attrValue[\"shortFoot\"]:\n aimBufferGrp = cmds.group(KnuckleRollGrp, KnuckleRollPivotGrp, n=\"{0}_AutoAim_02_Grp\".format(self.longAlias))\n else:\n aimBufferGrp = cmds.group(ikFootCtrlGrp, n=\"{0}_AutoAim_02_Grp\".format(self.longAlias))\n\n cmds.xform(aimBufferGrp, rp=(0, 0, 0), sp=(0, 0, 0), a=True, os=True)\n cmds.parent(aimBufferGrp, aimGrp) \n\n return True\n\n def createFK(self):\n super(hindLegRig, self).createFK()\n\n if self.attrValue[\"shortFoot\"]:\n cmds.parent(\"{0}_Grp\".format(self.ctrls[\"FK\"][-2]), \"{0}_Grp\".format(self.ctrls[\"FK\"][-4]))\n cmds.delete(self.ctrls[\"FK\"][-4])\n cmds.delete(\"{0}_Grp_parentConstraint1\".format(self.ctrls[\"FK\"][-4]))\n self.ctrls[\"FK\"].remove(self.ctrls[\"FK\"][-4])\n self.ctrls[\"FK\"].remove(self.ctrls[\"FK\"][-3])\n\n return True\n\n def mirrorProxy(self, mirrorJnt=True, mirrorShape=True):\n super(hindLegRig, self).mirrorProxy(mirrorJnt=mirrorJnt, mirrorShape=mirrorShape)\n\n if mirrorShape:\n sideDic = {self.left: self.right, self.right: self.left}\n for key in [\"BotBendy\", \"BotBendy2\", \"ToeIK2\", \"KnuckleIK\"]:\n ctrl = self.proxyCtrls[key]\n otherSideCtrl = ctrl.replace(self.side, sideDic[self.side])\n if cmds.objExists(otherSideCtrl):\n if key in [\"BotBendy\", \"BotBendy2\"]:\n cu.matchShape(ctrl, otherSideCtrl, flip=(1, 1, -1))\n else:\n cu.matchShape(ctrl, otherSideCtrl, flip=(-1, 1, 1))\n\n if key in [\"ToeIK2\", \"KnuckleIK\"]:\n t = cmds.getAttr(\"{0}.t\".format(otherSideCtrl))[0]\n cmds.setAttr(\"{0}.t\".format(ctrl), -t[0], t[1], t[2], type=\"double3\")\n\n","sub_path":"Rig_Module/hindLeg_rig.py","file_name":"hindLeg_rig.py","file_ext":"py","file_size_in_byte":30383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"568205533","text":"\"\"\"\n This spider is a Werkenineenhotel spider created on top of the ATSSpider\n scrapy crawl werkenineenhotel -a mining_job_id=9999 -a iteration=1 -a extract=1 -a url=\"https://www.werkenineenhotel.nl/hotel_vacatures_banen_bijbanen_stage/?distance=&company_group_id=&company_rating_id=&job_availability_id%5B%5D=&keyword=&company_type_id%5B%5D=3&job_type_id%5B%5D=&location=&search=1\"\n\n sampel seed url:\n https://www.werkenindehoreca.nl/horeca_vacatures_baan_bijbaan/?distance=&company_group_id=&company_rating_id=&job_availability_id%5B%5D=&keyword=&company_type_id%5B%5D=&job_type_id%5B%5D=&location=&search=Zoeken \n\n sample job url:\n https://www.werkenindehoreca.nl/vacature/bediening-bar-f-b/amsterdam/noord-holland/12109/\n\"\"\"\n\nfrom re import compile\nfrom scrapy.http import Request\nfrom scrapy.selector import Selector\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import Prefix, ConvertDateString\nfrom brightcorp.lib.utils import extract_first\n\n\nclass Werkenineenhotel(ATSSpider):\n\n name = \"werkenineenhotel\"\n domain = ''\n Domain_Url = compile(r\"://www\\.(\\S+)\\.nl\")\n\n def __init__(self, *args, **kwargs):\n super(Werkenineenhotel, self).__init__(*args, **kwargs)\n\n self.domain = self.Domain_Url.search(self.start_urls[0])\n if self.domain:\n self.domain = self.domain.group(1)\n\n def parse(self, response):\n selector = Selector(response)\n if not self.expected_job_count_set:\n job_count = selector.xpath(\n '//b[@id=\"results_count\"]/text()').extract()\n if job_count:\n self.expected_job_count = job_count\n\n jobs = selector.xpath(\n '//div[@class=\"job_results\"]/div/a[@class=\"job_result\"]')\n for job in jobs:\n url = job.xpath('./@href').extract()\n if url:\n yield Request(\n callback=self.parse_job_callback(),\n meta={\n 'title': job.xpath(\n './span[@class=\"job_title\"]/text()').extract(),\n 'company': job.xpath(\n './span[@class=\"company_logo\"]/img/@alt'\n ).extract(),\n },\n url=url[0]\n )\n\n next_page_url = extract_first(\n selector.xpath('//a[@class=\"next_page\"]/@href')\n )\n if next_page_url:\n yield Request(\n callback=self.parse,\n url=next_page_url\n )\n\n def parse_job(self, response):\n loader = BrightcorpItemLoader(response=response)\n\n details_xpath = '//td[text()=\"%s\"]/following-sibling::td/text()'\n\n loader.add_xpath(\n 'description',\n '//h3[text()=\"Algemene informatie\"]/following-sibling::node()[following::div[@class=\"job_company_info adbox-white\"]]'\n )\n loader.add_xpath(\n 'date', details_xpath % \"Geplaatst op:\",\n ConvertDateString('%d-%m-%Y')\n )\n loader.add_xpath('jobtype', details_xpath % \"Dienstverband:\")\n loader.add_xpath('location', details_xpath % \"Locatie:\")\n\n loader.add_xpath(\n 'referencenumber', details_xpath % \"Referentie nr.:\",\n Prefix('%s-' % self.domain)\n )\n\n loader.add_value('company', response.meta.get('company'))\n loader.add_value('title', response.meta.get('title'))\n loader.add_value('url', response.url)\n\n yield loader.load_item()\n","sub_path":"brightcorp/brightcorp/spiders/werkenineenhotel.py","file_name":"werkenineenhotel.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"311149504","text":"'''Python SpinPapi client\n\n File: SpinPapiClient.py\n Author Tom Worster\n Copyright (c) 2012 Spinitron, LLC. All rights reserved.\n Redistribution and use in source and binary forms, with or without modification, are permitted\n \tprovided that the following conditions are met:\n * Redistributions of source code must retain the above copyright notice, this list of conditions\n \t\tand the following disclaimer.\n * Redistributions in binary form must reproduce the above copyright notice, this list of\n \t\tconditions and the following disclaimer in the documentation and/or other materials provided\n \t\twith the distribution.\n * Neither the name of the Spinitron, LLC. nor the names of its contributors may be used to endorse\n \t\tor promote products derived from this software without specific prior written permission.\n THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR\n IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND\n FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\n CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\n IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT\n OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n '''\n\nimport hmac, hashlib, base64, sys, binascii \nfrom urllib.parse import quote\nfrom time import gmtime, strftime\nfrom operator import itemgetter\n\nclass SpinPapiClient:\n '''A SpinPapi client class.\n\n {@see http://spinitron.com/user-guide/pdf/SpinPapi-v2.pdf}\n NOTE: all SpinPapi client applications must cache result data. This\n class does not provide caching.\n '''\n\n def __init__(self, userid, secret, station=''):\n '''Client constructor.\n\n :param string userid: Authentication user ID issued by Spinitron\n :param string secret: Authentication secret issued by Spinitron\n :param string station: Station ID\n '''\n\n self.params = {}\n self.params['papiversion'] = '2'\n self.host = 'spinitron.com'\n self.url = '/public/spinpapi.php'\n self.params['papiuser'] = userid\n self.secret = secret\n self.params['station'] = station\n\n def query(self, params):\n '''Perform an uncached SpinPapi query.\n\n :param dict params: SpinPapi query parameters. At least method\n must be present in the dict.\n\n :return string: A complete signed query URL.\n '''\n\n # Add requested params to object default params\n all_params = self.params\n for key, val in params.items():\n all_params[key] = val\n\n # Add current GMT timestamp to params\n all_params['timestamp'] = strftime(\"%Y-%m-%dT%H:%M:%SZ\", gmtime())\n\n # Sort params alphabetically by dict key\n all_params = sorted(all_params.items(), key=itemgetter(0))\n\n # Build the urlencoded query in a list and join it with '&' chars\n the_query = []\n for key, val in all_params:\n the_query.append(quote(key) + '=' + quote(val))\n the_query = '&'.join(the_query)\n\n # Construct the query's HMAC signature.\n signature = bytes(self.host + '\\n' + self.url + '\\n' + the_query, 'utf-8')\n signature = hmac.new(self.secret, signature, hashlib.sha256)\n signature = signature.digest()\n signature = base64.b64encode(signature)\n signature = quote(signature, '')\n\n return ('http://' + self.host + self.url\n + '?' + the_query + '&signature=' + signature)\n","sub_path":"packages/SpinPapiClient.py","file_name":"SpinPapiClient.py","file_ext":"py","file_size_in_byte":3709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"466818494","text":"#!/usr/bin/env python\nimport requests\nimport xmlrpclib\nimport logging\nimport logging.handlers\n\n# Logging setup\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.DEBUG)\nhandler = logging.handlers.SysLogHandler(address = '/dev/log')\nformatter = logging.Formatter('%(module)s: %(message)s')\nhandler.setFormatter(formatter)\nlog.addHandler(handler)\n\n\n# Function to get the current IP address\ndef get_current_ip_address():\n url = 'http://ip.jsontest.com/'\n\n try:\n r = requests.get(url)\n except Exception as e:\n raise(e)\n\n if r.status_code == 200:\n if len(r.json()) > 0:\n return r.json()['ip']\n else:\n log.error('No data received back from the url \"{0}\"'.format(url))\n else:\n log.error('Invalid response code received. The received response code is \"{0}\"'.format(r.status_code))\n\n\n# Function to get the zone id\ndef get_zone_id_version(api, apiKey, zone_name):\n zone_list = api.domain.zone.list(apiKey)\n\n if len(zone_list) > 0:\n for zone in zone_list:\n if zone['name'] == zone_name:\n return zone['id'], zone['version']\n else:\n log.error('No items found in the zone list')\n\n\n# Function to get the A-Records from a zone\ndef get_a_records_from_zone(api, apiKey, zone_id, zone_version):\n record_list = api.domain.zone.record.list(apiKey, zone_id, zone_version)\n a_records = []\n\n if len(record_list) > 0:\n for record in record_list:\n if record['type'] == 'A':\n a_records.append(record)\n else:\n log.error('No records found in zone')\n\n return a_records\n\n\n# Function to check which records needs to be updated\ndef get_outdated_records(api, apiKey, current_ip_address, zone_id=0, zone_version=0):\n outdated_records = []\n\n if (zone_id == 0 and zone_version == 0):\n zone_id, zone_version = get_zone_id_version(api, apiKey, 'timmer.pro')\n\n a_records = get_a_records_from_zone(api, apiKey, zone_id, zone_version)\n\n for a_record in a_records:\n if a_record['value'] != current_ip_address:\n outdated_records.append(a_record)\n\n return outdated_records\n\n\n# Function to update the outdated records\ndef update_dns_zone(api, apiKey, zone_name, current_ip_address):\n updated_records = []\n\n # Get the current zone id version\n zone_id, zone_version = get_zone_id_version(api, apiKey, zone_name)\n\n # create a new version of the zone file\n new_zone_version = api.domain.zone.version.new(apiKey, zone_id)\n\n # Get the outdated records\n outdated_records = get_outdated_records(api, apiKey, current_ip_address, zone_id, new_zone_version)\n\n # Go through the outdated records\n for record in outdated_records:\n record_id = { 'id': record['id'] }\n data = { 'name': record['name'], 'type': record['type'], 'value': current_ip_address }\n\n # Update the record\n updated_records.append(api.domain.zone.record.update(apiKey, zone_id, new_zone_version, record_id, data))\n\n # If the number of outdated records is equal to the number of update records\n if len(outdated_records) == len(updated_records):\n # Set the new version as the current version\n api.domain.zone.version.set(apiKey, zone_id, new_zone_version)\n\n # Delete the old version\n api.domain.zone.version.delete(apiKey, zone_id, zone_version)\n log.info('Records updated for zone \"timmer.pro\", total {0} record(s) updated'.format(str(len(updated_records))))\n\n\n# If the script is called directly\nif __name__ == '__main__':\n current_ip_address = get_current_ip_address()\n #current_ip_address = '62.45.245.48'\n\n api = xmlrpclib.ServerProxy('https://rpc.gandi.net/xmlrpc/')\n apiKey = ''\n\n outdate_records = get_outdated_records(api, apiKey, current_ip_address)\n\n if len(outdate_records) > 0:\n update_dns_zone(api, apiKey, 'timmer.pro', current_ip_address)\n else:\n log.info('No records to be updated for zone \"timmer.pro\"')\n\n\n","sub_path":"DDNS.py","file_name":"DDNS.py","file_ext":"py","file_size_in_byte":3982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"406378966","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Notification',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('messageId', models.CharField(max_length=48)),\n ('topicArn', models.CharField(max_length=256)),\n ('subject', models.CharField(max_length=100)),\n ('message', models.TextField()),\n ('timestamp', models.DateTimeField()),\n ('status', models.CharField(default=b'PENDING', max_length=9, choices=[(b'PENDING', b'Pending'), (b'PROCESSED', b'Processed'), (b'ERROR', b'Error')])),\n ('modified', models.DateTimeField(auto_now=True)),\n ('errors', models.TextField(null=True, blank=True)),\n ],\n ),\n migrations.CreateModel(\n name='Subscription',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('topic', models.CharField(max_length=128)),\n ('messageId', models.CharField(max_length=48)),\n ('token', models.CharField(max_length=256, null=True, blank=True)),\n ('topicArn', models.CharField(max_length=256)),\n ('message', models.TextField()),\n ('subscribeURL', models.URLField(max_length=512, null=True, blank=True)),\n ('timestamp', models.DateTimeField()),\n ('signatureVersion', models.CharField(max_length=32, null=True, blank=True)),\n ('signature', models.TextField(null=True, blank=True)),\n ('signingCertURL', models.URLField(max_length=512, null=True, blank=True)),\n ('status', models.CharField(default=b'PENDING', max_length=7, choices=[(b'ACTIVE', b'Active'), (b'PENDING', b'Pending'), (b'RETIRED', b'Retired')])),\n ('modified', models.DateTimeField(auto_now=True)),\n ('errors', models.TextField(null=True, blank=True)),\n ],\n ),\n ]\n","sub_path":"sns/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"308452335","text":"# Revision 1.0 // PLEASE MAKE A COPY AND EDIT BEFORE MAKING CHANGES\r\n\r\nimport random\r\nimport math\r\n\r\n\r\nclass Sun:\r\n def __init__(self, seed):\r\n self.name = \"\"\r\n self.id = seed # int\r\n self.star_class = '' # Letter O, B, A, F, G, K, M\r\n self.mass = None # int, *10^26 kilograms\r\n self.temperature = None # Int, degrees kelvin\r\n self.radius = None # int, kilometers\r\n\r\n random.seed = self.id\r\n t_val = random.random() # Temp variable\r\n if t_val <= 0.0000003:\r\n self.star_class = 'O'\r\n self.mass = random.randrange(160000, 320000) # *10^26 kilograms\r\n self.radius = self.mass * 0.4125 # In Solar radii (6.975*10^5km)\r\n self.temperature = math.floor(4*math.pi*self.radius*self.radius * 42.6264386916 + 6666.67) # Kelvin (many thousands) The area of the sun multiplies by the gradient of the relationship between area and termperate for its class\r\n elif 0.0000003 < t_val <= 0.0013003:\r\n self.star_class = 'B'\r\n self.mass = random.randrange(21000, 160000) # *10^26 kilograms\r\n self.radius = self.mass * 0.345323741007 + 1.47842 # In Solar radii (6.975*10^5km)\r\n self.temperature = math.floor(4*math.pi*self.radius*self.radius * 39.4729521557 + 8392.86)\r\n elif 0.0013003 < t_val <= 0.0073003:\r\n self.star_class = 'A'\r\n self.mass = random.randrange(14000, 21000) # *10^26 kilograms\r\n self.radius = self.mass * 0.571428571429 + 0.6 # In Solar radii (6.975*10^5km)\r\n self.temperature = math.floor(4*math.pi*self.radius*self.radius * 155.424749113 + 3671.88)\r\n elif 0.0073003 < t_val <= 0.0373003:\r\n self.star_class = 'F'\r\n self.mass = random.randrange(10400, 14000) # *10^26 kilograms\r\n self.radius = self.mass * 0.694444444444 + 1.58333 # In Solar radii (6.975*10^5km)\r\n self.temperature = math.floor(4*math.pi*self.radius*self.radius * 187.24110952 + 2888.24)\r\n elif 0.0373003 < t_val <= 0.1133:\r\n self.star_class = 'G'\r\n self.mass = random.randrange(8000, 10400) # *10^26 kilograms\r\n self.radius = self.mass * 0.791666666667 + 0.326667 # In Solar radii (6.975*10^5km)\r\n self.temperature = math.floor(4*math.pi*self.radius*self.radius * 158.797648383 + 3360.94)\r\n elif 0.1133 < t_val <= 0.2343:\r\n self.star_class = 'K'\r\n self.mass = random.randrange(4500, 8000) # *10^26 kilograms\r\n self.radius = self.mass * 1.34615384615 + 0.365714 # In Solar radii (6.975*10^5km)\r\n self.temperature = math.floor(4*math.pi*self.radius*self.radius * 276.56674541 + 1997.03)\r\n else:\r\n self.star_class = 'M'\r\n self.mass = random.randrange(800, 4500) # *10^26 kilograms\r\n self.radius = self.mass * 1.75675675676 + -0.77973 # In Solar radii (6.975*10^5km)\r\n self.temperature = math.floor(4*math.pi*self.radius*self.radius * 212.206590789 + 2393.33)\r\n\r\n\r\nclass Planet:\r\n def __init__(self, seed, minimum_orbit, central_body):\r\n self.radius = None # Orbit radius\r\n self.p_type = None\r\n self.id = seed\r\n self.orbital_speed = None # How long it takes to orbit central body\r\n self.axis = None # Planets inclination relative to the orbital plane\r\n self.p_axis = None # Planet's axis tilt\r\n self.moons = None\r\n self.atmosphere = None\r\n self.temperature = None # Surface temperature\r\n self.mass = None\r\n self.p_radius = None # The base radius of the planet itself\r\n self.gravity = None # Gravitational pull at p_radius\r\n if minimum_orbit == 0 or minimum_orbit < 0 or minimum_orbit is None:\r\n minimum_orbit = central_body.radius * 1.05 + 0.5*central_body.radius\r\n self.radius = random.randrange(minimum_orbit, central_body.mass * 1000000000000000000000000000) # In Solar radii (6.975*10^5km) # Completely Arbitary, will need additional research but probably out of the scope of game\r\n random.seed = self.id\r\n t_val = random.random()\r\n if self.radius < central_body.radius * 300:\r\n if t_val <= 0.5:\r\n self.p_type = 'rock'\r\n else:\r\n self.p_type = 'gas'\r\n # Rocky or Gas (0% ice planet)\r\n if central_body.radius * 300 <= self.radius < central_body.radius * 700: # Semi habitable? Needs additional research\r\n if t_val <= 0.45:\r\n self.p_type = 'rock'\r\n elif 0.45 < t_val <= 0.8:\r\n self.p_type = 'gas'\r\n else:\r\n self.p_type = 'ice'\r\n # Rocky or Gas or ice (45% rocky, 35% gas, 20% ice\r\n else:\r\n if t_val <= 0.1:\r\n self.type = 'gas'\r\n if 0.1 < t_val <= 0.55:\r\n self.type = 'rock'\r\n else:\r\n self.type = 'ice'\r\n # Rocky or ice (~10% of gas, 45% other)\r\n self.orbital_speed = (2*math.pi*math.sqrt(self.radius*self.radius*self.radius))/(math.sqrt(6.67*10**-11)*math.sqrt(central_body.mass)) # How long it takes to orbit central body\r\n self.axis = random.randint(0, 10) # Planets inclination relative to the orbital plane\r\n self.p_axis = random.randrange(0, 90) # Planet's axis tilt in degrees from vertical\r\n self.p_radius = math.floor(random.randrange(900, 0.05 * central_body.radius)) # The base radius of the planet itself in km\r\n if self.p_type is 'gas':\r\n # gassy things\r\n self.mass = math.floor((4/3) * math.pi * self.p_radius**3 * random.randrange(0.9, 1.8)) # in kg\r\n self.atmosphere = None\r\n self.temperature = random.randint(800, 1400) # Surface temperature in kelvin\r\n if self.p_type is 'rock':\r\n # rocky things\r\n self.mass = math.floor((4/3) * math.pi * self.p_radius**3 * random.randrange(0.8, 1.1))\r\n self.atmosphere = random.randint(0, 1)\r\n if self.atmosphere == 1:\r\n self.temperature = random.randint(230, 380) # Surface temperature\r\n else:\r\n self.temperature = random.randint(125, 800)\r\n else:\r\n # icy things\r\n self.mass = math.floor((4/3) * math.pi * self.p_radius**3 * random.randrange(0.9, 1.1))\r\n self.atmosphere = random.randint(0, 1)\r\n if self.atmosphere == 1:\r\n self.temperature = random.randint(200, 270)\r\n else:\r\n self.temperature = random.randint(125, 270) # Surface temperature\r\n self.gravity = ((6.67*10**-11) * self.mass)/self.p_radius**2 # Gravitational pull at p_radius\r\n self.moons = {}\r\n for m in range(0, random.randint(0, 3)):\r\n self.moons[m] = Planet(self.id + 1, self.p_radius * 1.2, self)\r\n\r\n","sub_path":"solar_gen.py","file_name":"solar_gen.py","file_ext":"py","file_size_in_byte":6926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"168716441","text":"# File: ps_noise_sniffer.py\n# Description: Power Supply Noise Sniffer\n# Author: Mark Thoren (mark.thoren@analog.com)\n# Antoniu Miclaus (antoniu.miclaus@analog.com)\n#\n# Copyright 2018(c) Analog Devices, Inc.\n#\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n# - Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# - Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in\n# the documentation and/or other materials provided with the\n# distribution.\n# - Neither the name of Analog Devices, Inc. nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n# - The use of this software may or may not infringe the patent rights\n# of one or more patent holders. This license does not release you\n# from the requirement that you obtain separate licenses from these\n# patent holders to use this software.\n# - Use of the software either in source or binary form, must be run\n# on or directly connected to an Analog Devices Inc. component.\n#\n# THIS SOFTWARE IS PROVIDED BY ANALOG DEVICES \"AS IS\" AND ANY EXPRESS OR\n# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, NON-INFRINGEMENT,\n# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.\n# IN NO EVENT SHALL ANALOG DEVICES BE LIABLE FOR ANY DIRECT, INDIRECT,\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n# LIMITED TO, INTELLECTUAL PROPERTY RIGHTS, PROCUREMENT OF SUBSTITUTE GOODS OR\n# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n'''\nDerived from an example posted here:\nhttps://ez.analog.com/thread/99958-ad9361-libiio-api-python-binding\n\nA useful nugget for verifying throughput, from:\nhttps://wiki.analog.com/university/tools/pluto/devs/performance\nADALM-PLUTO Performance Metrics:\nData Throughput (USB)\n\nVia IIOD USB backend\niio_readdev -u usb:1.100.5 -b 100000 cf-ad9361-lpc | pv > /dev/null\n210GiB 1:19:51 [26,1MiB/s] [ <=> \n\nVia IIOD Network backend\niio_readdev -n 192.168.2.1 -b 100000 cf-ad9361-lpc | pv > /dev/null\n203MiB 0:00:10 [20,4MiB/s] [ <=> \n\n'''\n\nimport sys, os\nimport time\n\ntry:\n import tkinter as tk\n from tkinter import filedialog\n import tkinter.scrolledtext as tkscrolled\n\nexcept:\n print(\"Please install tkinter\")\ntry:\n import csv\nexcept:\n print(\"Please install csv\")\ntry:\n import pandas as pd\nexcept:\n print(\"Please install pandas\")\ntry:\n import numpy as np\nexcept:\n print(\"Please install numpy\")\n\ntry:\n import matplotlib.pyplot as plt\n from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\nexcept:\n print(\"Please install matplotlib\")\n\ntry: \n import iio\nexcept:\n sys.path.append('C:/Users/fhurgoi/Documents/libiio-master/bindings/python') #add own path to python bindings\n import iio\n\ntry:\n import serial\nexcept:\n print(\"Please install pywin32\")\n\nimport plotly\nimport plotly.graph_objs as go\n\ntry:\n import scipy.signal\nexcept:\n pritn(\"Please install scipy\")\n\n#Error\nERROR = -1\n\n#Success\nSUCCESS = 0\n\n# channel name index mapping\n# for phy:\nTMP = 2\n\n# buffer length index mapping\nBUFLEN = 2 ** 17\n\n# ADRV9009 operational parameters\nN_BITS = 16\n\nTRXLO = 1000e6\n\nTX0BW = 225e6\nTX0FS = 245.76e6\nRX0BW = 100e6\nRX0FS = 122.88e6#122.88e6\n\nFREQ_BAND = 100e6\n\n#TX tone frequency \nTXDAC_FREQ = 10e6 #MHz\n \n#Center frequencies, start frequency, stop frequency index mapping\nCENTER_FREQ = 0\nSTART_FREQ = 1\nSTOP_FREQ = 2\n\ndirname = os.path.dirname(__file__)\n\n# Clear Plot and Message Log\ndef clr_content():\n txt1.delete('1.0', tk.END)\n a.cla()\n canvas.draw()\n \n# Setup Context\ndef connect_device():\n \n global phy_a, phy_b, txdac, rxadc, ctx\n \n try:\n #ctx = iio.Context('ip:10.50.1.210')\n ctx = iio.Context('ip:192.168.1.146')\n except:\n return ERROR\n \n phy_a = ctx.find_device(\"adrv9009-phy\") # Register control\n phy_b = ctx.find_device(\"adrv9009-phy-b\") # Register control\n txdac = ctx.find_device(\"axi-adrv9009-tx-hpc\") # TX/DAC Core in HDL for DMA (plus DDS)\n rxadc = ctx.find_device(\"axi-adrv9009-rx-hpc\") # RX/ADC Core in HDL for DMA\n \n return phy_a, phy_b, txdac, rxadc, ctx\n\ndef connect_generator():\n\n global COMgen, ser\n\n COMgen = str('COM' + gen_COM.get())\n\n # configure the serial connections (the parameters differs on the device you are connecting to)\n ser = serial.Serial(\n port=COMgen,\n baudrate=115200,\n parity=serial.PARITY_NONE,\n stopbits=serial.STOPBITS_ONE,\n bytesize=serial.EIGHTBITS\n )\n\n try:\n port = ser.isOpen()\n print (str(port))\n except serial.SerialException:\n ser.close()\n print (\"port is closed\")\n ser.isOpen()\n print (\"port is open\")\n\n #ser.write(('*IDN?' + '\\r\\n').encode('utf-8'))\n ser.write(('FREQ 70e6' + '\\r\\n').encode('utf-8'))\n ser.write(('POW ' + str(int(tx_pow.get())) + '\\r\\n').encode('utf-8'))\n ser.write(('OUTP OFF' + '\\r\\n').encode('utf-8'))\n\ndef disconnect_generator():\n # configure the serial connections (the parameters differs on the device you are connecting to)\n ser.close()\n\n#Compute frequencies for each band\ndef comp_freq():\n center_freq = np.array([]) #array that holds the center frequencies\n \n #Convert input start/stop frequency values to integers\n try:\n start_f.get()\n stop_f.get()\n except:\n center_freq = np.append(center_freq, ERROR)\n return center_freq\n \n #Get start/stop frequency values\n start_freq = float(start_f.get()) * float(1e6) #MHz\n stop_freq = float(stop_f.get()) * float(1e6) #MHz\n \n #Check if start frequency is larger that input frequency\n if (start_freq > stop_freq or start_freq < 70e6 or stop_freq > 6e9):\n center_freq = np.append(center_freq, ERROR)\n else:\n nr_center_freq = int((stop_freq - start_freq)/FREQ_BAND) + 1 \n center_freq = np.append(center_freq, start_freq)\n \n #Compute center frequencies for each band\n for i in range(1, nr_center_freq):\n center_freq = np.append(center_freq, center_freq[i-1] + FREQ_BAND)\n return center_freq, start_freq, stop_freq\n\ndef configure_device():\n\n with open('Tx_BW200_IR245p76_Rx_BW100_OR122p88_ORx_BW200_OR245p76_DC245p76.txt', 'r') as myfile:\n filterfile = myfile.read()\n\n iio.Context.set_timeout(ctx, 30000);\n time.sleep(0.1)\n\n phy_a.attrs[\"profile_config\"].value = filterfile\n phy_b.attrs[\"profile_config\"].value = filterfile\n\n iio.Context.set_timeout(ctx, 3000);\n\ndef RXA1():\n\n rx_channel = phy_a.find_channel(\"voltage0\")\n return rx_channel\n\ndef RXA2():\n\n rx_channel = phy_a.find_channel(\"voltage1\")\n return rx_channel\n\ndef RXB1():\n\n rx_channel = phy_b.find_channel(\"voltage0\")\n return rx_channel\n\ndef RXB2():\n\n rx_channel = phy_b.find_channel(\"voltage1\")\n return rx_channel\n\n#Transciever settings \ndef setup_device():\n \n global tx, rx, TX1_I_F1, TX1_Q_F1, TX2_I_F1, TX2_Q_F1, TX2_Q_F1, TX3_I_F1, TX3_Q_F1, TX4_I_F1, TX4_Q_F1\n global TRX_LO_A, TRX_LO_B\n\n # Global configuration\n TRX_LO_A = phy_a.find_channel(\"altvoltage0\", True)\n TRX_LO_A.attrs[\"frequency\"].value = str(int(TRXLO))\n TRX_LO_A.attrs[\"frequency_hopping_mode_enable\"].value = str(0)\n\n TRX_LO_B = phy_b.find_channel(\"altvoltage0\", True)\n TRX_LO_B.attrs[\"frequency\"].value = str(int(TRXLO))\n TRX_LO_B.attrs[\"frequency_hopping_mode_enable\"].value = str(0)\n\n # Default RX Configuration\n\n #configure just the RX channel under test\n rx_chann = {\"RX1\": RXA1, \"RX2\": RXA2, \"RX3\": RXB1, \"RX4\": RXB2}\n func = rx_chann.get(rx_channel.get(), \"nothing\")\n rx = func()\n\n rx.attrs['gain_control_mode'].value = \"manual\"\n rx.attrs['hardwaregain'].value = rx_gain.get()\n #rx.attrs[\"rf_bandwidth\"].value = (str(int(RX0BW)))\n #rx.attrs[\"sampling_frequency\"].value = str(int(RX0FS))\n #rx.attrs[\"powerdown\"].value = str(0)\n\n #rx.attrs[\"gain_control_pin_mode_en\"].value = str(1)\n if(track_en.get() == True):\n rx.attrs['quadrature_tracking_en'].value = str(1)\n else:\n rx.attrs['quadrature_tracking_en'].value = str(0)\n\n # axi-adrv9009-tx-hpc (buffer capable) channels\n TX1_I_F1 = txdac.find_channel('altvoltage0',True)\n TX1_I_F2 = txdac.find_channel('altvoltage1',True)\n TX1_Q_F1 = txdac.find_channel('altvoltage2',True)\n TX1_Q_F2 = txdac.find_channel('altvoltage3',True)\n TX2_I_F1 = txdac.find_channel('altvoltage4',True)\n TX2_I_F2 = txdac.find_channel('altvoltage5',True)\n TX2_Q_F1 = txdac.find_channel('altvoltage6',True)\n TX2_Q_F2 = txdac.find_channel('altvoltage7',True)\n TX3_I_F1 = txdac.find_channel('altvoltage8',True)\n TX3_I_F2 = txdac.find_channel('altvoltage9',True)\n TX3_Q_F1 = txdac.find_channel('altvoltage10',True)\n TX3_Q_F2 = txdac.find_channel('altvoltage11',True)\n TX4_I_F1 = txdac.find_channel('altvoltage12',True)\n TX4_I_F2 = txdac.find_channel('altvoltage13',True)\n TX4_Q_F1 = txdac.find_channel('altvoltage14',True)\n TX4_Q_F2 = txdac.find_channel('altvoltage15',True)\n\n # Turn TX tone off\n TX1_I_F1.attrs[\"frequency\"].value = \"10000000\"\n TX1_I_F1.attrs[\"scale\"].value = \"0.0\"#\"0.031616\"#\"0.1\" = -20dBFS, \"0.031616 = -30dBFS = -20dBm\"\n TX1_Q_F1.attrs[\"frequency\"].value = \"10000000\"\n TX1_Q_F1.attrs[\"scale\"].value = \"0.0\"\n TX1_I_F2.attrs[\"frequency\"].value = \"10000000\"\n TX1_I_F2.attrs[\"scale\"].value = \"0.0\"#\"0.031616\"#\"0.1\" = -20dBFS, \"0.031616 = -30dBFS = -20dBm\"\n TX1_Q_F2.attrs[\"frequency\"].value = \"10000000\"\n TX1_Q_F2.attrs[\"scale\"].value = \"0.0\"\n TX2_I_F1.attrs[\"frequency\"].value = \"10000000\"\n TX2_I_F1.attrs[\"scale\"].value = \"0.0\"\n TX2_Q_F1.attrs[\"frequency\"].value = \"10000000\"\n TX2_Q_F1.attrs[\"scale\"].value = \"0.0\"\n TX2_I_F2.attrs[\"frequency\"].value = \"10000000\"\n TX2_I_F2.attrs[\"scale\"].value = \"0.0\"\n TX2_Q_F2.attrs[\"frequency\"].value = \"10000000\"\n TX2_Q_F2.attrs[\"scale\"].value = \"0.0\"\n TX3_I_F1.attrs[\"frequency\"].value = \"10000000\"\n TX3_I_F1.attrs[\"scale\"].value = \"0.0\"\n TX3_Q_F1.attrs[\"frequency\"].value = \"10000000\"\n TX3_Q_F1.attrs[\"scale\"].value = \"0.0\"\n TX3_I_F2.attrs[\"frequency\"].value = \"10000000\"\n TX3_I_F2.attrs[\"scale\"].value = \"0.0\"\n TX3_Q_F2.attrs[\"frequency\"].value = \"10000000\"\n TX3_Q_F2.attrs[\"scale\"].value = \"0.0\"\n TX4_I_F1.attrs[\"frequency\"].value = \"10000000\"\n TX4_I_F1.attrs[\"scale\"].value = \"0.0\"\n TX4_Q_F1.attrs[\"frequency\"].value = \"10000000\"\n TX4_Q_F1.attrs[\"scale\"].value = \"0.0\"\n TX4_I_F2.attrs[\"frequency\"].value = \"10000000\"\n TX4_I_F2.attrs[\"scale\"].value = \"0.0\"\n TX4_Q_F2.attrs[\"frequency\"].value = \"10000000\"\n TX4_Q_F2.attrs[\"scale\"].value = \"0.0\"\n\n v0_i = rxadc.find_channel(\"voltage0_i\")\n v0_q = rxadc.find_channel(\"voltage0_q\")\n v1_i = rxadc.find_channel(\"voltage1_i\")\n v1_q = rxadc.find_channel(\"voltage1_q\")\n v2_i = rxadc.find_channel(\"voltage2_i\")\n v2_q = rxadc.find_channel(\"voltage2_q\")\n v3_i = rxadc.find_channel(\"voltage3_i\")\n v3_q = rxadc.find_channel(\"voltage3_q\")\n v0_i.enabled = False\n v0_q.enabled = False\n v1_i.enabled = False\n v1_q.enabled = False\n v2_i.enabled = False\n v2_q.enabled = False\n v3_i.enabled = False\n v3_q.enabled = False\n\n # Enable I/Q channels to be associated with RX buffer\n if(rx_channel.get() == \"RX1\"):\n v0_i.enabled = True\n v0_q.enabled = True\n elif(rx_channel.get() == \"RX2\"):\n v1_i.enabled = True\n v1_q.enabled = True\n elif(rx_channel.get() == \"RX3\"):\n v2_i.enabled = True\n v2_q.enabled = True\n elif(rx_channel.get() == \"RX4\"):\n v3_i.enabled = True\n v3_q.enabled = True\n else:\n txt1.insert(tk.END, 'Ghinion. Nu-i bine canalu de RX!\\n\\n')\n\n #turn radio on\n phy_a.attrs[\"ensm_mode\"]. value = \"radio_on\"\n phy_b.attrs[\"ensm_mode\"]. value = \"radio_on\"\n\n\n#Die temperature\ndef die_temp():\n \n temp_a = int(phy_a.channels[TMP].attrs[\"input\"].value) / 1000.0\n txt1.insert(tk.END, 'Die Temperature A: ' + str(temp_a) + '\\n\\n')\n temp_b = int(phy_b.channels[TMP].attrs[\"input\"].value) / 1000.0\n txt1.insert(tk.END, 'Die Temperature B: ' + str(temp_b) + '\\n\\n')\n root.update_idletasks()\n root.update()\n\n#Get and plot data\ndef get_plot_data(dev, center_freq):\n \n global save_freq, save_data, save_noise, save_spur_number, save_max_spur\n global save_spur_freq, save_spur_data\n\n TX_offset = float(tone_offset.get())*1e6\n ylim = [0,0]\n # Create IIO RX Buffer\n rxbuf = iio.Buffer(dev[3], BUFLEN, False) \n \n # Window function, max value = unity\n window = np.hanning(BUFLEN)\n #window = np.blackman(BUFLEN)\n \n avg_step = 0\n avgband = np.array([])\n\n save_data = []\n save_freq = []\n save_noise = []\n save_spur_freq = []\n save_spur_data = []\n save_spur_number = []\n save_max_spur = []\n\n\n if(gen_en.get() == True):\n ser.write(('POW ' + str(int(tx_pow.get())) + '\\r\\n').encode('utf-8'))\n ser.write(('OUTP ON' + '\\r\\n').encode('utf-8'))\n\n ss = 0\n\n while(avg_step == 0):\n\n #acquire data\n for i in range(0, len(center_freq)):\n \n RXLO = center_freq[i]\n if(rx_channel.get() == 'RX1' or rx_channel.get() == 'RX2'):\n TRX_LO_A.attrs[\"frequency\"].value = str(int(RXLO))\n elif(rx_channel.get() == 'RX3' or rx_channel.get() == 'RX4'):\n TRX_LO_B.attrs[\"frequency\"].value = str(int(RXLO))\n else:\n print(\"Nu-i bine!\")\n\n time.sleep(0.2)\n root.update_idletasks()\n root.update()\n \n #generate test tone\n if(gen_en.get() == True):\n ser.write(('FREQ ' + str(RXLO + TX_offset) + '\\r\\n').encode('utf-8'))\n #delay before data acquisition\n time.sleep(0.5)\n \n btn_aq.config(bg = \"red\")\n root.update()\n for k in range(0, int(avg_nr.get())+1):\n #flush operations\n for j in range(5):\n rxbuf.refill()\n x = rxbuf.read()\n \n buff = np.frombuffer(x, dtype=np.int16)\n \n #apply window\n rxvals_i = buff[0::2] * window\n rxvals_q = buff[1::2] * window\n \n #construct complex IQ data\n rxvals_iq = rxvals_i + (1j * rxvals_q)\n\n # apply FFT and get amplitude of the IQ data \n newband = np.abs(np.fft.fft(rxvals_iq)) \n \n #apply averaging\n if(k != 0):\n avgband = (newband) * 1/k + avgband * (k-1) / k;#(newband)/int(avg_nr.get())\n else:\n avgband = newband\n\n btn_aq.config(bg = \"grey\")\n root.update()\n\n #compute cutoff thresholds \n low_th = int(((RX0FS - FREQ_BAND)/(RX0FS * 2)) * BUFLEN)\n high_th = int(BUFLEN - (((RX0FS - FREQ_BAND)/(RX0FS * 2)) * BUFLEN))\n\n #compute frequency bins\n # 0, 1, 2, ..., n/2-1, -n/2,..., -1\n freq_bins = (np.fft.fftfreq(BUFLEN, 1/RX0FS) + (center_freq[0] + FREQ_BAND * i)) /1e6\n\n # Create tuple such that frequencies and corresponding amplitudes can be manipulated together\n tuple1 = zip(freq_bins, avgband)\n \n # Rearrange such that the center frequency is in the middle.\n tuple1 = sorted(tuple1, key=lambda x: x[0])\n #print(tuple1)\n # Extract the passband of the FIR response\n tuple1 = tuple1[low_th : high_th]\n \n #iq = np.array([nr[1] for nr in tuple1]) / (10 ** (gain_calib/10))\n iq = np.array([nr[1] for nr in tuple1])\n fbin = np.array([nr[0] for nr in tuple1])\n\n fft_rxvals_iq_dbFS = 10 * np.log10(iq**2) + 20 * np.log10(2/2**(N_BITS-1))- 20 * np.log10(BUFLEN) + 20 * np.log10(1/(1.5**0.5))# IIO\n # Compute in dB, subtract hardware gain to normalize to absolute analog signal level at input\n\n fft_rxvals_iq_dbm = fft_rxvals_iq_dbFS - 6.7\n\n noise_f = compute_noise_floor(fft_rxvals_iq_dbFS)\n spurs = scipy.signal.find_peaks(fft_rxvals_iq_dbFS, ((noise_f + 6), 0), None, 5, 3, None, None, 0.5, None)\n\n n_bins = fft_rxvals_iq_dbm.size\n\n freqbin = FREQ_BAND/n_bins\n spurs_fund_th_low = int((FREQ_BAND/2 + TX_offset - 3e6)/freqbin)\n spurs_fund_th_high = int((FREQ_BAND/2 + TX_offset + 3e6)/freqbin)\n\n RX_crosstalk_ampl = np.max(fft_rxvals_iq_dbm[spurs_fund_th_low : spurs_fund_th_high])\n\n txt1.insert(tk.END, \"Center Frequency set to: \" + str(RXLO/1e6) + ' MHz\\n')\n txt1.insert(tk.END, \"RX crosstalk : \" + str('%04.3f'%(RX_crosstalk_ampl)+ \" dBm\\n\"))\n txt1.insert(tk.END, \"Noise floor: \" + str('%04.3f'%noise_f)+ \" dBFS\\n\")\n\n spurs_number_band = len(spurs[0])\n spurs_number_save = 0\n for i in range(0, spurs_number_band):\n if spurs[0][i] <= spurs_fund_th_low or spurs_fund_th_high <= spurs[0][i]:\n save_spur_freq.append((RXLO - (FREQ_BAND/2) + ((spurs[0][i])*freqbin))/1e6)#1875\n save_spur_data.append(spurs[1]['peak_heights'][i])\n txt1.insert(tk.END, \"Spurs freq: \" + str('%04.3f'%save_spur_freq[ss+spurs_number_save]) + \" MHz\\n\" +\" Spurs amplitude: \" + str('%04.3f'%save_spur_data[ss+spurs_number_save])+ \" dBm\\n\")\n spurs_number_save += 1\n ss += spurs_number_save\n\n txt1.see(\"end\")\n\n save_freq.append(RXLO/1e6)\n save_data.append(int(tx_pow.get()) - RX_crosstalk_ampl)\n save_noise.append(noise_f)\n save_spur_number.append(spurs_number_save)\n save_max_spur.append(np.max(spurs[1]['peak_heights']))\n\n # Plot data\n a.clear()\n a.grid(True)\n a.set_title('Frequency Spectrum')\n a.set_xlabel('Fequency (MHz)')\n a.set_ylabel('Magnitude')\n\n\n a.plot(fbin, fft_rxvals_iq_dbFS)\n\n canvas.draw()\n root.update_idletasks()\n root.update()\n\n #txt1.insert(tk.END, \"time: \" + str(time.time()) + \"\\n\")\n #txt1.see(\"end\")\n\n if(stop.get() == True):\n break\n \n avg_step += 1\n\n if(gen_en.get() == True):\n #disable TX\n ser.write(('OUTP OFF' + '\\r\\n').encode('utf-8'))\n\n trace0 = go.Scatter(\n x = save_freq,\n y = save_data,\n mode = 'lines+markers',\n name = 'RX Crosstalk'\n )\n\n x_title = 'Frequency (MHz)'\n\n layout = go.Layout(\n # width=1500, height=750,\n title='RX channel separation', titlefont=dict(family='Arial', size=28),\n xaxis=dict(title=x_title, titlefont=dict(family='Arial', size=28),\n tickangle=0, tickfont=dict(family='Arial', size=14), dtick=1000, range = [0, 6000]),\n yaxis=dict(title='Magnitude (dB)', titlefont=dict(family='Arial', size=28),\n tickfont=dict(family='Arial', size=14), dtick=10, range = [50, 120]),\n barmode='group',\n legend=dict(\n #orientation=\"h\", x=0.0, y=-0.25,\n font=dict(family='sans-serif', size=18)),\n showlegend = True)\n\n data = [trace0]\n plotly.offline.plot({\"data\": data, \"layout\": layout}, auto_open=False,\n filename = os.path.join(dirname, 'Crosstalk/RX_to_RX/ADRV9009_FullBW_crosstalk_' + rx_channel.get() + '_n11dBm_rx_gain_' + rx_gain.get() + '.html'))\n\n data_saving()\n\ndef compute_noise_floor(fft_data):\n #global noise_floor\n\n avg_size = fft_data.size/2\n noise_floor = fft_data[0] / avg_size\n\n for i in range(1, int(avg_size/2)):\n if(fft_data[i] < (noise_floor + 2)):\n noise_floor += fft_data[i] / avg_size\n else:\n noise_floor += noise_floor / avg_size\n\n for i in range(int(3*avg_size/2), fft_data.size):\n if(fft_data[i] < (noise_floor + 2)):\n noise_floor += fft_data[i] / avg_size\n else:\n noise_floor += noise_floor / avg_size\n\n return noise_floor\n\ndef data_saving():\n data = {'Frequency(MHz):': save_freq, 'Magnitude(dB):': save_data, 'Noise Floor (dBFS):': save_noise, 'Number of Spurs:': save_spur_number, 'Max Spur (dBFS):': save_max_spur}\n\n df = pd.DataFrame(data)\n df.to_csv(os.path.join(dirname, 'Crosstalk/RX_to_RX/ADRV9009_FullBW_crosstalk_' + rx_channel.get() + '_n11dBm_rx_gain_' + rx_gain.get() + '.csv'), index = False, sep = ',')\n\n data1 = {'Spur Freq (MHz):': save_spur_freq, 'Spur Data (dBFS):':save_spur_data}\n df = pd.DataFrame(data1)\n df.to_csv(os.path.join(dirname, 'Crosstalk/RX_to_RX/ADRV9009_FullBW_spurs_' + rx_channel.get() + '_n11dBm_rx_gain_' + rx_gain.get() + '.csv'), index = False, sep = ',')\n\ndef check_connection(dev):\n if(dev == ERROR):\n txt1.config(fg = \"red\")\n txt1.insert('1.0', \"No Device Detected.\\n\\n\")\n return ERROR\n else:\n txt1.config(fg = 'black')\n txt1.insert('1.0', \"Device Connected.\\n\\n\")\n return SUCCESS\n\ndef check_freq(freq):\n if(freq[0][0] == ERROR):\n txt1.config(fg = 'red')\n txt1.delete('1.0', tk.END)\n txt1.insert('1.0', \"Please insert valid start/stop frequencies.\")\n return ERROR\n else:\n txt1.config(fg = \"black\")\n txt1.insert(tk.END, \"Start frequency set to: \" + str(freq[START_FREQ]/1e6) + \" MHz\\n\\n\")\n txt1.insert(tk.END, \"Stop frequency set to: \" + str(freq[STOP_FREQ]/1e6) + \" MHz\\n\\n\")\n return SUCCESS\n\ndef check_avg_nr():\n try:\n int(avg_nr.get())\n return SUCCESS\n except:\n txt1.config(fg = \"red\")\n txt1.insert(tk.END, \"Please insert valid average number.\")\n btn_text.set(\"Start\")\n btn_start.config(bg = \"lime green\")\n return ERROR\n\n#Sweep Button\ndef sweep():\n \n sweep_en.set(True)\n \n #Clear GUI Content\n clr_content()\n\n #Check connection\n if(check_connection(dev) == ERROR):\n return\n\n # Read and print Die Temperature\n die_temp() \n \n #compute center frequencies\n freq = comp_freq()\n\n #Check for valid input frequencies\n if(check_freq(freq) == ERROR):\n return\n \n #Check for valid average number \n if(check_avg_nr() == ERROR):\n return\n \n if(load_profile.get() == str(1)):\n configure_device()\n time.sleep(0.1)\n\n setup_device()\n\n if(gen_en.get() == True):\n connect_generator()\n time.sleep(0.1)\n\n get_plot_data(dev, freq[CENTER_FREQ])\n\n if(gen_en.get() == True):\n disconnect_generator()\n\n sweep_en.set(False)\n\ndef stop():\n stop.set(true)\n\n#Create GUI\ndef gui():\n \n global root, txt1, start_f, stop_f, btn_stop, btn_aq, gen_COM, tx_pow, rx_channel, load_profile, tone_offset, rx_gain, track_en\n global a, canvas, sweep_en, avg_nr, gen_en, stop\n \n root = tk.Tk()\n root.iconbitmap(\"favicon.ico\")\n root.title(\"ADRV9009_RX_testing (Analog Devices, Inc.)\")\n\n start_f = tk.StringVar()\n stop_f = tk.StringVar()\n avg_nr = tk.StringVar()\n sweep_en = tk.BooleanVar()\n gen_en = tk.BooleanVar()\n gen_COM = tk.StringVar()\n tx_pow = tk.StringVar()\n rx_channel = tk.StringVar()\n load_profile = tk.StringVar()\n tone_offset = tk.StringVar()\n rx_gain = tk.StringVar()\n stop = tk.BooleanVar()\n track_en = tk.BooleanVar()\n\n #default values\n rx_channel.set(\"RX1\")\n gen_COM.set(\"13\")\n tx_pow.set(\"-11\")\n start_f.set(\"101\")\n stop_f.set(\"6000\")\n avg_nr.set(\"64\")\n sweep_en.set(False)\n gen_en.set(False)\n load_profile.set(False)\n tone_offset.set(\"10\")\n rx_gain.set(\"30\")\n stop.set(False)\n track_en.set(False)\n\n fr1 = tk.Frame(root)\n fr1.pack(side = tk.LEFT, anchor = 'n', padx = 10, pady = 10)\n\n fr2 = tk.Frame(fr1)\n fr2.grid(row = 0, column = 0, pady = 10)\n\n label1 = tk.Label(fr2, text = \"Start frequency (MHz): \")\n label1.grid(row = 0, column = 0)\n\n\n entry1 = tk.Entry(fr2, textvariable=start_f)\n entry1.grid(row = 0, column = 1)\n\n label2 = tk.Label(fr2, text = \"Stop frequency (MHz): \")\n label2.grid(row = 1, column = 0, pady = (0,5))\n\n entry2 = tk.Entry(fr2, textvariable=stop_f)\n entry2.grid(row = 1, column = 1)\n \n label3 = tk.Label(fr2, text = \"Load Profile: \")\n label3.grid(row = 2, column = 0)\n\n check1 = tk.Checkbutton(fr2, variable=load_profile)\n check1.grid(row = 2, column = 1)\n \n label4 = tk.Label(fr2, text = \"Average: \")\n label4.grid(row = 3, column = 0)\n\n entry3 = tk.Entry(fr2, textvariable=avg_nr)\n entry3.grid(row = 3, column = 1)\n \n label5 = tk.Label(fr2, text = \"Enable Generator: \")\n label5.grid(row = 4, column = 0)\n\n check2 = tk.Checkbutton(fr2, variable=gen_en)\n check2.grid(row = 4, column = 1)\n\n label7 = tk.Label(fr2, text = \"Generator COM: \")\n label7.grid(row = 6, column = 0)\n\n entry4 = tk.Entry(fr2, textvariable=gen_COM)\n entry4.grid(row = 6, column = 1)\n\n label8 = tk.Label(fr2, text = \"TX Power [dBm]: \")\n label8.grid(row = 7, column = 0)\n\n entry5 = tk.Entry(fr2, textvariable=tx_pow)\n entry5.grid(row = 7, column = 1)\n\n label6 = tk.Label(fr2, text = \"Tone Offset [MHz]: \")\n label6.grid(row = 8, column = 0)\n\n entry7 = tk.Entry(fr2, textvariable=tone_offset)\n entry7.grid(row = 8, column = 1)\n\n label9 = tk.Label(fr2, text = \"RX Channel: \")\n label9.grid(row = 9, column = 0)\n\n entry6 = tk.Entry(fr2, textvariable=rx_channel)\n entry6.grid(row = 9, column = 1)\n\n label10 = tk.Label(fr2, text = \"RX Gain [dB]: \")\n label10.grid(row = 10, column = 0)\n\n entry8 = tk.Entry(fr2, textvariable=rx_gain)\n entry8.grid(row = 10, column = 1)\n\n label11 = tk.Label(fr2, text = \"Tracking Enable: \")\n label11.grid(row = 11, column = 0)\n\n check3 = tk.Checkbutton(fr2, variable=track_en)\n check3.grid(row = 11, column = 1)\n\n fr3 = tk.Frame(fr1)\n fr3.grid(row = 1, column = 0)\n\n btn_sweep = tk.Button(fr3, text=\"Single Sweep\", command=sweep)\n btn_sweep.config(width = 13, height = 1, bg = \"orange\")\n btn_sweep.grid(row = 0, column = 0, pady = (10,0))\n\n btn_stop = tk.Button(fr3, text=\"Stop\", command=stop)\n btn_stop.config(width = 13, height = 1, bg = \"lime green\")\n btn_stop.grid(row = 0, column = 1, pady = (10,0))\n\n btn_aq = tk.Button(fr3, text=\"Acq&Proc\")\n btn_aq.config(width = 13, height = 1, bg = \"grey\")\n btn_aq.grid(row = 1, column = 0, pady = (10,0))\n\n fr4 = tk.Frame(fr1)\n fr4.grid(row = 2, column = 0)\n \n label9 = tk.Label(fr4, text = \"Message Log: \")\n label9.grid(row = 0, column = 0)\n \n txt1 = tkscrolled.ScrolledText(fr4, width = 40, height = 20)\n txt1.grid(row = 1, column = 0)\n \n fig = plt.figure()\n DPI = fig.get_dpi()\n fig.set_size_inches(1024.0/float(DPI), 640.0/float(DPI)) \n a = fig.add_subplot(111)\n a.grid(True)\n a.set_title('Frequency Spectrum')\n a.set_xlabel('Fequency (MHz)')\n a.set_ylabel('Magnitude')\n plt.tight_layout()\n plotFrame = tk.Frame(master=root)\n plotFrame.pack(side = tk.LEFT, pady = 10, padx = 10, anchor = 'n')\n\n toolbarFrame = tk.Frame(master=plotFrame)\n toolbarFrame.pack(anchor = 'w', pady = (0,10))\n \n canvas = FigureCanvasTkAgg(fig, master=plotFrame)\n canvas.get_tk_widget().pack(fill = tk.BOTH)\n canvas.draw()\n root.update_idletasks()\n\n toolbar = NavigationToolbar2TkAgg(canvas, toolbarFrame)\n toolbar.update()\n \n root.mainloop()\n\n#main function\ndef main():\n\n global dev\n\n #connect device\n dev = connect_device()\n\n configure_device()\n gui()\n\nmain()","sub_path":"ADRV9009_test_scripts/ADRV9009_crosstalk_RX.py","file_name":"ADRV9009_crosstalk_RX.py","file_ext":"py","file_size_in_byte":28358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"101217663","text":"#!/usr/bin/env python \n# -*- coding: utf-8 -*-\n# @Time : 2020-07-04 19:47\n# @Author : NingAnMe \nimport argparse\nfrom dateutil.relativedelta import relativedelta\n\nfrom lib.config import *\nfrom lib.dbkucun import *\nfrom lib.dbyingxiao import *\nfrom lib.path import *\n\n\ndef order_add(order_file, dt_s, dt_e):\n \"\"\"\n 将订单文件导入数据库\n :param order_file: csv文件\n :param dt_s: 发货时间-开始时间\n :param dt_e: 发货时间-结束时间\n :return:\n \"\"\"\n orders = PddOrder.csv2order(order_file)\n if len(orders) <= 0:\n print('没有有效的订单')\n return -1\n orders_filter_by_datetime = list()\n for order in orders:\n if dt_s <= order['fahuoshijian'] <= dt_e:\n orders_filter_by_datetime.append(order)\n\n if len(orders_filter_by_datetime) <= 0:\n print(f'没有有效的订单：{dt_s} to {dt_e}')\n return -1\n\n # 剔除已经录入的订单\n # 找到数据库里面这个时间范围的订单\n with session_scope() as session:\n orders_db = PddOrder.query_fahuoshijian(session, dt_s, dt_e)\n\n # 剔除已经录入的订单\n dingdanhao_db = set()\n if orders_db is not None:\n for order in orders_db:\n dingdanhao_db.add(order.dingdanhao)\n\n orders_filter = list()\n for order in orders_filter_by_datetime:\n if order[\"dingdanhao\"] not in dingdanhao_db:\n orders_filter.append(order)\n\n print(f\"有效订单数量：{len(orders_filter)}\")\n\n # 将过滤以后的订单入库\n with session_scope() as session:\n PddOrder.add(session, orders_filter)\n print(\"success\")\n\n\ndef goods_qukucun(dingdanhao):\n with session_scope() as session:\n order = PddOrder.query_dingdanhao(session, dingdanhao)\n if order is None or order.shifouqukucun == '是':\n return\n\n skubianma = order.skubianma\n sku = PddSku.query_bianma(session, skubianma)\n if sku is None:\n print(f\"没有对应的sku：{skubianma}\")\n return\n\n # 获取商品和商品需要减少的数量\n jianshu = order.shangpinshuliang\n for goods in sku.goods:\n goods.goods.shuliang -= goods.shuliang * jianshu\n order.shifouqukucun = '是'\n return True\n\n\ndef goods_detail_add(json_data, dt):\n datas = PddGoodsDetail.json2data(json_data)\n with session_scope() as session:\n result = PddGoodsDetail.query_datetime(session, dt)\n ids = set()\n for row in result:\n ids.add(row.goodsId)\n datas_filter = list()\n for i in datas:\n if str(i['goodsId']) not in ids:\n datas_filter.append(i)\n PddGoodsDetail.add(session, datas_filter)\n print(f\"goods_detail_add Success: {len(datas_filter)}\")\n\n\ndef ad_unit_add(json_data, dt, ad_type):\n datas = PddAdUnit.json2data(json_data, dt, ad_type)\n with session_scope() as session:\n result = PddAdUnit.query_datetime(session, dt)\n ids = set()\n for row in result:\n if row.adType == ad_type:\n ids.add(row.adId)\n datas_filter = list()\n for i in datas:\n if str(i['adId']) not in ids:\n datas_filter.append(i)\n PddAdUnit.add(session, datas_filter)\n print(f\"ad_unit_add Success: {ad_type} 处理数据量：{len(datas_filter)}\")\n\n\ndef deal_paid_free_order(dt):\n PddGoodsDetail.paid_free_order(dt)\n\n\n# def goods_detail_add(dt_s, dt_e):\n# if dt_s == dt_e:\n# dt_e = dt_e + relativedelta(days=1) - relativedelta(minutes=1)\n#\n# count = 0\n# with session_scope() as session:\n# orders = PddOrder.query_fahuoshijian(session, datetime_start=dt_s, datetime_end=dt_e)\n# print(f'本次有效订单数量：{len(orders)}')\n#\n# goods_xiaoliang = defaultdict(int)\n# for order in orders:\n# skubianma = order.skubianma\n# sku = PddSku.query_bianma(session, skubianma)\n# if sku is None:\n# print(f\"没有对应的sku：{skubianma}\")\n# continue\n#\n# # 获取商品和商品需要减少的数量\n# jianshu = order.shangpinshuliang\n# for goods in sku.goods:\n# goods_xiaoliang[goods.goods.bianma] += goods.shuliang * jianshu\n# count += 1\n# print(f'本次处理订单数量： {count}')\n# goods_details = list()\n# for k, v in goods_xiaoliang.items():\n# goods_details.append({\n# 'statDate': dt_s,\n# 'bianma': k,\n# 'xiaoliang': v,\n# })\n# GoodsDetail.add(session, goods_details)\n\n\ndef goods_qukucun_datetime(dt_s, dt_e):\n if dt_s == dt_e:\n dt_e = dt_e + relativedelta(days=1) - relativedelta(minutes=1)\n dingdanhaos = list()\n with session_scope() as session:\n orders = PddOrder.query_fahuoshijian(session, datetime_start=dt_s, datetime_end=dt_e)\n print(f'总订单数量：{len(orders)}')\n for order in orders:\n if order.shifouqukucun != '是':\n dingdanhaos.append(order.dingdanhao)\n print(f'本次有效订单数量：{len(dingdanhaos)}')\n count = 0\n for dingdanhao in dingdanhaos:\n r = goods_qukucun(dingdanhao)\n if r:\n count += 1\n print(f'本次处理订单数量： {count}')\n\n\ndef order_file_ruku_datetime(dt_s, dt_e):\n print(dt_s, dt_e)\n order_files = os.listdir(order_file_path)\n order_files.sort()\n while dt_s <= dt_e:\n dt_tmp_s = dt_s\n dt_tmp_e = dt_tmp_s + relativedelta(days=1) - relativedelta(seconds=1)\n dt_str = dt_tmp_s.strftime('%Y-%m-%d')\n # 处理订单并入库\n order_file = None\n for filename in order_files:\n if (\"orders_export\" + dt_str) in filename:\n order_file = os.path.join(order_file_path, filename)\n if order_file is not None:\n print(f'订单文件：{order_file}')\n order_add(order_file, dt_tmp_s, dt_tmp_e)\n dt_s += relativedelta(days=1)\n\n\ndef detail_file_ruku_datetime(dt_s, dt_e):\n json_data_files = os.listdir(json_file_path)\n json_data_files.sort()\n\n while dt_s <= dt_e:\n dt_tmp = dt_s\n dt_str = dt_tmp.strftime('%Y-%m-%d')\n dt = datetime.strptime(dt_str, \"%Y-%m-%d\")\n\n # 添加营销数据\n for filename in json_data_files:\n if dt_str in filename:\n json_data_file = os.path.join(json_file_path, filename)\n # 添加商品销售数据\n if 'detail' in filename:\n goods_detail_add(json_data_file, dt)\n\n # 添加搜索推广数据\n if 'search' in filename:\n ad_unit_add(json_data_file, dt, 'search')\n\n # 添加场景推广数据\n if 'scene' in filename:\n ad_unit_add(json_data_file, dt, 'scene')\n\n # 处理付费订单和免费数量\n deal_paid_free_order(dt)\n dt_s += relativedelta(days=1)\n\n\ndef set_all_order_qu_ku_cun_yes(dt_s, dt_e):\n # 将所有订单的状态设置为去库存\n with session_scope() as session:\n orders = session.query(PddOrder).filter(PddOrder.fahuoshijian >= dt_s,\n PddOrder.fahuoshijian <= dt_e,\n PddOrder.shifouqukucun != '是').all()\n for order in orders:\n order.shifouqukucun = '是'\n print(f\"set_all_order_qu_ku_cun Success: 处理数据量：{len(orders)}\")\n\n\ndef set_all_order_qu_ku_cun_no(dt_s, dt_e):\n # 将所有订单的状态设置为去库存\n with session_scope() as session:\n orders = session.query(PddOrder).filter(PddOrder.fahuoshijian >= dt_s,\n PddOrder.fahuoshijian <= dt_e,\n PddOrder.shifouqukucun != '否').all()\n for order in orders:\n order.shifouqukucun = '否'\n print(f\"set_all_order_qu_ku_cun Success: 处理数据量：{len(orders)}\")\n\n\nif __name__ == '__main__':\n # ######################### 业务运行 ###################################\n parser = argparse.ArgumentParser(description='地外太阳能数据生产工具')\n parser.add_argument('--addOrder', help='处理订单文件，数据入库')\n parser.add_argument('--quKuCun', help='销库存')\n parser.add_argument('--addGoodsDetail', help='处理营销Json文件，数据入库')\n parser.add_argument('--date', help='开始时间(北京时)，YYYY-mm-dd(2019-01-01)')\n parser.add_argument('--dateStart', '-s', help='开始时间(北京时)，YYYY-mm-dd(2019-01-01)')\n parser.add_argument('--dateEnd', '-e', help='结束时间（北京时），YYYY-mm-dd(2019-01-01)')\n parser.add_argument('--setAllOrderQuKuCunYes', help='将所有的订单状态设置为去库存', default=False)\n parser.add_argument('--setAllOrderQuKuCunNo', help='将所有的订单状态设置为去库存', default=False)\n parser.add_argument('--debug', help='是否DEBUG', default=False)\n parser.add_argument('--test', help='是否TEST', default=False)\n args = parser.parse_args()\n\n datetime_ = None\n if args.date is not None:\n datetime_ = datetime.strptime(args.date, '%Y-%m-%d')\n\n if args.date is not None and args.dateStart is None:\n args.dateStart = args.date\n\n datetime_s = datetime_e = None\n if args.dateStart is not None:\n datetime_s = datetime.strptime(args.dateStart, '%Y-%m-%d')\n if args.dateEnd is not None:\n datetime_e = datetime.strptime(args.dateEnd, '%Y-%m-%d')\n else:\n datetime_e = datetime_s\n\n # 处理订单文件\n if args.addOrder:\n print(f'addOrder: {args.addOrder}')\n order_file_ruku_datetime(datetime_s, datetime_e)\n\n # 销库存\n if args.quKuCun:\n goods_qukucun_datetime(datetime_s, datetime_e)\n\n # 营销数据入库且处理\n if args.addGoodsDetail:\n detail_file_ruku_datetime(datetime_s, datetime_e)\n\n # 将所有订单的状态设置为已经去库存的状态'是'\n if args.setAllOrderQuKuCunYes:\n set_all_order_qu_ku_cun_yes(datetime_s, datetime_e)\n\n # 将所有订单的状态设置为已经去库存的状态’否‘\n if args.setAllOrderQuKuCunNo:\n set_all_order_qu_ku_cun_no(datetime_s, datetime_e)\n\n # 销量统计\n goods_xiaoliang_tongji(datetime_s, datetime_e)\n","sub_path":"business.py","file_name":"business.py","file_ext":"py","file_size_in_byte":10571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"189171903","text":"# 1290. Convert Binary Number in a Linked List to Integer\n# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution(object):\n def getDecimalValue(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: int\n \"\"\"\n n = 0\n cur = head\n while cur != None:\n cur = cur.next\n n += 1\n \n number = 0\n while head != None:\n number += head.val * 2**(n-1)\n n = n - 1\n head = head.next\n return number\n","sub_path":"easy/1290.py","file_name":"1290.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"71661290","text":"from lxml import etree as ET\nfrom copy import deepcopy\n\n\ndef create_body():\n return ET.Element(\"body\")\n\n\nxml_body = ET.Element(\"body\")\nprint(xml_body)\n\n\ndef create_div(body, act):\n div = ET.SubElement(body, \"div\")\n div.set(\"act\", act)\n sp = ET.SubElement(div, \"sp\")\n sp.text = \"stuff\"\n\n\ncreate_div(xml_body, \"1\")\n\nxml_sp = ET.Element(\"sp\")\nspeaker = ET.SubElement(xml_sp, \"speaker\")\nspeaker.text = \"Jean\"\np = ET.SubElement(xml_sp, 'p')\np.text = \"Ta mère en slip\"\np = ET.SubElement(xml_sp, 'p')\np.text = \"ta mere a poil\"\nprint(ET.tostring(xml_sp))\n\n\ndef combine_xmltree_element(element_1, element_2):\n \"\"\"\n Recursively combines the given two xmltree elements. Common properties will be overridden by values of those\n properties in element_2.\n\n :param element_1: A xml Element\n :type element_1: L{Element}\n\n :param element_2: A xml Element\n :type element_2: L{Element}\n\n :return: A xml element with properties combined.\n \"\"\"\n\n if element_1 is None:\n return element_2.copy()\n\n if element_2 is None:\n return element_1.copy()\n\n if element_1.tag != element_2.tag:\n raise TypeError(\n \"The two XMLtree elements of type {t1} and {t2} cannot be combined\".format(\n t1=element_1.tag,\n t2=element_2.tag\n )\n )\n\n combined_element = ET.Element(tag=element_1.tag, attrib=element_1.attrib)\n combined_element.attrib.update(element_2.attrib)\n\n # Create a mapping from tag name to child element\n element_1_child_mapping = {child.tag: child for child in element_1}\n element_2_child_mapping = {child.tag: child for child in element_2}\n\n for child in element_1:\n if child.tag not in element_2_child_mapping:\n combined_element.append(child.copy())\n\n for child in element_2:\n if child.tag not in element_1_child_mapping:\n combined_element.append(child.copy())\n\n else:\n if len(child) == 0: # Leaf element\n combined_child = element_1_child_mapping[child.tag].copy()\n combined_child.text = child.text\n combined_child.attrib.update(child.attrib)\n\n else:\n # Recursively process the element, and update it in the same way\n combined_child = combine_xmltree_element(\n element_1_child_mapping[child.tag], child)\n\n combined_element.append(combined_child)\n\n return combined_element\n\n\nprint(xml_sp.tag)\n","sub_path":"test_tree.py","file_name":"test_tree.py","file_ext":"py","file_size_in_byte":2492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"232210832","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef gaussian(mu,sig,val):\n coef = 1.0/(sig*np.sqrt(2*np.pi))\n main = np.exp(-1*((val-mu)/(2*sig))**2)\n return coef*main\n\n\ndef quad(x,a,b,c):\n return a*x**2+b*x+c\n\n# Get \"observed\" quadratic\n# parameters for \"observed\" quadratic function\na = 0.3\nb = -1.8\nc = 1.8\nstp = 0.5\nx = np.arange(start=0,stop=10+stp,step=stp)\nvquad = np.vectorize(lambda x: quad(x,a,b,c))\ny = vquad(x)\n\ndef obsquad(ax=plt.gca()):\n spread = 1.5\n for _ in range(4):\n ax.plot(x,y+spread*np.random.randn(len(x)),linestyle='None',marker='o',c='blue',mfc='None')\n ax.set_xlabel('\"Input\" Variable')\n ax.set_ylabel('\"Response\" Variable')\n\ndef vobsquad(ax=plt.gca()):\n obsquad(ax)\n ax.plot(x,vquad(x),color='green')\n","sub_path":"whitty_meeting/quad.py","file_name":"quad.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"549152300","text":"import scrapy\r\nimport re\r\n\r\n\r\nclass FinnSpider(scrapy.Spider):\r\n \"\"\"Run 'scrapy crawl properties' in virtualenv\"\"\"\r\n allowed_domains = ['finn.no']\r\n name = \"FinnSpider\"\r\n\r\n # V70\r\n # start_urls = [\"https://www.finn.no/car/used/search.html?filters=&make=0.818&model=1.818.3077\"] \r\n\r\n # XC70\r\n # start_urls = [\"https://www.finn.no/car/used/search.html?filters=&make=0.818&model=1.818.7781\"]\r\n # start_urls = []\r\n\r\n def parse(self, response):\r\n ''' \r\n This method, as well as any other Request callback, must return an\r\n iterable of Request and/or dicts or Item objects.\r\n '''\r\n\r\n for ad_data, ad_title in zip(response.css('div.ads__unit__content__keys'), response.css('h2.ads__unit__content__title')):\r\n keys = ad_data.css('*::text').getall()\r\n title = ad_title.css('*::text').getall()\r\n\r\n if len(keys) > 1:\r\n item = dict()\r\n # parse year, remove non-numbers\r\n item['year'] = re.sub('[^0-9]', \"\", keys[0])\r\n # parse mileage, remove non-numbers\r\n item['mileage'] = re.sub('[^0-9]', \"\", keys[1])\r\n # parse price, remove non-numbers\r\n item['price'] = re.sub('[^0-9]', \"\", keys[2])\r\n\r\n try:\r\n # convert to integers\r\n item['year'] = int(item['year'])\r\n item['mileage'] = int(item['mileage'])\r\n item['price'] = int(item['price'])\r\n\r\n except ValueError:\r\n continue\r\n\r\n item['title'] = title[0]\r\n\r\n yield item\r\n\r\n for a in response.css('a.button--icon-right'):\r\n print('RESPONSE: ', a)\r\n yield response.follow(a, callback=self.parse)\r\n","sub_path":"properties/spiders/finn_spider.py","file_name":"finn_spider.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"458950856","text":"#!/usr/bin/env python3\n\nimport pandas as pd\nimport plotly.graph_objects as go\nfrom scipy.io import loadmat\n\ndef load_panda():\n d=pd.read_csv('excel/hfos_clean.csv')\n labels=[i for i in d]\n return d[labels[1:]]\n\ndef needles_list(patient=None, all=False):\n d=load_panda()\n if patient!=None:\n d=d.loc[d['Patient']==patient]\n res=[]\n if all:\n res.append('All')\n for e in d['Channel']:\n ee=''.join(filter(str.isalpha, e))\n if ee not in res:\n res.append(ee)\n return res\n\ndef pand():\n out,noOut= 0,0\n d=load_panda()\n #print(data[data.columns[0]])\n #print(data)\n for e in d['Zone']:\n if 'Outside' in e:\n out +=1\n else:\n noOut +=1\n total=out+noOut\n print(out, noOut, total)\n\ndef patients():\n d=load_panda()\n patients_list=[]\n for patient in d['Patient']:\n if patient not in patients_list:\n patients_list.append(patient)\n return patients_list\n\ndef pand_sensores(selected_patient):\n dic={}\n d=load_panda()\n for amplitude, patient, channel in zip(d['Amplitude'],d['Patient'],d['Channel']):\n if selected_patient in patient:\n if channel in dic:\n dic[channel].append(amplitude)\n else:\n dic[channel] = [amplitude]\n #print(dic, len(dic))\n return dic\n\ndef dic_to_data(d):\n x=[]\n y=[]\n for sensor in d:\n x.append(sensor)\n median=sum([float(e) for e in d[sensor]])/len(d[sensor])\n y.append(median)\n return x,y\n\ndef multiplot_soz1(selected_patient, *args):\n d=load_panda()\n filtered_d=d.loc[d['Patient']==selected_patient]\n dic={'Zone':[]}\n for zone in filtered_d['Zone']:\n if 'Outside' in zone:\n dic['Zone'].append(0)\n else:\n dic['Zone'].append(1) \n for e in args:\n dic[e]=[]\n for row_zone, row_e in zip(filtered_d['Zone'], filtered_d[e]):\n if 'Propagation' in row_zone:\n pass\n else:\n dic[e].append(row_e)\n return dic\n\n\ndef multiplot_soz(selected_patient, selector, args):\n d=load_panda()\n filtered_d=d.loc[d['Patient']==selected_patient]\n needles=needles_list(selected_patient)\n a=[]\n res=[]\n if selector != 'needles':\n for e in d[selector]:\n if e not in a:\n d={0:e}\n for h in args:\n d[h]=[]\n res.append(d)\n a.append(e)\n else:\n for e in needles:\n d={0:e}\n for h in args:\n d[h]=[]\n res.append(d)\n for y in args:\n for e in res:\n if selector != 'needles':\n for row_selector, row_e in zip(filtered_d[selector], filtered_d[y]):\n if row_selector == e[0]:\n e[y].append(row_e)\n else:\n for row_selector, row_e in zip(filtered_d['Channel'], filtered_d[y]):\n if e[0]==''.join(filter(str.isalpha, row_selector)):\n e[y].append(row_e)\n return res\n\ndef mp(selected_patient, selector, values):\n x=multiplot_soz(selected_patient, selector, values)\n data=[]\n for e in x:\n d=go.Splom(\n dimensions=[dict(label=k,values=e[k]) for k in e if k != 0],\n name=e[0],\n marker=dict(size=4),\n diagonal=dict(visible=False))\n data.append(d)\n layout=go.Layout(title=\"Multiplot prove\", dragmode='select', hovermode='closest', showlegend=True)\n fig=go.Figure(data=data,layout=layout)\n return fig\n\ndef scatter_soz(selected_patient, selector, args): #args=[x,y]\n d=load_panda()\n filtered_d=d.loc[d['Patient']==selected_patient]\n needles=needles_list(selected_patient)\n a=[]\n res=[]\n if selector != 'needles':\n for e in d[selector]:\n if e not in a:\n res.append({0:e, 'x':[], 'y':[]})\n a.append(e)\n else:\n for e in needles:\n res.append({0:e, 'x':[], 'y':[]})\n for e in res:\n if selector != 'needles':\n for row_selector, row_x, row_y in zip(filtered_d[selector], filtered_d[args[0]], filtered_d[args[1]]):\n if row_selector == e[0]:\n e['x'].append(row_x)\n e['y'].append(row_y)\n else:\n for row_selector, row_x, row_y in zip(filtered_d['Channel'], filtered_d[args[0]], filtered_d[args[1]]):\n if e[0]==''.join(filter(str.isalpha, row_selector)):\n e['x'].append(row_x)\n e['y'].append(row_y)\n return res\n\ndef scatter(selected_patient, selector, values):\n x=scatter_soz(selected_patient, selector, values)\n data=[]\n for e in x:\n d=go.Scatter(\n x=e['x'],\n y=e['y'],\n name=e[0],\n mode='markers',\n marker=dict(size=4))\n data.append(d)\n layout=go.Layout(title=\"Scatterplot prove\", hovermode='closest', showlegend=True, xaxis_title=values[0], yaxis_title=values[1])\n fig=go.Figure(data=data, layout=layout)\n return fig\n\ndef histogram_soz(selected_patient, selector, args): #args=[x,y]\n d=load_panda()\n filtered_d=d.loc[d['Patient']==selected_patient]\n needles=needles_list(selected_patient)\n a=[]\n res=[]\n if selector != 'needles':\n for e in d[selector]:\n if e not in a:\n res.append({0:e, 'd':[], 'len':0})\n a.append(e)\n else:\n for e in needles:\n res.append({0:e, 'd':[], 'len':0})\n for e in res:\n if selector != 'needles':\n for row_selector, row_e in zip(filtered_d[selector], filtered_d[args]):\n if row_selector == e[0]:\n e['d'].append(row_e)\n else:\n for row_selector, row_e in zip(filtered_d['Channel'], filtered_d[args]):\n if e[0]==''.join(filter(str.isalpha, row_selector)):\n e['d'].append(row_e)\n for e in res:\n e['len']=len(e['d'])\n res.sort(key= lambda i: i['len'], reverse=True)\n return res\n\ndef histogram(selected_patient, selector, value):\n x=histogram_soz(selected_patient, selector, value)\n data=[]\n for e in x:\n d=go.Histogram(\n x=e['d'],\n name=e[0],\n opacity=0.75,\n bingroup=1)\n data.append(d)\n layout=go.Layout(title_text=\"Histogram prove\", showlegend=True, xaxis_title_text=value, yaxis_title_text='Count', barmode='overlay')\n fig=go.Figure(data=data, layout=layout)\n return fig\n\ndef heatmap():\n mat = loadmat('excel/data.mat')\n mat_data = mat['stockwell']\n data=go.Heatmap(\n z=mat_data\n )\n layout=go.Layout(title_text=\"Heatmap prove\", showlegend=True, xaxis_title_text='X-axis', yaxis_title_text='Y-axis')\n fig=go.Figure(data=data, layout=layout)\n return fig\n\ndef scatter3d_soz(selected_patient, selector, args): #args=[x,y,z]\n d=load_panda()\n filtered_d=d.loc[d['Patient']==selected_patient]\n needles=needles_list(selected_patient)\n a=[]\n res=[]\n if selector != 'needles':\n for e in d[selector]:\n if e not in a:\n res.append({0:e, 'x':[], 'y':[], 'z':[]})\n a.append(e)\n else:\n for e in needles:\n res.append({0:e, 'x':[], 'y':[], 'z':[]})\n for e in res:\n if selector != 'needles':\n for row_selector, row_x, row_y, row_z in zip(filtered_d[selector], filtered_d[args[0]], filtered_d[args[1]], filtered_d[args[2]]):\n if row_selector == e[0]:\n e['x'].append(row_x)\n e['y'].append(row_y)\n e['z'].append(row_z)\n else:\n for row_selector, row_x, row_y, row_z in zip(filtered_d['Channel'], filtered_d[args[0]], filtered_d[args[1]], filtered_d[args[2]]):\n if e[0]==''.join(filter(str.isalpha, row_selector)):\n e['x'].append(row_x)\n e['y'].append(row_y)\n e['z'].append(row_z)\n return res\n\ndef scatter3d(selected_patient, selector, values):\n x=scatter3d_soz(selected_patient, selector, values)\n data=[]\n for e in x:\n d=go.Scatter3d(\n x=e['x'],\n y=e['y'],\n z=e['z'],\n name=e[0],\n mode='markers',\n marker=dict(size=4))\n data.append(d)\n layout=go.Layout(title=\"3D Scatterplot prove\", hovermode='closest', showlegend=True, scene=dict(xaxis_title=values[0], yaxis_title=values[1], zaxis_title=values[2]))\n fig=go.Figure(data=data, layout=layout)\n return fig\n\ndef list_needles_3dscatter(filtered_d, needles):\n res=[]\n for e in needles:\n dic={0:e, 'data':[]}\n a=[]\n for y, row_x, row_y, row_z in zip(filtered_d['Channel'], filtered_d['x'], filtered_d['y'], filtered_d['z']):\n if (e==''.join(filter(str.isalpha, y))) and (y not in a):\n dic['data'].append({0:y, 'xyz':[row_x, row_y, row_z], 'd':[], 'd_scale':[]})\n a.append(y)\n res.append(dic)\n return res\n\ndef scatter3d_soz_v1(selected_patient, args):\n d=load_panda()\n filtered_d=d.loc[d['Patient']==selected_patient]\n needles=needles_list()\n res=list_needles_3dscatter(filtered_d, needles_list(selected_patient))\n for e in res:\n for y in e['data']:\n for row_selector, value in zip(filtered_d['Channel'], filtered_d[args]):\n if y[0]==row_selector:\n y['d'].append(value)\n mini=0\n maxi=0\n for e in res:\n for y in e['data']:\n calc=sum(y['d'])/len(y['d'])\n y['d']=calc\n if calc>maxi:\n maxi=calc\n if calc%{text}
\" +\n \"%{customdata[1]} = %{customdata[0]:.02f}
\" +\n \"\", evaluate)\r\ne.grid(row=1)\r\n\r\nans = Label(root)\r\nans.grid(row=2,column=1)\r\n\r\nroot.mainloop()\r\n\r\n\r\n","sub_path":"Mini_calculator.py","file_name":"Mini_calculator.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"479021645","text":"import os\n\nfrom .settings import *\n\n# Google App Engine Settings\nimport sys\nsys.path.append('/usr/local/google_appengine')\nsys.path.append('/usr/local/google_appengine/lib/yaml/lib')\nsys.path.append('/usr/local/google_appengine/lib/fancy_urllib')\n\n\nDEBUG = False\n\nUSE_CACHEMANAGER = False\nENABLE_DB_LOGGER = True\n\n\nCACHES = {\n 'default': {\n 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',\n 'LOCATION': 'horizon_api',\n 'TIMEOUT': 60,\n }\n}\n\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),\n }\n}\n\n\n\nINSTALLED_APPS = ('django_nose',) + INSTALLED_APPS\nTEST_RUNNER = 'django_nose.NoseTestSuiteRunner'\nNOSE_ARGS = [\n '--with-gae',\n '-s',\n '--logging-level=WARNING',\n '--exclude-dir=gaenv_lib',\n '--with-coverage',\n '--cover-erase',\n ]\nAPPS_TO_COVER = [x for x in INSTALLED_APPS if not x.startswith('django') and x != 'jlib']\nNOSE_ARGS += ['--cover-package=%s'%x for x in APPS_TO_COVER]\n","sub_path":"food_trucks/test_settings.py","file_name":"test_settings.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"173903914","text":"from django.forms import ModelForm\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, Submit, Button, ButtonHolder\nfrom crispy_forms.bootstrap import FormActions\n\n# Models\nfrom blog.models import Article\n\nclass ArticleForm(ModelForm):\n \n class Meta:\n model = Article\n fields = '__all__'\n exclude = ('slug',)\n\n def __init__(self, *args, **kwargs):\n super(ArticleForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper(self)\n self.helper.layout = Layout(\n 'title',\n 'content',\n 'tags',\n ButtonHolder(\n Button('cancel', 'Cancelar'), \n Submit('save', 'Salvar'),\n css_class='float-right'\n )\n )","sub_path":"blog/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"415460297","text":"import os\nimport sys\nimport time\nimport subprocess\n\n# Use subprocess to Manage Child Processes\n\n# result = subprocess.run(\n# [\"echo\", \"Hello from the child!\"], capture_output=True, encoding=\"utf-8\"\n# )\n# result.check_returncode() # No exception means clean exit\n# print(result.stdout)\n\n## Decoupling the child process from the parent frees up the parent process to\n## run many child processes in parallel. Here I do this by starting all the child\n## processes together with Popen upfront\n# start = time.time()\n# sleep_procs = []\n# for _ in range(10):\n# proc = subprocess.Popen([\"sleep\", \"1\"])\n# sleep_procs.append(proc)\n\n##\n# for proc in sleep_procs:\n# proc.communicate()\n\n# end = time.time()\n# delta = end - start\n## Takes about 1.05 seconds to perform all the 10 tasks concurrently\n# print(f\"Process took: {delta:.3} seconds\")\n\n## If these processes ran in a sequential manner then the total delay would be\n## 10+ seconds rather than ~1 second\n\n## You can also pipe data from a Python program into a subprocess and retrieve\n## its output.\n\n\ndef run_encrypt(data):\n \"\"\"\n Use the openssl command-line tool to encrypt some data.\n This function starts the child process with CLI arguments and I/O pipes\n \"\"\"\n env = os.environ.copy()\n\n env[\"password\"] = \"zhsklsdfjw4e2888jsda/sfshnssd/m/KJ)2329#\"\n proc = subprocess.Popen(\n [\"openssl\", \"enc\", \"-des3\", \"-pass\", \"env:password\"],\n env=env,\n stdin=subprocess.PIPE, # Allow sending of data to process's stdin\n stdout=subprocess.PIPE, # To retrieve the result instead of None\n )\n proc.stdin.write(data)\n proc.stdin.flush() # Ensure that the child gets the input\n return proc\n\n\n# procs = []\n# for _ in range(3):\n# data = os.urandom(10)\n# proc = run_encrypt(data)\n# procs.append(proc)\n\n# for proc in procs:\n# out, _ = proc.communicate()\n# print(\"Out: \", out)\n\n\ndef run_hash(input_stdin):\n \"\"\"\n Start the openssl CLI tool as a subprocess to generate a Whirpool hash of\n the input stream\n\n Args:\n input_stdin -> Earlier subprocess that passes input to this process\n \"\"\"\n return subprocess.Popen(\n [\"openssl\", \"dgst\", \"-whirlpool\", \"-binary\"],\n stdin=input_stdin,\n stdout=subprocess.PIPE,\n )\n\n\ndef sleep_pipe(input_stdin):\n return subprocess.Popen([\"sleep\", \"1\"], stdin=input_stdin, stdout=subprocess.PIPE)\n\n\n# Now, one set of processes is used to encrypt some data and another set of\n# processes is used to hash their encrypted output.\n\nencrypt_procs = []\nhash_procs = []\nsleep_procs = []\n\nstart = time.time()\nfor _ in range(3):\n data = os.urandom(100)\n # Encrypt the data first\n encrypt_proc = run_encrypt(data)\n encrypt_procs.append(encrypt_proc)\n\n sleep_proc = sleep_pipe(encrypt_proc.stdout)\n sleep_procs.append(sleep_proc)\n\n # Pass the encrypted data stream to run_hash()\n hash_proc = run_hash(encrypt_proc.stdout)\n hash_procs.append(hash_proc)\n\n # Ensures that the child consumes the input stream and the communicate()\n # method doesn't inadvertently steal input from the child\n encrypt_proc.stdout.close()\n encrypt_proc.stdout = None\n\n sleep_proc.stdout.close()\n sleep_proc.stdout = None\n\n\nfor proc in encrypt_procs:\n try:\n proc.communicate(timeout=0.1)\n assert proc.returncode == 0\n except subprocess.TimeoutExpired:\n print(\"Exit status\", proc.poll())\n proc.terminate()\n proc.wait()\n sys.exit(1)\n\nfor proc in sleep_procs:\n try:\n # timeout of 0.1 should cause the entire process to end if the sleep is\n # longer than 0.1\n proc.communicate(timeout=2)\n assert proc.returncode == 0\n except subprocess.TimeoutExpired:\n print(\"Exit status\", proc.poll())\n proc.terminate()\n proc.wait()\n sys.exit(1)\n\nfor proc in hash_procs:\n try:\n out, _ = proc.communicate(timeout=0.1)\n print(out[-10:])\n assert proc.returncode == 0\n except subprocess.TimeoutExpired:\n print(\"Exit status\", proc.poll())\n proc.terminate()\n proc.wait()\n sys.exit(1)\n\nend = time.time()\ndelta = end - start\nprint(f\"Took {delta:.3} seconds\")\n","sub_path":"ConcurrencyAndParellelism/conc.py","file_name":"conc.py","file_ext":"py","file_size_in_byte":4197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"69396906","text":"from pyspark.sql import SparkSession\nfrom operator import add\nfrom itertools import combinations\nimport csv\nimport sys\nimport time\n\n\ndef candidateCount(partitionBasket, candidateItems):\n itemCount = {}\n\n for candidate in candidateItems:\n if type(candidate) is int:\n candidate = [candidate]\n item = tuple(sorted(candidate))\n candidate = set(candidate)\n for basket in partitionBasket:\n if candidate.issubset(basket):\n itemCount[item] = itemCount.get(item, 0) + 1\n\n return itemCount\n\n\ndef getFrequentItems(partitionBasket, candidateItems, basketSupport):\n itemCount = candidateCount(partitionBasket, candidateItems)\n filteredFrequentItems = [item for item in itemCount if itemCount[item] >= basketSupport]\n return filteredFrequentItems\n\n\ndef getCandidateItems(frequentItems, k):\n\n if k == 2:\n return list(combinations(frequentItems, 2))\n\n candidateItems = []\n\n for item in combinations(frequentItems, 2):\n new_item = set(item[0] + item[1])\n new_item_tuple = tuple(sorted(new_item))\n if len(new_item) == k and new_item_tuple not in candidateItems:\n flag = True\n for sub_item in combinations(new_item, k - 1):\n if tuple(sorted(sub_item)) not in frequentItems:\n flag = False\n break\n if flag:\n candidateItems.append(new_item_tuple)\n\n return candidateItems\n\n\ndef aprioriAlgorithm(partitionBasket, support, totalCount):\n\n partitionBasket = list(partitionBasket)\n basketSupport = support * (float(len(partitionBasket)) / float(totalCount))\n finalResult = list()\n\n candidateItems = []\n for basket in partitionBasket:\n candidateItems += list(basket)\n\n frequentItems = []\n for item in set(candidateItems):\n if candidateItems.count(item) >= basketSupport:\n frequentItems.append(item)\n\n frequentItems = sorted(frequentItems)\n\n finalResult.extend(frequentItems)\n\n k = 2\n while len(frequentItems) > 0:\n\n candidateItems = getCandidateItems(frequentItems, k)\n\n if len(candidateItems) > 0:\n frequentItems = getFrequentItems(partitionBasket, candidateItems, basketSupport)\n frequentItems = sorted(frequentItems)\n finalResult.extend(frequentItems)\n else:\n frequentItems = []\n\n k = k + 1\n\n return finalResult\n\n\ndef writeItemsToFile(outputItems, outFilePtr):\n\n length = 1\n output = {}\n\n nextList = list(filter(lambda l: len(l) == length, outputItems))\n\n while nextList:\n output[length] = nextList\n length += 1\n nextList = list(filter(lambda l: len(l) == length, outputItems))\n\n for key in sorted(output.keys()):\n\n outputList = output[key]\n outputItems = []\n for item in outputList:\n outputItems.append(tuple(sorted(str(i) for i in item)))\n\n outputItems = sorted(outputItems)\n\n if key == 1:\n outFilePtr.write(str(outputItems[0]).replace(\",\", \"\"))\n for i in outputItems[1:]:\n outFilePtr.write(\",\")\n outFilePtr.write(str(i).replace(\",\", \"\"))\n else:\n outFilePtr.write(\"\\n\\n\")\n outFilePtr.write(str(outputItems[0]))\n for i in outputItems[1:]:\n outFilePtr.write(\",\")\n outFilePtr.write(str(i))\n\n\ndef removeLeadingZeros(num):\n while num[0] == \"0\":\n num = num[1:]\n return int(num)\n\n\ndef update_year(date_string):\n date_list = date_string.split(\"/\")\n if len(date_list[2]) == 4:\n date_string = \"/\".join(date_list[:2]) + \"/\" + date_list[2][-2:]\n return date_string\n\n\nif __name__ == \"__main__\":\n\n start = time.time()\n\n filterThreshold = int(sys.argv[1])\n support = int(sys.argv[2])\n inputFile = sys.argv[3]\n outputFile = sys.argv[4]\n\n outFile = open(outputFile, \"w\")\n\n spark = SparkSession.builder.appName('inf-553-2b').getOrCreate()\n sc = spark.sparkContext\n\n dataset = sc.textFile(inputFile).mapPartitions(lambda x : csv.reader(x))\n\n # Data PreProcessing\n header = dataset.first()\n transactionIndex = header.index(\"TRANSACTION_DT\")\n customerIndex = header.index(\"CUSTOMER_ID\")\n productIndex = header.index(\"PRODUCT_ID\")\n dataset = dataset.filter(lambda x: x != header)\n\n dataset = dataset.map(lambda x: (update_year(x[transactionIndex]) + \"-\" + x[customerIndex], removeLeadingZeros(x[productIndex])))\n with open('divya_ramesha_preprocessed.csv', 'w') as csvFile:\n writer = csv.writer(csvFile)\n writer.writerows([['DATE-CUSTOMER_ID', 'PRODUCT_ID']] + dataset.collect())\n\n # Filter Baskets\n marketBaskets = dataset.groupByKey().mapValues(set).filter(lambda x: len(x[1]) > filterThreshold)\n marketBaskets = marketBaskets.map(lambda x: x[1])\n basketsCount = marketBaskets.count()\n\n # First Map\n firstMap = marketBaskets.mapPartitions(lambda partitionBasket: aprioriAlgorithm(partitionBasket, support, basketsCount)).map(lambda x: (x, 1))\n\n # First Reduce\n firstReduce = firstMap.reduceByKey(add).keys()\n\n outFile.write(\"Candidates:\\n\")\n singleItems = firstReduce.filter(lambda x: type(x) == int).collect()\n it = iter(singleItems)\n singleItems = list(zip(it))\n otherItems = firstReduce.filter(lambda x: type(x) != int).collect()\n writeItemsToFile(singleItems + otherItems, outFile)\n\n candidateKeys = firstReduce.collect()\n # Second Map\n secondMap = marketBaskets.mapPartitions(lambda partitionBasket: candidateCount(list(partitionBasket), candidateKeys).items())\n # Second Reduce\n secondReduce = secondMap.reduceByKey(add).filter(lambda x: x[1] >= support)\n\n outFile.write(\"\\n\\nFrequent Itemsets:\\n\")\n writeItemsToFile(secondReduce.keys().collect(), outFile)\n\n outFile.close()\n end = time.time()\n print(\"Duration: \", end - start)","sub_path":"project2/divya_ramesha_task2.py","file_name":"divya_ramesha_task2.py","file_ext":"py","file_size_in_byte":5916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"631445396","text":"# Given bottom left and top right points of two rectangles,\n# check if the rectangles intersect\n\ndef rect_intrs(rect1, rect2):\n if (rect2[0] in range(rect1[0], rect1[2]) \\\n or rect2[2] in range(rect1[0] + 1, rect1[2]) ) \\\n and (rect2[1] in range(rect1[1] + 1, rect1[3]) \\\n or rect2[3] in range(rect1[1] + 1, rect1[3]) ):\n return True\n else:\n return False\n\nprint (rect_intrs([5,15,8,18], [0,3,7,9]))\nprint(rect_intrs([0, 0, 1, 1], [2, 2, 3, 3]))\nprint (rect_intrs([0,0,1,1], [1,0,2,1]))\nprint (rect_intrs([0,0,2,2], [1,1,3,3]))\n","sub_path":"Problem Set One/rect_int.py","file_name":"rect_int.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"490427686","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.utils\n\nfrom utils import assert_expected_size\n\nclass Highway(nn.Module):\n def __init__(self, word_embed_size):\n '''Initializing Highway Model\n @param embed_size: word embed length\n '''\n super(Highway, self).__init__()\n self.word_embed_size = word_embed_size\n self.w_projection = nn.Linear(word_embed_size, word_embed_size, bias=True)\n self.w_gate = nn.Linear(word_embed_size, word_embed_size, bias=True)\n\n def forward(self, x_conv_out: torch.Tensor) -> torch.Tensor:\n '''\n @param x_conv_out (Tensor): Tensor of padded source sentences with shape (b, e_word), where\n b = batch_size, e_word = word embedding length. These are the outputs\n from convolutional neural network\n @returns x_highway (Tensor) : Tensor of padded source sentences with shape (b, e_word)\n '''\n batch_size, e_word = len(x_conv_out), self.word_embed_size\n assert_expected_size(x_conv_out, 'x_conv_out', [batch_size, e_word])\n\n relu = nn.ReLU()\n x_proj = relu(self.w_projection(x_conv_out)) # shape = (b, e_word)\n assert_expected_size(x_proj, 'x_proj', [batch_size, e_word])\n\n x_gate = torch.sigmoid(self.w_gate(x_conv_out)) # shape = (b, e_word)\n assert_expected_size(x_gate, 'x_gate', [batch_size, e_word])\n\n x_highway = x_gate*x_proj + (1-x_gate)*x_conv_out\n assert_expected_size(x_highway, 'x_highway', [batch_size, e_word])\n return x_highway\n\n","sub_path":"highway.py","file_name":"highway.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"308954210","text":"import sys\nimport dal\n\nfrom PyQt5.Qt import *\nfrom tableModel import *\nfrom frm_main import *\nfrom br_main import *\n\n# 全局变量\ndata_list = []\ndata_dic = {}\nphone_list = []\nphone_dic = {}\nphone_edit = PhEdit()\ngu_ke = Guke()\n\n\nclass MyWin(QMainWindow, Ui_MainWindow):\n # 窗口初始化\n def __init__(self, parent=None):\n super().__init__()\n self.setupUi(self)\n self.Cal.setVisible(False)\n self.br_listview()\n self.list_guzhang_view()\n self.lbl_danhao.setText(test.dan_hao())\n\n # 信号绑定\n self.list_br.clicked.connect(self.list_br_clicked)\n self.list_phone.clicked.connect(self.list_phone_clicked)\n self.btnAdd.clicked.connect(self.select_guzhang_clicked)\n self.btn_print.clicked.connect(self.prSave)\n self.btnDate.clicked.connect(self.E_clicked)\n self.Cal.clicked.connect(self.Cal_select)\n self.rd_gkSex1.toggled.connect(self.sex_select)\n self.rd_gkSex0.toggled.connect(self.sex_select)\n self.menu.triggered[QAction].connect(self.br_add)\n # 菜单点击\n def br_add(self,QAction):\n # print(QAction.text())\n if QAction.text()=='品牌维护':\n self.frm_br=DL_br()\n self.frm_br.my_Signal.connect(self.br_listview)\n self.frm_br.show()\n\n\n\n if QAction.text()=='机型维护':\n print('点击机型维护')\n if QAction.text()=='故障现象维护':\n print('点击故障现象维护')\n\n # 列表框填充数据\n def ref_listview(self, listName, listDate):\n slm = slm = QStringListModel()\n slm.setStringList(listDate)\n listName.setModel(slm)\n\n # 填充品牌列表\n def br_listview(self):\n global data_dic\n global data_list\n data_dic, data_list = dal.date_all(Br, 'brID', 'brName')\n self.ref_listview(self.list_br, data_list)\n\n # 填充故障选择列表\n def list_guzhang_view(self):\n global str_guzhang\n str_guzhang = \"\"\n # temp_list=[]\n #\n # for i in temp_list:\n # box = QCheckBox(i)\n # item = QListWidgetItem()\n # self.list_guzhang.addItem(item)\n # self.list_guzhang.setItemWidget(item, box)\n\n _, temp_list = dal.date_all(EditPs, 'id', 'Name')\n for i in temp_list:\n box = QCheckBox(i)\n item = QListWidgetItem()\n self.list_guzhang.addItem(item)\n self.list_guzhang.setItemWidget(item, box)\n\n # 品牌列表左键单击\n def list_br_clicked(self, index):\n\n global data_dic\n global data_list\n global phone_dic\n global phone_list\n global phone_edit\n\n phone_edit.phID = None\n\n temp_dic = {}\n for k, v in data_dic.items():\n temp_dic[v] = k\n\n str_br = data_list[index.row()]\n str_id = str(temp_dic[str_br])\n str_filter = \"Phone.brID==\" + str_id\n\n phone_dic, phone_list = dal.date_select(Phone, 'id', 'phName', str_filter)\n self.ref_listview(self.list_phone, phone_list)\n\n # 手机列表左键单击\n def list_phone_clicked(self, index):\n global phone_dic\n global phone_list\n global phone_edit\n\n temp_dic = {}\n for k, v in phone_dic.items():\n temp_dic[v] = k\n str_ph = phone_list[index.row()]\n # str_id=temp_dic[str_ph]\n phone_edit.phID = temp_dic[str_ph]\n\n # 选择故障后按钮事件\n def select_guzhang_clicked(self):\n \"\"\"\n 得到备选统计项的字段\n :return: list[str]\n \"\"\"\n global phone_edit\n\n count = self.list_guzhang.count() # 得到QListWidget的总个数\n cb_list = [self.list_guzhang.itemWidget(self.list_guzhang.item(i))\n for i in range(count)] # 得到QListWidget里面所有QListWidgetItem中的QCheckBox\n chooses = [] # 存放被选择的数据\n for cb in cb_list:\n if cb.isChecked():\n chooses.append(cb.text())\n choose = \"，\".join(chooses)\n str_t = self.txtGz.toPlainText()\n self.txtGz.setText(str_t + choose)\n # phone_edit.guzhang = choose\n\n # 显示日期选择窗口\n def E_clicked(self):\n self.Cal.setVisible(True)\n\n # 日期窗口点击\n def Cal_select(self):\n self.txt_date.setText(self.Cal.selectedDate().toString(\"yyyy-MM-dd\"))\n self.Cal.setVisible(False)\n\n # 顾客性别选择\n def sex_select(self):\n\n if self.rd_gkSex0.isChecked() == True:\n gu_ke.sex = '女士'\n if self.rd_gkSex1.isChecked() == True:\n gu_ke.sex = '先生'\n return gu_ke.sex\n\n # 控件初始化\n def view_init(self):\n self.br_listview()\n self.list_guzhang.clear()\n self.list_guzhang_view()\n self.ref_listview(self.list_phone, [])\n self.txtGz.setText(\"\")\n self.textEdit.setText(\"\")\n self.txt_gkName.setText(\"\")\n self.txt_date.setText(\"\")\n self.txt_cost.setText(\"\")\n self.txt_gkphone.setText(\"\")\n self.lbl_danhao.setText(test.dan_hao())\n\n # 检查所有控件的值\n def check_up(self):\n if self.list_br.currentIndex().row() == -1:\n QMessageBox.critical(self, \"请检查\", \"请选择品牌\", QMessageBox.Yes)\n return False\n if self.list_phone.currentIndex().row() == -1:\n QMessageBox.critical(self, \"请检查\", \"请选择机型\", QMessageBox.Yes)\n return False\n if len(self.txtGz.toPlainText()) == 0:\n QMessageBox.critical(self, \"请检查\", \"请生成故障现象\", QMessageBox.Yes)\n return False\n if len(self.textEdit.toPlainText()) == 0:\n QMessageBox.critical(self, \"请检查\", \"维修项目未录入\", QMessageBox.Yes)\n return False\n if len(self.txt_cost.text()) == 0:\n QMessageBox.critical(self, \"请检查\", \"请输入收费\", QMessageBox.Yes)\n return False\n if len(self.txt_date.text()) == 0:\n QMessageBox.critical(self, \"请检查\", \"请输入日期\", QMessageBox.Yes)\n return False\n if len(self.txt_gkName.text()) == 0:\n QMessageBox.critical(self, \"请检查\", \"请输入顾客姓名\", QMessageBox.Yes)\n return False\n if len(self.txt_gkphone.text()) == 0:\n QMessageBox.critical(self, \"请检查\", \"请输入顾客电话\", QMessageBox.Yes)\n return False\n if self.rd_gkSex1.isChecked() or self.rd_gkSex0.isChecked():\n return True\n else:\n QMessageBox.critical(self, \"请检查\", \"请选择顾客性别\", QMessageBox.Yes)\n return False\n\n # print(self.rd_gkSex0.isChecked())\n return True\n\n # 打印按钮单击\n def prSave(self):\n global phone_edit\n global gu_ke\n\n if self.check_up():\n gu_ke.Name = self.txt_gkName.text()\n gu_ke.dianhua = self.txt_gkphone.text()\n phone_edit.phedit = self.textEdit.toPlainText()\n phone_edit.jiage = self.txt_cost.text()\n phone_edit.rq = self.txt_date.text()\n phone_edit.id = self.lbl_danhao.text()\n phone_edit.guzhang = self.txtGz.toPlainText()\n gu_ke.sex = self.sex_select()\n # Sess = sessionmaker(bind=engine)\n # ss = Sess()\n # ss.add(phone_edit)\n # ss.add(gu_ke)\n # ss.commit()\n\n print(phone_edit.id,\n phone_edit.phID,\n phone_edit.guzhang,\n phone_edit.phedit,\n phone_edit.jiage,\n phone_edit.rq,\n gu_ke.Name,\n gu_ke.sex,\n gu_ke.dianhua\n )\n\n phone_edit = PhEdit()\n gu_ke = Guke()\n self.view_init()\n\n\nif __name__ == '__main__':\n QtCore.QCoreApplication.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling)\n app = QApplication(sys.argv)\n win = MyWin()\n win.show()\n sys.exit(app.exec_())\n","sub_path":"logMain.py","file_name":"logMain.py","file_ext":"py","file_size_in_byte":8098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"574241387","text":"\"\"\" Cosc264 Socket Assignment\n Author: Isaac Murtagh\n itm20, 78178123\n Date: 17/8/2019\n\"\"\"\n\nimport sys\nimport socket\nimport datetime\nimport os.path\n\nBUFF_SIZE = 4096\nSERVER_ADDRESS = \"127.0.0.1\"\n\n\nclass Server:\n \"\"\"A command line application that will act as a TCP Server to send and \n recieve data.\n \"\"\"\n\n def __init__(self, port_number):\n \"\"\"Accepts a port number which must be in the range 1,025 - 64,000,\n then begins calling functions to set up the socket.\n \"\"\"\n self.s = socket.socket()\n try:\n if port_number.isdigit() and 1024 < int(port_number) <= 64000:\n self.port_number = int(port_number)\n else:\n print(\"ERROR: Port number not in range 1,025 - 64,000\")\n raise Exception\n self.bind_address()\n self.start_listen()\n self.start_loop()\n except Exception:\n pass\n finally:\n self.s.close()\n sys.exit()\n\n\n def bind_address(self):\n \"\"\"Attempts to bind to an address with given port number, otherwise \n prints an error message and raises an error, closing the socket \n and exiting.\n \"\"\"\n try:\n self.s.bind((SERVER_ADDRESS, self.port_number))\n except Exception:\n print(f\"ERROR: Could not bind to port {self.port_number}\")\n raise Exception\n\n def start_listen(self):\n \"\"\"Attempts to execute the listen() call of the socket, if this does \n not work, then an error is printed and the socket is closed.\n \"\"\"\n try:\n self.s.listen()\n except Exception:\n print(\"ERROR: Server could not listen for connections\")\n raise Exception\n\n\n def start_loop(self):\n \"\"\"Starts an infinite loop where connections are accepted by clients. \n Once a connection is complete or fails some how, the client_socket \n is closed and the loops resumes.\n \"\"\"\n while True:\n client_socket, address = self.s.accept()\n self.s.settimeout(1)\n\n try:\n self.log(f\"Connection from {address}\")\n\n file_request = self.recieve_data(client_socket)\n filename = self.decode_request(file_request)\n\n self.send_file(filename, client_socket)\n except Exception:\n pass\n finally:\n self.s.settimeout(None)\n client_socket.close()\n print()\n\n\n def decode_request(self, arr):\n \"\"\"Decodes a byte array to determine if it is correct, returning the \n filename that has been recieved by the client.\n \"\"\"\n magic_number = (arr[0] << 8) + arr[1]\n if magic_number != 0x497E:\n print(\"ERROR: File request MAGIC NUMBER incorrect\")\n raise Exception\n\n file_type = arr[2]\n if file_type != 1:\n print(\"ERROR: File request FILE TYPE incorrect\")\n raise Exception\n\n file_len = (arr[3] << 8) + arr[4]\n\n filename = \"\"\n start = 5\n for ele in arr[start:start + file_len]:\n filename += chr(ele)\n\n return filename\n\n\n def send_file(self, filename, client):\n \"\"\"Attempts to send the requested file to the client, if it does not \n exist then an appropriate response is sent to the client informing\n them of this outcome. If the file exists, but was unable to be \n opened or read, an error occurs and the client is dropped.\n \"\"\"\n code = 1\n contents = \"\"\n\n if (os.path.exists(filename)):\n try:\n file = open(filename)\n contents = file.read()\n except Exception:\n print(\"ERROR: File is unable to be opened or read\")\n code = 0\n finally:\n file.close()\n else:\n code = 0\n print(f\"'{filename}' does not exist within the server.\")\n\n response = self.create_response(code, contents)\n\n try:\n client.send(response)\n except Exception:\n print(\"ERROR: Was unable to send response back to client\")\n raise Exception\n\n if code == 0:\n print(\"A response has been sent to client that the file \" +\n \"could not be found\")\n\n print(f\"File transfer complete, {len(response)} bytes \" +\n \"transferred to client.\")\n\n\n def create_response(self, code, contents):\n \"\"\"Returns a byte array payload for the file request\"\"\"\n file_len = len(contents)\n magic_num = 0x497E\n file_type = 2\n\n b = bytearray(bytes(8))\n b[0] = (magic_num >> 8)\n b[1] = magic_num - (b[0] << 8)\n b[2] = file_type\n b[3] = code\n b[4] = (file_len >> 24)\n b[5] = (file_len - (b[4] * 2**24)) >> 16\n b[6] = (file_len - (b[4] * 2**24 + b[5] * 2**16)) >> 8\n b[7] = (file_len - (b[4] * 2**24 + b[5] * 2**16 + b[6] * 2**8))\n\n return b + bytearray(contents.encode())\n\n\n def recieve_data(self, client):\n \"\"\"Attempts to recieve a file request and checks if it is correct. \n If it takes longer than 1 second to recieve data, a timeout\n error occurs.\n \"\"\"\n data = b''\n while True:\n try:\n part = client.recv(BUFF_SIZE)\n except:\n print(\"ERROR: Time out error has occured\")\n data += part\n if len(part) < BUFF_SIZE:\n break\n return bytearray(data)\n\n\n def log(self, message):\n \"\"\"logs the current time\"\"\"\n time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n print(f\"[{time}] {message}\")\n\n\nServer(input())","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"321023695","text":"#!/usr/bin/python3\n\n# discord-py requirements\nimport discord\nfrom discord.ext import commands\nimport asyncio\n\n# For DB Functionality\nimport sqlite3\nimport datetime\n\n# Other utilities\nimport random\nfrom .utils.paginator import Pages\n\n# For remindme functionality\nimport re\n\n\nclass Reminder:\n def __init__(self, bot):\n self.bot = bot\n self.frequencies = {\"daily\": 1, \"weekly\": 7, \"monthly\": 30}\n\n async def check_reminders(self):\n \"\"\"\n Co-routine that periodically checks if the bot must issue reminders to users.\n :return: None\n \"\"\"\n\n await self.bot.wait_until_ready()\n while not self.bot.is_closed():\n conn = sqlite3.connect(self.bot.config.db_path)\n c = conn.cursor()\n g = (guild for guild in self.bot.guilds\n if guild.name == 'McGill University')\n try:\n guild = next(g)\n except StopIteration:\n return\n reminders = c.execute('SELECT * FROM Reminders').fetchall()\n for i in range(len(reminders)):\n member = discord.utils.get(guild.members, id=reminders[i][0])\n\n # If non-repeating reminder is found\n if reminders[i][3] == 'once':\n # Check date to remind user\n reminder_activation_date = datetime.datetime.strptime(\n reminders[i][4], \"%Y-%m-%d %H:%M:%S.%f\")\n # Compute future_date-current_date and if <= 0:00:00, means time is due to remind user\n if reminder_activation_date - datetime.datetime.now(\n ) <= datetime.timedelta(0):\n await member.send(\"Reminding you to {}!\".format(\n reminders[i][2]))\n # Remove from from DB non-repeating reminder\n c.execute(\n 'DELETE FROM Reminders WHERE Reminder=? AND ID=? AND DATE=?',\n (reminders[i][2], reminders[i][0],\n reminder_activation_date))\n conn.commit()\n await asyncio.sleep(1)\n else:\n last_date = datetime.datetime.strptime(\n reminders[i][5], \"%Y-%m-%d %H:%M:%S.%f\")\n if datetime.datetime.now() - last_date > datetime.timedelta(\n days=self.frequencies[reminders[i][3]]):\n await member.send(\"Reminding you to {}! [{:d}]\".format(\n reminders[i][2], i + 1))\n\n c.execute(\n \"UPDATE 'Reminders' SET LastReminder = ? WHERE Reminder = ?\",\n (datetime.datetime.now(), reminders[i][2]))\n conn.commit()\n await asyncio.sleep(1)\n conn.close()\n await asyncio.sleep(60 * 1)\n\n @commands.command(aliases=['rm', 'rem'])\n async def remindme(self, ctx, *, quote: str = \"\"):\n \"\"\"\n Parses the reminder and adds a one-time reminder to the reminder database or\n calls remindme_repeating to deal with repetitive reminders when keyword\n \"daily\", \"weekly\" or \"monthly\" is found.\n \"\"\"\n\n if quote == \"\":\n await ctx.send(\n '**Usage:** \\n `?remindme in 1 hour and 20 minutes and 20 seconds to eat` **or** \\n '\n '`?remindme at 2020-04-30 11:30 to graduate` **or** \\n `?remindme daily to sleep`'\n )\n\n # Copies original reminder message and sets lowercase for regex.\n original_input_copy = quote.lower()\n\n # Letter numbers to number numbers\n replacements = [\n (r\"twenty[-\\s]one\", \"21\"), (r\"twenty[-\\s]two\",\n \"22\"), (r\"twenty[-\\s]three\", \"23\"),\n (r\"twenty[-\\s]four\",\n \"24\"), (r\"twenty[-\\s]five\",\n \"25\"), (r\"twenty[-\\s]six\",\n \"26\"), (r\"twenty[-\\s]seven\",\n \"27\"), (r\"twenty[-\\s]eight\",\n \"28\"), (r\"twenty[-\\s]nine\", \"29\"),\n (r\"thirty[-\\s]one\",\n \"31\"), (r\"thirty[-\\s]two\",\n \"32\"), (r\"thirty[-\\s]three\",\n \"33\"), (r\"thirty[-\\s]-four\",\n \"34\"), (r\"thirty[-\\s]four\",\n \"34\"), (r\"thirty[-\\s]five\", \"35\"),\n (r\"thirty[-\\s]six\",\n \"36\"), (r\"thirty[-\\s]seven\",\n \"37\"), (r\"thirty[-\\s]eight\",\n \"38\"), (r\"thirty[-\\s]nine\",\n \"39\"), (r\"forty[-\\s]one\",\n \"41\"), (r\"forty[-\\s]two\", \"42\"),\n (r\"forty[-\\s]three\",\n \"43\"), (r\"forty[-\\s]four\",\n \"44\"), (r\"forty[-\\s]five\",\n \"45\"), (r\"forty[-\\s]six\",\n \"46\"), (r\"forty[-\\s]seven\",\n \"47\"), (r\"forty[-\\s]eight\", \"48\"),\n (r\"forty[-\\s]nine\",\n \"49\"), (\"tomorrow\", \"1 day\"), (\"next week\",\n \"1 week\"), (\"later\",\n \"6 hours\"), (\"a\", \"1\"),\n (\"an\", \"1\"), (\"zero\",\n \"0\"), (\"no\", \"0\"), (\"none\",\n \"0\"), (\"one\", \"1\"), (\"two\", \"2\"),\n (\"three\", \"3\"), (\"four\", \"4\"), (\"five\",\n \"5\"), (\"six\", \"6\"), (\"seven\", \"7\"),\n (\"eight\",\n \"8\"), (\"nine\",\n \"9\"), (\"ten\",\n \"10\"), (\"eleven\",\n \"11\"), (\"twelve\",\n \"12\"), (\"thirteen\",\n \"13\"), (\"fourteen\",\n \"14\"), (\"fifteen\",\n \"15\"),\n (\"sixteen\",\n \"16\"), (\"seventeen\",\n \"17\"), (\"eighteen\",\n \"18\"), (\"nineteen\",\n \"19\"), (\"twenty\",\n \"20\"), (\"thirty\",\n \"30\"), (\"forty\",\n \"40\"), (\"fifty\",\n \"50\")\n ]\n\n # Regex for misspellings of time units\n units = {\n \"years\": \"ye?a?r?s?\",\n \"days\": \"da?y?s?\",\n \"hours\": \"ho?u?r?s?\",\n \"seconds\": \"se?c?o?n?d?s?\",\n \"minutes\": \"mi?n?u?t?e?s?\",\n \"weeks\": \"we?e?k?s?\"\n }\n\n # Stores units + number for calculating timedelta\n time_offset = {\n \"years\": 0,\n \"days\": 0,\n \"hours\": 0,\n \"seconds\": 0,\n \"minutes\": 0,\n \"weeks\": 0\n }\n\n punctuation_chars = \".,+&/ \"\n string_word_separator_regex = r\"(\\s|[\" + punctuation_chars + \"])+\"\n time_separator_regex = r\"(,|\\+|&|and|plus|in)\"\n # Regex to match units below (Which accounts for spelling mistakes!)\n unit_regex = r\"(\" + \"|\".join(list(units.values())) + \")\"\n # Matches a natural number\n number_regex = r\"[1-9]+[0-9]*(|\\.[0-9]+)\"\n # Regex for format YYYY-MM-DD\n ymd_regex = r\"(2[0-1][0-9][0-9])[\\s./-]((1[0-2]|0?[1-9]))[\\s./-](([1-2][0-9]|3[0-1]|0?[1-9]))\"\n # Regex for time HH:MM\n hm_regex = r\"\\b([0-1]?[0-9]|2[0-4]):([0-5][0-9])\"\n\n # Replaces word representation of numbers into numerical representation\n for k, v in replacements:\n original_input_copy = re.sub(r\"\\b\" + k + r\"\\b\", v,\n original_input_copy)\n\n # Split on spaces and other relevant punctuation\n input_segments = re.split(string_word_separator_regex,\n original_input_copy)\n input_segments = [x.strip(punctuation_chars) for x in input_segments]\n\n # Remove empty strings from list\n input_segments = [x for x in input_segments if x != \"\"]\n\n time_segments = []\n last_number = \"0\"\n first_reminder_segment = \"\"\n \"\"\" Checks the following logic:\n 1. If daily, weekly or monthly is specified, go to old reminder function for repetitive reminders\n for all input segments:\n 2. If one of the keywords commonly used for listing times is there, continue\n 3. If a number is found, save the number, mark that a number has been found for next iteration\n 4. Elif: A \"unit\" (years, days ... etc.) has been found, append the last number + its unit\n 5. Lastly: save beginning of \"reminder quote\" and end loop\n \"\"\"\n\n if len(input_segments) > 0 and (input_segments[0] == \"daily\"\n or input_segments[0] == \"weekly\"\n or input_segments[0] == \"monthly\"):\n await self.__remindme_repeating(\n ctx,\n input_segments[0],\n quote=quote[len(input_segments[0]) + 1:])\n return\n for segment in input_segments:\n if re.match(\"^\" + time_separator_regex + \"$\", segment):\n continue\n if re.match(\"^\" + number_regex + \"$\", segment):\n last_number = segment\n elif re.match(\"^\" + unit_regex + \"$\", segment):\n time_segments.append(last_number + \" \" + segment)\n else:\n first_reminder_segment = segment\n break\n\n # They probably dont want their reminder nuked of punctuation, spaces\n # and formatting, so extract from original string.\n reminder = quote[quote.index(first_reminder_segment):]\n\n # Date-based reminder triggered by \"at\" and \"on\" keywords\n if input_segments[0] == 'at' or input_segments[0] == 'on':\n date_result = re.search(ymd_regex,\n original_input_copy) # Gets YYYY-mm-dd\n time_result = re.search(hm_regex,\n original_input_copy) # Gets HH:MM\n\n # If both a date and a time is found, continue\n if date_result and time_result:\n # Compute datetime.Object\n absolute_duedate = datetime.datetime.strptime(\n date_result.group(1) + \"-\" + date_result.group(2) + \"-\" +\n date_result.group(4) + \"-\" + time_result.group(1) + \"-\" +\n time_result.group(2) + \"-\" + str(0.1),\n \"%Y-%m-%d-%H-%M-%S.%f\")\n\n # Strips \"to\" and dates from the reminder message\n time_input_end = time_result.span()[1]\n if re.match(\n \"to\",\n reminder[time_input_end:time_input_end + 4].strip(),\n re.IGNORECASE):\n reminder = reminder[time_input_end + 3:].strip()\n else:\n reminder = reminder[time_input_end + 1:].strip()\n\n # Add message to database\n conn = sqlite3.connect(self.bot.config.db_path)\n c = conn.cursor()\n t = (ctx.message.author.id, ctx.message.author.name, reminder,\n \"once\", absolute_duedate, datetime.datetime.now())\n try:\n c.execute(\n 'INSERT INTO Reminders VALUES (?, ?, ?, ?, ?, ?)', t)\n except sqlite3.OperationalError:\n c.execute(\n \"CREATE TABLE 'Reminders' ('ID'INTEGER,'Name'TEXT,'Reminder'TEXT,'Frequency'TEXT,'Date'\\\n TEXT,'LastReminder'TEXT)\")\n c.execute(\n 'INSERT INTO Reminders VALUES (?, ?, ?, ?, ?. ?)', t)\n\n # Send user information and close database\n reminders = c.execute('SELECT * FROM Reminders WHERE ID =?',\n (ctx.message.author.id, )).fetchall()\n await ctx.author.send(\n 'Hi {}! \\nI will remind you to {} on {} at {} unless you send me a message to stop '\n 'reminding you about it! [{:d}]'.format(\n ctx.author.name, reminder, date_result.group(0),\n time_result.group(0),\n len(reminders) + 1))\n\n await ctx.send('Reminder added.')\n\n conn.commit()\n conn.close()\n\n return\n\n # Wrong input feedback depending on what is missing.\n await ctx.send(\n \"Please check your private messages for information on correct syntax!\"\n )\n await ctx.author.send(\"Please double check the following: \")\n if not date_result:\n await ctx.author.send(\n \"Make sure you have specified a date in the format: `YYYY-mm-dd`\"\n )\n if not time_result:\n await ctx.author.send(\n \"Make sure you have specified a time in the 24H format: `HH:MM`\"\n )\n await ctx.author.send(\n \"E.g.: `?remindme on 2020-12-05 at 21:44 to feed Marty`\")\n return\n\n # Regex for the number and time units and store in \"match\"\n for segment in time_segments:\n match = re.match(\n \"^(\" + number_regex + \")\" + r\"\\s+\" + unit_regex + \"$\", segment)\n number = float(match.group(1))\n\n # Regex potentially misspelled time units and match to proper spelling\n for regex in units:\n if re.match(\"^\" + units[regex] + \"$\", match.group(3)):\n time_offset[regex] += number\n\n # Convert years to a unit that datetime will understand\n time_offset[\"days\"] = time_offset[\"days\"] + time_offset[\"years\"] * 365\n\n time_now = datetime.datetime.now() # Current time\n reminder_time = time_now + datetime.timedelta(\n days=time_offset[\"days\"],\n hours=time_offset[\"hours\"],\n seconds=time_offset[\"seconds\"],\n minutes=time_offset[\"minutes\"],\n weeks=time_offset[\"weeks\"]) # Time to be reminded on\n if time_now == reminder_time: # No time in argument, or it's zero.\n await ctx.send(\"Please specify a time! E.g.: `?remindme in 1 hour \"\n + reminder + \"`\")\n return\n # Strips the string \"to \" from reminder messages\n if reminder[:3].lower() == 'to ':\n reminder = reminder[3:]\n # DB: Date will hold TDELTA (When reminder is due), LastReminder will hold datetime.datetime.now()\n conn = sqlite3.connect(self.bot.config.db_path)\n c = conn.cursor()\n t = (ctx.message.author.id, ctx.message.author.name, reminder, \"once\",\n reminder_time, time_now)\n reminders = c.execute('SELECT * FROM Reminders WHERE ID =?',\n (ctx.message.author.id, )).fetchall()\n try:\n c.execute('INSERT INTO Reminders VALUES (?, ?, ?, ?, ?, ?)', t)\n except sqlite3.OperationalError:\n c.execute(\n \"CREATE TABLE 'Reminders' ('ID'INTEGER,'Name'TEXT,'Reminder'TEXT,'Frequency'TEXT,'Date'\\\n TEXT,'LastReminder'TEXT)\")\n c.execute('INSERT INTO Reminders VALUES (?, ?, ?, ?, ?. ?)', t)\n\n # Gets reminder date in YYYY-MM-DD format\n due_date = str(\n datetime.date(reminder_time.year, reminder_time.month,\n reminder_time.day))\n # Gets reminder time in HH:MM\n due_time = str(reminder_time).split()[1].split(\":\")[0] + \":\" + str(\n reminder_time).split()[1].split(\":\")[1]\n await ctx.author.send(\n 'Hi {}! \\nI will remind you to {} on {} at {} unless you send me a message to stop '\n 'reminding you about it! [{:d}]'.format(ctx.author.name, reminder,\n due_date, due_time,\n len(reminders) + 1))\n await ctx.send('Reminder added.')\n conn.commit()\n conn.close()\n\n @commands.command(aliases=['lr'])\n async def list_reminders(self, ctx):\n \"\"\"\n List reminders\n \"\"\"\n await ctx.trigger_typing()\n if not isinstance(ctx.message.channel, discord.DMChannel):\n await ctx.send(\n 'Slide into my DMs ;). \\n `List Reminder feature only available when DMing Marty.`'\n )\n return\n else:\n pass\n conn = sqlite3.connect(self.bot.config.db_path)\n c = conn.cursor()\n rem_author = ctx.message.author\n author_id = rem_author.id\n t = (author_id, )\n c.execute('SELECT * FROM Reminders WHERE ID = ?', t)\n rem_list = c.fetchall()\n if rem_list:\n quote_list_text = [\n ('[{}] (Frequency: {}' +\n (' at {}'.format(quote[4].split('.')[0])\n if quote[3] == 'once' else '') + ') - {}').format(\n i + 1, quote[3].capitalize(), quote[2])\n for i, quote in zip(range(len(rem_list)), rem_list)\n ]\n p = Pages(\n ctx,\n item_list=quote_list_text,\n title=\"{}'s reminders\".format(rem_author.display_name))\n await p.paginate()\n\n def msg_check(msg):\n try:\n return (0 <= int(msg.content) <= len(rem_list)\n and msg.author.id == author_id\n and msg.channel == ctx.message.channel)\n except ValueError:\n return False\n\n while p.delete:\n await ctx.send(\n 'Delete option selected. Enter a number to specify which '\n 'reminder you want to delete, or enter 0 to return.',\n delete_after=60)\n try:\n message = await self.bot.wait_for(\n 'message', check=msg_check, timeout=60)\n except asyncio.TimeoutError:\n await ctx.send(\n 'Command timeout. You may want to run the command again.',\n delete_after=60)\n break\n else:\n index = int(message.content) - 1\n if index == -1:\n await ctx.send('Exit delq.', delete_after=60)\n else:\n t = (rem_list[index][0], rem_list[index][2])\n del rem_list[\n index] # Remove deleted reminder from list.\n c.execute(\n 'DELETE FROM Reminders WHERE ID = ? AND '\n 'Reminder = ?', t)\n conn.commit()\n await ctx.send('Reminder deleted', delete_after=60)\n p.itemList = [('[{}] (Frequency: {}' + (\n ' at {}'.format(quote[4].split('.')[0])\n if quote[3] == 'once' else '') + ') - {}').format(\n i + 1, quote[3].capitalize(), quote[2])\n for i, quote in zip(\n range(len(rem_list)), rem_list)]\n await p.paginate()\n conn.commit()\n conn.close()\n else:\n await ctx.send('No reminder found.', delete_after=60)\n\n async def __remindme_repeating(self,\n ctx,\n freq: str = \"\",\n *,\n quote: str = \"\"):\n \"\"\"\n Called by remindme to add a repeating reminder to the reminder database.\n \"\"\"\n\n bad_input = False\n if freq not in self.frequencies.keys():\n await ctx.send(\n \"Please ensure you specify a frequency from the following list: `daily`, `weekly`, \"\n \"`monthly`, before your message!\")\n bad_input = True\n if quote == \"\":\n if bad_input and freq == \"\" or not bad_input:\n await ctx.send(\"Please specify a reminder message!\")\n else:\n pass\n bad_input = True\n if bad_input:\n return\n\n conn = sqlite3.connect(self.bot.config.db_path)\n c = conn.cursor()\n t = (ctx.message.author.id, ctx.message.author.name, quote, freq,\n datetime.datetime.now(), datetime.datetime.now())\n reminders = c.execute(\n 'SELECT * FROM Reminders WHERE Reminder =? AND ID = ?',\n (quote, ctx.message.author.id)).fetchall()\n if len(reminders) > 0:\n await ctx.send(\n \"The reminder `{}` already exists in your database. Please specify a unique reminder \"\n \"message!\".format(quote))\n return\n reminders = c.execute('SELECT * FROM Reminders WHERE ID =?',\n (ctx.message.author.id, )).fetchall()\n try:\n c.execute('INSERT INTO Reminders VALUES (?, ?, ?, ?, ?, ?)', t)\n except sqlite3.OperationalError:\n c.execute(\n \"CREATE TABLE 'Reminders' ('ID'INTEGER,'Name'TEXT,'Reminder'TEXT,'Frequency'TEXT,'Date'\\\n TEXT,'LastReminder'TEXT)\")\n c.execute('INSERT INTO Reminders VALUES (?, ?, ?, ?, ?. ?)', t)\n # Strips the string \"to \" from reminder messages\n if quote[:3].lower() == \"to \":\n quote = quote[3:]\n await ctx.author.send(\n 'Hi {}! \\nI will remind you to {} {} until you send me a message to stop '\n 'reminding you about it! [{:d}]'.format(ctx.author.name, quote,\n freq,\n len(reminders) + 1))\n await ctx.send('Reminder added.')\n conn.commit()\n conn.close()\n\n\ndef setup(bot):\n database = Reminder(bot)\n bot.add_cog(database)\n bot.loop.create_task(database.check_reminders())\n","sub_path":"cogs/reminder.py","file_name":"reminder.py","file_ext":"py","file_size_in_byte":22746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"336907034","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nimport sys\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nloaddatapath=os.getenv(\"PWD\")+'/../'\nsys.path.append(loaddatapath)\nimport loaddata as LD\nimport pdb \nplt.rc('font',family='Arial')\n\nif __name__==\"__main__\":\n columnsName_CPG=['RFO1','RFO2','RHO1','RHO2','LFO1','LFO2','LHO1','LKO2',\n 'RFSA','RHSA','LFSA','LHSA',\n 'RFACITerm0','RFACITerm1','RHACITerm0','RHACITerm1','LFACITerm0','LFACITerm1','LHACITerm0','LHACITerm1',\n 'RFSFTerm0','RFSFTerm1','RHSFTerm0','RHSFTerm1','LFSFTerm0','LFSFTerm1','LHSFTerm0','LHSFTerm1',\n 'RFFM','RHFM','LFFM','LHFM']\n fileName_CPG=\"controlfile_CPG\"\n columnsName_GRF=['RF','RH','LF','LH']\n fileName_GRF='sensorfile_GRF'\n fileName_joint='sensorfile_joint'\n columnsName_joint=['j1','j2','j3','j4','j5,''j6',\n 'j7','j8','j9','j10','j11','j12','s'\n ]*2\n fileName_POSE='sensorfile_POSE'\n columnsName_POSE=['roll','picth','yaw', 'x','y','z']\n freq=60.0 # 60Hz,\n joint_data=LD.loadData(fileName_joint,columnsName_joint,'1225222042')\n pose_data=LD.loadData(fileName_POSE,columnsName_POSE,'1225222042')\n grf_data=LD.loadData(fileName_GRF,columnsName_GRF,'1225222042')\n\n #2) postprecessing \n if len(sys.argv)>=2:\n run_id = int(sys.argv[1]) # The id of the experiments\n else:\n run_id = 0\n\n read_rows=min([40000,grf_data[run_id].shape[0], pose_data[run_id].shape[0]])\n start_point=1000\n end_point=read_rows\n time = np.linspace(int(start_point/freq),int(end_point/freq),end_point-start_point)\n\n #3) plot\n font_legend = {'family' : 'Arial',\n 'weight' : 'light',\n 'size' : 10,\n 'style' :'italic'\n }\n font_label = {'family' : 'Arial',\n 'weight' : 'light',\n 'size' : 12,\n 'style' :'normal'\n }\n\n font_title = {'family' : 'Arial',\n 'weight' : 'light',\n 'size' : 12,\n 'style' :'normal'\n }\n\n figsize=(10.5118,4.1244)\n fig,axs = plt.subplots(2,1,figsize=figsize,constrained_layout=False)\n fig.subplots_adjust(hspace=0.15)\n fig.subplots_adjust(left=0.14)\n fig.subplots_adjust(bottom=0.11)\n fig.subplots_adjust(right=0.98)\n fig.subplots_adjust(top=0.98)\n xticks=list(range(int(time[0]),int(time[-1])+1,1))\n\n idx=0\n axs[idx].plot(time,grf_data[run_id].iloc[start_point:end_point,0],'r')\n axs[idx].plot(time,grf_data[run_id].iloc[start_point:end_point,1],'g')\n axs[idx].plot(time,grf_data[run_id].iloc[start_point:end_point,2],'b')\n axs[idx].plot(time,grf_data[run_id].iloc[start_point:end_point,3],'k')\n #axs[idx].plot(time,joint_data[run_id].iloc[start_point:end_point,4],'b')\n axs[idx].legend([r'Pitch'], loc='upper left',prop=font_legend)\n axs[idx].grid(which='both',axis='x',color='k',linestyle=':')\n axs[idx].axis([time[0],time[-1],-3,3],'tight')\n axs[idx].set_xticks(xticks)\n axs[idx].set_xticklabels(labels=[])\n axs[idx].set_yticks([-1.0,0.0,1.0])\n #axs[idx].set_yticklabels(labels=['-1.0','0.0','1.0'],fontweight='light')\n axs[idx].set_ylabel('CPG',font_label)\n #axs[idx].set_title(\"CPG outputs of the right front leg\",font2)\n#\n# idx=1\n# axs[idx].plot(time,grf_data[run_id].iloc[start_point:end_point,1],'b')\n# axs[idx].legend([r'Pitch'], loc='upper left',prop=font_legend)\n# axs[idx].grid(which='both',axis='x',color='k',linestyle=':')\n# axs[idx].axis([time[0],time[-1],-1.0,1.0],'tight')\n# axs[idx].set_xticks(xticks)\n# axs[idx].set_xticklabels(labels=[])\n# axs[idx].set_yticks([-1.0,0.0,1.0])\n# axs[idx].set_yticklabels(labels=['-1.0','0.0','1.0'],fontweight='light')\n# axs[idx].set_ylabel('CPG',font_label)\n# #axs[idx].set_title(\"CPG outputs of the right front leg\",font2)\n# axs[idx].set_xlabel('Time [s]',font_label)\n# #axs[idx].set_title(\"Adaptive control input term of the right front leg\")\n#\n \n plt.savefig('/media/suntao/DATA/Research/P1_workspace/Figures/Fig13_source.svg')\n plt.show()\n\n","sub_path":"P1/Fig13.py","file_name":"Fig13.py","file_ext":"py","file_size_in_byte":4044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"287452285","text":"from lg_msg_defs.msg import WindowGeometry\nimport subprocess\nimport os\n\n\nclass WindowIdentityError(Exception):\n pass\n\n\ndef regex_escape(raw):\n \"\"\"Escape lua regex.\"\"\"\n escaped = raw\n escaped = escaped.replace(\"%\", \"%%\")\n escaped = escaped.replace(\"^\", \"%^\")\n escaped = escaped.replace(\"$\", \"%$\")\n escaped = escaped.replace(\"(\", \"%(\")\n escaped = escaped.replace(\")\", \"%)\")\n escaped = escaped.replace(\"[\", \"%[\")\n escaped = escaped.replace(\"]\", \"%]\")\n escaped = escaped.replace(\".\", \"%.\")\n escaped = escaped.replace(\"*\", \"%*\")\n escaped = escaped.replace(\"+\", \"%+\")\n escaped = escaped.replace(\"-\", \"%-\")\n escaped = escaped.replace(\"?\", \"%?\")\n escaped = escaped.replace(\"\\0\", \"%z\")\n return escaped\n\n\ndef get_rule_types(window):\n \"\"\"Get a dict of rule types and values for the window.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n Dict[str,str]\n\n Raises:\n WindowIdentityError: If the window identity can not be determined.\n \"\"\"\n rules = {}\n if window.w_name is not None:\n rules['name'] = regex_escape(window.w_name)\n if window.w_instance is not None:\n rules['instance'] = regex_escape(window.w_instance)\n if window.w_class is not None:\n rules['class'] = regex_escape(window.w_class)\n if not rules:\n raise WindowIdentityError('Could not determine window identity')\n return rules\n\n\ndef get_rule_pattern(window):\n \"\"\"Get a rule to match the given window.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n str: Comma-separated awesome rules to match the window.\n \"\"\"\n def pattern(r):\n return \"%s = '%s'\" % r\n patternized = list(map(pattern, iter(get_rule_types(window).items())))\n return ', '.join(patternized)\n\n\ndef get_properties(window, chrome_kiosk_workaround=False):\n \"\"\"Get a properties string to converge the window to its state.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n str: Comma-separated awesome properties for the window.\n \"\"\"\n prop_list = [\n \"border_width = 0\",\n \"size_hints_honor = false\",\n \"floating = true\",\n # Chrome Kiosk Workaround:\n # Trick Chrome in kiosk mode by setting fullscreen to true here.\n # In the callback we will set it to false.\n \"fullscreen = {}\".format('true' if chrome_kiosk_workaround else 'false'),\n \"maximized = false\",\n \"hidden = {}\".format('false' if window.is_visible else 'true'),\n \"minimized = {}\".format('false' if window.is_visible else 'true'),\n \"opacity = {}\".format(1 if window.is_visible else 0),\n ]\n if window.geometry is not None:\n prop_list.extend([\n \"width = {}\".format(window.geometry.width),\n \"height = {}\".format(window.geometry.height),\n ])\n return ', '.join(prop_list)\n\n\ndef get_callback(window):\n \"\"\"Get an awesome callback to move the window to its proper spot.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n str: awesome callback for window geometry.\n \"\"\"\n if window.geometry is not None:\n return \"function(c) awful.client.property.set(c, 'fullscreen', false) c.fullscreen = false c:geometry({x=%d, y=%d, width=%d, height=%d}) c:connect_signal('property::fullscreen', function() if c.fullscreen then c.fullscreen = false end end) end\" % (\n window.geometry.x,\n window.geometry.y,\n window.geometry.width,\n window.geometry.height,\n )\n else:\n return \"\"\n\n\ndef get_entry(window, chrome_kiosk_workaround=False):\n \"\"\"Get the full awesome (awful) rule that will converge the window.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n str: awesome rule for the window.\n \"\"\"\n rule = get_rule_pattern(window)\n properties = get_properties(window, chrome_kiosk_workaround)\n callback = get_callback(window)\n return \"{ rule = { %s }, properties = { %s }, callback = %s }\" % (rule, properties, callback)\n\n\ndef get_subtractive_script(window):\n \"\"\"Get a script that will remove existing awesome (awful) rules for the\n window.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n str: Lua code to remove existing rules for the window.\n \"\"\"\n rules = get_rule_types(window)\n\n def rule(r):\n return \"rule['rule']['%s'] == '%s'\" % r\n checks = ' and '.join(map(rule, iter(rules.items())))\n return \"for key,rule in pairs(awful.rules.rules) do if rule['rule'] ~= nil and {checks} then table.remove(awful.rules.rules, key) end end\".format(\n checks=checks\n )\n\n\ndef get_additive_script(window, chrome_kiosk_workaround=False):\n \"\"\"Get a script that will add an awesome (awful) rule for the window.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n str: Lua code to add the window rule.\n \"\"\"\n entry = get_entry(window, chrome_kiosk_workaround)\n return \"table.insert(awful.rules.rules, 1, {entry})\".format(entry=entry)\n\n\ndef get_apply_script(window):\n \"\"\"Get a script that will apply rules to all awesome window clients that\n match the window's identity.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n str: Lua code to apply rules to all matching windows.\n \"\"\"\n pattern = get_rule_pattern(window)\n return \"for k,c in pairs(client.get()) do if awful.rules.match(c, {%s}) then awful.rules.apply(c) end end\" % pattern\n\n\ndef get_script(window, chrome_kiosk_workaround=False):\n \"\"\"Combine scripts to form a mega-script that fixes everything.\n\n Args:\n window (ManagedWindow)\n\n Returns:\n str: Lua code to make awesome put the window in the right place.\n \"\"\"\n return ' '.join([\n \"local awful = require('awful') awful.rules = require('awful.rules')\",\n get_subtractive_script(window),\n get_additive_script(window, chrome_kiosk_workaround),\n get_apply_script(window)\n ])\n\n\ndef get_awesome_pid():\n awesome_pid = None\n\n try:\n awesome_pid = int(subprocess.check_output(['pidof', 'x-window-manager']))\n except Exception:\n pass\n try:\n awesome_pid = int(subprocess.check_output(['pidof', 'awesome']))\n except Exception:\n pass\n try:\n awesome_pid = int(subprocess.check_output(['pidof', '/usr/bin/awesome']))\n except Exception:\n pass\n\n return awesome_pid\n\n\ndef get_environ():\n \"\"\"Attempt to copy relevant environment of the window manager.\"\"\"\n awesome_pid = get_awesome_pid()\n if awesome_pid is None:\n raise Exception('Could not find awesome pid')\n\n with open('/proc/{}/environ'.format(awesome_pid), 'r') as f:\n awesome_environ_raw = f.read().strip('\\0')\n\n def split_environ(raw):\n pair = raw.split('=', 1)\n return pair[0], pair[1]\n pairs = list(map(split_environ, awesome_environ_raw.split('\\0')))\n awesome_environ = dict((p[0], p[1]) for p in pairs)\n\n env = os.environ.copy()\n\n def copy_environ(k):\n env[k] = awesome_environ[k]\n copy_environ('DISPLAY')\n copy_environ('DBUS_SESSION_BUS_ADDRESS')\n copy_environ('XAUTHORITY')\n\n return env\n\n# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4\n","sub_path":"lg_common/src/lg_common/awesome.py","file_name":"awesome.py","file_ext":"py","file_size_in_byte":7145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"443356208","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Apr 5 13:58:31 2021\r\n\r\n@author: ustc\r\n\"\"\"\r\n# this is a programmon which searches restaurants in a city,and then rank them\r\n\r\nfrom flask import Flask, render_template\r\nimport requests\r\nimport json\r\nimport time\r\nimport random\r\nimport re\r\nfrom bs4 import BeautifulSoup\r\n\r\ndef getjson(location,page_num=0):#get data from the API of baidu map\r\n headers = {'User-Agent' : 'Mozilla/5.0 (Windows; U; Windows NT 5.1; zh-CN; rv:1.9.1.16) Gecko/20201130 Firefox/3.5.16'}\r\n restaurant = {'q':'美食',\r\n #keyword is the yummy food\r\n \r\n 'region': location,\r\n 'scope': '2',\r\n 'page-size':20,\r\n 'page_num': page_num,\r\n 'output':'json',\r\n #'ak':'8Aoqw1l43V8GTCniw0coFWVPVBgPYRjO' #a backup for 'ak',which is an access to API\r\n 'ak':'DDtVK6HPruSSkqHRj5gTk0rc' \r\n }\r\n r = requests.get(\"http://api.map.baidu.com/place/v2/search\",params=restaurant,headers= headers)\r\n decodejson = json.loads(r.text)\r\n #print(decodejson)\r\n return decodejson\r\n\r\n\r\nclass resturants_of_city:\r\n def __init__ (self,city,region1,region2,region3,region4):\r\n self.city = city\r\n self.region1 = region1\r\n self.region2 = region2\r\n self.region3 = region3\r\n self.region4 = region4 \r\n \r\n def search_and_show(self):\r\n region_list = {self.region1,self.region2,self.region3,self.region4}\r\n \r\n #initial the list\r\n best1=most_popular1=most_expensive1={'rank':1,'name':'0','image':'0','address':'0','price':0,'overall_rating':0,'comment_num':0}\r\n best2=most_popular2=most_expensive2={'rank':2,'name':'0','image':'0','address':'0','price':0,'overall_rating':0,'comment_num':0}\r\n best3=most_popular3=most_expensive3={'rank':3,'name':'0','image':'0','address':'0','price':0,'overall_rating':0,'comment_num':0}\r\n \r\n for each_region in region_list:\r\n not_last_page = True\r\n page_num = 0\r\n while not_last_page:\r\n decodejson = getjson(self.city + each_region,page_num)\r\n print(each_region,page_num)\r\n time.sleep(random.random())#avoid the restriction from baidu API\r\n \r\n try:\r\n decodejson['total'] #something goes wrong when it turns the last page\r\n \r\n if decodejson['total']!=0:\r\n for each_place in decodejson['results']:#get information of a restaurant\r\n place = each_place['name']\r\n address = each_place['address']\r\n try:\r\n price = float(each_place['detail_info']['price'] ) \r\n except:\r\n price = 0\r\n try:\r\n rating = float(each_place['detail_info']['overall_rating'] ) \r\n except:\r\n rating = 0\r\n try:\r\n comment_num = int(each_place['detail_info']['comment_num'] ) \r\n except:\r\n comment_num = 0\r\n \r\n output = '\\t'.join([place,address,str(price),str(rating),str(comment_num)]) + '\\r\\n'\r\n #print(output)\r\n \r\n #store all the information in a file named \"restaurants.txt\"\r\n with open('restaurants.txt', 'a+', encoding='UTF-8') as f:\r\n f.write(output)\r\n f.close()\r\n \r\n new={'rank':0,'name':place,'image':'0','address':address,'price':price,'overall_rating':rating,'comment_num':comment_num}\r\n \r\n #rank by price/rating/comment number\r\n if price>=most_expensive1['price']:\r\n most_expensive3 = most_expensive2\r\n most_expensive2 = most_expensive1\r\n most_expensive1 = new\r\n elif price>=most_expensive2['price']:\r\n most_expensive3 = most_expensive2\r\n most_expensive2 = new \r\n elif price>=most_expensive3['price']:\r\n most_expensive3 = new \r\n \r\n if rating>=best1['overall_rating']:\r\n best3 = best2\r\n best2 = best1\r\n best1 = new\r\n elif rating>=best2['overall_rating']:\r\n best3 = best2\r\n best2 = new\r\n elif rating>=best3['overall_rating']:\r\n best3 =new\r\n \r\n if comment_num>=most_popular1['comment_num']:\r\n most_popular3 = most_popular2\r\n most_popular2 = most_popular1\r\n most_popular1 = new\r\n elif comment_num>=most_popular2['comment_num']:\r\n most_popular3 = most_popular2\r\n most_popular2 = new\r\n elif comment_num>=most_popular3['comment_num']:\r\n most_popular3 = new \r\n \r\n #after collecting all the information in a page ,turn to next page\r\n page_num=page_num+1\r\n \r\n else:\r\n not_last_page = False\r\n except:\r\n not_last_page = False\r\n \r\n #intial the rank\r\n best1['rank']=most_popular1['rank']=most_expensive1['rank']=1\r\n best2['rank']=most_popular2['rank']=most_expensive2['rank']=2\r\n best3['rank']=most_popular3['rank']=most_expensive3['rank']=3\r\n \r\n #collect all the names of the three top restaurants,and then go to \"www.dianping.com\" to find their images\r\n new = [most_expensive1['name'],most_expensive2['name'],most_expensive3['name'],most_popular1['name'],most_popular2['name'],most_popular3['name'],best1['name'],best2['name'],best3['name']]\r\n \r\n headers = {'User-Agent' : 'Mozilla/5.0 (Windows; U; Windows NT6.1; en-US; rv:1.9.1.6) Gecko/20201201 Firefox/3.5.6'}\r\n \r\n #the cookie may fail, need to be changed from time to time \r\n cookie=\"fspop=test; cye=hefei; _lxsdk_cuid=178970e9127c8-0a7e2614614ad4-4c3f237d-144000-178970e9127c8; _lxsdk=178970e9127c8-0a7e2614614ad4-4c3f237d-144000-178970e9127c8; Hm_lvt_602b80cf8079ae6591966cc70a3940e7=1617540449,1617603737,1617612171,1617712454; _hc.v=2d291f0f-f5e3-68c0-5121-15b43625688f.1617442019; s_ViewType=10; cy=110; _lx_utm=utm_source%3DBaidu%26utm_medium%3Dorganic; _lxsdk_s=178a72d1497-b7-792-6c3%7C%7C105; Hm_lpvt_602b80cf8079ae6591966cc70a3940e7=1617712942\"\r\n cookie_dict = {i.split(\"=\")[0]:i.split(\"=\")[-1] for i in cookie.split(\"; \")}\r\n \r\n i=0\r\n str2 = ['0']*10\r\n for name in new:\r\n link = 'https://www.dianping.com/search/keyword/110/0_' + name \r\n proxies ={'http':'http://49.70.17.135'}\r\n response =requests.get(link, proxies=proxies)\r\n r = requests.get(link, headers =headers, cookies =cookie_dict, timeout=1)\r\n soup = BeautifulSoup(r.text,\"lxml\")\r\n time.sleep(random.random())\r\n html =r.text\r\n #print(html)\r\n \r\n #search the sites of the photo,which exist in two ways below\r\n try:\r\n result = re.search('src=\"https://.*?/>',html)\r\n str1 = result.group()\r\n except:\r\n result = re.search('src=\"http://.*?/>',html)\r\n str1 = result.group()\r\n\r\n #absorb the surplus chars\r\n str1=str1.lstrip('src=\"')\r\n str1=str1.rstrip('\"/>')\r\n \r\n print(name)\r\n print(str1)\r\n \r\n#store the sites of photos of each restaurant in list str2[]\r\n str2[i]=str1\r\n i=i+1\r\n \r\n #create lists:most_popular,most_expensive,best\r\n most_popular = [most_popular1,most_popular2,most_popular3]\r\n most_expensive = [most_expensive1,most_expensive2,most_expensive3]\r\n best = [best1,best2,best3]\r\n \r\n i=0\r\n \r\n for a in most_expensive: \r\n a['image']=str2[i]\r\n i=i+1\r\n \r\n for a in most_popular: \r\n a['image']=str2[i]\r\n i=i+1 \r\n \r\n for a in best: \r\n a['image']=str2[i]\r\n i=i+1 \r\n \r\n print(most_popular)\r\n print(most_expensive)\r\n print(best)\r\n \r\n app = Flask(__name__)\r\n \r\n @app.route(\"/\")\r\n def index():\r\n return render_template('index.html')\r\n @app.route(\"/best\")\r\n def index1():\r\n return render_template('module1.html', restaurants=best)\r\n @app.route(\"/most_popular\")\r\n def index2():\r\n return render_template('module1.html', restaurants=most_popular)\r\n @app.route(\"/most_expensive\")\r\n def index3():\r\n return render_template('module1.html', restaurants=most_expensive) \r\n\r\n app.run()\r\n","sub_path":"another_test.py","file_name":"another_test.py","file_ext":"py","file_size_in_byte":9900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"179149799","text":"from tkinter import *\n\nroot = Tk()\nroot.title(\"Nado GUI\")\nroot.geometry(\"640x480\") # \"가로 x 세로 + x좌표 + y좌표\"\nroot.resizable(True, True)\n\nchkvar = IntVar() #chkvar 에 int 형으로 값을 저장\nchkbox = Checkbutton(root, text = \"오늘하루 보지 않기\", variable = chkvar)\n# chkbox.select()\n# chkbox.deselect()\nchkbox.pack()\n\nchkvar2 = IntVar()\nchkbox2 = Checkbutton(root, text = \"일주일동안 보지 않기\", variable = chkvar2)\nchkbox2.pack()\n\n\n\ndef btncmd():\n print(chkvar.get()) # 0 : 체크 해제, 1: 체크\n print(chkvar2.get())\n\n \nbtn = Button(root, text=\"click\", command=btncmd)\nbtn.pack()\n\nroot.mainloop()\n","sub_path":"GUI/6_checkbox.py","file_name":"6_checkbox.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"486057106","text":"import csv\nimport sys\nimport sklearn\nimport numpy\n\n\nfrom sklearn import svm\n\nfrom sklearn.neural_network import MLPClassifier\n\nfrom sklearn import tree\n\nfrom sklearn.ensemble import RandomForestClassifier\n\nfrom sklearn.model_selection import ShuffleSplit\nfrom sklearn import preprocessing as pp\nfrom sklearn.preprocessing import StandardScaler, RobustScaler\n\nfr = 0.95\n\n\n\n# read input\nreader = csv.reader(open(\"dataset1.csv\"), delimiter=\",\")\nx = list(reader)\n\ndata = numpy.array(x).astype(\"float\")\nlabel_idx = data.shape[1]-1\nlabels = data[:, label_idx].astype(int)\ndata = data[:, 0:label_idx]\n\n\n# create cross-validation sets\nsample_size = data.shape[0]-1\norder = numpy.arange(sample_size)\nrs = ShuffleSplit(n_splits = 5, test_size=0.20)\n\noverall = 0\nfor train_index, test_index in rs.split(order):\n\n lim = int(train_index.shape[0] * fr)\n\n train_main_idx = train_index[0:lim]\n train_seco_idx = train_index[lim:]\n\n train_main_data = data[train_main_idx][:]\n train_main_label = labels[train_main_idx]\n\n train_seco_data = data[train_seco_idx][:]\n train_seco_label = labels[train_seco_idx]\n\n robust_scaler = RobustScaler()\n fold_train = robust_scaler.fit_transform(train_main_data)\n train_seco_data = robust_scaler.transform(train_seco_data)\n fold_test = robust_scaler.transform(data[test_index, :])\n\n # train the basic classifier with fold train\n #linear svm\n classifier_svm = svm.LinearSVC()\n classifier_svm = classifier_svm.fit(fold_train, train_main_label)\n # mlp\n classifier_mlp = MLPClassifier( hidden_layer_sizes = (72,28) )\n classifier_mlp = classifier_mlp.fit(fold_train, train_main_label)\n # random forrest\n classifier_raf = RandomForestClassifier(n_estimators=13)\n classifier_raf = classifier_raf.fit(fold_train, train_main_label)\n\n\n\n\n # boosting classifier\n label_svm = classifier_svm.predict(train_seco_data)\n label_mlp = classifier_mlp.predict(train_seco_data)\n label_raf = classifier_raf.predict(train_seco_data)\n tmp = numpy.vstack((label_svm, label_mlp, label_raf))\n boosting_features = numpy.transpose(tmp)\n\n # boosting mlp\n #classifier = MLPClassifier( hidden_layer_sizes = (5,5) )\n classifier = RandomForestClassifier(n_estimators=14)\n classifier = classifier.fit(boosting_features, train_seco_label)\n\n label_svm = classifier_svm.predict(fold_test)\n label_mlp = classifier_mlp.predict(fold_test)\n label_raf = classifier_raf.predict(fold_test)\n tmp = numpy.vstack((label_svm, label_mlp, label_raf))\n boosting_test = numpy.transpose(tmp)\n\n z = classifier.score(boosting_test, labels[test_index])\n\n\n\n overall += z\n\n\n\nprint (overall/5*100)\n","sub_path":"combined.py","file_name":"combined.py","file_ext":"py","file_size_in_byte":2667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"623644886","text":"# Copyright 2020-, Gavin E. Crooks and the QuantumFlow contributors\n#\n# This source code is licensed under the Apache License, Version 2.0 found in\n# the LICENSE.txt file in the root directory of this source tree.\n\n\"\"\"\nQuantumFlow: Gates peculiar to Rigetti's Forest\n\"\"\"\n\nfrom typing import List\n\nimport numpy as np\n\nfrom .. import tensors, var\nfrom ..config import CTRL, NCTRL\nfrom ..future import cached_property\nfrom ..paulialgebra import Pauli, sZ\nfrom ..qubits import Qubit\nfrom ..tensors import QubitTensor\nfrom ..var import Variable\nfrom .stdgates import StdGate\n\n__all__ = (\"CPhase\", \"CPhase00\", \"CPhase01\", \"CPhase10\", \"PSwap\")\n\n\nclass CPhase(StdGate):\n r\"\"\"A 2-qubit 11 phase-shift gate\n\n .. math::\n \\text{CPhase}(\\theta) \\equiv \\text{diag}(1, 1, 1, e^{i \\theta})\n \"\"\"\n cv_interchangeable = True\n cv_tensor_structure = \"diagonal\"\n\n def __init__(self, theta: Variable, q0: Qubit = 0, q1: Qubit = 1) -> None:\n super().__init__(params=[theta], qubits=[q0, q1])\n\n @property\n def hamiltonian(self) -> Pauli:\n q0, q1 = self.qubits\n theta = self.param(\"theta\")\n return -theta * (1 + sZ(q0) * sZ(q1) - sZ(q0) - sZ(q1)) / 4\n\n @cached_property\n def tensor(self) -> QubitTensor:\n theta = var.asfloat(self.param(\"theta\"))\n unitary = [\n [1.0, 0, 0, 0],\n [0, 1.0, 0, 0],\n [0, 0, 1.0, 0],\n [0, 0, 0, np.exp(1j * theta)],\n ]\n return tensors.asqutensor(unitary)\n\n @property\n def H(self) -> \"CPhase\":\n return self**-1\n\n def __pow__(self, t: Variable) -> \"CPhase\":\n theta = self.param(\"theta\") * t\n return CPhase(theta, *self.qubits)\n\n def _diagram_labels_(self) -> List[str]:\n return [CTRL, \"P({theta})\"]\n\n\n# end class CPhase\n\n\nclass CPhase00(StdGate):\n r\"\"\"A 2-qubit 00 phase-shift gate\n\n .. math::\n \\text{CPhase00}(\\theta) \\equiv \\text{diag}(e^{i \\theta}, 1, 1, 1)\n \"\"\"\n cv_interchangeable = True\n cv_tensor_structure = \"diagonal\"\n\n def __init__(self, theta: Variable, q0: Qubit = 0, q1: Qubit = 1) -> None:\n super().__init__(params=[theta], qubits=[q0, q1])\n\n @property\n def hamiltonian(self) -> Pauli:\n q0, q1 = self.qubits\n theta = self.param(\"theta\")\n return -theta * (1 + sZ(q0) * sZ(q1) + sZ(q0) + sZ(q1)) / (4)\n\n @cached_property\n def tensor(self) -> QubitTensor:\n theta = var.asfloat(self.param(\"theta\"))\n unitary = [\n [np.exp(1j * theta), 0, 0, 0],\n [0, 1.0, 0, 0],\n [0, 0, 1.0, 0],\n [0, 0, 0, 1.0],\n ]\n return tensors.asqutensor(unitary)\n\n @property\n def H(self) -> \"CPhase00\":\n return self**-1\n\n def __pow__(self, t: Variable) -> \"CPhase00\":\n theta = self.param(\"theta\")\n return CPhase00(theta * t, *self.qubits)\n\n def _diagram_labels_(self) -> List[str]:\n return [NCTRL + \"({theta})\", NCTRL + \"({theta})\"]\n\n\n# end class CPhase00\n\n\nclass CPhase01(StdGate):\n r\"\"\"A 2-qubit 01 phase-shift gate\n\n .. math::\n \\text{CPhase01}(\\theta) \\equiv \\text{diag}(1, e^{i \\theta}, 1, 1)\n \"\"\"\n cv_tensor_structure = \"diagonal\"\n\n def __init__(self, theta: Variable, q0: Qubit = 0, q1: Qubit = 1) -> None:\n super().__init__(params=[theta], qubits=[q0, q1])\n\n @property\n def hamiltonian(self) -> Pauli:\n q0, q1 = self.qubits\n return -self.param(\"theta\") * (1 - sZ(q0) * sZ(q1) + sZ(q0) - sZ(q1)) / (4)\n\n @cached_property\n def tensor(self) -> QubitTensor:\n unitary = [\n [1.0, 0, 0, 0],\n [0, np.exp(1j * var.asfloat(self.param(\"theta\"))), 0, 0],\n [0, 0, 1.0, 0],\n [0, 0, 0, 1.0],\n ]\n return tensors.asqutensor(unitary)\n\n @property\n def H(self) -> \"CPhase01\":\n return self**-1\n\n def __pow__(self, t: Variable) -> \"CPhase01\":\n theta = self.param(\"theta\")\n return CPhase01(theta * t, *self.qubits)\n\n def _diagram_labels_(self) -> List[str]:\n return [NCTRL + \"({theta})\", CTRL + \"({theta})\"]\n\n\n# end class CPhase01\n\n\nclass CPhase10(StdGate):\n r\"\"\"A 2-qubit 10 phase-shift gate\n\n .. math::\n \\text{CPhase10}(\\theta) \\equiv \\text{diag}(1, 1, e^{i \\theta}, 1)\n \"\"\"\n cv_tensor_structure = \"diagonal\"\n\n def __init__(self, theta: Variable, q0: Qubit = 0, q1: Qubit = 1) -> None:\n super().__init__(params=[theta], qubits=[q0, q1])\n\n @property\n def hamiltonian(self) -> Pauli:\n q0, q1 = self.qubits\n theta = self.param(\"theta\")\n return -theta * (1 - sZ(q0) * sZ(q1) - sZ(q0) + sZ(q1)) / (4)\n\n @cached_property\n def tensor(self) -> QubitTensor:\n unitary = [\n [1.0, 0, 0, 0],\n [0, 1.0, 0, 0],\n [0, 0, np.exp(1j * var.asfloat(self.param(\"theta\"))), 0],\n [0, 0, 0, 1.0],\n ]\n return tensors.asqutensor(unitary)\n\n @property\n def H(self) -> \"CPhase10\":\n return self**-1\n\n def __pow__(self, t: Variable) -> \"CPhase10\":\n theta = self.param(\"theta\")\n return CPhase10(theta * t, *self.qubits)\n\n def _diagram_labels_(self) -> List[str]:\n return [CTRL + \"({theta})\", NCTRL + \"({theta})\"]\n\n\n# end class CPhase10\n\n\nclass PSwap(StdGate):\n r\"\"\"A 2-qubit parametric-swap gate, as defined by Quil.\n Interpolates between SWAP (theta=0) and iSWAP (theta=pi/2).\n\n Locally equivalent to ``CAN(1/2, 1/2, 1/2 - theta/pi)``\n\n .. math::\n \\text{PSwap}(\\theta) \\equiv \\begin{pmatrix} 1&0&0&0 \\\\\n 0&0&e^{i\\theta}&0 \\\\ 0&e^{i\\theta}&0&0 \\\\ 0&0&0&1 \\end{pmatrix}\n \"\"\"\n cv_interchangeable = True\n cv_tensor_structure = \"monomial\"\n\n def __init__(self, theta: Variable, q0: Qubit = 0, q1: Qubit = 1) -> None:\n super().__init__(params=[theta], qubits=[q0, q1])\n\n @cached_property\n def tensor(self) -> QubitTensor:\n theta = var.asfloat(self.param(\"theta\"))\n unitary = [\n [[[1, 0], [0, 0]], [[0, 0], [np.exp(theta * 1.0j), 0]]],\n [[[0, np.exp(theta * 1.0j)], [0, 0]], [[0, 0], [0, 1]]],\n ]\n return tensors.asqutensor(unitary)\n\n @property\n def H(self) -> \"PSwap\":\n theta = self.param(\"theta\")\n theta = 2.0 * np.pi - theta % (2.0 * np.pi)\n return PSwap(theta, *self.qubits)\n\n\n# end class PSwap\n\n\n# fin\n","sub_path":"quantumflow/stdgates/stdgates_forest.py","file_name":"stdgates_forest.py","file_ext":"py","file_size_in_byte":6341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"219684982","text":"import RPi.GPIO as GPIO\nimport os\n\npowerLed = 4\nled1 = 2\nled2 = 3\n\npowerSwitch = 17\nswitchMode1 = 27\nswitchMode2 = 22\n\n# Quick gpio setup method.\nledList = [powerLed, led1, led2]\nswitchList = [powerSwitch, switchMode1, switchMode2]\n\nGPIO.setmode(GPIO.BCM)\n\nfor i in range(len(switchList)):\n GPIO.setup(switchList[i], GPIO.IN, pull_up_down=GPIO.PUD_UP)\n print('Switch:', switchList[i], 'has been setup as an input')\n\nfor i in range(len(ledList)):\n GPIO.setup(ledList[i], GPIO.OUT)\n print('LED:', ledList[i], 'Has been setup as an output')\n\ntry:\n while True:\n for i in range(len(switchList)):\n inputState = GPIO.input(switchList[i])\n if not inputState and int(switchList[i]) == switchMode1:\n GPIO.output(led2, GPIO.LOW)\n GPIO.output(led1, GPIO.HIGH)\n os.system(\"omxplayer -o local Audio1.mp3\")\n elif not inputState and int(switchList[i]) == switchMode2:\n GPIO.output(led1, GPIO.LOW)\n GPIO.output(led2, GPIO.HIGH)\n os.system(\"omxplayer -o local Audio2.mp3\")\nexcept KeyboardInterrupt:\n for i in range(len(ledList)):\n GPIO.output(ledList[i], GPIO.LOW)\nfinally:\n print('Now exiting')\n GPIO.cleanup()\n\n","sub_path":"TESTING/audio_test.py","file_name":"audio_test.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"375136528","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 21 10:35:39 2018\n\n@author: user\n\"\"\"\nfrom tensorflow.python.framework import ops\nops.reset_default_graph()\nimport tensorflow as tf\nimport numpy as np\nimport os\n\nx1 = np.random.randn(5,5).astype('float32')\ny1 = np.random.randn(5,5).astype('float32')\nu1 = np.random.randn(5,5).astype('float32')\nv1 = np.random.randn(5,5).astype('float32')\n\n#print(x1,y1,u1,v1)\n\n#with tf.name_scope('sum') as scope:\nu = tf.placeholder(tf.float32,shape = [None,5])\nv = tf.placeholder(tf.float32,shape = [None,5])\nw = tf.reduce_sum(u + v)\nt = tf.reduce_sum(u - v)\n\nwriter = tf.summary.FileWriter('tb2',tf.get_default_graph())\ntf.summary.scalar('w',w)\ntf.summary.scalar('t',t)\ntf.summary.histogram('u',u)\ntf.summary.histogram('v',v)\nsumm = tf.summary.merge_all()\n\ninit = tf.global_variables_initializer()\nwith tf.Session() as sess:\n sess.run(init)\n for i in range(5): \n b,c,s = sess.run([w,t,summ],feed_dict = {u:u1+i , v:v1+2*i })\n print(b,c)\n writer.add_summary(s, int(i))\n\nwriter.close()\nos.system('tensorboard --logdir=/home/user/atulmlpython/TF_assign/tb2/ --port=6006') \n","sub_path":"TestAddSummary.py","file_name":"TestAddSummary.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"209230737","text":"from random import randint \n\nclass Player(object):\n def __init__(self, lifes):\n self.lifes = lifes\n self.scores = 0\n self.did_hit = False\n self.is_hitted = False\n\n def fire(self):\n sikta = randint(1, 5) \n if sikta in {1,2,3}: \n self.did_hit=True\n undvika = randint(1, 10)\n if undvika in {1,2,3}:\n self.is_hitted = True\n \n def inc_scores(self):\n self.scores= self.scores + 1\n self.did_hit= False\n\n def reduce_lifes(self):\n self.lifes = self.lifes - 1\n self.is_hitted = False\n\na_player = Player(3) \n\nwhile True:\n input('Tryck Enter för att skjuta')\n a_player.fire()\n if a_player.did_hit:\n print('Träff!')\n a_player.inc_scores() \n else:\n print('Miss, sikta bättre')\n if a_player.is_hitted:\n print('Aaaaaah, du blev träffad')\n a_player.reduce_lifes() \n else:\n print('Du klarade dig denna gång!')\n print(f\"{a_player.scores} poäng\")\n print(f\"{a_player.lifes} liv\")\n if a_player.lifes <= 0:\n print(\"du dog\")\n break\n","sub_path":"pang-pang.py","file_name":"pang-pang.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"595385887","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Codec:\n\n def serialize(self, root):\n \"\"\"Encodes a tree to a single string.\n\n :type root: TreeNode\n :rtype: str\n \"\"\"\n data = []\n q = [root]\n \n while q:\n node = q.pop(0)\n if node is None:\n data.append(None)\n else:\n data.append(node.val)\n q.append(node.left)\n q.append(node.right)\n return data\n\n def deserialize(self, data):\n \"\"\"Decodes your encoded data to tree.\n \n :type data: str\n :rtype: TreeNode\n \"\"\"\n if not data:\n return None\n n = len(data)\n nums = [0 for i in range(n)]\n nodes = [None for i in range(n)]\n \n for i in range(n):\n if i > 0:\n nums[i] = nums[i - 1]\n if data[i] is None:\n nums[i] += 1\n else:\n nodes[i] = TreeNode(data[i])\n \n for i in range(n):\n if nodes[i] is None:\n continue\n else:\n l = 2 * (i - nums[i]) + 1\n r = 2 * (i - nums[i]) + 2\n nodes[i].left = nodes[l]\n nodes[i].right = nodes[r]\n return nodes[0]\n \n","sub_path":"Tree Recur backtracking/297-Serialize and Deserialize Binary Tree/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"619110421","text":"'''\nFunctions for creating and connecting PyLGN networks.\n'''\n\nimport quantities as pq\nfrom matplotlib import pyplot as plt\n\nimport pylgn\nimport pylgn.kernels.spatial as spl\nimport pylgn.kernels.temporal as tpl\n\nfrom . import util \n\n\ndef create_staticnetwork(params=None):\n \"\"\"\n Create a PyLGN network where all temporal kernels are delta functions.\n \n Parameters\n ----------\n params : None, dict, str\n passed to util._parse_parameters\n \"\"\"\n \n params = util.parse_parameters(params)\n \n # network\n network = pylgn.Network()\n integrator = network.create_integrator(\n nt=0, \n nr=params['nr'], \n dt=params['dt'], \n dr=params['dr']\n )\n \n # neurons\n ganglion = network.create_ganglion_cell()\n relay = network.create_relay_cell()\n cortical = network.create_cortical_cell()\n \n # RGC impulse-response function\n delta_t = tpl.create_delta_ft()\n Wg_r = spl.create_dog_ft(\n A=params['A_g'], \n a=params['a_g'], \n B=params['B_g'], \n b=params['b_g']\n )\n ganglion.set_kernel((Wg_r, delta_t))\n \n # excitatory FF connection\n Krg_r = spl.create_gauss_ft(A=params['A_rg_ex'], a=params['a_rg_ex'])\n network.connect(ganglion, relay, (Krg_r, delta_t), weight=params['w_rg_ex'])\n \n # inhibitory FF\n Krig_r = spl.create_gauss_ft(A=params['A_rg_in'], a=params['a_rg_in'])\n network.connect(ganglion, relay, (Krig_r, delta_t), weight=params['w_rg_in'])\n\n # excitatory FB\n Krc_ex_r = spl.create_gauss_ft(A=params['A_rc_ex'], a=params['a_rc_ex'])\n network.connect(cortical, relay, (Krc_ex_r, delta_t), weight=params['w_rc_ex'])\n \n # inhibitory FB\n Krc_in_r = spl.create_gauss_ft(A=params['A_rc_in'], a=params['a_rc_in'])\n network.connect(cortical, relay, (Krc_in_r, delta_t), weight=params['w_rc_in'])\n \n # TC feed-forward\n Kcr_r = spl.create_delta_ft()\n network.connect(relay, cortical, (Kcr_r, delta_t), weight=1)\n \n return network\n\n\ndef create_dynamicnetwork(params=None):\n \"\"\"\n Create PyLGN network with temporal kernels.\n \n Parameters\n ----------\n params : None, dict, str\n passed to util._parse_parameters\n \"\"\"\n \n params = util.parse_parameters(params)\n\n # network\n network = pylgn.Network()\n integrator = network.create_integrator(\n nt=params['nt'], \n nr=params['nr'], \n dt=params['dt'], \n dr=params['dr']\n )\n \n # neurons\n ganglion = network.create_ganglion_cell()\n relay = network.create_relay_cell()\n cortical = network.create_cortical_cell()\n \n # RGC impulse-response function\n Wg_r = spl.create_dog_ft(\n A=params['A_g'], \n a=params['a_g'], \n B=params['B_g'], \n b=params['b_g']\n )\n Wg_t = tpl.create_biphasic_ft(\n phase=params['phase_g'], \n damping=params['damping_g']\n )\n ganglion.set_kernel((Wg_r, Wg_t))\n \n # excitatory FF connection\n Krg_r = spl.create_gauss_ft(A=params['A_rg_ex'], a=params['a_rg_ex'])\n Krg_t = tpl.create_exp_decay_ft(\n tau=params['tau_rg_ex'], \n delay=params['delay_rg_ex']\n )\n network.connect(ganglion, relay, (Krg_r, Krg_t), weight=params['w_rg_ex'])\n \n # inhibitory FF\n Krig_r = spl.create_gauss_ft(A=params['A_rg_in'], a=params['a_rg_in'])\n Krig_t = tpl.create_exp_decay_ft(\n tau=params['tau_rg_in'], \n delay=params['delay_rg_in']\n )\n network.connect(ganglion, relay, (Krig_r, Krig_t), weight=params['w_rg_in'])\n \n # excitatory FB\n Krc_ex_r = spl.create_gauss_ft(A=params['A_rc_ex'], a=params['a_rc_ex'])\n Krc_ex_t = tpl.create_exp_decay_ft(\n tau=params['tau_rc_ex'], \n delay=params['delay_rc_ex'])\n network.connect(cortical, relay, (Krc_ex_r, Krc_ex_t), weight=params['w_rc_ex'])\n \n # inhibitory FB\n Krc_in_r = spl.create_gauss_ft(A=params['A_rc_in'], a=params['a_rc_in'])\n Krc_in_t = tpl.create_exp_decay_ft(\n tau=params['tau_rc_in'], \n delay=params['delay_rc_in'])\n network.connect(cortical, relay, (Krc_in_r, Krc_in_t), weight=params['w_rc_in'])\n \n # TC feed-forward\n Kcr_r = spl.create_delta_ft()\n Kcr_t = tpl.create_delta_ft()\n network.connect(relay, cortical, (Kcr_r, Kcr_t), weight=params['w_cr'])\n\n return network\n\n\ndef get_neuron(name, network):\n \"\"\"\n Returns requested pylgn.Neuron object. Taken from \n https://github.com/miladh/edog-simulations.\n\n Parameters\n ----------\n name : string\n name of the neuron\n\n network : pylgn.Network\n\n Returns\n -------\n out : pylgn.Neuron\n \"\"\"\n neuron = [neuron for neuron in network.neurons if type(neuron).__name__ == name]\n \n if not neuron:\n raise NameError(\"neuron not found in network\", name)\n \n elif len(neuron) > 1 and name == \"Relay\":\n raise ValueError(\"more than one Relay cell found in network\")\n \n return neuron\n\n","sub_path":"dlgnsim/networks.py","file_name":"networks.py","file_ext":"py","file_size_in_byte":4979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"362235790","text":"from tables.common import parse_file, grid_search_eval\n\nPRETRAIN_EVAL_RESULTS_TEMPLATE = \"\"\"global_step = {global_step:d}\nloss = {loss:f}\nmasked_lm_accuracy = {masked_lm_accuracy:f}\nmasked_lm_loss = {masked_lm_loss:f}\nnext_sentence_accuracy = {next_sentence_accuracy:f}\nnext_sentence_loss = {next_sentence_loss:f}\n\"\"\"\n\ntable_rows = []\nfor sparsity in [0, .1, .2, .3, .4, .5, .6, .7, .8, .9]:\n\n eval_entries, train_losses = grid_search_eval(lambda task, lr: f'models/{task}/gradual_prune_{int(sparsity*100)}_less_embed_lr_{lr}/eval_results.txt')\n\n pretrain_results = parse_file(f'models/pretrain/gradual_prune_0_head_pruned_{int(sparsity*100)}/eval_results.txt', PRETRAIN_EVAL_RESULTS_TEMPLATE)\n pretrain_loss = pretrain_results['loss'] if pretrain_results else float('inf')\n\n avg_eval = sum(eval_entries) / len(eval_entries)\n avg_loss = sum(train_losses) / len(train_losses)\n table_rows.append(\n f\"{sparsity} & {pretrain_loss:.2f} & \" #+ \" & \".join([f\"{acc:.2f}|{loss:.2f}\" for acc, loss in zip(eval_entries, train_losses)]) + f\" & {avg_eval:.2f}|{avg_loss:.2f}\"\n )\n\nrows = \"\\\\\\\\\\n\".join(table_rows)\nprint(f\"\"\"\n\\\\begin{{tabular}}{{ccccccccccc}}\nPruned & Pre-train Loss & MNLI & QQP & QNLI & SST-2 & CoLA & STS-B & MRPC & RTE & AVG\\\\\\\\\n\\hline\n{rows}\\\\\\\\\n\\\\end{{tabular}}\n\"\"\")\n","sub_path":"tables/head_pruned.py","file_name":"head_pruned.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"635699395","text":"\"\"\"Support for Ezviz binary sensors.\"\"\"\nimport logging\n\nfrom pyezviz.constants import BinarySensorType\n\nfrom homeassistant.components.binary_sensor import BinarySensorEntity\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.entity import DeviceInfo\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\nfrom homeassistant.helpers.update_coordinator import CoordinatorEntity\n\nfrom .const import DATA_COORDINATOR, DOMAIN, MANUFACTURER\nfrom .coordinator import EzvizDataUpdateCoordinator\n\n_LOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup_entry(\n hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\n) -> None:\n \"\"\"Set up Ezviz sensors based on a config entry.\"\"\"\n coordinator: EzvizDataUpdateCoordinator = hass.data[DOMAIN][entry.entry_id][\n DATA_COORDINATOR\n ]\n sensors = []\n\n for idx, camera in enumerate(coordinator.data):\n for name in camera:\n # Only add sensor with value.\n if camera.get(name) is None:\n continue\n\n if name in BinarySensorType.__members__:\n sensor_type_name = getattr(BinarySensorType, name).value\n sensors.append(\n EzvizBinarySensor(coordinator, idx, name, sensor_type_name)\n )\n\n async_add_entities(sensors)\n\n\nclass EzvizBinarySensor(CoordinatorEntity, BinarySensorEntity):\n \"\"\"Representation of a Ezviz sensor.\"\"\"\n\n coordinator: EzvizDataUpdateCoordinator\n\n def __init__(\n self,\n coordinator: EzvizDataUpdateCoordinator,\n idx: int,\n name: str,\n sensor_type_name: str,\n ) -> None:\n \"\"\"Initialize the sensor.\"\"\"\n super().__init__(coordinator)\n self._idx = idx\n self._camera_name = self.coordinator.data[self._idx][\"name\"]\n self._name = name\n self._sensor_name = f\"{self._camera_name}.{self._name}\"\n self.sensor_type_name = sensor_type_name\n self._serial = self.coordinator.data[self._idx][\"serial\"]\n\n @property\n def name(self) -> str:\n \"\"\"Return the name of the Ezviz sensor.\"\"\"\n return self._name\n\n @property\n def is_on(self) -> bool:\n \"\"\"Return the state of the sensor.\"\"\"\n return self.coordinator.data[self._idx][self._name]\n\n @property\n def unique_id(self) -> str:\n \"\"\"Return the unique ID of this sensor.\"\"\"\n return f\"{self._serial}_{self._sensor_name}\"\n\n @property\n def device_info(self) -> DeviceInfo:\n \"\"\"Return the device_info of the device.\"\"\"\n return {\n \"identifiers\": {(DOMAIN, self._serial)},\n \"name\": self.coordinator.data[self._idx][\"name\"],\n \"model\": self.coordinator.data[self._idx][\"device_sub_category\"],\n \"manufacturer\": MANUFACTURER,\n \"sw_version\": self.coordinator.data[self._idx][\"version\"],\n }\n\n @property\n def device_class(self) -> str:\n \"\"\"Device class for the sensor.\"\"\"\n return self.sensor_type_name\n","sub_path":"homeassistant/components/ezviz/binary_sensor.py","file_name":"binary_sensor.py","file_ext":"py","file_size_in_byte":3090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"150075485","text":"from tkinter import *\r\n#导入tkinter模块所有内容\r\n\r\n#创建一个toplevel的根窗口,并把它作为参数实例化下列的textLabel、imgLabel对象\r\nroot = Tk()\r\nroot.title(\"温馨提示\")\r\n#穿件一个文本label对象\r\ntextLabel = Label(root,\\\r\ntext = \"您所下载的影片含有未成年人限制的内容，请满18周岁后再点击观看！\")\r\ntextLabel.pack(side=LEFT)\r\n#创建一个图像Label对象\r\n#用PhotosImage实例化一个图片对象（支持gif格式的图片）\r\nphoto = PhotoImage(file = \"18.gif\")\r\nimgLabel = Label(root,image = photo)\r\nimgLabel.pack(side=RIGHT)\r\n\r\nmainloop()","sub_path":"p15_3.py","file_name":"p15_3.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"565827513","text":"import os\r\nimport re\r\n\r\npath = 'C:/ShareSSD/scop/maxcluster_filtered/'\r\npath_to_summaries = 'C:/ShareSSD/scop/maxcluster_summaries/'\r\n\r\ndef startSummaries():\r\n\r\n #start with gdt_2 files\r\n\r\n for filename in os.listdir(path):\r\n print(filename)\r\n if 'gdt_2' in filename:\r\n\r\n with open(path+filename,'r') as fp:\r\n\r\n line = fp.readline()\r\n while line:\r\n if 'RMSD' in line:\r\n parsed = re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", line)\r\n rmsd = parsed[5]\r\n maxsub = parsed[3]\r\n tmscore = parsed[6]\r\n\r\n #gdt-ha\r\n if 'GDT' in line:\r\n for n in line.split():\r\n try: \r\n gdt = float(n)\r\n except ValueError:\r\n pass\r\n\r\n line = fp.readline()\r\n\r\n new_name = str(filename).replace('gdt_2','summary')\r\n\r\n with open(path_to_summaries+new_name,'w') as nf:\r\n nf.write('RMSD= '+rmsd+'\\n')\r\n nf.write('MaxSub= '+maxsub+'\\n')\r\n nf.write('TM-score= '+tmscore+'\\n')\r\n nf.write('GDT-HA= '+str(gdt)+'\\n')\r\n\r\n appendGDTTS()\r\n\r\ndef appendGDTTS():\r\n\r\n for filename in os.listdir(path):\r\n print(filename)\r\n if 'gdt_4' in filename:\r\n\r\n parsed = str(filename).split('_')\r\n\r\n structure1 = parsed[0]\r\n if '.ent' not in structure1:\r\n structure1 = structure1+'_.ent'\r\n structure2 = parsed[2]\r\n if '.ent' not in structure2:\r\n structure2 = structure2+'_.ent'\r\n\r\n print(structure1)\r\n print(structure2)\r\n\r\n with open(path+filename) as fp:\r\n line = fp.readline()\r\n while line:\r\n if 'GDT' in line:\r\n for n in line.split():\r\n try: \r\n gdt = float(n)\r\n except ValueError:\r\n pass\r\n\r\n line = fp.readline()\r\n\r\n summary_name = structure1+'_'+structure2+'_summary'\r\n\r\n with open(path_to_summaries+summary_name,'a') as nf:\r\n nf.write('GDT-TS= '+str(gdt)+'\\n')\r\n\r\nstartSummaries() \r\n\r\n","sub_path":"Alignments/ParseMaxCluster.py","file_name":"ParseMaxCluster.py","file_ext":"py","file_size_in_byte":2533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"182918977","text":"from itertools import chain\nfrom typing import Optional\nimport logging\nimport pathlib\nimport os\nimport json\nimport shutil\n\nimport click\n\nfrom libs import google_sheet_helpers, wide_dates_df\nfrom libs.datasets import statistical_areas\nfrom libs.datasets.combined_datasets import (\n ALL_TIMESERIES_FEATURE_DEFINITION,\n US_STATES_FILTER,\n ALL_FIELDS_FEATURE_DEFINITION,\n)\nfrom libs.datasets.latest_values_dataset import LatestValuesDataset\nfrom libs.datasets.timeseries import MultiRegionTimeseriesDataset\nfrom libs.datasets.timeseries import add_new_cases\nfrom libs.qa import dataset_summary\nfrom libs.qa import data_availability\nfrom libs.datasets.timeseries import TimeseriesDataset\nfrom libs.datasets import dataset_utils\nfrom libs.datasets import combined_dataset_utils\nfrom libs.datasets import combined_datasets\nfrom libs.datasets.sources import forecast_hub\nfrom pyseir import DATA_DIR\nimport pyseir.icu.utils\nfrom pyseir.icu import infer_icu\n\n\nPROD_BUCKET = \"data.covidactnow.org\"\n\n_logger = logging.getLogger(__name__)\n\n\n@click.group(\"data\")\ndef main():\n pass\n\n\ndef _save_field_summary(\n timeseries_dataset: MultiRegionTimeseriesDataset, output_path: pathlib.Path\n):\n\n _logger.info(\"Starting dataset summary generation\")\n summary = dataset_summary.summarize_timeseries_fields(timeseries_dataset.data)\n summary.to_csv(output_path)\n _logger.info(f\"Saved dataset summary to {output_path}\")\n\n\n@main.command()\n@click.option(\"--filename\", default=\"external_forecasts.csv\")\ndef update_forecasts(filename):\n \"\"\"Updates external forecasts to the current checked out covid data public commit\"\"\"\n path_prefix = dataset_utils.DATA_DIRECTORY.relative_to(dataset_utils.REPO_ROOT)\n data_root = dataset_utils.LOCAL_PUBLIC_DATA_PATH\n data_path = forecast_hub.ForecastHubDataset.DATA_PATH\n shutil.copy(data_root / data_path, path_prefix / filename)\n _logger.info(f\"Updating External Forecasts at {path_prefix / filename}\")\n\n\n@main.command()\n@click.option(\"--summary-filename\", default=\"timeseries_summary.csv\")\n@click.option(\"--wide-dates-filename\", default=\"multiregion-wide-dates.csv\")\n@click.option(\"--aggregate-to-msas\", is_flag=True, help=\"Aggregate counties to MSAs\")\ndef update(summary_filename, wide_dates_filename, aggregate_to_msas: bool):\n \"\"\"Updates latest and timeseries datasets to the current checked out covid data public commit\"\"\"\n path_prefix = dataset_utils.DATA_DIRECTORY.relative_to(dataset_utils.REPO_ROOT)\n\n data_source_classes = set(\n chain(\n chain.from_iterable(ALL_FIELDS_FEATURE_DEFINITION.values()),\n chain.from_iterable(ALL_TIMESERIES_FEATURE_DEFINITION.values()),\n )\n )\n data_sources = {\n data_source_cls.SOURCE_NAME: data_source_cls.local()\n for data_source_cls in data_source_classes\n }\n timeseries_dataset: TimeseriesDataset = combined_datasets.build_from_sources(\n TimeseriesDataset, data_sources, ALL_TIMESERIES_FEATURE_DEFINITION, filter=US_STATES_FILTER\n )\n latest_dataset: LatestValuesDataset = combined_datasets.build_from_sources(\n LatestValuesDataset, data_sources, ALL_FIELDS_FEATURE_DEFINITION, filter=US_STATES_FILTER,\n )\n multiregion_dataset = MultiRegionTimeseriesDataset.from_timeseries_and_latest(\n timeseries_dataset, latest_dataset\n )\n multiregion_dataset = add_new_cases(multiregion_dataset)\n if aggregate_to_msas:\n aggregator = statistical_areas.CountyToCBSAAggregator.from_local_public_data()\n multiregion_dataset = multiregion_dataset.append_regions(\n aggregator.aggregate(multiregion_dataset)\n )\n\n _, multiregion_pointer = combined_dataset_utils.update_data_public_head(\n path_prefix, latest_dataset, multiregion_dataset,\n )\n\n # Write DataSource objects that have provenance information, which is only set when significant\n # processing of the source data is done in this repo before it is combined. The output is not\n # used downstream, it is for debugging only.\n for data_source in data_sources.values():\n if data_source.provenance is not None:\n wide_dates_df.write_csv(\n data_source.timeseries().get_date_columns(),\n path_prefix / f\"{data_source.SOURCE_NAME}-wide-dates.csv\",\n )\n\n if wide_dates_filename:\n wide_dates_df.write_csv(\n timeseries_dataset.get_date_columns(),\n multiregion_pointer.path.with_name(wide_dates_filename),\n )\n\n if summary_filename:\n _save_field_summary(multiregion_dataset, path_prefix / summary_filename)\n\n\n@main.command()\n@click.option(\"--output-dir\", type=pathlib.Path, required=True)\n@click.option(\"--filename\", type=pathlib.Path, default=\"timeseries_field_summary.csv\")\ndef save_summary(output_dir: pathlib.Path, filename: str):\n \"\"\"Saves summary of timeseries dataset indexed by fips and variable name.\"\"\"\n us_timeseries = combined_datasets.load_us_timeseries_dataset()\n _save_field_summary(us_timeseries, output_dir / filename)\n\n\n@main.command()\n@click.option(\"--name\", envvar=\"DATA_AVAILABILITY_SHEET_NAME\", default=\"Data Availability - Dev\")\n@click.option(\"--share-email\")\ndef update_availability_report(name: str, share_email: Optional[str]):\n sheet = google_sheet_helpers.open_or_create_spreadsheet(name, share_email=share_email)\n info_worksheet = google_sheet_helpers.update_info_sheet(sheet)\n data_sources_by_source_name = data_availability.load_all_latest_sources()\n\n for name, dataset in data_sources_by_source_name.items():\n _logger.info(f\"Updating {name}\")\n report = data_availability.build_data_availability_report(dataset)\n data_availability.update_multi_field_availability_report(\n sheet, report, name, columns_to_drop=[\"source\", \"fips\"]\n )\n\n # Reorder sheets with combined data first and metadata last\n COLUMN_ORDER_OVERRIDE = {data_availability.COMBINED_DATA_KEY: -5, info_worksheet.title: 5}\n worksheets = sheet.worksheets()\n worksheets = sorted(worksheets, key=lambda x: (COLUMN_ORDER_OVERRIDE.get(x.title, 0), x.title))\n sheet.reorder_worksheets(worksheets)\n\n _logger.info(\"Finished updating data availability report\")\n\n\n@main.command()\ndef update_case_based_icu_utilization_weights():\n \"\"\"\n Calculate the updated States to Counties disaggregation weights and save to disk. These\n weights are used to estimate county level ICU heads-in-beds as an input for the ICU Utilization\n metric.\n\n The output is callable with county aggregation-level fips keys and returns a normalized [0,1]\n value such that the weights for all counties in a given state sum to unity.\n \"\"\"\n output_path = os.path.join(DATA_DIR, infer_icu.ICUWeightsPath.ONE_MONTH_TRAILING_CASES.value)\n output = pyseir.icu.utils.calculate_case_based_weights()\n _logger.info(f\"Saved case-based ICU Utilization weights to {output_path}\")\n with open(output_path, \"w\") as f:\n json.dump(output, f)\n","sub_path":"cli/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":6986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"321603653","text":"#scraping code modified from Mark Needham's Python NLTK/Neo4j: Analysing the transcripts of How I Met Your Mother\nimport requests\nfrom bs4 import BeautifulSoup\nfrom soupsieve import select\n\nepisodes = {}\n\nfor i in range(5,7):\n \n page = open(\"data/html/page-\" + str(i) + \".html\", 'r')\n soup = BeautifulSoup(page.read(), features=\"lxml\")\n doc = soup.select(\".topic-titles.row2 h3 a\")\n for row in doc:\n if row != doc[0] and row != doc[1]: \n parts = row.text.split(\" - \")\n episodes[parts[0]] = {\"title\": parts[1], \"link\": row.get(\"href\")}\n\nwebsite = \"http://transcripts.foreverdreaming.org\"\nfor key, value in episodes.items():\n\n parts = key.split(\"x\")\n season = int(parts[0])\n episode = int(parts[1])\n\n filename = \"data/transcripts/S%d-Ep%d.txt\" %(season, episode)\n link = value[\"link\"] #for pages 3-14\n link = website+ link[1:] #for pages 3-14\n \n with open(filename, 'wb') as handle:\n headers = {'User-Agent': 'Chrome/78.0.3904.106'}\n response = requests.get(link,headers = headers) #use value[\"link\"] instead of link for pages 1 and 2 #use link for other pages\n if response.ok:\n for block in response.iter_content(1024):\n if not block:\n break\n\n handle.write(block)\n","sub_path":"code/scraping.py","file_name":"scraping.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"557225462","text":"'''\n2017/04/20\nWritten By Eunjin Cho\nDetermining the importance of proteins using PageRank\n'''\nimport sys\nimport numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\n\n\nclass PR:\n\tdef __init__(self, filename, beta, iters):\n\t\tself.filename = filename\n\t\tself.beta = float(beta)\n\t\tself.iters = int(iters)\n\n\n\tdef parse_matrix(self):\n\t\t'''\n\t\tRead protein data and convert it to dictionaries to later create a matrix\n\t\t'''\n\n\t\tidx = -1\n\t\t# key: idx, value: a real protein values\n\t\tself.idx_protein_dict = dict()\n\t\t# key: source, value: set of destinations\n\t\tself.protein_link_dict = dict()\n\t\tfor line in self.filename.readlines():\n\t\t\tif (idx == -1):\n\t\t\t\tidx += 1\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tline = line.strip()\n\t\t\t\tline_split = line.split('\\t')\n\t\t\t\tsrc_protein = line_split[0]\n\t\t\t\tdest_protein = line_split[1]\n\t\t\t\tif (src_protein not in self.idx_protein_dict.values()):\n\t\t\t\t\tself.idx_protein_dict[idx] = src_protein\n\t\t\t\t\tidx += 1\n\t\t\t\tif (dest_protein not in self.idx_protein_dict.values()):\n\t\t\t\t\tself.idx_protein_dict[idx] = dest_protein\n\t\t\t\t\tidx += 1\n\t\t\t\tif src_protein in self.protein_link_dict:\n\t\t\t\t\tself.protein_link_dict[src_protein].add(dest_protein)\n\t\t\t\tif src_protein not in self.protein_link_dict:\n\t\t\t\t\tself.protein_link_dict[src_protein] = set()\n\t\t\t\t\tself.protein_link_dict[src_protein].add(dest_protein)\n\n\t\t# key: protein, value: idx\n\t\tself.protein_idx_dict = dict()\n\t\tfor k, v in self.idx_protein_dict.items():\n\t\t\tself.protein_idx_dict[v] = k\n\n\t\tself.protein_idx_dict_new = self.protein_idx_dict.copy()\n\t\tself.idx_protein_dict_new = self.idx_protein_dict.copy()\n\n\n\tdef create_matrix(self):\n\t\t'''\n\t\tCreate a numpy matrix for faster matrix multiplication\n\t\t'''\n\n\t\t# initialize the matrix\n\t\t# rows are destinations and columns are sources\n\t\tlist_matrix = []\n\t\tfor i in range(len(self.idx_protein_dict)):\n\t\t\tlist_inside = [0]*len(self.idx_protein_dict)\n\t\t\tlist_matrix.append(list_inside)\n\n\t\tfor src, dest_set in self.protein_link_dict.items():\n\t\t\tsrc_idx = self.protein_idx_dict[src]\n\t\t\tfor dest in dest_set:\n\t\t\t\tdest_idx = self.protein_idx_dict[dest]\n\t\t\t\tlist_matrix[dest_idx][src_idx] = 1/len(dest_set)\n\t\t\n\t\t# Create a numpy array\n\t\tself.np_protein_mat_orig = np.array(list_matrix)\n\t\tself.np_protein_mat = np.array(list_matrix)\t\n\n\n\tdef remove_dead_ends(self):\n\t\t'''\n\t\tIteratively remove all the dead ends (vertices with no links coming out)\n\t\t'''\n\t\tself.removed_vertices = set()\n\t\tcanStop = False # stop when all of the dead ends are removed\n\t\twhile (not canStop):\n\t\t\tcols_to_remove = []\n\t\t\tfor col in range(len(self.np_protein_mat)):\n\t\t\t\tcolumn = self.np_protein_mat[:, col]\n\t\t\t\t# if there's no out links, remove\n\t\t\t\tif (not np.any(column)):\n\t\t\t\t\tcols_to_remove.append(col)\n\t\t\t\t\tself.mat_remove(col)\n\t\t\t\t\tbreak\n\t\t\t\t\t\n\t\t\t# if there's no dead ends\n\t\t\tif (len(cols_to_remove) == 0):\n\t\t\t\tcanStop = True\n\n\t\tprint (\"Number of unique proteins after removing dead ends: \", len(self.np_protein_mat))\n\n\n\tdef mat_remove(self, col):\n\t\tself.removed_vertices.add(self.idx_protein_dict_new[col])\n\t\tself.update_bidict(col) \n\t\tself.np_protein_mat = scipy.delete(self.np_protein_mat, col, 1)\n\t\tself.np_protein_mat = scipy.delete(self.np_protein_mat, col, 0)\n\t\tself.recreate_matrix()\n\t\tprint (\"removed the dead end: \", col)\n\n\n\tdef update_bidict(self, i):\n\t\t'''\n\t\tUpdate two dictionaries after removing dead ends\n\t\t@ param\n\t\t\t- i : the column number that is removed\n\t\t'''\n\t\tnew_idx_protein_dict = dict()\n\t\tfor k, v in self.idx_protein_dict_new.items():\n\t\t\tif k > i:\n\t\t\t\tk -= 1\n\t\t\t\tnew_idx_protein_dict[k] = v\n\t\t\tif k < i:\n\t\t\t\tnew_idx_protein_dict[k] = v\n\n\t\tself.idx_protein_dict_new = new_idx_protein_dict\n\n\t\tnew_protein_idx_dict = dict()\n\t\tfor k, v in self.protein_idx_dict_new.items():\n\t\t\tif v > i:\n\t\t\t\tv -= 1\n\t\t\t\tnew_protein_idx_dict[k] = v\n\t\t\tif v < i:\n\t\t\t\tnew_protein_idx_dict[k] = v\n\t\tself.protein_idx_dict_new = new_protein_idx_dict\n\n\n\tdef recreate_matrix(self):\n\t\t'''\n\t\tRe-create a numpy matrix after dead ends are removed\n\t\t'''\n\n\t\t# initialize the matrix\n\t\t# rows are destinations and columns are sources\n\t\tlist_matrix = []\n\t\tfor i in range(len(self.np_protein_mat)):\n\t\t\tlist_inside = [0]*len(self.np_protein_mat)\n\t\t\tlist_matrix.append(list_inside)\n\t\t\n\t\tfor src, dest_set in self.protein_link_dict.items():\n\t\t\t# print (\"src: \", src, \" dest: \", dest_set)\n\t\t\tif (src not in self.removed_vertices):\n\t\t\t\tsrc_idx = self.protein_idx_dict_new[src]\n\t\t\t\tlen_dest_set_minus_removed = dest_set.difference(self.removed_vertices)\n\t\t\t\tfor dest in dest_set:\n\t\t\t\t\tif (dest not in self.removed_vertices):\n\t\t\t\t\t\tdest_idx = self.protein_idx_dict_new[dest]\n\t\t\t\t\t\tlist_matrix[dest_idx][src_idx] = 1/len(len_dest_set_minus_removed)\n\t\t\n\t\t# Create a numpy array\n\t\tself.np_protein_mat = np.array(list_matrix)\n\n\n\tdef iterate_mat(self, beta_param):\n\t\t'''\n\t\tUsing iterative method using the equation during the class\n\t\tdo this iteration for self.iters time\n\n\t\t'''\n\t\tinit_vec = [1/len(self.np_protein_mat)]*(len(self.np_protein_mat))\n\t\tinit_vec = np.array(init_vec)\n\t\te = [1/len(self.np_protein_mat)]*(len(self.np_protein_mat))\n\t\te = np.array(e)\n\n\t\tmy_vec = np.array(init_vec)\n\t\tfor i in range(self.iters):\n\t\t\tfirst = beta_param*(self.np_protein_mat.dot(my_vec))\n\t\t\tsecond = (1-(beta_param))*e\n\t\t\tmy_vec = first + second\n\t\t\n\t\t# my_vec is the rank for each protein\n\t\tself.my_protein_rank = my_vec\n\t\tprint (\"final rank: \")\n\t\tprint (self.my_protein_rank)\n\t\tprint (len(self.my_protein_rank))\n \n\n\tdef num_highest(self, number):\n\t\t'''\n\t\tFind the top 5 ranks\n\t\t'''\n\t\tnum_high = -number\n\t\ttop_ind = np.argpartition(self.my_protein_rank, num_high)[num_high:]\n\t\treal_value = self.my_protein_rank[top_ind]\n\t\tprint (\"real_value: \", real_value)\n\t\tprint ()\n\t\tprint (\" ---------------------------------------------- \")\n\t\ti = 0\n\t\tfor ind in top_ind:\n\t\t\tprint (\"Protein: \", self.idx_protein_dict_new[ind], \" pageRank: \", real_value[i])\n\t\t\ti += 1\n\t\tprint (\" ---------------------------------------------- \")\n\n\n\tdef draw_histogram(self):\n\t\t''' \n\t\tDraw histogram of the distribution\n\t\t''' \n\t\tx_axis = []\n\t\tfor i in range(len(self.my_protein_rank)):\n\t\t\t# x_axis.append(self.idx_protein_dict_new[i])\n\t\t\tx_axis.append(i)\n\n\t\t# Barplot\n\t\tplt.bar(x_axis, self.my_protein_rank, width=1, align='center', color='brown')\n\t\tplt.ylabel(\"pageRank\")\n\t\tplt.xlabel(\"proteins (index of proteins)\")\n\t\tplt.title(\"Bar plot of pageRank\")\n\t\tplt.show()\n\n\t\t# Histogram\n\t\tplt.hist(self.my_protein_rank)\n\t\tplt.xlabel(\"pageRank\")\n\t\tplt.ylabel(\"Frequency\")\n\t\tplt.title(\"Histogram of pageRank\")\n\t\tplt.show()\n\n\ndef main():\n\tprotein_data = open(\"test2.txt\", \"r\")\n\tbeta = sys.argv[1]\n\titers = sys.argv[2]\n\tPR_class = PR(protein_data, beta, iters)\n\tPR_class.parse_matrix()\n\tPR_class.create_matrix()\n\t# PR_class.remove_dead_ends()\n\ta_value = []\n\tfor i in range(1, 9):\n\t\tbeta = i/10\n\t\tPR_class.iterate_mat(beta)\n\t\ta_value.append(PR_class.my_protein_rank[0])\n\tplt.scatter([1,2,3,4,5,6,7,8],a_value)\n\tplt.show()\n\tPR_class.num_highest(3)\n\t# PR_class.draw_histogram()\n\n\nif __name__ == \"__main__\":\n\tmain()","sub_path":"page_rank/pageRank.py","file_name":"pageRank.py","file_ext":"py","file_size_in_byte":6910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"77681595","text":"import mortgage\n\n# read inputs from command line\nprice = float(input('Home price: '))\ndown_min = float(input('Minimum down payment: '))\ndown_max = float(input('Maximum down payment: '))\nrate = float(input('Interest rate: '))\nterm = float(input('Loan term (years): '))\n\nrate = rate / 100 / 12\n\n# use range function to enumerate down payments\nfor down in range(int(down_min), int(down_max), 1000):\n loan = price - down\n payment = mortgage.mortgage_payment(loan, rate, term*12)\n\n print(down, round(payment, 2))\n","sub_path":"PS_2/s18-a02-solutions-master/table.py","file_name":"table.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"649518882","text":"from fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\nimport nest_asyncio\nfrom pyngrok import ngrok\nimport uvicorn\n\napp = FastAPI()\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=['*'],\n allow_credentials=True,\n allow_methods=['*'],\n allow_headers=['*'],\n)\n\n@app.get('/')\nasync def root():\n return {'hello': 'world'}\n\n# Request body\n\nclass Item(BaseModel):\n name: str\n description: str = None\n price: float\n tax: float = None\n\n\n@app.post(\"/items/\")\nasync def create_item(item: Item):\n item_dict = item.dict()\n if item.tax:\n total = item.price + item.tax\n item_dict.update({\"TotalAmount\" : total})\n return item_dict\n\nngrok_tunnel = ngrok.connect(8000)\nprint('Public URL:', ngrok_tunnel.public_url)\nnest_asyncio.apply()\nuvicorn.run(app, port=8000)\n","sub_path":"RequestBody.py","file_name":"RequestBody.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"341652252","text":"from scrapy import Spider\nfrom scrapy.selector import Selector\nfrom scrapy.http import HtmlResponse, Request\nimport json\nfrom techreview.items import TecSpecsItem\n\nclass GsmArenaSpider(Spider):\n\tname = \"phonearena\"\n\tallowed_domains = [\"http://www.phonearena.com/\", \"www.phonearena.com\", \"phonearena.com\"]\n\tstart_urls = [\"http://www.phonearena.com/phones/manufacturers\"]\n\n\tdef parse(self, response):\t\t\n\t\tselector = Selector(response=response)\n\n\t\t# extract brand on current page\n\t\tbrands = selector.xpath('//div[@id=\"brands\"]/div[@class=\"s_listing\"]/div[contains(@class, \"s_block_4\")]/div[@class=\"s_hover\"]')\n\t\tfor brand in brands:\n\t\t\tbrandLink = (brand.xpath('.//a[@class=\"s_thumb\"]/@href').extract())[0]\n\t\t\tbrandName = (brand.xpath('(.//a)[2]//text()').extract())[0]\n\n\t\t\tyield Request(\"http://www.phonearena.com\" + brandLink + \"/?filter_class[]=1223\", meta={'product': 'Cellphones', 'brand': brandName}, callback=self.parseBrandItems)\n\t\t\tyield Request(\"http://www.phonearena.com\" + brandLink + \"/?filter_class[]=1612\", meta={'product': 'Tablets','brand': brandName}, callback=self.parseBrandItems)\n\t\t\n\tdef parseBrandItems(self, response):\n\t\tproduct = response.meta['product']\n\t\tbrand = response.meta['brand']\n\n\t\tselector = Selector(response=response)\n\n\t\tphones = selector.xpath('//div[@id=\"phones\"]/div[@class=\"s_listing\"]/div[contains(@class, \"s_block_4\")]/h3')\n\t\tfor phone in phones:\n\t\t\tphoneLink = (phone.xpath('.//a[not(*)]/@href').extract())[0]\n\t\t\tphoneName = (phone.xpath('.//a[not(*)]//text()').extract())[0]\n\n\t\t\titem = TecSpecsItem()\n\t\t\titem[\"product\"] = product\n\t\t\titem[\"brand\"] = brand\n\t\t\titem[\"name\"] = phoneName\n\t\t\titem[\"source\"] = 0\n\n\t\t\tyield Request(\"http://www.phonearena.com\" + phoneLink, meta={'item': item}, callback=self.parseItemSpecs)\n\n\n\t\tnextPageLink = selector.xpath('//li[@class=\"s_next\"]/a/@href').extract()\n\t\tif len(nextPageLink) > 0:\n\t\t\tyield Request(\"http://www.phonearena.com\" + nextPageLink[0], meta={'product': product, 'brand': brand}, callback=self.parseBrandItems)\n\n\tdef callnext(self, response):\n\t\tmeta = response.request.meta\n\n\t\tif len(meta[\"callstack\"]) > 0:\n\t\t\ttarget = meta[\"callstack\"].pop()\n\t\t\tyield Request(\"http://www.phonearena.com\" + target['url'], meta = meta, callback=target['callback'])\n\t\telse:\n\t\t\tyield meta[\"item\"]\n\n\tdef parseItemSpecs(self, response):\n\t\titem = response.meta[\"item\"]\n\t\t\n\t\tselector = Selector(response=response)\n\n\n\t\t# get pros and cons\n\t\tpros = []\n\t\tcons = []\n\n\t\tproscons_node = selector.xpath('//div[@class=\"proscons\"]')\n\t\tpros_nodes = proscons_node.xpath('.//ul[@class=\"s_pros_list\"]/li')\n\t\tfor node in pros_nodes:\n\t\t\tpros.append((node.xpath('.//text()').extract())[0])\n\n\t\tcons_nodes = proscons_node.xpath('.//ul[@class=\"s_cons_list\"]/li')\n\t\tfor node in cons_nodes:\n\t\t\tcons.append((node.xpath('.//text()').extract())[0])\t\t\n\n\t\titem[\"pros\"] = pros\n\t\titem[\"cons\"] = cons\n\n\t\t# get images\n\t\titem[\"images\"] = selector.xpath('//div[@class=\"lcimg\"]/a[@class=\"pics\"]/@href').extract()\n\t\titem[\"thumbnail\"] = selector.xpath('//div[@class=\"lcimg\"]/a[@class=\"pics\"]/img/@src').extract()\n\n\t\t# get stats\n\t\titem[\"stats\"] = selector.xpath('//div[@id=\"have_it\"]/div/a[contains(@class, \"number\")]//text()').extract()\n\n\t\t# getting urls to parse other information\n\t\tphonetabs = selector.xpath('//div[@class=\"phonetabs\"]/div[contains(@class, \"s_tabs\")]/ul')\n\t\t\n\t\tsizeUrl = phonetabs.xpath('.//li/a[text()[contains(.,\"Size\")]]/@href').extract()\n\t\tbenchmarksUrl = phonetabs.xpath('.//li/a[text()[contains(.,\"Benchmarks\")]]/@href').extract()\n\t\tvideosUrl = phonetabs.xpath('.//li/a[text()[contains(.,\"Video\")]]/@href').extract()\n\n\t\t# building up callstack\n\t\tcallstack = []\n\t\tif len(sizeUrl) > 0 and sizeUrl[0] != \"javascript:;\":\n\t\t\tcallstack.append({'url': sizeUrl[0], 'callback': self.parseItemSize})\n\t\t\n\t\tif len(benchmarksUrl) > 0 and benchmarksUrl[0] != \"javascript:;\":\n\t\t\tcallstack.append({'url': benchmarksUrl[0], 'callback': self.parseItemBenchmarks})\n\t\t\n\t\tif len(videosUrl) > 0 and videosUrl[0] != \"javascript:;\":\n\t\t\tcallstack.append({'url': videosUrl[0], 'callback': self.parseItemVideos})\n\n\t\tresponse.request.meta[\"item\"] = item\n\t\tresponse.request.meta[\"callstack\"] = callstack\n\t\treturn self.callnext(response)\n\n\tdef parseItemSize(self, response):\n\t\titem = response.meta['item']\n\n\t\tselector = Selector(response=response)\n\n\t\tsz_phone = selector.xpath('//div[@class=\"standart_view\"]/ul[contains(@class, \"sizecompare\")]/li[@class=\"sz_phone\"]')\n\t\tif len(sz_phone) > 0:\n\t\t\titem[\"size_images\"] = sz_phone[0].xpath('.//img/@src').extract()\n\n\t\treturn self.callnext(response)\n\n\tdef parseItemBenchmarks(self, response):\n\t\titem = response.meta['item']\n\n\t\tphoneName = \"{0} {1}\".format(item[\"brand\"], item[\"name\"])\n\t\t\n\t\tselector = Selector(response=response)\n\t\tbenchmark_topaccordeon = selector.xpath('//li[a[@class=\"acc_toggle\" and following-sibling::div[contains(@class, \"accordeon \")]]]')\n\n\t\tbenchmarks = []\n\t\tfor i in range(len(benchmark_topaccordeon)):\n\t\t\taccordeon = benchmark_topaccordeon[i]\n\t\t\t\n\t\t\tbench = {}\n\t\t\tbench[\"title\"] = (accordeon.xpath('.//a/span[@class=\"title\"]//text()').extract())[0].strip()\n\t\t\tbench[\"data\"] = []\n\n\t\t\tif i == 0 or i == 1 or i == 4:\n\t\t\t\ttable = accordeon.xpath('.//table[contains(@class, \"benchmark_table\")]')\n\t\t\t\tif table:\n\t\t\t\t\ttb_header = table.xpath('.//thead/tr/th[descendant::*]')\n\t\t\t\t\t\n\t\t\t\t\tphones = table.xpath('.//tbody/tr')\n\t\t\t\t\tfor phone in phones:\n\t\t\t\t\t\tname = (phone.xpath('.//th//text()').extract())[0].strip()\n\t\t\t\t\t\tif name == phoneName:\n\t\t\t\t\t\t\ttb_benchmark = phone.xpath('.//td[@class=\"benchmark_tb\"]')\n\t\t\t\t\t\n\t\t\t\t\t\t\tfor header, benchmark in zip(tb_header, tb_benchmark):\n\t\t\t\t\t\t\t\tscore = benchmark.xpath('.//strong/text()').extract()\n\t\t\t\t\t\t\t\tscore_des = benchmark.xpath('.//span/text()').extract()\n\n\t\t\t\t\t\t\t\ttitle = header.xpath('.//*[self::span[contains(@class,\"title\")] | self::a[contains(@class,\"title\")]]/text()').extract()\n\t\t\t\t\t\t\t\ttitle_des = header.xpath('.//span[@class=\"wichisbetter\"]//text()').extract()\n\n\t\t\t\t\t\t\t\tbench[\"data\"].append({\"title\": title[0].strip() if len(title) > 0 else \"\", \\\n\t\t\t\t\t\t\t\t\t\t\t\"title_des\": title_des[0].strip() if len(title_des) > 0 else \"\", \\\n\t\t\t\t\t\t\t\t\t\t\t\"score\": score[0].strip() if len(score) > 0 else \"\" , \\\n\t\t\t\t\t\t\t\t\t\t\t\"score_des\": score_des[0].strip() if len(score_des) > 0 else \"\"})\n\t\t\telif i == 3 or i == 5:\n\t\t\t\tcharts = accordeon.xpath('.//table/tr')\n\t\t\t\tfor chart in charts:\n\t\t\t\t\tch_header = chart.xpath('.//td[1]')\n\t\t\t\t\tch_benchmark = chart.xpath('.//td[2]')\n\n\t\t\t\t\ttitle = ch_header.xpath('.//div[contains(@class,\"name\")]/text()').extract()\n\t\t\t\t\ttitle_des = ch_header.xpath('.//span[@class=\"wichisbetter\"]//text()').extract()\n\n\t\t\t\t\tphones = ch_benchmark.xpath('.//div[@class=\"score\"]')\n\t\t\t\t\tfor phone in phones:\n\t\t\t\t\t\tname = (phone.xpath('.//span[contains(@class, \"bar\")]//text()').extract())[0].strip()\n\t\t\t\t\t\tif name == phoneName:\n\t\t\t\t\t\t\tscore = (phone.xpath('.//span[contains(@class, \"stext\")]/text()').extract())\n\t\t\t\t\t\t\tbench[\"data\"].append({\"title\": title[0].strip() if len(title) > 0 else \"\",\\\n\t\t\t\t\t\t\t\t\t\t\"title_des\": title_des[0].strip() if len(title_des) > 0 else \"\", \\\n\t\t\t\t\t\t\t\t\t\t\"score\": score[0].strip() if len(score) > 0 else \"\", \"score_des\": \"\"})\n\t\t\t\t\t\t\tbreak\n\t\t\telif i == 6:\n\t\t\t\ttitles = [\"\", \"Battery Life\", \"\", \"Charging Time\"]\n\t\t\t\ttables = accordeon.xpath('.//table')\n\t\t\t\tfor i in range(len(tables)):\n\t\t\t\t\tif i == 0 or i == 2:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\t\n\t\t\t\t\ttable = tables[i]\n\t\t\t\t\ttb_benchmark = table.xpath('.//tr')\n\t\t\t\t\tfor benchmark in tb_benchmark:\n\t\t\t\t\t\tname = (benchmark.xpath('.//div[@class=\"name\"]//text()').extract())[0].strip()\n\t\t\t\t\t\tif name == phoneName:\n\t\t\t\t\t\t\tscore = (benchmark.xpath('.//span[contains(@class, \"stext\")]/text()').extract())\n\t\t\t\t\t\t\tscore_des = (benchmark.xpath('.//span[contains(@class, \"stext\")]/span/text()').extract())\n\n\t\t\t\t\t\t\tbench[\"data\"].append({\"score\": score[0].strip() if len(score) > 0 else \"\", \\\n\t\t\t\t\t\t\t\t\t\t\t\t\"score_des\": score_des[0].strip() if len(score_des) > 0 else \"\", \\\n\t\t\t\t\t\t\t\t\t\t\t\t\"title\" : titles[i]})\n\t\t\t\t\t\t\tbreak\n\n\n\n\t\t\tbenchmarks.append(bench)\n\t\t\n\t\titem[\"benchmarks\"] = benchmarks\n\n\t\treturn self.callnext(response)\n\n\tdef parseItemVideos(self, response):\n\t\titem = response.meta['item']\n\t\t\n\t\tselector = Selector(response=response)\n\t\tvideo_nodes = selector.xpath('//*[@id=\"yt-thumbs\"]/div')\n\n\t\tyt_videos = []\n\t\tfor node in video_nodes:\n\t\t\tyt_id = node.xpath('.//a[contains(@class, \"yt_show\")]/@vid').extract()\n\t\t\ttitle = node.xpath('.//a[contains(@class, \"yt_show\")]/@title').extract()\n\t\t\tthumbnail = node.xpath('.//img/@src').extract()\n\n\t\t\tyt_videos.append({'yt_id': yt_id[0] if len(yt_id) > 0 else \"\", \\\n\t\t\t\t\t\t\t'title': title[0] if len(title) > 0 else \"\", \\\n\t\t\t\t\t\t\t'thumbnail': thumbnail[0] if len(thumbnail) > 0 else \"\"})\n\n\t\titem[\"yt_videos\"] = yt_videos\n\n\t\treturn self.callnext(response)","sub_path":"build/lib.linux-x86_64-2.7/techreview/spiders/PhoneArenaSpider.py","file_name":"PhoneArenaSpider.py","file_ext":"py","file_size_in_byte":8677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"625888177","text":"from __future__ import annotations\nfrom datetime import timedelta\nfrom json import load\nfrom os import path\nfrom typing import Any, Mapping\n\nfrom wgups.structures.clock import Clock\nfrom wgups.structures.hash_set import HashSet\nfrom wgups.routing.package import Package\n\nDistances = HashSet[str, HashSet[str, str]]\nPackages = HashSet[int, Package]\nPrompts = HashSet[str, str]\n\n\nclass DataLoader:\n \"\"\"A class for loading external data into the application. Utilizes a cache to ensure\n that data is ever only loaded once.\n\n Class Attributes\n ----------------\n cache : HashSet[str, Any]\n The cache which handles storing file data.\n \"\"\"\n\n cache = HashSet[str, Any]()\n\n @classmethod\n def load_json(cls, filename) -> Mapping[Any, Any]:\n \"\"\"Attempts to retrieve the file from the cache. Loads the file data if it is not\n present in the cache.\n\n Returns\n -------\n Mapping[Any, Any]\n The JSON file data.\n\n Space Complexity\n ---------------\n O(n)\n\n Time Complexity\n ---------------\n O(n)\n \"\"\"\n file_path = path.join(path.dirname(__file__), filename)\n\n with open(file_path, 'r') as file:\n return load(file)\n\n @classmethod\n def get_packages(cls) -> Packages:\n \"\"\"Attempts to retrieve the packages from the cache. Loads the package data from a file\n if it is not present in the cache.\n\n Returns\n -------\n HashSet[int, str]\n The mapping of package identifiers to package objects.\n\n Space Complexity\n ---------------\n O(n)\n\n Time Complexity\n ---------------\n O(n)\n \"\"\"\n if 'packages' not in cls.cache:\n cls.cache.set('packages', cls.load_packages())\n\n return cls.cache.get('packages')\n\n @classmethod\n def load_packages(cls) -> Packages:\n \"\"\"Loads the package data from a file.\n\n Returns\n -------\n HashSet[int, str]\n The mapping of package identifiers to package objects.\n\n Space Complexity\n ---------------\n O(n)\n\n Time Complexity\n ---------------\n O(n)\n \"\"\"\n data = cls.load_json('data/package_data.json')\n size = len(data)\n packages = HashSet(size)\n\n for key, value in data.items():\n identifier = int(key)\n\n (hours, minutes) = map(int, value['deadline'].split(':'))\n deadline = Clock(hours, minutes)\n\n package = Package(\n identifier,\n value['address'],\n value['city'],\n value['state'],\n value['zip'],\n value['kg'],\n deadline,\n )\n\n # Delayed packages - will not arrive at depot until 09:05\n if package.id in [6, 25, 28, 32]:\n package.arrival_time = Clock(9, 5)\n\n # Incorrect address - will be corrected at 10:20\n if package.id == 9:\n package.street = '410 S State St'\n package.arrival_time = Clock(10, 20)\n\n # Package must be delivered via truck two\n if package.id in [3, 18, 36, 38]:\n package.deliverable_by = [2]\n package.is_priority = True\n\n # Package must be delivered with linked packages\n if package.id in [13, 14, 15, 16, 19, 20]:\n package.linked = True\n package.is_priority = True\n\n packages.set(identifier, package)\n\n return packages\n\n @classmethod\n def get_distances(cls) -> Distances:\n \"\"\"Attempts to retrieve the distances from the cache. Loads the distance data from a file\n if it is not present in the cache.\n\n Returns\n -------\n HashSet[str, HashSet[str, str]]\n The mapping of from and to addresses and the corresponding distance between them.\n\n Space Complexity\n ---------------\n O(n)\n\n Time Complexity\n ---------------\n O(n)\n \"\"\"\n if 'distances' not in cls.cache:\n cls.cache.set('distances', cls.load_distances())\n\n return cls.cache.get('distances')\n\n @classmethod\n def load_distances(cls) -> Distances:\n \"\"\"Loads the distance data from a file.\n\n Returns\n -------\n HashSet[str, HashSet[str, str]]\n The mapping of from and to addresses and the corresponding distance between them.\n\n Space Complexity\n ---------------\n O(n)\n\n Time Complexity\n ---------------\n O(n)\n \"\"\"\n data = cls.load_json('data/distance_data.json')\n size = len(data)\n distances = HashSet(size)\n\n for from_address, destinations in data.items():\n if from_address not in distances:\n distances.set(from_address, HashSet(size))\n\n for to_address, miles in destinations.items():\n distances.get(from_address).set(to_address, miles)\n\n return distances\n\n @classmethod\n def get_prompts(cls) -> Prompts:\n \"\"\"Attempts to retrieve the prompts from the cache. Loads the prompt data from a file\n if it is not present in the cache.\n\n Returns\n -------\n HashSet[str, str]\n The mapping of prompt names to prompt values.\n\n Space Complexity\n ---------------\n O(n)\n\n Time Complexity\n ---------------\n O(n)\n \"\"\"\n if 'prompts' not in cls.cache:\n cls.cache.set('prompts', cls.load_prompts())\n\n return cls.cache.get('prompts')\n\n @classmethod\n def load_prompts(cls) -> Prompts:\n \"\"\"Loads the prompt data from a file.\n\n Returns\n -------\n HashSet[str, str]\n The mapping of prompt names to prompt values.\n\n Space Complexity\n ---------------\n O(n)\n\n Time Complexity\n ---------------\n O(n)\n \"\"\"\n data = cls.load_json('data/prompts.json')\n size = len(data)\n prompts = HashSet(size)\n\n for key, value in data.items():\n prompts.set(key, value)\n\n return prompts\n","sub_path":"wgups/data/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":6360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"256487073","text":"from keras.models import Sequential\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.layers.core import Activation\nfrom keras.layers.core import Flatten\nfrom keras.layers.core import Dense\nfrom keras.layers import Concatenate\nfrom keras import backend as K\nimport matplotlib\nfrom keras.layers import LeakyReLU\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.callbacks import EarlyStopping\nfrom keras.optimizers import Adam, SGD, RMSprop, Adagrad, Adadelta\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nimport sys\nfrom math import ceil\n# sys.path.append('..')\n# matplotlib.use(\"Agg\")\nfrom keras.models import Model\nfrom keras.layers import Input, Dense, concatenate\n\n\ndef args_parse():\n ap = argparse.ArgumentParser()\n ap.add_argument(\"-m\", \"--model\", required=False,\n help=\"path to output model\")\n ap.add_argument(\"-p\", \"--plot\", type=str, default=\"plot.png\",\n help=\"path to output accuracy/loss plot\")\n args = vars(ap.parse_args())\n return args\n\n\nclass LeNet:\n @staticmethod\n def build(width, height, depth, classes = 5):\n model = Sequential()\n inputShape = (height, width, depth)\n if K.image_data_format() == \"channels_first\":\n inputShape = (depth, height, width)\n\n # first set of CONV => RELU => POOL layers\n model.add(Conv2D(20, (5, 5), padding=\"same\", input_shape=inputShape))\n model.add(Activation(\"relu\"))\n model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n\n # second set of CONV => RELU => POOL layers\n model.add(Conv2D(50, (5, 5), padding=\"same\"))\n model.add(Activation(\"relu\"))\n model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n\n # first (and only) set of FC => RELU layers\n model.add(Flatten())\n model.add(Dense(500))\n model.add(Activation(\"relu\"))\n\n # activation function\n model.add(Dense(CLASS_NUM))\n model.add(Activation(\"softmax\"))\n return model\n\ndef generator_three_img(gen1):\n\n while True:\n X1i = gen1.next()\n X2i = X1i\n X3i = X1i\n yield [X1i[0], X2i[0], X3i[0]], X1i[1]\n\ndef model1(width, height, depth):\n inputShape = (height, width, depth)\n if K.image_data_format() == \"channels_first\":\n inputShape = (depth, height, width)\n\n\n top_branch_input = Input(shape=inputShape, name='Top')\n top_branch_output = Conv2D(4, (7, 3), padding='same',strides=(2, 2),activation='relu')(top_branch_input)\n\n middle_branch_input = Input(shape=inputShape, name='Middle')\n middle_branch_output = Conv2D(8, (5, 5), padding='same', strides=(2, 2),activation='relu')(middle_branch_input)\n\n bottom_branch_input = Input(shape=inputShape, name='Bottom')\n bottom_branch_output = Conv2D(4, (3, 7), padding='same', strides=(2, 2), activation='relu')(bottom_branch_input)\n\n concat = concatenate([top_branch_output, middle_branch_output,bottom_branch_output], name='Concatenate')\n\n conv1 = Conv2D(32, (3, 3), strides=(2, 2), padding='same', activation='relu')(concat)\n conv2 = Conv2D(64, (3, 3), strides=(2, 2), padding='same', activation='relu')(conv1)\n flattern = Flatten()(conv2)\n dense1 = Dense(500, activation='relu')(flattern)\n dense2 = Dense(128, activation='relu')(dense1)\n final_model_output = Dense(CLASS_NUM, activation='softmax')(dense2)\n final_model = Model(inputs=[top_branch_input, middle_branch_input,bottom_branch_input], outputs=final_model_output,\n name='Final_output')\n return final_model\n \n \ndef model2(width, height, depth):\n inputShape = (height, width, depth)\n if K.image_data_format() == \"channels_first\":\n inputShape = (depth, height, width)\n\n\n top_branch_input = Input(shape=inputShape, name='Top')\n top_branch_output = Conv2D(4, (7, 3), padding='same',strides=(2, 2))(top_branch_input)\n top_branch_output = LeakyReLU(0.2)(top_branch_output)\n\n middle_branch_input = Input(shape=inputShape, name='Middle')\n middle_branch_output = Conv2D(8, (5, 5), padding='same', strides=(2, 2))(middle_branch_input)\n middle_branch_output = LeakyReLU(0.2)(middle_branch_output)\n\n bottom_branch_input = Input(shape=inputShape, name='Bottom')\n bottom_branch_output = Conv2D(4, (3, 7), padding='same', strides=(2, 2))(bottom_branch_input)\n bottom_branch_output = LeakyReLU(0.2)(bottom_branch_output)\n\n concat = concatenate([top_branch_output, middle_branch_output,bottom_branch_output], name='Concatenate')\n\n conv1 = Conv2D(32, (3, 3), strides=(2, 2), padding='same', activation='relu')(concat)\n conv2 = Conv2D(64, (3, 3), strides=(2, 2), padding='same', activation='relu')(conv1)\n flattern = Flatten()(conv2)\n dense1 = Dense(500, activation='relu')(flattern)\n dense2 = Dense(128, activation='relu')(dense1)\n final_model_output = Dense(CLASS_NUM, activation='softmax')(dense2)\n final_model = Model(inputs=[top_branch_input, middle_branch_input,bottom_branch_input], outputs=final_model_output,\n name='Final_output')\n return final_model\n\n\n\nclass AKNet:\n @staticmethod\n def build(width, height, depth, classes):\n kernel1 = Sequential()\n kernel2 = Sequential()\n kernel3 = Sequential()\n model = Sequential()\n\n\n inputShape = (height, width, depth)\n if K.image_data_format() == \"channels_first\":\n inputShape = (depth, height, width)\n\n # first set of CONV => RELU => POOL layers\n kernel1.add(Conv2D(8, (5, 5), padding='same',strides=(2,2), input_shape=inputShape, activation='relu'))\n kernel2.add(Conv2D(4, (3, 7), padding='same',strides=(2,2),input_shape=inputShape, activation='relu'))\n kernel3.add(Conv2D(4, (7, 3), padding='same',strides=(2,2), input_shape=inputShape, activation='relu'))\n\n merged = Concatenate([kernel1, kernel2,kernel3])\n\n model.add(merged)\n model.add(Conv2D(32,(3, 3), padding=\"same\",strides=(2,2), activation='relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n\n model.add(Conv2D(32, (3, 3), padding=\"same\",strides=(2,2), activation='relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n\n\n model.add(Flatten())\n model.add(Dense(96))\n model.add(Dense(16))\n\n\n model.add(Dense(CLASS_NUM))\n model.add(Activation(\"softmax\"))\n return model\n\n\n# parameters\nEPOCHS = 2000\nINIT_LR = 1e-3\nBS = 128\nCLASS_NUM = 5\nnorm_size = 32\nsteps_per_epoch = ceil(1126618 / BS)\nvalidation_step= ceil(281651 / BS)\ndef eachFile(filepath):\n out = []\n for allDir in filepath:\n child = allDir\n return out\n\n\n\n\n\nif __name__ == '__main__':\n args = args_parse()\n file_path = '/data2/Lucy_dataset_HEVC/'\n checkpoint_filepath = '/data2/minh_hevc/HEVC_dataset_test/TrainingData/checkpoint/5_classes_Kevin.h5'\n model_checkpoint_callback = ModelCheckpoint(\n filepath=checkpoint_filepath,\n save_weights_only=False,\n monitor='val_acc',\n mode='max',\n save_best_only=True)\n es = EarlyStopping(monitor='val_loss', mode='min', verbose=1,patience=5)\n # train_file_path = './TRAIN/'\n # test_file_path = './TEST/'\n train_datagen = ImageDataGenerator(validation_split=0.25)\n train_generator = train_datagen.flow_from_directory(directory=file_path, target_size=(32, 32),\n classes=eachFile(file_path),subset='training')\n # test_datagen = ImageDataGenerator()\n test_generator = train_datagen.flow_from_directory(directory=file_path, target_size=(32, 32),\n classes=eachFile(file_path),subset='validation')\n\n print(train_generator.class_indices)\n\n # model = AKNet.build(width=norm_size, height=norm_size, depth=3, classes=CLASS_NUM)\n model = LeNet.build(width=norm_size, height=norm_size, depth=3)\n # opt = SGD(lr=0.001, momentum=0.9)\n\n opt = Adam(lr=0.001)\n #opt = RMSprop(lr=0.001, rho=0.9, decay=0.0)\n #opt = Adagrad(lr=0.01, decay=0.0)\n # opt = Adadelta(lr=0.01, rho=0.95, decay=0.0)\n\n model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])\n model.summary()\n # H = model.fit_generator(generator_three_img(train_generator,), epochs=EPOCHS, validation_data=generator_three_img(test_generator,),steps_per_epoch=steps_per_epoch,validation_steps=validation_step,callbacks=[model_checkpoint_callback,es])\n H = model.fit_generator(train_generator, epochs=EPOCHS,\n validation_data=test_generator, steps_per_epoch=steps_per_epoch,\n validation_steps=validation_step, callbacks=[model_checkpoint_callback, es])\n\n print(\"[INFO] serializing network...\")\n model.save('5_classes_Kevin.h5')\n\n # plot the training loss and accuracy\n plt.style.use(\"ggplot\")\n plt.figure()\n N = es.stopped_epoch + 1\n plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n plt.plot(np.arange(0, N), H.history[\"acc\"], label=\"train_acc\")\n plt.plot(np.arange(0, N), H.history[\"val_acc\"], label=\"val_acc\")\n plt.title(\"Training Loss and Accuracy\")\n plt.xlabel(\"Epoch #\")\n plt.ylabel(\"Loss/Accuracy\")\n plt.legend(loc=\"lower left\")\n plt.savefig(args[\"plot\"])","sub_path":"AK-CNN.py","file_name":"AK-CNN.py","file_ext":"py","file_size_in_byte":9615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"111045665","text":"from flask import Flask,render_template,request\r\nimport pickle\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\napp=Flask(__name__)\r\n\r\n@app.route(\"/\")\r\ndef home():\r\n return render_template('index.html')\r\n\r\n@app.route('/predict',methods=['POST'])\r\ndef predict():\r\n if request.method=='POST':\r\n spl=request.form['spl']\r\n spw=request.form['spw']\r\n ptl=request.form['ptl']\r\n ptw=request.form['ptw']\r\n\r\n data=[[float(spl),float(spw),float(ptl),float(ptw)]]\r\n lr=pickle.load(open('iris.pkl','rb'))\r\n prediction=lr.predict(data)[0]\r\n\r\n return render_template('index.html',prediction=prediction)\r\n\r\nif __name__=='__main__':\r\n app.run(debug=True)\r\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"447667353","text":"from django.urls import path\nfrom . import views\n\nfrom django.views.generic import TemplateView\n\napp_name = 'pyconafrica2019'\nurlpatterns = [\n path('', view=views.home19, name='home19'),\n path('about/', view=views.about, name='about'),\n path('schedule/', view=views.schedule, name='schedule'),\n path('conduct/', view=views.conduct, name='conduct'),\n path('coc/eporting-guidelines/', TemplateView.as_view(template_name='conduct/eporting-guidelines/eporting-guidelines.html')),\n path('coc/guidelines/', TemplateView.as_view(template_name='conduct/guidelines/guidelines.html')),\n path('sponsor-us/', view=views.sponsor_us, name='sponsor_us'),\n path('our-sponsors/', view=views.sponsors, name='sponsors'),\n path('register/', view=views.register, name='register'),\n path('travel/', view=views.traveladvice, name='traveladvice'),\n path('fin-aid/', view=views.fin_aid, name='fin_aid'),\n path('team/', view=views.team, name='team'),\n path('report/', view=views.report, name='report'),\n]\n\n","sub_path":"pyconafrica2019/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"324111928","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Oct 10 19:09:44 2019\r\n\r\n@author: CLIENTE\r\n\"\"\"\r\n\r\nfrom sympy import symbols, diff, evalf\r\nimport numpy as np\r\nfrom moresorensen import ms\r\nx1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11 = symbols('x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11')\r\nvar = [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11]\r\nimport time\r\n\r\ndef MétodoDeRegiãoDeConfiança(dado):\r\n x = dado[4]\r\n função = dado[0]\r\n gradiente = dado[1]\r\n hessiana = dado[2]\r\n n = dado[3]\r\n def f(X):\r\n return função.subs([(var[i],X[i]) for i in range(n)])\r\n def grad(X):\r\n return np.asarray([expr.subs([(var[i],X[i]) for i in range(n)]) for expr in gradiente],dtype=float)\r\n def hess(X):\r\n return np.asarray([[expr.subs([(var[i],X[i]) for i in range(n)]) for expr in hessiana[j]] for j in range(n)],dtype=float) \r\n delta = 1\r\n mi1 = 0.01\r\n mi2 = 0.9\r\n lambda1 = 1/2 \r\n lambda2 = 1/2\r\n k = 0\r\n\r\n while np.dot(grad(x),grad(x)) > 10**(-4):\r\n s = ms(grad(x),hess(x),delta)\r\n p = (-1)*(f(x) - f(x+s))/(np.dot(s,grad(x))+(1/2)*(np.dot(s,np.dot(hess(x),s))))\r\n if p >= mi1:\r\n x = x+s\r\n if p >= mi2:\r\n delta = delta/lambda1\r\n elif p >= mi1:\r\n delta = delta\r\n else:\r\n delta = lambda1*delta\r\n if k >= 50:\r\n break\r\n return [x, f(x),grad(x)]","sub_path":"Método_de_Região_de_Confiança.py","file_name":"Método_de_Região_de_Confiança.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"268401293","text":"import tensorflow as tf\n\n\nclass Shakespeare(object):\n def __init__(self):\n self._vocab_size = 11302\n self._embed_size = 200\n self._cell_size = 300\n self._embedding_matrix = tf.get_variable(\n 'embedding',\n shape=[self._vocab_size, self._embed_size],\n initializer=tf.truncated_normal_initializer())\n\n def BuildTrainModel(self, word_ids):\n '''\n Args:\n words: a Tensor of shape [1, ?], each is a word(token) id.\n Returns:\n loss: a scalar Tensor.\n '''\n single_cell = tf.nn.rnn_cell.BasicLSTMCell(self._cell_size)\n\n concat_words = tf.concat(concat_dim=1, values=word_ids)\n word_embeds = tf.nn.embedding_lookup([self._embedding_matrix],\n concat_words)\n word_embed_list = tf.unstack(word_embeds, num=None, axis=1)\n outputs, final_state = tf.nn.rnn(single_cell,\n inputs=word_embed_list,\n dtype=tf.float32)\n w, b = self._GetWeightsAndBias()\n output_batch = tf.concat(concat_dim=0, values=outputs[:-1])\n labels = tf.reshape(word_ids[0, 1:], [-1, 1])\n loss = self._NceLoss(\n weights=w, biases=b, inputs=output_batch, labels=labels)\n return loss\n\n def BuildInferenceModel(self, word):\n '''\n Args:\n word: a scalar Tensor of int32, representing a word id.\n Returns:\n next_word: a scalar Tensor of int32, representing a word id.\n state_feed: a LSTMStateTuple of two placeholders, c and h.\n state_next: a LSTMStateTuple of two tensors, c and h.\n '''\n\n single_cell = tf.nn.rnn_cell.BasicLSTMCell(self._cell_size)\n state_feed_c = tf.placeholder(\n tf.float32, shape=[1, self._cell_size], name='c_feed')\n state_feed_h = tf.placeholder(\n tf.float32, shape=[1, self._cell_size], name='h_feed')\n state_feed = tf.nn.rnn_cell.LSTMStateTuple(state_feed_c, state_feed_h)\n\n word_embeds = tf.nn.embedding_lookup([self._embedding_matrix], word)\n word_embeds = tf.reshape(word_embeds, [1, self._embed_size])\n w, b = self._GetWeightsAndBias()\n output, state_next = single_cell(\n word_embeds, state_feed, scope='RNN/BasicLSTMCell')\n\n logits = tf.matmul(output, tf.transpose(w)) + b\n next_word_id = tf.argmax(logits, axis=1)\n\n # next_word_id = tf.Print(\n # next_word_id, [state_next.c, state_next.h, logits],\n # message='state')\n\n return next_word_id, state_feed, state_next\n\n def _NceLoss(self, weights, biases, inputs, labels):\n losses = tf.nn.nce_loss(\n weights=weights,\n biases=biases,\n inputs=inputs,\n labels=labels,\n num_sampled=500,\n num_classes=self._vocab_size)\n return tf.reduce_sum(losses)\n\n def _GetWeightsAndBias(self):\n softmax_w = tf.get_variable(\n 'W',\n shape=[self._vocab_size, self._cell_size],\n dtype=tf.float32,\n initializer=tf.truncated_normal_initializer())\n softmax_b = tf.get_variable(\n 'B',\n shape=[self._vocab_size],\n dtype=tf.float32,\n initializer=tf.truncated_normal_initializer())\n return softmax_w, softmax_b","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"98344655","text":"# uncompyle6 version 3.6.7\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.linux-x86_64/egg/ditz/editor.py\n# Compiled at: 2016-11-27 06:27:17\n__doc__ = '\\nText editor.\\n'\nfrom __future__ import print_function\nimport os, yaml, tempfile\nfrom . import util\nfrom .files import read_file, read_yaml_file\nfrom .util import DitzError\n\nclass DitzEditor(object):\n \"\"\"\n An editor for Ditz objects.\n \"\"\"\n prefix = 'tmp-'\n suffix = '.yaml'\n textmode = False\n filetag = ''\n\n def __init__(self, path):\n self.original = read_file(path)\n self.text = self.original\n self.error = None\n return\n\n def edit(self):\n \"\"\"Edit the text and return it.\"\"\"\n program = util.editor()\n if not program:\n raise DitzError('no text editor is configured')\n fp, filename = tempfile.mkstemp(prefix=self.prefix, suffix=self.suffix, text=self.textmode)\n os.write(fp, self.text.encode('utf-8'))\n os.close(fp)\n try:\n util.run_editor(program, filename)\n self.text = read_file(filename)\n err = self.validate(filename)\n if err:\n self.error = err.replace(filename, self.filetag)\n else:\n self.error = None\n finally:\n os.remove(filename)\n\n return self.text\n\n def validate(self, path):\n \"\"\"Return validation error text or None.\"\"\"\n try:\n obj = read_yaml_file(path)\n obj.normalize()\n except yaml.error.YAMLError as err:\n return str(err)\n except DitzError as err:\n return str(err)\n\n return\n\n @property\n def modified(self):\n \"\"\"Whether text has been modified.\"\"\"\n return self.text != self.original\n\n\nif __name__ == '__main__':\n path = '../bugs/issue-3d1596d37cbd170f512eb939b97f5f22a3834c79.yaml'\n from ditz.objects import DitzObject\n editor = DitzEditor(path)\n print(editor.edit())\n print('Modified:', editor.modified)\n print('Error:', editor.error)","sub_path":"pycfiles/pydiv-2.1.tar/editor.py","file_name":"editor.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"619148613","text":"import scrapy\nimport re\nimport time\nimport math\nimport random\nfrom scrapy.linkextractors import LinkExtractor\nfrom javbus.items import JavbusItem\n\nclass JavSpider(scrapy.Spider):\n name = 'jav'\n start_urls = ['https://www.fanbus.in']\n web='https://www.fanbus.in'\n\n def parse(self, response):\n le=LinkExtractor(restrict_xpaths='//*[@id=\"waterfall\"]')\n links=le.extract_links(response)\n for link in links:\n yield scrapy.Request(url=link.url,callback=self.parse_info)\n nextpage=response.xpath('//*[@id=\"next\"]/@href').extract_first()\n if nextpage:\n nextpage=self.web+nextpage\n yield scrapy.Request(url=nextpage,callback=self.parse)\n\n\n def parse_info(self,response):\n item=JavbusItem()\n item['jav_sn']=response.xpath('//*[@class=\"row movie\"]/div/p/span[2]/text()').extract_first()\n item['jav_name']=response.xpath('//*[@class=\"container\"]/h3/text()').extract_first()\n\n patten_time=re.compile('長度: (.*?)分鐘')\n item['jav_time']=int(patten_time.search(response.text).group(1))\n item['jav_date']=int(time.strftime('%Y%m%d',time.localtime(time.time())))\n\n patten_open_date=re.compile('發行日期: (.*?)=1:\n for i in range(len(elements)):\n sourceInfo = {}\n #磁力链接\n mangetUrl = elements[i].xpath(\"td[1]/a/@href\").extract_first()\n sourceInfo[\"magnetUrl\"] = mangetUrl\n # #番号\n fanhao = item['jav_sn']\n sourceInfo[\"fanhao\"] = fanhao\n\n #视频大小\n size = elements[i].xpath(\"td[2]/a/text()\").extract_first().strip() if elements[i].xpath(\"td[2]/a/text()\").extract_first() else \"\"\n sourceInfo[\"size\"] = size\n # #时间\n openTime = elements[i].xpath(\"td[3]/a/text()\").extract_first().strip() if elements[i].xpath(\"td[3]/a/text()\").extract_first() else \"\"\n sourceInfo[\"openTime\"] = openTime\n info.append(sourceInfo)\n item[\"jav_cili\"] = info\n yield item\n\n\n","sub_path":"javbus/javbus/spiders/jav.py","file_name":"jav.py","file_ext":"py","file_size_in_byte":2946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"445701659","text":"import tensorflow as tf\nfrom fc_data_ff import get_run_op as get_ff_run_op\nfrom fc_data_ff import get_var_device\nfrom fc_data_ff import get_all_devices\n\nFLAGS = tf.app.flags.FLAGS\n\ndef all_reduce_gradients(tower_grads, devices):\n average_grads = []\n for grad_and_vars in zip(*tower_grads):\n # Note that each grad_and_vars looks like the following:\n # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n grads = []\n split_grads = []\n assert len(grad_and_vars) == FLAGS.num_workers\n # Each GPU splits its own grad\n for i, (g, _) in enumerate(grad_and_vars):\n with tf.device(devices[i]):\n split_grads.append(tf.split(0, FLAGS.num_workers, g))\n # Each GPU gatheres slices of grad from other GPUs to do average.\n for i, dev in enumerate(devices):\n with tf.device(dev):\n x = split_grads[i][i]\n for j in range(FLAGS.num_workers):\n if i == j:\n continue\n x += split_grads[j][i]\n grads.append(x / FLAGS.num_workers)\n grad = tf.concat(0, grads)\n\n # Keep in mind that the Variables are redundant because they are shared\n # across towers. So .. we will just return the first tower's pointer to\n # the Variable.\n v = grad_and_vars[0][1]\n grad_and_var = (grad, v)\n average_grads.append(grad_and_var)\n return average_grads\n\n\ndef get_run_op():\n losses = get_ff_run_op()\n tower_grads = []\n opt = tf.train.GradientDescentOptimizer(learning_rate=0.5)\n for i in range(FLAGS.num_workers):\n tower_grads.append(opt.compute_gradients(losses[i]))\n grads = all_reduce_gradients(tower_grads, get_all_devices())\n apply_gradient_op = opt.apply_gradients(grads)\n train_op = tf.group(apply_gradient_op)\n return train_op\n","sub_path":"benchmark/mini/models/fc_data_slice.py","file_name":"fc_data_slice.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"84600072","text":"s = input()\nk = int(input())\na = 0 \nb = 0\nfor i in range(len(s)):\n if(s[i] != \"1\"):\n a = s[i]\n b = i + 1\n break\nif(a == 0 or k < b):\n print(1)\nelse:\n print(a)\n","sub_path":"abc/abc106/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"118668149","text":"# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom google.api import resource_pb2\nfrom google.protobuf.descriptor_pb2 import FieldDescriptorProto\nfrom src.findings.finding_container import FindingContainer\nfrom src.findings.utils import FindingCategory\nfrom src.comparator.wrappers import Field\n\n\nclass FieldComparator:\n # global_resources: file-level resource definitions.\n # local_resource: message-level resource definition.\n # We need the resource database information to determine if the resource_reference\n # annotation removal or change is breaking or not.\n def __init__(\n self,\n field_original: Field,\n field_update: Field,\n ):\n self.field_original = field_original\n self.field_update = field_update\n\n def compare(self):\n # 1. If original FieldDescriptor is None, then a\n # new FieldDescriptor is added.\n if self.field_original is None:\n FindingContainer.addFinding(\n category=FindingCategory.FIELD_ADDITION,\n location=f\"{self.field_update.proto_file_name} Line: {self.field_update.source_code_line}\",\n message=f\"A new Field {self.field_update.name} is added.\",\n actionable=False,\n )\n return\n\n # 2. If updated FieldDescriptor is None, then\n # the original FieldDescriptor is removed.\n if self.field_update is None:\n FindingContainer.addFinding(\n category=FindingCategory.FIELD_REMOVAL,\n location=f\"{self.field_original.proto_file_name} Line: {self.field_original.source_code_line}\",\n message=f\"A Field {self.field_original.name} is removed\",\n actionable=True,\n )\n return\n\n self.global_resources_original = self.field_original.file_resources\n self.global_resources_update = self.field_update.file_resources\n self.local_resource_original = self.field_original.message_resource\n self.local_resource_update = self.field_update.message_resource\n\n # 3. If both FieldDescriptors are existing, check\n # if the name is changed.\n if self.field_original.name != self.field_update.name:\n FindingContainer.addFinding(\n category=FindingCategory.FIELD_NAME_CHANGE,\n location=f\"{self.field_update.proto_file_name} Line: {self.field_update.source_code_line}\",\n message=f\"Name of the Field is changed, the original is {self.field_original.name}, but the updated is {self.field_update.name}\",\n actionable=True,\n )\n return\n\n # 4. If the FieldDescriptors have the same name, check if the\n # repeated state of them stay the same.\n # TODO(xiaozhenliu): add location information for field.label\n if self.field_original.label != self.field_update.label:\n FindingContainer.addFinding(\n category=FindingCategory.FIELD_REPEATED_CHANGE,\n location=\"\",\n message=f\"Repeated state of the Field is changed, the original is {self.field_original.label}, but the updated is {self.field_update.label}\",\n actionable=True,\n )\n\n # 5. If the FieldDescriptors have the same repeated state,\n # check if the type of them stay the same.\n # TODO(xiaozhenliu): add location information for field.type\n if self.field_original.proto_type != self.field_update.proto_type:\n FindingContainer.addFinding(\n category=FindingCategory.FIELD_TYPE_CHANGE,\n location=\"\",\n message=f\"Type of the field is changed, the original is {self.field_original.proto_type}, but the updated is {self.field_update.proto_type}\",\n actionable=True,\n )\n # 6. Check the oneof_index of the field.\n if self.field_original.oneof != self.field_update.oneof:\n location = f\"{self.field_update.proto_file_name} Line: {self.field_update.source_code_line}\"\n if self.field_original.oneof:\n msg = f\"The existing field {self.field_original.name} is moved out of One-of.\"\n FindingContainer.addFinding(\n category=FindingCategory.FIELD_ONEOF_REMOVAL,\n location=location,\n message=msg,\n actionable=True,\n )\n else:\n msg = f\"The existing field {self.field_original.name} is moved into One-of.\"\n FindingContainer.addFinding(\n category=FindingCategory.FIELD_ONEOF_ADDITION,\n location=location,\n message=msg,\n actionable=True,\n )\n\n # 6. Check `google.api.resource_reference` annotation.\n # TODO(xiaozhenliu): add location information for field.resource_reference.\n self._compare_resource_reference(self.field_original, self.field_update)\n\n def _compare_resource_reference(self, field_original, field_update):\n resource_ref_original = self.field_original.resource_reference\n resource_ref_update = self.field_update.resource_reference\n # No resource_reference annotations found for the field in both versions.\n if not resource_ref_original and not resource_ref_update:\n return\n # A `google.api.resource_reference` annotation is added.\n if not resource_ref_original and resource_ref_update:\n FindingContainer.addFinding(\n FindingCategory.RESOURCE_REFERENCE_ADDITION,\n \"\",\n f\"A resource reference option is added to the field {field_original.name}\",\n False,\n )\n return\n # Resource annotation is removed, check if it is added as a message resource.\n if resource_ref_original and not resource_ref_update:\n if not self._resource_ref_in_local(resource_ref_original):\n FindingContainer.addFinding(\n FindingCategory.RESOURCE_REFERENCE_REMOVAL,\n \"\",\n f\"A resource reference option of field '{field_original.name}' is removed.\",\n True,\n )\n return\n # Resource annotation is both existing in the field for original and update versions.\n # They both use `type` or `child_type`.\n if field_original.child_type == field_update.child_type:\n original_type = (\n resource_ref_original.type or resource_ref_original.child_type\n )\n update_type = resource_ref_update.type or resource_ref_update.child_type\n if original_type != update_type:\n FindingContainer.addFinding(\n FindingCategory.RESOURCE_REFERENCE_CHANGE,\n \"\",\n f\"The type of resource reference option in field '{field_original.name}' is changed from '{original_type}' to '{update_type}'.\",\n True,\n )\n return\n # The `type` is changed to `child_type` or `child_type` is changed to `type`, but\n # resulting referenced resource patterns can be resolved to be identical,\n # in that case it is not considered breaking.\n # Register the message-level resource into the global resource database,\n # so that we can query the parent resources for child_type.\n self._register_local_resource()\n if field_original.child_type:\n self._is_parent_type(\n resource_ref_original.child_type, resource_ref_update.type, True\n )\n if field_update.child_type:\n self._is_parent_type(\n resource_ref_update.child_type, resource_ref_original.type, False\n )\n\n def _resource_ref_in_local(self, resource_ref):\n \"\"\"Check if the resource type is in the local resources defined by a message option.\"\"\"\n if not self.local_resource_update:\n return False\n checked_type = resource_ref.type or resource_ref.child_type\n if not checked_type:\n raise TypeError(\n \"In a resource_reference annotation, either `type` or `child_type` field should be defined\"\n )\n if self.local_resource_update.type != checked_type:\n return False\n return True\n\n def _register_local_resource(self):\n # Add message-level resource definition to the global resource database for query.\n self.global_resources_original.register_resource(self.local_resource_original)\n self.global_resources_update.register_resource(self.local_resource_update)\n\n def _is_parent_type(self, child_type, parent_type, original_is_child):\n if original_is_child:\n parent_resources = (\n self.global_resources_original.get_parent_resources_by_child_type(\n child_type\n )\n )\n else:\n parent_resources = (\n self.global_resources_update.get_parent_resources_by_child_type(\n child_type\n )\n )\n if parent_type not in [parent.type for parent in parent_resources]:\n # Resulting referenced resource patterns cannot be resolved identical.\n FindingContainer.addFinding(\n FindingCategory.RESOURCE_REFERENCE_CHANGE,\n \"\",\n f\"The child_type '{child_type}' and type '{parent_type}' of \"\n f\"resource reference option in field '{self.field_original.name}' \"\n \"cannot be resolved to the identical resource.\",\n True,\n )\n","sub_path":"src/comparator/field_comparator.py","file_name":"field_comparator.py","file_ext":"py","file_size_in_byte":10262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"639231818","text":"# check whether the result of 2^n+1 is prime number or not when n is a prime number\n# ref. stackoverflow.com\n#\n# pip3 install sympy!\n#\n# 2022.09.15 by neibc96@gmail.com & GD\n\nimport math\nfrom datetime import datetime\nimport sys\nimport platform\nimport sympy\n\n#global variables\nispause = 0\n\ndef is_prime(n):\n if n % 2 == 0 and n > 2: \n return False\n return all(n % i for i in range(3, int(math.sqrt(n)) + 1, 2))\n \nprint('environment_uname : ', end='')\nprint(platform.platform())\n\nmaxnum = 1000\nmaxsquarenum = 10000\n\n#startnum = 3 \n\nwhile maxnum > 0:\n pnum1 = 0\n noftrue = 0\n noffalse = 0\n\n maxnum = int(input(\"input max target investigation value(>1, 0 to end) ex> 2020123212321223 : \"))\n print('-------------------------------------------------------')\n print('start time : ' + datetime.utcnow().strftime('%Y/%m/%d_%H:%M:%S.%f'))\n starta = datetime.now()\n\n for i in range(2, maxsquarenum):\n if sympy.isprime(i) == True:\n tval = pow(2, i) + 1\n if tval > maxnum:\n break\n pcheckresult = sympy.isprime(tval)\n if pcheckresult == True:\n noftrue=noftrue+1\n else: \n noffalse=noffalse+1\n print('2^%d + 1 = %d : %s' %(i, tval, pcheckresult))\n\n startb = datetime.now()\n print('\\nend time : ' + datetime.utcnow().strftime('%Y/%m/%d_%H:%M:%S.%f'))\n print('-------------------------------------------------------')\n print('time diff : ', end='')\n print(startb-starta)\n print('max number : %d' %maxnum)\n print('\\nnumber of true samples : %d' %noftrue)\n print('number of false samples : %d' %noffalse)\n print('ratio of true samples : %f' %(noftrue/(noftrue+noffalse)))\n\nif ispause > 0:\n input('enter key to end')\n","sub_path":"checkmersenne_prime_plus1.py","file_name":"checkmersenne_prime_plus1.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"489161639","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2019/10/24 3:28 下午\n# @Author : Frankie\n# @File : leetcode_102_层次遍历.py\n\n\nfrom typing import List\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 levelOrder(self, root: TreeNode) -> List[List[int]]:\n \"\"\"\n 思路：二叉树 => 递归\n :param root:\n :return:\n \"\"\"\n # 递归终止\n if not root:\n return []\n if root.left is None and root.right is None:\n return [[root.val]]\n\n res = []\n left_tree = self.levelOrder(root.left)\n right_tree = self.levelOrder(root.right)\n\n res.append([root.val])\n len_left = len(left_tree)\n len_right = len(right_tree)\n if len_left >= len_right:\n for i in range(len_left):\n right_tem = right_tree[i] if i < len_right else []\n tem = left_tree[i] + right_tem\n res.append(tem)\n else:\n for i in range(len_right):\n left_tem = left_tree[i] if i < len_left else []\n tem = left_tem + right_tree[i]\n res.append(tem)\n return res\n\n\n\n\n","sub_path":"Python/二叉树/二叉树的构造及遍历/leetcode_102_层次遍历.py","file_name":"leetcode_102_层次遍历.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"456540084","text":"from turtle import *\r\nimport math\r\n\r\n# Name your Turtle.\r\nt = Turtle()\r\n\r\n\r\n# Set Up your screen and starting position.\r\nt.penup()\r\nsetup(850,850)\r\nbgcolor(\"black\")\r\n\r\nt.penup()\r\nt.setx(200)\r\nt.sety(200)\r\n\r\n\r\nsides = input('How many sides?: ')\r\nsides= int(sides)\r\nangle = 360 / sides\r\ncolor = input('What color?: ')\r\nscale = input('Big, medium or small? Type B for big, M for medium, S for small: ')\r\nif scale == 'B':\r\n length = 300\r\nelif scale == 'M':\r\n length = 200\r\nelif scale == 'S':\r\n length = 100\r\n\r\n\r\n#define draw shape\r\ndef drawshape(sides, color):\r\n t.pendown()\r\n t.fillcolor(color)\r\n t.begin_fill()\r\n for sides in range(sides):\r\n t.forward(length)\r\n t.right(angle)\r\n t.end_fill()\r\n\r\n#move!\r\ndrawshape(sides, color)\r\n\r\n# Close window on click.\r\nexitonclick()\r\n","sub_path":"draw_polygon_game.py","file_name":"draw_polygon_game.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"112319074","text":"\nfrom tkinter import *\nfrom tkinter import ttk\nfrom trello import TrelloClient\nimport requests\n\n\ndef create_board(board_name):\n url = \"https://api.trello.com/1/boards/\"\n querystring = {\"name\": board_name, \"key\": api_key, \"token\": token}\n response = requests.request(\"POST\", url, params=querystring)\n board_id = response.json()[\"shortUrl\"].split(\"/\")[-1].strip()\n return board_id\n \ndef create_card(list_id, card_name):\n url = \"https://api.trello.com/1/cards\"\n querystring = {\"name\": card_name, \"idList\": list_id, \"key\": key, \"token\": token}\n response = requests.request(\"POST\", url, params=querystring)\n card_id = response.json()[\"id\"]\n return card_id\n \n# on change dropdown value\ndef change_dropdown(*args):\n print( tkvar.get() )\n \ndef clickMe():\n boardName = name.get()\n create_board(boardName)\n\n#last_board = all_boards[-1]\n#print(last_board.name)\n\n#print(all_boards[0].name)\n\n#lists = all_boards[0].all_lists()\n#print(lists)\n\n#all_boards[1].add_label(\"concept\", \"blue\")\n\n#create_board(\"randomTest\")\n\nroot = Tk()\nroot.title(\"Tk dropdown example\")\nroot.geometry('300x400')\n# Add a grid\nmainframe = Frame(root)\nmainframe.grid(column=0,row=0, sticky=(N,W,E,S) )\nmainframe.columnconfigure(0, weight = 2)\nmainframe.rowconfigure(0, weight = 2)\nmainframe.pack(padx = 0, pady = 0)\n\n\n# Create a Tkinter variable\ntkvar = StringVar(root)\n\nall_boards = client.list_boards()\nboardList = []\n\nfor i in all_boards:\n boardList.append(i)\nprint(list)\n\nboardList = list(dict.fromkeys(boardList))\nprint(boardList)\n# Dictionary with options\nchoices = boardList\ntkvar.set(choices[-1]) # set the default option\n\npopupMenu = OptionMenu(mainframe, tkvar, *choices)\ntkvar.trace('w', change_dropdown)\nLabel(mainframe, text=\"Choose a Board:\").grid(row = 0, column = 1)\npopupMenu.grid(row = 1, column =1)\n\n# link function to change dropdown\ntkvar.trace('w', change_dropdown)\n\nlabel = ttk.Label(mainframe, text = \"Create Board:\").grid(column = 0, row = 2)\n\nname = StringVar()\nnameEntered = ttk.Entry(mainframe, width = 15, textvariable = name)\nnameEntered.grid(column = 1, row = 2)\n\nbutton = ttk.Button(mainframe, text = \"Click Me\", command = clickMe).grid(column= 2, row = 2)\n\n\nroot.mainloop()\n\ndel boardList, tkvar, root\n","sub_path":"program.py","file_name":"program.py","file_ext":"py","file_size_in_byte":2245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"40839414","text":"from random import sample\nimport time\nfrom getContigs import getContigs\n\n'''\nThis script evaluates the getContigs function I wrote up\nI scale up the length of the list I get contigs from.\nIt's assumed that the list you pass into getContigs()\nis already sorted, so no such check is currently in place\nfor getContigs for sorted state of the list.\n'''\n\n# unit test\nmyList = [1, 2, 4, 6, 8, 9, 10]\nprint(getContigs(myList))\n\n# generative test\nN_iter = [10, 100, 1000]\ncontigsDict = {str(n):{} for n in N_iter}\n# for reproducibility, I'd normally place a seed here.\nfor n in N_iter:\n thisLineup = sorted(sample(range(1,n), round(0.7*n)))\n start = time.perf_counter()\n theseContigs = getContigs(thisLineup)\n end = time.perf_counter()\n delta = end - start\n contigsDict[str(n)] = {\"time\":delta, \"contigs\":theseContigs}\n \nprint([[contigsDict[k][\"time\"], len(contigsDict[k][\"contigs\"])] for k in contigsDict.keys()])\n","sub_path":"getContigs_evaluation.py","file_name":"getContigs_evaluation.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"311186572","text":"from __future__ import print_function # Python 2 and 3 print compatibility\n\nfrom hail.expr.ast import *\nfrom hail.expr.types import *\nfrom hail.utils.java import *\nfrom hail.utils.linkedlist import LinkedList\nfrom hail.genetics import Locus, Variant, Interval, Call, AltAllele\nfrom hail.typecheck import *\nfrom collections import Mapping\n\nclass Indices(object):\n @typecheck_method(source=anytype, axes=setof(strlike))\n def __init__(self, source=None, axes=set()):\n self.source = source\n self.axes = axes\n\n def __eq__(self, other):\n return isinstance(other, Indices) and self.source is other.source and self.axes == other.axes\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n @staticmethod\n def unify(*indices):\n axes = set()\n src = None\n for ind in indices:\n if src is None:\n src = ind.source\n else:\n if ind.source is not None and ind.source is not src:\n raise ExpressionException()\n\n both = axes.intersection(ind.axes)\n left_only = axes - both\n right_only = ind.axes - both\n\n axes = axes.union(ind.axes)\n\n return Indices(src, axes)\n\n def __str__(self):\n return 'Indices(axes={}, source={})'.format(self.axes, self.source)\n\n def __repr__(self):\n return 'Indices(axes={}, source={})'.format(repr(self.axes), repr(self.source))\n\nclass Aggregation(object):\n def __init__(self, indices, refs):\n self.indices = indices\n self.refs = refs\n\n\nclass Join(object):\n def __init__(self, join_function, temp_vars):\n self.join_function = join_function\n self.temp_vars = temp_vars\n\n\n@typecheck(ast=AST, type=Type, indices=Indices, aggregations=LinkedList, joins=LinkedList, refs=LinkedList)\ndef construct_expr(ast, type, indices=Indices(), aggregations=LinkedList(Aggregation), joins=LinkedList(Join),\n refs=LinkedList(tuple)):\n if isinstance(type, TArray) and type.element_type.__class__ in elt_typ_to_array_expr:\n return elt_typ_to_array_expr[type.element_type.__class__](ast, type, indices, aggregations, joins, refs)\n elif isinstance(type, TSet) and type.element_type.__class__ in elt_typ_to_set_expr:\n return elt_typ_to_set_expr[type.element_type.__class__](ast, type, indices, aggregations, joins, refs)\n elif type.__class__ in typ_to_expr:\n return typ_to_expr[type.__class__](ast, type, indices, aggregations, joins, refs)\n else:\n raise NotImplementedError(type)\n\n@typecheck(name=strlike, type=Type, indices=Indices, prefix=nullable(strlike))\ndef construct_reference(name, type, indices, prefix=None):\n if prefix is not None:\n ast = Select(Reference(prefix, True), name)\n else:\n ast = Reference(name, True)\n return construct_expr(ast, type, indices, refs=LinkedList(tuple).push((name, indices)))\n\ndef to_expr(e):\n if isinstance(e, Expression):\n return e\n elif isinstance(e, str) or isinstance(e, unicode):\n return construct_expr(Literal('\"{}\"'.format(Env.jutils().escapePyString(e))), TString())\n elif isinstance(e, bool):\n return construct_expr(Literal(\"true\" if e else \"false\"), TBoolean())\n elif isinstance(e, int):\n return construct_expr(Literal(str(e)), TInt32())\n elif isinstance(e, long):\n return construct_expr(ClassMethod('toInt64', Literal('\"{}\"'.format(e))), TInt64())\n elif isinstance(e, float):\n return construct_expr(Literal(str(e)), TFloat64())\n elif isinstance(e, Variant):\n return construct_expr(ApplyMethod('Variant', Literal('\"{}\"'.format(str(e)))), TVariant(e.reference_genome))\n elif isinstance(e, Locus):\n return construct_expr(ApplyMethod('Locus', Literal('\"{}\"'.format(str(e)))), TLocus(e.reference_genome))\n elif isinstance(e, Interval):\n return construct_expr(ApplyMethod('LocusInterval', Literal('\"{}\"'.format(str(e)))), TInterval(TLocus(e.reference_genome)))\n elif isinstance(e, Call):\n return construct_expr(ApplyMethod('Call', Literal(str(e.gt))), TCall())\n elif isinstance(e, AltAllele):\n return construct_expr(ApplyMethod('AltAllele', to_expr(e.ref)._ast, to_expr(e.alt)._ast), TAltAllele())\n elif isinstance(e, Struct):\n attrs = e._fields.items()\n cols = [to_expr(x) for _, x in attrs]\n names = [k for k, _ in attrs]\n indices, aggregations, joins, refs = unify_all(*cols)\n t = TStruct(names, [col._type for col in cols])\n return construct_expr(StructDeclaration(names, [c._ast for c in cols]),\n t, indices, aggregations, joins, refs)\n elif isinstance(e, list):\n cols = [to_expr(x) for x in e]\n types = list({col._type for col in cols})\n if len(cols) == 0:\n raise ExpressionException('Cannot convert empty list to expression.')\n elif len(types) == 1:\n t = TArray(types[0])\n elif all([is_numeric(t) for t in types]):\n t = TArray(convert_numeric_typ(*types))\n else:\n raise ExpressionException('Cannot convert list with heterogeneous key types to expression.'\n '\\n Found types: {}.'.format(types))\n indices, aggregations, joins, refs = unify_all(*cols)\n return construct_expr(ArrayDeclaration([col._ast for col in cols]),\n t, indices, aggregations, joins, refs)\n elif isinstance(e, set):\n cols = [to_expr(x) for x in e]\n types = list({col._type for col in cols})\n if len(cols) == 0:\n raise ExpressionException('Cannot convert empty set to expression.')\n elif len(types) == 1:\n t = TSet(types[0])\n elif all([is_numeric(t) for t in types]):\n t = TSet(convert_numeric_typ(*types))\n else:\n raise ExpressionException('Cannot convert set with heterogeneous key types to expression.'\n '\\n Found types: {}.'.format(types))\n indices, aggregations, joins, refs = unify_all(*cols)\n return construct_expr(ClassMethod('toSet', ArrayDeclaration([col._ast for col in cols])), t, indices,\n aggregations, joins, refs)\n elif isinstance(e, dict):\n key_cols = []\n value_cols = []\n keys = []\n values = []\n for k, v in e.items():\n key_cols.append(to_expr(k))\n keys.append(k)\n value_cols.append(to_expr(v))\n values.append(v)\n key_types = list({col._type for col in key_cols})\n value_types = list({col._type for col in value_cols})\n\n if len(key_types) == 0:\n raise ExpressionException('Cannot convert empty dictionary to expression.')\n elif len(key_types) == 1:\n key_type = key_types[0]\n elif all([is_numeric(t) for t in key_types]):\n key_type = convert_numeric_typ(*key_types)\n else:\n raise ExpressionException('Cannot convert dictionary with heterogeneous key types to expression.'\n '\\n Found types: {}.'.format(key_types))\n\n if len(value_types) == 1:\n value_type = value_types[0]\n elif all([is_numeric(t) for t in value_types]):\n value_type = convert_numeric_typ(*value_types)\n else:\n raise ExpressionException('Cannot convert dictionary with heterogeneous value types to expression.'\n '\\n Found types: {}.'.format(value_types))\n\n kc = to_expr(keys)\n vc = to_expr(values)\n\n indices, aggregations, joins, refs = unify_all(kc, vc)\n\n assert key_type == kc._type.element_type\n assert value_type == vc._type.element_type\n\n ast = ApplyMethod('Dict',\n ArrayDeclaration([k._ast for k in key_cols]),\n ArrayDeclaration([v._ast for v in value_cols]))\n return construct_expr(ast, TDict(key_type, value_type), indices, aggregations, joins, refs)\n else:\n raise ExpressionException(\"Cannot implicitly convert value '{}' of type '{}' to expression.\".format(\n e, e.__class__))\n\n\n_lazy_int32 = lazy()\n_lazy_numeric = lazy()\n_lazy_array = lazy()\n_lazy_set = lazy()\n_lazy_bool = lazy()\n_lazy_struct = lazy()\n_lazy_string = lazy()\n_lazy_variant = lazy()\n_lazy_locus = lazy()\n_lazy_altallele = lazy()\n_lazy_interval = lazy()\n_lazy_call = lazy()\n_lazy_expr = lazy()\n\nexpr_int32 = transformed((_lazy_int32, identity),\n (integral, to_expr))\nexpr_numeric = transformed((_lazy_numeric, identity),\n (integral, to_expr),\n (float, to_expr))\nexpr_list = transformed((list, to_expr),\n (_lazy_array, identity))\nexpr_set = transformed((set, to_expr),\n (_lazy_set, identity))\nexpr_bool = transformed((bool, to_expr),\n (_lazy_bool, identity))\nexpr_struct = transformed((Struct, to_expr),\n (_lazy_struct, identity))\nexpr_str = transformed((strlike, to_expr),\n (_lazy_string, identity))\nexpr_variant = transformed((Variant, to_expr),\n (_lazy_variant, identity))\nexpr_locus = transformed((Locus, to_expr),\n (_lazy_locus, identity))\nexpr_altallele = transformed((AltAllele, to_expr),\n (_lazy_altallele, identity))\nexpr_interval = transformed((Interval, to_expr),\n (_lazy_interval, identity))\nexpr_call = transformed((Call, to_expr),\n (_lazy_call, identity))\nexpr_any = transformed((_lazy_expr, identity),\n (anytype, to_expr))\n\ndef unify_all(*exprs):\n assert len(exprs) > 0\n try:\n new_indices = Indices.unify(*[e._indices for e in exprs])\n except ExpressionException:\n # source mismatch\n from collections import defaultdict\n sources = defaultdict(lambda: [])\n for e in exprs:\n for name, inds in e._refs:\n sources[inds.source].append(str(name))\n raise ExpressionException(\"Cannot combine expressions from different source objects.\"\n \"\\n Found fields from {n} objects:{fields}\".format(\n n=len(sources),\n fields=''.join(\"\\n {}: {}\".format(src, fds) for src, fds in sources.items())\n ))\n first, rest = exprs[0], exprs[1:]\n agg = first._aggregations\n joins = first._joins\n refs = first._refs\n for e in rest:\n agg = agg.push(*e._aggregations)\n joins = joins.push(*e._joins)\n refs = refs.push(*e._refs)\n return new_indices, agg, joins, refs\n\n\n__numeric_types = [TInt32, TInt64, TFloat32, TFloat64]\n\n\n@typecheck(t=Type)\ndef is_numeric(t):\n return t.__class__ in __numeric_types\n\n\n@typecheck(types=Type)\ndef convert_numeric_typ(*types):\n priority_map = {t: p for t, p in zip(__numeric_types, range(len(__numeric_types)))}\n priority = 0\n for t in types:\n assert (is_numeric(t)), t\n t_priority = priority_map[t.__class__]\n if t_priority > priority:\n priority = t_priority\n return __numeric_types[priority]()\n\n\nclass Expression(object):\n \"\"\"Base class for Hail expressions.\"\"\"\n\n @typecheck_method(ast=AST, type=Type, indices=Indices, aggregations=LinkedList, joins=LinkedList, refs=LinkedList)\n def __init__(self, ast, type, indices=Indices(), aggregations=LinkedList(Aggregation), joins=LinkedList(Join), refs=LinkedList(tuple)):\n self._ast = ast\n self._type = type\n self._indices = indices\n self._aggregations = aggregations\n self._joins = joins\n self._refs = refs\n\n self._init()\n\n def __str__(self):\n return repr(self)\n\n def __repr__(self):\n s = \"{super_repr}\\n Type: {type}\".format(\n super_repr=super(Expression, self).__repr__(),\n type=str(self._type),\n )\n\n indices = self._indices\n if len(indices.axes) == 0:\n s += '\\n Index{agg}: None'.format(agg=' (aggregated)' if self._aggregations else '')\n else:\n s += '\\n {ind}{agg}:\\n {index_lines}'.format(ind=plural('Index', len(indices.axes), 'Indices'),\n agg=' (aggregated)' if self._aggregations else '',\n index_lines='\\n '.join('{} of {}'.format(\n axis, indices.source) for axis in indices.axes))\n if self._joins:\n s += '\\n Dependent on {} {}'.format(len(self._joins),\n plural('broadcast/join', len(self._joins), 'broadcasts/joins'))\n return s\n\n def __lt__(self, other):\n raise NotImplementedError(\"'<' comparison with expression of type {}\".format(str(self._type)))\n\n def __le__(self, other):\n raise NotImplementedError(\"'<=' comparison with expression of type {}\".format(str(self._type)))\n\n def __gt__(self, other):\n raise NotImplementedError(\"'>' comparison with expression of type {}\".format(str(self._type)))\n\n def __ge__(self, other):\n raise NotImplementedError(\"'>=' comparison with expression of type {}\".format(str(self._type)))\n\n def _init(self):\n pass\n\n def __nonzero__(self):\n raise NotImplementedError(\n \"The truth value of an expression is undefined\\n Hint: instead of if/else, use 'f.cond'\")\n\n def _unary_op(self, name):\n return construct_expr(UnaryOperation(self._ast, name),\n self._type, self._indices, self._aggregations, self._joins, self._refs)\n\n def _bin_op(self, name, other, ret_typ):\n other = to_expr(other)\n indices, aggregations, joins, refs = unify_all(self, other)\n return construct_expr(BinaryOperation(self._ast, other._ast, name), ret_typ, indices, aggregations, joins, refs)\n\n def _bin_op_reverse(self, name, other, ret_typ):\n other = to_expr(other)\n indices, aggregations, joins, refs = unify_all(self, other)\n return construct_expr(BinaryOperation(other._ast, self._ast, name), ret_typ, indices, aggregations, joins, refs)\n\n def _field(self, name, ret_typ):\n return construct_expr(Select(self._ast, name), ret_typ, self._indices,\n self._aggregations, self._joins, self._refs)\n\n def _method(self, name, ret_typ, *args):\n args = tuple(to_expr(arg) for arg in args)\n indices, aggregations, joins, refs = unify_all(self, *args)\n return construct_expr(ClassMethod(name, self._ast, *(a._ast for a in args)),\n ret_typ, indices, aggregations, joins, refs)\n\n def _index(self, ret_typ, key):\n key = to_expr(key)\n indices, aggregations, joins, refs = unify_all(self, key)\n return construct_expr(Index(self._ast, key._ast),\n ret_typ, indices, aggregations, joins, refs)\n\n def _slice(self, ret_typ, start=None, stop=None, step=None):\n if start is not None:\n start = to_expr(start)\n start_ast = start._ast\n else:\n start_ast = None\n if stop is not None:\n stop = to_expr(stop)\n stop_ast = stop._ast\n else:\n stop_ast = None\n if step is not None:\n raise NotImplementedError('Variable slice step size is not currently supported')\n\n non_null = [x for x in [start, stop] if x is not None]\n indices, aggregations, joins, refs = unify_all(self, *non_null)\n return construct_expr(Index(self._ast, Slice(start_ast, stop_ast)),\n ret_typ, indices, aggregations, joins, refs)\n\n def _bin_lambda_method(self, name, f, inp_typ, ret_typ_f, *args):\n args = (to_expr(arg) for arg in args)\n new_id = Env._get_uid()\n lambda_result = to_expr(\n f(construct_expr(Reference(new_id), inp_typ, self._indices, self._aggregations, self._joins, self._refs)))\n indices, aggregations, joins, refs = unify_all(self, lambda_result)\n ast = LambdaClassMethod(name, new_id, self._ast, lambda_result._ast, *(a._ast for a in args))\n return construct_expr(ast, ret_typ_f(lambda_result._type), indices, aggregations, joins, refs)\n\n @property\n def dtype(self):\n \"\"\"The data type of the expression.\n\n Returns\n -------\n :class:`.Type`\n Data type.\n \"\"\"\n return self._type\n\n def __eq__(self, other):\n \"\"\"Returns ``True`` if the two epressions are equal.\n\n Examples\n --------\n\n .. doctest::\n\n >>> x = functions.capture(5)\n >>> y = functions.capture(5)\n >>> z = functions.capture(1)\n\n >>> eval_expr(x == y)\n True\n\n >>> eval_expr(x == z)\n False\n\n Notes\n -----\n This method will fail with an error if the two expressions are not\n of comparable types.\n\n Parameters\n ----------\n other : :class:`.Expression`\n Expression for equality comparison.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the two expressions are equal.\n \"\"\"\n return self._bin_op(\"==\", other, TBoolean())\n\n def __ne__(self, other):\n \"\"\"Returns ``True`` if the two expressions are not equal.\n\n Examples\n --------\n\n .. doctest::\n\n >>> x = functions.capture(5)\n >>> y = functions.capture(5)\n >>> z = functions.capture(1)\n\n >>> eval_expr(x != y)\n False\n\n >>> eval_expr(x != z)\n True\n\n Notes\n -----\n This method will fail with an error if the two expressions are not\n of comparable types.\n\n Parameters\n ----------\n other : :class:`.Expression`\n Expression for inequality comparison.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the two expressions are not equal.\n \"\"\"\n return self._bin_op(\"!=\", other, TBoolean())\n\n\nclass CollectionExpression(Expression):\n \"\"\"Expression of type :py:class:`hail.expr.types.TArray` or :py:class:`hail.expr.types.TSet`\n\n >>> a = functions.capture([1, 2, 3, 4, 5])\n\n >>> s = functions.capture({'Alice', 'Bob', 'Charlie'})\n \"\"\"\n\n def exists(self, f):\n \"\"\"Returns ``True`` if `f` returns ``True`` for any element.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.exists(lambda x: x % 2 == 0))\n True\n\n >>> eval_expr(s.exists(lambda x: x[0] == 'D'))\n False\n\n Notes\n -----\n This method always returns ``False`` for empty collections.\n\n Parameters\n ----------\n f : callable\n Function to evaluate for each element of the collection. Must return a\n :py:class:`BooleanExpression`.\n\n Returns\n -------\n :py:class:`BooleanExpression`.\n ``True`` if `f` returns ``True`` for any element, ``False`` otherwise.\n \"\"\"\n return self._bin_lambda_method(\"exists\", f, self._type.element_type, lambda t: TBoolean())\n\n def filter(self, f):\n \"\"\"Returns a new collection containing elements where `f` returns ``True``.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.filter(lambda x: x % 2 == 0))\n [2, 4]\n\n >>> eval_expr(s.filter(lambda x: ~(x[-1] == 'e')))\n {'Bob'}\n\n Notes\n -----\n Returns a same-type expression; evaluated on a :py:class:`SetExpression`, returns a\n :py:class:`SetExpression`. Evaluated on an :py:class:`ArrayExpression`,\n returns an :py:class:`ArrayExpression`.\n\n Parameters\n ----------\n f : callable\n Function to evaluate for each element of the collection. Must return a\n :py:class:`BooleanExpression`.\n\n Returns\n -------\n :py:class:`CollectionExpression`\n Expression of the same type as the callee.\n \"\"\"\n return self._bin_lambda_method(\"filter\", f, self._type.element_type, lambda t: self._type)\n\n def find(self, f):\n \"\"\"Returns the first element where `f` returns ``True``.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.find(lambda x: x ** 2 > 20))\n 5\n\n >>> eval_expr(s.find(lambda x: x[0] == 'D'))\n None\n\n Notes\n -----\n If `f` returns ``False`` for every element, then the result is missing.\n\n Parameters\n ----------\n f : callable\n Function to evaluate for each element of the collection. Must return a\n :py:class:`BooleanExpression`.\n\n Returns\n -------\n :py:class:`.Expression`\n Expression whose type is the element type of the collection.\n \"\"\"\n return self._bin_lambda_method(\"find\", f, self._type.element_type, lambda t: self._type.element_type)\n\n def flatmap(self, f):\n \"\"\"Map each element of the collection to a new collection, and flatten the results.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.flatmap(lambda x: functions.range(0, x)))\n [0, 0, 1, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 4]\n\n >>> eval_expr(s.flatmap(lambda x: functions.range(0, x.length()).map(lambda i: x[i]).to_set()))\n {'A', 'B', 'C', 'a', 'b', 'c', 'e', 'h', 'i', 'l', 'o', 'r'}\n\n Parameters\n ----------\n f : callable\n Function from the element type of the collection to the type of the\n collection. For instance, `flatmap` on a ``Set[String]`` should take\n a ``String`` and return a ``Set``.\n\n Returns\n -------\n :py:class:`CollectionExpression`\n \"\"\"\n return self._bin_lambda_method(\"flatMap\", f, self._type.element_type, lambda t: t)\n\n def forall(self, f):\n \"\"\"Returns ``True`` if `f` returns ``True`` for every element.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.forall(lambda x: x < 10))\n True\n\n Notes\n -----\n This method returns ``True`` if the collection is empty.\n\n Parameters\n ----------\n f : callable\n Function to evaluate for each element of the collection. Must return a\n :py:class:`BooleanExpression`.\n\n Returns\n -------\n :py:class:`BooleanExpression`.\n ``True`` if `f` returns ``True`` for every element, ``False`` otherwise.\n \"\"\"\n return self._bin_lambda_method(\"forall\", f, self._type.element_type, lambda t: TBoolean())\n\n def group_by(self, f):\n \"\"\"Group elements into a dict according to a lambda function.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.group_by(lambda x: x % 2 == 0))\n {False: [1, 3, 5], True: [2, 4]}\n\n >>> eval_expr(s.group_by(lambda x: x.length()))\n {3: {'Bob'}, 5: {'Alice'}, 7: {'Charlie'}}\n\n Parameters\n ----------\n f : callable\n Function to evaluate for each element of the collection to produce a key for the\n resulting dictionary.\n\n Returns\n -------\n :py:class:`DictExpression`.\n Dictionary keyed by results of `f`.\n \"\"\"\n return self._bin_lambda_method(\"groupBy\", f, self._type.element_type, lambda t: TDict(t, self._type))\n\n def map(self, f):\n \"\"\"Transform each element of a collection.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.map(lambda x: x ** 3))\n [1.0, 8.0, 27.0, 64.0, 125.0]\n\n >>> eval_expr(s.map(lambda x: x.length()))\n {3, 5, 7}\n\n Parameters\n ----------\n f : callable\n Function to transform each element of the collection.\n\n Returns\n -------\n :py:class:`CollectionExpression`.\n Collection where each element has been transformed according to `f`.\n \"\"\"\n return self._bin_lambda_method(\"map\", f, self._type.element_type, lambda t: self._type.__class__(t))\n\n def length(self):\n \"\"\"Returns the size of a collection.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.length())\n 5\n\n >>> eval_expr(s.length())\n 3\n\n Returns\n -------\n :py:class:`hail.expr.Int32Expression`\n The number of elements in the collection.\n \"\"\"\n return self._method(\"size\", TInt32())\n\n def size(self):\n \"\"\"Returns the size of a collection.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.size())\n 5\n\n >>> eval_expr(s.size())\n 3\n\n Returns\n -------\n :py:class:`hail.expr.Int32Expression`\n The number of elements in the collection.\n \"\"\"\n return self._method(\"size\", TInt32())\n\n\nclass CollectionNumericExpression(CollectionExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` or :class:`hail.expr.types.TSet` with numeric element type.\n\n >>> a = functions.capture([1, 2, 3, 4, 5])\n \"\"\"\n\n def max(self):\n \"\"\"Returns the maximum element.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.max())\n 5\n\n Returns\n -------\n :py:class:`NumericExpression`\n The maximum value in the collection.\n \"\"\"\n return self._method(\"max\", self._type.element_type)\n\n def min(self):\n \"\"\"Returns the minimum element.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.min())\n 1\n\n Returns\n -------\n :py:class:`NumericExpression`\n The miniumum value in the collection.\n \"\"\"\n return self._method(\"min\", self._type.element_type)\n\n def mean(self):\n \"\"\"Returns the mean of all values in the collection.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.mean())\n 3.0\n\n Note\n ----\n Missing elements are ignored.\n\n Returns\n -------\n :py:class:`Float64Expression`\n The mean value of the collection.\n \"\"\"\n return self._method(\"mean\", TFloat64())\n\n def median(self):\n \"\"\"Returns the median element.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.median())\n 3\n\n Note\n ----\n Missing elements are ignored.\n\n Returns\n -------\n :py:class:`NumericExpression`\n The median value in the collection.\n \"\"\"\n return self._method(\"median\", self._type.element_type)\n\n def product(self):\n \"\"\"Returns the product of all elements in the collection.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.product())\n 120\n\n Note\n ----\n Missing elements are ignored.\n\n Returns\n -------\n :py:class:`NumericExpression`\n The product of the collection.\n \"\"\"\n return self._method(\"product\",\n TInt64() if isinstance(self._type.element_type, TInt32) or\n isinstance(self._type.element_type, TInt64) else TFloat64())\n\n def sum(self):\n \"\"\"Returns the sum of all elements in the collection.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.sum())\n 15\n\n Note\n ----\n Missing elements are ignored.\n\n Returns\n -------\n :py:class:`NumericExpression`\n The sum of the collection.\n \"\"\"\n return self._method(\"sum\", self._type.element_type)\n\n\nclass ArrayExpression(CollectionExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray`.\n\n >>> a = functions.capture(['Alice', 'Bob', 'Charlie'])\n \"\"\"\n def __getitem__(self, item):\n \"\"\"Index into or slice the array.\n\n Examples\n --------\n\n Index with a single integer:\n\n .. doctest::\n\n >>> eval_expr(a[1])\n 'Bob'\n\n >>> eval_expr(a[-1])\n 'Charlie'\n\n Slicing is also supported:\n\n .. doctest::\n\n >>> eval_expr(a[1:])\n ['Bob', 'Charlie']\n\n Parameters\n ----------\n item : slice or :class:`Int32Expression`\n Index or slice.\n\n Returns\n -------\n :class:`.Expression`\n Element or array slice.\n \"\"\"\n if isinstance(item, slice):\n return self._slice(self._type, item.start, item.stop, item.step)\n elif isinstance(item, int) or isinstance(item, Int32Expression):\n return self._index(self._type.element_type, item)\n else:\n raise NotImplementedError\n\n @typecheck_method(item=expr_any)\n def contains(self, item):\n \"\"\"Returns a boolean indicating whether `item` is found in the array.\n\n Examples\n --------\n\n .. doctest::\n\n >>> eval_expr(a.contains('Charlie'))\n True\n\n >>> eval_expr(a.contains('Helen'))\n False\n\n Parameters\n ----------\n item : :class:`.Expression`\n Item for inclusion test.\n\n Warning\n -------\n This method takes time proportional to the length of the array. If a\n pipeline uses this method on the same array several times, it may be\n more efficient to convert the array to a set first\n (:meth:`ArrayExpression.to_set`).\n\n Returns\n -------\n :py:class:`BooleanExpression`\n ``True`` if the element is found in the array, ``False`` otherwise.\n \"\"\"\n return self.exists(lambda x: x == item)\n\n @typecheck_method(x=expr_any)\n def append(self, x):\n \"\"\"Append an element to the array and return the result.\n\n Examples\n --------\n\n .. doctest::\n\n >>> eval_expr(a.append('Dan'))\n ['Alice', 'Bob', 'Charlie', 'Dan']\n\n Parameters\n ----------\n x : :class:`.Expression`\n Element to append, same type as the array element type.\n\n Returns\n -------\n :py:class:`ArrayExpression`\n \"\"\"\n return self._method(\"append\", self._type, x)\n\n @typecheck_method(a=expr_list)\n def extend(self, a):\n \"\"\"Concatenate two arrays and return the result.\n\n Examples\n --------\n\n .. doctest::\n\n >>> eval_expr(a.extend(['Dan', 'Edith']))\n ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith']\n\n Parameters\n ----------\n a : :class:`ArrayExpression`\n Array to concatenate, same type as the callee.\n\n Returns\n -------\n :py:class:`ArrayExpression`\n \"\"\"\n return self._method(\"extend\", self._type, a)\n\n def sort_by(self, f, ascending=True):\n \"\"\"Sort the array according to a function.\n\n Examples\n --------\n\n .. doctest::\n\n >>> eval_expr(a.sort_by(lambda x: x, ascending=False))\n ['Charlie', 'Bob', 'Alice']\n\n Parameters\n ----------\n f : callable\n Function to evaluate per element to obtain the sort key.\n ascending : bool or :py:class:`BooleanExpression`\n Sort in ascending order.\n\n Returns\n -------\n :py:class:`ArrayExpression`\n \"\"\"\n return self._bin_lambda_method(\"sortBy\", f, self._type.element_type, lambda t: self._type, ascending)\n\n def to_set(self):\n \"\"\"Convert the array to a set.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.to_set())\n {'Alice', 'Bob', 'Charlie'}\n\n Returns\n -------\n :py:class:`SetExpression`\n Set of all unique elements.\n \"\"\"\n return self._method(\"toSet\", TSet(self._type.element_type))\n\n\nclass ArrayNumericExpression(ArrayExpression, CollectionNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` with a numeric type.\n\n Numeric arrays support arithmetic both with scalar values and other arrays.\n Arithmetic between two numeric arrays requires that the length of each array\n is identical, and will apply the operation positionally (``a1 * a2`` will\n multiply the first element of ``a1`` by the first element of ``a2``, the\n second element of ``a1`` by the second element of ``a2``, and so on).\n Arithmetic with a scalar will apply the operation to each element of the\n array.\n\n >>> a1 = functions.capture([0, 1, 2, 3, 4, 5])\n\n >>> a2 = functions.capture([1, -1, 1, -1, 1, -1])\n\n \"\"\"\n\n def _bin_op_ret_typ(self, other):\n if isinstance(self, ArrayNumericExpression) and isinstance(other, float):\n return TArray(TFloat64())\n\n elif isinstance(self, ArrayNumericExpression) and isinstance(other, Float64Expression):\n return TArray(TFloat64())\n elif isinstance(self, Float64Expression) and isinstance(other, ArrayNumericExpression):\n return TArray(TFloat64())\n elif isinstance(self, ArrayFloat64Expression) and isinstance(other, ArrayNumericExpression):\n return TArray(TFloat64())\n elif isinstance(self, ArrayNumericExpression) and isinstance(other, ArrayFloat64Expression):\n return TArray(TFloat64())\n\n elif isinstance(self, Float32Expression) and isinstance(other, ArrayNumericExpression):\n return TArray(TFloat32())\n elif isinstance(self, ArrayNumericExpression) and isinstance(other, Float32Expression):\n return TArray(TFloat32())\n elif isinstance(self, ArrayFloat32Expression) and isinstance(other, ArrayNumericExpression):\n return TArray(TFloat32())\n elif isinstance(self, ArrayNumericExpression) and isinstance(other, ArrayFloat32Expression):\n return TArray(TFloat32())\n\n elif isinstance(self, ArrayInt64Expression) and isinstance(other, int):\n return TArray(TInt64())\n elif isinstance(self, Int64Expression) and isinstance(other, ArrayNumericExpression):\n return TArray(TInt64())\n elif isinstance(self, ArrayNumericExpression) and isinstance(other, Int64Expression):\n return TArray(TInt64())\n elif isinstance(self, ArrayInt64Expression) and isinstance(other, ArrayNumericExpression):\n return TArray(TInt64())\n elif isinstance(self, ArrayNumericExpression) and isinstance(other, ArrayInt64Expression):\n return TArray(TInt64())\n\n elif isinstance(self, ArrayInt32Expression) and isinstance(other, int):\n return TArray(TInt32())\n elif isinstance(self, Int32Expression) and isinstance(other, ArrayNumericExpression):\n return TArray(TInt32())\n elif isinstance(self, ArrayNumericExpression) and isinstance(other, Int32Expression):\n return TArray(TInt32())\n elif isinstance(self, ArrayInt32Expression) and isinstance(other, ArrayNumericExpression):\n return TArray(TInt32())\n elif isinstance(self, ArrayNumericExpression) and isinstance(other, ArrayInt32Expression):\n return TArray(TInt32())\n\n else:\n raise NotImplementedError('''Error in return type for numeric conversion.\n self = {}\n other = {}'''.format(type(self), type(other)))\n\n def _bin_op_numeric(self, name, other):\n other = to_expr(other)\n ret_typ = self._bin_op_ret_typ(other)\n return self._bin_op(name, other, ret_typ)\n\n def _bin_op_numeric_reverse(self, name, other):\n other = to_expr(other)\n ret_typ = self._bin_op_ret_typ(other)\n return self._bin_op_reverse(name, other, ret_typ)\n\n def __add__(self, other):\n \"\"\"Positionally add an array or a scalar.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a1 + 5)\n [5, 6, 7, 8, 9, 10]\n\n >>> eval_expr(a1 + a2)\n [1, 0, 3, 2, 5, 4]\n\n Parameters\n ----------\n other : :class:`NumericExpression` or :class:`ArrayNumericExpression`\n Value or array to add.\n\n Returns\n -------\n :class:`ArrayNumericExpression`\n Array of positional sums.\n \"\"\"\n return self._bin_op_numeric(\"+\", other)\n\n def __radd__(self, other):\n return self._bin_op_numeric_reverse(\"+\", other)\n\n def __sub__(self, other):\n \"\"\"Positionally subtract an array or a scalar.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a2 - 1)\n [0, -2, 0, -2, 0, -2]\n\n >>> eval_expr(a1 - a2)\n [-1, 2, 1, 4, 3, 6]\n\n Parameters\n ----------\n other : :class:`NumericExpression` or :class:`ArrayNumericExpression`\n Value or array to subtract.\n\n Returns\n -------\n :class:`ArrayNumericExpression`\n Array of positional differences.\n \"\"\"\n return self._bin_op_numeric(\"-\", other)\n\n def __rsub__(self, other):\n return self._bin_op_numeric_reverse(\"-\", other)\n\n def __mul__(self, other):\n \"\"\"Positionally multiply by an array or a scalar.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a2 * 5)\n [5, -5, 5, -5, 5, -5]\n\n >>> eval_expr(a1 * a2)\n [0, -1, 2, -3, 4, -5]\n\n Parameters\n ----------\n other : :class:`NumericExpression` or :class:`ArrayNumericExpression`\n Value or array to multiply by.\n\n Returns\n -------\n :class:`ArrayNumericExpression`\n Array of positional products.\n \"\"\"\n return self._bin_op_numeric(\"*\", other)\n\n def __rmul__(self, other):\n return self._bin_op_numeric_reverse(\"*\", other)\n\n def __div__(self, other):\n \"\"\"Positionally divide by an array or a scalar.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a1 / 10)\n [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]\n\n >>> eval_expr(a2 / a1)\n [inf, -1.0, 0.5, -0.3333333333333333, 0.25, -0.2]\n\n Parameters\n ----------\n other : :class:`NumericExpression` or :class:`ArrayNumericExpression`\n Value or array to divide by.\n\n Returns\n -------\n :class:`ArrayNumericExpression`\n Array of positional quotients.\n \"\"\"\n return self._bin_op(\"/\", other, TArray(TFloat64()))\n\n def __rdiv__(self, other):\n return self._bin_op_reverse(\"/\", other, TArray(TFloat64()))\n\n def __pow__(self, other):\n \"\"\"Positionally raise to the power of an array or a scalar.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a1 ** 2)\n [0.0, 1.0, 4.0, 9.0, 16.0, 25.0]\n\n >>> eval_expr(a1 ** a2)\n [0.0, 1.0, 2.0, 0.3333333333333333, 4.0, 0.2]\n\n Parameters\n ----------\n other : :class:`NumericExpression` or :class:`ArrayNumericExpression`\n Value or array to exponentiate by.\n\n Returns\n -------\n :class:`ArrayNumericExpression`\n Array of positional exponentiations.\n \"\"\"\n return self._bin_op('**', other, TArray(TFloat64()))\n\n def __rpow__(self, other):\n return self._bin_op_reverse('**', other, TArray(TFloat64()))\n\n def sort(self, ascending=True):\n \"\"\"Returns a sorted array.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a1.sort())\n [-1, -1, -1, 1, 1, 1]\n\n Parameters\n ----------\n ascending : bool or :py:class:`BooleanExpression`\n Sort in ascending order.\n\n Returns\n -------\n :py:class:`ArrayNumericExpression`\n Sorted array.\n \"\"\"\n return self._method(\"sort\", self._type, ascending)\n\n\nclass ArrayFloat64Expression(ArrayNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` with element type :class:`hail.expr.types.TFloat64`.\"\"\"\n pass\n\n\nclass ArrayFloat32Expression(ArrayNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` with element type :class:`hail.expr.types.TFloat32`.\"\"\"\n pass\n\n\nclass ArrayInt32Expression(ArrayNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` with element type :class:`hail.expr.types.TInt32`\"\"\"\n pass\n\n\nclass ArrayInt64Expression(ArrayNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` with element type :class:`hail.expr.types.TInt64`.\"\"\"\n pass\n\n\nclass ArrayStringExpression(ArrayExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` with element type :class:`hail.expr.types.TString`.\n\n >>> a = functions.capture(['Alice', 'Bob', 'Charles'])\n \"\"\"\n def mkstring(self, delimiter):\n \"\"\"Joins the elements of the array into a single string delimited by `delimiter`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.mkstring(','))\n 'Alice,Bob,Charles'\n\n Parameters\n ----------\n delimiter : str or :py:class:`StringExpression`\n Field delimiter.\n\n Returns\n -------\n :py:class:`StringExpression`\n Joined string expression.\n \"\"\"\n return self._method(\"mkString\", TString(), delimiter)\n\n def sort(self, ascending=True):\n \"\"\"Returns a sorted array.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.sort(ascending=False))\n ['Charles', 'Bob', 'Alice']\n\n Parameters\n ----------\n ascending : bool or :py:class:`BooleanExpression`\n Sort in ascending order.\n\n Returns\n -------\n :py:class:`ArrayStringExpression`\n Sorted array.\n \"\"\"\n return self._method(\"sort\", self._type, ascending)\n\n\nclass ArrayStructExpression(ArrayExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` with element type :class:`hail.expr.types.TStruct`.\"\"\"\n pass\n\n\nclass ArrayArrayExpression(ArrayExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TArray` with element type :class:`hail.expr.types.TArray`.\n\n >>> a = functions.capture([[1, 2], [3, 4]])\n \"\"\"\n def flatten(self):\n \"\"\"Flatten the nested array by concatenating subarrays.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(a.flatten())\n [1, 2, 3, 4]\n\n Returns\n -------\n :py:class:`ArrayExpression`\n Array generated by concatenating all subarrays.\n \"\"\"\n return self._method(\"flatten\", self._type.element_type)\n\n\nclass SetExpression(CollectionExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TSet`.\n\n >>> s1 = functions.capture({1, 2, 3})\n >>> s2 = functions.capture({1, 3, 5})\n \"\"\"\n @typecheck_method(x=expr_any)\n def add(self, x):\n \"\"\"Returns a new set including `x`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s1.add(10))\n {1, 2, 3, 10}\n\n Parameters\n ----------\n x : :class:`.Expression`\n Value to add.\n\n Returns\n -------\n :class:`SetExpression`\n Set with `x` added.\n \"\"\"\n return self._method(\"add\", self._type, x)\n\n @typecheck_method(x=expr_any)\n def remove(self, x):\n \"\"\"Returns a new set excluding `x`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s1.remove(1))\n {2, 3}\n\n Parameters\n ----------\n x : :class:`.Expression`\n Value to remove.\n\n Returns\n -------\n :py:class:`SetExpression`\n Set with `x` removed.\n \"\"\"\n return self._method(\"remove\", self._type, x)\n\n @typecheck_method(x=expr_any)\n def contains(self, x):\n \"\"\"Returns ``True`` if `x` is in the set.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s1.contains(1))\n True\n\n >>> eval_expr(s1.contains(10))\n False\n\n Parameters\n ----------\n x : :class:`.Expression`\n Value for inclusion test..\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if `x` is in the set.\n \"\"\"\n return self._method(\"contains\", TBoolean(), x)\n\n @typecheck_method(s=expr_set)\n def difference(self, s):\n \"\"\"Return the set of elements in the set that are not present in set `s`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s1.difference(s2))\n {2}\n\n >>> eval_expr(s2.difference(s1))\n {5}\n\n Parameters\n ----------\n s : :class:`SetExpression`\n Set expression of the same type.\n\n Returns\n -------\n :class:`SetExpression`\n Set of elements not in `s`.\n \"\"\"\n return self._method(\"difference\", self._type, s)\n\n @typecheck_method(s=expr_set)\n def intersection(self, s):\n \"\"\"Return the intersection of the set and set `s`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s1.intersection(s2))\n {1, 3}\n\n Parameters\n ----------\n s : :class:`SetExpression`\n Set expression of the same type.\n\n Returns\n -------\n :class:`SetExpression`\n Set of elements present in `s`.\n \"\"\"\n return self._method(\"intersection\", self._type, s)\n\n @typecheck_method(s=expr_set)\n def is_subset(self, s):\n \"\"\"Returns ``True`` if every element is contained in set `s`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s1.is_subset(s2))\n False\n\n >>> eval_expr(s1.remove(2).is_subset(s2))\n True\n\n Parameters\n ----------\n s : :class:`SetExpression`\n Set expression of the same type.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if every element is contained in set `s`.\n \"\"\"\n return self._method(\"isSubset\", TBoolean(), s)\n\n @typecheck_method(s=expr_set)\n def union(self, s):\n \"\"\"Return the union of the set and set `s`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s1.union(s2))\n {1, 2, 3, 5}\n\n Parameters\n ----------\n s : :class:`SetExpression`\n Set expression of the same type.\n\n Returns\n -------\n :class:`SetExpression`\n Set of elements present in either set.\n \"\"\"\n return self._method(\"union\", self._type, s)\n\n def to_array(self):\n \"\"\"Convert the set to an array.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s1.to_array())\n [1, 2, 3]\n\n Notes\n -----\n The order of elements in the array is not guaranteed.\n\n Returns\n -------\n :class:`ArrayExpression`\n Array of all elements in the set.\n \"\"\"\n return self._method(\"toArray\", TArray(self._type.element_type))\n\n\nclass SetFloat64Expression(SetExpression, CollectionNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TSet` with element type :class:`hail.expr.types.TFloat64`.\"\"\"\n pass\n\n\nclass SetFloat32Expression(SetExpression, CollectionNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TSet` with element type :class:`hail.expr.types.TFloat32`.\"\"\"\n pass\n\n\nclass SetInt32Expression(SetExpression, CollectionNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TSet` with element type :class:`hail.expr.types.TInt32`.\"\"\"\n pass\n\n\nclass SetInt64Expression(SetExpression, CollectionNumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TSet` with element type :class:`hail.expr.types.TInt64`.\"\"\"\n pass\n\n\nclass SetStringExpression(SetExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TSet` with element type :class:`hail.expr.types.TString`.\n\n >>> s = functions.capture({'Alice', 'Bob', 'Charles'})\n \"\"\"\n @typecheck_method(delimiter=expr_str)\n def mkstring(self, delimiter):\n \"\"\"Joins the elements of the set into a single string delimited by `delimiter`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s.mkstring(','))\n 'Bob,Charles,Alice'\n\n Notes\n -----\n The order of the elements in the string is not guaranteed.\n\n Parameters\n ----------\n delimiter : str or :py:class:`StringExpression`\n Field delimiter.\n\n Returns\n -------\n :py:class:`StringExpression`\n Joined string expression.\n \"\"\"\n return self._method(\"mkString\", TString(), delimiter)\n\n\nclass SetSetExpression(SetExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TSet` with element type :class:`hail.expr.types.TSet`.\n\n >>> s = functions.capture({1, 2, 3}).map(lambda s: {s, 2 * s})\n \"\"\"\n def flatten(self):\n \"\"\"Flatten the nested set by concatenating subsets.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s.flatten())\n {1, 2, 3, 4, 6}\n\n Returns\n -------\n :py:class:`SetExpression`\n Set generated by concatenating all subsets.\n \"\"\"\n return self._method(\"flatten\", self._type.element_type)\n\n\nclass DictExpression(Expression):\n \"\"\"Expression of type :class:`hail.expr.types.TDict`.\n\n >>> d = functions.capture({'Alice': 43, 'Bob': 33, 'Charles': 44})\n \"\"\"\n def _init(self):\n self._key_typ = self._type.key_type\n self._value_typ = self._type.value_type\n\n @typecheck_method(item=expr_any)\n def __getitem__(self, item):\n \"\"\"Get the value associated with key `item`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(d['Alice'])\n 43\n\n Notes\n -----\n Raises an error if `item` is not a key of the dictionary. Use\n :meth:`DictExpression.get` to return missing instead of an error.\n\n Parameters\n ----------\n item : :class:`.Expression`\n Key expression.\n\n Returns\n -------\n :class:`.Expression`\n Value associated with key `item`.\n \"\"\"\n return self._index(self._value_typ, item)\n\n @typecheck_method(k=expr_any)\n def contains(self, k):\n \"\"\"Returns whether a given key is present in the dictionary.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(d.contains('Alice'))\n True\n\n >>> eval_expr(d.contains('Anne'))\n False\n\n Parameters\n ----------\n k : :class:`.Expression`\n Key to test for inclusion.\n\n Returns\n -------\n :py:class:`BooleanExpression`\n ``True`` if `k` is a key of the dictionary, ``False`` otherwise.\n \"\"\"\n return self._method(\"contains\", TBoolean(), k)\n\n @typecheck_method(k=expr_any)\n def get(self, k):\n \"\"\"Returns the value associated with key `k`, or missing if that key is not present.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(d.get('Alice'))\n 43\n\n >>> eval_expr(d.get('Anne'))\n None\n\n Parameters\n ----------\n k : :class:`.Expression`\n Key.\n\n Returns\n -------\n :py:class:`.Expression`\n The value associated with `k`, or missing.\n \"\"\"\n return self._method(\"get\", self._value_typ, k)\n\n def key_set(self):\n \"\"\"Returns the set of keys in the dictionary.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(d.key_set())\n {'Alice', 'Bob', 'Charles'}\n\n Returns\n -------\n :py:class:`SetExpression`\n Set of all keys.\n \"\"\"\n return self._method(\"keySet\", TSet(self._key_typ))\n\n def keys(self):\n \"\"\"Returns an array with all keys in the dictionary.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(d.keys())\n ['Bob', 'Charles', 'Alice']\n\n Returns\n -------\n :py:class:`ArrayExpression`\n Array of all keys.\n \"\"\"\n return self._method(\"keys\", TArray(self._key_typ))\n\n def map_values(self, f):\n \"\"\"Transform values of the dictionary according to a function.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(d.map_values(lambda x: x * 10))\n {'Alice': 430, 'Bob': 330, 'Charles': 440}\n\n Parameters\n ----------\n f : callable\n Function to apply to each value.\n\n Returns\n -------\n :py:class:`DictExpression`\n Dictionary with transformed values.\n \"\"\"\n return self._bin_lambda_method(\"mapValues\", f, self._value_typ, lambda t: TDict(self._key_typ, t))\n\n def size(self):\n \"\"\"Returns the size of the dictionary.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(d.size())\n 3\n\n Returns\n -------\n :py:class:`Int32Expression`\n Size of the dictionary.\n \"\"\"\n return self._method(\"size\", TInt32())\n\n def values(self):\n \"\"\"Returns an array with all values in the dictionary.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(d.values())\n [33, 44, 43]\n\n Returns\n -------\n :py:class:`ArrayExpression`\n All values in the dictionary.\n \"\"\"\n return self._method(\"values\", TArray(self._value_typ))\n\n\nclass Aggregable(object):\n \"\"\"Expression that can only be aggregated.\n\n An :class:`Aggregable` is produced by the :meth:`explode` or :meth:`filter`\n methods. These objects can be aggregated using aggregator functions, but\n cannot otherwise be used in expressions.\n \"\"\"\n def __init__(self, ast, type, indices, aggregations, joins, refs):\n self._ast = ast\n self._type = type\n self._indices = indices\n self._aggregations = aggregations\n self._joins = joins\n self._refs = refs\n\n def __nonzero__(self):\n raise NotImplementedError('Truth value of an aggregable collection is undefined')\n\n def __eq__(self, other):\n raise NotImplementedError('Comparison of aggregable collections is undefined')\n\n def __ne__(self, other):\n raise NotImplementedError('Comparison of aggregable collections is undefined')\n\n\nclass StructExpression(Mapping, Expression):\n \"\"\"Expression of type :class:`hail.expr.types.TStruct`.\n\n >>> s = functions.capture(Struct(a=5, b='Foo'))\n\n Struct fields are accessible as attributes and keys. It is therefore\n possible to access field `a` of struct `s` with dot syntax:\n\n .. doctest::\n\n >>> eval_expr(s.a)\n 5\n\n It is possible to have fields whose names violate Python identifier syntax.\n For these, use the :meth:`StructExpression.__getitem__` syntax.\n \"\"\"\n def _init(self):\n self._fields = OrderedDict()\n\n for fd in self._type.fields:\n expr = construct_expr(Select(self._ast, fd.name), fd.typ, self._indices,\n self._aggregations, self._joins, self._refs)\n self._set_field(fd.name, expr)\n\n def _set_field(self, key, value):\n self._fields[key] = value\n if key not in self.__dict__:\n self.__dict__[key] = value\n\n def __len__(self):\n return len(self._fields)\n\n @typecheck_method(item=strlike)\n def __getitem__(self, item):\n \"\"\"Access a field of the struct.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s['a'])\n 5\n\n Parameters\n ----------\n item : :obj:`str`\n Field name.\n\n Returns\n -------\n :class:`.Expression`\n Struct field.\n \"\"\"\n if not item in self._fields:\n raise KeyError(\"Struct has no field '{}'\\n\"\n \" Fields: [ {} ]\".format(item, ', '.join(\"'{}'\".format(x) for x in self._fields)))\n return self._fields[item]\n\n def __iter__(self):\n return iter(self._fields)\n\n def __hash__(self):\n return object.__hash__(self)\n\n def __eq__(self, other):\n return Expression.__eq__(self, other)\n\n def __ne__(self, other):\n return Expression.__ne__(self, other)\n\n def __nonzero__(self):\n return Expression.__nonzero__(self)\n\nclass AtomicExpression(Expression):\n \"\"\"Abstract base class for numeric and logical types.\"\"\"\n\n def to_float64(self):\n \"\"\"Convert to a 64-bit floating point expression.\n\n Returns\n -------\n :py:class:`Float64Expression`\n \"\"\"\n return self._method(\"toFloat64\", TFloat64())\n\n def to_float32(self):\n \"\"\"Convert to a 32-bit floating point expression.\n\n Returns\n -------\n :py:class:`Float32Expression`\n \"\"\"\n return self._method(\"toFloat32\", TFloat32())\n\n def to_int64(self):\n \"\"\"Convert to a 64-bit integer expression.\n\n Returns\n -------\n :py:class:`Int64Expression`\n \"\"\"\n return self._method(\"toInt64\", TInt64())\n\n def to_int32(self):\n \"\"\"Convert to a 32-bit integer expression.\n\n Returns\n -------\n :py:class:`Int32Expression`\n \"\"\"\n return self._method(\"toInt32\", TInt32())\n\n\nclass BooleanExpression(AtomicExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TBoolean`.\n\n >>> t = functions.capture(True)\n >>> f = functions.capture(False)\n >>> na = functions.null(TBoolean())\n\n .. doctest::\n\n >>> eval_expr(t)\n True\n\n >>> eval_expr(f)\n False\n\n >>> eval_expr(na)\n None\n\n \"\"\"\n\n def _bin_op_logical(self, name, other):\n other = to_expr(other)\n return self._bin_op(name, other, TBoolean())\n\n @typecheck_method(other=expr_bool)\n def __and__(self, other):\n \"\"\"Return ``True`` if the left and right arguments are ``True``.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(t & f)\n False\n\n >>> eval_expr(t & na)\n None\n\n >>> eval_expr(f & na)\n False\n\n The ``&`` and ``|`` operators have higher priority than comparison\n operators like ``==``, ``<``, or ``>``. Parentheses are often\n necessary:\n\n .. doctest::\n\n >>> x = functions.capture(5)\n\n >>> eval_expr((x < 10) & (x > 2))\n True\n\n Parameters\n ----------\n other : :class:`BooleanExpression`\n Right-side operation.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if both left and right are ``True``.\n \"\"\"\n return self._bin_op_logical(\"&&\", other)\n\n def __or__(self, other):\n \"\"\"Return ``True`` if at least one of the left and right arguments is ``True``.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(t | f)\n True\n\n >>> eval_expr(t | na)\n True\n\n >>> eval_expr(f | na)\n None\n\n The ``&`` and ``|`` operators have higher priority than comparison\n operators like ``==``, ``<``, or ``>``. Parentheses are often\n necessary:\n\n .. doctest::\n\n >>> x = functions.capture(5)\n\n >>> eval_expr((x < 10) | (x > 20))\n True\n\n Parameters\n ----------\n other : :class:`BooleanExpression`\n Right-side operation.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if either left or right is ``True``.\n \"\"\"\n return self._bin_op_logical(\"||\", other)\n\n def __invert__(self):\n \"\"\"Return the boolean inverse.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(~t)\n False\n\n >>> eval_expr(~f)\n True\n\n >>> eval_expr(~na)\n None\n\n Returns\n -------\n :class:`BooleanExpression`\n Boolean inverse.\n \"\"\"\n return self._unary_op(\"!\")\n\n\nclass NumericExpression(AtomicExpression):\n \"\"\"Expression of numeric type.\n\n >>> x = functions.capture(3)\n\n >>> y = functions.capture(4.5)\n \"\"\"\n def _bin_op_ret_typ(self, other):\n if isinstance(self, NumericExpression) and isinstance(other, float):\n return TFloat64()\n elif isinstance(self, Float64Expression) and isinstance(other, NumericExpression):\n return TFloat64()\n elif isinstance(self, NumericExpression) and isinstance(other, Float64Expression):\n return TFloat64()\n elif isinstance(self, Float32Expression) and isinstance(other, NumericExpression):\n return TFloat32()\n elif isinstance(self, NumericExpression) and isinstance(other, Float32Expression):\n return TFloat32()\n elif isinstance(self, Int64Expression) and (\n isinstance(other, NumericExpression) or isinstance(other, int) or isinstance(other, long)):\n return TInt64()\n elif isinstance(self, NumericExpression) and isinstance(other, Int64Expression):\n return TInt64()\n elif isinstance(self, Int32Expression) and (isinstance(other, NumericExpression) or isinstance(other, int)):\n return TInt32()\n elif isinstance(self, NumericExpression) or isinstance(other, Int32Expression):\n return TInt32()\n else:\n raise NotImplementedError(\"Error in return type for numeric conversion.\",\n \"\\nself =\", type(self),\n \"\\nother =\", type(other))\n\n def _bin_op_numeric(self, name, other):\n ret_typ = self._bin_op_ret_typ(other)\n return self._bin_op(name, other, ret_typ)\n\n def _bin_op_numeric_reverse(self, name, other):\n ret_typ = self._bin_op_ret_typ(other)\n return self._bin_op_reverse(name, other, ret_typ)\n\n @typecheck_method(other=expr_numeric)\n def __lt__(self, other):\n \"\"\"Less-than comparison.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(x < 5)\n True\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Right side for comparison.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the left side is smaller than the right side.\n \"\"\"\n return self._bin_op(\"<\", other, TBoolean())\n\n @typecheck_method(other=expr_numeric)\n def __le__(self, other):\n \"\"\"Less-than-or-equals comparison.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(x <= 3)\n True\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Right side for comparison.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the left side is smaller than or equal to the right side.\n \"\"\"\n return self._bin_op(\"<=\", other, TBoolean())\n\n @typecheck_method(other=expr_numeric)\n def __gt__(self, other):\n \"\"\"Greater-than comparison.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(y > 4)\n True\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Right side for comparison.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the left side is greater than the right side.\n \"\"\"\n return self._bin_op(\">\", other, TBoolean())\n\n @typecheck_method(other=expr_numeric)\n def __ge__(self, other):\n \"\"\"Greater-than-or-equals comparison.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(y >= 4)\n True\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Right side for comparison.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the left side is greater than or equal to the right side.\n \"\"\"\n return self._bin_op(\">=\", other, TBoolean())\n\n\n def __pos__(self):\n return self\n\n def __neg__(self):\n \"\"\"Negate the number (multiply by -1).\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(-x)\n -3\n\n Returns\n -------\n :class:`NumericExpression`\n Negated number.\n \"\"\"\n return self._unary_op(\"-\")\n\n def __add__(self, other):\n \"\"\"Add two numbers.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(x + 2)\n 5\n\n >>> eval_expr(x + y)\n 7.5\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Number to add.\n\n Returns\n -------\n :class:`NumericExpression`\n Sum of the two numbers.\n \"\"\"\n return self._bin_op_numeric(\"+\", other)\n\n def __radd__(self, other):\n return self._bin_op_numeric_reverse(\"+\", other)\n\n def __sub__(self, other):\n \"\"\"Subtract the right number from the left.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(x - 2)\n 1\n\n >>> eval_expr(x - y)\n -1.5\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Number to subtract.\n\n Returns\n -------\n :class:`NumericExpression`\n Difference of the two numbers.\n \"\"\"\n return self._bin_op_numeric(\"-\", other)\n\n def __rsub__(self, other):\n return self._bin_op_numeric_reverse(\"-\", other)\n\n def __mul__(self, other):\n \"\"\"Multiply two numbers.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(x * 2)\n 6\n\n >>> eval_expr(x * y)\n 9.0\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Number to multiply.\n\n Returns\n -------\n :class:`NumericExpression`\n Product of the two numbers.\n \"\"\"\n return self._bin_op_numeric(\"*\", other)\n\n def __rmul__(self, other):\n return self._bin_op_numeric_reverse(\"*\", other)\n\n def __div__(self, other):\n \"\"\"Divide two numbers.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(x / 2)\n 1.5\n\n >>> eval_expr(y / 0.1)\n 45.0\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Dividend.\n\n Returns\n -------\n :class:`NumericExpression`\n The left number divided by the left.\n \"\"\"\n return self._bin_op_numeric(\"/\", other)\n\n def __rdiv__(self, other):\n return self._bin_op_numeric_reverse(\"/\", other)\n\n def __mod__(self, other):\n \"\"\"Compute the left modulo the right number.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(32 % x)\n 2\n\n >>> eval_expr(7 % y)\n 2.5\n\n Parameters\n ----------\n other : :class:`NumericExpression`\n Dividend.\n\n Returns\n -------\n :class:`NumericExpression`\n Remainder after dividing the left by the right.\n \"\"\"\n return self._bin_op_numeric('%', other)\n\n def __rmod__(self, other):\n return self._bin_op_numeric_reverse('%', other)\n\n def __pow__(self, power, modulo=None):\n \"\"\"Raise the left to the right power.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(x ** 2)\n 9.0\n\n >>> eval_expr(x ** -2)\n 0.1111111111111111\n\n >>> eval_expr(y ** 1.5)\n 9.545941546018392\n\n Parameters\n ----------\n power : :class:`NumericExpression`\n modulo\n Unsupported argument.\n\n Returns\n -------\n :class:`Float64Expression`\n Result of raising left to the right power.\n \"\"\"\n return self._bin_op('**', power, TFloat64())\n\n def __rpow__(self, other):\n return self._bin_op_reverse('**', other, TFloat64())\n\n def signum(self):\n \"\"\"Returns the sign of the callee, ``1`` or ``-1``.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(y.signum())\n 1\n\n Returns\n -------\n :py:class:`Int32Expression`\n ``1`` or ``-1``.\n \"\"\"\n return self._method(\"signum\", TInt32())\n\n def abs(self):\n \"\"\"Returns the absolute value of the callee.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(y.abs())\n 4.5\n\n Returns\n -------\n :py:class:`.NumericExpression`\n Absolute value of the number.\n \"\"\"\n return self._method(\"abs\", self._type)\n\n @typecheck_method(other=expr_numeric)\n def max(self, other):\n \"\"\"Returns the maximum value between the callee and `other`.\n\n Parameters\n ----------\n other : :py:class:`NumericExpression`\n Value to compare against.\n\n Returns\n -------\n :py:class:`.NumericExpression`\n Maximum value.\n \"\"\"\n assert (isinstance(other, self.__class__))\n return self._method(\"max\", self._type, other)\n\n @typecheck_method(other=expr_numeric)\n def min(self, other):\n \"\"\"Returns the minumum value between the callee and `other`.\n\n Parameters\n ----------\n other : :py:class:`NumericExpression`\n Value to compare against.\n\n Returns\n -------\n :py:class:`.NumericExpression`\n Minimum value.\n \"\"\"\n assert (isinstance(other, self.__class__))\n return self._method(\"min\", self._type, other)\n\n\nclass Float64Expression(NumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TFloat64`.\"\"\"\n pass\n\n\nclass Float32Expression(NumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TFloat32`.\"\"\"\n pass\n\n\nclass Int32Expression(NumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TInt32`.\"\"\"\n pass\n\n\nclass Int64Expression(NumericExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TInt64`.\"\"\"\n pass\n\n\nclass StringExpression(AtomicExpression):\n \"\"\"Expression of type :class:`hail.expr.types.TString`.\n\n >>> s = functions.capture('The quick brown fox')\n \"\"\"\n @typecheck_method(item=oneof(slice, expr_int32))\n def __getitem__(self, item):\n \"\"\"Slice or index into the string.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s[:15])\n 'The quick brown'\n\n >>> eval_expr(s[0])\n 'T'\n\n Parameters\n ----------\n item : slice or :class:`Int32Expression`\n Slice or character index.\n\n Returns\n -------\n :class:`StringExpression`\n Substring or character at index `item`.\n \"\"\"\n if isinstance(item, slice):\n return self._slice(TString(), item.start, item.stop, item.step)\n elif isinstance(item, int) or isinstance(item, Int32Expression):\n return self._index(TString(), item)\n else:\n raise NotImplementedError()\n\n def __add__(self, other):\n \"\"\"Concatenate strings.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s + ' jumped over the lazy dog')\n 'The quick brown fox jumped over the lazy dog'\n\n Parameters\n ----------\n other : :class:`StringExpression`\n String to concatenate.\n\n Returns\n -------\n :class:`StringExpression`\n Concatenated string.\n \"\"\"\n other = to_expr(other)\n if not isinstance(other, StringExpression):\n raise TypeError(\"cannot concatenate 'TString' expression and '{}'\".format(other._type.__class__))\n return self._bin_op(\"+\", other, TString())\n\n def __radd__(self, other):\n assert (isinstance(other, StringExpression) or isinstance(other, str))\n return self._bin_op_reverse(\"+\", other, TString())\n\n def length(self):\n \"\"\"Returns the length of the string.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s.length())\n 19\n\n Returns\n -------\n :class:`Int32Expression`\n Length of the string.\n \"\"\"\n return self._method(\"length\", TInt32())\n\n @typecheck_method(pattern1=expr_str, pattern2=expr_str)\n def replace(self, pattern1, pattern2):\n \"\"\"Replace substrings matching `pattern1` with `pattern2` using regex.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s.replace(' ', '_'))\n 'The_quick_brown_fox'\n\n Notes\n -----\n The regex expressions used should follow\n `Java regex syntax `_\n\n Parameters\n ----------\n pattern1 : str or :class:`StringExpression`\n pattern2 : str or :class:`StringExpression`\n\n Returns\n -------\n\n \"\"\"\n return self._method(\"replace\", TString(), pattern1, pattern2)\n\n @typecheck_method(delim=expr_str, n=nullable(expr_int32))\n def split(self, delim, n=None):\n \"\"\"Returns an array of strings generated by splitting the string at `delim`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(s.split('\\\\s+'))\n ['The', 'quick', 'brown', 'fox']\n\n >>> eval_expr(s.split('\\\\s+', 2))\n ['The', 'quick brown fox']\n\n Notes\n -----\n The delimiter is a regex using the\n `Java regex syntax `_\n delimiter. To split on special characters, escape them with double\n backslash (``\\\\\\\\``).\n\n Parameters\n ----------\n delim : str or :class:`StringExpression`\n Delimiter regex.\n n : :class:`Int32Expression`, optional\n Maximum number of splits.\n\n Returns\n -------\n :class:`ArrayExpression`\n Array of split strings.\n \"\"\"\n if n is None:\n return self._method(\"split\", TArray(TString()), delim)\n else:\n return self._method(\"split\", TArray(TString()), delim, n)\n\n @typecheck_method(regex=strlike)\n def matches(self, regex):\n \"\"\"Returns ``True`` if the string contains any match for the given regex.\n\n Examples\n --------\n\n >>> string = functions.capture('NA12878')\n\n The `regex` parameter does not need to match the entire string:\n\n .. doctest::\n\n >>> eval_expr(string.matches('12'))\n True\n\n Regex motifs can be used to match sequences of characters:\n\n .. doctest::\n\n >>> eval_expr(string.matches(r'NA\\\\\\\\d+'))\n True\n\n Notes\n -----\n The `regex` argument is a\n `regular expression `__,\n and uses\n `Java regex syntax `__.\n\n Parameters\n ----------\n regex: :obj:`str`\n Pattern to match.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the string contains any match for the regex, otherwise ``False``.\n \"\"\"\n return construct_expr(RegexMatch(self._ast, regex), TBoolean(),\n self._indices, self._aggregations, self._joins, self._refs)\n\n\nclass CallExpression(Expression):\n \"\"\"Expression of type :class:`hail.expr.types.TCall`.\n\n >>> call = functions.capture(Call(1))\n \"\"\"\n @property\n def gt(self):\n \"\"\"Returns the triangle number of :py:meth:`Call.gtj` and :py:meth:`Call.gtk`.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.gt)\n 1\n\n Returns\n -------\n :class:`Int32Expression`\n Triangle number of the two alleles.\n \"\"\"\n return self._field(\"gt\", TInt32())\n\n def gtj(self):\n \"\"\"Returns the allele index of the first allele.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.gtj())\n 0\n\n Returns\n -------\n :class:`Int32Expression`\n First allele index.\n \"\"\"\n return self._method(\"gtj\", TInt32())\n\n def gtk(self):\n \"\"\"Returns the allele index of the second allele.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.gtk())\n 1\n\n Returns\n -------\n :class:`Int32Expression`\n Second allele index.\n \"\"\"\n return self._method(\"gtk\", TInt32())\n\n def is_non_ref(self):\n \"\"\"Evaluate whether the call includes one or more non-reference alleles.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.is_non_ref())\n True\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if at least one allele is non-reference, ``False`` otherwise.\n \"\"\"\n return self._method(\"isNonRef\", TBoolean())\n\n def is_het(self):\n \"\"\"Evaluate whether the call includes two different alleles.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.is_het())\n True\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the two alleles are different, ``False`` if they are the same.\n \"\"\"\n return self._method(\"isHet\", TBoolean())\n\n def is_het_nonref(self):\n \"\"\"Evaluate whether the call includes two different alleles, neither of which is reference.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.is_het_nonref())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the call includes two different alternate alleles, ``False`` otherwise.\n \"\"\"\n return self._method(\"isHetNonRef\", TBoolean())\n\n def is_het_ref(self):\n \"\"\"Evaluate whether the call includes two different alleles, one of which is reference.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.is_het_ref())\n True\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the call includes one reference and one alternate allele, ``False`` otherwise.\n \"\"\"\n return self._method(\"isHetRef\", TBoolean())\n\n def is_hom_ref(self):\n \"\"\"Evaluate whether the call includes two reference alleles.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.is_hom_ref())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the call includes two reference alleles, ``False`` otherwise.\n \"\"\"\n return self._method(\"isHomRef\", TBoolean())\n\n def is_hom_var(self):\n \"\"\"Evaluate whether the call includes two identical alternate alleles.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.is_hom_var())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the call includes two identical alternate alleles, ``False`` otherwise.\n \"\"\"\n return self._method(\"isHomVar\", TBoolean())\n\n def num_alt_alleles(self):\n \"\"\"Returns the number of non-reference alleles (0, 1, or 2).\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.num_alt_alleles())\n 1\n\n Returns\n -------\n :class:`Int32Expression`\n The number of non-reference alleles (0, 1, or 2).\n \"\"\"\n return self._method(\"nNonRefAlleles\", TInt32())\n\n @typecheck_method(v=expr_variant)\n def one_hot_alleles(self, v):\n \"\"\"Returns an array containing the summed one-hot encoding of the two alleles.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.one_hot_alleles(Variant('1', 1, 'A', 'T')))\n [1, 1]\n\n This one-hot representation is the positional sum of the one-hot\n encoding for each called allele. For a biallelic variant, the one-hot\n encoding for a reference allele is ``[1, 0]`` and the one-hot encoding\n for an alternate allele is ``[0, 1]``. Diploid calls would produce the\n following arrays: ``[2, 0]`` for homozygous reference, ``[1, 1]`` for\n heterozygous, and ``[0, 2]`` for homozygous alternate.\n\n Parameters\n ----------\n v : :class:`hail.genetics.Variant` or :class:`VariantExpression`\n Variant, used to determine the number of possible alleles.\n\n Returns\n -------\n :class:`ArrayInt32Expression`\n An array of summed one-hot encodings of allele indices.\n \"\"\"\n return self._method(\"oneHotAlleles\", TArray(TInt32()), v)\n\n @typecheck_method(v=expr_variant)\n def one_hot_genotype(self, v):\n \"\"\"Returns the triangle number of the genotype one-hot encoded into an integer array.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(call.one_hot_genotype(Variant('1', 1, 'A', 'T')))\n [0, 1, 0]\n\n A one-hot encoding uses a vector to represent categorical data, like a\n genotype call, by using an array with one ``1`` and many ``0`` s. In\n this case, the array will have a ``1`` at the position of the triangle\n number of the genotype (:py:meth:`Call.gt`).\n\n This is useful for manipulating counts of categorical variables.\n\n Parameters\n ----------\n v : :class:`hail.genetics.Variant` or :class:`VariantExpression`\n Variant, used to determine the number of possible alleles.\n Returns\n -------\n :class:`ArrayInt32Expression`\n An array with a one-hot encoding of the call.\n \"\"\"\n return self._method(\"oneHotGenotype\", TArray(TInt32()), v)\n\n\nclass LocusExpression(Expression):\n \"\"\"Expression of type :class:`hail.expr.types.TLocus`.\n\n >>> locus = functions.capture(Locus('1', 100))\n \"\"\"\n @property\n def contig(self):\n \"\"\"Returns the chromosome.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(locus.contig)\n '1'\n\n Returns\n -------\n :class:`StringExpression`\n The chromosome for this locus.\n \"\"\"\n return self._field(\"contig\", TString())\n\n @property\n def position(self):\n \"\"\"Returns the position along the chromosome.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(locus.position)\n 100\n\n Returns\n -------\n :class:`Int32Expression`\n This locus's position along its chromosome.\n \"\"\"\n return self._field(\"position\", TInt32())\n\n\nclass IntervalExpression(Expression):\n \"\"\"Expression of type :class:`hail.expr.types.TInterval`.\n\n >>> interval = functions.capture(Interval.parse('X:1M-2M'))\n \"\"\"\n @typecheck_method(locus=expr_locus)\n def contains(self, locus):\n \"\"\"Tests whether a locus is contained in the interval.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(interval.contains(Locus('X', 3000000)))\n False\n\n >>> eval_expr(interval.contains(Locus('X', 1500000)))\n True\n\n Parameters\n ----------\n locus : :class:`hail.genetics.Locus` or :class:`LocusExpression`\n Locus to test for interval membership.\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if `locus` is contained in the interval, ``False`` otherwise.\n \"\"\"\n locus = to_expr(locus)\n if self._type.point_type != locus._type:\n raise TypeError('expected {}, found: {}'.format(self._type.point_type, locus._type))\n return self._method(\"contains\", TBoolean(), locus)\n\n @property\n def end(self):\n \"\"\"Returns the end locus.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(interval.end)\n Locus(contig=X, position=2000000, reference_genome=GRCh37)\n\n Returns\n -------\n :class:`LocusExpression`\n End locus.\n \"\"\"\n return self._field(\"end\", TLocus())\n\n @property\n def start(self):\n \"\"\"Returns the start locus.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(interval.start)\n Locus(contig=X, position=1000000, reference_genome=GRCh37)\n\n Returns\n -------\n :class:`LocusExpression`\n Start locus.\n \"\"\"\n return self._field(\"start\", TLocus())\n\n\nclass AltAlleleExpression(Expression):\n \"\"\"Expression of type :class:`hail.expr.types.TAltAllele`.\n\n >>> altallele = functions.capture(AltAllele('A', 'AAA'))\n \"\"\"\n @property\n def alt(self):\n \"\"\"Returns the alternate allele string.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.alt)\n 'AAA'\n\n Returns\n -------\n :class:`StringExpression`\n Alternate base string.\n \"\"\"\n return self._field(\"alt\", TString())\n\n @property\n def ref(self):\n \"\"\"Returns the reference allele string.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.ref)\n 'A'\n\n Returns\n -------\n :class:`StringExpression`\n Reference base string.\n \"\"\"\n return self._field(\"ref\", TString())\n\n def category(self):\n \"\"\"Returns the string representation of the alternate allele class.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.category())\n 'Insertion'\n\n Returns\n -------\n :class:`StringExpression`\n One of ``\"SNP\"``, ``\"MNP\"``, ``\"Insertion\"``, ``\"Deletion\"``, ``\"Star\"``, ``\"Complex\"``.\n \"\"\"\n return self._method(\"category\", TString())\n\n def is_complex(self):\n \"\"\"Returns ``True`` if the polymorphism is not a SNP, MNP, star, insertion, or deletion.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_complex())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the allele is a complex event.\n \"\"\"\n return self._method(\"isComplex\", TBoolean())\n\n def is_deletion(self):\n \"\"\"Returns ``True`` if the polymorphism is a deletion.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_deletion())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the event is a a deletion.\n \"\"\"\n return self._method(\"isDeletion\", TBoolean())\n\n def is_indel(self):\n \"\"\"Returns ``True`` if the polymorphism is an insertion or deletion.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_insertion())\n True\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the event is an insertion or deletion.\n \"\"\"\n return self._method(\"isIndel\", TBoolean())\n\n def is_insertion(self):\n \"\"\"Returns ``True`` if the polymorphism is an insertion.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_insertion())\n True\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the event is an insertion.\n \"\"\"\n return self._method(\"isInsertion\", TBoolean())\n\n def is_mnp(self):\n \"\"\"Returns ``True`` if the polymorphism is a MNP.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_mnp())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the event is a multiple nucleotide polymorphism.\n \"\"\"\n return self._method(\"isMNP\", TBoolean())\n\n def is_snp(self):\n \"\"\"Returns ``True`` if the polymorphism is a SNP.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_snp())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the event is a single nucleotide polymorphism.\n \"\"\"\n return self._method(\"isSNP\", TBoolean())\n\n def is_star(self):\n \"\"\"Returns ``True`` if the polymorphism is a star (upstream deletion) allele.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_star())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the event is a star (upstream deletion) allele.\n \"\"\"\n return self._method(\"isStar\", TBoolean())\n\n def is_transition(self):\n \"\"\"Returns ``True`` if the polymorphism is a transition SNP.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_transition())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the event is a SNP and a transition.\n \"\"\"\n return self._method(\"isTransition\", TBoolean())\n\n def is_transversion(self):\n \"\"\"Returns ``True`` if the polymorphism is a transversion SNP.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(altallele.is_transversion())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the event is a SNP and a transversion.\n \"\"\"\n return self._method(\"isTransversion\", TBoolean())\n\n\nclass VariantExpression(Expression):\n \"\"\"Expression of type :class:`hail.expr.types.TVariant`.\n\n >>> variant = functions.capture(Variant('16', 123055, 'A', 'C'))\n \"\"\"\n def alt(self):\n \"\"\"Returns the alternate allele string.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.alt())\n 'C'\n\n Warning\n -------\n Assumes biallelic. If the variant is multiallelic, throws an error.\n\n Returns\n -------\n :class:`StringExpression`\n Alternate allele base string.\n \"\"\"\n return self._method(\"alt\", TString())\n\n def alt_allele(self):\n \"\"\"Returns the alternate allele.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.alt_allele())\n AltAllele(ref='A', alt='C')\n\n Warning\n -------\n Assumes biallelic. If the variant is multiallelic, throws an error.\n\n Returns\n -------\n :class:`AltAlleleExpression`\n Alternate allele.\n \"\"\"\n return self._method(\"altAllele\", TAltAllele())\n\n @property\n def alt_alleles(self):\n \"\"\"Returns the alternate alleles in the polymorphism.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.alt_alleles)\n [AltAllele(ref='A', alt='C')]\n\n Returns\n -------\n :class:`ArrayExpression` with element type :class:`AltAlleleExpression`\n Alternate alleles.\n \"\"\"\n return self._field(\"altAlleles\", TArray(TAltAllele()))\n\n @property\n def contig(self):\n \"\"\"Returns the chromosome.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.contig)\n '16'\n\n Returns\n -------\n :class:`StringExpression`\n Contig.\n \"\"\"\n return self._field(\"contig\", TString())\n\n def in_x_nonpar(self):\n \"\"\"Returns ``True`` if the polymorphism is in a non-pseudoautosomal region of chromosome X.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.in_x_nonpar())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the variant is found in a non-pseudoautosomal region of chromosome X.\n \"\"\"\n return self._method(\"inXNonPar\", TBoolean())\n\n def in_x_par(self):\n \"\"\"Returns ``True`` if the polymorphism is in the pseudoautosomal region of chromosome X.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.in_x_par())\n False\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the variant is found in a pseudoautosomal region of chromosome X.\n \"\"\"\n return self._method(\"inXPar\", TBoolean())\n\n def in_y_nonpar(self):\n \"\"\"Returns ``True`` if the polymorphism is in a non-pseudoautosomal region of chromosome Y.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.in_y_nonpar())\n False\n\n Note\n ----\n Many variant callers only generate variants on chromosome X for the\n pseudoautosomal region. In this case, all variants mapped to chromosome\n Y are non-pseudoautosomal.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the variant is found in a non-pseudoautosomal region of chromosome Y.\n \"\"\"\n return self._method(\"inYNonPar\", TBoolean())\n\n def in_y_par(self):\n \"\"\"Returns ``True`` if the polymorphism is in a pseudoautosomal region of chromosome Y.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.in_y_par())\n False\n\n Note\n ----\n Many variant callers only generate variants on chromosome X for the\n pseudoautosomal region. In this case, all variants mapped to chromosome\n Y are non-pseudoautosomal.\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the variant is found in a pseudoautosomal region of chromosome Y.\n \"\"\"\n return self._method(\"inYPar\", TBoolean())\n\n def is_autosomal(self):\n \"\"\"Returns ``True`` if the polymorphism is found on an autosome.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.is_autosomal())\n True\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the variant is found on an autosome.\n \"\"\"\n return self._method(\"isAutosomal\", TBoolean())\n\n def is_biallelic(self):\n \"\"\"Returns ``True`` if there is only one alternate allele.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.is_biallelic())\n True\n\n Returns\n -------\n :class:`BooleanExpression`\n ``True`` if the variant has only one alternate allele.\n \"\"\"\n return self._method(\"isBiallelic\", TBoolean())\n\n def locus(self):\n \"\"\"Returns the locus at which the polymorphism occurs.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.locus())\n Locus(contig=16, position=123055, reference_genome=GRCh37)\n\n Returns\n -------\n :class:`LocusExpression`\n Locus associated with this variant.\n \"\"\"\n return self._method(\"locus\", TLocus())\n\n def num_alleles(self):\n \"\"\"Returns the number of alleles in the polymorphism, including the reference.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.num_alleles())\n 2\n\n Returns\n -------\n :class:`Int32Expression`\n Total number of alleles, including the reference.\n \"\"\"\n return self._method(\"nAlleles\", TInt32())\n\n def num_alt_alleles(self):\n \"\"\"Returns the number of alleles in the polymorphism, excluding the reference.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.num_alt_alleles())\n 1\n\n Returns\n -------\n :class:`Int32Expression`\n Total number of non-reference alleles.\n \"\"\"\n return self._method(\"nAltAlleles\", TInt32())\n\n def num_genotypes(self):\n \"\"\"Returns the number of possible genotypes given the number of total alleles.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.num_genotypes())\n 3\n\n Returns\n -------\n :class:`Int32Expression`\n Total number of possible diploid genotype configurations.\n \"\"\"\n return self._method(\"nGenotypes\", TInt32())\n\n @property\n def ref(self):\n \"\"\"Returns the reference allele string.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.ref)\n 'A'\n\n Returns\n -------\n :class:`StringExpression`\n Reference base string.\n \"\"\"\n return self._field(\"ref\", TString())\n\n @property\n def start(self):\n \"\"\"Returns the chromosomal position of the polymorphism.\n\n Examples\n --------\n .. doctest::\n\n >>> eval_expr(variant.start)\n 123055\n\n Returns\n -------\n :class:`Int32Expression`\n \"\"\"\n return self._field(\"start\", TInt32())\n\n\ntyp_to_expr = {\n TBoolean: BooleanExpression,\n TInt32: Int32Expression,\n TInt64: Int64Expression,\n TFloat64: Float64Expression,\n TFloat32: Float32Expression,\n TLocus: LocusExpression,\n TInterval: IntervalExpression,\n TVariant: VariantExpression,\n TCall: CallExpression,\n TAltAllele: AltAlleleExpression,\n TString: StringExpression,\n TDict: DictExpression,\n TArray: ArrayExpression,\n TSet: SetExpression,\n TStruct: StructExpression\n}\n\nelt_typ_to_array_expr = {\n TInt32: ArrayInt32Expression,\n TFloat64: ArrayFloat64Expression,\n TInt64: ArrayInt64Expression,\n TFloat32: ArrayFloat32Expression,\n TString: ArrayStringExpression,\n TStruct: ArrayStructExpression,\n TArray: ArrayArrayExpression\n}\n\nelt_typ_to_set_expr = {\n TInt32: SetInt32Expression,\n TFloat64: SetFloat64Expression,\n TFloat32: SetFloat32Expression,\n TInt64: SetFloat32Expression,\n TString: SetStringExpression,\n TSet: SetSetExpression\n}\n\n\nclass ExpressionException(Exception):\n def __init__(self, msg=''):\n self.msg = msg\n super(ExpressionException, self).__init__(msg)\n\n\nclass ExpressionWarning(Warning):\n def __init__(self, msg=''):\n self.msg = msg\n super(ExpressionWarning, self).__init__(msg)\n\n\n@typecheck(caller=strlike, expr=Expression,\n expected_indices=Indices,\n aggregation_axes=setof(strlike))\ndef analyze(caller, expr, expected_indices, aggregation_axes=set()):\n indices = expr._indices\n source = indices.source\n axes = indices.axes\n aggregations = expr._aggregations\n refs = expr._refs\n\n warnings = []\n errors = []\n\n expected_source = expected_indices.source\n expected_axes = expected_indices.axes\n\n if source is not None and source is not expected_source:\n errors.append(\n ExpressionException('{} expects an expression from source {}, found expression derived from {}'.format(\n caller, expected_source, source\n )))\n\n # check for stray indices by subtracting expected axes from observed\n unexpected_axes = axes - expected_axes\n\n if unexpected_axes:\n # one or more out-of-scope fields\n assert len(refs) > 0\n bad_refs = []\n for name, inds in refs:\n bad_axes = inds.axes.intersection(unexpected_axes)\n if bad_axes:\n bad_refs.append((name, inds))\n\n assert len(bad_refs) > 0\n errors.append(ExpressionException(\n \"scope violation: '{caller}' expects an expression indexed by {expected}\"\n \"\\n Found indices {axes}, with unexpected indices {stray}. Invalid fields:{fields}{agg}\".format(\n caller=caller,\n expected=list(expected_axes),\n axes=list(indices.axes),\n stray=list(unexpected_axes),\n fields=''.join(\"\\n '{}' (indices {})\".format(name, list(inds.axes)) for name, inds in bad_refs),\n agg='' if (unexpected_axes - aggregation_axes) else\n \"\\n '{}' supports aggregation over axes {}, \"\n \"so these fields may appear inside an aggregator function.\".format(caller, list(aggregation_axes))\n )))\n\n if aggregations:\n if aggregation_axes:\n\n # the expected axes of aggregated expressions are the expected axes + axes aggregated over\n expected_agg_axes = expected_axes.union(aggregation_axes)\n\n for agg in aggregations:\n agg_indices = agg.indices\n agg_axes = agg_indices.axes\n if agg_indices.source is not None and agg_indices.source is not expected_source:\n errors.append(\n ExpressionException(\n 'Expected an expression from source {}, found expression derived from {}'\n '\\n Invalid fields: [{}]'.format(\n expected_source, source, ', '.join(\"'{}'\".format(name) for name, _ in agg.refs)\n )))\n\n # check for stray indices\n unexpected_agg_axes = agg_axes - expected_agg_axes\n if unexpected_agg_axes:\n # one or more out-of-scope fields\n bad_refs = []\n for name, inds in agg.refs:\n bad_axes = inds.axes.intersection(unexpected_agg_axes)\n if bad_axes:\n bad_refs.append((name, inds))\n\n errors.append(ExpressionException(\n \"scope violation: '{caller}' supports aggregation over indices {expected}\"\n \"\\n Found indices {axes}, with unexpected indices {stray}. Invalid fields:{fields}\".format(\n caller=caller,\n expected=list(aggregation_axes),\n axes=list(agg_axes),\n stray=list(unexpected_agg_axes),\n fields=''.join(\"\\n '{}' (indices {})\".format(\n name, list(inds.axes)) for name, inds in bad_refs)\n )\n ))\n else:\n errors.append(ExpressionException(\"'{}' does not support aggregation\".format(caller)))\n\n for w in warnings:\n warn('{}'.format(w.msg))\n if errors:\n for e in errors:\n error('{}'.format(e.msg))\n raise errors[0]\n\n\n@typecheck(expression=expr_any)\ndef eval_expr(expression):\n \"\"\"Evaluate a Hail expression, returning the result.\n\n This method is extremely useful for learning about Hail expressions and understanding\n how to compose them.\n\n Expressions that refer to fields of :class:`hail.api2.Table` or :class:`hail.api.MatrixTable`\n objects cannot be evaluated.\n\n Examples\n --------\n Evaluate a conditional:\n\n .. doctest::\n\n >>> x = 6\n >>> eval_expr(functions.cond(x % 2 == 0, 'Even', 'Odd'))\n 'Even'\n\n Parameters\n ----------\n expression : :class:`hail.expr.expression.Expression`\n Any expression, or a Python value that can be implicitly interpreted as an expression.\n\n Returns\n -------\n any\n Result of evaluating `expression`.\n \"\"\"\n return eval_expr_typed(expression)[0]\n\n\n@typecheck(expression=expr_any)\ndef eval_expr_typed(expression):\n \"\"\"Evaluate a Hail expression, returning the result and the type of the result.\n\n This method is extremely useful for learning about Hail expressions and understanding\n how to compose them.\n\n Expressions that refer to fields of :class:`hail.api2.Table` or :class:`hail.api.MatrixTable`\n objects cannot be evaluated.\n\n Examples\n --------\n Evaluate a conditional:\n\n .. doctest::\n\n >>> x = 6\n >>> eval_expr_typed(functions.cond(x % 2 == 0, 'Even', 'Odd'))\n ('Odd', TString())\n\n Parameters\n ----------\n expression : :class:`hail.expr.expression.Expression`\n Any expression, or a Python value that can be implicitly interpreted as an expression.\n\n Returns\n -------\n (any, :class:`hail.expr.Type`)\n Result of evaluating `expression`, and its type.\n \"\"\"\n analyze('eval_expr_typed', expression, Indices())\n if not expression._joins.empty():\n raise ExpressionException(\"'eval_expr' methods do not support joins or broadcasts\")\n r, t = Env.hc().eval_expr_typed(expression._ast.to_hql())\n return r, t\n\n\n_lazy_int32.set(Int32Expression)\n_lazy_numeric.set(NumericExpression)\n_lazy_array.set(ArrayExpression)\n_lazy_set.set(SetExpression)\n_lazy_bool.set(BooleanExpression)\n_lazy_struct.set(StructExpression)\n_lazy_string.set(StringExpression)\n_lazy_variant.set(VariantExpression)\n_lazy_locus.set(LocusExpression)\n_lazy_altallele.set(AltAlleleExpression)\n_lazy_interval.set(IntervalExpression)\n_lazy_call.set(CallExpression)\n_lazy_expr.set(Expression)\n","sub_path":"python/hail/expr/expression.py","file_name":"expression.py","file_ext":"py","file_size_in_byte":106717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"350780306","text":"# Import QRCode from pyqrcode\nimport pyqrcode\nimport png\n\n# String which represents the QR code\nurl = \"https://www.youtube.com/watch?v=dQw4w9WgXcQ\"\n\n# Variable conatining link\nurl = \"https://www.youtube.com/watch?v=choF2JRgjHY\"\n\n# Generating QR code\nqrcode = pyqrcode.create(url)\n\n# Create and save the png file naming \"myqr.png\"\nqrcode.png('MustScan.png', scale = 6)\n\n# Create and save the png file\nqrcode.png('QRCODE.png', scale = 6)\n\n","sub_path":"QR.py","file_name":"QR.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"223093062","text":"import re\nline = \"::'''Herr Guld''' (kunnt hinte-n-ine hintersi un schint ebbes z'bschaüe duss).\"\nline1 = \"::'''Herr Kalt.'''\"\nline2 = \"Ich mag noch so nogeh un nolüege, 's hilft alles nit meh!\"\n\nline3 = \"::'''Herr Guld''' (fir sich).\"\nline4 =\"Wie schön as se doch g'wachse-n-isch!\"\n\nregex = r'()(.+)()'\n\nreg_parenthesis = r\"\\(([^)]+)\\)\"\nre_obj = re.compile(reg_parenthesis)\nmatch = re_obj.search(line)\nprint(match)\nprint(match.group())\n# print(match.groups())\n\n\nreg_name = r\"(\\'\\'\\')([^\\']+)(\\'\\'\\')\"\nre_name = re.compile(reg_name)\na = re_name.search(line)\nprint(a.groups())\nprint(a.group(2))\nprint(\"\\nNew stuff\")\nreg_start_line = r\"\\:\\:\"\nmatch = re.match(reg_start_line,line1)\nif match:\n print(re.search(regex, line1).group())\n\n\nt1 = \"::'''Pierrot, Domino.'''\"\nt2 = \"::'''Pierrot''' (no-n-em Nachtesse im Restaurant).\"\nt3 = \"'s isch Zit glaüb, liewer Domino,\"\nt4 = \"== I. Uftritt. ==\"\nt5 = \"::(er b'schaüt's g'naüer)\"\nt6 = \"\"\nt7 = \"{{ImDruckVerbergen|[[Datei:ALustig SämtlicheWerke ZweiterBand page724 725.pdf|thumb|724 - 725]]}}\"\n\nseveral_chars = re.compile(r\"::\\'\\'\\'([^,]+,[^,]+)\\'\\'\\'\")\nact = re.compile(r\"==\\s(.+)\\s==\")\nsingle_char = re.compile(r\"::\\'\\'\\'([^']+)\\'\\'\\'\\s*\"\n r\"(\\([^)]+\\))\\.*\")\nstage_line = r\"[:]*(\\([^)]+\\))\"\nline = r\"[\\:]*([^(<:]+)\"\nre_page = r\"{{.+page(\\d+\\s\\d+)\\.pdf.+]]}}\"\nm = re.match(re_page, t7)\nif m:\n print(m.group(1))\n\n","sub_path":"test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"425948667","text":"import os\nimport pandas as pd\nimport numpy as np\n\nclass DataLoader:\n '''\n Splits the raw dataset into windowed chunks of data. Also helps to easily load the chunks into memory\n '''\n def __init__(self, read_chunk_size=20000000, window_size=150000, step=50000, raw_data_path='../input/train.csv', window_data_path='./'):\n '''\n :param int read_chunk_size: The number of sample to read from the raw input in every chunk\n :param int window_size: Number of samples in a single window of data\n :param int step: Step to shift when splitting into windows\n :param str raw_data_path: The path to the raw data\n :param str window_data_path: The path of the dir to place the files with windowed data\n '''\n self.read_chunk_size = read_chunk_size\n self.window_size = window_size\n self.step = step\n self.raw_data_path = raw_data_path\n self.window_data_path = window_data_path\n self.mem = None\n\n def onlineGenerator(self, time_to_failure_dtype=np.float32):\n '''\n Instead of saving splits to memory, this function creates a generator that yields batches in an online way. This\n should prevent loading all data into memory at the same time.\n :return: X, y.\n :rtype: (numpy.array), (numpy.array)\n '''\n data_iterator = pd.read_csv(self.raw_data_path, iterator=True, chunksize=self.read_chunk_size,\n dtype={'acoustic_data': np.int16, 'time_to_failure': time_to_failure_dtype})\n # get features by iterating over all chunks\n for chunk in data_iterator:\n ind_range = np.arange(chunk.shape[0]-self.window_size+1, step=self.step)[:, None]\n indexer = np.arange(self.window_size)[None, :] + ind_range\n X = chunk.acoustic_data.values\n X = X[indexer]\n y = chunk.time_to_failure.values\n y = y[ind_range].flatten()\n yield X, y\n\n def _splitRawChunk(self, index):\n print('Loading data from {} to {}'.format(index*self.read_chunk_size, (index+1)*self.read_chunk_size))\n # Load part of the file\n raw = pd.read_csv(self.raw_data_path, nrows=self.read_chunk_size, skiprows=index*self.read_chunk_size+1, names=['acoustic_data', 'time_to_failure'], dtype={'acoustic_data': np.int16, 'time_to_failure': np.float32})\n\n # Use memory\n if (self.mem is not None) and (index > 0):\n raw = pd.concat([self.mem,raw],axis=0)\n self.mem = raw.tail(self.window_size-1)\n\n # Get features\n ind_range = np.arange(raw.shape[0]-self.window_size+1, step=self.step)[:, None]\n indexer = np.arange(self.window_size)[None, :] + ind_range\n X = raw['acoustic_data'].values\n X = X[indexer]\n y = raw['time_to_failure'].values\n y = y[ind_range].flatten()\n\n return X, y\n\n def splitRaw(self):\n '''\n Splits the original trainset into smaller files of overlapping windows.\n Saves the files in the window_data_path in the format X_{i}.csv and y_{i}.csvself.\n :return: The number of files created\n :rtype: int\n '''\n index = 0\n run = True\n while(run):\n print(' Iteration',index)\n X, y = self._splitRawChunk(index)\n if (X.shape[0] == 0):\n run = False\n else:\n np.savetxt('X_{}.csv'.format(index), X, delimiter=',', fmt='%i')\n np.savetxt('y_{}.csv'.format(index), y, delimiter=',', fmt='%.9e')\n index += 1\n return index-1\n\n def loadData(self, index):\n '''\n Splits the original trainset into smaller files of overlapping windows.\n Saves the files in the window_data_path in the format X_{i}.csv and y_{i}.csvself.\n :param int index: The index of the files to load\n :return: X, y\n :rtype: (numpy.array, numpy.array)\n '''\n X = np.loadtxt(os.path.join(self.window_data_path, 'X_{}.csv'.format(index)), delimiter=',', dtype=np.int16)\n y = np.loadtxt(os.path.join(self.window_data_path, 'y_{}.csv'.format(index)), delimiter=',', dtype=np.float32)\n\n return X, y\n\nif __name__ == \"__main__\":\n d = DataLoader(read_chunk_size=80000000)\n d.splitRaw()\n","sub_path":"lanl/working/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":4304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"463666318","text":"from odoo import http\nfrom odoo.addons.website.controllers.main import Website\nfrom odoo.http import request\nfrom odoo.addons.portal.controllers.web import Home\n\nclass Website(Website):\n\n @http.route(auth='public')\n def index(self, data={}, **kw):\n super(Website, self).index(**kw)\n return http.request.render('intranet_home.new_homepage_vardhman', data)\n\n\n\nclass Academy(http.Controller):\n\n @http.route('/usefullinks/', auth='public', website=True)\n def index(self, **kw):\n Teachers = http.request.env['vardhman.useful.links']\n Employees = http.request.env['hr.employee']\n return http.request.render('intranet_home.use_links', {\n 'teachers': Teachers.search([])\n })\n\n\nclass home(Home):\n\n @http.route('/', type='http', auth=\"public\", website=True)\n def index(self, **kw):\n print(\"<<<<<<<<<<<<<<<<<<<\")\n links = request.env['vardhman.useful.links'].sudo().search([])\n print(\"----------\",links)\n if links:\n values = [{\n 'name':x['name'],\n 'url':x['url'],\n #'icon':links.icon\n } for x in links]\n return request.render('intranet_home.new_homepage_vardhman', {'values':values})","sub_path":"intranet_home1/controllers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"302364977","text":"import pandas as pd\nfrom functools import reduce\nfrom itertools import chain as ch\nfrom trees.node import Node\n\n'''\ngrow_tree(): an ID3 implementation for binary classification;\n\tgiven a dataset with binary attribute and target labels,\n\tgrow_tree recursively divides the dataset via the\n\tminimal conditional entropy (i.e., maximum gain) heuristic.\n\tEach subdivision creates a node holding attribute name and \n\tdata subset information until division is no longer possible,\n\tat which case the node is a leaf node.\n\tRETURN: a binary decision tree\n'''\n\ndef grow_tree(data, glob_freq_label = None, name = \"root\", branch = None):\n\t\n\t'''\n\tdata: a pandas DataFrame with a subset of the data; \n\t\t the data is corralled by an attribute with minimal \n\t\t conditional entropy in the previous iteration of grow_tree();\n\t\t 1st iteration: all data\n\t\t \n\tglob_freq_label: the most frequent target class;\n\t\t \n\tname: the attribute criterion the node branched from; \n\t\t each name is the attribute with minimal conditional entropy\n\t\t in the previous iteration;\n\t\t 1st iteration: \"root\"\n\t\t \n\tbranch: the attribute criterion's label;\n\t\t T (1) or F (0), from the last iteration's \n\t\t attribute with minimal conditional entropy;\n\t\t 1st iteration: None\n\t'''\n\t\n\tnode = Node(data)\t\t\t\t# node with data subset\n\tnode.name = name\t\t\t\t# name it via its branching attribute & label\n\tnode.branch_case = str(branch) if branch is not None else None\n\trows, cols = node.data.shape\t# grab the dimensions of the ata\n\tH = node.entropy()\t\t\t\t# compute target label entropy\n\t\n\tif name is \"root\":\t\t\t\t\t\t# calculate most common label for entire dataset\n\t\tglob_freq_label = 1 if float(sum(data.iloc[:, cols - 1])) / len(data) >= 0.5 else 0\t\n\tloc_freq_label = (1 if float(sum(node.data.iloc[:, cols - 1])) \n\t\t/ len(node.data) >= .5 else 0)\t\t# calculate most common label within subset of data\n\t\n\tif H == 0:\t\t\t\t\t\t\t\t\t\t\t\t\t\t# if there is 0 uncertainty, then\n\t\tnode.label = 1 if node.data.iloc[0, cols - 1] == 1 else 0 \t# the current node is a leaf, and\n\t\treturn node\t\t\t\t\t\t\t\t\t\t\t\t ### its label is the target label\n\t\t\n\tif cols == 1:\t\t\t\t\t\t\t\t\t\t\t\t\t# if there are no more attributes, then the current\n\t\tnode.label = glob_freq_label if H == 1 else loc_freq_label\t# node's label is the most common label in the data subset,\n\t\treturn node\t\t\t\t\t\t\t\t\t\t\t\t ### unless impurity is maximum, then it is the most common\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# label in the entire data\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\tidentical_attr = (map(lambda x, y: x == y, node.data.iloc[0, :(cols-1)], node.data.iloc[i, :(cols-1)]) for i in range(rows))\n\tidentical_attr = list(ch.from_iterable(identical_attr))\t\t\t# compare each row with the first via map and de-nest\n\tidentical_attr = reduce(lambda x, y: x and y, identical_attr)\t# the nested T and F values via from_iterable so that\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# reduce can determine if all rows are identical\n\tif identical_attr:\n\t\tnode.label = glob_freq_label if H == 1 else loc_freq_label\t# if all examples' attributes are identical,\n\t\treturn node\t\t\t\t\t\t\t\t\t\t\t\t ### return the node and set its label depending on H\n\t\t\n\tbest_attr, H_cond = node.min_conditional_entropy()\t\t\t\t# compute the value and attribute of minimal conditional H\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\tdata_true = pd.DataFrame(node.data[node.data.iloc[:, best_attr] == 1])\t\t\t\t# split data into two new datasets via true and\n\tdata_true = pd.DataFrame(data_true.drop(data_true.columns[best_attr], axis = 1))\t# false labels of the best attribute, then\n\tdata_false = pd.DataFrame(node.data[node.data.iloc[:, best_attr] == 0])\t\t\t\t# drop the attribute from the datasets, preventing\n\tdata_false = pd.DataFrame(data_false.drop(data_false.columns[best_attr], axis = 1))\t# the tree from reusing redundant attributes\n\t\n\tname_true = node.data.columns[best_attr]\t\t\t\t\t# set the branching names for the true \n\tname_false = node.data.columns[best_attr]\t\t\t\t\t# and false branches of the next iteration\t\n\t\n\tnode.branch_1 = grow_tree(data_true, glob_freq_label, name_true, 1)\t\t\t\t\t# and create the branches via grow_tree\n\tnode.branch_0 = grow_tree(data_false, glob_freq_label, name_false, 0)\n\t\n\treturn node\n\t\n\n\t\n","sub_path":"f_growtree.py","file_name":"f_growtree.py","file_ext":"py","file_size_in_byte":4074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"412584327","text":"#!/usr/bin/env python3\n#Author: Suman Sewani\n#Author ID: sksewani\n#Date Created: 2021/01/24\nimport sys\ntimer = 10\nwhile timer != 0:\n\tprint(timer)\n\ttimer = timer - 1\n\nprint('blast off!')\n\n","sub_path":"lab2e.py","file_name":"lab2e.py","file_ext":"py","file_size_in_byte":187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"391456261","text":"# coding: utf-8\n\n'''\n equiv.py\n\n Construct each of the .beleq files, given a particular dataset.\n This involves gathering all the terms and determining whether\n or not there are existing terms that refer to the same thing.\n Terms are either then equivalenced to something else, or given\n a new uuid (but UUIDs should be preserved after the first run).\n\n'''\n\nimport uuid\nimport namespaces\nimport csv\nimport parsed\nimport os\nfrom collections import deque, defaultdict\n\nhgnc_list = []\nmgi_list = []\nrgd_list = []\nsp_list = []\n\nentrez_eq = {}\nhgnc_eq = {}\nmgi_eq = {}\nrgd_eq = {}\nsp_eq = {}\nentrez_converter = {}\naffy_eq = {}\nrefseq = {}\nsd = {}\nchebi_id_eq = {}\nchebi_name_eq = {}\npub_eq_dict = {}\ngobp_eq_dict = {}\ngocc_eq_dict = {}\ndo_eq_dict = {}\n\nref_status = {'REVIEWED' : 0,\n 'VALIDATED' : 1,\n 'PROVISIONAL' : 2,\n 'PREDICTED' : 3,\n 'MODEL' : 4,\n 'INFERRED' : 5,\n '-' : 6}\n\ndelim = '|'\n\n# create a reference, used in MeSH equivalencing\ncurdir = os.getcwd()\nmesh_to_gocc = curdir+'/datasets/meshcs_to_gocc.csv'\nmg_eq = {}\nwith open(mesh_to_gocc, 'r') as meshf:\n mg_eq = dict([(rec[0], rec[2]) for rec in csv.reader(meshf, delimiter=',', quotechar='\"')])\n\n# this method is called once, to build an equivalence dict used by SwissProt\ndef build_equivs():\n\n ns_dicts = [namespaces.hgnc_ns_dict, namespaces.mgi_ns_dict, \\\n namespaces.rgd_ns_dict]\n for d in ns_dicts:\n for k, v in d.items():\n if d is namespaces.hgnc_ns_dict:\n equiv(k, 'hgnc')\n if d is namespaces.mgi_ns_dict:\n equiv(k, 'mgi')\n if d is namespaces.rgd_ns_dict:\n equiv(k, 'rgd')\n\ndef make_eq_dict(d):\n temp_dict = d.get_dictionary()\n for entry in temp_dict:\n entrez_id = entry\n mapping = temp_dict.get(entrez_id)\n tax_id = mapping.get('tax_id')\n symbol = mapping.get('Symbol_from_nomenclature_authority')\n if tax_id == '9606':\n entrez_converter['HGNC:'+symbol] = entrez_id\n if tax_id == '10116':\n entrez_converter['RGD:'+symbol] = entrez_id\n if tax_id == '10090':\n entrez_converter['MGI:'+symbol] = entrez_id\n\n# 'd' is a DataObject, which wraps a nested dictionary. Currently, a copy\n# of each .beleq file is stored in a dictionary as well, (like entrez_eq).\n# This may not be needed in final implementation.\ndef equiv(d, verbose):\n if str(d) == 'entrez_info':\n with open('entrez-gene-ids.beleq', 'w') as fp:\n for gene_id in d.get_eq_values():\n uid = uuid.uuid4()\n fp.write(delim.join((gene_id, str(uid)))+'\\n')\n entrez_eq[gene_id] = uuid.uuid4()\n make_eq_dict(d)\n\n elif str(d) == 'hgnc':\n with open('hgnc-approved-symbols.beleq', 'w') as fp:\n for approved_symbol in d.get_eq_values():\n if '~withdrawn' in approved_symbol:\n continue\n new_id = to_entrez('HGNC:'+approved_symbol)\n if new_id is None:\n # keep track of which hgnc genes need new uuids (dont map to entrez)\n hgnc_list.append(approved_symbol)\n # generate new uuid\n uid = uuid.uuid4()\n fp.write(delim.join((approved_symbol, str(uid)))+'\\n')\n hgnc_eq[approved_symbol] = uid\n else:\n # use the entrez uuid\n uid = entrez_eq.get(new_id)\n fp.write(delim.join((approved_symbol, str(uid)))+'\\n')\n hgnc_eq[approved_symbol] = uid\n\n elif str(d) == 'mgi':\n with open('mgi-approved-symbols.beleq', 'w') as fp:\n for marker_symbol in d.get_eq_values():\n new_id = to_entrez('MGI:'+marker_symbol)\n if new_id is None:\n # keep track of which genes need new uuids (dont map to entrez)\n mgi_list.append(marker_symbol)\n # generate new uuid\n uid = uuid.uuid4()\n fp.write(delim.join((marker_symbol, str(uid)))+'\\n')\n mgi_eq[marker_symbol] = uid\n else:\n # use the entrez uuid\n uid = entrez_eq.get(new_id)\n fp.write(delim.join((marker_symbol, str(uid)))+'\\n')\n mgi_eq[marker_symbol] = uid\n\n elif str(d) == 'rgd':\n with open('rgd-approved-symbols.beleq', 'w') as fp:\n for symbol in d.get_eq_values():\n new_id = to_entrez('RGD:'+symbol)\n if new_id is None:\n # keep track of which genes need new uuids (dont map to entrez)\n rgd_list.append(symbol)\n # generate new uuid\n uid = uuid.uuid4()\n fp.write(delim.join((symbol, str(uid)))+'\\n')\n rgd_eq[symbol] = uid\n else:\n # use the entrez uuid\n uid = entrez_eq.get(new_id)\n fp.write(delim.join((symbol, str(uid)))+'\\n')\n rgd_eq[symbol] = uid\n\n elif str(d) == 'swiss':\n with open('swissprot-entry-names.beleq', 'w') as fp:\n # dbrefs is a dict, i.e { reference_type : id_of_that_gene}\n for name, dbrefs, accessions in d.get_eq_values():\n target_pool = ['HGNC', 'MGI', 'RGD']\n gene_ids = []\n alt_ids = []\n\n if dbrefs is not None:\n for k, v in dbrefs.items():\n if k == 'GeneId':\n gene_ids.extend(v)\n if k in target_pool:\n # could be MGI or RGD or HGNC ids\n alt_ids.extend(v)\n if len(gene_ids) == 1:\n temp_id = entrez_eq.get(gene_ids[0])\n if temp_id is None:\n uid = uuid.uuid4()\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n else:\n uid = entrez_eq.get(gene_ids[0])\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n elif len(gene_ids) == 0:\n # are there hgnc, mgi or rgd refs?\n if len(alt_ids) == 0:\n uid = uuid.uuid4()\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n sp_list.append(name)\n elif len(alt_ids) == 1:\n a_id = alt_ids[0]\n if 'HGNC' in a_id:\n hgnc_key = namespaces.hgnc_map.get(a_id)\n uid = hgnc_eq.get(hgnc_key)\n # SwissProt may be referring to a since-removed gene.\n if uid is None:\n uid = uuid.uuid4()\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n else:\n sp_eq[name] = uid\n elif 'MGI' in a_id:\n mgi_key = namespaces.mgi_map.get(a_id)\n uid = mgi_eq.get(mgi_key)\n # SwissProt may be referring to a since-removed gene.\n if uid is None:\n uid = uuid.uuid4()\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n else:\n sp_eq[name] = uid\n else:\n rgd_key = namespaces.rgd_map.get(a_id)\n uid = rgd_eq.get(rgd_key)\n # SwissProt may be referring to a since-removed gene.\n if uid is None:\n uid = uuid.uuid4()\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n else:\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n # > 1 alt_id then generate a new uuid\n else:\n uid = uuid.uuid4()\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n # > 1 entrez id than generate a new uuid\n else:\n uid = uuid.uuid4()\n fp.write(delim.join((name, str(uid)))+'\\n')\n sp_eq[name] = uid\n # finally, generate .beleq for accession data also\n build_acc_data(accessions, name)\n swiss_accessions_eq()\n\n elif str(d) == 'affy':\n with open('affy-probeset-ids.beleq', 'w') as fp:\n for probe_id, gene_id in d.get_eq_values():\n\n if gene_id is not None and '---' not in gene_id:\n\n # need the space before and after '///' because that is how it is parsed.\n entrez_ids = gene_id.split(' /// ')\n\n # for 1 entrez mapping, use the entez uuid\n if len(entrez_ids) == 1:\n status = entrez_eq.get(entrez_ids[0])\n if status is None:\n uid = uuid.uuid4()\n fp.write(delim.join((probe_id, str(uid)))+'\\n')\n affy_eq[probe_id] = uid\n else:\n uid = status\n fp.write(delim.join((probe_id, str(uid)))+'\\n')\n affy_eq[probe_id] = uid\n # we have > 1 entrez mapping, resolve to one.\n else:\n adjacent_list = []\n for entrez_gene in entrez_ids:\n refstatus = refseq.get(entrez_gene)\n adjacent_list.append(ref_status.get(refstatus))\n\n # zipping yields a list of tuples like [('5307',0), ('104',2), ('3043',None)]\n # i.e. [(entrez_id, refseq_status)]\n list_of_tuples = list(zip(entrez_ids, adjacent_list))\n\n # get rid of all 'None' tuples (No entrez mapping)\n list_of_tuples = [tup for tup in list_of_tuples if tup[1] is not None]\n\n # no mapping, generate new uuid\n if len(list_of_tuples) == 0:\n uid = uuid.uuid4()\n fp.write(delim.join((probe_id, str(uid)))+'\\n')\n affy_eq[probe_id] = uid\n\n # multiple entrez, resolve by refseq status\n else:\n # min tuple is highest refseq status (0 the best)\n min_tuple = min(list_of_tuples, key=lambda x: x[1])\n min_refseq = min_tuple[1]\n lowest_tuples = []\n\n for item in list_of_tuples:\n if item[1] == min_refseq:\n lowest_tuples.append(item)\n\n # if mutiple genes with same refseq, resolve by lowest gene #\n target_tuple = min(lowest_tuples)\n uid = entrez_eq.get(target_tuple[0])\n fp.write(delim.join((probe_id, str(uid)))+'\\n')\n affy_eq[probe_id] = uid\n # no entrez mapping, create a new uuid\n else:\n uid = uuid.uuid4()\n fp.write(delim.join((probe_id, str(uid)))+'\\n')\n affy_eq[probe_id] = uid\n\n # equiv for alt ids and names relies on the equivalence for\n # primary ids being completely generated.\n elif str(d) == 'chebi':\n with open('chebi-ids.beleq', 'w') as fp, open('chebi-names_eq.beleq', 'w') as f:\n # like Entrez, new uuid for primary ids only the FIRST time.\n for primary_id in d.get_primary_ids():\n uid = uuid.uuid4()\n fp.write(delim.join((primary_id, str(uid)))+'\\n')\n chebi_id_eq[primary_id] = uid\n for alt_id in d.get_alt_ids():\n if alt_id not in chebi_id_eq:\n # get its primary equivalent and use its uuid\n primary = d.alt_to_primary(alt_id)\n uid = chebi_id_eq[primary]\n fp.write(delim.join((alt_id, str(uid)))+'\\n')\n chebi_id_eq[alt_id] = uid\n for name in d.get_names():\n primary = d.name_to_primary(name)\n uid = chebi_id_eq.get(primary)\n f.write(delim.join((name, str(uid)))+'\\n')\n chebi_name_eq[name] = uid\n\n elif str(d) == 'pubchem_equiv':\n with open('pubchem_eq.beleq', 'w') as fp:\n for sid, source, cid in d.get_eq_values():\n if 'ChEBI' in source and cid is not None: # <-- verify that NO PubChem CID == 'None'\n # use the CHEBI uuid\n chebi_equiv = source.split(':')[1]\n uid = chebi_id_eq.get(chebi_equiv)\n fp.write(delim.join((sid, str(uid)))+'\\n')\n pub_eq_dict[sid] = uid\n else:\n # generate a new uuid\n uid = uuid.uuid4()\n fp.write(delim.join((sid, str(uid)))+'\\n')\n\n elif str(d) == 'gobp':\n with open('go-biological-processes-names.beleq', 'w') as gobp, \\\n open('go-biological-processes-ids.beleq', 'w') as gobp_id:\n for vals in d.get_eq_values():\n termid, termname = vals\n uid = uuid.uuid4()\n gobp_id.write(delim.join((termid, str(uid)))+'\\n')\n gobp.write(delim.join((termname, str(uid)))+'\\n')\n gobp_eq_dict[termname] = uid\n\n # GO is the baseline for processes, so new uuids the first time.\n elif str(d) == 'gocc':\n\n with open('go-cellular-component-terms.beleq', 'w') as gocc, \\\n open('go-cellular-component-ids.beleq', 'w') as gocc_id:\n\n for vals in d.get_eq_values():\n termid, termname = vals\n uid = uuid.uuid4()\n gocc_id.write(delim.join((termid, str(uid)))+'\\n')\n gocc.write(delim.join((termname, str(uid)))+'\\n')\n gocc_eq_dict[termid] = uid\n\n elif str(d) == 'do':\n # assign DO a new uuid and use as the primary for diseases\n with open('disease-ontology-names.beleq', 'w') as dn, \\\n open('disease-ontology-ids.beleq', 'w') as di:\n for vals in d.get_eq_values():\n name, id = vals\n uid = uuid.uuid4()\n dn.write(delim.join((name, str(uid)))+'\\n')\n di.write(delim.join((id, str(uid)))+'\\n')\n do_eq_dict[name] = uid\n\n elif str(d) == 'sdis_to_do':\n # try to resolve sdis terms to DO. If there is not one,\n # assign a new uuid.\n count = 0\n sdis = parsed.load_data('sdis')\n with open('selventa-legacy-diseases.beleq', 'w') as dof:\n for vals in sdis.get_eq_values():\n uid = None\n sdis_term = vals\n if d.has_equivalence(sdis_term):\n count = count + 1\n do_term = d.get_equivalence(sdis_term)\n if do_term in do_eq_dict:\n uid = do_eq_dict[do_term]\n else:\n uid = do_eq_dict[do_term.lower()]\n else:\n uid = uuid.uuid4()\n dof.write(delim.join((sdis_term, str(uid)))+'\\n')\n if verbose:\n print('Able to resolve ' +str(count)+ ' legacy disease terms to DO.')\n\n elif str(d) == 'schem_to_chebi':\n # try to resolve schem terms to CHEBI. If there is not one,\n # assign a new uuid.\n count = 0\n schem = parsed.load_data('schem')\n with open('selventa-legacy-chemical-names.beleq', 'w') as schemf:\n for vals in schem.get_eq_values():\n uid = None\n schem_term = vals\n if d.has_equivalence(schem_term):\n count = count + 1\n chebi_term = d.get_equivalence(schem_term)\n if chebi_term in chebi_name_eq:\n uid = chebi_name_eq[chebi_term]\n elif chebi_term.lower() in chebi_name_eq:\n uid = chebi_name_eq[chebi_term.lower()]\n else:\n uid = uuid.uuid4()\n schemf.write(delim.join((schem_term, str(uid)))+'\\n')\n if verbose:\n print('Able to resolve ' +str(count)+ ' legacy chemical terms to CHEBI.')\n\n elif str(d) == 'mesh':\n\n with open('mesh-cellular-locations.beleq', 'w') as mesha, \\\n open('mesh-diseases.beleq', 'w') as meshc, \\\n open('mesh-biological-processes.beleq', 'w') as meshg:\n do_data = parsed.load_data('do')\n for vals in d.get_eq_values():\n ui, mh, mns, synonyms = vals\n if any('A11.284' in branch for branch in mns):\n # get GO equiv if there is one\n uid = None\n go_id = mg_eq.get(mh)\n # meshcs_to_gocc contains OBSOLETE GO terms at the moment.\n # It is possible this lookup will return None, in that\n # case generate a new uuid.\n if go_id is not None:\n uid = gocc_eq_dict.get(go_id)\n # try to find out why lookups fail - maybe OBSOLETE?\n if uid is None:\n if verbose:\n print('Lookup failed for: '+str(go_id))\n uid = uuid.uuid4()\n else:\n uid = uuid.uuid4()\n mesha.write(delim.join((mh, str(uid)))+'\\n')\n elif any('C' in branch for branch in mns):\n # does UI exist as a Xref in DO?\n xref = do_data.get_xrefs('MSH:'+ui)\n if xref:\n uid = do_eq_dict[xref]\n else:\n uid = uuid.uuid4()\n meshc.write(delim.join((mh, str(uid)))+'\\n')\n elif any('G' in branch for branch in mns):\n # synonyms for MeSH\n uid = None\n for syn in synonyms:\n # root 'G' branch in GOBP\n for name in gobp_eq_dict:\n if syn.lower() == name.lower():\n uid = gobp_eq_dict.get(name)\n if uid is None:\n uid = uuid.uuid4()\n meshg.write(delim.join((mh, str(uid)))+'\\n')\n\nacc_helper_dict = defaultdict(list)\nsp_acc_eq = {}\ndef build_acc_data(accessions, gene_name):\n with open('swissprot-accession-numbers.beleq', 'w') as fp:\n # turn list into a queue\n q = deque(accessions)\n # primary accession name is first one on the queue\n primary = q.popleft()\n for item in q:\n acc_helper_dict[item].append(gene_name)\n # map every primary one-to-one with SP entry uuid\n uid = sp_eq.get(gene_name)\n fp.write(delim.join((primary, str(uid)))+'\\n')\n sp_acc_eq[primary] = uid\n\n# try to factor this out and merge into build_acc_data\ndef swiss_accessions_eq():\n with open('swissprot-accession-numbers.beleq', 'a') as fp:\n for sec_acc_id, v in acc_helper_dict.items():\n # only maps to one primary accession, same uuid\n if len(v) == 1:\n uid = sp_eq.get(v[0])\n fp.write(delim.join((sec_acc_id, str(uid)))+'\\n')\n sp_acc_eq[sec_acc_id] = uid\n # maps to > 1 primary accession, give it a new uuid.\n else:\n uid = uuid.uuid4()\n fp.write(delim.join((sec_acc_id, str(uid)))+'\\n')\n sp_acc_eq[sec_acc_id] = uid\n\n# fills a dict mapping (entrez_gene -> refseq status)\ndef build_refseq(d):\n for entrez_gene, status, taxid in d.get_eq_values():\n target_pool = ['9606', '10116', '10090']\n valid_status = ['REVIEWED', 'VALIDATED', 'PROVISIONAL', 'PREDICTED',\n 'MODEL', 'INFERRED']\n\n if taxid in target_pool and status in valid_status:\n refseq[entrez_gene] = status\n\ndef to_entrez(gene_id):\n converted_id = entrez_converter.get(gene_id)\n return converted_id\n","sub_path":"equiv.py","file_name":"equiv.py","file_ext":"py","file_size_in_byte":21292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"497661542","text":"#!/usr/bin/env python\n\n\"\"\"\n\n All Emoncms code is released under the GNU Affero General Public License.\n See COPYRIGHT.txt and LICENSE.txt.\n\n ---------------------------------------------------------------------\n Emoncms - open source energy visualisation\n Part of the OpenEnergyMonitor project:\n http://openenergymonitor.org\n\n\"\"\"\n\n\"\"\"\nTODO : \n- I18n : translate strings (gettext ?)\n- add new parameters instead of hardcoding (log level, sending interval...)\n- allow any number of servers (instead of hardcoding 1 local and 1 remote) ?\n- save samples for later when connection is down\n\"\"\"\n\nimport serial\nimport MySQLdb, MySQLdb.cursors\nimport urllib2, httplib\nimport time, datetime\nimport logging, logging.handlers\nimport re\nimport signal\nimport os\nimport csv\nimport argparse\nimport urlparse\n\n\"\"\"class ServerDataBuffer\n\nStores server parameters and buffers the data between two HTTP requests\n\n\"\"\"\nclass ServerDataBuffer():\n\n def __init__(self, protocol, domain, path, apikey, period, active, logger=None):\n \"\"\"Create a server data buffer initialized with server settings.\n \n domain (string): domain name (eg: 'domain.tld')\n path (string): emoncms path with leading slash (eg: '/emoncms')\n apikey (string): API key with write access\n period (int): sending interval in seconds\n active (bool): whether the data buffer is active\n logger (string): the logger's name (default None)\n \n \"\"\"\n self._protocol = protocol\n self._domain = domain\n self._path = path\n self._apikey = apikey\n self._period = period\n self._data_buffer = []\n self._last_send = time.time()\n self._active = active\n self._logger = logging.getLogger(logger)\n\n def update_settings(self, protocol=None, domain=None, path=None, apikey=None, period=None, active=None):\n \"\"\"Update server settings.\"\"\"\n if protocol is not None:\n self._protocol = protocol\n if domain is not None:\n self._domain = domain\n if path is not None:\n self._path = path\n if apikey is not None:\n self._apikey = apikey\n if period is not None:\n self._period = period\n if active is not None:\n self._active = active\n\n def add_data(self, data):\n \"\"\"Append timestamped dataset to buffer.\n\n data (list): node and values (eg: '[node,val1,val2,...]')\n\n \"\"\"\n \n if not self._active:\n return\n \n self._logger.debug(\"Server \" + self._domain + self._path + \" -> add data: \" + str(data))\n \n # Insert timestamp before data\n dataset = list(data) # Make a distinct copy: we don't want to modify data\n dataset.insert(0,time.time())\n # Append new data set [timestamp, node, val1, val2, val3,...] to _data_buffer\n self._data_buffer.append(dataset)\n\n def send_data(self):\n \"\"\"Send data to server.\"\"\"\n \n if not self._active:\n return\n\n # Prepare data string with the values in data buffer\n now = time.time()\n data_string = '['\n for data in self._data_buffer:\n data_string += '['\n data_string += str(int(round(data[0]-now)))\n for sample in data[1:]:\n data_string += ','\n data_string += str(sample)\n data_string += '],'\n data_string = data_string[0:-1]+']' # Remove trailing comma and close bracket \n self._data_buffer = []\n self._logger.debug(\"Data string: \" + data_string)\n \n # Prepare URL string of the form\n # 'http://domain.tld/emoncms/input/bulk.json?apikey=12345&data=[[-10,10,1806],[-5,10,1806],[0,10,1806]]'\n url_string = self._protocol+self._domain+self._path+\"/input/bulk.json?apikey=\"+self._apikey+\"&data=\"+data_string\n self._logger.debug(\"URL string: \" + url_string)\n\n # Send data to server\n self._logger.info(\"Sending to \" + self._domain + self._path)\n try:\n result = urllib2.urlopen(url_string)\n except urllib2.HTTPError as e:\n self._logger.warning(\"Couldn't send to server, HTTPError: \" + str(e.code))\n except urllib2.URLError as e:\n self._logger.warning(\"Couldn't send to server, URLError: \" + str(e.reason))\n except httplib.HTTPException:\n self._logger.warning(\"Couldn't send to server, HTTPException\")\n except Exception:\n import traceback\n self._logger.warning(\"Couldn't send to server, Exception: \" + traceback.format_exc())\n else:\n if (result.readline() == 'ok'):\n self._logger.info(\"Send ok\")\n else:\n self._logger.warning(\"Send failure\")\n \n # Update _last_send\n self._last_send = time.time()\n\n def check_time(self):\n \"\"\"Check if it is time to send data to server.\n \n Return True if sending interval has passed since last time\n\n \"\"\"\n now = time.time()\n if (now - self._last_send > self._period):\n return True\n \n def has_data(self):\n \"\"\"Check if buffer has data\n \n Return True if data buffer is not empty.\n \n \"\"\"\n return (self._data_buffer != [])\n\n\n\"\"\"class RFM2PiGateway\n\nMonitors the serial port for data from RFM2Pi and sends data to local or remote \nemoncms servers through ServerDataBuffer instances.\n\n\"\"\"\nclass RFM2PiGateway():\n \n def __init__(self, logpath=None, local_url='http://localhost/emoncms'):\n \"\"\"Setup an RFM2Pi gateway.\n \n logpath (path): Path to the file the log should be written into.\n If Null, log to STDERR.\n local_domain (string): domain name of local emonCMS server\n local_path (string): path to emonCMS on local server\n\n \"\"\"\n\n # Initialize exit request flag\n self._exit = False\n\n # Initialize logging\n self.log = logging.getLogger(__name__)\n if (logpath is None):\n # If no path was specified, everything goes to sys.stderr\n loghandler = logging.StreamHandler()\n else:\n # Otherwise, rotating logging over two 5 MB files\n loghandler = logging.handlers.RotatingFileHandler(logpath,\n 'a', 5000 * 1024, 1)\n loghandler.setFormatter(logging.Formatter(\n '%(asctime)s %(levelname)s %(message)s'))\n self.log.addHandler(loghandler)\n self.log.setLevel(logging.DEBUG)\n \n # Initialize local server settings\n url = urlparse.urlparse(local_url)\n self._local_protocol = url.scheme + '://'\n self._local_domain = url.netloc\n self._local_path = url.path\n \n # Open serial port\n self._ser = self._open_serial_port()\n if self._ser is None:\n self.log.critical(\"COM port opening failed. Exiting...\")\n self.close()\n raise Exception('COM port opening failed.')\n \n # Initialize serial RX buffer\n self._serial_rx_buf = ''\n \n # Initialize target emoncms server buffer set\n self._server_buffers = {}\n \n # Declare timers\n self._status_update_timestamp = 0\n self._time_update_timestamp = 0\n\n # Get emoncms server buffers and RFM2Pi settings\n # (force_RFM2Pi_update forces RFM2Pi parameters to be sent)\n self._settings = None\n self._update_settings()\n \n # If settings can't be obtained, exit\n while (self._settings is None):\n self.log.warning(\"Couldn't get settings. Retrying in 10 sec...\")\n time.sleep(10)\n self._update_settings()\n \n def run(self):\n \"\"\"Launch the gateway.\n \n Monitor the COM port and process data.\n Check settings on a regular basis.\n\n \"\"\"\n\n # Set signal handler to catch SIGINT and shutdown gracefully\n signal.signal(signal.SIGINT, self._sigint_handler)\n \n # Until asked to stop\n while not self._exit:\n \n # Update settings and status every second\n now = time.time()\n if (now - self._status_update_timestamp > 1):\n # Update \"running\" status to inform emoncms the script is running\n self._raspberrypi_running()\n # Update settings\n self._update_settings()\n # \"Thanks for the status update. You've made it crystal clear.\"\n self._status_update_timestamp = now\n \n # Broadcast time to synchronize emonGLCD\n if (int(self._settings['sendtimeinterval'])):\n if (now - self._time_update_timestamp > \n int(self._settings['sendtimeinterval'])):\n self._send_time()\n self._time_update_timestamp = now\n\n # Read serial RX\n self._serial_rx_buf = self._serial_rx_buf + self._ser.readline()\n \n # If full line was read, process\n if ((self._serial_rx_buf != '') and \n (self._serial_rx_buf[len(self._serial_rx_buf)-1] == '\\n')):\n \n # Remove CR,LF\n self._serial_rx_buf = re.sub('\\\\r\\\\n', '', self._serial_rx_buf)\n \n # Log data\n self.log.info(\"Serial RX: \" + self._serial_rx_buf)\n \n # Get an array out of the space separated string\n received = self._serial_rx_buf.strip().split(' ')\n \n # Empty serial_rx_buf\n self._serial_rx_buf = ''\n \n # If information message, discard\n if ((received[0] == '>') or (received[0] == '->')):\n continue\n\n # Else,frame should be of the form \n # [node val1_lsb val1_msb val2_lsb val2_msb ...]\n # with number of elements odd and at least 3\n elif ((not (len(received) & 1)) or (len(received) < 3)):\n self.log.warning(\"Misformed RX frame: \" + str(received))\n \n # Else, process frame\n else:\n try:\n received = [int(val) for val in received]\n except Exception:\n self.log.warning(\"Misformed RX frame: \" + str(received))\n else:\n # Get node ID\n node = received[0]\n \n # Recombine transmitted chars into signed int\n values = []\n for i in range(1,len(received),2):\n value = received[i] + 256 * received[i+1]\n if value > 32768:\n value -= 65536\n values.append(value)\n \n self.log.debug(\"Node: \" + str(node))\n self.log.debug(\"Values: \" + str(values))\n \n # Add data to send buffers\n values.insert(0,node)\n for server_buf in self._server_buffers.itervalues():\n server_buf.add_data(values)\n \n # Send data if time has come\n for server_buf in self._server_buffers.itervalues():\n if server_buf.check_time():\n if server_buf.has_data():\n server_buf.send_data()\n \n # Sleep until next iteration\n time.sleep(0.2);\n \n def close(self):\n \"\"\"Close gateway. Do some cleanup before leaving.\"\"\"\n \n # Close serial port\n if self._ser is not None:\n self.log.debug(\"Closing serial port.\")\n self._ser.close()\n\n self.log.info(\"Exiting...\")\n logging.shutdown()\n\n def _sigint_handler(self, signal, frame):\n \"\"\"Catch SIGINT (Ctrl+C).\"\"\"\n \n self.log.debug(\"SIGINT received.\")\n # gateway should exit at the end of current iteration.\n self._exit = True\n\n def get_settings(self):\n \"\"\"Get settings\n \n Returns a dictionnary\n\n \"\"\"\n try:\n result = urllib2.urlopen(self._local_protocol +\n self._local_domain +\n self._local_path +\n \"/raspberrypi/get.json\")\n result = result.readline()\n # result is of the form\n # {\"userid\":\"1\",\"sgroup\":\"210\",...,\"remoteprotocol\":\"http:\\\\/\\\\/\"}\n result = result[1:-1].split(',')\n # result is now of the form\n # ['\"userid\":\"1\"',..., '\"remoteprotocol\":\"http:\\\\/\\\\/\"']\n settings = {}\n # For each setting, separate key and value\n for s in result:\n # We can't just use split(':') as there can be \":\" inside a value \n # (eg: \"http://\")\n s = csv.reader([s], delimiter=':').next() \n settings[s[0]] = s[1].replace(\"\\\\\",\"\")\n return settings\n\n except Exception:\n import traceback\n self.log.warning(\"Couldn't get settings, Exception: \" + traceback.format_exc())\n return\n\n def _set_rfm2pi_setting(self, setting, value):\n \"\"\"Send a configuration parameter to the RFM2Pi through COM port.\n \n setting (string): setting to be sent, can be one of the following:\n baseid, frequency, sgroup\n value (string): value for this setting\n \n \"\"\"\n \n self.log.info(\"Setting RFM2Pi | %s: %s\" % (setting, value))\n if setting == 'baseid':\n self._ser.write(value+'i')\n elif setting == 'frequency':\n self._ser.write(value+'b')\n elif setting == 'sgroup':\n self._ser.write(value+'g')\n time.sleep(1);\n \n def _update_settings(self):\n \"\"\"Check settings and update if needed.\"\"\"\n \n # Get settings\n s_new = self.get_settings()\n\n # If s_new is None, no answer to settings request\n if s_new is None:\n return\n\n # If self._settings is None, this is the first call\n # Send all RFM2Pi settings\n if self._settings is None:\n for param in ['baseid', 'frequency', 'sgroup']:\n self._set_rfm2pi_setting(param,str(s_new[param]))\n\n # General case, send RFM2Pi settings only if they changed\n else:\n for param in ['baseid', 'frequency', 'sgroup']:\n if ((s_new[param] != self._settings[param])):\n self._set_rfm2pi_setting(param,str(s_new[param]))\n\n # Server settings\n if 'local' not in self._server_buffers:\n self._server_buffers['local'] = ServerDataBuffer(\n protocol = self._local_protocol,\n domain = self._local_domain,\n path = self._local_path, \n apikey = s_new['apikey'], \n period = 0, \n active = True,\n logger = __name__)\n else:\n self._server_buffers['local'].update_settings(\n apikey = s_new['apikey'])\n \n if 'remote' not in self._server_buffers:\n self._server_buffers['remote'] = ServerDataBuffer(\n protocol = s_new['remoteprotocol'], \n domain = s_new['remotedomain'], \n path = s_new['remotepath'],\n apikey = s_new['remoteapikey'],\n period = 30,\n active = bool(s_new['remotesend']),\n logger = __name__)\n else: \n self._server_buffers['remote'].update_settings(\n protocol = s_new['remoteprotocol'], \n domain = s_new['remotedomain'],\n path = s_new['remotepath'],\n apikey = s_new['remoteapikey'],\n active = bool(int(s_new['remotesend'])))\n \n self._settings = s_new\n \n def _open_serial_port(self):\n \"\"\"Open serial port.\"\"\"\n\n self.log.debug(\"Opening serial port: /dev/ttyAMA0\")\n \n try:\n ser = serial.Serial('/dev/ttyAMA0', 9600, timeout = 0)\n except serial.SerialException as e:\n self.log.error(e)\n except Exception:\n import traceback\n self.log.error(\n \"Couldn't open serial port, Exception: \" \n + traceback.format_exc())\n else:\n return ser\n\n def _raspberrypi_running(self):\n \"\"\"Update \"script running\" status.\"\"\"\n \n try:\n result = urllib2.urlopen(self._local_protocol +\n self._local_domain +\n self._local_path +\n \"/raspberrypi/setrunning.json\")\n except Exception:\n import traceback\n self.log.warning(\"Couldn't update \\\"running\\\" status, Exception: \" + traceback.format_exc())\n \n def _send_time(self):\n \"\"\"Send time over radio link to synchronize emonGLCD.\"\"\"\n\n now = datetime.datetime.now()\n\n self.log.debug(\"Sending time for emonGLCD: %d:%d\" % (now.hour, now.minute))\n\n self._ser.write(\"%02d,00,%02d,00,s\" % (now.hour, now.minute))\n\n\nif __name__ == \"__main__\":\n\n # Command line arguments parser\n parser = argparse.ArgumentParser(description='RFM2Pi Gateway')\n parser.add_argument('--logfile', action='store', type=argparse.FileType('a'),\n help='path to optional log file (default: log to Standard error stream STDERR)')\n parser.add_argument('--local-url', action='store', default='http://localhost/emoncms',\n help='custom local URL (default: \\'http://localhost/emoncms\\')')\n parser.add_argument('--show-settings', action='store_true',\n help='show RFM2Pi settings and exit (for debugging purposes)')\n args = parser.parse_args()\n \n # If logfile is supplied, argparse opens the file in append mode, \n # this ensures it is writable\n # Close the file for now and get its path\n if args.logfile is None:\n logfile = None\n else:\n args.logfile.close()\n logfile = args.logfile.name\n\n # Create, run, and close RFM2Pi Gateway instance\n try:\n gateway = RFM2PiGateway(logfile, args.local_url)\n except Exception as e:\n print(e)\n else: \n # If in \"Show settings\" mode, print RFM2Pi settings and exit\n if args.show_settings:\n print(gateway.get_settings())\n # Else, run normally\n else:\n gateway.run()\n # When done, close gateway\n gateway.close()\n \n","sub_path":"rfm2pigateway.py","file_name":"rfm2pigateway.py","file_ext":"py","file_size_in_byte":18999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"202711373","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 ('contenttypes', '0002_remove_content_type_name'),\n ('auth', '0006_require_contenttypes_0002'),\n ('organizations', '0001_initial'),\n ('guardian', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='OrganizationObjectPermission',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('object_pk', models.CharField(max_length=255, verbose_name='object ID')),\n ('permission_expiry', models.DateTimeField(null=True, blank=True)),\n ('content_type', models.ForeignKey(to='contenttypes.ContentType', on_delete=models.CASCADE)),\n ('organization', models.ForeignKey(to='organizations.Organization', on_delete=models.CASCADE)),\n ('permission', models.ForeignKey(to='auth.Permission', on_delete=models.CASCADE)),\n ],\n ),\n migrations.AddField(\n model_name='groupobjectpermission',\n name='permission_expiry',\n field=models.DateTimeField(null=True, blank=True),\n ),\n migrations.AddField(\n model_name='userobjectpermission',\n name='permission_expiry',\n field=models.DateTimeField(null=True, blank=True),\n ),\n migrations.AlterUniqueTogether(\n name='organizationobjectpermission',\n unique_together=set([('organization', 'permission', 'object_pk')]),\n ),\n ]\n\n","sub_path":"guardian/migrations/0002_auto_20150922_1355.py","file_name":"0002_auto_20150922_1355.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"368228311","text":"from store.models import *\n\n\n\ndef get_all_possible_filters(item_category):\n\t\"\"\"Returns a list of 'FilterOption' PKs for each 'FilterCategory' set within the\n\tcurrent 'ItemCategory'\n\t\"\"\"\n\n\tpk_lists = []\n\n\tfor filter_category in FilterCategory.objects.filter(item_category=item_category):\n\t\tfilter_option_set = filter_category.filteroption_set.all()\n\t\ttemp_list = list(filter_option_set.values_list('pk', flat=True))\n\n\t\tpk_lists.append(temp_list)\n\n\treturn pk_lists\n\n\ndef filter_item_list(item_category, item_list, chosen_filters):\t\n\t\"\"\"First for loop: 'and' filter.\n\t Second for loop: 'or' filter.\n\t\"\"\"\n\t\n\tfor filter_option_set in get_all_possible_filters(item_category):\n\t\ttemp_list = []\n\n\t\tfor pk in chosen_filters:\n\t\t\tif pk in filter_option_set:\n\t\t\t\ttemp_list.append(pk)\n\n\t\tif len(temp_list) > 0:\n\t\t\titem_list = item_list.filter(filter_option__pk__in=temp_list).distinct()\n\n\treturn item_list","sub_path":"apps/store/utils/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"500721238","text":"# -*- coding: utf-8 -*-\nimport sqlite3 as sq\nimport pymongo\n\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\n\nclass OlxSQLitePipeline(object):\n def process_item(self, item, spider):\n self.c.execute(\n 'INSERT INTO cars('\n 'title, year, type, km, fuel, wheel, doors, engine,'\n 'gear, color, plate, price'\n ') VALUES('\n ':title, :year, :type, :km, :fuel,'\n ':wheel, :doors, :engine, :gear, :color,'\n ':plate, :price'\n ')', item\n )\n self.conn.commit()\n return item\n\n def create_table(self):\n res = self.c.execute (\n 'SELECT NAME FROM sqlite_master WHERE TYPE = \"table\" AND NAME = \"cars\"'\n )\n try:\n value = next(res)\n except StopIteration as ex:\n self.c.execute(\n 'CREATE TABLE cars ('\n 'id INTEGER PRIMARY KEY,'\n 'title TEXT,'\n 'year TEXT,'\n 'type TEXT,'\n 'km TEXT,'\n 'fuel TEXT,'\n 'wheel TEXT,'\n 'doors TEXT,'\n 'engine TEXT,'\n 'gear TEXT,'\n 'color TEXT,'\n 'plate TEXT,'\n 'price TEXT'\n ')'\n )\n\n\n\n def open_spider(self, spider):\n self.conn = sq.connect('db.sqlite3')\n self.c = self.conn.cursor()\n self.create_table()\n\n def close_spider(self, spider):\n self.conn.close()\n\n\n\nclass OlxMongoPipeline(object):\n\n collection_name = 'cars'\n\n def __init__(self, mongo_uri, mongo_db):\n self.mongo_uri = mongo_uri\n self.mongo_db = mongo_db\n\n @classmethod\n def from_crawler(cls, crawler):\n return cls(\n mongo_uri=crawler.settings.get('MONGO_URI'),\n mongo_db=crawler.settings.get('MONGO_DATABASE', 'items')\n )\n\n def open_spider(self, spider):\n self.client = pymongo.MongoClient(self.mongo_uri)\n self.db = self.client[self.mongo_db]\n\n def close_spider(self, spider):\n self.client.close()\n\n def process_item(self, item, spider):\n self.db[self.collection_name].insert_one(dict(item))\n return item","sub_path":"olx/olx/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"627858446","text":"\"\"\"\n This file is part of the cream web paste libs.\n See ../paster.py for license details.\n\"\"\"\n\nfrom cream.util import cached_property\n\nfrom basic_service import *\n\nclass DPaste(PasteService):\n __name__ = \"DPaste\"\n fields = ('content', 'language', 'name', 'title', 'hold')\n host = 'http://dpaste.com'\n\n @cached_property\n def languages(self):\n languages_regexp = \"\"\n content = '\\n'.join([line for line in \\\n urllib.urlopen(self.host).read().split(\"\\n\") \\\n if line.startswith(\" 0)\n self.Est = Est\n self.est_params = est_params\n if type(self.est_params) == dict:\n self.est_params = [self.est_params] * self.steps\n\n self.step_models = {(i): self.Est(**p) for i, p\n in enumerate(self.est_params)}\n self.step_cutoffs = {(i): min((i + 1) * self.d, self.n)\n for i, p in enumerate(self.step_models)}\n\n def assert_input(self, features, target=None):\n assert type(features.index.get_level_values(-1)) == DatetimeIndex\n assert int(features.isnull().sum().sum()) == 0\n if target is not None:\n assert type(target.index.get_level_values(-1)) == DatetimeIndex\n assert features.shape[0] == target.shape[0]\n assert int(target.isnull().sum()) == 0\n\n def build_data(self, features, target=None):\n self.build_features(features)\n if target is not None:\n self.build_target(target)\n\n self.step_preds = DataFrame(index=features.index,\n columns=self.step_targets.columns)\n\n def build_features(self, features):\n self.all_fits = DataFrame({'step_{}'.format(i): nan for i in range(1, self.n + 1)},\n index=features.index,\n columns=RangeIndex(self.steps).rename('step'))\n\n def build_target(self, target):\n if hasattr(target.index, 'levels'):\n step_targets = target.groupby(level=range(target.index.nlevels - 1))\\\n .apply(lambda g: lag_multiseries(g.reset_index(level=0, drop=True),\n lags=range(-self.n + 1, 1), freq='H'))\n else:\n step_targets = lag_multiseries(target, lags=range(-self.n + 1, 1), freq='H')\n\n step_targets = step_targets[sorted(step_targets.columns, reverse=True)]\n if self.d > 1:\n step_targets = self.aggregate_step_targets(step_targets)\n\n self.step_targets = step_targets\n self.step_targets.columns = RangeIndex(self.steps)\n\n def aggregate_step_targets(self, step_targets):\n cutoffs = deepcopy(self.step_cutoffs)\n cutoffs[-1] = 0\n aggregated_targets = []\n for step in range(self.steps):\n int_col_slice = slice(cutoffs[step - 1], cutoffs[step])\n col_agg = step_targets.iloc[:, int_col_slice].mean(axis=1).rename(step)\n aggregated_targets.append(col_agg)\n\n return concat(aggregated_targets, axis=1)\n\n def expand_step_preds(self, step_preds):\n expanded_preds = []\n cutoffs = deepcopy(self.step_cutoffs)\n cutoffs[-1] = 0\n for step in range(self.steps):\n expanded_count = cutoffs[step] - cutoffs[step - 1]\n col = step_preds.iloc[:, step]\n expanded_preds.extend([col] * expanded_count)\n\n expanded_preds = concat(expanded_preds, axis=1)\n return expanded_preds\n\n def add_predicted_features(self, features, step, test=False):\n if test:\n pred_features = self.step_preds.loc[features.index, :step - 1]\n else:\n pred_features = self.all_fits.loc[features.index, :step - 1]\n\n return concat([features, pred_features], axis=1)\n\n @measure_time\n def fit(self, features, target):\n self.assert_input(features, target)\n self.build_data(features, target)\n for step in range(self.steps):\n self.fit_step(features, target, step)\n\n def fit_step(self, features, target, step):\n step_features = self.add_predicted_features(features, step).dropna()\n step_target = self.step_targets.loc[step_features.index, step].dropna()\n step_features = step_features.loc[step_target.index.tolist()]\n self.step_models[step]\\\n .fit(*self.preprocess_data(step_features, step_target))\n step_fit = self.step_models[step]\\\n .predict(self.preprocess_data(step_features))\n self.all_fits.loc[step_features.index.tolist(), step] = step_fit\n # fill unpredicted values with 0 (FISHY)\n self.all_fits.loc[:, step].fillna(0, inplace=True)\n\n @measure_time\n def predict(self, features):\n self.assert_input(features)\n self.build_data(features)\n for step in range(self.steps):\n self.predict_step(features, step)\n\n pred = self.step_preds\n if self.d > 1:\n pred = self.expand_step_preds(pred)\n\n pred.columns = RangeIndex(1, self.n + 1)\n return pred\n\n def predict_step(self, features, step):\n step_features = self.add_predicted_features(features, step, test=True)\n step_features = step_features.loc[self.step_preds.index.tolist()]\n self.step_preds.loc[self.step_preds.index, step] =\\\n self.step_models[step].predict(self.preprocess_data(step_features))\n\n # fill unpredicted values with 0 (FISHY)\n self.step_preds.loc[:, step].fillna(0, inplace=True)\n\n @measure_time\n def validate(self, features, target, metric_func=SMAPE, predictions=True):\n self.assert_input(features, target)\n pred = self.predict(features)\n metric = Series(index=RangeIndex(self.n))\n for i in range(self.n):\n metric.iloc[i] = SMAPE(target.loc[pred.index],\n pred.iloc[:, i])\n\n if predictions:\n return metric, pred\n else:\n return metric\n\n\n def preprocess_data(self, features, target=None):\n \"Renames all columns to strings e.g. for `XGBRegressor`\"\n features.columns = [str(c) for c in features.columns]\n if target is not None:\n target = target.to_frame()\n target.columns = [str(c) for c in target.columns]\n return features.values, target.values\n else:\n return features.values\n\n\nclass StepTimeSeriesModelTestCase(TestCase):\n data_datetime_idx = date_range('2018-03-01', '2018-04-01', freq='H')\n data_idx = MultiIndex.from_product([['A', 'B', 'C'],\n data_datetime_idx])\n features = DataFrame({'feature_0': [e * random.normal(1) for e in range(data_idx.shape[0])],\n 'feature_1': [e / 2 * random.normal(1) for e in range(data_idx.shape[0])]},\n index=data_idx)\n target = DataFrame({'target': [e ** 2 for e in range(data_idx.shape[0])]},\n index=data_idx)\n\n def fit_topline(self, **init_args):\n nsm = StepTimeSeriesModel(**init_args)\n nsm.fit(self.features, self.target)\n return nsm\n\n def fit_granular(self, **init_args):\n nsm = StepTimeSeriesModel(**init_args)\n nsm.assert_input(self.features, self.target)\n nsm.build_data(self.features, self.target)\n for step in range(nsm.steps):\n nsm.fit_step(self.features, self.target, step)\n nan_predictions = nsm.all_fits.loc[:, step].isnull().sum()\n self.assertEqual(nan_predictions, 0)\n\n return nsm\n\n def test(self, **init_args):\n if 'Est' not in init_args.keys():\n init_args['Est'] = LinearRegression\n\n nsmI = self.fit_topline(**init_args)\n nsmII = self.fit_granular(**init_args)\n\n for nsm in [nsmI, nsmII]:\n\n if 'n' in init_args.keys():\n self.assertEqual(nsm.n, init_args['n'])\n else:\n self.assertEqual(nsm.n, 3)\n\n if 'd' in init_args.keys():\n self.assertEqual(nsm.d, init_args['d'])\n else:\n self.assertEqual(nsm.d, 1)\n\n expected_steps = int(nsm.n / nsm.d) +\\\n int(nsm.n % nsm.d > 0)\n\n # target\n self.assertEqual(nsm.step_targets.shape[1], expected_steps)\n\n # steps\n self.assertEqual(nsm.steps, expected_steps)\n self.assertEqual(len(nsm.est_params), expected_steps)\n self.assertEqual(len(nsm.step_models), expected_steps)\n self.assertEqual(len(nsm.step_cutoffs), expected_steps)\n\n # fits\n self.assertEqual(nsm.all_fits.transpose()\n .drop_duplicates().shape[0],\n expected_steps)\n self.assertEqual(nsm.all_fits.shape[0], self.features.shape[0])\n self.assertEqual(nsm.all_fits.shape[1], nsm.steps)\n\n # predict\n preds = nsm.predict(self.features)\n self.assertEqual(type(preds.columns), RangeIndex)\n self.assertEqual(preds.columns.min(), 1)\n self.assertEqual(preds.columns.max(), nsm.n)\n self.assertEqual(nsm.step_preds.transpose()\n .drop_duplicates().shape[0],\n expected_steps)\n self.assertEqual(preds.shape[0], self.features.shape[0])\n self.assertEqual(preds.shape[1], nsm.n)\n\n def test_other_n(self):\n self.test(n=1)\n self.test(n=4)\n\n def test_other_d(self):\n self.test(d=2)\n self.test(n=4, d=2)\n self.test(n=8, d=3)\n\n def test_other_freq(self):\n self.test(freq='M')\n\n def test_other_est(self):\n self.test(Est=XGBRegressor)\n","sub_path":"src/models/time_series.py","file_name":"time_series.py","file_ext":"py","file_size_in_byte":9877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"160882593","text":"import pytest\nimport flask\nimport subprocess\nimport multiprocessing\n\nfrom bci.defaults import DEFAULT_SERVER_ACTUAL_HOST, DEFAULT_SERVER_PORT\nfrom bci.client import upload_sample\n\n_HOST = DEFAULT_SERVER_ACTUAL_HOST\n_PORT = DEFAULT_SERVER_PORT\n\n\n@pytest.fixture\ndef get_message():\n parent, child = multiprocessing.Pipe()\n process = multiprocessing.Process(target=_run_server,\n args=(child,))\n process.start()\n parent.recv()\n try:\n def get_message():\n if not parent.poll(1):\n raise TimeoutError()\n return parent.recv()\n\n yield get_message\n finally:\n process.terminate()\n process.join()\n\n\ndef test_upload_sample(get_message, raw_data_and_path):\n data, path = raw_data_and_path\n upload_sample(path=path,\n host=_HOST,\n port=_PORT)\n assert get_message() == data\n\n\ndef test_cli(get_message, raw_data_and_path):\n data, path = raw_data_and_path\n process = subprocess.Popen(\n ['python', '-m', 'bci.client', 'upload-sample',\n '-h', _HOST, '-p', str(_PORT), str(path)],\n stdout=subprocess.PIPE,\n )\n stdout, _ = process.communicate()\n assert b'done' in stdout.lower()\n assert get_message() == data\n\n\ndef test_cli_error(raw_data_and_path):\n data, path = raw_data_and_path\n process = subprocess.Popen(\n ['python', '-m', 'bci.client', 'upload-sample',\n '-h', _HOST, '-p', str(_PORT), str(path)],\n stdout=subprocess.PIPE,\n )\n stdout, _ = process.communicate()\n assert b'error' in stdout.lower()\n\n\ndef _run_server(pipe):\n app = flask.Flask(__name__)\n\n @app.route('/config', methods=['GET'])\n def get_config():\n return flask.jsonify({\n 'fields': ['feelings', 'color_image', 'pose', 'depth_image'],\n 'error': None})\n\n @app.route('/snapshot', methods=['POST'])\n def snapshot():\n pipe.send(flask.request.data)\n return \"\"\n\n pipe.send('read')\n app.run(host=_HOST, port=_PORT)\n","sub_path":"tests/test_client.py","file_name":"test_client.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"131328677","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 04.05.2016\n\n@author: rdebeerst\n'''\n\n\nimport bkt\nimport bkt.library.powerpoint as pplib\n\nimport os #for os.path, listdir and makedirs\nimport shelve\nimport time\n\nimport logging\nimport traceback\n\nfrom System import Array\n\nfrom bkt import dotnet\nDrawing = dotnet.import_drawing()\nBitmap = Drawing.Bitmap\nColorTranslator = Drawing.ColorTranslator\n\nForms = dotnet.import_forms() #required for clipboard functions\n\n\nfrom threading import Thread #used for non-blocking gallery thumbnails refresh\nfrom contextlib import contextmanager\n\n@contextmanager\ndef open_presentation_without_window(context, filename):\n ''' opens and returns presentation file '''\n try:\n presentation = context.app.Presentations.Open(filename, True, False, False) #readonly, untitled, withwindow\n yield presentation\n finally:\n presentation.Saved = True\n presentation.Close()\n\n\n# TODO\n# ChartLib --> ChartLibMenu\n# ChartLib class with\n# file-buttons\n# copy shape/slide action\n\nTHUMBNAIL_POSTFIX = '_thumbnails'\n\n\nclass ChartLibCache(object):\n cache = {}\n\n @classmethod\n def init_cache(cls):\n cache_file = os.path.join( bkt.helpers.get_cache_folder(), \"chartlib.cache\" )\n cls.cache = shelve.open(cache_file, protocol=2)\n\n\n\nclass ChartLib(object):\n \n def __init__(self, copy_shapes=False):\n '''Constructor. Configures options and slide_action'''\n\n # configuration of library\n self.library_folders = []\n self.library_files = []\n \n # option: show gallery or menu for presentations\n self.show_gallery = True\n # callback method for menu-actions\n self.slide_action = None\n \n # caches\n self.cached_presentation_menus = {}\n self.cached_presentation_galleries = {}\n\n # folder for favorites\n # self.fav_folder = os.path.dirname(os.path.realpath(__file__))\n # self.fav_folder = bkt.helpers.get_fav_folder()\n self.fav_folder = None\n \n # init slide_action\n self.copy_shapes_setting = copy_shapes\n # if copy_shapes:\n # # copy shapes\n # self.slide_action = self.copy_shapes_callback\n # self.fav_folder = os.path.join(self.fav_folder, \"shapelib\")\n # else:\n # # copy full slide\n # self.slide_action = self.copy_slide_callback\n # self.fav_folder = os.path.join(self.fav_folder, \"chartlib\")\n \n # # add favorite folder as first folder\n # self.library_folders.insert(0, {'title': \"Favoriten\", 'folder': self.fav_folder} )\n \n\n def init_chartlib(self):\n logging.debug('initializing chartlib')\n\n #load gallery items cache\n ChartLibCache.init_cache()\n\n if self.copy_shapes_setting:\n # copy shapes\n subfolder = \"shapelib\"\n self.slide_action = self.copy_shapes_callback\n\n self.library_folders.extend( bkt.config.shape_library_folders or [] )\n self.library_files.extend( bkt.config.shape_libraries or [] )\n\n else:\n # copy full slide\n subfolder = \"chartlib\"\n self.slide_action = self.copy_slide_callback\n\n self.library_folders.extend( bkt.config.chart_library_folders or [] )\n self.library_files.extend( bkt.config.chart_libraries or [] )\n\n # add from feature-folders\n for folder in bkt.config.feature_folders:\n chartlib_folder = os.path.join(folder, subfolder)\n if os.path.exists(chartlib_folder):\n self.library_folders.append( { 'title':os.path.basename(os.path.realpath(folder)), 'folder':os.path.join(folder, subfolder)})\n \n # add favorite folder as first folder\n self.fav_folder = os.path.join(bkt.helpers.get_fav_folder(), subfolder)\n self.library_folders.insert(0, {'title': \"Favoriten\", 'folder': self.fav_folder} )\n \n \n # ===========\n # = Helpers =\n # ===========\n \n # @classmethod\n # def open_presentation_file(cls, context, file):\n # ''' opens and returns presentation file '''\n # # if self.presentations.has_key(file):\n # # presentation = self.presentations[file]\n # # else:\n # # # # Parameter: schreibgeschützt, ohne Titel, kein Fenster\n # # # presentation = context.app.Presentations.Open(file, True, False, False)\n # # # Parameter: rw-access, ohne Titel, kein Fenster\n # # presentation = context.app.Presentations.Open(file, False, False, False)\n # # self.presentations[file] = presentation\n # return context.app.Presentations.Open(file, True, False, False)\n \n @classmethod\n def create_or_open_presentation(cls, context, file):\n if os.path.exists(file):\n return context.app.Presentations.Open(file, False, False, False)\n else:\n newpres = context.app.Presentations.Add(False) #WithWindow=False\n if not os.path.isdir(os.path.dirname(file)):\n os.makedirs(os.path.dirname(file))\n newpres.SaveAs(file)\n return newpres\n \n @staticmethod\n def _save_paste(obj, *args, **kwargs):\n for _ in range(3):\n try:\n return obj.paste(*args, **kwargs)\n except EnvironmentError:\n logging.debug(\"chartlib pasting error, waiting for 50ms\")\n #wait some time to avoid EnvironmentError (run ahead bug if clipboard is busy, see https://stackoverflow.com/questions/54028910/vba-copy-paste-issues-pptx-creation)\n time.sleep(.50)\n #FIXME: maybe better way to check if clipboard actually contains \"something\"\n else:\n raise EnvironmentError(\"pasting not successfull\")\n \n # ====================================\n # = Menus for folders and subfolders =\n # ====================================\n \n ##Overview of UI parts for chart library\n ###Chartlib-Files\n # - fixed chartlib-menu (Menu) from file/presentation ***Will open presentation***\n # get_chartlib_menu_from_file ***caches***\n # get_chartlib_menu_from_presentation\n # - dynamic chartlib-menu (DynamicMenu) ***Will only open presentation if menu is opened***\n # get_dynamic_file_menu\n # --> maps to get_chartlib_menu_from_file / get_chartlib_menu_callback\n # - dynamic chartlib-gallery (Gallery) ***Will only open presentation if gallery is opened***\n # get_chartlib_gallery_from_file\n # --> uses ChartLibGallery\n ###Chartlib-Folders\n # - fixed folder-menu (Menu) from folder\n # get_folder_menu\n # --> uses get_dynamic_file_menu for files\n # --> uses get_dynamic_folder_menu for subfolders\n # - dynamic folder-menu (DynamicMenu)\n # get_dynamic_folder_menu\n # --> maps to get_folder_menu / get_folder_menu_callback\n # - root library menu (Menu)\n # get_root_menu\n # --> uses get_folder_menu for library folders to create menu-sections\n # --> uses get_dynamic_file_menu for library files\n #\n \n def get_root_menu(self):\n '''\n Returns static menu-control for root-chartlib-dir\n with entries for every root-directory and root-file.\n If just one root-directory is configured, it lists its contents directly instead.\n \n Menu\n --- root-folder 1 ---\n file 1\n file 2\n ...\n --- root-folder 2 ---\n ...\n ---------------------\n root-file 1\n root-file 2\n '''\n \n #initialize chartlib on first call\n if not self.fav_folder:\n self.init_chartlib()\n\n # create menu-items for chartlibrary-directories\n if len(self.library_folders) == 0:\n # create empty menu\n menu = bkt.ribbon.Menu(\n xmlns=\"http://schemas.microsoft.com/office/2009/07/customui\",\n id=None\n )\n elif len(self.library_folders) == 1:\n # create menu with contents of directory\n folder = self.library_folders[0]\n if type(folder) == dict:\n folder = folder['folder']\n menu = self.get_folder_menu(folder, id=None)\n menu.label=None\n else:\n # create menu with sections for each directory\n folders = [ folder if type(folder) == dict else {'folder':folder, 'title':os.path.basename(folder)} for folder in self.library_folders]\n logging.debug(\"ChartLib root menu with folders: %s\" % folders)\n \n children = [ self.get_folder_menu(folder['folder'], label=folder['title']) for folder in folders]\n # make list flat \n flat_children = []\n for folder_menu in children:\n if len(folder_menu.children) > 0:\n flat_children.append( bkt.ribbon.MenuSeparator(title=folder_menu['label'] or None) )\n flat_children += folder_menu.children\n \n menu = bkt.ribbon.Menu(\n xmlns=\"http://schemas.microsoft.com/office/2009/07/customui\",\n id=None,\n children=flat_children\n )\n \n \n # create menu-items for chartlibrary-files\n menu.children.append( bkt.ribbon.MenuSeparator() )\n for filename in self.library_files:\n menu.children.append( self.get_dynamic_file_menu(filename) )\n \n # buttons\n menu.children += [\n bkt.ribbon.MenuSeparator(title=\"Settings\"),\n bkt.ribbon.Button(label=\"Zu Favoriten hinzufügen\",\n screentip=\"Chart zu Favoriten-Library hinzufügen\",\n supertip=\"Ausgewählte Slides/Shapes zu Standard-Favoriten-Library hinzufügen. Falls diese Library noch nicht existiert, wird diese neu angelegt.\",\n image_mso='SourceControlCheckIn',\n on_action=bkt.Callback(self.add_chart_to_lib)\n ),\n bkt.ribbon.Button(label=\"Library erneut indizieren\",\n screentip=\"Library erneut indizieren\",\n supertip=\"Alle Cashes werden zurückgesetzt. Library-Order und -Dateien werden jeweils beim nächsten Öffnen erneut indiziert.\\n\\nEs werden nur Thumbnails bereits geöffneter Libraries aktualisiert. Eine Neugenerierung der Thumbnails kann auch für jede Shape-Library-Datei separat angestoßen werden (im jeweiligen Menü).\",\n image_mso='AccessRefreshAllLists',\n on_action=bkt.Callback(self.update_thumbnails_and_reset_cashes)\n )\n ]\n \n logging.debug(\"ChartLib root menu: %s\" % menu.xml())\n return menu\n \n def get_root_menu_xml(self):\n ''' returns xml of root-chartlib-dir menu'''\n return self.get_root_menu().xml_string()\n \n def get_folder_menu_callback(self, current_control):\n ''' callback for dynamic menu, returns menu-control for folder represented by control '''\n folder = current_control['tag']\n if not self.cached_presentation_menus.has_key(folder):\n folder_menu = self.get_folder_menu(folder, id=None)\n self.cached_presentation_menus[folder] = folder_menu\n # else:\n # logging.debug('get_folder_menu_callback: reuse menu for %s' % folder)\n return self.cached_presentation_menus[folder]\n \n def get_folder_menu(self, folder, **kwargs):\n ''' returns static menu-control for given folder. menu contains entries for subfolders and pptx-files. '''\n\n files = []\n subfolders = []\n \n if os.path.isdir(folder):\n # find pptx-Files\n for filename in os.listdir(folder):\n if filename.endswith(\".pptx\") and not filename.startswith(\"~$\"):\n files.append(filename)\n logging.debug('get_folder_menu files: %s' % files)\n \n # find subfolders\n for filename in os.listdir(folder):\n if os.path.isdir(os.path.join(folder, filename)):\n if not filename.endswith(THUMBNAIL_POSTFIX):\n subfolders.append(filename)\n logging.debug('get_folder_menu folders: %s' % subfolders)\n \n else:\n logging.warning(\"Chartlib. No such directory: %s\" % folder)\n \n # FIXME: no folders which belong to files\n \n if len(files) + len(subfolders) == 0:\n children = []\n else:\n # create DynamicMenus / Galleries\n children = [\n self.get_chartlib_gallery_from_file(folder + \"\\\\\" + filename) if self.show_gallery else self.get_dynamic_file_menu(folder + \"\\\\\" + filename) \n for filename in files\n ] + ([bkt.ribbon.MenuSeparator()] if len(files) > 0 else [] )+ [\n self.get_dynamic_folder_menu(folder + \"\\\\\" + subfolder)\n for subfolder in subfolders\n ]\n \n # return Menu\n return bkt.ribbon.Menu(\n xmlns=\"http://schemas.microsoft.com/office/2009/07/customui\",\n children = children,\n **kwargs\n )\n \n def get_dynamic_folder_menu(self, folder):\n ''' returns dynamic menu for folder. if menu unfolds, menu content is obtained by get_folder_menu '''\n basename = os.path.basename(folder)\n return bkt.ribbon.DynamicMenu(label=basename, tag=folder, get_content=bkt.Callback(self.get_folder_menu_callback, current_control=True))\n\n \n def get_dynamic_file_menu(self, filename):\n ''' returns dynamic menu for file. if menu unfolds, menu content is obtained by get_chartlib_menu_from_file '''\n file_basename = os.path.splitext(os.path.basename(filename))[0]\n return bkt.ribbon.DynamicMenu(\n label=file_basename, tag=filename, \n get_content=bkt.Callback(self.get_chartlib_menu_callback, current_control=True, context=True)\n )\n \n def update_thumbnails_and_reset_cashes(self, context):\n if not bkt.helpers.confirmation(\"Dieser Vorgang kann bei vielen Libraries einige Minuten dauern und nicht abgebrochen werden. Trotzdem fortsetzen?\"):\n return\n\n def loop(worker):\n try:\n total = len(self.cached_presentation_galleries)+1\n current = 1.0\n worker.ReportProgress(1, \"Lade Dateien\")\n for gal in self.cached_presentation_galleries.itervalues():\n if worker.CancellationPending:\n break\n if len(gal.filename) > 50:\n worker.ReportProgress(current/total*100, \"...\" + gal.filename[-50:])\n else:\n worker.ReportProgress(current/total*100, gal.filename)\n \n gal.reset_gallery_items(context)\n current += 1.0\n worker.ReportProgress(100, \"Cache löschen\")\n except:\n logging.error(\"Error on refreshing chartlib libraries\")\n logging.debug(traceback.format_exc())\n finally:\n self.reset_cashes()\n \n bkt.ui.execute_with_progress_bar(loop, context, modal=False) #modal=False important so main thread can handle app events and all presentations close properly\n\n def reset_cashes(self):\n ''' cashes for library menus and galleries are deleted '''\n self.cached_presentation_menus = {}\n self.cached_presentation_galleries = {}\n \n \n \n # ====================================\n # = Quick add to favorites =\n # ====================================\n\n # def add_fav_to_config(self):\n # if not self.copy_shapes_setting:\n # folders = bkt.config.chart_library_folders or []\n # if self.fav_folder not in folders:\n # folders.append(self.fav_folder)\n # self.library_folders.insert(0, self.fav_folder)\n # bkt.config.set_smart(\"chart_library_folders\", folders)\n # else:\n # folders = bkt.config.shape_library_folders or []\n # if self.fav_folder not in folders:\n # folders.append(self.fav_folder)\n # self.library_folders.insert(0, self.fav_folder)\n # bkt.config.set_smart(\"shape_library_folders\", folders)\n\n def add_chart_to_lib(self, context):\n #Open default file\n #FIXME: read list of files in fav folder and ask user to select file\n file = os.path.join(self.fav_folder, \"Favorites.pptx\")\n pres = self.create_or_open_presentation(context, file)\n #Ensure fav folder is in config\n # self.add_fav_to_config()\n \n try:\n if not self.copy_shapes_setting:\n #Copy slides\n context.selection.SlideRange.Copy()\n # pres.Slides.Paste()\n self._save_paste(pres.Slides)\n else:\n #Copy each shape individually\n for shape in context.shapes:\n title = bkt.ui.show_user_input(\"Bitte Shape-Titel eingeben:\", \"Shape-Titel\", shape.Name)\n if title is None:\n break\n shape.Copy()\n slide = pres.Slides.Add(pres.Slides.Count+1, 11) #11=ppTitleOnly\n \n #set slide background color\n slide.FollowMasterBackground = False\n orig_bg = context.slides[0].Background.Fill.ForeColor\n if orig_bg.Type == pplib.MsoColorType['msoColorTypeScheme'] and orig_bg.ObjectThemeColor > 0:\n slide.Background.Fill.ForeColor.ObjectThemeColor = orig_bg.ObjectThemeColor\n slide.Background.Fill.ForeColor.Brightness = orig_bg.Brightness\n else:\n slide.Background.Fill.ForeColor.RGB = orig_bg.RGB\n \n # new_shp = slide.Shapes.Paste()\n new_shp = self._save_paste(slide.Shapes)\n new_shp.Left = (pres.PageSetup.SlideWidth - new_shp.Width)*0.5\n new_shp.Top = (pres.PageSetup.SlideHeight - new_shp.Height)*0.5\n slide.Shapes.Title.Textframe.TextRange.Text = title\n\n pres.Save()\n except:\n bkt.helpers.exception_as_message()\n finally:\n pres.Saved = True\n pres.Close()\n\n #Regenerate thumbnails\n gallery = self.get_chartlib_gallery_from_file(file)\n gallery.reset_gallery_items(context)\n self.reset_cashes()\n\n\n \n # ====================================\n # = Menu-items for ChartLib-sections =\n # ====================================\n \n def reset_chartlib_menu_callback(self, context, current_control):\n ''' callback to reload menu for presentation-file represented by control '''\n return self.reset_chartlib_menu_from_file(context, current_control['tag'])\n \n def reset_chartlib_menu_from_file(self, context, filename):\n ''' reloads menu for presentation-file. resets caches and relads chartlib-menu '''\n self.cached_presentation_menus.pop(filename, None)\n self.get_chartlib_menu_from_file(context, filename)\n return None\n \n def get_chartlib_menu_callback(self, context, current_control):\n ''' callback for dynamic menu, return Menu-control for presentation represented by control '''\n return self.get_chartlib_menu_from_file(context, current_control['tag'])\n \n def get_chartlib_menu_from_file(self, context, filename):\n ''' returns static menu for presentation-file. uses cached menu or generates menu using get_chartlib_menu_from_presentation '''\n if not self.cached_presentation_menus.has_key(filename):\n # presentation = self.open_presentation_file(context, filename)\n with open_presentation_without_window(context, filename) as presentation:\n menu = self.get_chartlib_menu_from_presentation(presentation)\n # presentation.Close()\n self.cached_presentation_menus[filename] = menu\n # else:\n # logging.debug('get_chartlib_menu_from_presentation: reuse menu for %s' % filename)\n return self.cached_presentation_menus[filename]\n \n def get_chartlib_menu_from_presentation(self, presentation):\n ''' returns static menu for presentation. \n Opens prensentation and creates menu-buttons for every slide.\n If presentation has sections, these are reused to structure menu:\n either by menu-sections or sub-menus; latter if presentation has 40 slides or more.\n '''\n children = [ ]\n \n # items per section\n num_sections = presentation.sectionProperties.Count\n if num_sections == 0:\n # only one section, list slide-Buttons\n children += self.get_section_menu(presentation.slides, 1, presentation.slides.count)\n elif presentation.Slides.Count < 40:\n # list-seperator per section, with slide-Buttons\n for idx in range(1,num_sections+1):\n children += [ bkt.ribbon.MenuSeparator(title = presentation.sectionProperties.Name(idx)) ]\n children += self.get_section_menu(presentation.slides,\n presentation.sectionProperties.FirstSlide(idx),\n presentation.sectionProperties.SlidesCount(idx) )\n else:\n # list Menu per section, with slide-Buttons\n children += [\n bkt.ribbon.Menu(\n label = presentation.sectionProperties.Name(idx),\n children = self.get_section_menu(presentation.slides,\n presentation.sectionProperties.FirstSlide(idx),\n presentation.sectionProperties.SlidesCount(idx) )\n )\n for idx in range(1,num_sections+1)\n ]\n\n # open-file-Button\n children += self.get_file_buttons(presentation.FullName)\n \n # return Menu\n menu = bkt.ribbon.Menu(\n #label=presentation.Name,\n xmlns=\"http://schemas.microsoft.com/office/2009/07/customui\",\n id=None,\n children = children\n )\n return menu\n \n \n def get_chartlib_gallery_from_file(self, filename):\n ''' returns dynamic gallery for presentation-file. uses cached gallery or generates gallery using ChartLibGallery class '''\n if not self.cached_presentation_galleries.has_key(filename):\n gallery = ChartLibGallery(filename, copy_shapes=self.copy_shapes_setting)\n self.cached_presentation_galleries[filename] = gallery\n return self.cached_presentation_galleries[filename]\n \n \n \n \n # ==================================\n # = Menu-items for ChartLib-slides =\n # ==================================\n \n def get_section_menu(self, slides, offset, count):\n ''' return menu-buttons for given slide selection '''\n \n # control chars are removed from labels\n control_chars = dict.fromkeys(range(32))\n \n return [\n bkt.ribbon.Button(\n label = slides.item(idx).Shapes.Title.Textframe.TextRange.text[:40].translate(control_chars) if slides.item(idx).Shapes.Title.Textframe.TextRange.text != \"\" else \"slide\" + str(idx),\n tag=slides.parent.FullName + \"|\" + str(idx),\n on_action=bkt.Callback(self.slide_action, context=True, current_control=True)\n )\n for idx in range(offset, offset+count)\n if slides.item(idx).shapes.hastitle != False\n ]\n \n\n\n \n # ===========\n # = actions =\n # ===========\n \n def get_file_buttons(self, filename, show_menu_separator=True):\n return ([bkt.ribbon.MenuSeparator(title = \"Library\")] if show_menu_separator else []) + [\n bkt.ribbon.Button(label=\"Datei öffnen und Library bearbeiten\",\n image_mso='FileSaveAsPowerPointPptx',\n tag=filename,\n on_action=bkt.Callback(self.open_file, context=True, current_control=True)\n ),\n bkt.ribbon.Button(label=\"Datei erneut indizieren\",\n image_mso='AccessRefreshAllLists',\n tag=filename,\n on_action=bkt.Callback(self.reset_chartlib_menu_callback, context=True, current_control=True)\n )\n ]\n \n @classmethod\n def open_file(cls, context, current_control):\n ''' Open library file with window and write access '''\n filename = current_control['tag']\n # Parameter: rw-access, ohne Titel, mit Fenster\n presentation = context.app.Presentations.Open(filename, False, False, True)\n \n @classmethod\n def copy_slide_callback(cls, context, current_control):\n filename, slide_index = current_control['tag'].split(\"|\")\n cls.copy_slide(context, filename, slide_index)\n \n @classmethod\n def copy_slide(cls, context, filename, slide_index):\n ''' Copy slide from chart lib '''\n # open presentation\n # template_presentation = cls.open_presentation_file(context, filename)\n with open_presentation_without_window(context, filename) as template_presentation:\n # copy slide\n template_presentation.slides.item(int(slide_index)).copy()\n # paste slide\n position = context.app.activeWindow.View.Slide.SlideIndex\n # context.app.activeWindow.presentation.slides.paste(position+1)\n cls._save_paste(context.app.activeWindow.presentation.slides, position+1)\n # template_presentation.Close()\n \n @classmethod\n def copy_shapes_callback(cls, context, current_control):\n filename, slide_index = current_control['tag'].split(\"|\")\n cls.copy_shapes(context, filename, slide_index)\n \n @classmethod\n def copy_shapes(cls, context, filename, slide_index):\n ''' Copy shape from shape lib '''\n # open presentation\n # template_presentation = cls.open_presentation_file(context, filename)\n with open_presentation_without_window(context, filename) as template_presentation:\n template_slide = template_presentation.slides.item(int(slide_index))\n # current slide\n cur_slide = context.app.activeWindow.View.Slide\n shape_count = cur_slide.shapes.count\n # find relevant shapes\n shape_indices = []\n shape_index = 1\n for shape in template_slide.shapes:\n if shape.type != 14 and shape.visible == -1:\n # shape is not a placeholder and visible\n shape_indices.append(shape_index)\n shape_index+=1\n # select and copy shapes\n template_slide.shapes.Range(Array[int](shape_indices)).copy()\n # cur_slide.shapes.paste()\n cls._save_paste(cur_slide.shapes)\n \n # group+select shapes\n if cur_slide.shapes.count - shape_count > 1:\n cur_slide.shapes.Range(Array[int](range(shape_count+1, cur_slide.shapes.count+1))).group().select()\n else:\n cur_slide.shapes.item(cur_slide.shapes.count).select()\n # template_presentation.Close()\n \n \n \n\n\ndef filename_as_ui_id(filename):\n from string import maketrans\n ''' creates customui-id from filename by removing unsupported characters '''\n # characters to remove\n chr_numbers = range(45) + range(46,48) + range(58,65) + range(91,95) + [96] + range(123,256)\n # translation tab\n transtab = maketrans(\"\".join( chr(i) for i in chr_numbers ), '|'*len(chr_numbers))\n \n return 'id_' + filename.translate(transtab).replace('|', '__')\n\n\n\nclass ChartLibGallery(bkt.ribbon.Gallery):\n \n # size of items\n # item_width should not be used in chart lib (copy_shapes=False); will be overwritten with respect to slide-format\n item_height = 150\n item_width = 200\n \n def __init__(self, filename, copy_shapes=False, **user_kwargs):\n '''Constructor\n Initializes Gallery for chart/shape-library\n '''\n self.filename = filename\n self.items_initialized = False\n \n self.labels = []\n self.slide_indices = []\n \n parent_id = user_kwargs.get('id') or \"\"\n \n self.this_id = filename_as_ui_id(filename) + \"--\" + str(copy_shapes)\n \n \n \n # default settings\n kwargs = dict(\n #label = u\"Test Gallery\",\n id = self.this_id,\n label = os.path.splitext(os.path.basename(filename))[0],\n show_label=False,\n #screentip=\"\",\n #supertip=\"\",\n get_image=bkt.Callback(lambda: self.get_chartlib_item_image(0)),\n )\n \n self.copy_shapes = copy_shapes\n if self.copy_shapes:\n # shape lib settings\n kwargs.update(dict(\n item_height=50,\n item_width=50,\n columns=3\n ))\n else:\n # slide lib settings\n kwargs.update(dict(\n item_height=100,\n #item_width=177, item_height=100, # 16:9\n #item_width=133, item_height=100, # 4:3\n columns=2\n ))\n \n # user kwargs\n if len(user_kwargs) > 0:\n kwargs.update(user_kwargs)\n \n # initialize gallery\n super(ChartLibGallery, self).__init__(\n children = self.get_file_buttons(),\n **kwargs\n )\n \n \n \n # ================\n # = ui callbacks =\n # ================\n \n def on_action_indexed(self, selected_item, index, context, current_control):\n '''CustomUI-callback: copy shape/slide from library to current presentation'''\n if self.copy_shapes:\n # copy shapes / shape library\n ChartLib.copy_shapes(context, self.filename, self.slide_indices[index])\n else:\n # copy slide / slide library\n ChartLib.copy_slide(context, self.filename, self.slide_indices[index])\n \n def get_item_count(self, context):\n '''CustomUI-callback'''\n if not self.items_initialized:\n self.init_gallery_items(context)\n return len(self.labels)\n\n def get_item_height(self):\n '''CustomUI-callback: return item-height\n item-height is initialized by init_gallery_items\n '''\n return self.item_height\n\n def get_item_width(self):\n '''CustomUI-callback: return item-width\n item-width is initialized by init_gallery_items\n '''\n return self.item_width\n \n def get_item_id(self, index):\n '''CustomUI-callback: returns corresponding item id'''\n return self.this_id + \"--\" + str(index)\n \n def get_item_label(self, index):\n '''CustomUI-callback: returns corresponding item label\n labels are initialized by init_gallery_items\n '''\n if not self.items_initialized:\n self.init_gallery_items(context)\n return self.labels[index][:40]\n \n def get_item_screentip(self, index):\n '''CustomUI-callback'''\n #return \"Shape aus Shape-library einfügen\"\n if self.copy_shapes:\n # copy shapes / shape library\n return \"Shape »\" + self.labels[index] + \"« aus Shape-Library einfügen\"\n else:\n # copy slide / slide library\n return \"Folie »\" + self.labels[index] + \"« aus Chart-Library einfügen\"\n \n # def get_item_supertip(self, index):\n # return \"tbd\"\n \n def get_item_image(self, index):\n '''CustomUI-callback: calls get_chartlib_item_image'''\n if not self.items_initialized:\n self.init_gallery_items(context)\n return self.get_chartlib_item_image(index)\n \n \n \n # ===========\n # = methods =\n # ===========\n \n def get_file_buttons(self):\n return [\n bkt.ribbon.Button(label=\"Datei öffnen und Library bearbeiten\",\n image_mso='FileSaveAsPowerPointPptx',\n tag=self.filename,\n on_action=bkt.Callback(ChartLib.open_file, context=True, current_control=True)\n ),\n bkt.ribbon.Button(label=\"Datei erneut indizieren und Thumbnails aktualisieren\",\n image_mso='AccessRefreshAllLists',\n on_action=bkt.Callback(self.reset_gallery_items, context=True)\n )\n ]\n \n def reset_gallery_items(self, context):\n '''Forces Gallery to re-initialize and generate thumbnail-images'''\n # reset gallery in a separate thread so core thread is still able to handle the app-events (i.e. presentation open/close).\n # otherwise when this function is called in a loop, some presentations remain open and block ppt process from being quit.\n t = Thread(target=self.init_gallery_items, args=(context, True))\n t.start()\n t.join()\n # self.init_gallery_items(context, force_thumbnail_generation=True)\n \n def init_gallery_items(self, context, force_thumbnail_generation=False):\n try:\n if force_thumbnail_generation:\n raise KeyError(\"Thumbnail generation not possible from cache\")\n self.init_gallery_items_from_cache()\n except KeyError as e:\n if str(e) == \"CACHE_FILEMTIME_INVALID\":\n force_thumbnail_generation = True\n with open_presentation_without_window(context, self.filename) as presentation:\n try:\n self.init_gallery_items_from_presentation(presentation, force_thumbnail_generation=force_thumbnail_generation)\n except:\n logging.error('error initializing gallery')\n logging.debug(traceback.format_exc())\n \n # try:\n # presentation = Charlib.open_presentation_file(context, self.filename)\n # # presentation = context.app.Presentations.Open(self.filename, True, False, True) #filename, readonly, untitled, withwindow\n # self.init_gallery_items_from_presentation(presentation, force_thumbnail_generation=force_thumbnail_generation)\n # except:\n # logging.error('error initializing gallery')\n # logging.debug(traceback.format_exc())\n # finally:\n # # logging.debug('closing presentation: %s' % self.filename)\n # presentation.Saved = True\n # presentation.Close()\n \n def init_gallery_items_from_cache(self):\n ''' initialize gallery items from cache if file has not been modified.\n '''\n cache = ChartLibCache.cache[self.filename]\n if os.path.getmtime(self.filename) != cache[\"file_mtime\"]:\n raise KeyError(\"CACHE_FILEMTIME_INVALID\")\n self.item_width = cache[\"item_width\"]\n self.slide_indices = cache[\"slide_indices\"]\n self.labels = cache[\"labels\"]\n self.item_count = cache[\"item_count\"]\n self.items_initialized = True\n \n def init_gallery_items_from_presentation(self, presentation, force_thumbnail_generation=False):\n ''' initialize gallery items (count, labels, item-widht, item-height... ).\n Also generates thumbnail-image-files if needed.\n '''\n \n # item width should respect slide-format\n if not self.copy_shapes:\n self.item_width = int(self.item_height * presentation.SlideMaster.Width / presentation.SlideMaster.Height)\n \n # init labels\n control_chars = dict.fromkeys(range(32))\n item_count = presentation.Slides.Count\n self.slide_indices = [\n idx\n for idx in range(1, 1+item_count)\n if presentation.slides.item(idx).shapes.hastitle != False\n ]\n self.labels = [\n # presentation.slides.item(idx).Shapes.Title.Textframe.TextRange.text[:40].translate(control_chars) \n presentation.slides.item(idx).Shapes.Title.Textframe.TextRange.text.translate(control_chars) \n if presentation.slides.item(idx).Shapes.Title.Textframe.TextRange.text != \"\" else \"slide\" + str(idx)\n for idx in self.slide_indices\n ]\n # logging.debug(\"labels %s\" % self.labels)\n\n # init items\n self.item_count = len(self.labels)\n\n # items are initialized and can be displayed\n self.items_initialized = True\n\n #create cache\n ChartLibCache.cache[self.filename] = dict(\n # cache_time = time.time(),\n file_mtime = os.path.getmtime(self.filename),\n item_width = self.item_width,\n slide_indices = self.slide_indices,\n labels = self.labels,\n item_count = self.item_count,\n )\n ChartLibCache.cache.sync()\n\n # init images, if first thumbnail does not exist\n image_filename = self.get_image_filename(1)\n if force_thumbnail_generation or not os.path.exists(image_filename):\n if self.copy_shapes:\n self.generate_gallery_images_from_shapes(presentation)\n else:\n self.generate_gallery_images_from_slides(presentation)\n \n # # cache items for next call on library\n # # image loading still necessary\n # self.children = [\n # bkt.ribbon.Item(\n # id=self.get_item_id(idx),\n # label=self.get_item_label(idx),\n # #image=self.get_item_image(idx),\n # )\n # for idx in range(self.get_item_count(context=None))\n # ] + self.children\n \n \n \n def get_image_filename(self, index, postfix=\"\"):\n ''' returns path of thumbnail for given chart-lib slide '''\n return os.path.join(os.path.splitext(self.filename)[0] + THUMBNAIL_POSTFIX, str(index) + postfix + '.png')\n \n \n def generate_gallery_images_from_slides(self, presentation):\n ''' generate thumbnail images for all chart-lib items '''\n filename = presentation.FullName\n item_count = presentation.Slides.Count\n control_chars = dict.fromkeys(range(32))\n \n # make sure, directory exists\n directory = os.path.split( self.get_image_filename(1) )[0]\n if not os.path.exists(directory):\n os.makedirs(directory)\n \n for slide in presentation.slides:\n if slide.shapes.hastitle != False:\n # select shapes\n # slide_range = presentation.slides.Range(Array[int]([ slide.SlideIndex ]))\n image_filename = self.get_image_filename(slide.SlideIndex)\n try:\n # export image as PNG, 2 = ppShapeFormatPNG, 0 = ppShapeFormatGIF\n # width 600, auto height (450 for 4:3)\n # slide_range.Export(image_filename, 'PNG', 600)\n # slide.Export(image_filename, 'PNG', 600) #FIXME: this line closes the chartlib menu, so every time the images are generated, the user needs to re-open the chartlib\n slide.Copy()\n Forms.Clipboard.GetImage().Save(image_filename, Drawing.Imaging.ImageFormat.Png)\n except:\n logging.warning('Creation of thumbnail image failed: %s' % image_filename)\n Forms.Clipboard.Clear()\n \n \n def generate_gallery_images_from_shapes(self, presentation, with_placeholders=False):\n ''' generate thumbnail images for all shape-lib items '''\n filename = presentation.FullName\n item_count = presentation.Slides.Count\n control_chars = dict.fromkeys(range(32))\n \n # make sure, directory exists\n directory = os.path.split( self.get_image_filename(1) )[0]\n if not os.path.exists(directory):\n os.makedirs(directory)\n \n for slide in presentation.slides:\n if slide.shapes.hastitle != False:\n # find relevant shapes\n shape_indices = []\n shape_index = 1\n for shape in slide.shapes:\n if shape.visible == -1 and (with_placeholders or shape.type != 14):\n # shape is visible and not a placeholder (or placeholder are allowed)\n shape_indices.append(shape_index)\n shape_index+=1\n # select shapes\n shape_range = slide.shapes.Range(Array[int](shape_indices))\n image_filename = self.get_image_filename(slide.SlideIndex)\n # WAS: image_filename = os.path.join(os.path.splitext(self.filename)[0], str(slide.SlideIndex) + '.png')\n \n try:\n # export image as PNG, \n # ppShapeFormatGIF = 0, ppShapeFormatJPG = 1, ppShapeFormatPNG = 2, ppShapeFormatBMP = 3, ppShapeFormatWMF = 4, ppShapeFormatEMF = 5;\n shape_range.Export(image_filename, 2) \n except:\n logging.warning('Creation of thumbnail image failed: %s' % image_filename)\n \n # resize thumbnail image to square\n if os.path.exists(image_filename):\n try:\n # init croped image\n width = 100\n height = 100\n image = Bitmap(image_filename)\n bmp = Bitmap(width, height)\n graph = Drawing.Graphics.FromImage(bmp)\n #set background color of thumbnails\n background_color = Drawing.ColorTranslator.FromOle(slide.Background.Fill.ForeColor.RGB)\n if background_color != pplib.PowerPoint.XlRgbColor.rgbWhite.value__:\n graph.Clear(background_color)\n # compute scale\n scale = min(float(width) / image.Width, float(height) / image.Height)\n scaleWidth = int(image.Width * scale)\n scaleHeight = int(image.Height * scale)\n # set quality\n graph.InterpolationMode = Drawing.Drawing2D.InterpolationMode.High\n graph.CompositingQuality = Drawing.Drawing2D.CompositingQuality.HighQuality\n graph.SmoothingMode = Drawing.Drawing2D.SmoothingMode.AntiAlias\n # redraw and save\n # logging.debug('crop image from %sx%s to %sx%s. rect %s.%s-%sx%s' % (image.Width, image.Height, width, height, int((width - scaleWidth)/2), int((height - scaleHeight)/2), scaleWidth, scaleHeight))\n graph.DrawImage(image, Drawing.Rectangle(int((width - scaleWidth)/2), int((height - scaleHeight)/2), scaleWidth, scaleHeight))\n cropped_image_filename = self.get_image_filename(slide.SlideIndex, postfix=\"-cropped\")\n bmp.Save(cropped_image_filename)\n # close files\n image.Dispose()\n bmp.Dispose()\n # move files\n original_image_filename = self.get_image_filename(slide.SlideIndex, postfix=\"-original\")\n if os.path.exists(original_image_filename):\n os.remove(original_image_filename)\n os.rename(image_filename, original_image_filename)\n os.rename(cropped_image_filename, image_filename)\n \n except:\n logging.error('Creation of croped thumbnail image failed: %s' % image_filename)\n logging.debug(traceback.format_exc())\n finally:\n if image:\n image.Dispose()\n if bmp:\n bmp.Dispose()\n \n \n def get_chartlib_item_image(self, index):\n ''' load item image from corresponding image-file and return Bitmap-object '''\n #logging.debug('get_chartlib_item_image %s -- %s' % (filename, index))\n image_filename = self.get_image_filename(index+1)\n #logging.debug('get_chartlib_item_image %s' % (image_filename))\n if os.path.exists(image_filename):\n # return empty from file\n #return Bitmap.FromFile(image_filename)\n \n #version that should not lock the file, which prevents updating of thumbnails:\n with Bitmap.FromFile(image_filename) as img:\n new_img = Bitmap(img)\n img.Dispose()\n return new_img\n else:\n # return empty white image\n img = Bitmap(50, 50)\n color = ColorTranslator.FromHtml('#ffffff00')\n img.SetPixel(0, 0, color);\n return img\n\n\n\n\n\ncharts = ChartLib()\n# charts.library_folders.extend( bkt.config.chart_library_folders or [] )\n# charts.library_files.extend( bkt.config.chart_libraries or [] )\n\nshapes = ChartLib( copy_shapes=True )\n# shapes.library_folders.extend( bkt.config.shape_library_folders or [] )\n# shapes.library_files.extend( bkt.config.shape_libraries or [] )\n\n\n# add from feature-folders\n# for folder in bkt.config.feature_folders:\n# chartlib_folder = os.path.join(folder, \"chartlib\")\n# charts.library_folders.append( { 'title':os.path.basename(os.path.realpath(folder)), 'folder':chartlib_folder})\n# shapelib_folder = os.path.join(folder, \"shapelib\")\n# shapes.library_folders.append( { 'title':os.path.basename(os.path.realpath(folder)), 'folder':shapelib_folder})\n\n\n\nchartlib_button = bkt.ribbon.DynamicMenu(\n id='menu-add-chart',\n label=\"Templatefolie einfügen\",\n show_label=False,\n screentip=\"Folie aus Slide-Library einfügen\",\n supertip=\"Aus den hinterlegten Slide-Templates kann ein Template als neue Folie eingefügt werden.\",\n image_mso=\"SlideMasterInsertLayout\",\n #image_mso=\"CreateFormBlankForm\",\n get_content = bkt.Callback(\n charts.get_root_menu\n )\n)\nshapelib_button = bkt.ribbon.DynamicMenu(\n id='menu-add-shape',\n label=\"Personal Shape Library\",\n show_label=False,\n screentip=\"Shape aus Shape-Library einfügen\",\n supertip=\"Aus den hinterlegten Shape-Templates kann ein Shape auf die aktuelle Folie eingefügt werden.\",\n image_mso=\"ActionInsert\",\n #image_mso=\"ShapesInsertGallery\",\n #image_mso=\"OfficeExtensionsGallery\",\n get_content = bkt.Callback(\n shapes.get_root_menu\n )\n)\n\n# chartlibgroup = bkt.ribbon.Group(\n# label=\"chartlib\",\n# children=[ chartlib_button, shapelib_button]\n# )\n\n# bkt.powerpoint.add_tab(\n# bkt.ribbon.Tab(\n# label=\"chartlib\",\n# children = [\n# chartlibgroup\n# ]\n# )\n# )\n\n\n\n\n\n","sub_path":"features/toolbox/chartlib.py","file_name":"chartlib.py","file_ext":"py","file_size_in_byte":47248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"2828393","text":"#import bibliotek\nimport os\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\n#wyświetlenie zawartości katalogu z opiniami\nprint(*os.listdir(\"./opinions_json\"))\n\n#wczytanie identyfikatora produktu, którego opinie będą analizowane\nproduct_id = input(\"Podaj kod produktu: \")\n\n#wczytanie do ramki danych opinii z pliku\nopinions = pd.read_json(\"./opinions_json/\"+product_id+'.json') \nopinions = opinions.set_index(\"opinion_id\") \n\n#częstość występowania poszczególnej liczby gwiazdek\nstars = opinions[\"stars\"].value_counts().sort_index().reindex(list(np.arange(0, 5.1, 0.5)), fill_value=0)\nfig, ax = plt.subplots()\nstars.plot.bar(color=\"deepskyblue\")\nplt.xticks(rotation=0)\nax.set_title(\"Częstość występowania poszczególnych ocen\")\nax.set_xlabel(\"liczba gwiazdek\")\nax.set_ylabel(\"liczba opinii\")\nplt.savefig(\"./figures_png/\"+product_id+'_bar.png')\nplt.close()\n\n#udział poszczególnych rekomandacji w ogólnej liczbie opinii\nrecommendation = opinions[\"recommendation\"].value_counts()\nfig, ax = plt.subplots()\nrecommendation.plot.pie(label=\"\", autopct=\"%.1f%%\", colors=['mediumseagreen', 'coral'])\nax.set_title(\"Udział poszczególnych rekomandacji w ogólnej liczbie opinii\")\nplt.savefig(\"./figures_png/\"+product_id+'_pie.png')\nplt.close()\n\n#podstawowe startstyki\nstars_everage = opinions[\"stars\"].mean()\npros = opinions[\"pros\"].count()\ncons = opinions[\"cons\"].count()\npurchased = opinions[\"purchased\"].sum()\nprint(stars_everage, pros, cons, purchased)\n\nstars_purchased = pd.crosstab(opinions[\"stars\"],opinions[\"purchased\"])\nprint(stars_purchased)","sub_path":"app/analyzer.py","file_name":"analyzer.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"420692689","text":"'''\nutils.py: part of singularity package\n\nThe MIT License (MIT)\n\nCopyright (c) 2016-2017 Vanessa Sochat\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n'''\n\nimport collections\nimport os\nimport re\nimport requests\n\nimport shutil\nimport json\nfrom singularity.logger import bot\nfrom singularity.utils import (\n write_json,\n write_file\n)\nimport sys\n\nimport subprocess\n\nimport tempfile\nimport zipfile\n\n\n############################################################################\n## FILE OPERATIONS #########################################################\n############################################################################\n\n\ndef zip_up(file_list,zip_name,output_folder=None):\n '''zip_up will zip up some list of files into a package (.zip)\n :param file_list: a list of files to include in the zip.\n :param output_folder: the output folder to create the zip in. If not \n :param zip_name: the name of the zipfile to return.\n specified, a temporary folder will be given.\n '''\n tmpdir = tempfile.mkdtemp()\n \n # Make a new archive \n output_zip = \"%s/%s\" %(tmpdir,zip_name)\n zf = zipfile.ZipFile(output_zip, \"w\", zipfile.ZIP_DEFLATED, allowZip64=True)\n\n # Write files to zip, depending on type\n for filename,content in file_list.items():\n\n bot.debug(\"Adding %s to package...\" %filename)\n\n # If it's the files list, move files into the archive\n if filename.lower() == \"files\":\n if not isinstance(content,list): \n content = [content]\n for copyfile in content:\n zf.write(copyfile,os.path.basename(copyfile))\n os.remove(copyfile)\n\n else:\n\n output_file = \"%s/%s\" %(tmpdir, filename)\n \n # If it's a list, write to new file, and save\n if isinstance(content,list):\n write_file(output_file,\"\\n\".join(content))\n \n # If it's a dict, save to json\n elif isinstance(content,dict):\n write_json(content,output_file)\n\n # If bytes, need to decode\n elif isinstance(content,bytes):\n write_file(output_file,content.decode('utf-8'))\n \n # String or other\n else: \n output_file = write_file(output_file,content)\n\n if os.path.exists(output_file):\n zf.write(output_file,filename)\n os.remove(output_file)\n\n # Close the zip file \n zf.close()\n\n if output_folder is not None:\n shutil.copyfile(output_zip,\"%s/%s\"%(output_folder,zip_name))\n shutil.rmtree(tmpdir)\n output_zip = \"%s/%s\"%(output_folder,zip_name)\n\n return output_zip\n\n\n\n############################################################################\n## OTHER MISC. #############################################################\n############################################################################\n\n\ndef get_container_contents(container=None,gets=None,split_delim=None,image_package=None):\n '''get_container_contents will return a list of folders and or files\n for a container. The environmental variable SINGULARITY_HUB being set\n means that container objects are referenced instead of packages\n :param container: the container to get content for\n :param gets: a list of file names to return, without parent folders\n :param split_delim: if defined, will split text by split delimiter\n :param image_package: if defined, user has provided an image_package\n '''\n from singularity.package import package, load_package\n\n if container == None and image_package == None:\n bot.error(\"You must define an image package or container.\")\n sys.exit(1)\n\n # Default returns are the list of files and folders\n if gets == None:\n gets = ['files.txt','folders.txt']\n if not isinstance(gets,list):\n gets = [gets]\n\n # We will look for everything in guts, then return it\n guts = dict()\n\n SINGULARITY_HUB = os.environ.get('SINGULARITY_HUB',\"False\")\n\n # Visualization deployed local or elsewhere\n if SINGULARITY_HUB == \"False\":\n tmpdir = tempfile.mkdtemp()\n if image_package == None:\n image_package = package(image_path=container,\n output_folder=tmpdir,\n remove_image=True)\n \n guts = load_package(image_package,get=gets)\n shutil.rmtree(tmpdir)\n\n # Visualization deployed by singularity hub\n else: \n \n # user has provided a package, but not a container\n if container == None:\n guts = load_package(image_package,get=gets)\n\n # user has provided a container, but not a package\n else:\n for sfile in container.files:\n for gut_key in gets: \n if os.path.basename(sfile['name']) == gut_key:\n if split_delim == None:\n guts[gut_key] = requests.get(sfile['mediaLink']).text\n else:\n guts[gut_key] = requests.get(sfile['mediaLink']).text.split(split_delim)\n\n return guts\n","sub_path":"singularity/package/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"281604479","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 6 20:16:56 2017\n\n@author: wuxiong\n\"\"\"\n#得到AER每一期issues的url\nimport requests\nfrom bs4 import BeautifulSoup\nfrom requests.exceptions import RequestException\nfrom multiprocessing import Pool\nimport re\nimport json\ndef get_onepage_url(url):\n try:\n user_agent = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36\"\n headers = {\"User-Agent\": user_agent}\n response = requests.get(url,headers=headers)\n if response.status_code==200:\n return response.content\n return None\n except RequestException:\n return None\ndef parse_one_page(url_html):\n soup = BeautifulSoup(url_html,'lxml')\n text = soup.find_all('div',style=\"margin-top:5px;\")\n pattern = re.compile('',re.S)\n new_results = re.findall(pattern,str(text))\n text = []\n print(new_results)\n print(len(new_results))\n url = 'https://www.aeaweb.org'\n for result in new_results:\n text.append( url + str(result))\n return text\ndef write_to_file(content):\n with open('AER_urls_only.txt','a') as f:\n f.write(content + '\\n')\n f.close()\n# def main(number):\ndef main(url_real):\n '''\n url = \"https://www.aeaweb.org/issues/\"\n url_real = url + str(number)\n html = get_onepage_url(url_real)\n urls = parse_one_page(html)\n for item in urls:\n write_to_file(item)\n '''\n html = get_onepage_url(url_real)\n for item in parse_one_page(html):\n write_to_file(item)\nif __name__==\"__main__\":\n url = \"https://www.aeaweb.org/journals/aer/issues\"\n #pool = Pool()\n #pool.map(main,[i for i in range(0,500,1)])\n main(url)\n print('Done')\n\n","sub_path":"Spider_AER_1_GetEveryAerIssuesUrls.py","file_name":"Spider_AER_1_GetEveryAerIssuesUrls.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"334862145","text":"import numpy as np\nimport os\n\nfpath = ''\nfile_list = os.listdir(fpath)\nras_files = [f[:-3] for f in file_list if f[-3:] == 'ras']\nxye_files = [f[:-3] for f in file_list if f[-3:] == 'xye']\nras_notxye = [rf for rf in ras_files if rf not in xye_files]\nsizes = [(os.stat(r + 'ras')).st_size for r in ras_notxye]\nras_todo = [rn for i, rn in enumerate(ras_notxye) if sizes[i] > 0]\n\nfor rf in ras_todo:\n fname = rf + 'ras'\n new_fname = fname[:-3] + 'xye'\n data = np.genfromtxt(fname, comments='*', usecols=[0, 1])\n new_data = np.column_stack((data, data[:, 1]**0.5))\n np.savetxt(new_fname, new_data)\n print(f'Written {new_fname}')\n\n","sub_path":"ras_to_xye.py","file_name":"ras_to_xye.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"543335770","text":"import json\n\nimport numpy as np\n\nfrom sklearn.preprocessing import MinMaxScaler\n\nsc = MinMaxScaler(feature_range=(-.5, .5))\n\n\ndef noise_norm():\n r = corpus_normus()\n k, v = zip(*r.items())\n v = sc.fit_transform(np.asarray(v).reshape(-1, 1))\n v = [_.tolist()[0] for _ in v]\n return dict(zip(k, v))\n # s = sum(r.values())\n # return {k: v / s for k, v in r.items()}\n\n\ndef corpus_normus():\n r = json.load(open('./noise_200.json'))\n return {k: v - r['corpus'] for k, v in r.items()}\n\n\nprint(noise_norm())\n# print(corpus_normus())\n","sub_path":"get_process_data/calc_noise_ratio.py","file_name":"calc_noise_ratio.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"611657800","text":"#!/usr/bin/env python3\nimport time\nimport re\nimport sys\nimport textwrap\n\nimport mailsh\nimport mailsh.mail\n\n\n@mailsh.register_command(\"head\", dry_run_safe=True)\ndef head_command(mdc, inputs, argv):\n \"\"\"\n Usage: head [COUNT]\n\n Return, at most, the specified number of messages or lines. When\n unspecified, this defaults to 10.\n \"\"\"\n if argv:\n if len(argv) > 1:\n raise ValueError(\"Command expects one argumnet or none.\")\n try:\n limit = int(argv[0])\n except ValueError:\n raise ValueError(\"%r is not a number.\" % (argv[0],))\n else:\n limit = 10\n\n for index, item in enumerate(inputs):\n if index >= limit:\n break\n yield item\n\n\n@mailsh.register_command(\"invert\", dry_run_safe=True)\ndef invert_command(mdc, inputs, argv):\n inputs = set(inputs)\n for message in mdc.listmessages(poll_new=False):\n if message not in inputs:\n yield message\n\n\n@mailsh.register_command(\"help\", dry_run_safe=True)\ndef help_command(mdc, inputs, argv):\n \"\"\"\n Usage: help [COMMAND]...\n\n Display documentation for interpreter commands.\n \"\"\"\n if argv:\n for command in argv or (\"help\",):\n yield command + \":\"\n if command in mailsh.runtime.Interpreter.commands:\n function = mailsh.runtime.Interpreter.commands[command]\n docstring = function.__doc__ or \"This command is undocumented.\"\n dedented = textwrap.dedent(docstring)\n formatted = dedented.replace(\"\\n\", \"\\n \").strip()\n else:\n formatted = \" Unrecognized command.\"\n\n yield formatted\n else:\n yield \"Valid commands:\"\n for command in sorted(mailsh.runtime.Interpreter.commands):\n yield \"- \" + command\n\n\n@mailsh.register_command(\"count\", dry_run_safe=True)\ndef count_command(mdc, inputs, argv):\n \"\"\"\n Usage: count\n\n Display number of items piped into command.\n \"\"\"\n yield len(tuple(inputs))\n\n\n@mailsh.register_command(\"cd\")\ndef cd_command(mdc, inputs, argv):\n \"\"\"\n Usage: cd FOLDER\n\n Change current working directory.\n \"\"\"\n if not argv:\n raise Exception(\"Expected argument.\")\n elif len(argv) != 1:\n raise Exception(\"Too many arguments passed.\")\n else:\n mdc.chdir(argv[0])\n\n\n@mailsh.register_command(\"dirs\", dry_run_safe=True)\ndef dirs_command(mdc, inputs, argv):\n \"\"\"\n Usages: dirs [FOLDER]\n\n List directories in folder. If FOLDER is unspecified, the current working\n directory is used.\n \"\"\"\n yield from mdc.listdirs()\n\n\n@mailsh.register_command(\"pwd\", dry_run_safe=True)\ndef pwd_command(mdc, inputs, argv):\n \"\"\"\n Usage: pwd\n\n Display current (present) working directory.\n \"\"\"\n yield mdc.cwd\n\n\n@mailsh.register_filter(\"grep\", dry_run_safe=True)\ndef grep_filter(mdc, value, argv):\n if argv:\n expression = \"|\".join(argv)\n else:\n return True\n\n if isinstance(value, mailsh.mail.Message):\n value = str(value.headers)\n\n return bool(re.search(expression, value, re.I | re.U))\n\n\ndef message_date(message):\n date = message.headers.get(\"date\", \"\")\n timestamps = mailsh.mail.extract_timestamps(date)\n if timestamps:\n return timestamps[0]\n else:\n return 0\n\n\ndef time_received(message):\n received = message.headers.get_all(\"received\", tuple())\n timestamps = mailsh.mail.extract_timestamps(\" \".join(received))\n if timestamps:\n return max(timestamps)\n else:\n return 0\n\n\n@mailsh.register_command(\"sort\", dry_run_safe=True)\ndef sort_command(mdc, inputs, argv):\n if not argv:\n raise Exception(\"Expected argument.\")\n elif len(argv) != 1:\n raise Exception(\"Too many arguments passed.\")\n else:\n field = argv[0].lower()\n\n if field == \"date\":\n key = message_date\n elif field == \"received\":\n key = time_received\n else:\n key = lambda m: m.headers.get(field, \"\")\n\n yield from sorted(inputs, key=key, reverse=True)\n\n\n@mailsh.register_command(\"pick\")\ndef pick_command(mdc, inputs, argv):\n choices = set(map(int, argv))\n if choices:\n for i, item in enumerate(inputs, 1):\n if i in choices:\n yield item\n else:\n yield from inputs\n","sub_path":"src/runcontrol.py","file_name":"runcontrol.py","file_ext":"py","file_size_in_byte":4304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"161019393","text":"#!/usr/bin/env python3.7\n# -*- coding: utf-8 -*-\n\n\"\"\"\nAdd a description ....\n\n\"\"\"\n\n__author__ = \"Dylan Hamel\"\n__maintainer__ = \"Dylan Hamel\"\n__version__ = \"1.0\"\n__email__ = \"dylan.hamel@protonmail.com\"\n__status__ = \"Prototype\"\n__copyright__ = \"Copyright 2019\"\n\n########################################################################################################################\n#\n# HEADERS\n#\n\nERROR_HEADER = \"Error import [mtu_gets.py]\"\nHEADER_GET = \"[netests - get_mtu]\"\n\n########################################################################################################################\n#\n# Import Library\n#\n\ntry:\n from const.constants import *\nexcept ImportError as importError:\n print(f\"{ERROR_HEADER} const.constants\")\n exit(EXIT_FAILURE)\n print(importError)\n\ntry:\n from functions.mtu.mtu_converters import _napalm_mtu_converter\n from functions.mtu.mtu_converters import _ios_mtu_converter\n from functions.mtu.mtu_converters import _nexus_mtu_converter\n from functions.mtu.mtu_converters import _cumulus_mtu_converter\n from functions.mtu.mtu_converters import _iosxr_mtu_converter\n from functions.mtu.mtu_converters import _arista_mtu_converter\n from functions.mtu.mtu_converters import _juniper_mtu_converter\n from functions.mtu.mtu_converters import _extreme_vsp_mtu_converter\nexcept ImportError as importError:\n print(f\"{ERROR_HEADER} functions.mtu.mtu_converters\")\n print(importError)\n exit(EXIT_FAILURE)\n\ntry:\n from nornir.core import Nornir\n # To use advanced filters\n from nornir.core.filter import F\n # To execute netmiko commands\n from nornir.plugins.tasks.networking import netmiko_send_command\n # To execute napalm get config\n from nornir.plugins.tasks.networking import napalm_get\n # To print task results\n from nornir.plugins.functions.text import print_result\nexcept ImportError as importError:\n print(f\"{ERROR_HEADER} nornir\")\n print(importError)\n exit(EXIT_FAILURE)\n\ntry:\n import json\nexcept ImportError as importError:\n print(f\"{ERROR_HEADER} json\")\n exit(EXIT_FAILURE)\n print(importError)\n\ntry:\n import textfsm\nexcept ImportError as importError:\n print(f\"{ERROR_HEADER} textfsm\")\n exit(EXIT_FAILURE)\n print(importError)\n\n########################################################################################################################\n#\n# Functions\n#\ndef get_mtu(nr: Nornir, filters={}, level=None, own_vars={}):\n\n devices = nr.filter()\n\n if len(devices.inventory.hosts) == 0:\n raise Exception(f\"[{HEADER_GET}] no device selected.\")\n\t\t\n data = devices.run(\n task=generic_mtu_get,\n on_failed=True,\n num_workers=10\n )\n #print_result(data)\n\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Generic function\n#\ndef generic_mtu_get(task):\n\n use_ssh = False\n\n if NEXUS_PLATEFORM_NAME in task.host.platform or ARISTA_PLATEFORM_NAME in task.host.platform or \\\n CISCO_IOSXR_PLATEFORM_NAME in task.host.platform or CISCO_IOS_PLATEFORM_NAME in task.host.platform or \\\n JUNOS_PLATEFORM_NAME in task.host.platform:\n if 'connexion' in task.host.keys():\n if task.host.data.get('connexion', NOT_SET) == 'ssh' or task.host.get('connexion', NOT_SET):\n use_ssh = True\n\n if task.host.platform == CUMULUS_PLATEFORM_NAME:\n _cumulus_get_mtu(task)\n\n elif task.host.platform == EXTREME_PLATEFORM_NAME:\n _extreme_vsp_get_mtu(task)\n\n elif task.host.platform in NAPALM_COMPATIBLE_PLATEFORM :\n if use_ssh and NEXUS_PLATEFORM_NAME == task.host.platform:\n _nexus_get_mtu(task)\n\n elif use_ssh and CISCO_IOS_PLATEFORM_NAME == task.host.platform:\n _ios_get_mtu(task)\n\n elif use_ssh and CISCO_IOSXR_PLATEFORM_NAME == task.host.platform:\n _iosxr_get_mtu(task)\n\n elif use_ssh and ARISTA_PLATEFORM_NAME == task.host.platform:\n _arista_get_mtu(task)\n \n elif use_ssh and JUNOS_PLATEFORM_NAME == task.host.platform:\n _juniper_get_mtu(task)\n\n else:\n _generic_mtu_napalm(task)\n\n else:\n # RAISE EXCEPTIONS\n print(f\"{HEADER_GET} No plateform selected for {task.host.name}...\")\n\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Generic Napalm\n#\ndef _generic_mtu_napalm(task):\n pass\n \"\"\"\n print(f\"Start _generic_mtu_napalm with {task.host.name} \")\n\n output = task.run(\n name=f\"NAPALM get_interfaces {task.host.platform}\",\n task=napalm_get,\n getters=[\"interfaces\"],\n retrieve=\"all\"\n )\n # print(output.result)\n\n if output.result != \"\":\n bgp_sessions = _napalm_mtu_converter(\n task.host.name,\n output.result\n )\n\n task.host[MTU_DATA_HOST_KEY] = bgp_sessions\n \"\"\"\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Cumulus Networks\n#\ndef _cumulus_get_mtu(task):\n\n output = task.run(\n name=f\"{CUMULUS_GET_MTU}\",\n task=netmiko_send_command,\n command_string=CUMULUS_GET_MTU\n )\n # print_result(output)\n\n mtu_interfaces = _cumulus_mtu_converter(\n hostname=task.host.name,\n cmd_output=json.loads(output.result)\n )\n\n task.host[MTU_DATA_HOST_KEY] = mtu_interfaces\n\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Cisco Nexus (NXOS)\n#\ndef _nexus_get_mtu(task):\n\n output = task.run(\n name=f\"{NEXUS_GET_MTU}\",\n task=netmiko_send_command,\n command_string=NEXUS_GET_MTU\n )\n # print_result(output)\n\n mtu_interfaces = _nexus_mtu_converter(\n hostname=task.host.name,\n cmd_output=json.loads(output.result)\n )\n\n task.host[MTU_DATA_HOST_KEY] = mtu_interfaces\n\t\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Cisco IOS\n#\ndef _ios_get_mtu(task):\n\n output = task.run(\n name=f\"{IOS_GET_MTU}\",\n task=netmiko_send_command,\n command_string=IOS_GET_MTU\n )\n #print_result(output)\n\n if output.result != \"\":\n template = open(\n f\"{TEXTFSM_PATH}cisco_ios_show_interface.textfsm\")\n results_template = textfsm.TextFSM(template)\n \n parsed_results = results_template.ParseText(output.result)\n # Result Example =\n #\n #\n # type = list() of list()\n\n mtu_interfaces = _ios_mtu_converter(\n hostname=task.host.name,\n cmd_output=parsed_results\n )\n\n task.host[MTU_DATA_HOST_KEY] = mtu_interfaces\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Cisco IOSXR\n#\ndef _iosxr_get_mtu(task):\n pass\n\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Arista vEOS\n#\ndef _arista_get_mtu(task):\n\n output = task.run(\n name=f\"{ARISTA_GET_MTU}\",\n task=netmiko_send_command,\n command_string=ARISTA_GET_MTU\n )\n # print_result(output)\n\n mtu_interfaces = _arista_mtu_converter(\n hostname=task.host.name,\n cmd_output=json.loads(output.result)\n )\n\n task.host[MTU_DATA_HOST_KEY] = mtu_interfaces\n\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Juniper Networks\n#\ndef _juniper_get_mtu(task):\n\n output = task.run(\n name=f\"{JUNOS_GET_MTU}\",\n task=netmiko_send_command,\n command_string=JUNOS_GET_MTU\n )\n # print_result(output)\n\n mtu_interfaces = _juniper_mtu_converter(\n hostname=task.host.name,\n cmd_output=json.loads(output.result)\n )\n\n task.host[MTU_DATA_HOST_KEY] = mtu_interfaces\n\t\n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Extreme Networks\n#\ndef _extreme_vsp_get_mtu(task):\n\n output = task.run(\n name=f\"{EXTREME_VSP_GET_MTU}\",\n task=netmiko_send_command,\n command_string=EXTREME_VSP_GET_MTU,\n enable=True\n )\n # print_result(output)\n\n if output.result != \"\":\n template = open(\n f\"{TEXTFSM_PATH}extreme_vsp_show_interfaces_gigabitethernet.textfsm\")\n results_template = textfsm.TextFSM(template)\n\n parsed_results = results_template.ParseText(output.result)\n # Result Example =\n #\n #\n # type = list() of list()\n\n mtu_interfaces = _extreme_vsp_mtu_converter(\n hostname=task.host.name,\n cmd_output=parsed_results\n )\n\n task.host[MTU_DATA_HOST_KEY] = mtu_interfaces\n \n# ----------------------------------------------------------------------------------------------------------------------\n#\n# Next Device\n#\n","sub_path":"functions/mtu/mtu_get.py","file_name":"mtu_get.py","file_ext":"py","file_size_in_byte":9092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"504843632","text":"from io import BytesIO\n\nimport numpy\nimport networkx as nx\n\nimport matplotlib as mpl\n\nmpl.use(\"svg\")\nfrom matplotlib import pyplot as plt\n\n\ndef render(\n g, request_nodes, subgraph_nodes, xs=None, ys=None, node_size=None, zoom=None\n):\n if xs is None:\n xs = 1\n if ys is None:\n ys = 1\n if node_size is None:\n node_size = 10\n if zoom is None:\n zoom = 1\n\n ns = numpy.pi * ((node_size * zoom) ** 2) / 4\n\n fig, ax = plt.subplots(figsize=(8 * zoom, 8 * zoom))\n\n ax.set_aspect(float(ys) / float(xs))\n ax.set_xticks([])\n ax.set_yticks([])\n ax.axis(\"off\")\n\n pos = nx.nx_pydot.pydot_layout(g, prog=\"dot\")\n\n sgs = [[n[\"id\"] for n in ng[\"nodes\"]] for ng in subgraph_nodes]\n req = [n[\"id\"] for n in request_nodes]\n\n basenodes = nx.draw_networkx(\n g,\n pos=pos,\n ax=ax,\n node_size=ns,\n node_color=\"lightsteelblue\",\n with_labels=True,\n font_size=5 * zoom,\n label=\"all nodes\",\n )\n\n for i, _nodes in enumerate(sgs):\n subgraphs = nx.draw_networkx_nodes(\n g.subgraph(_nodes),\n ax=ax,\n pos=pos,\n node_size=ns,\n node_color=f\"C{i}\",\n with_labels=True,\n font_size=5 * zoom,\n label=f\"subgraph {i+1}\",\n )\n\n selection = nx.draw_networkx_nodes(\n g.subgraph(req),\n ax=ax,\n pos=pos,\n node_shape=\"o\",\n node_color=\"none\",\n node_size=ns * 2,\n with_labels=False,\n label=\"requested nodes\",\n )\n\n selection.set_edgecolor(\"firebrick\")\n selection.set_linewidths(1.5)\n\n ax.legend(loc=\"upper center\", ncol=4, bbox_to_anchor=(0.5, -0.05))\n ax.set_title(\"User requested nodes: \" + \", \".join(req))\n\n return fig\n\n\ndef fig_to_image(fig):\n img = BytesIO()\n fig.savefig(img, bbox_inches=\"tight\")\n img.seek(0)\n\n return img\n","sub_path":"nereid/nereid/network/render.py","file_name":"render.py","file_ext":"py","file_size_in_byte":1898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"552630821","text":"from __future__ import absolute_import\nimport types\nimport re\nimport cgi\nfrom functools import wraps\nimport datetime\n\nfrom decorators import FuncDecorator\n\nfrom ..exception import CallError, AccessDenied\nfrom . import auth\nfrom .base import TargetDecorator\n\n\nclass ratelimit(TargetDecorator):\n \"\"\"Rate limit a certain endpoint\n\n example --\n\n from endpoints import Controller\n from endpoints.decorators import ratelimit\n\n class Default(Controller):\n @ratelimit(10, 3600) # you can make 10 requests per hour\n def GET(self):\n return \"hello world\"\n \"\"\"\n def target(self, request, key, limit, ttl):\n \"\"\"this is what is run to check if requests should be throttled\n\n you should override this method if you want to customize the implementation\n\n request -- Request -- the request instance\n key -- string -- the unique key for the endpoint\n limit -- int -- max requests that should be received in ttl\n ttl -- int -- how many seconds they should be throttled for (3600 = 1 hour)\n \"\"\"\n now = datetime.datetime.utcnow()\n count = 1\n calls = getattr(self, \"_calls\", {})\n if not calls:\n calls = {}\n\n if key in calls:\n count = calls[key][\"count\"] + 1\n if count > limit:\n td = now - calls[key][\"date\"]\n if td.total_seconds() < ttl:\n raise ValueError(\n \"Please wait {} seconds to make another request\".format(ttl - td.seconds)\n )\n\n else:\n count = 1 # we are starting over\n\n calls[key] = {\n \"count\": count,\n \"date\": now,\n }\n\n self._calls = calls\n return True\n\n def normalize_target_params(self, request, controller_args, controller_kwargs):\n kwargs = {\n \"request\": request,\n \"key\": self.normalize_key(\n request,\n controller_args=controller_args,\n controller_kwargs=controller_kwargs,\n ),\n \"limit\": self.limit,\n \"ttl\": self.ttl,\n }\n return [], kwargs\n\n def normalize_key(self, request, *args, **kwargs):\n \"\"\"if you don't want to override target but do want to customize the key,\n override this method, this is mainly for convenience of child classes\"\"\"\n return \"{}.{}\".format(request.ip, request.path)\n\n def handle_error(self, e):\n \"\"\"all exceptions should generate 429 responses\"\"\"\n raise CallError(429, e.message)\n\n def decorate(self, func, limit, ttl, *anoop, **kwnoop):\n \"\"\"see target for an explanation of limit and ttl\"\"\"\n self.limit = int(limit)\n self.ttl = int(ttl)\n return super(ratelimit, self).decorate(func, target=None, *anoop, **kwnoop)\n\n\nclass httpcache(FuncDecorator):\n \"\"\"\n sets the cache headers so the response can be cached by the client\n\n link -- https://developers.google.com/web/fundamentals/performance/optimizing-content-efficiency/http-caching\n\n ttl -- integer -- how many seconds to have the client cache the request\n \"\"\"\n def decorate(self, func, ttl):\n def decorated(self, *args, **kwargs):\n self.response.add_headers({\n \"Cache-Control\": \"max-age={}\".format(ttl),\n })\n return func(self, *args, **kwargs)\n # TODO -- figure out how to set ETag\n #if not self.response.has_header('ETag')\n\n return decorated\n\n\nclass nohttpcache(FuncDecorator):\n \"\"\"\n sets all the no cache headers so the response won't be cached by the client\n \"\"\"\n def decorate(self, func):\n def decorated(self, *args, **kwargs):\n self.response.add_headers({\n \"Cache-Control\": \"no-cache, no-store, must-revalidate\",\n \"Pragma\": \"no-cache\", \n \"Expires\": \"0\"\n })\n return func(self, *args, **kwargs)\n\n return decorated\n\n\nclass _property(object):\n \"\"\"A memoized @property that is only evaluated once, and then stored at _property\n and retrieved from there unless deleted, in which case this would be called\n again and stored in _property again.\n\n See http://www.reddit.com/r/Python/comments/ejp25/cached_property_decorator_that_is_memory_friendly/\n see https://docs.python.org/2/howto/descriptor.html\n see http://stackoverflow.com/questions/17330160/python-how-does-the-property-decorator-work\n see https://docs.python.org/2/howto/descriptor.html\n\n options you can use to further customize the property\n\n read_only -- boolean (default False) -- set to de-activate set and del methods\n allow_empty -- boolean (default True) -- False to not cache empty values (eg, None, \"\")\n setter -- boolean -- set this to true if you want the decorated method to act as the setter\n instead of the getter, this will cause a default getter to be created that just returns\n _name (you should set self._name in your setter)\n deleter -- boolean -- same as setter, set to True to have the method act as the deleter\n \"\"\"\n def __init__(self, *args, **kwargs):\n self.allow_empty = kwargs.get('allow_empty', True)\n self.has_setter = kwargs.get('setter', False)\n self.has_deleter = kwargs.get('deleter', False)\n\n has_no_write = not self.has_setter and not self.has_deleter\n self.has_getter = kwargs.get('getter', has_no_write)\n\n # mutually exclusive to having defined a setter or deleter\n self.read_only = kwargs.get('read_only', False) and has_no_write\n\n if args:\n if isinstance(args[0], bool):\n self.read_only = args[0]\n\n else:\n # support _property(fget, fset, fdel, desc) also\n total_args = len(args)\n self.set_method(args[0])\n if total_args > 1:\n self.setter(args[1])\n if total_args > 2:\n self.deleter(args[2])\n if total_args > 3:\n self.__doc__ = args[3]\n\n # support _property(fget=getter, fset=setter, fdel=deleter, doc=\"\")\n if \"fget\" in kwargs:\n self.set_method(kwargs[\"fget\"])\n if \"fset\" in kwargs:\n self.setter(kwargs[\"fset\"])\n if \"fdel\" in kwargs:\n self.deleter(kwargs[\"fdel\"])\n if \"doc\" in kwargs:\n self.__doc__ = kwargs[\"doc\"]\n\n def set_method(self, method):\n self.fget = method if self.has_getter else self.default_get\n self.fset = method if self.has_setter else self.default_set\n self.fdel = method if self.has_deleter else self.default_del\n self.__doc__ = method.__doc__\n self.__name__ = method.__name__\n self.name = '_{}'.format(self.__name__)\n\n def __call__(self, *args, **kwargs):\n if args:\n self.set_method(args[0])\n\n return self\n\n def __get__(self, instance, cls):\n # return the cached value if it exists\n val = None\n name = self.name\n if name in instance.__dict__:\n val = instance.__dict__[name]\n\n else:\n try:\n val = self.fget(instance)\n if val or self.allow_empty:\n # We don't do fset here because that causes unexpected bahavior\n # if you ever override the setter, causing the setter to be fired\n # every time the getter is called, which confused me for about\n # an hour before I figured out what was happening\n self.default_set(instance, val)\n\n except Exception:\n # make sure no value gets set no matter what\n instance.__dict__.pop(name, None)\n raise\n\n return val\n\n def default_get(self, instance):\n try:\n return instance.__dict__[self.name]\n except KeyError:\n raise AttributeError(\"can't get attribute {}\".format(self.__name__))\n\n def default_set(self, instance, val):\n instance.__dict__[self.name] = val\n\n def __set__(self, instance, val):\n if self.read_only:\n raise AttributeError(\"can't set attribute {}\".format(self.__name__))\n\n if val or self.allow_empty:\n self.fset(instance, val)\n\n def default_del(self, instance):\n instance.__dict__.pop(self.name, None)\n\n def __delete__(self, instance, *args):\n if self.read_only:\n raise AttributeError(\"can't delete attribute {}\".format(self.__name__))\n\n self.fdel(instance)\n\n def getter(self, fget):\n self.fget = fget\n self.has_getter = True\n return self\n\n def setter(self, fset):\n self.fset = fset\n self.has_setter = True\n return self\n\n def deleter(self, fdel):\n self.fdel = fdel\n self.has_deleter = True\n return self\n\n\nclass _propertyset(_property):\n def set_method(self, method):\n super(_propertyset, self).set_method(method)\n\n # switch get and set since this method is \n self.fget = self.default_get\n self.fset = method\n\n def set_method(self, method):\n self.fget = method\n self.fset = self.default_set\n self.fdel = self.default_del\n self.__doc__ = method.__doc__\n self.__name__ = method.__name__\n self.name = '_{}'.format(self.__name__)\n\n\n\nclass param(object):\n \"\"\"\n decorator to allow setting certain expected query/body values and options\n\n this tries to be as similar to python's built-in argparse as possible\n\n This checks both POST and GET query args, if you would like to only check POST,\n use post_param, if you only want to check GET, then use get_param\n\n example --\n\n @decorators.param('name', type=int, nargs='+', action='store_list')\n\n name -- string -- the name of the query_param\n **flags -- dict\n type -- type -- a python type like int or float\n action -- string --\n store -- default\n store_false -- set if you want default to be true, and false if param is\n passed in\n store_true -- opposite of store_false\n store_list -- set to have a value like 1,2,3 be blown up to ['1', '2', '3']\n append -- if multiple param values should be turned into an array\n eg, foo=1&foo=2 would become foo=[1, 2]\n append_list -- it's store_list + append, so foo=1&foo=2,3 would be foo=[1, 2, 3]\n default -- mixed -- the value that should be set if query param isn't there, if this is\n callable (eg, time.time or datetime.utcnow) then it will be called every time the\n decorated method is called\n required -- boolean -- True if param is required, default is true\n choices -- set() -- a set of values to be in tested against (eg, val in choices)\n matches -- regex -- TODO -- a regular expression that the value needs to match\n allow_empty -- boolean -- True allows values like False, 0, '' through,\n default False, this will also let through any empty value that was set\n via the default flag\n max_size -- int -- the maximum size of the param\n min_size -- int -- the minimum size of the param\n regex -- regexObject -- if you would like the param to be validated with a regular\n exception, uses the re.search() method\n\n raises -- \n CallError -- with 400 status code on any param validation failures\n \"\"\"\n def __init__(self, *names, **flags):\n self.name = names[0]\n self.names = names\n self.flags = flags\n\n def normalize_default(self, default):\n ret = default\n if isinstance(default, dict):\n ret = dict(default)\n\n elif isinstance(default, list):\n ret = list(default)\n\n return ret\n\n def find_param(self, names, required, default, request, args, kwargs):\n \"\"\"actually try to retrieve names key from params dict\"\"\"\n val = default\n found_name = ''\n for name in names:\n if name in kwargs:\n val = kwargs[name]\n found_name = name\n break\n\n if not found_name and required:\n raise CallError(400, \"required param {} was not present\".format(self.name))\n\n return found_name, val\n\n def normalize_param(self, slf, args, kwargs):\n \"\"\"this is where all the magic happens, this will try and find the param and\n put its value in kwargs if it has a default and stuff\"\"\"\n flags = self.flags\n name = self.name\n dest_name = flags.get('dest', name)\n ptype = flags.get('type', None)\n paction = flags.get('action', 'store')\n if paction == 'store_false':\n flags['default'] = True \n ptype = bool\n\n elif paction == 'store_true':\n flags['default'] = False\n ptype = bool\n\n pdefault = self.normalize_default(flags.get('default', None))\n if callable(pdefault): pdefault = pdefault()\n\n prequired = False if 'default' in flags else flags.get('required', True)\n pchoices = flags.get('choices', None)\n allow_empty = flags.get('allow_empty', False)\n min_size = flags.get('min_size', None)\n max_size = flags.get('max_size', None)\n regex = flags.get('regex', None)\n\n normalize = True\n request = slf.request\n found_name, val = self.find_param(self.names, prequired, pdefault, request, args, kwargs)\n if found_name:\n kwargs.pop(found_name)\n else:\n normalize = 'default' in flags\n\n if normalize:\n if paction in set(['store_list']):\n if isinstance(val, list) and len(val) > 1:\n raise CallError(400, \"too many values for param {}\".format(name))\n\n if isinstance(val, basestring):\n val = val.split(',')\n\n else:\n val = list(val)\n\n elif paction in set(['append', 'append_list']):\n if not isinstance(val, list):\n val = [val]\n\n if paction == 'append_list':\n vs = []\n for v in val:\n if isinstance(v, basestring):\n vs.extend(v.split(','))\n else:\n vs.append(v)\n\n val = vs\n\n else:\n if paction not in set(['store', 'store_false', 'store_true']):\n raise ValueError('unknown param action {}'.format(paction))\n\n if ptype:\n if isinstance(val, list) and ptype != list:\n val = map(ptype, val)\n\n else:\n if isinstance(ptype, type):\n if issubclass(ptype, bool):\n if val in set(['true', 'True', '1']):\n val = True\n elif val in set(['false', 'False', '0']):\n val = False\n else:\n val = ptype(val)\n\n elif issubclass(ptype, str):\n charset = request.charset\n if charset and isinstance(val, unicode):\n val = val.encode(charset)\n else:\n val = ptype(val)\n\n else:\n val = ptype(val)\n\n else:\n val = ptype(val)\n\n if pchoices:\n if val not in pchoices:\n raise CallError(400, \"param {} with value {} not in choices {}\".format(name, val, pchoices))\n\n # at some point this if statement is just going to be too ridiculous\n # FieldStorage check is because of this bug https://bugs.python.org/issue19097\n if not allow_empty and val is not False and not val and not isinstance(val, cgi.FieldStorage):\n if 'default' not in flags:\n raise CallError(400, \"param {} was empty\".format(name))\n\n if min_size is not None:\n failed = False\n if isinstance(val, (int, float)):\n if val < min_size: failed = True\n else:\n if len(val) < min_size: failed = True\n\n if failed:\n raise CallError(400, \"param {} was smaller than {}\".format(name, min_size))\n\n if max_size is not None:\n failed = False\n if isinstance(val, (int, float)):\n if val > max_size: failed = True\n else:\n if len(val) > max_size: failed = True\n\n if failed:\n raise CallError(400, \"param {} was bigger than {}\".format(name, max_size))\n\n if regex:\n failed = False\n if isinstance(regex, basestring):\n if not re.search(regex, val): failed = True\n else:\n if not regex.search(val): failed = True\n\n if failed:\n raise CallError(400, \"param {} failed regex check\".format(name))\n\n kwargs[dest_name] = val\n\n return slf, args, kwargs\n\n def __call__(slf, func):\n position = func.__dict__.get('param_position', 0)\n @wraps(func)\n def wrapper(self, *args, **kwargs):\n slf.position = wrapper.__dict__.get('param_position', 0)\n self, args, kwargs = slf.normalize_param(self, args, kwargs)\n return func(self, *args, **kwargs)\n\n position = wrapper.__dict__.setdefault('param_position', position + 1)\n return wrapper\n\n\nclass get_param(param):\n \"\"\"same as param, but only checks GET params\"\"\"\n def find_param(self, names, required, default, request, args, kwargs):\n try:\n return super(get_param, self).find_param(\n names,\n required,\n default,\n request,\n args,\n request.query_kwargs\n )\n\n except CallError:\n raise CallError(400, \"required param {} was not present in GET params\".format(self.name))\n\n\nclass post_param(param):\n \"\"\"same as param but only checks POST params\"\"\"\n def find_param(self, names, required, default, request, args, kwargs):\n try:\n return super(post_param, self).find_param(\n names,\n required,\n default,\n request,\n args,\n request.body_kwargs\n )\n\n except CallError:\n raise CallError(400, \"required param {} was not present in POST params\".format(self.name))\n\n\nclass require_params(object):\n \"\"\"\n if you want to make sure that certain params are present in the request\n\n If the request is a GET request, then the params checked are in the query string,\n if the method is POST, PUT, then the params checked are in the body\n\n example --\n # make request fail if foo and bar aren't in the request body\n @require_params(\"foo\", \"bar\")\n def POST(self, *args, **kwargs):\n pass\n\n **param_options -- dict\n allow_empty -- boolean -- True if passed in param names can have values\n that evaluate to False (like 0 or \"\")\n \"\"\"\n def __init__(self, *req_param_names, **param_options):\n self.req_param_names = req_param_names\n self.param_options = param_options\n\n def __call__(slf, func):\n param_options = slf.param_options\n req_param_names = slf.req_param_names\n not_empty = not param_options.pop('allow_empty', False)\n\n @wraps(func)\n def decorated(self, *args, **kwargs):\n for req_param_name in req_param_names:\n if req_param_name not in kwargs:\n raise CallError(400, \"required param {} was not present\".format(req_param_name))\n\n if not_empty and not kwargs[req_param_name]:\n raise CallError(400, \"required param {} was empty\".format(req_param_name))\n\n return func(self, *args, **kwargs)\n\n return decorated\n\n","sub_path":"endpoints/decorators/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":20427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"573060564","text":"import matplotlib.pyplot as plt\nfrom tensorflow import keras\nimport numpy as np\nimage_number = 23 \ndata = keras.datasets.mnist\n(train_images, train_labels), (test_images, test_labels) = data.load_data()\nmodel = keras.Sequential([\n keras.layers.Flatten(input_shape=(28, 28)),\n keras.layers.Dense(128, activation=\"relu\"),\n keras.layers.Dense(10, activation=\"softmax\")\n])\nmodel.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\nmodel.fit(train_images, train_labels, epochs=1)\nloss, acc = model.evaluate(test_images, test_labels)\nprint(f\"{acc*100}% accuracy\")\npredictions = model.predict(test_images)\nprint(np.argmax(predictions[image_number]))\nplt.imshow(test_images[image_number].reshape(28, 28), cmap='gray')\nplt.show()\n\n","sub_path":"numbers_tensorflow.py","file_name":"numbers_tensorflow.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"601592895","text":"import os\nimport six\n\nimport traitlets\nimport itk\nimport numpy as np\ntry:\n import zstandard as zstd\nexcept ImportError:\n import zstd\ntry:\n from functools import reduce\nexcept ImportError:\n pass\n\ndef is_arraylike(arr):\n return hasattr(arr, 'shape') and \\\n hasattr(arr, 'dtype') and \\\n hasattr(arr, '__array__') and \\\n hasattr(arr, 'ndim')\n\nhave_imglyb = False\ntry:\n import imglyb\n have_imglyb = True\nexcept ImportError:\n pass\nhave_vtk = False\ntry:\n import vtk\n have_vtk = True\nexcept ImportError:\n pass\n\nclass ITKImage(traitlets.TraitType):\n \"\"\"A trait type holding an itk.Image object\"\"\"\n\n info_text = 'An N-dimensional, potentially multi-component, scientific ' + \\\n 'image with origin, spacing, and direction metadata'\n\n # Hold a reference to the source object to use with shallow views\n _source_object = None\n\n def validate(self, obj, value):\n if is_arraylike(value):\n array = np.asarray(value)\n self._source_object = array\n if array.flags['OWNDATA']:\n image_from_array = itk.GetImageViewFromArray(array)\n else:\n image_from_array = itk.GetImageFromArray(array)\n return image_from_array\n elif have_vtk and isinstance(value, vtk.vtkImageData):\n from vtk.util import numpy_support as vtk_numpy_support\n array = vtk_numpy_support.vtk_to_numpy(value.GetPointData().GetScalars())\n array.shape = tuple(value.GetDimensions())[::-1]\n self._source_object = array\n image_from_array = itk.GetImageViewFromArray(array)\n image_from_array.SetSpacing(value.GetSpacing())\n image_from_array.SetOrigin(value.GetOrigin())\n return image_from_array\n elif have_imglyb and isinstance(value,\n imglyb.util.ReferenceGuardingRandomAccessibleInterval):\n array = imglyb.to_numpy(value)\n self._source_object = array\n image_from_array = itk.GetImageViewFromArray(array)\n return image_from_array\n\n try:\n # an itk.Image or a filter that produces an Image\n # return itk.output(value)\n # Working around traitlets / ipywidgets update mechanism to\n # force an update. While the result of __eq__ can indicate it is\n # the same object, the actual contents may have changed, as\n # indicated by image.GetMTime()\n value = itk.output(value)\n self._source_object = value\n grafted = value.__New_orig__()\n grafted.Graft(value)\n return grafted\n except:\n self.error(obj, value)\n\ndef _image_to_type(itkimage):\n component_str = repr(itkimage).split('itkImagePython.')[1].split(';')[0][8:]\n if component_str[:2] == 'UL':\n if os.name == 'nt':\n return 'uint32_t',\n else:\n return 'uint64_t',\n mangle = None\n pixelType = 1\n if component_str[:2] == 'SL':\n if os.name == 'nt':\n return 'int32_t', 1,\n else:\n return 'int64_t', 1,\n if component_str[0] == 'V':\n # Vector\n mangle = component_str[1]\n pixelType = 5\n elif component_str[:2] == 'CF':\n # complex flot\n return 'float', 10\n elif component_str[:2] == 'CD':\n # complex flot\n return 'double', 10\n elif component_str[0] == 'C':\n # CovariantVector\n mangle = component_str[1]\n pixelType = 7\n elif component_str[0] == 'O':\n # Offset\n return 'int64_t', 4\n elif component_str[:2] == 'FA':\n # FixedArray\n mangle = component_str[2]\n pixelType = 11\n elif component_str[:4] == 'RGBA':\n # RGBA\n mangle = component_str[4:-1]\n pixelType = 3\n elif component_str[:3] == 'RGB':\n # RGB\n mangle = component_str[3:-1]\n pixelType = 2\n elif component_str[:4] == 'SSRT':\n # SymmetricSecondRankTensor\n mangle = component_str[4:-1]\n pixelType = 8\n else:\n mangle = component_str[:-1]\n _python_to_js = {\n 'SC':'int8_t',\n 'UC':'uint8_t',\n 'SS':'int16_t',\n 'US':'uint16_t',\n 'SI':'int32_t',\n 'UI':'uint32_t',\n 'F':'float',\n 'D':'double',\n 'B':'uint8_t'\n }\n return _python_to_js[mangle], pixelType\n\ndef itkimage_to_json(itkimage, manager=None):\n \"\"\"Serialize a Python itk.Image object.\n\n Attributes of this dictionary are to be passed to the JavaScript itkimage\n constructor.\n \"\"\"\n if itkimage is None:\n return None\n else:\n direction = itkimage.GetDirection()\n directionMatrix = direction.GetVnlMatrix()\n directionList = []\n dimension = itkimage.GetImageDimension()\n pixelArr = itk.GetArrayViewFromImage(itkimage)\n compressor = zstd.ZstdCompressor(level=3)\n compressed = compressor.compress(pixelArr.data)\n pixelArrCompressed = memoryview(compressed)\n for col in range(dimension):\n for row in range(dimension):\n directionList.append(directionMatrix.get(row, col))\n componentType, pixelType = _image_to_type(itkimage)\n imageType = dict(\n dimension=dimension,\n componentType=componentType,\n pixelType=pixelType,\n components=itkimage.GetNumberOfComponentsPerPixel()\n )\n return dict(\n imageType=imageType,\n origin=tuple(itkimage.GetOrigin()),\n spacing=tuple(itkimage.GetSpacing()),\n size=tuple(itkimage.GetBufferedRegion().GetSize()),\n direction={'data': directionList,\n 'rows': dimension,\n 'columns': dimension},\n compressedData=pixelArrCompressed\n )\n\n\ndef _type_to_image(jstype):\n _pixelType_to_prefix = {\n 1:'',\n 2:'RGB',\n 3:'RGBA',\n 4:'O',\n 5:'V',\n 7:'CV',\n 8:'SSRT',\n 11:'FA'\n }\n pixelType = jstype['pixelType']\n dimension = jstype['dimension']\n if pixelType == 10:\n if jstype['componentType'] == 'float':\n return itk.Image[itk.complex, itk.F], np.float32\n else:\n return itk.Image[itk.complex, itk.D], np.float64\n\n def _long_type():\n if os.name == 'nt':\n return 'LL'\n else:\n return 'L'\n prefix = _pixelType_to_prefix[pixelType]\n _js_to_python = {\n 'int8_t':'SC',\n 'uint8_t':'UC',\n 'int16_t':'SS',\n 'uint16_t':'US',\n 'int32_t':'SI',\n 'uint32_t':'UI',\n 'int64_t':'S' + _long_type(),\n 'uint64_t':'U' + _long_type(),\n 'float': 'F',\n 'double': 'D'\n }\n _js_to_numpy_dtype = {\n 'int8_t': np.int8,\n 'uint8_t': np.uint8,\n 'int16_t': np.int16,\n 'uint16_t': np.uint16,\n 'int32_t': np.int32,\n 'uint32_t': np.uint32,\n 'int64_t': np.int64,\n 'uint64_t': np.uint64,\n 'float': np.float32,\n 'double': np.float64\n }\n dtype = _js_to_numpy_dtype[jstype['componentType']]\n if pixelType != 4:\n prefix += _js_to_python[jstype['componentType']]\n if pixelType not in (1, 2, 3, 10):\n prefix += str(dimension)\n prefix += str(dimension)\n return getattr(itk.Image, prefix), dtype\n\ndef itkimage_from_json(js, manager=None):\n \"\"\"Deserialize a Javascript itk.js Image object.\"\"\"\n if js is None:\n return None\n else:\n ImageType, dtype = _type_to_image(js['imageType'])\n decompressor = zstd.ZstdDecompressor()\n if six.PY2:\n asBytes = js['compressedData'].tobytes()\n pixelBufferArrayCompressed = np.frombuffer(asBytes, dtype=dtype)\n else:\n pixelBufferArrayCompressed = np.frombuffer(js['compressedData'], dtype=dtype)\n pixelCount = reduce(lambda x, y: x*y, js['size'], 1)\n numberOfBytes = pixelCount * js['imageType']['components'] * np.dtype(dtype).itemsize\n pixelBufferArray = \\\n np.frombuffer(decompressor.decompress(pixelBufferArrayCompressed,\n numberOfBytes),\n dtype=dtype)\n pixelBufferArray.shape = js['size'][::-1]\n image = itk.PyBuffer[ImageType].GetImageFromArray(pixelBufferArray)\n Dimension = image.GetImageDimension()\n image.SetOrigin(js['origin'])\n image.SetSpacing(js['spacing'])\n direction = image.GetDirection()\n directionMatrix = direction.GetVnlMatrix()\n directionJs = js['direction']['data']\n for col in range(Dimension):\n for row in range(Dimension):\n directionMatrix.put(row, col, directionJs[col + row * Dimension])\n return image\n\nitkimage_serialization = {\n 'from_json': itkimage_from_json,\n 'to_json': itkimage_to_json\n}\n","sub_path":"itkwidgets/trait_types.py","file_name":"trait_types.py","file_ext":"py","file_size_in_byte":8916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"383991699","text":"# -*- coding: utf-8 -*- \r\nimport numpy as np\r\n\r\nn = int(input())\r\nt = list(map(int,input().split()))\r\nv = list(map(int,input().split()))\r\n\r\n\r\n#dp = [[0 for i in range(w+1)] for j in range(n+1)]\r\ndp = np.zeros((n+1,w+1),dtype='int')\r\n\r\nfor i in range(1,n+1):\r\n for j in range(1,w+1):\r\n if(j < a[i-1]):\r\n dp[i][j] = dp[i-1][j]\r\n else:\r\n dp[i][j] = max(dp[i-1][j],dp[i-1][j-a[i-1]]+b[i-1])\r\nprint(dp) ","sub_path":"BeginnerContest/077D.py","file_name":"077D.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"358898368","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\nimport os\nimport sys\n\nfrom mock import MagicMock, patch\n\nfrom splunk_eventgen.__main__ import parse_args\nfrom splunk_eventgen.eventgen_core import EventGenerator\nfrom splunk_eventgen.lib.plugins.output.syslogout import SyslogOutOutputPlugin\n\nFILE_DIR = os.path.dirname(os.path.abspath(__file__))\n\n\nclass TestSyslogOutputPlugin(object):\n def test_output_data_to_syslog(self):\n configfile = \"tests/sample_eventgen_conf/medium_test/eventgen.conf.syslogoutput\"\n testargs = [\"eventgen\", \"generate\", configfile]\n with patch.object(sys, \"argv\", testargs):\n with patch(\"logging.getLogger\"):\n pargs = parse_args()\n assert pargs.subcommand == \"generate\"\n assert pargs.configfile == configfile\n eventgen = EventGenerator(args=pargs)\n\n sample = MagicMock()\n sample.name = \"test\"\n sample.syslogDestinationHost = \"127.0.0.1\"\n sample.syslogDestinationPort = 9999\n syslogoutput = SyslogOutOutputPlugin(sample)\n\n eventgen.start()\n for i in range(1, 6):\n appearance = False\n for logger_call in syslogoutput._l.info.call_args_list:\n if \"WINDBAG Event {} of 5\".format(i) in str(logger_call):\n appearance = True\n if not appearance:\n assert False\n","sub_path":"tests/medium/plugins/test_syslog_output.py","file_name":"test_syslog_output.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"354720650","text":"'''\nConvert the raw ID3.txt to a csv file\nRemove the boolean value\n'''\n\nfile = open(\"Raw_Data\\\\ID3.txt\", \"r\")\ndest = open(\"Trans_Data\\\\ID3_new.csv\", \"w\")\ndest.write(\"latitude,longitude,time\\n\")\ndestList= []\n# Number of line to keep\nnbLineTODo = 10000000\ni = 0\n\nfor l in file:\n if i > nbLineTODo:\n break\n elif i == 0:\n i += 1\n continue\n pieces = l.split(\",\")\n newL = \"+\" + \",\".join([x for i, x in enumerate(pieces) if i in (0, 1, 3)])\n destList.append(newL)\n\ndestList.reverse()\nfor l in destList:\n dest.write(l)\n\nfile.close()\ndest.close()\n","sub_path":"convertID3.py","file_name":"convertID3.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"135090912","text":"name = 'Река'\n\nactions = [ 'Грести руками' ]\n\n\n\n\ndef get_actions(user):\n if user.has_item('m79'):\n actions.append('Использовать М79')\n if user.has_item('m-16'):\n actions.append('Использовать М-16')\n return actions\n\n\ndef enter(user, reply):\n reply( 'Дверь, через которую вы вошли, магическим образом исчезла.\\n'\n 'Вы решаете осмотреться и замечаете лодку, стоящую неподалёку.\\n'\n 'Вам ничего не остаётся, кроме как сесть в лодку и переплыть препятствие.\\n')\n user.set_room_temp('rvr', 0)\n\n\ndef action(user, reply, text):\n global rvr\n if text == actions[0]:\n rvr += 1\n reply('Вы гребёте руками. Кажется, что скоро конец')\n print(rvr)\n if rvr > 8:\n msg = ''\n if user.has_item('m-16'):\n user.remove_item('m-16')\n msg += 'Вы уронили свой М-16, пока плыли. Ну хоть себя не выронили.\\n'\n msg += 'Вы пристаёте к другому концу берега, вылезаете из лодки.\\n'\n msg += 'Единственное место, куда вы можете пойти здесь - едва приоткрытая дверь.\\n'\n msg += 'Вы открываете дверь... И выхо��ите в коридор!'\n reply(msg)\n rvr = 0\n user.leave(reply)\n elif user.has_item('m79'):\n rvr += 1\n reply('Вы вспоминаете, что у вас есть М79, берёте его в руки и гребёте им.')\n if rvr > 8:\n msg = ''\n if user.has_item('m-16'):\n user.remove_item('m-16')\n msg += 'Вы уронили свой М-16, пока плыли. Ну хоть себя не выронили.\\n'\n msg += 'Вы пристаёте к другому концу берега, вылезаете из лодки.\\n'\n msg += 'Единственное место, куда вы можете пойти здесь - едва приоткрытая дверь.\\n'\n msg += 'Вы открываете дверь... И выходите в коридор!'\n reply(msg)\n rvr = 0\n user.leave(reply)\n else:\n rvr += 1\n reply('Вы вспоминаете, что у вас есть М-16, берёте его в руки и гребёте им.')\n if rvr > 8:\n user.remove_item('m-16')\n reply('Вы пристаёте к другому концу берега, вылезаете из лодки. Ваш М-16 не выдержал такого нахальства и рассыпался прямо у вас в руках.\\n'\n '- Ну и чёрт с ним, - подумали вы.\\n'\n 'Единственное место, куда вы можете пойти здесь - едва приоткрытая дверь.\\n'\n 'Вы открываете дверь... И выходите в коридор!')\n user.leave(reply)\n rvr = 0\n","sub_path":"rooms/monster/vietnam/river.py","file_name":"river.py","file_ext":"py","file_size_in_byte":3637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"325678333","text":"\nfrom odoo import models, fields, api\n\n\nclass ProductAttributeWizard(models.TransientModel):\n _name = 'product.attribute.wizard'\n\n value_ids = fields.Many2many('product.attribute.value')\n attribute_id = fields.Many2one('product.attribute')\n\n def action_create_attribute(self):\n model = self.env.context.get('active_model')\n rec_model = self.env[model].browse(self.env.context.get('active_id'))\n if rec_model.attribute_line_ids:\n for rec in rec_model.attribute_line_ids:\n if rec.attribute_id.id == self.attribute_id.id:\n att_list = []\n for attr in rec.value_ids:\n att_list.append(attr.id)\n for x in self.value_ids:\n att_list.append(x.id)\n rec.write({\n 'value_ids': att_list,\n })\n else:\n vals = {\n 'attribute_id': self.attribute_id.id,\n 'value_ids': self.value_ids.ids,\n 'product_tmpl_id': rec_model.id\n }\n # rec_model.attribute_line_ids = val_list\n # for res in rec_model.attribute_line_ids:\n # if res.attribute_id.id == self.attribute_id.id:\n # # att_list = []\n # # for attr in rec.value_ids:\n # # att_list.append(attr.id)\n # # for x in self.value_ids:\n # # att_list.append(x.id)\n # res.update({\n # 'value_ids': self.value_ids.ids,\n # })\n pro = self.env['product.template.attribute.line'].create(vals)\n self.assign_sequence_in_variant()\n\n def assign_sequence_in_variant(self):\n model = self.env.context.get('active_model')\n rec_model = self.env[model].browse(self.env.context.get('active_id'))\n count = 0\n print(rec_model.product_variant_ids)\n for variant in rec_model.product_variant_ids:\n count += 1\n variant.product_seq = str(00) + str(00) + str(0) + str(count)\n variant.item_code = rec_model.product_temp_seq + variant.product_seq\n return\n\n","sub_path":"product_sequence/models/product_attribute_wizard.py","file_name":"product_attribute_wizard.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"513553385","text":"def copy_path(root_src_dir, root_dst_dir, exclude_files=[]):\r\n\r\n from shutil import copy\r\n import os\r\n\r\n for src_dir, dirs, files in os.walk(root_src_dir):\r\n dst_dir = src_dir.replace(root_src_dir, root_dst_dir)\r\n if not os.path.exists(dst_dir):\r\n os.makedirs(dst_dir)\r\n for f in files:\r\n if f not in exclude_files:\r\n src_file = os.path.join(src_dir, f)\r\n dst_file = os.path.join(dst_dir, f)\r\n copy(src_file, dst_file)\r\n","sub_path":"project/SciProgHF/test/runtest/copy.py","file_name":"copy.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"283971547","text":"\"\"\"\nProblem:\n\nGiven a m x n grid filled with non-negative numbers, find a path from top left to bottom right which minimizes the sum of all numbers along its path.\n\nNote: You can only move either down or right at any point in time.\n\"\"\"\n\n\"\"\"\nSolution:\n\nThis is a typical dynamic programming problem. \n\nThe minimum path to position (i,j) is the minimum among the choice of going down from (i-1,j) and going right from (i,j-1).\n\nIn order to reduce the space for the problem, only a row of the dynamic table is needed instead of the whole dynamic programming table\nwith the same size of grid.\n\"\"\"\n\nclass Solution(object):\n def minPathSum(self, grid):\n \"\"\"\n :type grid: List[List[int]]\n :rtype: int\n \"\"\"\n m = len(grid)\n n = len(grid[0])\n pathSum = [[None]*n for _ in range(m)]\n pathSum[0][0] = grid[0][0]\n for j in range(1,n):\n pathSum[0][j] = grid[0][j] + pathSum[0][j-1]\n for i in range(1,m):\n pathSum[i][0] = grid[i][0] + pathSum[i-1][0]\n for i in range(1,m):\n for j in range(1,n):\n pathSum[i][j] = min(pathSum[i-1][j], pathSum[i][j-1]) + grid[i][j]\n return pathSum[-1][-1]\n \n","sub_path":"64_Minimum_Path_Sum.py","file_name":"64_Minimum_Path_Sum.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"246452759","text":"import sys\nimport CheshirePy as c\n\nif c.init('../index/testconfig.new') == 0:\n print (\"Opened Cheshire config file %s\" % c.showconfig())\nelse:\n print(\"failed to open configuration file - exiting\")\n exit()\n \nif c.setdb('bibfile') == 0:\n print (\"Set database to bibfile\")\nelse:\n print(\"failed to set database\")\n exit()\n\nsearchstr = 'topic @ mathematics programming'\n\nprint (\"searching for '%s'\" % searchstr)\n\nr = c.Search(searchstr)\n\nn = c.getnumfound(r)\nprint (\"%d records found\" % n)\n\ni = 0\n\nwhile i < n :\n rec = c.getrecord(r,i)\n rel = c.getrelevance(r,i)\n print(\"*********************************************\")\n print(\"Rank: %d Relevance: %f\" % (i, rel))\n print(rec)\n print(\"*********************************************\")\n i += 1\n\nc.closeresult(r)\nprint(\"End of record set\")\n\nexit()\n","sub_path":"CheshirePy/testscp.py","file_name":"testscp.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"151736200","text":"\"\"\"\nGiven 3 int values, a, b, c, return their sum.\nHowever, if one of the values is 13 then it does not count towards the sum and values to its right do not count.\n\"\"\"\n\ndef lucky_sum(a, b, c):\n l = [a, b, c]\n total = 0\n i = 0\n while i < 3:\n if l[i] == 13:\n break\n else:\n total += l[i]\n i += 1\n return total\n\nprint(lucky_sum(1, 13, 2))","sub_path":"Logic-2/3. lucky_sum.py","file_name":"3. lucky_sum.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"589365815","text":"import pandas as pd\nfrom scipy.stats import trim_mean, kurtosis\nfrom fastai.imports import *\nfrom fastai.structured import *\nfrom tabulate import tabulate\nfrom sklearn.ensemble import RandomForestClassifier\nfrom matplotlib import pyplot\nfrom sklearn.metrics import f1_score,\\\n accuracy_score, confusion_matrix,\\\n precision_score, recall_score,\\\n roc_curve, roc_auc_score,\\\n cohen_kappa_score, mean_absolute_error,\\\n precision_recall_curve, auc,\\\n average_precision_score\n\n# Constants\nPROBABILITY_CUTOFF = 0.50\n\ndef split_vals(a,n): return a[:n], a[n:]\n\ndef display_all(df):\n with pd.option_context(\"display.max_rows\", 1000, \"display.max_columns\", 1000):\n display(df)\n\ndef general_stats(df, feature):\n array = data_summary_feature(df, feature)\n print(array[0])\n print(\"\")\n count = 1\n for heading in ['Min', 'Max', 'Mean', 'Trimmed Mean', 'Median', 'Std', 'CV']:\n print(f\"{heading}: {array[count]}\")\n count += 1\n\n# Utility functions\ndef plot_roc_pr(m, X_valid, y_valid):\n # Generate the probabilities\n #y_pred_prob = generate_predictions(X_valid)\n y_pred_prob = m.predict_proba(X_valid)\n y_pred_prob = y_pred_prob[:, 1]\n\n # Calculate the roc metrics\n fpr, tpr, thresholds = roc_curve(y_valid, y_pred_prob)\n\n fig, ax = plt.subplots(figsize=(10,10))\n\n # Add labels and diagonal line\n plt.xlabel(\"False Positive Rate / Recall\")\n plt.ylabel(\"True Positive Rate / Precision\")\n plt.plot([0, 1], [0, 1], \"k--\", label='no skill')\n plt.plot([0,0,1,1],[0,1,1,1],'g-',label='perfect')\n\n # Plot a precision-recall curve\n precision, recall, thresholds = precision_recall_curve(y_valid, y_pred_prob)\n area_under_curve = auc(recall, precision)\n ap = average_precision_score(y_valid, y_pred_prob)\n\n # Plot the ROC curve\n plt.plot(fpr,tpr, label='AUC: %.3f'%area_under_curve)\n\n # plot no skill\n pyplot.plot([0, 1], [0.5, 0.5], linestyle='--', color='magenta', label='no skill')\n # plot the precision-recall curve for the model\n pyplot.plot(recall, precision, marker='.', color='orange', label='precision-recall')\n\n legend = ax.legend(loc='best', shadow=True, fontsize='medium')\n\n # show the plot\n pyplot.show()\n\n # Output AUC and average precision score\n print('auc=%.3f ap=%.3f' % (area_under_curve, ap))\n\ndef uber_score(y_valid, validate_predictions):\n print(f\"Precision: {precision_score(y_valid,validate_predictions)}\")\n print(f\"Recall: {recall_score(y_valid,validate_predictions)}\")\n print(f\"f1_score: {f1_score(y_valid,validate_predictions)}\")\n print(f\"Accuracy: {accuracy_score(y_valid,validate_predictions)}\")\n print(f\"kappa: {cohen_kappa_score(y_valid,validate_predictions)}\")\n print(f\"mean abs error: {mean_absolute_error(y_valid,validate_predictions)}\")\n\ndef graph_corr(data_frame):\n fig, ax = plt.subplots(figsize=(20,10))\n corr = data_frame.corr()\n sns.heatmap(corr, cmap='YlGnBu', annot_kws={'size':30}, ax=ax)\n ax.set_title(\"Correlation Matrix\", fontsize=14)\n plt.show()\n\ndef data_summary_feature(data_frame, feature):\n \"\"\"\n Doc string\n \"\"\"\n\n max_value = data_frame[feature].max()\n min_value = data_frame[feature].min()\n mean = data_frame[feature].mean()\n median = data_frame[feature].median()\n mode = data_frame[feature].mode()\n std = data_frame[feature].std()\n co_variant = data_frame[feature].std()/data_frame[feature].mean()\n trimmed_mean = trim_mean(data_frame[feature].values, 0.1)\n return [feature, min_value, max_value, mean, trimmed_mean, median, mode, std, co_variant]\n\ndef data_summary_dataframe(data_frame):\n array = []\n for feature in data_frame.select_dtypes(include=['float64', 'int64']).columns:\n array.append(data_summary_feature(data_frame, feature))\n\n print(tabulate(array, headers=['Column', 'Min', 'Max',\n 'Mean', 'Trimmed Mean',\n 'Median', 'Std', 'cv'], tablefmt=\"simple\"))\n\ndef conf_matrix(y_valid, validate_predictions):\n confusion_matrix_data = confusion_matrix(y_valid, validate_predictions)\n print(\"tp, fn, fp, tn\")\n print(confusion_matrix_data)\n","sub_path":"lib/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4169,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"63739303","text":"# coding=utf-8\n\nfrom datetime import datetime\nfrom dateutil import rrule\nfrom django.utils.encoding import python_2_unicode_compatible\nfrom django.utils.functional import cached_property\nfrom django.db import models\nfrom django.core.urlresolvers import reverse\nfrom imagekit.models import ImageSpecField\nfrom imagekit.processors import ResizeToFill\nfrom connection.models import EnjoyTodayUser\nfrom location.models import City\nfrom core.models import Topic\nfrom django.contrib.sites.models import Site\nfrom django.contrib.sites.managers import CurrentSiteManager\n\n\n__all__ = (\n 'EventType',\n 'Event',\n 'Occurrence',\n)\n\n\n# ==============================================================================\n@python_2_unicode_compatible\nclass EventType(models.Model):\n \"\"\"\n Simple ``Event`` classification.\n \"\"\"\n topic = models.ForeignKey(Topic,\n verbose_name='Thematique',\n default=None,\n null=True,\n on_delete=models.SET_DEFAULT)\n\n label = models.CharField(verbose_name='label',\n max_length=50,\n default='autres')\n\n image = models.ImageField(default=None,\n null=True,\n upload_to='event_types/')\n\n #image_main = ImageSpecField(source='image',\n #processors=[ResizeToFit(450, 300)],\n #format='JPEG',\n #options={'quality': 100})\n\n # ==========================================================================\n class Meta:\n verbose_name = 'event type'\n verbose_name_plural = 'event types'\n\n # --------------------------------------------------------------------------\n def __str__(self):\n return self.label\n\n def get_absolute_url(self):\n return reverse('topic:event_type_coming_days',\n kwargs={'event_type_id_string': str(self.pk)},\n current_app=self.topic.name)\n\n# ==============================================================================\n@python_2_unicode_compatible\nclass Event(models.Model):\n \"\"\"\n Container model for general metadata and associated ``Occurrence`` entries.\n \"\"\"\n image = models.ImageField(verbose_name=\"Image\",\n default=None,\n null=True,\n upload_to='events/')\n\n title = models.CharField(verbose_name=\"Titre\",\n max_length=100,\n default=None,\n null=True)\n\n description = models.TextField(verbose_name=\"Description\",\n default=None,\n null=True)\n\n event_type = models.ForeignKey(EventType,\n verbose_name=\"Catégorie\",\n default=None,\n null=True,\n on_delete=models.SET_DEFAULT,\n )\n\n price = models.PositiveSmallIntegerField(verbose_name=\"Prix en euros\",\n default=0)\n\n contact = models.CharField(verbose_name=\"Coordonnées du contact éventuel\",\n max_length=150,\n default=\"non précisé\",\n null=True,\n blank=True)\n\n website = models.CharField(verbose_name=\"Lien vers le site officiel de l'événement\",\n max_length=150,\n default=\"non précisé\",\n null=True,\n blank=True)\n\n image_main = ImageSpecField(source='image',\n processors=[ResizeToFill(800, 300)],\n format='JPEG',\n options={'quality': 100},\n )\n\n event_planner = models.ForeignKey(EnjoyTodayUser,\n on_delete=models.CASCADE,\n default=None,\n null=True,\n verbose_name='annonceur',\n )\n\n address = models.CharField(verbose_name=\"Adresse\",\n max_length=150,\n default=\"non précisé\")\n\n #public_transport = models.CharField(verbose_name=\"Arrêt transport en commun (métro,...)\",\n # max_length=150,\n # default=\"non précisé\",\n # null=True,\n # blank=True\n # )\n\n site = models.ForeignKey(Site,\n default=None,\n null=True,\n on_delete=models.SET_DEFAULT,\n # if affected, breaks\n # default=None,\n )\n\n objects = models.Manager()\n on_site = CurrentSiteManager()\n\n # ===========================================================================\n class Meta:\n verbose_name = 'event'\n verbose_name_plural = 'events'\n ordering = ('title', )\n\n # ---------------------------------------------------------------------------\n def __str__(self):\n return self.title\n\n # --------------------------------------------------------------------------\n #def get_absolute_url(self):\n #return reverse('', kwargs={'event_id': self.pk})\n\n def delete_url(self):\n return reverse('catho:crud:delete_event', kwargs={'event_id': self.pk})\n\n def update_url(self):\n return reverse('catho:crud:update_event', kwargs={'event_id': self.pk})\n\n\n # --------------------------------------------------------------------------\n def add_occurrences(self, start_time, end_time, is_multiple, **rrule_params):\n \"\"\"\n Add one or more occurences to the event using a comparable API to\n ``dateutil.rrule``.\n\n Because ``rrule.rrule`` returns an iterator that can essentially be\n unbounded, we need to slightly alter the expected behavior here in order\n to enforce a finite number of occurrence creation.\n\n If ``until`` entry is missing from ``rrule_params``,\n only a single ``Occurrence`` instance will be created using the exact\n ``start_time`` and ``end_time`` values.\n \"\"\"\n\n until = rrule_params.get('until')\n if not until:\n self.occurrence_set.create(start_time=start_time, end_time=end_time)\n else:\n delta_hour = end_time - start_time\n occurrences = []\n for ev in rrule.rrule(dtstart=start_time, **rrule_params):\n occurrences.append(Occurrence(start_time=ev,\n end_time=ev + delta_hour,\n event=self,\n is_multiple=is_multiple))\n self.occurrence_set.bulk_create(occurrences)\n\n # --------------------------------------------------------------------------\n def upcoming_occurrences(self):\n \"\"\"\n Return all occurrences that are set to start on or after the current\n time.\n \"\"\"\n return self.occurrence_set.filter(start_time__gte=datetime.now())\n\n # --------------------------------------------------------------------------\n def next_occurrence(self):\n \"\"\"\n Return the single occurrence set to start on or after the current time\n if available, otherwise ``None``.\n \"\"\"\n upcoming = self.upcoming_occurrences()\n return upcoming[0] if upcoming else None\n\n\n\n# ==============================================================================\nclass OccurrenceManager(models.Manager):\n\n use_for_related_fields = True\n\n # --------------------------------------------------------------------------\n def daily_occurrences(self, dt=None, event=None):\n \"\"\"\n Returns a queryset of for instances that have any overlap with a\n particular day.\n\n * ``dt`` may be either a datetime.datetime, datetime.date object, or\n ``None``. If ``None``, default to the current day.\n\n * ``event`` can be an ``Event`` instance for further filtering.\n \"\"\"\n\n dt = dt or datetime.now()\n start = datetime(dt.year, dt.month, dt.day)\n end = start.replace(hour=23, minute=59, second=59)\n qs = self.filter(\n models.Q(\n start_time__gte=start,\n start_time__lte=end,\n ) |\n models.Q(\n end_time__gte=start,\n end_time__lte=end,\n ) |\n models.Q(\n start_time__lt=start,\n end_time__gt=end\n )\n )\n\n return qs.filter(event=event) if event else qs\n\n\n# ==============================================================================\n@python_2_unicode_compatible\nclass Occurrence(models.Model):\n \"\"\"\n Represents the start end time for a specific occurrence of a master ``Event``\n object.\n \"\"\"\n start_time = models.DateTimeField()\n end_time = models.DateTimeField()\n event = models.ForeignKey(Event,\n editable=False,\n on_delete=models.CASCADE)\n objects = OccurrenceManager()\n is_multiple = models.BooleanField(default=False)\n\n # ==========================================================================\n class Meta:\n verbose_name = 'occurrence'\n verbose_name_plural = 'occurrences'\n ordering = ('start_time', 'end_time')\n\n # --------------------------------------------------------------------------\n def __str__(self):\n return u'{}: {}'.format(self.event.title, self.start_time.isoformat())\n\n # --------------------------------------------------------------------------\n def get_absolute_url(self):\n return reverse('topic:get_occurrence', kwargs={'pk': self.pk}, current_app=self.event.event_type.topic.name)\n\n def delete_url(self):\n return reverse('topic:crud:delete_occurrence', kwargs={'occurrence_id': self.pk}, current_app=self.event.event_type.topic.name)\n\n def update_url(self):\n return reverse('topic:crud:update_occurrence', kwargs={'occurrence_id': self.pk}, current_app=self.event.event_type.topic.name)\n\n # --------------------------------------------------------------------------\n def __lt__(self, other):\n return self.start_time < other.start_time\n","sub_path":"topic/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":10906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"220636450","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 31 13:38:44 2019\n\n@author: tyler\n\"\"\"\n\n\nimport pandas as pd\nimport numpy as np\n\n\ndata = pd.read_csv('/home/tyler/Desktop/Dolezal_Python_Practice/rome_data.csv', index_col = 'DATE', dtype={'TempF':str, 'WEATHER':str}, parse_dates=True)\n\n''' Like the AZ data our temp readings have 's' attached to them as well as a few columns marked '*', we can\neliminate these items and then plot the data '''\n\ntemp_data = data['TempF']\nrmv = temp_data == '*'\ntemp_data.loc[rmv] = np.nan\ntemp_data = temp_data.dropna() \ntemp_data = temp_data.map(lambda x: x.rstrip('s'))\n\ntemp_data = temp_data.astype(float)\n\n\ntemp = temp_data.resample('M').apply(np.median)\n\n#temp_data.plot(title = 'Rome NY Temp (F) Data over 10 Years', figsize = (15,7.5))\n\n\n''' Now lets look at snowfall over the past 10 years '''\n\nsnow = data['WEATHER'].str.contains('SN')\nsnow = snow.dropna()\nmost_snow = snow.astype(float).resample('M').apply(np.mean)\n\nmost_snow.plot(title = 'Snowiest times of the year')","sub_path":"rome_climate.py","file_name":"rome_climate.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"516342049","text":"class Solution:\n def countPrimes(self, n: int) -> int:\n counter = 0\n isPrime = [True] * (n+1)\n for i in range(2, n):\n if isPrime[i]:\n counter += 1\n for j in range(1, (n // i)+1):\n isPrime[i * j] = False\n \n return counter","sub_path":"204. Count Primes/solution1.py","file_name":"solution1.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"630119248","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport sys\n\nfrom flask import Flask\n\n\nCONFIG_NAME_MAPPER = {\n 'development': 'config.DevelopmentConfig',\n 'testing': 'config.TestingConfig',\n 'production': 'config.ProductionConfig',\n 'local': 'local_config.LocalConfig',\n}\n\n\ndef create_app(flask_env=None, *args, **kwargs) -> Flask:\n \"\"\"\n Create a Flask application using the app factory pattern\n \"\"\"\n\n # create the base app\n app = Flask(__name__, instance_relative_config=True)\n\n # load correct flask env definition\n if not flask_env:\n env_flask_env = os.getenv('FLASK_ENV')\n\n if env_flask_env:\n flask_env = env_flask_env\n else:\n msg = 'could not identify which environment the application should use'\n app.logger.error(msg)\n sys.exit(1)\n\n # load app configurations\n app.config.from_object(CONFIG_NAME_MAPPER[flask_env])\n\n # initialize modules\n from app import modules\n\n modules.init_app(app)\n\n return app\n","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"99488070","text":"from main.server import db\nfrom main.server.models import Announcement, MultiGallery, Gallery, Games, Message, Video, SetMetadata\nfrom main.utils.dto import AnnouncementDTO, MultiGalleryDTO, GalleryDTO, GameDTO, MessageDTO, VideoDTO\nfrom main.server.status import Status\nfrom main.utils.blur import getBlurCodeFromImage\n\ndef insertAnnouncement(data):\n if not isinstance(data, AnnouncementDTO):\n raise TypeError('data must be of type AnnouncementDTO')\n if not data.message: \n return Status.FAIL\n res = Announcement.query.filter_by(message=data.message).first() \n if res:\n return Status.WARN\n res = Announcement(message=data.message)\n db.session.add(res) \n return Status.OK\n\ndef insertMultiGallery(data): \n if not isinstance(data, MultiGalleryDTO):\n raise TypeError('data must be of type MultiGalleryDTO')\n if not data.artworkLink:\n return Status.FAIL\n if not SetMetadata.query.filter_by(setID=data.setID).first():\n metadata_entry = SetMetadata(\n setID=data.setID,\n artistLink=data.artistLink,\n username=data.username,\n message=data.message,\n )\n db.session.add(metadata_entry)\n \n if not MultiGallery.query.filter_by(artworkLink=data.artworkLink).first():\n multigallery_entry = MultiGallery(\n setID=data.setID,\n artworkLink=data.artworkLink,\n blurhash=getBlurCodeFromImage(data.artworkLink),\n )\n db.session.add(multigallery_entry)\n else:\n # return from this insert with dupe warn\n return Status.WARN\n\n return Status.OK\n\ndef insertGallery(data): \n if not isinstance(data, GalleryDTO):\n raise TypeError('data must be of type GalleryDTO')\n if not data.artworkLink:\n return Status.FAIL\n res = Gallery.query.filter_by(artworkLink=data.artworkLink).first() \n if res:\n return Status.WARN\n res = Gallery(artworkLink=data.artworkLink,\n blurhash=getBlurCodeFromImage(data.artworkLink),\n artistLink=data.artistLink,\n username=data.username,\n title=data.title)\n db.session.add(res)\n return Status.OK\n\ndef insertGame(data):\n if not isinstance(data, GameDTO):\n raise TypeError('data must be of type GameDTO')\n if not data.gameLink:\n return Status.FAIL\n res = Games.query.filter_by(gameLink=data.gameLink).first()\n if res:\n return Status.WARN\n res = Games(gameLink=data.gameLink,\n gitLink=data.gitLink,\n description=data.description,\n title=data.title,\n thumbnail=data.thumbnail,\n blurhash=getBlurCodeFromImage(data.thumbnail))\n db.session.add(res)\n\ndef insertMessage(data):\n if not isinstance(data, MessageDTO):\n raise TypeError('data must be of type MessageDTO')\n if not data.orig_msg:\n return Status.FAIL\n res = Message.query.filter_by(orig_msg=data.orig_msg).first()\n if res:\n return Status.WARN\n res = Message(orig_msg=data.orig_msg,\n tl_msg=data.tl_msg,\n country=data.country,\n username=data.username)\n db.session.add(res)\n return Status.OK\n\ndef insertVideo(data):\n if not isinstance(data, VideoDTO):\n raise TypeError('data must be of type VideoDTO')\n if not data.videoLink:\n return Status.FAIL\n res = Video.query.filter_by(\n videoLink=data.videoLink).first()\n if res:\n return Status.WARN\n res = Video(videoLink=data.videoLink,\n artistLink=data.artistLink,\n username=data.username,\n title=data.title)\n db.session.add(res)\n","sub_path":"backend/main/utils/insert.py","file_name":"insert.py","file_ext":"py","file_size_in_byte":4063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"416549418","text":"import sys \r\nimport pandas as pd\r\nimport numpy as np\r\nimport math\r\nimport matplotlib.pyplot as plt\r\nimport pickle\r\n\r\n\r\nclass Solution:\r\n \r\n \r\n def __init__(self,df_TSP,filename):\r\n self.filename = filename\r\n self.df_TSP = df_TSP\r\n self.n = len(self.df_TSP) # nr of cities\r\n root = math.sqrt(self.n)\r\n if (int(root + 0.5)**2 != self.n):\r\n print(\"n=\" + str(self.n) + \" is not a perfect square!\")\r\n sys.exit() \r\n # distance from each city to the depot\r\n self.dist_depot = [((self.df_TSP.iloc[i,1]- 0)**2 + (self.df_TSP.iloc[i,2]-0)**2)**0.5 for i in range(self.n)]\r\n # symmetric nxn matrix with distances between each city\r\n self.dist = [[((self.df_TSP.iloc[i,1]-self.df_TSP.iloc[j,1])**2+(self.df_TSP.iloc[i,2]-self.df_TSP.iloc[j,2])**2)**0.5 for j in range(self.n)] for i in range(self.n)]\r\n self.dim = int(self.n**0.5) # nr of rows and columns\r\n self.sol = [[ (-1) for i in range(self.dim)] for j in range(self.dim)] # solution\r\n \r\n # This function intialises the solution using the algorithm GREEDY\r\n # the first column of my solution is first filled with random cities (without replacement)\r\n # then each row is filled with the city that is closest in location to the previous city \r\n # with respect to each row (without replacement)\r\n def InitialSolution(self):\r\n print(\"n\",self.n)\r\n if (self.n == 1):\r\n self.sol = [0]\r\n print(\"sol\",self.sol)\r\n print(self.dist)\r\n print(self.dist_depot)\r\n return \r\n cities = list(range(self.n)) # this is a list I store my cities in in order to be able to fill in \r\n # unique cities in my solution\r\n li = np.random.choice(self.n,self.dim, replace = False) # dim random cities to fill first column of my solution\r\n # here li stands for list but do not want to confuse this \r\n # variable with function list so called it li\r\n for i in range(self.dim):\r\n cities[li[i]] = -1 # when cities are allocated to my solution\r\n # set these cities equal to -1 in cities array to be able to draw without permuation\r\n for i in range(self.dim):\r\n self.sol[i][0] = li[i] # filling in first column of my solution with cities\r\n city0 = 0 # this will be my first city of reference from cities \r\n for c in cities:\r\n if c >= 0:\r\n city0 = c\r\n break \r\n d0 = self.dist[self.sol[0][0]][city0] # this will be the corresponding distance with respect to city0\r\n count = self.n - self.dim # count nr of cities left that have not been allocated to solution\r\n while (count != 0): # as long as there are still cities to allocate\r\n for i in range(self.dim): # for each column\r\n for j in range(self.dim-1): # for each row\r\n if (count == 2): # this is to check when there are only 2 cities left to allocate\r\n count = 0\r\n if (self.dim > 2): # if dimension of solution is greater than 2\r\n city3 = 0 # this variable stores last city \r\n dist3 = 0 # this variable stores last distance\r\n for c in cities: \r\n if ((c >= 0) and (c != city0)):\r\n city3 = c \r\n dist3 = self.dist[self.sol[self.dim-1][-3]][city3]\r\n break \r\n # compare the distances of the last 2 cities and allocate these to solution \r\n if dist3 < d0:\r\n self.sol[self.dim-1][-2] = city3 \r\n self.sol[self.dim-1][-1] = city0 \r\n else:\r\n self.sol[self.dim-1][-2] = city0 \r\n self.sol[self.dim-1][-1] = city3 \r\n elif (self.dim == 2): # if dimension of solution is 2x2 matrix \r\n city3 = 0\r\n for c in cities: \r\n if (c>= 0 and c!= city0):\r\n city3 = c \r\n break \r\n dist3 = self.dist[self.sol[0][0]][city3]\r\n d0 = self.dist[self.sol[0][0]][city0]\r\n # compare distances of last 2 cities and allocate these to solution\r\n if (dist3 < d0):\r\n self.sol[0][1] = city3 \r\n self.sol[1][1] = city0 \r\n else: \r\n self.sol[0][1] = city0 \r\n self.sol[1][1] = city3 \r\n # else: # if dimension of solution is 1\r\n # p = 0 \r\n if count > 2: # if there are more than 2 cities to allocate\r\n # find smallest distance and allocate city to solution\r\n for c in cities:\r\n if (c >= 0): \r\n dist1 = self.dist[self.sol[i][j]][c] \r\n if dist1 < d0:\r\n city0 = c \r\n d0 = dist1\r\n cities[city0] = -1 \r\n self.sol[i][j+1] = city0 \r\n count = count - 1\r\n for c in cities: \r\n if c >= 0:\r\n city0 = c\r\n d0 = self.dist[self.sol[i][j+1]][c] \r\n break \r\n if (j+1 == self.dim-1): \r\n for c in cities: \r\n if c >= 0:\r\n city0 = c\r\n d0 = self.dist[self.sol[i+1][0]][c] \r\n break \r\n else: \r\n for c in cities: \r\n if c >= 0:\r\n city0 = c \r\n d0 = self.dist[self.sol[i][j+1]][c] \r\n print(\"\\nSolution initialised according to greedy algorithm\")\r\n test = self.Test()\r\n if (test == True):\r\n print(\"Initial solution is unique!\") \r\n \r\n \r\n # swap operation over rows and columns\r\n def Swap(self,x1, y1, x2, y2): \r\n temp = self.sol[x1][y1]\r\n self.sol[x1][y1] = self.sol[x2][y2]\r\n self.sol[x2][y2] = temp\r\n \r\n # insert operation over rows only\r\n def Insert(self,x,y1,y2): \r\n # y2 will be inserted \r\n # if cities are adjacent or equal \r\n if (y2 == y1 or abs(y2 -y1) == 1):\r\n self.Swap(x,y1,x,y2)\r\n # if y1 is to the left of y2 \r\n elif y1 > y2: # elements of solution will have to be shifted left when y2 is inserted at y1\r\n temp = self.sol[x][y2]\r\n for i in range(abs(y2-y1)): \r\n self.sol[x][y2+i] = self.sol[x][y2+1+i]\r\n self.sol[x][y1] = temp\r\n else: # y1 is to the right of y2 so elements in solution will have to be shifted to the right\r\n # when y2 is inserted at y1\r\n temp = self.sol[x][y2] \r\n for i in range(abs(y2-y1)): \r\n self.sol[x][y2-i] = self.sol[x][y2-1-i]\r\n self.sol[x][y1] = temp\r\n\r\n # reverse operation\r\n def Reverse(self,y1,y2,x):\r\n if( abs(y2 - y1) == 1 or abs(y2-y1) == 2 or abs(y2 - y1) == 0): # 1 or no elements in between y1 and y2\r\n # normal swap \r\n self.Swap(x,y1,x,y2)\r\n else: \r\n if (abs(y2 -y1)%2 != 0): # nr of elements between y1 and y2 is even\r\n for i in range(abs(y2-y1)-2):\r\n if y1 < y2:\r\n self.Swap(x,y1+i,x,y2-i)\r\n else: \r\n self.Swap(x,y1-i,x,y2+i)\r\n else: # nr of elements between y1 and y2 is uneven \r\n for i in range(abs(y2-y1)-3):\r\n if y1 < y2:\r\n self.Swap(x,y1+i,x,y2-i)\r\n else: \r\n self.Swap(x,y1-i,x,y2+i) \r\n\r\n # calculate cost\r\n def Cost(self):\r\n cost = 0 # total cost \r\n if (self.n == 1):\r\n cost = self.dist_depot[0] + self.dist_depot[0]\r\n else: \r\n TotalCost = [0 for x in range(len(self.sol[0]))] # to stor sums of distances of each column\r\n count = 0 # to retrieve index to store sums of costs for each row of solution\r\n for j in range(len(self.sol[0])): # for each row of solution\r\n cost = cost +self.dist_depot[self.sol[j][0]] # add distance from depot to first city\r\n for i in range(len(self.sol[0])-1): \r\n cost = cost + self.dist[self.sol[j][i]][self.sol[j][i+1]] # then add distances between cities\r\n cost = cost + self.dist_depot[self.sol[j][-1]] # distance from last city to depot\r\n TotalCost[count] = cost\r\n count = count + 1\r\n cost = 0\r\n cost = sum(TotalCost) + max(TotalCost) - min(TotalCost) \r\n return cost\r\n\r\n # Calculate feasibility\r\n def Feasibility(self):\r\n f = 0 # feasibility\r\n if (self.n == 1):\r\n f = 1 \r\n else: \r\n for i in range(len(self.sol)): # i = 0,1\r\n if (self.sol[i][0]%2 == 0):\r\n f = f + 1\r\n if (self.sol[i][-1]%2 != 0):\r\n f = f + 1 \r\n return f\r\n\r\n # Method 1: Hill Climbing method\r\n def HC(self):\r\n x = int(100000/100)\r\n X = list(range(1,(x+1))) \r\n Y = [0 for i in range(x)] \r\n count = 0\r\n cost = self.Cost()\r\n if (self.n == 1):\r\n test = True\r\n Y = [cost for i in range(x)]\r\n else: \r\n x = int(100000/100)\r\n X = list(range(1,(x+1))) \r\n Y = [0 for i in range(x)] \r\n count = 0\r\n cost = self.Cost()\r\n for i in range(100000):\r\n x1 = np.random.randint(0,self.dim)\r\n y1 = np.random.randint(0,self.dim)\r\n x2 = np.random.randint(0,self.dim)\r\n y2 = np.random.randint(0,self.dim)\r\n self.Swap(x1, y1, x2, y2)\r\n new_cost = self.Cost()\r\n if new_cost <= cost:\r\n cost = new_cost\r\n else:\r\n self.Swap(x2, y2, x1, y1)\r\n if ((i+1)%100 == 0):\r\n Y[count] =cost\r\n count = count + 1\r\n test = self.Test() \r\n print(\"\\n HILL CLIMBING METHOD: \")\r\n print(\"Is solution correct: \",test)\r\n print(\"Cost: \",cost)\r\n f = self.Feasibility()\r\n print(\"Feasibility: \",f)\r\n answer1 = self.Write()\r\n if (answer1 == 1): \r\n self.WriteToFile(cost,f)\r\n elif (answer1 == 2): \r\n self.WB_to_file(cost,f)\r\n elif (answer1 == 3): \r\n self.WriteToFile(cost,f) \r\n self.WB_to_file(cost,f) \r\n answer2 = self.GraphSol()\r\n if (answer2 == 1):\r\n self.Graph_Fig1(\" hill climbing method\") \r\n if (answer2 == 2):\r\n self.Graph_Fig2(X,Y,\" hill climbing method\") \r\n if (answer2 == 3): \r\n self.Graph_Fig1(\" hill climbing method\")\r\n self.Graph_Fig2(X,Y,\" hill climbing method\") \r\n return cost, self.sol,test\r\n\r\n # User input/output for graphing solution\r\n def GraphSol(self):\r\n print(\"\\nWould you like see graphs of solution ? \")\r\n print(\"Input <0>: NO\")\r\n print(\"Input <1>: Fig (1): Visual representation of solution\")\r\n print(\"Input <2>: Fig (2): Iterations vs Cost line graph\")\r\n print(\"Input <3>: Fig (1) and Fig (2) \")\r\n try: \r\n answer = input(\"INPUT: \") \r\n answer = int(answer)\r\n except ValueError:\r\n print(\"Wrong input given!\")\r\n exit() \r\n return answer\r\n\r\n # User input/output for writing solution to csv/bs files\r\n def Write(self):\r\n print(\"\\nWould you like to save solution? \")\r\n print(\"Input <0>: No saving of file\")\r\n print(\"Input <1>: Save file as csv file\")\r\n print(\"Input <2>: Save file as binary file\")\r\n print(\"Input <3>: Save both binary and csv file\")\r\n try: \r\n answer = input(\"INPUT: \") \r\n answer = int(answer)\r\n except ValueError:\r\n print(\"Wrong input given!\")\r\n sys.exit() \r\n return answer\r\n \r\n\r\n # Method 2: HC by using swap, insert and revert operations at random\r\n def HC_Swap_Insert_Reverse(self): \r\n x = int(100000/100)\r\n X = list(range(1,(x+1))) \r\n Y = [0 for i in range(x)] \r\n count = 0\r\n cost = self.Cost()\r\n if (self.n == 1):\r\n test = True\r\n Y = [cost for i in range(x)]\r\n else: \r\n for i in range(100000):\r\n operation = np.random.randint(0,4)\r\n # 1: swap \r\n # 2: insert \r\n # 3: reverse\r\n x1 = np.random.randint(0,self.dim)\r\n y1 = np.random.randint(0,self.dim)\r\n x2 = np.random.randint(0,self.dim)\r\n y2 = np.random.randint(0,self.dim)\r\n x = np.random.randint(0,self.dim)\r\n if (operation == 1):\r\n self.Swap(x1, y1, x2, y2)\r\n elif (operation == 2):\r\n self.Insert(x,y1,y2)\r\n else: \r\n self.Reverse(y1,y2,x) \r\n new_cost = self.Cost()\r\n if new_cost <= cost:\r\n cost = new_cost\r\n else:\r\n if (operation == 1):\r\n self.Swap(x2, y2, x1, y1)\r\n elif (operation == 2):\r\n self.Reverse(y2,y1,x)\r\n else: \r\n self.Insert(x,y2,y1) \r\n if ((i+1)%100 == 0):\r\n Y[count] =cost\r\n count = count + 1 \r\n test = self.Test()\r\n print(\"\\n HILL CLIMBING METHOD USING SWAP INSERT REVERT \")\r\n print(\"Is solution correct: \",test)\r\n print(\"Cost: \",cost)\r\n f = self.Feasibility()\r\n print(\"Feasibility: \",f)\r\n answer1 = self.Write()\r\n if (answer1 == 1): \r\n self.WriteToFile(cost,f)\r\n elif (answer1 == 2): \r\n self.WB_to_file(cost,f)\r\n elif (answer1 == 3): \r\n self.WriteToFile(cost,f) \r\n self.WB_to_file(cost,f) \r\n answer2 = self.GraphSol()\r\n if (answer2 == 1):\r\n self.Graph_Fig1(\" hill climbing with swap insert revert\") \r\n if (answer2 == 2):\r\n self.Graph_Fig2(X,Y,\" hill climbing with swap insert revert\") \r\n if (answer2 == 3): \r\n self.Graph_Fig1(\" hill climbing with swap insert revert\")\r\n self.Graph_Fig2(X,Y,\" hill climbing with swap insert revert\") \r\n return cost,self.sol,test \r\n\r\n # Method 3: HC by implementing simulated annealing\r\n def SimulatedAnnealing(self):\r\n X = list(0 for i in range(1037)) \r\n for i in range(1037):\r\n X[i] = (i*2002 + 1)\r\n Y = [0 for i in range(1037)] # cost\r\n T0 = 10000\r\n coolingRate = 0.99999\r\n index = 0 # index to insert cost in Y \r\n value = 0 # variable to help store cost at index t\r\n NrIter = 0 # nr of iterations \r\n absoluteTemp = 0.00001\r\n cost0 = self.Cost() # initial cost\r\n if (self.n == 1):\r\n test = True\r\n Y = [cost0 for i in range(1037)]\r\n else: \r\n while (T0 > absoluteTemp):\r\n # count = count + 1\r\n x1 = np.random.randint(0,self.dim)\r\n y1 = np.random.randint(0,self.dim)\r\n x2 = np.random.randint(0,self.dim)\r\n y2 = np.random.randint(0,self.dim)\r\n self.Swap(x1, y1, x2, y2)\r\n cost1 = self.Cost() # new cost\r\n if cost1 < cost0:\r\n cost0 = cost1 #accept new solution\r\n else: \r\n q = np.random.randint(0,2)\r\n if q < math.exp(-(cost1-cost0)/T0): # accept solution if relation holds\r\n cost0 = cost1 \r\n else:\r\n self.Swap(x2,y2,x1,y1) \r\n T0 = T0*coolingRate # cool temperature\r\n if (NrIter == value):\r\n value = value + 2001 \r\n Y[index] = cost0\r\n index = index + 1\r\n NrIter = NrIter + 1 \r\n test = self.Test() \r\n X[-1] = NrIter\r\n Y[-1] = cost0\r\n print(\"\\n HILL CLIMBING METHOD USING SIMULATED ANNEALING \")\r\n print(\"Is solution correct \",test)\r\n print(\"Cost: \",cost0)\r\n f = self.Feasibility()\r\n print(\"Feasibility: \",f)\r\n answer1 = self.Write()\r\n if (answer1 == 1): \r\n self.WriteToFile(cost0,f)\r\n elif (answer1 == 2): \r\n self.WB_to_file(cost0,f)\r\n elif (answer1 == 3): \r\n self.WriteToFile(cost0,f) \r\n self.WB_to_file(cost0,f) \r\n answer2 = self.GraphSol()\r\n if (answer2 == 1):\r\n self.Graph_Fig1( \" simulated annealing\") \r\n if (answer2 == 2):\r\n self.Graph_Fig2(X,Y,\" simulated annealing\") \r\n if (answer2 == 3): \r\n self.Graph_Fig1(\" simulated annealing\")\r\n self.Graph_Fig2(X,Y,\" simulated annealing\") \r\n return cost0,self.sol,test \r\n\r\n # To test whether the solution has unique elements\r\n def Test(self):\r\n test = True \r\n cities = list(range(self.n))\r\n count = [0 for i in range(self.n)]\r\n for i in range(self.dim):\r\n for j in range(self.dim):\r\n for c in cities:\r\n if c == self.sol[i][j]:\r\n count[c] = count[c] + 1\r\n if count[c] > 1: \r\n test = False\r\n return test\r\n \r\n # Fig 2: Iterations Vs Cost line-graph \r\n def Graph_Fig2(self,X,Y,title):\r\n fig2 = plt.figure()\r\n plt.grid(b = \"True\",axis = \"both\",color=\"lightgrey\",linestyle = \"-\", linewidth = 0.5)\r\n plt.plot(X,Y,linestyle=\"-\",linewidth = 1)\r\n plt.title(\"Iterations Vs Cost line-graph for\" + title)\r\n plt.xlabel(\"Nr of iterations\")\r\n plt.ylabel(\"Cost\") \r\n print(\"\\nFig (2): Iterations Vs Cost line-graph for\" + title)\r\n filename = input(\"Save file as: \")\r\n plt.savefig(filename)\r\n\r\n # Fig 1: Visual representation of solution\r\n def Graph_Fig1(self,title): \r\n fig1 = plt.figure()\r\n colors = []\r\n for i in range(self.dim):\r\n colors.append('#%06X' % np.random.randint(0, 0xFFFFFF))\r\n x = [[0 for i in range(self.dim + 2)] for j in range(self.dim)]\r\n y = [[0 for i in range(self.dim + 2)] for j in range(self.dim)]\r\n if (self.n == 1):\r\n x[0][1] = self.df_TSP.iloc[self.sol[0],1] \r\n y[0][1] = self.df_TSP.iloc[self.sol[0],2] \r\n else: \r\n for i in range(self.dim):\r\n for j in range(self.dim): \r\n x[i][j+1] = self.df_TSP.iloc[self.sol[i][j],1]\r\n y[i][j+1] = self.df_TSP.iloc[self.sol[i][j],2] \r\n plt.grid(b = \"True\",axis = \"both\",color=\"lightgrey\",linestyle = \"-\", linewidth = 0.5)\r\n plt.axhline(y=0,color = \"grey\", linewidth = 1)\r\n plt.axvline(x = 0, color = \"grey\", linewidth = 1)\r\n trucks = [\"\" for i in range(self.dim)]\r\n for i in range(self.dim):\r\n trucks[i] = \"Truck \" + str(i+1)\r\n for i in range(self.dim): \r\n plt.plot(x[i][:],y[i][:], linestyle =\"-\", marker = \"o\", color = colors[i], linewidth = 1,label = trucks[i]) \r\n plt.legend(loc = \"upper left\")\r\n plt.title(\"Visual representation of solution for\" + title)\r\n print(\"\\nFig (1): Visual representation of solution\" + title)\r\n filename = input(\"Save file as: \")\r\n plt.savefig(filename)\r\n \r\n \r\n # write to csv file \r\n def WriteToFile(self,cost,f):\r\n filename = input(\"\\nSave file as < filename.csv >\")\r\n with open(filename,'w') as writeFile:\r\n if (self.n == 1):\r\n writeFile.write(str(0) + \"\\n\")\r\n else: \r\n for i in range(self.dim):\r\n writeFile.write(','.join(str(s) for s in self.sol[i]))\r\n writeFile.write(\"\\n\") \r\n writeFile.write(str(cost) +\"\\n\" + str(f)) \r\n\r\n # write to binary file\r\n def WB_to_file(self,cost,f):\r\n filename = input(\"\\nSave file as \")\r\n found = False\r\n try :\r\n file = open(filename,\"rb\")\r\n prev_run = pickle.load(file)\r\n found = True\r\n except FileNotFoundError :\r\n print(\"New file with name \" + filename + \" is created!\")\r\n file = open(filename,\"wb\")\r\n if (found == True):\r\n pickle.dump(prev_run,file)\r\n pickle.dump(self.sol,file)\r\n pickle.dump(f,file)\r\n pickle.dump(cost,file)\r\n file.close()\r\n \r\n\r\n\r\n ","sub_path":"Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":22138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"163360922","text":"\"\"\"\nClass that handles logins.\n\"\"\"\nimport typing\n\nimport functools\n\nimport asyncio\n\nfrom kyokai import Kyokai, Request\nfrom kyokai.response import redirect, Response\n\ntry:\n import itsdangerous\nexcept ImportError:\n raise ImportError(\"itsdangerous is not installed - cannot use session extension\")\n\n\nclass SessionUser(object):\n \"\"\"\n This is a stub class, that is used for KyoukaiSession.\n\n You can override this class and use it with KyoukaiSession.\n \"\"\"\n def __init__(self):\n \"\"\"\n The only attribute that is required here is `self.id`.\n \"\"\"\n self.id = None\n\n\nclass KyoukaiSession(object):\n \"\"\"\n A KyoukaiSession object provides a few useful methods for handling client logins.\n\n The class takes a callable, which will then be called to get a user upon a request.\n\n This user will be passed in to your route, as the second argument.\n\n To require login on a route, decorate the route with `@sess.login_required`, for example:\n\n ```python\n @app.route(\"/\")\n @session.login_required\n async def index(request: Request, user: User):\n return \"You are \" + repr(user)\n ```\n\n If the user is not logged in, then it will redirect them to `redirect_uri`. This can be passed as the second param.\n\n This ext is Flask-like in terms of handling the application object - you can either pass it as the last item of\n the args, or call `init_app` later to initialize it.\n\n To login a user, return `session.login(id, redirect_uri=\"/\")`, which will return a response that logs in the\n specified ID, and redirects them.\n \"\"\"\n def __init__(self, user_f, redirect_uri: str=\"/login\", app: Kyokai=None):\n \"\"\"\n Create a new KyoukaiSession object.\n \"\"\"\n\n self._callable_f = user_f\n self.redirect_uri = redirect_uri\n\n self.secret_key = b\"\"\n self.signer = None\n\n if app:\n self.init_app(app)\n\n def init_app(self, app: Kyokai):\n \"\"\"\n Loads the secret key from the config.\n \"\"\"\n try:\n self.secret_key = app.config[\"secret_key\"].encode()\n except KeyError:\n raise Exception(\"You must set a secret key in `config.yml` for sessions to work.\")\n\n self.signer = itsdangerous.Signer(self.secret_key)\n\n def _get_id(self, request: Request):\n \"\"\"\n Checks a request for the cookie headers.\n \"\"\"\n cook = request.cookies.get(\"KySess\")\n if not cook:\n return None\n # Load, and unsign.\n try:\n id = self.signer.unsign(cook)\n except itsdangerous.BadSignature:\n return None\n else:\n return id\n\n def login_required(self, func):\n \"\"\"\n Decorator for checking if the login is correct.\n \"\"\"\n @functools.wraps(func)\n async def _login_required_fake_func(request: Request, *args):\n \"\"\"\n You will never see this.\n\n Enforces login.\n \"\"\"\n id = self._get_id(request)\n if not id:\n return redirect(self.redirect_uri)\n # Get the user object.\n if asyncio.iscoroutine(self._callable_f):\n # Don't know why, but ok\n u = await self._callable_f\n elif asyncio.iscoroutinefunction(self._callable_f):\n u = await self._callable_f(id)\n else:\n u = self._callable_f(id)\n # Await the underlying function.\n return await func(request, u, *args)\n\n return _login_required_fake_func\n\n def login(self, id, redirect_uri=\"/\"):\n \"\"\"\n Logs in a user, and returns a Response.\n \"\"\"\n r = redirect(redirect_uri)\n r.cookies[\"KySess\"] = self.signer.sign(id)\n return r","sub_path":"kyokai/ext/sessions/sessionhandler.py","file_name":"sessionhandler.py","file_ext":"py","file_size_in_byte":3812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"642017241","text":"import csv\nimport os\nfrom functools import partial\n\nimport pytest\n\nfrom finance.web import create_app\nfrom finance.importers import import_stock_values\nfrom finance.models import db as _db\nfrom finance.models import (\n Account,\n AccountType,\n Asset,\n AssetType,\n CurrencyAsset,\n FundAsset,\n P2PBondAsset,\n Portfolio,\n StockAsset,\n)\n\n\n@pytest.fixture(scope=\"session\")\ndef app(request):\n \"\"\"Session-wide test `Flask` application.\"\"\"\n settings_override = {\n \"TESTING\": True,\n }\n settings_override[\"SQLALCHEMY_DATABASE_URI\"] = os.environ[\"TEST_DB_URL\"]\n app = create_app(__name__, config=settings_override)\n\n # Establish an application context before running the tests.\n # ctx = app.app_context()\n # ctx.push()\n\n # def teardown():\n # ctx.pop()\n\n # request.addfinalizer(teardown)\n return app\n\n\n@pytest.fixture\ndef testapp(app, db):\n with app.app_context():\n yield app.test_client()\n\n\n@pytest.fixture(scope=\"module\", autouse=True)\ndef db(app, request):\n \"\"\"Session-wide test database.\"\"\"\n\n def teardown():\n with app.app_context():\n _db.session.close()\n _db.drop_all()\n\n request.addfinalizer(teardown)\n\n _db.app = app\n with app.app_context():\n _db.create_all()\n\n yield _db\n\n\n@pytest.fixture(scope=\"module\")\ndef stock_assets():\n with open(\"tests/samples/stocks.csv\") as fin:\n reader = csv.reader(fin, delimiter=\",\")\n for row in reader:\n isin, code, name = row\n if isin.startswith(\"#\"):\n continue\n Asset.create(type=AssetType.stock, isin=isin, code=code, name=name)\n\n\n@pytest.fixture(scope=\"function\")\ndef account_checking(request, db):\n account = Account.create(type=\"checking\", name=\"신한은행 입출금\")\n request.addfinalizer(partial(teardown, db=db, record=account))\n return account\n\n\n@pytest.fixture(scope=\"function\")\ndef account_savings(request, db):\n account = Account.create(type=\"savings\", name=\"신한은행 적금\")\n request.addfinalizer(partial(teardown, db=db, record=account))\n return account\n\n\n@pytest.fixture(scope=\"function\")\ndef account_8p(request, db):\n account = Account.create(type=\"virtual\", name=\"8퍼센트\")\n request.addfinalizer(partial(teardown, db=db, record=account))\n return account\n\n\n@pytest.fixture(scope=\"function\")\ndef account_hf(request, db):\n account = Account.create(type=\"virtual\", name=\"어니스트펀드\")\n request.addfinalizer(partial(teardown, db=db, record=account))\n return account\n\n\n@pytest.fixture(scope=\"function\")\ndef account_sp500(request, db):\n account = Account.create(type=\"investment\", name=\"S&P500 Fund\")\n request.addfinalizer(partial(teardown, db=db, record=account))\n return account\n\n\n@pytest.fixture(scope=\"function\")\ndef account_stock(request, db):\n account = Account.create(\n type=AccountType.investment,\n institution=\"Miraeasset\",\n number=\"ACCOUNT1\",\n name=\"미래에셋대우 1\",\n )\n request.addfinalizer(partial(teardown, db=db, record=account))\n return account\n\n\n@pytest.fixture(scope=\"module\")\ndef asset_hf1(request, db):\n asset = P2PBondAsset.create(name=\"포트폴리오 투자상품 1호\")\n request.addfinalizer(partial(teardown, db=db, record=asset))\n assert asset.type == \"p2p_bond\"\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef asset_krw(request, db):\n asset = CurrencyAsset.create(code=\"KRW\", description=\"Korean Won\")\n request.addfinalizer(partial(teardown, db=db, record=asset))\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef asset_sp500(request, db):\n asset = FundAsset.create(\n name=\"KB Star S&P500\", description=\"\", data={\"code\": \"KR5223941018\"}\n )\n request.addfinalizer(partial(teardown, db=db, record=asset))\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef asset_usd(request, db):\n asset = CurrencyAsset.create(code=\"USD\", description=\"United States Dollar\")\n request.addfinalizer(partial(teardown, db=db, record=asset))\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef stock_asset_ncsoft(request, db):\n asset = StockAsset.create(\n name=\"NCsoft Corporation\",\n code=\"036570.KS\",\n description=\"NCsoft Corporation\",\n data={\"bps\": 88772, \"eps\": 12416},\n )\n request.addfinalizer(partial(teardown, db=db, record=asset))\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef stock_asset_spy(request, db, asset_usd):\n asset = StockAsset.create(\n name=\"SPY\",\n code=\"SPY\",\n isin=\"US78462F1030\",\n description=\"SPDR S&P 500 ETF Trust Fund\",\n )\n request.addfinalizer(partial(teardown, db=db, record=asset))\n\n with open(\"tests/samples/SPY.csv\") as fin:\n for av in import_stock_values(fin, \"SPY\", base_asset=asset_usd):\n request.addfinalizer(partial(teardown, db=db, record=av))\n\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef stock_asset_amd(request, db, asset_usd):\n asset = StockAsset.create(\n name=\"AMD\",\n code=\"AMD\",\n isin=\"US0079031078\",\n description=\"Advanced Micro Devices, Inc\",\n )\n request.addfinalizer(partial(teardown, db=db, record=asset))\n\n with open(\"tests/samples/AMD.csv\") as fin:\n for av in import_stock_values(fin, \"AMD\", base_asset=asset_usd):\n request.addfinalizer(partial(teardown, db=db, record=av))\n\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef stock_asset_nvda(request, db, asset_usd):\n asset = StockAsset.create(\n name=\"NVDA\", code=\"NVDA\", isin=\"US67066G1040\", description=\"NVIDIA Corporation\"\n )\n request.addfinalizer(partial(teardown, db=db, record=asset))\n\n with open(\"tests/samples/NVDA.csv\") as fin:\n for av in import_stock_values(fin, \"NVDA\", base_asset=asset_usd):\n request.addfinalizer(partial(teardown, db=db, record=av))\n\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef stock_asset_amzn(request, db, asset_usd):\n asset = StockAsset.create(\n name=\"AMZN\", code=\"AMZN\", isin=\"US0231351067\", description=\"Amazon\"\n )\n request.addfinalizer(partial(teardown, db=db, record=asset))\n\n with open(\"tests/samples/AMZN.csv\") as fin:\n for av in import_stock_values(fin, \"AMZN\", base_asset=asset_usd):\n request.addfinalizer(partial(teardown, db=db, record=av))\n\n return asset\n\n\n@pytest.fixture(scope=\"module\")\ndef stock_asset_sbux(request, db, asset_usd):\n asset = StockAsset.create(\n name=\"SBUX\", code=\"SBUX\", isin=\"US8552441094\", description=\"Starbucks\"\n )\n request.addfinalizer(partial(teardown, db=db, record=asset))\n\n with open(\"tests/samples/SBUX.csv\") as fin:\n for av in import_stock_values(fin, \"SBUX\", base_asset=asset_usd):\n request.addfinalizer(partial(teardown, db=db, record=av))\n\n return asset\n\n\n@pytest.fixture(scope=\"function\")\ndef portfolio(request, db, asset_krw, account_checking, account_sp500):\n p = Portfolio.create(base_asset=asset_krw)\n p.add_accounts(account_checking, account_sp500)\n\n def teardown():\n # NOTE: The following statement is necessary because the scope of\n # `asset_krw` is a module, whereas the scope of `p` is a function.\n p.base_asset = None\n db.session.delete(p)\n db.session.commit()\n\n request.addfinalizer(teardown)\n return p\n\n\ndef teardown(db, record):\n db.session.delete(record)\n db.session.commit()\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":7436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"256495441","text":"# Copyright 2020, 2021 Intel Corporation\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\n\nimport dpctl\nimport numba\nimport numpy as np\n\nimport numba_dppy as dppy\n\n\ndef func(param_a, param_b):\n param_c = param_a + 10 # Set breakpoint\n param_d = param_b * 0.5\n result = param_c + param_d\n return result\n\n\ndppy_func = dppy.func(debug=True)(func)\nnumba_func = numba.njit(debug=True)(func)\n\n\n@dppy.kernel(debug=True)\ndef dppy_kernel(a_in_kernel, b_in_kernel, c_in_kernel):\n i = dppy.get_global_id(0)\n c_in_kernel[i] = dppy_func(a_in_kernel[i], b_in_kernel[i])\n\n\n@numba.njit(debug=True)\ndef numba_func_driver(a, b, c):\n for i in range(len(c)):\n c[i] = numba_func(a[i], b[i])\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--api\",\n required=False,\n default=\"numba\",\n choices=[\"numba\", \"numba-dppy\"],\n help=\"Start the version of functions using numba or numba-dppy API\",\n )\n\n args = parser.parse_args()\n\n print(\"Using API:\", args.api)\n\n global_size = 10\n N = global_size\n\n a = np.arange(N, dtype=np.float32)\n b = np.arange(N, dtype=np.float32)\n c = np.empty_like(a)\n\n if args.api == \"numba-dppy\":\n device = dpctl.select_default_device()\n with dppy.offload_to_sycl_device(device):\n dppy_kernel[global_size, dppy.DEFAULT_LOCAL_SIZE](a, b, c)\n else:\n numba_func_driver(a, b, c)\n\n print(\"Done...\")\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"numba_dppy/examples/debug/dppy_numba_basic.py","file_name":"dppy_numba_basic.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"612796420","text":"from collections import Counter\nimport spacy\n\n\ndef complex_words_PoS_count(dataset):\n tag_count = Counter()\n for sent in dataset:\n word = sent['target_word']\n tokenized_word = word.split()\n sentence = sent['sentence']\n gold_label = sent['gold_label']\n nlp = spacy.load('en_core_web_sm')\n doc = nlp(sentence)\n\n tags = [(token.text, token.tag_) for token in doc]\n if gold_label == \"1\":\n if len(tokenized_word) == 1:\n tokenized_word = word\n for tag in tags:\n if tag[0] == word:\n tag_count[tag[1]] += 1\n # And sometimes you get more than one word\n elif (len(tokenized_word) > 1):\n for word in tokenized_word:\n for tag in tags:\n if tag[0] == word:\n tag_count[tag[1]] += 1\n\n return tag_count\n","sub_path":"pos_counts.py","file_name":"pos_counts.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"439891390","text":"import numpy as np\nimport pandas as pd\n\ndef mirror_mat(matrix):\n grid = [row[:] for row in matrix]\n n = len(grid)\n m = len(grid[0])\n m2 = round(m/2)\n for i in range(n):\n for j in range(m2):\n temp = grid[i][j]\n grid[i][j] = grid[i][m-j-1]\n grid[i][m-j-1] = temp\n return grid\n\ndef transpose_mat(matrix):\n grid = [row[:] for row in matrix]\n n = len(grid)\n m = len(grid[0])\n for i in range(n):\n for j in range(m):\n if(i= threshold)\n corners = [[0,0],[0,m-1],[n-1,0],[n-1,m-1]]\n score = 0\n for r in range(len(relevant)):\n dists = []\n for c in range(len(corners)):\n a = relevant[r]\n b = corners[c]\n dists.append(np.linalg.norm(a-b))\n print(dists)\n min_distance = min(dists)\n score += min_distance\n return score\n\ndef avg_dist_between_largest(matrix, num_sigma=2):\n grid = np.array(matrix)\n miu = grid.mean()\n sigma = grid.std()\n threshold = round(miu + num_sigma * sigma)\n relevant = np.argwhere(grid >= threshold)\n distances = []\n done = []\n for r0 in range(len(relevant)):\n for r1 in range(len(relevant)):\n if [r0,r1] not in done and [r1,r0] not in done:\n distances.append(np.linalg.norm(relevant[r0]-relevant[r1]))\n done.append([[r0,r1]])\n avg_dist = sum(distances)/len(distances)\n return avg_dist\n\ngrid = [[4, 8, 8, 64],\n [0, 0, 0, 0 ],\n [0, 0, 0, 2 ],\n [4, 0, 0, 2 ]]\n\n\n\nprint(avg_dist_between_largest(grid))","sub_path":"other/2048.py","file_name":"2048.py","file_ext":"py","file_size_in_byte":3644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"575347638","text":"from django.shortcuts import render, redirect, get_object_or_404\r\nfrom django.contrib import messages\r\nfrom django.contrib.auth.models import User\r\nfrom django.views.generic import ListView, DetailView, CreateView, UpdateView, DeleteView\r\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\r\nfrom .forms import UserRegisterForm, UserUpdateForm, ProfileUpdateForm\r\nfrom .models import Post\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.views import View\r\nfrom django.http import HttpResponse\r\n\r\n\r\n\r\ndef index(request):\r\n return render(request, 'bashmycode/index.html')\r\n\r\nclass PostListViewBash(ListView):\r\n model = Post\r\n queryset = Post.objects.filter(post_type='BASH').order_by('-likes')\r\n template_name = 'bashmycode/bash.html' # /_.html\r\n context_object_name = 'posts'\r\n paginate_by = 5\r\n\r\n# SOMETHING WRONG WITH PAGINATION ON HELP\r\nclass PostListViewHelp(ListView):\r\n model = Post\r\n queryset = Post.objects.filter(post_type='HELP').order_by('-likes')\r\n template_name = 'bashmycode/help.html' # /_.html\r\n context_object_name = 'posts'\r\n paginate_by = 5\r\n\r\n\r\nclass UserPostListView(ListView):\r\n model = Post\r\n template_name = 'bashmycode/user_posts.html' # /_.html\r\n context_object_name = 'posts'\r\n paginate_by = 5\r\n\r\n def get_queryset(self):\r\n user = get_object_or_404(User, username=self.kwargs.get('username'))\r\n return Post.objects.filter(author=user).order_by('-likes')\r\n\r\nclass PostDetailView(DetailView):\r\n model = Post\r\n\r\nclass PostCreateView(LoginRequiredMixin, CreateView):\r\n model = Post\r\n fields = ['title', 'content', 'post_type']\r\n\r\n def form_valid(self, form):\r\n form.instance.author = self.request.user\r\n return super().form_valid(form)\r\n\r\nclass PostUpdateView(LoginRequiredMixin, UserPassesTestMixin, UpdateView):\r\n model = Post\r\n fields = ['title', 'content', 'post_type']\r\n\r\n def form_valid(self, form):\r\n form.instance.author = self.request.user\r\n return super().form_valid(form)\r\n \r\n def test_func(self):\r\n post = self.get_object()\r\n if self.request.user == post.author:\r\n return True\r\n return False\r\n\r\nclass PostDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\r\n model = Post\r\n success_url = '/bashmycode/'\r\n\r\n def test_func(self):\r\n post = self.get_object()\r\n if self.request.user == post.author:\r\n return True\r\n return False\r\n\r\n\r\nclass LikePostView(View):\r\n def get(self, request):\r\n post_id = request.GET['post_id']\r\n try:\r\n post = Post.objects.get(id=int(post_id))\r\n except Post.DoesNotExist:\r\n return HttpResponse(-1)\r\n except ValueError:\r\n return HttpResponse(-1)\r\n post.likes = post.likes + 1\r\n post.save()\r\n \r\n return HttpResponse(post.likes)\r\n\r\n\r\ndef bash(request):\r\n context = {\r\n 'posts': Post.objects.filter(post_type='BASH').order_by('-date_posted')\r\n }\r\n return render(request, 'bashmycode/bash.html', context)\r\n\r\ndef help(request):\r\n context = {\r\n 'posts': Post.objects.filter(post_type='HELP').order_by('-date_posted')\r\n }\r\n return render(request, 'bashmycode/bash.html', context)\r\ndef register(request):\r\n if request.method == 'POST':\r\n form = UserRegisterForm(request.POST)\r\n if form.is_valid():\r\n form.save()\r\n username = form.cleaned_data.get('username')\r\n messages.success(request, f'Account created for {username}!')\r\n return redirect('/bashmycode/login')\r\n else:\r\n form = UserRegisterForm()\r\n return render(request, 'bashmycode/register.html', {'form': form})\r\n\r\n@login_required\r\ndef profile(request):\r\n if request.method == 'POST':\r\n u_form = UserUpdateForm(request.POST, instance=request.user)\r\n p_form = ProfileUpdateForm(request.POST, request.FILES, instance=request.user.profile)\r\n if u_form.is_valid() and p_form.is_valid():\r\n u_form.save()\r\n p_form.save()\r\n messages.success(request, f'Your account has been updated!')\r\n return redirect('/bashmycode/profile')\r\n else:\r\n u_form = UserUpdateForm(instance=request.user)\r\n p_form = ProfileUpdateForm(instance=request.user.profile)\r\n\r\n context = {\r\n 'u_form': u_form,\r\n 'p_form': p_form\r\n }\r\n\r\n return render(request, 'bashmycode/profile.html', context)","sub_path":"bashmycode/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"645230283","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport math\nimport os\nimport random\nimport sys\nimport time\n\nimport numpy as np\nfrom six.moves import xrange # pylint: disable=redefined-builtin\nimport tensorflow as tf\nfrom tensorflow.contrib import learn\nimport logging\nimport data_utils\nfrom cnnLstmSerialModel import SerialModel\n\nimport data_iterator\nfrom data_iterator import DataIterator\nfrom tensorflow.python.client import timeline\n\nfrom summary import ModelSummary, variable_summaries\n\nimport argparse\n\n############################\n######## MARK:FLAGS ########\n############################\nparser = argparse.ArgumentParser(description=\"Sequence to Sequence Using Tensorflow.\")\n# mode\nparser.add_argument(\"--mode\", type=str, default=\"TRAIN\", help=\"TRAIN|DECODE\")\n\n# datasets, paths, and preprocessing\nparser.add_argument(\"--model_dir\", type=str, default=\"./model\", help=\"model_dir/data_cache/n model_dir/saved_model; model_dir/log.txt .\")\nparser.add_argument(\"--train_path_from\", type=str, default=\"./train.src\", help=\"the absolute path of raw source train file.\")\nparser.add_argument(\"--dev_path_from\", type=str, default=\"./dev.src\", help=\"the absolute path of raw source dev file.\")\nparser.add_argument(\"--test_path_from\", type=str, default=\"./test.src\", help=\"the absolute path of raw source test file.\")\nparser.add_argument(\"--word_vec_path\", type=str, default=\"./word2vec.txt\", help=\"the absolute path of word vectors file.\")\n\nparser.add_argument(\"--train_path_to\", type=str, default=\"./train.tgt\", help=\"the absolute path of raw target train file.\")\nparser.add_argument(\"--dev_path_to\", type=str, default=\"./dev.tgt.tgt\", help=\"the absolute path of raw target dev file.\")\nparser.add_argument(\"--test_path_to\", type=str, default=\"./test.tgt\", help=\"the absolute path of raw target test file.\")\n\nparser.add_argument(\"--decode_output\", type=str, default=\"./test.output\", help=\"beam search decode output.\")\n\n# tuning hypers\nparser.add_argument(\"--learning_rate\", type=float, default=0.5, help=\"Learning rate.\")\nparser.add_argument(\"--learning_rate_decay_factor\", type=float, default=0.83, help=\"Learning rate decays by this much.\")\nparser.add_argument(\"--max_gradient_norm\", type=float, default=5.0, help=\"Clip gradients to this norm.\")\nparser.add_argument(\"--keep_prob\", type=float, default=0.8, help=\"dropout rate.\")\nparser.add_argument(\"--batch_size\", type=int, default=128, help=\"Batch size to use during training/evaluation.\")\nparser.add_argument(\"--dilation_rate\",type=int,default=1,help=\"the dilatation of dilated convoluational kernel\")\n\nparser.add_argument(\"--from_vocab_size\", type=int, default=10000, help=\"from vocabulary size.\")\nparser.add_argument(\"--to_vocab_size\", type=int, default=10000, help=\"to vocabulary size.\")\n\nparser.add_argument(\"--size\", type=int, default=128, help=\"Size of each model layer.\")\nparser.add_argument(\"--num_layers\", type=int, default=2, help=\"Number of layers in the model.\")\nparser.add_argument(\"--n_epoch\", type=int, default=50, help=\"Maximum number of epochs in training.\")\n\nparser.add_argument(\"--L\", type=int, default=2000, help=\"max length\")\nparser.add_argument(\"--patience\", type=int, default=10, help=\"exit if the model can't improve for $patence / 2 epochs\")\n\n# devices\nparser.add_argument(\"--N\", type=str, default=\"000\", help=\"GPU layer distribution: [input_embedding, lstm, output_embedding]\")\n\n# training parameter\nparser.add_argument(\"--withAdagrad\", type=bool, default=True, help=\"withAdagrad.\")\nparser.add_argument(\"--fromScratch\", type=bool, default=True, help=\"fromScratch.\")\nparser.add_argument(\"--saveCheckpoint\", type=bool, default=False, help=\"save Model at each checkpoint.\")\nparser.add_argument(\"--profile\", type=bool, default=False, help=\"False = no profile, True = profile\")\n\n# GPU configuration\nparser.add_argument(\"--allow_growth\", type=bool, default=False, help=\"allow growth\")\n\n# Summary\nparser.add_argument(\"--with_summary\", type=bool, default=False, help=\"with_summary\")\nparser.add_argument(\"--data_cache_dir\", type=str, default=\"data_cache\", help=\"data_cache\")\n\nFLAGS = parser.parse_args()\n\ndef mylog(msg):\n print(msg)\n sys.stdout.flush()\n logging.info(msg)\n\n\ndef mylog_section(section_name):\n mylog(\"======== {} ========\".format(section_name)) \n\ndef mylog_subsection(section_name):\n mylog(\"-------- {} --------\".format(section_name)) \n\ndef mylog_line(section_name, message):\n mylog(\"[{}] {}\".format(section_name, message))\n\n\ndef get_device_address(s):\n add = []\n if s == \"\":\n for i in xrange(3):\n add.append(\"/cpu:0\")\n else:\n add = [\"/gpu:{}\".format(int(x)) for x in s]\n\n return add\n\ndef show_all_variables():\n all_vars = tf.global_variables()\n for var in all_vars:\n mylog(var.name)\n\n\ndef log_flags():\n mylog_section(\"FLAGS\")\n for attr, value in sorted(FLAGS.__dict__.items()):\n mylog(\"{}={}\".format(attr.upper(), value))\n\ndef create_model(session, run_options, run_metadata, max_len):\n devices = get_device_address(FLAGS.N)\n dtype = tf.float32\n model = SerialModel(FLAGS.size,\n FLAGS.real_vocab_size_from,\n FLAGS.real_vocab_size_to,\n FLAGS.num_layers,\n FLAGS.max_gradient_norm,\n FLAGS.batch_size,\n max_len,\n FLAGS.learning_rate,\n FLAGS.learning_rate_decay_factor,\n withAdagrad = FLAGS.withAdagrad,\n dropoutRate = FLAGS.keep_prob,\n dtype = dtype,\n devices = devices,\n run_options = run_options,\n run_metadata = run_metadata,\n word_vector = FLAGS.word_vector,\n dilation_rate=FLAGS.dilation_rate,\n )\n\n ckpt = tf.train.get_checkpoint_state(FLAGS.saved_model_dir)\n\n if FLAGS.mode == \"DECODE\":\n mylog(\"Reading model parameters from %s\" % ckpt.model_checkpoint_path)\n model.saver.restore(session, ckpt.model_checkpoint_path)\n else:\n mylog(\"Created model with fresh parameters.\")\n session.run(tf.global_variables_initializer())\n return model\n\ndef train():\n\n # Read Data\n mylog_section(\"READ DATA\")\n\n from_train = None\n to_train = None\n from_dev = None\n to_dev = None\n \n from_train, to_train, from_dev, to_dev, _, _ = data_utils.prepare_data(\n FLAGS.data_cache_dir,\n FLAGS.train_path_from,\n FLAGS.train_path_to,\n FLAGS.dev_path_from,\n FLAGS.dev_path_to,\n FLAGS.from_vocab_size,\n FLAGS.to_vocab_size)\n\n train_data, train_max_len = data_utils.read_data(from_train,to_train)\n dev_data, dev_max_len = data_utils.read_data(from_dev,to_dev)\n max_len = max(train_max_len, dev_max_len)\n _,_,real_vocab_size_from,real_vocab_size_to = data_utils.get_vocab_info(FLAGS.data_cache_dir)\n \n FLAGS.real_vocab_size_from = real_vocab_size_from\n FLAGS.real_vocab_size_to = real_vocab_size_to\n\n if FLAGS.word_vector:\n \tword_embedding, _ = data_utils.read_word_vec(FLAGS.word_vec_path, FLAGS.data_cache_dir)\n else:\n \tword_embedding = None\n\n train_n_tokens = np.sum([len(items[1]) for items in train_data])\n train_total_size = len(train_data)\n dev_total_size = len(dev_data)\n\n mylog_section(\"REPORT\")\n # steps\n batch_size = FLAGS.batch_size\n n_epoch = FLAGS.n_epoch\n steps_per_epoch = int(train_total_size / batch_size)\n steps_per_dev = int(dev_total_size / batch_size)\n steps_per_checkpoint = int(steps_per_epoch / 2)\n total_steps = steps_per_epoch * n_epoch\n\n # reports\n mylog(\"from_vocab_size: {}\".format(FLAGS.from_vocab_size))\n mylog(\"to_vocab_size: {}\".format(FLAGS.to_vocab_size))\n mylog(\"Train:\")\n mylog(\"total: {}\".format(train_total_size))\n mylog(\"Dev:\")\n mylog(\"total: {}\".format(dev_total_size))\n mylog(\"Steps_per_epoch: {}\".format(steps_per_epoch))\n mylog(\"Total_steps:{}\".format(total_steps))\n mylog(\"Steps_per_checkpoint: {}\".format(steps_per_checkpoint))\n\n\n mylog_section(\"IN TENSORFLOW\")\n \n with tf.Graph().as_default():\n tf.set_random_seed(23)\n config = tf.ConfigProto(allow_soft_placement=True, log_device_placement = False)\n config.gpu_options.allow_growth = FLAGS.allow_growth\n config.gpu_options.per_process_gpu_memory_fraction = 1.0\n with tf.Session(config=config) as sess:\n \n # runtime profile\n if FLAGS.profile:\n run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_metadata = tf.RunMetadata()\n else:\n run_options = None\n run_metadata = None\n \n mylog_section(\"MODEL/SUMMARY/WRITER\")\n \n mylog(\"Creating Model.. (this can take a few minutes)\")\n model = create_model(sess, run_options, run_metadata, max_len)\n \n if FLAGS.with_summary:\n mylog(\"Creating ModelSummary\")\n modelSummary = ModelSummary()\n \n mylog(\"Creating tf.summary.FileWriter\")\n summaryWriter = tf.summary.FileWriter(os.path.join(FLAGS.summary_dir , \"train.summary\"), sess.graph)\n \n mylog_section(\"All Variables\")\n show_all_variables() #convenient for checking model variables\n \n # Data Iterators\n mylog_section(\"Data Iterators\")\n \n dite = DataIterator(model, train_data, batch_size)\n \n iteType = 0\n if iteType == 0:\n mylog(\"Itetype: withRandom\")\n ite = dite.next_random()\n elif iteType == 1:\n mylog(\"Itetype: withSequence\")\n ite = dite.next_sequence()\n \n # statistics during training\n step_time, loss = 0.0, 0.0\n correct = 0\n current_step = 0\n previous_losses = []\n high_acc = -1\n high_acc_step = 0\n steps_per_report = 30\n n_targets_report = 0\n report_time = 0\n n_valid_sents = 0\n n_valid_words = 0\n patience = FLAGS.patience\n \n mylog_section(\"TRAIN\")\n \n \n while current_step < total_steps:\n \n # start\n start_time = time.time()\n \n # data and train\n source_inputs, source_lengths, target_outputs = ite.next()\n\n L, unary_scores, transition_matrix = model.step(sess, source_inputs, target_outputs, source_lengths, word_embedding)\n \n # loss and time\n step_time += (time.time() - start_time) / steps_per_checkpoint\n \n _, correct_labels = CRF_viterbi_decode(unary_scores, transition_matrix, source_lengths, target_outputs)\n correct += correct_labels\n loss += L\n \n current_step += 1\n n_valid_sents += np.sum(np.sign(source_lengths))\n n_valid_words += np.sum(source_lengths)\n \n # for report\n report_time += (time.time() - start_time)\n n_targets_report += np.sum(source_lengths)\n \n if current_step % steps_per_report == 0:\n sect_name = \"STEP {}\".format(current_step)\n msg = \"StepTime: {:.2f} sec Speed: {:.2f} targets/s Total_targets: {}\".format(report_time/steps_per_report, n_targets_report*1.0 / report_time, train_n_tokens)\n mylog_line(sect_name,msg)\n \n report_time = 0\n n_targets_report = 0\n \n \n # Create the Timeline object, and write it to a json\n if FLAGS.profile:\n tl = timeline.Timeline(run_metadata.step_stats)\n ctf = tl.generate_chrome_trace_format()\n with open('timeline.json', 'w') as f:\n f.write(ctf)\n exit()\n\n \n if current_step % steps_per_checkpoint == 0:\n \n i_checkpoint = int(current_step / steps_per_checkpoint)\n \n # train_acc\n loss = loss / n_valid_words\n train_acc = correct * 1.0 / n_valid_words\n learning_rate = model.learning_rate.eval()\n \n # dev_acc\n dev_loss, dev_acc = evaluate(sess, model, dev_data, word_embedding)\n \n # report\n sect_name = \"CHECKPOINT {} STEP {}\".format(i_checkpoint, current_step)\n msg = \"Learning_rate: {:.4f} Dev_acc: {:.4f} Train_acc: {:.4f}\".format(learning_rate, dev_acc, train_acc)\n mylog_line(sect_name, msg)\n \n if FLAGS.with_summary:\n # save summary\n _summaries = modelSummary.step_record(sess, train_acc, dev_acc)\n for _summary in _summaries:\n summaryWriter.add_summary(_summary, i_checkpoint)\n \n # save model per checkpoint\n if False: # FLAGS.saveCheckpoint\n checkpoint_path = os.path.join(FLAGS.saved_model_dir, \"model\")\n s = time.time()\n model.saver.save(sess, checkpoint_path, global_step=i_checkpoint, write_meta_graph = False)\n msg = \"Model saved using {:.2f} sec at {}\".format(time.time()-s, checkpoint_path)\n mylog_line(sect_name, msg)\n \n # save best model\n if dev_acc > high_acc:\n patience = FLAGS.patience\n high_acc = dev_acc\n high_acc_step = current_step\n checkpoint_path = os.path.join(FLAGS.saved_model_dir, \"model\")\n s = time.time()\n model.saver.save(sess, checkpoint_path, global_step=i_checkpoint, write_meta_graph = False)\n msg = \"Model saved using {:.2f} sec at {}\".format(time.time()-s, checkpoint_path)\n mylog_line(sect_name, msg)\n \n else:\n patience -= 1\n \n if patience <= 0:\n mylog(\"Training finished. Running out of patience.\")\n break\n \n \n # Save checkpoint and zero timer and loss.\n step_time, loss, n_valid_sents, n_valid_words, correct = 0.0, 0.0, 0, 0, 0\n \n\ndef evaluate(sess, model, data_set, word_embedding):\n # Run evals on development set and print their perplexity/loss or accuracy.\n dropoutRateRaw = FLAGS.keep_prob\n\n start_id = 0\n loss = 0.0\n n_steps = 0\n n_valids = 0\n n_correct = 0\n batch_size = FLAGS.batch_size\n \n dite = DataIterator(model, data_set, batch_size)\n ite = dite.next_sequence()\n\n for sources, lengths, outputs in ite:\n L, unary_scores, transition_matrix= model.step(sess, sources, outputs, lengths, word_embedding, forward_only = True)\n _, correct_labels = CRF_viterbi_decode(unary_scores, transition_matrix, lengths, outputs)\n n_correct += correct_labels\n loss += L\n n_steps += 1\n n_valids += np.sum(lengths)\n\n loss = loss/(n_valids)\n acc = n_correct * 1.0 /(n_valids)\n\n\n return loss, acc\n\n\ndef CRF_viterbi_decode(unary_scores, transition_matrix, lengths, target_outputs=None):\n correct_labels = 0\n viterbi_seqs = []\n for i, (unary_score, length) in enumerate(zip(unary_scores, lengths)):\n unary_score = unary_score[:length]\n if length:\n viterbi_seq, _ = tf.contrib.crf.viterbi_decode(unary_score, transition_matrix)\n else:\n viterbi_seq = []\n viterbi_seqs.append(viterbi_seq)\n if target_outputs is not None:\n target = target_outputs[i][:length]\n correct_labels += np.sum(np.equal(viterbi_seq, target))\n return viterbi_seqs, correct_labels\n\n\ndef decode(ans=False):\n mylog_section(\"READ DATA\")\n\n from_vocab_path, to_vocab_path, real_vocab_size_from, real_vocab_size_to = data_utils.get_vocab_info(FLAGS.data_cache_dir)\n FLAGS.real_vocab_size_from = real_vocab_size_from\n FLAGS.real_vocab_size_to = real_vocab_size_to\n\n from_test = data_utils.prepare_test_data(FLAGS.data_cache_dir, FLAGS.test_path_from, from_vocab_path)\n if ans:\n to_test = os.path.join(FLAGS.data_cache_dir, \"test.tgt.ids\")\n data_utils.data_to_token_ids(FLAGS.test_path_to, to_test, to_vocab_path)\n test_data, test_max_len = data_utils.read_data(from_test, to_test)\n else:\n test_data, test_max_len = data_utils.read_test_data(from_test)\n\n test_total_size = len(test_data)\n\n # reports\n mylog_section(\"REPORT\")\n mylog(\"from_vocab_size: {}\".format(FLAGS.from_vocab_size))\n mylog(\"to_vocab_size: {}\".format(FLAGS.to_vocab_size))\n mylog(\"DECODE:\")\n mylog(\"total: {}\".format(test_total_size))\n \n config = tf.ConfigProto(allow_soft_placement=True, log_device_placement = False)\n config.gpu_options.allow_growth = FLAGS.allow_growth\n config.gpu_options.per_process_gpu_memory_fraction = 1.0\n\n mylog_section(\"IN TENSORFLOW\")\n with tf.Session(config=config) as sess:\n\n # runtime profile\n if FLAGS.profile:\n run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_metadata = tf.RunMetadata()\n else:\n run_options = None\n run_metadata = None\n\n mylog(\"Creating Model\")\n model = create_model(sess, run_options, run_metadata, test_max_len)\n \n mylog_section(\"All Variables\")\n show_all_variables()\n \n #sess.run(model.dropoutRate.assign(1.0))\n\n start_id = 0\n n_steps = 0\n batch_size = FLAGS.batch_size\n\n mylog_section(\"Data Iterators\")\n dite = DataIterator(model, test_data, batch_size)\n ite = dite.next_sequence(test = (not ans))\n\n i_sent = 0\n\n mylog_section(\"DECODING\")\n results = []\n sources = []\n targets = []\n\n n_valids = 0\n n_correct = 0\n\n if ans:\n n_valids = 0\n n_correct = 0\n for inputs, lengths, outputs in ite:\n \n mylog(\"--- decoding {}/{} sent ---\".format(i_sent, test_total_size))\n i_sent += 1\n \n L, unary_scores,transition_matrix = model.step(sess, inputs, outputs, lengths, forward_only = True)\n viterbi_seqs, correct_labels = CRF_viterbi_decode(unary_scores, transition_matrix, lengths, outputs)\n results += viterbi_seqs\n n_correct += correct_labels\n n_valids += np.sum(lengths)\n\n mylog(\"Correct Labels / Total Labels: {} / {}\".format(correct_labels, np.sum(lengths)))\n n_valids += np.sum(lengths)\n n_correct += correct_labels\n \n sources += [inputs[i][:length] for i, length in enumerate(lengths)]\n targets += [outputs[i][:length] for i, length in enumerate(lengths)]\n\n else:\n outputs = np.zeros([batch_size, test_max_len], dtype=int)\n for inputs, lengths in ite:\n # inputs: [[_GO],[1],[2],[3],[_EOS],[pad_id],[pad_id]]\n # positions: [4]\n mylog(\"--- decoding {}/{} sent ---\".format(i_sent, test_total_size))\n i_sent += 1\n \n L, unary_scores,transition_matrix = model.step(sess, inputs, outputs, lengths, forward_only = True)\n viterbi_seqs, _ = CRF_viterbi_decode(unary_scores, transition_matrix, lengths)\n results += viterbi_seqs\n mylog(\"LOSS: {}\".format(L))\n \n sources += [inputs[i][:length] for i, length in enumerate(lengths)]\n \n # do the following convert:\n # inputs: [[pad_id],[1],[2],[pad_id],[pad_id],[pad_id]]\n # positions:[2]\n data_utils.ids_to_tokens(results, to_vocab_path, FLAGS.decode_output)\n data_utils.ids_to_tokens(sources, from_vocab_path, FLAGS.decode_output+'.src')\n if ans:\n data_utils.ids_to_tokens(targets, to_vocab_path, FLAGS.decode_output+'.tgt')\n msg = \"Test_acc: {:.4f}\".format(n_correct * 1.0 /(n_valids))\n mylog(msg)\n mylog(\"Decoding finished.\")\n\n\ndef mkdir(path):\n if not os.path.exists(path):\n os.mkdir(path)\n\n\ndef parsing_flags():\n # saved_model\n\n FLAGS.data_cache_dir = os.path.join(FLAGS.model_dir, \"data_cache\")\n FLAGS.saved_model_dir = os.path.join(FLAGS.model_dir, \"saved_model\")\n FLAGS.summary_dir = FLAGS.saved_model_dir\n FLAGS.word_vector = True if os.path.exists(FLAGS.word_vec_path) else False\n\n mkdir(FLAGS.model_dir)\n mkdir(FLAGS.data_cache_dir)\n mkdir(FLAGS.saved_model_dir)\n mkdir(FLAGS.summary_dir)\n\n # for logs\n log_path = os.path.join(FLAGS.model_dir,\"log.{}.txt\".format(FLAGS.mode))\n filemode = 'w' if FLAGS.fromScratch else \"a\"\n logging.basicConfig(filename=log_path,level=logging.DEBUG, filemode = filemode)\n \n log_flags()\n\ndef main(_):\n \n parsing_flags()\n\n os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(FLAGS.N)\n \n if FLAGS.mode == \"TRAIN\":\n train()\n\n \n if FLAGS.mode == 'DECODE': \n FLAGS.batch_size = 1\n FLAGS.word_vector = False\n answer = True if os.path.exists(FLAGS.test_path_to) else False\n decode(answer)\n \n logging.shutdown()\n \nif __name__ == \"__main__\":\n tf.app.run()\n","sub_path":"chenhangting/py/runCNNLSTMSerial.py","file_name":"runCNNLSTMSerial.py","file_ext":"py","file_size_in_byte":22047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"289002657","text":"import tkinter\r\nfrom tkinter import *\r\n\r\n#Características de wroot\r\n\r\nclass frame():\r\n \r\n \r\n def __init__(self):\r\n \r\n \r\n #CREACIÓN DE LA VENANA PRINCIPAL\r\n self.wroot = tkinter.Tk()\r\n self.wroot.title('calculadora')\r\n self.wroot.geometry('300x450+800+150')\r\n self.wroot.pack_propagate(False)\r\n \r\n \r\n self.createLayout()\r\n \r\n def createLayout(self):\r\n \r\n \r\n \r\n \r\n #CREACIÓN DE CUADRO DE TEXTO\r\n\r\n #self.entrada = tk.Entry(self.wroot ,textvariable = self.caracter, width = 300, height = 75, bg = 'lightgreen').place(x = 0, y=0)\r\n \r\n #CREACIÓN DE LOS FRAMES. VOY A CREAR 7 FRAMES.\r\n\r\n self.fr_text = tkinter.Frame(self.wroot, width=300, height=75, bg ='lightgreen')\r\n self.fr_text.place(x=0,y=0)\r\n self.fr_text.pack_propagate(False)\r\n\r\n self.fr_hor = tkinter.Frame(self.wroot,width=300, height = 75, bg = 'orange')\r\n self.fr_hor.place(x=0, y=75)\r\n self.fr_hor.pack_propagate(False)\r\n\r\n self.fr_ver = tkinter.Frame(self.wroot,width=75, height = 300, bg = 'orange')\r\n self.fr_ver.place(x=225, y=150)\r\n self.fr_ver.pack_propagate(False)\r\n\r\n self.fr_1 = tkinter.Frame(self.wroot, width =225, height = 75, bg ='black')\r\n self.fr_1.place(x=0, y= 150)\r\n self.fr_1.pack_propagate(False)\r\n\r\n self.fr_2 = tkinter.Frame(self.wroot, width =225, height = 75, bg ='black')\r\n self.fr_2.place(x=0, y= 225)\r\n self.fr_2.pack_propagate(False)\r\n\r\n self.fr_3 = tkinter.Frame(self.wroot, width =225, height = 75, bg ='black')\r\n self.fr_3.place(x=0, y= 300)\r\n self.fr_3.pack_propagate(False)\r\n\r\n self.fr_4 = tkinter.Frame(self.wroot, width =225, height = 75, bg ='black')\r\n self.fr_4.place(x=0, y= 375)\r\n self.fr_4.pack_propagate(False)\r\n\r\n\r\n #BOTONES DE LA PRIMERA HORIZONTAL // C / X DELETE\r\n self.btn_c = tkinter.Button(self.fr_hor, text ='C', fg ='black', bg = 'orange')\r\n self.btn_c.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_dividir = tkinter.Button(self.fr_hor, text ='/', fg ='black', bg = 'orange')\r\n self.btn_dividir.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_multiplicar = tkinter.Button(self.fr_hor, text ='x', fg ='black', bg = 'orange')\r\n self.btn_multiplicar.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_delete = tkinter.Button(self.fr_hor, text ='«', fg ='black', bg = 'orange')\r\n self.btn_delete.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n #BOTONES DE LA VERTICAL // - + =\r\n\r\n self.btn_suma = tkinter.Button(self.fr_ver, text ='+', fg ='black', bg = 'orange')\r\n self.btn_suma.pack(side= tkinter.TOP, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_resta = tkinter.Button(self.fr_ver, text ='-', fg ='black', bg = 'orange')\r\n self.btn_resta.pack(side= tkinter.TOP, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_result = tkinter.Button(self.fr_ver, text ='=', fg ='black', bg = 'orange')\r\n self.btn_result.pack(side= tkinter.TOP, expand = True, fill = tkinter.BOTH)\r\n\r\n #BOTONES PRIMER LÍNEA DE NUMEROS // 7 8 9\r\n\r\n self.btn_7 = tkinter.Button(self.fr_1, text ='7', fg ='white', bg = 'black')\r\n self.btn_7.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n\r\n self.btn_8 = tkinter.Button(self.fr_1, text ='8', fg ='white', bg = 'black')\r\n self.btn_8.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_9 = tkinter.Button(self.fr_1, text ='9', fg ='white', bg = 'black')\r\n self.btn_9.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n #BOTONES SEGUNDA LÍNEA DE NUMEROS // 4 5 6\r\n\r\n self.btn_4 = tkinter.Button(self.fr_2, text ='4', fg ='white', bg = 'black')\r\n self.btn_4.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_5 = tkinter.Button(self.fr_2, text ='5', fg ='white', bg = 'black')\r\n self.btn_5.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_6 = tkinter.Button(self.fr_2, text ='6', fg ='white', bg = 'black')\r\n self.btn_6.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n #BOTONES TERCERA LÍNEA DE NUMEROS // 1 2 3\r\n\r\n self.btn_1 = tkinter.Button(self.fr_3, text ='1', fg ='white', bg = 'black')\r\n self.btn_1.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_2 = tkinter.Button(self.fr_3, text ='2', fg ='white', bg = 'black')\r\n self.btn_2.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_3 = tkinter.Button(self.fr_3, text ='3', fg ='white', bg = 'black')\r\n self.btn_3.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n\r\n #BOTONES CUARTA LÍNEA DE NUMEROS // % 0 ,\r\n\r\n self.btn_percent = tkinter.Button(self.fr_4, text ='%', fg ='white', bg = 'black')\r\n self.btn_percent.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_0 = tkinter.Button(self.fr_4, text ='0 ', fg ='white', bg = 'black')\r\n self.btn_0.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n self.btn_coma = tkinter.Button(self.fr_4, text =' , ', fg ='white', bg = 'black')\r\n self.btn_coma.pack(side= tkinter.LEFT, expand = True, fill = tkinter.BOTH)\r\n\r\n \r\n def press(self, *args):\r\n \r\n self.entrada.config(textvariable = key)\r\n \r\n \r\n def start(self):\r\n \r\n self.wroot.mainloop()\r\n\r\nclass main():\r\n \r\n def __init__(self):\r\n \r\n self.f = frame()\r\n self.f.press()\r\n self.f.start()\r\n \r\n\r\nif __name__ == '__main__':\r\n \r\n juego= main()","sub_path":"calculadoraInterfaz.py","file_name":"calculadoraInterfaz.py","file_ext":"py","file_size_in_byte":5922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"631862719","text":"from .room import SlackChannel, SlackGroup, SlackIM\nfrom .user import SlackUser\n\n\nclass SlackEvent(object):\n \"\"\"Encapsulates an event received from the RTM socket\"\"\"\n def __init__(self, sc=None, **kwargs):\n\n self._sc = sc\n self._channel = None\n self._user = None\n\n for k, v in kwargs.iteritems():\n setattr(self, k, v)\n\n @property\n def channel(self):\n return self._channel\n\n @channel.setter\n def channel(self, value):\n if isinstance(value, basestring):\n if value[0] == 'G':\n # Slack groups have an ID starting with 'G'\n self._channel = SlackGroup(value, sc=self._sc)\n elif value[0] == 'D':\n # Slack IMs have an ID starting with 'D'\n self._channel = SlackIM(value, sc=self._sc)\n else:\n self._channel = SlackChannel(value, sc=self._sc)\n else:\n self._channel = value\n\n @property\n def user(self):\n return self._user\n\n @user.setter\n def user(self, value):\n if isinstance(value, basestring):\n self._user = SlackUser(value, sc=self._sc)\n else:\n self._user = value\n","sub_path":"slackminion/slack/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"255944518","text":"from logger import *\nimport os\nimport json\n\nfrom lib.db import MemeChainDB\nfrom lib.blockchain import *\nfrom lib.memechain import MemeTx, Validate\n\n# Load configuration file\nwith open(\"config.json\", \"r\") as f:\n config = json.loads(f.read())\n\n\nclass MemechainParser(object):\n \"\"\"\n Wrapper class for various blockchain parsing functions\n used to construct the local memechain metadata database\n \"\"\"\n\n def __init__(self, block_height):\n self.block_height = block_height\n self._memetxs_raw = []\n self.memetxs = []\n\n def collect_memetxs(self):\n \"\"\"\n Method used to parse the op_return data\n for the transactions in the block\n \"\"\"\n block_txs = get_block_txs(block_height)\n\n for txid in block_txs:\n memetx = get_op_return_data(txid)\n self._memetxs_raw.append(memetx)\n\n def parse_memetxs(self):\n \"\"\"\n Method used to parse the raw memetx metadata\n \"\"\"\n for memetx in self._memetxs_raw:\n if memetx[:4] == '3ae4':\n ipfs_id = memetx[4:][:len(memetx) - 4 - 16]\n hashlink = memetx[4:][len(memetx) - 4 - 16:]\n\n self.memetxs.append({\n 'ipfs_id': ipfs_id,\n 'hashlink': hashlink\n })\n\n def return_memetxs(self):\n \"\"\"\n Method used to return the potentially valid memetxs\n\n Returns:\n Memetxs at block_height (array)\n \"\"\"\n return self.memetxs\n\n\nif __name__ == '__main__':\n # Sync logic\n pass\n","sub_path":"sync.py","file_name":"sync.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"232009823","text":"\"\"\"Utility functions used by the CLI.\"\"\"\n\nimport os\nimport fnmatch\nfrom pathlib import Path\n\nimport click\nimport re\nfrom google.cloud import bigquery\n\nfrom bigquery_etl.util.common import project_dirs\n\n\ndef is_valid_dir(ctx, param, value):\n \"\"\"Check if the parameter provided via click is an existing directory.\"\"\"\n if not os.path.isdir(value) or not os.path.exists(value):\n raise click.BadParameter(f\"Invalid directory path to {value}\")\n return value\n\n\ndef is_valid_file(ctx, param, value):\n \"\"\"Check if the parameter provided via click is an existing file.\"\"\"\n if not os.path.isfile(value) or not os.path.exists(value):\n raise click.BadParameter(f\"Invalid file path to {value}\")\n return value\n\n\ndef is_authenticated(project_id=None):\n \"\"\"Check if the user is authenticated to GCP and can access the project.\"\"\"\n client = bigquery.Client()\n\n if project_id:\n return client.project == project_id\n\n return True\n\n\ndef is_valid_project(ctx, param, value):\n \"\"\"Check if the provided project_id corresponds to an existing project.\"\"\"\n if value is None or value in [Path(p).name for p in project_dirs()]:\n return value\n raise click.BadParameter(f\"Invalid project {value}\")\n\n\ndef table_matches_patterns(pattern, invert, table):\n \"\"\"Check if tables match pattern.\"\"\"\n pattern = re.compile(fnmatch.translate(pattern))\n return (pattern.match(table) is not None) != invert\n","sub_path":"bigquery_etl/cli/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"221266842","text":"#!/usr/bin/python3\nimport RPi.GPIO as GPIO\nimport subprocess\nimport shlex\nimport time, datetime\nimport telepot\nfrom telepot.loop import MessageLoop\nfrom telepot.namedtuple import ReplyKeyboardMarkup, KeyboardButton\n\nnow = datetime.datetime.now()\ntelegram_bot = telepot.Bot('597228802:AAGHvtTRo3LWoP3gI4BdddvpHxGhC5iPsYM')\nbandera = \"False\"\n\n\ndef greeting():\n command_line = 'aplay --device plughw:CARD=Device,DEV=0 /home/pi/greeting.wav'\n args = shlex.split(command_line)\n subprocess.call(args)\n print(\"Preguntando quien es\")\n\ndef answer():\n\tcommand_line = 'sudo arecord -D plughw:1 --duration=3 -f cd /home/pi/answer.wav'\n\targs = shlex.split(command_line)\n\tsubprocess.call(args)\n\tgetCurrentPicture()\n\n\ttelegram_bot.sendPhoto(569563829, photo=open('/home/pi/visitante.jpg','rb'))\n\ttelegram_bot.sendAudio(569563829, audio=open('/home/pi/answer.wav','rb'))\n\t\n#Cuando el intercom no esta en automatico \ndef telegram():\n\ttelegram_bot.sendMessage (569563829, str(\"Estan tocando el intercom!!! y el automatico esta\" + str(bandera)))\n\ttelegram_bot.sendAudio(569563829, audio=open('/home/pi/answer.wav','rb'))\n\ndef action(msg):\n\tchat_id = msg['chat']['id']\n\tcommand = msg['text']\n\tglobal bandera\n\tif command == '/hi':\n\t telegram_bot.sendMessage (chat_id, str(\"IntercomPI Funcionando\"))\n\telif command == 'Abrir':\n\t\tGPIO.output(11, 0)\n\t\tGPIO.output(7, 0)\n\t\tGPIO.output(13, 1)\n\t\ttime.sleep(2)\n\t\tGPIO.output(13, 0)\n\t\tprint(\"tamo abriendo\")\n\t\ttelegram_bot.sendMessage (chat_id, str(\"Abriendo intercom!!!\"))\n\telif command == '/on':\n\t\tbandera = \"True\"\n\t\tprint(\"Automatico: \" + str(bandera))\n\t\ttelegram_bot.sendMessage (chat_id, str(\"Automatico: \" + str(bandera)))\n\telif command == '/off':\n\t\tbandera = \"False\"\n\t\tprint(\"Automatico: \" + str(bandera))\n\t\ttelegram_bot.sendMessage (chat_id, str(\"Automatico: \" + str(bandera)))\n\telif command == 'Status':\n\t\tprint(\"Automatico: \" + str(bandera))\n\t\ttelegram_bot.sendMessage (chat_id, str(\"Automatico: \" + str(bandera)))\n\telif command == 'Repetir':\n\t\tprint(\"Enviando Nota de Voz\")\n\t\tanswer()\n\telif command == '/probando':\n\t\tanswer()\n\t\tprint(\"Grabando y Enviando\")\n\t\ttelegram_bot.sendAudio(chat_id, audio=open('/home/pi/answer.wav','rb'))\n\t\tprint(\"Enviado\")\n\n#lee una imagen del url de la camara de seguridad\ndef getCurrentPicture():\n\timport requests\n\tfrom requests.auth import HTTPBasicAuth\n\n\timage_url = 'http://10.0.0.246/ISAPI/Streaming/channels/0700/picture?videoResolutionWidth=1920&videoResolutionHeight=1080'\n\timg_data = requests.get(image_url, auth=HTTPBasicAuth('admin', 'Acropolis12')).content\n\twith open('/home/pi/visitante.jpg', 'wb') as handler:\n\t handler.write(img_data)\n\t\n\n \ndef pulsador():\n\tif (GPIO.input(16)):\n\t\tprint(\"Tocaron la puerta\")\n\t\tGPIO.output(11, 1)\n\t\tGPIO.output(7, 1)\n\t\ttime.sleep(2)\n\t\tgreeting()\n\t\tprint(\"Respondi\")\n\t\tanswer()\n\t\tGPIO.output(11, 0)\n\t\tGPIO.output(7, 0)\n\t\tif (bandera == \"False\"):\n\t\t\tprint(\"Esperando telegram para abir\")\n\t\t\ttelegram()\n\t\tif (bandera == \"True\"):\n\t\t\tGPIO.output(13, 1)\n\t\t\ttime.sleep(2)\n\t\t\tGPIO.output(13, 0)\n\t\t\ttelegram_bot.sendMessage (569563829, str(\"Abriendo intercom!!!\"))\n\t\t\tprint(\"Abriendo\")\n\n \n \nif __name__ == '__main__':\n MessageLoop(telegram_bot, action).run_as_thread()\n try: \n GPIO.setmode(GPIO.BOARD)\n GPIO.setup(16, GPIO.IN)\n GPIO.setwarnings(False)\n GPIO.setup(7, GPIO.OUT, initial=0) \n GPIO.setup(11, GPIO.OUT, initial=0)\n GPIO.setup(13, GPIO.OUT, initial=0)\n print(\"Try\")\n telegram_bot.sendMessage (569563829, str(\"Bienvenido a Ana Paula 801A v2\"), \n\t\treply_markup=ReplyKeyboardMarkup(\n\t\tkeyboard=[[KeyboardButton(text=\"Abrir\"), KeyboardButton(text=\"China\"), KeyboardButton(text=\"Repetir\")],[KeyboardButton(text=\"Status\"), KeyboardButton(text=\"/on\"), KeyboardButton(text=\"/off\")]])\n \t)\n while True:\n pulsador()\n \n except KeyboardInterrupt:\n GPIO.cleanup()\n exit(0)\n except Exception as e:\n print(\"Error durante ejecucion:\")\n print(e) \n finally:\n GPIO.cleanup()\n exit(1)","sub_path":"s1.py","file_name":"s1.py","file_ext":"py","file_size_in_byte":4088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"276564275","text":"from django.conf import settings\nfrom django.urls import include, path\n\nfrom shared.oidc.views.eauth_views import (\n EauthAuthenticationCallbackView,\n EauthAuthenticationRequestView,\n)\nfrom shared.oidc.views.hki_views import (\n HelsinkiOIDCAuthenticationCallbackView,\n HelsinkiOIDCAuthenticationRequestView,\n HelsinkiOIDCBackchannelLogoutView,\n HelsinkiOIDCLogoutView,\n HelsinkiOIDCUserInfoView,\n)\n\nurlpatterns = []\n\nif settings.MOCK_FLAG:\n from shared.oidc.views.mock_views import (\n MockAuthenticationRequestView,\n MockLogoutView,\n MockUserInfoView,\n )\n\n urlpatterns += [\n path(\n \"authenticate/\",\n MockAuthenticationRequestView.as_view(),\n name=\"oidc_authentication_init\",\n ),\n path(\n \"logout/\",\n MockLogoutView.as_view(),\n name=\"oidc_logout\",\n ),\n path(\n \"userinfo/\",\n MockUserInfoView.as_view(),\n name=\"oidc_userinfo\",\n ),\n ]\nelse:\n urlpatterns += [\n path(\n \"authenticate/\",\n HelsinkiOIDCAuthenticationRequestView.as_view(),\n name=\"oidc_authentication_init\",\n ),\n path(\n \"callback/\",\n HelsinkiOIDCAuthenticationCallbackView.as_view(),\n name=\"oidc_authentication_callback\",\n ),\n path(\n \"logout/\",\n HelsinkiOIDCLogoutView.as_view(),\n name=\"oidc_logout\",\n ),\n path(\n \"userinfo/\",\n HelsinkiOIDCUserInfoView.as_view(),\n name=\"oidc_userinfo\",\n ),\n path(\n \"backchannel/logout/\",\n HelsinkiOIDCBackchannelLogoutView.as_view(),\n name=\"oidc_backchannel_logout\",\n ),\n path(\"\", include(\"mozilla_django_oidc.urls\")),\n path(\n \"eauthorizations/authenticate/\",\n EauthAuthenticationRequestView.as_view(),\n name=\"eauth_authentication_init\",\n ),\n path(\n \"eauthorizations/callback/\",\n EauthAuthenticationCallbackView.as_view(),\n name=\"eauth_authentication_callback\",\n ),\n ]\n","sub_path":"backend/shared/shared/oidc/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"74415845","text":"from setuptools import setup, find_packages\nfrom tag_manager import __version__\n\nversion_str = \".\".join(str(n) for n in __version__)\n\nrequires = ['django>=1.8']\n\nsetup(name='django-tag-manager',\n version=version_str,\n description='Tag management system for Django',\n url='https://github.com/cvng/django-tag-manager',\n author='cvng',\n author_email='mail@cvng.io',\n license='MIT',\n packages=find_packages(),\n install_requires=requires,\n zip_safe=False,\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Environment :: Web Environment\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python\",\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n \"Framework :: Django\",\n ])\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"590132210","text":"import json\nimport logging\n\nfrom channels import Channel, Group\nfrom channels.auth import channel_session_user_from_http, channel_session_user\nfrom channels.generic.websockets import JsonWebsocketConsumer\n\nfrom .conf import MSG, ERR\nfrom .models import Game\nfrom .exceptions import ClientError\nfrom .decorators import catch_client_error\nfrom .serializers import GameUpdateSerializer\n\nlogger = logging.getLogger('game')\n\n\nclass BaseWSConsumer(JsonWebsocketConsumer):\n http_user = True\n channel_session_user = True\n\n command_handlers = {\n 'send': 'chat_send',\n 'role': 'choose_role',\n 'move': 'game_move',\n 'reset': 'game_reset',\n }\n\n def get_command_handler(self, text, **kwargs):\n handler = self.command_handlers[text['command']]\n handler = getattr(self, handler)\n return handler(text, **kwargs)\n\n def connection_groups(self, **kwargs):\n if Game.objects.get(pk=kwargs.get('room')):\n return ['game-%s' % kwargs['room'], ]\n return []\n\n @catch_client_error\n def connect(self, message, **kwargs):\n if message.user.is_anonymous:\n raise ClientError(ERR.UNAUTHENTICATED)\n\n try:\n game = kwargs.get('room')\n except AttributeError:\n raise ClientError(ERR.NO_ROOM_ID)\n\n try:\n game = Game.objects.get(pk=game)\n except Game.DoesNotExist():\n raise ClientError(ERR.ROOM_ID_INCORRECT)\n\n super().connect(message, **kwargs)\n\n if not all(game.players_dict.values()) and message.user.username not in game.players_dict.values():\n # if there are vacant roles and the user has no role, offer them to pick a role\n self.send(dict(\n type=MSG.ROLE_CHOICE,\n board=game.board,\n players=game.players_dict\n ))\n else:\n # just send an update\n self.send_update(game, individual=True)\n\n @catch_client_error\n def receive(self, text=None, bytes=None, **kwargs):\n self.get_command_handler(text, **kwargs)\n\n def chat_send(self, message, **kwargs):\n \"\"\"\n Send a message to game chat.\n \"\"\"\n message.update({'type': MSG.CHAT,\n 'user': self.message.user.username})\n\n for group in self.connection_groups(**kwargs):\n Group(group).send({'text': json.dumps(message)})\n\n def choose_role(self, message, **kwargs):\n \"\"\"\n Set a given role field (red/black) of the game to the current session\n user if it is vacant.\n \"\"\"\n game = Game.objects.get(pk=kwargs['room'])\n role = message.get('role')\n\n vacant_roles = [r for (r, p) in game.players_dict.items() if not p]\n\n if role in vacant_roles:\n setattr(game, role, self.message.user)\n game.save()\n response = dict(type=MSG.ROLE_GRANTED, role=role)\n self.send(response)\n else:\n raise ClientError(ERR.WRONG_ROLE_CHOICE)\n\n def game_move(self, message, **kwargs):\n \"\"\"\n Perform a move.\n \"\"\"\n game = Game.objects.get(pk=kwargs['room'])\n try:\n x, y = message['x'], message['y']\n for i in (x, y):\n if not (isinstance(i, int) and i < 12):\n raise KeyError\n except KeyError:\n raise ClientError(ERR.WRONG_COORDINATES)\n\n if (game.red == self.message.user and game.red_turn) or \\\n (game.black == self.message.user and not game.red_turn):\n game.move(x, y)\n\n else:\n raise ClientError(ERR.ROLE_INACTIVE)\n\n def send_update(self, game, individual=False):\n \"\"\"\n Update clients' game data. If individual == True, send message only\n to the current reply channel. Otherwise, broadcast it to\n everyone in the group.\n \"\"\"\n serializer = GameUpdateSerializer(game)\n response = dict(type=MSG.GAME_UPDATE, **serializer.data)\n\n if individual:\n self.send(response)\n else:\n game.send(**response)\n","sub_path":"game/consumers.py","file_name":"consumers.py","file_ext":"py","file_size_in_byte":4126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"446571009","text":"import os\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.backends.cudnn as cudnn\nfrom utils.trainer import *\nfrom nets.my_vgg import vgg_diy\nfrom nets.resnet_pre_activation import *\nfrom nets.se_resnet import *\nfrom utils.convert_DataParallel_Model import convert_DataParallel_Model_to_Common_Model\nfrom command_parameter import *\nfrom dataset_factory import dataset_factory\n\n\n# parser from command_parameter\nargs = parser.parse_args()\n\n# setting command parameters\nargs.cuda = (not args.no_cuda) and torch.cuda.is_available()\ntorch.manual_seed(args.seed)\nif args.cuda:\n torch.cuda.manual_seed(args.seed)\ncudnn.benchmark = True\n\n# loading model and choose model\nmodel_class = [vgg_diy,preactivation_resnet164,se_resnet_34]\nmodel_dict = dict(list(zip(model_name,model_class)))\nif args.fine_tune:\n load_pkl = torch.load(args.fine_tune)\n model = model_dict[args.model](\n num_classes=args.num_classes, cfg=load_pkl['cfg'])\n model.load_state_dict(load_pkl['model_state_dict'])\n if args.teacher_model is not None:\n teacher_model = model_dict[args.model](\n num_classes=args.num_classes)\n teacher_model.load_state_dict(torch.load(args.teacher_model))\n else:\n pass\n #model = model_dict[args.model](num_classes=args.num_classes)\n # model.load_state_dict(load_pkl)\n args.save_path = os.path.join(\n args.save_path,\n 'fine_tune/' + args.model,\n args.dataset)\nelse:\n model = model_dict[args.model](num_classes=args.num_classes)\n args.save_path = os.path.join(args.save_path, args.model, args.dataset)\n\n\n# dataset choice\nkwargs = {'num_workers': args.num_workers,\n 'pin_memory': True} if args.cuda else {}\n'''\nif args.dataset == 'cifar10':\n normalize = transforms.Normalize(\n mean=[0.491, 0.482, 0.447],\n std=[0.247, 0.243, 0.262])\n train_loader = torch.utils.data.DataLoader(\n datasets.CIFAR10('./data', train=True, download=False,\n transform=transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n #transforms.ColorJitter(brightness=1),\n transforms.ToTensor(),\n normalize\n ])\n ), batch_size=args.batch_size, shuffle=True, **kwargs\n )\n validate_loader = torch.utils.data.DataLoader(\n datasets.CIFAR10('./data', train=False,\n transform=transforms.Compose([\n transforms.ToTensor(),\n normalize\n ])\n ),\n batch_size=args.validate_batch_size, shuffle=False, **kwargs\n )\nelif args.dataset == 'cifar100':\n normalize = transforms.Normalize(\n mean=[0.507, 0.487, 0.441],\n std=[0.267, 0.256, 0.276])\n # normalize = transforms.Normalize(mean=[.5,.5,.5], std=[.5,.5,.5])\n # normalize = transforms.Normalize((.5,.5,.5),(.5,.5,.5))\n train_loader = torch.utils.data.DataLoader(\n datasets.CIFAR100('./data', train=True, download=True,\n transform=transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n normalize\n ])\n ), batch_size=args.batch_size, shuffle=True, **kwargs\n )\n validate_loader = torch.utils.data.DataLoader(\n datasets.CIFAR100('./data', train=False,\n transform=transforms.Compose([\n transforms.ToTensor(),\n normalize\n ])\n ),\n batch_size=args.validate_batch_size, shuffle=False, **kwargs\n )\nelse:\n train_loader = torch.utils.data.DataLoader(\n ImageList(root=args.image_root_path, fileList=args.image_train_list,\n transform=transforms.Compose([\n transforms.Resize(size=(args.img_size, args.img_size)),\n transforms.RandomCrop(args.crop_size),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n ])\n ),\n batch_size=args.batch_size, shuffle=True, **kwargs\n )\n validate_loader = torch.utils.data.DataLoader(\n ImageList(\n root=args.image_root_path, fileList=args.image_validate_list,\n transform=transforms.Compose(\n [transforms.Resize(\n size=(args.crop_size, args.crop_size)),\n transforms.ToTensor(), \n ])\n ),\n batch_size=args.validate_batch_size, shuffle=False, **kwargs\n )\n'''\n\ntest_path = '/home/leolau/pytorch/data' #just for test, when parameter confirm this might delete\ndataset_f = dataset_factory(args.dataset,args.batch_size,args.validate_batch_size,kwargs,root_path=test_path)\ntrain_loader = dataset_f.train_loader\nvalidate_loader = dataset_f.validate_loader\n\noptimizer = optim.SGD(\n filter(\n lambda p: p.requires_grad,\n model.parameters()),\n lr=args.lr,\n weight_decay=args.weight_decay,\n momentum=args.momentum,\n nesterov=True)\n# optimizer = optim.Adam(\n# filter(\n# lambda p: p.requires_grad,\n# model.parameters()),\n# lr=args.lr,\n# weight_decay=args.weight_decay)\ncriterion = nn.CrossEntropyLoss()\ntransfer_criterion = nn.MSELoss()\nif args.sr:\n print('\\nSparsity Training \\n')\n trainer = Network_Slimming_Trainer(\n model=model,\n optimizer=optimizer,\n lr=args.lr,\n criterion=criterion,\n start_epoch=args.start_epoch,\n epochs=args.epochs,\n cuda=args.cuda,\n log_interval=args.log_interval,\n train_loader=train_loader,\n validate_loader=validate_loader,\n root=args.save_path,\n penalty=args.p,\n )\nelif args.se:\n print('\\nSE_ResNet Training \\n')\n trainer = SE_Trainer(\n model=model,\n optimizer=optimizer,\n lr=args.lr,\n criterion=criterion,\n start_epoch=args.start_epoch,\n epochs=args.epochs,\n cuda=args.cuda,\n log_interval=args.log_interval,\n train_loader=train_loader,\n validate_loader=validate_loader,\n root=args.save_path,\n SEBlock=SEBlock\n )\n\nelif args.fine_tune is not None and args.teacher_model is not None: # other 3 00 01 10\n print('\\nTraining with Knowledge Distillation \\n')\n trainer = Trainer(\n model=model,\n teacher_model=teacher_model,\n optimizer=optimizer,\n lr=args.lr,\n criterion=criterion,\n start_epoch=args.start_epoch,\n epochs=args.epochs,\n cuda=args.cuda,\n log_interval=args.log_interval,\n train_loader=train_loader,\n validate_loader=validate_loader,\n root=args.save_path,\n loss_ratio=args.loss_ratio,\n transfer_criterion=transfer_criterion,\n\n )\n\nelse:\n print('\\nNormal Training \\n')\n trainer = Trainer(\n model=model,\n optimizer=optimizer,\n lr=args.lr,\n criterion=criterion,\n start_epoch=args.start_epoch,\n epochs=args.epochs,\n cuda=args.cuda,\n log_interval=args.log_interval,\n train_loader=train_loader,\n validate_loader=validate_loader,\n root=args.save_path,\n\n )\ntrainer.start()\n","sub_path":"main_bk.py","file_name":"main_bk.py","file_ext":"py","file_size_in_byte":7752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"340640825","text":"from selenium import selenium\nimport unittest, time, re\n\nclass abc(unittest.TestCase):\n def setUp(self):\n self.verificationErrors = []\n self.selenium = selenium(\"localhost\", 4444, \"*chrome\", \"http://www.alpha.ablesky.com/\")\n self.selenium.start()\n \n def test_abc(self):\n sel = self.selenium\n sel.open(\"/index.do\")\n sel.click(\"id=J_PopupLogin\")\n time.sleep(2)\n sel.type(\"id=login_username\", \"lyang_test\")\n sel.type(\"id=login_password\", \"2165165\")\n sel.click(\"id=ext-gen63\")\n time.sleep(2)\n sel.click(\"id=headMyOffice\")\n sel.wait_for_page_to_load(\"30000\")\n sel.click(\"id=headLogoutId\")\n sel.wait_for_page_to_load(\"30000\")\n \n def tearDown(self):\n self.selenium.stop()\n self.assertEqual([], self.verificationErrors)\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"new python/testscripts/ab.py","file_name":"ab.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"167706218","text":"# Example of a gizmo that activates an operator\n# using the predefined dial gizmo to change the camera roll.\n#\n# Usage: Run this script and select a camera in the 3D view.\n#\nimport bpy\nfrom bpy.types import (\n GizmoGroup,\n)\n\n\nclass MyCameraWidgetGroup(GizmoGroup):\n bl_idname = \"OBJECT_GGT_test_camera\"\n bl_label = \"Object Camera Test Widget\"\n bl_space_type = 'VIEW_3D'\n bl_region_type = 'WINDOW'\n bl_options = {'3D', 'PERSISTENT'}\n\n @classmethod\n def poll(cls, context):\n ob = context.object\n return (ob and ob.type == 'CAMERA')\n\n def setup(self, context):\n # Run an operator using the dial gizmo\n ob = context.object\n mpr = self.gizmos.new(\"GIZMO_GT_dial_3d\")\n props = mpr.target_set_operator(\"transform.rotate\")\n props.constraint_axis = False, False, True\n props.constraint_orientation = 'LOCAL'\n props.release_confirm = True\n\n mpr.matrix_basis = ob.matrix_world.normalized()\n mpr.line_width = 3\n\n mpr.color = 0.8, 0.8, 0.8\n mpr.alpha = 0.5\n\n mpr.color_highlight = 1.0, 1.0, 1.0\n mpr.alpha_highlight = 1.0\n\n self.roll_widget = mpr\n\n def refresh(self, context):\n ob = context.object\n mpr = self.roll_widget\n mpr.matrix_basis = ob.matrix_world.normalized()\n\n\nbpy.utils.register_class(MyCameraWidgetGroup)\n","sub_path":"engine/2.80/scripts/templates_py/gizmo_operator_target.py","file_name":"gizmo_operator_target.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"319058681","text":"import calendar\n\nfrom django.http import JsonResponse\nfrom django.shortcuts import render\nfrom .models import Feedback, Giver, ENTRUSTABILITY_SCORES, ACTIVITIES\n\n\n# primary view, calls the html script\ndef bootstrap_view(request):\n # no filtering done on initial request\n qs_inputs = filter_request(request, 'None')\n # store the filter input from the form so users will not have to reenter data upon each filter request\n url_filter = request.META['QUERY_STRING']\n context = {\n 'queryset': qs_inputs['qs'],\n 'activities': qs_inputs['activities'],\n 'givers': qs_inputs['givers'],\n 'url_filter': url_filter,\n 'selected_inputs': qs_inputs['selected_inputs'],\n 'entrustability_dict': qs_inputs['entrustability_dict'],\n 'activities_dict': qs_inputs['activities_dict']\n }\n return render(request, \"bootstrap_form.html\", context)\n\n\n# primary filtering method used by views\n# request is the html request\n# config is to disable certain filtering for specific chart types, can be empty, 'GiverPie', or 'ActivityType'\ndef filter_request(request, config):\n # order by date and store all queries\n qs = Feedback.objects.order_by('feedbackDate').all()\n start_date_query = request.GET.get('startDate')\n end_date_query = request.GET.get('endDate')\n giver_name_query = request.GET.get('giverName')\n complicated_query = request.GET.get('complicated')\n complex_query = request.GET.get('complex')\n not_complicated_query = request.GET.get('notComplicated')\n not_complex_query = request.GET.get('notComplex')\n activity_query = request.GET.get('activity')\n sorting_query = request.GET.get('byTime')\n\n # save initial inputs\n selected_start_date = start_date_query\n selected_end_date = end_date_query\n selected_giver = giver_name_query\n selected_complicated = complicated_query\n selected_complex = complex_query\n selected_not_complicated = not_complicated_query\n selected_not_complex = not_complex_query\n selected_activity = activity_query\n selected_sorting = sorting_query\n\n # filtering\n if is_valid_queryparam(giver_name_query) and giver_name_query != 'All' and config != 'GiverPie':\n qs = qs.filter(giver__name__icontains=giver_name_query)\n else:\n selected_giver = 'All'\n if is_valid_queryparam(start_date_query):\n qs = qs.filter(feedbackDate__gte=start_date_query)\n else:\n selected_start_date = \"2017-01-01\" # preset date for filling\n if is_valid_queryparam(end_date_query):\n qs = qs.filter(feedbackDate__lt=end_date_query)\n else:\n selected_end_date = \"2019-12-31\" # preset date for filling\n # if complicated is on, set complicated to true (regardless of if not_complicated is also on)\n if complicated_query == 'on':\n qs = qs.filter(complicated=True)\n elif not_complicated_query == 'on':\n qs = qs.filter(complicated=False)\n else:\n selected_complicated = 'off'\n selected_not_complicated = 'off'\n if complex_query == 'on':\n qs = qs.filter(complex=True)\n elif not_complex_query == 'on':\n qs = qs.filter(complex=False)\n else:\n selected_complex = 'off'\n selected_not_complex = 'off'\n if is_valid_queryparam(activity_query) and activity_query != 'All' and config != 'ActivityRadar':\n qs = qs.filter(activity=activity_query)\n else:\n selected_activity = 'All'\n\n activities = Feedback.objects.order_by('activity').values('activity').distinct()\n givers = Feedback.objects.order_by('giver__name').values('giver__name').distinct()\n\n # store selected inputs\n selected_inputs = {\n 'selected_start_date': selected_start_date,\n 'selected_end_date': selected_end_date,\n 'selected_giver': selected_giver,\n 'selected_complicated': selected_complicated,\n 'selected_complex': selected_complex,\n 'selected_not_complicated': selected_not_complicated,\n 'selected_not_complex': selected_not_complex,\n 'selected_activity': selected_activity,\n 'selected_sorting': selected_sorting\n }\n qs_inputs = {\n 'qs': qs,\n 'selected_inputs': selected_inputs,\n 'entrustability_dict': dict(ENTRUSTABILITY_SCORES),\n 'activities_dict': dict(ACTIVITIES),\n 'givers': givers,\n 'activities': activities\n }\n return qs_inputs\n\n\n# primary method for generating the appropriate data for the feedback chart\ndef feedback_chart(request):\n # run all filters\n qs_inputs = filter_request(request, 'None')\n qs = qs_inputs['qs']\n selected_inputs = qs_inputs['selected_inputs']\n data = []\n labels = []\n # averages the entrustability score by year and month\n data_month, data_year, labels_month, labels_year = averaging_helper(qs, selected_inputs['selected_start_date'],\n selected_inputs['selected_end_date'])\n\n # display appropriate output based on user input, default to month\n if selected_inputs['selected_sorting'] == 'byYear':\n data = data_year\n labels = labels_year\n elif selected_inputs['selected_sorting'] == 'byIndividual':\n for entry in qs:\n data.append(entry.entrustability)\n labels.append(entry.to_month_day_year())\n else:\n data = data_month\n labels = labels_month\n\n return JsonResponse(data={\n 'labels': labels,\n 'data': data,\n 'selected_inputs': selected_inputs,\n 'entrustability_dict': qs_inputs['entrustability_dict'],\n 'activities_dict': qs_inputs['activities_dict']\n })\n\n\n# primary method for generating the appropriate data for the giver pie chart\ndef giver_pie_chart(request):\n # run all filters using config: 'GiverPie'\n qs_inputs = filter_request(request, 'GiverPie')\n qs = qs_inputs['qs']\n selected_inputs = qs_inputs['selected_inputs']\n\n # primary helper function that counts the amount of feedback given be each giver\n labels, data_count = count_by_giver(qs, qs_inputs['givers'])\n\n return JsonResponse(data={\n 'labels': labels,\n 'data': data_count,\n 'selected_inputs': selected_inputs,\n 'entrustability_dict': qs_inputs['entrustability_dict'],\n 'activities_dict': qs_inputs['activities_dict']\n })\n\n\n# primary method for generating the appropriate data for the activity radar chart\ndef activity_radar_chart(request):\n # run all filters using config: 'ActivityRadar'\n qs_inputs = filter_request(request, 'ActivityRadar')\n qs = qs_inputs['qs']\n selected_inputs = qs_inputs['selected_inputs']\n\n # primary helper function that averages entrustability scores by activities\n labels, data = average_by_activity(qs, qs_inputs['activities'])\n\n return JsonResponse(data={\n 'labels': labels,\n 'data': data,\n 'selected_inputs': selected_inputs,\n 'entrustability_dict': qs_inputs['entrustability_dict'],\n 'activities_dict': qs_inputs['activities_dict']\n })\n\n\n# primary helper function for generating the average entrustability score by year and month\ndef averaging_helper(qs, start_date, end_date):\n # this only works for the given data model\n start_year = int(start_date[0:4])\n end_year = int(end_date[0:4])\n start_month = int(start_date[5:7])\n end_month = int(end_date[5:7])\n labels_month = []\n labels_year = []\n data_month = []\n data_year = []\n\n # exception handling\n if start_year == end_year and start_month > end_month:\n return data_month, data_year, labels_month, labels_year\n if start_year > end_year:\n return data_month, data_year, labels_month, labels_year\n\n # creating labels\n cur_year = start_year\n cur_month = start_month\n labels_year_temp = []\n while cur_year != end_year or cur_month != end_month:\n labels_month.append(calendar.month_abbr[cur_month] + ' ' + str(cur_year))\n labels_year_temp.append(str(cur_year))\n if cur_month == 12:\n cur_month = 1\n cur_year = cur_year + 1\n else:\n cur_month = cur_month + 1\n labels_month.append(calendar.month_abbr[cur_month] + ' ' + str(cur_year))\n labels_year_temp.append(str(cur_year))\n for i in labels_year_temp:\n if i not in labels_year:\n labels_year.append(i)\n\n data_sum_year = [0] * len(labels_year)\n data_sum_month = [0] * len(labels_month)\n data_count_year = [0] * len(labels_year)\n data_count_month = [0] * len(labels_month)\n\n # iterating through queryset to sum entrustability scores\n for entry in qs:\n index_year = labels_year.index(entry.to_year())\n data_sum_year[index_year] = data_sum_year[index_year] + entry.entrustability\n data_count_year[index_year] = data_count_year[index_year] + 1\n index_month = labels_month.index(entry.to_month_year())\n data_sum_month[index_month] = data_sum_month[index_month] + entry.entrustability\n data_count_month[index_month] = data_count_month[index_month] + 1\n\n # average the values ofr month and year\n for index in range(0, len(labels_year)):\n if data_count_year[index] == 0:\n data_year.append(None)\n else:\n data_year.append(data_sum_year[index] / data_count_year[index])\n for index in range(0, len(labels_month)):\n if data_count_month[index] == 0:\n data_month.append(None)\n else:\n data_month.append(data_sum_month[index] / data_count_month[index])\n\n return data_month, data_year, labels_month, labels_year\n\n\n# primary helper function for the giver pie chart, counts the amount of feedback given by each giver\ndef count_by_giver(qs, givers):\n labels = []\n for giver in givers:\n labels.append(giver['giver__name'])\n data_count = [0] * len(givers)\n\n for entry in qs:\n index = labels.index(entry.giver.name)\n data_count[index] = data_count[index] + 1\n\n return labels, data_count\n\n\n# primary helper function for the activity radar chart, calculates the average entrustability score by activity\ndef average_by_activity(qs, activities):\n # creating initial variables\n labels = []\n labels_temp = []\n for activity in activities:\n labels.append(dict(ACTIVITIES)[activity['activity']])\n labels_temp.append(activity['activity'])\n data_sum = [0] * len(activities)\n data_count = [0] * len(activities)\n data = []\n\n # iterating through all entries and queryset and summing the entrustability\n for entry in qs:\n index = labels_temp.index(entry.activity)\n data_sum[index] = data_sum[index] + entry.entrustability\n data_count[index] = data_count[index] + 1\n\n # averaging the scores\n for index in range(0, len(labels)):\n if data_count[index] == 0:\n data.append(None)\n else:\n data.append(data_sum[index] / data_count[index])\n\n return labels, data\n\n\n# checks if the given query parameter is valid\ndef is_valid_queryparam(param):\n return param != '' and param is not None\n","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"427436655","text":"N = int(input())\narr = []\nfor i in range(N):\n arr.append(list(map(int, input())))\n\nfor h in range(1, N): #높이를 1씩 높여가면서 맵을 반복적으로 탐색\n flag=0\n for i in range(1, N - 1):\n for j in range(1, N - 1):\n if arr[i][j]==h:\n flag = 1 #현재 높이가 있으면 체크\n if arr[i-1][j]>=h and arr[i+1][j]>=h and arr[i][j-1]>=h and arr[i][j+1]>=h:\n arr[i][j]+=1\n\n if flag==0: break\nprint (h-1)\n\n\n","sub_path":"algorithm/codexpert/마을인공위성.py","file_name":"마을인공위성.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"308983261","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nimport argparse\nimport sys\nimport os\nimport glob\nimport subprocess\nimport pandas as pd\nimport pdb\n\n\n\n#Arguments for argparse module:\nparser = argparse.ArgumentParser(description = '''A program that runs TMalign and tree-puzzle and\n\t\t\t\t\t\treceives the resulting output.''')\n\nparser.add_argument('outdir', nargs=1, type= str, default=sys.stdin, help = 'Path to output directory.')\nparser.add_argument('hgroup_file', nargs=1, type= str, default=sys.stdin, help = 'File with selected uids for the specific H-group.')\nparser.add_argument('puzzle', nargs=1, type= str, default=sys.stdin, help = 'Path to tree-puzzle.')\nparser.add_argument('TMalign', nargs=1, type= str, default=sys.stdin, help = 'Path to TMalign.')\nparser.add_argument('address', nargs=1, type= str, default=sys.stdin, help = 'Web adress to download from.') #www.cathdb.info/version/v4_2_0/api/rest/id/\n\n#FUNCTIONS\ndef run_TMalign(outdir, TMalign, hgroup, hgroup_file, address):\n\t'''Download pdb files from CATH api and run TMalign.\n\t'''\n\t\n\tmeasures = {} #Save RMSD to add with MLAA distance from tree-puzzle\n\tuids = pd.read_csv(hgroup_file, sep ='\\n', header=None)\n\tuids = uids[0]\n\t#Get pdb files for domains\n\tfor uid in uids:\n\t\t#Get pdb file. Make sure they are saved to outdir\n\t\tsubprocess.call([\"wget\",address+uid+'.pdb'])\t\t\t\n\n\t#Run TMalign\n\tfor i in range(len(uids)):\n\t\tstr_i = outdir+uids[i]+'.pdb'\n\t\tfor j in range(i+1, len(uids)):\n\t\t\tstr_j = outdir+uids[j]+'.pdb'\n\t\t\t#Run TMalign and parse output\n\t\t\ttmalign_out = subprocess.check_output([TMalign, str_i , str_j]) #Performs optimal structural alignment \n\t\t\t(tm_aligned_len, rmsd, tmscores, tm_identity, chain_lens, tm_sequences)= parse_tm(tmalign_out)\t\n\t\t\tmeasures[uids[i]+'_'+uids[j]] = [rmsd, tmscores[0], tmscores[1]]\n\t\t\t#Make .phy file of aligned sequences\n\t\t\tmake_phylip([uids[i], uids[j]], tm_sequences[0], tm_sequences[1]) \n\n\treturn measures\n\ndef parse_tm(tmalign_out):\n\t'''A function that gets the uids and the corresponding scores\n\tand prints them in tsv.\n\t'''\n\t\n\ttmalign_out = tmalign_out.decode(\"utf-8\")\n\ttmalign_out = tmalign_out.split('\\n')\n\ttmscores = [] #Save TMscores\n\tfor i in range(0, len(tmalign_out)): #Step through all items in list\n\t\t\t\n\t\tif 'Aligned length' and 'RMSD' and 'Seq_ID' in tmalign_out[i]:\n\t\t\trow = tmalign_out[i].split(',')\n\t\t\taligned_len = row[0].split('=')[1].lstrip()\n\t\t\trmsd = row[1].split('=')[1].lstrip()\n\t\t\tidentity = row[2].split('=')[2].lstrip() \n\t\t\n\t\tif 'Length of Chain_1:' in tmalign_out[i]:\n\t\t\tlen_1 = tmalign_out[i].split(':')[1].split()[0]\n\t\t\t\t\n\t\tif 'Length of Chain_2:' in tmalign_out[i]:\n len_2 = tmalign_out[i].split(':')[1].split()[0]\n\t\tif 'TM-score=' in tmalign_out[i]:\n\t\t\ttmscores.append(tmalign_out[i].split('(')[0].split('=')[1].strip())\n\n\t#Get per residue sequence alignments from structural alignment\n\tsequences = [tmalign_out[-5], tmalign_out[-3]]\n\n\tchain_lens = [int(len_1), int(len_2)]\n\treturn(aligned_len, rmsd, tmscores, identity, chain_lens, sequences)\n\ndef make_phylip(uids, query_aln, template_aln):\n '''Print phylip format for tree-puzzle calculations\n '''\n #Create text in phylip format\n text = (' 4 ' + str(len(query_aln)) + '\\n'\n + uids[0] + '00|' + query_aln + '\\n'\n + 'copy11111' + '|' + query_aln + '\\n'\n + uids[1] + '00|' + template_aln + '\\n'\n + 'copy22222' + '|' + template_aln + '\\n')\n\n #Define file name\n file_name = uids[0] + '_' + uids[1] + '.phy'\n #Open file and write text to it\n with open(file_name, \"w\") as file:\n file.write(text)\n\n return None\n\ndef run_puzzle(indir, puzzle):\n '''Run tree-puzzle and retrieve output\n '''\n for name in glob.glob(indir+\"*.phy\"): #Use all .phy files\n uid_pairs = name.split('/')[-1].split('.')[0].split('_')\n try:\n p = subprocess.Popen([puzzle, name], stdin=subprocess.PIPE)\n p.communicate(b'y\\nn\\n')[0]\n except:\n raise IOError(name)\n\n\n return None\n\ndef parse_puzzle(measures, indir):\n\t'''Parse output from tree-puzzle and write to dict\n\t'''\n\tkeys = [*measures] #Make list of keys in dict\n\tfor key in keys:\n\t\tuids = key.split('_')\n\t\trmsd, tmscore1, tmscore2 = measures[key] #Get rmsd\n\t\ttry:\n\t\t\tdist_file = open(indir + key + '.phy.dist')\n\t\texcept:\n\t\t\tuids = key.split('_')\n\t\t\tdist_file = open(indir + uids[1] + '_' + uids[0] + '.phy.dist')\n\t\t\tmeasures.pop(key)\n\t\t\t#change key to match other file names\n\t\t\tkey = uids[1] + '_' + uids[0]\n\t\tfor line in dist_file:\n\t\t\tline = line.rstrip()\n\t\t\tline = line.split(\" \") #split on double space\n\t\t\tline = list(filter(None, line)) #Filter away empty strings\n\n\t\t\tif len(line)>2:\n\t\t\t\tseq_dist = line[-1] #Get ML evolutionary distance between sequences\n\t\t\t\tmeasures[key] = [rmsd, tmscore1, tmscore2, seq_dist] \n\t\t\t\tbreak\n\t\tdist_file.close()\n\n\treturn measures\n\n\ndef print_tsv(measures, hgroup):\n\t'''Print measures in tsv to file\n\t'''\n\twith open(hgroup+'.tsv', 'w') as file:\n\t\tfile.write('uid1\\tuid2\\tMLAAdist\\tRMSD\\tTMscore_high\\tTMscore_low\\n')\n\t\tfor key in measures:\n\t\t\tuids = key.split('_')\n\t\t\trmsd, tmscore1, tmscore2, seq_dist = measures[key]\n\t\t\thigh_score = max(float(tmscore1), float(tmscore2))\n\t\t\tlow_score = min(float(tmscore1), float(tmscore2)) \n\t\t\tfile.write(uids[0]+'\\t'+uids[1]+'\\t'+seq_dist+'\\t'+rmsd+'\\t'+str(high_score)+'\\t'+str(low_score)+'\\n')\n\n\treturn None\n\n#####MAIN#####\nargs = parser.parse_args()\n\noutdir = args.outdir[0]\nhgroup_file = args.hgroup_file[0]\npuzzle = args.puzzle[0]\nTMalign = args.TMalign[0]\naddress = args.address[0]\n\n#Download pdb files from CATH api and run TMalign\nhgroup = hgroup_file.split('/')[-1].split('.txt')[0]\nmeasures = run_TMalign(outdir, TMalign, hgroup, hgroup_file, address)\n#Run tree-puzzle on .phy files created from extracted sequence alignments from TMalign struvtural alignments\nrun_puzzle(outdir, puzzle)\n#Parse dist files from tree-puzzle and match to TMalign results\nmeasures = parse_puzzle(measures, outdir)\nprint_tsv(measures, hgroup)\n","sub_path":"CATH/str_aln/str_match.py","file_name":"str_match.py","file_ext":"py","file_size_in_byte":6151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"163738201","text":"# coding=utf-8\n\"\"\"Launch several services in a Rancher platform.\"\"\"\nimport json\nimport requests\nimport itertools\nfrom subprocess import Popen, PIPE\nimport threading\nimport yaml\nimport numpy\nimport os\nimport signal\nimport shutil\nfrom operator import methodcaller\nimport time\nimport re\nfrom launcherApp.dbConnection import dbConnector\nimport datetime\nfrom bson.objectid import ObjectId\n\n# TO SEE DEBUG AND INFO\n# TODO: Check Error Handling\n# TODO: Fix it for local usage.\n# TODO: reformta del nombre de las colecciones\n# NOTE: name of the collections -> experiments, queue, execution\n\n\nclass lanzador:\n \"\"\".\"\"\"\n\n def __init__(self, url_entradas, access_key, secret_key, db_password, logger):\n \"\"\"Init the object.\"\"\"\n self.url_entradas = url_entradas\n self.access_key = access_key\n self.secret_key = secret_key\n self.logger = logger\n self.namespaces_running = 0\n self.namespaces_limit = 0\n self.time_out = 0\n self.db_password = db_password\n self.db = None\n self.MODULE_DIR = os.path.dirname(__file__)\n self.logger.info(self.MODULE_DIR)\n self.connect_db()\n self.clean_directories()\n\n def launch_experiment(self, experiment_id):\n \"\"\"Launch the experiments in the execution queue.\"\"\"\n self.logger.info(type(experiment_id))\n experiment = self.db.get_document(\n doc_query={'_id': experiment_id},\n coll_name='experiments')\n self.logger.debug(type(experiment))\n self.logger.info(experiment['files'])\n\n # for file_name, text in experiment['files']:\n # self.logger.info(name + '=' + value)\n for file_name in experiment['files']:\n if(file_name != 'rancher-compose.yml'):\n with open(\n self.MODULE_DIR + '/files/'\n + experiment['experiment_group'] + '/'\n + file_name,\n 'r') as f:\n text = f.read()\n for name, value in experiment['parameters'].items():\n text = text.replace('${' + name + '}', value)\n # Set by default the namespace\n text = text.replace(\n '${' + 'NAMESPACE' + '}',\n experiment['name'])\n text = text.replace(\n '${' + 'ROOT_TOPIC' + '}',\n experiment['name'])\n with open(\n self.MODULE_DIR + '/files/'\n + experiment['experiment_group']\n + '/launch/' + file_name,\n 'w') as f:\n f.write(text)\n self.logger.info(\n 'Preparado para lanzar namespace ' + experiment['name'])\n # Se crea un namespace por cada combinacion\n self.create_namespace(experiment['name'])\n for file_name in experiment['files']:\n if(file_name != 'rancher-compose.yml'):\n self.start_service(\n experiment['name'],\n self.MODULE_DIR + '/files/'\n + experiment['experiment_group']\n + '/launch/' + file_name)\n # NOTE: Guarda cada experimento como documento en la coll de executions\n self.db.update_document(\n doc_query={'_id': experiment_id},\n doc_update={'launch_time': datetime.datetime.utcnow()},\n coll_name='experiments')\n self.db.push_document(\n doc_query={}, key='running',\n element=experiment_id, coll_name='execution')\n pid = self.startKafka(experiment['name'])\n thread = threading.Thread(\n target=self.checkResults,\n args=[experiment['name'], pid])\n thread.start()\n\n def launch_experiments(self):\n \"\"\".\"\"\"\n print('COMIENZA PROCESO DE LANZAMIENTO EXPERIMENTOS')\n entradas = requests.get(url=self.url_entradas, verify=False)\n entradas = yaml.load(entradas.text)\n self.logger.info('Obtenido el fichero de configuracion ' +\n 'para los parametros')\n self.logger.debug(entradas)\n self.time_out = entradas[\"time_out\"]\n self.namespaces_limit = entradas[\"limit_namespaces\"]\n\n for catalog_name, catalog_param in entradas['catalog_services'].items():\n self.create_directories(catalog_name)\n files, url_rancher = self.getConfiguration(\n catalog_param,\n catalog_name)\n self.configurateKubectl(url_rancher)\n parametros_nombre, parametros = self.getDefinedParams(\n catalog_param['PARAMS'])\n parametros_nombre, parametros = self.addDefaultParams(\n parametros_nombre,\n parametros,\n catalog_name)\n self.save_grid_combinations(\n catalog_name,\n files,\n parametros,\n parametros_nombre)\n self.logger.info('Combinations save for the catalog')\n # while(self.namespaces_running >= self.namespaces_limit):\n # continue\n experiment_id = self.db.pop_document({}, 'queue', 'queue')\n\n while (experiment_id and self.namespaces_running < self.namespaces_limit):\n self.launch_experiment(experiment_id)\n self.namespaces_running += 1\n # while(self.namespaces_running >= self.namespaces_limit):\n # continue\n experiment_id = self.db.pop_document({}, 'queue', 'queue')\n\n def connect_db(self):\n \"\"\"Establish a connection with the database.\"\"\"\n cont = 0\n while cont < 10:\n try:\n self.db = dbConnector(\n db_name='automodelingDB')\n except Exception:\n self.logger.warning('NO DATABASE CONNECTION')\n cont += 1\n time.sleep(5)\n else:\n self.logger.info('Database succesfuly connected')\n break\n if cont is 10:\n self.logger.critical('FAILED TO CONNECT THE DATABASE')\n\n def create_directories(self, dir_name):\n \"\"\"Create the directory with the specified name.\"\"\"\n if not os.path.isdir(self.MODULE_DIR + '/files/' + dir_name):\n os.mkdir(self.MODULE_DIR + '/files/' + dir_name)\n if not os.path.isdir(self.MODULE_DIR + '/files/' + dir_name + '/launch'):\n os.mkdir(self.MODULE_DIR + '/files/' + dir_name + '/launch')\n\n def clean_directories(self):\n \"\"\"Clean the files directory.\"\"\"\n if(os.path.isdir(self.MODULE_DIR + '/files')):\n shutil.rmtree(self.MODULE_DIR + '/files')\n os.mkdir(self.MODULE_DIR + '/files')\n\n def getConfiguration(self, configuration, catalog_name):\n \"\"\"\n Extrae de un yaml toda la configuracion para el lanzador.\n\n Devuelve la lista de archivos necesarios para la plantilla del catalogo\n y escribe en una subcarpeta dentro de files con el nombre del catalogo\n los archivos necesarios para lanzar los experimentos.\n \"\"\"\n # Peticion a la API para obtener el dockercompose\n url_catalog = configuration[\"URL_API\"]\n url_rancher = configuration[\"URL_RANCHER\"]\n auth = requests.auth.HTTPBasicAuth(self.access_key, self.secret_key)\n r = requests.get(url=url_catalog, auth=auth)\n content_all = r.json()\n self.logger.info('Obtenido el objeto JSON de la API')\n self.logger.debug(content_all)\n\n # Obtención de los ficheros de los servicios que hay que arrancar\n files = content_all['files']\n for file_name in files:\n with open(\n self.MODULE_DIR + '/files/'\n + catalog_name + '/' + file_name,\n 'w') as file_service:\n file_service.write(str(content_all['files'][file_name]))\n\n return (files, url_rancher)\n\n def configurateKubectl(self, rancher_url):\n \"\"\"Configurate the kubectl client in the container.\"\"\"\n # TODO: Dejar configurable los parametros que llevan el nombre ml-kube\n\n # Configuramos kubectl\n self.logger.debug('Empezamos a configurar kubectl')\n\n # calculo de la ruta relativa donde se encuentra la carpeta .kube\n ##filepath = '/root/.kube/'\n # if args.local: #TODO: New local configuration needed\n filepath = '/home/ignacio/.kube/'\n\n os.system('cp ' + self.MODULE_DIR + '/config ' + filepath)\n\n # Obtenemos la plantilla para el config\n with open(filepath + 'config', 'r') as f:\n text = f.read()\n self.logger.debug('Plantilla del config\\n' + text)\n kubeConfig = yaml.load(text)\n\n # https://rancher.default.svc.cluster.local:80/r/projects/1a8238/kubernetes\n kubeConfig['clusters'][0]['cluster']['server'] = rancher_url\n kubeConfig['users'][0]['user']['username'] = self.access_key\n kubeConfig['users'][0]['user']['password'] = self.secret_key\n\n self.logger.info('Configuration set')\n\n with open(filepath + 'config', 'w') as f:\n yaml.dump(kubeConfig, f)\n\n def getDefinedParams(self, parametros_yml):\n \"\"\"\n Obtiene los parametros para un stack del catalogo.\n\n Devuelve los parametros en forma de dos listas: una con el nombre de\n los parametros y otra con los parametros en si.\n \"\"\"\n # FIXME: En vez de devolver una tupla devolver diccionario???\n parametros_nombre = []\n parametros = []\n self.logger.debug(parametros_yml)\n # Las distintas formas que se consideran son: parametroNombre->n\n # 1. [valorInicial:valorFinal:Salto] -> Lineal\n # 2. TODO: [valorInicial:valorFinal:Función] -> Otro tipo de funcion\n # 3. TODO: HIDDEN_SIZE debe aceptar parametros que no fueran absolute\n for parametro in parametros_yml:\n self.logger.info(parametro)\n self.logger.debug(type(parametro))\n parametros_nombre.append(parametro)\n # Obtiene el parametro HIDDEN_SIZE, que es especial\n if(parametro == 'HIDDEN_SIZE'):\n layers = [parametros_yml[parametro]['number_units'] for i in\n range(parametros_yml[parametro]['number_layers'][0])]\n combinations = []\n for combination in itertools.product(*layers):\n combination = ','.join(map(str, combination))\n combinations.append(combination)\n parametros.append(combinations)\n continue\n\n self.logger.info(\"LOS PARAMETROS RECIBIDOS EN GETDEFINEDPARAMS:\")\n self.logger.info(parametros_yml)\n\n opcion = parametros_yml[parametro]['type']\n # parametro[parametro.index(\"{\"):parametro.index(\"}\")]\n if(opcion == 'lineal'):\n valorInicial = parametros_yml[parametro]['initial-value']\n valorFinal = parametros_yml[parametro][\"final-value\"]\n valorSalto = parametros_yml[parametro][\"interval\"]\n opcionesParametro = numpy.arange(\n valorInicial, valorFinal, valorSalto)\n parametros.append(opcionesParametro.tolist())\n elif(opcion == 2):\n # opcionesParametro\n pass\n elif(opcion == \"absolute\"):\n parametros.append(parametros_yml[parametro][\"param\"])\n else:\n self.logger.critical('ERROR: FORMATO DE PARAMETROS INCORRECTO')\n raise SyntaxError('Parametros en el yml incorectos')\n\n parametros_nombre = parametros_nombre[::-1]\n parametros = parametros[::-1]\n self.logger.info('Obtenida la lista de posibles parametros')\n\n return (parametros_nombre, parametros)\n\n def addDefaultParams(self, parametros_nombre, parametros, catalog_name):\n \"\"\"\n Add default values to the params remaining.\n\n The func adds the defaults values in the parameters not specified in\n the parameter list given in the arguments.\n \"\"\"\n with open(\n self.MODULE_DIR + '/files/'\n + catalog_name + '/rancher-compose.yml',\n 'r') as f:\n fileContent = f.read()\n rancherComposeContent = yaml.load(fileContent)\n\n questions = rancherComposeContent['.catalog']['questions']\n\n for element in questions:\n if(element['variable'] in parametros_nombre\n or element['variable'] == 'NAMESPACE'\n or element['variable'] == 'ROOT_TOPIC'):\n continue\n else:\n parametros_nombre.append(element['variable'])\n listElem = list()\n listElem.append(element['default'])\n parametros.append(listElem)\n\n return (parametros_nombre, parametros)\n\n def save_grid_combinations(\n self, catalog_name, files, parametros, parametros_nombre):\n \"\"\"Store in the db the execution queue and the parameters.\"\"\"\n cont = 1\n for param in itertools.product(*parametros):\n # Substitucion de las variables en los ficheros\n # NOTE: Los nombres de los paramentros deben ser exactamente\n # los mismos que en los ficheros.\n # El namespace no admite mayusculas\n namespace = ''.join([catalog_name, 'model{num}'.format(num=cont)])\n namespace_document = {}\n namespace_document['files'] = []\n namespace_document['name'] = namespace\n namespace_document['experiment_group'] = catalog_name\n namespace_document['parameters'] = {}\n for index in range(len(parametros_nombre)):\n self.logger.info(\n parametros_nombre[index] + '=' +\n str(param[index]) + '\\n')\n namespace_document['parameters'][\n parametros_nombre[index]] = str(param[index])\n namespace_document['create_time'] = datetime.datetime.utcnow()\n for file_name in files:\n namespace_document['files'].append(file_name)\n self.logger.debug(namespace_document)\n id_experiment = self.db.save_document(\n namespace_document,\n coll_name='experiments').inserted_id\n self.db.push_document(\n doc_query={},\n key='queue',\n element=id_experiment,\n coll_name='queue')\n cont += 1\n # FIXME: Comprobar la forma de hacer esto\n\n def get_execution_queue(self):\n \"\"\"Return the execution queue in a list form.\"\"\"\n return self.db.get_document({}, 'queue')['queue']\n\n def get_running_list(self):\n \"\"\"Return the current running experiments in a list form.\"\"\"\n return self.db.get_document({}, 'running')['running']\n\n def create_namespace(self, namespace):\n \"\"\"Crea un namespace con el nombre dado.\"\"\"\n os.system(\n self.MODULE_DIR + '/exec/kubectl create namespace ' + namespace)\n self.namespaces_running += 1\n\n def rm_namespace(self, namespace, pid):\n \"\"\"Borra el namespace con el nombre dado y su contenido.\"\"\"\n self.killProcess(pid)\n # Llama a kafka para obtener los resultados\n self.getResults(namespace, 1)\n # Delete namespace content\n os.system(\n self.MODULE_DIR + '/exec/kubectl delete ' +\n '--all service,rc,ingress,pod --namespace=' +\n namespace +\n ' --now'\n )\n # Delete the namespace itself\n os.system(\n self.MODULE_DIR + '/exec/kubectl delete namespace ' + namespace)\n self.namespaces_running -= 1\n\n def start_service(self, namespace, serviceFile):\n \"\"\"Launch one service or rc from one file.\"\"\"\n self.logger.info(\n 'Lanzando servicio ' + serviceFile +\n ' en el namespace ' + namespace)\n os.system(self.MODULE_DIR + '/exec/kubectl --namespace=' + namespace +\n ' create -f ' + serviceFile)\n\n def startKafka(self, namespace):\n \"\"\"Start the kafka consumer process.\"\"\"\n pid = 0\n with open(self.MODULE_DIR + '/results/' + namespace, 'w') as file_results:\n kafkaConsumer = Popen(\n [self.MODULE_DIR + '/exec/kafka-console-consumer'],\n env={\"KAFKA_SERVICE\": \"kafka.default.svc.cluster.local\",\n \"TOPIC\": namespace+\"-metrics\",\n \"OFFSET\": \"oldest\",\n \"KAFKA_GROUP\": namespace},\n stdout=file_results,\n shell=True,\n preexec_fn=os.setsid)\n pid = kafkaConsumer.pid\n self.logger.info(pid)\n return pid\n\n def checkResults(self, namespace, pid):\n \"\"\"Check the results till it reaches accuracy 1.\"\"\"\n time_finish = time.time() + self.time_out\n last_time = time.time()\n start_time = time.time()\n while (time.time() <= time_finish):\n self.logger.debug('Está en le bucle de acceso a resultados')\n lastResults = self.getResults(namespace, 10)\n if(len(lastResults) == 0):\n time.sleep(10)\n continue\n if(lastResults[len(lastResults)-1]['accuracy'] == 1.0):\n self.logger.info('Resultados:')\n self.logger.info(lastResults)\n break\n time.sleep(10)\n last_time = time.time()\n\n self.db.pull_document(\n doc_query={}, key='running',\n element=namespace, coll_name='execution')\n self.rm_namespace(namespace, pid)\n self.logger.debug('Debería pasar por aquí para guardar los resultados')\n results = {\n 'time': last_time - start_time, 'last_results': lastResults}\n self.logger.info('Guardando resultados:')\n self.logger.info(results)\n self.db.update_document(\n doc_query={'name': namespace},\n doc_update=results,\n coll_name='experiments')\n\n def getResults(self, namespace, numberResults):\n \"\"\"Obtiene el numero de resultados especificadas como parametro.\"\"\"\n process1 = Popen(['cat', self.MODULE_DIR + '/results/'+namespace], stdout=PIPE)\n process2 = Popen(\n ['tail', '-'+str(numberResults)],\n stdin=process1.stdout,\n stdout=PIPE)\n (out, err) = process2.communicate()\n out = out.decode('UTF-8')\n self.logger.info(out)\n\n results = str(out).split('\\n')[:-1]\n self.logger.info(results)\n\n if(len(results) <= 1):\n return []\n\n prog = re.compile('[(\\d|\\.)+\\s]+')\n if(not (prog.match(results[len(results)-1]) and\n prog.match(results[0]))):\n self.logger.warning(\"Incorrect results format\")\n self.logger.warning(results[0])\n self.logger.warning(results[len(results)-1])\n return []\n\n results = list(map(methodcaller(\"split\"), results))\n\n if(len(results) <= 1):\n return []\n\n self.logger.info(results)\n resultsList = [{'cost': float(result[3]),\n 'accuracy': float(result[4])} for result in results]\n self.logger.info(resultsList)\n return resultsList\n\n # self.logger.info(\"Ejecutando cat directamente:\")\n # os.system('cat ./results/'+namespace+' | tail -'+str(numberResults))\n\n def killProcess(self, pid):\n \"\"\"Mata el proceso kafka creado por popen.\"\"\"\n os.killpg(os.getpgid(pid), signal.SIGTERM)\n\n# TODO: funcion que envie al monitor la orden de dejar de escuchar.\n# A esta funcion hay que llamarla desde el rm_namespace\n\n def stop_experiment(self, id):\n \"\"\"Stop one of the current experiments execution and start the next.\"\"\"\n # Llama a rm_namespace\n # Primero obtiene el nombre del namespace\n namespace = self.db.get_document(\n {\"_id\": ObjectId(id)}, \"experiments\")[\"name\"]\n self.rm_namespace(namespace)\n # Elimina de la lista de experimentos ejecutandose\n self.db.delete_document({\"id_experiment\": ObjectId(id)}, \"running\")\n self.execute_next(id)\n\n def execute_next(self, id):\n \"\"\"Launch the first experiment in the queue.\"\"\"\n # Primero lo saca de la cola de espera\n experiment = self.db.pop_document(\n {\"id_experiment\": ObjectId(id)}, \"queue\")\n if not experiment:\n return False\n # Luego lo guarda en la cola de ejecución\n self.db.push_document(\n {}, \"running\", experiment[\"id_experiment\"], \"running\")\n # Ejecuta el experimento nuevo\n self.launch_experiment(experiment[\"id_experiment\"])\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n # def launchExperiments(self, files, catalog_name, parametros, parametros_nombre):\n # \"\"\"\n # Lanza las combinaciones entre los parametros de entrada.\n #\n # Guarda cada combinacion de parametros como un documento en la misma\n # coleccion. No debe devolver nada. Gestiona la cola?\n # (Inicializa la cola?).\n # \"\"\"\n # cont = 1\n # threadsCheckResults = []\n # # Se guardan los parametros en el fichero answers.txt\n # for param in itertools.product(*parametros):\n # # Substitucion de las variables en los ficheros\n # # Check -> Los nombres de los paramentros deben ser exactamente\n # # los mismos que en los ficheros.\n # # El namespace no admite mayusculas\n # namespace = ''.join([catalog_name, 'model{num}'.format(num=cont)])\n # # param_record[namespace] = {}\n # namespace_document = {}\n # namespace_document['name'] = namespace\n # for file_name in files:\n # if(file_name != 'rancher-compose.yml'):\n # with open('./files/' + file_name, 'r') as f:\n # text = f.read()\n # for index in range(len(parametros_nombre)):\n # self.logger.info(\n # parametros_nombre[index] + '=' +\n # str(param[index]) + '\\n')\n # text = text.replace(\n # '${' + parametros_nombre[index] + '}',\n # str(param[index]))\n # namespace_document['parameters'][\n # parametros_nombre[index]] = param[index]\n # # Set by default the namespace\n # text = text.replace(\n # '${' + 'NAMESPACE' + '}',\n # namespace)\n # text = text.replace(\n # '${' + 'ROOT_TOPIC' + '}',\n # namespace)\n # with open('./files/launch/' + file_name, 'w') as f:\n # f.write(text)\n # self.db.save_document(namespace_document, coll_name='experiments')\n # while(self.namespaces_running >= self.namespaces_limit):\n # continue\n #\n # self.logger.info('Preparado para lanzar namespace ' + namespace)\n # # Llamadas a kubectl\n # # Se crea un namespace por cada combinacion\n # self.create_namespace(namespace)\n # # Por cada fichero en ./files, se lanza un start_service\n # # dentro de un namespace\n # for file in files:\n # if(file != 'rancher-compose.yml'):\n # self.start_service(namespace, './files/launch/' + file)\n #\n # pid = self.startKafka(namespace)\n #\n # threadsCheckResults.append(threading.Thread(\n # target=self.checkResults,\n # args=[namespace, pid]))\n # threadsCheckResults[cont-1].start()\n #\n # cont = cont + 1\n #\n # return namespace_document # FIXME: Return innecesario ahora\n\n\n\n\n\n # def main(self):\n # \"\"\"Main execution of the class.\"\"\"\n # print('COMIENZA PROCESO DE LANZAMIENTO EXPERIMENTOS')\n # entradas = requests.get(url=self.url_entradas, verify=False)\n # entradas = yaml.load(entradas.text)\n # self.logger.info('Obtenido el fichero de configuracion ' +\n # 'para los parametros')\n # self.logger.debug(entradas)\n # self.time_out = entradas[\"time_out\"]\n # self.namespaces_limit = entradas[\"limit_namespaces\"]\n #\n # self.prepareDirectories()\n # catalogs = [catalog for catalog in entradas[\"catalog_services\"]][::-1]\n # self.logger.info(catalogs)\n # param_record = {}\n #\n # for catalog in catalogs:\n # self.logger.info(catalog)\n # files, url, url_catalog = self.getConfiguration(\n # configuration=entradas[\"catalog_services\"][catalog])\n # self.configurateKubectl(rancher_url=url)\n # os.system(self.MODULE_DIR + '/exec/kubectl version')\n # parametros_nombre, parametros = self.getDefinedParams(\n # entradas[\"catalog_services\"][catalog]['PARAMS'])\n # parametros_nombre, parametros = self.addDefaultParams(\n # parametros_nombre, parametros)\n # param_record[catalog] = self.launchExperiments(\n # files=files,\n # catalog_name=catalog,\n # parametros=parametros,\n # parametros_nombre=parametros_nombre)\n # # self.db.save_document(param_record, coll_name='parameter_records')\n","sub_path":"service/webApp/launcherApp/lanzadorServicios.py","file_name":"lanzadorServicios.py","file_ext":"py","file_size_in_byte":25840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"430286659","text":"# A Python interpretation of Steve Jackson's Zombie Dice\n# Rules for the game can be found at http://www.sjgames.com/dice/zombiedice/img/ZDRules_English.pdf\n# This script has been written such that the entire game is completely customisable.\n\nimport random\n\n# Number of players\nnum_players=2\n\n# The total number of turns in a game\nmax_turns=num_players * 500\n\n# The score required to win\nwinning_score=13\n\n# The total number of dice\nmax_dice=13\n\n# The maximum number of sides a die has\nmax_sides=6\n\n# Initialise the scores (assuming a two player game)\nscores = [0] * num_players\n\n# The number of dice selected each turn\ndice_per_turn = 3\n\n# Define the different basic dice types\n# The final element in the array is the die colour\nmaster_green_die = [\"brain\", \"runner\", \"shotgun\", \"brain\", \"brain\", \"runner\", \"green\"]\nmaster_yellow_die = [\"runner\", \"brain\", \"shotgun\", \"runner\", \"shotgun\", \"brain\", \"yellow\"]\nmaster_red_die = [\"shotgun\", \"runner\", \"brain\", \"shotgun\", \"shotgun\", \"runner\", \"red\"]\n\n# Define the dice that the game will use based on the maximum number of die sides\ngreen_die = []\nyellow_die = []\nred_die = []\nfor i in range(0, max_sides):\n green_die.append(master_green_die[i % (len(master_green_die) - 1)])\n yellow_die.append(master_yellow_die[i % (len(master_yellow_die) - 1)])\n red_die.append(master_red_die[i % (len(master_red_die) - 1)])\ngreen_die.append(\"green\")\nyellow_die.append(\"yellow\")\nred_die.append(\"red\")\n\n# Define the available dice in the game\n# Typically, six green dice, four yellow dice and three red dice\ndice=[green_die, green_die, green_die, green_die, green_die, green_die, yellow_die, yellow_die, yellow_die, yellow_die, red_die, red_die, red_die]\n\n# Ensure that the values selected for the game variables won't seriously break anything\nif dice_per_turn > max_dice:\n print (\"The number of dice selected per turn exceeds the total number of dice in the game (\", dice_per_turn, \">\", max_dice, \")\")\n raise SystemExit\n\nfor j in range(0, max_turns):\n print (\"SCORES\")\n for i in range(0, num_players):\n print (\"Player\", i+1, \":\", scores[i])\n print (\"Player \",(j%num_players)+1,\"'s turn...\")\n input (\"Press Enter to continue...\")\n \n brains = 0\n shotguns = 0\n\n # Dice selections are predetermined, but the players are unaware of this\n dice_selections = random.sample(range(0, max_dice), max_dice)\n\n # Select the dice to roll\n # The first time the dice being rolled are simply the first three selected\n dice_to_roll = []\n for dice_pulled in range(0, dice_per_turn):\n dice_to_roll.append(dice_selections[dice_pulled])\n\n roll_again = \"yes\"\n # While the player wants to keep rolling...\n while roll_again == \"yes\" or roll_again == \"y\":\n # Roll the three dice...\n print (\"You rolled: \")\n for i in range(0, dice_per_turn):\n roll = dice[dice_to_roll[i]][(random.randint(0, max_sides - 1))]\n print(dice[dice_to_roll[i]][max_sides], \" - \", roll)\n # If the player got a brain, add one to their brain total for this round and select another die\n # If the player got a shotgun, add one to their shotgun total for this round and select another die\n # If the player got a runner, do nothing and do not select another die\n if roll == \"brain\":\n brains+=1\n dice_to_roll[i] = dice_selections[dice_pulled]\n dice_pulled+=1\n elif roll == \"shotgun\":\n shotguns+=1\n dice_to_roll[i] = dice_selections[dice_pulled]\n dice_pulled+=1\n \n print (\"brains = \", brains, \"; shotguns = \", shotguns)\n # If the player has not rolled three shotguns, then ask them if they want to keep rolling\n if shotguns < 3:\n roll_again = input(\"Do you want to roll again?\")\n else:\n break\n \n # If the player has not rolled three shotguns, then add the number of brains they rolled to their total score\n if shotguns < 3:\n scores[j%num_players]+=brains\n # Check whether the player has enough brains to win\n if scores[j%num_players] >= winning_score:\n print (\"Player\", j%num_players+1, \"wins!\")\n raise SystemExit\n else:\n print (\"Your score is\", scores[j%num_players])\n else:\n print(\"Argh! You got shot!!!\")\n\n print (\"\")\n","sub_path":"zd.py","file_name":"zd.py","file_ext":"py","file_size_in_byte":4405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"275373629","text":"from tkinter import messagebox\n\nfrom selenium.webdriver import ActionChains\n\nfrom features.Locators.Locators import Locators\n\n\nclass ConfirmationPage():\n def __init__(self, driver):\n self.driver = driver\n\n self.Booking_summary_location_xpath = Locators.Booking_summary_location_xpath\n self.Booking_summary_pickup_xpath = Locators.Booking_summary_pickup_xpath\n self.Booking_summary_dropoff_xpath = Locators.Booking_summary_dropoff_xpath\n self.GPS_item_xpath = Locators.GPS_item_xpath\n self.Baby_seat_xpath = Locators.Baby_seat_xpath\n self.Booking_summary_item_price_xpath = Locators.Booking_summary_item_price_xpath\n self.Booking_summary_subtotal_xpath = Locators.Booking_summary_subtotal_xpath\n self.Booking_summary_total_amount_xpath = Locators.Booking_summary_total_amount_xpath\n\n def booking_summary_maintained(self, location, pickup, dropoff):\n pickup_location = self.driver.find_element_by_xpath(self.Booking_summary_location_xpath).text\n pickup_date = self.driver.find_element_by_xpath(self.Booking_summary_pickup_xpath).text\n drop_off_date = self.driver.find_element_by_xpath(self.Booking_summary_dropoff_xpath).text\n assert (pickup_location, pickup_date, drop_off_date) == (location, pickup, dropoff)\n\n def add_extra_item(self, driver):\n gps = self.driver.find_element_by_xpath(self.GPS_item_xpath)\n baby_seat = self.driver.find_element_by_xpath(self.Baby_seat_xpath)\n move = ActionChains(driver)\n move.click_and_hold(gps).move_by_offset(10, 0).release().perform()\n move.click_and_hold(baby_seat).move_by_offset(10, 0).release().perform()\n\n def adding_item_price(self):\n item = self.driver.find_element_by_xpath(self.Booking_summary_item_price_xpath).text\n subtotal = self.driver.find_element_by_xpath(self.Booking_summary_subtotal_xpath).text\n total = self.driver.find_element_by_xpath(self.Booking_summary_total_amount_xpath).text\n a = item.split(\"$\")\n item_price = a[1]\n b = subtotal.split(\"$\")\n subtotal_price = b[1]\n total_price = float(total)\n booking_total = int(item_price) + float(subtotal_price.replace(',', ''))\n assert total_price == booking_total\n","sub_path":"features/Pages/ConfirmationPage.py","file_name":"ConfirmationPage.py","file_ext":"py","file_size_in_byte":2276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"282238721","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2020/3/21 下午3:52\n# @Author : serendipity-xp\n# @FileName: two_add.py\n# @Software: PyCharm\n# @GitHub ：https://github.com/serendipity-xp\n\n\n# Definition for singly-linked list.\nclass ListNode(object):\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution(object):\n def addTwoNumbers(self, l1, l2):\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n dummy_head = ListNode(0)\n cur_l1, cur_l2 = l1, l2\n cur_node = dummy_head\n carry = 0 # 表示进位\n while cur_l1 is not None or cur_l2 is not None:\n x = (cur_l1.val if cur_l1 else 0)\n y = (cur_l2.val if cur_l2 else 0)\n sum_val = x + y + carry\n carry = sum_val / 10\n cur_node.next = ListNode(sum_val % 10)\n cur_node = cur_node.next\n cur_l1 = (cur_l1.next if cur_l1 else None)\n cur_l2 = (cur_l2.next if cur_l2 else None)\n if carry > 0:\n cur_node.next = ListNode(carry)\n return dummy_head.next\n\n\nif __name__ == '__main__':\n l1 = ListNode(2)\n l1.next = ListNode(4)\n l1.next.next = ListNode(3)\n l2 = ListNode(5)\n l2.next = ListNode(6)\n l2.next.next = ListNode(4)\n Solution().addTwoNumbers(l1, l2)\n\n\n","sub_path":"library/two_add/two_add.py","file_name":"two_add.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"620823493","text":"from tkinter import StringVar, messagebox\nimport tkinter.font as tkFont\nimport tkinter as tk\n\nfrom ..config import *\nfrom ..TopLevelObject import *\n\n\nclass Account(TopLevelObject):\n def __init__(self, master):\n super().__init__(master)\n\n def Grid(self, **options):\n super().Grid(options)\n\n self.menuFrame = tk.Frame(self.frame)\n self.openOrders = tk.Button(self.menuFrame, text='Open Orders').grid(row=1, column=1, sticky='NWE')\n self.oderHistory = tk.Button(self.menuFrame, text='Order History').grid(row=1, column=2, sticky='NWE')\n self.tradeHistory = tk.Button(self.menuFrame, text='Trade History').grid(row=1, column=3, sticky='NWE')\n self.funds = tk.Button(self.menuFrame, text='Funds').grid(row=1, column=4, sticky='NWE')\n self.menuFrame.grid(row=1, column=1)\n\n for label in range(7):\n tk.Button(self.frame, text='Account button ' + str(label).zfill(2)).grid(row=label+2, column=1)\n \n\n def Pack(self, **options):\n super().Pack(options)\n\n #tk.Button(self.master, text='Header button1').pack(side='top')\n \n self.button1 = tk.Button(self.frame, text='Account button top')\n self.button1.pack(side='top')\n\n self.button2 = tk.Button(self.frame, text='Account button bottom')\n self.button2.pack(side='bottom')\n","sub_path":"GUI/Account/Account.py","file_name":"Account.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"36793622","text":"#encoding=utf-8\r\nimport os\r\nimport struct\r\n\r\ndef PackAFile(fname):\r\n fs=open(fname,'rb')\r\n stm=fs.read()\r\n fs.close()\r\n entry=bytearray()\r\n entry+=b'abimgdat10'.ljust(16,b'\\0')\r\n p=fname.rfind('\\\\')\r\n if p==-1:\r\n pn=fname.encode('932')\r\n else:\r\n pn=fname[p+1:].encode('932')\r\n entry+=struct.pack('H',len(pn))\r\n entry+=pn\r\n entry+=b'\\x03'\r\n entry+=struct.pack('I',len(stm))\r\n entry+=stm\r\n return entry\r\n\r\ndef GetOriHdr(fname):\r\n fs=open(fname,'rb')\r\n stm=fs.read(0x24)\r\n hdrlen,=struct.unpack('32xI',stm)\r\n hdrs=fs.read(hdrlen)\r\n return stm+hdrs\r\n\r\ndef WriteB(path1,oriname,newname):\r\n cnt=0\r\n nnames=[]\r\n for f in os.listdir(path1):\r\n nnames.append(f)\r\n cnt+=1\r\n hdr=GetOriHdr(oriname)\r\n fs=open(oriname,'rb')\r\n fs.seek(len(hdr))\r\n imgsect=fs.read(17)\r\n magic,cnnt=struct.unpack('<16sB',imgsect)\r\n if magic.strip(b'\\0')!=b'abimage10':\r\n print(magic)\r\n asdgawer\r\n if cnnt!=cnt:\r\n print('not match,count')\r\n afasef\r\n\r\n imgstm=bytearray(imgsect)\r\n for i in range(cnt):\r\n magic,namelen=struct.unpack('16sH',fs.read(18))\r\n na=fs.read(namelen)\r\n if magic.strip(b'\\0')==b'abimgdat13':\r\n hashlen,=struct.unpack('H',fs.read(2))\r\n hashc=fs.read(hashlen)\r\n na+=struct.pack('H',hashlen)+hashc\r\n na+=fs.read(13)\r\n elif magic.strip(b'\\0')==b'abimgdat11':\r\n awerfgasdf\r\n else:\r\n na+=fs.read(1)\r\n imgstm+=magic+struct.pack('H',namelen)+na\r\n\r\n olen,=struct.unpack('I',fs.read(4))\r\n fs.seek(olen,1)\r\n\r\n nstm=open(os.path.join(path1,nnames[i]),'rb').read()\r\n imgstm+=struct.pack('I',len(nstm))+nstm\r\n nfs=open(newname,'wb')\r\n nfs.write(hdr)\r\n nfs.write(imgstm)\r\n nfs.write(fs.read())\r\n nfs.close()\r\n fs.close()\r\n\r\nnewfolders='bfiles'\r\noribf='orib'\r\nnewbf='newb'\r\n\r\nfor f in os.listdir(oribf):\r\n WriteB(os.path.join(newfolders,f),\r\n os.path.join(oribf,f),\r\n os.path.join(newbf,f))\r\n\r\n##WriteB(r'D:\\chinesize\\zeas\\美少女万華鏡\\exp\\button_yes.b',\r\n## r'D:\\Program Files\\美少女万華鏡\\GameData\\da7\\library\\dialog\\button_yes.b',\r\n## r'nbutton_yes.b')\r\n","sub_path":"zeas/美少女万華鏡/packb.py","file_name":"packb.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"557429877","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n'''\n@author: zhaogao\n@license: (C) Copyright 2013-2018.\n@contact: gaozhao89@qq.com\n@software: learn-py\n@file: len206_给简单脚本增加日志功能.py\n@time: 28/05/2018 10:07 AM\n'''\n\n# 你希望在脚本和程序中将诊断信息写入日志文件。\n# 打印日志最简单方式是使用logging���块\n\nimport logging, os\n\n\ndef main():\n # Configure the logging system\n # basicConfig() 在程序中只能被执行一次。如果你稍后想改变日志配置，就需要先获取 root logger ，然后直接修改它\n logging.basicConfig(\n filename=os.path.expanduser('~/PycharmProjects/learn-py/cook/data/len206/app.log'),\n # 如果你想改变输出等级，你可以修改 basicConfig() 调用中的参数\n # level=logging.ERROR\n # 如 果 你 想 要 你 的 日 志 消 息 写 到 标 准 错 误 中， 而 不 是 日 志 文 件 中， 调 用 basicConfig() 时不传文件名参数即可\n level=logging.WARNING,\n format='%(levelname)s:%(asctime)s:%(message)s'\n )\n\n # Variables (to make the calls that follow work)\n hostname = 'www.python.org'\n item = 'spam'\n filename = 'data.csv'\n mode = 'r'\n\n # Example logging calls (insert into your program)\n logging.critical('host %s unknown', hostname)\n logging.error(\"couldn't find %r\", item)\n logging.warning('feature is deprecated')\n logging.info('opening file %r, mode=%r', filename, mode)\n logging.debug('got here')\n\n\nif __name__ == '__main__':\n main()\n\n# 上面五个日志调用(critical(), error(), warning(), info(), debug())以降序方式表示 不同的严重级别。 basicConfig() 的 level 参数是一个过滤器。所有级别低于此级别 的日志消息都会被忽略掉。每个 logging 操作的参数是一个消息字符串，后面再跟一个 或多个参数。构造最终的日志消息的时候我们使用了% 操作符来格式化消息字符串\n","sub_path":"cook/len206_给简单脚本增加日志功能.py","file_name":"len206_给简单脚本增加日志功能.py","file_ext":"py","file_size_in_byte":1971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"33291966","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n'''\nAuthor : 612957\nFile : 判断文件编码.py\nTime : 2019/9/28 16:41\nDesc : \n'''\n\nimport chardet\nfrom bs4 import BeautifulSoup\n\nif __name__ == '__main__':\n with open('hwar.html','r',encoding='utf-8')as fr:\n content = fr.read()\n print(content)\n\n response_analyze = BeautifulSoup(content, \"html5lib\")\n app_name = response_analyze.find_all(name=\"h4\", attrs={\"class\": \"title\"})\n for app in app_name:\n\n print(app.a.string)","sub_path":"pythoncode/判断文件编码.py","file_name":"判断文件编码.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"584481813","text":"import numpy as np\nimport match_projid_coreid as dutils\nimport compare_keywords_with_rcdc as dutils2 # for keywords\n\n\n###########################################################\n# how many articles are there in total\narticle_data = dutils.load_articles_json()\nnum_articles = len(article_data) # 231435\n\n# medline_id to abstracts #231435\narticle_med2abs = dutils.get_article_med2abstract()\n\n\n###########################################################\n# Dictionary of lists\n# Keys: coreid\n# Values: list of predictions\n# 20421 core ids\narticle_core2predictions = dutils.get_article_core2pred() \n\n# 205555 articles that matches with 20421 core ids. \n# count\ncount = 0\nfor key in article_core2predictions.keys():\n count += len(article_core2predictions[key])\n\nprint(count)\ntotnum_articles_in_predictions = count\n\n\n###########################################################\n# Number of articles that have links to grant core ids.\narticle_medid2core = dutils.get_article_medid2core()\nprint(len(article_medid2core)) #210890\ntotnum_medline_articles_coreid = len(article_medid2core)\n\n\n###########################################################\n# Number of articles in keywords file\n# id includes WOS ids and MEDLINE ids. \nid2keywords = dutils2.load_keywords_json()\nprint(len(id2keywords)) #3893154\ntotnum_articles = len(id2keywords)\n\n# Number of articles with keywords\nnum_articles_w_keywords =0 # 1972024\nfor k in id2keywords.keys():\n if len(id2keywords[k]['keywords']) > 1:\n num_articles_w_keywords += 1\n\n# Number of MEDLINE articles\n# Number of MEDLINE articles with keywords \nnum_medline_articles_w_keywords = 0 # 0\nmedline_ids = set()\nfor k in id2keywords.keys():\n if \"MEDLINE\" in k:\n medline_ids.add(k)\n if len(id2keywords[k]['keywords']) > 1:\n num_medline_articles_w_keywords += 1\n\ntotnum_medline_articles = len(medline_ids)\n# 232149\nprint('Number of MEDLINE articles: ' + totnum_medline_articles)\n\n# 0\nprint('Number of MEDLINE articles with keywords: ' + num_medline_articles_w_keywords)\n\n###########################################################\n# totnum_articles : number of articles \n\n\n","sub_path":"datascripts/article_stats.py","file_name":"article_stats.py","file_ext":"py","file_size_in_byte":2137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"3589250","text":"import unittest\nimport xml.dom\n\nfrom fs.path import basename\n\nfrom pdart.db.bundle_db import create_bundle_db_in_memory\nfrom pdart.db.fits_file_db import populate_database_from_fits_file\nfrom pdart.labels.lookup import DictLookup\nfrom pdart.labels.time_coordinates import get_start_stop_times, get_time_coordinates\nfrom pdart.labels.utils import path_to_testfile\nfrom pdart.xml.pretty import pretty_print\n\n\nclass TestTimeCoordinates(unittest.TestCase):\n def test_get_default_target(self) -> None:\n fits_product_lidvid = \"urn:nasa:pds:hst_13012:data_acs_raw:jbz504eoq_raw::2.3\"\n card_dicts = [\n {\"DATE-OBS\": \"2001-01-02\", \"TIME-OBS\": \"08:20:00\", \"EXPTIME\": \"1.0\"}\n ]\n nb = get_time_coordinates(\n get_start_stop_times(DictLookup(\"test_get_default_target\", card_dicts))\n )\n doc = xml.dom.getDOMImplementation().createDocument(None, None, None)\n str: bytes = nb(doc).toxml().encode()\n str = pretty_print(str)\n expected = b\"\"\"\n\n 2001-01-02T08:20:00Z \n 2001-01-02T08:20:01Z \n \n\"\"\"\n self.assertEqual(expected, str)\n\n def test_get_time_coordinates(self) -> None:\n db = create_bundle_db_in_memory()\n db.create_tables()\n fits_product_lidvid = \"urn:nasa:pds:hst_13012:data_acs_raw:jbz504eoq_raw::2.3\"\n os_filepath = path_to_testfile(\"jbz504eoq_raw.fits\")\n\n populate_database_from_fits_file(db, os_filepath, fits_product_lidvid)\n\n file_basename = basename(os_filepath)\n\n card_dicts = db.get_card_dictionaries(fits_product_lidvid, file_basename)\n\n nb = get_time_coordinates(\n get_start_stop_times(DictLookup(\"test_get_time_coordinates\", card_dicts))\n )\n doc = xml.dom.getDOMImplementation().createDocument(None, None, None)\n str: bytes = nb(doc).toxml().encode()\n str = pretty_print(str)\n\n expected = b\"\"\"\n\n 2012-09-27T20:23:28Z \n 2012-09-27T20:27:58Z \n \n\"\"\"\n self.assertEqual(expected, str)\n","sub_path":"pdart/labels/test_time_coordinates.py","file_name":"test_time_coordinates.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"488974469","text":"from django.shortcuts import render\nfrom AppTwo.models import User\nfrom . import forms\n# Create your views here.\n\ndef home(request):\n h = {'Home_Page': ''}\n return render(request, 'home.html', h)\n\ndef user(request):\n form = forms.User()\n\n if request.method == 'POST':\n form = forms.User(request.POST)\n\n if form.is_valid():\n p = form.save()\n print('VALIDATION SUCCESS')\n return render(request, 'user.html', {'forms':form})","sub_path":"DJANGO SHIT/ProTwo/AppTwo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"446505555","text":"from django.conf.urls import patterns, include, url\nfrom django.views.generic import TemplateView\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'Django_SCCOP.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n\n url(r'^admin/', include(admin.site.urls)),\n url(r'^about$', TemplateView.as_view(template_name='about.html'), name=\"about\"),\n\turl(r'^contact$', TemplateView.as_view(template_name='contact.html'), name=\"contact\"),\n)\n \nurlpatterns += patterns('sccop.views',\n\turl(r'^$', \"master\", name=\"home\"),\n\turl(r'^signup$', \"signup\", name=\"signup\"),\n\turl(r'^signin$', \"signin\", name=\"signin\"),\n\turl(r'^signout$', \"signout\", name=\"signout\"),\n\turl(r'^email/send$', \"email_send\", name=\"email_send\"),\n\turl(r'^dashboard$', \"dashboard\", name=\"dashboard\"),\n\turl(r'^log$', \"log\", name=\"log\"),\n)\n\nurlpatterns += patterns('sccop.api',\n\turl(r'^api/update/state/$', \"updateState\", name=\"update_state\"),\n\turl(r'^api/update/location/$', \"updateLocation\", name=\"update_location\"),\n)\n","sub_path":"Django_SCCOP/Django_SCCOP/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"518906457","text":"import unittest\nimport subprocess\nimport json\nimport os\nimport tempfile\n\n\nclass PdnsSysrepoBasicTest(unittest.TestCase):\n webserver_port = 8081\n api_key = \"foobar\"\n dbdir = os.environ.get('PDNS_DB_DIR', '/var/lib/pdns')\n\n default_config = {\n \"pdns-server:pdns-server\": {\n \"backend\": [\n {\n \"name\": \"backend1\",\n \"backendtype\": \"lmdb\",\n \"filename\": \"{}/pdns.lmdb\".format(dbdir)\n }\n ],\n \"listen-addresses\": [\n {\n \"name\": \"main IP\",\n \"ip-address\": \"127.0.0.1\",\n \"port\": \"5300\"\n }\n ],\n \"webserver\": {\n \"port\": webserver_port,\n \"api-key\": api_key\n },\n \"master\": False,\n \"slave\": True\n }\n }\n\n @classmethod\n def importConfig(cls, config: dict):\n p = subprocess.Popen(['sysrepocfg', '--format', 'json', '--import',\n '-m', 'pdns-server'], stdin=subprocess.PIPE)\n config_str = json.dumps(config)\n p.communicate(input=config_str.encode('utf-8'))\n\n @classmethod\n def editConfig(cls, config: dict):\n with tempfile.NamedTemporaryFile() as fp:\n fp.write(json.dumps(config).encode('utf-8'))\n fp.flush()\n subprocess.call(['sysrepocfg', '--format', 'json',\n '--edit={}'.format(fp.name),\n '-m', 'pdns-server'])\n\n @classmethod\n def getRunningDS(cls) -> dict:\n output = subprocess.run(['sysrepocfg', '-X', '--format=json',\n '--module=pdns-server',\n '--datastore=running'],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n print(output)\n return json.loads(output.stdout)\n\n @classmethod\n def getOperationalDS(cls) -> dict:\n output = subprocess.run(['sysrepocfg', '-X', '--format=json',\n '--module=pdns-server',\n '--datastore=operational'],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n print(output)\n return json.loads(output.stdout)\n\n def testImported(self):\n self.importConfig(self.default_config)\n data = self.getRunningDS()\n self.assertFalse(data[\"pdns-server:pdns-server\"][\"master\"])\n self.assertTrue(data[\"pdns-server:pdns-server\"][\"slave\"])\n self.assertEqual(data[\"pdns-server:pdns-server\"][\"webserver\"][\"api-key\"], self.api_key)\n self.assertEqual(data[\"pdns-server:pdns-server\"][\"webserver\"][\"port\"], self.webserver_port)\n self.assertEqual(len(data[\"pdns-server:pdns-server\"][\"listen-addresses\"]), 1)\n\n def testUpdateConfig(self):\n self.importConfig(self.default_config)\n self.editConfig({\n \"pdns-server:pdns-server\": {\n \"slave\": False\n }\n })\n data = self.getRunningDS()\n self.assertFalse(data[\"pdns-server:pdns-server\"][\"slave\"])\n\n def testAddZone(self):\n self.importConfig(self.default_config)\n zonename = \"foo.example.com.\"\n zonetype = \"native\"\n self.editConfig({\n \"pdns-server:zones\": [\n {\n \"name\": zonename,\n \"class\": \"IN\",\n \"zonetype\": zonetype\n }\n ]\n })\n\n data = self.getOperationalDS()\n self.assertEqual(len(data[\"pdns-server:zones-state\"][\"zones\"]), 1)\n self.assertEqual(\n data[\"pdns-server:zones-state\"][\"zones\"][0][\"name\"], zonename)\n self.assertEqual(\n data[\"pdns-server:zones-state\"][\"zones\"][0][\"zonetype\"],\n zonetype)\n\n def testChangeZoneType(self):\n self.importConfig(self.default_config)\n zonename = \"edit.example.com.\"\n zonetype = \"native\"\n self.editConfig({\n \"pdns-server:zones\": [\n {\n \"name\": zonename,\n \"class\": \"IN\",\n \"zonetype\": zonetype\n }\n ]\n })\n\n zonetype = \"master\"\n self.editConfig({\n \"pdns-server:zones\": [\n {\n \"name\": zonename,\n \"class\": \"IN\",\n \"zonetype\": zonetype\n }\n ]\n })\n\n data = self.getOperationalDS()\n self.assertEqual(len(data[\"pdns-server:zones-state\"][\"zones\"]), 1)\n self.assertEqual(\n data[\"pdns-server:zones-state\"][\"zones\"][0][\"name\"], zonename)\n self.assertEqual(\n data[\"pdns-server:zones-state\"][\"zones\"][0][\"zonetype\"],\n zonetype)\n","sub_path":"end-to-end-tests/test_01-initial.py","file_name":"test_01-initial.py","file_ext":"py","file_size_in_byte":4926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"237667415","text":"# Copyright 2018 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import absolute_import\n\nfrom kfp_component import BaseOp\n\nfrom kubernetes import client, config\nfrom kubernetes.client.rest import ApiException\n\nimport mock\nimport unittest\n\n@mock.patch('kubernetes.config.load_incluster_config')\n@mock.patch('kubernetes.client.CoreV1Api')\nclass BaseOpTest(unittest.TestCase):\n\n def test_execute_success(self, mock_k8s_client, mock_load_config):\n BaseOp().execute()\n\n @mock.patch.dict('os.environ', {\n 'KFP_POD_NAME': 'mock-pod-id'\n })\n def test__exit_gracefully_cancel(self, mock_k8s_client, mock_load_config):\n mock_pod = mock_k8s_client().read_namespaced_pod.return_value\n mock_pod.metadata.annotations = {\n 'workflows.argoproj.io/execution': '{\"deadline\": \"1970-01-01T00:00:00Z\"}'\n }\n\n op = BaseOp()\n op.on_cancelling = mock.MagicMock()\n\n op._exit_gracefully(0, 0)\n\n op.on_cancelling.assert_called_once()\n\n @mock.patch.dict('os.environ', {\n 'KFP_POD_NAME': 'mock-pod-id'\n })\n def test__exit_gracefully_no_cancel(self, mock_k8s_client, mock_load_config):\n mock_pod = mock_k8s_client().read_namespaced_pod.return_value\n mock_pod.metadata.annotations = {}\n\n op = BaseOp()\n op.on_cancelling = mock.MagicMock()\n\n op._exit_gracefully(0, 0)\n\n op.on_cancelling.assert_not_called()","sub_path":"component_sdk/python/tests/test_base_op.py","file_name":"test_base_op.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"513842825","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('expenses', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='AdtExpenses',\n fields=[\n ('vExpenseId', models.AutoField(primary_key=True, serialize=False, db_column='vExpenseId')),\n ('vExpenseDescription', models.CharField(verbose_name='Descripcion', max_length=100)),\n ('vExpenseBalance', models.CharField(verbose_name='Balance', max_length=100)),\n ],\n ),\n migrations.DeleteModel(\n name='AdtUsers',\n ),\n ]\n","sub_path":"AdTominApps/expenses/migrations/0002_auto_20200329_0242.py","file_name":"0002_auto_20200329_0242.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"347761751","text":"# -*- coding:utf-8 -*-\nimport os\n\nif __name__ == '__main__':\n path = os.getcwd()\n sku_id = '10001'\n file = path + os.sep + 'test.txt'\n f = open(file, 'w')\n for i in range(1, 10):\n f.write(sku_id)\n f.write('\\t')\n f.write('2017-01-0{0}'.format(i))\n f.write('\\t')\n f.write('{0}'.format(i * 10))\n f.write('\\n')\n f.close()\n\nimport pandas as pd\n\npd.set_option('display.max_colwidth', 20)\npd.set_option('display.width', 150) # 150\n\ntrain = pd.read_csv(r'/Users/longguangbin/Downloads/train_20181125105600.csv', sep='|')\nflag = pd.read_csv(r'/Users/longguangbin/Downloads/flag_20181125105020.csv', sep='|')\ngranu = pd.read_csv(r'/Users/longguangbin/Downloads/granu_split_20181125175310.csv', sep='|')\n\ngranu['sku_id_s'] = granu['sku_id'].apply(lambda x: x.split('$')[1])\ngranu_s = granu.loc[:, ['sku_id_s', 'granu_split']].drop_duplicates()\ngranu_s.index = range(len(granu_s))\ngranu_s = granu_s.groupby(['sku_id_s']).count().reset_index().rename(columns={'granu_split': 'cnt'})\ngranu_m = granu.merge(granu_s, on=['sku_id_s'])\ncheck_df = granu_m[granu_m['cnt'] == 2]\ncheck_df.index = range(len(check_df))\ncheck_df.to_csv(r'/Users/longguangbin/Downloads/check_1.csv', index=False)\n\nflag = flag.drop_duplicates()\nflag.index = range(len(flag))\n\nflag_2 = flag.copy()\nflag_2.columns = ['a', 'b']\nflag_2['b'] = 'this is a long test' + flag_2['b']\n\ntrain.drop_duplicates()\n","sub_path":"longgb/Scripts/PyCode/scripts/test_01.py","file_name":"test_01.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"647270924","text":"# 02_return_something.py\n# \n# Functions sometimes need to return something. If you have a function\n# that requires information, then you will need to use arguments. \n\n\ndef main():\n\t\n\t# The print function needs an argument. THe argument is the string\n\t# you put in it.\n\tprint(\"I am an argument\")\n\t\n\t# This function returns stuff!\n\ta = subtract(10, 2)\n\tprint(a)\n\t\n\t# Try making your own function that returns something. \n\treturn 0\n\ndef subtract(a, b):\n\treturn a - b\nif __name__ == \"__main__\":\n\timport sys\n\tmain()\n\t\n\tsys.exit()\n\n\n","sub_path":"Lesson_03 How to function/03_return_something.py","file_name":"03_return_something.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"589523426","text":"from typing import List, Callable, Dict, Iterable, Tuple\nimport logging\nimport numpy as np\nimport csv\nfrom os import linesep, sep, system\nfrom collections import Counter\nfrom datetime import datetime\nfrom constants import PATH_TO_DATA, SPLIT_FILES, PATH_TO_CLN_DATA, PATH_TO_PRPD_DATA, PATH_TO_LOG_NORM_DATA, TRAIN, TEST,\\\n VALIDATION, FULL_TRAIN, CLASSES_MAPPING, PATH_TO_NORM_DATA, TRAIN_SIZE, FULL_TRAIN_SIZE,\\\n VALIDATION_SIZE, TEST_SIZE, SMALL_TRAIN_SIZE, ORIGINAL_FILES_WITH_SAME_HEADER\nfrom math import isinf, isnan, floor, ceil, log\nfrom functools import partial\n\n\nclass Transformer:\n '''\n Class Transformer provides transformations of dataset.\n It can drop columns, replace values or transform values.\n\n Before running it has to be configured and then it\n applies all defined transformations.\n '''\n\n _logger = logging.getLogger(\"Transformer\")\n\n def __init__(self, csv_sep: str = \",\"):\n self._replacement = []\n self._transformation = []\n self._drop = []\n self._csv_sep = csv_sep\n self._header = None\n\n def _extract_header_mapping(self, header: List[str]) -> Dict[str, int]:\n header_mapping = dict()\n\n for i, column in enumerate(header):\n if column not in self._drop:\n header_mapping.update([(column, i)])\n\n return header_mapping\n\n def _make_transformation(self, header_mapping: Dict[str, int], row: List[str]) -> List[str]:\n for col, trans in self._transformation:\n row[header_mapping[col]] = trans(row[header_mapping[col]])\n\n return row\n\n def _make_replacing(self, header_mapping: Dict[str, int], row: List[str]) -> List[str]:\n for col, rplc in self._replacement:\n row[header_mapping[col]] = rplc(row[header_mapping[col]])\n\n return row\n\n def _make_drop(self, header_mapping: Dict[str, int], row: List[str]) -> List[str]:\n return [row[i] for _, i in header_mapping.items()]\n\n def add_replacement(self, column: str, to_replace: str, replace_by: str) -> 'Transformer':\n '''\n Adds a replacement.\n\n Parameters\n ----------\n column : str\n A column where the replacement should be applied.\n to_replace : str\n A value that should be replaced.\n replace_by : str\n A value that should replace the original value.\n\n Returns\n -------\n self\n '''\n\n self._replacement.append((column, lambda value: replace_by if value == to_replace else value))\n\n return self\n\n def add_transformation(self, column: str, transformation: Callable[[str], str]) -> 'Transformer':\n '''\n Adds a transformation of column.\n\n Parameters\n ----------\n column : str\n A column to be transformed.\n transformation: Callable[[str], str]\n A transformation that gets an original value and returns a transformed value.\n\n Returns\n -------\n self\n '''\n\n self._transformation.append((column, transformation))\n\n return self\n\n def add_drop(self, column: str) -> 'Transformer':\n '''\n Adds a column that should be drop.\n\n Parameters\n ----------\n column : str\n A column to be drop.\n\n Returns\n -------\n self\n '''\n\n self._drop.append(column)\n\n return self\n\n def set_drop(self, columns: List[str]) -> 'Transformer':\n '''\n Sets columns that should be drop.\n\n Parameters\n ----------\n columns : List[str]\n Columns to be drop.\n\n Returns\n -------\n self\n '''\n\n self._drop = columns\n\n return self\n\n def transform(self, row: List[str], first_line: bool = False) -> List[str]:\n '''\n Transforms a single row.\n\n Parameters\n ----------\n row : List[str]\n A row to be transformed.\n first_line : bool\n If it is first line it extracts a header mapping that is\n required in the later transformation.\n\n Returns\n -------\n row : List[str]\n A transformed row.\n '''\n\n if first_line:\n self._header = self._extract_header_mapping(row)\n return [col for col, _ in self._header.items()]\n else:\n transformed = self._make_transformation(self._header, row)\n replaced = self._make_replacing(self._header, transformed)\n dropped = self._make_drop(self._header, replaced)\n\n return dropped\n\n def run(self, path_to_files: str, files: List[str], results_dir: str):\n '''\n Runs a transformation of the files and stores results into result_dir.\n\n Parameters\n ----------\n path_to_files : str\n A path to the directory with the CSV files.\n files : List[str]\n A list of CSV files to be transformed\n results_dir : str\n A directory where results should be stored.\n '''\n for file in files:\n self._logger.info(\"Processing file: {}\".format(file))\n with open(path_to_files + sep + file, \"r\") as in_file:\n with open(results_dir + sep + file, \"w\") as out_file:\n first_line = True\n for row in csv.reader(in_file, delimiter=self._csv_sep):\n row_to_write = self.transform(row, first_line)\n\n out_file.write(self._csv_sep.join(row_to_write))\n out_file.write(linesep)\n\n if first_line:\n first_line = False\n\n\ndef load_header() -> List[str]:\n '''\n Loads a header of files.\n\n Returns\n -------\n header : List[str]\n A header of files.\n '''\n\n with open(PATH_TO_DATA + sep + SPLIT_FILES[0], \"r\") as f:\n return f.readline()[:-1].split(\",\")\n\n\ndef split_files_with_more_headers(path_to_data: str, files: List[str]):\n '''\n Splits files with more headers that are contained in middle of files.\n\n Parameters\n ----------\n path_to_data : str\n A path to the directory with the CSV files.\n files : List[str]\n A list of files that should be split.\n '''\n\n for file in files:\n logging.info(\"Processing file: {}\".format(file))\n index = 0\n with open(path_to_data + sep + file, \"r\") as original:\n header = None\n split = None\n for line in original:\n if header is None:\n header = line\n split = open(path_to_data + sep + file.replace(\".csv\", \"_\" + str(index) + \".csv\"), \"w\")\n split.write(line)\n elif header == line:\n split.flush()\n split.close()\n index += 1\n split = open(path_to_data + sep + file.replace(\".csv\", \"_\" + str(index) + \".csv\"), \"w\")\n split.write(line)\n else:\n split.write(line)\n split.flush()\n split.close()\n\n\ndef get_number_of_classes_in_file(iterator: Iterable[str]) -> Counter:\n '''\n Gets a number of samples for each class in a file.\n\n Parameters\n ----------\n iterator : Iterable[str]\n An iterator over the file.\n\n Returns\n -------\n counter : Counter\n A counter with a number of samples for each class in a file.\n '''\n\n counter = Counter()\n\n first_line = True\n for row in iterator:\n if first_line:\n first_line = False\n else:\n counter.update([row[-1]])\n\n return counter\n\n\ndef write_row(out_file, row_to_write: List[str], csv_del: str = \",\"):\n '''\n Writes a row into a file.\n\n Parameters\n ----------\n out_file\n A file writer.\n row_to_write : List[str]\n A row that should be written into the file.\n csv_del : str\n A CSV delimiter. Default value is comma \",\".\n\n '''\n\n out_file.write(csv_del.join(row_to_write))\n out_file.write(linesep)\n\n\ndef split_train_test(path_to_data: str, files: List[str], path_to_results: str, transformer: Transformer = None,\n train_ratio: float = 0.8, val: bool = False):\n '''\n Splits files to a train set and test set or train set and validation set.\n The files are split based on the train_ratio. By default the train set\n consists of 80% data and test sets consists of 20% data. The classes\n ratio is preserved.\n\n Parameters\n ----------\n path_to_data : str\n A path to the directory with the CSV files.\n files : List[str]\n A list of CSV files to be split.\n path_to_results : str\n A path to the directory with results.\n transformer : Transformer\n A row transformer. It is optional, by default None.\n train_ratio : float\n A train ratio. It means that test ratio is 1 - train_ratio. By default 0.8.\n val : bool\n If it is train/validation split.\n '''\n\n train = path_to_results + sep + FULL_TRAIN if not val else path_to_results + sep + TRAIN\n test = path_to_results + sep + TEST if not val else path_to_results + sep + VALIDATION\n with open(train, \"w\") as train:\n with open(test, \"w\") as test:\n header_is_written = False\n for file in files:\n logging.info(\"Processing file: {}\".format(file))\n\n number_fo_classes = dict()\n with open(path_to_data + sep + file, \"r\") as in_file:\n logging.info(\"Checking classes counts.\")\n\n for cls, count in get_number_of_classes_in_file(csv.reader(in_file, delimiter=',')).items():\n number_fo_classes.update([(cls, int(count * train_ratio))])\n\n with open(path_to_data + sep + file, \"r\") as in_file:\n logging.info(\"Splitting into train and test.\")\n\n first_line = True\n\n if transformer is not None:\n for row in csv.reader(in_file, delimiter=','):\n if first_line:\n if not header_is_written:\n row_to_write = transformer.transform(row, first_line)\n write_row(train, row_to_write)\n write_row(test, row_to_write)\n\n header_is_written = True\n\n first_line = False\n else:\n row_to_write = transformer.transform(row, first_line)\n\n number_fo_classes[row_to_write[-1]] -= 1\n\n if number_fo_classes[row_to_write[-1]] > 0:\n write_row(train, row_to_write)\n else:\n write_row(test, row_to_write)\n else:\n for row in csv.reader(in_file, delimiter=','):\n if first_line:\n if not header_is_written:\n write_row(train, row)\n write_row(test, row)\n\n header_is_written = True\n\n first_line = False\n else:\n number_fo_classes[row[-1]] -= 1\n\n if number_fo_classes[row[-1]] > 0:\n write_row(train, row)\n else:\n write_row(test, row)\n\n\ndef check_time(path_to_data: str, files: List[str]):\n '''\n Checks if samples are sorted by timestamp.\n\n Parameters\n ----------\n path_to_data : str\n A path to the directory with CSV files.\n files : List[str]\n A list of CSV files that should be checked.\n '''\n\n for file in files:\n logging.info(\"Processing file: {}\".format(file))\n with open(path_to_data + sep + file, \"r\") as in_file:\n prev_time = 0\n first_line = True\n line_number = 0\n for row in csv.reader(in_file, delimiter=','):\n if first_line:\n first_line = False\n else:\n current_time = float(row[2])\n\n if current_time < prev_time:\n logging.error(\"Line {} - Current time {} is smaller than previous time {}\".format(line_number,\n current_time, prev_time))\n\n prev_time = current_time\n\n line_number += 1\n\n\ndef get_size(dataset: str) -> Tuple[int, int, int]:\n '''\n Computes a size of dataset.\n\n Parameters\n ----------\n dataset : str\n A path to the dataset.\n\n Returns\n -------\n size : Tuple[int, int, int]\n The first is number of samples, the second is number of features\n and the last is number of negative samples.\n '''\n\n negatives = 0\n size = 0\n features = 0\n i = 0\n\n logging.info(\"Getting shape of data.\")\n\n with open(dataset, \"r\") as in_data:\n first_line = True\n for row in csv.reader(in_data, delimiter=','):\n if first_line:\n features = len(row) - 1\n first_line = False\n else:\n if CLASSES_MAPPING[row[-1]] == 0:\n negatives += 1\n\n size += 1\n\n if i % 100000 == 0:\n logging.info(\"Row {}\".format(i))\n\n i += 1\n\n logging.info(\"X = ({}, {}), Y = {}, number of negative values = {}\".format(size, features, size, negatives))\n\n return size, features, negatives\n\n\ndef load_dataset_as_array(dataset: str, number_of_negatives: int = None) -> Tuple[np.ndarray, np.ndarray, int, int]:\n '''\n Loads a dataset numpy array. It is possible to control\n a number of negative samples.\n\n Parameters\n ----------\n dataset : str\n A path to the dataset.\n number_of_negatives : int\n A number of negative samples to be loaded.\n\n Returns\n -------\n result : Tuple[np.ndarray, np.ndarray, int, int]\n It contains in this order the X data (samples), the Y data (labels),\n the number of samples and the number of features.\n '''\n\n to_be_skipped = 0\n\n size, features, all_negatives = get_size(dataset)\n\n if number_of_negatives is not None:\n to_be_skipped = all_negatives - number_of_negatives\n else:\n number_of_negatives = all_negatives\n\n logging.info(\"Initializing numpy arrays.\")\n\n data = np.zeros((size - to_be_skipped, features), dtype=np.float)\n labels = np.zeros(size - to_be_skipped, dtype=np.int)\n\n logging.info(\"Starting to read the dataset.\")\n\n step = int(all_negatives / number_of_negatives)\n negatives = 0\n with open(dataset, \"r\") as in_data:\n first_line = True\n i = 0\n for row in csv.reader(in_data, delimiter=','):\n if first_line:\n first_line = False\n else:\n if CLASSES_MAPPING[row[-1]] == 0 and (negatives % step > 0 or negatives / step >= number_of_negatives):\n negatives += 1\n continue\n\n labels[i] = CLASSES_MAPPING[row[-1]]\n\n if CLASSES_MAPPING[row[-1]] == 0:\n negatives += 1\n\n for j in range(features):\n value = np.float(row[j])\n\n if not isinf(value) and not isnan(value):\n data[i][j] = value\n else:\n logging.error(\"Value on line {} in column {} is inf or nan after converting.\".format(i + 1, j + 1))\n\n if i % 100000 == 0:\n logging.info(\"Row {}\".format(i))\n\n i += 1\n\n logging.info(\"Dataset is converted into numpy array.\")\n\n return data, labels, size - to_be_skipped, features\n\n\ndef convert_to_npy(path_to_data: str, type: str, path_to_result: str, number_of_negatives: int = None):\n '''\n Converts a dataset into numpy array and then stores it as memory map.\n\n The path to the result is constructed as:\n path_to_data + sep + type + sep + \"X_{}.npy\".format(type)\n path_to_data + sep + type + sep + \"Y_{}.npy\".format(type)\n\n Parameters\n ----------\n path_to_data : str\n A path to the dataset.\n type : str\n A type of the dataset - train, test, validation...\n path_to_result : str\n A path to the directory with result.\n number_of_negatives : str\n A number of negative samples to be loaded.\n '''\n\n logging.info(\"Converting dataset into numpy array.\")\n\n X, Y, size, features = load_dataset_as_array(path_to_data + sep + type + \".csv\", number_of_negatives)\n\n logging.info(\"Storing X into memmap X.npy.\")\n\n X_memmap = np.memmap(path_to_result + sep + type + sep + \"X_{}.npy\".format(type), dtype=np.float32, mode=\"write\", shape=(size, features))\n X_memmap[:] = X[:]\n del X_memmap\n\n del X\n\n logging.info(\"Storing X is completed.\")\n logging.info(\"Storing Y into memmap Y.npy.\")\n\n Y_memmap = np.memmap(path_to_result + sep + type + sep + \"Y_{}.npy\".format(type), dtype=np.int, mode=\"write\", shape=size)\n Y_memmap[:] = Y[:]\n del Y_memmap\n\n del Y\n\n logging.info(\"Storing Y is completed.\")\n\n\ndef create_labels_for_each_class_separately(path_to_data: str, type: str, size: int):\n '''\n Converts multiclass classification into binary classification.\n It means that for each class it creates new labels that contains\n only {1, 0} where 1 is the current class and 0 is the rest.\n\n The path to the data is constructed as:\n path_to_data + sep + type + sep + \"Y_{}.npy\".format(type)\n\n The path to the result is constructed as:\n path_to_data + sep + type + sep + \"Y_{}_cls_{}.npy\".format(type, class)\n\n Parameters\n ----------\n path_to_data : str\n A path to the directory with data (e.g. normalized_data).\n type : str\n A type of the data (e.g. train or test).\n size : int\n A size of the dataset.\n '''\n\n for cls in range(0, 15):\n logging.info(\"Processing class: {}\".format(cls))\n y_old = np.memmap(path_to_data + sep + type + sep + \"Y_{}.npy\".format(type), dtype=np.int, mode=\"r\", shape=size)\n y_new = np.memmap(path_to_data + sep + type + sep + \"Y_{}_cls_{}.npy\".format(type, cls), dtype=np.int, mode=\"write\", shape=size)\n\n for i in range(size):\n if y_old[i] == cls:\n y_new[i] = 1\n else:\n y_new[i] = 0\n\n del y_new\n\n logging.info(\"Processing is completed.\")\n\n\ndef normalize(value: float, mean: float, std: float, digit: int = 4) -> float:\n '''\n Makes standardization with specific precision.\n\n Parameters\n ----------\n value : float\n A value to be normalized.\n mean : float\n A mean of values.\n std : float\n A standard deviation of values.\n digit : int\n A number of digits after floating point.\n\n Returns\n -------\n results : float\n A normalized value by standardization.\n '''\n\n return round((value - mean) / std, digit)\n\n\ndef load_normalizer() -> Transformer:\n '''\n Loads a normalizer that does standardization\n based on the train statistics.\n\n Returns\n -------\n normalizer : Transformer\n A loaded normalizer that does standardization.\n '''\n\n def normalize_str(value: str, mean: float, std: float) -> str:\n return str(normalize(float(value), mean, std))\n\n normalizer = Transformer()\n\n with open(\"statistics/train/train_results.csv\", mode=\"r\") as stat:\n column_stats = dict()\n first_row = True\n for row in csv.reader(stat, delimiter=\",\"):\n if first_row:\n first_row = False\n else:\n if row[1] == \"Mean\":\n column_stats.update([(\"Mean\", float(row[2]))])\n elif row[1] == \"StandardDeviation\":\n column_stats.update([(\"StandardDeviation\", float(row[2]))])\n\n if len(column_stats) == 2:\n normalizer.add_transformation(row[0], partial(normalize_str, mean=column_stats[\"Mean\"], std=column_stats[\"StandardDeviation\"]))\n column_stats = dict()\n\n return normalizer\n\n\ndef load_log_normalizer() -> Transformer:\n '''\n Loads a normalizer that takes logarithm of the data.\n\n Returns\n -------\n normalizer : Transformer\n A normalizer that takes logarithm of the data.\n '''\n\n def normalize_str(value: str):\n return str(log(float(value) + 1))\n\n normalizer = Transformer()\n\n skip = [1, 2, 3, 18, 20, 21, 22, 23, 25, 26, 32, 33, 34, 35, 45, 46, 47, 48, 49, 50, 51, 52, 57, 58, 59, 60, 61, 62,\n 67, 68, 79]\n\n for index, column in enumerate(load_header()):\n if index not in skip:\n normalizer.add_transformation(column, normalize_str)\n\n return normalizer\n\n\ndef select_samples(path_to_data: str, type: str, ratio: float, size: int, number_of_negatives: int = 1000000) -> int:\n '''\n Selects only part of the data based on specified ratio and number of negative samples.\n\n Parameters\n ----------\n path_to_data : str\n A path to the directory with data.\n type : str\n A type of data (e.g. train or test)\n ratio : float\n How many percent of data should be selected with preserving\n classes ratio if it is possible. At least 10 samples for\n each class is preserved.\n size : int\n A number of samples.\n number_of_negatives : int\n A number of negative samples that is loaded before selecting.\n\n Returns\n -------\n result : int\n A final number of samples.\n '''\n\n y_old = np.memmap(path_to_data + sep + type + sep + \"Y_{}.npy\".format(type), dtype=np.int, mode=\"r\", shape=size)\n\n logging.info(\"Counting classes.\")\n\n cls_counts = Counter()\n for cls in y_old:\n cls_counts.update([cls])\n\n cls_steps = dict()\n cntr_steps = dict()\n for cls in range(15):\n if cls == 0 and cls_counts[0] >= number_of_negatives:\n final_counts = ceil(number_of_negatives * ratio)\n else:\n final_counts = ceil(cls_counts[cls] * ratio)\n\n final_counts = 10 if final_counts < 10 else final_counts\n cls_steps[cls] = floor(cls_counts[cls] / final_counts)\n cls_counts[cls] = int(final_counts)\n cntr_steps[cls] = 0\n\n x_old = np.memmap(path_to_data + sep + type + sep + \"X_{}.npy\".format(type), dtype=np.float32, mode=\"r\", shape=(size, 70))\n\n new_size = sum(cls_counts.values())\n\n logging.info(\"New size of dataset is: {}\".format(new_size))\n logging.info(\"Ratio classes is (in order 0,1,2...): {}\".format(\",\".join([str(x) for _, x in cls_counts.items()])))\n\n y_new = np.memmap(path_to_data + sep + type + sep + \"Y_smaller_{}.npy\".format(type), dtype=np.int, mode=\"write\", shape=new_size)\n x_new = np.memmap(path_to_data + sep + type + sep + \"X_smaller_{}.npy\".format(type), dtype=np.float32, mode=\"write\", shape=(new_size, 70))\n\n logging.info(\"Creating dataset.\")\n\n i_new = 0\n for i in range(size):\n if cntr_steps[y_old[i]] % cls_steps[y_old[i]] == 0 and cntr_steps[y_old[i]] < cls_counts[y_old[i]]:\n y_new[i_new] = y_old[i]\n x_new[i_new] = x_old[i]\n\n i_new += 1\n\n cntr_steps[y_old[i]] += 1\n\n logging.info(\"Dataset is completed.\")\n\n return new_size\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(format=\"%(levelname)s %(name)s: %(message)s\", level=logging.INFO)\n\n # removing headers in middle of file (preparation for data analysis)\n # split_files_with_more_headers(PATH_TO_DATA, ORIGINAL_FILES_WITH_SAME_HEADER[0])\n # transformer = Transformer()\\\n # .add_drop(\"Flow ID\")\\\n # .add_drop(\"Src IP\")\\\n # .add_drop(\"Src Port\")\\\n # .add_drop(\"Dst IP\")\n # transformer.run(PATH_TO_DATA, ORIGINAL_FILES_WITH_SAME_HEADER[1], \"../\")\n # system(\"mv ../\" + ORIGINAL_FILES_WITH_SAME_HEADER[1][0] + \" \" + PATH_TO_DATA + sep + ORIGINAL_FILES_WITH_SAME_HEADER[1][0].replace(\".csv\", \"_dropped.csv\"))\n # split_files_with_more_headers(PATH_TO_DATA, [ORIGINAL_FILES_WITH_SAME_HEADER[1][0].replace(\".csv\", \"_dropped.csv\")])\n\n\n # datetime is converted to timestamp\n # NaN value is replaced by most common value in a column\n # Infinity value is replaced by maximum value in a column\n # columns, that have only one distinct value, are dropped\n # transformer = Transformer()\\\n # .add_transformation(\"Timestamp\", lambda s: str(datetime.strptime(s, \"%d/%m/%Y %H:%M:%S\").timestamp()))\\\n # .add_replacement(\"Flow Byts/s\", \"NaN\", \"0\")\\\n # .add_replacement(\"Flow Byts/s\", \"Infinity\", \"1806642857.14286\")\\\n # .add_replacement(\"Flow Pkts/s\", \"NaN\", \"1000000\")\\\n # .add_replacement(\"Flow Pkts/s\", \"Infinity\", \"6000000.0\")\\\n # .set_drop([\"Bwd Blk Rate Avg\", \"Bwd Byts/b Avg\", \"Bwd PSH Flags\", \"Bwd Pkts/b Avg\", \"Bwd URG Flags\",\n # \"Fwd Blk Rate Avg\", \"Fwd Byts/b Avg\", \"Fwd Pkts/b Avg\"])\n #\n # transformer.run(PATH_TO_DATA, SPLIT_FILES, PATH_TO_CLN_DATA)\n\n # check_time(PATH_TO_CLN_DATA, SPLIT_FILES)\n\n # dropping Timestamp and spit to full train and test\n # transformer = Transformer().set_drop([\"Timestamp\"])\n # split_train_test(PATH_TO_CLN_DATA, SPLIT_FILES, PATH_TO_PRPD_DATA, transformer)\n\n # normalizing data by subtracting mean and dividing by std\n # normalizer = load_normalizer()\n # normalizer.run(PATH_TO_PRPD_DATA, [FULL_TRAIN, TEST], PATH_TO_NORM_DATA)\n\n # normalizing data by log\n # normalizer = load_log_normalizer()\n # normalizer.run(PATH_TO_PRPD_DATA, [FULL_TRAIN, TEST], PATH_TO_LOG_NORM_DATA)\n\n # split to train and validation\n # split_train_test(PATH_TO_NORM_DATA, [FULL_TRAIN], PATH_TO_NORM_DATA, val=True)\n # split_train_test(PATH_TO_LOG_NORM_DATA, [FULL_TRAIN], PATH_TO_LOG_NORM_DATA, val=True)\n\n # converting normalized data to numpy array (memmap)\n # convert_to_npy(PATH_TO_NORM_DATA, \"train\", PATH_TO_NORM_DATA + sep + \"train\")\n # convert_to_npy(PATH_TO_NORM_DATA, \"full_train\", PATH_TO_NORM_DATA + sep + \"full_train\")\n # convert_to_npy(PATH_TO_NORM_DATA, \"test\", PATH_TO_NORM_DATA + sep + \"test\")\n # convert_to_npy(PATH_TO_NORM_DATA, \"validation\", PATH_TO_NORM_DATA + sep + \"validation\")\n # convert_to_npy(PATH_TO_NORM_DATA, \"train\", PATH_TO_NORM_DATA, 600000) # for visualization -> size 2798546\n # create_labels_for_each_class_separately(PATH_TO_NORM_DATA, \"train\", TRAIN_SIZE)\n # create_labels_for_each_class_separately(PATH_TO_NORM_DATA, \"full_train\", FULL_TRAIN_SIZE)\n # create_labels_for_each_class_separately(PATH_TO_NORM_DATA, \"validation\", VALIDATION_SIZE)\n # create_labels_for_each_class_separately(PATH_TO_NORM_DATA, \"test\", TEST_SIZE)\n # convert_to_npy(PATH_TO_NORM_DATA, \"train\", PATH_TO_NORM_DATA + sep + \"small_train\", 1000000) # for training SVM\n # create_labels_for_each_class_separately(PATH_TO_NORM_DATA, \"small_train\", SMALL_TRAIN_SIZE) # for training SVM\n # size = select_samples(PATH_TO_NORM_DATA, \"small_train\", 0.3, SMALL_TRAIN_SIZE, number_of_negatives=1000000) # for training SVM\n # create_labels_for_each_class_separately(PATH_TO_NORM_DATA, \"small_train\", size) # for training SVM\n\n # converting log normalized data to numpy array (memmap)\n # convert_to_npy(PATH_TO_LOG_NORM_DATA, \"train\", PATH_TO_LOG_NORM_DATA + sep + \"train\")\n # convert_to_npy(PATH_TO_LOG_NORM_DATA, \"full_train\", PATH_TO_LOG_NORM_DATA + sep + \"full_train\")\n # convert_to_npy(PATH_TO_LOG_NORM_DATA, \"test\", PATH_TO_LOG_NORM_DATA + sep + \"test\")\n # convert_to_npy(PATH_TO_LOG_NORM_DATA, \"validation\", PATH_TO_LOG_NORM_DATA + sep + \"validation\")\n # create_labels_for_each_class_separately(PATH_TO_LOG_NORM_DATA, \"train\", TRAIN_SIZE)\n # create_labels_for_each_class_separately(PATH_TO_LOG_NORM_DATA, \"full_train\", FULL_TRAIN_SIZE)\n # create_labels_for_each_class_separately(PATH_TO_LOG_NORM_DATA, \"validation\", VALIDATION_SIZE)\n # create_labels_for_each_class_separately(PATH_TO_LOG_NORM_DATA, \"test\", TEST_SIZE)\n","sub_path":"data_transformation.py","file_name":"data_transformation.py","file_ext":"py","file_size_in_byte":28482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"456614298","text":"tmp1 = {\"k1\": \"\",\n \"k2\": \"\",\n \"k3\": \"\"\n }\n\nstring_word = \"8x6.7x2.5\"\nsplit_word = string_word.split(\"x\")\ntruck_key = (\"body_length\", \"body_width\", \"body_height\")\n# truck_dict = dict(zip(truck_key, split_word))\ntmp1[\"k1\"]=split_word[0]\ntmp1[\"k2\"]=split_word[1]\ntmp1[\"k3\"]=split_word[2]\nprint(tmp1)\n# print(\"body_height is {}\".format(truck_dict.get('body_height')))\n\n\ndef __init__(self, start, end):\n self.current = start\n self.end = end\n\n\ndef __iter__(self):\n return self\n\n\ndef __next__(self):\n if self.current >= self.end:\n raise StopIteration\n\n result = self.current ** 2\n self.current += 1\n return result","sub_path":"week_4/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"415315711","text":"# X FIRST, Y SECOND!!!\n\n###########\n# Convention is to use underscores for variables instead of camel case in Python, however i'm not gonna \n# touch it in case they are standard functions from libtcod\n# Note to Self: end of fuctions and control-flow constructs is determined by indentation, not curly braces!\n#\n# Python is not object oriented like Java! define functions before you call them, even when using classes \n# and/or inheritance!\n###########\nimport libtcodpy as libtcod # Can the name be arbitrary?\n\nSCREEN_WIDTH=80\nSCREEN_HEIGHT=50\n#MAX_FPS=20\n\nclass Object:\n def __init__(self, x, y, char, color): # This is basically a constructor # there's two underscores!\n self.x=x\n self.y=y\n self.char=char\n self.color=color\n\n def move (self, distance_x, distance_y):\n self.x += distance_x\n self.y += distance_y\n\n def draw(self):\n libtcod.console_set_default_foreground(con, self.color)\n libtcod.console_put_char(con, self.x, self.y, self.char, libtcod.BKGND_NONE)\n\n def clear(self):\n libtcod.console_put_char(con, self.x, self.y, ' ', libtcod.BKGND_NONE)\n\n# Function (Java's \"method\") to define wich key correspond to what action. Does it modify the player_*pos outside the function aswell? is it limited in scope?\ndef handle_keys():\n key = libtcod.console_wait_for_keypress(True) \n if key.vk == libtcod.KEY_ESCAPE:\n return True\n\n if libtcod.console_is_key_pressed(libtcod.KEY_UP):\n player.move(0, -1)\n elif libtcod.console_is_key_pressed(libtcod.KEY_DOWN):\n player.move(0, 1)\n elif libtcod.console_is_key_pressed(libtcod.KEY_LEFT):\n player.move(-1, 0)\n elif libtcod.console_is_key_pressed(libtcod.KEY_RIGHT):\n player.move(1, 0) \n\n\n# SCREEN INIT\nlibtcod.console_set_custom_font('arial10x10.png', libtcod.FONT_TYPE_GREYSCALE | libtcod.FONT_LAYOUT_TCOD)\nlibtcod.console_init_root(SCREEN_WIDTH, SCREEN_HEIGHT, 'Test', False) # the 'root' console will be number 0 (stdout). 3rd parameters is window title, 4th is fullscreen bool\ncon=libtcod.console_new(SCREEN_WIDTH, SCREEN_HEIGHT) # secondary console\n# Setting up player and npc\nplayer=Object(SCREEN_WIDTH/2, SCREEN_HEIGHT/2, '@', libtcod.white)\nnpc=Object(SCREEN_WIDTH/2-5, SCREEN_HEIGHT/2, '@', libtcod.yellow)\nobjects=[npc, player] \n\n#MAIN LOOP: setup console, place @ and wait for presses. The second console_put_char applies to the previous coordinates, to avoid having a trail of @.\nwhile not libtcod.console_is_window_closed():\n libtcod.console_set_default_foreground(0, libtcod.white) # the 0 parameter should be output device, aka stdout\n for object in objects:\n \tobject.draw()\n\n libtcod.console_blit(con, 0, 0, SCREEN_WIDTH, SCREEN_HEIGHT, 0, 0, 0)\n libtcod.console_flush() #look this up\n \n for object in objects:\n \tobject.clear()\n \n exit=handle_keys()\n if exit:\n break\n\n#####\n# I don't understand why exit=handle_keys() is after the flush() function, and also what's up with the way we call the handle_keys() function\n#####\n\n\n\n#MAIN LOOP: setup console, place @ and wait for presses. The second console_put_char applies to the previous coordinates, to avoid having a trail of @.\n# while not libtcod.console_is_window_closed():\n# libtcod.console_set_default_foreground(0, libtcod.white) # the 0 parameter should be output device, aka stdout\n# libtcod.console_put_char(con, player_xpos, player_ypos, '@', libtcod.BKGND_NONE) # X FIRST, Y SECOND!\n# libtcod.console_blit(con, 0, 0, SCREEN_WIDTH, SCREEN_HEIGHT, 0, 0, 0)\n# libtcod.console_flush() #look this up\n# libtcod.console_put_char(con, player_xpos, player_ypos, ' ', libtcod.BKGND_NONE)\n# exit=handle_keys()\n# if exit:\n# break\n","sub_path":"OLD/roguelike.py","file_name":"roguelike.py","file_ext":"py","file_size_in_byte":3752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"24019129","text":"# ===========================================\n# @Time : 2019/3/27 14:10\n# @Author : antony\n# @Email : 502202879@qq.com\n# @File : win32kb_auto.py\n# @Software: PyCharm\n# ===========================================\nimport win32api\nimport win32con\nimport win32clipboard as w3c\nimport ctypes\nimport threading\n\n\ndef set_clipboard(astring): # 写入剪切板 目前这方法有bug\n w3c.OpenClipboard()\n w3c.EmptyClipboard()\n w3c.SetClipboardData(win32con.CF_TEXT, astring)\n w3c.CloseClipboard()\n\n\nclass Hotkey:\n\n def __init__(self):\n self.stop_flag = False # 置为True时终止热键进程\n self.keys ={\n 10: win32con.VK_F10,\n 98: (win32con.VK_F10, win32con.MOD_CONTROL),\n 99: (win32con.VK_F10, win32con.MOD_CONTROL, win32con.MOD_ALT)}\n self.callbacks = {\n 10: self.print_test,\n 98: self.print_test,\n 99: self.print_test}\n self.args = {\n 10: (),\n 98: ('token to run out ~',),\n 99: ('token to run out ~', '额外信息测试 id:99')} # 一般默认的传参都是 () 特定传参需要注册\n\n def add(self, des_list):\n # des_list = [10, (win32con.VK_F10, win32con.MOD_CONTROL), hotkey_print_test, *()]\n # 4个元素 分别是 id 热键 回调函数 和 回调函数的参数\n if des_list[0] in self.keys:\n print(f'key_id: {des_list[0]} is already used')\n return -1\n else:\n id, key, callback, *args = des_list\n self.keys.update({id: key})\n self.callbacks.update({id: callback})\n self.args.update({id: args if args else ()})\n\n def __iter__(self):\n return iter([(i, self.keys[i], self.callbacks[i], self.args[i]) for i in self.keys])\n\n def print_test(self, x='this is a hotkey run test~', *args):\n print(x, *args)\n if x == 'token to run out ~':\n self.stop_flag = True\n print(' >> 热键监控到此为止~!!')\n\n\nclass HotkeyThread(threading.Thread): # 创建一个Thread.threading的扩展类\n user32 = ctypes.windll.user32 # 加载user32.dll\n\n def __init__(self, hotkey_configs):\n super(HotkeyThread, self).__init__()\n self.useable = True\n if type(hotkey_configs) is not Hotkey:\n print('hotkey_configs type error')\n self.useable = False\n self.hotkey_configs = hotkey_configs\n\n def run(self):\n if not self.useable:\n return -1\n for id, key, callback, args in self.hotkey_configs:\n if type(key) is not int and len(key) > 1:\n mod, vk = eval('|'.join([str(x) for x in key[1:]])), key[0] # 辅助按键的特殊处理例子\n else:\n mod, vk = 0, key\n # 注册快捷键mod是辅助按键，vk是像F10这样的按键\n if not self.user32.RegisterHotKey(None, id, mod, vk):\n print(\"Unable to register id\", id) # 返回一个错误信息\n else:\n print(f'成功注册热键：mod({mod}) vk({vk})')\n try:\n # 以下为检测热键是否被按下，并在最后释放快捷键\n msg = ctypes.wintypes.MSG()\n while True and not self.hotkey_configs.stop_flag:\n if self.user32.GetMessageA(ctypes.byref(msg), None, 0, 0) != 0:\n if msg.message == win32con.WM_HOTKEY:\n # print(msg.message, msg.wParam)\n if msg.wParam in self.hotkey_configs.keys:\n func = self.hotkey_configs.callbacks[msg.wParam]\n args = self.hotkey_configs.args[msg.wParam]\n if args:\n res = func(*args)\n else:\n res = func()\n self.user32.TranslateMessage(ctypes.byref(msg))\n self.user32.DispatchMessageA(ctypes.byref(msg))\n finally:\n # 必须得释放热键，否则下次就会注册失败，所以当程序异常退出，没有释放热键，\n # 那么下次很可能就没办法注册成功了，这时可以换一个热键测试\n for id, key, callback, args in self.hotkey_configs:\n self.user32.UnregisterHotKey(None, id)\n\n\nif __name__ == '__main__':\n # 一个通过热键触发自动root登录(通过pyautogui)的实例\n hot = Hotkey()\n # 增加自己真正需要配置的热键\n from win32kb_auto import typing_root, dobjsylogin\n # 68 对应D键\n hot.add([11, (67, win32con.MOD_CONTROL, win32con.MOD_ALT), dobjsylogin, ()]) #\n hot.add([12, (68, win32con.MOD_CONTROL, win32con.MOD_ALT), typing_root, ()]) #\n p = HotkeyThread(hot)\n p.start()\n","sub_path":"exam/win32keys_comp.py","file_name":"win32keys_comp.py","file_ext":"py","file_size_in_byte":4799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"255213661","text":"import logging\n# from datetime import *\nfrom datetime import datetime, timedelta\nimport time\nimport os\nimport sys\nfrom tokenize import Token\nimport pytz\nimport re\nimport requests\n\nfrom HELPME.bot_sql_integration import *\nfrom HELPME.helperFunctions import *\n\nfrom uuid import uuid4, uuid1\nfrom telegram.utils.helpers import escape_markdown\nfrom telegram.ext import InlineQueryHandler, Updater, CommandHandler, CallbackQueryHandler, CallbackContext, Filters, MessageHandler, ConversationHandler\nfrom telegram import Bot, InlineQueryResultArticle, ParseMode, InputTextMessageContent, InlineKeyboardButton, InlineKeyboardMarkup, Update, message, replymarkup\n\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n level=logging.INFO)\n\ntz = pytz.timezone('Asia/Singapore')\nnow = datetime.now(tz).replace(microsecond=0) # the current time in your local timezone\n\nlogger = logging.getLogger(__name__)\nTOKEN = os.environ[\"API_TOKEN\"]\nPORT = int(os.environ.get('PORT', '8443'))\nBOT_ID = os.environ['BOT_ID']\n\ndef startGroup(update, context):\n \"\"\"Send the welcome message when the command /start is issued in a group.\"\"\"\n # The registration keyboard used to register groups into our Group Database.\n keyboard = [\n [\n InlineKeyboardButton(\"Register\", callback_data='groupRegister'),\n ],\n [\n InlineKeyboardButton(\"Don't Register\", callback_data='groupDontRegister')\n ],\n ]\n\n ## This section is to send a logo before the registration process!\n # context.bot.send_photo(\n # chat_id=update.effective_chat.id,\n # photo='https://res.cloudinary.com/jianoway/image/upload/v1621939877/O%24P%24_Logo.png',\n # )\n\n # Sets up the InlineKeyboardMarkup as the reply_markup to be used in the message.\n reply_markup = InlineKeyboardMarkup(keyboard)\n\n # Sends the welcome message for the group.\n message = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=\n \"Hello this is O$P$, your personal Telegram loan chaser and debt tracker!\\n\\n\" +\n \"We aim to make the process of tracking which of your 'friends' still owe you \" +\n \"and reminding them as impersonal as possible so you won't feel the paiseh!\"\n \"Along with that, you can now also notify people who you've returned money to\" +\n \"with a simple click of a button.\\n\\n\" +\n \"Simply register your group with us by pressing the button below!\",\n reply_markup=reply_markup,\n )\n return message\n\ndef startPrivate(update, context):\n \"\"\"Send the welcome message when the command /start is issued via PM\"\"\"\n chat_id = update.message.chat_id\n username = update.message.chat.username\n firstname = update.message.chat.first_name\n user = (chat_id, username, 1, firstname)\n\n keyboard = [\n [\n InlineKeyboardButton(\"Register\", callback_data='userRegister'),\n ],\n [\n InlineKeyboardButton(\"Don't Register\", callback_data='userDontRegister')\n ],\n ]\n # Sets up the InlineKeyboardMarkup as the reply_markup to be used in the message.\n reply_markup = InlineKeyboardMarkup(keyboard)\n\n message = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=\n \"Hi! Thank you for choosing O$P$, your one stop debt chaser!\\n\\n\" +\n \"Simply register with us by clicking pressing the button below!\",\n reply_markup=reply_markup,\n )\n return message\n\ndef scanReceiptPrivateMessage(update, context):\n \"\"\"Prompt the user to send their receipt.\"\"\"\n chat_id = update.effective_chat.id\n context.bot.send_message(\n chat_id=chat_id,\n text=\n \"Please send in the receipt to be scanned! Alternatively, to cancel please type /cancelreceipt\"\n )\n return \"waitingonpicprivate\"\n\ndef scanReceiptPrivatePicture(update, context):\n \"\"\"Parses the image if possible, otherwise will prompt user to send a valid image.\"\"\"\n photos = update.message.photo\n length = len(photos)\n if length == 0:\n context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=\n \"Please send in the receipt to be scanned! Alternatively, to cancel please type /cancelreceipt\"\n )\n return \"waitingonpicprivate\"\n else:\n photo = photos[length - 1]\n fileID = photo.file_id\n filePathURL = \"https://api.telegram.org/bot%s/getfile?file_id=%s\" % (TOKEN, fileID)\n filePath = requests.get(filePathURL).json()[\"result\"][\"file_path\"]\n fileURL = \"https://api.telegram.org/file/bot%s/%s\" % (TOKEN, filePath)\n text = receiptParser(fileURL)\n context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=text\n )\n return ConversationHandler.END\n\ndef cancelReceipt(update, context):\n \"\"\"Cancels the receipt scanning procedure.\"\"\"\n context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=\n \"Your receipt scan has been cancelled!\"\n )\n print('ok')\n return ConversationHandler.END\n\n\ndef getDebtors(update, context):\n \"\"\"Sends the user the message in response to the /whoowesme command.\"\"\"\n userID = update.effective_chat.id\n if not isNotifiable(userID):\n message = context.bot.send_message(\n chat_id=userID,\n text=\n \"Please register with us first by using /start!\"\n )\n return message\n \n \n unsettledTransactions = getUnsettledTransactionsForCreditor(userID)\n keyboardMarkup = formatTransactionsForCreditorKeyboardMarkup(unsettledTransactions)\n\n if len(unsettledTransactions) < 1:\n message = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text='No one owes you money! What great friends you have!!!'\n )\n return message\n\n message = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text='The baddies who have your cash money! >:(',\n reply_markup=keyboardMarkup\n )\n return message\n\ndef getCreditors(update, context):\n \"\"\"Sends the user the message in response to the /whomeowes command.\"\"\"\n userID = update.effective_chat.id\n if not isNotifiable(userID):\n message = context.bot.send_message(\n chat_id=userID,\n text=\n \"Please register with us first by using /start!\"\n )\n return message\n \n unsettledTransactions = getUnsettledTransactionsForDebtor(userID)\n keyboardMarkup = formatTransactionsForDebtorKeyboardMarkup(unsettledTransactions)\n\n if len(unsettledTransactions) < 1:\n message = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=\"Wow! Amazing! You don't owe anyone any money!\"\n )\n return message\n message = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=\"The kind people who you've taken from:\",\n reply_markup=keyboardMarkup\n )\n return message\n \ndef cancel(update, context):\n \"\"\"Cancels any ongoing operation for the user in a group.\"\"\"\n groupID = update.effective_chat.id\n userID = update.message.from_user.id\n messageID = update.message.message_id\n setUserStateInactive(userID, groupID)\n resetUserTempAmount(userID, groupID)\n resetUserTempOrderID(userID, groupID)\n resetUserTempMessageID(userID, groupID)\n context.bot.send_message(\n chat_id=groupID,\n reply_to_message_id=messageID,\n text=\"I've been cancelled!\"\n )\n\ndef help(update, context):\n \"\"\"Sends the user the help message.\"\"\"\n return context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=\n \"List of commands:\\n\\n\" +\n \"/start Initialise and register with us.\\n\" +\n \"/help For the confused souls.\\n\" +\n \"/whoowesme To see your debtors (only in private message).\\n\" +\n \"/whomeowes To see your creditors (only in private message).\\n\"+\n \"/cancel To cancel any creation of order.\\n\"\n \"\\nAfter running /start and registering in the group you wish to split bills in, you can start splitting your bills by simply typing @OwePay_bot followed by name of the order.\" +\n \"\\n\\nDue to the nature of Telegram Bots, our bot will only be able to detect users if they have either sent a message in the group after I've been added or users added after me!\" \n )\n\ndef error(update, context):\n \"\"\"Log Errors caused by Updates.\"\"\"\n logger.warning('Update \"%s\" caused error \"%s\"', update, context.error)\n\ndef inline(update, context):\n \"\"\"Handles the Inline Queries sent by the user.\"\"\"\n query = update.inline_query.query\n if query == \"\":\n return\n results = inlineQueryHelper(update)\n update.inline_query.answer(results)\n\ndef button(update, context):\n \"\"\"Handles the button presses for Inline Keyboard Callbacks\"\"\"\n query = update.callback_query\n choice = query.data\n username = query.message.chat.username\n\n if username == None or not 'test,bot' in username: # this is safe because ',' is an invalid character for telegram usernames\n query.answer()\n \n if choice == 'groupRegister':\n groupRegister(update, context)\n return query\n\n if choice == 'groupDontRegister':\n groupDontRegister(update, context)\n return query\n\n if choice == 'userRegister':\n userRegister(update, context)\n return query\n\n if choice == 'userDontRegister':\n userDontRegister(update, context)\n return query\n\n if choice == 'debtorEvenlyPaid':\n debtorEvenlyPaid(update, context)\n return query\n\n if choice == 'debtorEvenlyUnpaid':\n debtorEvenlyUnpaid(update, context)\n return query\n\n if choice == 'SplitEvenlyFinalise':\n splitEvenly(update, context)\n return query\n\n if 'splitevenlycallbackdata' in choice:\n user = choice.replace('splitevenlycallbackdata', '', 1)\n if userAlreadyAdded(user): #split some\n editMessageForSplitEvenly(update, context)\n return query\n \n if 'settledebtforcreditor' in choice:\n settleDebtForCreditor(update, context)\n\n if 'settledebtfordebtor' in choice:\n settleDebtForDebtor(update, context)\n \n if 'notifydebtorcallbackdata' in choice:\n notifyUserFromPrivateMessage(update, context)\n \n if 'splitunevenlycallbackdata' in choice:\n editUnevenlyMessageIndividual(update, context)\n\n if choice == 'splitevenlyaddeveryonecallbackdata':\n editSplitEvenlyAddEveryone(update, context)\n\n if choice == 'splitunevenlyaddeveryonecallbackdata':\n editSplitUnevenlyAddEveryone(update, context)\n\n\n if choice == 'splitunevenlynextitem':\n splitUnevenlyNextItem(update, context)\n \n if choice == 'goodservicetax':\n splitGST(update, context)\n \n if choice == 'servicechargecallbackdata':\n splitSVC(update, context)\n \n if choice == 'splitunevenlyfinalise':\n splitUnevenlyFinalise(update, context)\n \n if choice == 'debtorUnevenlyPaid':\n debtorUnevenlyPaid(update, context)\n\n if choice == 'debtorUnevenlyUnpaid':\n debtorUnevenlyUnpaid(update, context)\n\n if choice == 'newordersplitevenly':\n newOrderSplitEvenly(update, context)\n\n if choice == 'newordersplitunevenly':\n newOrderSplitUnevenly(update, context)\n\ndef newOrderSplitUnevenly(update, context):\n \"\"\"Prompts the user to send in the list of items to be added to the order.\"\"\"\n query = update.callback_query\n groupID = query.message.chat_id\n message_id = query.message.message_id\n userID = query.from_user.id\n\n if not userStateNewOrder(userID, groupID) and userIsMessageCreator(userID, groupID, message_id):\n return\n \n order = catchSplitUnevenlyOrder(userID, groupID)\n orderID = order.orderID\n updateOrderIDToUserGroupRelational(userID, groupID, orderID)\n setOrderDifferentAmountsFromOrderID(orderID)\n message = context.bot.editMessageText(\n chat_id=groupID,\n message_id=message_id,\n text='Please send in the items in the following format:\\nItem Name - Price\\n\\nFor example:\\nChicken Rice - 5\\nCurry Chicken - 5.50\\nNasi Lemak - 4'\n )\n updateUserStateSplitUnevenly(userID, groupID)\n return message\n\n\ndef newOrderSplitEvenly(update, context):\n \"\"\"Prompts the user to send in the amount to be split in the order.\"\"\"\n query = update.callback_query\n groupID = query.message.chat_id\n message_id = query.message.message_id\n userID = query.from_user.id\n\n if not userStateNewOrder(userID, groupID) and userIsMessageCreator(userID, groupID, message_id):\n return\n \n message = context.bot.editMessageText(\n chat_id=groupID,\n message_id=message_id,\n text='Please send in the amount to split!'\n )\n updateUserStateSplitEvenly(userID, groupID)\n return message\n \n\n \n \ndef debtorUnevenlyPaid(update, context):\n \"\"\"\"\"\"\n query = update.callback_query\n groupID = query.message.chat_id\n message_id = query.message.message_id\n debtorID = query.from_user.id\n debtorUsername = getUsername(debtorID)\n text = query.message.text\n orderID = frOrderIDFromMessageAndGroupID(message_id, groupID)\n creditorID = getCreditorIDFromMessageAndGroupID(message_id, groupID)\n\n if str(creditorID) == str(debtorID):\n return\n\n transactionID = getTransactionIDFromOrderIDCreditorIDDebtorID(orderID, creditorID, debtorID)\n amountOwed = getAmountOwedFromTransactionID(transactionID)\n\n textList = text.split('\\n')\n edited = False\n newTextList = []\n for item in textList:\n if not '(@' + debtorUsername + ')' in item:\n newTextList.append(item + '\\n')\n else:\n edited = True\n markTransactionAsSettled(creditorID, debtorID, orderID)\n newText = ''.join(newTextList)\n if edited:\n context.bot.editMessageText(\n chat_id=groupID,\n message_id=message_id,\n text=newText,\n reply_markup=splitUnevenlyFinalisedKeyboardMarkup()\n )\n\ndef debtorUnevenlyUnpaid(update, context):\n query = update.callback_query\n groupID = query.message.chat_id\n message_id = query.message.message_id\n debtorID = query.from_user.id\n debtorUsername = getUsername(debtorID)\n text = query.message.text\n orderID = getOrderIDFromMessageAndGroupID(message_id, groupID)\n creditorID = getCreditorIDFromMessageAndGroupID(message_id, groupID)\n\n if str(creditorID) == str(debtorID):\n return\n\n transactionID = getTransactionIDFromOrderIDCreditorIDDebtorID(orderID, creditorID, debtorID)\n amountOwed = getAmountOwedFromTransactionID(transactionID)\n\n textList = text.split('\\n')\n alreadyInside = False\n newTextList = []\n for item in textList:\n if '(@' + debtorUsername + ')' in item:\n newTextList.append(item + '\\n')\n alreadyInside = True\n else:\n newTextList.append(item + '\\n')\n\n if not alreadyInside:\n debtorName = getFirstName(debtorID)\n stringToAdd = '%s (@%s) - $%s\\n' %(debtorName, debtorUsername, amountOwed)\n newTextList.append(stringToAdd)\n newText = ''.join(newTextList)\n context.bot.editMessageText(\n chat_id=groupID,\n message_id=message_id,\n text=newText,\n reply_markup=splitUnevenlyFinalisedKeyboardMarkup()\n )\n \n\ndef splitUnevenlyFinalise(update, context):\n query = update.callback_query\n groupID = query.message.chat_id\n message_id = query.message.message_id\n userID = query.from_user.id\n text = query.message.text\n date = datetime.now(tz).replace(microsecond=0)\n\n if not (userIsCreditorForMessage(message_id, groupID, userID)):\n return\n \n orderID = getOrderIDFromUserIDAndGroupID(userID, groupID)\n creditorUsername = getUsername(userID)\n textList = text.split('\\n')\n userList = []\n debtList = []\n for item in textList:\n if '-' in item:\n if '(@' + creditorUsername + ')' in item: \n continue\n debtList.append(item)\n noBrackets = item.replace('(', '`')\n noBracketsList = noBrackets.split('`')\n for noBrack in noBracketsList:\n user = ''\n if '@' in noBrack:\n user = noBrack\n \n if user != '':\n user = user.split(') - $')\n userList.append(user)\n\n for user in userList:\n username = user[0]\n debtorID = getUserIDFromUsername(username.replace('@', '', 1))\n if username.replace('@', '', 1) == creditorUsername:\n continue\n amountOwed = user[1]\n transactionID = str(uuid1())\n addTransaction((transactionID, orderID, amountOwed, userID, debtorID, date))\n \n newDebtorList = []\n for debtor in debtList:\n newDebtorList.append(debtor + '\\n')\n debtorString = ''.join(newDebtorList)\n orderName = getOrderNameFromOrderID(orderID)\n messageText = \"Please return %s their cash money for %s!\\n\\n%s\" % ('@' + creditorUsername, orderName, debtorString)\n orderMessage = context.bot.editMessageText(\n chat_id=groupID,\n message_id=message_id,\n text=messageText,\n reply_markup=splitUnevenlyFinalisedKeyboardMarkup()\n )\n resetUserTempOrderID(userID, groupID)\n resetUserTempMessageID(userID, groupID)\n\n \ndef splitGST(update, context):\n query = update.callback_query\n userID = query.from_user.id\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n text = query.message.text\n textList = text.split('\\n')\n newTextList = []\n if 'w/ GST' in text:\n for itemWithoutPara in textList:\n newItem = itemWithoutPara\n if 'People paying for ' in itemWithoutPara:\n if 'and SVC' in itemWithoutPara:\n newItem = itemWithoutPara.replace('w/ GST and ', 'w/ ', 1)\n else:\n newItem = itemWithoutPara.replace(' w/ GST:', ':', 1)\n tempSplitList = itemWithoutPara.split('-')\n tempSplitItem = tempSplitList[len(tempSplitList) - 1]\n if len(tempSplitList) > 0 and tempSplitList[0] != '':\n currentAmount = re.sub(r\"[^\\d.]+\", \"\", str(tempSplitItem))\n if (isValidAmount(currentAmount)):\n newAmount = getFormattedAmountFromString(float(currentAmount) / 1.07)\n newItem = newItem.replace('$%s' % str(currentAmount), '$%s' % newAmount, 1)\n newTextList.append(newItem + '\\n')\n else:\n for itemWithoutPara in textList:\n newItem = itemWithoutPara\n if 'People paying for ' in itemWithoutPara:\n if 'w/ SVC' in itemWithoutPara:\n newItem = itemWithoutPara.replace('w/ ', 'w/ GST and ', 1)\n else:\n newItem = itemWithoutPara.replace(':', ' w/ GST:', 1)\n tempSplitList = itemWithoutPara.split('-')\n tempSplitItem = tempSplitList[len(tempSplitList) - 1]\n if len(tempSplitList) > 0 and tempSplitList[0] != '':\n currentAmount = re.sub(r\"[^\\d.]+\", \"\", str(tempSplitItem))\n if (isValidAmount(currentAmount)):\n newAmount = getFormattedAmountFromString(float(currentAmount) * 1.07)\n newItem = newItem.replace('$%s' % str(currentAmount), '$%s' % newAmount, 1)\n newTextList.append(newItem + '\\n')\n \n newMessage = ''.join(newTextList)\n\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=newMessage,\n reply_markup=query.message.reply_markup\n )\n \n \ndef splitSVC(update, context):\n query = update.callback_query\n userID = query.from_user.id\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n text = query.message.text\n textList = text.split('\\n')\n newTextList = []\n\n if not userIsCreditorForMessage(message_id, chat_id, userID):\n return\n\n if ' SVC:' in text:\n for itemWithoutPara in textList:\n newItem = itemWithoutPara\n if 'People paying for ' in itemWithoutPara:\n if 'w/ GST' in itemWithoutPara:\n newItem = itemWithoutPara.replace(' and SVC:', ':', 1)\n else:\n newItem = itemWithoutPara.replace(' w/ SVC:', ':', 1)\n tempSplitList = itemWithoutPara.split('-')\n tempSplitItem = tempSplitList[len(tempSplitList) - 1]\n if len(tempSplitList) > 0 and tempSplitList[0] != '':\n currentAmount = re.sub(r\"[^\\d.]+\", \"\", str(tempSplitItem))\n if (isValidAmount(currentAmount)):\n newAmount = getFormattedAmountFromString(float(currentAmount) / 1.1)\n newItem = newItem.replace('$%s' % str(currentAmount), '$%s' % newAmount, 1)\n newTextList.append(newItem + '\\n')\n else:\n for itemWithoutPara in textList:\n newItem = itemWithoutPara\n if 'People paying for ' in itemWithoutPara:\n if 'w/ GST:' in itemWithoutPara:\n newItem = itemWithoutPara.replace(':', ' and SVC:', 1)\n else:\n newItem = itemWithoutPara.replace(':', ' w/ SVC:')\n tempSplitList = itemWithoutPara.split('-')\n tempSplitItem = tempSplitList[len(tempSplitList) - 1]\n if len(tempSplitList) > 0 and tempSplitList[0] != '':\n currentAmount = re.sub(r\"[^\\d.]+\", \"\", str(tempSplitItem))\n if (isValidAmount(currentAmount)):\n newAmount = getFormattedAmountFromString(float(currentAmount) * 1.1)\n newItem = newItem.replace('$%s' % str(currentAmount), '$%s' % newAmount, 1)\n newTextList.append(newItem + '\\n')\n \n newMessage = ''.join(newTextList)\n\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=newMessage,\n reply_markup=query.message.reply_markup\n )\n \n\ndef splitUnevenlyNextItem(update, context):\n query = update.callback_query\n userID = query.from_user.id\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n if not (userIsCreditorForMessage(message_id, chat_id, userID)):\n return\n orderID = getOrderIDFromUserIDAndGroupID(userID, chat_id)\n orderName = getOrderNameFromOrderID(orderID)\n text = query.message.text\n textList = text.split('\\n')\n itemToRemove = ''\n for item in textList:\n if 'People paying for ' in item:\n itemToRemove = item\n \n itemToRemovePosition = textList.index(itemToRemove)\n itemsLeftToSplitTitlePosition = textList.index('Items left to split:')\n count = -1\n userListToAdd = []\n for item in textList:\n count += 1\n if count <= itemToRemovePosition:\n continue\n else:\n userListToAdd.append(item)\n\n numOfUsersToAdd = len(userListToAdd)\n if numOfUsersToAdd < 1:\n return\n \n currentSplitList = []\n cleanAmountList = itemToRemove.split('(')\n cleanAmount = cleanAmountList[len(cleanAmountList) - 1]\n amountBeforeSplit = float(re.sub(r\"[^\\d.]+\", \"\", cleanAmount))\n amountAfterSplit = amountBeforeSplit / numOfUsersToAdd\n\n count = -1\n for item in textList:\n count += 1\n if count < itemsLeftToSplitTitlePosition and count != 0 and item != '':\n currentSplitList.append(item)\n else:\n continue\n for userToAdd in userListToAdd:\n inside = False\n for splitUser in currentSplitList:\n if userToAdd in splitUser:\n splitUserPosition = currentSplitList.index(splitUser)\n inside = True\n splitUserList = splitUser.split('-')\n tempSplitUser = splitUserList[len(splitUserList) - 1]\n currentAmount = float(re.sub(r\"[^\\d.]+\", \"\", tempSplitUser))\n\n newAmount = getFormattedAmountFromString(currentAmount + amountAfterSplit)\n print(newAmount)\n currentSplitList[splitUserPosition] = userToAdd + ' - $' + newAmount\n if not inside:\n currentSplitList.append(userToAdd + ' - $' + getFormattedAmountFromString(amountAfterSplit))\n itemList = []\n count = -1\n for item in textList:\n count += 1\n if count > itemsLeftToSplitTitlePosition and count < itemToRemovePosition and item != '':\n itemList.append(item)\n numOfItemsLeft = len(itemList)\n last = numOfItemsLeft < 1\n \n \n if not last:\n nextItem = itemList.pop(0)\n newItemList = []\n for item in itemList:\n newItemList.append(item + '\\n')\n itemListString = ''.join(newItemList)\n newSplitUserList = []\n for splitUser in currentSplitList:\n newSplitUserList.append(splitUser + '\\n')\n splitUserString = ''.join(newSplitUserList)\n messageText = 'Current split for %s:\\n%s\\nItems left to split:\\n%s\\nPeople paying for %s:' % (orderName, splitUserString, itemListString, nextItem)\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=messageText,\n reply_markup=splitUnevenlyKeyboardMarkup(chat_id, last)\n )\n else:\n newSplitUserList = []\n totalAmount = float(0)\n for splitUser in currentSplitList:\n newSplitUserList.append(splitUser + '\\n')\n tempSplitUserList = splitUser.split('-')\n tempSplitUser = tempSplitUserList[len(tempSplitUserList) - 1]\n currentAmount = float(re.sub(r\"[^\\d.]+\", \"\", tempSplitUser))\n totalAmount += currentAmount\n formattedTotal = getFormattedAmountFromString(totalAmount)\n splitUserString = ''.join(newSplitUserList)\n messageText = 'People paying for %s:\\n%s\\nTotal: $%s' % (orderName, splitUserString, formattedTotal)\n context.bot.editMessageText(\n chat_id=chat_id,\n text=messageText,\n message_id=message_id,\n reply_markup=splitUnevenlyKeyboardMarkup(chat_id, last),\n )\n\n\ndef editUnevenlyMessageIndividual(update, context):\n query = update.callback_query\n debtorID = query.data.replace('splitunevenlycallbackdata', '', 1)\n userID = query.from_user.id\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n \n if not (userIsCreditorForMessage(message_id, chat_id, userID)):\n return\n \n text = query.message.text\n textList = text.split('\\n')\n debtorName = getFirstName(debtorID)\n debtorUsername = getUsername(debtorID)\n debtorToAdd = debtorName + ' (@' + debtorUsername + ')'\n if debtorToAdd in textList:\n textList.remove(debtorToAdd)\n else:\n textList.append(debtorToAdd)\n newTextList = []\n for text in textList:\n newTextList.append(text + '\\n')\n\n newText = ''.join(newTextList)\n\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=newText,\n reply_markup=query.message.reply_markup\n )\n\ndef editSplitEvenlyAddEveryone(update, context):\n query = update.callback_query\n userID = query.from_user.id\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n text = query.message.text\n if not (userIsCreditorForMessage(message_id, chat_id, userID)):\n return\n \n userList = getAllUsersFromGroup(chat_id)\n listOfUsersWithNameAndUsername = []\n \n for user in userList:\n username = getUsername(user)\n firstName = getFirstName(user)\n entry = '\\n' + firstName + ' (@' + username + ')'\n if entry not in text:\n listOfUsersWithNameAndUsername.append(entry)\n \n newText = text + ''.join(listOfUsersWithNameAndUsername)\n \n if newText == text:\n return\n\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=newText,\n reply_markup=query.message.reply_markup\n )\n\ndef editSplitUnevenlyAddEveryone(update, context):\n query = update.callback_query\n userID = query.from_user.id\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n text = query.message.text\n if not (userIsCreditorForMessage(message_id, chat_id, userID)):\n return\n \n # relevantHalf = text.split('People paying for ')[1]\n userList = getAllUsersFromGroup(chat_id)\n listOfUsersWithNameAndUsername = []\n \n splitByPara = text.split('\\n')\n\n for user in userList:\n username = getUsername(user)\n firstName = getFirstName(user)\n entry = firstName + ' (@' + username + ')'\n if entry not in splitByPara:\n listOfUsersWithNameAndUsername.append('\\n' + entry)\n \n newText = text + ''.join(listOfUsersWithNameAndUsername)\n\n if text == newText:\n return\n\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=newText,\n reply_markup=query.message.reply_markup\n )\n\n # text = query.message.text\n\n\n\ndef notifyUserFromPrivateMessage(update, context):\n query = update.callback_query\n transactionID = query.data.replace('notifydebtorcallbackdata', '', 1)\n\n lastNotifiedTime = getLastNotifiedTimeFromTransactionID(transactionID)\n currentTime = datetime.now(tz).replace(microsecond=0)\n to_add = timedelta(minutes=60)\n thresholdTime = lastNotifiedTime + to_add \n\n debtorID = getDebtorIDFromTransactionID(transactionID)\n creditorID = getCreditorIDFromTransactionID(transactionID)\n debtorName = getFirstName(debtorID)\n debtorUsername = getUsername(debtorID)\n orderID = getOrderIDFromTransactionID(transactionID)\n orderName = getOrderNameFromOrderID(orderID)\n orderDate = getOrderDateFromOrderID(orderID)\n formattedDate = orderDate.strftime(\"%d %B %Y\")\n groupID = getGroupIDFromOrder(orderID)\n groupName = getGroupNameFromGroupID(groupID)\n \n if not isNotifiable(debtorID):\n context.bot.send_message(\n chat_id=creditorID,\n text = '%s (@%s) is not notifiable.' % (debtorName, debtorUsername)\n )\n return\n\n if currentTime.replace(tzinfo=None) < thresholdTime.replace(tzinfo=None):\n timediff = currentTime.replace(tzinfo=None) - lastNotifiedTime.replace(tzinfo=None)\n timeTillNextSend = 60 - int(timediff.total_seconds()/60)\n context.bot.send_message(\n chat_id=creditorID,\n text = 'You have notified %s (@%s) too recently for %s on %s in %s. Please try again in %s minutes!' % (debtorName, debtorUsername, orderName, formattedDate, groupName, timeTillNextSend)\n ) \n return \n updateLastNotifiedTimeWithTransactionID(transactionID, currentTime)\n\n creditorName = getFirstName(creditorID)\n creditorUsername = getUsername(creditorID)\n amountOwed = getAmountOwedFromTransactionID(transactionID)\n formattedAmount = getFormattedAmountFromString(amountOwed)\n\n context.bot.send_message(\n chat_id=debtorID,\n text='Hi %s! Your friend %s (@%s) from the group %s is asking you to return them their $%s for %s %s.' % (debtorName, creditorName, creditorUsername,groupName, formattedAmount, orderName, formattedDate)\n )\n context.bot.send_message(\n chat_id=creditorID,\n text='%s (@%s) has been notified to return $%s for %s in %s' % (debtorName, debtorUsername, formattedAmount, orderName, groupName)\n )\n\ndef updateOrderMessageAsSettledWhenTransactionSettled(transactionID):\n if transactionAlreadySettled(transactionID):\n return\n bot = Bot(TOKEN)\n orderID = getOrderIDFromTransactionID(transactionID)\n debtorID = getDebtorIDFromTransactionID(transactionID)\n messageID = getMessageIDFromOrder(orderID)\n groupID = getGroupIDFromOrder(orderID)\n \n debtorName = getFirstName(debtorID)\n debtorUsername = getUsername(debtorID)\n entry = '%s (@%s)' % (debtorName, debtorUsername)\n\n isEven = orderIsEvenlySplit(orderID)\n\n placeholderGroupMessage = bot.forward_message(\n chat_id = -528685244,\n from_chat_id=groupID,\n message_id=messageID,\n )\n text = placeholderGroupMessage.text\n\n textList = text.split('\\n')\n\n newTextList =[]\n for item in textList:\n if entry in item:\n continue\n else:\n newTextList.append(item + '\\n')\n\n newText = ''.join(newTextList)\n\n reply_markup = ''\n\n if isEven:\n reply_markup = splitEvenlyFinalisedKeyboardMarkup()\n else:\n reply_markup = splitUnevenlyFinalisedKeyboardMarkup()\n \n bot.editMessageText(\n chat_id=groupID,\n message_id=messageID,\n text=newText,\n reply_markup=reply_markup,\n )\n \n\ndef settleDebtForCreditor(update, context):\n query = update.callback_query\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n userID = query.from_user.id\n text = query.message.text\n\n transactionID = query.data.replace('settledebtforcreditor', '', 1)\n updateTransactionAsSettledWithTransactionID(transactionID)\n updateOrderMessageAsSettledWhenTransactionSettled(transactionID)\n\n unsettledTransactions = getUnsettledTransactionsForCreditor(userID)\n keyboardMarkup = formatTransactionsForCreditorKeyboardMarkup(unsettledTransactions)\n if (keyboardMarkup!=None):\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=text,\n reply_markup = keyboardMarkup\n )\n else:\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text='All debts settled! You have such responsible friends... Unlike me ):'\n )\n\ndef settleDebtForDebtor(update, context):\n query = update.callback_query\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n userID = query.from_user.id\n text = query.message.text\n\n transactionID = query.data.replace('settledebtfordebtor', '', 1)\n try:\n updateTransactionAsSettledWithTransactionID(transactionID)\n updateOrderMessageAsSettledWhenTransactionSettled(transactionID)\n finally:\n unsettledTransactions = getUnsettledTransactionsForDebtor(userID)\n keyboardMarkup = formatTransactionsForDebtorKeyboardMarkup(unsettledTransactions)\n\n if (keyboardMarkup!=None):\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=text,\n reply_markup=keyboardMarkup\n )\n else:\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text='All debts settled! How responsible of you...'\n )\n \n\ndef editMessageForSplitEvenly(update, context):\n query = update.callback_query\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n userID = query.from_user.id\n text = query.message.text\n\n if not (userIsCreditorForMessage(message_id, chat_id, userID)):\n return\n \n debtorID = query.data.replace('splitevenlycallbackdata', '', 1)\n debtorUsername = getUsername(debtorID)\n debtorName = getFirstName(debtorID)\n entry = debtorName + ' (@' + debtorUsername + ')'\n \n if entry in text:\n text = text.replace('\\n' + entry, '', 1)\n else:\n text = text + '\\n' + entry\n \n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=text,\n reply_markup=query.message.reply_markup,\n )\n\n \ndef debtorEvenlyPaid(update, context):\n query = update.callback_query\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n username = query.from_user.username\n debtorID = query.from_user.id\n text = query.message.text\n debtorName = getFirstName(debtorID)\n entry = '%s (@%s)' % (debtorName, username)\n textList = text.split('\\n')\n newTextList = []\n\n creditorID = getCreditorIDFromMessageAndGroupID(message_id, chat_id)\n\n if str(creditorID) == str(debtorID):\n return\n\n for item in textList:\n if entry in item:\n continue\n else:\n newTextList.append(item + '\\n')\n\n textAfterRemove = ''.join(newTextList)\n\n orderID = getOrderIDFromMessageAndGroupID(message_id, chat_id)\n \n\n if textAfterRemove != text:\n markTransactionAsSettled(creditorID, debtorID, orderID)\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=textAfterRemove,\n reply_markup=query.message.reply_markup\n )\n \n return None\n\ndef debtorEvenlyUnpaid(update, context):\n query = update.callback_query\n chat_id = query.message.chat_id\n message_id = query.message.message_id\n username = query.from_user.username\n debtorID = query.from_user.id\n debtorName = getFirstName(debtorID)\n text = query.message.text\n creditorID = getCreditorIDFromMessageAndGroupID(message_id, chat_id)\n\n if str(creditorID) == str(debtorID):\n return\n entry = '%s (@%s)' % (debtorName, username)\n textList = text.split('\\n')\n newTextList = []\n for item in textList:\n if entry in item:\n return\n else:\n newTextList.append(item + '\\n')\n\n newTextList.append(entry + '\\n')\n textAfterAdd = ''.join(newTextList)\n\n orderID = getOrderIDFromMessageAndGroupID(message_id, chat_id)\n creditorID = getCreditorIDFromMessageAndGroupID(message_id, chat_id)\n\n if textAfterAdd != text:\n markTransactionAsUnsettled(creditorID, debtorID, orderID)\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=textAfterAdd,\n reply_markup=query.message.reply_markup\n )\n\n return None\n \n\ndef groupRegister(update, context):\n query = update.callback_query\n chat_id = query.message.chat_id\n groupname = query.message.chat.title\n message_id = query.message.message_id\n group = (chat_id, groupname)\n if (groupAlreadyAdded(chat_id)):\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=\"Your group has already been registered with us.\"\n )\n else:\n addGroup(group)\n print('donkey')\n context.bot.editMessageText(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=\"Your group is now registered!\\n\\nBegin splitting bills by sending a message starting with @OwePay_bot followed by the name of the bill. For example:\\n\\n@OwePay_bot Assorted Sausages\\n\\nDue to the nature of Telegram Bots, our bot will only be able to detect users if they have either sent a message in the group after I've been added or users added after me!\",\n )\n\n\n\ndef splitEvenly(update, context):\n query = update.callback_query\n groupID = query.message.chat_id\n message_id = query.message.message_id\n userID = query.from_user.id\n text = query.message.text\n date = datetime.now(tz).replace(microsecond=0)\n\n if not (userIsCreditorForMessage(message_id, groupID, userID)):\n return\n \n totalAmount = getUserTempAmount(userID, groupID)\n listOfUsers = tuple(text.replace(\"People who have your cash money:\\n\", \"\", 1).replace(\"\\n\",\",\",text.count(\"\\n\")).split(','))\n numberOfUsers = len(listOfUsers)\n\n if numberOfUsers == 0:\n return\n \n listOfUsernames = []\n for user in listOfUsers:\n tempList = []\n tempList.append(user.split('@')[1])\n for tempItem in tempList:\n listOfUsernames.append(tempItem.split(')')[0])\n \n\n splitAmount = totalAmount / numberOfUsers\n orderID = getOrderIDFromUserIDAndGroupID(userID, groupID)\n orderName = getOrderNameFromOrderID(orderID)\n creditorUsername = getUsername(userID)\n creditorName = getFirstName(userID)\n \n listOfUserID = getUserIDListFromUsernameList(listOfUsernames)\n\n if len(listOfUserID) < 1:\n return\n \n setUserStateInactive(userID, groupID)\n resetUserTempAmount(userID, groupID)\n resetUserTempOrderID(userID, groupID)\n resetUserTempMessageID(userID, groupID)\n\n messageText = \"Please return @%s $%s each for %s\" % (creditorUsername, getFormattedAmountFromString(splitAmount), orderName)\n for username in listOfUsers:\n messageText = messageText + '\\n' + username\n\n if \"%s (@%s)\" % (creditorName, creditorUsername) in messageText:\n messageText = messageText.replace(\"\\n%s (@%s)\" % (creditorName, creditorUsername), \"\", 1)\n\n orderMessage = context.bot.editMessageText(\n chat_id=update.effective_chat.id,\n message_id=message_id,\n text=messageText,\n reply_markup=splitEvenlyFinalisedKeyboardMarkup(),\n )\n order = Order(orderID, groupID, orderName, splitAmount, userID, date)\n messageID = orderMessage.message_id\n addMessageIDToOrder(str(orderID), messageID)\n createTransactionBetweenSomeUsers(order, listOfUserID)\n\n\ndef splitDifferentAmounts(update, context, userID, groupID):\n textString = update.message.text\n textStringWithoutParagraphs = textString.split('\\n')\n textListSeparated = []\n messageID = update.message.message_id\n itemList = []\n orderID = getOrderIDFromUserIDAndGroupID(userID, groupID)\n orderName = getOrderNameFromOrderID(orderID)\n for text in textStringWithoutParagraphs:\n tempText = text.split ('-')\n tempList = []\n for t in tempText:\n tempList.append(removeCrustFromString(t))\n textListSeparated.append(tempList)\n for item in textListSeparated:\n tempList = []\n if len(item) != 2 or not isValidAmount(item[1]):\n context.bot.send_message(\n chat_id=groupID,\n reply_to_message_id=messageID,\n text='Please send your order in a valid format!'\n )\n return\n tempList.append(item[0])\n if (isValidAmount(item[1])):\n tempList.append(getFormattedAmountFromString(item[1]))\n else:\n context.bot.send_message(\n chat_id=groupID,\n reply_to_message_id=messageID,\n text='%s is not a valid amount' % (item[1])\n )\n itemList.append(tempList)\n\n firstItem = itemList.pop(0)\n firstItemString = firstItem[0] + ' ($' + firstItem[1] + ')'\n itemListString = itemListToString(itemList)\n messageText = 'Current split for %s:\\n\\nItems left to split:%s\\n\\nPeople paying for %s:' % (orderName, itemListString, firstItemString)\n orderMessage = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=messageText,\n reply_markup=splitUnevenlyKeyboardMarkup(groupID, False)\n )\n print(splitUnevenlyKeyboardMarkup(groupID, False))\n messageID = orderMessage.message_id\n addMessageIDToOrder(orderID, messageID)\n setUserStateInactive(userID, groupID)\n return orderMessage\n \ndef getTotalAmountFromMessage(update, context):\n chat_message=update.message.text\n value = int(''.join(filter(str.isdigit, chat_message)))\n total_amount = float(value/100)\n return total_amount\n\n\n#############################\n# when split among us is called this will update regisfter the userid and the\n# amount of money\n##############\n\n\ndef getOrderNameFromMessage(update):\n text = update.message.text\n return str(text.split('New Order: ')[1])\n\ndef messageContainsNewOrder(update, context):\n if \"New Order:\" in update.message.text: \n orderName = getOrderNameFromMessage(update)\n user_id = update.message.from_user.id\n GroupID = update.message.chat_id\n updateUserStateNewOrder(user_id, GroupID)\n updateOrderIDToUserGroupRelational(user_id, GroupID, orderName)\n message = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text=\n \"Please choose if you wish to split %s evenly or unevenly\" % orderName,\n reply_markup=waitingForUserToChooseSplitKeyboardMarkup()\n )\n updateMessageIDToUserGroupRelational(user_id, GroupID, message.message_id)\n return message\n\ndef catchSplitEvenlyOrderFromUpdate(update):\n order_id = str(uuid1())\n user_id = update.message.from_user.id\n group_id = update.message.chat_id\n order_amount= update.message.text.replace('$', '', 1)\n date = datetime.now(tz).replace(microsecond=0)\n order_name = getOrderIDFromUserIDAndGroupID(user_id, group_id)\n addOrder((order_id, group_id, order_name, order_amount, user_id, date))\n return Order(order_id, group_id, order_name, order_amount, user_id, date)\n\ndef catchSplitUnevenlyOrder(user_id, group_id):\n order_id = str(uuid1())\n order_amount = 0\n date = datetime.now(tz).replace(microsecond=0)\n order_name = getOrderIDFromUserIDAndGroupID(user_id, group_id)\n addOrder((order_id, group_id, order_name, order_amount, user_id, date))\n return Order(order_id, group_id, order_name, order_amount, user_id, date)\n\n\ndef waitingForSomeNames(update, context, user_id, group_id):\n order = catchSplitEvenlyOrderFromUpdate(update)\n orderID = order.orderID\n orderAmount = order.orderAmount\n updateUserTempAmount(user_id, group_id, orderAmount)\n updateUserStateWaitingForSomeNames(user_id, group_id)\n updateOrderIDToUserGroupRelational(user_id, group_id, orderID)\n\n message = context.bot.send_message(\n chat_id=update.effective_chat.id,\n text='People who have your cash money:',\n reply_markup=splitEvenlyKeyboardMarkup(update.effective_chat.id)\n )\n messageID = message.message_id\n addMessageIDToOrder(orderID, messageID)\n return message\n\ndef createTransactionBetweenSomeUsers(order, userIDList):\n creditorID = order.creditorID\n orderID = order.orderID\n users = userIDList\n splitAmount = order.orderAmount\n date = order.date\n\n for userID in users:\n if str(userID) == str(creditorID):\n None\n else:\n transaction_id = str(uuid1())\n addTransaction((transaction_id, orderID, splitAmount, creditorID, userID, date))\n\n return UsersAndSplitAmount(users, splitAmount)\n\n\n\n#order123 = (\"b62aa85b-cb98-11eb-baef-d0509938caba\",-524344128,'thai food',123.45,339096917)\n#createTransactionbetweenallusers(order123)\n\ndef groupDontRegister(update, context):\n query = update.callback_query\n context.bot.editMessageText(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=\n \"Thank you for your interest in our bot! We hope to see you soon!\\n\\n\" +\n \"If you ever feel like registering your Group with our bot in the future,\" +\n \" simply run /start to get started!\",\n )\n\ndef userRegister(update, context):\n query = update.callback_query\n chat_id = query.message.chat_id\n username = query.message.chat.username\n message_id = query.message.message_id\n firstname = query.message.chat.first_name\n user = (chat_id, username, 1, firstname)\n if (userAlreadyAdded(chat_id)):\n if not(isNotifiable(chat_id)):\n makeNotifiable(chat_id)\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=\"You are now registered!\\n\\nAdd this bot to your telegram groups to split bills among your friends! Bills can be split by sending a message starting with @OwePay_bot followed by the name of the order after registering the bot in the group with the /start command. For example:\\n\\n@OwePay_bot Assorted Meats\\n\\nDue to the nature of Telegram Bots, our bot will only be able to detect users if they have either sent a message in the group after I've been added or users added after me!\",\n )\n else:\n context.bot.editMessageText(\n chat_id=chat_id,\n message_id=message_id,\n text=\"You have already been registered with us.\",\n )\n else:\n addUser(user)\n context.bot.editMessageText(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=\"You are now registered!\\n\\nAdd this bot to your telegram groups to split bills among your friends! Bills can be split by sending a message starting with @OwePay_bot followed by the name of the order after registering the bot in the group with the /start command. For example:\\n\\n@OwePay_bot Assorted Meats\\n\\nDue to the nature of Telegram Bots, our bot will only be able to detect users if they have either sent a message in the group after I've been added or users added after me!\",\n )\n\ndef userDontRegister(update, context):\n query = update.callback_query\n context.bot.editMessageText(\n chat_id=query.message.chat_id,\n message_id=query.message.message_id,\n text=\n \"Thank you for your interest in our bot! We hope to see you soon!\\n\\n\" +\n \"If you ever feel like registering with our bot in the future, simply run /start\" +\n \" to get started!\",\n )\n#############################\n# checks if msg is sent from a bot\n##############\ndef viabot_check(update, context):\n return (update.message.via_bot!=None and str(update.message.via_bot.id) == str(BOT_ID))\n\n# def echo(update: Update, _: CallbackContext) -> None:\ndef echo(update, context):\n \"\"\"Echo the update for debugging purposes.\"\"\"\n print(update)\n\ndef askUserToSendValidAmount(update, context):\n group_id = update.message.chat_id\n user_id = update.message.from_user.id\n text = update.message.text\n message = context.bot.send_message(\n chat_id=group_id,\n text=\"%s is not a valid amount.\\n\\nPlease key in a valid amount e.g $123, 123, 123.10 or /cancel to stop the creation of the order.\" % text\n )\n return message\n\ndef groupMemberScanner(update, context):\n \"\"\"\"Constantly monitors group chat to check if members are counted in the group or not\"\"\"\n group_id = update.message.chat_id\n message_id = update.message.message_id\n user_id = update.message.from_user.id\n username = update.message.from_user.username\n firstname = update.message.from_user.first_name\n \n if len(update.message.new_chat_members) > 0:\n for newMember in update.message.new_chat_members:\n if newMember.is_bot:\n continue\n newUserID = newMember.id\n addUserToGroup(newUserID, group_id)\n if not userAlreadyAdded(newUserID):\n newUsername = newMember.username\n newFirstname = newMember.first_name\n user = (newUserID, newUsername, 0, newFirstname)\n addUser(user)\n \n if update.message.left_chat_member != None:\n print(update.message.left_chat_member.id)\n leftMember = update.message.left_chat_member\n leftMemberID = leftMember.id\n if userInGroup(leftMemberID, group_id):\n deleteUserFromGroup(leftMemberID, group_id)\n if not userAlreadyAdded(leftMemberID):\n leftUsername = leftMember.username\n leftFirstname = leftMember.first_name\n user = (leftMemberID, leftUsername, 0, leftFirstname)\n addUser(user)\n\n if not(userAlreadyAdded(user_id)):\n user = (user_id, username, 0, firstname)\n addUser(user)\n\n\n if not(userInGroup(user_id, group_id)):\n increaseGroupMemberCount(group_id)\n addUserToGroup(user_id, group_id)\n \n\n if not(groupAlreadyAdded(group_id)):\n return 'Group with id %s not added' % group_id\n \n if userStateSplitEvenly(user_id, group_id):\n text = update.message.text\n if isValidAmount(text):\n waitingForSomeNames(update, context, user_id, group_id)\n return \"User %s has state 'splitevenly'\" % user_id\n else:\n askUserToSendValidAmount(update, context)\n return \"User %s sending invalid amount\" % user_id\n\n if userStateSplitUnevenly(user_id, group_id):\n splitDifferentAmounts(update, context, user_id, group_id)\n return \"User %s has state 'splitunevenly'\" % user_id\n\n if viabot_check(update, context):\n bot = update.message.via_bot.id\n messageContainsNewOrder(update, context)\n return \"Bot found %s\" % bot\n\n\n\ndef main():\n \"\"\"Start the bot.\"\"\"\n updater = Updater(\n TOKEN, use_context=True)\n # Get the dispatcher to register handlers\n dp = updater.dispatcher\n\n # on different commands - answer in Telegram\n dp.add_handler(CommandHandler(\"start\", startGroup, Filters.chat_type.groups))\n dp.add_handler(CommandHandler(\"start\", startPrivate, Filters.chat_type.private))\n dp.add_handler(CommandHandler(\"whoowesme\", getDebtors, Filters.chat_type.private))\n dp.add_handler(CommandHandler(\"whomeowes\", getCreditors, Filters.chat_type.private))\n dp.add_handler(CommandHandler(\"help\", help))\n dp.add_handler(CommandHandler(\"cancel\", cancel, Filters.chat_type.groups))\n dp.add_handler(CallbackQueryHandler(button))\n dp.add_handler(InlineQueryHandler(inline))\n #dp.add_handler(MessageHandler(Filters.chat_type.groups, echo))\n dp.add_handler(MessageHandler(Filters.chat_type.groups, groupMemberScanner))\n dp.add_handler(ConversationHandler(\n [\n CommandHandler(\"scanreceipt\", scanReceiptPrivateMessage)\n ], \n {\n \"waitingonpicprivate\": [CommandHandler(\"cancelreceipt\", cancelReceipt), MessageHandler(Filters.chat_type.private, scanReceiptPrivatePicture)],\n\n }, \n [\n CommandHandler(\"cancelreceipt\", cancelReceipt)\n ],\n allow_reentry=True\n ))\n\n # log all errors\n dp.add_error_handler(error)\n\n updater.start_webhook(listen=\"0.0.0.0\",\n port=PORT,\n url_path=TOKEN,\n webhook_url=\"https://owepaybot.herokuapp.com/\" + TOKEN)\n # updater.start_polling()\n updater.idle()\n\nif __name__ == '__main__':\n main()\n","sub_path":"owepaybot.py","file_name":"owepaybot.py","file_ext":"py","file_size_in_byte":54362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"540601629","text":"import numpy as np\nfrom keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Add, Multiply\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.core import SpatialDropout2D, Activation\nfrom keras import backend as K\nimport tensorflow as tf\nfrom keras.layers.merge import concatenate\nfrom keras import Sequential\nfrom keras.layers import Lambda\n# Number of output masks (1 in case you predict only one type of objects)\nINPUT_CHANNELS = 3\n# Pretrained weights\n\ndef double_conv_layer(x, size, dropout=0.0, batch_norm=True):\n if K.image_dim_ordering() == 'th':\n axis = 1\n else:\n axis = 3\n conv = Conv2D(size, (3, 3), padding='same',)(x)\n if batch_norm is True:\n conv = BatchNormalization(axis=axis)(conv)\n conv = Activation('relu')(conv)\n conv = Conv2D(size, (3, 3), padding='same')(conv)\n if batch_norm is True:\n conv = BatchNormalization(axis=axis)(conv)\n conv = Activation('relu')(conv)\n if dropout > 0:\n conv = SpatialDropout2D(dropout)(conv)\n return conv\ndef UpSampling2DBilinear(stride, **kwargs):\n def layer(x):\n input_shape = K.int_shape(x)\n output_shape = (stride * input_shape[1], stride * input_shape[2])\n return tf.image.resize_bilinear(x, output_shape, align_corners=True)\n return Lambda(layer, **kwargs)\n\n\ndef dsv(x, size,scale_factor, dropout=0.0, batch_norm=True):\n if K.image_dim_ordering() == 'th':\n axis = 1\n else:\n axis = 3\n conv = Conv2D(size, (3, 3), padding='same')(x)\n if batch_norm is True:\n conv = BatchNormalization(axis=axis)(conv)\n conv = Activation('relu')(conv)\n conv = Conv2D(size, (3, 3), padding='same')(conv)\n if batch_norm is True:\n conv = BatchNormalization(axis=axis)(conv)\n conv = Activation('relu')(conv)\n if dropout > 0:\n conv = SpatialDropout2D(dropout)(conv)\n\n upsampler = UpSampling2DBilinear(stride=scale_factor)(conv)\n\n return upsampler\ndef gating(x, size, dropout=0.0, batch_norm=True):\n if K.image_dim_ordering() == 'th':\n axis = 1\n else:\n axis = 3\n conv = Conv2D(size, (1, 1), padding='valid')(x)\n if batch_norm is True:\n conv = BatchNormalization(axis=axis)(conv)\n conv = Activation('relu')(conv)\n\n if dropout > 0:\n conv = SpatialDropout2D(dropout)(conv)\n return conv\n\ndef grid_attention(input_signal,gating_signal,inter_channels):\n if K.image_dim_ordering() == 'th':\n axis = 1\n else:\n axis = 3\n theta_x = Conv2D(inter_channels,\n kernel_size=(1,1), strides=(2,2), padding='valid',\n use_bias=False)(input_signal)\n theta_x_size = theta_x.shape\n phi_g = Conv2D(inter_channels,\n kernel_size=(1,1), padding='valid', use_bias=True)(gating_signal)\n\n cancat =Add()([phi_g, theta_x])\n f = Activation('relu')(cancat)\n psi = Conv2D(filters=1, kernel_size=(1,1), padding='valid', use_bias=True)(f)\n sigm_psi_f = Activation('sigmoid')(psi)\n\n input_signal_shape=input_signal.shape\n # upsample the attentions and multiply\n #print('sigm_psi_f is {0}'.format(sigm_psi_f))\n scale_factor=2\n upsampler = UpSampling2DBilinear(stride=scale_factor)(sigm_psi_f)\n #print('upsampled is {0}'.format(upsampler.shape))\n upsampler_conc = concatenate([upsampler, upsampler], axis = axis)\n for i in range(input_signal_shape[3]-2):\n upsampler_conc = concatenate([upsampler_conc, upsampler], axis=axis)\n #print('upsampler_conc is {0}'.format(upsampler_conc .shape))\n y = Multiply()([input_signal,upsampler_conc])\n #final_dimention=input_signal_shape[3]\n W_y =Conv2D (inter_channels, kernel_size=(1,1), strides=(1,1), padding='valid')(y)\n w_y = BatchNormalization(axis=axis)(W_y)\n\n return w_y, sigm_psi_f\n\ndef build(inp, nc):\n if K.image_dim_ordering() == 'th':\n inputs = inp\n axis = 1\n else:\n inputs =inp\n axis = 3\n filters = 32\n OUTPUT_MASK_CHANNELS = nc\n\n dropout_val = 0.2\n layer_name = 'first_conv_layer'\n conv_224 = double_conv_layer(inputs, filters)\n pool_112 = MaxPooling2D(pool_size=(2, 2))(conv_224)\n #print('pool_112 is {0}'.format(pool_112.shape))\n layer_name = 'second_conv_layer'\n conv_112 = double_conv_layer(pool_112, 2 * filters)\n pool_56 = MaxPooling2D(pool_size=(2, 2))(conv_112)\n #print('pool_56 is {0}'.format(pool_56.shape))\n layer_name = 'third_conv_layer'\n conv_56 = double_conv_layer(pool_56, 4 * filters)\n pool_28 = MaxPooling2D(pool_size=(2, 2))(conv_56)\n #print('pool_28 is {0}'.format(pool_28.shape))\n layer_name = 'fourth_conv_layer'\n conv_28 = double_conv_layer(pool_28, 8 * filters)\n pool_14 = MaxPooling2D(pool_size=(2, 2))(conv_28)\n #print('pool_14 is {0}'.format(pool_14.shape))\n layer_name = 'fifth_conv_layer'\n conv_14 = double_conv_layer(pool_14, 16 * filters,)\n #print('conv_14 is {0}'.format(conv_14.shape))\n pool_7 = MaxPooling2D(pool_size=(2, 2))(conv_14)\n #print('pool_7 is {0}'.format(pool_7.shape))\n layer_name = 'six_conv_layer'\n center = double_conv_layer(pool_7,32* filters)\n #print('center is {0}'.format(center.shape))\n gate_signal= gating(center,32*filters) \n #print('gate_signal is {0}'.format(gate_signal.shape))\n g_conv2, att2 = grid_attention(conv_14, gate_signal,inter_channels=16*filters)\n #print('g_conv2 is {0}'.format(g_conv2.shape))\n up_14 = concatenate([UpSampling2D(size=(2, 2))(center), g_conv2], axis=axis)\n layer_name = 'first_upconv_layer'\n up_14 = double_conv_layer(up_14, filters*16)\n #print('up_14 is {0}'.format(up_14.shape))\n g_conv3, att3 = grid_attention(conv_28, up_14,inter_channels=8*filters)\n up_28 = concatenate([UpSampling2D(size=(2, 2))(up_14), g_conv3], axis=axis)\n layer_name = 'second_upconv_layer'\n up_28 = double_conv_layer(up_28, filters*8)\n #print('up_28 is {0}'.format(up_28.shape))\n g_conv4, att4 = grid_attention(conv_56, up_28,inter_channels=4*filters)\n up_56 = concatenate([UpSampling2D(size=(2, 2))(up_28), g_conv4], axis=axis)\n layer_name = 'fourth_upconv_layer'\n up_56 = double_conv_layer(up_56, filters*4,)\n #print('up_56 is {0}'.format(up_56.shape))\n g_conv5, att5 = grid_attention(conv_112, up_56, inter_channels=2*filters)\n up_112 = concatenate([UpSampling2D(size=(2, 2))(up_56), g_conv5], axis=axis)\n layer_name = 'fifth_upconv_layer'\n up_112 = double_conv_layer(up_112, filters*2,)\n #print('up_112 is {0}'.format(up_112.shape))\n up_224 = concatenate([UpSampling2D(size=(2, 2))(up_112), conv_224], axis=axis)\n layer_name = 'sixth_upconv_layer'\n up_224 = double_conv_layer(up_224, filters)\n #print('up_224 is {0}'.format(up_224.shape))\n\n up_conv_14 = dsv(up_14, OUTPUT_MASK_CHANNELS,16)\n up_conv_28 = dsv(up_28, OUTPUT_MASK_CHANNELS,8)\n up_conv_56 = dsv(up_56, OUTPUT_MASK_CHANNELS,4)\n up_conv_112 = dsv(up_112, OUTPUT_MASK_CHANNELS,2)\n up_conv_224 = dsv(up_224, OUTPUT_MASK_CHANNELS,1, dropout_val)\n final = concatenate([up_conv_14, up_conv_28,up_conv_56,up_conv_112,up_conv_224], axis=axis)\n\n\n return final","sub_path":"models/weakly_sup/base_unet.py","file_name":"base_unet.py","file_ext":"py","file_size_in_byte":7142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"310629675","text":"# Copyright (C) 2018-2021 Intel Corporation\n# SPDX-License-Identifier: Apache-2.0\n\nimport numpy as np\n\nfrom mo.front.common.partial_infer.utils import is_fully_defined\nfrom mo.graph.graph import Node, Graph\nfrom mo.ops.op import Op\nfrom mo.utils.broadcasting import bi_directional_shape_broadcasting, bi_directional_broadcasting\n\n\nclass Select(Op):\n op = 'Select'\n\n def __init__(self, graph: Graph, attrs: dict):\n mandatory_props = {\n 'op': self.op,\n 'type': self.op,\n 'version': 'opset1',\n 'in_ports_count': 3,\n 'out_ports_count': 1,\n 'infer': self.infer,\n 'type_infer': self.type_infer,\n 'auto_broadcast': 'numpy'\n }\n super().__init__(graph, mandatory_props, attrs)\n\n def backend_attrs(self):\n return ['auto_broadcast']\n\n @staticmethod\n def infer(node: Node):\n node_name = node.soft_get('name', node.id)\n assert len([port for port in node.in_ports().values() if not port.disconnected()]) == 3, \\\n \"Select operation must have 3 inputs: 'condition', 'then' and 'else' tensors for node {}\".format(node_name)\n\n condition_value = node.in_port(0).data.get_value()\n resulting_tensors = [node.in_port(1).data.get_value(), node.in_port(2).data.get_value()]\n\n a_shape = node.in_port(1).data.get_shape()\n b_shape = node.in_port(2).data.get_shape()\n output_shape = bi_directional_shape_broadcasting(a_shape, b_shape)\n assert output_shape is not None, 'Input shapes for node {} are not broadcast-able'.format(node_name)\n node.out_port(0).data.set_shape(output_shape)\n\n if condition_value is not None:\n if resulting_tensors[0] is not None:\n resulting_tensors[0] = bi_directional_broadcasting(resulting_tensors[0], b_shape)\n if resulting_tensors[1] is not None:\n resulting_tensors[1] = bi_directional_broadcasting(resulting_tensors[1], a_shape)\n condition_value = bi_directional_broadcasting(condition_value, output_shape)\n\n output_value = np.ma.where(condition_value, resulting_tensors[0], resulting_tensors[1])\n if condition_value.size != 1:\n if np.any(output_value == None):\n # If any element of output value is None that means that we use the value from the 'then' or the\n # 'else' tensor which is not defined, this means that we cannot perform value propagation.\n output_value = None\n else:\n output_value = output_value.astype(resulting_tensors[not np.bool(condition_value.item(0))].dtype)\n\n if output_value is not None:\n node.out_port(0).data.set_value(output_value)\n\n @staticmethod\n def type_infer(node: Node):\n assert node.in_port(1).get_source().get_data_type() == node.in_port(2).get_source().get_data_type(), \\\n 'The data type of the second and the third inputs must be equal for the node {}'.format(node.name)\n node.out_port(0).set_data_type(node.in_port(1).get_source().get_data_type())\n","sub_path":"model-optimizer/extensions/ops/select.py","file_name":"select.py","file_ext":"py","file_size_in_byte":3139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"98415771","text":"import requests\nfrom pprint import pprint\n\ncoindesk_url = 'https://api.coindesk.com/v1/bpi/currentprice.json'\n\n\ndef main():\n num_of_bitcoin = get_number_of_bitcoin()\n data = request_exchange_rate()\n dollars_exchange_rate = get_exchange_rate(data)\n bitcoin_value_in_dollars = calculate_bitcoin_to_dollar(dollars_exchange_rate, num_of_bitcoin)\n display_result(num_of_bitcoin, bitcoin_value_in_dollars)\n\ndef get_number_of_bitcoin():\n while True:\n try:\n num_of_bitcoin = float(input('Enter the number bitcoins you want to convert: '))\n if num_of_bitcoin<=0:\n raise ValueError('Number of bitcoin must be >0')\n return num_of_bitcoin\n except:\n print('Enter a number greater than 0')\n\n\ndef request_exchange_rate():\n try:\n response = requests.get(coindesk_url)\n response.raise_for_status() #raises an exception for 400 or 500 status code\n data = response.json() #convert response to json (like python dictionary)\n return data\n except Exception as e:\n print(e)\n print('There was an error making the request')\n\ndef get_exchange_rate(data): #extra info from data returned from request\n dollars_exchange_rate = data['bpi']['USD']['rate_float']\n return dollars_exchange_rate\n \n\ndef calculate_bitcoin_to_dollar(dollars_exchange_rate, num_of_bitcoin):\n bitcoin_value_in_dollars= dollars_exchange_rate * num_of_bitcoin\n return bitcoin_value_in_dollars\n\ndef display_result( num_of_bitcoin,bitcoin_value_in_dollars):\n print(f'$ {bitcoin_value_in_dollars:.2f} is the equivalent of {num_of_bitcoin} bitcoins')\n\n\n\n\nif __name__ == \"__main__\":\n main()\n\n #APIpowered by Coindesk","sub_path":"bitcoin.py","file_name":"bitcoin.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"373092883","text":"# method_1\nclass Solution:\n def productExceptSelf(self, nums: List[int]) -> List[int]:\n n = len(nums)\n L = [1]*n\n R = [1]*n\n ans = [0]*n\n # 计算前缀后缀乘积\n for i in range(1,n):\n L[i] = L[i-1]*nums[i-1]\n\n for i in range(n-2,-1,-1):\n R[i] = R[i+1]*nums[i+1]\n \n for i in range(n):\n ans[i] = L[i]*R[i]\n\n return ans\n\n# method_2\nclass Solution:\n def productExceptSelf(self, nums: List[int]) -> List[int]:\n n = len(nums)\n ans = [1]*n\n left = 1\n right = 1\n \n for i in range(n):\n ans[i] *= right\n ans[-1-i] *= left\n right *= nums[i]\n left *= nums[-1-i]\n\n return ans","sub_path":"Qustion Code/238. 除自身以外数组的乘积.py","file_name":"238. 除自身以外数组的乘积.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"388226047","text":"##############################################################################\n#\n# Copyright (c) 2001, 2002 Zope Foundation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"ZCML directive handlers for browser resources\n\"\"\"\nimport os\n\nfrom zope.component import queryUtility\nfrom zope.component.interface import provideInterface\nfrom zope.component.zcml import handler\nfrom zope.configuration.exceptions import ConfigurationError\nfrom zope.interface import Interface\nfrom zope.interface import implementer\nfrom zope.interface import provider\nfrom zope.publisher.interfaces.browser import IBrowserRequest\nfrom zope.publisher.interfaces.browser import IDefaultBrowserLayer\nfrom zope.security.checker import Checker\nfrom zope.security.checker import CheckerPublic\nfrom zope.security.checker import NamesChecker\nfrom zope.security.proxy import Proxy\n\nfrom zope.browserresource.directory import DirectoryResourceFactory\nfrom zope.browserresource.file import File\nfrom zope.browserresource.file import FileResourceFactory\nfrom zope.browserresource.i18nfile import I18nFileResourceFactory\nfrom zope.browserresource.icon import IconViewFactory\nfrom zope.browserresource.interfaces import IResourceFactory\nfrom zope.browserresource.interfaces import IResourceFactoryFactory\n\n\nallowed_names = ('GET', 'HEAD', 'publishTraverse', 'browserDefault',\n 'request', '__call__')\n\n\n@implementer(IResourceFactory)\n@provider(IResourceFactoryFactory)\nclass ResourceFactoryWrapper:\n\n def __init__(self, factory, checker, name):\n self.__factory = factory\n self.__checker = checker\n self.__name = name\n\n def __call__(self, request):\n resource = self.__factory(request)\n resource.__Security_checker__ = self.__checker\n resource.__name__ = self.__name\n return resource\n\n\ndef resource(_context, name, layer=IDefaultBrowserLayer,\n permission='zope.Public', factory=None,\n file=None, image=None, template=None):\n\n if permission == 'zope.Public':\n permission = CheckerPublic\n\n checker = NamesChecker(allowed_names, permission)\n\n too_many = bool(factory) + bool(file) + bool(image) + bool(template)\n if too_many > 1:\n raise ConfigurationError(\n \"Must use exactly one of factory or file or image or template\"\n \" attributes for resource directives\"\n )\n\n if image or template:\n import warnings\n warnings.warn_explicit(\n 'The \"template\" and \"image\" attributes of resource '\n 'directive are deprecated in favor of pluggable '\n 'file resource factories based on file extensions. '\n 'Use the \"file\" attribute instead.',\n DeprecationWarning,\n _context.info.file, _context.info.line)\n if image:\n file = image\n elif template:\n file = template\n\n _context.action(\n discriminator=('resource', name, IBrowserRequest, layer),\n callable=resourceHandler,\n args=(name, layer, checker, factory, file, _context.info),\n )\n\n\ndef resourceHandler(name, layer, checker, factory, file, context_info):\n if factory is not None:\n factory = ResourceFactoryWrapper(factory, checker, name)\n else:\n ext = os.path.splitext(os.path.normcase(file))[1][1:]\n factory_factory = queryUtility(IResourceFactoryFactory, ext,\n FileResourceFactory)\n factory = factory_factory(file, checker, name)\n handler('registerAdapter', factory, (layer,),\n Interface, name, context_info)\n\n\ndef resourceDirectory(_context, name, directory, layer=IDefaultBrowserLayer,\n permission='zope.Public'):\n if permission == 'zope.Public':\n permission = CheckerPublic\n\n checker = NamesChecker(allowed_names + ('__getitem__', 'get'),\n permission)\n\n if not os.path.isdir(directory):\n raise ConfigurationError(\n \"Directory %s does not exist\" % directory\n )\n\n factory = DirectoryResourceFactory(directory, checker, name)\n _context.action(\n discriminator=('resource', name, IBrowserRequest, layer),\n callable=handler,\n args=('registerAdapter',\n factory, (layer,), Interface, name, _context.info),\n )\n\n\ndef icon(_context, name, for_, file=None, resource=None,\n layer=IDefaultBrowserLayer, title=None,\n width=16, height=16):\n\n iname = for_.getName()\n\n if title is None:\n title = iname\n if title.startswith('I'):\n title = title[1:] # Remove leading 'I'\n\n if file is not None and resource is not None:\n raise ConfigurationError(\n \"Can't use more than one of file, and resource \"\n \"attributes for icon directives\"\n )\n elif file is not None:\n resource = '-'.join(for_.__module__.split('.'))\n resource = \"{}-{}-{}\".format(resource, iname, name)\n ext = os.path.splitext(file)[1]\n if ext:\n resource += ext\n\n # give this module another name, so we can use the \"resource\" directive\n # in it that won't conflict with our local variable with the same name.\n from zope.browserresource import metaconfigure\n metaconfigure.resource(_context, file=file, name=resource, layer=layer)\n elif resource is None:\n raise ConfigurationError(\n \"At least one of the file, and resource \"\n \"attributes for resource directives must be specified\"\n )\n\n vfactory = IconViewFactory(resource, title, width, height)\n\n _context.action(\n discriminator=('view', name, vfactory, layer),\n callable=handler,\n args=('registerAdapter',\n vfactory, (for_, layer), Interface, name, _context.info)\n )\n\n _context.action(\n discriminator=None,\n callable=provideInterface,\n args=(for_.__module__ + '.' + for_.getName(),\n for_)\n )\n\n\nclass I18nResource:\n\n type = IBrowserRequest\n default_allowed_attributes = '__call__'\n\n def __init__(self, _context, name=None, defaultLanguage='en',\n layer=IDefaultBrowserLayer, permission=None):\n self._context = _context\n self.name = name\n self.defaultLanguage = defaultLanguage\n self.layer = layer\n self.permission = permission\n self.__data = {}\n\n def translation(self, _context, language, file=None, image=None):\n\n if file is not None and image is not None:\n raise ConfigurationError(\n \"Can't use more than one of file, and image \"\n \"attributes for resource directives\"\n )\n elif file is None and image is None:\n raise ConfigurationError(\n \"At least one of the file, and image \"\n \"attributes for resource directives must be specified\"\n )\n\n if image is not None:\n import warnings\n warnings.warn_explicit(\n 'The \"image\" attribute of i18n-resource directive is '\n 'deprecated in favor of simple files. Use the \"file\" '\n 'attribute instead.',\n DeprecationWarning,\n _context.info.file, _context.info.line)\n file = image\n\n self.__data[language] = File(_context.path(file), self.name)\n\n def __call__(self, require=None):\n if self.name is None:\n return\n\n if self.defaultLanguage not in self.__data:\n raise ConfigurationError(\n \"A translation for the default language (%s) \"\n \"must be specified\" % self.defaultLanguage\n )\n\n permission = self.permission\n factory = I18nFileResourceFactory(self.__data, self.defaultLanguage)\n\n if permission:\n if require is None:\n require = {}\n\n if permission == 'zope.Public':\n permission = CheckerPublic\n\n if require:\n checker = Checker(require)\n\n factory = self._proxyFactory(factory, checker)\n\n self._context.action(\n discriminator=('i18n-resource', self.name, self.type, self.layer),\n callable=handler,\n args=('registerAdapter',\n factory, (self.layer,), Interface, self.name,\n self._context.info)\n )\n\n def _proxyFactory(self, factory, checker):\n def proxyView(request,\n factory=factory, checker=checker):\n resource = factory(request)\n\n # We need this in case the resource gets unwrapped and\n # needs to be rewrapped\n resource.__Security_checker__ = checker\n\n return Proxy(resource, checker)\n\n return proxyView\n","sub_path":"src/zope/browserresource/metaconfigure.py","file_name":"metaconfigure.py","file_ext":"py","file_size_in_byte":9241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"74748546","text":"# -*- coding: utf-8 -*-\r\n# @Time : 2021/03/19 10:03 下午\r\n# @Author : lishouxian\r\n# @Email : gzlishouxian@gmail.com\r\n# @File : word2vec_textrnn.py\r\n# @Software: PyCharm\r\nfrom abc import ABC\r\nimport tensorflow as tf\r\nfrom config import classifier_config\r\n\r\n\r\nclass TextRNN(tf.keras.Model, ABC):\r\n \"\"\"\r\n TextRNN模型\r\n \"\"\"\r\n\r\n def __init__(self, num_classes, embedding_dim, vocab_size, embeddings_matrix=None):\r\n super(TextRNN, self).__init__()\r\n hidden_dim = classifier_config['hidden_dim']\r\n if classifier_config['embedding_method'] is '':\r\n self.embedding = tf.keras.layers.Embedding(vocab_size + 1, embedding_dim, mask_zero=True)\r\n else:\r\n self.embedding = tf.keras.layers.Embedding(vocab_size + 1, embedding_dim, weights=[embeddings_matrix],\r\n trainable=False)\r\n self.bilstm = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(hidden_dim, return_sequences=True))\r\n self.dropout = tf.keras.layers.Dropout(classifier_config['dropout_rate'], name='dropout')\r\n if classifier_config['use_attention']:\r\n self.attention_w = tf.Variable(tf.zeros([1, 2 * hidden_dim]))\r\n self.dense = tf.keras.layers.Dense(num_classes,\r\n activation='softmax',\r\n kernel_regularizer=tf.keras.regularizers.l2(0.1),\r\n bias_regularizer=tf.keras.regularizers.l2(0.1),\r\n name='dense')\r\n\r\n @tf.function\r\n def call(self, inputs, training=None):\r\n inputs = self.embedding(inputs)\r\n bilstm_outputs = self.bilstm(inputs)\r\n if classifier_config['use_attention']:\r\n output = tf.nn.tanh(bilstm_outputs)\r\n output = tf.matmul(output, self.attention_w, transpose_b=True)\r\n alpha = tf.nn.softmax(output, axis=1)\r\n outputs = alpha * bilstm_outputs\r\n bilstm_outputs = tf.nn.tanh(outputs)\r\n dropout_outputs = self.dropout(bilstm_outputs, training)\r\n outputs = tf.reduce_sum(dropout_outputs, axis=1)\r\n outputs = self.dense(outputs)\r\n return outputs\r\n","sub_path":"engines/models/TextRNN.py","file_name":"TextRNN.py","file_ext":"py","file_size_in_byte":2241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"204853241","text":"import requests\nimport threading\nimport time\nfrom collections import deque\nfrom Queue import Empty\nfrom solariat_bottle.daemons.exc import ApplicationError, FeedAPIError, \\\n InfrastructureError, UnauthorizedRequestError\nfrom solariat_bottle.settings import LOGGER, get_var\n\n\nclass RequestsClient(object):\n \"\"\"FeedAPI http client\"\"\"\n\n authtoken = None\n authtoken_expired = set()\n lock = threading.Lock()\n\n def __init__(self, options=None, sleep_timeout=30, user_agent='FeedApi'):\n self.headers = {}\n self.new_session()\n self.sleep_timeout = sleep_timeout\n self.user_agent = user_agent\n self.options = options\n self.headers = {'Content-Type': 'application/json',\n 'User-Agent': self.user_agent}\n\n def get_authtoken(self, expired=None):\n with self.lock:\n if expired is None:\n self.__class__.authtoken_expired = set()\n if self.__class__.authtoken:\n return self.__class__.authtoken\n return self.__gen_authtoken()\n\n if expired not in self.__class__.authtoken_expired:\n self.__class__.authtoken_expired.add(expired)\n return self.__gen_authtoken()\n return self.__class__.authtoken\n\n def __gen_authtoken(self):\n post_data = {\n 'username': self.options.username,\n 'password': self.options.password,\n }\n #url = '/api/v1.2/authtokens'\n url = '/api/%s/authenticate' % get_var('API_VERSION')\n\n while True:\n try:\n url_str = '%s%s' % (self.options.url, url)\n LOGGER.debug('Trying to authenticate using url: %s %s', url_str, self.options)\n response = self.post(\n url_str,\n json=post_data,\n headers={'Content-Type': 'application/json',\n 'User-Agent': self.user_agent})\n LOGGER.debug('Got auth token: %s' % response)\n except FeedAPIError as err:\n LOGGER.warning(err, exc_info=True)\n time.sleep(self.sleep_timeout)\n else:\n if not ('token' in response or 'item' in response):\n LOGGER.error(\"Bad auth response %s\", response)\n time.sleep(self.sleep_timeout)\n continue\n\n if 'item' in response:\n response = response['item']\n\n try:\n authtoken = response['token'].encode('utf-8')\n except KeyError:\n LOGGER.exception(response)\n time.sleep(self.sleep_timeout)\n else:\n self.__class__.authtoken = authtoken\n return authtoken\n\n def new_session(self):\n self.session = requests.Session()\n self.session.headers = self.headers\n\n def post(self, url, data=None, json=None, **kwargs):\n from requests.compat import json as json_lib\n\n try:\n response = self.session.request(\n 'POST', url, data=data, json=json,\n stream=False, # return connection to pool not waiting for response\n **kwargs)\n except requests.ConnectionError as e:\n self.new_session()\n raise InfrastructureError(unicode(e))\n\n if response.status_code == 401:\n raise UnauthorizedRequestError(response.text)\n\n if response.status_code != 200:\n raise InfrastructureError(u\"HTTP status: {} Response: {}\".format(\n response.status_code, response.text))\n\n try:\n data = response.json()\n except (TypeError, json_lib.JSONDecodeError):\n raise ApplicationError(u\"Bad response: {}\".format(response.text))\n else:\n if not data.get('ok'):\n raise ApplicationError(data.get('error', 'Unknown Error'))\n return data\n\n def api_posts(self, post_data=None, number_of_retries=None):\n api_url = '/api/%s/posts' % get_var('API_VERSION')\n\n payload = {\"serialized_to_json\": True,\n \"return_response\": False,\n \"post_object_data\": post_data,\n \"channel\": 'no',\n \"content\": 'no'}\n url = '%s%s' % (self.options.url, api_url)\n return self.post_authenticated(url, json=payload, number_of_retries=number_of_retries)\n\n def apply_token(self, url, post_data, authtoken):\n if not ('token' in url or (post_data and 'token' in post_data)):\n return '%s?token=%s' % (url, authtoken)\n return url\n\n def post_authenticated(self, url, json=None, number_of_retries=None):\n assert self.options and self.options.username and self.options.password\n\n authtoken = None\n expired = None\n\n while True:\n if not authtoken:\n authtoken = self.get_authtoken(expired)\n expired = None\n auth_url = self.apply_token(url, json, authtoken)\n try:\n return self.post(auth_url, json=json)\n except ApplicationError as err:\n if str(err) == 'Auth token %s is expired' % authtoken:\n LOGGER.info(err)\n expired = authtoken\n authtoken = None\n else:\n LOGGER.exception(err)\n break\n except UnauthorizedRequestError as err:\n LOGGER.warning(err, exc_info=True)\n expired = authtoken\n authtoken = None\n except InfrastructureError as err:\n LOGGER.exception(err)\n if number_of_retries is None:\n time.sleep(self.sleep_timeout)\n elif isinstance(number_of_retries, int) and number_of_retries > 0:\n number_of_retries -= 1\n else:\n break\n\n\nclass FeedApiThread(threading.Thread):\n \"\"\"FeedApi implementation with injectable http client,\n compatible with daemons.feedapi_old.FeedAPI\n \"\"\"\n _client = None\n\n @property\n def client(self):\n if self._client is None:\n _client = RequestsClient(self.options, self.sleep_timeout, self.user_agent)\n self._client = _client\n return self._client\n\n _counter = 0\n QUIT = object()\n\n def __init__(self, args=(), kwargs=None):\n\n self.__class__._counter += 1\n threading.Thread.__init__(self, name='FeedAPI-%d' % self._counter)\n if kwargs is None:\n kwargs = {}\n self.task_queue = args[0]\n self.options = args[1]\n self.user_agent = kwargs.get('User-Agent', self.name)\n self.sleep_timeout = 30\n self._busy = False\n self._elapsed_times = deque([], 25)\n self._stopped = threading.Event()\n\n def stop(self):\n self._stopped.set()\n\n def stopped(self):\n return self._stopped.isSet()\n\n def run(self):\n post_data = None\n start_time = time.time()\n wait_timeout = 0.1 if get_var('ON_TEST') else 30\n\n while not self.stopped():\n try:\n post_data = self.task_queue.get(block=True, timeout=wait_timeout)\n except Empty:\n self.stop()\n self._busy = False\n break\n start_time = time.time()\n if post_data is self.QUIT or post_data is None or post_data == 'QUIT':\n LOGGER.debug('received QUIT signal')\n self.task_queue.task_done()\n break\n\n self._busy = True\n self.client.api_posts(post_data)\n self.task_queue.task_done()\n self._elapsed_times.append(time.time() - start_time)\n self._busy = False\n\n @property\n def average_processing_time(self):\n size = len(self._elapsed_times)\n if size > 0:\n return sum(self._elapsed_times) / (size * 1.0)\n else:\n return -1\n\n def is_busy(self):\n return self._busy\n","sub_path":"daemons/feedapi.py","file_name":"feedapi.py","file_ext":"py","file_size_in_byte":8122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"185059222","text":"#!/usr/bin/env python\n# -*-coding:utf-8-*-\n\nimport urllib\nimport urllib.request\n\nlocal_path = r'/home/arthur/Desktop/wps/temp/'\nfile_name = r'aa/bb.jpg'\n\nr_file_path = local_path + file_name\nr_file_path = r_file_path.encode(\"utf-8\")\npic_url = \"http://www.jsxiaoguo.com/Content/Style/Default/Images/rd-a-hh-DefaultImg.jpg\"\n\nprint(r_file_path)\n\nurllib.request.urlretrieve(pic_url,r_file_path)\n","sub_path":"temp/t2.py","file_name":"t2.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"296793324","text":"import pandas as pd \n\npd.set_option('max_rows',5)\n\na = pd.DataFrame({'Yes':[50,21],'No':[131,2]})\nb = pd.DataFrame({'Bob':['I liked it.','It was awful.'],\n\t\t\t\t\t'Sue':['Pretty good','Bland.']},\n\t\t\t\t\tindex=['Product A','Product B'])\nc = pd.Series([1,2,3,4,5,])\nd = pd.Series([30,35,40],index=['2015 Sales','2016 Sales','2017 Sales'],name='Sales')\ne = pd.read_csv('mel_data.csv')\n#e.select_dtypes(include='int?',exclude='object') #剔除非数字列\ne1 = ['Rooms', 'Price', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n 'Landsize', 'BuildingArea', 'YearBuilt', 'Lattitude', 'Longtitude',\n 'Propertycount']\nprint(e.groupby('Price').Price.count())\n\n\n\n\n\n\n\n\n\n\n#import sqlites\n#conn = sqlites.connect(\"file path .sqlite\")\n#fires = pd.read_sql_query(\"SELECT * FROM fires\",conn)\n\n#conn = sqlites3.connect('fires.sqlite')\n#fires.head(10).to_sql('fires',conn)\n\n\n\n\n'''\npd.DataFrame()\npd.Series()\ndata =pd.read_csv(,index_col=) 默认是数字，可选择是第几列\ndata.to_csv(\"data.csv\")\npd.read_excel(,sheet_name=) 导入EXCEL要加表名。 \ndata.to_excel('data.xlsx',sheet_name='Total Woman') 导出也需要表名\ndata.shape()\ndata.head()\ndata.columns()\ndata.loc[[],[]] 行列用字符表示\ndata.iloc[[],[]] 行列用数字表示 eg:.iloc[0:100,[0,5,9]]\n\t\t\t\t\t\t\t\t\tiloc[[0,3,4],[3,4,5]]\n\t\t\t\t\t\t\t\t\tiloc[3,4] 直接选定第几行\n\ndata.loc[(data.att == some)&(data.att1 >= some)] 选出条件匹配的两项\n & ： 与 | ： 或 ！ ： 非\n data.loc[data.col.isin(['att1','att2'])] 选出某col的att1,att2属性的数据\n data.loc[data.col.isnull()] isnull() notnull()\n 选出某col的is null (NaN)的数据 （或 not null）\n data['col1'] = 'some' 新建一个Series,都是some\n data.col.describe() 一个简单的描述，根据dtype的不同会有变化\n data.col.mean() 求平均数\n data.col.unique() 唯一值\n data.col.value_counts() 每个值出现的次数统计\n .map() .apply() 没太懂，31醒了看\n\n data.group('str1').str1.count() groupby() 将数据分组\n eg : data.group('str1').str2.min()\n agg \n'''","sub_path":"machine_learn/P_pandes.py","file_name":"P_pandes.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"615360001","text":"#\n# * A predicate is a Boolean combination of terms.\n# * @author Edward Sciore\n# *\n#\nfrom simpledb.record.Schema import Schema\n\n\nclass Predicate(object):\n\n #\n # * Create an empty predicate, corresponding to \"true\".\n #\n def __init__(self, *args):\n self.terms = []\n #\n # * Create a predicate containing a single term.\n # * @param t the term\n #\n if len(args) == 1:\n t = args[0]\n self.terms.append(t)\n\n #\n # * Modifies the predicate to be the conjunction of\n # * itself and the specified predicate.\n # * @param pred the other predicate\n #\n def conjoinWith(self, pred):\n for t in pred.terms:\n self.terms.append(t)\n\n #\n # * Returns true if the predicate evaluates to true\n # * with respect to the specified scan.\n # * @param s the scan\n # * @return true if the predicate is true in the scan\n #\n def isSatisfied(self, s):\n\n for t in self.terms:\n if not t.isSatisfied(s):\n return False\n return True\n\n #\n # * Calculate the extent to which selecting on the predicate\n # * reduces the number of records output by a query.\n # * For example if the reduction factor is 2, then the\n # * predicate cuts the size of the output in half.\n # * @param p the query's plan\n # * @return the integer reduction factor.\n #\n def reductionFactor(self, p):\n factor = 1\n for t in self.terms:\n factor *= t.reductionFactor(p)\n return factor\n\n #\n # * Return the subpredicate that applies to the specified schema.\n # * @param sch the schema\n # * @return the subpredicate applying to the schema\n #\n def selectSubPred(self, sch):\n result = Predicate()\n for t in self.terms:\n if t.appliesTo(sch):\n result.terms.append(t)\n if len(result.terms) == 0:\n return None\n else:\n return result\n\n #\n # * Return the subpredicate consisting of terms that apply\n # * to the union of the two specified schemas,\n # * but not to either schema separately.\n # * @param sch1 the first schema\n # * @param sch2 the second schema\n # * @return the subpredicate whose terms apply to the union of the two schemas but not either schema separately.\n #\n def joinSubPred(self, sch1, sch2):\n result = Predicate()\n newsch = Schema()\n newsch.addAll(sch1)\n newsch.addAll(sch2)\n for t in self.terms:\n if not t.appliesTo(sch1) and not t.appliesTo(sch2) and t.appliesTo(newsch):\n result.terms.append(t)\n if len(result.terms) == 0:\n return None\n else:\n return result\n\n #\n # * Determine if there is a term of the form \"F=c\"\n # * where F is the specified field and c is some constant.\n # * If so, the method returns that constant.\n # * If not, the method returns null.\n # * @param fldname the name of the field\n # * @return either the constant or null\n #\n def equatesWithConstant(self, fldname):\n for t in self.terms:\n c = t.equatesWithConstant(fldname)\n if c is not None:\n return c\n return None\n\n #\n # * Determine if there is a term of the form \"F1=F2\"\n # * where F1 is the specified field and F2 is another field.\n # * If so, the method returns the name of that field.\n # * If not, the method returns null.\n # * @param fldname the name of the field\n # * @return the name of the other field, or null\n #\n def equatesWithField(self, fldname):\n for t in self.terms:\n s = t.equatesWithField(fldname)\n if s is not None:\n return s\n return None\n\n def __str__(self):\n if len(self.terms) == 0:\n return \"\"\n result = self.terms[0].__str__()\n for i in range(1, len(self.terms)):\n result += \" and \" + self.terms[i].__str__()\n return result\n","sub_path":"simpledb/query/Predicate.py","file_name":"Predicate.py","file_ext":"py","file_size_in_byte":4160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"419482868","text":"from historia.utils import unique_id, random_country_colors\nfrom historia.country.models.province import Province\nfrom historia.log import LogAction\n\nclass Country(object):\n \"\"\"\n Top level of government.\n Representation of a Country.\n \"\"\"\n def __init__(self, manager, initial_hex, ancestor=None):\n super(self.__class__, self).__init__()\n self.manager = manager\n self.id = unique_id('co')\n\n # ancestor country (mother country)\n # The country this country was formed out of\n self.ancestor = ancestor\n\n # hexes this country controls\n # create a province\n capital = Province(self.manager, initial_hex, self, is_capital=True)\n self.provinces = [capital]\n self.capital = capital\n self.manager.logger.log(self, {\n 'capital': capital.id\n })\n self.manager.logger.log(self, {\n 'provinces': capital.id\n }, LogAction.extend)\n\n # Vassal countries under this country\n self.vassals = []\n self.is_vassal = False\n\n # tuple of Country, relation int\n self.relations = []\n\n map_color, border_color = random_country_colors()\n self.display = {\n 'map_color': map_color.hex,\n 'border_color': border_color.hex\n }\n\n self.name = ''\n\n def settle_hex(self, hex_inst):\n \"Settles a new hex, creating a province and returning it\"\n province = Province(self.manager, hex_inst, self, is_capital=False)\n self.provinces.append(province)\n return province\n\n @property\n def pops(self):\n pops = []\n for p in self.provinces:\n pops.extend(p.pops)\n return pops\n\n def __repr__(self):\n return \"\".format(self.name, self.id)\n\n def __eq__(self, other):\n return self.id == other.id\n\n def __key__(self):\n return self.id\n\n def __hash__(self):\n return hash(self.__key__())\n\n\n def export(self):\n \"Export country data\"\n return {\n 'display': self.display,\n 'name': self.name,\n 'provinces': [p.id for p in self.provinces]\n }\n","sub_path":"historia/country/models/country.py","file_name":"country.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"411923942","text":"import os\nimport json\n\nimport oss2\n\nfrom oss_command import config\n\n\ndef load_config():\n config_file = config.config_file\n cfg = dict()\n if not os.path.exists(config_file):\n key = os.environ.get(\"oss_command_key\", None)\n secret = os.environ.get(\"oss_command_secret\", None)\n if not key and not secret:\n raise Exception(\"Run oss_command config first\")\n cfg[\"key\"] = key\n cfg[\"secret\"] = secret\n return cfg\n with open(config_file, \"r\") as f:\n cfg = json.load(f)\n return cfg\n\n\ndef get_bucket(endpoint):\n cfg = load_config()\n auth = oss2.Auth(cfg[\"key\"], cfg[\"secret\"])\n bucket_name, endpoint = endpoint.split(\".\", 1)\n bucket = oss2.Bucket(auth, endpoint, bucket_name)\n return bucket\n","sub_path":"oss_command/commands/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"285689939","text":"count=0\ndef hanoi(n,src,dst,mid):\n global count\n if n == 1:\n print(\"{}号圆盘:{}->{}\".format(1,src,dst))\n count += 1\n else:\n hanoi(n-1,src,mid,dst)\n print(\"{}号圆盘:{}->{}\".format(n,src,dst))\n count+=1\n hanoi(n-1,mid,dst,src)\nh = eval(input(\"请输入圆盘数量：\"))\nhanoi(h,\"A\",\"C\",\"B\")\nprint(\"一共搬运了{}次\".format(count))","sub_path":"PythonProgramming/class5/example5.3.py","file_name":"example5.3.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"557364134","text":"# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is regenerated.\n# --------------------------------------------------------------------------\n\nfrom azure.identity import DefaultAzureCredential\nfrom azure.mgmt.iothub import IotHubClient\n\n\"\"\"\n# PREREQUISITES\n pip install azure-identity\n pip install azure-mgmt-iothub\n# USAGE\n python iothub_create_or_update.py\n\n Before run the sample, please set the values of the client ID, tenant ID and client secret\n of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,\n AZURE_CLIENT_SECRET. For more info about how to get the value, please see:\n https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal\n\"\"\"\n\n\ndef main():\n client = IotHubClient(\n credential=DefaultAzureCredential(),\n subscription_id=\"91d12660-3dec-467a-be2a-213b5544ddc0\",\n )\n\n response = client.iot_hub_resource.begin_create_or_update(\n resource_group_name=\"myResourceGroup\",\n resource_name=\"testHub\",\n iot_hub_description={\n \"etag\": \"AAAAAAFD6M4=\",\n \"location\": \"centraluseuap\",\n \"properties\": {\n \"cloudToDevice\": {\n \"defaultTtlAsIso8601\": \"PT1H\",\n \"feedback\": {\"lockDurationAsIso8601\": \"PT1M\", \"maxDeliveryCount\": 10, \"ttlAsIso8601\": \"PT1H\"},\n \"maxDeliveryCount\": 10,\n },\n \"enableDataResidency\": False,\n \"enableFileUploadNotifications\": False,\n \"eventHubEndpoints\": {\"events\": {\"partitionCount\": 2, \"retentionTimeInDays\": 1}},\n \"features\": \"None\",\n \"ipFilterRules\": [],\n \"messagingEndpoints\": {\n \"fileNotifications\": {\n \"lockDurationAsIso8601\": \"PT1M\",\n \"maxDeliveryCount\": 10,\n \"ttlAsIso8601\": \"PT1H\",\n }\n },\n \"minTlsVersion\": \"1.2\",\n \"networkRuleSets\": {\n \"applyToBuiltInEventHubEndpoint\": True,\n \"defaultAction\": \"Deny\",\n \"ipRules\": [\n {\"action\": \"Allow\", \"filterName\": \"rule1\", \"ipMask\": \"131.117.159.53\"},\n {\"action\": \"Allow\", \"filterName\": \"rule2\", \"ipMask\": \"157.55.59.128/25\"},\n ],\n },\n \"routing\": {\n \"endpoints\": {\n \"eventHubs\": [],\n \"serviceBusQueues\": [],\n \"serviceBusTopics\": [],\n \"storageContainers\": [],\n },\n \"fallbackRoute\": {\n \"condition\": \"true\",\n \"endpointNames\": [\"events\"],\n \"isEnabled\": True,\n \"name\": \"$fallback\",\n \"source\": \"DeviceMessages\",\n },\n \"routes\": [],\n },\n \"storageEndpoints\": {\n \"$default\": {\"connectionString\": \"\", \"containerName\": \"\", \"sasTtlAsIso8601\": \"PT1H\"}\n },\n },\n \"sku\": {\"capacity\": 1, \"name\": \"S1\"},\n \"tags\": {},\n },\n ).result()\n print(response)\n\n\n# x-ms-original-file: specification/iothub/resource-manager/Microsoft.Devices/stable/2021-07-02/examples/iothub_createOrUpdate.json\nif __name__ == \"__main__\":\n main()\n","sub_path":"sdk/iothub/azure-mgmt-iothub/generated_samples/iothub_create_or_update.py","file_name":"iothub_create_or_update.py","file_ext":"py","file_size_in_byte":3854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"88173872","text":"n1=int(input('Um valor:'))\r\nn2=int(input('Outro valor:'))\r\n#print('A soma vale {}'.format(n1+n2))\r\ns=n1+n2\r\nm=n1*n2\r\nd=n1/n2\r\ndi=n1/n2\r\ne=n1**n2\r\nprint('A soma é {}, o produto é {} \\n e a divisão é {:.3f}' .format(s, m, d), end=' ')\r\nprint('Divisão inteira {} \\n e potência {}'.format(di, e))\r\n","sub_path":"testes_aulas/aula07b.py","file_name":"aula07b.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"313761988","text":"from flask import url_for\nfrom flask.ext.testing import TestCase\n\nfrom webapp import app, db\nfrom webapp.models import Monkey, Friendship, BestFriend\nfrom config import HEROKU_POSTGRESQL_CHARCOAL_URL\n\n\ndef setup_module():\n app.config['SQLALCHEMY_DATABASE_URI'] = HEROKU_POSTGRESQL_CHARCOAL_URL\n app.config['WTF_CSRF_ENABLED'] = False\n db.create_all()\n\n\ndef teardown_module():\n db.session.remove()\n db.drop_all()\n\n\nclass TestProfilePage(TestCase):\n def create_app(self):\n return app\n\n def test_profile_page_is_working(self):\n monkey = Monkey(name='Sajjad', age='21', email='Sajjad@nono.com')\n db.session.add(monkey)\n db.session.commit()\n response = self.client.get(url_for('profile',\n monkey_id=monkey.monkey_id))\n self.assert_200(response)\n\n def test_edit_info_inside_profile_page_valid_input_works(self):\n monkey = Monkey(name='Sajjad', age='21', email='Sajjad@nono.com')\n db.session.add(monkey)\n db.session.commit()\n response = self.client.get(url_for('profile',\n monkey_id=monkey.monkey_id))\n assert b'Sajjad@nono.com' in response.data\n\n data = {\n 'name': 'Sajjad',\n 'age': '21',\n 'email': 'Sajjad@gmail.com'\n }\n response = self.client.post(url_for('profile',\n monkey_id=monkey.monkey_id),\n data=data,\n follow_redirects=True)\n self.assert_200(response)\n assert b'Sajjad@gmail.com' in response.data\n\n def test_best_friend_status_not_yet_and_name_of_bf_works(self):\n monkey = Monkey(name='Sajjad', age='21', email='Sajjad@nono.com')\n db.session.add(monkey)\n db.session.commit()\n response = self.client.get(url_for('profile',\n monkey_id=monkey.monkey_id))\n assert b'Best friend: Not yet' in response.data\n\n bf = Monkey(name='Alice', age='100', email='Alice@nono.com')\n db.session.add(bf)\n db.session.commit()\n best_friend = BestFriend(monkey_id=monkey.monkey_id,\n best_friend_id=bf.monkey_id)\n db.session.add(best_friend)\n db.session.commit()\n response = self.client.get(url_for('profile',\n monkey_id=monkey.monkey_id))\n assert b'Best friend: Alice' in response.data\n\n def test_terminate_monkey_works(self):\n monkey = Monkey(name='About2Terminate', age='21', email='ATT@no.com')\n monkey2 = Monkey(name='PoorFriend', age='21', email='ATT@no.com')\n monkey3 = Monkey(name='EvenPoorer', age='21', email='ATT@no.com')\n db.session.add(monkey)\n db.session.add(monkey2)\n db.session.add(monkey3)\n db.session.commit()\n friendship = Friendship(monkey_id=monkey.monkey_id,\n friend_id=monkey2.monkey_id)\n friendship_reciprocal = Friendship(monkey_id=monkey2.monkey_id,\n friend_id=monkey.monkey_id)\n best_friend = BestFriend(monkey_id=monkey.monkey_id,\n best_friend_id=monkey3.monkey_id)\n db.session.add(friendship)\n db.session.add(best_friend)\n db.session.commit()\n response = self.client.get(url_for('terminate',\n monkey_id=monkey.monkey_id),\n follow_redirects=True)\n self.assert_200(response)\n assert b'About2Terminate was terminated' in response.data\n assert monkey not in Monkey.query.all()\n assert friendship not in Friendship.query.all()\n assert best_friend not in BestFriend.query.all()\n\n def test_edit_info_a_non_existent_monkey_gets_handled(self):\n data = {\n 'name': 'Sajjad',\n 'age': '21',\n 'email': 'Sajjad@gmail.com'\n }\n response = self.client.post(url_for('profile',\n monkey_id=999999999),\n data=data)\n self.assert_200(response)\n assert b'500: Internal Server Error' in response.data\n","sub_path":"tests/test_profile_page.py","file_name":"test_profile_page.py","file_ext":"py","file_size_in_byte":4326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"389845667","text":"'''\nIn this exercise, you will practice both File I/O as well as using Exceptions\nin a real-world scenario.\n\nYou have a folder containing three text files of books from Project Gutenberg:\n- war_and_peace.txt\n- pride_and_prejudice.txt\n- crime_and_punishment.txt\n\n1) Open war_and_peace.txt, read the whole file content and store it in a variable\n\n2) Open crime_and_punishment.txt and overwrite the whole content with an empty string\n\n3) Loop over all three files and print out only the first character each. Your program\n should NEVER terminate with a Traceback.\n\n a) Which Exception can you expect to encounter? Why?\n\n b) How do you catch it to avoid the program from terminating with a Traceback?\n\n\nBONUS CHALLENGE: write a custom Exception that inherits from Exception and raise it if the\nfirst 100 characters of any of the files contain the string \"Prince\".\n\n'''\n\nimport os\npath = (\"/Users/ridinhome/Documents/CodingNomads/labs/09_exceptions/books/\")\nwar_and_peace = []\ncrime_and_punishment = (\"\")\nfirst_letter = {}\nos.chdir(path)\nfile_list = [\"war_and_peace.txt\",\"crime_and_punishment.txt\",\"pride_and_prejudice.txt\"]\n\n###,\n\n# with open(\"war_and_peace.txt\",\"r\") as tolstoy:\n# for item in tolstoy:\n# war_and_peace.append(item)\n#\n# print (war_and_peace)\n\n# with open(\"crime_and_punishment.txt\",\"w\") as dostoyevsky:\n# dostoyevsky.write(crime_and_punishment)\n\nfor item in file_list:\n with open(item,\"r\") as book:\n lines = book.readline()\n first_letter[item] = lines[0][0]\n\nfor value in first_letter.values():\n print (value)\n\n\n\n\n\n","sub_path":"09_exceptions/09_04_files_exceptions.py","file_name":"09_04_files_exceptions.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"411221294","text":"from time import sleep\r\nprint('\\033[1;36m-=-\\033[m' * 20)\r\nprint('\\033[1;36m *-*-*-*-* CLASSE CACHORRO! *-*-*-*-*\\033[m')\r\nprint('\\033[1;36m-=-\\033[m' * 20)\r\nsleep(2)\r\n\r\nclass Cachorro:\r\n def __init__(self, nome, raca, idade, peso):\r\n self.nome = nome\r\n self.raca = raca\r\n self.idade = idade\r\n self.peso = peso\r\n\r\n def adora_comer(self):\r\n self.peso = self.peso + 0.5\r\n\r\n\r\nc1 = Cachorro(nome = 'Charlotte', raca = 'S.R.D', idade = 7, peso = 9.7)\r\nprint('Oi, esta é {}, um cão da raça: {}. \\nSua idade é {} anos e de tanto comer pesa {}kg! '.format(c1.nome, c1.raca, c1.idade, c1.peso))\r\nsleep(5)\r\nprint('\\n{} adora comer.... Quer ver??'.format(c1.nome))\r\nsleep(3)\r\nprint('EI!! Psiu! Vem!!')\r\nsleep(2)\r\nprint('{}!!!'.format(c1.nome))\r\nsleep(3)\r\nprint('MmMmM... {} está comendo...'.format(c1.nome))\r\nsleep(5)\r\nc1.adora_comer()\r\nprint('Uau!!! Agora {} está pesando {}kg!'.format(c1.nome, c1.peso))","sub_path":"Charlotte_SamuelMachado.py","file_name":"Charlotte_SamuelMachado.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"133254494","text":"from business_logic import *\n\ndef main():\n\n print('GUIDE MANUAL\\n---------------------------')\n print('PLEASE SELECT NUMBER BETWEEN 1 - 6 TO RETRIEVE MESSAGE FOR GUEST')\n print('AND NUMBER BETWEEN 1 - 5 TO WELCOME AND ASSIGN GUEST TO HOTEL\\n')\n while True:\n try:\n guest_id = user_input_guest()\n instance_of_guest = get_guest_data(guest_id)\n hotel_id = user_input_hotel()\n instance_of_hotel = get_hotel_data(hotel_id)\n\n #required message\n print(f'{instance_of_guest.greeting()} {instance_of_hotel.get_hotel_name()} {instance_of_guest.get_room()}')\n except:\n print('Invalid operation!')\n\nif __name__ == \"__main__\":\n main()\n \n\n","sub_path":"main_menu.py","file_name":"main_menu.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"22026937","text":"'''\nAuthor: Brian Loi\nDate: September 14, 2017\nClass: ISTA 130\nSection Leader: Sebastion Andrews\n\nDescription:\nThis program uses turtle graphics to draw a castle.\n'''\n\nimport turtle\n\ndef house(a_turtle, size):\n '''\n Draw a simple house of the given size.\n\n Parameters:\n a_turtle: a turtle that is used to draw the house\n size: an int that determines the size of the house\n\n Returns: None\n '''\n polygon(a_turtle, 4, size)\n a_turtle.left(90)\n a_turtle.forward(size)\n a_turtle.right(90)\n polygon(a_turtle, 3, size)\n a_turtle.left(90)\n a_turtle.backward(size)\n a_turtle.right(90)\n return None\n\ndef castle(a_turtle, size):\n '''\n Draw a simple castle of the given size.\n\n Parameters:\n a_turtle: a turtle that is used to draw the castle\n size: an int that determines the size of the castle\n\n Returns: None\n '''\n polygon(a_turtle, 4, size)\n a_turtle.penup()\n a_turtle.backward(size * 0.25)\n a_turtle.left(90)\n a_turtle.forward(size)\n a_turtle.right(90)\n a_turtle.pendown()\n house(a_turtle, size * 0.5)\n a_turtle.forward(size * 0.5)\n house(a_turtle, size * 0.5)\n a_turtle.forward(size * 0.5)\n house(a_turtle, size * 0.5)\n a_turtle.penup()\n a_turtle.backward(size * 0.75)\n a_turtle.left(90)\n a_turtle.backward(size)\n a_turtle.right(90)\n a_turtle.pendown()\n return None\n\ndef polygon(some_turtle, sides, length):\n '''\n Draws a regular polygon using a turtle depending on the specified number\n of sides and length of the sides.\n\n Parameters:\n some_turtle: a turtle used to draw the polygon\n sides: an int that determines the number of sides in the polgyon\n length: an int that determines the length of the sides in the polygon\n\n Returns: None\n '''\n for i in range(sides):\n some_turtle.pendown()\n some_turtle.forward(length)\n some_turtle.left(360/sides)\n some_turtle.penup()\n return None\n\n#==============================================\ndef main():\n '''\n Draws a castle\n '''\n\n #Creates a turtle object to draw the castle. Sets the turtle's settings.\n yertle = turtle.Turtle()\n yertle.speed(0)\n yertle.penup()\n yertle.backward(200)\n yertle.pendown()\n yertle.pencolor('dark slate grey')\n yertle.pensize(5)\n\n #Repeatedly draws simple castles 4 times to create a larger castle drawing\n for i in range (4):\n castle(yertle, 100)\n yertle.forward(100)\n yertle.right(180)\n castle(yertle, 100)\n yertle.right(180)\n\n yertle.getscreen().exitonclick() #Keeps the turtle graphics window open\n\nif __name__ == '__main__':\n main()\n","sub_path":"ISTA130/hw2/castles.py","file_name":"castles.py","file_ext":"py","file_size_in_byte":2669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"242519380","text":"\"\"\"\r\n===========\r\nGroup analysis file\r\n===========\r\nScript for group level analyses.\r\n(Source reconstruction for all subjects and statistical tests)\r\n\"\"\"\r\n\r\nimport mne\r\nimport os\r\nimport numpy as np\r\nimport open_files as op \r\nfrom mne.channels import find_ch_adjacency\r\n\r\nimport source_reconstruction as source\r\nfrom tools import (add_to_array, zscore, save_results, \r\n check_list_cond, get_epochs_in_condition, \r\n sujets_exclusion_psds)\r\nfrom open_files import recover_epochs_sonetaa\r\nfrom spectral_density import (power_spectral_density_multitapper, power_spectral_density_welch)\r\nfrom morphing import (morph_surface_source_space, morph_volume_source_space, \r\n compute_morph_surface, compute_morph_volume)\r\nfrom statistical_tests import (cluster_permutation_test_1sample, \r\n cluster_permutation_test_2sample) \r\nfrom parameters import (subjects_dir)\r\n\r\n#global exclus_file\r\n#global subjects_dir\r\n\r\n################################################################################################################\r\n#### SOURCE RECONSTRUCTION\r\n################################################################################################################\r\n\r\n\r\ndef source_reconstruction_group(path_to_eeg_group, path_results_source_rec, list_subjects, \r\n method, lambda2, coreg, plot, redo):\r\n \"\"\"Reconstruct sources for all EEG subjects. \r\n Automatically uses subject MRI or a template MRI.\r\n \r\n Parameters\r\n ----------\r\n liste_participants : list\r\n list of list of subjects path to eeg and path to mri.\r\n method : str\r\n method for source reconstruction\r\n \"\"\"\r\n\r\n for (path_or_name_eeg, path_to_mri) in list_subjects:\r\n epochs_HDC = recover_epochs_sonetaa(path_or_name_eeg, 'HDC', path_to_mri, True)\r\n epochs_RS = recover_epochs_sonetaa(path_or_name_eeg, 'RS', path_to_mri, True)\r\n\r\n #set folders to save results of source reconstruction\r\n if os.path.isdir(path_or_name_eeg):\r\n #save results in subject folder\r\n folder_to_save_HDC = path_or_name_eeg + '/HDC'\r\n folder_to_save_RS = path_or_name_eeg + '/RS'\r\n else:\r\n #create a subject folder to save results\r\n path_results_subject = os.path.join(path_results_source_rec, path_or_name_eeg)\r\n if not os.path.exists(path_results_subject):\r\n os.mkdir(path_results_subject)\r\n folder_to_save_HDC = os.path.join(path_results_subject, 'HDC')\r\n folder_to_save_RS = os.path.join(path_results_subject, 'RS')\r\n #source rec for RS\r\n _, noise_cov = source.source_reconstruction(epochs_RS, method, lambda2, folder_to_save_RS, \r\n path_to_mri = path_to_mri, coreg = coreg, \r\n plot = plot, redo = redo)\r\n #source rec for HDC, using RS noise cov matrix\r\n _, _ = source.source_reconstruction(epochs_HDC, method, lambda2, folder_to_save_HDC,\r\n path_to_mri = path_to_mri, coreg = coreg, \r\n plot = plot, redo = redo, noise_cov = noise_cov)\r\n \r\n print('Source reconstruction computed for all subjects with method ' + method + '!')\r\n\r\n################################################################################################################\r\n#### POWER ANALYSIS\r\n################################################################################################################\r\n\r\ndef set_function_names(level):\r\n if level == \"cortex\":\r\n compute_morph, morph_source_space = compute_morph_surface, morph_surface_source_space\r\n elif level in [\"cereb\", \"cerebellum\"]:\r\n compute_morph, morph_source_space = compute_morph_volume, morph_volume_source_space \r\n \r\n return compute_morph, morph_source_space\r\n\r\ndef power_analysis_sensor_groups_psds(liste_ASD, liste_TD, path_to_figures, \r\n liste_cond, frequency):\r\n \"\"\"PSD analyses at sensor level between TD and ASD, between 2 conditions\r\n Save only the PSDs for later statistical analyses\r\n \"\"\"\r\n liste_cond, cond1 = check_list_cond(liste_cond)\r\n mean_psd_asd1, mean_psd_asd2 = None, None\r\n mean_psd_td1, mean_psd_td2 = None, None\r\n std_psd_asd1, std_psd_asd2 = None, None\r\n std_psd_td1, std_psd_td2 = None, None\r\n\r\n for i, liste in enumerate([liste_ASD, liste_TD]):\r\n for (path_or_name_eeg, path_to_mri) in liste:\r\n epochs_RS = recover_epochs_sonetaa(path_or_name_eeg, 'RS', path_to_mri)\r\n epochs_HDC = recover_epochs_sonetaa(path_or_name_eeg, 'HDC', path_to_mri)\r\n \r\n #compute PSD in cond 1 and cond 2\r\n for cond in liste_cond:\r\n epochs_condition = get_epochs_in_condition(cond, epochs_HDC, epochs_RS)\r\n psd, _, _ = power_spectral_density_welch(epochs_condition, frequency)\r\n \r\n #psd : variable nb of epochs (1) x n sensors x frequencies\r\n # average over frequencies n epochs x n sensors\r\n psd = np.mean(psd, axis = 2)\r\n moy_epochs = np.mean(psd, axis =0) #shape n sensors\r\n moy_epochs = np.reshape(moy_epochs, (1, moy_epochs.shape[0]))\r\n std_epochs = np.std(psd, axis = 0)\r\n std_epochs = np.reshape(std_epochs, (1, std_epochs.shape[0]))\r\n\r\n if cond is cond1 and i == 0:\r\n mean_psd_asd1 = add_to_array(mean_psd_asd1, moy_epochs)\r\n std_psd_asd1 = add_to_array(std_psd_asd1, std_epochs)\r\n elif cond is cond1 and i != 0:\r\n mean_psd_td1 = add_to_array(mean_psd_td1, moy_epochs)\r\n std_psd_td1 = add_to_array(std_psd_td1, std_epochs)\r\n elif cond is not cond1 and i == 0:\r\n mean_psd_asd2 = add_to_array(mean_psd_asd2, moy_epochs)\r\n std_psd_asd2 = add_to_array(std_psd_asd2, std_epochs)\r\n elif cond is not cond1 and i != 0:\r\n mean_psd_td2 = add_to_array(mean_psd_td2, moy_epochs)\r\n std_psd_td2 = add_to_array(std_psd_td2, std_epochs)\r\n\r\n save_results(\"PSD_sensors\", liste_cond, frequency, liste_TD+liste_ASD, \r\n path_to_figures = path_to_figures, Mtd1 = mean_psd_td1, Mtd2 = mean_psd_td2, \r\n Masd1 = mean_psd_asd1, Masd2 = mean_psd_asd2, \r\n Std1 = std_psd_td1, Std2= std_psd_td2,\r\n Sasd1 = std_psd_asd1, Sasd2 = std_psd_asd2)\r\n\r\ndef power_analysis_source_groups_psds(path_results_subjects, liste_ASD, liste_TD, path_to_figures, \r\n method, lambda2, subject_to, area, liste_cond,\r\n frequency, tfce = True, n_epochs = None):\r\n \"\"\" \r\n PSD analyses at source level between TD and ASD, between 2 conditions\r\n Write the Power Spectral densities for later statistical analyses\r\n\r\n path_results_subjects: path to where the subjects informations are stored\r\n liste_ASD: list of ASD subjects\r\n liste_TD: list of TD subjects\r\n path_to_figures: path to the folder where the figures and files are saved\r\n method: method for source reconstruction (MNE or eLORETA)\r\n lambda2: parameter to solve inverse problem\r\n subject_to: name of the subject to morph the subjects PSDs to a common source space (used fsaverage)\r\n area: cerebellum or cortex\r\n liste_cond: list of conditions of interest (HDC / RS)\r\n frequency: frequency of interest (alpha, theta...)\r\n \"\"\"\r\n liste_cond, cond1 = check_list_cond(liste_cond)\r\n compute_morph, morph_source_space = set_function_names(area)\r\n\r\n #compute zscore for each participant between HDC and RS\r\n mean_psd_asd1, mean_psd_asd2 = None, None\r\n mean_psd_td1, mean_psd_td2 = None, None\r\n std_psd_asd1, std_psd_asd2 = None, None\r\n std_psd_td1, std_psd_td2 = None, None\r\n\r\n for i, liste in enumerate([liste_ASD, liste_TD]):\r\n for (path_or_name_eeg, path_to_mri) in liste:\r\n epochs_RS = recover_epochs_sonetaa(path_or_name_eeg, 'RS', path_to_mri)\r\n epochs_HDC = recover_epochs_sonetaa(path_or_name_eeg, 'HDC', path_to_mri) \r\n \r\n #recover inverse operator and compute morph matrix for later morphing\r\n inverse_op = op.recover_inverse_op(path_or_name_eeg, 'RS', method, path_results_subjects)\r\n if i == 0:\r\n morph, src = compute_morph(subjects_dir, inverse_op, subject_to)\r\n \r\n for cond in liste_cond:\r\n #recover inverse operator\r\n if cond in [\"RS\", \"eo\", \"eyeo\", \"ec\", \"eyec\"]:\r\n inverse_op = op.recover_inverse_op(path_or_name_eeg, 'RS', method, path_results_subjects)\r\n else:\r\n inverse_op = op.recover_inverse_op(path_or_name_eeg, 'HDC', method, path_results_subjects)\r\n epochs_condition = get_epochs_in_condition(cond, epochs_HDC, epochs_RS)\r\n #compute PSD : a generator of PSDs over epochs (psd = sources x freq)\r\n psd = power_spectral_density_multitapper(epochs_condition[:n_epochs], inverse_op, \r\n method, lambda2, frequency, return_type='stc')\r\n \r\n #compute the mean and std of the subject PSDs over epochs\r\n array_epochs = None\r\n for (_, stc) in enumerate(psd):\r\n #transport PSD to common source space\r\n psd_morphed = stc.mean()\r\n psd_morphed = morph_source_space(morph, src, psd_morphed, subjects_dir, \r\n inverse_op, subject_to, path_to_figures)\r\n array_epochs = add_to_array(array_epochs, psd_morphed.data, axis = 1)\r\n\r\n moy_epochs = np.mean(array_epochs, axis = 1)\r\n moy_epochs = np.expand_dims(moy_epochs, axis = 0)\r\n std_epochs = np.std(array_epochs, axis = 1)\r\n std_epochs = np.expand_dims(std_epochs, axis = 0)\r\n \r\n #add the mean and std to the relevant list \r\n if cond is cond1 and i == 0:\r\n mean_psd_asd1 = add_to_array(mean_psd_asd1, moy_epochs)\r\n std_psd_asd1 = add_to_array(std_psd_asd1, std_epochs)\r\n elif cond is cond1 and i != 0:\r\n mean_psd_td1 = add_to_array(mean_psd_td1, moy_epochs)\r\n std_psd_td1 = add_to_array(std_psd_td1, std_epochs)\r\n elif cond is not cond1 and i == 0:\r\n mean_psd_asd2 = add_to_array(mean_psd_asd2, moy_epochs)\r\n std_psd_asd2 = add_to_array(std_psd_asd2, std_epochs)\r\n elif cond is not cond1 and i != 0:\r\n mean_psd_td2 = add_to_array(mean_psd_td2, moy_epochs)\r\n std_psd_td2 = add_to_array(std_psd_td2, std_epochs)\r\n\r\n save_results(\"PSD_\"+area, liste_cond, frequency, liste_TD + liste_ASD, tfce, path_to_figures, \r\n method, Mtd1 = mean_psd_td1, Mtd2 = mean_psd_td2, \r\n Masd1 = mean_psd_asd1, Masd2 = mean_psd_asd2, \r\n Std1 = std_psd_td1, Std2= std_psd_td2,\r\n Sasd1 = std_psd_asd1, Sasd2 = std_psd_asd2)\r\n \r\ndef power_analysis_statistics(path_results_subjects, psd_file, path_to_figures, list_subjects,\r\n liste_TD, liste_ASD, method, subject_to, level, liste_cond,\r\n tfce = True, compare = \"all\", score = \"zscore\", frequency = \"theta\"):\r\n \"\"\"\r\n Open an array of PSDs and compute statistics (inter and/or intra group)\r\n \"\"\"\r\n #exclusions\r\n indices_td, indices_asd = sujets_exclusion_psds(path_to_figures)\r\n indices_asd, indices_td = np.asarray(indices_asd), np.asarray(indices_td)\r\n\r\n liste_cond, _ = check_list_cond(liste_cond)\r\n (path_or_name_eeg, path_to_mri) = list_subjects[0] \r\n epochs_RS = recover_epochs_sonetaa(path_or_name_eeg, 'RS', path_to_mri) \r\n\r\n if level not in [\"sensor\", \"sensors\"]:\r\n inverse_op = op.recover_inverse_op(path_or_name_eeg, 'RS', method, path_results_subjects)\r\n compute_morph, _ = set_function_names(level)\r\n _, src = compute_morph(subjects_dir, inverse_op, subject_to)\r\n\r\n parent_folder = os.path.dirname(psd_file)\r\n\r\n # Load dara\r\n npzfile = np.load(psd_file, allow_pickle = True)\r\n\r\n # Exclusions\r\n Mtd1 = np.delete(npzfile[\"Mtd1\"], obj = indices_td, axis = 0)\r\n Mtd2 = np.delete(npzfile[\"Mtd2\"], obj = indices_td, axis = 0)\r\n Masd1 = np.delete(npzfile[\"Masd1\"], obj = indices_asd, axis = 0)\r\n Masd2 = np.delete(npzfile[\"Masd2\"], obj = indices_asd, axis = 0)\r\n Std1 = np.delete(npzfile[\"Std1\"], obj = indices_td, axis = 0)\r\n Sasd1 = np.delete(npzfile[\"Sasd1\"], obj = indices_asd, axis = 0)\r\n\r\n #compute z score\r\n score_td = zscore(Mtd2, Mtd1, Std1)\r\n score_asd = zscore(Masd2, Masd1, Sasd1)\r\n \r\n if level in [\"sensor\", \"sensors\"]:\r\n connectivity, _ = find_ch_adjacency(epochs_RS.info, ch_type='eeg')\r\n else:\r\n connectivity = mne.spatial_src_connectivity(src)\r\n if compare == \"all\":\r\n clu = cluster_permutation_test_1sample(score_td, connectivity, tfce = tfce)\r\n save_results(level, liste_cond, frequency, liste_TD, tfce, parent_folder, \r\n method, score = score, Xtd=score_td, T_obs = clu[0], clusters = clu[1],\r\n clusters_pvalues = clu[2], participants = \"td\")\r\n \r\n clu = cluster_permutation_test_1sample(score_asd, connectivity, tfce = tfce)\r\n save_results(level, liste_cond, frequency, liste_ASD, tfce, parent_folder, method, \r\n score = score, Xasd=score_asd, T_obs = clu[0], clusters = clu[1],\r\n clusters_pvalues = clu[2], participants = \"asd\")\r\n\r\n clu = cluster_permutation_test_2sample(score_asd, score_td, connectivity, tfce = tfce)\r\n save_results(level, liste_cond, frequency, liste_ASD + liste_TD, tfce, parent_folder, method, \r\n score = score, Xasd=score_asd, Xtd=score_td, T_obs = clu[0], clusters = clu[1],\r\n clusters_pvalues = clu[2])\r\n\r\n","sub_path":"HDC/EEG/analyses_groupes.py","file_name":"analyses_groupes.py","file_ext":"py","file_size_in_byte":14494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"319534008","text":"# DO *NOT* WRITE YOUR NAME TO MAINTAIN ANONYMITY FOR PLAGIARISM DETECTION\n#\n# Call multibase a nonempty finite sequence of strictly positive\n# integers (a_n, ..., a_0) all at most equal to 10.\n# A positive integer is said to be a valid representation in the\n# multibase if it is of the form x_k...x_0 with k <= n and for all\n# i in {0,...,k}, x_i < a_i. Its representation in base 10 is defined as\n# x_0 + x_1*a_0 + x_2*a_0*a_1 + ... + x_k*a_0*a_1*...*a_{k-1}.\n#\n# Determines the largest integer with a valid representation\n# in the multibase, and converts representations between base\n# 10 and the multibase.\n#\n# For instance, consider the multibase (8, 10, 6, 1).\n# - 3820 is a valid representation in the multibase; it is the number\n# that in base 10 reads as 0 + 2*1 + 8*6*1 + 3*10*6*1 = 230.\n# - 432, as a decimal number, can be represented in the multibase,\n# reading as 7200, since 2*6 + 7*10*6 = 432.\n\nimport sys\n\ntry: \n multibase = [int(x) for x in input('Enter a nonempty sequence of integers '\n 'between 1 and 10: '\n ).split()\n ]\n if not len(multibase) or any(b < 1 or b > 10 for b in multibase): \n raise ValueError\nexcept ValueError:\n print('Incorrect input, giving up.')\n sys.exit()\ntry: \n input_multibase_representation =\\\n int(input('Enter a first positive number: '))\n if input_multibase_representation < 0: \n raise ValueError\nexcept ValueError:\n print('Incorrect input, giving up.')\n sys.exit()\ntry: \n input_base_10_representation =\\\n int(input('Enter a second positive number: '))\n if input_base_10_representation < 0: \n raise ValueError\nexcept ValueError:\n print('Incorrect input, giving up.')\n sys.exit()\n\nvalid_multibase_representation = True\nvalid_base_10_representation = True\nmax_number = 0\noutput_multibase_representation = 0\noutput_base_10_representation = 0\n\n# INSERT YOUR CODE HERE\nr_multibase=multibase[::-1]\nfor i in range(1,len(r_multibase)):\n r_multibase[i]=r_multibase[i]*r_multibase[i-1]\nmax_num_base=multibase[::-1]\n\nfor i in range(1,len(max_num_base)):\n max_number=(max_num_base[i]-1)*r_multibase[i-1]+max_number\nmax_number=max_number+max_num_base[0]-1\n#print(max_num_base,r_multibase)\n\nm1=multibase[::-1]\nm2=[int(x) for x in str(input_multibase_representation)[::-1]]\nif len(m2)>len(m1):\n valid_multibase_representation=False\nelse:\n for i in range(len(m2)) :\n if m2[i]>=m1[i]:\n valid_multibase_representation=False\n break\n else:\n if i==0:\n output_base_10_representation=output_base_10_representation+m2[0]\n elif i >0:\n output_base_10_representation=output_base_10_representation+m2[i]*r_multibase[i-1]\n\n\nif input_base_10_representation > max_number:\n valid_base_10_representation=False\nelse:\n b=''\n num=input_base_10_representation\n for i in range(len(m1)):\n a=num//m1[i]\n #print(b,num % m1[i])\n b=str(num % m1[i])+b\n num=a\n #print(b,str(num % m1[i]))\n output_multibase_representation=int(b)\n\n\n\n\n\n\n\nprint('The largest number that can be represented in this multibase is:',\n max_number\n )\nif not valid_multibase_representation:\n print(input_multibase_representation,\n 'is not a valid representation in the given multibase.'\n )\nelse:\n print('In base 10,', input_multibase_representation,\n 'reads as:', output_base_10_representation\n )\nif not valid_base_10_representation:\n print(input_base_10_representation, 'cannot be represented in the '\n 'given multibase.'\n )\nelse:\n print('In the given multibase,', input_base_10_representation,\n 'reads as:', output_multibase_representation\n )\n\n\n\n\n\n","sub_path":"quiz3/quiz_3.py","file_name":"quiz_3.py","file_ext":"py","file_size_in_byte":3836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"632510674","text":"\"\"\"\r\n基于python和selenium实现的大麦网自动刷新抢票脚本\r\n用户要提前添加好个人信息和收货地址\r\n\"\"\"\r\n\r\nimport requests\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.common.exceptions import TimeoutException\r\nimport time\r\n\r\n# 设置抢票链接和开票时间\r\nURL = \"https://piao.damai.cn/138382.html?spm=a2o6e.search.0.0.2b5328df7CB40F\"\r\nHOUR = 11\r\nMIN = 18\r\n\r\ndriver = webdriver.Chrome()\r\n# 设置等待时间\r\nwait = WebDriverWait(driver, 5)\r\n# 以周杰伦的为例\r\ndriver.get(URL)\r\n\r\n# test\r\n# driver.get(\"https://piao.damai.cn/141699.html?spm=a2o6e.search.0.0.10f94d15pF81cd\")\r\n\r\n\r\ndef choose(seletor):\r\n try:\r\n # 控件可点击时才选定\r\n choice = wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, seletor)))\r\n return choice\r\n except TimeoutException as e:\r\n print(\"Time out!\")\r\n return None\r\n except Exception:\r\n print(\"Not found!\")\r\n return None\r\n\r\n# 点击登录\r\nlogin = choose(\"#userLoginInfo > span > a:nth-child(1)\")\r\nlogin.click()\r\nusername = choose(\"#login_email\")\r\nusername.send_keys(\"13112390306\")\r\n\"\"\"\r\n由于密码框控件被设置为不可见\r\n先自行输入密码并记住密码\r\n方便刷新\r\n（也可用cookie实现）\r\n\"\"\"\r\n\r\n# 10秒等待用户输入密码后再开始刷\r\ntime.sleep(10)\r\n\r\nwhile 1:\r\n if HOUR == time.localtime().tm_hour:\r\n while 1:\r\n if MIN == time.localtime().tm_min:\r\n print(\"开始抢票\")\r\n driver.get(URL)\r\n price = None\r\n plus = None\r\n buybtn = None\r\n submit = None\r\n # 点击价格\r\n while None == price:\r\n # 这里选的是580票面的，如果选其他票面，修改对应的选择器内容即可\r\n price = choose(\"li.itm-oos:nth-child(2)\")\r\n print(price)\r\n price.click()\r\n # 数量加1\r\n while None == plus:\r\n plus = choose(\"a.btn:nth-child(3)\")\r\n plus.click()\r\n # 立即抢购\r\n while None == buybtn:\r\n buybtn = choose(\"#btnBuyNow\")\r\n buybtn.click()\r\n # 提交订单\r\n while None == submit:\r\n submit = choose(\"#orderConfirmSubmit\")\r\n submit.click()\r\n break\r\n \r\ntime.sleep(10)\r\n# driver.quit()\r\nprice(\"抢票成功\")\r\n","sub_path":"damai.py","file_name":"damai.py","file_ext":"py","file_size_in_byte":2711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"250318820","text":"import xlsxwriter\nimport xlrd\nexcel = xlrd.open_workbook(\"mainbasket_daftarsteaming.xlsx\")\nsheetdata = excel.sheet_by_index(0)\n'''\ndata.name = nama sheet\ndata.nrows = jumah baris\ndata.ncols = jumlah kolom\n'''\n\n\nworkbook = xlsxwriter.Workbook('mainbasket_vsmsatufitur.xlsx')\nworksheet1 = workbook.add_worksheet()\n\ndata=[]\nkumpulan_katadasar=\"\"\nfor i in range(1,sheetdata.nrows):\n\n value = sheetdata.cell_value(rowx=i, colx=2)\n\n kumpulan_katadasar=kumpulan_katadasar+value\n data.append(value)\n\nprint (kumpulan_katadasar)\n\n\ntemp = {}\nkal = kumpulan_katadasar.split(\" \")\nfor i in kal:\n if i not in temp:\n temp[i] = 1\n else:\n hasil_sementara = temp[i]\n temp[i] = hasil_sementara + 1\n\n\n# print (temp)\n\n\ndef list_fitur():\n t = {}\n semua_kata = \"\"\n for i in data:\n #print (i)\n semua_kata = semua_kata + i + \" \"\n\n for j in semua_kata.split(\" \"):\n t[j] = 0\n\n del t[\"\"]\n return t\n\nprint (len(list_fitur()))\n\nprint ((list_fitur()))\n\ndef hapus_fitur():\n temp=[]\n tampung_data=[]\n for baris in range(1, sheetdata.nrows):\n value = sheetdata.cell_value(rowx=baris, colx=2)\n tempkat = value.split(\" \")\n tampung_data.append(tempkat)\n\n kolom=0\n for kata in list_fitur():\n count=0\n for i in range(len(tampung_data)):\n if kata in tampung_data[i] :\n count+=1\n\n if count>1:\n if kata not in temp:\n temp.append(kata)\n worksheet1.write(0,kolom+1,kata)\n\n\n kolom+=1\n\n break\n return temp\n\nprint (\"panjang\",len(hapus_fitur()))\nprint (\"temp_fitur\",(hapus_fitur()))\n\n\n\ndef hitung_katatiapbaris():\n tampung_data = []\n for baris in range(1, sheetdata.nrows):\n value = sheetdata.cell_value(rowx=baris, colx=2)\n temp = value.split(\" \")\n dict_baris = {}\n for i in temp:\n if i == '':\n continue\n temp_count = 0\n\n for j in temp:\n\n if i == j:\n temp_count += 1\n dict_baris[i] = temp_count\n tampung_data.append(dict_baris)\n\n return tampung_data\n\nfor baris in range(1,sheetdata.nrows):\n kolom=1\n for temp_fitur in hapus_fitur():\n\n if temp_fitur not in hitung_katatiapbaris()[baris-1]:\n hasil = 0\n #continue\n else:\n hasil=hitung_katatiapbaris()[baris-1][temp_fitur]\n\n print (\"data ke>>\",baris,\"kata>>\",temp_fitur,\"ada>>\",hasil)\n\n worksheet1.write(baris, kolom, int(hasil))\n\n kolom+=1\nworkbook.close()\n\n\n\n","sub_path":"mainbasket_sastrawi_seringmuncul.py","file_name":"mainbasket_sastrawi_seringmuncul.py","file_ext":"py","file_size_in_byte":2619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"194432152","text":"import time\nimport sys\n\n'''\n Progress Bar\n Parameters: width = number of iterations\n'''\n\nclass progressBar():\n\n ''' Init function '''\n def __init__(self, width):\n if width is None:\n self.width = 40\n else:\n self.width = width\n\n sys.stdout.write(\"[%s]\" % (\" \" * self.width))\n sys.stdout.flush()\n sys.stdout.write(\"\\b\" * (self.width+1)) # return to start of line, after '['\n \n self.iter = 0\n \n ''' Update function > use this inside for loops '''\n def update(self):\n sys.stdout.write(\"-\")\n sys.stdout.flush()\n \n self.iter = self.iter + 1\n \n if self.iter == self.width:\n sys.stdout.write(\"]\\n\") # this ends the progress bar\n \n\nif __name__ == '__main__':\n\n pBar = progressBar(40)\n\n for i in range(pBar.width):\n time.sleep(0.1) # do real work here\n # update the bar\n pBar.update()","sub_path":"progressBar.py","file_name":"progressBar.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"13370866","text":"import math\n\nN = 5\nARR = [2, 4, 4, 0, 2]\n\nN = 7\nARR = [6, 4, 0, 2, 4, 0, 2]\n#\n# N = 8\n# ARR = [7, 5, 1, 1, 7, 3, 5, 3]\n#\nN = int(input())\nARR = list(map(int, input().split()))\n\n\ndef calculate(n, arr):\n if n % 2 == 0:\n n = n // 2\n start = 1\n finalResult = pow(2, n, 1000000000 + 7)\n else:\n n = n // 2 + 1\n start = 0\n finalResult = pow(2, (n - 1), 1000000000 + 7)\n\n result = {}\n for i in range(n):\n index = start + 2 * i\n if index == 0:\n result.__setitem__(0, 1)\n else:\n result.__setitem__(index, 2)\n\n isOk = True\n for i in range(len(arr)):\n if result.get(arr[i]) == None:\n isOk = False\n break\n\n if result.get(arr[i]) == 0:\n isOk = False\n break\n\n result.__setitem__(arr[i], result.get(arr[i])-1)\n\n if isOk == False:\n print(0)\n return\n\n if sum(result.values()) == 0:\n print(finalResult)\n else:\n print(0)\n\n\ncalculate(N, ARR)\n","sub_path":"Python_codes/p03846/s527402211.py","file_name":"s527402211.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"70215043","text":"#!/usr/bin/env python\n\nfrom collections import Counter\n\ncounts = Counter() # <1>\n\nwith open(\"../DATA/breakfast.txt\") as breakfast_in:\n for line in breakfast_in:\n item = line.rstrip('\\n\\r') # <2>\n counts[item] += 1 # <3>\n\n# for item, count in counts.items(): # <4>\n# print(item, count)\n\nprint(counts)\n\n\nwith open('../DATA/words.txt') as words_in:\n all_letters = [w[0] for w in words_in]\n\nword_counter = Counter(all_letters)\n\nprint(word_counter.most_common(6))\n\nprint(Counter(w[0] for w in open('../DATA/words.txt')).most_common(6))\n","sub_path":"EXAMPLES/count_with_counter.py","file_name":"count_with_counter.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"473924029","text":"import sys\nimport os\n \ndef createEntityModel(_entityName):\n header = \"\"\"#ifndef INCLUDED_EXPEDITION_COMPONENTS_MODELS_{upperEntityName}_MODEL_HPP_\n#define INCLUDED_EXPEDITION_COMPONENTS_MODELS_{upperEntityName}_MODEL_HPP_\n\n#include \n\nnamespace expd {{\n\n class {entityName}Model : public ModelBase {{\n public:\n {entityName}Model(void);\n ~{entityName}Model(void);\n }};\n\n}}\n\n#endif // INCLUDED_EXPEDITION_COMPONENTS_MODELS_{upperEntityName}_MODEL_HPP_\n\"\"\".format(\n upperEntityName = _entityName.upper(), \n entityName = _entityName\n )\n source = \"\"\"#include \n\nnamespace expd {{\n\n {entityName}Model::{entityName}Model(void) {{\n \n }}\n\n {entityName}Model::~{entityName}Model(void) {{\n \n }}\n\n}}\n\"\"\".format(\n entityName = _entityName\n)\n \n return (source, header)\n\ndef createEntityView(_entityName):\n header = \"\"\"#ifndef INCLUDED_EXPEDITION_COMPONENTS_VIEWS_{upperEntityName}_VIEW_HPP_\n#define INCLUDED_EXPEDITION_COMPONENTS_VIEWS_{upperEntityName}_VIEW_HPP_\n\n#include \n\nnamespace expd {{\n class {entityName}Model;\n class {entityName}View : public ViewBase {{\n public:\n {entityName}View({entityName}Model *_model);\n ~{entityName}View(void) override;\n\n\t\tvoid update(float _delta) override;\n\t\tvoid draw(sf::RenderTarget& _target, sf::RenderStates _states) const override;\n\n private:\n {entityName}Model *m_Model;\n }};\n\n}}\n\n#endif // INCLUDED_EXPEDITION_COMPONENTS_VIEWS_{upperEntityName}_VIEW_HPP_\n\"\"\".format(\n upperEntityName = _entityName.upper(), \n entityName = _entityName\n)\n source = \"\"\"#include \n#include \n\nnamespace expd {{\n\n {entityName}View::{entityName}View({entityName}Model *_model) :\n m_Model(_model) {{\n \n }}\n {entityName}View::~{entityName}View(void) {{\n \n }}\n\n void {entityName}View::update(float _delta) {{\n\n }}\n void {entityName}View::draw(sf::RenderTarget& _target, sf::RenderStates _states) const {{\n\n }}\n}}\n\"\"\".format(\n entityName = _entityName\n)\n \n return (source, header)\n\ndef createEntityController(_entityName):\n header = \"\"\"#ifndef INCLUDED_EXPEDITION_COMPONENTS_CONTROLLERS_{upperEntityName}_CONTROLLER_HPP_\n#define INCLUDED_EXPEDITION_COMPONENTS_CONTROLLERS_{upperEntityName}_CONTROLLER_HPP_\n\n#include \n\nnamespace expd {{\n\tclass {entityName}Model;\n\tclass {entityName}Controller : public ControllerBase {{\n\tpublic:\n\t\t{entityName}Controller({entityName}Model *_model);\n\t\t~{entityName}Controller(void) override;\n\n\t\tvoid update(float _delta) override;\n\t\tbool handleMessage(const GameMessage& _message) override;\n\t\tbool handleEvent(const sf::Event& _event) override;\n\n\tprivate:\n\t\t{entityName}Model * m_Model;\n\t}};\n}}\n\n#endif // INCLUDED_EXPEDITION_COMPONENTS_CONTROLLERS_{upperEntityName}_CONTROLLER_HPP_\n\"\"\".format(\n upperEntityName = _entityName.upper(), \n entityName = _entityName\n)\n source = \"\"\"#include \n#include \n\nnamespace expd {{\n\n\t{entityName}Controller::{entityName}Controller({entityName}Model *_model) :\n\t\tControllerBase(_model),\n\t\tm_Model(_model) {{\n\n\t}}\n\t{entityName}Controller::~{entityName}Controller(void) {{\n\n\t}}\n\n\tvoid {entityName}Controller::update(float _delta) {{\n\n\t}}\n\tbool {entityName}Controller::handleMessage(const GameMessage& _message) {{\n\t\treturn false;\n\t}}\n\tbool {entityName}Controller::handleEvent(const sf::Event& _event) {{\n\t\treturn false;\n\t}}\n}}\n\"\"\".format(\n entityName = _entityName\n)\n return (source, header)\n\nclass EntityCreation:\n 'Class to help creating entities'\n\n def __init__(self, projectName, projectPath):\n self.ProjectName = projectName\n self.ProjectPath = projectPath\n self.SourceLocation = \"src\"\n self.HeaderLocation = \"include\"\n self.ModelFolder = \"Models\"\n self.ViewFolder = \"Views\"\n self.ControllerFolder = \"Controllers\"\n self.ComponentsFolder = \"Components\"\n pass\n\n def createEntity(self, _entityName):\n (self.modelSource, self.modelHeader) = createEntityModel(_entityName)\n (self.viewSource, self.viewHeader) = createEntityView(_entityName)\n (self.controllerSource, self.controllerHeader) = createEntityController(_entityName)\n\n self.ModelSourceFilePath = os.path.join(self.ProjectPath, self.SourceLocation, self.ProjectName, self.ComponentsFolder, self.ModelFolder, _entityName + \"Model.cpp\")\n self.ModelHeaderFilePath = os.path.join(self.ProjectPath, self.HeaderLocation, self.ProjectName, self.ComponentsFolder, self.ModelFolder, _entityName + \"Model.hpp\")\n\n self.ViewSourceFilePath = os.path.join(self.ProjectPath, self.SourceLocation, self.ProjectName, self.ComponentsFolder, self.ViewFolder, _entityName + \"View.cpp\")\n self.ViewHeaderFilePath = os.path.join(self.ProjectPath, self.HeaderLocation, self.ProjectName, self.ComponentsFolder, self.ViewFolder, _entityName + \"View.hpp\")\n\n self.ControllerSourceFilePath = os.path.join(self.ProjectPath, self.SourceLocation, self.ProjectName, self.ComponentsFolder, self.ControllerFolder, _entityName + \"Controller.cpp\")\n self.ControllerHeaderFilePath = os.path.join(self.ProjectPath, self.HeaderLocation, self.ProjectName, self.ComponentsFolder, self.ControllerFolder, _entityName + \"Controller.hpp\")\n\n pass\n\n def validateEntityFilesExist(self, _entityName):\n\n if (os.path.exists(self.ModelSourceFilePath)):\n print('Model Source file exists')\n return False\n if (os.path.exists(self.ModelHeaderFilePath)):\n print('Model Header file exists')\n return False\n\n if (os.path.exists(self.ViewSourceFilePath)):\n print('View Source file exists')\n return False\n if (os.path.exists(self.ViewHeaderFilePath)):\n print('View Header file exists')\n return False\n\n if (os.path.exists(self.ControllerSourceFilePath)):\n print('Controller Source file exists')\n return False\n if (os.path.exists(self.ControllerHeaderFilePath)):\n print('Controller Header file exists')\n return False\n\n return True\n \n def createEntityFiles(self, _entityName):\n with open(self.ModelSourceFilePath,'w+') as f:\n f.write(self.modelSource)\n with open(self.ModelHeaderFilePath,'w+') as f:\n f.write(self.modelHeader)\n \n with open(self.ViewSourceFilePath,'w+') as f:\n f.write(self.viewSource)\n with open(self.ViewHeaderFilePath,'w+') as f:\n f.write(self.viewHeader)\n \n with open(self.ControllerSourceFilePath,'w+') as f:\n f.write(self.controllerSource)\n with open(self.ControllerHeaderFilePath,'w+') as f:\n f.write(self.controllerHeader)\n\n\nif __name__ == '__main__':\n EntityName = \"TileBound\"\n ProjectName = \"Expedition\"\n\n Path = os.getcwd()\n res = Path[:Path.find(ProjectName) + len(ProjectName)]\n\n entityCreate = EntityCreation(ProjectName, res)\n\n entityCreate.createEntity(EntityName)\n\n if (not entityCreate.validateEntityFilesExist(EntityName)):\n exit()\n\n entityCreate.createEntityFiles(EntityName)\n ","sub_path":"utility/Python/CreateEntity.py","file_name":"CreateEntity.py","file_ext":"py","file_size_in_byte":7571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"483542236","text":"import json2dat\nimport os\nimport re\nimport multiprocessing\n\nGraph_Json_Path = '../data/graph/'\nGraph_Json_Data = '../data/graph_json_simple'\nGraph_Binary_Path = '../data/graph_binary'\nMeta_File = '../data/meta'\nThread_Count = 8\n\n\ndef convert(graph_files, meta_info, out_path):\n for gf in graph_files:\n filename, _ = os.path.splitext(os.path.split(gf)[1])\n output_file = os.path.join(out_path, filename + '.dat')\n if os.path.exists(output_file):\n print(\"Skip: %s\" % output_file)\n else:\n c = json2dat.Converter(meta_info, gf, output_file+'.tmp')\n c.do()\n os.rename(output_file+'.tmp', output_file)\n print(multiprocessing.current_process().name + ' Finished')\n\n\ndef get_graph_files(path):\n graph_files = []\n re_gf = re.compile(\"part-\\d+\")\n for root, _, files in os.walk(path):\n for name in files:\n m = re_gf.match(name)\n if not m: continue\n if m.end(0) == len(name):\n graph_files.append(os.path.join(root, name))\n return graph_files\n\n\n#def create_graph_files(path):\n\n\nif __name__ == '__main__':\n gf = [Graph_Json_Data]\n c = len(gf)\n print(c)\n t_c = int(c / Thread_Count)\n tasks = []\n for i in range(0, c):\n p_gf = gf[:]\n t = multiprocessing.Process(name='Task-%d-%d'% (i, i+len(p_gf)-1),\n target=convert, args=(p_gf, Meta_File, Graph_Binary_Path,))\n tasks.append(t)\n t.start()\n for t in tasks:\n t.join()\n","sub_path":"data_process/convert_to_binary.py","file_name":"convert_to_binary.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"495164947","text":"import pytest\nimport torch\n\nfrom baal.utils import array_utils\nfrom baal.utils.iterutils import map_on_tensor\n\n\n@pytest.fixture()\ndef a_tensor():\n return torch.randn([10, 3, 32, 32])\n\n\ndef test_stack_in_memory_single(a_tensor):\n iterations = 10\n out = array_utils.stack_in_memory(a_tensor, iterations=iterations)\n assert out.shape == (10 * iterations, 3, 32, 32)\n\n\ndef test_stack_in_memory_multi(a_tensor):\n iterations = 10\n t = [a_tensor, a_tensor]\n out = map_on_tensor(lambda ti: array_utils.stack_in_memory(ti, iterations=iterations), t)\n assert out[0].shape == (10 * iterations, 3, 32, 32)\n assert out[1].shape == (10 * iterations, 3, 32, 32)\n\n\nif __name__ == '__main__':\n pytest.main()\n","sub_path":"tests/utils/test_array_utils.py","file_name":"test_array_utils.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"26713796","text":"import json\nfrom pymongo import MongoClient\nimport math\nimport pandas as pd\nfrom bson import ObjectId\n\nDB_URI = 'mongodb:///admin'\nDB_NAME = 'interactive_cloze'\n\nmongo_cursor = MongoClient(DB_URI, connect = False)\ndb = mongo_cursor[DB_NAME]\n\nCSV_FILE = \"raw_survey\"\n\nCOLLECTION_NAME = \"survey\"\n\ndef get_survey_key():\n survey_key = None\n with open('survey_key2.json') as f:\n survey_key = json.load(f)\n \n if survey_key:\n return survey_key\n\n return False\n\ndef populate_db():\n print (\"populating db with \" + CSV_FILE + \".csv\")\n db[COLLECTION_NAME].remove({})\n df = pd.read_csv( CSV_FILE + \".csv\")\n for i,row in df.iterrows():\n doc = dict()\n for column in df.columns:\n if (not (type(row[column]) is str)):\n if (not math.isnan(row[column])):\n doc[column] = row[column]\n else:\n doc[column] = row[column]\n db[COLLECTION_NAME].update({'ResponseId':doc['ResponseId']}, doc, upsert = True)\n print ('[' + CSV_FILE + '][' + str(i) + \"] inserted into db...\")\n print (\"COMPLETE: populated db with \" + CSV_FILE + \".csv\")\n\ndef clean_and_process():\n print (\"cleaning and processing db...\")\n survey_key = get_survey_key()\n \n if survey_key:\n cnt = 0\n total = db[COLLECTION_NAME].find().count()\n for doc in db[COLLECTION_NAME].find(no_cursor_timeout=True):\n for key in doc.keys():\n if key[0] == 'Q':\n question_label = key[1:]\n #iterate categories\n for i in range(len(survey_key)):\n category_label = survey_key[i]['category_label']\n #iterate docs\n for j in range(len(survey_key[i]['docnames'])):\n docname = survey_key[i]['docnames'][j]['docname']\n timing_qno = survey_key[i]['docnames'][j]['timing_qno']\n\n if '_' in question_label:\n if timing_qno == question_label.split('_')[0]:\n db[COLLECTION_NAME].update({'_id':doc['_id']}, {'$set':{category_label + '_docname': docname}}, upsert=True)\n db[COLLECTION_NAME].update({'_id':doc['_id']}, {'$set':{category_label + '_' + question_label.split('_')[1]: doc[key]}}, upsert=True)\n break\n \n else:\n #iterate questions\n # print (survey_key[i]['docnames'][j])\n for k in range(len(survey_key[i]['docnames'][j]['questions'])):\n qno = survey_key[i]['docnames'][j]['questions'][k]['qno']\n answer = survey_key[i]['docnames'][j]['questions'][k]['answer']\n\n if qno == question_label:\n if doc[key] == answer:\n db[COLLECTION_NAME].update({'_id':doc['_id']}, {'$set':{category_label + '_docname': docname}}, upsert=True)\n db[COLLECTION_NAME].update({'_id':doc['_id']}, {'$set':{category_label + '_Q' + str(k+1): True}}, upsert=True)\n else:\n db[COLLECTION_NAME].update({'_id':doc['_id']}, {'$set':{category_label + '_docname': docname}}, upsert=True)\n db[COLLECTION_NAME].update({'_id':doc['_id']}, {'$set':{category_label + '_Q' + str(k+1): False}}, upsert=True)\n\n cnt += 1\n print (str(cnt) + '/' + str(total))\n print (\"COMPLETE: db processed...\")\n\ndef create_csv():\n print (\"creating csv...\")\n survey_key = get_survey_key()\n\n attrs = ['MID']\n \n for i in range(len(survey_key)):\n category_label = survey_key[i]['category_label']\n attrs.append(category_label + '_Q1')\n attrs.append(category_label + '_Q2')\n attrs.append(category_label + '_Q3')\n attrs.append(category_label + '_First Click')\n attrs.append(category_label + '_Last Click')\n attrs.append(category_label + '_Page Submit')\n attrs.append(category_label + '_Click Count')\n attrs.append(category_label + '_docname')\n\n \n file_name = \"survey_results.csv\"\n\n results = dict()\n for attr in attrs:\n results[attr] = list()\n\n for doc in db[COLLECTION_NAME].find({'MID':{'$exists':True}},no_cursor_timeout=True):\n if (doc['StartDate'] == 'Start Date') or (doc['StartDate'] == '{\"ImportId\":\"startDate\",\"timeZone\":\"America/New_York\"}'):\n continue\n for attr in attrs:\n if attr in doc:\n results[attr].append(doc[attr])\n else:\n results[attr].append(None)\n\n # for attr in attrs:\n # print (attr + ' ' +str(len(results[attr])))\n # print (results)\n df = pd.DataFrame(data=results)\n df.to_csv(file_name)\n print (file_name + \" saved.\")\n\npopulate_db()\nclean_and_process()\ncreate_csv()\n","sub_path":"comprehensionQuestions/score_survey.py","file_name":"score_survey.py","file_ext":"py","file_size_in_byte":5220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"522282538","text":"\"\"\"Pendulum solution.\"\"\"\n\nimport tensorflow as tf\nimport gym\n\n\nclass BipedalWalkerAgent:\n def __init__(self):\n self.actor, self.critic = (None, None)\n self.load_models()\n assert((self.actor, self.critic) != (None, None))\n\n def load_models(self):\n try:\n self.critic = tf.keras.models \\\n .load_model('./bipedal_walker_solution/critic')\n self.actor = tf.keras.models \\\n .load_model('./bipedal_walker_solution/actor')\n return True\n except Exception as err:\n print(err)\n\n def get_action(self, state):\n return self.actor(state)*2\n\n\ndef play(steps):\n env = gym.make('BipedalWalker-v3')\n agent = BipedalWalkerAgent()\n state = env.reset()\n agent.actor.summary()\n agent.critic.summary()\n for i in range(steps):\n action = agent.get_action(state[None])[0]\n state, reward, done, _ = env \\\n .step(action)\n state = state\n env.render()\n\n\nplay(steps=1000)\n","sub_path":"bipedal_walker_solution/play.py","file_name":"play.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"462841942","text":"import netaddr\nfrom oslo_config import cfg\nfrom oslo_log import log\n\nfrom ovn_k8s import constants\nfrom ovn_k8s.lib import ovn\nfrom ovn_k8s import utils\n\nLOG = log.getLogger(__name__)\n\n\ndef init_conf(args):\n # Register options\n opts = [\n cfg.StrOpt('lrouter_name', default=constants.DEFAULT_LROUTER_NAME),\n cfg.StrOpt('k8s_api_server_host', default='127.0.0.1'),\n cfg.IntOpt('k8s_api_server_port', default='8080'),\n cfg.StrOpt('ovn_nb_remote',\n default='unix:/var/run/openvswitch/nb_db.sock'),\n cfg.FloatOpt('coalesce_interval', default='0.1',\n help=('Interval in seconds for coalescing events.'\n 'There will be a delay in event processing equal '\n 'to the value of this parameter')),\n cfg.BoolOpt('enable_networkpolicy', default=False,\n help=('Set to True to enable watching and processing '\n 'network policy objects'))]\n cfg.CONF.register_opts(opts)\n cfg.CONF(args=args, project='ovn-k8s')\n\n\ndef _check_vswitch(lswitch_name):\n lswitch_raw_data = ovn.ovn_nbctl('find', 'Logical_Switch',\n 'name=%s' % lswitch_name)\n lswitch_data = ovn.parse_ovn_nbctl_output(lswitch_raw_data)\n if len(lswitch_data) > 1:\n LOG.warn(\"I really was not expecting more than one switch... I'll \"\n \"pick the first, there's a %.2f\\% chance I'll get it right\" %\n (100 / len(lswitch_data)))\n if lswitch_data:\n lswitch_data = lswitch_data[0]\n LOG.debug(\"OVN Logical Switch for K8S host found. Skipping creation\")\n return lswitch_data\n\n\ndef init_host(host_name, host_subnet):\n \"\"\"Initializes a host adding it to the logical topology\"\"\"\n # Check for logical router, if not found create one\n lrouter_name = cfg.CONF.lrouter_name\n lrouter_raw_data = ovn.ovn_nbctl('find', 'Logical_Router',\n 'name=%s' % lrouter_name)\n lrouter_data = ovn.parse_ovn_nbctl_output(lrouter_raw_data)\n if len(lrouter_data) > 1:\n LOG.warn(\"I really was not expecting more than one router... I'll \"\n \"pick the first, there's a %.2f\\% chance I'll get it right\",\n (100 / len(lrouter_data)))\n if lrouter_data:\n lrouter_data = lrouter_data[0]\n LOG.debug(\"Logical router for K8S networking found. \"\n \"Skipping creation\")\n else:\n LOG.debug(\"Creating Logical Router for K8S networking with name:%s\",\n lrouter_name)\n output = ovn.ovn_nbctl('create', 'Logical_Router',\n 'name=%s' % lrouter_name)\n LOG.debug(\"Will use OVN Logical Router:%s\", output)\n # Check for host logical switch. If not found create one\n lswitch_name = host_name\n LOG.info(\"OVN lswitch for the host: %s\", lswitch_name)\n lswitch_data = _check_vswitch(lswitch_name)\n if lswitch_data:\n LOG.debug(\"OVN Logical Switch for K8S host found. Skipping creation\")\n else:\n LOG.debug(\"Creating LogicalSwitch for K8S host with name: %s\",\n lswitch_name)\n ovn.ovn_nbctl('ls-add', lswitch_name)\n\n # Check for logical router port connecting local logical switch to\n # kubernetes router.\n # If not found create one, and connect it to both router and switch\n lrp_raw_data = ovn.ovn_nbctl('find', 'Logical_Router_port',\n 'name=%s' % lswitch_name)\n lrp_data = ovn.parse_ovn_nbctl_output(lrp_raw_data)\n if len(lrp_data) > 1:\n LOG.warn(\"I really was not expecting more than one router port... \"\n \"I'll pick the first, there's a %.2f\\% chance I'll get it \"\n \"right\", (100 / len(lrp_data)))\n if lrp_data:\n lrp_data = lrp_data[0]\n LOG.debug(\"OVN logical router port for K8S host found.\"\n \"Skipping creation\")\n # TODO: worry about changes in IP address and subnet\n else:\n lrp_mac = utils.generate_mac()\n cidr = netaddr.IPNetwork(host_subnet)\n ip_address = netaddr.IPAddress(cidr.first + 1)\n lrp_uuid = ovn.ovn_nbctl('--', '--id=@lrp', 'create',\n 'Logical_Router_port',\n 'name=%s' % lswitch_name,\n 'network=%s/%s' % (ip_address,\n cidr.prefixlen),\n 'mac=\"%s\"' % lrp_mac, '--', 'add',\n 'Logical_Router', lrouter_name, 'ports',\n '@lrp', '--', 'lsp-add',\n lswitch_name, 'rp-%s' % lswitch_name)\n ovn.ovn_nbctl('set', 'Logical_Switch_port', 'rp-%s' % lswitch_name,\n 'type=router', 'options:router-port=%s' % lswitch_name,\n 'addresses=\"%s\"' % lrp_mac)\n LOG.debug(\"Configured logical router port: %s\", lrp_uuid)\n","sub_path":"ovn_k8s/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":5020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"637708609","text":"#!/usr/bin/env python\nimport re\nimport sys\n\nclass AccessPoint:\n\t\"\"\"\n\thold access point data and gps coords\n\t\"\"\"\n\n\tdef __init__(self, bssid):\n\t\t#capture ID and starting info\n\t\tself.bssid = bssid\n\t\t\n\t\t#create arrays to hold lat/long/sig strength\n\t\tself.lat_a = []\n\t\tself.lon_a = []\n\t\tself.strength = []\n\n\tdef get_bssid(self):\n\t\t\"\"\"return bssid of access point\"\"\"\n\t\tif self.bssid:\n\t\t\treturn self.bssid\n\t\telse:\n\t\t\treturn -1\n\n\tdef update(self, lat_u, lon_u, dbm_u):\n\t\t\"\"\"update access point's lat/long/signal strength\n\t\t\t:param lat_u: latitude update\n\t\t\t:param lon_u: longitude update\n\t\t\t:param dbm_u: signal strength update\n\t\t\"\"\"\n\t\tself.lat_a.append(lat_u)\n\t\tself.lon_a.append(lon_u)\n\t\tself.strength.append(dbm_u)\n\n\tdef get_current_lat(self):\n\t\t\"\"\":returns: last recorded latitude of access point\"\"\"\n\t\tif self.lat_a[-1]:\n\t\t\treturn self.lat_a[-1]\n\t\telse:\n\t\t\treturn -1\n\n\tdef get_current_lon(self):\n\t\t\"\"\":returns: last recorded longitude of access point\"\"\"\n\t\tif self.lon_a[-1]:\n\t\t\treturn self.lon_a[-1]\n\t\telse:\n\t\t\treturn -1\n\n\tdef get_current_strength(self):\n\t\t\"\"\":returns: last recorded signal strength of access point\"\"\"\n\t\tif self.strength[-1]:\n\t\t\treturn self.strength[-1]\n\t\telse:\n\t\t\treturn -1\n\ndef ap_exists(bssid, ap_list):\n\t\"\"\"check if ap object is in master list of AccessPoints\n\t\t:param bssid: access point BSSID from .gpsxml FILE\n\t\t:param ap_list: master list of APs to check against\n\t\t:returns: boolean value - False if AP is not in list/True if Ap is in list\n\t\"\"\"\n\tsize = len(ap_list)\n\n\tif size > 0:\n\t\tfor x in range(size):\n\t\t\tif bssid == ap_list[x].get_bssid():\n\t\t\t\treturn True\n\telse:\n\t\treturn False\n\n\treturn False\n\ndef inflate_ap(ap, line):\n\t\"\"\"pull data from gpsxml file and add to new AccessPoint object\n\t\t:param ap: AccessPoint object\n\t\t:param line: next line in gpsxml file to pull data from\n\t\t:returns: AccessPoint object with initial location/signal data\n\t\"\"\"\n\tlat = re.search(lat_re,line)\n\tlat = lat.group(0)[5:-1]\n\n\tlon = re.search(lon_re, line)\n\tlon= lon.group(0)[5:-1]\n\n\tsig = re.search(sig_re, line)\n\tsig = sig.group(0)[12:-1]\n\n\tap.update(lat, lon, sig)\n\n\treturn ap\n\n\n\n#build out regular expressions for each piece of data in the gpsxml file\nbssid_re = '(bssid=\"([0-9A-F]{2}[:]){5}([0-9A-F]{2})\")'\nlat_re = '(lat=\"([0-9]{2})[.]([0-9]{6})\")'\nlon_re = '(lon=\"[-?]([0-9]{2}[0-9]?)[.]([0-9]{6})\")'\nsig_re = '(signal_dbm=\"[-]([0-9]{2})\")'\n\n#open gpsxml file (how to do this dynamically?) - could just run through periodically\ntry:\n\tf = open('gpstest', 'r')\nexcept IOError:\n\tprint(\"DEBUG: ERROR - COULD NOT READ FILE\")\n\tsys.exit()\n\n#initalize global list of access point objects\nap_list = []\n\n#should this be a function? will it be run often? how to pull updates from file continuiously? \nfor line in f:\n\tbssid = re.search(bssid_re, line)\n\tif bssid:\n\t\tbssid = bssid.group(0)[7:-1]\n\t\t#filter out probe packets\n\t\tif not bssid == '00:00:00:00:00:00':\n\t\t\tif not ap_exists(bssid, ap_list):\n\t\t\t\t#create new access point\n\t\t\t\tpt = AccessPoint(bssid)\n\t\t\t\t#add to global AccessPoint object list\n\t\t\t\tap_list.append(inflate_ap(pt, line))\n\nfor x in range(len(ap_list)):\n\tprint(ap_list[x].get_bssid())\n\tprint(ap_list[x].get_current_strength())\n\n\n\n#for line in f:\n\n\n#ap = AccessPoint(match)\n\n#print(ap.get_bssid())\n\n\n\n\n","sub_path":"final_proj/testbed_trilat.py","file_name":"testbed_trilat.py","file_ext":"py","file_size_in_byte":3213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"507770282","text":"from Algorithm.RAISLD import Alg_RAISLD\nfrom Load_Dataset import load_dataset,load_ref\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\n\n\ndef _RAISLDe_it(trainSet,ref,save_path,**kw):\n x_list=[]\n train_num=trainSet.shape[0]\n dim=trainSet.shape[1]\n inner_loops=round(train_num/kw['batchSize'])\n model=Alg_RAISLD(\n np.zeros(dim),\n train_num,kw['alpha'],kw['d'])\n curr_iter_count=0\n model.initialize(trainSet)\n for epoch in tqdm(range((int)(kw['num_epochs']))):\n for i in range(inner_loops):\n curr_iter_count+=1\n model.Sample_Datas(\n trainSet,\n train_num,\n kw['batchSize'],\n model.p)\n model.Grads_Calc()\n model.average_grads()\n grad=model.grad_avg\n model.update()\n #eta=kw['lr_a']*(round(model.t.item())+kw['lr_b'])**(-kw['lr_gamma'])*model.r.item()\n eta=kw['lr_a']*(curr_iter_count+kw['lr_b'])**(-kw['lr_gamma'])\n noise=np.random.normal(0,1,dim)*np.sqrt(2*eta)\n model.curr_x=model.curr_x-grad*eta+noise\n x_list.append(model.curr_x)\n x_list=np.array(x_list).astype(np.float32)\n model.save_figure(x_list,ref,save_path,'RAISLDe')\n np.save(save_path,x_list)\n\ndef RAISLDe_sample(**kw):\n # Creat the save folder and name for result\n save_name='RAISLDe'+' '+\\\n 'lr[{:.2e},{:.2f},{:.2f}] alpha[{:.2f}] d[{:.1f}]'.format(\\\n kw['lr_a'],kw['lr_b'],kw['lr_gamma'],kw['alpha'],kw['d'])\n if not os.path.exists(kw['save_folder']+'RAISLDe'):\n os.makedirs(kw['save_folder']+'RAISLDe')\n save_path=kw['save_folder']+'RAISLDe/'+save_name\n # Print information before train\n print(save_name)\n # Set the random seed\n np.random.seed(kw['random_seed'])\n # Load DataSet as sparse matrix\n trainSet=load_dataset()\n ref=load_ref()\n # Main function\n _RAISLDe_it(trainSet,ref,save_path,**kw)\n","sub_path":"graduation_project/GM/Train/RAISLDe.py","file_name":"RAISLDe.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"233490805","text":"class Solution(object):\n def findLexSmallestString(self, s, a, b):\n \"\"\"\n :type s: str\n :type a: int\n :type b: int\n :rtype: str\n \"\"\"\n n = len(s)\n nums = []\n for c in s:\n nums.append(ord(c) - ord('0'))\n \n # enumerate rotate\n memo = set()\n best = s\n idx = 0\n for i in range(n):\n idx = (i * b) % n\n if idx in memo:\n break\n # change odd only to get min\n besto = nums[1]\n for offset in range(10):\n besto = min(besto, (nums[1] + offset * a) % 10)\n offset = besto - nums[1]\n for j in range(n):\n if j % 2 == 1:\n nums[j] = (nums[j] + offset) % 10\n print(nums)\n nums_ = [str(i) for i in nums]\n tmp = \"\".join(nums_)\n best = min(best, tmp)\n print(i, tmp, best)\n memo.add(idx)\n nums = nums[b:] + nums[:b]\n\n return best\n \n\nif __name__ == \"__main__\":\n a = Solution()\n print(a.findLexSmallestString(\"43987654\", 7, 3))","sub_path":"python/leetcode/1625_lexico.py","file_name":"1625_lexico.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"590240562","text":"# ввод размерности матрицы\ns = input().split()\nrow, col = int(s[0]), int(s[1])\n\n# формирование нулевой матрицы введенной размерности\nmat = [[0] * col for _ in range(row)]\n\nst = 'up'\nn1 = 0\ni = 1\n\nwhile i < row * col:\n \n if st == 'up':\n for j in range(n1, col - n1 - 1):\n if mat[n1][j] == 0:\n mat[n1][j] = i\n i += 1\n st = 'right'\n \n if st == 'right':\n for j in range(n1, row - n1 - 1):\n if mat[j][col - n1 - 1] == 0:\n mat[j][col - n1 - 1] = i\n i += 1\n st = 'down'\n \n if st == 'down':\n for j in range(n1, col - n1 - 1):\n if mat[row - n1 - 1][col - j - 1] == 0:\n mat[row - n1 - 1][col - j - 1] = i\n i += 1\n st = 'left' \n \n if st == 'left':\n for j in range(n1, row - n1 - 1):\n if mat[row - j - 1][n1] == 0:\n mat[row - j - 1][n1] = i\n i += 1\n st = 'up'\n n1 += 1\nif row * col % 2 == 1 and mat[row // 2][col // 2] == 0:\n mat[row // 2][col // 2] = row * col \n \n\n# вывод матрицы\n[print(*i) for i in mat]\n","sub_path":"Python_advanced/spiral_matrix.py","file_name":"spiral_matrix.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"440855937","text":"\nn_features = 200\nlatent_dim=100\n\n\n\nimport scipy.io as sio\n\n\nimport numpy as np\nnp.random.seed(666)\nimport tensorflow as tf\n#from tensorflow.math import reduce_sum\n#import tensorflow.math\nimport os\nfrom keras.backend.tensorflow_backend import set_session\nconfig = tf.ConfigProto()\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\nconfig.gpu_options.per_process_gpu_memory_fraction = 0.5\nset_session(tf.Session(config=config))\n\n\nimport keras\nfrom keras.datasets import mnist\nfrom keras.layers.merge import _Merge\nfrom keras.layers import Input, Dense, Reshape, Flatten, Dropout, Average\nfrom keras.layers import BatchNormalization, Activation, ZeroPadding2D,Concatenate\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.layers.convolutional import UpSampling2D, Conv2D\nfrom keras.models import Sequential, Model\nfrom keras.optimizers import RMSprop,SGD, Adam\nfrom functools import partial\nfrom keras import layers, models\nfrom keras.models import load_model\n\n\n\ndef loadData():\n \n msdata = np.load('/home/SharedData/saurabh/HS/multispectralData.npy')\n pcdata = np.load('/home/SharedData/saurabh/HS/panchromaticData.npy')\n labels = np.load('/home/SharedData/saurabh/HS/labelsData.npy')\n \n return msdata, pcdata, labels\n\n\n\n\n\n\nmsdata, pcdata, gt = loadData()\n\nmsdata=np.transpose(msdata,(0,2,3,1))\npcdata=np.transpose(pcdata,(0,2,3,1))\ngt=np.transpose(gt,(1,0))\ngt=np.ravel(gt)\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import OneHotEncoder\n\nlabel_encoder = LabelEncoder()\ninteger_encoded = label_encoder.fit_transform(gt)\n\nonehot_encoder = OneHotEncoder(sparse=False)\ninteger_encoded = integer_encoded.reshape(len(integer_encoded), 1)\nY = onehot_encoder.fit_transform(\n integer_encoded)\n\n\n#preprocessing b/w -1 and +1\n\nmsdata = msdata.astype(np.float32)\npcdata = pcdata.astype(np.float32)\n\nit2 = 5000\nfor it1 in range(0,80000,5000):\n\n print(it1,it2)\n msdata[it1:it2,:,:,:] = (msdata[it1:it2,:,:,:] - msdata[it1:it2,:,:,:].mean(axis=(0, 1, 2), keepdims=True))/(msdata[it1:it2,:,:,:].std(axis=(0, 1,2), keepdims=True))\n pcdata[it1:it2,:,:,:] = (pcdata[it1:it2,:,:,:] - pcdata[it1:it2,:,:,:].mean(axis=(0,1, 2), keepdims=True))/(pcdata[it1:it2,:,:,:].std(axis=(0,1,2), keepdims=True))\n it2 += 5000\n\nprint(msdata.shape, pcdata.shape)\n\n#from sklearn.model_selection import train_test_split\n\n#(X_train_one, Xtest_one, Y, Ytest) = train_test_split(msdata, gt, random_state = 666, test_size= 0.75)\n#(X_train_two, Xtest_two, Y, Ytest) = train_test_split(pcdata, gt, random_state = 666, test_size = 0.75 )\n\nclassifier = load_model('/home/SharedData/Avinandan/TeacherStudentExperiments/MultiModal/twostream.h5')\nstreamone = Model(inputs=classifier.get_layer('MultiModal_Input_1').input,\n outputs=classifier.get_layer('MultiModal_Output_1').output)\nstreamtwo = Model(inputs=classifier.get_layer('Multimodal_Input_2').input,\n outputs=classifier.get_layer('MultiModal_Output_2').output)\n#generator = load_model('/home/SharedData/Avinandan/Semi Supervised GAN/2N/generator2N.h5')\n#discriminator = load_model('/home/SharedData/Avinandan/Semi Supervised GAN/2N/discriminator.h5')\n\nprint(classifier.summary())\n \n\nfinalclassifierinput = Input(shape=(2*n_features,))\n\nfinalclassifier = finalclassifierinput\n\nfor layer in classifier.layers[35:]:\n finalclassifier = layer(finalclassifier)\n\n# create the model\n#new_model = Model(layer_input, x)\n\nfinalclassifier = Model(inputs=finalclassifierinput,\n outputs=finalclassifier)\nprint(finalclassifier.summary())\n\n\ndata1 = streamone.predict(msdata)\ndata2 = streamtwo.predict(pcdata)\n\n\ndata=np.concatenate((data1, data2), axis = 1)\n\nrandom_latent_vectors = np.random.normal(0,1, size = (data1.shape[0],latent_dim)) #sample random points\n\n#print(random_latent_vectors.shape,data2.shape,data1.shape)\n\n\ntopvalid = 0\ntoploss = 5000\n\n#610\n#2025 98.28\n#2517 98.51\n\nfor x in range(4815,4845,1):\n\n generator = load_model('/home/SharedData/Avinandan/TeacherStudentExperiments/MultiModal/models/generator-{0}.h5'.format(x))\n #generator = load_model('/home/SharedData/Avinandan/Semi Supervised GAN/2N/generator2N.h5')\n #latent, conditional\n generated_images = generator.predict([random_latent_vectors,data2])\n #generated_images = generator.predict([random_latent_vectors,data1],verbose=1)\n\n #generated_images = np.reshape(generated_images,(,100))\n #print(generated_images.shape)\n\n X_MultiModal1 = np.concatenate((generated_images,data2),axis=1)\n\n #print(X_MultiModal1.shape,Y.shape)\n\n from sklearn.model_selection import train_test_split\n\n\n #(Xtrain_MultiModal, Xtest_MultiModal,Ytrain_MultiModal,Ytest_MultiModal,) = train_test_split(X_MultiModal,Y_MultiModal, random_state = 666)\n\n #(Xtrain_MultiModal, Xtest_MultiModal,Ytrain_MultiModal,Ytest_MultiModal,) = train_test_split(data,Y, random_state = 666)\n\n #(generated_images_train, generated_images_test,Ytrain_MultiModal,Ytest_MultiModal,) = train_test_split(generated_images,Y, random_state = 666)\n #(secondstream_train, secondstream_test,Ytrain_MultiModal,Ytest_MultiModal,) = train_test_split(data2,Y, random_state = 666)\n #(firststream_train, firststream_test,Ytrain_MultiModal,Ytest_MultiModal,) = train_test_split(data1,Y, random_state = 666)\n\n #results = onestream.evaluate([Xtest_MultiModal, Xtest_MultiModal],Ytest_MultiModal, batch_size=32)\n\n #print('test loss, test acc:', results)\n\n (Xtrain_MultiModal1, Xtest_MultiModal1,Ytrain_MultiModal,Ytest_MultiModal,) = train_test_split(X_MultiModal1,Y, random_state = 666, test_size = 0.75)\n\n sgd = SGD(lr=0.0001, momentum=0.9, decay=1e-6)\n finalclassifier.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])\n\n #results = finalclassifier.evaluate(Xtest_MultiModal,Ytest_MultiModal, batch_size=32)\n results1 = finalclassifier.evaluate(Xtest_MultiModal1,Ytest_MultiModal, batch_size=32)\n\n print(\" \")\n #print('REAL DATA %d test loss, test acc:', results)\n print('EPOCH GENERATED DATA test loss, test acc:',x, results1)\n\n if results1[1]> topvalid and results1[0]< toploss:\n topvalid = results1[1]\n toploss = results1[0]\n index = x\n\n\nprint(\"%d EPOCH, %f acc\",index,topvalid)\n\n\n\n\n\n\n\n\n\n","sub_path":"MultiModal/testmodelsone.py","file_name":"testmodelsone.py","file_ext":"py","file_size_in_byte":6307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"47407668","text":"\"\"\"\n===========\nHexbin plot\n===========\n\nThis example shows how to plot the location of events occurring in a match\nusing hexbins.\n\"\"\"\nfrom urllib.request import urlopen\n\nfrom matplotlib.colors import LinearSegmentedColormap\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom highlight_text import ax_text\n\nfrom mplsoccer import VerticalPitch, add_image, FontManager, Sbopen\n\n##############################################################################\n# Load the first game that Messi played as a false-9 and the match before.\nparser = Sbopen()\ndf_false9 = parser.event(69249)[0] # 0 index is the event file\ndf_before_false9 = parser.event(69251)[0] # 0 index is the event file\n# filter messi's actions (starting positions)\ndf_false9 = df_false9.loc[df_false9.player_id == 5503, ['x', 'y']]\ndf_before_false9 = df_before_false9.loc[df_before_false9.player_id == 5503, ['x', 'y']]\n\n##############################################################################\n# Create a custom colormap.\n# Note see the `custom colormaps\n# `_\n# example for more ideas.\nflamingo_cmap = LinearSegmentedColormap.from_list(\"Flamingo - 10 colors\",\n ['#e3aca7', '#c03a1d'], N=10)\n\n##############################################################################\n# Plot Messi's first game as a false-9.\npitch = VerticalPitch(line_color='#000009', line_zorder=2, pitch_color='white')\nfig, ax = pitch.draw(figsize=(4.4, 6.4))\nhexmap = pitch.hexbin(df_false9.x, df_false9.y, ax=ax, edgecolors='#f4f4f4',\n gridsize=(8, 8), cmap=flamingo_cmap)\n\n##############################################################################\n# Load a custom font.\nURL = 'https://raw.githubusercontent.com/google/fonts/main/apache/roboto/Roboto%5Bwdth,wght%5D.ttf'\nURL2 = 'https://raw.githubusercontent.com/google/fonts/main/apache/robotoslab/RobotoSlab%5Bwght%5D.ttf'\nrobotto_regular = FontManager(URL)\nrobboto_bold = FontManager(URL2)\n\n##############################################################################\n# Load images.\n\n# Load the StatsBomb logo and Messi picture\nMESSI_URL = 'https://upload.wikimedia.org/wikipedia/commons/b/b8/Messi_vs_Nigeria_2018.jpg'\nmessi_image = Image.open(urlopen(MESSI_URL))\nSB_LOGO_URL = ('https://raw.githubusercontent.com/statsbomb/open-data/'\n 'master/img/SB%20-%20Icon%20Lockup%20-%20Colour%20positive.png')\nsb_logo = Image.open(urlopen(SB_LOGO_URL))\n\n##############################################################################\n# Plot the chart again with a title.\n# We will use mplsoccer's grid function to plot a pitch with a title and endnote axes.\n\nfig, axs = pitch.grid(figheight=10, title_height=0.08, endnote_space=0,\n title_space=0,\n # Turn off the endnote/title axis. I usually do this after\n # I am happy with the chart layout and text placement\n axis=False,\n grid_height=0.82, endnote_height=0.03)\nhexmap = pitch.hexbin(df_false9.x, df_false9.y, ax=axs['pitch'], edgecolors='#f4f4f4',\n gridsize=(8, 8), cmap=flamingo_cmap)\naxs['endnote'].text(1, 0.5, '@your_twitter_handle', va='center', ha='right', fontsize=15,\n fontproperties=robotto_regular.prop)\naxs['title'].text(0.5, 0.7, \"Lionel Messi's Actions\", color='#000009',\n va='center', ha='center', fontproperties=robotto_regular.prop, fontsize=30)\naxs['title'].text(0.5, 0.25, \"First game as a false nine\", color='#000009',\n va='center', ha='center', fontproperties=robotto_regular.prop, fontsize=20)\nax_sb_logo = add_image(sb_logo, fig,\n # set the left, bottom and height to align with the endnote\n left=axs['endnote'].get_position().x0,\n bottom=axs['endnote'].get_position().y0,\n height=axs['endnote'].get_position().height)\n\n##############################################################################\n# Plot Messi's actions in the matches before and after becoming a false-9.\n# We will use mplsoccer's grid function, which is a convenient way to plot a grid\n# of pitches with a title and endnote axes.\n\nfig, axs = pitch.grid(ncols=2, axis=False, endnote_height=0.05)\nhexmap_before = pitch.hexbin(df_before_false9.x, df_before_false9.y, ax=axs['pitch'][0],\n edgecolors='#f4f4f4',\n gridsize=(8, 8), cmap='Reds')\nhexmap2_after = pitch.hexbin(df_false9.x, df_false9.y, ax=axs['pitch'][1], edgecolors='#f4f4f4',\n gridsize=(8, 8), cmap='Blues')\nax_sb_logo = add_image(sb_logo, fig,\n # set the left, bottom and height to align with the endnote\n left=axs['endnote'].get_position().x0,\n bottom=axs['endnote'].get_position().y0,\n height=axs['endnote'].get_position().height)\nax_messi = add_image(messi_image, fig, interpolation='hanning',\n # set the left, bottom and height to align with the title\n left=axs['title'].get_position().x0,\n bottom=axs['title'].get_position().y0,\n height=axs['title'].get_position().height)\n\n# titles using highlight_text and a google font (Robotto)\n\nTITLE_STR1 = 'The Evolution of Lionel Messi'\nTITLE_STR2 = 'Actions in the match and\\n becoming a False-9'\ntitle1_text = axs['title'].text(0.5, 0.7, TITLE_STR1, fontsize=28, color='#000009',\n fontproperties=robotto_regular.prop,\n ha='center', va='center')\nhighlight_text = [{'color': '#800610', 'fontproperties': robboto_bold.prop},\n {'color': '#08306b', 'fontproperties': robboto_bold.prop}]\nax_text(0.5, 0.3, TITLE_STR2, ha='center', va='center', fontsize=18, color='#000009',\n fontproperties=robotto_regular.prop, highlight_textprops=highlight_text, ax=axs['title'])\n\n# sphinx_gallery_thumbnail_path = 'gallery/pitch_plots/images/sphx_glr_plot_hexbin_003.png'\n\n# Messi Photo from: https://en.wikipedia.org/wiki/Lionel_Messi#/media/File:Messi_vs_Nigeria_2018.jpg\n# License: https://creativecommons.org/licenses/by-sa/3.0/;\n# Creator: Кирилл Венедиктов\n\nplt.show() # If you are using a Jupyter notebook you do not need this line\n","sub_path":"examples/pitch_plots/plot_hexbin.py","file_name":"plot_hexbin.py","file_ext":"py","file_size_in_byte":6477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"430380245","text":"def reader(file):\r\n f_out = file[:-2] + 'out'\r\n f = open(file,'r')\r\n T = int(f.readline())\r\n cases = []\r\n for t in range(T):\r\n KCS = f.readline()\r\n KCS_list = KCS.split(' ')\r\n cases.append(KCS_list)\r\n return cases, f_out\r\n\r\ndef solver(cases, f_out):\r\n f_out = open(f_out,'w')\r\n for c in range(len(cases)):\r\n case = cases[c]\r\n K = int(case[0]) \r\n C = int(case[1])\r\n result = [i*K**(C-1)+1 for i in range(K)]\r\n #ZAMIENIC RESULT W STRINGA\r\n string = ''\r\n for i in result:\r\n string += str(i) + ' '\r\n print(\"Case #{}: {}\".format(c + 1, string), file = f_out)\r\n f_out.close()\r\n\r\ndef main():\r\n cases, f_out = reader('D-small-attempt0.in')\r\n solver(cases, f_out)\r\n \r\nmain()\r\n","sub_path":"codes/CodeJamCrawler/16_0_4/marcinowski/Fractiles_2.py","file_name":"Fractiles_2.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"598503619","text":"'''problem 7\n\n\nBy listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13.\n\nWhat is the 10 001st prime number?\n'''\n\nimport time\nstart_time = time.time()\n\n\nprimes = [2]\nchecked = [2]\ncomposites = []\n\n#print(len(primes))\n\npin = 3\n\nwhile len(primes)<10001:\n for p in range(0,len(primes)-1):\n if pin%primes[p] == 0:\n composites.append(pin)\n # if pin in composites:\n # continue\n if pin not in composites:\n primes.append(pin)\n if len(primes)%1000==0:\n print('Passing prime # {}'.format(len(primes)))\n print('Computation has taken {} seconds so far.'.format(time.time()-start_time))\n print('Prime density: {}'.format(len(primes)/pin))\n pin+=2\n composites = []\n\nprint('The {}th prime is {}.'.format(len(primes),primes[len(primes)-1]))\nprint('Computation has taken {} seconds so far.'.format(time.time()-start_time))\nprint('Prime density: {}'.format(len(primes)/primes[len(primes)-1]))","sub_path":"problem_007.py","file_name":"problem_007.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"488685385","text":"from .EmbConstant import *\r\nfrom .WriteHelper import write_string_utf8\r\nfrom .PecGraphics import get_graphic_as_string\r\nimport math\r\n\r\nSTRIP_SPEEDS = False\r\nSEQUIN_CONTINGENCY = CONTINGENCY_SEQUIN_UTILIZE\r\nMAX_JUMP_DISTANCE = 121\r\nMAX_STITCH_DISTANCE = 121\r\n\r\n\r\ndef csv(f, values):\r\n string = \"\"\r\n for v in values:\r\n if len(string) > 0:\r\n string += ','\r\n string += ('\\\"%s\\\"' % v)\r\n write_string_utf8(f, string + \"\\n\")\r\n\r\n\r\ndef distance(dx, dy):\r\n dx *= dx\r\n dy *= dy\r\n return math.sqrt(dx + dy)\r\n\r\n\r\ndef angle(dx, dy):\r\n tau = math.pi * 2\r\n angle = math.atan2(dy, dx)\r\n angle += tau / float(2)\r\n angle /= tau\r\n return angle\r\n\r\n\r\ndef write(pattern, f, settings=None):\r\n names = get_common_name_dictionary()\r\n\r\n deltas = settings is not None and \"deltas\" in settings\r\n displacement = settings is not None and \"displacement\" in settings\r\n\r\n extends = pattern.extends()\r\n width = extends[2] - extends[0]\r\n height = extends[3] - extends[1]\r\n\r\n csv(f, ('#', '[VAR_NAME]', '[VAR_VALUE]'))\r\n count_stitches = pattern.count_stitches()\r\n csv(f, ('>', 'STITCH_COUNT:', str(count_stitches)))\r\n count_threads = pattern.count_color_changes()\r\n csv(f, ('>', 'THREAD_COUNT:', str(count_threads)))\r\n csv(f, ('>', 'EXTENTS_LEFT:', str(extends[0])))\r\n csv(f, ('>', 'EXTENTS_TOP:', str(extends[1])))\r\n csv(f, ('>', 'EXTENTS_RIGHT:', str(extends[2])))\r\n csv(f, ('>', 'EXTENTS_BOTTOM:', str(extends[3])))\r\n csv(f, ('>', 'EXTENTS_WIDTH:', str(width)))\r\n csv(f, ('>', 'EXTENTS_HEIGHT:', str(height)))\r\n\r\n stitch_counts = {}\r\n for s in pattern.stitches:\r\n command = s[2]\r\n if command in stitch_counts:\r\n stitch_counts[command] += 1\r\n else:\r\n stitch_counts[command] = 1\r\n\r\n if len(stitch_counts) != 0:\r\n for the_key, the_value in stitch_counts.items():\r\n try:\r\n name = \"COMMAND_\" + names[the_key]\r\n except (IndexError, KeyError):\r\n name = \"COMMAND_UNKNOWN_\" + str(the_key)\r\n csv(f, (\r\n '>',\r\n name,\r\n str(the_value)\r\n ))\r\n\r\n write_string_utf8(f, \"\\n\")\r\n\r\n if len(pattern.extras) > 0:\r\n csv(f, (\r\n '#',\r\n '[METADATA_NAME]',\r\n '[METADATA]'\r\n ))\r\n for the_key, the_value in pattern.extras.items():\r\n if isinstance(the_value, tuple):\r\n the_value = \"\\n\" + get_graphic_as_string(the_value)\r\n csv(f, (\r\n '@',\r\n str(the_key),\r\n str(the_value)\r\n ))\r\n write_string_utf8(f, \"\\n\")\r\n\r\n if len(pattern.threadlist) > 0:\r\n csv(f, (\r\n '#',\r\n '[THREAD_NUMBER]',\r\n '[HEX_COLOR]',\r\n '[DESCRIPTION]',\r\n '[BRAND]',\r\n '[CATALOG_NUMBER]',\r\n '[DETAILS]',\r\n '[WEIGHT]'\r\n ))\r\n for i, thread in enumerate(pattern.threadlist):\r\n csv(f, (\r\n '$',\r\n str(i),\r\n thread.hex_color(),\r\n thread.description,\r\n thread.brand,\r\n thread.catalog_number,\r\n thread.details,\r\n thread.weight,\r\n ))\r\n write_string_utf8(f, \"\\n\")\r\n\r\n if len(pattern.stitches) > 0:\r\n if displacement:\r\n csv(f, (\r\n '#',\r\n '[STITCH_INDEX]',\r\n '[STITCH_TYPE]',\r\n '[X]',\r\n '[Y]',\r\n '[DX]',\r\n '[R]',\r\n '[ANGLE]'\r\n ))\r\n elif deltas:\r\n csv(f, (\r\n '#',\r\n '[STITCH_INDEX]',\r\n '[STITCH_TYPE]',\r\n '[X]',\r\n '[Y]',\r\n '[DX]',\r\n '[DY]'\r\n ))\r\n else:\r\n csv(f, (\r\n '#',\r\n '[STITCH_INDEX]',\r\n '[STITCH_TYPE]',\r\n '[X]',\r\n '[Y]'\r\n ))\r\n current_x = 0\r\n current_y = 0\r\n for i, stitch in enumerate(pattern.stitches):\r\n try:\r\n name = names[stitch[2]]\r\n except (IndexError, KeyError):\r\n name = \"UNKNOWN \" + str(stitch[2])\r\n if displacement:\r\n dx = stitch[0] - current_x\r\n dy = stitch[1] - current_y\r\n csv(f, (\r\n '*',\r\n str(i),\r\n name,\r\n str(stitch[0]),\r\n str(stitch[1]),\r\n str(dx),\r\n str(dy),\r\n str(distance(dx, dy)),\r\n str(angle(dx, dy))\r\n ))\r\n elif deltas:\r\n dx = stitch[0] - current_x\r\n dy = stitch[1] - current_y\r\n csv(f, (\r\n '*',\r\n str(i),\r\n name,\r\n str(stitch[0]),\r\n str(stitch[1]),\r\n str(dx),\r\n str(dy)\r\n ))\r\n else:\r\n csv(f, (\r\n '*',\r\n str(i),\r\n name,\r\n str(stitch[0]),\r\n str(stitch[1]),\r\n ))\r\n current_x = stitch[0]\r\n current_y = stitch[1]\r\n\r\n\r\ndef get_common_name_dictionary():\r\n return {\r\n NO_COMMAND: \"NO_COMMAND\",\r\n STITCH: \"STITCH\",\r\n JUMP: \"JUMP\",\r\n TRIM: \"TRIM\",\r\n STOP: \"STOP\",\r\n END: \"END\",\r\n SLOW: \"SLOW\",\r\n FAST: \"FAST\",\r\n COLOR_CHANGE: \"COLOR_CHANGE\",\r\n SEQUIN_MODE: \"SEQUIN_MODE\",\r\n SEQUIN_EJECT: \"SEQUIN_EJECT\",\r\n SEW_TO: \"SEW_TO\",\r\n NEEDLE_AT: \"NEEDLE_AT\",\r\n STITCH_BREAK: \"STITCH_BREAK\",\r\n SEQUENCE_BREAK: \"SEQUENCE_BREAK\",\r\n COLOR_BREAK: \"COLOR_BREAK\",\r\n TIE_ON: \"TIE_ON\",\r\n TIE_OFF: \"TIE_OFF\",\r\n FRAME_EJECT: \"FRAME_EJECT\",\r\n MATRIX_TRANSLATE: \"MATRIX_TRANSLATE\",\r\n MATRIX_SCALE: \"MATRIX_SCALE\",\r\n MATRIX_ROTATE: \"MATRIX_ROTATE\",\r\n MATRIX_RESET: \"MATRIX_RESET\",\r\n OPTION_ENABLE_TIE_ON: \"OPTION_ENABLE_TIE_ON\",\r\n OPTION_ENABLE_TIE_OFF: \"OPTION_ENABLE_TIE_OFF\",\r\n OPTION_DISABLE_TIE_ON: \"OPTION_DISABLE_TIE_ON\",\r\n OPTION_DISABLE_TIE_OFF: \"OPTION_DISABLE_TIE_OFF\",\r\n OPTION_MAX_STITCH_LENGTH: \"OPTION_MAX_STITCH_LENGTH\",\r\n OPTION_MAX_JUMP_LENGTH: \"OPTION_MAX_JUMP_LENGTH\",\r\n OPTION_IMPLICIT_TRIM: \"OPTION_IMPLICIT_TRIM\",\r\n OPTION_EXPLICIT_TRIM: \"OPTION_EXPLICIT_TRIM\",\r\n CONTINGENCY_NONE: \"CONTINGENCY_NONE\",\r\n CONTINGENCY_JUMP_NEEDLE: \"CONTINGENCY_JUMP_NEEDLE\",\r\n CONTINGENCY_SEW_TO: \"CONTINGENCY_SEW_TO\",\r\n }\r\n","sub_path":"pyembroidery/CsvWriter.py","file_name":"CsvWriter.py","file_ext":"py","file_size_in_byte":6934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"177916738","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sys\n\n# Запись о багаже пассажира авиарейса содержит следующие поля:\n# номер рейса, дата вылета, пункт назначения, фамилия пассажира, количество мест багажа,\n# суммарный вес багажа.\n# Поиск выполнять по номеру рейса, дате вылета, пункту назначения, весу багажа (превышение максимально допустимого).\n\n\nclass SimpleIterator:\n\n def __init__(self, limit):\n self.limit = limit\n self.counter = 0\n\n def __next__(self):\n if self.counter < self.limit:\n self.counter += 1\n return 1\n else:\n raise StopIteration\n\n\nif __name__ == '__main__':\n # Список данных о багаже.\n baggage = []\n # Организовать бесконечный цикл запроса команд.\n while True:\n # Запросить команду из терминала.\n command = input(\">>> \").lower()\n # Выполнить действие в соответствие с командой.\n if command == 'exit':\n break\n elif command == 'add':\n # Запросить запись о багаже.\n second_name = input(\"Введите фамилию пассажира: \")\n number_of_seats = int(input(\"Введите кол-во мест багажа: \"))\n flight_number = list(map(int, input(\"Введите номер рейса: \").split(\".\")))\n date = list(map(int, input(\"Введите дату вылета: \").split(\".\")))\n location = list(map(str, input(\"Введите пункт назначения: \").split(\".\")))\n weight = list(map(int, input(\"Введите суммарный вес багажа: \").split(\".\")))\n\n # Создать словарь.\n information = {\n 'second_name': second_name,\n 'number_of_seats': number_of_seats,\n 'flight_number': flight_number,\n 'date': date,\n 'location': location,\n 'weight': weight\n }\n\n baggage.append(information)\n if len(baggage) > 1:\n baggage.sort(key=lambda item: item.get('name', ''))\n elif command == 'list':\n line = '+-{}-+-{}-+-{}-+-{}-+-{}-+-{}-+-{}-+-{}-+-{}-+'.format(\n '-' * 4,\n '-' * 30,\n '-' * 20,\n '-' * 8,\n '-' * 8,\n '-' * 8,\n '-' * 8,\n '-' * 8,\n '-' * 11\n )\n print(line)\n print(\n '| {:^3} | {:^30} | {:^20} | {:^8} | {:^8} | {:^8} | {:^8} | {:^8} |'.format(\n \"№\",\n \"Ф.И.О.\",\n \"Группа\",\n \"1-ая оценка\",\n \"2-ая оценка\",\n \"3-ая оценка\",\n \"4-ая оценка\",\n \"5-ая оценка\"\n )\n )\n print(line)\n # Вывести данные о всех сотрудниках.\n for idx, person in enumerate(students, 1):\n print(\n '| {:>3} | {:<30} | {:<20} | {:>11} | {:>11} | {:>11} | {:>11} | {:>11} |'.format(\n idx,\n person.get('name', ''),\n person.get('group', ''),\n person.get('marks[0]', f'{marks[0]}'),\n person.get('marks[1]', f'{marks[1]}'),\n person.get('marks[2]', f'{marks[2]}'),\n person.get('marks[3]', f'{marks[3]}'),\n person.get('marks[4]', f'{marks[4]}')\n )\n )\n print(line)\n elif command.startswith('select '):\n parts = command.split(' ', maxsplit=2)\n period = int(parts[1])\n\n count = 0\n for person in students:\n if 2 in marks:\n count += 1\n print(\n '{:>4}: {}'.format(count, person.get('name', ''))\n )\n # Если счетчик равен 0, то работники не найдены.\n if count == 0:\n print(\"Нет сту��ентов, которые получили оценку - 2.\")\n elif command == 'help':\n # Вывести справку о работе с программой.\n print(\"Список команд:\\n\")\n print(\"add - добавить студента;\")\n print(\"list - вывести список студентов;\")\n print(\"select <оценка> - найти студентов которые получили такую оценку;\")\n print(\"help - отобразить справку;\")\n print(\"exit - завершить работу с программой.\")\n else:\n print(f\"Неизвестная команда {command}\", file=sys.stderr)","sub_path":"individ_1.py","file_name":"individ_1.py","file_ext":"py","file_size_in_byte":5390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"317159445","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimg = cv2.imread(\"t_shape.png\")\nrows,coloums,channels = img.shape\nM1 = np.float32([[1,0,50],[0,1,50]])\ntrans1 = cv2.warpAffine(img,M1,(coloums,rows))\nM2 = np.float32([[1,0,0],[0,1,100]])\ntrans2 = cv2.warpAffine(img,M2,(coloums,rows))\nM3 = np.float32([[1,0,50],[0,1,-50]])\ntrans3 = cv2.warpAffine(img,M3,(coloums,rows))\nM4 = np.float32([[1,0,-50],[0,1,-50]])\ntrans4 = cv2.warpAffine(img,M4,(coloums,rows))\n\n\nM5 = cv2.getRotationMatrix2D((coloums/2,rows/2),45,1)\nrot1 = cv2.warpAffine(img,M5,(coloums,rows))\nM6 = cv2.getRotationMatrix2D((coloums/2,rows/2),90,1)\nrot2 = cv2.warpAffine(img,M6,(coloums,rows))\nM7 = cv2.getRotationMatrix2D((coloums/2,rows/2),180,1)\nrot3 = cv2.warpAffine(img,M7,(coloums,rows))\nM8 = cv2.getRotationMatrix2D((coloums/2,rows/2),135,1)\nrot4 = cv2.warpAffine(img,M8,(coloums,rows))\n\n\nblur1 = cv2.blur(img,(3,3))\nblur2 = cv2.GaussianBlur(img,(3,3),0)\n\n\nfig, axs = plt.subplots(2, 5)\naxs[0,0].imshow(cv2.cvtColor(trans1,cv2.COLOR_BGR2RGB))\naxs[0,1].imshow(cv2.cvtColor(trans2,cv2.COLOR_BGR2RGB))\naxs[0,2].imshow(cv2.cvtColor(trans3,cv2.COLOR_BGR2RGB))\naxs[0,3].imshow(cv2.cvtColor(trans4,cv2.COLOR_BGR2RGB))\naxs[0,4].imshow(cv2.cvtColor(rot1,cv2.COLOR_BGR2RGB))\naxs[1,0].imshow(cv2.cvtColor(rot2,cv2.COLOR_BGR2RGB))\naxs[1,1].imshow(cv2.cvtColor(rot3,cv2.COLOR_BGR2RGB))\naxs[1,2].imshow(cv2.cvtColor(rot4,cv2.COLOR_BGR2RGB))\naxs[1,3].imshow(cv2.cvtColor(blur1,cv2.COLOR_BGR2RGB))\naxs[1,4].imshow(cv2.cvtColor(blur2,cv2.COLOR_BGR2RGB))\nplt.show()","sub_path":"OpenCV/assignment2_1.py","file_name":"assignment2_1.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"256035923","text":"#정렬된 배열에서 특정 수의 개수 구하기\n\n#정렬된 수열에서 길이 x인 원소의 개수를 세는 메서드\ndef count_by_value(array,x):\n n = len(array) # 데이터의 개수\n\n # x가 처음 등장한 인덱스 계산\n a = first(array,x,0,n-1)\n\n if a == None:\n return 0 # 값이 x인 원소의 개수는 0개이므로 0 반환\n \n # x가 마지막으로 등장한 인덱스 계산\n b = last(array,x,0,n-1)\n\n return b - a + 1\n\n#처음 위치를 찾는 이진 탐색 메서드\n\ndef first(array,target,start,end):\n if start>end:\n return None\n mid = (start+end)//2\n # 해당 값을 가지는 원소 중에서 가장 왼쪽에 있는 경우에만 인덱스 반환\n if (mid == 0 or target > array[mid-1]) and array[mid] == target:\n return mid\n # 중간점의 값 보다 찾고자 하는 값이 적거나 같은 경우 왼쪽 확인\n elif array[mid] >= target:\n return first(array,target,start,mid-1)\n # 중간점의 값 보다 찾고자 하는 값이 크거나 같은 경우 오른쪽 확인\n else:\n return first(array,target,mid+1,end)\n\ndef last(array,target,start,end):\n if start>end:\n return None\n mid = (start+end)//2\n # 해당 값을 가지는 원소 중에서 가장 왼쪽에 있는 경우에만 인덱스 반환\n if (mid == n-1 or target < array[mid+1]) and array[mid] == target:\n return mid\n # 중간점의 값 보다 찾고자 하는 값이 적거나 같은 경우 왼쪽 확인\n elif array[mid] > target:\n return last(array,target,start,mid-1)\n # 중간점의 값 보다 찾고자 하는 값이 크거나 같은 경우 오른쪽 확인\n else:\n return last(array,target,mid+1,end)\n\nn,x = map(int,input().split())\narray = list(map(int,input().split()))\n\ncount = count_by_value(array,x)\n\nif count == 0:\n print(-1)\nelse:\n print(count)\n","sub_path":"이코테/이진탐색/정렬된 배수에서 특정 수의 개수 구하기-2.py","file_name":"정렬된 배수에서 특정 수의 개수 구하기-2.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"157961448","text":"# -*- encoding: UTF-8 -*-\n\nimport psutil\nimport datetime\n\nclass ServerInfo:\n\n @staticmethod\n def cpu_percent():\n return psutil.cpu_percent()\n\n @staticmethod\n def cpu_times():\n return psutil.cpu_times()\n\n @staticmethod\n def cpu_info():\n info = {}\n info[\"cpu_count\"] = psutil.cpu_count(logical=False)\n info[\"cpu_logical_count\"] = psutil.cpu_count()\n info[\"cpu_freq\"] = psutil.cpu_freq()._asdict()\n return info\n\n @staticmethod\n def mem_info():\n return psutil.virtual_memory()._asdict()\n\n @staticmethod\n def disk_info():\n partitions = psutil.disk_partitions()\n disks = []\n for partition in partitions:\n usage = None\n if partition.fstype:\n usage = psutil.disk_usage(partition.device)\n item = {\n \"device\": partition.device,\n \"mountpoint\": partition.mountpoint,\n \"fstype\": partition.fstype,\n \"opts\": partition.opts,\n \"usage\": None\n }\n if usage:\n item[\"usage\"] = {\n \"total\": usage.total,\n \"used\": usage.used,\n \"free\": usage.free,\n \"percent\": usage.percent\n }\n disks.append(item)\n return disks\n\n @staticmethod\n def processes():\n processes = []\n for proc in psutil.process_iter():\n try:\n pinfo = proc.as_dict(attrs=['pid', 'name', \"cpu_percent\", \"memory_info\", \"username\", \"exe\"])\n pinfo[\"memory_info\"] = proc.memory_info()\n except Exception as e:\n print(e)\n pass\n else:\n processes.append(pinfo)\n return processes\n\n @staticmethod\n def boot_time():\n time = datetime.datetime.fromtimestamp(psutil.boot_time()).strftime(\"%Y-%m-%d %H:%M:%S\")\n return time","sub_path":"server_info.py","file_name":"server_info.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"330842741","text":"from typing import List\n\n\nclass Solution:\n def minPathSum(self, grid: List[List[int]]) -> int:\n row, col = len(grid), len(grid[0])\n dp = [[0]*col for _ in range(row)]\n dp[row-1][col-1] = grid[row-1][col-1]\n for i in range(col-2, -1, -1):\n dp[row-1][i] = dp[row-1][i+1] + grid[row-1][i]\n for i in range(row-2, -1, -1):\n dp[i][col-1] = dp[i+1][col-1] + grid[i][col-1]\n for i in range(row-2, -1, -1):\n for j in range(col-2, -1, -1):\n dp[i][j] = grid[i][j] + min(dp[i][j+1], dp[i+1][j])\n return dp[0][0]\n\n\ns = Solution()\nprint(s.minPathSum([[1, 2, 3], [4, 5, 6]]))\n","sub_path":"Python/DP/64_MinimumPathSum/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"371528273","text":"from __future__ import annotations\n\nimport logging\nimport time\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass, field\nfrom typing import (\n Callable,\n Generic,\n Mapping,\n MutableMapping,\n MutableSequence,\n Optional,\n Sequence,\n TypeVar,\n)\n\nfrom snuba.utils.metrics import MetricsBackend\nfrom snuba.utils.streams.processing.strategies.abstract import (\n ProcessingStrategy,\n ProcessingStrategyFactory,\n)\nfrom snuba.utils.streams.types import Message, Partition, TPayload\n\n\nlogger = logging.getLogger(__name__)\n\n\nTResult = TypeVar(\"TResult\")\n\n\nclass AbstractBatchWorker(ABC, Generic[TPayload, TResult]):\n \"\"\"\n The ``BatchProcessingStrategy`` requires an instance of this class to\n handle user provided work such as processing raw messages and flushing\n processed batches to a custom backend.\n \"\"\"\n\n @abstractmethod\n def process_message(self, message: Message[TPayload]) -> Optional[TResult]:\n \"\"\"\n Called with each raw message, allowing the worker to do incremental\n (preferably local!) work on events. The object returned is put into\n the batch maintained internally by the ``BatchProcessingStrategy``.\n\n If this method returns `None` it is not added to the batch.\n\n A simple example would be decoding the message payload value and\n extracting a few fields.\n \"\"\"\n pass\n\n @abstractmethod\n def flush_batch(self, batch: Sequence[TResult]) -> None:\n \"\"\"\n Called with a list of processed (by ``process_message``) objects.\n The worker should write the batch of processed messages into whatever\n store(s) it is maintaining. Afterwards the offsets are committed by\n the ``BatchProcessingStrategy``.\n\n A simple example would be writing the batch to another topic.\n \"\"\"\n pass\n\n\n@dataclass\nclass Offsets:\n __slots__ = [\"lo\", \"hi\"]\n\n lo: int # inclusive\n hi: int # exclusive\n\n\n@dataclass\nclass Batch(Generic[TResult]):\n results: MutableSequence[TResult] = field(default_factory=list)\n offsets: MutableMapping[Partition, Offsets] = field(default_factory=dict)\n created: float = field(default_factory=lambda: time.time())\n messages_processed_count: int = 0\n # the total amount of time, in milliseconds, that it took to process\n # the messages in this batch (does not included time spent waiting for\n # new messages)\n processing_time_ms: float = 0.0\n\n\nclass BatchProcessingStrategy(ProcessingStrategy[TPayload]):\n \"\"\"\n The ``BatchProcessingStrategy`` is a processing strategy that accumulates\n processed message values, periodically flushing them after a given\n duration of time has passed or number of output values have been\n accumulated. Users need only provide an implementation of what it means\n to process a raw message and flush a batch of events via an\n ``AbstractBatchWorker`` instance.\n\n Messages are processed as they are read from the consumer, then added to\n an in-memory batch. These batches are flushed based on the batch size or\n time sent since the first message in the batch was recieved (e.g. \"500\n items or 1000ms\"), whichever threshold is reached first. When a batch of\n items is flushed, the consumer offsets are synchronously committed.\n \"\"\"\n\n def __init__(\n self,\n commit: Callable[[Mapping[Partition, int]], None],\n worker: AbstractBatchWorker[TPayload, TResult],\n max_batch_size: int,\n max_batch_time: int,\n metrics: MetricsBackend,\n ) -> None:\n self.__commit = commit\n self.__worker = worker\n self.__max_batch_size = max_batch_size\n self.__max_batch_time = max_batch_time\n self.__metrics = metrics\n\n self.__batch: Optional[Batch[TResult]] = None\n self.__closed = False\n\n def poll(self) -> None:\n \"\"\"\n Check if the current in-flight batch should be flushed.\n \"\"\"\n assert not self.__closed\n\n if self.__batch is not None and (\n len(self.__batch.results) >= self.__max_batch_size\n or time.time() > self.__batch.created + self.__max_batch_time / 1000.0\n ):\n self.__flush()\n\n def submit(self, message: Message[TPayload]) -> None:\n \"\"\"\n Process a message.\n \"\"\"\n assert not self.__closed\n\n start = time.time()\n\n self.__metrics.timing(\n \"receive_latency\",\n (start - message.timestamp.timestamp()) * 1000,\n tags={\n \"topic\": message.partition.topic.name,\n \"partition\": str(message.partition.index),\n },\n )\n\n # Create the batch only after the first message is seen.\n if self.__batch is None:\n self.__batch = Batch()\n\n result = self.__worker.process_message(message)\n\n # XXX: ``None`` is indistinguishable from a potentially valid return\n # value of ``TResult``!\n if result is not None:\n self.__batch.results.append(result)\n\n duration = (time.time() - start) * 1000\n self.__batch.messages_processed_count += 1\n self.__batch.processing_time_ms += duration\n self.__metrics.timing(\"process_message\", duration)\n\n if message.partition in self.__batch.offsets:\n self.__batch.offsets[message.partition].hi = message.next_offset\n else:\n self.__batch.offsets[message.partition] = Offsets(\n message.offset, message.next_offset\n )\n\n def close(self) -> None:\n self.__closed = True\n\n def terminate(self) -> None:\n self.__closed = True\n\n def join(self, timeout: Optional[float] = None) -> None:\n # The active batch is discarded when exiting without attempting to\n # write or commit, so this method can exit immediately without\n # blocking.\n pass\n\n def __flush(self) -> None:\n \"\"\"\n Flush the active batch and reset the batch state.\n \"\"\"\n assert not self.__closed\n assert self.__batch is not None, \"cannot flush without active batch\"\n\n logger.info(\n \"Flushing %s items (from %r)\",\n len(self.__batch.results),\n self.__batch.offsets,\n )\n\n self.__metrics.timing(\n \"process_message.normalized\",\n self.__batch.processing_time_ms / self.__batch.messages_processed_count,\n )\n\n batch_results_length = len(self.__batch.results)\n if batch_results_length > 0:\n logger.debug(\"Flushing batch via worker\")\n flush_start = time.time()\n self.__worker.flush_batch(self.__batch.results)\n flush_duration = (time.time() - flush_start) * 1000\n logger.info(\"Worker flush took %dms\", flush_duration)\n self.__metrics.timing(\"batch.flush\", flush_duration)\n self.__metrics.timing(\n \"batch.flush.normalized\", flush_duration / batch_results_length\n )\n\n logger.debug(\"Committing offsets for batch\")\n commit_start = time.time()\n offsets = {\n partition: offsets.hi for partition, offsets in self.__batch.offsets.items()\n }\n self.__commit(offsets)\n logger.debug(\"Committed offsets: %s\", offsets)\n commit_duration = (time.time() - commit_start) * 1000\n logger.debug(\"Offset commit took %dms\", commit_duration)\n\n self.__batch = None\n\n\nclass BatchProcessingStrategyFactory(ProcessingStrategyFactory[TPayload]):\n def __init__(\n self,\n worker: AbstractBatchWorker[TPayload, TResult],\n max_batch_size: int,\n max_batch_time: int,\n metrics: MetricsBackend,\n ) -> None:\n self.__worker = worker\n self.__max_batch_size = max_batch_size\n self.__max_batch_time = max_batch_time\n self.__metrics = metrics\n\n def create(\n self, commit: Callable[[Mapping[Partition, int]], None]\n ) -> ProcessingStrategy[TPayload]:\n return BatchProcessingStrategy(\n commit,\n self.__worker,\n self.__max_batch_size,\n self.__max_batch_time,\n self.__metrics,\n )\n","sub_path":"snuba/utils/streams/processing/strategies/batching.py","file_name":"batching.py","file_ext":"py","file_size_in_byte":8214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"646569231","text":"'''\nredis实现的分布式锁\n'''\nimport redis_pool\nimport redis\nimport threading\nimport time\n\n\nredis_client = redis.Redis(connection_pool=redis_pool.pool)\n\n\ndef task():\n print(threading.current_thread().name)\n\n\ndef doSomething():\n '''\n 分布式锁实现，没有时设置并设置超时时间，完成任务后删除\n 会有任务超过锁超时时间的问题。。。\n '''\n try:\n redis_client.set('lock', 1, ex=20, nx=True)\n for i in range(3):\n task()\n time.sleep(5)\n except Exception as e:\n print(e)\n finally:\n redis_client.delete('lock')\n\n\nif __name__ == '__main__':\n threads = []\n for i in range(3):\n t = threading.Thread(\n name='thread-' + str(i),\n target=doSomething,\n daemon=True)\n threads.append(t)\n for t in threads:\n t.start()\n for t in threads:\n t.join()\n","sub_path":"redis/distributed_lock_redis.py","file_name":"distributed_lock_redis.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"173876335","text":"\n# my package\nfrom param import Param\nfrom run import run\nfrom systems.consensus import Consensus\nfrom other_policy import LCP_Policy, WMSR_Policy\n\n# standard package\nimport numpy as np \nfrom torch import nn as nn \nfrom torch import tanh\nimport torch \n\nclass ConsensusParam(Param):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.env_name = 'Consensus'\n\t\tself.env_case = None\n\n\t\t#\n\t\tself.pomdp_on = True\n\t\tself.r_comm = 5\n\t\tself.n_agents = 5\n\t\tself.n_malicious = 1\n\t\tself.agent_memory = 2\n\t\tself.n_neighbors = 4\n\t\tself.single_agent_sim = False\n\t\tself.multi_agent_sim = True \n\n\t\t# dim \n\t\tself.state_dim_per_agent = 1 \n\t\tself.state_dim = self.n_agents*self.state_dim_per_agent\n\t\tself.action_dim_per_agent = 1\n\t\tself.action_dim = self.n_agents*self.action_dim_per_agent\n\n\t\t# RL\n\t\tself.rl_train_model_fn = '../models/consensus/rl_current.pt'\n\t\tself.rl_continuous_on = False\n\t\tself.rl_lr_schedule_on = False\n\t\tself.rl_gpu_on = False\n\t\tself.rl_max_episodes = 50000\n\t\tself.rl_batch_size = 500\n\t\tself.rl_gamma = 0.98\n\t\tself.rl_K_epoch = 5\n\t\tself.rl_num_actions = 5\n\t\tself.a_min = -1\n\t\tself.a_max = 1\n\t\tself.rl_discrete_action_space = np.linspace(self.a_min, self.a_max, self.rl_num_actions)\n\t\tself.rl_warm_start_on = False \n\t\tself.rl_warm_start_fn = '../models/consensus/rl_current.pt'\n\t\tself.rl_module = \"PPO\" # PPO_w_DeepSet, DDPG, PPO, (DDPG_w_DeepSet)\n\t\tself.rl_log_interval = 20\n\t\tself.rl_scale_reward = 0.01\n\n\t\th_s = 32 # hidden layer\n\t\tself.rl_activation = tanh\n\t\tif self.rl_module is 'DDPG':\n\t\t\t# ddpg param\n\t\t\tself.rl_lr_mu = 5e-5\n\t\t\tself.rl_lr_q = 5e-4\n\t\t\tself.rl_buffer_limit = 5e6\n\t\t\tself.rl_action_std = 1\n\t\t\tself.rl_max_action_perturb = 0.5\n\t\t\tself.rl_tau = 0.995\n\t\t\t# network architecture\n\t\t\tself.rl_mu_network_architecture = nn.ModuleList([\n\t\t\t\tnn.Linear(n,h_mu), \n\t\t\t\tnn.Linear(h_mu,h_mu),\n\t\t\t\tnn.Linear(h_mu,m)])\n\t\t\tself.rl_q_network_architecture = nn.ModuleList([\n\t\t\t\tnn.Linear(n+m,h_q),\n\t\t\t\tnn.Linear(h_q,h_q),\n\t\t\t\tnn.Linear(h_q,1)])\n\t\t\tself.rl_network_activation = tanh \t\t\n\n\t\telif self.rl_module is 'PPO':\n\t\t\t# ppo param\n\t\t\tself.rl_lr = 5e-4\n\t\t\tself.rl_lmbda = 0.95\n\t\t\tself.rl_eps_clip = 0.2\n\n\t\t\tself.rl_layers = nn.ModuleList([\n\t\t\t\tnn.Linear(self.agent_memory*self.n_neighbors,h_s),\n\t\t\t\tnn.Linear(h_s,h_s),\n\t\t\t\tnn.Linear(h_s,len(self.rl_discrete_action_space)),\n\t\t\t\tnn.Linear(h_s,1)\n\t\t\t\t])\n\n\t\t# IL\n\t\tself.il_train_model_fn = '../models/consensus/il_current.pt'\n\t\tself.il_imitate_model_fn = '../models/consensus/rl_current.pt'\n\t\tself.il_controller_class = None \n\n\t\t# Sim\n\t\tself.sim_rl_model_fn = '../models/consensus/rl_current.pt'\n\t\tself.sim_il_model_fn = '../models/consensus/il_current.pt'\n\t\tself.sim_render_on = True\n\t\tself.sim_t0 = 0\n\t\tself.sim_tf = 8\n\t\tself.sim_dt = 0.01\n\t\tself.sim_times = np.arange(self.sim_t0,self.sim_tf,self.sim_dt)\n\t\tself.sim_nt = len(self.sim_times)\n\n\t\tself.plots_fn = 'plots.pdf'\n\n\n\nif __name__ == '__main__':\n\tparam = ConsensusParam()\n\tenv = Consensus(param)\n\n\t# x0 = np.array([0.4, np.pi/2, 0.5, 0])\n\t# x0 = np.array([0.07438156, 0.33501733, 0.50978889, 0.52446423])\n\n\n\tcontrollers = {\n\t\t'LCP': LCP_Policy(env),\n\t\t# 'WMSR': WMSR_Policy(env),\n\t\t'RL':\ttorch.load(param.sim_rl_model_fn),\n\t\t# 'IL':\ttorch.load(param.sim_il_model_fn),\n\t\t# 'SCP':\tFilePolicy(scp_file),\n\t}\n\n\trun(param, env, controllers)","sub_path":"code/examples/run_consensus.py","file_name":"run_consensus.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"426927533","text":"# coding: utf8\nfrom __future__ import unicode_literals\n\nimport Queue\nimport time\n\nimport requests\n\nimport IRC\nimport message_parsing\nimport utils\n\n\nclass Troll(IRC.Bot):\n \"\"\"\n Troll class who respond with random quote\n \"\"\"\n\n def __init__(self, parent, username, Target, server, channel_to_reply):\n IRC.Bot.__init__(self, parent, Target, username, server)\n self.queue = Queue.Queue()\n self.channel_to_reply = channel_to_reply\n\n def init(self):\n \"\"\"\n init the bot (send i can help you to target)\n\n :return: Nothing what did you expect\n \"\"\"\n self.reply(\"I can help you {} :)\".format(self.target), \"PRIVMSG\")\n\n def main(self):\n \"\"\"\n main loop of bot , reply to every Message with a quote and send the conversation to main channel\n\n :return: Nothing what did you expect\n \"\"\"\n message = self.queue.get()\n res = self.get_citation()\n self.reply(\"{}=>{}\".format(message.pseudo, message.content), \"PUBMSG\", target=self.channel_to_reply)\n time.sleep(0.1 * len(res))\n self.reply(\"{}<={}\".format(message.pseudo, res), \"PUBMSG\", target=self.channel_to_reply)\n self.reply(res, \"PRIVMSG\")\n\n @staticmethod\n def get_citation():\n \"\"\"\n get a random quote\n\n :return: random quote\n \"\"\"\n r = requests.get(\"http://www.quotationspage.com/random.php3\")\n return utils.parse_html_balise(\"a\", utils.parse_html_balise(\"dt\", r.text))\n\n\ndef create_troll(message, bot):\n param = message.content.split()\n if message.pseudo not in [\"Tagashy\", \"ghozt\"]:\n return\n if len(param) != 2:\n if message.msg_type == \"PRIVMSG\":\n bot.reply(\"!troll \", \"PRIVMSG\", target=message.pseudo)\n else:\n bot.reply(\"!troll \", \"PUBMSG\")\n else:\n if param[1].lower() not in {\"hackira\", \"arod\", \"dazax\", \"dvor4x\", \"notfound\", \"sambecks\", \"thanat0s\", \"gelu\",\n \"geluchat\", bot.username, \"#root-me\"}:\n troll = Troll(bot.parent, bot.username, param[1], bot.server, bot.target)\n reg = message_parsing.Message(pseudo=param[1], msg_type=\"PRIVMSG\")\n bot.parent.add_bot(reg, troll, bot.username, bot.server, bot.target)\n if message.msg_type == \"PRIVMSG\":\n bot.reply(\"trolling {} :P\".format(param[1]), \"PRIVMSG\", target=message.pseudo)\n else:\n bot.reply(\"trolling {} :P\".format(param[1]), \"PUBMSG\")\n","sub_path":"RandomQuote.py","file_name":"RandomQuote.py","file_ext":"py","file_size_in_byte":2534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"355061135","text":"#!/usr/bin/env python\n\nimport os\nimport time\n#import serial\nimport rospy\nimport math\nfrom std_msgs.msg import Float64\nfrom std_msgs.msg import UInt16\n\nclass Motors:\n def __init__(self):\n self.thrust = True\n\n self.maxPowerValue = 1900\n self.minPowerValue = 1100\n\n self.powerR = 0 \n self.powerL = 0 \n\n rospy.Subscriber(\"right_thruster\", Float64, self.right_callback)\n rospy.Subscriber(\"left_thruster\", Float64, self.left_callback)\n\n self.r_pwm_pub = rospy.Publisher(\"rpwm\", UInt16, queue_size=10)\n self.l_pwm_pub = rospy.Publisher(\"lpwm\", UInt16, queue_size=10)\n\n def right_callback(self, right_t):\n self.powerR = right_t.data\n #rospy.logwarn(self.powerR)\n\n def left_callback(self, left_t):\n self.powerL = left_t.data\n #rospy.logwarn(self.powerL)\n\n def move_thrusters(self,powerR=1500, powerL=1500):\n #validate the pwm range\n if powerR < 1100 or powerR > 1900 or powerL < 1100 or powerL > 1900:\n rospy.logwarn(\"Thruster power must be between 1100 - 1900\")\n else:\n pwmR = powerR\n pwmL = powerL\n self.r_pwm_pub.publish(pwmR)\n self.l_pwm_pub.publish(pwmL)\n\n def newtons(self, powerR=0, powerL=0):\n #validate the Newtons range\n if (powerR < -30 or powerR > 36.5 or powerL < -30 or powerL > 36.5):\n rospy.logwarn(\"Saturation range\")\n realPowerValueR = powerR * 0.5\n realPowerValueL = powerL * 0.5\n\n else:\n if (powerR >= 0):\n realPowerValueR = round((powerR / 36.5 * 385)+1515)\n elif (powerR < 0):\n realPowerValueR = round((powerR / 30 * 385)+1485)\n if (powerL <= 0):\n realPowerValueL = round((powerL / 30 * 385)+1485)\n elif(powerL > 0):\n realPowerValueL = round((powerL / 36.5 * 385)+1515)\n\n self.move_thrusters(realPowerValueR,realPowerValueL)\n #print('moving')\n\n def run(self, powerR = 0, powerL = 0):\n self.newtons(powerR, powerL)\n\ndef main():\n rospy.init_node('ardumotors', anonymous=True)\n rate = rospy.Rate(250) # 100hz\n m = Motors()\n while not rospy.is_shutdown() and m.thrust:\n #if m.thrust:\n m.run(m.powerR, m.powerL)\n rate.sleep()\n rospy.spin()\n\nif __name__ == \"__main__\":\n try:\n main()\n except rospy.ROSInterruptException:\n pass","sub_path":"scripts/ardumotors.py","file_name":"ardumotors.py","file_ext":"py","file_size_in_byte":2453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"287244467","text":"class Disponibilidad:\r\n\r\n def __init__(self, pId=None, pDescripcion=None):\r\n self.id = pId\r\n self.descripcion = pDescripcion\r\n\r\n def __getitem__(self, item):\r\n return self.__dict__[item]\r\n\r\n def __str__(self):\r\n return 'Descripción: {}'.format(self.descripcion)\r\n\r\n\r\ndef getDisponibilidadesRegistradas(bd):\r\n try:\r\n cursor = bd.connection.cursor()\r\n cursor.execute('SELECT id, descripcion FROM disponibilidad')\r\n retorno = cursor.fetchall()\r\n bd.connection.commit()\r\n cursor.close()\r\n disponibilidades = list()\r\n for tuplaDisponibilidad in retorno:\r\n disponibilidad = Disponibilidad(\r\n tuplaDisponibilidad[0], tuplaDisponibilidad[1])\r\n disponibilidades.append(disponibilidad)\r\n return disponibilidades\r\n except Exception as e:\r\n print(\"Error en getDisponibilidadesRegistradas \", e)\r\n\r\n\r\ndef agregarDisponibilidadEmpleado(bd, id_disponibilidad, id_empleado):\r\n try:\r\n cursor = bd.connection.cursor()\r\n cursor.execute('INSERT INTO empleado_disponibilidad (id_empleado, id_disponibilidad) VALUES ({}, {})'.format(\r\n id_empleado, id_disponibilidad))\r\n bd.connection.commit()\r\n cursor.close()\r\n #print('Disponibilidad \"', id_disponibilidad, '\" agregada en el empleado')\r\n except Exception as e:\r\n print(\"Error en agregarDisponibilidadEmpleado \", e)\r\n\r\n\r\ndef quitarDisponibilidadEmpleado(bd, id_disponibilidad, id_empleado):\r\n try:\r\n cursor = bd.connection.cursor()\r\n cursor.execute('DELETE FROM empleado_disponibilidad WHERE id_empleado = {} AND id_disponibilidad = {}'.format(\r\n id_empleado, id_disponibilidad))\r\n bd.connection.commit()\r\n cursor.close()\r\n #print('Disponibilidad quitada del empleado')\r\n except Exception as e:\r\n print(\"Error en quitarDisponibilidadEmpleado \", e)\r\n\r\n\r\ndef quitarTodaLaDisponibilidadDelEmpleado(bd, id_empleado):\r\n try:\r\n cursor = bd.connection.cursor()\r\n cursor.execute(\r\n 'DELETE FROM empleado_disponibilidad WHERE id_empleado = {}'.format(id_empleado))\r\n bd.connection.commit()\r\n cursor.close()\r\n #print('Toda disponibilidad quitada del empleado')\r\n except Exception as e:\r\n print(\"Error en quitarTodaLaDisponibilidadDelEmpleado \", e)\r\n\r\n\r\ndef agregarDisponibilidadAnuncio(bd, id_disponibilidad, id_anuncio):\r\n try:\r\n cursor = bd.connection.cursor()\r\n cursor.execute('INSERT INTO anuncio_disponibilidad (id_anuncio, id_disponibilidad) VALUES ({}, {})'.format(\r\n id_anuncio, id_disponibilidad))\r\n bd.connection.commit()\r\n cursor.close()\r\n #print('Disponibilidad \"', id_disponibilidad, '\" agregada en el anuncio')\r\n except Exception as e:\r\n print(\"Error en agregarDisponibilidadAnuncio \", e)\r\n\r\n\r\ndef quitarDisponibilidadAnuncio(bd, id_disponibilidad, id_anuncio):\r\n try:\r\n cursor = bd.connection.cursor()\r\n cursor.execute('DELETE FROM anuncio_disponibilidad WHERE id_anuncio = {} AND id_disponibilidad = {}'.format(\r\n id_anuncio, id_disponibilidad))\r\n bd.connection.commit()\r\n cursor.close()\r\n #print('Disponibilidad quitada del anuncio')\r\n except Exception as e:\r\n print(\"Error en quitarDisponibilidadAnuncio \", e)\r\n\r\n\r\ndef quitarTodaLaDisponibilidadDelAnuncio(bd, id_anuncio):\r\n try:\r\n cursor = bd.connection.cursor()\r\n cursor.execute(\r\n 'DELETE FROM anuncio_disponibilidad WHERE id_anuncio = {}'.format(id_anuncio))\r\n bd.connection.commit()\r\n cursor.close()\r\n #print('Toda disponibilidad quitada del anuncio')\r\n except Exception as e:\r\n print(\"Error en quitarTodaLaDisponibilidadDelAnuncio \", e)\r\n","sub_path":"Implementacion/Disponibilidad.py","file_name":"Disponibilidad.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"512938387","text":"from Board import *\nfrom Bank import *\nfrom Title import Property, Utility\nimport math \nimport util\n\nclass Player():\n #Dual means both Regular and Banker, used only if there are less than 5 players; Bank can be automated\n #PLAYER_TITLES = [\"Regular\", \"Banker/Auctioneer\", \"Dual\"] \n\n PLAYER_MINIMUM = 2\n PLAYER_MAXIMUM = 8\n \n def __init__(self, id, name):\n self.id = id #Identification Number\n self.name = name #Name of player\n self.money = 0 #Cash on hand\n self.position = 0 #Position ranges from 0 - 40 (for game's 41 spaces) - Start at \"Go\" tile\n self.bankrupt = False #Bankrupt status\n \n self.inJail = False #Check if user is in jail \n self.jail_cards = [] #Jail cards in possession\n self.jail_turns = 0 #Number of remaining turns in jail\n\n self.debt = [] #Records of debts - money owned to other players \n\n self.consecutiveDoubles = 0 #Start with no doubles \n self.order = None #Determine the order of play in game - default is None. \n\n self.titleDeeds = [] #Start with no title deeds owned\n self.propertyOwned = 0 #Number of properties owned \n self.colorMonopoly = [] #List of color group monopolies owned \n self.utilityOwned = 0 #Number of utilities owned\n self.transportsOwned = 0 #Number of transports owned\n\n self.num_homes = 0 #Number of homes in total \n self.num_hotels = 0 #Number of hotels in total \n\n\n #Get the id of user\n def getuserID(self): \n return self.id \n \n #Get the name of user\n def getName(self): \n return self.name \n\n #Get the monetary value of the player\n def getMonetaryValue(self): \n return self.money \n \n #Add money to or subtract money from player\n def changeMonetaryValue(self, amount): \n self.money += amount\n \n #Get the position of the player on the board\n def getPosition(self): \n return self.position \n\n #Set the position of player on board\n def setPosition(self, position): \n self.position = position \n\n #Get the order of play of player\n def getOrder(self):\n return self.order \n\n #Set the order of play for player\n def setOrder(self, order): \n self.order = order \n \n #Get the list of properties of player\n def getTitleDeeds(self): \n return self.titleDeeds\n \n #Add a title deed to the player's list of possessions\n def addTitleDeed(self, titleDeed, mortgaged = False): \n titleType = titleDeed.getTitleType()\n titleName = titleDeed.getName()\n\n if (titleType == \"Property\"): \n self.titleDeeds.append({\"Title Deed\": titleDeed, \"Mortgaged\": mortgaged, \"Houses\": 0, \"Hotels\": 0, \"Color Group\": titleDeed.getColorGroup()})\n self.propertyOwned += 1\n \n #Check if adding the property means the player has a monopoly\n colorGroupName = titleDeed.getColorGroup() \n colorGroupList = Property.getColorGroup(colorGroupName)\n\n createMonopoly = True\n for propertyItem in colorGroupList:\n if (propertyItem == titleName):\n #Property item refers to the title deed being added \n continue\n for titleDeed in self.titleDeeds:\n titleDeedInList = False \n\n if (propertyItem == titleDeed[\"Title Deed\"].getName()): \n titleDeedInList = True \n \n if (not titleDeedInList): \n #If given property item is not part of the owner's list of title deeds, the new item would not result in a monopoly\n createMonopoly = False\n break\n\n if (createMonopoly): \n self.colorMonopoly.append({\"Color Group\": colorGroupName, \"Number Houses Built\": 0, \"Number Hotels Built\": 0})\n\n elif (titleType == \"Utility\"):\n self.titleDeeds.append({\"Title Deed\": titleDeed, \"Mortgaged\": mortgaged, \"Houses\": None, \"Hotels\": None, \"Color Group\": None}) \n self.utilityOwned += 1\n elif(titleType == \"Transports\"): \n self.titleDeeds.append({\"Title Deed\": titleDeed, \"Mortgaged\": mortgaged, \"Houses\": None, \"Hotels\": None, \"Color Group\": None}) \n self.transportsOwned += 1\n \n #Remove a title deed from a player's list of possessions \n def removeTitleDeed(self, titleDeedName): \n titleDeedCard = None \n record = None \n\n #Remove the title deed from the list\n for titleDeed in self.titleDeeds: \n if titleDeed[\"Title Deed\"].getName() == titleDeedName:\n titleDeedCard = titleDeed[\"Title Deed\"]\n record = self.titleDeeds.remove(titleDeed)\n \n #Reduce the number of properties owned by this player\n if (titleDeedCard.getTitleType() == \"Property\"): \n colorGroup = titleDeedCard.getColorGroup()\n\n #Check if removing property would result in player to lose monopoly\n #This already assumes the color group has no property already\n monopolyList = self.getColorMonopoly() \n\n #If color group is in the monopoly list, the property being removed results in monopoly loss\n if (colorGroup in monopolyList): \n for monopolyItem in monopolyList:\n if (monopolyItem[\"Color Group\"] == colorGroup): \n monopolyList.remove(monopolyItem)\n\n self.propertyOwned -= 1 \n elif (titleDeedCard.getTitleType() == \"Utility\"):\n self.utilityOwned -= 1\n elif (titleDeedCard.getTitleType() == \"Transports\"): \n self.transportsOwned -= 1\n\n return titleDeedCard\n\n def getPropertyOwned(self): \n return self.propertyOwned\n \n def getColorMonopoly(self):\n return self.colorMonopoly\n \n def getUtilityOwned(self):\n return self.utilityOwned\n \n def getTransportsOwned(self):\n return self.transportsOwned\n\n #Get total number of homes owned\n def getNumHomes(self): \n return self.num_homes\n \n #Get total number of hotels owned \n def getNumHotels(self): \n return self.num_hotels \n\n #Get the status of player in jail\n def getInJailStatus(self):\n return self.inJail\n \n #Set the in jail status\n def setInJailStatus(self, status): \n self.inJail = status\n\n #Add a jail-free card to player's possession\n def addEscapeJailCard(self, card): \n self.jail_cards.append(card)\n\n #Remove a jail-free card from player's possession, starting with the most recent card first\n def removeEscapeJailCard(self):\n card = self.jail_cards.pop()\n return card\n \n #Check if there are jail cards available \n def jailCardsAvailable(self): \n if (len(self.jail_cards) > 0): \n return True\n \n return False\n\n #Get the current number of jail turns\n def getJailTurns(self): \n return self.jail_turns\n \n #Set the number of jail turns\n def setJailTurns(self, turns): \n self.jail_turns = turns\n \n #Subtract the number of jail turns\n def subtractJailTurns(self): \n self.jail_turns -= 1\n\n #Get the bankrupt status\n def getBankruptStatus(self):\n return self.bankrupt\n \n #Set the bankrupt status\n def setBankruptStatus(self, status = False): \n self.bankrupt = status\n\n #Get the debt records of player\n def getDebtRecord(self): \n return self.debt\n \n #Set the debt records of player\n def addDebtRecord(self, playerName, amount): \n #Check if there is an existing debt to that player, and if so update record\n existingRecord = False \n \n #If there is existing debt, just increase debt amount. \n for debtRecord in self.debt: \n if (debtRecord[\"Player\"] == playerName):\n debtRecord[\"Debt Owned\"] = debtRecord[\"Debt Owned\"] + amount\n existingRecord = True\n \n #If there is no record, add new debt to record \n if (not existingRecord): \n self.debt.append({\"Player\": playerName, \"Debt Owned\": amount})\n \n #Reduce debt records of player (if reducing debt)\n def reduceDebt(self, playerName, amount): \n \n #Search for the player to update debt or remove record altogether\n for debtRecord in self.debt: \n if (debtRecord[\"Player\"] == playerName):\n debtRecord[\"Debt Owned\"] = debtRecord[\"Debt Owned\"] - amount\n \n #Should the debt amount reaches below or at zero, remove record\n if (debtRecord[\"Debt Owned\"] <= 0):\n self.debt.remove(debtRecord) \n\n \"\"\"Methods associated with the player interacting in the game \"\"\"\n #Move the player across the board when it is their turn \n def move(self, moveNum, dice, bank = None):\n #TODO: Add logic regarding if user has jail-free cards or has money to self-bail \n if (self.jail_turns > 0):\n return 0\n\n #Check if player rolled doubles \n if dice.getDoubleStatus(): \n self.consecutiveDoubles += 1\n\n #Check if user goes to jail if 3 doubles in a row\n if self.consecutiveDoubles >= 3: \n self.setInJailStatus(True)\n self.setPosition(Board.TILES_JAIL[0])\n self.consecutiveDoubles = 0 \n\n #Reset the dice double status\n dice.resetDoubleStatus()\n\n return #If going to jail, end the turn right here\n\n else: \n #Reset consecutive doubles if different numbers are rolled\n self.consecutiveDoubles = 0 \n \n #Calculate new position of player\n newPosition = self.getPosition() + moveNum \n \n #Add one to position if went past jail, assuming player was starting next to jail at minimum.\n #Number 35 is the highest possible new position if user rolled doubles twice and was closest to jail by one spot\n if (newPosition >= Board.TILES_JAIL[0] and newPosition < 35) and (self.getPosition() < Board.TILES_JAIL[0] or self.getPosition() > 35):\n newPosition += 1\n\n #Check if player has cycled through the board, assuming player does not go to jail directly\n if (newPosition > len(Board.TILES_LIST)):\n newPosition -= len(Board.TILES_LIST)\n \n #Collect more money by passing GO\n self.changeMonetaryValue(Bank.PASS_GO)\n if (bank != None): \n bank.subtract(Bank.PASS_GO)\n \n #Apply new position \n self.setPosition(newPosition)\n \n #Read and do the chance card \n def doChanceCard(self, card, board, bank):\n #Get user's position \n position = self.getPosition() \n\n #Get the type of chance card and the associated value\n cardKind = card.getKind()\n cardValue = card.getValue() \n\n #Do actions based on chance card \n if cardKind == \"Advance\":\n #Record user was on this space first\n board.hit(position)\n\n #Read the values, and do the actions based on the value\n if cardValue == \"Go\": \n self.setPosition(Board.TILES_GO[0])\n\n #Collect money\n self.changeMonetaryValue(Bank.PASS_GO)\n bank.subtract(Bank.PASS_GO)\n\n elif cardValue == \"Utility\":\n #Keep track if nearest utility is found \n found = False \n newPositionUtility = None\n\n #Go to the nearest utility\n for posUtil in Board.TILES_UTILITY: \n if position < posUtil: \n newPositionUtility = posUtil\n found = True\n break \n \n #If not nearest utility found, go to first utility. \n #Note it may be possible that user does not collect cash if passing Go\n if not found:\n newPositionUtility = Board.TILES_UTILITY[0]\n\n #If player would cycle the board, re-calibrate the position number and collect 200 if necessary\n if (newPositionUtility < position):\n #Collect more money by passing GO\n self.changeMonetaryValue(Bank.PASS_GO)\n bank.subtract(Bank.PASS_GO)\n\n self.setPosition(newPositionUtility)\n \n elif cardValue == \"Transports\":\n #Keep track if nearest transports is found \n found = False \n newPositionTransports = None\n\n #Go to the nearest transport\n for posTransports in Board.TILES_TRANSPORTS: \n if position < posTransports: \n newPositionTransports = posTransports\n found = True\n break \n \n #If not nearest utility found, go to first transports. \n #Note it may be possible that user does not collect cash if passing Go\n if not found:\n newPositionTransports = Board.TILES_TRANSPORTS[0]\n \n #If player would cycle the board, re-calibrate the position number and collect 200 if necessary\n if (newPositionTransports < position):\n #Collect more money by passing GO\n self.changeMonetaryValue(Bank.PASS_GO)\n bank.subtract(Bank.PASS_GO)\n\n self.setPosition(newPositionTransports)\n \n elif cardValue <= 0: \n #If negative, this means player must go back\n newPosition = position + cardValue \n self.setPosition(newPosition)\n\n elif cardValue == \"Go to Jail\": \n #Player goes to jail directly, do not collect amount of passing GO\n self.setInJailStatus(True)\n self.setPosition(Board.TILES_JAIL[0])\n \n else: \n #If player would cycle the board, re-calibrate the position number and collect 200 if necessary\n if (cardValue < position):\n #Collect more money by passing GO\n self.changeMonetaryValue(Bank.PASS_GO)\n bank.subtract(Bank.PASS_GO)\n \n #Set new position\n self.setPosition(cardValue)\n\n elif cardKind == \"Credit\": \n bank.subtract(cardValue)\n self.changeMonetaryValue(cardValue)\n elif cardKind == \"Debit\": \n self.changeMonetaryValue(cardValue) \n bank.add(abs(cardValue))\n elif cardKind == \"Debit Complex\": \n #debit from each home and hotel \n numHomePay = cardValue[0] * self.getNumHomes()\n numHotelPay = cardValue[1] * self.getNumHotels() \n \n #Debit the amount from player \n self.changeMonetaryValue(numHomePay + numHotelPay)\n\n #Add the amount to the bank \n bank.add(abs(numHomePay + numHotelPay))\n elif cardKind == \"Escape Jail\":\n self.addEscapeJailCard(card)\n \n #Read and do the community card \n def doCommunityCard(self, card, board, bank):\n #Get user's position \n position = self.getPosition() \n\n #Get the type of chance card and the associated value\n cardKind = card.getKind()\n cardValue = card.getValue() \n\n #Do actions based on community card \n if cardKind == \"Advance\": \n #Record user was on this space first\n board.hit(position)\n\n #Read the values, and do the actions based on the value\n if cardValue == \"Go\": \n self.setPosition(Board.TILES_GO[0])\n \n #Collect money\n self.changeMonetaryValue(Bank.PASS_GO)\n bank.subtract(Bank.PASS_GO)\n\n elif cardValue == \"Go to Jail\": \n print(\"You have been sent to jail.\")\n self.setInJailStatus(True)\n self.setPosition(Board.TILES_JAIL[0])\n \n elif cardKind == \"Credit\": \n bank.subtract(cardValue)\n self.changeMonetaryValue(cardValue)\n elif cardKind == \"Debit\": \n self.changeMonetaryValue(cardValue) \n bank.add(abs(cardValue))\n elif cardKind == \"Debit Complex\": \n #debit from each home and hotel \n numHomePay = cardValue[0] * self.getNumHomes()\n numHotelPay = cardValue[1] * self.getNumHotels() \n \n #Debit the amount from player \n self.changeMonetaryValue(numHomePay + numHotelPay)\n\n #Add the amount to the bank \n bank.add(abs(numHomePay + numHotelPay)) \n elif cardKind == \"Escape Jail\":\n self.addEscapeJailCard(card)\n\n #User pays tax if lands on a tax tile\n def payTax(self, bank): \n #Get position of player to get tax type, and create value to hold tax amount\n position = self.getPosition()\n taxCharged = 0 \n \n #Get the type of tax \n if (Board.TILES_LIST[position] == \"Income Tax\"):\n taxCharged = Bank.INCOME_TAX\n elif (Board.TILES_LIST[position] == \"Luxury Tax\"):\n taxCharged = Bank.LUXURY_TAX\n\n #Check current amounts of player before paying tax\n monetaryAmount = self.getMonetaryValue() \n newPossibleMonetaryAmount = monetaryAmount - abs(taxCharged)\n \n #Check if the player already has no money currently\n if (monetaryAmount <= 0): \n #Record the amount of money owned to the bank\n self.addDebtRecord(\"Bank\", abs(taxCharged))\n\n #Check if the new monetary value causes player to go below zero. If so, the remaining amount must be recorded as debt\n elif (newPossibleMonetaryAmount < 0): \n #Make payment that can be paid now \n payableNow = newPossibleMonetaryAmount + abs(taxCharged)\n self.changeMonetaryValue(-1 * payableNow)\n bank.add(abs(payableNow))\n\n #Add the amount to debt record\n amountDebt = newPossibleMonetaryAmount\n self.addDebtRecord(\"Bank\", amountDebt)\n else: \n #If sufficient amount, pay the tax to the bank\n #Tax the player, and add the money to bank\n self.changeMonetaryValue(taxCharged)\n bank.add(abs(taxCharged))\n\n #Options to escape jail - Note player can collect rent (provided not mortgaged) or make changes to the title deeds\n def escapeJailOptions(self, bank, dice): \n #Return the Get Out of Jail Free card to the deck in game if used \n card = None\n validOptionSelected = False\n\n jailMessage = \"You are currently in jail, and you have \" + str(self.getJailTurns()) + \" left in jail.\" + \"\\nYou do have the following options if you wish to get out of jail early.\" + \"\\n1. Pay 50 dollar fine - Type 'Pay 50' to use this option.\" + \"\\n2. Roll a double - Type 'Roll Dice' to use this option.\"\n \n if (self.jailCardsAvailable()): \n jailMessage += \"\\n3. Use a get out of jail free card - Type 'Jail Free Card' to use this option.\"\n \n while (not validOptionSelected): \n print(jailMessage)\n jailOption = input(\"Select an option based on the options listed.\") #This needs to be validated\n \n #Pay 50 fine, user may not go forward until the next turn\n if (jailOption == \"Pay 50\"): \n #User must have sufficient funds to pay the bank \n if (self.getMonetaryValue() > 0):\n self.changeMonetaryValue(-1 * Bank.JAIL_PAYMENT)\n bank.add(Bank.JAIL_PAYMENT)\n\n #Reset the statuses \n self.setInJailStatus(False)\n self.setJailTurns(0)\n\n #Player goes to just visiting jail \n self.setPosition(Board.TILES_LIST[\"Just Visiting\"])\n\n validOptionSelected = True\n else: \n print(\"You do not have sufficient funds to get out of jail.\")\n\n #Use get out of jail free card\n elif (jailOption == \"Jail Free Card\" and self.jailCardsAvailable()): \n card = self.removeEscapeJailCard() \n\n #Reset the statuses \n self.setInJailStatus(False)\n self.setJailTurns(0)\n\n #Player goes to just visiting jail \n self.setPosition(Board.TILES_LIST[\"Just Visiting\"])\n\n validOptionSelected = True\n \n #Roll a double\n elif (jailOption == \"Roll Dice\"):\n total = dice.rollDice()\n\n #If player rolls a double \n if (dice.getDoubleStatus()):\n #Advance to the tile according to your roll\n #Calculate new position of player\n newPosition = self.getPosition() + total\n\n #Apply new position \n self.setPosition(newPosition)\n\n #Reset the statuses \n self.setInJailStatus(False)\n self.setJailTurns(0)\n\n #Reset the double status - user does not do another dice roll\n dice.resetDoubleStatus()\n\n validOptionSelected = True\n else: \n print(\"You did not roll a double.\") \n\n #Reduce one jail turn \n self.subtractJailTurns()\n\n #If this results in no more jail turns left, player must pay 50. If so reset statuses. \n if (self.getInJailStatus() == 0):\n print(\"\\nYou may now escape jail as a result, but you must pay 50 to the bank.\")\n self.changeMonetaryValue(-1 * Bank.JAIL_PAYMENT)\n bank.add(Bank.JAIL_PAYMENT)\n #Reset the statuses \n self.setInJailStatus(False)\n self.setJailTurns(0)\n self.setPosition(Board.TILES_LIST[\"Just Visiting\"])\n \n #By virtue, valid option does not need to be selected\n validOptionSelected = True\n else: \n print(\"Currently, you must remain in jail.\")\n else:\n if (jailOption == \"Jail Free Card\"): \n print(\"You do not have any jail free cards you can use at this time.\")\n \n #Print message reporting user did not type in a valid response. \n print(\"You did not select a valid option to escape jail. Please try again.\")\n \n return card \n\n #User pays the rent to the other player\n #Check if the current player has run out of money but still has property - i.e. owes debt\n def payRent(self, owner, titleDeedName, boardTile, dice):\n titleDeedCard = None\n titleDeedRecord = None\n rentAmount = 0\n\n #Get the title deed information \n titleDeedsList = owner.getTitleDeeds() \n for titleDeedItem in titleDeedsList: \n if (titleDeedName == titleDeedItem[\"Title Deed\"].getName()):\n titleDeedRecord = titleDeedItem\n titleDeedCard = titleDeedItem[\"Title Deed\"]\n \n #No payment can be made given no information is available \n if (titleDeedCard == None): \n print(\"There is no title deed card information available for \" + titleDeedName)\n return \n\n #Check if the title deed is in mortgages, if so do not collect rent\n if (titleDeedRecord[\"Mortgaged\"]):\n print(\"This property \" + titleDeedCard.getName() + \" is currently mortgaged, and rent cannot be collected at this time.\") \n return #Go back to the caller function; do not collect rent\n\n #If title deed is a property, additional checking is needed to determine the rent value.\n if (boardTile == \"Property\"): \n #Check if the owner owns a monopoly - that is owns all title deeds of that color group \n colorGroup = titleDeedCard.getColorGroup() \n\n #If the owner has a monopoly on that color group\n if (colorGroup in owner.getColorMonopoly()): \n #Get the number of houses and hotels of that property\n num_Houses = titleDeedRecord[\"Houses\"]\n num_Hotels = titleDeedRecord[\"Hotels\"]\n\n if (num_Houses): \n #There is at least one house on the property\n rentAmount = titleDeedCard.getRentCosts(Property.HOMES_RENT[num_Houses - 1])\n elif (num_Hotels): \n #There may be no homes on the property, but there is a hotel \n rentAmount = titleDeedCard.getRentCosts(Property.HOTELS_COST)\n else: \n #This is an undeveloped site \n rentAmount = titleDeedCard.getRentValue() * Property.DOUBLE_RENT\n else: \n #There is no monopoly on the color group associated for that property\n rentAmount = titleDeedCard.getRentValue() \n\n #If utility, roll dice, and the rental amount based on number of utilities owned\n elif (boardTile == \"Utility\"): \n #Roll the dice\n diceRoll = dice.rollDice()\n \n #If owner has both utilities - use 10 \n if (owner.getUtilityOwned() == Utility.UTILITY_BOTH):\n rentAmount = diceRoll * titleDeedCard.getRentCosts[Utility.UTILITY_BOTH - 1] * Bank.RENT_MULTIPLIER\n else: #If owner owns this utility only - use 4\n rentAmount = diceRoll * titleDeedCard.getRentCosts[Utility.UTILITY_ONE - 1] * Bank.RENT_MULTIPLIER\n\n #If transports, calculate the rent amount based on number of transports title deeds owned by owner\n elif (boardTile == \"Transports\"):\n #Get the number of transports owned by the owner\n transportsOwned = owner.getTransportsOwned() \n\n #Calculate the rental payment\n rentAmount = titleDeedCard.getRentCosts[transportsOwned - 1] \n\n #Check current amounts of player before making rental payment \n monetaryAmount = self.getMonetaryValue() \n newPossibleMonetaryAmount = monetaryAmount - rentAmount\n \n #Check if the player already has no money currently\n if (monetaryAmount <= 0): \n #Record the amount of money owned to that player\n self.addDebtRecord(owner.getName(), rentAmount)\n\n #Since there is no tangible object of monetary value being exchanged, there is no additional debt accumulated. \n\n #Check if the new monetary value causes player to go below zero. If so, the remaining amount must be recorded as debt\n elif (newPossibleMonetaryAmount < 0): \n #Make payment that can be paid now \n payableNow = newPossibleMonetaryAmount + rentAmount\n self.changeMonetaryValue(-1 * payableNow)\n owner.changeMonetaryValue(payableNow) \n\n #Add the amount to debt record\n amountDebt = newPossibleMonetaryAmount\n self.addDebtRecord(owner.getName(), amountDebt)\n \n #If player has sufficient funds\n else: \n self.changeMonetaryValue(-1 * rentAmount)\n owner.changeMonetaryValue(rentAmount) \n\n #Check if there is any existing debt to that player, and if so reduce debt. \n debtOwned = self.getDebtRecord() \n\n for record in debtOwned: \n if (record[\"Player\"] == owner.getName()): \n self.reduceDebt(owner.getName(), rentAmount)\n \n #Reset the dice doubles\n dice.resetDoubleStatus() \n\n \"\"\"Methods associated with adding by purchasing or auctioning from the bank (or by bankruptcy of another player) \"\"\"\n #Add a title deed to player's possession (as a result of the bank)\n def acquireTitleDeed(self, titleDeed, purchaseValue, bank = None): \n #Add title deed to possession\n self.addTitleDeed(titleDeed)\n\n #Remove card from bank's possession\n if (bank != None): \n bank.removeTitleDeed(titleDeed)\n\n #Set the owner to the title deed \n titleDeed.setOwner(self.getName())\n \n #Check current amounts of player before repaying mortgage\n monetaryAmount = self.getMonetaryValue() \n newPossibleMonetaryAmount = monetaryAmount - purchaseValue\n \n #Check if the player already has no money currently\n if (monetaryAmount <= 0): \n #Record the amount of money owned to the bank\n self.addDebtRecord(\"Bank\", purchaseValue)\n\n self.changeMonetaryValue(-1 * purchaseValue)\n if (bank != None): \n bank.add(purchaseValue)\n\n #Check if the new monetary value causes player to go below zero. If so, the remaining amount must be recorded as debt (on the condition has title deeds)\n elif (newPossibleMonetaryAmount < 0): \n #Make payment that can be paid now \n payableNow = newPossibleMonetaryAmount + purchaseValue\n self.changeMonetaryValue(-1 * payableNow)\n if (bank != None): \n bank.changeMonetaryValue(payableNow) \n\n #Add the amount to debt record\n amountDebt = newPossibleMonetaryAmount\n self.addDebtRecord(\"Bank\", amountDebt)\n else: \n #If there are sufficient funds to pay mortgage w/ interest in full \n #Make the purchase\n self.changeMonetaryValue(-1 * purchaseValue)\n if (bank != None): \n bank.add(purchaseValue)\n\n #Check if there is any existing debt to the bank, and if so reduce debt. \n debtOwned = self.getDebtRecord() \n\n for record in debtOwned: \n if (record[\"Player\"] == \"Bank\"): \n self.reduceDebt(\"Bank\", purchaseValue)\n\n print(\"The title deed \" + titleDeed.getName() + \" has been added.\")\n \n \"\"\"Methods associated with existing properties or making changes including selling and mortgaging\"\"\" \n #Add a mortgage to a property\n def addMortgage(self, titleDeedName, mortgageValue, bank): \n for titleDeed in self.titleDeeds: \n if (titleDeed[\"Title Deed\"].getName() == titleDeedName): \n titleDeed[\"Mortgaged\"] = True\n \n bank.giveMortgageLoan(mortgageValue)\n self.changeMonetaryValue(mortgageValue)\n\n #Remove and repay a mortgage from a property\n def removeMortgage(self, titleDeedName, repayAmount, bank): \n for titleDeed in self.titleDeeds: \n if (titleDeed[\"Title Deed\"].getName() == titleDeedName): \n titleDeed[\"Mortgaged\"] = False\n \n #Check current amounts of player before repaying mortgage\n monetaryAmount = self.getMonetaryValue() \n newPossibleMonetaryAmount = monetaryAmount - repayAmount\n \n #Check if the player already has no money currently \n if (monetaryAmount <= 0): \n #Record the amount of money owned to the bank\n self.addDebtRecord(\"Bank\", repayAmount)\n\n #Check if the new monetary value causes player to go below zero. If so, the remaining amount must be recorded as debt\n elif (newPossibleMonetaryAmount < 0): \n #Make payment that can be paid now \n payableNow = newPossibleMonetaryAmount + repayAmount\n self.changeMonetaryValue(-1 * payableNow)\n bank.changeMonetaryValue(payableNow) \n\n #Add the amount to debt record\n amountDebt = newPossibleMonetaryAmount\n self.addDebtRecord(\"Bank\", amountDebt)\n else: \n #If there are sufficient funds to pay mortgage w/ interest in full \n self.changeMonetaryValue(-1 * repayAmount)\n bank.creditMortgagePayment(repayAmount)\n\n #Purchase a home for the property\n def purchaseHome(self, propertyName, buildingCost, bank):\n propertyFound = False\n colorGroup = None\n propertyRecord = None\n\n #Search for the property \n for titleDeed in self.titleDeeds: \n if (titleDeed[\"Title Deed\"].getName() == propertyName):\n propertyFound = True\n colorGroup = titleDeed[\"Title Deed\"].getColorGroup()\n propertyRecord = titleDeed\n \n #Add the house to the property name if found\n if (propertyFound): \n propertyRecord[\"Houses\"] = propertyRecord[\"Houses\"] + 1\n self.num_homes += 1\n \n #Add the number of houses built for that monopoly\n for monopolyColor in self.colorMonopoly:\n if (colorGroup == monopolyColor[\"Color Group\"]):\n monopolyColor[\"Number of Houses Built\"] = monopolyColor[\"Number of Houses Built\"] + 1\n\n #Check current amounts of player before repaying mortgage\n monetaryAmount = self.getMonetaryValue() \n newPossibleMonetaryAmount = monetaryAmount - buildingCost\n \n #Check if the player already has no money currently\n if (monetaryAmount <= 0): \n #Record the amount of money owned to the bank\n self.addDebtRecord(\"Bank\", buildingCost)\n\n #Make the purchase\n self.changeMonetaryValue(-1 * buildingCost) \n bank.purchaseHome(buildingCost)\n\n #Check if the new monetary value causes player to go below zero. If so, the remaining amount must be recorded as debt\n elif (newPossibleMonetaryAmount < 0): \n #Make payment that can be paid now \n payableNow = newPossibleMonetaryAmount + buildingCost\n self.changeMonetaryValue(-1 * payableNow)\n bank.purchaseHome(payableNow) \n\n #Add the amount to debt record\n amountDebt = newPossibleMonetaryAmount\n self.addDebtRecord(\"Bank\", amountDebt)\n else: \n #If there are sufficient funds to pay mortgage w/ interest in full \n #Make the purchase\n self.changeMonetaryValue(-1 * buildingCost) \n bank.purchaseHome(buildingCost)\n\n #Check if there is any existing debt to the bank, and if so reduce debt. \n debtOwned = self.getDebtRecord() \n\n for record in debtOwned: \n if (record[\"Bank\"] == \"Bank\"): \n self.reduceDebt(\"Bank\", buildingCost)\n \n #Purchase a hotel for the property, and return four houses to the bank\n def purchaseHotel(self, propertyName, buildingCost, bank): \n propertyFound = False\n colorGroup = None\n propertyRecord = None\n\n #Search for the property \n for titleDeed in self.titleDeeds: \n if (titleDeed[\"Title Deed\"].getName() == propertyName):\n propertyFound = True\n colorGroup = titleDeed[\"Title Deed\"].getColorGroup()\n propertyRecord = titleDeed\n \n #Add the hotel to the property name if found, return four houses\n if (propertyFound): \n propertyRecord[\"Hotels\"] = propertyRecord[\"Hotels\"] + 1\n self.num_hotels += 1\n\n propertyRecord[\"Houses\"] = 0\n self.num_homes -= Property.HOMES_MAX\n\n #Add the number of hotels built for that monopoly, and reflect four houses returned\n for monopolyColor in self.colorMonopoly:\n if (colorGroup == monopolyColor[\"Color Group\"]):\n monopolyColor[\"Number of Hotels Built\"] = monopolyColor[\"Number of Hotels Built\"] + 1\n monopolyColor[\"Number of Houses Built\"] = monopolyColor[\"Number of Hotels Built\"] - Property.HOMES_MAX\n \n #Check current amounts of player before repaying mortgage\n monetaryAmount = self.getMonetaryValue() \n newPossibleMonetaryAmount = monetaryAmount - buildingCost\n \n #Check if the player already has no money currently (on the condition has title deeds)\n if (monetaryAmount <= 0): \n #Record the amount of money owned to that player, assuming player has title deeds\n self.addDebtRecord(\"Bank\", buildingCost)\n\n self.changeMonetaryValue(-1 * buildingCost) \n bank.purchaseHotel(buildingCost)\n\n #Check if the new monetary value causes player to go below zero. If so, the remaining amount must be recorded as debt (on the condition has title deeds)\n elif (newPossibleMonetaryAmount < 0): \n #Make payment that can be paid now \n payableNow = newPossibleMonetaryAmount + buildingCost\n self.changeMonetaryValue(-1 * payableNow)\n bank.purchaseHotel(payableNow)\n\n #Add the amount to debt record\n amountDebt = newPossibleMonetaryAmount\n self.addDebtRecord(\"Bank\", amountDebt)\n else: \n #If there are sufficient funds to pay mortgage w/ interest in full \n #Make the purchase\n self.changeMonetaryValue(-1 * buildingCost) \n bank.purchaseHotel(buildingCost)\n\n #Check if there is any existing debt to the bank, and if so reduce debt. \n debtOwned = self.getDebtRecord() \n\n for record in debtOwned: \n if (record[\"Player\"] == \"Bank\"): \n self.reduceDebt(\"Bank\", buildingCost)\n \n #Return four houses back to the bank\n bank.returnHomesWithHotel()\n \n #Sell a home from the property\n def sellHome(self, propertyName, sellingAmount, bank): \n propertyFound = False\n colorGroup = None\n propertyRecord = None\n\n #Search for the property \n for titleDeed in self.titleDeeds: \n if (titleDeed[\"Title Deed\"].getName() == propertyName):\n propertyFound = True \n colorGroup = titleDeed[\"Title Deed\"].getColorGroup()\n propertyRecord = titleDeed\n \n #Remove the house to the property name if found\n if (propertyFound): \n propertyRecord[\"Houses\"] = propertyRecord[\"Houses\"] - 1\n self.num_homes -= 1\n \n #Add the number of houses built for that monopoly\n for monopolyColor in self.colorMonopoly:\n if (colorGroup == monopolyColor[\"Color Group\"]):\n monopolyColor[\"Number of Houses Built\"] = monopolyColor[\"Number of Houses Built\"] - 1\n \n #Make the sell\n self.changeMonetaryValue(sellingAmount) \n bank.sellHome(sellingAmount)\n\n #Check if there is any existing debt to the bank, and if so reduce debt. \n debtOwned = self.getDebtRecord() \n\n for record in debtOwned: \n if (record[\"Player\"] == \"Bank\"): \n amountOwned = record[\"Debt Owned\"]\n currentMonetaryAmount = self.getMonetaryValue()\n\n #To reduce debt, the sell must result in positive monetary cash amount\n if (currentMonetaryAmount > 0):\n if (currentMonetaryAmount >= amountOwned): \n #Player has enough cash to clear the debt, and has at least some money\n self.changeMonetaryValue(-1 * amountOwned) \n self.reduceDebt(\"Bank\", amountOwned) \n else: \n #Player does not have enough cash to clear debt fully \n self.changeMonetaryValue(-1 * amountOwned) \n self.reduceDebt(\"Bank\", amountOwned - currentMonetaryAmount)\n \n #Sell a hotel from the property, and get four houses back from the bank\n def sellHotel(self, propertyName, sellingAmount, bank): \n propertyFound = False\n colorGroup = None\n propertyRecord = None\n\n #Search for the property \n for titleDeed in self.titleDeeds: \n if (titleDeed[\"Title Deed\"].getName() == propertyName):\n propertyFound = True\n colorGroup = titleDeed[\"Title Deed\"].getColorGroup()\n propertyRecord = titleDeed\n \n #Add the hotel to the property name if found, return four houses\n if (propertyFound): \n propertyRecord[\"Hotels\"] = propertyRecord[\"Hotels\"] - 1\n self.num_hotels -= 1\n\n propertyRecord[\"Houses\"] = Property.HOMES_MAX\n self.num_homes += Property.HOMES_MAX\n\n #Add the number of hotels built for that monopoly, and reflect four houses returned\n for monopolyColor in self.colorMonopoly:\n if (colorGroup == monopolyColor[\"Color Group\"]):\n monopolyColor[\"Number of Hotels Built\"] = monopolyColor[\"Number of Hotels Built\"] - 1\n monopolyColor[\"Number of Houses Built\"] = monopolyColor[\"Number of Hotels Built\"] + Property.HOMES_MAX\n \n #Make the purchase\n self.changeMonetaryValue(sellingAmount) \n bank.sellHotel(sellingAmount)\n\n #Return four houses back to the bank\n bank.getHomesWithHotel()\n\n #Check if there is any existing debt to the bank, and if so reduce debt. \n debtOwned = self.getDebtRecord() \n\n for record in debtOwned: \n if (record[\"Player\"] == \"Bank\"): \n amountOwned = record[\"Debt Owned\"]\n currentMonetaryAmount = self.getMonetaryValue()\n\n #To reduce debt, the sell must result in positive monetary cash amount\n if (currentMonetaryAmount > 0):\n if (currentMonetaryAmount >= amountOwned): \n #Player has enough cash to clear the debt, and has at least some money\n self.changeMonetaryValue(-1 * amountOwned) \n self.reduceDebt(\"Bank\", amountOwned) \n else: \n #Player does not have enough cash to clear debt fully \n self.changeMonetaryValue(-1 * amountOwned) \n self.reduceDebt(\"Bank\", amountOwned - currentMonetaryAmount)\n\n #Sell the title deed to another player\n def sellTitle(self, titleDeedName, sellingAmount = 0):\n titleDeed = self.removeTitleDeed(titleDeedName)\n self.changeMonetaryValue(sellingAmount)\n \n #Return the card to allow receiver to gain possession\n return titleDeed\n\n #Inherit a title deed from a sell by another player (i.e. original owner)\n def inheritTitle(self, titleDeedCard, purchaseAmount, mortgaged = False, bank = None): \n self.addTitleDeed(titleDeedCard, mortgaged)\n self.changeMonetaryValue(-1 * purchaseAmount)\n\n if (mortgaged): \n #Get the mortgage value\n mortgageValue = titleDeedCard.getMortgageValue()\n\n print(\"This title deed \" + titleDeedCard.getName() + \" is mortgaged. You have two options.\\n\" + \n \"1. Repay the Mortgage Now\\n\" + \n \"2. Pay 10\\% Interest Now\\n\" + \n \"Please type either 'Repay Mortgage' or 'Pay Interest Only'\")\n\n option = input(\"Type in your option: \") #This requires validation \n\n if (option == \"Repay Mortgage\"): \n #Calculate repayment amount with interest\n repayAmount = int(math.ceil((mortgageValue + (mortgageValue * Bank.MORTGAGE_INTEREST))/100.0) * 100)\n\n #Make the repayment mortgage to the bank \n self.removeMortgage(titleDeedCard.getName(), repayAmount, bank)\n\n print(self.getName() + \", your repayment for \" + titleDeedCard.getName() + \" was successful.\\n\")\n\n elif (option == \"Pay Interest Only\"): \n #Calculate interest amount\n interestAmount = int(math.ceil(mortgageValue * Bank.MORTGAGE_INTEREST)/100.0) * 100\n\n #Pay the interest only, user retains mortgage \n self.changeMonetaryValue(-1 * interestAmount)\n if (bank != None): \n bank.add(interestAmount)\n \n #Check if user has run out of cash\n def runOutOfCash(self): \n if (self.getMonetaryValue() <= 0): \n return True #User does not have cash \n return False \n \n #Provide amount for auctioning or selling (if receiver)\n def provideAmount(self, currentPlayStatus, titleDeedName, bidAmount = 0):\n amount = 0 \n inputValid = False\n\n while (not inputValid): \n if (currentPlayStatus == \"Auction\"): \n \n #auctionMessage = \"Please enter your bidding bid for \" + titleDeedName + \". If you wish to skip the bid, please enter amount as '0'.\"\n #auctionMessage += \"\\nEnter bid here: \"\n\n #name of some function to send to Controller....\n #util.dataHandling(auctionMessage) \n\n print(\"Please enter your bidding bid for \" + titleDeedName + \". If you wish to skip the bid, please enter amount as '0'.\") \n\n #Validate the input \n amount = input(\"\\nEnter bid here: \") \n \n elif (currentPlayStatus == \"Selling\"):\n print(\"Please enter the amount you wish to purchase for \" + titleDeedName + \".\")\n\n #Validate the input \n amount = input(\"\\nEnter bid here: \") \n \n if (amount < 0 or amount == None): \n print(\"This is not an acceptable value. Please try again.\")\n elif (currentPlayStatus == \"Auction\" and amount < bidAmount):\n print(\"You must enter a bid amount higher than or equal to the starting amount. Please enter a higher value.\")\n elif (currentPlayStatus == \"Selling\" and amount <= 0):\n print(\"You must enter a non-zero positive value for selling. Please enter a higher value.\")\n else: \n inputValid = True\n\n return amount\n \n #Accept or reject an amount for selling a title deed\n def decideAmount(self, receiverName, titleDeedName, sellingOfferAmount): \n print(\"Player \" + receiverName + \" can purchase your property for \" + str(sellingOfferAmount) + \n \". Do you wish to sell \" + titleDeedName + \" at this amount?\")\n\n #Validate the input\n response = input(\"Enter either 'Accept' or 'Decline': \")\n\n if (response == \"Accept\"):\n return True\n \n #Default amount if not accepted or some other response\n return False\n \n \n\n \n\n\n\n\n","sub_path":"src/Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":47147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"21967637","text":"'''\nfilename: graph.py\nconsisting: functions to create graph and subgraph\naffliation: milestone 1\n'''\n\nimport networkx as nx\nfrom a_star import *\nfrom load import *\n\ndef graphMaker(nodeFile, edgeFile):\n # initiates a graph \n G = nx.Graph()\n\n # nodes\n node = nodeLoader(nodeFile)\n for parse in node:\n G.add_node(int(parse[0]), pos=(float(parse[1]), float(parse[2])))\n\n # edges\n edge = edgeLoader(edgeFile)\n for parse in edge:\n G.add_edge(int(parse[1]), int(parse[2]), weight=float(parse[3]))\n\n # return graph\n return G\n\ndef subgraphMaker(G, posts):\n # initiates the subgraph\n subgraph = nx.Graph()\n\n #nodes\n node_pos = G.nodes().data('pos')\n for post in posts:\n subgraph.add_node(post, pos=node_pos[post])\n\n #edges\n for i in range(len(posts)):\n for j in range(i+1, len(posts)):\n subgraph.add_edge(posts[i], posts[j], weight= astar_path_length(G, posts[i], posts[j]))\n \n #return subgraph\n return subgraph\n\n","sub_path":"src/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"61105516","text":"import unittest\nimport numpy as np\nimport pytest\nfrom scipy.optimize import OptimizeResult\nfrom zquantum.core.circuit import ParameterGrid\nfrom zquantum.core.interfaces.optimizer_test import OptimizerTests\nfrom .grid_search import GridSearchOptimizer\n\n\n@pytest.fixture(params=[ParameterGrid([[0, 1.5, 0.1], [0, 1.5, 0.1]])])\ndef optimizer(request):\n return GridSearchOptimizer(request.param)\n\n\nclass TestGridSearchOptimizer(OptimizerTests):\n\n def test_get_values_grid(self):\n # Given\n param_ranges = [(0, 1.1, 0.5)] * 2\n grid = ParameterGrid(param_ranges)\n grid_search_optimizer = GridSearchOptimizer(grid)\n optimization_results = OptimizeResult()\n history = [\n {\"value\": 0},\n {\"value\": 1},\n {\"value\": 2},\n {\"value\": 3},\n {\"value\": 4},\n {\"value\": 5},\n {\"value\": 6},\n {\"value\": 7},\n {\"value\": 8},\n ]\n optimization_results[\"history\"] = history\n correct_values_grid = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])\n\n # When\n values_grid = grid_search_optimizer.get_values_grid(optimization_results)\n\n # Then\n np.testing.assert_array_equal(values_grid, correct_values_grid)\n","sub_path":"src/python/zquantum/optimizers/grid_search_test.py","file_name":"grid_search_test.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"645671465","text":"import os\nimport sys\nimport copy\nimport datetime\nimport collections\nimport robel\nimport gym\nimport torch\nimport torch.nn as nn\nimport gym\nimport numpy as np\nimport argparse\nimport itertools\n\n\nfrom modules import *\nimport utils\n\nfrom tensorboardX import SummaryWriter\n\n\nimport pdb\n\ndef eval_policy(policy, env_name, broken_info = False, eval_episodes=3, real_robot = False, seed = 0):\n env_seed = 2 ** 32 - 1 - seed\n if real_robot:\n eval_env = gym.make(env_name, device_path='/dev/tty.usbserial-FT3WI485')\n else:\n eval_env = gym.make(env_name)\n eval_env.seed(env_seed)\n\n avg_reward = 0.\n for _ in range(eval_episodes):\n state, done = eval_env.reset(), False\n if args.trim_state:\n state = utils.trim_state(state)\n if broken_info:\n state = np.concatenate((state, np.ones(9)))\n while not done:\n action = policy.select_action(np.array(state), evaluate=True)\n state, reward, done, _ = eval_env.step(action)\n if args.trim_state:\n state = utils.trim_state(state)\n avg_reward += reward\n if broken_info:\n state = np.concatenate((state, np.ones(9)))\n \n avg_reward /= eval_episodes\n\n print(\"---------------------------------------\")\n print(\"Evaluation over {} episodes: {:.3f}\".format(eval_episodes, avg_reward))\n print(\"---------------------------------------\")\n return avg_reward\n\nenv = gym.make('DClawTurnFixed-v0')\n# env = gym.make('DClawTurnFixed-v0', device_path='/dev/tty.usbserial-FT3WI485')\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--start-timesteps\", type=int, default=int(1e4))\n # parser.add_argument(\"--start-timesteps\", type=int, default=int(256))\n parser.add_argument(\"--max-timesteps\", type=int, default=int(1e7))\n parser.add_argument(\"--eval-freq\", type=int, default=20)\n parser.add_argument(\"--save-freq\", type=int, default=5000)\n parser.add_argument(\"--record-freq\", type=int, default=5000)\n # parser.add_argument(\"--eval-freq\", type=int, default=1)\n # parser.add_argument(\"--save-freq\", type=int, default=1)\n # parser.add_argument(\"--record-freq\", type=int, default=1)\n parser.add_argument(\"--seed\", type=int, default=0)\n parser.add_argument(\"--buffer-max-size\", type=int, default=int(1e6))\n parser.add_argument(\"--agent-training-episodes\", type=int, default=int(10))\n parser.add_argument(\"--restore-step\", type=int, default=0)\n parser.add_argument(\"--broken-timesteps\", type=int, default=1)\n parser.add_argument(\"--hidden-size\", type=int, default=512)\n parser.add_argument(\"--broken-info\", type=bool, default=True,\n help=\"whether use broken joints indice as a part of state\")\n parser.add_argument(\"--broken-angle\", type=float, default=-0.6)\n parser.add_argument(\"--std\", type=float, default=0.1)\n parser.add_argument('--gamma', type=float, default=0.99, metavar='G',\n help='discount factor for reward (default: 0.99)')\n parser.add_argument('--tau', type=float, default=0.005, metavar='G',\n help='target smoothing coefficient(τ) (default: 0.005)')\n parser.add_argument('--env-name', default='DClawTurnFixed-v0',\n help='Mujoco Gym environment (default: HalfCheetah-v2)')\n parser.add_argument('--policy', default=\"Gaussian\",\n help='Policy Type: Gaussian | Deterministic (default: Gaussian)')\n parser.add_argument('--eval', type=bool, default=True,\n help='Evaluates a policy a policy every 10 episode (default: True)')\n parser.add_argument('--alpha', type=float, default=0.2, metavar='G',\n help='Temperature parameter α determines the relative importance of the entropy\\\n term against the reward (default: 0.2)')\n parser.add_argument('--automatic_entropy_tuning', type=bool, default=False, metavar='G',\n help='Automaically adjust α (default: False)')\n parser.add_argument('--target_update_interval', type=int, default=1, metavar='N',\n help='Value target update per no. of updates per step (default: 1)')\n parser.add_argument('--lr', type=float, default=0.0003, metavar='G',\n help='learning rate (default: 0.0003)')\n parser.add_argument('--batch_size', type=int, default=256, metavar='N',\n help='batch size (default: 256)')\n parser.add_argument('--trim-state', type=bool, default=True)\n args = parser.parse_args()\n env.seed(args.seed)\n if not os.path.exists('./logs'):\n os.system('mkdir logs')\n if not os.path.exists('./saved_models'):\n os.system('mkdir saved_models')\n outdir = datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n outdir = os.path.join('./saved_models', outdir)\n os.system('mkdir ' + outdir)\n with open(outdir+'/setting.txt','w') as f:\n # f.writelines(\"fix the broken info bug\\n\")\n f.writelines(\"using random adversary sac\\n\")\n for each_arg, value in args.__dict__.items():\n f.writelines(each_arg + \" : \" + str(value)+\"\\n\")\n writer = SummaryWriter(logdir=('logs/gac{}').format(datetime.datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")))\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n state_dim = env.reset().shape[0]\n if args.trim_state:\n state_dim -= 9\n original_state_dim = state_dim\n\n if args.broken_info:\n state_dim += 9\n action_dim = env.action_space.sample().shape[0]\n max_action = env.action_space.high[0]\n agent = SAC(num_inputs=state_dim,\n action_space=env.action_space,\n args=args,\n writer=writer,\n outdir=outdir,\n device=device)\n \"advesarial agent code\"\n # if args.restore_step:\n # print(\"restoring the model {}\".format(args.restore_step))\n # ddpg.restore_model_for_train(args.restore_step)\n # ddpg.index = 0\n # adversary.restore_model(args.restore_step)\n current_state = env.reset()\n if args.broken_info:\n joint_info = np.ones(9)\n current_state = np.concatenate((current_state, joint_info))\n def step(adversarial_action: int, current_state):\n \"input obs obs is processed and its broken info must be all 1s\"\n \"return unprocessed next_state\"\n if args.broken_info:\n current_state = np.concatenate((current_state, np.ones(9)))\n current_state[original_state_dim + adversarial_action] = 0\n # broken_timesteps = 1\n \n total_done = False\n reward_list = []\n for i in range(args.broken_timesteps):\n agent_action = agent.select_action(current_state, evaluate=True)\n agent_action[adversarial_action] = -0.6\n next_state, reward, done, info = env.step(agent_action)\n original_next_state = next_state\n if args.trim_state:\n next_state = utils.trim_state(next_state)\n if args.broken_info:\n joint_info = np.ones(9)\n joint_info[adversarial_action] = 0\n next_state = np.concatenate((next_state, joint_info))\n reward_list.append(reward)\n if done:\n total_done = done\n break\n current_state = next_state\n avg_reward = np.array(reward_list).mean()\n return original_next_state, avg_reward, total_done, info\n episode = 0\n t = 0\n agent_t = 0\n adversary_t = 0\n \n done = False\n minimal_index = 0\n \n for i_episode in itertools.count(1):\n if t > args.max_timesteps:\n break\n for agent_episode in range(args.agent_training_episodes):\n done = False\n \" the agent training loop\"\n broken_joints = collections.deque(maxlen=1)\n current_state = env.reset()\n if args.trim_state:\n current_state = utils.trim_state(current_state)\n if args.broken_info:\n joint_info = np.ones(9)\n current_state = np.concatenate((current_state, joint_info))\n episode_steps = 0\n \n while not done:\n t += 1\n agent_t += 1\n if args.broken_info:\n current_state[original_state_dim + minimal_index] = 0\n \n if agent_t == args.start_timesteps:\n print(\"start ddpg learning\")\n if agent_t < args.start_timesteps:\n original_action = env.action_space.sample()\n else:\n original_action = agent.select_action(current_state, evaluate=False)\n action = copy.deepcopy(original_action)\n action[minimal_index] = args.broken_angle\n next_state, reward, done, info = env.step(action)\n episode_steps += 1\n mask = 1 if episode_steps == env._max_episode_steps else float(not done)\n if args.trim_state:\n next_state = utils.trim_state(next_state)\n if args.broken_info:\n next_state = np.concatenate((next_state, np.ones(9)))\n next_state[original_state_dim + minimal_index] = 0\n # suc = info['score/success']\n agent.add_buffer(current_state, original_action, next_state, reward, mask)\n if agent_t > args.start_timesteps:\n agent.update_parameters()\n current_state = next_state\n\n minimal_index = random.randint(0,8)\n\n if i_episode % args.eval_freq == 0:\n print(\"-------------------------------------------\")\n print(\"steps:{:07d}\".format(t))\n print(\"episode:{:07d}\".format(i_episode))\n avg_reward = eval_policy(agent, 'DClawTurnFixed-v0', broken_info = args.broken_info)\n writer.add_scalar('/eval/avg_reward',avg_reward, i_episode)\n\n","sub_path":"train/train_random_sac.py","file_name":"train_random_sac.py","file_ext":"py","file_size_in_byte":10110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"180116996","text":"from random import shuffle, randint\nfrom peewee import fn, JOIN\n\nfrom config import *\nfrom business_objects.Models import Definition as Definition\nfrom business_objects.Models import Word as Word\n\n# TODO: This is still giving duplicates - change it so that it does not\n# TODO: probably has something to do with querying definitions\n# TODO: try querying words instead\n\n\nclass DefinitionQuestion:\n word_index = None\n question_text = None\n answer_choices = None\n chapter_index = None\n question_type = None\n\n def make_definition_question(\n self,\n chapter_index,\n question_type=None,\n cumulative=False,\n ):\n # if no question type is requested, flip a coin to determine the question type\n self.chapter_index = chapter_index\n if question_type not in [0, 1]:\n self.question_type = randint(0, 1)\n else:\n self.question_type = question_type\n\n # question type 1 is word prompt with definition choices\n # this is pretty inefficient currently as it pings the database 5 times\n # question type 0 only makes one query\n # TODO: make this more efficient\n if self.question_type == 1:\n\n if cumulative:\n query = (\n Word\n .select()\n .where(Word.chapter_index <= chapter_index)\n .order_by(fn.Rand())\n .limit(number_of_multiple_choices)\n )\n\n else:\n query = (\n Word\n .select()\n .where(Word.chapter_index == chapter_index)\n .order_by(fn.Rand())\n .limit(number_of_multiple_choices)\n )\n\n definition_list = []\n for word in query:\n random_definition = (\n Definition\n .select()\n .where(Definition.word_index == word.word_index)\n .order_by(fn.Rand())\n .get()\n )\n definition = {\n \"text\": random_definition.definition,\n \"index\": random_definition.word_index_id\n }\n definition_list.append(definition)\n self.answer_choices = definition_list\n self.question_text = query[0].word\n self.word_index = query[0].word_index\n\n # question type 0 is definition prompt with word choices\n elif self.question_type == 0:\n\n if cumulative:\n query = (Definition\n .select(Definition, Word)\n .join(Word)\n .where(Word.chapter_index <= chapter_index)\n .order_by(fn.Rand())\n .limit(number_of_multiple_choices)\n )\n else:\n query = (Definition\n .select(Definition, Word)\n .join(Word)\n .where(Word.chapter_index == chapter_index)\n .order_by(fn.Rand())\n .limit(number_of_multiple_choices)\n )\n\n word_list = []\n for element in query:\n word = {\n \"text\": element.word_index.word,\n \"index\": element.word_index_id\n }\n word_list.append(word)\n\n self.answer_choices = word_list\n self.question_text = query[0].definition\n self.word_index = query[0].word_index_id\n\n shuffle(self.answer_choices)\n\n return self\n\n def get_json_min(self):\n return {\n \"prompt\": self.question_text,\n \"answers\": self.answer_choices,\n \"chapter_index\": self.chapter_index,\n \"question_type\": self.question_type\n }\n","sub_path":"business_objects/DefinitionQuestion.py","file_name":"DefinitionQuestion.py","file_ext":"py","file_size_in_byte":3972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"236493924","text":"from csptools import *\nimport logging\n\nlogging.getLogger().setLevel(logging.INFO)\n\ndomain = {1,2,3}\nE = [ [1,2],[2,3] ] # list of pairs...\nF = [ [3,1], [1,3] ]\nstructure = RelationalStructure(domain, [[E,2], [F,2]])\n\n# outputs a majority polymorphism\nfor m in majority(structure):\n print(m)\n break\n \nfor o in olsak(structure,structure):\n print(o)\n break\n","sub_path":"example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"367315290","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Mar 18 11:43:18 2020\r\n\r\n@author: salam_000\r\n\"\"\"\r\n\r\nimport re\r\n\r\n\r\ndef morse(text):\r\n encrypt = {\r\n 'A': '.-', 'B': '-...', 'C': '-.-.', \r\n 'D': '-..', 'E': '.', 'F': '..-.', \r\n 'G': '--.', 'H': '....', 'I': '..', \r\n 'J': '.---', 'K': '-.-', 'L': '.-..', \r\n 'M': '--', 'N': '-.', 'O': '---', \r\n 'P': '.--.', 'Q': '--.-', 'R': '.-.', \r\n 'S': '...', 'T': '-', 'U': '..-', \r\n 'V': '...-', 'W': '.--', 'X': '-..-',\r\n 'Y': '-.--', 'Z': '--..', ' ': '.....'\r\n }\r\n decrypt = {value: key for key, value in encrypt.items()}\r\n \r\n if re.match(r'(\\s|-|\\.) +', text):\r\n return ' '.join(decrypt[i] for i in text.split())\r\n return ' '.join(encrypt[i] for i in text.upper())\r\nprint(morse('shaxi'))\r\n\"\"\"\r\nimport sys\r\nif __name__ == \"__main__\":\r\n print(morse(sys.argv[0])) \"\"\"\r\n \r\n\"\"\"Running the script directly from the console, it will be the main program.\r\n In this case, to pass the argument to the morse function and print result \r\n is wanted. To import the more function in another Python file, no need to\r\n to run this part of code. sys.arg[0] contains the file name (morse.py)\"\"\"","sub_path":"morse.py","file_name":"morse.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"23681323","text":"#! /usr/bin/evn python\nimport netfilterqueue\nimport subprocess\nimport scapy.all as scapy\n\n\nclass DNS_spoofing:\n def __init__(self, target_address, spoofed_address):\n self.target_address = target_address\n self.spoofed_address = spoofed_address\n self.queue = netfilterqueue.NetfilterQueue()\n self.queue.bind(0, self.process_packet)\n\n def process_packet(self, packet):\n scapy_packet = scapy.IP(packet.get_payload())\n if scapy_packet.haslayer(scapy.DNSRR):\n print(\"\\r\"+scapy_packet.summary(), end=\"\")\n qname = scapy_packet[scapy.DNSQR].qname\n if self.target_address in str(qname):\n answer = scapy.DNSRR(rrname=qname, rdata=self.spoofed_address)\n scapy_packet[scapy.DNS].an = answer\n scapy_packet[scapy.DNS].ancount = 1\n del scapy_packet[scapy.IP].len\n del scapy_packet[scapy.IP].chksum\n del scapy_packet[scapy.UDP].len\n del scapy_packet[scapy.UDP].chksum\n packet.set_payload(bytes(scapy_packet))\n packet.accept()\n\n def run(self):\n subprocess.call(\"iptables -I FORWARD -j NFQUEUE --queue-num 0\", shell=True)\n self.queue.run()\n","sub_path":"MITM-Framework/files/DNS_Spoofing.py","file_name":"DNS_Spoofing.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"24125934","text":"import itertools, os, re\nimport tempfile, subprocess\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torchtext\nfrom torchtext.vocab import Vectors, GloVe\nuse_gpu = torch.cuda.is_available()\n\nclass Logger():\n '''Prints to a log file and to standard output''' \n def __init__(self, path):\n if os.path.exists(path):\n self.path = path\n else:\n raise Exception('path does not exist')\n \n def log(self, info, stdout=True):\n with open(os.path.join(self.path, 'log.log'), 'a') as f:\n print(info, file=f)\n if stdout:\n print(info)\n \n def save_model(self, model_dict):\n #with open(os.path.join(self.path, 'model.pkl'), 'w') as f:\n torch.save(model_dict, os.path.join(self.path, 'model.pkl'))\n\nclass AverageMeter():\n '''Computes and stores the average and current value. \n Taken from the PyTorch ImageNet tutorial'''\n def __init__(self):\n self.reset()\n \n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n \n def update(self, val, n=1):\n self.val = val\n self.sum = self.sum + val * n\n self.count = self.count + n\n self.avg = self.sum / self.count\n\ndef moses_multi_bleu(outputs, references, lw=False):\n '''Outputs, references are lists of strings. Calculates BLEU score using https://raw.githubusercontent.com/moses-smt/mosesdecoder/master/scripts/generic/multi-bleu.perl -- Python function from Google '''\n \n # Save outputs and references as temporary text files\n out_file = tempfile.NamedTemporaryFile()\n out_file.write('\\n'.join(outputs).encode('utf-8'))\n out_file.write(b'\\n')\n out_file.flush() #?\n ref_file = tempfile.NamedTemporaryFile()\n ref_file.write('\\n'.join(references).encode('utf-8'))\n ref_file.write(b'\\n')\n ref_file.flush() #?\n \n # Use moses multi-bleu script\n with open(out_file.name, 'r') as read_pred:\n bleu_cmd = ['scripts/multi-bleu.perl']\n bleu_cmd = bleu_cmd + ['-lc'] if lw else bleu_cmd\n bleu_cmd = bleu_cmd + [ref_file.name]\n try: \n bleu_out = subprocess.check_output(bleu_cmd, stdin=read_pred, stderr=subprocess.STDOUT)\n bleu_out = bleu_out.decode('utf-8')\n #print(bleu_out)\n bleu_score = float(re.search(r'BLEU = (.+?),', bleu_out).group(1))\n except subprocess.CalledProcessError as error:\n print(error)\n raise Exception('Something wrong with bleu script')\n bleu_score = 0.0\n \n # Close temporary files\n out_file.close()\n ref_file.close()\n \n return bleu_score\n\n","sub_path":"utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"123056432","text":"import argparse\nimport glob\nimport os\nimport sys\nimport random\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data.dataloader import DataLoader\nfrom torch.autograd import Variable\nfrom torch import cuda\n\nimport baseline as encoder\nimport pr_dataset\n\n\ndef test(model, data, num_per_class, cuda_dev):\n correct_total, xy_count = 0, 0\n labels = data.idx2name.items()\n num_classes = len(data.get_all_labels())\n correct = np.zeros(num_classes)\n confusion = np.zeros((num_classes, num_classes))\n predict_confusion = np.zeros((num_classes, num_classes))\n for i, label in labels:\n # get (x, y)'s from dataset object, select num_per_class of them\n class_datapoints = list(data.get_from_class(label))\n random.shuffle(class_datapoints)\n num_d = min(num_per_class, len(class_datapoints))\n probs = np.zeros(num_classes) # [0. for j in range(len(data.get_all_labels()))]\n preds = np.zeros(num_classes)\n for x, y in class_datapoints[:num_d]:\n model.eval()\n z = model([x])\n # record softmax \"probabilities\" for each label\n p = torch.nn.functional.softmax(z).cpu().data.numpy()\n probs = probs + p\n # make prediction\n _, y_hat = torch.max(z, 1)\n preds[y_hat.data[0]] += 1\n c = int(y_hat.data[0] == y)\n correct[i] += c\n correct_total += c\n xy_count += 1\n # column i shows probabilities that composer i is classified as...\n confusion[:, i] = probs / num_d\n correct[i] /= num_d\n predict_confusion[:, i] = preds / num_d\n\n print(\"*\"*10, \"Probabilities\", \"*\"*10, \"\\n\")\n print(\" \" * 11, end=\"\")\n short_label = []\n for _, label in labels:\n l = min(8, len(label))\n short_label.append(\"{:>8}\".format(label[:l]))\n for sl in short_label:\n print(\"{}\".format(sl), end=\" \")\n print()\n for j, sl in enumerate(short_label):\n print(sl, end=\" \")\n for i in range(confusion.shape[1]):\n print(\"{:8.1%}\".format(confusion[j][i]), end=\" \")\n print()\n\n print(\"\\n\" + \"*\"*10, \"Predictions\", \"*\"*10, \"\\n\")\n print(\" \" * 11, end=\"\")\n short_label = []\n for _, label in labels:\n l = min(8, len(label))\n short_label.append(\"{:>8}\".format(label[:l]))\n for sl in short_label:\n print(\"{}\".format(sl), end=\" \")\n print()\n for j, sl in enumerate(short_label):\n print(sl, end=\" \")\n for i in range(predict_confusion.shape[1]):\n print(\"{:8.1%}\".format(predict_confusion[j][i]), end=\" \")\n print()\n print()\n for sl in short_label:\n print(sl, end=\" \")\n print()\n for i in range(correct.shape[0]):\n print(\"{:8.2%}\".format(correct[i]), end=\" \")\n\n print(\"\\n\\nAverage accuracy: {:.3%}\\n\".format(float(correct_total) / xy_count))\n\n\ndef main(opts):\n if opts.use_cuda is not None:\n print(\"using CUDA device {}\".format(opts.use_cuda), file=sys.stderr)\n cuda_dev = int(opts.use_cuda)\n else:\n cuda_dev = None\n\n # load the data\n dataset = pr_dataset.PianoRollDataset(os.getcwd() + \"/\" + opts.data_dir, \"labels.csv\", \"val\")\n\n # load the model\n enc = encoder.Encoder(\n dataset.get_y_count(),\n opts.batch_size,\n rnn_size=opts.rnn_size,\n num_rnn_layers=opts.rnn_layers,\n use_cuda=cuda_dev,\n max_w=opts.max_w\n )\n\n if opts.load:\n enc.load_state_dict(torch.load(opts.load))\n if cuda_dev is not None:\n enc = enc.cuda(cuda_dev)\n\n test(enc, dataset, 100, opts.use_cuda)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--rnn_size\", type=int, default=128)\n parser.add_argument(\"--rnn_layers\", type=int, default=3)\n parser.add_argument(\"--data_dir\", default=\"preprocessed\")\n parser.add_argument(\"--max_epochs\", type=int, default=1000)\n parser.add_argument(\"--max_w\", type=int, default=5000)\n parser.add_argument(\"--test\", action=\"store_true\")\n parser.add_argument(\"--batch_size\", type=int, default=1)\n parser.add_argument(\"-m\", \"--model_dir\", default=\"model\")\n parser.add_argument(\"-e\", \"--num_epochs\", type=int, default=5)\n parser.add_argument(\"--num_batch_valid\", type=int, default=1)\n parser.add_argument(\"--beam_size\", type=int, default=5)\n parser.add_argument(\"-s\", \"--seed\", type=int, default=0)\n parser.add_argument(\"-c\", \"--use_cuda\", type=int, default=None)\n parser.add_argument(\"-l\", \"--init_lr\", type=int, default=5)\n parser.add_argument(\"--load\", default=None)\n\n args = parser.parse_args()\n main(args)","sub_path":"baseline_test.py","file_name":"baseline_test.py","file_ext":"py","file_size_in_byte":4668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"295556017","text":"from twilio.rest import Client\nimport config\n\n# Your Account SID from twilio.com/console\naccount_sid = config.ACCOUNT_SID\n# Your Auth Token from twilio.com/console\nauth_token = config.AUTH_TOKEN\n\ndef sendMessage(recipient, body):\n client = Client(account_sid, auth_token)\n message = client.messages.create(\n to=recipient, \n from_=config.MESSAGE_FROM,\n body=body)\n print (\"Sent to \" + recipient + \": \" + body)\n # print (message.sid)\n\nif __name__ == \"__main__\":\n recipient = \"+14084203598\"\n body = \"THERE IS A DISASTER DUDE\"\n sendMessage(recipient, body)\n","sub_path":"send_sms.py","file_name":"send_sms.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"396881846","text":"# -*- coding: utf-8 -*-\nimport cv2\nimport numpy as np\n# try:\n# from sklearn.externals import joblib\n# except ImportError:\nimport joblib\nimport pandas as pd\nimport os\nimport re\nfrom pyzbar.pyzbar import decode\nimport inout\nfrom csv_process import read_csv, write_csv\n\n\ndef qr_inout_csv(csv_file,stream_size=(320,240), font=cv2.FONT_HERSHEY_SIMPLEX):\n \"\"\"\n grコードを読み込み、その人の入退室データを書き込む\n \"\"\"\n data = read_csv(csv_file)\n cap = cv2.VideoCapture(0)\n while True: \n # 映像データを読み込んでサイズ変更\n rst, stream = cap.read()\n stream = cv2.resize(stream, stream_size)\n \n # streamからqrを検出する\n decoded_objs = decode(stream)\n if decoded_objs != []:\n for obj in decoded_objs:\n print('Type: ', obj)\n str_dec_obj = obj.data.decode('utf-8', 'ignore')\n # 文字列から数値を抽出\n id = int(re.sub(\"\\\\D\",\"\", str_dec_obj))\n # csvにidの人の入退室時間をpandasの形式で書き込む\n inout.inout_to_csv(id, csv_file)\n\n print('QR code: {}'.format(str_dec_obj))\n x, y, w, h = obj.rect\n # qrコードを四角で囲う\n cv2.rectangle(stream, (x,y), (x+w,y+h), (0,0,255), 1)\n # 文字列を表示\n cv2.putText(stream, str_dec_obj,(x,y-10), font, 1, (0,255,0), 3, cv2.LINE_AA)\n\n # 画像をウインドウに表示\n cv2.imshow(\"img\", stream)\n \n # 'q'を入力でアプリケーション終了\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n \n #終了処理\n cap.release()\n cv2.destroyAllWindows()\n\n\nif __name__ == \"__main__\":\n now = pd.Timestamp.now()\n today_csv = now.strftime(\"%Y-%m-%d\") + \".csv\"\n base_csv = \"data/train_data_arasi.csv\"\n if os.path.exists(today_csv) == False:\n today_data = read_csv(base_csv)\n write_csv(today_data, today_csv)\n\n qr_inout_csv(today_csv)","sub_path":"inout_system/qr_inout.py","file_name":"qr_inout.py","file_ext":"py","file_size_in_byte":2082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"333569494","text":"import datetime\nimport re\nimport time\nimport redis\nfrom urllib.parse import urlparse, urlencode, parse_qs\n\nimport scrapy\nfrom lxml.etree import HTML\nfrom selenium import webdriver\nfrom selenium.webdriver import ChromeOptions\n\noption = ChromeOptions()\noption.add_experimental_option('excludeSwitches', ['enable-automation'])\n\n\ndef get_cookie(url):\n driver = webdriver.Chrome(options=option)\n driver.get(url)\n time.sleep(3)\n cookies = driver.get_cookies()\n driver.quit()\n items = []\n dict_ = {}\n for i in range(len(cookies)):\n cookie_value = cookies[i]\n item = cookie_value['name'] + '=' + cookie_value['value']\n items.append(item)\n dict_[cookie_value['name']] = cookie_value['value']\n return dict_\n\n\ndef format_doc_no(doc_no: str):\n doc = ''\n if doc_no and len(doc_no) <= 30 and re.findall(r'^[\\u4e00-\\u9fa5]', doc_no):\n doc = doc_no\n return doc\n\n\ndef format_file_type(doc_no: str):\n file_type = ''\n if '〔' in doc_no:\n file_type = obj_first(re.findall('^(.*?)〔', doc_no))\n elif '[' in doc_no:\n file_type = obj_first(re.findall(r'^(.*?)\\[', doc_no))\n elif obj_first(re.findall(r'^(.*?)\\d{4}', doc_no)):\n file_type = obj_first(re.findall(r'^(.*?)\\d{4}', doc_no))\n elif '第' in doc_no:\n file_type = obj_first(re.findall('^(.*?)第', doc_no))\n elif obj_first(re.findall(r'^(.*?)\\d', doc_no)):\n file_type = obj_first(re.findall(r'^(.*?)\\d', doc_no))\n return '' if re.findall(r'^\\d', file_type) else file_type\n\n\ndef time_map(t_str, error=''):\n \"\"\"\n >>>time_map('发布日期：2020年2月18日')\n 2020-02-18\n \"\"\"\n try:\n year, month, day = re.findall(r'(\\d{4})\\D(\\d{1,2})\\D(\\d{1,2})', t_str.strip())[0]\n return '{}-{:0>2}-{:0>2}'.format(year, month, day)\n except:\n return error\n\n\ndef obj_first(obj, error=''):\n return obj[0] if obj else error\n\n\ndef get_html_content(response: scrapy.http.Response, con_xpath: str) -> str:\n \"\"\"\n 返回网页响应中的正文主体html代码\n :param response: 网页响应\n :param con_xpath: 正文的爬取范围 eg:'//*[@id=\"mainText\"]'\n :return: 正文主体html代码\n \"\"\"\n html_content = response.xpath(con_xpath).get()\n html_content = re.sub(r'|\\<\\!--[\\s\\S]*?--\\>|', '', html_content)\n return re.sub(r'<([a-z]+)[\\s\\S]*?>', r'<\\1>', html_content)\n\n\ndef query2dict(url):\n \"\"\"\n >>>url = 'http://www.zjwjw.gov.cn/col/col1202101/index.html?uid=4978845&pageNum=1'\n >>>query2dict(url)\n {'uid': '4978845', 'pageNum': '1'}\n \"\"\"\n return {k: obj_first(v) for k, v in parse_qs(urlparse(url).query).items()}\n\n\ndef parse2query(parse_data=None, url_join='', url_replace=''):\n query = urlencode(parse_data)\n if url_join:\n return url_join + query\n elif url_replace:\n return re.sub(r'\\?.*', '?{}'.format(query), url_replace)\n else:\n return query\n\n\ndef xpath_from_remove(response: scrapy.http.Response or str, xpath_str):\n \"\"\"获取xpath_str部分的页面数据（移除script和style节点的干扰）\"\"\"\n if not response:\n return ''\n elif isinstance(response, str):\n content = HTML(response)\n else:\n content = HTML(response.text)\n for dom in content.xpath('//script'):\n new_content = dom.getparent()\n new_content.remove(dom)\n for dom in content.xpath('//style'):\n new_content = dom.getparent()\n new_content.remove(dom)\n return re.sub(r'\\<\\!--[\\s\\S]*?--\\>', '', content.xpath(xpath_str).strip())\n\n\ndef effective(start, end):\n if not start:\n return ''\n end = time_map(end, error='9999-12-31')\n start = time_map(start, error='1000-01-01')\n now = datetime.datetime.now().strftime('%Y-%m-%d')\n if now < start:\n return '尚未生效'\n elif start <= now < end:\n return '现行有效'\n else:\n return '失效'\n\n\ndef find_effective_start(content, publish_time):\n return time_map(obj_first(re.findall(r'[自从]\\d{4}\\D\\d{1,2}\\D\\d{1,2}\\D{0,5}(?:实施|施行)', content)), error=publish_time)\n\n\nclass PageHTMLControl:\n \"\"\"\n 翻页器： pageControl(4, 2, \"index\", \"shtml\", 10, 'pages-nav');\n http://www.hubei.gov.cn/zhuanti/2020/gzxxgzbd/zy/index_2.shtml\n \"\"\"\n def __init__(self, response: str, re_str='createPageHTML(.*?);', count=False):\n # createPageHTML(89, 1,\"index\", \"shtml\", \"black2\",621);\n self.find = re.findall(r'{}'.format(re_str), response)\n try:\n if count:\n self.total, self.count, self.now, self.default, self.type, *_ = eval(self.find[0])\n else:\n self.total, self.now, self.default, self.type, *_ = eval(self.find[0])\n except:\n self.total = self.now = self.default = self.type = None\n\n def next_page(self, url):\n if not self.find:\n return None\n elif self.total - 1 > self.now:\n base_url = url.split('/')\n base_url[-1] = '{}_{}.{}'.format(self.default, self.now + 1, self.type)\n return '/'.join(base_url)\n else:\n return None\n\n\nclass PageFormControl:\n \"\"\"\n \n \"\"\"\n def __init__(self, response):\n self.find = response.xpath('//*[@id=\"pageForm\"]/input')\n self.base_url = response.xpath('//*[@id=\"pageForm\"]/@action').extract_first()\n self.form_data = {}\n for item in self.find:\n name = item.xpath('./@name').extract_first()\n value = item.xpath('./@value').extract_first()\n self.form_data[name] = value\n msg = response.xpath('//div[@class=\"control\"]/span/text()').extract_first()\n if msg:\n self.end, self.now = re.findall(r'(\\d+)页', msg)\n else:\n self.end = self.now = None\n\n def next_page(self):\n if self.end == self.now:\n return None\n else:\n self.form_data[\"pageNum\"] = str(int(self.form_data[\"pageNum\"]) + 1)\n return {'url': self.base_url, 'data': self.form_data}\n\n\nclass GovBeiJingPageControl:\n \"\"\"\n 翻页器： Pager({size:266, current:0, prefix:'index',suffix:'html'});\n http://www.beijing.gov.cn/zhengce/zhengcefagui/index_1.html\n \"\"\"\n def __init__(self, response):\n try:\n self.find = re.findall(r'Pager\\(\\{.*?\\}\\)\\;', response)[0]\n self.total = int(re.findall(r'size\\:(\\d+)', self.find)[0])\n self.now = int(re.findall(r'current\\:(\\d+)', self.find)[0])\n self.default = re.findall(r'prefix\\:\\'(.*?)\\'', self.find)[0]\n self.type = re.findall(r'suffix\\:\\'(.*?)\\'', self.find)[0]\n except:\n self.find = self.total = self.now = self.default = self.type = None\n\n def next_page(self, url):\n if not self.find:\n return None\n elif self.total - 1 > self.now:\n base_url = url.split('/')\n base_url[-1] = '{}_{}.{}'.format(self.default, self.now + 1, self.type)\n return '/'.join(base_url)\n else:\n return None\n\n\nclass GovSiChuangPageHTMLControl:\n \"\"\"\n 翻页器：\n {createPageHTML('page_div',15, 1,'list_ft','shtml',450);}\n pageControl(4, 2, \"index\", \"shtml\", 10, 'pages-nav');\n http://www.sc.gov.cn/10462/10464/10684/12419/list_ft.shtml\n \"\"\"\n def __init__(self, response: str, re_str='createPageHTML(.*?);', count=False):\n # createPageHTML(89, 1,\"index\", \"shtml\", \"black2\",621);\n self.find = re.findall(r'{}'.format(re_str), response)\n try:\n if count:\n self.total, self.count, self.now, self.default, self.type, *_ = eval(self.find[0])\n else:\n _, self.total, self.now, self.default, self.type, *_ = eval(self.find[0])\n except:\n self.total = self.now = self.default = self.type = None\n\n def next_page(self, url):\n if not self.find:\n return None\n elif self.total - 1 >= self.now:\n base_url = url.split('/')\n base_url[-1] = '{}_{}.{}'.format(self.default, self.now + 1, self.type)\n return '/'.join(base_url)\n else:\n return None\n\n\nclass RedisConnect:\n def __init__(self):\n pool = redis.ConnectionPool(host='127.0.0.1', port=6379, db=0)\n self.conn = redis.StrictRedis(connection_pool=pool)\n\n\nclass TimeShow:\n @staticmethod\n def to_second(time_str):\n second = 0\n try:\n m, s = time_str.split(':')\n second = int(m) * 60 + int(s)\n except:\n pass\n return second\n\n @staticmethod\n def to_min_second(time_str):\n min_second = '0'\n try:\n time_str = int(time_str)\n m = time_str // 60\n s = time_str % 60\n min_second = '{:0>2}:{:0>2}'.format(m, s)\n except:\n pass\n return min_second\n\n def median(self, time_list):\n time_median = sorted(self.to_second(i) for i in time_list)\n return self.to_min_second(time_median[1])\n\n\nif __name__ == '__main__':\n # ts = TimeShow()\n # print(ts.median(['00:30', '10:21', '01:51']))\n # print(find_effective_start('政策从2020年10月111日起正式施行', '2020-10-11'))\n print(format_file_type('2014国办函2020第34号'))","sub_path":"policy_business/policy_business/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":9857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"383366770","text":"import json\nimport time\nimport logging\nimport collections\n\nMAX_RETRIES = 30\nRETRY_WAIT = 0.1\n\ndef wait_until_zone_exists(vinyldns_client, zone_id):\n \"\"\"\n Waits until the zone exists\n \"\"\"\n zone = vinyldns_client.get_zone(zone_id)\n retries = MAX_RETRIES\n while (zone is None) and retries > 0:\n zone = vinyldns_client.get_zone(zone_id)\n time.sleep(RETRY_WAIT)\n retries -= 1\n\n if zone is None:\n print(\"Issue on zone create: {}\".format(json.dumps(zone)))\n\n assert zone is not None\n\n\ndef wait_until_zone_deleted(vinyldns_client, zone_id):\n \"\"\"\n Waits until the zone no longer exists\n \"\"\"\n zone = vinyldns_client.get_zone(zone_id)\n retries = MAX_RETRIES\n while (zone is not None) and retries > 0:\n zone = vinyldns_client.get_zone(zone_id)\n time.sleep(RETRY_WAIT)\n retries -= 1\n\n if zone is not None:\n print(\"Zone was not deleted: {}\".format(json.dumps(zone)))\n\n assert zone is None\n","sub_path":"func_tests/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"295233206","text":"import kdigo_funcs as kf\r\nimport numpy as np\r\nimport datetime\r\nimport os\r\n\r\n#------------------------------- PARAMETERS ----------------------------------#\r\nbasePath = \"/Users/taylorsmith/Google Drive/Documents/Work/Workspace/Kidney Pathology/KDIGO_eGFR_traj/\"\r\nt_analyze = 'ICU'\r\nxl_file = \"KDIGO_test.xlsx\"\r\ntimescale = 6 #in hours\r\nid_ref = 'all_ids.csv'#default is all\r\n\r\n#-----------------------------------------------------------------------------#\r\n\r\nsort_id = 'STUDY_PATIENT_ID'\r\nsort_id_date = 'SCR_ENTERED'\r\ndataPath = basePath + \"DATA/\"\r\noutPath = dataPath + t_analyze.lower() + '/'\r\nresPath = basePath + 'RESULTS/' + t_analyze.lower() + '/'\r\ninFile = dataPath + xl_file\r\nid_ref = outPath + id_ref\r\n\r\ndef main():\r\n if not os.path.exists(outPath):\r\n os.makedirs(outPath)\r\n if not os.path.exists(resPath):\r\n os.makedirs(resPath)\r\n\r\n #Try to load previously extracted data\r\n try:\r\n ids = np.loadtxt(id_ref,dtype=int)\r\n #try to load final KDIGO values\r\n try:\r\n _,kdigo = kf.load_csv(outPath+'kdigo.csv',ids)\r\n print('Loaded previously extracted KDIGO vectors')\r\n #try to load extracted raw data\r\n except:\r\n _,scr = kf.load_csv(outPath+'scr_raw.csv',ids)\r\n _,dates = kf.load_csv(outPath+'dates.csv',ids,fmt=datetime.datetime)\r\n _,masks = kf.load_csv(outPath+'masks.csv',ids,fmt='%d')\r\n _,dmasks = kf.load_csv(outPath+'dialysis.csv',ids,fmt='%d')\r\n _,baselines = kf.load_csv(outPath+'baselines.csv',ids)\r\n print('Loaded previously extracted raw data')\r\n\r\n #Interpolate missing values\r\n print('Interpolating missing values')\r\n interpo_log = open(outPath+'interpo_log.txt','w')\r\n post_interpo,dmasks_interp=kf.linear_interpo(scr,ids,dates,masks,dmasks,timescale,interpo_log)\r\n kf.arr2csv(outPath+'scr_interp.csv',post_interpo,ids)\r\n kf.arr2csv(outPath+'dmasks_interp.csv',dmasks_interp,ids,fmt='%d')\r\n interpo_log.close()\r\n print('Converting to KDIGO')\r\n #Convert SCr to KDIGO\r\n kdigo = kf.scr2kdigo(post_interpo,baselines,dmasks_interp)\r\n kf.arr2csv(outPath+'kdigo.csv',kdigo,ids)\r\n #If data loading unsuccesful start from scratch\r\n except:\r\n ############ Get Indices for All Used Values ################\r\n print('Loading encounter info...')\r\n #Get IDs and find indices of all used metrics\r\n date_m = kf.get_mat(inFile,'ADMISSION_INDX',[sort_id])\r\n id_loc=date_m.columns.get_loc(\"STUDY_PATIENT_ID\")\r\n hosp_locs=[date_m.columns.get_loc(\"HOSP_ADMIT_DATE\"),date_m.columns.get_loc(\"HOSP_DISCHARGE_DATE\")]\r\n icu_locs=[date_m.columns.get_loc(\"ICU_ADMIT_DATE\"),date_m.columns.get_loc(\"ICU_DISCHARGE_DATE\")]\r\n adisp_loc = date_m.columns.get_loc('DISCHARGE_DISPOSITION')\r\n date_m=date_m.as_matrix()\r\n\r\n ### GET SURGERY SHEET AND LOCATION\r\n print('Loading surgery information...')\r\n surg_m = kf.get_mat(inFile,'SURGERY_INDX',[sort_id])\r\n surg_loc = surg_m.columns.get_loc(\"SURGERY_CATEGORY\")\r\n surg_des_loc = surg_m.columns.get_loc(\"SURGERY_DESCRIPTION\")\r\n surg_m = surg_m.as_matrix()\r\n\r\n ### GET DIAGNOSIS SHEET AND LOCATION\r\n print('Loading diagnosis information...')\r\n dx_m = kf.get_mat(inFile,'DIAGNOSIS',[sort_id])\r\n dx_loc = dx_m.columns.get_loc(\"DIAGNOSIS_DESC\")\r\n dx_m = dx_m.as_matrix()\r\n\r\n print('Loading ESRD status...')\r\n #ESRD status\r\n esrd_m = kf.get_mat(inFile,'ESRD_STATUS',[sort_id])\r\n esrd_locs = [esrd_m.columns.get_loc(\"AT_ADMISSION_INDICATOR\"),esrd_m.columns.get_loc(\"DURING_INDEXED_INDICATOR\"),esrd_m.columns.get_loc(\"BEFORE_INDEXED_INDICATOR\")]\r\n esrd_m = esrd_m.as_matrix()\r\n\r\n #Dialysis dates\r\n print('Loading dialysis dates...')\r\n dia_m = kf.get_mat(inFile,'RENAL_REPLACE_THERAPY',[sort_id])\r\n crrt_locs = [dia_m.columns.get_loc('CRRT_START_DATE'),dia_m.columns.get_loc('CRRT_STOP_DATE')]\r\n hd_locs = [dia_m.columns.get_loc('HD_START_DATE'),dia_m.columns.get_loc('HD_STOP_DATE')]\r\n pd_locs = [dia_m.columns.get_loc('PD_START_DATE'),dia_m.columns.get_loc('PD_STOP_DATE')]\r\n dia_m = dia_m.as_matrix()\r\n\r\n #All SCR\r\n print('Loading SCr values (may take a while)...')\r\n scr_all_m = kf.get_mat(inFile,'SCR_ALL_VALUES',[sort_id,sort_id_date])\r\n scr_date_loc = scr_all_m.columns.get_loc('SCR_ENTERED')\r\n scr_val_loc = scr_all_m.columns.get_loc('SCR_VALUE')\r\n scr_all_m = scr_all_m.as_matrix()\r\n\r\n #Baselines\r\n print('Loading baselines...')\r\n bsln_m = kf.get_mat(inFile,'BASELINE_SCR',[sort_id])\r\n bsln_scr_loc = bsln_m.columns.get_loc('BASELINE_VALUE')\r\n bsln_date_loc = bsln_m.columns.get_loc('BASELINE_DATE')\r\n bsln_m = bsln_m.as_matrix()\r\n\r\n #Demographics\r\n print('Loading demographics...')\r\n dem_m = kf.get_mat(inFile,'DEMOGRAPHICS_INDX',[sort_id])\r\n sex_loc = dem_m.columns.get_loc('GENDER')\r\n eth_loc = dem_m.columns.get_loc('RACE')\r\n dem_m = dem_m.as_matrix()\r\n\r\n #DOB\r\n print('Loading birthdates...')\r\n dob_m = kf.get_mat(inFile,'DOB',[sort_id])\r\n birth_loc = dob_m.columns.get_loc(\"DOB\")\r\n dob_m = dob_m.as_matrix()\r\n ###### Get masks for ESRD, dialysis, etc.\r\n\r\n #Get mask inidicating which points are during dialysis\r\n dia_mask=kf.get_dialysis_mask(scr_all_m,scr_date_loc,dia_m,crrt_locs,hd_locs,pd_locs)\r\n\r\n #Get mask indicating whether each point was in hospital or ICU\r\n t_mask=kf.get_t_mask(scr_all_m,scr_date_loc,scr_val_loc,date_m,hosp_locs,icu_locs)\r\n\r\n #Get mask for all patients with ESRD\r\n #esrd_mask=kf.get_esrd_mask(scr_all_m,id_loc,esrd_m,esrd_locs)\r\n\r\n #Get mask for the desired data\r\n mask=np.zeros(len(scr_all_m))\r\n for i in range(len(scr_all_m)):\r\n if t_analyze == 'ICU':\r\n if t_mask[i] == 2:\r\n if dia_mask[i]:\r\n mask[i]=-1\r\n else:\r\n mask[i]=1\r\n elif t_analyze == 'HOSP':\r\n if t_mask[i] >= 1:\r\n if dia_mask[i]:\r\n mask[i]=-1\r\n else:\r\n mask[i]=1\r\n\r\n count_log = open(outPath+'record_counts.csv','w')\r\n #Extract patients into separate list elements\r\n ids,scr,dates,masks,dmasks,baselines,bsln_gfr,d_disp = kf.get_patients(scr_all_m,scr_val_loc,scr_date_loc,adisp_loc,\\\r\n mask,dia_mask,\\\r\n dx_m,dx_loc,\\\r\n esrd_m,esrd_locs,\\\r\n bsln_m,bsln_scr_loc,bsln_date_loc,\\\r\n date_m,id_loc,icu_locs,\\\r\n surg_m,surg_loc,surg_des_loc,\\\r\n dem_m,sex_loc,eth_loc,\\\r\n dob_m,birth_loc,count_log)\r\n count_log.close()\r\n kf.arr2csv(outPath+'scr_raw.csv',scr,ids)\r\n kf.str2csv(outPath+'dates.csv',dates,ids)\r\n kf.arr2csv(outPath+'masks.csv',masks,ids,fmt='%d')\r\n kf.arr2csv(outPath+'dialysis.csv',dmasks,ids,fmt='%d')\r\n kf.arr2csv(outPath+'baselines.csv',baselines,ids)\r\n kf.arr2csv(outPath+'baseline_gfr.csv',bsln_gfr,ids)\r\n kf.str2csv(outPath+'disch_disp.csv',d_disp,ids)\r\n np.savetxt(id_ref,ids,fmt='%d')\r\n\r\n #Interpolate missing values\r\n print('Interpolating missing values')\r\n interpo_log = open(outPath+'interpo_log.txt','w')\r\n post_interpo,dmasks_interp=kf.linear_interpo(scr,ids,dates,masks,dmasks,timescale,interpo_log)\r\n kf.arr2csv(outPath+'scr_interp.csv',post_interpo,ids)\r\n kf.arr2csv(outPath+'dmasks_interp.csv',dmasks_interp,ids,fmt='%d')\r\n interpo_log.close()\r\n\r\n #Convert SCr to KDIGO\r\n print('Converting to KDIGO')\r\n kdigo = kf.scr2kdigo(post_interpo,baselines,dmasks_interp)\r\n kf.arr2csv(outPath+'kdigo.csv',kdigo,ids)\r\n\r\n #Get KDIGO Distance Matrix\r\n kf.pairwise_dtw_dist(kdigo,ids,resPath+'kdigo_dm.csv',resPath+'kdigo_dtwlog.csv')\r\n\r\nmain()","sub_path":"main_kdigo.py","file_name":"main_kdigo.py","file_ext":"py","file_size_in_byte":8574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"382821810","text":"# 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 # @param head, a ListNode\n # @param k, an integer\n # @return a ListNode\n def reverseKGroup(self, head, k):\n if not head: return None\n newhead = ListNode(0)\n newhead.next = head\n start = newhead\n while start.next:\n end = start\n for i in range(k):\n end = end.next\n if not end: return newhead.next\n \n [left, right] = self.reverse(start, end)\n start.next = left\n start = right\n return newhead.next \n def reverse(self, start, end):\n head = start.next\n while start.next != end:\n temp = head.next\n head.next = temp.next\n temp.next = start.next\n start.next = temp\n \n \n return [start.next,head]# not [start.next, end], because end is now in the front of list","sub_path":"lc/LC3/15 +Reverse Nodes in k-Group.py","file_name":"15 +Reverse Nodes in k-Group.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"513246234","text":"from src.bayes import Bayes\n\n\ndef die_likelihood(roll, die):\n \"\"\" \n Args:\n roll (int): result of a single die roll\n die (int): number of sides of the die that produced the roll\n \n Returns:\n likelihood (float): the probability of the roll given the die.\n \"\"\"\n \n if roll in range(1,die+1): \n return 1/die\n else:\n return 0\n\n\nif __name__ == '__main__':\n prior_1 = {4:1/5,6:1/5,8:1/5,12:1/5,20:1/5} ## balanced\n prior_2 = {4:.08,6:.12,8:.16,12:.24,20:.4} ## unbalanced\n die_bayes_1 = Bayes(prior_1.copy(), die_likelihood)\n die_bayes_2 = Bayes(prior_2.copy(), die_likelihood) \n # ... you take it from here ... \n data =[8,2,1,2,5,8,2,4,3,7,6,5,1,6,2,5,8,8,5,3,4,2,4,3,8,8,7,8,8,8,5,5,1,3,8,7,8,5,2,5,1,4,1,2,1,3,1,3,1,5] #[10, 10, 10, 10, 8, 8] #\n for d in data:\n die_bayes_1.update(d)\n die_bayes_2.update(d)\n \n print('Data: ',data)\n print('\\nBalanced: ',prior_1)\n die_bayes_1.print_distribution()\n die_bayes_1.plot()\n print('\\nUnbalanced: ',prior_2)\n die_bayes_2.print_distribution()","sub_path":"bayes-intro-ASSIGNMENT/src/dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"350439831","text":"\"\"\"\nCreated on Thu Jan 19 18:25:27 2019\n\n@author: jonathanroth\n\"\"\"\n\nimport numpy as np\nimport pickle\nimport pandas as pd\nimport os\nimport timeit\nimport re\nimport matplotlib.pyplot as plt\nfrom ubem_simulation_pso import UBEM_Simulator, get_simulation_errors, scale_all_doe_datasets, plot_2x2_hourly_load, \\\n create_hourly_load, create_one_building_timeseries, plot_all_hourly_loads\n\n\ndef create_all_buildings(ubem, training_hours, sim_num=65):\n a_rand, buildings = ubem.create_a_rand(sim_ind=sim_num).values()\n amx = ubem.create_amx(a_rand=a_rand, training_hours=training_hours)\n prepared_buildings = ubem.create_prepared_buildings(amx=amx,\n a_rand=a_rand,\n buildings=buildings,\n training_hours=training_hours,\n sim_ind=sim_num)\n print('Finished creating prepared_buildings...')\n return prepared_buildings\n\n\ndef calculate_error(betas, Ec, prepared_buildings, sim_num=65):\n beta = betas[sim_num-15, :]\n num_buildings = prepared_buildings.shape[0]\n modeling_hours = prepared_buildings.shape[2]\n\n # Optimization prediction\n Eaj_hat = np.array([prepared_buildings[j, :, :].T * beta for j in range(num_buildings)])\n Ea_hat = np.sum(Eaj_hat, axis=2)\n Ea_hat = np.sum(Ea_hat, axis=0).reshape(modeling_hours, )\n\n error = np.abs(Ea_hat - Ec[:modeling_hours]) / (Ec[:modeling_hours]) * 100\n return np.mean(error)\n\n\ndef out_of_sample_errors(ubem, betas, Ec, training_hours):\n error_vec = []\n start = timeit.default_timer()\n for sim_num in np.arange(15, 515):\n prepared_buildings = create_all_buildings(ubem, training_hours, sim_num=sim_num)\n error = calculate_error(betas, Ec, prepared_buildings, sim_num=sim_num)\n end = timeit.default_timer()\n\n print('sim_num: ', sim_num, ' | Error: ', error, ' | Time: ', start-end)\n error_vec.append(error)\n\n return error_vec\n\n\nif __name__ == '__main__':\n starting_hour = 1000\n total_hours = 1000\n\n ubem = UBEM_Simulator(sample_buildings=1000, modeling_hours=total_hours) # 6148\n # Extract simulations and data\n list_of_simulations = [pickle.load(open(os.getcwd() + '/Data/test005_1000_1000_50_' + str(15 + num*50) + '.obj', 'rb'))\n for num in np.arange(10)]\n\n betas = np.array([sim[1] for simulations in list_of_simulations for sim in simulations])\n indices = np.array([sim[1] for simulations in list_of_simulations for sim in simulations])\n training_hours = np.arange(starting_hour, starting_hour+total_hours)\n Ec = np.reshape(ubem.city_electricity_scaled[training_hours], (ubem.modeling_hours,))\n all_errors = get_simulation_errors() # UNORDERED!\n\n # CHECK ERRORS: OUT-OF-SAMPLE\n # all_errors = np.array(all_errors).flatten()\n # prepared_buildings = create_all_buildings(ubem, training_hours, sim_num=77)\n # error = calculate_error(betas, Ec, prepared_buildings, sim_num=77)\n # print(error)\n\n # RUN 500 TIMES: OUT-OF-SAMPLE ERROR FOR CONVX. SIMULATION\n # error_vec = out_of_sample_errors(ubem, betas, Ec, training_hours)\n # print(np.mean(error_vec))\n pickle.dump(error_vec, open(os.getcwd() + '/Data/of_of_sample_1000hrs.obj', 'wb'))\n\n\n # PLOT ONE BUILDING, ALL ENERGY CURVES\n ubem2 = UBEM_Simulator(sample_buildings=1000, modeling_hours=8784) # 6148\n doe_list = np.array(scale_all_doe_datasets(calculate=False))\n all_buildings = [create_one_building_timeseries(ubem, betas, '1012970023', doe_list, beta_num=i, modeling_hours=8784, ll84=True) for i in np.arange(betas.shape[0])]\n plot_all_hourly_loads(all_buildings, start=100, end=100+168)\n\n\n","sub_path":"building_block_plotting.py","file_name":"building_block_plotting.py","file_ext":"py","file_size_in_byte":3784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"539810179","text":"# %matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport numpy as np\n\nclass IsingGrid:\n\tdef __init__(self, height, width, extfield, invtemp):\n\t\tself.width, self.height, self.extfield, self.invtemp = height, width, extfield, invtemp\n\t\tself.grid = np.zeros([self.width, self.height], dtype=np.int8) + 1\n\t\t\n\tdef plot(self):\n\t\tplt.imshow(self.grid, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\n\t\n\tdef make_random(self):\n\t\tself.grid = (np.random.randint(0, 2, size = self.width * self.height).reshape(self.width, self.height) * 2) - 1\n\t\n\tdef neighbours(self, x, y):\n\t\tn = []\n\t\tif x == 0:\n\t\t\tn.append( (self.width-1, y) )\n\t\telse:\n\t\t\tn.append( (x-1, y) )\n\t\tif x == self.width-1:\n\t\t\tn.append( (0, y) )\n\t\telse:\n\t\t\tn.append( (x+1, y) )\n\t\tif y == 0:\n\t\t\tn.append( (x, self.height-1) )\n\t\telse:\n\t\t\tn.append( (x, y-1) )\n\t\tif y == self.height-1:\n\t\t\tn.append( (x, 0) )\n\t\telse:\n\t\t\tn.append( (x, y+1) )\n\t\treturn n\n\t\n\tdef local_energy(self, x, y):\n\t\treturn self.extfield + sum( self.grid[xx,yy] for (xx, yy) in self.neighbours(x, y) )\n\t\n\tdef total_energy(self):\n\t\t# Could maybe do some numpy games here, but periodic boundary conditions make this tricky.\n\t\t# This function is only ever useful for very small grids anyway.\n\t\tenergy = - self.extfield * np.sum(self.grid)\n\t\tenergy += - sum( self.grid[x, y] * sum( self.grid[xx, yy] for (xx, yy) in self.neighbours(x, y) )\n\t\t\t\t\t\tfor x in range(self.width) for y in range(self.height) ) / 2\n\t\treturn energy\n\t\n\tdef probability(self):\n\t\treturn np.exp( - self.invtemp * self.total_energy() )\n\t\n\tdef gibbs_move(self):\n\t\tn = np.random.randint(0, self.width * self.height)\n\t\ty = n // self.width\n\t\tx = n % self.width\n\t\tp = 1 / (1 + np.exp(-2 * self.invtemp * self.local_energy(x,y)))\n\t\tif np.random.random() <= p:\n\t\t\tself.grid[x,y] = 1\n\t\telse:\n\t\t\tself.grid[x,y] = -1\n\t\t\t\n\tdef from_number(self, n):\n\t\t\"\"\"Convert an integer 0 <= n < 2**(width*height) into a grid.\"\"\"\n\t\tbinstring = bin(n)[2:]\n\t\tbinstring = \"0\" * (N - len(binstring)) + binstring\n\t\tself.grid = np.array([int(x)*2-1 for x in binstring], dtype=np.int8).reshape(self.width, self.height)\n\t\n\tdef to_number(self):\n\t\t\"\"\"Convert grid into an integer.\"\"\"\n\t\tflat = [self.grid[x, y] for x in range(self.width) for y in range(self.height)]\n\t\treturn sum(2**n * (int(x)+1)//2 for n, x in enumerate(reversed(flat)))\n\n\ngg = IsingGrid(30, 50, 0, .3)\ngg.make_random()\ngg.plot()\n\n\nfor _ in range(100000):\n\tgg.gibbs_move()\n\tgg.plot()\n\n\n\nclass IsingGridVaryingField(IsingGrid):\n\tdef __init__(self, height, width, extfield, invtemp):\n\t\tsuper().__init__(height, width, 0, invtemp)\n\t\tself.vextfield = extfield\n\t\t\n\tdef local_energy(self, x, y):\n\t\treturn self.vextfield[x,y] + sum( self.grid[xx,yy] for (xx, yy) in self.neighbours(x, y) )\n\nimport skimage.io\nimage = skimage.io.imread(\"noisy-yingyang.png\")\nimage = (image[:,:,0].astype(np.int) * 2) - 1 # Black and white so just grab one RGB channel\nfig, axes = plt.subplots(figsize=(10,6))\naxes.imshow(image, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\n\nq = 0.9\nnoise = np.random.random(size = image.size).reshape(image.shape) > q\nnoisy = np.array(image)\nnoisy[noise] = -noisy[noise]\nfig, axes = plt.subplots(figsize=(10,6))\naxes.imshow(noisy, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\n\n\ndef IsingDeNoise(noisy, q, burnin = 50000, loops = 500000):\n\th = 0.5 * np.log(q / (1-q))\n\tgg = IsingGridVaryingField(noisy.shape[0], noisy.shape[1], h*noisy, 2)\n\tgg.grid = np.array(noisy)\n\t\n\t# Burn-in\n\tfor _ in range(burnin):\n\t\tgg.gibbs_move()\n\t\n\t# Sample\n\tavg = np.zeros_like(noisy).astype(np.float64)\n\tfor _ in range(loops):\n\t\tgg.gibbs_move()\n\t\tavg += gg.grid\n\treturn avg / loops\n\n\navg = IsingDeNoise(noisy, 0.9)\navg[avg >= 0] = 1\navg[avg < 0] = -1\navg = avg.astype(np.int)\n\nfig, axes = plt.subplots(ncols=2, figsize=(11,6))\naxes[0].imshow(avg, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\naxes[1].imshow(noisy, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\n\n\n\navg = IsingDeNoise(noisy, 0.99)\navg[avg >= 0] = 1\navg[avg < 0] = -1\navg = avg.astype(np.int)\n\nfig, axes = plt.subplots(ncols=2, figsize=(11,6))\naxes[0].imshow(avg, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\naxes[1].imshow(noisy, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\n\navg = IsingDeNoise(noisy, 0.5)\navg[avg >= 0] = 1\navg[avg < 0] = -1\navg = avg.astype(np.int)\n\nfig, axes = plt.subplots(ncols=2, figsize=(11,6))\naxes[0].imshow(avg, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\naxes[1].imshow(noisy, cmap=cm.gray, aspect=\"equal\", interpolation=\"none\", vmin=-1, vmax=1)\n\n","sub_path":"3rd_sem_grad/PROBMOD/Hwk4/Ising.py","file_name":"Ising.py","file_ext":"py","file_size_in_byte":4658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"68073956","text":"from math import ceil, log2\nMAX = 10**9\n\ndef build(arr, segTree, lo, hi, idx):\n if lo == hi:\n segTree[idx] = arr[lo]\n else:\n mid = lo + (hi-lo)//2\n build(arr, segTree, lo, mid, 2*idx+1)\n build(arr, segTree, mid+1, hi, 2*idx+2)\n segTree[idx] = min(segTree[2*idx+1], segTree[2*idx+2])\n\n\ndef query(segTree, lo, hi, l, r, idx):\n if lo > r or hi < l:\n return MAX\n elif l <= lo and hi <= r:\n return segTree[idx]\n else:\n mid = lo + (hi-lo)//2\n left = query(segTree, lo, mid, l, r, 2*idx +1)\n right = query(segTree, mid+1, hi, l, r, 2*idx +2)\n return min(left, right)\n\n\nn, q = map(int, input().split())\narr = list(map(int, input().split()))\npower = ceil(log2(n)) + 1\nlength = 2**power\nsegTree = [None for i in range(length)]\nbuild(arr, segTree, 0, n-1, 0)\n\nfor i in range(q):\n l, r = map(int, input().split())\n l -= 1\n r -= 1\n ans = query(segTree, 0, n-1, l, r, 0)\n print(ans)\n","sub_path":"1647.py","file_name":"1647.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"412300417","text":"'''\nA wrapper for original OASC evaluation\n\n$: python stats.py ../path/to/result.json\n$: python stats.py ../path/to/result.json scenario_name\n\ncomments\n========\nAuthor: HearNest\n\n'''\n\nimport os\nimport csv\nimport sys\nfrom subprocess import Popen\n\n\ndef main(args):\n\n scnearios = [\n 'ASP-POTASSCO'\n ,'BNSL-2016'\n ,'CPMP-2015'\n ,'CSP-Minizinc-Time-2016'\n ,'GLUHACK-2018'\n ,'GRAPHS-2015'\n ,'MAXSAT-PMS-2016'\n ,'MAXSAT-WPMS-2016'\n ,'MAXSAT19-UCMS'\n ,'MIP-2016'\n ,'QBF-2016'\n ,'SAT03-16_INDU'\n ,'SAT12-ALL'\n ,'SAT18-EXP'\n ,'TSP-LION2015']\n\n for scen in scnearios:\n for i in list(range(1,11)):\n a_scn = scen+'_'+str(i)\n\n cmd = 'python3 rf.py --scen ' + a_scn\n print(\"Exe cmd:\",cmd)\n proc = Popen(cmd.split())\n proc.communicate()\n\n\n\nif __name__ == '__main__':\n main(sys.argv[1:])\n ","sub_path":"validation/run_rf.py","file_name":"run_rf.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"301698465","text":"from django.urls import path\nfrom .import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\napp_name = 'newapp'\nurlpatterns=[\n path('', views.index, name='index'),\n path('blogPage/', views.blogPage, name='blogPage'),\n path('registered/', views.registered, name='registered'),\n path('login/', views.login, name='login'),\n path('registry/', views.registry, name='registry'),\n path('pannelpage/', views.pannelpage, name='pannelpage'),\n path('writepage/', views.writepage, name='writepage'),\n path('savemessage/', views.savemessage, name='savemessage'),\n path('personalpage/', views.personalpage, name='personalpage'),\n # path('purchasing/',views.purchasing,name='purchasing'),\n # path('purchased/',views.purchased,name='purchased'),\n # path('page_list/',views.page_split,name='page_list')\n\n]+static(settings.MEDIA_URL,document_root=settings.MEDIA_ROOT)","sub_path":"newapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"279736573","text":"\"\"\"\nWrapper class for Baidu PCS API\n\"\"\"\n\nimport urllib.request\nimport urllib.parse\nimport urllib.error\nimport ssl\nimport json\nimport http.cookiejar\nimport time\nimport env\nimport mimetypes\nimport os\nimport re\n\nclass BaiduPan:\n access_token = env.access_token\n rooturl = 'https://pcs.baidu.com/rest/2.0/pcs/'\n \n def __init__(self):\n proxies = {'http':r'http://proxy.statestr.com:80', 'https':r'http://proxy.statestr.com:80'}\n proxy_support = urllib.request.ProxyHandler(proxies)\n cookie_support = urllib.request.HTTPCookieProcessor(http.cookiejar.CookieJar())\n ssl_support = urllib.request.HTTPSHandler(context=ssl.SSLContext(ssl.PROTOCOL_TLSv1))\n auth = urllib.request.HTTPBasicAuthHandler()\n opener = urllib.request.build_opener(ssl_support, auth, proxy_support, cookie_support)\n urllib.request.install_opener(opener)\n \n def httpget(self,url):\n res = urllib.request.urlopen(url)\n return res.read().decode('utf-8')\n \n def httppost(self,url,data={}):\n #print(url)\n res = urllib.request.urlopen(url,urllib.parse.urlencode(data).encode('utf-8'))\n return res.read().decode('utf-8')\n \n def geturl(self,apitype,query_str):\n return BaiduPan.rooturl+apitype+'?access_token='+BaiduPan.access_token+'&'+urllib.parse.urlencode(query_str)\n\n def quota(self):\n url = self.geturl('quota',{'method':'info'})\n return self.httpget(url);\n\n def mkdir(self,path):\n url = self.geturl('file', {'method':'mkdir'}) \n return self.httppost(url, {'path':path})\n \n def list(self,path):\n url = self.geturl('file', {'method':'list','path':path})\n res = self.httpget(url)\n pattern = r'\"path\":\"([\\w\\\\\\/.]+)\".+?\"mtime\":(\\d+).+?\"size\":(\\d+)'\n items = re.findall(pattern, res)\n for name, mtime, size in items:\n print ('{0:50} {1:30} {2}'.format(os.path.normpath(name), time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(int(mtime))), int(size)))\n \n def meta(self,path):\n url = self.geturl('file', {'method':'meta','path':path})\n return self.httpget(url)\n \n def meta_multi(self,paths):\n url = self.geturl('file', {'method':'meta'})\n postdata = {'param':json.dumps({'list':[{'path':x} for x in paths]})}\n return self.httppost(url, postdata)\n\n def move(self,ffrom,fto):\n url = self.geturl('file', {'method':'move'})\n postdata = {'from':ffrom,'to':fto}\n return self.httppost(url, postdata)\n\n def move_multi(self,param):\n url = self.geturl('file', {'method':'move'})\n postdata = {'param':json.dumps({'list':param})}\n return self.httppost(url, postdata)\n\n def copy(self,ffrom,fto):\n url = self.geturl('file', {'method':'copy'})\n postdata = {'from':ffrom,'to':fto}\n return self.httppost(url, postdata)\n\n def copy_multi(self,param):\n url = self.geturl('file', {'method':'copy'})\n postdata = {'param':json.dumps({'list':param})}\n return self.httppost(url, postdata)\n\n def delete(self,path):\n url = self.geturl('file', {'method':'delete'})\n return self.httppost(url, {'path':path})\n\n def delete_multi(self,paths):\n url = self.geturl('file', {'method':'delete'})\n postdata = {'param':json.dumps({'list':[{'path':x} for x in paths]})}\n return self.httppost(url, postdata)\n\n def search(self,path,wd,re=0):\n url = self.geturl('file', {'method':'search','path':path,'wd':wd,'re':re })\n return self.httpget(url)\n\n def upload(self, pathr, pathlocal):\n url=self.geturl('file',{'path':pathr,'method':'upload'})\n files={'file':pathlocal}\n \n body, content_type = self.__encode_multipart({}, files)\n req = urllib.request.Request(url, body.encode('ISO-8859-1'))\n req.add_header('Content-Type', content_type)\n try:\n resp = urllib.request.urlopen(req)\n resp_body = resp.read().decode('UTF-8')\n print(resp_body)\n except urllib.error.HTTPError as e:\n print(e.fp.read())\n\n def download(self,path,local):\n f=open(local,'wb')\n url = self.geturl('file', {'method':'download','path':path})\n f.write(self.httpget(url))\n f.close()\n\n def diff(self,cursor='null'):\n url = self.geturl('file', {'method':'diff','cursor':cursor})\n return self.httpget(url)\n \n def __encode_multipart(self, fields, files={}):\n '''\n Build a multipart/form-data body with generated random boundary.\n '''\n CRLF = '\\r\\n'\n boundary = '----------%s' % hex(int(time.time() * 1000))\n data = []\n for (k, v) in fields.items():\n data.append('--%s' % boundary)\n data.append('Content-Disposition: form-data; name=\"%s\"' % k)\n data.append('')\n data.append(v if isinstance(v, str) else v.decode('ISO-8859-1'))\n for (k, filepath) in files.items():\n data.append('--%s' % boundary)\n data.append('Content-Disposition: form-data; name=\"%s\"; filename=\"%s\"' % (k, os.path.basename(filepath)))\n data.append('Content-Type: %s' % self.__get_content_type(filepath)) \n data.append('') \n data.append(open(filepath, 'rb').read().decode('ISO-8859-1')) \n data.append('--%s--' % boundary)\n data.append('') \n \n content_type = 'multipart/form-data; boundary=%s' % boundary\n return CRLF.join(data), content_type \n\n def __get_content_type(self, filepath): \n return mimetypes.guess_type(filepath)[0] or 'application/octet-stream' ","sub_path":"cloudstore/baidu.py","file_name":"baidu.py","file_ext":"py","file_size_in_byte":5246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"239592615","text":"#encoding=utf-8\nfrom pymongo import MongoClient\nimport time\nimport pandas as pd\nimport email_util\n\nimport sys\nreload(sys)\nsys.setdefaultencoding( \"utf-8\" )\n\n\n# 获取数据库链接\nclient = MongoClient('localhost', 27017)\n\n# 获取数据库\ndb = client.get_database(\"Spark\")\n\n'''\n###################################################################################################\n插入记录\n参数一：记录\n###################################################################################################\n'''\ndef insertRecord(record):\n #获取表\n table = db.get_collection(\"report_\" + time.strftime('%Y-%m-%d', time.localtime(time.time())))\n table.insert(record)\n\n'''\n###################################################################################################\n转化成DataFrame文件\n###################################################################################################\n'''\ndef toDataFrame(query):\n #获取表\n table = db.get_collection(\"report_\" + time.strftime('%Y-%m-%d', time.localtime(time.time())))\n cursor = table.find(query)\n df = pd.DataFrame(list(cursor))\n df.to_csv(\"./report/\" + \"report_\" + time.strftime('%Y-%m-%d', time.localtime(time.time())) + \".csv\")\n email_util.sendMailAttatch(email_util.template2(\"\"),\"./report/\" + \"report_\" + time.strftime('%Y-%m-%d', time.localtime(time.time())) + \".csv\", \"report_\" + time.strftime('%Y-%m-%d', time.localtime(time.time())) + \".csv\")\n #df.to_csv(\"C:\\\\\" + \"report_\" + time.strftime('%Y-%m-%d', time.localtime(time.time())) + \".csv\")\n #email_util.sendMailAttatch(email_util.template2(\"\"),\"C:\\\\\" + \"report_\" + time.strftime('%Y-%m-%d', time.localtime(time.time())) + \".csv\", \"report_\" + time.strftime('%Y-%m-%d', time.localtime(time.time())) + \".csv\")\n return df\n\n\n#df = ts.get_hist_data('600848',ktype='D')\n#insertRecord(json.loads(df.to_json(orient='records')))\n\n#json_str = { \"course\" : \"chinese\", \"score\" : 91, \"name\" : \"wubiao\"}\n#insertRecord(json_str)\n#print 'success'\n\n#print toDataFrame({})\n","sub_path":"com/alex/spark002/mongo_util.py","file_name":"mongo_util.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"370055352","text":"# -*- coding: utf-8 -*-\nfrom five import grok\nfrom zope import schema\nfrom zope.interface import alsoProvides\nfrom plone.autoform.interfaces import IFormFieldProvider\nfrom plone.autoform import directives as form\nfrom plone.supermodel import model\nfrom collective.z3cform.datagridfield import DataGridField, DictRow\nfrom plone.registry.interfaces import IRegistry\nfrom zope.component import getUtility\nfrom z3c.form.browser.radio import RadioFieldWidget\nfrom zope.schema.interfaces import IContextSourceBinder\nfrom zope.schema.vocabulary import SimpleVocabulary\n\nfrom ric.core import RICMessageFactory as _\n\n\nclass MultimailTypes(object):\n grok.implements(IContextSourceBinder)\n\n def __call__(self, context):\n registry = getUtility(IRegistry)\n\n terms = []\n\n if registry is not None:\n types = registry.get('ric.core.multimail', {})\n i = 0\n for type in types:\n terms.append(SimpleVocabulary.createTerm(type, i, types[type]))\n i += 1\n return SimpleVocabulary(terms)\n\n def getTermByToken(self, token):\n registry = getUtility(IRegistry)\n if registry is not None:\n types = registry.get('ric.core.multimail', {})\n return SimpleVocabulary.createTerm(types[token])\n\n\nclass IRICPerson(model.Schema):\n\n invalidmail = schema.Bool(title=_(u\"E-mail invalide\"),\n required=True)\n\n form.read_permission(invalidmail='RIC.ActualPersonOwner')\n form.write_permission(invalidmail='RIC.Administrator')\n form.widget('invalidmail', RadioFieldWidget)\n\n multimail = schema.List(title=_(u\"Envoi mail\"),\n required=False,\n value_type=schema.Choice(source=MultimailTypes()),\n )\n\n userid = schema.TextLine(title=_(u\"Identifiant de l'utilisateur\"),\n required=False)\n\n form.read_permission(userid='RIC.Administrator')\n form.write_permission(userid='RIC.Administrator')\n\n\nalsoProvides(IRICPerson, IFormFieldProvider)\n\n\nclass ICotisationRow(model.Schema):\n\n year = schema.Int(title=_(u\"Année\"),\n required=True)\n\n payment = schema.Bool(title=_(u\"Versement\"),\n required=True)\n\n\nclass IRICOrganization(model.Schema):\n\n citizen = schema.TextLine(\n title=_(u\"Nombre d'habitants\"),\n required=True\n )\n\n servers = schema.TextLine(\n title=_(u\"Serveurs\"),\n required=True\n )\n\n softwares = schema.TextLine(\n title=_(u\"Logiciels\"),\n required=True\n )\n\n subscriptions = schema.List(\n title=_(u\"Cotisations\"),\n value_type=DictRow(title=_(u\"Cotisation\"),\n schema=ICotisationRow),\n required=False,\n )\n form.read_permission(subscriptions='RIC.ActualOrganizationMember')\n form.write_permission(subscriptions='RIC.Administrator')\n form.widget('subscriptions', DataGridField)\n\n\nalsoProvides(IRICOrganization, IFormFieldProvider)\n","sub_path":"ric/core/content/behaviors.py","file_name":"behaviors.py","file_ext":"py","file_size_in_byte":3044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"400134033","text":"# Copyright (c) 2016, 2017, Oracle and/or its affiliates. All rights reserved.\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License, version 2.0, as\n# published by the Free Software Foundation.\n#\n# This program is also distributed with certain software (including\n# but not limited to OpenSSL) that is licensed under separate terms,\n# as designated in a particular file or component or in included license\n# documentation. The authors of MySQL hereby grant you an\n# additional permission to link the program and your derivative works\n# with the separately licensed software that they have included with\n# MySQL.\n#\n# Without limiting anything contained in the foregoing, this file,\n# which is part of MySQL Connector/Python, is also subject to the\n# Universal FOSS Exception, version 1.0, a copy of which can be found at\n# http://oss.oracle.com/licenses/universal-foss-exception.\n#\n# This program is distributed in the hope that it will be useful, but\n# WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n# See the GNU General Public License, version 2.0, for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation, Inc.,\n# 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA\n\n\"\"\"Translations\"\"\"\n\n__all__ = [\"get_client_error\"]\n\nfrom .. import errorcode\n\n\ndef get_client_error(error, language=\"eng\"):\n \"\"\"Lookup client error\n\n This function will lookup the client error message based on the given\n error and return the error message. If the error was not found,\n None will be returned.\n\n Error can be either an integer or a string. For example:\n error: 2000\n error: CR_UNKNOWN_ERROR\n\n The language attribute can be used to retrieve a localized message, when\n available.\n\n Returns a string or None.\n \"\"\"\n try:\n tmp = __import__(\"mysqlx.locales.{0}\".format(language),\n globals(), locals(), [\"client_error\"])\n except ImportError:\n raise ImportError(\"No localization support for language '{0}'\"\n \"\".format(language))\n client_error = tmp.client_error\n\n if isinstance(error, int):\n errno = error\n for key, value in errorcode.__dict__.items():\n if value == errno:\n error = key\n break\n\n if isinstance(error, (str)):\n try:\n return getattr(client_error, error)\n except AttributeError:\n return None\n\n raise ValueError(\"Error argument needs to be either an integer or string\")\n","sub_path":"venv/lib/python3.6/site-packages/mysqlx/locales/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"402907427","text":"from django.shortcuts import render, redirect, get_list_or_404, get_object_or_404\r\nfrom articles.models import Article\r\nfrom articles.forms import ArticleForm, CommentForm\r\nfrom django.views.decorators.http import require_safe, require_POST, require_http_methods\r\n\r\n# Create your views here.\r\n@require_safe\r\ndef index(request):\r\n articles = get_list_or_404(Article)\r\n context = {\r\n 'articles': articles\r\n }\r\n return render(request, 'articles/index.html', context)\r\n\r\n@require_safe\r\ndef detail(request, pk):\r\n article = get_object_or_404(Article, pk=pk)\r\n context = {\r\n 'article': article\r\n }\r\n return render(request, 'articles/detail.html', context)\r\n\r\n@require_http_methods(['GET','POST'])\r\ndef create(request):\r\n if request.method == 'POST':\r\n form = ArticleForm(request.POST, request.FILES)\r\n if form.is_valid():\r\n article = form.save()\r\n return redirect('articles:detail', article.pk)\r\n else:\r\n form = ArticleForm()\r\n context = {\r\n 'form': form,\r\n }\r\n return render(request, 'articles/form.html', context)\r\n\r\n@require_http_methods(['GET','POST'])\r\ndef update(request, pk):\r\n article = get_object_or_404(Article, pk=pk)\r\n if request.method == 'POST':\r\n form = ArticleForm(request.POST, request.FILES, instance=article)\r\n if form.is_valid():\r\n form.save()\r\n return redirect('articles:detail', article.pk)\r\n else:\r\n form = ArticleForm(instance=article)\r\n context = {\r\n 'form': form,\r\n }\r\n return render(request, 'articles/form.html', context)\r\n\r\n@require_POST\r\ndef delete(request, pk):\r\n article = get_object_or_404(Article, pk=pk)\r\n article.delete()\r\n return redirect('articles:index')\r\n\r\n@require_POST\r\ndef comments_create(request, pk):\r\n article = get_object_or_404(Article, pk=pk)\r\n form = CommentForm(request.POST)\r\n if form.is_valid():\r\n comment = form.save(commit=False)\r\n comment.article = article\r\n comment.save()\r\n return redirect('articles:detail', article.pk)","sub_path":"articles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"145307388","text":"\n# ----------------------------------------------------------------------- #\n# Functions of genAij routine \n# ----------------------------------------------------------------------- #\n#\n#\t\t:: getElements\n#\t\tGets expectation values from [x] mode output files to populate temp\n# \tB matrix\n#\n# ----------------------------------------------------------------------- #\n\n# Python packages\nimport os \nimport fileinput as fi \nimport linecache as lc \nimport numpy as np\nimport argparse as ap \n\n# ----------------------------------------------------------------------- #\ndef getElements( numParams, numStates, nuclide ):\n# ----------------------------------------------------------------------- #\n\n\tBmatrix = np.zeros(( numParams, numStates ))\n\n\tfor i in range( numParams ):\n\t\tnum = i + 1\n\n\t\t# Open file to read from\n\t\tfileName = \"out_expt\" + str( nuclide ) + \"_\" + str( num ) + \".dat\"\n\t\ttemp = fi.input( fileName )\n\n\t\t# Finds the line before the state and expectation value output\n\t\tfor line in temp:\n\t\t\tif line.find(' STATE') != -1:\n\t\t\t\tstartLine = fi.lineno()\n\t\t\t\tbreak \n\n\t\t# Extract data following ' STATE' line\n\t\tfor j in range( numStates ):\n\t\t\tlineNum = ( startLine + 1 ) + j \n\t\t\telement = ( lc.getline( fileName, lineNum )).split()[3]\n\n\t\t\tBmatrix[i][j] = element \n\n\t\ttemp.close()\n\n\treturn ( Bmatrix )","sub_path":"inputClAr/genAijMods.py","file_name":"genAijMods.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"398312329","text":"import sys\n\nmonkeys = []\nops = []\ndivs = []\nedges = []\nfor info in sys.stdin.read().split(\"\\n\\n\"):\n lines = info.split(\"\\n\")\n items = list(map(int, lines[1][18:].split(\", \")))\n op_str = lines[2][13:]\n if op_str.startswith(\"new = old + \"):\n other = int(op_str[len(\"new = old + \"):])\n ops.append([\"add\", other])\n elif op_str.startswith(\"new = old * \"):\n other = op_str[len(\"new = old * \"):]\n if other == \"old\":\n ops.append([\"square\"])\n else:\n other = int(other)\n ops.append([\"mult\", other])\n div = int(lines[3][len(\" Test: divisible by \"):])\n n1 = int(lines[4][len(\" If true: throw to monkey \"):])\n n2 = int(lines[5][len(\" If false: throw to monkey \"):])\n\n monkeys.append(items)\n divs.append(div)\n edges.append((n1, n2))\n\ndef apply_op(op, num):\n if op[0] == \"square\":\n return num*num\n if op[0] == \"add\":\n return num+op[1]\n return num*op[1]\n\ncnts = [0 for _ in range(len(monkeys))]\nfor _ in range(20):\n for i in range(len(monkeys)):\n for item in monkeys[i]:\n cnts[i] += 1\n new_item = apply_op(ops[i], item)//3\n if new_item % divs[i] == 0:\n monkeys[edges[i][0]].append(new_item)\n else:\n monkeys[edges[i][1]].append(new_item)\n monkeys[i] = []\n\ncnts.sort()\nprint(cnts[-1]*cnts[-2])\n","sub_path":"2022/day11/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"78130225","text":"import boto3\n\nclient = boto3.client('sagemaker-runtime')\n\nendpoint_name = \"mia-mytask-endpoint\" # Your endpoint name.\ncontent_type = \"application/jsonlines\" # The MIME type of the input data in the request body.\naccept = \"text/json\" # The desired MIME type of the inference in the response.\n#payload = '{\"source\":\"帮 我 尽快 发货 ， 家里 没有 了\"}' # Payload for inference.\npayload = '{\"source\":\"能不能 快点 发货\"}' # Payload for inference.\n\n \nresponse = client.invoke_endpoint(\n EndpointName=endpoint_name, \n ContentType=content_type,\n Accept=accept,\n Body=payload\n )\n\nprint(response)\nprint(response['Body'].read())\n","sub_path":"test_endpoint.py","file_name":"test_endpoint.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"545749794","text":"#!/usr/bin/env python\n\nimport os\n\nfrom setuptools import setup\n\n\ndef read(fname):\n with open(os.path.join(os.path.dirname(__file__), fname)) as f:\n x = f.read()\n return x\n\nsetup(\n name='dccclient',\n version='0.1.1',\n description='DCC Send for Twisted',\n author='Alexander Walters',\n author_email='tritium@sdamon.com',\n url='http://tritium21.github.io/dccclient/',\n bugtrack_url=\"https://github.com/tritium21/dccclient/issues\",\n license=\"MIT\",\n install_requires=['Twisted>=13.0.0'],\n py_modules=['dccclient'],\n long_description=read('README.rst'),\n classifiers=[\n \"Development Status :: 3 - Alpha\",\n \"Topic :: Communications :: Chat :: Internet Relay Chat\",\n \"Framework :: Twisted\",\n \"License :: OSI Approved :: MIT License\",\n \"Programming Language :: Python :: 2 :: Only\",\n ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"484850797","text":"import re\nfrom typechecker.typecheck import *\nimport unidecode\n\nclass Cleaner:\n good_chars = \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\"\n good_chars += \"ÀÁÂÄÈÉÊËÍÌÎÏÓÒÔÖÚÙÛÜàáâäèéêëìíîïôöòóüùúûÿ\"\n good_chars += \" -,.?!0123456789%&\\\"\\'()/$*+:;<=>[]\\\\^_{}|\\\\~€°²\"\n good_chars = set(good_chars)\n\n html_entities = [\"<\", \">\", \"≤\", \"≥\", \"&\",\n \"<\", \">\", \"&le\", \"&ge\", \"&\"]\n\n smileys = ['-\\\\_(ツ)_/-', '\\\\_(ツ)_/', \"(ツ)\", '\\\\^o^/', '^o^', '^~^', \"^^'\",\n '^^', \"^ ^'\", '^ ^', '*-*', '*^*', '*~*', '*.*', \"-.-'\", '-.-',\n '\\\\m/', '<3', ' XD', '^.^', '\\\\o/', '\\\\(. _. )/', '/o\\\\',\n '\\\\(\\'o\\')/', '\\\\(~_~)/', '\\\\*O*/', '\\\\/', '/\\\\',\n '-\\\\_( •-•)_/-', '-\\\\_( -)_/-', '-\\\\_(-)_/-', '-\\\\_(- )_/-']\n\n punc_set = set(\".,:;!?\")\n punc_set_no_period = set(\",:;!?\")\n\n @staticmethod\n @accepts(str, List[Tuple[str, str]])\n @returns(str)\n def preprocess(sentence, regexs):\n \"\"\"Preprocess a given sentence by replacing specific patterns. This is\n useful e.g. for removing mentions, hashtags, urls, and so on.\n\n Parameters\n sentence | str\n The sentence to process\n regexs | List[Tuple[str, str]]\n A list of tuple with first element being the pattern to match\n and the second element being the string that replaces the\n pattern.\n \"\"\"\n for regex, replacement in regexs:\n sentence = re.sub(regex, replacement, sentence)\n return sentence\n\n @staticmethod\n @accepts(str)\n @returns(str)\n def remove_smileys(sentence):\n \"\"\"Remove smileys from text. This is different from the emojis because\n the smileys are made by combining characters (mostly ascii), e.g. :-)\n \"\"\"\n regex = r\"[\\:\\;\\=]{1}-?([\\\\\\/DpPdoO0\\*)(\\]\\[\\]])\\1*\"\n sentence = re.sub(regex, \" \", sentence)\n regex = r\"[\\:\\;\\=]{1}\\s?-\\s?([\\\\\\/\\*)(\\]\\[\\]])\\1*\"\n sentence = re.sub(regex, \" \", sentence)\n\n for smiley in Cleaner.smileys:\n sentence = sentence.replace(smiley, \" \")\n return sentence\n\n @staticmethod\n @accepts(str)\n @returns(str)\n def remove_hat_element(sentence):\n \"\"\"Remove some hat elements (^gf, ^chs, ^ds and so on) that appears\n sometimes at the end of sentences. The meaning and origin of these\n elements are unknown.\n \"\"\"\n sentence = re.sub(r\"\\^\\w+$\", \"\", sentence)\n return sentence\n\n @staticmethod\n @accepts(str)\n @returns(str)\n def remove_html_entities(sentence):\n \"\"\"Remove some html entities\n \"\"\"\n for x in Cleaner.html_entities:\n sentence = sentence.replace(x, \" \")\n return sentence\n\n @staticmethod\n @accepts(str, int, special=set)\n @returns(str)\n def remove_groups_of_special_chars(\n sentence,\n from_size,\n special=set(\"-,%&\\\"'()/$*+:;<=>[]^_{}|\\\\~€°²\")):\n \"\"\"Remove tokens of consecutive special characters, if the length is\n at least 'from_size'. The tokens are space-separated\n \"\"\"\n final_words = []\n sentence = Cleaner.clean_spaces(sentence)\n for word in sentence.split():\n special_count = len([c for c in word if c in special])\n if special_count < from_size or len(word) != special_count:\n final_words.append(word)\n return ' '.join(final_words)\n\n @staticmethod\n @accepts(str, special=set)\n @returns(str)\n def remove_isolated_special_chars(sentence,\n special=set(\"+<=>^_\\\\°²\")):\n \"\"\"Remove isolated special characters.\n \"\"\"\n final_words = []\n sentence = Cleaner.clean_spaces(sentence)\n for word in sentence.split():\n if len(word) > 1 or word not in special:\n final_words.append(word)\n return ' '.join(final_words)\n\n @staticmethod\n @accepts(str)\n @returns(List[str])\n def split_parenthesis(sentence):\n \"\"\"Split the text containing parenthesis ()[]{}| by considering the\n different parenthesis as delimiters.\n \"\"\"\n for x in list(\"()[]{}\"):\n sentence = sentence.replace(x, '|')\n return [x.strip() for x in sentence.split('|') if len(x.strip())>0]\n\n @staticmethod\n @accepts(str, Set[str])\n @returns(str)\n def remove_not_good_chars(sentence, good_chars):\n \"\"\"Replace by a space all characters that are not in good_chars\"\"\"\n chars = set(list(sentence))\n for c in chars:\n if c not in good_chars:\n sentence = sentence.replace(c, \" \")\n return sentence\n\n @staticmethod\n @returns(str)\n def remove_special_chars(sentence, special_chars=set(\"#@[]{}<>=^\\\\_~\")):\n \"\"\"Replace by a space all given special characters from a list of\n sentences\"\"\"\n chars = set(list(sentence))\n for c in chars:\n if c in special_chars:\n sentence = sentence.replace(c, \" \")\n return sentence\n\n @staticmethod\n @accepts(str)\n @returns(str)\n def remove_special_words(sentence):\n \"\"\"Remove words containing one or more character that are not in\n the good character list\"\"\"\n final = []\n for word in sentence.split():\n if len(set(word).difference(Cleaner.good_chars)) == 0:\n final.append(word)\n return ' '.join(final)\n\n @staticmethod\n @accepts(str)\n @returns(str)\n def clean_punc(sentence):\n \"\"\"Clean the punctuation : no space before, space after, remove\n duplicate punctuation except for point. Several points is\n mapped to three points. Note that we try to use as less regex as\n possible because regex are time consuming. \"\"\"\n\n # remove duplicated spaces\n sentence = Cleaner.clean_spaces(sentence)\n # remove any space before punctuation\n for c in Cleaner.punc_set:\n sentence = sentence.replace(\" \"+c, c)\n # remove duplicated punctuation\n for c in Cleaner.punc_set_no_period:\n while c+c in sentence:\n sentence = sentence.replace(c+c, c)\n # replace two or more points by three points\n sentence = re.sub(r\"\\.{2,}\", \"...\", sentence)\n # replace a weird combination of punctuation by a simple one\n sentence = re.sub(r\"[\\?\\!\\.\\,\\;\\:]*\\?[\\?\\!\\.\\,\\;\\:]*\", \"?\", sentence)\n sentence = re.sub(r\"[\\!\\.\\,\\;\\:]*\\![\\!\\.\\,\\;\\:]*\", \"!\", sentence)\n sentence = re.sub(r\"[\\,\\;\\:]*\\.[\\,\\;\\:]*\", \".\", sentence)\n sentence = re.sub(r\"[\\,\\;\\:]*\\,[\\,\\;\\:]*\", \",\", sentence)\n # remove punctuation at the beginning\n while len(sentence) > 0 and sentence[0] in Cleaner.punc_set:\n sentence = sentence[1:]\n # add space after punctuation\n sentence = re.sub(r\"\\.(?!\\.)\", \". \", sentence)\n for c in Cleaner.punc_set_no_period:\n sentence = sentence.replace(c, c+\" \")\n # remove duplicated space\n sentence = Cleaner.clean_spaces(sentence)\n return sentence\n\n @staticmethod\n @accepts(str)\n @returns(str)\n def clean_spaces(sentence):\n \"\"\"Remove duplicated spaces and spaces at the beginning or end of a\n sentence\"\"\"\n sentence = re.sub(r\"\\s+\", \" \", sentence)\n sentence = sentence.strip()\n return sentence\n\n def add_num_token(sentence):\n \"\"\"Convert all numbers in a token and isolate the\n token by adding a space before and after.\n \"\"\"\n sentence = re.sub(r\"[0-9][0-9\\.\\,\\'/]*[0-9]\", \" \", sentence)\n sentence = re.sub(r\"\\(\\s+\\)+\", \"\", sentence)\n return sentence\n\n def add_punc_token(sentence):\n \"\"\"Convert all punctuation signs in a token and isolate the\n token by adding a space before and after. Make sure to run this after\n add_num_token.\n \"\"\"\n return re.sub(r\"[\\.\\,\\:\\;\\?\\!\\]\\(\\)\\\"]+\", \" \", sentence)\n sentence = re.sub(r\"\\(\\s+\\)+\", \"\", sentence)\n return sentence\n\n def remove_special_duplication(sentence,\n special=set(\"-,?!%&\\\"\\'()/$*+:;<=>[]\\\\^_{}|~€°²\")):\n \"\"\"Remove the duplication of special characters\n \"\"\"\n\n # would do the job but takes slightly more time\n special = r\"[\\-\\,\\?\\!\\%\\&\\\"\\'\\(\\)\\/\\$\\*\\+\\:\\;\\<\\=\\>\\[\\]\\\\\\^\" + \\\n r\"\\_\\{\\}\\|\\~\\€\\°\\²]\"\n regex = r\"(\" + special + r\")\\1*\"\n sentence = re.sub(regex, r\"\\1\", sentence)\n return sentence\n\n # slightly faster but need to check escaped characters\n # for c in list(special) + [\" \"]:\n # if c in sentence:\n # regex = \"\\\\\" + c + \"+\"\n # sentence = re.sub(regex, c, sentence)\n # return sentence\n\n def remove_non_gsw_accent(sentence, exclude={'Ä','Ö','Ü','ä','ö','ü'}):\n \"\"\"Replace all non gsw accent by the letter without the accent\"\"\"\n res = []\n for c in sentence:\n if not c in exclude:\n c = unidecode.unidecode(c)\n res.append(c)\n return ''.join(res)\n","sub_path":"preprocessing/cleaner.py","file_name":"cleaner.py","file_ext":"py","file_size_in_byte":9360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"202572511","text":"# -*- coding: utf-8 -*-\n\"\"\"\n flaskext.session.sessions\n ~~~~~~~~~~~~~~~~~~~~~~~~~\n\n Server-side Sessions and SessionInterfaces.\n\n :copyright: (c) 2014 by Shipeng Feng.\n :license: BSD, see LICENSE for more details.\n\"\"\"\nimport sys\nimport time\nfrom uuid import uuid4\ntry:\n import cPickle as pickle\nexcept ImportError:\n import pickle\n\nfrom flask.sessions import SessionInterface, SessionMixin\nfrom itsdangerous import URLSafeTimedSerializer, BadSignature\nfrom werkzeug.datastructures import CallbackDict\n\n\nclass ServerSideSession(CallbackDict, SessionMixin):\n \"\"\"Baseclass for server-side based sessions.\"\"\"\n\n def __init__(self, initial=None, sid=None):\n def on_update(self):\n self.modified = True\n CallbackDict.__init__(self, initial, on_update)\n self.sid = sid\n self.permanent = True\n self.modified = False\n\n\nclass RedisSession(ServerSideSession):\n pass\n\n\nclass MemcachedSession(ServerSideSession):\n pass\n\n\nclass FileSystemSession(ServerSideSession):\n pass\n\n\nclass MongoDBSession(ServerSideSession):\n pass\n\n\nclass BaseSessionInterface(SessionInterface):\n\n def __init__(self, client, key_prefix):\n self.client = self._get_client(client)\n self.key_prefix = key_prefix\n\n def _get_client(client):\n raise NotImplemented()\n\n def get_value(self, key):\n return self.client.get(key)\n\n def set_value(self, key, value, timeout):\n return self.client.set(key, value, timeout)\n\n def delete_value(self, key):\n return self.client.delete(key)\n\n def _generate_sid(self, app):\n return self._signer(app).dumps(str(uuid4()))\n\n def _signer(self, app):\n if not hasattr(self, '_signer_serializer'):\n self._signer_serializer = URLSafeTimedSerializer(app.secret_key)\n return self._signer_serializer\n\n def _encode_key(self, key, encoding='utf-8'):\n if sys.version_info.major == 2:\n if isinstance(key, unicode):\n return key.encode(encoding)\n else:\n if isinstance(key, bytes):\n return key.decode(encoding)\n return key\n\n def _session_key(self, app, raw_sid):\n max_age = app.permanent_session_lifetime.total_seconds()\n sid = self._signer(app).loads(raw_sid, max_age=max_age)\n return self._encode_key('{}:{}'.format(self.key_prefix, sid))\n\n def _session_timeout(self, app):\n return int(app.permanent_session_lifetime.total_seconds())\n\n def open_session(self, app, request):\n sid = request.cookies.get(app.session_cookie_name)\n if not sid:\n sid = self._generate_sid(app)\n return self.session_class(sid=sid)\n try:\n val = self.get_value(self._session_key(app, sid))\n except BadSignature:\n val = None\n if val is not None:\n try:\n data = self.serializer.loads(val)\n return self.session_class(data, sid=sid)\n except:\n pass\n return self.session_class(sid=sid)\n\n def save_session(self, app, session, response):\n domain = self.get_cookie_domain(app)\n path = self.get_cookie_path(app)\n try:\n full_session_key = self._session_key(app, session.sid)\n except BadSignature:\n response.delete_cookie(app.session_cookie_name,\n domain=domain, path=path)\n return\n\n if not session:\n if session.modified:\n self.delete_value(full_session_key)\n response.delete_cookie(app.session_cookie_name,\n domain=domain, path=path)\n return\n\n # Modification case. There are upsides and downsides to\n # emitting a set-cookie header each request. The behavior\n # is controlled by the :meth:`should_set_cookie` method\n # which performs a quick check to figure out if the cookie\n # should be set or not. This is controlled by the\n # SESSION_REFRESH_EACH_REQUEST config flag as well as\n # the permanent flag on the session itself.\n #if not self.should_set_cookie(app, session):\n # return\n\n httponly = self.get_cookie_httponly(app)\n secure = self.get_cookie_secure(app)\n expires = self.get_expiration_time(app, session)\n val = self.serializer.dumps(dict(session))\n self.set_value(full_session_key, val, self._session_timeout(app))\n response.set_cookie(app.session_cookie_name, session.sid,\n expires=expires, httponly=httponly,\n domain=domain, path=path, secure=secure)\n\n\nclass NullSessionInterface(SessionInterface):\n \"\"\"Used to open a :class:`flask.sessions.NullSession` instance.\n \"\"\"\n\n def open_session(self, app, request):\n return None\n\n\nclass RedisSessionInterface(BaseSessionInterface):\n \"\"\"Uses the Redis key-value store as a session backend.\n\n :param redis: A ``redis.Redis`` instance.\n :param key_prefix: A prefix that is added to all Redis store keys.\n \"\"\"\n\n serializer = pickle\n session_class = RedisSession\n\n def _get_client(self, client):\n if client is None:\n from redis import Redis\n client = Redis()\n return client\n\n def set_value(self, key, value, timeout):\n return self.client.setex(key, value, timeout)\n\n\nclass MemcachedSessionInterface(BaseSessionInterface):\n \"\"\"A Session interface that uses memcached as backend.\n\n :param client: A ``memcache.Client`` instance.\n :param key_prefix: A prefix that is added to all Memcached store keys.\n \"\"\"\n\n serializer = pickle\n session_class = MemcachedSession\n\n def _get_client(self, client):\n if client is not None:\n return client\n\n servers = ['127.0.0.1:11211']\n try:\n import pylibmc\n except ImportError:\n pass\n else:\n return pylibmc.Client(servers)\n\n try:\n import memcache\n except ImportError:\n pass\n else:\n return memcache.Client(servers)\n\n def _session_timeout(self, app):\n \"\"\"\n Memcached deals with long (> 30 days) timeouts in a special\n way. Call this function to obtain a safe value for your timeout.\n \"\"\"\n timeout = super(MemcachedSessionInterface, self)._session_timeout(app)\n if timeout > 2592000: # 60*60*24*30, 30 days\n # See http://code.google.com/p/memcached/wiki/FAQ\n # \"You can set expire times up to 30 days in the future. After that\n # memcached interprets it as a date, and will expire the item after\n # said date. This is a simple (but obscure) mechanic.\"\n #\n # This means that we have to switch to absolute timestamps.\n timeout += int(time.time())\n return timeout\n\n\nclass FileSystemSessionInterface(BaseSessionInterface):\n \"\"\"Uses the :class:`werkzeug.contrib.cache.FileSystemCache` as a session\n backend.\n\n :param cache_dir: the directory where session files are stored.\n :param threshold: the maximum number of items the session stores before it\n starts deleting some.\n :param mode: the file mode wanted for the session files, default 0600\n :param key_prefix: A prefix that is added to FileSystemCache store keys.\n \"\"\"\n\n session_class = FileSystemSession\n serializer = pickle\n\n def __init__(self, cache_dir, threshold, mode, key_prefix):\n self._cache_dir = cache_dir\n self._threshold = threshold\n self._mode = mode\n super(FileSystemSessionInterface, self).__init__(None, key_prefix)\n\n def _get_client(self, client):\n from werkzeug.contrib.cache import FileSystemCache\n return FileSystemCache(self._cache_dir, threshold=self._threshold, mode=self._mode)\n\n\nclass MongoDBSessionInterface(BaseSessionInterface):\n \"\"\"A Session interface that uses mongodb as backend.\n\n :param client: A ``pymongo.MongoClient`` instance.\n :param db: The database you want to use.\n :param collection: The collection you want to use.\n :param key_prefix: A prefix that is added to all MongoDB store keys.\n \"\"\"\n\n serializer = pickle\n session_class = MongoDBSession\n\n def __init__(self, client, db, collection, key_prefix):\n self._db = db\n self._collection = collection\n super(MongoDBSessionInterface, self).__init__(client, key_prefix)\n\n def _get_client(self, client):\n if client is None:\n from pymongo import MongoClient\n client = MongoClient()\n return client[self._db][self._collection]\n\n def get_value(self, key):\n document = self.client.find_one({'id': key})\n if not document:\n return None\n return str(document['val'])\n\n def set_value(self, key, value, timeout):\n return self.client.update({'id': key},\n {'id': key,\n 'val': value}, True)\n\n def delete_value(self, key):\n super(MongoDBSessionInterface, self).delete_value({'id': key})\n","sub_path":"flask_session/sessions.py","file_name":"sessions.py","file_ext":"py","file_size_in_byte":9191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"380301061","text":"# hack to be able to load modules in Python3\nimport sys, os\nsys.path.insert(1, os.path.join(os.getcwd(),'.'))\n\nimport argparse\n\nfrom train_rl import train_rl\nfrom train_il import train_il\nfrom sim import sim\nfrom planning.rrt import rrt\nfrom planning.scp import scp\n\ndef run(param, env, controllers, initial_state = None):\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\"--rl\", action='store_true')\n\tparser.add_argument(\"--il\", action='store_true')\n\tparser.add_argument(\"--rrt\", action='store_true')\n\tparser.add_argument(\"--scp\", action='store_true')\n\tparser.add_argument(\"--animate\", action='store_true')\n\targs = parser.parse_args()\n\n\tif args.rl:\n\t\ttrain_rl(param, env)\n\telif args.il:\n\t\ttrain_il(param, env)\n\telif args.rrt:\n\t\trrt(param, env)\n\telif args.scp:\n\t\tscp(param, env)\n\telse:\n\t\tsim(param, env, controllers, initial_state, args.animate)\n","sub_path":"code/examples/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"169745108","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 12 07:22:28 2019\n\n@author: walker\n\"\"\"\n\n'''1) Create a function that will sum the squares of one array,\nsum the cubes of another, and return true or false value on whether sum a is greater than sum b'''\n#my sol:\ndef array_madness(a,b):\n sum_squares = []\n sum_cubes = []\n for n in a:\n sum_squares = sum(n**2)\n \n for x in b:\n sum_cubes = sum(x**3)\n \n return sum_squares == sum_cubes\n\n#solution found\ndef array_madness(a,b):\n return sum(x ** 2 for x in a) > sum(x **3 for x in b)\n\n'''2) Complete the solution so that it reverses all of the words within the string passed in.'''\n\ndef reverseWords(str):\n str_spl = str.split()\n reversd = []\n \n for i in range(len(str_spl), -1, -1, -1):\n reversd.append(str_spl[i])\n \n return ' '.join(reversd) \n\n\n'''3) Lost pencil game!'''\n#pre load end:\nend = range(0, 21, 1)\n\ndef lostPencils(end):\n result = 0\n current = 1\n\n while current <= end:\n result += current\n current += 1\n \n return result\n\ntest.assert_equals(lostPencils(10), 55)\ntest.assert_equals(lostPencils(2), 3)\ntest.assert_equals(lostPencils(5), 15)","sub_path":"cw_june12.py","file_name":"cw_june12.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"516729897","text":"#занесение в базу из папки\n\nimport numpy as np\nimport dlib\nimport cv2\nimport os\n\nDIR = \"photos_of_people\"\n\nif not os.path.isdir(DIR):\n\tos.makedirs(DIR)\n\nsp = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\nfacerec = dlib.face_recognition_model_v1('dlib_face_recognition_resnet_model_v1.dat')\ndetector = dlib.get_frontal_face_detector()\n\ndescriptors = np.load('descriptors1.npy')\n\nname = 'Руслан'\n\nimage_from_dir = os.listdir(DIR)[0]\n\nimg = cv2.imread(os.path.join(DIR, image_from_dir))\n\n#img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\ndets = detector(img, 1)\ntry:\n\td = dets[0]\n\n\tcv2.rectangle(img,(d.left(),d.top()),(d.right(),d.bottom()),(255,0,0),2)\n\n\tshape = sp(img, d)\n\tface_descriptor1 = facerec.compute_face_descriptor(img, shape)\n\n\tcv2.imshow('img',img)\n\tk = cv2.waitKey(0) & 0xff\n\tif k == 32:\n\t\tpass\n\telif k == 27:\n\t\t# np.save('descriptors1.npy', np.append(\tdescriptors, \t\t\t\t\t#признаки из базы\n\t\t# \t\t\t\t\t\t\t\t\t\t[[[face_descriptor1],name]],\t#новые признаки\n\t\t# \t\t\t\t\t\t\t\t\t\taxis=0))\t\n\t\tprint('записано')\nexcept:\n\tprint('Лицо человека не найдено, или же его там вообще нет')","sub_path":"n_p/skid_from_folder.py","file_name":"skid_from_folder.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"97806052","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = 'Ben Carr'\nSITENAME = 'Carr B Coding'\nSITEURL = ''#'http://carrben.github.io'\n\nPATH = 'content'\n\nDIRECT_TEMPLATES = ('index', 'categories', 'archives')\n\nTIMEZONE = 'Europe/London'\n\nDEFAULT_LANG = '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 = (('Github', 'http://github.com/carrben/'),\n ('LinkedIn', 'http://uk.linkedin.com/in/carrben'),)\n\nGITHUB_URL = 'http://github.com/carrben'\nLINKEDIN_URL = 'http://uk.linkedin.com/in/carrben'\n\n# Social widget\nSOCIAL = ()#(('You can add links in your config file', '#'),\n #('Another social link', '#'),)\n\nDEFAULT_PAGINATION = False\n\n# Uncomment following line if you want document-relative URLs when developing\n#RELATIVE_URLS = True\n","sub_path":"site/pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"74096080","text":"#\n# Copyright (c) 2021 Cisco Systems, Inc and its affiliates\n# All rights reserved\n#\nimport hvac\nimport logging\n\nfrom hvac.exceptions import InvalidPath\n\nfrom config import VaultConfig\n\n\nclass VaultHelper(object):\n def __init__(self, config: VaultConfig):\n self._client = hvac.Client(\n url=config.scheme + \"://\" + config.host + \":\" + config.port,\n token=config.token,\n verify=config.cacert)\n\n def get_string(self, secret, key, default):\n try:\n response = self._client.secrets.kv.v1.read_secret(secret)\n if response and response[\"data\"] and key in response[\"data\"]:\n return response[\"data\"][key]\n except InvalidPath as e:\n logging.error(str(e))\n return default\n\n def test(self, prefix):\n # Read a secret from Vault and it to the console.\n # Do not leak secrets in production as it is a security violation.\n secret_squirrel_location = self.get_string(f\"{prefix}/helloworldservice/\", \"secret.squirrel.location\", \"UNKNOWN\")\n logging.info(\"Where are the acorns buried?\")\n logging.info(secret_squirrel_location)\n","sub_path":"python-hello-world-service-7/helpers/vault_helper.py","file_name":"vault_helper.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"183860746","text":"# myAgentP3.py\n# ---------\n# Licensing Information: You are free to use or extend these projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.\n#\n# Attribution Information: The Pacman AI projects were developed at UC Berkeley.\n# The core projects and autograders were primarily created by John DeNero\n# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).\n# Student side autograding was added by Brad Miller, Nick Hay, and\n# Pieter Abbeel (pabbeel@cs.berkeley.edu).\n# This file was based on the starter code for student bots, and refined\n# by Mesut (Xiaocheng) Yang\n\n\nfrom captureAgents import CaptureAgent\nimport random, time, util\nfrom game import Directions, Actions\nfrom util import Counter\nimport game\nfrom util import nearestPoint\n\n\n#########\n# Agent #\n#########\nclass BaseAgent(CaptureAgent):\n \"\"\"\n YOUR DESCRIPTION HERE\n \"\"\"\n\n def registerInitialState(self, gameState):\n \"\"\"\n This method handles the initial setup of the\n agent to populate useful fields (such as what team\n we're on).\n\n A distanceCalculator instance caches the maze distances\n between each pair of positions, so your agents can use:\n self.distancer.getDistance(p1, p2)\n\n IMPORTANT: This method may run for at most 15 seconds.\n \"\"\"\n\n # Make sure you do not delete the following line.\n # If you would like to use Manhattan distances instead\n # of maze distances in order to save on initialization\n # time, please take a look at:\n # CaptureAgent.registerInitialState in captureAgents.py.\n CaptureAgent.registerInitialState(self, gameState)\n self.start = gameState.getAgentPosition(self.index)\n self.weights = [1, 1, -0.5, -1]\n\n def chooseAction(self, gameState):\n \"\"\"\n Picks among actions randomly.\n \"\"\"\n print(gameState.data.time)\n print(len(gameState.getFood().asList()))\n teammateActions = self.receivedBroadcast\n # Process your teammate's broadcast!\n # Use it to pick a better action for yourself\n\n actions = gameState.getLegalActions(self.index)\n\n filteredActions = actionsWithoutReverse(actionsWithoutStop(actions), gameState, self.index)\n\n # currentAction = random.choice(filteredActions) # Change this!\n currentAction = self.actionHelper(gameState)\n return currentAction\n\n def getLimitedActions(self, state, index):\n actions = state.getLegalActions(index)\n filteredActions = actionsWithoutReverse(actionsWithoutStop(actions), state, index)\n return filteredActions\n\n def actionHelper(self, state):\n actions = self.getLimitedActions(state, self.index)\n\n val = float('-inf')\n best = None\n for action in actions:\n new_state = state.generateSuccessor(self.index, action)\n new_state_val = self.evaluationFunction(new_state)\n\n if new_state_val > val:\n val = new_state_val\n best = action\n\n return best\n\n def evaluationFunction(self, state):\n foods = state.getFood().asList()\n ghosts = [state.getAgentPosition(ghost) for ghost in state.getGhostTeamIndices()]\n friends = [state.getAgentPosition(pacman) for pacman in state.getPacmanTeamIndices() if pacman != self.index]\n\n pacman = state.getAgentPosition(self.index)\n\n closestFood = min(self.distancer.getDistance(pacman, food) for food in foods) + 2.0 \\\n if len(foods) > 0 else 1.0\n closestGhost = min(self.distancer.getDistance(pacman, ghost) for ghost in ghosts) + 1.0 \\\n if len(ghosts) > 0 else 1.0\n closestFriend = min(self.distancer.getDistance(pacman, friend) for friend in friends) + 1.0 \\\n if len(friends) > 0 else 1.0\n\n closestFoodReward = 1.0 / closestFood\n closestGhostPenalty = 1.0 / (closestGhost ** 2) if closestGhost < 20 else 0\n closestFriendPenalty = 1.0 / (closestFriend ** 2) if closestFriend < 5 else 0\n\n numFood = len(foods)\n\n features = [-numFood, closestFoodReward, closestGhostPenalty, closestFriendPenalty]\n\n myState = state.getAgentState(self.index)\n\n value = sum(feature * weight for feature, weight in zip(features, self.weights))\n return value\n\nclass MyAgent(BaseAgent):\n def toString(self):\n return \"MyAgent\"\n\nclass ReinforcementAgent(BaseAgent):\n\n def registerInitialState(self, gameState):\n CaptureAgent.registerInitialState(self,gameState)\n self.start = gameState.getAgentPosition(self.index)\n self.walls = gameState.getWalls()\n\n self.readWeights()\n self.lastNeighbor = []\n self.lastFeature = Counter()\n\n self.alpha = 0.001\n self.gamma = 0.99\n self.reverse_prob=0.5\n\n self.bornHardLevel = 0\n for i in range(1, 4):\n col = 2 * i\n walls = 0\n for j in range(1, self.walls.height - 1):\n if self.walls[col][j] == True:\n walls += 1\n if walls < self.walls.height - 3:\n break\n self.bornHardLevel += 1\n\n def getFeatures(self, gameState, action):\n feats = Counter()\n feats['bias'] = 1\n if action == Directions.REVERSE[gameState.getAgentState(self.index).configuration.direction]:\n feats['reverse'] = 1\n else: feats['reverse'] = 0\n\n foods = gameState.getFood().asList()\n ghosts = [gameState.getAgentPosition(ghost) for ghost in gameState.getGhostTeamIndices()]\n friends = [gameState.getAgentPosition(pacman) for pacman in gameState.getPacmanTeamIndices() if pacman != self.index]\n pacman = gameState.getAgentPosition(self.index)\n closestFood = min(self.distancer.getDistance(pacman, food) for food in foods) + 2.0 \\\n if len(foods) > 0 else 1.0\n closestGhost = min(self.distancer.getDistance(pacman, ghost) for ghost in ghosts) + 1.0 \\\n if len(ghosts) > 0 else 1.0\n closestFriend = min(self.distancer.getDistance(pacman, friend) for friend in friends) + 1.0 \\\n if len(friends) > 0 else 1.0\n closestFoodReward = 1.0 / closestFood\n closestGhostPenalty = 1.0 / (closestGhost ** 2) if closestGhost < 20 else 0\n closestFriendPenalty = 1.0 / (closestFriend ** 2) if closestFriend < 5 else 0\n numFood = len(foods)\n feats['numFood'] = -numFood/60\n feats['closestFood'] = closestFoodReward\n feats['closestFriend'] = closestFriendPenalty\n feats['closestGhost'] = closestGhostPenalty\n\n pacActions = gameState.getLegalActions(gameState.getGhostTeamIndices()[0])\n pacActions = actionsWithoutStop(pacActions)\n feats['pacInTunnel'] = 0\n if len(pacActions) == 2:\n feats['pacInTunnel'] = 1\n\n futureMoves = 0\n for a in gameState.getLegalActions(self.index):\n s = gameState.generateSuccessor(self.index, a)\n futureMoves += 1 + len(s.getLegalActions(self.index))\n feats['5*5space'] = futureMoves / 30\n return feats\n\n def getQValue(self, state, action):\n \"\"\"\n Should return Q(state,action) = w * featureVector\n where * is the dotProduct operator\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n #print(self.weights)\n #print(self.featExtractor.getFeatures(state,action))\n return self.weight*self.getFeatures(state,action)\n\n def computeValueFromQValues(self, state):\n \"\"\"\n Returns max_action Q(state,action)\n where the max is over legal actions. Note that if\n there are no legal actions, which is the case at the\n terminal state, you should return a value of 0.0.\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n actions=state.getLegalActions(self.index)\n actions = actionsWithoutStop(actions)\n best_Qvalue=0\n if len(actions)!=0:\n Qvalues = [self.getQValue(state,action) for action in actions]\n best_Qvalue=max(Qvalues)\n return best_Qvalue\n\n def computeActionFromQValues(self, state):\n \"\"\"\n Compute the best action to take in a state. Note that if there\n are no legal actions, which is the case at the terminal state,\n you should return None.\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n actions=state.getLegalActions(self.index)\n actions = actionsWithoutStop(actions)\n best_action=None\n if len(actions)!=0:\n #print(\"actions\",actions,\" size:\",len(actions))\n Qvalues = [self.getQValue(state,action) for action in actions]\n best_Qvalue=max(Qvalues)\n #print(\"bestQvalue\",best_Qvalue)\n best_indices=[index for index in range(len(Qvalues)) if Qvalues[index]==best_Qvalue]\n #print(\"best_indicies\",best_indices,\" size:\",len(best_indices))\n chosen_index=random.choice(best_indices)\n best_action=actions[chosen_index]\n return best_action\n\n def chooseAction(self, state):\n \"\"\"\n Compute the action to take in the current state. With\n probability self.epsilon, we should take a random action and\n take the best policy action otherwise. Note that if there are\n no legal actions, which is the case at the terminal state, you\n should choose None as the action.\n\n HINT: You might want to use util.flipCoin(prob)\n HINT: To pick randomly from a list, use random.choice(list)\n \"\"\"\n pacX, pacY = state.getAgentPosition(self.index)\n # Pick Action\n legalActions = state.getLegalActions(self.index)\n if util.flipCoin(self.reverse_prob):\n legalActions=actionsWithoutReverse(legalActions,state,self.index)\n\n action = None\n \"*** YOUR CODE HERE ***\"\n if pacX <= 2 * self.bornHardLevel - 1:\n if ((pacX + 1) / 2) % 2 == 1:\n action = Directions.NORTH if Directions.NORTH in legalActions else None\n else:\n action = Directions.SOUTH if Directions.SOUTH in legalActions else None\n if len(legalActions)!=0 and action == None:\n if util.flipCoin(self.epsilon):\n action=random.choice(legalActions)\n else:\n action=self.computeActionFromQValues(state)\n self.update(state)\n self.lastFeature=self.getFeatures(state,action)\n self.saveWeights(state)\n return action\n\n def update(self, state):\n \"\"\"\n Should update your weights based on transition\n \"\"\"\n \"*** YOUR CODE HERE ***\"\n reward=self.getReward(state)\n if len(self.lastFeature.keys()) != 0:\n #print('weight:',self.weight,\" lastfeature\",self.lastFeature)\n difference=reward+self.gamma*self.computeValueFromQValues(state)-self.weight*self.lastFeature\n #print(\"difference:\",difference)\n for feature in self.lastFeature:\n self.weight[feature]+=self.alpha*difference*self.lastFeature[feature]\n\n\n def getReward(self, gameState):\n eatenPenalty = 0\n if gameState.getAgentPosition(self.index) == self.start:\n eatenPenalty = -10 * self.bornHardLevel\n eatFoodReward = 0\n pacman = gameState.getAgentPosition(self.index)\n\n if len(self.lastNeighbor) != 0:\n x, y = pacman\n if (x, y) in self.lastNeighbor:\n eatFoodReward = 10\n\n foodDecreaseReward = 0\n if len(self.observationHistory) != 0:\n lastFoodNum = len(self.observationHistory[-1].getFood().asList())\n presentFoodNum = len(gameState.getFood().asList())\n foodDecreaseReward = 10 * (lastFoodNum - presentFoodNum)\n\n # 储存我的四邻域食物情况\n self.lastNeighbor = []\n food = gameState.getFood()\n legalNeighbors = Actions.getLegalNeighbors(pacman, self.walls)\n for i in range(len(legalNeighbors)):\n x, y = legalNeighbors[i]\n if food[x][y] == True:\n self.lastNeighbor.append((x, y))\n\n return eatenPenalty + eatFoodReward + foodDecreaseReward\n\n def getPolicy(self, state):\n return self.computeActionFromQValues(state)\n\n def getValue(self, state):\n return self.computeValueFromQValues(state)\n\n def getWeights(self):\n return self.weight\n\n def saveWeights(self, gameState):\n if len(gameState.getFood().asList()) or gameState.data.time >= 1190:\n f = open(\"train.txt\", \"w\")\n for key in self.weight.keys():\n value = str(self.weight[key])\n toPrint = key + ':' + value\n print(toPrint, file=f)\n\n def readWeights(self):\n self.weight = Counter()\n with open(\"train.txt\", \"r\") as f:\n for line in f.readlines():\n content = line.split(':')\n self.weight[content[0]] = float(content[1])\n with open(\"epsilon.txt\", \"r\")as f:\n self.epsilon = float(f.read())\n\nclass TutoredRLAgent(ReinforcementAgent):\n def update(self, state):\n ReinforcementAgent.update(self, state)\n if self.receivedBroadcast != None:\n features, rewards, lastFeatures = self.receivedBroadcast\n difference = rewards + self.gamma * (self.weight * features) - self.weight * lastFeatures\n for feature in lastFeatures:\n self.weight[feature] += self.alpha / 2 * difference * lastFeatures[feature]\n\n\n\ndef actionsWithoutStop(legalActions):\n \"\"\"\n Filters actions by removing the STOP action\n \"\"\"\n legalActions = list(legalActions)\n if Directions.STOP in legalActions:\n legalActions.remove(Directions.STOP)\n return legalActions\n\n\ndef actionsWithoutReverse(legalActions, gameState, agentIndex):\n \"\"\"\n Filters actions by removing REVERSE, i.e. the opposite action to the previous one\n \"\"\"\n legalActions = list(legalActions)\n reverse = Directions.REVERSE[gameState.getAgentState(agentIndex).configuration.direction]\n if len(legalActions) > 1 and reverse in legalActions:\n legalActions.remove(reverse)\n return legalActions\n","sub_path":"PacPack_Fall_2018/myAgent.py","file_name":"myAgent.py","file_ext":"py","file_size_in_byte":14379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"550326691","text":"import tkinter as tk\r\nimport cv2 as cv\r\nimport tensorflow as tf\r\nfrom tkinter import filedialog #文件打开\r\nfrom model_reload import net_structure\r\nimport numpy as np\r\nfrom PIL import ImageTk,Image,ImageGrab\r\n\r\nfilename = None\r\n\r\n#网络结构与模型加载\r\nnet = net_structure()\r\n\r\n#画图方法\r\ndef paint(event):\r\n canv.create_rectangle(event.x,event.y,event.x+5,event.y+5,fill=\"black\")\r\n\r\n#画板清空方法\r\ndef clear_all():\r\n\r\n canv.create_rectangle(0,0,200,200,fill='white')\r\n v.set(\" \")\r\n label1.update()\r\n\r\n#识别方法\r\ndef recognition():\r\n img = ImageGrab.grab((window.winfo_x()+840,window.winfo_y()+33,window.winfo_x()+824+204,window.winfo_y()+233))\r\n img.save('1.png')\r\n\r\n img1 = cv.imread('1.png')\r\n img1 = cv.resize(img1,(28,28))\r\n img_res = cv.cvtColor(img1,cv.COLOR_RGB2GRAY)\r\n #二值化\r\n ret,binary1 = cv.threshold(img_res,0,255,cv.THRESH_BINARY|cv.THRESH_TRIANGLE)\r\n for i in range(28):\r\n for j in range(28):\r\n binary1[i][j]=255-binary1[i][j]\r\n #cv.imshow(\"4\",binary1)\r\n\r\n #平滑滤波\r\n blu_img = cv.blur(binary1,(5,5))\r\n\r\n #img_res = cv.blur(img_res,(5,5))\r\n res = np.reshape(blu_img,784)\r\n y_pre = tf.argmax(net.prediction,1)\r\n predint = y_pre.eval(feed_dict={net.xs:[res],net.keep_prob:1},session=net.sess)\r\n\r\n v.set(str(predint[0]))\r\n label1.update()\r\n\r\n#图片打开\r\ndef open_img():\r\n global filename\r\n global o_img\r\n filename = filedialog.askopenfilename(title=u'选择文件')\r\n o_img = Image.open(filename)\r\n o_img = o_img.resize((200,200))\r\n o_img = ImageTk.PhotoImage(o_img)\r\n\r\n canv.create_image(100,100,image=o_img)\r\n\r\n\r\n#窗口设置\r\nwindow = tk.Tk()\r\nscreenwidth = window.winfo_screenwidth()\r\nscreenheight = window.winfo_screenheight()\r\nsize = '%dx%d+%d+%d' % (1024, 618, (screenwidth - 1024)/2, (screenheight - 618)/2)\r\nwindow.resizable(False,False)#大小不可变\r\nwindow.config(bg='AliceBlue')\r\nwindow.geometry(size)\r\nwindow.iconbitmap(\"图标.ico\")\r\nv = tk.StringVar()\r\n\r\n#画布设置\r\ncanv = tk.Canvas(window,width=200,height=200,bg='white')\r\n\r\n# if filename == None:\r\n# print(\"ddd\")\r\n# pass\r\n# else:\r\n# o_img = Image.open('1.png')\r\n# o_img = o_img.resize((200,200))\r\n# o_img = ImageTk.PhotoImage(o_img)\r\n# canv.create_image(100,100,image=o_img)\r\n\r\n# o_img = Image.open('1.png')\r\n# o_img = o_img.resize((200,200))\r\n# o_img = ImageTk.PhotoImage(o_img)\r\n# canv.create_image(100,100,image=o_img)\r\n\r\ncanv.place(x=824,y=0)\r\ncanv.bind('',paint)\r\n\r\n#画布2设置\r\ncanv1 = tk.Canvas(window,width=826,height=618,bg='AliceBlue')\r\nimg_path = \"模型结构图2.png\"\r\nimg = Image.open(img_path)\r\nphoto = ImageTk.PhotoImage(img)\r\ncanv1.create_image(411,250,image=photo)\r\ncanv1.create_line(823,0,823,618,width=3,fill='gray')\r\ncanv1.create_text((410,520),text=\"网络---结构图\",font=(\"楷体\",20))\r\ncanv1.place(x=0,y=0)\r\n\r\n#按钮设置\r\nbutton1 = tk.Button(window,text='清空',width=10,height=1,command=clear_all)\r\nbutton2 = tk.Button(window,text='识别',width=10,height=1,command=recognition)\r\nbutton3 = tk.Button(window,text='打开图片',width=10,height=1,command=open_img)\r\nbutton1.place(x=890,y=210)\r\nbutton2.place(x=890,y=250)\r\nbutton3.place(x=890,y=290)\r\n\r\n#文本窗口\r\nlabel = tk.Label(window,text=\"识别结果为：\",width=13,height=1,bg='AliceBlue',font=(\"宋体\",13))\r\nlabel.place(x=830,y=330)\r\n\r\n#text 窗口\r\nlabel1 = tk.Label(window,textvariable=v,width=6,height=1,font=(\"宋体\",13))\r\nlabel1.place(x=940,y=330)\r\n\r\nwindow.mainloop()\r\n","sub_path":"GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":3533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"336900335","text":"import os\n\n# Intro to CLI\nprint(\"Welcome to Director!\\n\\nUse this tool to automatically find files and copy them to a new folder.\")\n\n# Allows you to specificy a sub folder\nsource_path = './' + raw_input(\"Enter the source directory (blank if already there): \")\n\n# Choose a name for the folder you want to move the new files to\ntarget_path = './' + raw_input(\"Enter a name for the new folder: \")\n\n# Creates that new folder\nos.mkdir(target_path)\n\n# Choose the search term to grab target files with\nkeyword = raw_input(\"Enter the file keyword you wish to target: \")\n\n# Create a list of all the files in the\nall_files = os.listdir(source_path)\n\n# filter out the files that match your keyword -- lower the case the it is case insensitive\nto_relocate = [file for file in all_files if keyword.lower() in file.lower()]\n\n# iterate over the name matches and move the files, while keeping track of how many\nfile_count = 0\n\nfor file in to_relocate:\n os.rename(source_path + '/' + file, target_path + '/' + file)\n file_count += 1\n\nprint(\"\\n\\n\\n-------------------------------------------\")\nprint(\"Director successfully relocated \" + str(file_count) + \" files\")\nprint(\"-------------------------------------------\")\nfor file in to_relocate:\n print(file)\n","sub_path":"director.py","file_name":"director.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"72676057","text":"\n\nfrom xai.brain.wordbase.nouns._tracing import _TRACING\n\n#calss header\nclass _TRACINGS(_TRACING, ):\n\tdef __init__(self,): \n\t\t_TRACING.__init__(self)\n\t\tself.name = \"TRACINGS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"tracing\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_tracings.py","file_name":"_tracings.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"508134070","text":"import pywifi\nfrom pywifi import *\nimport time\n\n\nclass WifiHelper:\n def __init__(self, wait_time):\n self.wait_time = wait_time\n self.wifi = pywifi.PyWiFi()\n self.interfaces = self.wifi.interfaces()\n self.interface = self.interfaces[0]\n self.status = self.interface.status()\n scan_results = self.interface.scan_results()\n ssid_list = []\n for result in scan_results:\n ssid_list.append((result.ssid, result.signal))\n self.ssid_list = sorted(ssid_list, key=lambda r: r[1], reverse=True)\n\n def print_wifi_info(self):\n print(\"网卡状态\", self.status)\n print(\"序号 wifi名字 信号值\")\n index = 0\n for ssid in self.ssid_list:\n print(index, \" \", ssid[0], \" \", ssid[1])\n index += 1\n\n def get_ssid_by_index(self, index):\n return self.ssid_list[index][0]\n\n def run_keys(self, ssid_index, keys):\n ssid = self.ssid_list[ssid_index][0]\n for key in keys:\n print(key)\n if self.connect(ssid, key):\n return True\n return False\n\n def connect(self, ssid, key):\n pf = pywifi.Profile()\n pf.ssid = ssid\n pf.auth = const.AUTH_ALG_OPEN\n pf.akm.append(const.AKM_TYPE_WPA2PSK)\n pf.cipher = const.CIPHER_TYPE_CCMP\n pf.key = key\n self.interface.remove_all_network_profiles()\n tmp_profile = self.interface.add_network_profile(pf)\n self.interface.connect(tmp_profile)\n time.sleep(self.wait_time)\n if self.interface.status() == const.IFACE_CONNECTED:\n self.save_file(ssid, key)\n print(\"密码 \", key, \" 已保存到wifi.txt\")\n return True\n self.interface.disconnect()\n return False\n\n @staticmethod\n def save_file(ssid, pw):\n file = open(\"wifi.txt\", \"a+\")\n file.write(\"\\nssid:\")\n file.write(ssid)\n file.write(\"\\n密码:\")\n file.write(pw)\n","sub_path":"WiFiHelper/wifi_helper.py","file_name":"wifi_helper.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"611931567","text":"import numpy as np\nimport cv2\n\ndef overlay_rect_with_opacity(overlaid_image, rect):\n x, y, w, h = rect\n sub_img = overlaid_image[y:y+h, x:x+w]\n white_rect = np.ones(sub_img.shape, dtype=np.uint8) * 255\n\n res = cv2.addWeighted(sub_img, 0.5, white_rect, 0.5, 1.0)\n\n # Putting the image back to its position\n overlaid_image[y:y+h, x:x+w] = res","sub_path":"pikapi/utils/opencv.py","file_name":"opencv.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"371908564","text":"from collections import defaultdict\nfrom itertools import chain\n\nfrom django.db.models import Q\n\nfrom restraint import models\n\n\n# A global variable for holding the configuration of django restraint\nRESTRAINT_CONFIG = {}\n\n\ndef register_restraint_config(restraint_config):\n RESTRAINT_CONFIG.clear()\n RESTRAINT_CONFIG.update(restraint_config)\n\n\ndef get_restraint_config():\n if RESTRAINT_CONFIG:\n return RESTRAINT_CONFIG\n else:\n raise RuntimeError('No restraint config has been registered')\n\n\ndef update_restraint_db(flush_default_access=False):\n \"\"\"\n Updates the restraint db based on the restraint config.\n Can optionally flush the previous default access configuration.\n \"\"\"\n config = get_restraint_config()\n models.PermSet.objects.sync_perm_sets(config['perm_sets'])\n updated_perms, new_perms = models.Perm.objects.sync_perms(config['perms'])\n models.PermLevel.objects.sync_perm_levels(config['perms'])\n models.PermAccess.objects.update_perm_set_access(config.get('default_access', {}), new_perms, flush_default_access)\n\n\nclass Restraint(object):\n \"\"\"\n The primary way of accessing permissions. The programmer loads a restraint object with the\n permission object and which permissions they want to load. One permissions are loaded for\n that account, the user may check if a user has certain permissions and also restrict\n querysets based on access levels that a user has.\n \"\"\"\n def __init__(self, user, which_perms=None):\n \"\"\"\n Initializes the Restraint object.\n\n :type user: Any object\n :param user: A user in a project\n\n :type which_perms: list\n :param which_perms: The permissions to be loaded for the user, or all permissions if None.\n \"\"\"\n self._config = get_restraint_config()\n self._user = user\n self._load_perms(user, which_perms)\n\n @property\n def perms(self):\n return self._perms\n\n def _load_perms(self, account, which_perms):\n perm_set_names = self._config['perm_set_getter'](account)\n perm_levels = models.PermLevel.objects.filter(\n Q(permaccess__perm_set__name__in=perm_set_names) | Q(\n permaccess__perm_user_id=account.id,\n permaccess__perm_user_type__app_label=account._meta.app_label,\n permaccess__perm_user_type__model=account._meta.model_name)).select_related('perm')\n if which_perms:\n perm_levels = perm_levels.filter(perm__name__in=which_perms)\n\n self._perms = defaultdict(dict)\n for l in perm_levels:\n self._perms[l.perm.name].update({\n l.name: self._config['perms'][l.perm.name]['levels'][l.name]['id_filter']\n })\n\n def has_perm(self, perm, level=None):\n \"\"\"\n Returns true if the restraint object has the perm. If a level is not specified, it returns\n true if that perm exists for any level.\n\n :type perm: string\n :param perm: The permission to check\n\n :type level: string\n :param level: The level to check, or any level if None\n \"\"\"\n return perm in self._perms and level in self._perms[perm] if level else perm in self._perms\n\n def filter_qset(self, qset, perm, restrict_kwargs=None):\n \"\"\"\n Given a permission, filter the queryset by its levels.\n\n :type qset: A Django QuerySet\n :param qset: The queryset to be filtered\n\n :type perm: string\n :param perm: The permission over which to do the filtering\n \"\"\"\n if not self.has_perm(perm):\n # if this restraint only protects a certain subset of the queryset, return the rest\n if restrict_kwargs is not None:\n return qset.exclude(**restrict_kwargs)\n # else return nothing\n else:\n return qset.none()\n elif None in self._perms[perm].values():\n # If any levels are none, return the full queryset\n return qset\n else:\n # Filter the queryset by the union of all filters\n return qset.filter(id__in=set(chain(*[l(self._user) for l in self._perms[perm].values()])))\n","sub_path":"restraint/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"355568303","text":"import pandas as pd\nimport numpy as np\nfrom babel.numbers import format_currency, format_decimal, default_locale\nfrom decimal import Decimal\n\n\nclass Calculation:\n def __init__(self, data):\n self.account_number = data[\"account_number\"]\n self.months_to_maturity = data[\"months_to_maturity\"]\n self.amortization_term = data[\"amortization_term\"]\n self.payment_type = data[\"payment_type\"]\n self.coupon_type = data[\"coupon_type\"]\n self.recovery_lag = data[\"recovery_lag\"]\n self.bank_balance = data[\"bank_balance\"]\n self.expected_cf = data[\"expected_cf\"]\n self.carrying_value = data[\"carrying_value\"]\n self.lgd = data[\"lgd\"]\n self.smm = data[\"smm\"]\n self.cdr = data[\"cdr\"]\n self.discount_rate = data[\"discount_rate\"]\n self.coupon_rate = data[\"coupon_rate\"]\n\n @property\n def _raw_actual_total(self):\n expected_default = self.bank_balance * self.cdr\n expected_principal = -np.ppmt(self.coupon_rate / 12, 1, self.months_to_maturity, self.bank_balance -\n expected_default)\n expected_interest = (self.bank_balance - expected_default) * (self.coupon_rate / 12)\n prepaid_principal = (self.bank_balance - expected_default - expected_principal) * self.smm\n ending_principal = (self.bank_balance - expected_default - expected_principal - prepaid_principal)\n recovery = 0\n expected_cash_flows = (expected_principal + expected_interest + prepaid_principal)\n pv_of_cash_flows = expected_cash_flows / (1 + (self.discount_rate / 12))\n\n # Create data frame top row\n initial_cf = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n cf_df = pd.DataFrame(initial_cf, columns=['Period', 'Beginning Principal', 'Expected Default',\n 'Expected Principal', 'Interest Rate', 'Expected Interest',\n 'Prepaid Principal', 'Ending Principal', 'Recovery',\n 'Expected Cash Flows', 'PV of Cash Flows'])\n\n # Append period 1 CF\n append_df = [[1, self.bank_balance, expected_default, expected_principal,\n self.coupon_rate, expected_interest, prepaid_principal, ending_principal, recovery,\n expected_cash_flows, pv_of_cash_flows]]\n append_df_mod = pd.DataFrame(append_df, columns=['Period', 'Beginning Principal', 'Expected Default',\n 'Expected Principal', 'Interest Rate', 'Expected Interest',\n 'Prepaid Principal', 'Ending Principal', 'Recovery',\n 'Expected Cash Flows', 'PV of Cash Flows'])\n cf_df_appended = cf_df.append(append_df_mod, ignore_index=True)\n # loop\n count = 0\n while count < self.months_to_maturity - 1:\n period = int(cf_df_appended[\"Period\"].tail(1)) + 1\n bank_balance = Decimal(float(cf_df_appended[\"Ending Principal\"].tail(1)))\n expected_default = bank_balance * self.cdr\n expected_principal = -np.ppmt(self.coupon_rate / 12, 1, self.months_to_maturity - period + 1,\n bank_balance - expected_default)\n expected_interest = (bank_balance - expected_default) * (self.coupon_rate / 12)\n prepaid_principal = (bank_balance - expected_default - expected_principal) * self.smm\n ending_principal = (bank_balance - expected_default - expected_principal - prepaid_principal)\n recovery = 0\n expected_cash_flows = (expected_principal + expected_interest + prepaid_principal)\n pv_of_cash_flows = expected_cash_flows / ((1 + (self.discount_rate / 12)) ** period)\n append_df2 = [[period, bank_balance, expected_default, expected_principal,\n self.coupon_rate, expected_interest, prepaid_principal, ending_principal, recovery,\n expected_cash_flows, pv_of_cash_flows]]\n append_df2_mod = pd.DataFrame(append_df2, columns=['Period', 'Beginning Principal', 'Expected Default',\n 'Expected Principal', 'Interest Rate',\n 'Expected Interest',\n 'Prepaid Principal', 'Ending Principal', 'Recovery',\n 'Expected Cash Flows', 'PV of Cash Flows'])\n cf_df_appended = cf_df_appended.append(append_df2_mod, ignore_index=True)\n count += 1\n return pd.DataFrame(cf_df_appended, columns=['Period', 'Beginning Principal', 'Expected Default',\n 'Expected Principal', 'Interest Rate',\n 'Expected Interest',\n 'Prepaid Principal', 'Ending Principal', 'Recovery',\n 'Expected Cash Flows', 'PV of Cash Flows'])\n\n @property\n def _cf_df_appended_df(self):\n return str(np.nansum(self._raw_actual_total[\"Expected Cash Flows\"]))\n\n def _format_as_dollars(self, value):\n return format_currency(value, \"USD\", locale=\"en_US\")\n\n def _format_as_decimal(self, value):\n return int(value)\n\n @property\n def actual_total(self):\n # expected cf at the moment\n return self._format_as_dollars(self._cf_df_appended_df)\n\n def appended_df(self):\n return self._raw_actual_total\n\n @property\n def _raw_inflation_adjusted_total(self):\n return str(np.nansum(self._raw_actual_total[\"PV of Cash Flows\"]))\n\n @property\n def inflation_adjusted_total(self):\n return self._format_as_dollars(self._raw_inflation_adjusted_total)","sub_path":"individual_cf/calculation.py","file_name":"calculation.py","file_ext":"py","file_size_in_byte":6070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"437753077","text":"# -*- coding:utf-8 -*-\n# @Time: 2019-09-15 22:32\n# @Author: duiya duiyady@163.com\n\n\n\"\"\"\n将一个给定字符串根据给定的行数，以从上往下、从左到右进行 Z 字形排列。\n比如输入字符串为 \"LEETCODEISHIRING\" 行数为 3 时，排列如下：\nL C I R\nE T O E S I I G\nE D H N\n你的输出需要从左往右逐行读取，产生出一个新的字符串，比如：\"LCIRETOESIIGEDHN\"\n\"\"\"\n\n\ndef convert(s, numRows):\n if numRows == 1:\n return s\n result = []\n one_line = 2*numRows-2\n i = 0\n while i < len(s):\n result.append(s[i])\n i = i + one_line\n for i in range(1, numRows-1):\n flag = 0 # 0���示单数， 1表示奇数\n j = i\n add_0 = 2 * (numRows - 1 - i)\n add_1 = 2 * i\n while j < len(s):\n result.append(s[j])\n if flag == 0:\n j = j + add_0\n flag = 1\n else:\n j = j + add_1\n flag = 0\n i = numRows-1\n while i < len(s):\n result.append(s[i])\n i = i + one_line\n return ''.join(result)\n\n\nif __name__ == '__main__':\n print(convert('\"PAYPALISHIRIdasfgaNG',5))\n","sub_path":"src/main/num001_100/6_Z字形变换.py","file_name":"6_Z字形变换.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"519218836","text":"# -*- coding : utf-8 -*- \n'''\n@Author: Tomas Wu\nDate: 2019-01-29 22:31:26\n@Desc: this is a spyder for fetching the data of entrance score line for college entrance examination. \n'''\n# import urllib,re\n\n# the goal web:\n# http://college.gaokao.com/schpoint/a16/p1\n\n# def getHTML(url):\n# f=urllib.request.urlopen(url)\n# print(f.read().decode('gbk'))\n# It is sad that the urlopen can't deal with the ajax.\n\nfrom selenium import webdriver\nimport re,time,json\nimport multiprocessing as mpi\nimport os\n\ndef getContent(url,m=0,*args, **kwargs):\n opt=webdriver.FirefoxOptions()\n opt.headless=True # headless mode.\n prf=webdriver.FirefoxProfile()\n # prf.set_preference('permissions.default.image',2) #not loading image.\n # It is very weird that browser can't stop connection with the server if not loading images.\n browser=webdriver.Firefox(options=opt,firefox_profile=prf)\n browser.implicitly_wait(30)\n browser.get(url)\n # the parameter m means getting the college info (m=0) or scores info (m=1). \n if m==0:\n content=browser.find_element_by_class_name('scores_List').get_attribute('outerHTML')\n elif m==1:\n content=browser.find_element_by_id('pointbyarea').get_attribute('outerHTML')\n browser.quit()\n return content\n\n\ndef parseContent(content,m=0,*args, **kwargs):\n result=[]\n if m==0:\n r=r'.*?
'\n total_data=re.findall(r,content,re.DOTALL)\n for i in total_data:\n college=re.findall(r'k\\\">.{,20}?.*?.{1,15}?<',i)]\n keys=['年份','最低','最高','平均','录取人数','录取批次']\n ye={}\n for i in range(len(keys)):\n try:\n ye[keys[i]]=data[i]\n except:\n ye[keys[i]]='------'\n data_json=json.dumps(ye)\n result.append(data_json)\n return result\n\ndef save_data(data):\n print('saving data...')\n with open('saved_data.txt','a',encoding='utf-8') as t:\n t.write(json.dumps(data)+'\\n')\n print('Done')\n\n\n# test url:'http://college.gaokao.com/school/tinfo/34/result/16/2/'\n \ndef urls():\n for i in range(1,4):\n yield 'http://college.gaokao.com/schpoint/a16/p{}'.format(i)\n\ndef process(i):\n print('I am %s, satrt work'%(os.getpid()))\n d=getContent(i)\n pd=parseContent(d)\n print('I am {},have {} urls to fetch.'.format(os.getpid(),len(pd)))\n for j in range(len(pd)):\n ur=pd[j][-1]\n print('{} fetching {}'.format(os.getpid(),ur))\n try:\n pd[j].append(parseContent(getContent(ur,1),1))\n except:\n print('fetch error {}'.format(ur))\n continue\n print('I am {},saving data.'.format(os.getpid()))\n pd_json={}\n for i in range(len(pd)):\n pd_json['college'+str(i)]=pd[i]\n return pd_json\n\nif __name__==\"__main__\":\n \n # p=[mpi.Process(target=process,args=(i,),daemon=True) for i in urls()]\n # for i in p:\n # i.start()\n # i.join()\n # print('all done!') \n\n p=mpi.Pool()\n [p.apply_async(process,(i,),callback=save_data) for i in urls()]\n p.close()\n p.join()\n print('all done!')\n\n\n \n","sub_path":"spider/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":3693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"10375559","text":"# Copyright 2013 Jake Basile\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# \n# http://www.apache.org/licenses/LICENSE-2.0\n# \n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Handles authentication to the Github API v3.\"\"\"\n\nfrom __future__ import print_function\nimport json\nimport urlparse\nimport getpass\nimport base64\nimport os.path\nimport requests\n\nCLIENT_ID = '3ce2846348573b3e08b3'\nCLIENT_SECRET = '0883a9d4842b6da210d79bc3322c31c2bffd2bc5'\n\ndef init_subparser(subparsers):\n login = subparsers.add_parser('login', help='Login to GitHub.')\n login.set_defaults(func=perform_login)\n\ndef perform_login(args):\n if os.path.exists(os.path.expanduser('~/.ghcl')):\n print('Already logged in. Please delete ~/.ghcl to change users.')\n return\n if args.user != None:\n print('Cannot authenticate as another user.')\n return\n if args.org != None:\n print('Cannot authenticate as an organization.')\n return\n request_url = urlparse.urljoin(args.uri, '/authorizations')\n payload = {\n 'scopes': ['user','repo','gist'],\n 'client_id': CLIENT_ID,\n 'client_secret': CLIENT_SECRET,\n }\n un = raw_input('Github Username: ')\n pw = getpass.getpass('Github Password: ')\n unpw = base64.b64encode('%s:%s' % (un, pw))\n result = requests.post(\n request_url,\n headers = {\n 'Authorization': 'Basic ' + unpw,\n 'Accept': 'application/json',\n 'Content-Type': 'application/json',\n },\n data=json.dumps(payload),\n )\n if result.status_code != 201:\n print('Something went wrong, try again.')\n print(result.reason)\n else:\n token = result.json()['token']\n with open(os.path.expanduser('~/.ghcl'), 'w') as cache:\n print(token, file=cache)\n print(un, file=cache)\n print('Logged in.')\n\ndef get_token():\n path = os.path.expanduser('~/.ghcl')\n if os.path.exists(path):\n with open(path, 'r') as cache:\n return cache.readline().strip()\n print('Please login first.')\n return None\n\ndef get_user():\n path = os.path.expanduser('~/.ghcl')\n if os.path.exists(path):\n with open(path, 'r') as cache:\n cache.readline()\n return cache.readline().strip()\n print('Please login first')\n return None\n","sub_path":"ghcl/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"481037359","text":"from flask import Flask, render_template, request\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n\n\ndef randWord(): \n import requests\n import random\n dictionary = requests.get('http://norvig.com/ngrams/sowpods.txt').text \n listOfWords = dictionary.split() # create a list of words from dictionary\n choice = random.choice(listOfWords) # chose a random word from the list of words\n return choice\nword = randWord()\nlength = len(word)\n\n# convert the word to a dictionary, where the keys are numbers 0 to length and the values are the \n# letters that correspond to that number\n# eg. word = \"cats\" ---> wordDict = {0:'c', 1:'a', 2:'t', 3:'s'}\n## wordDict = {letter: word[letter] for letter in list(range(length))}\n\n# create list of empty spaces the length of the word to be guessed, \n# this will be the argument that goes into the guessLetter() function\nwordGuessBlank = []\nfor i in list(range(length)): \n wordGuessBlank.append('_')\n i = i # not necessary, but prevents the unused variable error\n\n# a function that makes the list of letters pretty by removing [] and commas\ndef prettifyList(wordGuessBlank):\n wordGuessOut = \" \".join(wordGuessBlank) # this one has spaces, to make it look nice when printing out hangman word for the game\n return (wordGuessOut) \nblankSpaces = prettifyList(wordGuessBlank)\n\n\n@app.route(\"/play\", methods=[\"POST\",\"GET\"])\ndef play():\n username = request.form.get(\"name\")\n if not request.form.get(\"name\"):\n return \"failure to enter username\"\n else:\n pass\n\n return render_template(\"play.html\",name=username,word=word,blankSpaces=blankSpaces)\n\n\n@app.route(\"/continue\", methods=[\"POST\",\"GET\"])\ndef continuePlay():\n letter = request.form.get(\"q\")\n if not letter:\n return \"You must guess a letter.\"\n else:\n pass\n return render_template(\"continue.html\",letter=letter,blankSpaces=blankSpaces,word=word)\n\n\n\nif __name__ == '__main__':\n app.run(debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"85391080","text":"\"\"\"\nnohup python3 -u apply.py --dataroot /opt/data/private/datasets/mgtv/apply/A --name mgtv_mgtv2_05_31_16_42 --model mgtv2 --block_size 2_2 --load_epoch epoch_500 --scenedetect True --ch1 72 --ch2 48 >> /opt/data/private/mgtv2_epoch500_scenedetect.log 2>&1 &\nnohup python3 -u apply.py --dataroot /opt/data/private/datasets/mgtv/apply/A --name mgtv_mgtv2_05_31_16_42 --model mgtv2 --block_size 2_2 --load_epoch epoch_1000 --scenedetect True --ch1 72 --ch2 48 >> /opt/data/private/mgtv2_epoch1000_scenedetect.log 2>&1 &\nnohup python3 -u apply.py --dataroot /opt/data/private/datasets/mgtv/apply/A --name mgtv_mgtv2_05_31_16_42 --model mgtv2 --block_size 2_2 --load_epoch epoch_2000 --scenedetect True --ch1 72 --ch2 48 >> /opt/data/private/mgtv2_epoch2000_scenedetect.log 2>&1 &\nnohup python3 -u apply.py --dataroot /opt/data/private/datasets/mgtv/apply/A --name mgtv_mgtv2_05_31_16_42 --model mgtv2 --block_size 2_2 --load_epoch epoch_3000 --scenedetect True --ch1 72 --ch2 48 >> /opt/data/private/mgtv2_epoch3000_scenedetect.log 2>&1 &\n\nnohup python3 -u apply.py --dataroot /opt/data/private/datasets/mgtv/apply/A --name mgtv_mgtv2_large_06_06_11_49 --model mgtv2 --block_size 2_2 --load_epoch epoch_3000 --scenedetect True --ch1 64 --ch2 64 >> /opt/data/private/mgtv2_large_epoch3000_scenedetect.log 2>&1 &\n\ndataset_images2video(datasetpath=\"./results/mgtv_mgtv2_05_31_16_42/apply-A-epoch_3000-block_size_2_2\", fps=25, suffix=\".y4m\")\ndataset_images2video(datasetpath=\"./results/mgtv_mgtv2_large_06_06_11_49/apply-A-epoch_3000-block_size_2_2\", fps=25, suffix=\".y4m\")\n\naimax:\n gpu:\n\n v1:\n python3 train.py\n --dataroot /opt/data/private/datasets/mgtv\n --name mgtv_mgtv2\n --model mgtv2\n --display_freq 2400\n --print_freq 240\n --save_epoch_freq 500\n --gpu_ids 0,1,2\n --batch_size 12\n --suffix 05_31_16_42\n --crop_size 256\n --imgseqlen 5\n --seed 1\n --max_consider_len 125\n --scenedetect True\n --ch1 72\n --ch2 48\n --num_threads 11\n --continue_train True\n --load_epoch epoch_2000\n --epoch_count 2001\n\n v2:\n python3 train.py\n --dataroot /opt/data/private/datasets/mgtv\n --name mgtv_mgtv2_large\n --model mgtv2\n --display_freq 2700\n --print_freq 270\n --save_epoch_freq 500\n --gpu_ids 0,1,2\n --batch_size 9\n --suffix 06_06_11_49\n --crop_size 256\n --imgseqlen 5\n --seed 1\n --max_consider_len 125\n --scenedetect True\n --ch1 64\n --ch2 64\n --num_threads 11\n\"\"\"\nimport torch\nfrom .base_model import BaseModel\nfrom . import mgtv2_networks\nfrom util import remove_pad_for_tensor\n\n\nclass MGTV2Model(BaseModel):\n \"\"\" This class implements the mgtv2 model\n\n The model training requires '--dataset_mode aligned_video' dataset.\n\n mgtv train dataset size: 600.\n \"\"\"\n @staticmethod\n def modify_commandline_options(parser, is_train=True):\n \"\"\"Add new dataset-specific options, and rewrite default values for existing options.\n\n Parameters:\n parser -- original option parser\n is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.\n\n Returns:\n the modified parser.\n\n \"\"\"\n parser.set_defaults(dataset_mode='aligned_video')\n parser.set_defaults(batch_size=48)\n parser.set_defaults(preprocess='crop')\n parser.set_defaults(SR_factor=1)\n parser.set_defaults(crop_size=256)\n parser.set_defaults(beta1='0.9')\n parser.set_defaults(lr=0.0001)\n parser.set_defaults(init_type='kaiming')\n parser.set_defaults(lr_policy='step')\n parser.set_defaults(lr_decay_iters=2000)\n parser.set_defaults(lr_gamma=0.1)\n parser.set_defaults(n_epochs=5000)\n parser.set_defaults(multi_base=32)\n parser.set_defaults(max_consider_len=125)\n parser.set_defaults(scenedetect=True)\n parser.set_defaults(block_size=\"1_1\")\n parser.set_defaults(pre_crop_num=0)\n parser.add_argument('--ch1', type=int, default=64)\n parser.add_argument('--ch2', type=int, default=48)\n parser.add_argument('--nframes', type=int, default=5, help='frames used by model') # used for assert, imgseqlen should set equal to this when train\n\n return parser\n\n def __init__(self, opt):\n BaseModel.__init__(self, opt)\n self.SR_factor = opt.SR_factor\n\n # specify the training losses you want to print out. The training/test scripts will call \n self.loss_names = ['pd', 'st']\n\n # specify the images you want to save/display. The training/test scripts will call \n if self.opt.phase == \"apply\":\n self.visual_names = ['HR_G']\n else:\n self.visual_names = ['LR', 'HR_GroundTruth', 'HR_G']\n\n # specify the models you want to save to the disk. The training/test scripts will call and \n if self.isTrain:\n self.model_names = ['G']\n else:\n self.model_names = ['G']\n\n self.netG = mgtv2_networks.define_G(opt)\n\n if self.isTrain:\n self.criterionL2 = torch.nn.MSELoss()\n self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n self.optimizers.append(self.optimizer_G)\n\n def set_input(self, input):\n \"\"\"Unpack input data from the dataloader and perform necessary pre-processing steps.\n\n Parameters:\n input (dict): include the data itself and its metadata information.\n \"\"\"\n self.A_paths = input['A_paths']\n # input['A']: e.g. [4, 5, 3, 256, 256]\n # input['B']: e.g. [4, 5, 3, 256, 256]\n self.LR = input['A'].to(self.device, non_blocking=True)\n assert self.LR.shape[1] == self.opt.nframes, \"input image length {} should equal to opt.nframes {}\".format(self.LR.shape[1], self.opt.nframes)\n\n if self.opt.phase in ('train', 'test'):\n self.B_paths = input['B_paths']\n self.HR_GroundTruth = input['B'].to(self.device, non_blocking=True)\n\n if self.opt.phase == \"apply\":\n self.HR_GT_h, self.HR_GT_w = input['gt_h_w']\n\n def forward(self):\n \"\"\"Run forward pass; called by both functions and .\n \"\"\"\n self.HR_Gs, self.HR_G = self.netG(self.LR)\n\n def compute_visuals(self):\n mid = self.opt.nframes//2\n self.LR = self.LR[:, mid, ...]\n\n if self.opt.phase in (\"train\", \"test\"):\n self.HR_GroundTruth = self.HR_GroundTruth[:, mid, ...]\n\n if self.opt.phase in (\"test\", \"apply\"):\n # remove pad for LR\n if self.opt.phase == \"test\":\n h, w = self.HR_GroundTruth.shape[-2], self.HR_GroundTruth.shape[-1]\n else: # apply\n h, w = self.HR_GT_h, self.HR_GT_w\n self.LR = remove_pad_for_tensor(tensor=self.LR,\n HR_GT_h_w=(h, w),\n factor=self.SR_factor, LR_flag=True)\n # remove pad for HR_G\n self.HR_G = remove_pad_for_tensor(tensor=self.HR_G,\n HR_GT_h_w=(h, w),\n factor=self.SR_factor, LR_flag=False)\n\n # 将HR_G的边缘部分替换为LR的 两个像素\n assert self.LR.shape == self.HR_G.shape # [1,3,H,W]\n self.HR_G[..., 0:2, :] = self.LR[..., 0:2, :]\n self.HR_G[..., -2:, :] = self.LR[..., -2:, :]\n self.HR_G[..., :, 0:2] = self.LR[..., :, 0:2]\n self.HR_G[..., :, -2:] = self.LR[..., :, -2:]\n\n\n def backward(self):\n \"\"\"Calculate loss\"\"\"\n _, _, C, H, W = self.HR_Gs.shape\n self.loss_pd = self.criterionL2(self.HR_Gs.view(-1, C, H, W), self.HR_GroundTruth.view(-1, C, H, W))\n self.loss_st = self.criterionL2(self.HR_G, self.HR_GroundTruth[:, self.opt.nframes//2, ...])\n if self.nowepoch < 2000:\n self.loss = self.loss_pd + self.nowepoch / 1000 * self.loss_st\n else:\n self.loss = 2 * self.loss_st\n self.loss.backward()\n\n def optimize_parameters(self):\n self.forward() # compute fake images: G(A)\n # update\n self.optimizer_G.zero_grad()\n self.backward()\n self.optimizer_G.step()\n","sub_path":"models/mgtv2_model.py","file_name":"mgtv2_model.py","file_ext":"py","file_size_in_byte":8948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"168342274","text":"'''\nCreated on Jul 27, 2014\n\n@author: ANBU\n'''\nimport socket\n\n# s = socket.socket()\n# s.bind(('127.0.0.1', 1234))\n# s.listen(5)\n# print(\"Server is Listening\")\n# while True:\n# c, addr = s.accept();\n# print(\"Connected With\", addr)\n# c.send('Hello Sir!! ')\n# c.close()\n\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Create a socket object\nhost = socket.gethostname() # Get local machine name\nport = 12345 # Reserve a port for your service.\ns.bind((host, port)) # Bind to the port\n\ns.listen(5) # Now wait for client connection.\nwhile True:\n c, addr = s.accept() # Establish connection with client.\n print('Got connection from', addr)\n c.send(\"Hi\".__str__())\n c.close() \n","sub_path":"PythonWorkspace/FirstPgm/org/srm/kalai/ClientServer_01.py","file_name":"ClientServer_01.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"627326887","text":"from typing import Any, Hashable, Mapping, Tuple\n\nimport pandas as pd\nimport xarray as xr\n\n\nclass GenotypeDisplay:\n \"\"\"\n A printable object to display genotype information.\n \"\"\"\n\n def __init__(\n self,\n df: pd.DataFrame,\n shape: Tuple[int, int],\n max_variants: int,\n max_samples: int,\n ):\n self.df = df\n self.shape = shape\n self.max_variants = max_variants\n self.max_samples = max_samples\n self.pd_options = [\n \"display.max_rows\",\n self.max_variants,\n \"display.min_rows\",\n self.max_variants,\n \"display.max_columns\",\n self.max_samples,\n \"display.show_dimensions\",\n False,\n ]\n\n def __repr__(self) -> Any:\n with pd.option_context(*self.pd_options):\n if (\n len(self.df) > self.max_variants\n or len(self.df.columns) > self.max_samples\n ):\n return (\n self.df.__repr__()\n + f\"\\n\\n[{self.shape[0]} rows x {self.shape[1]} columns]\"\n )\n return self.df.__repr__()\n\n def _repr_html_(self) -> Any:\n with pd.option_context(*self.pd_options):\n if (\n len(self.df) > self.max_variants\n or len(self.df.columns) > self.max_samples\n ):\n return self.df._repr_html_().replace(\n \"\",\n f\"{self.shape[0]} rows x {self.shape[1]} columns
\",\n )\n return self.df._repr_html_()\n\n\ndef truncate(ds: xr.Dataset, max_sizes: Mapping[Hashable, int]) -> xr.Dataset:\n \"\"\"Truncate a dataset along two dimensions into a form suitable for display.\n\n Truncation involves taking four rectangles from each corner of the dataset array\n (or arrays) and combining them into a smaller dataset array (or arrays).\n\n Parameters\n ----------\n ds\n The dataset to be truncated.\n max_sizes : Mapping[Hashable, int]\n A dict with keys matching dimensions and integer values indicating\n the maximum size of the dimension after truncation.\n\n Returns\n -------\n Dataset\n A truncated dataset.\n\n Warnings\n --------\n A maximum size of `n` may result in the array having size `n + 2` (and not `n`).\n The reason for this is so that pandas can be used to display the array as a table,\n and correctly truncate rows or columns (shown as ellipses ...).\n \"\"\"\n\n if len(max_sizes) != 2:\n raise ValueError(\"Truncation is only supported for two dimensions\")\n\n dims = list(max_sizes.keys())\n max_dim = max_sizes[dims[0]], max_sizes[dims[1]]\n n_dim = ds.sizes[dims[0]], ds.sizes[dims[1]]\n\n if n_dim[0] <= max_dim[0] + 2 and n_dim[1] <= max_dim[1] + 2:\n # No truncation required\n return ds\n\n if n_dim[0] <= max_dim[0] + 1:\n # Truncate dim1 only\n m_dim = n_dim[0], max_dim[1] // 2 + 1\n rows = [[(0, 0), (0, m_dim[1])]]\n elif n_dim[1] <= max_dim[1] + 1:\n # Truncate dim0 only\n m_dim = max_dim[0] // 2 + 1, n_dim[1]\n rows = [[(0, 0)], [(m_dim[0], 0)]]\n else:\n # Truncate both dimensions\n m_dim = max_dim[0] // 2 + 1, max_dim[1] // 2 + 1\n rows = [[(0, 0), (0, m_dim[1])], [(m_dim[0], 0), (m_dim[0], m_dim[1])]]\n\n limits = {dims[0]: m_dim[0], dims[1]: m_dim[1]}\n slices = {k: slice(v) for k, v in limits.items()}\n ds_abbr: xr.Dataset = xr.combine_nested( # type: ignore[no-untyped-call]\n [\n [\n # Roll all of these simultaneously along with any indexes/coords\n # and then clip them using the same slice for each corner\n ds.roll(dict(zip(limits, roll)), roll_coords=True).isel(**slices)\n for roll in row\n ]\n for row in rows\n ],\n concat_dim=limits.keys(),\n )\n\n assert ds_abbr.sizes[dims[0]] <= max_dim[0] + 2\n assert ds_abbr.sizes[dims[1]] <= max_dim[1] + 2\n\n return ds_abbr\n\n\ndef display_genotypes(\n ds: xr.Dataset, max_variants: int = 60, max_samples: int = 10\n) -> GenotypeDisplay:\n \"\"\"Display genotype calls.\n\n Display genotype calls in a tabular format, with rows for variants,\n and columns for samples. Genotypes are displayed in the same manner\n as in VCF. For example, `1/0` is a diploid call of the first alternate\n allele and the reference allele (0). Phased calls are denoted by a `|`\n separator. Missing values are denoted by `.`.\n\n Parameters\n ----------\n ds\n The dataset containing genotype calls in the `call/genotype`\n variable, and (optionally) phasing information in the\n `call/genotype_phased` variable. If no phasing information is\n present genotypes are assumed to be unphased.\n max_variants\n The maximum number of variants (rows) to display. If there are\n more variants than this then the table is truncated.\n max_samples\n The maximum number of samples (columns) to display. If there are\n more samples than this then the table is truncated.\n\n Returns\n -------\n A printable object to display genotype information.\n \"\"\"\n\n # Create a copy to avoid clobbering original indexes\n ds_calls = ds.copy()\n\n # Set indexes only if not already set (allows users to have different row/col labels)\n if isinstance(ds_calls.get_index(\"samples\"), pd.RangeIndex):\n ds_calls = ds_calls.set_index(samples=\"sample_id\")\n if isinstance(ds_calls.get_index(\"variants\"), pd.RangeIndex):\n variant_index = \"variant_id\" if \"variant_id\" in ds_calls else \"variant_position\"\n ds_calls = ds_calls.set_index(variants=variant_index)\n\n # Restrict to genotype call variables\n if \"call_genotype_phased\" in ds_calls:\n ds_calls = ds_calls[\n [\"call_genotype\", \"call_genotype_mask\", \"call_genotype_phased\"]\n ]\n else:\n ds_calls = ds_calls[[\"call_genotype\", \"call_genotype_mask\"]]\n\n # Truncate the dataset then convert to a dataframe\n ds_abbr = truncate(\n ds_calls, max_sizes={\"variants\": max_variants, \"samples\": max_samples}\n )\n df = ds_abbr.to_dataframe().unstack(level=\"ploidy\")\n\n # Convert each genotype to a string representation\n def calls_to_str(r: pd.DataFrame) -> str:\n gt = r[\"call_genotype\"].astype(str)\n gt_mask = r[\"call_genotype_mask\"].astype(bool)\n gt[gt_mask] = \".\"\n if \"call_genotype_phased\" in r and r[\"call_genotype_phased\"][0]:\n return \"|\".join(gt)\n return \"/\".join(gt)\n\n df = df.apply(calls_to_str, axis=1).unstack(\"samples\")\n\n return GenotypeDisplay(\n df,\n (ds.sizes[\"variants\"], ds.sizes[\"samples\"]),\n max_variants,\n max_samples,\n )\n","sub_path":"sgkit/display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":6833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"549110288","text":"'''\r\n\r\nNew Integration Test for VM vga mode.\r\n\r\n@author: quarkonics\r\n'''\r\n\r\nimport zstackwoodpecker.test_lib as test_lib\r\nimport zstackwoodpecker.operations.config_operations as conf_ops\r\nimport zstackwoodpecker.test_util as test_util\r\nfrom vncdotool import api\r\nfrom PIL import Image\r\n\r\n_config_ = {\r\n 'timeout' : 360,\r\n 'noparallel' : False\r\n }\r\n\r\ntest_stub = test_lib.lib_get_test_stub()\r\nvm = None\r\ndefault_mode = None\r\n\r\ndef test():\r\n global vm\r\n global default_mode\r\n# default_mode = conf_ops.get_global_config_value('kvm', 'videoType')\r\n default_mode = conf_ops.change_global_config('vm', 'videoType', 'vga')\r\n vm = test_stub.create_sg_vm()\r\n console = test_lib.lib_get_vm_console_address(vm.get_vm().uuid)\r\n test_util.test_logger('[vm:] %s console is on %s:%s' % (vm.get_vm().uuid, console.hostIp, console.port))\r\n display = str(int(console.port)-5900)\r\n vm.check()\r\n vm_mode = test_lib.lib_get_vm_video_type(vm.get_vm())\r\n if vm_mode != 'vga':\r\n test_util.test_fail('VM is expected to work in vga mode instead of %s' % (vm_mode))\r\n client = api.connect(console.hostIp+\":\"+display)\r\n client.captureScreen('tmp.png')\r\n image = Image.open('tmp.png')\r\n if image.width != 1024 or image.height != 768:\r\n test_util.test_fail(\"VM is expected to work in 1024x768 while its %sx%s\" % (image.width, image.height))\r\n box = image.getbbox()\r\n if box != (0, 18, 359, 79) and box != (0, 18, 359, 80):\r\n test_util.test_fail(\"VM is expected to display text in area (0, 18, 359, 79) while it's actually: (%s, %s, %s, %s)\" % (box[0], box[1], box[2], box[3]))\r\n\r\n# test_util.test_logger('[vm:] change vga mode to vga=794 which is 1280x1024')\r\n# cmd = 'sed -i \"s/115200$/115200 vga=794/g\" /boot/grub2/grub.cfg'\r\n# test_lib.lib_execute_command_in_vm(vm.get_vm(), cmd)\r\n# vm.reboot()\r\n# vm.check()\r\n# client = api.connect(console.hostIp+\":\"+display)\r\n# client.captureScreen('tmp.png')\r\n# image = Image.open('tmp.png')\r\n# if image.width != 1280 or image.height != 1024:\r\n# test_util.test_fail(\"VM is expected to work in 1280x1024 while its %sx%s\" % (image.width, image.height))\r\n#\r\n# test_util.test_logger('[vm:] change vga mode to vga=907 which is 2560x1600')\r\n# cmd = 'sed -i \"s/vga=794/vga=907/g\" /boot/grub2/grub.cfg'\r\n# test_lib.lib_execute_command_in_vm(vm.get_vm(), cmd)\r\n# vm.reboot()\r\n# vm.check()\r\n# client = api.connect(console.hostIp+\":\"+display)\r\n# client.captureScreen('tmp.png')\r\n# image = Image.open('tmp.png')\r\n# if image.width != 2560 or image.height != 1600:\r\n# test_util.test_fail(\"VM is expected to work in 2560x1600 while its %sx%s\" % (image.width, image.height))\r\n\r\n vm.destroy()\r\n vm.check()\r\n conf_ops.change_global_config('vm', 'videoType', default_mode)\r\n test_util.test_pass('Create VM Test Success')\r\n\r\n#Will be called only if exception happens in test().\r\ndef error_cleanup():\r\n conf_ops.change_global_config('vm', 'videoType', default_mode)\r\n global vm\r\n if vm:\r\n vm.destroy()\r\n","sub_path":"integrationtest/vm/virtualrouter/vga/test_vm_vga.py","file_name":"test_vm_vga.py","file_ext":"py","file_size_in_byte":3081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"583350896","text":"# Selection sort\ndef Selection(inList=[7, 6, 8, 9, 3, 2, 10, 5, 1]):\n n = len(inList)\n swaps, comparison = 0, 0\n for key in range(n):\n min = key\n for j in range(key+1, n):\n comparison += 1\n if inList[min] > inList[j]:\n min = j\n swaps += 1\n inList[key], inList[min] = inList[min], inList[key]\n return 'SelectionSorted List: {} \\nComparison: {} \\nSwap: {} '.format(\n inList, comparison, swaps)\n\n# Bubble sort \ndef Bubble(inList=[7, 6, 8, 9, 3, 2, 10, 5, 1]):\n n = len(inList)\n swaps, comparison = 0, 0\n for i in range(n):\n for j in range(0, n-i-1):\n comparison += 1\n if inList[j] > inList[j+1]:\n swaps += 1\n inList[j], inList[j+1] = inList[j+1], inList[j]\n\n return 'BubbleSorted List: {} \\nComparison: {} \\nSwap: {} '.format(\n inList, comparison, swaps)\n","sub_path":"Hafta_01/180401060_Ders02.py","file_name":"180401060_Ders02.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"240508053","text":"import random\nfrom stdnum import imei\n\n\ndef luhn_residue(digits):\n return sum(sum(divmod(int(d) * (1 + i % 2), 10))\n for i, d in enumerate(digits[::-1])) % 10\n\n\ndef get_imei(N=15):\n part = ''.join(str(random.randrange(0, 9)) for _ in range(N - 1))\n res = luhn_residue('{}{}'.format(part, 0))\n return '{}{}'.format(part, -res % 10)\n\nif __name__ == \"__main__\":\n str_imei = get_imei()\n print(str_imei, imei.is_valid(str_imei))\n","sub_path":"hhmcds/imei.py","file_name":"imei.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"439517136","text":"\"\"\"empty message\n\nRevision ID: 3e6280aa61c9\nRevises: None\nCreate Date: 2015-01-15 18:20:35.546802\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '3e6280aa61c9'\ndown_revision = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.create_table('users',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('username', sa.String(length=80), nullable=False),\n sa.Column('email', sa.String(length=80), nullable=False),\n sa.Column('password', sa.String(length=128), nullable=True),\n sa.Column('created_at', sa.DateTime(), nullable=False),\n sa.Column('first_name', sa.String(length=30), nullable=True),\n sa.Column('last_name', sa.String(length=30), nullable=True),\n sa.Column('active', sa.Boolean(), nullable=True),\n sa.Column('is_admin', sa.Boolean(), nullable=True),\n sa.PrimaryKeyConstraint('id'),\n sa.UniqueConstraint('email'),\n sa.UniqueConstraint('username')\n )\n op.create_table('company_purchases',\n sa.Column('purchase_id', sa.Integer(), nullable=False),\n sa.Column('item', sa.Text(), nullable=True),\n sa.Column('company_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['company_id'], ['company.company_id'], ),\n sa.PrimaryKeyConstraint('purchase_id')\n )\n op.create_table('roles',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('name', sa.String(length=80), nullable=False),\n sa.Column('user_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),\n sa.PrimaryKeyConstraint('id'),\n sa.UniqueConstraint('name')\n )\n op.add_column(u'company_contact', sa.Column('contact_name', sa.String(length=255), nullable=True))\n op.create_foreign_key(None, 'company_contact', 'company', ['company_id'], ['company_id'])\n op.create_foreign_key(None, 'contracts', 'company', ['company_id'], ['company_id'])\n op.drop_column(u'contracts', 'contact_name')\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column(u'contracts', sa.Column('contact_name', sa.VARCHAR(length=255), autoincrement=False, nullable=True))\n op.drop_constraint(None, 'contracts', type_='foreignkey')\n op.drop_constraint(None, 'company_contact', type_='foreignkey')\n op.drop_column(u'company_contact', 'contact_name')\n op.drop_table('roles')\n op.drop_table('company_purchases')\n op.drop_table('users')\n ### end Alembic commands ###\n","sub_path":"migrations/versions/3e6280aa61c9_.py","file_name":"3e6280aa61c9_.py","file_ext":"py","file_size_in_byte":2540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"50679685","text":"#!/usr/bin/env python\n\"\"\"\nVery simple HTTP server in python.\nUsage::\n ./dummy-web-server.py []\nSend a GET request::\n curl http://localhost\nSend a HEAD request::\n curl -I http://localhost\nSend a POST request::\n curl -d \"foo=bar&bin=baz\" http://localhost\n\"\"\"\nfrom BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer\nimport SocketServer\nimport json \nimport random\n\nclass S(BaseHTTPRequestHandler):\n def _set_headers(self):\n self.send_response(200)\n self.send_header('Content-type', 'javascript/json')\n self.end_headers()\n\n def do_GET(self):\n self._set_headers()\n \n self.wfile.write(getRandomVerb())\n\n def do_HEAD(self):\n self._set_headers()\n \n def do_POST(self):\n # Doesn't do anything with posted data\n self._set_headers()\n self.wfile.write(\"POST!
\")\n \ndef run(server_class=HTTPServer, handler_class=S, port=80):\n server_address = ('', port)\n httpd = server_class(server_address, handler_class)\n print('Starting httpd...')\n httpd.serve_forever()\n\nif __name__ == \"__main__\":\n from sys import argv\n\n if len(argv) == 2:\n run(port=int(argv[1]))\n else:\n run()\n\n\n\ndef getRandomVerb():\n try:\n # We get the json file content (phrasal verbs)\n with open('phrasal_verbs.json') as dataFile:\n phrasalVerbs = json.load(dataFile)\n\n # We get a random position to get the daily phrasal verb\n #for x in range(len(phrasalVerbs)):\n # randomPosition = random.randint(0,len(phrasalVerbs))\n randomPosition = random.randint(0,len(phrasalVerbs))\n\n # Now, we have the choosen item\n #print(\"Content-Type: javascript/json\")\n print(phrasalVerbs[randomPosition])\n #web.header('Content-Type', 'application/json')\n return json.dumps(phrasalVerbs[randomPosition])\n except ValueError:\n print(\"Ups, there is an error: \",ValueError)\n\n#getRandomVerb()","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"350713457","text":"import os \r\n#workDir=os.chdir(\"C:\\Users\\Ralitza\\OneDrive - Seattle University\\SeattleU\\IS 5201\\week1\")\r\nsetDir=os.chdir(\"C:/Users/Ralitza/OneDrive - Seattle University/SeattleU/IS 5201/week1&2\")\r\n\r\ncount=0\r\nfileName=input(\"Please enter the file name:\")\r\nf=open(fileName,\"r\")\r\nfor wordline in f:\r\n lines=wordline.split()\r\n count=count+1\r\nprint(\"The number of words are\" , count)","sub_path":"ForWhile/practicecountingnoOf word.py","file_name":"practicecountingnoOf word.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"377731288","text":"import os\nimport numpy as np\nimport scipy.misc\nimport pandas as pd\nfrom constants import *\nfrom PIL import Image\n\n#столько написал, а из всего этого пригодится только readCsvFile\n\nclass Converter:\n # Метод перевода csv в реальные изображения\n # лейблы сохраняются также в файлик\n def csvFileToImageFiles(self, csvFilePath, outputDirectory = '', saveLabels = False):\n (images, labels) = self.readCsvFile(csvFilePath)\n self.saveImages(images, outputDirectory=outputDirectory)\n\n if saveLabels:\n self.saveLabels(labels)\n\n def saveImages(self, images, outputDirectory = ''):\n index = 0\n for index, image in enumerate(images):\n imageFile = os.path.join(outputDirectory, str(index) + '.jpg')\n scipy.misc.imsave(imageFile, image)\n print(\"Saving image \", index)\n index += 1\n print(\"{} images were saved\".format(index))\n\n def saveLabels(self, labels, outputDirectory = ''):\n pathLabel = NPY_LABEL_FILE\n if outputDirectory and not os.path.exists(outputDirectory):\n if not outputDirectory.endswith('/'):\n outputDirectory += '/'\n os.makedirs(outputDirectory)\n pathLabel = outputDirectory + pathLabel\n np.save(pathLabel, labels)\n\n # Метод переводит csv в .npy файлы (настройка именования файлов в constants.py\n def csvToNpy(self, csvFilePath, outputDirectory = ''):\n (images, labels) = self.readCsvFile(csvFilePath)\n pathImage = NPY_IMAGE_FILE\n pathLabel = NPY_LABEL_FILE\n if outputDirectory and not os.path.exists(outputDirectory):\n if not outputDirectory.endswith('/'):\n outputDirectory += '/'\n os.makedirs(outputDirectory)\n pathImage = outputDirectory + pathImage\n pathLabel = outputDirectory + pathLabel\n np.save(pathImage, images)\n np.save(pathLabel, labels)\n\n # Метод чтения csv файла\n def readCsvFile(self, csvFilePath):\n data = pd.read_csv(csvFilePath)\n labels = []\n images = []\n index = 1\n for index, row in data.iterrows():\n emotion = self.convertEmotion(row['emotion'])\n image = self.convertImageData(row['pixels'])\n if image is not None:\n labels.append(emotion)\n images.append(image)\n else:\n print(\"Error converting image data \", index)\n index += 1\n return (images, labels)\n\n # Создание вектора эмоций\n def convertEmotion(self, emotion):\n d = np.zeros(len(EMOTIONS))\n d[emotion] = 1.0\n return d\n\n #преобразование массива в RGB изорабражение 3x48x48 в оттенках серого\n def convertImageData(self, imageArray):\n dataImage = np.fromstring(str(imageArray), dtype=np.uint8, sep=' ')\n dataImage = dataImage.reshape((WIDTH, HEIGHT))\n dataImage = Image.fromarray(dataImage).convert('RGB')\n dataImage = np.array(dataImage)[:, :, ::-1].copy()\n return dataImage","sub_path":"converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"192619630","text":"def isprime(n):\n for i in range(2,int(n//2)):\n if n%i == 0:\n return False\n return True\n\nprimes = [2,3,5,7,11]\ntemps = sum(primes)\n\nc = 6\nno = 13\nwhile c <= 2000000:\n if isprime(no):\n primes.append(no)\n c+=1\n temps += no\n no+=1\n\nprint(temps)","sub_path":"10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"266506378","text":"\"\"\"\n\n\"\"\"\nimport logging\nimport pandas as pd\n\n\nclass ExcelConnector:\n def __init__(self, file_name, sheet_name):\n logging.info(\"reading excel\")\n self.file_name=file_name\n self.sheet_name=sheet_name\n\n def read_excel(self, spark):\n try:\n excel_data_df = pd.read_excel(\n self.file_name,\n sheet_name=self.sheet_name,\n ).dropna(how='all', axis=1)\n excel_data_df = excel_data_df.where(pd.notnull(excel_data_df), None)\n df = spark.createDataFrame(excel_data_df)\n return df\n except:\n return spark.range(0).drop(\"id\") # Empty DF\n\n\n def write_excel(self, df):\n pandas_df = df.toPandas()\n pandas_df.to_excel(self.file_name)\n","sub_path":"excel/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"446991322","text":"from typing import Tuple, List\n\nimport re\nimport json\nimport time\nimport logging\nimport urllib.parse\nimport os.path\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nWEBSITE = \"http://lewisdavidsonartist.co.uk\"\nSLEEP_DURATION = 10\n\nlogger = logging.getLogger(__name__)\n\n\ndef load_json(filename: str):\n with open(filename) as open_file:\n return json.load(open_file)\n\n\ndef get_cache_filename(path: str):\n return \".cache\" + path + \".html\"\n\n\ndef load_cache(path: str):\n filename = get_cache_filename(path)\n with open(filename) as open_file:\n return open_file.read()\n\n\ndef write_cache(path: str, content: str):\n filename = get_cache_filename(path)\n with open(filename, \"w\") as open_file:\n open_file.write(content)\n\n\ndef download_page(page_path: str):\n try:\n content = load_cache(page_path)\n logger.debug(\"using cached version\")\n except FileNotFoundError:\n page_address = WEBSITE + page_path\n logger.debug(\"downloading non cached version\")\n content = requests.get(page_address).text\n write_cache(page_path, content)\n logger.info(\"sleep for %s seconds\", SLEEP_DURATION)\n time.sleep(SLEEP_DURATION)\n\n return content\n\n\nclass PageData:\n title: str\n description: str\n image_url: str\n\n\nTITLE_MAP = {\n \"Puzzles (Close-up)\": \"Puzzles\",\n \"Bowling Ball (Close-up)\": \"Bowling Balls\",\n \"Tag\": \"Tags\",\n}\n\n\ndef lookup_title(title: str) -> str:\n return TITLE_MAP.get(title, title)\n\n\ndef process_page(content: str):\n soup = BeautifulSoup(content, \"html.parser\")\n main = soup.find(\"div\", id=\"main\")\n page_data = PageData()\n page_data.title = lookup_title(main.find(\"h2\").text)\n page_data.description = main.find(\"p\").text\n page_data.image_url = main.find(\"img\").get(\"src\")\n return page_data\n\n\ndef load_shows():\n shows = {}\n for page in load_json(\"./data/pages.json\"):\n page_without_slash = page[1:]\n content = download_page(page)\n page_data = process_page(content)\n if page_data.title in shows:\n show = shows[page_data.title]\n show[\"pages\"].append(page_without_slash)\n show[\"descriptions\"].append(page_data.description)\n show[\"image_urls\"].append(page_data.image_url)\n else:\n shows[page_data.title] = {\n \"title\": page_data.title,\n \"pages\": [page_without_slash],\n \"descriptions\": [page_data.description],\n \"image_urls\": [page_data.image_url],\n }\n return shows.values()\n\n\ndef download_show_image(images_folder: str, image_url: str) -> str:\n parsed_image_url = urllib.parse.urlparse(image_url)\n image_name = os.path.basename(parsed_image_url.path)\n local_image_path = os.path.join(images_folder, image_name)\n if os.path.exists(local_image_path):\n logger.info(\"image exists %s\", local_image_path)\n return local_image_path\n # Create the local folder\n local_folder = os.path.split(local_image_path)[0]\n os.makedirs(local_folder, exist_ok=True)\n # Download the image to the local file\n logger.info(\"downloading %s\", local_image_path)\n with open(local_image_path, \"wb\") as open_file:\n response = requests.get(image_url, stream=True)\n for chunk in response.iter_content(chunk_size=128):\n open_file.write(chunk)\n # When download complete sleep to not get blocked\n logger.info(\"sleep for %s seconds\", SLEEP_DURATION)\n time.sleep(SLEEP_DURATION)\n return local_image_path\n\n\ndef download_show_images(name: str, show: dict) -> List[str]:\n local_image_paths = []\n local_folder = \"./images/{}\".format(name)\n for image_url in show[\"image_urls\"]:\n local_image_path = download_show_image(local_folder, image_url)\n local_image_paths.append(local_image_path)\n return local_image_paths\n\n\ndef convert_image_paths(local_paths: List[str], prefix: str = \"/\") -> List[str]:\n return [local_path.replace(\"./\", prefix) for local_path in local_paths]\n\n\npost_template = \"\"\"---\nlayout: post\ntitle: {title}\ncover_image: {cover_image}\nimages_folder: {images_folder}\n---\n\n{description}\n\"\"\"\n\n\ndef save_post(filename, post_data):\n\n post_content = post_template.format(**post_data)\n\n filepath = \"_posts/\" + filename\n with open(filepath, \"w\") as open_file:\n open_file.write(post_content)\n\n\npattern = re.compile(r\"/wp-content/uploads/(?P\\d+)/(?P\\d+)\")\n\n\ndef parse_image_date(image_url: str) -> Tuple[str, str]:\n match = pattern.search(image_url)\n year = match.group(\"year\")\n month = match.group(\"month\")\n return (year, month)\n\n\ndef create_name(year, month, title):\n title_name = re.sub(r\"\\W+\", \"-\", title).lower()\n return \"{}-{}-01-{}\".format(year, month, title_name)\n\n\ndef export_posts(shows):\n posts = []\n for show in shows:\n logger.info(\"processing %s\", show[\"title\"])\n year, month = parse_image_date(show[\"image_urls\"][0])\n name = create_name(year, month, show[\"title\"])\n descriptons = [d.replace(\"\\n\", \" - \") for d in show[\"descriptions\"]]\n local_image_paths = download_show_images(name, show)\n image_urls = convert_image_paths(local_image_paths)\n images = zip(descriptons, image_urls)\n save_post(\n \"{}.markdown\".format(name),\n {\n \"name\": name,\n \"title\": show[\"title\"],\n \"description\": descriptons[0],\n \"cover_image\": image_urls[0],\n \"images_folder\": os.path.commonpath(image_urls),\n },\n )\n\n\ndef main():\n shows = load_shows()\n export_posts(shows)\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.INFO)\n main()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"320975338","text":"import re\nfrom datetime import datetime as dt\nfrom urlparse import urljoin, urlsplit\n\nfrom scrapy.exceptions import DropItem\nfrom scrapy.http import Request, FormRequest\nfrom scrapy.selector import XmlXPathSelector\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\n\nclass Allianz(ATSSpider):\n \"\"\" Crawler for Allianz based on `ATSSpider`\n\n scrapy crawl allianz \\\n -a url=\"https://www.allianz.com/en/careers/job_search/global-job-search.html#\\!c2146b15b-9fdd-4ccb-bd93-066f4936dd6c\" \\\n -a mining_job=9999 \\\n -a iterator=1 \\\n -a extract=1\n \n scrapes the following sites:\n https://www.allianz.com/en/careers/job_search/global-job-search.html#!c2146b15b-9fdd-4ccb-bd93-066f4936dd6c\n \n \"\"\"\n\n name = 'allianz'\n\n def start_requests(self):\n \"\"\" Grab data mappings resource to identify location, contract type, \n and job category. \"\"\"\n\n mappings_url = \"https://jobs.allianz.com/sap/bc/srt/rfc/sap/zercuix_get_customization/100/zercuix_get_customization/zercuix_get_customization_na\"\n mappings_query = \"\"\"\n \n \n \n \n \n \"\"\"\n\n return [Request(mappings_url,\n method='POST',\n body=mappings_query,\n headers={'Content-Type': 'text/xml'}\n )]\n\n def parse(self, response):\n \"\"\" Get XML resource containing job listings. \"\"\"\n\n self.location_map = {}\n self.jobtype_map = {}\n self.jobcategory_map = {}\n\n xxs = XmlXPathSelector(response)\n for item in xxs.xpath(\"//EtLocations/item\"):\n location_id = item.xpath(\"Location/text()\").extract()[0]\n location_name = item.xpath(\"Description/text()\").extract()[0]\n \n location_parent = item.xpath(\"Parent/text()\").extract()[0]\n if location_parent != '0':\n location_name += \", \" + self.location_map[location_parent]\n \n self.location_map[location_id] = location_name\n\n for item in xxs.xpath(\"//EtEmploymentTypes/item\"):\n jobtype_id = item.xpath(\"EmplTypeId/text()\").extract()[0]\n jobtype_name = item.xpath(\"EmplTypeDescr/text()\").extract()[0]\n self.jobtype_map[jobtype_id] = jobtype_name\n\n for item in xxs.xpath(\"//EtFunctionalAreas/item\"):\n jobcategory_id = item.xpath(\"FunctAreaId/text()\").extract()[0]\n jobcategory_name = item.xpath(\"FunctAreaDescription/text()\").extract()[0]\n self.jobcategory_map[jobcategory_id] = jobcategory_name\n\n self.joblist_url = \"https://jobs.allianz.com/sap/bc/srt/rfc/sap/zercuix_search_ext/100/zercuix_search_ext/zercuix_search_ext_na\"\n \n self.joblist_query = \"\"\"\n \n \n \n \n \n \n \"\"\"\n\n self.jobdetails_query = \"\"\"\n \n \n \n %(PinstGuid)s \n \n \n \n \"\"\"\n\n yield Request(self.joblist_url,\n method='POST',\n body=self.joblist_query,\n headers={'Content-Type': 'text/xml'},\n callback=self.parse_joblist\n )\n\n\n def parse_joblist(self, response):\n \"\"\" Parse job listings recursively. \"\"\"\n\n self.set_meta_language(response)\n\n xxs = XmlXPathSelector(response)\n\n jobs = xxs.xpath(\"//EtHitlist/item\")\n for job in jobs:\n pinst_guid = job.xpath('PinstGuid/text()').extract()\n soap_query = self.jobdetails_query % {'PinstGuid': pinst_guid[0]}\n\n request = Request(self.joblist_url, \n method='POST',\n body=soap_query,\n headers={'Content-Type': 'text/xml'},\n callback=self.parse_job_callback())\n \n yield request\n\n def parse_job(self, response):\n \"\"\" Scrape job details and returns all the fields found. \"\"\"\n\n error = self.validate_parse_job(response)\n if error: raise DropItem(error)\n\n xxs = XmlXPathSelector(response)\n loader = BrightcorpItemLoader()\n\n loader.add_value('url', self.get_url(xxs))\n loader.add_value('date', self.get_date(xxs))\n loader.add_value('referencenumber', \n \"%s-%s\" % (self.name, self.get_refno(xxs)))\n\n loader.add_value('title', self.get_title(xxs))\n loader.add_value('company', self.get_company(xxs))\n loader.add_value('location', self.get_location(xxs))\n loader.add_value('description', self.get_description(xxs))\n loader.add_value('jobtype', self.get_jobtype(xxs))\n loader.add_value('jobcategory', self.get_jobcategory(xxs))\n\n return loader.load_item()\n\n def get_refno(self, selector):\n \"\"\" Extract the job reference number from the page. Fall back to using\n the URL if it cannot be found on the page. \"\"\"\n\n referencenumber = selector.xpath(\"//RefCode/text()\")\n if referencenumber: return referencenumber.extract()[0]\n\n def get_url(self, selector):\n \"\"\" Extract URL from details page. \"\"\"\n\n url = selector.xpath(\"//JobDetailsUrl/text()\")\n if url: return urljoin(selector.response.url, url.extract()[0])\n \n return \"\"\n\n def get_title(self, selector):\n \"\"\" Extract the job title from the page. \"\"\"\n \n title = selector.xpath(\"//Title/text()\")\n if title: return title.extract()\n\n return \"\"\n\n def get_company(self, selector):\n \"\"\" Extract the company name from the page. Hard-code the name based on\n the URL if it cannot be found on the page. \"\"\"\n\n company = selector.xpath(\"//CompanyTxt/text()\")\n if company: return company.extract()\n\n return \"\"\n\n def get_location(self, selector):\n \"\"\" Extract the job location or workplace from the page. \"\"\"\n\n location_id = selector.xpath(\"//Location/text()\")\n if location_id: \n return self.location_map.get(location_id.extract()[0])\n\n return \"\"\n\n def get_date(self, selector):\n \"\"\" Extract the date when the job ad is posted. Default to the\n current `datetime` if cannot be found. \"\"\"\n\n # field not found\n\n return str(dt.now())\n\n def get_description(self, selector):\n \"\"\" Extract the raw HTML description from the page. \"\"\"\n\n description = selector.xpath(\"//ProjectDesc/text()\")\n if len(description) > 1: return ''.join(description.extract())\n if description: return description.extract()\n\n return \"\"\n\n def get_jobtype(self, selector):\n \"\"\" Extract the job type or contract from the page. \"\"\"\n\n jobtype_id = selector.xpath(\"//EmploymentType/text()\").extract()\n if int(jobtype_id[0]): \n return self.jobtype_map.get(jobtype_id[0])\n\n return \"\"\n\n def get_jobcategory(self, selector):\n \"\"\" Extract the category or nature of the job from the page. \"\"\"\n\n jobcategory_id = selector.xpath(\"//FunctionalArea/text()\").extract()\n if int(jobcategory_id[0]):\n return self.jobcategory_map.get(jobcategory_id[0])\n\n return \"\"\n\n def set_custom_item(self, response):\n \"\"\" Load values that cannot be scraped from job pages for\n raw extraction `-a extract=0` \"\"\"\n\n xxs = XmlXPathSelector(response)\n\n self.loader.add_value('referencenumber', \n \"%s-%s\" % (self.name, self.get_refno(xxs)))\n\n def validate_parse_job(self, response):\n \"\"\" Ensure that there are details to be scraped from the page. \"\"\"\n\n xxs = XmlXPathSelector(response)\n if not self.get_refno(xxs):\n return \"Job reference number missing.\"\n\n errors = [\n \"An exception occurred in the SAP E-Recruiting application\",\n \"No suitable entry was found in a Customizing table\",\n \"Processing error\"\n ]\n\n for error in errors:\n if error in response.body:\n return error\n\n return \"\"","sub_path":"brightcorp/brightcorp/spiders/allianz.py","file_name":"allianz.py","file_ext":"py","file_size_in_byte":9446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"290522734","text":"import json\nimport os\nimport sys\n\nclass awscontext(object):\n def __init__(self,ctx_file=None, ctx_version=\"2.0\", verbose = False):\n self.verbose = verbose\n self.ctx_file = os.path.join(os.path.dirname(os.path.abspath(sys.argv[0])),\n \"awscontext.json\")\n if self.verbose:\n print(\">>>awscontext: ctx file is: \" + self.ctx_file)\n if ctx_file != None:\n self.ctx_file = ctx_file\n # open the json ctx file\n with open(self.ctx_file) as cfh:\n ctxinfo = json.load(cfh)\n # check version\n key = \"version\"\n if key in ctxinfo:\n if ctxinfo[key] != ctx_version:\n print(\"Error: version of : \" + self.ctx_file + \" should be \" + ctx_version +\n \" not \" + ctxinfo[key])\n sys.exit(2)\n else:\n print(\"Error: \" + key + \" key not found in \" + self.ctx_file)\n sys.exit(2)\n # get the contexts\n key = \"context\"\n if key in ctxinfo:\n self.contexts = ctxinfo[key]\n self.ctxnames = self.contexts.keys()\n else:\n print(\"Error: \" + key + \" key not found in \" + self.ctx_file)\n sys.exit(2)\n if self.verbose:\n for key,value in self.contexts.iteritems():\n print( \"\\t>>>awscontext: key: \" + key + \" value: \" + str(value))\n # create list of profiles and bucket names\n self.profile_names = []\n self.bucket_names = []\n for ctxname,ctxdict in self.contexts.iteritems():\n if self.verbose:\n print( \"\\t>>>awscontext: key: \" + ctxname + \" value: \" + str(ctxdict))\n for key,value in ctxdict.iteritems():\n if key == \"s3bucket\":\n self.bucket_names.append(value)\n elif key == \"profile\":\n self.profile_names.append(value)\n\n def getctxnames(self):\n return self.ctxnames\n def getprofilenames(self):\n return self.profile_names\n def getbucketnames(self):\n return self.bucket_names\n def getctx(self, name_a):\n ctx = None\n if name_a in self.ctxnames:\n ctx = self.contexts[name_a]\n return ctx\n def getdefaultctx(self):\n key = 'default`'\n if key not in self.ctxnames:\n print(\"Error: \" + key + \" key not found in \" + self.ctx_file)\n sys.exit(2)\n return self.contexts[key]\n def getbucketname(self, name_a):\n bn = None\n if name_a in self.ctxnames:\n bn = self.contexts[name_a]['s3bucket']\n return bn\n def getprofile(self, name_a):\n profile = None\n if name_a in self.ctxnames:\n profile = self.contexts[name_a]['profile']\n return profile\n","sub_path":"awscontext.py","file_name":"awscontext.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"385020386","text":"import parameter\nfrom parameter import pprint\n\n\nclass actuator:\n def __init__(self, pin, dc=0, freq=1000):\n if parameter.emulate:\n pprint(\"createt actuator at pin \" + str(pin))\n\n else:\n import pigpio\n global pi\n pi = pigpio.pi()\n #pi = pigpio.pi('soft',8888)\n\n self.GPIO = pin\n pi.set_mode(self.GPIO, pigpio.OUTPUT)\n pi.set_PWM_frequency(self.GPIO, freq)\n pi.set_PWM_dutycycle(self.GPIO, dc)\n self.prevDC = 0\n\n def setdc(self, dc):\n self.prevDC = dc\n dc = int(max(min(dc, 255.0), 0.0))\n if parameter.emulate:\n pprint(\"dutycycle set to \" + str(dc))\n\n else:\n pi.set_PWM_dutycycle(self.GPIO, dc)\n #print(\"SET ACTUATOR VAL: \", dc)\n \n def getdc(self):\n return self.prevDC\n","sub_path":"controling/actuator.py","file_name":"actuator.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"83134452","text":"\"\"\"\nSliding-window-based job/task queue class (& example of use.)\n\nMay use ``multiprocessing.Process`` or ``threading.Thread`` objects as queue\nitems, though within Fabric itself only ``Process`` objects are used/supported.\n\"\"\"\n\nfrom __future__ import with_statement\nimport time\nimport Queue\n\nfrom collections import deque\n\nfrom progressbar import Bar, ETA, Percentage, ProgressBar, SimpleProgress\n\nfrom fabric.network import ssh\n\n\nclass JobQueue(object):\n \"\"\"\n The goal of this class is to make a queue of processes to run, and go\n through them running X number at any given time.\n\n So if the bubble is 5 start with 5 running and move the bubble of running\n procs along the queue looking something like this:\n\n Start\n ...........................\n [~~~~~]....................\n ___[~~~~~].................\n _________[~~~~~]...........\n __________________[~~~~~]..\n ____________________[~~~~~]\n ___________________________\n End\n \"\"\"\n def __init__(self, max_running, comms_queue, role_limits=None, debug=False):\n \"\"\"\n Setup the class to resonable defaults.\n \"\"\"\n self._max = max_running\n self._comms_queue = comms_queue\n self._debug = debug\n\n if role_limits is None:\n role_limits = {}\n role_limits.setdefault('default', self._max)\n\n self._pools = {}\n for role, limit in role_limits.iteritems():\n self._pools[role] = {\n 'running': [],\n 'queue': deque(),\n 'limit': limit,\n }\n\n self._completed = []\n self._num_of_jobs = 0\n self._finished = False\n self._closed = False\n\n widgets = ['Running tasks: ', Percentage(), ' ', Bar(), ' ', SimpleProgress(), ETA()]\n self.pbar = ProgressBar(widgets=widgets)\n\n def __len__(self):\n \"\"\"\n Just going to use number of jobs as the JobQueue length.\n \"\"\"\n return self._num_of_jobs\n\n def close(self):\n \"\"\"\n A sanity check, so that the need to care about new jobs being added in\n the last throws of the job_queue's run are negated.\n \"\"\"\n if self._debug:\n print(\"JOB QUEUE: closed\")\n\n self._closed = True\n\n def append(self, process):\n \"\"\"\n Add the Process() to the queue, so that later it can be checked up on.\n That is if the JobQueue is still open.\n\n If the queue is closed, this will just silently do nothing.\n\n To get data back out of this process, give ``process`` access to a\n ``multiprocessing.Queue`` object, and give it here as ``queue``. Then\n ``JobQueue.run`` will include the queue's contents in its return value.\n \"\"\"\n if not self._closed:\n r = process.name.split('|')[0]\n role = r if r in self._pools else 'default'\n\n self._pools[role]['queue'].appendleft(process)\n\n self._num_of_jobs += 1\n\n self.pbar.maxval = self._num_of_jobs\n\n if self._debug:\n print(\"JOB QUEUE: %s: added %s\" % (role, process.name))\n\n def run(self):\n \"\"\"\n This is the workhorse. It will take the intial jobs from the _queue,\n start them, add them to _running, and then go into the main running\n loop.\n\n This loop will check for done procs, if found, move them out of\n _running into _completed. It also checks for a _running queue with open\n spots, which it will then fill as discovered.\n\n To end the loop, there have to be no running procs, and no more procs\n to be run in the queue.\n\n This function returns an iterable of all its children's exit codes.\n \"\"\"\n if not self._closed:\n raise Exception(\"Need to close() before starting.\")\n\n if self._debug:\n print(\"JOB QUEUE: starting\")\n\n def _consume_result(comms_queue, results, ignore_empty=False):\n \"\"\"\n Helper function to attempt to get results from the comms queue\n and put them into the results dict\n \"\"\"\n try:\n datum = self._comms_queue.get_nowait()\n except Queue.Empty:\n if not ignore_empty:\n raise\n else:\n results[datum['name']]['result'] = datum['result']\n\n\n results = {}\n\n self.pbar.start()\n\n while len(self._completed) < self._num_of_jobs:\n for pool_name, pool in self._pools.iteritems():\n while len(pool['queue']) and len(pool['running']) < pool['limit']:\n job = pool['queue'].pop()\n if self._debug:\n print(\"JOB QUEUE: %s: %s: start\" % (pool_name, job.name))\n job.start()\n pool['running'].append(job)\n\n # job.name contains role so split that off and discard\n host_string = job.name.split('|')[-1]\n # Place holder for when the job finishes\n results[host_string] = {\n 'exit_code': None,\n 'result': None\n }\n\n for i, job in enumerate(pool['running']):\n if not job.is_alive():\n if self._debug:\n print(\"JOB QUEUE: %s: %s: finish\" % (pool_name, job.name))\n\n job.join() # not necessary for Process but is for Thread\n self._completed.append(job)\n pool['running'].pop(i)\n\n host_string = job.name.split('|')[-1]\n results[host_string]['exit_code'] = job.exitcode\n\n # Let's consume a result so the queue doesn't get big\n _consume_result(self._comms_queue, results, True)\n\n if self._debug:\n print(\"JOB QUEUE: %s: %d running jobs\" % (pool_name, len(pool['running'])))\n\n if len(pool['queue']) == 0:\n print(\"JOB QUEUE: %s: depleted\" % pool_name)\n\n # Allow some context switching\n time.sleep(ssh.io_sleep)\n\n self.pbar.update(len(self._completed))\n\n # Make sure to drain the comms queue since all jobs are completed\n while True:\n try:\n _consume_result(self._comms_queue, results)\n except Queue.Empty:\n break\n\n self.pbar.finish()\n\n return results\n\n\n#### Sample\n\ndef try_using(parallel_type):\n \"\"\"\n This will run the queue through it's paces, and show a simple way of using\n the job queue.\n \"\"\"\n\n def print_number(number):\n \"\"\"\n Simple function to give a simple task to execute.\n \"\"\"\n print(number)\n\n if parallel_type == \"multiprocessing\":\n from multiprocessing import Process as Bucket # noqa\n\n elif parallel_type == \"threading\":\n from threading import Thread as Bucket # noqa\n\n # Make a job_queue with a bubble of len 5, and have it print verbosely\n jobs = JobQueue(5)\n jobs._debug = True\n\n # Add 20 procs onto the stack\n for x in range(20):\n jobs.append(Bucket(\n target=print_number,\n args=[x],\n kwargs={},\n ))\n\n # Close up the queue and then start it's execution\n jobs.close()\n jobs.run()\n\n\nif __name__ == '__main__':\n try_using(\"multiprocessing\")\n try_using(\"threading\")\n","sub_path":"fabric/job_queue.py","file_name":"job_queue.py","file_ext":"py","file_size_in_byte":7582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"268019690","text":"from datetime import date\nfrom flask import Flask, session, redirect, url_for, request, render_template, abort, flash\nfrom functools import wraps\n\nfrom db import get_flights, get_hotels, get_hotel, get_rooms\nimport utils\n\napp = Flask(__name__)\napp.secret_key = b'dklsdjf@#423f8_#942;3['\n\n### General views ###\n\n@app.route('/')\ndef home():\n\tsession_username = session.get('username')\n\n\treturn render_template('index.html', username=session_username)\n\n@app.errorhandler(404)\ndef page_not_found(e):\n\tusername = session.get('username')\n\treturn render_template('404.html', username=username), 404\n\n@app.after_request\ndef add_header(r):\n\t\"\"\"\n\tAdd headers to both force latest IE rendering engine or Chrome Frame,\n\tand also to cache the rendered page for 10 minutes.\n\t\"\"\"\n\tr.headers[\"Cache-Control\"] = \"no-cache, no-store, must-revalidate\"\n\tr.headers[\"Pragma\"] = \"no-cache\"\n\tr.headers[\"Expires\"] = \"0\"\n\tr.headers['Cache-Control'] = 'public, max-age=0'\n\treturn r\n\ndef login_required(f):\n\t@wraps(f)\n\tdef decorated_function(*args, **kwargs):\n\t\tsession_username = \tsession.get('username')\n\t\tif session_username is None:\n\t\t\treturn redirect(url_for('login', next=request.url))\n\t\treturn f(*args, **kwargs)\n\treturn decorated_function\n\t\n\n### Auth views ###\n\n@app.route('/logout')\ndef logout():\n\tsession.pop('username', None)\n\treturn redirect(url_for('home'))\n\n\n@app.route('/login', methods=[\"GET\", \"POST\"])\ndef login():\n\terror = None\n\tsession_username = \tsession.get('username')\n\n\tif session_username is not None:\n\t\terror = \"please logout first\"\n\telif request.method == \"POST\":\n\t\tusername = request.form.get(\"username\", \"\")\n\t\tpassword = request.form.get(\"password\", \"\")\n\t\tnext_url = request.form.get(\"next\")\n\t\ttry:\n\t\t\tutils.login_user(username, password)\n\t\t\tsession['username'] = username\n\t\t\tif next_url:\n\t\t\t\treturn redirect(next_url)\n\t\t\treturn redirect(url_for('home'))\n\t\texcept ValueError as err:\n\t\t\terror = err\n\n\tif error:\n\t\tflash(error, 'error')\n\t\n\treturn render_template('login.html', username=session_username)\n\n\n@app.route('/register', methods=[\"GET\", \"POST\"])\ndef register():\n\terror = None\n\tsession_username = session.get('username')\n\n\tif session_username is not None:\n\t\terror = \"please logout first\"\n\telif request.method == \"POST\":\n\t\tusername = request.form.get(\"username\", None)\n\t\tpassword = request.form.get(\"password\", None)\n\t\tnational_id = request.form.get(\"national_id\", None)\n\t\tname = request.form.get(\"name\", None)\n\t\taddress\t= request.form.get(\"address\", None)\n\t\tphone = request.form.get(\"phone\", None)\n\t\temail = request.form.get(\"email\", None)\n\t\ttry:\n\t\t\terror = utils.register_customer({\n\t\t\t\t'username':username,\n\t\t\t\t'password':password,\n\t\t\t\t'national_id':national_id,\n\t\t\t\t'name':name,\n\t\t\t\t'address':address,\n\t\t\t\t'phone':phone,\n\t\t\t\t'email':email\n\t\t\t\t})\n\n\t\t\tif not error:\n\t\t\t\tsession['username'] = username\n\t\t\t\treturn redirect(url_for('home'))\n\t\texcept ValueError as err:\n\t\t\terror = err\n\tif error:\n\t\tflash(error, 'error')\n\n\treturn render_template('register.html', username=session_username)\n\n\n### Hotel views ###\n\n\n@app.route('/hotels', methods=[\"GET\"])\ndef hotels():\n\tcountry_name = request.args.get('country_name', '')\n\tcity_name = request.args.get('city_name', '')\n\tname = request.args.get('name', '')\n\n\thotels = get_hotels(country=country_name, city=city_name, name=name)\n\tsession_username = session.get('username')\n\n\treturn render_template('hotels.html', hotels=hotels, username=session_username)\n\n@app.route('/hotels/', methods=[\"GET\", \"POST\"])\ndef hotel(id):\n\tsession_username = session.get('username')\n\n\tbooking_id = request.args.get('booking_id', -1)\n\trate=False\n\tif booking_id != -1:\n\t\trate = True\n\terror = None\n\tif request.method == \"POST\":\n\t\trating = request.form.get(\"rating\", None)\n\t\ttry:\n\t\t\terror = utils.add_rating(\n\t\t\t\tusername=session_username,\n\t\t\t\trating=rating,\n\t\t\t\thotel_id=id,\n\t\t\t\tbooking_id=booking_id\n\t\t\t)\n\n\t\t\tif not error:\n\t\t\t\tflash('Your rating was submitted!', 'success')\n\t\texcept ValueError as err:\n\t\t\terror = err\n\n\t\tif error:\n\t\t\tflash(error, 'error')\n\n\n\thotel = get_hotel(id)\n\tif not hotel:\n\t\tabort(404)\n\trooms = get_rooms(id)\n\treturn render_template('hotel_info.html', hotel=hotel, rooms=rooms, rate=rate, username=session_username)\n\n\n@app.route('/hotels//room/', methods=[\"GET\", \"POST\"])\n@login_required\ndef reserve_room(hotel_id, room_number):\n\tsession_username = session.get('username')\n\t\n\terror = None\n\tif request.method == \"POST\":\n\t\tpassword = request.form.get(\"password\", None)\n\t\tfrom_date = request.form.get(\"from_date\", None)\n\t\tto_date = request.form.get(\"to_date\", None)\n\t\ttry:\n\t\t\terror = utils.reserve_room(\n\t\t\t\tusername=session_username, \n\t\t\t\tpassword=password,\n\t\t\t\tfrom_date=from_date,\n\t\t\t\tto_date=to_date,\n\t\t\t\thotel_id=hotel_id,\n\t\t\t\troom_number=room_number\n\t\t\t)\n\n\t\t\tif not error:\n\t\t\t\tflash('Room reserved successfully!', 'success')\n\t\t\t\treturn redirect(url_for('my_bookings'))\n\t\texcept ValueError as err:\n\t\t\terror = err\n\t\t\n\tif error:\n\t\tflash(error, 'error')\n\t\n\treturn render_template('reserve_room.html', username=session_username)\n\n\n\n### Flight views ###\n\n@app.route('/flights', methods=[\"GET\"])\ndef flights():\n\n\torigin_country = request.args.get('origin_country', '')\n\tdestination_city = request.args.get('destination_city', '')\n\torigin_city = request.args.get('origin_city', '')\n\tdestination_country = request.args.get('destination_country', '')\n\tdeparture_date = request.args.get('departure_date', '')\n\n\tflights = get_flights(origin_country=origin_country, destination_city=destination_city, origin_city=origin_city,\n\t destination_country=destination_country, departure_date=departure_date)\n\t \n\tsession_username = session.get('username')\n\n\treturn render_template('flights.html', flights=flights, username=session_username)\n\n\n@app.route('/flights//', methods=[\"GET\", \"POST\"])\n@login_required\ndef reserve_flight(airline_id, number):\n\tsession_username = session.get('username')\n\t\n\terror = None\n\tif request.method == \"POST\":\n\t\tpassword = request.form.get(\"password\", None)\n\t\ttry:\n\t\t\terror = utils.reserve_flight(\n\t\t\t\tusername=session_username, \n\t\t\t\tpassword=password,\n\t\t\t\tairline_id=airline_id,\n\t\t\t\tflight_number=number\n\t\t\t)\n\n\t\t\tif not error:\n\t\t\t\tflash('Flight reserved successfully!', 'success')\n\t\t\t\treturn redirect(url_for('my_bookings'))\n\n\t\texcept ValueError as err:\n\t\t\terror = err\n\n\tif error:\n\t\tflash(error, 'error')\n\n\treturn render_template('reserve_flight.html', username=session_username)\n\n\n### Booking for users view ###\n\n@app.route('/bookings', methods=['GET'])\n@login_required\ndef my_bookings():\n\tsession_username = session.get('username')\n\n\tbookings = utils.get_customer_booking(session_username)\n\n\tfor booking in bookings:\n\t\tif date.today() >= booking['from_date']:\n\t\t\tbooking['started'] = True\n\t\tif date.today() >= booking['to_date']:\n\t\t\tbooking['ended'] = True\n\n\treturn render_template('bookings.html', bookings=bookings, username=session_username)\n\n\n@app.route('/payment/', methods=['GET', 'POST'])\n@login_required\ndef payment(booking_id):\n\tsession_username = session.get('username')\n\tbooking = utils.get_customer_booking(session_username, booking_id)[0]\n\n\terror = None\n\tif request.method == \"POST\":\n\t\ttry:\n\t\t\tpassword = request.form.get(\"password\", None)\n\t\t\tdiscount_code = request.form.get(\"discount\", None)\n\t\t\terror = utils.handle_payment(session_username, password, discount_code ,booking)\n\n\t\t\tif not error:\n\t\t\t\tflash('Reservation completed succesfully!', 'success')\n\t\t\t\treturn redirect(url_for('my_bookings'))\n\n\t\texcept ValueError as err:\n\t\t\terror = err\n\n\tif error:\n\t\tflash(error, 'error')\n\t\n\treturn render_template('payment.html', booking=booking, username=session_username)\n\n\n@app.route('/cancel/', methods=['GET', 'POST'])\n@login_required\ndef cancel(booking_id):\n\tsession_username = session.get('username')\n\tbooking = utils.get_customer_booking(session_username, booking_id)[0]\n\n\terror = None\n\ttry:\n\t\terror = utils.handle_cancel(session_username ,booking)\n\n\t\tif not error:\n\t\t\tflash('Reservation cancelled succesfully!', 'success')\n\t\t\treturn redirect(url_for('my_bookings'))\n\n\texcept ValueError as err:\n\t\terror = err\n\n\tif error:\n\t\tflash(error, 'error')\n\n\treturn redirect(url_for('my_bookings'))\n\t\n","sub_path":"flitel/flitel.py","file_name":"flitel.py","file_ext":"py","file_size_in_byte":8160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"447935901","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jul 14 09:44:10 2021\r\n\r\n@author: eden\r\n\"\"\"\r\n\r\ndef TcyclesAnalyzer2(filename,dataTcycuploaded):\r\n #%%Import libs\r\n #import external libs\r\n # import pandas as pd\r\n import matplotlib.pyplot as plt\r\n from matplotlib.widgets import Cursor\r\n import numpy as np\r\n from sklearn.cluster import KMeans\r\n #from sklearn.preprocessing import StandardScaler\r\n from sklearn.neighbors import LocalOutlierFactor\r\n import statistics\r\n from scipy.signal import find_peaks\r\n import math\r\n import re\r\n import shutil\r\n import time\r\n from scipy.signal import decimate\r\n from mpl_toolkits.axes_grid1 import host_subplot\r\n from copy import deepcopy\r\n \r\n # import PL own function from path\r\n import os\r\n #os.chdir('W:\\Operation\\Production\\Production_tools_environment\\PTE')\r\n from FourPointIndex import FourPointCalc\r\n from math_oper import smooth\r\n from stdout_sup import suppress_stdout_stderr\r\n #%% Load data\r\n devNameInd = filename.find('L1')\r\n deviceName = filename[devNameInd:devNameInd+14]\r\n # dataTcyc = pd.read_table(os.path.join(filepath, filename),header=None)\r\n dataTcyc = dataTcycuploaded\r\n #%% output inits (from main - temporary)\r\n temps = dict(High1=85,High2=85,Low=-40) #[C]\r\n temps = dict(High1=75,High2=75,Low=-54) #[C]\r\n external_Tsensor = dict(sensitivity=0.008,offset = 66)# [V/C] ; [C]\r\n external_poly = dict(SFp=[0,0,0,0,0,13],Bp = [0,0,0,0,0,0],MAp = [0,0,0,0,0,0])\r\n\r\n\r\n #%% init\r\n \r\n decimation_factor = 30\r\n f_sample = 5 #Hz\r\n poly_rank = 5 # polynomial rank\r\n \r\n if re.search(\"cyc_L\",filename):\r\n cycle1 = 1\r\n label = 'cyc1_'\r\n if re.search(\"cyc_2_L\",filename):\r\n cycle1 = 0\r\n label = 'cyc2_'\r\n \r\n # loc_sep = [m.start() for m in re.finditer('/', filename)]\r\n # directoy_path = filename[:loc_sep[-1]]\r\n if not os.path.exists(os.path.join (os.path.expanduser (\"~\"), \"Desktop\",\"Analyzed_Tcyc\")):\r\n Analyzed_folder_path =os.path.join (os.path.expanduser (\"~\"), \"Desktop\",\"Analyzed_Tcyc\")\r\n os.mkdir(Analyzed_folder_path)\r\n directoy_path = os.path.join (Analyzed_folder_path,deviceName)\r\n if not os.path.exists(directoy_path):\r\n os.mkdir(directoy_path) \r\n if cycle1:\r\n Folder_path = [directoy_path+'/Tcycle1_'+deviceName+'_py'] #'_py' for comparison purpose only\r\n else:\r\n Folder_path = [directoy_path+'/Tcycle2_'+deviceName+'_py'] #'_py' for comparison purpose only\r\n if os.path.exists(Folder_path[0]): # delete existing folder\r\n shutil.rmtree(Folder_path[0])\r\n \r\n os.mkdir(Folder_path[0])\r\n os.chdir(directoy_path)\r\n\r\n \r\n #%% preprocess data\r\n \r\n dataTcyc = dataTcyc.iloc[:,:2].values\r\n \r\n if 1: # trim beginning of Tcycle (clear junk data at turn ON)\r\n tempeVdiff = np.gradient(dataTcyc[:1000,1])\r\n tempeVtrim=np.where(abs(tempeVdiff)>0.004)\r\n \r\n if (tempeVtrim[0].size > 0):\r\n dataTcyc = dataTcyc[tempeVtrim[0][-1]+1:,:] # clear junk data\r\n \r\n #Tsensor_resolution = max(abs((np.gradient(dataTcyc[:1000,1]))))\r\n \r\n\r\n\r\n #%% Tcycle test analyzer\r\n \"\"\"\"\"\r\n Tcycle test analyzer - this part will check whether its cont/steps, number of 4p, orientation and \r\n finds the start to end points of each 4p measurements.\r\n input: dataTcyc - Temp cycle data(acc,temp)\r\n outputs:\r\n startEnd4p - list of starting and ending indexes of each 4point\r\n indexesOf4Ps - all the indexes of the same clustered 4p\r\n \"\"\"\"\"\r\n #dataFrameTcyc = pd.DataFrame(data=dataTcyc, columns=[\"acc\", \"temp\"])\r\n\r\n cont = False\r\n if('cont' in filename):\r\n cont = True\r\n \r\n delta = 10\r\n Neg4p = np.where(dataTcyc[:,0]<0.8*np.min(dataTcyc[:,0]))[0]#finds indexes of the -1g areas for all 4p\r\n \r\n #filtering outliers:\r\n clf = LocalOutlierFactor(n_neighbors=10)\r\n Neg4pOutLiers= clf.fit_predict(Neg4p.reshape(-1, 1))\r\n Neg4p = Neg4p[np.where(Neg4pOutLiers==1)]\r\n #Number of 4p:\r\n GradNeg4p=np.gradient(Neg4p)\r\n peaks= find_peaks(GradNeg4p,distance=40) # distance should be smaller than 4point samples at -1g\r\n numOf4p = int(len(peaks[0]))+1\r\n #size of areas of interest:\r\n meanArea = int(np.ceil(np.mean(np.diff(peaks[0]))))\r\n \r\n #%% find 4points indexes for beginning and end of each 4point section\r\n \r\n startEnd4p=[]\r\n if(cont):\r\n #clustering gradients indexes for each -1g area of 4p \r\n kmeans = KMeans(init = 'k-means++',n_clusters = numOf4p)\r\n Neg4p_y_kmeans = kmeans.fit_predict(Neg4p.reshape(-1,1))\r\n #orientation:\r\n orientation =1\r\n for i in range(numOf4p):\r\n midNeg4p= math.ceil(np.median(Neg4p[np.where(Neg4p_y_kmeans==i)]))#middle of Neg4p\r\n startEnd4p.append([midNeg4p-int(meanArea*2.5)-2*delta,midNeg4p+int(1.5*meanArea)+4*delta])\r\n else:\r\n grad =np.gradient(dataTcyc[:,0])\r\n #clustering grad into 2 clusters to find indForCalc \r\n kmeans = KMeans(init = 'k-means++',n_clusters = 2)\r\n indForCalc_y_kmeans = kmeans.fit_predict(abs(grad).reshape(-1,1)) \r\n indForCalc = np.where(indForCalc_y_kmeans==1)[0] #first filter\r\n #clustering gradients indexes for each 4p \r\n kmeans = KMeans(init = 'k-means++',n_clusters = numOf4p)\r\n y_kmeans = kmeans.fit_predict(indForCalc.reshape(-1,1)) \r\n #orientation:\r\n orientation = int(len(find_peaks(grad[indForCalc[0]-500:indForCalc[0]+1500],height=0.75*max(abs(grad[indForCalc[0]:indForCalc[0]+1200])))[0])==3) \r\n #LOF: Local Outliers Factor to find outliers areas with high gradients other than requested 20/06/2021\r\n clf = LocalOutlierFactor(n_neighbors=8)\r\n indexesOf4Ps = []\r\n for i in range(numOf4p):\r\n temp = indForCalc[np.where(y_kmeans==i)]\r\n outliersGradients = clf.fit_predict(temp.reshape(-1, 1))\r\n temp = temp[np.where(outliersGradients==1)]\r\n indexesOf4Ps.append(temp) \r\n startEnd4p.append([indexesOf4Ps[i][0]-2*delta,indexesOf4Ps[i][-1]+2*delta])\r\n startEnd4p.sort()\r\n startEnd4p[-1][1] = min(startEnd4p[-1][1],len(dataTcyc))\r\n \r\n #%% creates allfpa and indexes:\r\n gradIndexes = []\r\n Indexes = []\r\n allfpa = []\r\n for i in range(numOf4p):\r\n with suppress_stdout_stderr(): # using the func. while avoiding stdout printing in console\r\n FourPointareas, allfpares = FourPointCalc(dataTcyc[startEnd4p[i][0]:startEnd4p[i][1]],startEnd4p[i][0],orientation,cont)\r\n Indexes.append([FourPointareas[0][0],FourPointareas[0][1],FourPointareas[1][0],FourPointareas[1][1],FourPointareas[2][0],FourPointareas[2][1],FourPointareas[3][0],FourPointareas[3][1]]) #stars&end points for all 4 areas of each 4p test\r\n #indexes of data to calculate 4p measurements - SEND TO IF 0\r\n gradIndexes.append(list(range(int(Indexes[i][0]),int(Indexes[i][1])))+list(range(int(Indexes[i][2]),int(Indexes[i][3])))+list(range(int(Indexes[i][4]),int(Indexes[i][5])))+list(range(int(Indexes[i][6]),int(Indexes[i][7])))) \r\n allfpa.append(allfpares)\r\n allfpa=np.array(allfpa) # [mbit/g]; [g]; [mrad]; [V]\r\n print('Total of four points in Tcycle:',numOf4p)\r\n \r\n #%% Tsensor:\r\n \r\n tv = allfpa[:,-1]\r\n \r\n T_sesnitivity = (max(tv)-min(tv))/(temps[\"High1\"]-temps[\"Low\"]) # [V/C]\r\n T_offset = max(tv)/T_sesnitivity-temps[\"High1\"] # [C]\r\n \r\n if cycle1: # generate Tsensor info\r\n Tsensor =dict(sensitivity=T_sesnitivity,offset=T_offset)\r\n np.save('Tsensor.npy',Tsensor)\r\n else: # use Tsensor info from first cycle\r\n external_Tsensor = np.load('Tsensor.npy',allow_pickle='TRUE').item()\r\n Tsensor = external_Tsensor.copy()\r\n \r\n print('Temperature sensor before calibration: Offset',round(Tsensor[\"offset\"]),'[C] ; Sensitivty',round(1000*Tsensor[\"sensitivity\"],1),'[mV/C]')\r\n \r\n tc = tv/Tsensor[\"sensitivity\"]-Tsensor[\"offset\"]\r\n allfpa = np.append(allfpa,tc[:,None],axis=1) # [mbit/g] [g] [mrad] [tV] [tC]\r\n \r\n #temp_v = dict(min_tv = min(tv),fp1_tv = tv[0],max_tv = max(tv))\r\n \r\n \r\n # Tsensor.update(sensitivity=T_sesnitivity,offset=T_offset)\r\n #read_dictionary = np.load('Tsensor.npy',allow_pickle='TRUE').item()\r\n \r\n #%% filtering 4p areas\r\n fpIndToFilter=[]\r\n dataTcycFiltered = dataTcyc\r\n for i in range(numOf4p):\r\n fpIndToFilter.extend(list(range(startEnd4p[i][0],startEnd4p[i][1])))\r\n dataTcycFiltered = np.delete(dataTcyc, np.array(fpIndToFilter),axis = 0)\r\n \r\n alldatag = 1000000*dataTcycFiltered[:,0]/np.mean(allfpa[:,0]) # [mg]\r\n alltempe = dataTcycFiltered[:,1] # [V]\r\n alltempec = alltempe/Tsensor[\"sensitivity\"]-Tsensor[\"offset\"] # [C]\r\n \r\n #%% spikes finder\r\n # =============================================================================\r\n # filteredGrad = np.gradient(dataTcycFiltered[:,0])\r\n # # filtering local outliers from dataTcycFiltered\r\n # clf = LocalOutlierFactor(n_neighbors=30)\r\n # outliersfilteredGrad = clf.fit_predict(filteredGrad.reshape(-1, 1))\r\n # dataTcycFiltered=dataTcycFiltered[np.where(outliersfilteredGrad==-1)]\r\n # =============================================================================\r\n \r\n #%% Poly calculations\r\n SFp = np.polyfit(tv,allfpa[:,0],poly_rank) # SF poly\r\n Bp=np.polyfit(tv,allfpa[:,1],poly_rank) # Bias poly\r\n MAp=np.polyfit(tv,allfpa[:,2],poly_rank) # MA poly\r\n \r\n if cycle1: # save poly ana allfpa if it is first cycle\r\n poly = dict(SFp = SFp,Bp = Bp,MAp = MAp)\r\n np.save('poly.npy',poly)\r\n np.save('allfpa.npy',allfpa)\r\n else: # use poly from first cycle. load first cycle allfpa as well for reference\r\n external_allfpa = np.load('allfpa.npy')\r\n external_poly = np.load('poly.npy',allow_pickle='TRUE').item()\r\n poly = external_poly.copy()\r\n SFp = poly[\"SFp\"]\r\n Bp = poly[\"Bp\"]\r\n MAp = poly[\"MAp\"]\r\n \r\n SFcom = np.polyval(SFp,tv) # SF compensating values [bit/g]\r\n SFer = 1000000*(allfpa[:,0]-SFcom)/allfpa[:,0] # SF error [ppm]\r\n Bcom = np.polyval(Bp,tv) # Bias compensating values [g]\r\n Ber = 1000000*(allfpa[:,1] - Bcom) # Bias error [ug]\r\n MAcom = np.polyval(MAp,tv) # MA compensating values\r\n MAer = 1*(allfpa[:,2] - MAcom) # MA error [mrad]\r\n \r\n # Residual error, mean error, max error:\r\n SFerr = dict(std_err = np.std(SFer),mean_err = np.mean(SFer),max_err = max(abs(SFer))) # [ppm]\r\n Berr = dict(std_err = np.std(Ber),mean_err = np.mean(Ber),max_err = max(abs(Ber))) # [ug]\r\n MAerr = dict(std_err = 1000*np.std(MAer),mean_err = 1000*np.mean(MAer),max_err = 1000*max(abs(MAer))) # [urad]\r\n \r\n print('Bias residual error STD is:',round(Berr[\"std_err\"]),'[ug]')\r\n if not(cycle1):\r\n print('Bias residual mean error is:',round(Berr[\"mean_err\"]),'[ug]')\r\n \r\n #%% Tcycle Bias Delta:\r\n \r\n Tcycle_delta_bias_err = Ber[-1] - Ber[0] # [ug]\r\n Tcycle_p2p_bias_err = max(Ber) - min(Ber) # [ug]\r\n TcycleDeltaBias = dict(TDB_err = Tcycle_delta_bias_err,TDB_abs = abs(Tcycle_delta_bias_err),TDB_p2p = Tcycle_p2p_bias_err)\r\n print('Tcycle delta bias error is:',round(min(Ber)),'[ug]')\r\n \r\n #%% FULL temperature range sensitivities calculation (from poly):\r\n if cycle1:\r\n tv2 = list(range(temps[\"Low\"],temps[\"High1\"]+1))\r\n else:\r\n tv2 = list(range(temps[\"Low\"],temps[\"High2\"]+1))\r\n \r\n tv2 = (tv2+Tsensor[\"offset\"])*Tsensor[\"sensitivity\"]\r\n SFbyPol = np.polyval(SFp,tv2) # [mbit/g]\r\n BiasbyPol = np.polyval(Bp,tv2) # [g]\r\n MAbyPol = np.polyval(MAp,tv2); # [mrad]\r\n \r\n SensPoly_SF = 1000000*np.diff(SFbyPol)/np.mean(allfpa[:,0]) # SF sensitivities [ppm/C]\r\n SensPoly_Bias = 1000000*np.diff(BiasbyPol) # Bias sensitivities [ug/C]\r\n SensPoly_MA = 1000*np.diff(MAbyPol) # MA sensitivities [urad/C]\r\n \r\n MaxSensPoly_SF = max(abs(SensPoly_SF)); # max SF sensitivity [ppm/C]\r\n MaxSensPoly_Bias = max(abs(SensPoly_Bias)); # max Bias sensitivity [ug/C]\r\n MaxSensPoly_MA = max(abs(SensPoly_MA)); # max MA sensitivity [urad/C]\r\n \r\n MaxSensPoly = dict(SF = MaxSensPoly_SF,Bias = MaxSensPoly_Bias,MA = MaxSensPoly_MA)\r\n \r\n #%% FULL temperature range sensitivities calculation (between adjacent fpoints):\r\n \r\n SensSteps_Temp = np.diff(allfpa[:,4]) # delta temp C between steps\r\n SensSteps_SF = 1000000*np.diff(allfpa[:,0])/(SensSteps_Temp)/np.mean(allfpa[:,0]) # SF sensitivities [ppm/C]\r\n SensSteps_Bias = 1000000*np.diff(allfpa[:,1])/(SensSteps_Temp) # Bias sensitivities [ug/C]\r\n SensSteps_MA = 1000*np.diff(allfpa[:,2])/(SensSteps_Temp) # MA sensitivities [urad/C]\r\n \r\n if cont: # Remove steps sensitivity calculation for Max Temp due to high temperature drift:\r\n max_T_step = np.where(allfpa[:,4] == max(allfpa[:,4]))[0][0]\r\n SensSteps_Temp = np.delete(SensSteps_Temp,max_T_step-1)\r\n SensSteps_SF = np.delete(SensSteps_SF,max_T_step-1)\r\n SensSteps_Bias = np.delete(SensSteps_Bias,max_T_step-1)\r\n SensSteps_MA = np.delete(SensSteps_MA,max_T_step-1)\r\n \r\n # Remove steps where temperature change is smaller than 1C, to avoid dividing by small value\r\n RemoveFromSteps = np.where(abs(SensSteps_Temp)<1)[0]\r\n SensSteps_Temp = np.delete(SensSteps_Temp,RemoveFromSteps)\r\n SensSteps_SF = np.delete(SensSteps_SF,RemoveFromSteps)\r\n SensSteps_Bias = np.delete(SensSteps_Bias,RemoveFromSteps)\r\n SensSteps_MA = np.delete(SensSteps_MA,RemoveFromSteps) \r\n SensSteps = dict(Temp = SensSteps_Temp,SF = SensSteps_SF,Bias = SensSteps_Bias,MA = SensSteps_MA)\r\n \r\n MaxSensSteps_SF = max(abs(SensSteps[\"SF\"])) # max SF sensitivity [ppm/C]\r\n MaxSensSteps_Bias = max(abs(SensSteps[\"Bias\"])) # max Bias sensitivity [ug/C]\r\n MaxSensSteps_MA = max(abs(SensSteps[\"MA\"])) # max MA sensitivity [urad/C]\r\n MaxSensSteps = dict(SF = MaxSensSteps_SF,Bias = MaxSensSteps_Bias,MA = MaxSensSteps_MA)\r\n print('Bias sensitivity:',round(MaxSensSteps[\"Bias\"]),'[ug/C]')\r\n \r\n #%% saving results: added at 20/06/2021 11:20\r\n if cycle1:\r\n Tcyc_Results = np.array([MaxSensSteps_SF,SFerr[\"std_err\"],MaxSensSteps_Bias,Berr[\"std_err\"],MaxSensSteps_MA,MAerr[\"std_err\"],SFerr[\"mean_err\"],Berr[\"mean_err\"],MAerr[\"mean_err\"],0,0,0,Tcycle_delta_bias_err]).reshape(1, -1)\r\n np.save('Tcyc_1_Results.npy',Tcyc_Results)\r\n else:\r\n Tcyc_Results = np.array([SFerr[\"std_err\"],Berr[\"std_err\"],MAerr[\"std_err\"],SFerr[\"mean_err\"],Berr[\"mean_err\"],MAerr[\"mean_err\"],Tcycle_delta_bias_err]).reshape(1, -1)\r\n np.save('Tcyc_2_Results.npy',Tcyc_Results)\r\n \r\n #%% smoothed data for plots:\r\n \r\n all_sf = np.polyval(SFp,alltempe) # [bit/g]\r\n all_bias = np.polyval(Bp,alltempe) # [g]\r\n datacomp = 1000*(1000*dataTcycFiltered[:,0]/all_sf - all_bias) # [mg] compensated\r\n \r\n #%% plots:\r\n \r\n if 0: # perform decimation for displayed alldata plots\r\n alldatag = decimate(alldatag,decimation_factor,20,'fir')\r\n alltempec = decimate(alltempec,decimation_factor,20,'fir') \r\n datacomp = decimate(datacomp,decimation_factor,20,'fir')\r\n \r\n alldatag_s = smooth(alldatag,100) # [mg]\r\n alltempec_s = smooth(alltempec,100) # [C]\r\n datacompds = smooth(datacomp,100*decimation_factor) # [mg] compensated\r\n \r\n t = time.time()\r\n \r\n plot_folder = Folder_path[0]\r\n timeline_all = np.arange(len(dataTcyc[:,0]))/f_sample/60/60 #Hours\r\n timeline_filt = np.arange(len(alldatag))/f_sample/60/60 #Hours\r\n plt.rcParams['axes.formatter.useoffset'] = False\r\n \r\n # All Temp. Cycle raw data (acc and temp)\r\n host = host_subplot(111)\r\n par = host.twinx()\r\n p1, = host.plot(timeline_all,dataTcyc[:,0]/abs(allfpa[0,0])*1000000,'royalblue',linewidth=0.5,label='Acceleration')\r\n p2, = par.plot(timeline_all,dataTcyc[:,1],'coral',linewidth=0.5,label='Temperature')\r\n host.set_xlabel('Time [Hours]')\r\n host.set_ylabel('Acceleration [mg]')\r\n par.set_ylabel('Temperature [V]')\r\n plt.title('Temp. Cycle Raw Data - %s' %deviceName)\r\n cursor = Cursor(host, useblit=True, color='red', linewidth=2)\r\n host.grid(color='lightgrey',linewidth=0.5)\r\n host.yaxis.get_label().set_color(p1.get_color())\r\n par.yaxis.get_label().set_color(p2.get_color())\r\n # leg = plt.legend(loc='best')\r\n # leg.texts[0].set_color(p1.get_color())\r\n # leg.texts[1].set_color(p2.get_color())\r\n w=host.figure.get_figwidth()\r\n h=host.figure.get_figheight()\r\n host.figure.set_size_inches(1.5*w,h*1.5)\r\n host.figure.savefig(os.path.join(plot_folder,'Tcyc_all_raw_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # All Temp. Cycle data filtered w/o 4p\r\n host = host_subplot(111)\r\n par = host.twinx()\r\n p1, = host.plot(timeline_filt,alldatag_s,'royalblue',linewidth=0.5,label='Acceleration')\r\n p2, = par.plot(timeline_filt,alltempec_s,'coral',linewidth=0.5,label='Temperature')\r\n host.set_xlabel('Time [Hours]')\r\n host.set_ylabel('Acceleration [mg]')\r\n par.set_ylabel('Temperature [$^\\circ$C]')\r\n plt.title('Temp. Cycle All Data w/o 4p - %s' %deviceName)\r\n cursor = Cursor(host, useblit=True, color='red', linewidth=2)\r\n host.grid(color='lightgrey',linewidth=0.5)\r\n host.yaxis.get_label().set_color(p1.get_color())\r\n par.yaxis.get_label().set_color(p2.get_color())\r\n # leg = plt.legend(loc='best')\r\n # leg.texts[0].set_color(p1.get_color())\r\n # leg.texts[1].set_color(p2.get_color())\r\n w=host.figure.get_figwidth()\r\n h=host.figure.get_figheight()\r\n host.figure.set_size_inches(1.5*w,h*1.5)\r\n host.figure.savefig(os.path.join(plot_folder,'Tcyc_all_data_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # All Temp. Cycle Acc Vs Temp\r\n fig = plt.figure()\r\n plt.plot(alltempec_s,alldatag_s,'royalblue',linewidth=0.4)\r\n plt.title('Temp. Cycle Acc vs. Temp - %s'%deviceName)\r\n plt.xlabel('Temperature [$^\\circ$C]')\r\n plt.ylabel('Output [mg]')\r\n plt.grid(color='lightgrey',linewidth=0.5)\r\n # plt.ylim([math.floor(min(alldatag_s)),math.ceil(1+max(alldatag_s))])\r\n # plt.yticks(np.arange(math.floor(min(alldatag_s)),math.ceil(1+max(alldatag_s)),1))\r\n w=fig.get_figwidth()\r\n h=fig.get_figheight()\r\n fig.set_size_inches(1.5*w,h*1.5)\r\n fig.savefig(os.path.join(plot_folder,'Tcyc_all_vs_temp_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # Tcycle All Bias\r\n if cycle1: # use own poly\r\n fig = plt.figure()\r\n plt.plot(allfpa[:,4],1000*allfpa[:,1],'royalblue',linewidth=0.5,label='Temp. Cycle')\r\n plt.plot(allfpa[:,4],1000*Bcom,'magenta',dashes=[8, 4],linewidth=0.5,label='Poly. order: %d' %poly_rank)\r\n else: # use external poly\r\n if 0: # delete, for debug while creating script\r\n external_allfpa = deepcopy(allfpa)\r\n external_allfpa[:,1] = external_allfpa[:,1]+2/1000\r\n fig = plt.figure()\r\n plt.plot(external_allfpa[:,4],1000*external_allfpa[:,1],'royalblue',linewidth=0.5,label='Temp. Cycle 1')\r\n plt.plot(allfpa[:,4],1000*allfpa[:,1],'green',linewidth=0.5,label='Temp. Cycle 2')\r\n plt.plot(allfpa[:,4],1000*Bcom,'magenta',dashes=[8, 4],linewidth=0.5,label='Poly. order: %d' %poly_rank)\r\n plt.title('Temp. Cycle All Bias - %s'%deviceName)\r\n plt.xlabel('Temperature [$^\\circ$C]')\r\n plt.ylabel('Bias [mg]')\r\n plt.grid(color='lightgrey',linewidth=0.5)\r\n leg = plt.legend(loc='best')\r\n w=fig.get_figwidth()\r\n h=fig.get_figheight()\r\n fig.set_size_inches(1.5*w,h*1.5)\r\n fig.savefig(os.path.join(plot_folder,'Tcyc_Bias_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.plot(allfpa[0,4],1000*allfpa[0,1],'r.-',linewidth=0.5,label='')\r\n plt.close()\r\n # plt.show()\r\n \r\n # Tcycle All SF\r\n if cycle1: # use own poly\r\n fig = plt.figure()\r\n plt.plot(allfpa[:,4],allfpa[:,0],'royalblue',linewidth=0.5,label='Temp. Cycle')\r\n plt.plot(allfpa[:,4],SFcom,'magenta',dashes=[8, 4],linewidth=0.5,label='Poly. order: %d' %poly_rank)\r\n else: # use external poly\r\n if 0: # delete, for debug while creating script\r\n external_allfpa = deepcopy(allfpa)\r\n external_allfpa[:,0] = external_allfpa[:,0]+0.01\r\n fig = plt.figure()\r\n plt.plot(external_allfpa[:,4],external_allfpa[:,0],'royalblue',linewidth=0.5,label='Temp. Cycle 1')\r\n plt.plot(allfpa[:,4],allfpa[:,0],'green',linewidth=0.5,label='Temp. Cycle 2')\r\n plt.plot(allfpa[:,4],SFcom,'magenta',dashes=[8, 4],linewidth=0.5,label='Poly. order: %d' %poly_rank)\r\n plt.title('Temp. Cycle All SF - %s'%deviceName)\r\n plt.xlabel('Temperature [$^\\circ$C]')\r\n plt.ylabel('SF [0.5mLSB/g]')\r\n plt.grid(color='lightgrey',linewidth=0.5)\r\n leg = plt.legend(loc='best')\r\n w=fig.get_figwidth()\r\n h=fig.get_figheight()\r\n fig.set_size_inches(1.5*w,h*1.5)\r\n fig.savefig(os.path.join(plot_folder,'Tcyc_SF_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # Tcycle All MA\r\n if cycle1: # use own poly\r\n fig = plt.figure()\r\n plt.plot(allfpa[:,4],allfpa[:,2],'royalblue',linewidth=0.5,label='Temp. Cycle')\r\n plt.plot(allfpa[:,4],MAcom,'magenta',dashes=[8, 4],linewidth=0.5,label='Poly. order: %d' %poly_rank)\r\n else: # use external poly\r\n if 0: # delete, for debug while creating script\r\n external_allfpa = deepcopy(allfpa)\r\n external_allfpa[:,2] = external_allfpa[:,2]+0.2\r\n fig = plt.figure()\r\n plt.plot(external_allfpa[:,4],external_allfpa[:,2],'royalblue',linewidth=0.5,label='Temp. Cycle 1')\r\n plt.plot(allfpa[:,4],allfpa[:,2],'green',linewidth=0.5,label='Temp. Cycle 2')\r\n plt.plot(allfpa[:,4],MAcom,'magenta',dashes=[8, 4],linewidth=0.5,label='Poly. order: %d' %poly_rank)\r\n plt.title('Temp. Cycle All MA - %s'%deviceName)\r\n plt.xlabel('Temperature [$^\\circ$C]')\r\n plt.ylabel('MA [mrad]')\r\n plt.grid(color='lightgrey',linewidth=0.5)\r\n leg = plt.legend(loc='best')\r\n w=fig.get_figwidth()\r\n h=fig.get_figheight()\r\n fig.set_size_inches(1.5*w,h*1.5)\r\n fig.savefig(os.path.join(plot_folder,'Tcyc_MA_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # SF error Vs Temp\r\n fig = plt.figure()\r\n plt.plot(allfpa[:,4],SFer,'royalblue',linewidth=0.4)\r\n plt.title('Temp. Cycle SF Error - %s'%deviceName)\r\n plt.xlabel('Temperature [$^\\circ$C]')\r\n plt.ylabel('SF Error [ppm]')\r\n plt.grid(color='lightgrey',linewidth=0.5)\r\n # plt.ylim([math.floor(min(alldatag_s)),math.ceil(1+max(alldatag_s))])\r\n # plt.yticks(np.arange(math.floor(min(alldatag_s)),math.ceil(1+max(alldatag_s)),1))\r\n w=fig.get_figwidth()\r\n h=fig.get_figheight()\r\n fig.set_size_inches(1.5*w,h*1.5)\r\n fig.savefig(os.path.join(plot_folder,'Tcyc_ER_SF_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # Bias error Vs Temp\r\n fig = plt.figure()\r\n plt.plot(allfpa[:,4],Ber,'royalblue',linewidth=0.4)\r\n plt.title('Temp. Cycle Bias Error - %s'%deviceName)\r\n plt.xlabel('Temperature [$^\\circ$C]')\r\n plt.ylabel('Bias Error [$\\mu$g]')\r\n plt.grid(color='lightgrey',linewidth=0.5)\r\n # plt.ylim([math.floor(min(alldatag_s)),math.ceil(1+max(alldatag_s))])\r\n # plt.yticks(np.arange(math.floor(min(alldatag_s)),math.ceil(1+max(alldatag_s)),1))\r\n w=fig.get_figwidth()\r\n h=fig.get_figheight()\r\n fig.set_size_inches(1.5*w,h*1.5)\r\n fig.savefig(os.path.join(plot_folder,'Tcyc_ER_Bias_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # MA error Vs Temp\r\n fig = plt.figure()\r\n plt.plot(allfpa[:,4],1000*MAer,'royalblue',linewidth=0.4)\r\n plt.title('Temp. Cycle MA Error - %s'%deviceName)\r\n plt.xlabel('Temperature [$^\\circ$C]')\r\n plt.ylabel('MA Error [$\\mu$rad]')\r\n plt.grid(color='lightgrey',linewidth=0.5)\r\n # plt.ylim([math.floor(min(alldatag_s)),math.ceil(1+max(alldatag_s))])\r\n # plt.yticks(np.arange(math.floor(min(alldatag_s)),math.ceil(1+max(alldatag_s)),1))\r\n w=fig.get_figwidth()\r\n h=fig.get_figheight()\r\n fig.set_size_inches(1.5*w,h*1.5)\r\n fig.savefig(os.path.join(plot_folder,'Tcyc_ER_MA_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # All Temp. Cycle data filtered w/o 4p - compensated\r\n host = host_subplot(111)\r\n par = host.twinx()\r\n p1, = host.plot(timeline_filt,datacompds,'royalblue',linewidth=0.5,label='Acceleration')\r\n p2, = par.plot(timeline_filt,alltempec_s,'coral',linewidth=0.5,label='Temperature')\r\n host.set_xlabel('Time [Hours]')\r\n host.set_ylabel('Acceleration [mg]')\r\n par.set_ylabel('Temperature [$^\\circ$C]')\r\n plt.title('Temp. Cycle Acceleration (Compensated) - %s' %deviceName)\r\n cursor = Cursor(host, useblit=True, color='red', linewidth=2)\r\n host.grid(color='lightgrey',linewidth=0.5)\r\n host.yaxis.get_label().set_color(p1.get_color())\r\n par.yaxis.get_label().set_color(p2.get_color())\r\n # leg = plt.legend(loc='best')\r\n # leg.texts[0].set_color(p1.get_color())\r\n # leg.texts[1].set_color(p2.get_color())\r\n w=host.figure.get_figwidth()\r\n h=host.figure.get_figheight()\r\n host.figure.set_size_inches(1.5*w,h*1.5)\r\n host.figure.savefig(os.path.join(plot_folder,'Tcyc_data_comp_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n # All Temp. Cycle data filtered w/o 4p - compensated residual\r\n host = host_subplot(111)\r\n par = host.twinx()\r\n p1, = host.plot(timeline_filt,1000*(datacompds-statistics.mean(datacompds)),'royalblue',linewidth=0.5,label='Acceleration')\r\n p2, = par.plot(timeline_filt,alltempec_s,'coral',linewidth=0.5,label='Temperature')\r\n host.set_xlabel('Time [Hours]')\r\n host.set_ylabel('Acceleration Residual Error [$\\mu$g]')\r\n par.set_ylabel('Temperature [$^\\circ$C]')\r\n plt.title('Temp. Cycle Acceleration Residual Error - %s' %deviceName)\r\n cursor = Cursor(host, useblit=True, color='red', linewidth=2)\r\n host.grid(color='lightgrey',linewidth=0.5)\r\n host.yaxis.get_label().set_color(p1.get_color())\r\n par.yaxis.get_label().set_color(p2.get_color())\r\n # leg = plt.legend(loc='best')\r\n # leg.texts[0].set_color(p1.get_color())\r\n # leg.texts[1].set_color(p2.get_color())\r\n w=host.figure.get_figwidth()\r\n h=host.figure.get_figheight()\r\n host.figure.set_size_inches(1.5*w,h*1.5)\r\n host.figure.savefig(os.path.join(plot_folder,'Tcyc_data_res_%s%s.png'%(label,deviceName)),transparent=True,dpi=200)\r\n plt.close()\r\n # plt.show()\r\n \r\n elpse2 = time.time()-t\r\n print('plots time:',round(elpse2,2),'[sec]')\r\n \r\n #%%Output res\r\n return plot_folder","sub_path":"TcyclesAnalyzerA02.py","file_name":"TcyclesAnalyzerA02.py","file_ext":"py","file_size_in_byte":26989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"460259323","text":"def decimal(Bin):\r\n power = 0\r\n Dec = 0\r\n while Bin > 0:\r\n rem = Bin % 10\r\n Dec = Dec + rem * 2**(power)\r\n Bin //= 10\r\n power += 1\r\n return Dec\r\n \r\n\r\nif __name__ == \"__main__\":\r\n Bin = int(input(\"Enter the binary number.\"))\r\n print(decimal(Bin))\r\n","sub_path":"Python programs/BinaryToDecimal.py","file_name":"BinaryToDecimal.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"578591784","text":"#import mdbread\nimport maidenhead\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport numpy as np\nimport pandas as pd\nimport reverse_geocode\nfrom matplotlib.collections import PatchCollection\nfrom matplotlib.patches import Polygon\nfrom mpl_toolkits.basemap import Basemap\nimport pypyodbc\n\n'''\ndb = mdbread.MDB(\"LogData.mdb\")\ntabs = db.tables\ntab = db['tblContacts']\ndf = tab.to_data_frame()\n\n'''\nconn = pypyodbc.win_connect_mdb('C:\\\\Users\\\\rhkin\\\\Documents\\\\Affirmatech\\\\N3FJP Software\\\\ACLog\\\\LogData.mdb')\n\ndf = pd.io.sql.read_sql('SELECT fldgridr, fldqslr, fldmode FROM tblContacts', con=conn)\ndf = df.convert_objects(convert_numeric=True)\n#df.dropna(how='all', inplace=True)\n\nconn.close()\n\n\n\n#print(df.columns)\n'''\ndf = df[df['fldrstr']<20.0]\nplt.figure()\ndf['fldrsts'].plot.hist(bins=50)\nplt.figure()\ndf['fldrstr'].plot.hist(bins=50)\n#plt.figure()\ndf.plot.scatter('fldrsts','fldrstr')\nplt.show()\n'''\n\nlat = []\nlon = []\ncountries = []\ncountries2 = []\ncolors = []\nsymbols = []\n\n# m = Basemap(projection='npaeqd', llcrnrlat= -90, urcrnrlat= 90, \\\n# resolution='i', llcrnrlon=-180+offset, urcrnrlon=180+offset, \\\n# boundinglat=10, lon_0=-122)\nm = Basemap(projection='cyl', resolution='l', llcrnrlat=-90, urcrnrlat=90, \\\n llcrnrlon=-180, urcrnrlon=180)\n\nfor index, row in df.iterrows():\n grid = row['fldgridr']\n qsl = row['fldqslr']\n sgrid = str(grid)\n if (len(sgrid) > 1 and len(sgrid) < 9):\n x, y = maidenhead.toLoc(sgrid)\n if len(sgrid)==4:\n x += (-1 if x>=0 else 1)\n y += (-2 if y>=0 else 2)\n elif len(sgrid)==6:\n x += (-0.04166667 if x>=0 else 0.04166667)\n y += (-0.08333333 if y>=0 else 0.08333333)\n\n c = reverse_geocode.search([(x, y)])\n # x, y = m(x, y)\n lat.append(x)\n lon.append(y)\n if (qsl == 'Y'):\n colors.append('green')\n if(~countries.__contains__(c[0]['country_code'])):\n countries.append(c[0]['country_code'])\n if (countries2.__contains__(c[0]['country_code'])):\n countries2.remove(c[0]['country_code'])\n else:\n colors.append('yellow')\n if countries2.__contains__(c[0]['country_code']) == False and \\\n countries.__contains__(c[0]['country_code']) == False:\n countries2.append(c[0]['country_code'])\n mode = row['fldmode']\n if mode == 'SSB':\n s = 'o'\n else:\n s = 'D'\n symbols.append(s)\n\ndf2 = pd.DataFrame(\n {'lat': lat,\n 'lon': lon,\n 'colors': colors,\n 'symbols': symbols\n })\n\ncountries = list(set(countries))\ncountries.append('AQ')\n#print countries\n\n#fig = plt.figure(figsize=(17, 9))\nfig = plt.figure()\n\nax = fig.add_subplot(111, axisbg='w', frame_on=False)\n\nm.readshapefile('ne_10m_admin_0_countries_lakes', 'units', color='#444444', linewidth=.2)\n\nfor info, shape in zip(m.units_info, m.units):\n iso2 = info['ISO_A2']\n patches = [Polygon(np.array(shape), True)]\n pc = PatchCollection(patches)\n #pc.set_edgecolor('#FFFFFFFF')\n pc.set_facecolor('#00000000')\n #pc.set_color('#00000030')\n #pc.set_linewidth(20)\n mpl.rcParams['hatch.linewidth'] = 2\n if iso2 in countries:\n #pc.set_edgecolor('#00FF00FF')\n #mpl.rcParams['hatch.color']='#00FF0080'\n pc.set_facecolor('#00FF0080')\n #pc.set_hatch('////')\n if iso2 in countries2:\n #pc.set_edgecolor('#FFFF00FF')\n #mpl.rcParams['hatch.color']='#FFFF0080'\n pc.set_facecolor('#00000060')\n #pc.set_hatch('\\\\\\\\\\\\\\\\')\n ax.add_collection(pc)\n\ndf3 = df2[df2.symbols == 'o']\nm.scatter(df3.lon, df3.lat, s=50, c=df3.colors, marker='o', edgecolors='black', linewidths=1, alpha=.5, zorder=99)\ndf3 = df2[df2.symbols != 'o']\nm.scatter(df3.lon, df3.lat, s=40, c=df3.colors, marker='D', edgecolors='black', linewidths=1, alpha=.5, zorder=98)\n\nm.drawcountries(color='black', zorder=1)\nm.drawstates(color='black', linestyle='dotted', zorder=2)\nm.drawcoastlines(color='black', zorder=3)\n#m.etopo(scale=0.5, alpha=1, zorder=0)\n#m.shadedrelief(zorder=0)\n#m.bluemarble(scale=1, zorder=0)\n\nplt.legend(loc='lower left', handles=[\n mpl.lines.Line2D([], [], color='green', markeredgecolor='black', marker='o', linewidth=0, markersize=10, label='QSL SSB'),\n mpl.lines.Line2D([], [], color='green', markeredgecolor='black', marker='D', linewidth=0, markersize=8, label='QSL Digital'),\n mpl.lines.Line2D([], [], color='yellow', markeredgecolor='black', marker='o', linewidth=0, markersize=10, label='SSB'),\n mpl.lines.Line2D([], [], color='yellow', markeredgecolor='black', marker='D', linewidth=0, markersize=8, label='Digital'),\n mpl.patches.Patch(edgecolor='#00FF00FF', linewidth=1, label='Confirmed Country', hatch='////'),\n mpl.patches.Patch(edgecolor='#FFFF00FF', linewidth=1, label='Unconfirmed Country', hatch='\\\\\\\\\\\\\\\\')])\n\n\nplt.suptitle('KK6VGE Contacts')\nfig.tight_layout()\nplt.show()\n# plt.savefig('test.png')\n","sub_path":"dashmdb.py","file_name":"dashmdb.py","file_ext":"py","file_size_in_byte":5019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"203517071","text":"\"\"\"index.bzl provides the packaging ruleset\"\"\"\n\nload(\"@rules_python//python:defs.bzl\", \"PyInfo\")\nload(\"//tools:utils.bzl\", \"get_transitive_deps\")\nload(\"//tools:providers.bzl\", \"PackageInfo\")\n\ndef _package_api(ctx):\n language = None\n if PyInfo in ctx.attr.srcs[0]:\n language = \"python\"\n output_file = ctx.actions.declare_file(ctx.attr.name + \".zip\")\n files = ctx.files._packaging_bin + ctx.files._python_bin + ctx.files.srcs + ctx.files.data + get_transitive_deps(ctx.attr.srcs)\n args = ctx.actions.args()\n args.add_joined([s.path for s in get_transitive_deps(ctx.attr.srcs)], join_with = \",\")\n args.add(\"--output-file\", output_file.path)\n args.add(\"--runtime\", language)\n ctx.actions.run(\n mnemonic = \"Packaging\",\n executable = ctx.executable._packaging_bin,\n arguments = [args],\n inputs = files,\n outputs = [output_file],\n )\n\n return [\n DefaultInfo(\n files = depset([output_file]),\n ),\n PackageInfo(language = language),\n ]\n\npackage_api = rule(\n implementation = _package_api,\n attrs = {\n \"srcs\": attr.label_list(\n providers = [PyInfo],\n ),\n \"data\": attr.label_list(\n allow_files = True,\n ),\n \"_packaging_bin\": attr.label(\n default = \"//tools/package:package_api_bin\",\n executable = True,\n cfg = \"exec\",\n ),\n \"_python_bin\": attr.label(\n default = \"@python_interpreter//:python_bin\",\n executable = True,\n cfg = \"exec\",\n allow_files = True,\n ),\n },\n)\n","sub_path":"tools/package/index.bzl","file_name":"index.bzl","file_ext":"bzl","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"391677606","text":"\"\"\"Inventory module, update, a CSV file that lists which furniture is rented to which customer \"\"\"\nimport csv\nfrom functools import partial\n\ndef add_furniture(invoice_file, customer_name, item_code, item_description, item_monthly_price):\n \"\"\"add new entry to invoice_file\"\"\"\n with open(invoice_file, 'a', newline='') as csvfile:\n writer = csv.writer(csvfile)\n entry = [customer_name, item_code, item_description, item_monthly_price]\n writer.writerow(entry)\n\ndef single_customer(customer_name, invoice_file):\n \"\"\"Using closure and currying, \"\"\"\n add_rental_item = partial(add_furniture, invoice_file, customer_name)\n def read_customer_invoice(rental_items):\n with open(rental_items, newline='') as rentfile:\n reader = csv.reader(rentfile, delimiter=',')\n for row in reader:\n item_code = row[0]\n item_description = row[1]\n item_price = row[2]\n add_rental_item(item_code, item_description, item_price)\n return read_customer_invoice\n","sub_path":"students/pooria_k/Lesson08/inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"29251674","text":"#!/usr/bin/python\n\ndef dheanamh(lib, ctx):\n \n cppObjGroupOne = lib.ObjGroup(\"cppObj\", \"C++\")\n cppObjGroupOne.setHeaders([\"Globals.h\"])\n cppObjGroupOne.setClasses([\"Engine\", \"Nexus\"])\n cppObjGroupOne.mapHeaderToSource(\"driver.h\", [\"driver.cpp\"])\n cppObjGroupOne.setSource([\"app.cpp\"])\n \n project = lib.Project(\"Alpha\")\n project.addBuildGroup(cppObjGroupOne)\n \n return project\n #\n#\n","sub_path":"Examples/CppPrototype/Alpha/dh.build.py","file_name":"dh.build.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"574746430","text":"# Calculate pay\n# Created by: Martin Alonso\n# Date created: 2019-04-06\n# Purpose: calculate pay from hours worked and rate of pay \n\n# Take inputs from the user \nhours = input(\"How many hours worked? \")\npay_rate = input(\"What is the pay rate? \")\n\n# Convert hours and pay_rate to float; calculate pay\npay = float(hours) * float(pay_rate)\n\n# Return pay \nprint(\"Your payment is ${:.2f}\".format(pay))\n","sub_path":"Week2/firstProgram.py","file_name":"firstProgram.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"309943087","text":"# *-* coding: utf-8 *-*\n\na = 2\nb = 10\nc = 15\n\num = b / a\ndois = b * c\ntres = um + c\nquatro = c / b \ncinco = c / float(b)\nseis = quatro - c\nsete = (a+b) * (b+c)\noito = \"willie\"\n\n","sub_path":"teste.py","file_name":"teste.py","file_ext":"py","file_size_in_byte":177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"518486230","text":"\n\n#Code for a valentines day madlib for Dario.\n# 2/5/2016\n\nprint (\"Hi Dario! Happy Valentine's Day.\")\n\ndoing = input(\"How are you doing?\")\n\nprint(\"I'm glad you're \"+ doing + \". Me too!\")\n# Let Dario say Yes or No\n\nanswer = input(\"Do you want to see the awesome card I made you?!\")\nif answer == \"yes\" or \"Yes\" : \n print(\"Great! I'm gonna need some help though. I dozed off as I was writing, and I forgot a few words. I need your help filling them in.\")\nelse :\n print(\"Oh, cmon, lazyface! Please say yes!\")\n\nmoons = input(\"Gimme a plural noun.\")\nbrunch = input(\"Name a party occasion.\")\narrived = input(\"And a past tense verb.\")\nnice = input(\"Adjective.\")\npeople = input(\"Type of animal, plural.\")\ncharmed = input(\"Emotion.\")\nclothes = input(\"Type of clothing.\")\nhandsome = input(\"Adjective.\")\nC3PO = input(\"Celebrity.\")\nHan = input(\"Another celebrity.\")\nanimal = input(\"Animal.\")\nkilos = input(\"Unit of measurement, plural.\")\nthreaded = input(\"Past tense verb.\")\nneedle = input(\"Noun.\")\nexhibit = input(\"Type of party or event.\")\nbeers = input(\"Beverage.\")\nMatching = input(\"Verb ending in -ing. Capitalized, please.\")\nHalf = input(\"Noun, also capitalized.\")\nhead = input(\"Body part.\")\nheels = input(\"Another body part.\")\nnickname = input(\"Nickname.\")\n\n\nanswer2 = input(\"Thanks a lot! That oughtta do the trick. Ready for your message?\")\n\nif answer2 == \"yes\" or \"Yes\" :\n print(\"One Saturday morning many \" + moons + \" ago, I went to a \" + brunch + \" party hoping to meet some nice \" + people + \" . And there you were in your fancy \" +clothes+ \", more \" + handsome + \" than \" + C3PO + \" and more fascinating than \" + Han + \"--although, I admit, I didn't realize it at the time. In contrast, you thought I was as beautiful as a \" + animal + \" , and you spent several \" + kilos + \" of time constructing a message that \" + threaded + \" the \" + needle + \" of flirtation and friendliness. It worked! We went to that Georgia O'Keeffe \" + exhibit + \" at the De Young and sipped some \" + beers + \" at the \" + Matching +\" \"+ Half + \" Cafe. Honestly, from then on I've been \" + head + \" over \" + heels + \". Happy Valentine's Day, \" +nickname+ \"!\" ) \nelse: \n print(\"Ha, ha, very funny. Cmon, say yes :) \")\n\n\n\n\n","sub_path":"RunMeXoXo.py","file_name":"RunMeXoXo.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"73877261","text":"from django.http import HttpResponse, JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework import generics\nfrom rest_framework.parsers import JSONParser\nfrom apps.handbooks.models import Category, Genre, City, Country\nfrom apps.handbooks.serializers import (\n CategorySerializer, GenreSerializer,\n CountrySerializer, CityWithCountrySerializer\n)\n\n\n@csrf_exempt\ndef category_list(request):\n \"\"\"\n Список всех категории или создание новой категории\n \"\"\"\n if request.method == 'GET':\n categories = Category.objects.all()\n serializer = CategorySerializer(categories, many=True)\n return JsonResponse(serializer.data, safe=False)\n\n elif request.method == 'POST':\n data = JSONParser().parse(request)\n serializer = CategorySerializer(data=data)\n if serializer.is_valid():\n serializer.save()\n return JsonResponse(serializer.data, status=201)\n return JsonResponse(serializer.errors, status=400)\n\n\n@csrf_exempt\ndef category_detail(request, pk):\n \"\"\"\n Выборка, обновление или удаление одной категории\n \"\"\"\n try:\n category = Category.objects.get(pk=pk)\n except Category.DoesNotExist:\n return HttpResponse(status=404)\n\n if request.method == 'GET':\n serializer = CategorySerializer(category)\n return JsonResponse(serializer.data)\n\n elif request.method == 'PUT':\n data = JSONParser().parse(request)\n serializer = CategorySerializer(category, data=data)\n if serializer.is_valid():\n serializer.save()\n return JsonResponse(serializer.data)\n return JsonResponse(serializer.errors, status=400)\n\n elif request.method == 'DELETE':\n category.delete()\n return HttpResponse(status=204)\n\n\n@csrf_exempt\ndef genre_list(request):\n \"\"\"\n Список всех категории или создание новой жанра\n \"\"\"\n if request.method == 'GET':\n genres = Genre.objects.all()\n serializer = GenreSerializer(genres, many=True)\n return JsonResponse(serializer.data, safe=False)\n\n elif request.method == 'POST':\n data = JSONParser().parse(request)\n serializer = GenreSerializer(data=data)\n if serializer.is_valid():\n serializer.save()\n return JsonResponse(serializer.data, status=201)\n return JsonResponse(serializer.errors, status=400)\n\n\n@csrf_exempt\ndef genre_detail(request, pk):\n \"\"\"\n Выборка, обновление или удаление одной жанра\n \"\"\"\n try:\n genre = Genre.objects.get(pk=pk)\n except Genre.DoesNotExist:\n return HttpResponse(status=404)\n\n if request.method == 'GET':\n serializer = GenreSerializer(genre)\n return JsonResponse(serializer.data)\n\n elif request.method == 'PUT':\n data = JSONParser().parse(request)\n serializer = GenreSerializer(genre, data=data)\n if serializer.is_valid():\n serializer.save()\n return JsonResponse(serializer.data)\n return JsonResponse(serializer.errors, status=400)\n\n elif request.method == 'DELETE':\n genre.delete()\n return HttpResponse(status=204)\n\n\nclass CountryList(generics.ListCreateAPIView):\n queryset = Country.objects.all()\n serializer_class = CountrySerializer\n\n\nclass CountryDetail(generics.RetrieveUpdateDestroyAPIView):\n queryset = Country.objects.all()\n serializer_class = CountrySerializer\n\n\nclass CityList(generics.ListCreateAPIView):\n queryset = City.objects.all().select_related('country')\n serializer_class = CityWithCountrySerializer\n\n\nclass CityDetail(generics.RetrieveUpdateDestroyAPIView):\n queryset = City.objects.all().select_related('country')\n serializer_class = CityWithCountrySerializer\n","sub_path":"apps/handbooks/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"266093524","text":"\"\"\"\nStart from integer 1, remove any integer that contains 9 such as 9, 19, 29...\n\nSo now, you will have a new integer sequence: 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, ...\n\nGiven a positive integer n, you need to return the n-th integer after removing. Note that 1 will be the first integer.\n\nExample 1:\nInput: 9\nOutput: 10\nHint: n will not exceed 9 x 10^8.\n\"\"\"\n\n# switch from decimal base to nineth-base\n\nclass Solution(object):\n def newInteger(self, n):\n \"\"\"\n :type n: int\n :rtype: int\n \"\"\"\n ans = ''\n while n > 0:\n ans = str(n%9) + ans\n n /= 9\n return int(ans)","sub_path":"leetcode/660_remove_9.py","file_name":"660_remove_9.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"320775963","text":"from sklearn import datasets\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn import metrics\r\nfrom sklearn.model_selection import cross_val_predict\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n\r\n\r\ndef dataprocess(df):\r\n xlist=[]\r\n ylist=[]\r\n df=df.replace(['NR'],[0.0])\r\n array=np.array(df).astype(float)\r\n\r\n for i in range(0,4320,18):\r\n for j in range(24-9):\r\n mat=array[i:i+18,j:j+9]\r\n label=array[i+9,j+9]\r\n xlist.append(mat[0:10].reshape(90))\r\n ylist.append(label)\r\n x=np.array(xlist)\r\n y=np.array(ylist)\r\n\r\n return x, y\r\n\r\ndf = pd.read_csv('pm2.5.csv', usecols=range(3, 27))\r\nx, y = dataprocess(df)\r\n\r\n\r\n\r\nloaded_data=datasets.load_boston()\r\ndata_X=loaded_data.data\r\ndata_y=loaded_data.target\r\n\r\nX_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2)#训练和测试集划分\r\nmodel=LinearRegression()\r\nmodel.fit(X_train,y_train)\r\nprint(model.coef_)\r\nprint(model.intercept_)\r\n\r\ny_pred=model.predict(X_test)\r\n\r\nprint(\"MSE is:\",metrics.mean_squared_error(y_test,y_pred))#LOSS=MSE均方差\r\n\r\n#10折交叉验证\r\npredicted=cross_val_predict(model,x,y,cv=10)\r\nprint(\"MSE is(with_cvp):\",metrics.mean_squared_error(y,predicted))\r\nplt.scatter(y,predicted,color='y',marker='o')\r\nplt.scatter(y,y,color='g',marker='+')\r\nplt.title('pm2.5_prediction')\r\nplt.xlabel('real_pm2.5')\r\nplt.ylabel('predict_pm2.5')\r\nplt.show()\r\n\r\n","sub_path":"linear_regression/linear_regression/two_factors_sklearn.py","file_name":"two_factors_sklearn.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"61762951","text":"## 1. Introduction ##\n\nimport pandas as pd\n\nhouses = pd.read_table('AmesHousing_1.txt',sep='\\t')\n\nland_slope = houses['Land Slope']\nland_slope.head()\nscale_land = 'ordinal'\n\nroof_style = houses['Roof Style']\nroof_style.unique()\nscale_roof = 'nominal'\n\nkitchen = houses['Kitchen AbvGr']\nkitchen.unique()\nkitchen_variable = 'discrete'\n\n\n## 2. The Mode for Ordinal Variables ##\n\ndef mode_function(array):\n dict_values={}\n unique_array = set(array)\n \n for value in unique_array:\n dict_values[value]=0\n \n for row in array:\n dict_values[row]+=1\n \n return max(dict_values.keys(),key=(lambda k: dict_values[k]))\n \nmode_function = mode_function(houses['Land Slope'])\n\nmode_method = houses['Land Slope'].mode()\n\nsame = mode_function == mode_method\n\n\n\n## 3. The Mode for Nominal Variables ##\n\n# The function we wrote (you can copy-paste yours from the previous screen)\ndef mode(array):\n counts = {}\n \n for value in array:\n if value in counts:\n counts[value] += 1\n else:\n counts[value] = 1\n \n return max(counts, key = counts.get)\ndef mode(array):\n counts = {}\n \n for value in array:\n if value in counts:\n counts[value] += 1\n else:\n counts[value] = 1\n \n return (max(counts, key = counts.get),\n counts\n )\n\nmode, value_counts = mode(houses['Roof Style'])\n\n## 4. The Mode for Discrete Variables ##\n\nbedroom_variable = 'discrete'\n\nbedroom_mode = houses['Bedroom AbvGr'].mode()\n\nprice_variable = 'continuous'\n\nprice_mode = houses['SalePrice'].mode()\n\n## 5. Special Cases ##\n\nintervals = pd.interval_range(start = 0, end = 800000, freq = 100000)\ngr_freq_table = pd.Series([0,0,0,0,0,0,0,0], \n index = intervals)\n\nfor value in houses['SalePrice']:\n for interval in intervals:\n if value in interval:\n gr_freq_table.loc[interval] += 1\n break\n\nprint(gr_freq_table)\n\neff = gr_freq_table.reset_index()\nmaxi = 0\nprint(eff)\n\nfor row in gr_freq_table.items(): \n if row[1] > maxi:\n maxi = row[1]\n mode = int((row[0].left + row[0].right) / 2)\n\nmidpoint = mode\n\nmean = houses['SalePrice'].mean()\n\nmedian = houses['SalePrice'].median()\n\nsentence_1 = True\nsentence_2 = True\n\n \n \n\n## 6. Skewed Distributions ##\n\ndistribution_1 = {'mean': 3021 , 'median': 3001, 'mode': 2947}\ndistribution_2 = {'median': 924 , 'mode': 832, 'mean': 962}\ndistribution_3 = {'mode': 202, 'mean': 143, 'median': 199}\n\nshape_1 = 'right skew'\nshape_2 = 'right skew'\nshape_3 = 'left skew'\n\n## 7. Symmetrical Distributions ##\n\nimport matplotlib.pyplot as plt\n\nhouses['Mo Sold'].plot.kde(xlim=(1,12))\n\nplt.axvline(x=houses['Mo Sold'].mode().item(),label='Mode')\n\nplt.axvline(x=houses['Mo Sold'].median(),label='Median')\n\nplt.axvline(x=houses['Mo Sold'].mean(),label='Mean')\n\nplt.legend()\n\nplt.show()\n\n","sub_path":"Python - Projets/DataQuest - 2018-08/statistics-intermediate/The Mode-307.py","file_name":"The Mode-307.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"306161025","text":"from Cell import Cell\nfrom Coordinate import Coordinate\nfrom Rule import Rule\n\nimport copy;\n\n\nclass GameField:\n def __init__(self, width, height):\n # self.mapSize = mapSize; #22\n self.listContainsPreviousState = [];\n self.listOfCells = [];\n self.ruleName = \"\";\n self.rule = Rule();\n self.state1 = False;\n self.state2 = False;\n self.width = width;\n self.height = height;\n self.rowCounter = 0;\n self.columnCounter = 0;\n self.x = 0;\n self.y = 0;\n self.MAX = 100;\n self.valOfRightExtremeCoordinateX = 0;\n self.valOfLeftExtremeCoordinateX = 0;\n self.xCoordinateRight = 0;\n self.xCoordinateLeft = 0;\n\n self.setMapValues();\n\n def setMapValues(self):\n\n if self.width % 2 != 0 and self.height % 2 != 0:\n self.x = -(self.width - 1) / 2;\n self.y = (self.height - 1) / 2;\n\n\n\n\n elif self.width % 2 == 0 and self.height % 2 != 0:\n self.x = -(self.width - 2) / 2;\n self.y = (self.height - 1) / 2;\n\n elif self.width % 2 != 0 and self.height % 2 == 0:\n self.x = -(self.width - 1) / 2;\n self.y = (self.height - 2) / 2;\n\n\n\n else:\n self.x = -(self.width - 2) / 2;\n self.y = (self.height - 2) / 2;\n\n self.columnCounter = self.width;\n self.rowCounter = self.height;\n\n x1 = self.x;\n\n for i in range(self.rowCounter):\n\n new = [];\n for j in range(self.columnCounter):\n new.append(Cell(False, Coordinate(self.x, self.y)));\n # print(str(self.listOfCells[i][j])+\" \\n\");\n # self.listOfCells.append(Cell(False,Coordinate(x,y)))\n self.x = self.x + 1;\n self.y = self.y - 1;\n # self.x = -self.mapSize / 2;\n self.x = x1;\n self.listOfCells.append(new)\n\n self.valOfRightExtremeCoordinateX = self.listOfCells[0][0].coordinate.x + (self.width - 1);\n self.valOfLeftExtremeCoordinateX = self.listOfCells[0][len(self.listOfCells[0]) - 1].coordinate.x - (\n self.width - 1);\n print(str(self.valOfRightExtremeCoordinateX) + \" \" + str(self.valOfLeftExtremeCoordinateX));\n\n def showMap(self):\n for i in range(self.rowCounter):\n for j in range(self.columnCounter):\n print(\"{:->7}\".format(\n str(self.listOfCells[i][j].coordinate.x) + \" \" + str(self.listOfCells[i][j].coordinate.y)) + \" \",\n end='')\n\n print(\"\\n\");\n\n def setNumberOfCellsNeighbours(self, cell, i, j):\n\n state = [];\n\n self.xCoordinateRight = cell.coordinate.x;\n self.xCoordinateLeft = cell.coordinate.x;\n\n if (self.xCoordinateRight == self.valOfRightExtremeCoordinateX):\n self.xCoordinateRight = self.valOfLeftExtremeCoordinateX;\n else:\n self.xCoordinateRight = cell.coordinate.x + 1;\n\n if (self.xCoordinateLeft == self.valOfLeftExtremeCoordinateX):\n self.xCoordinateLeft = self.valOfRightExtremeCoordinateX;\n else:\n self.xCoordinateLeft = cell.coordinate.x - 1;\n\n if any(\n (\n cellExample.coordinate.x == self.xCoordinateRight and cellExample.coordinate.y == cell.coordinate.y and cellExample.previousState == True)\n for cellExample in self.listOfCells[i]):\n state.append(True);\n\n if any(\n (\n cellExample.coordinate.x == self.xCoordinateLeft and cellExample.coordinate.y == cell.coordinate.y and cellExample.previousState == True)\n for cellExample in self.listOfCells[i]):\n state.append(True);\n\n if (i - 1 >= 0):\n # 3\n if any(\n (\n cellExample.coordinate.x == cell.coordinate.x and cellExample.coordinate.y == cell.coordinate.y + 1 and cellExample.previousState == True)\n for cellExample in self.listOfCells[i - 1]):\n state.append(True);\n\n # 5\n #do wziecia\n if any(\n (\n cellExample.coordinate.x == self.xCoordinateRight and cellExample.coordinate.y == cell.coordinate.y + 1 and cellExample.previousState == True)\n for cellExample in self.listOfCells[i - 1]):\n state.append(True);\n\n if any(\n (\n cellExample.coordinate.x == self.xCoordinateLeft and cellExample.coordinate.y == cell.coordinate.y + 1 and cellExample.previousState == True)\n for cellExample in self.listOfCells[i - 1]):\n state.append(True);\n\n if i + 1 < self.rowCounter:\n # 4\n if any(\n (\n cellExample.coordinate.x == cell.coordinate.x and cellExample.coordinate.y == cell.coordinate.y - 1 and cellExample.previousState == True)\n for cellExample in self.listOfCells[i + 1]):\n state.append(True);\n\n #do wziecia\n # 6\n if any(\n (\n cellExample.coordinate.x == self.xCoordinateRight and cellExample.coordinate.y == cell.coordinate.y - 1 and cellExample.previousState == True)\n for cellExample in self.listOfCells[i + 1]):\n state.append(True);\n\n # 7\n if any(\n (\n cellExample.coordinate.x == self.xCoordinateLeft and cellExample.coordinate.y == cell.coordinate.y - 1 and cellExample.previousState == True)\n for cellExample in self.listOfCells[i + 1]):\n state.append(True);\n\n # if any(\n # (\n # cellExample.coordinate.x == cell.coordinate.x + 1 and cellExample.coordinate.y == cell.coordinate.y and cellExample.previousState == True)\n # for cellExample in self.listOfCells[i]):\n # state.append(True);\n #\n # if any(\n # (\n # cellExample.coordinate.x == cell.coordinate.x - 1 and cellExample.coordinate.y == cell.coordinate.y and cellExample.previousState == True)\n # for cellExample in self.listOfCells[i]):\n # state.append(True);\n #\n # if (i - 1 >= 0):\n # # 3\n # if any(\n # (\n # cellExample.coordinate.x == cell.coordinate.x and cellExample.coordinate.y == cell.coordinate.y + 1 and cellExample.previousState == True)\n # for cellExample in self.listOfCells[i - 1]):\n # state.append(True);\n #\n # # 5\n # if any(\n # (\n # cellExample.coordinate.x == cell.coordinate.x + 1 and cellExample.coordinate.y == cell.coordinate.y + 1 and cellExample.previousState == True)\n # for cellExample in self.listOfCells[i - 1]):\n # state.append(True);\n #\n # if any(\n # (\n # cellExample.coordinate.x == cell.coordinate.x - 1 and cellExample.coordinate.y == cell.coordinate.y + 1 and cellExample.previousState == True)\n # for cellExample in self.listOfCells[i - 1]):\n # state.append(True);\n #\n # if i + 1 < self.rowCounter:\n # # 4\n # if any(\n # (\n # cellExample.coordinate.x == cell.coordinate.x and cellExample.coordinate.y == cell.coordinate.y - 1 and cellExample.previousState == True)\n # for cellExample in self.listOfCells[i + 1]):\n # state.append(True);\n #\n # # 6\n # if any(\n # (\n # cellExample.coordinate.x == cell.coordinate.x + 1 and cellExample.coordinate.y == cell.coordinate.y - 1 and cellExample.previousState == True)\n # for cellExample in self.listOfCells[i + 1]):\n # state.append(True);\n #\n # # 7\n # if any(\n # (\n # cellExample.coordinate.x == cell.coordinate.x - 1 and cellExample.coordinate.y == cell.coordinate.y - 1 and cellExample.previousState == True)\n # for cellExample in self.listOfCells[i + 1]):\n # state.append(True);\n\n for l in range(len(state)):\n cell.numberOfNeighbours = cell.numberOfNeighbours + 1;\n\n print(\"{:->7}\".format(\n str(i) + \" \" + str(j) + \" \" + str(cell.coordinate.x) + \" \" + str(\n cell.coordinate.y)) + \" :liczba zywych sasiadow: \" + str(cell.numberOfNeighbours)\n + \"\\n\")\n\n if (cell.numberOfNeighbours == 3 and cell.previousState == False):\n cell.isAlive = True;\n elif ((cell.numberOfNeighbours == 2 or cell.numberOfNeighbours == 3) and cell.previousState == True):\n cell.isAlive = True;\n\n elif (\n (\n cell.numberOfNeighbours == 0 or cell.numberOfNeighbours == 1 or cell.numberOfNeighbours == 4 or cell.numberOfNeighbours == 5 or cell.numberOfNeighbours == 6 or cell.numberOfNeighbours == 7\n ) and cell.previousState == True\n ):\n cell.isAlive = False;\n\n def startTheGame(self):\n\n # for i in range(len(self.listOfCells)):\n # it = False;\n # it1 = False;\n # cell=copy.copy(cell);\n\n # for i in range(len(self.listOfCells)):\n # # it = False;\n # # it1 = False;\n # # cell=copy.copy(cell);\n # cell=self.listOfCells[0][1];\n # if cell.coordinate.x == (self.mapSize / 2):\n # # cell1 = Cell(cell.isAlive, Coordinate(-cell.coordinate.x, cell.coordinate.y));\n # cell1 = next((obj for obj in self.listOfCells[i] if obj.coordinate.x == -cell.coordinate.x and obj.coordinate.y == cell.coordinate.y), None)\n #\n # it = cell1 in self.listOfCells[i] and cell1.previousState == True;\n #\n #\n # else:\n # # cell1 = Cell(cell.isAlive, Coordinate(cell.coordinate.x + 1, cell.coordinate.y))\n # cell1 = next((obj for obj in self.listOfCells[i] if obj.coordinate.x == cell.coordinate.x + 1 and obj.coordinate.y == cell.coordinate.y),None)\n # it = cell1 in self.listOfCells[i] and cell1.previousState==True\n # if it:\n # print(it)\n #\n # #print(\"xzc\");\n #\n # if cell.coordinate.x == (-self.mapSize / 2):\n # # cell1 = Cell(cell.isAlive, Coordinate(-cell.coordinate.x, cell.coordinate.y));\n # cell1 = next((obj for obj in self.listOfCells[i] if obj.coordinate.x == -cell.coordinate.x and obj.coordinate.y == cell.coordinate.y), None)\n #\n # it1 = cell1 in self.listOfCells[i] and cell1.previousState == True;\n #\n #\n # else:\n # # cell2 = Cell(cell.isAlive, Coordinate(cell.coordinate.x + 1, cell.coordinate.y))\n # cell2 = next((obj for obj in self.listOfCells[i] if obj.coordinate.x == cell.coordinate.x - 1 and obj.coordinate.y == cell.coordinate.y),None)\n # it1 = cell2 in self.listOfCells[i] and cell2.previousState == True\n #\n # # if (it):\n # # cell.numberOfNeighbours = cell.numberOfNeighbours + 1\n # #\n # # if (it1):\n # # cell.numberOfNeighbours = cell.numberOfNeighbours + 1\n #\n # if (it):\n # print(\"podnsze\\n\");\n #\n # if (it1):\n # print(\"podnsze\\n\");\n\n # for i in range(len(self.listOfCells)):\n # #cell=copy.copy(cell);\n #\n #\n # #it = Cell(cell.isAlive, Coordinate(-cell.coordinate.x, cell.coordinate.y)) in self.listOfCells[i] and cell.previousState == True;\n #\n #\n #\n # #if Cell(True,Coordinate(0,4)) == obj:\n # cell=Cell(True,Coordinate( self.listOfCells[1][4].coordinate.x,self.listOfCells[1][4].coordinate.y))\n #\n # print( cell in self.listOfCells[i] and cell.previousState== True)\n\n # print( Cell(True,Coordinate(0,4)) in self.listOfCells[i])\n # cell=Cell(True,Coordinate(0,4))\n # print(cell.coordinate.y)\n # it=cell in self.listOfCells[i];\n # print(it);\n #\n # check = True;\n #\n # for i in range(self.rowCounter):\n #\n # for j in range(self.columnCounter):\n # if self.listOfCells[i][j].previousState:\n # # print(\"[]\", end='')\n #\n # if (check):\n # self.listContainsPreviousState.append(copy.deepcopy(self.listOfCells[i]));\n # check = False;\n #\n # # else:\n # # #print(\"-\", end='')\n # self.setNumberOfCellsNeighbours(self.listOfCells[i][j], i, j)\n # # print(\"\\n\");\n #\n # # print(\"druga petla\\n\")\n # #\n # # for i in range(len(self.listContainsPreviousState)):\n # # # self.listContainsPreviousState.append(self.listOfCells[i]);\n # # for j in range(len(self.listContainsPreviousState[i])):\n # # if self.listContainsPreviousState[i][j].previousState:\n # # print(\"[]\", end='')\n # # else:\n # # print(\"-\", end='')\n # #\n # # #self.setNumberOfCellsNeighbours(self.listOfCells[i][j])\n # # print(\"\\n\");\n #\n # for i in range(self.rowCounter):\n # for j in range(self.columnCounter):\n # self.listOfCells[i][j].previousState = self.listOfCells[i][j].isAlive;\n #\n # self.listOfCells[i][j].numberOfNeighbours = 0;\n print(1);\n","sub_path":"GameOfLife/CellularAutomaton/classes/GameField.py","file_name":"GameField.py","file_ext":"py","file_size_in_byte":14190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"430332203","text":"#!python3\n\n# https://forum.omz-software.com/topic/4178/tableview-button-color/2\n\nimport ui\nfrom objc_util import *\n\n#\n# UITableViewDelegate\n#\n\ndef handler(_cmd, action, index_path):\n\tprint('Trash it!')\n\t\nblock_handler = ObjCBlock(handler, restype=None, argtypes=[c_void_p, c_void_p, c_void_p])\n\ndef tableView_editActionsForRowAtIndexPath_(self, cmd, table_view, index_path):\n\tyellow = ObjCClass('UIColor').yellowColor()\n\t\n\taction = ObjCClass('UITableViewRowAction').rowActionWithStyle_title_handler_(0, ns('Trash it!'), block_handler)\n\taction.setBackgroundColor(yellow)\n\t\n\treturn ns([action]).ptr\n\t\n\t\ndef make_table_view_delegate():\n\tmethods = [tableView_editActionsForRowAtIndexPath_]\n\tprotocols = ['UITableViewDelegate']\n\tdelegate = create_objc_class('CustomTableViewDelegate', methods=methods, protocols=protocols)\n\treturn delegate.alloc().init()\n\t\n#\n# UITableView\n#\n\ntable_view = ui.TableView(name='Custom Action', frame=(0, 0, 300, 480))\ntable_view.data_source = ui.ListDataSource(['Hallo', 'Hi', 'Ciao'])\n\ntable_view_objc = ObjCInstance(table_view)\ntable_view_objc.setDelegate(make_table_view_delegate())\n\ntable_view.present('sheet')\n\n","sub_path":"_2017/using-uitableviewrowaction.py","file_name":"using-uitableviewrowaction.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"197123592","text":"#--gcloud tts\nfrom google.cloud import texttospeech\n\n#--mediaplayer\nimport os\n\n#--firebase import\nfrom datetime import datetime\nimport firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import firestore\nimport record_data\n\ndb = firestore.client()\n\n#gcloud tts\nclient = texttospeech.TextToSpeechClient()\n\n\n\nclass read_data:\n def __init__(self):\n self.temp = 0\n self.humi = 0\n self.wave = 0\n self.decibel = 0\n self.pir = 0\n self.people= ''\n self.date = ''\n self.result = ''\n self.txt = \"\" #for tts\n self.txt_pir_add = \"\" #pir txt\n self.txt_temp_add = \"\" #temp text\n self.txt_humi_add = \"\" #humi text\n self.txt_people_add = \"\" #people text\n self.txt_decibel_add =\"\" #decibel text\n \n \n def get_data(self):\n print(self.people)\n #-- get latest document ID\n user_ref = db.collection(u'hanium_db')\n docs = user_ref.stream()\n *_, last = docs\n self.date = last.id\n\n #-- get values from document\n doc_ref = db.collection('hanium_db').document(f'{self.date}')\n #get data from firestore\n doc = doc_ref.get().to_dict()\n \n print(f'DATE: {self.date}')\n print(f'from databas: {doc}')\n \n # dict to string \n self.result = doc.get('result')\n self.people = doc.get('PEOPLE')\n\n def txt_people(self):\n result_people = self.people\n hour = self.date[11:13]\n minute = self.date[14:16]\n print(result_people)\n if result_people == \"0\":\n self.txt_people_add = \"지금은 사람이 없습니다. 소등 및 각종 디바이스들을 종료합니다. \"\n else:\n self.txt_people_add = \"현재 시간 {}시 {}분,\".format(hour,minute)\n \n\n# def txt_pir(self):\n# result_pir = self.result[:1]\n \n def txt_decibel(self):\n result_decibel = self.result[1:2]\n if result_decibel == \"0\":\n self.txt_decibel_add = \"지금은 소음이 없습니다.\"\n else:\n self.txt_decibel_add = \"소음이 발생하고 있습니다. 필요에 따라 ��문을 닫아주세요.\"\n \n def txt_temp(self):\n result_temp = self.result[2:3]\n \n if result_temp == \"2\":\n self.txt_temp_add = \"온도가 높습니다. 에어컨을 작동 시킬게요. 야외활동을 되도록이면 자제해주시길 바랍니다.\"\n elif result_temp == \"1\":\n self.txt_temp_add = \"적정 온도입니다.\"\n else:\n self.txt_temp_add = \"온도가 낮습니다. 난방기를 작동합니다. 나가실 때 적절한 겉옷 챙겨주시길 바랍니다.\"\n \n def txt_humi(self):\n result_humi = self.result[3:]\n \n if result_humi== \"2\":\n self.txt_humi_add = \"습도가 높습니다. 제습기를 실행시킬게요.\"\n elif result_humi == \"1\":\n self.txt_humi_add = \"적정 습도입니다.\"\n else:\n self.txt_humi_add = \"습도가 낮습니다. 가습기를 실행시킬게요.\"\n \n def add_txt(self):\n \n if self.people == \"0\":\n self.txt = self.txt_people_add\n else:\n self.txt = self.txt_people_add + self.txt_decibel_add + self.txt_temp_add + self.txt_humi_add\n print(self.txt)\n \n def make_tts(self):\n print(\"make_tts start\")\n synthesis_input = texttospeech.SynthesisInput(text=self.txt)\n print(self.txt)\n voice = texttospeech.VoiceSelectionParams(\n language_code=\"ko-KR\", ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL)\n \n #audio \n audio_config = texttospeech.AudioConfig(\n audio_encoding=texttospeech.AudioEncoding.MP3)\n\n #all together\n response = client.synthesize_speech(\n input=synthesis_input, voice=voice, audio_config=audio_config)\n\n\n with open('output.mp3', 'wb') as out:\n out.write(response.audio_content)\n print('Audio content written to file \"output.mp3\"')\n \n #speaker\n #os.system('omxplayer -o local output.mp3 \"{}\" /&'.format(address))\n print(\"gcloud end\")\n\n \n def tts_data(self):\n print('read_data start')\n self.get_data()\n self.txt_people()\n self.txt_decibel()\n self.txt_temp()\n self.txt_humi()\n self.add_txt()\n #self.make_tts()\n\nif __name__ == '__main__':\n x= read_data()\n x.tts_data()\n","sub_path":"read_to_gcloud.py","file_name":"read_to_gcloud.py","file_ext":"py","file_size_in_byte":4553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"98091597","text":"#!/usr/bin/env python3\n\"\"\"Map stage 2.\"\"\"\n\nimport sys\n\n# INPUT:\n# doc_id[TAB]term[TAB]term_frequency[NEW_LINE]\n\nfor line in sys.stdin:\n split_line = line.strip().split('\\t')\n key = split_line[0]\n split_values = split_line[1].split(' ')\n # print(split_line)\n # print(split_line)\n doc = key\n word = split_values[0]\n freq = split_values[1]\n print(word + '\\t' + doc + ' ' + freq)\n\n# OUTPUT:\n# term[TAB]doc_id[TAB]term_frequency[NEW_LINE]\n","sub_path":"index/hadoop/exec/inverted_index/map2.py","file_name":"map2.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"292411210","text":"import pygame, diep_sprite\nfrom constants import *\n\nclass Sparkle(diep_sprite.DiepSprite):\n def __init__(self, screen, x, y, color):\n super().__init__(screen, x, y, color, 0, 0,\n sprite_type=TYPE_ANIMATION,\n draw_healthbar=False)\n #Use hp as timeout\n self.hit_points = 15\n\n #Override super class's update function\n def update(self):\n #Use hp as timeout\n self.hit_points -= 1\n\n def draw(self, povx, povy, center_on_screen=False):\n #Get coordinate adjustments\n x_adjust = self.screen.get_width()/2 - povx\n y_adjust = self.screen.get_height()/2 - povy\n x = self.x + x_adjust\n y = self.y + y_adjust\n #If this sprite is not on screen, don't draw it\n if self.isOffScreen(x, y):\n return\n length = 5\n pygame.draw.line(self.screen, self.color,\n (x-length,y-length), (x+length,y+length), 1) #1 is width\n pygame.draw.line(self.screen, self.color,\n (x-length,y+length), (x+length,y-length), 1) #1 is width","sub_path":"CS2/9900_diepio/PygameDiepio/sparkle.py","file_name":"sparkle.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"253990566","text":"#!/usr/bin/env python \n'''Add associate feature info to glif lib based on a csv file\n csv format glyphname,featurename[,featurevalue]'''\n__url__ = 'http://github.com/silnrsi/pysilfont'\n__copyright__ = 'Copyright (c) 2015, SIL International (http://www.sil.org)'\n__license__ = 'Released under the MIT License (http://opensource.org/licenses/MIT)'\n__author__ = 'David Raymond'\n__version__ = '0.0.1'\n\nfrom silfont.UFOlib import *\n\nsuffix = \"_AssocFeat\"\nargspec = [\n ('ifont',{'help': 'Input font file'}, {'type': 'infont'}),\n ('ofont',{'help': 'Output font file','nargs': '?' }, {'type': 'outfont', 'def': suffix}),\n ('-i','--input',{'help': 'Input csv file'}, {'type': 'infile', 'def': suffix+'.csv'}), \n ('-l','--log',{'help': 'Log file'}, {'type': 'outfile', 'def': suffix+'.log'}),\n ('-v','--version',{'help': 'UFO version to output'},{}),\n ('-p','--params',{'help': 'Font output parameters','action': 'append'}, {'type': 'optiondict'})]\n\ndef doit(args) :\n font = args.ifont\n csv = args.input\n glyphlist = font.deflayer.keys() # Identify which glifs have not got an AssocFeat set\n \n for l in csv.readlines() :\n l = l.strip()\n if l == \"\" or l[0] == \"#\" : continue # Skip blank lines and comments\n line = l.split(\",\")\n if len(line) < 2 or len(line) > 3 : font.logger.log(\"Invalid line in csv: \" + l,\"E\") ; continue\n glyphn = line[0]\n feature = line[1]\n value = line[2] if len(line) == 3 else \"\"\n \n if glyphn in glyphlist :\n glyph = font.deflayer[glyphn]\n if glyph[\"lib\"] is None : glyph.add(\"lib\")\n glyph[\"lib\"].setval(\"org.sil.assocFeature\",\"string\",feature)\n if value is not \"\" :\n glyph[\"lib\"].setval(\"org.sil.assocFeatureValue\",\"integer\",value)\n else :\n if \"org.sil.assocFeatureValue\" in glyph[\"lib\"] : glyph[\"lib\"].remove(\"org.sil.assocFeatureValue\")\n glyphlist.remove(glyphn)\n else :\n font.logger.log(\"No glyph in font for \" + glyphn,\"E\")\n\n for glyphn in glyphlist : # Remove any values from remaining glyphs\n glyph = font.deflayer[glyphn]\n if glyph[\"lib\"] :\n if \"org.sil.assocFeatureValue\" in glyph[\"lib\"] : glyph[\"lib\"].remove(\"org.sil.assocFeatureValue\")\n if \"org.sil.assocFeature\" in glyph[\"lib\"] :\n glyph[\"lib\"].remove(\"org.sil.assocFeature\")\n font.logger.log(\"Feature info removed for \" + glyphn,\"I\")\n \n return font\n \nexecute(\"PSFU\",doit, argspec)","sub_path":"scripts/UFOsetAssocFeat.py","file_name":"UFOsetAssocFeat.py","file_ext":"py","file_size_in_byte":2547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"172256286","text":"from collections import defaultdict\nimport re\n\n\ndef parse_file():\n bots = defaultdict(list)\n instructions = []\n\n with open(\"input.txt\") as f:\n for line in f.readlines():\n found = re.search(r'value ([\\d ]+) goes to ([\\w ]+)', line)\n if found:\n value = int(found.group(1))\n bot_id = found.group(2)\n bots[bot_id].append(value)\n else:\n instructions.append(line)\n return bots, instructions\n\n\ndef proceed(bots, instructions):\n bot = None\n\n while not all(x is None for x in instructions):\n for i, instruction in enumerate(instructions):\n if instruction is None:\n continue\n found = re.search(r'([\\w ]+) gives low to ([\\w ]+) and high to ([\\w ]+)', instruction)\n if found:\n giver = found.group(1)\n receive_low = found.group(2)\n receive_high = found.group(3)\n\n if 61 in bots[giver] and 17 in bots[giver]:\n bot = giver\n\n if len(bots[giver]) < 2:\n continue\n\n bots[giver].sort()\n\n bots[receive_high].append(bots[giver].pop())\n bots[receive_low].append(bots[giver].pop(0))\n instructions[i] = None\n return bot\n\n\nif __name__ == \"__main__\":\n bots, instructions = parse_file()\n bot = proceed(bots, instructions)\n\n print(\"Puzzle 1: {}\".format(bot))\n\n value = bots['output 0'][0] * bots['output 1'][0] * bots['output 2'][0]\n print(\"Puzzle 2: {}\".format(value))\n","sub_path":"AOC-2016/Day-10/day-10.py","file_name":"day-10.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"240698486","text":"import nltk.util\nfrom re import sub\nfrom lib import FeatureExtractor as FE\nfrom lib import aux\nimport pickle\n\n\nclass PrepareData:\n\n def __init__ (self):\n # self.svm = svm.svm()\n self.data = []\n self.feat_space = []\n self.training_data = []\n self.test_data = []\n self.FE = FE.FeatureExtractor(retrain=False)\n\n def all_features(self, n=3, ignore_unknown = True, filter=[0,1,2,3], grams=\"token\"):\n \"\"\" \n @input n : number of grams (1 - unigram, 2 - bigram, 3 -trigram, etc)\n @input ignore_unknown : ignore the 'unknown' labelled data\n @input ngrams : type of n-grams, options 'char', 'token' or 'linguistic'.\n tokens are word-like sequences\n chars are just that\n linguistic uses linguistic extraction features (see FeatureExtractor module)\n\"\"\"\n if not self.data:\n raise Exception(\"No data loaded, aborting...\")\n\n if not (grams == 'char' or grams == 'token' or grams == 'linguistic'):\n raise Exception(\"Unsupported value for parameter 'grams': %s\" % grams)\n\n\n ngrams = []\n feature_vectors = []\n for d in self.data[1:]:\n doc_label = int(d[1])\n if not doc_label == 0 or ignore_unknown == False: ## labelled as 'unknown'\n if doc_label == 0:\n doc_label = 9 #change unknown class to 9... because then the resulting matrices will be correct\n doc_id = d[0]\n doc_title = d[2]\n doc_abstract = d[3]\n doc_abstract_title = doc_abstract + \" \" + doc_title\n doc_abstract_title = sub(r'\\.([A-Z])', r'. \\1', doc_abstract_title) # fixes some (if not all) cases with having no space between sentences.\n doc_grams = self.FE.feature_extractor(doc_abstract_title, n, filter, 2, grams)\n\n feature_vectors.append([doc_id, doc_label, doc_grams, []])\n ngrams += doc_grams\n unique_ngrams = aux.unique_items(ngrams) # remove duplicates across all documents\n\n self.dict_ngrams_gramnum = dict(zip(unique_ngrams, range(1,len(unique_ngrams)+1)))\n self.dict_gramnum_ngrams = dict(zip(range(1,len(unique_ngrams)+1),unique_ngrams))\n\n for feature_vector in feature_vectors:\n # create vectors of: (doc_id, doc_label, [(, ), ...])\n feats = []\n for gram in feature_vector[2]:\n gramcount = feature_vector[2].count(gram)\n gramnum = self.dict_ngrams_gramnum[gram]\n featnum_feat_set = (gramnum, gramcount)\n if featnum_feat_set not in feats:\n feats.append(featnum_feat_set)\n\n feature_vector[3] = (feature_vector[1], feats)\n\n self.feature_vectors = feature_vectors\n for feat in self.feature_vectors:\n self.feat_space.append((feat[0], feat[-1])) #(id, [feature-tuples])\n\n return feature_vectors #dict_ngrams_gramnum, dict_gramnum_ngrams, feature_vectors #unique_ngrams, feature_vectors\n\n # def prep_featurespace(self):\n\n\n def load_csv_file(self, filename, path=\"./preliminary_experiments/\"):\n fd = open(path+filename, 'r')\n file_lines = fd.readlines()\n fd.close()\n\n for line in file_lines:\n split_line = line.split('|')\n self.data.append(split_line)\n\n\n return\n\n def experiment_prepare(self, filename, save_file, input_path=\"./preliminary_experiments/\", output_path=\"./experiments/\", ignore_unknown=True, ngrams=3, grams=\"token\", filter=[0,1,2,3]):\n self.load_csv_file(filename, input_path)\n self.all_features(ngrams, ignore_unknown, filter=filter, grams=grams)\n\n # self.prep_featurespace()\n\n outputlines = \"\"\n for item in self.feat_space:\n outputlines += \"%s,\" % item[0]\n outputlines += str(item[1][0])\n for feat_pair in item[1][1]:\n outputlines += \" %s:%s\" %(str(feat_pair[0]), str(feat_pair[1]))\n outputlines += \"\\n\"\n\n fd = open(output_path+save_file, 'w')\n fd.write(outputlines)\n fd.close()\n\n fd1 = open(output_path+save_file+'_dictof_ngrams_gramnum', 'w')\n pickle.dump(self.dict_ngrams_gramnum, fd1)\n fd1.close()\n\n fd2 = open(output_path+save_file+'_dictof_gramnum_ngrams', 'w')\n pickle.dump(self.dict_gramnum_ngrams, fd2)\n fd2.close()\n","sub_path":"source/PrepareData.py","file_name":"PrepareData.py","file_ext":"py","file_size_in_byte":4468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"136809774","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n'''\nThis is a bare bone code for linear interpolation\nwritten by Dr. Md. Enamul Hoque, Department of Physics, SUST.\n(c) mjonyh@gmail.com 2019\n\nFormulae:\n f(x) = fi + (fi+1 - fi) * (x - xi) / (xi+1 - xi)\n'''\n\n# 1. import necessary package, library or module here\nimport matplotlib.pyplot as plt\n\ndef linearinterpolation(xGiven, yGiven, xline, yline):\n stop = xGiven[-1]\n x = xGiven[0]\n\n fi = yGiven[0]\n fl = yGiven[1]\n xi = xGiven[0]\n xl = xGiven[1]\n\n while (x <= stop):\n f = fi + (fl - fi) * (x - xi) / (xl - xi)\n xline.append(x)\n yline.append(f)\n x = x + 0.001\n\n return xline, yline\n\n# 2. define data set (list of x and y) and iteration number\nxGiven = [2, 4, 5, 8, 9, 10]\nyGiven = [3, 5, 3, 8, 7, 10]\nxline = []\nyline = []\n\n# 3. user defined function call\n\nfor i in range(len(xGiven)-1):\n xlist = [xGiven[i], xGiven[i+1]]\n ylist = [yGiven[i], yGiven[i+1]]\n xline, yline = linearinterpolation(xlist, ylist, xline, yline)\n\n# print(xline, yline)\n\n# 4. plot data point in red and linear fit in green\nplt.plot(xGiven, yGiven, 'ro', label='Given data point')\nplt.plot(xline, yline, 'g--', label='Linear fit')\n\nplt.xlabel(\"x-axis\")\nplt.ylabel(\"y-axis\")\nplt.legend()\n\nplt.show()\n","sub_path":"Slide Codes/5. linear_interpolation_barebone_complete.py","file_name":"5. linear_interpolation_barebone_complete.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"468207191","text":"from flask import Flask, request, abort, jsonify\nfrom sqlalchemy import and_, or_\nfrom sqlalchemy.orm import aliased\nfrom sqlalchemy.orm.exc import NoResultFound\nfrom server_root.models.users.models import User, aliases\nfrom server_root import app, db, regional_db\nfrom server_root.utilities import file_utilities\nfrom server_root.models.users.models import check_cur_user_token\nfrom server_root.config import S3_PROFILE_PIC_FOLDER\n\n@app.route('/api/v1/contacts', methods=['GET'])\ndef contacts():\n cur_user_id = request.args.get('cur_user_id', None)\n if cur_user_id:\n cur_user_id = str(cur_user_id)\n else:\n abort(404)\n try:\n cur_user = regional_db.session.query(User).filter(and_(User.id==cur_user_id, User.is_deleted==False)).one()\n except NoResultFound:\n abort(404)\n\n auth_token = request.args.get('auth_token', None)\n if not auth_token:\n abort(404)\n\n check_cur_user_token(cur_user_id=cur_user.id, cur_user_token=auth_token)\n cur_user.update_located_region()\n cur_user.update_last_time_use_app()\n\n contact_object_list = cur_user.contact_list()\n\n contact_list = []\n\n for contact in contact_object_list:\n contact_dict = dict(\n contact_id = contact.contact_id,\n contact_phone_number_hash = contact.contact_phone_number_hash,\n session_id = cur_user.get_session_id_with_contact(contact.contact_id),\n )\n last_time_interacted_with_user_object = cur_user.get_last_time_interacted_with_contact(contact_id=contact.contact_id)\n if last_time_interacted_with_user_object:\n contact_dict[\"last_time_interacted_with_user\"] = last_time_interacted_with_user_object.strftime('%a, %d %b %Y %H:%M:%S -0000')\n contact_list.append(contact_dict)\n\n result = {}\n result['objects'] = contact_list\n return jsonify(result)\n\n\n\n","sub_path":"server_root/apis/v1/contacts/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"540276656","text":"class Contact:\r\n def __init__(self,p_name,p_no,email_add):\r\n self.p_name=p_name\r\n self.p_no=p_no\r\n self.email_add=email_add\r\n def get_name(self):\r\n return input('Enter The Name of The Person')\r\n def set_name(self):\r\n self.p_name=self.get_name()\r\n return self.p_name\r\n def get_phone(self):\r\n return input('Enter The Contact Number')\r\n def set_phone(self):\r\n self.p_no=self.get_phone()\r\n return self.p_no\r\n def get_email(self):\r\n return input('Enter The E-mail Id of The Person')\r\n def set_email(self):\r\n self.email_add=self.get_email()\r\n return self.email_add\r\n def insert_record(self):\r\n contact[self.set_name()]={'phone':self.set_phone(),'email':self.set_email()}\r\n print(contact)\r\n print(self.__str__())\r\n p=input('Do You Want To Store this Record.\\nPress 1 To Yes\\nPress 2 To No')\r\n if p=='1':\r\n with open('contact-dict.txt','a') as f:\r\n for c, v in contact.items():\r\n f.write('name : '+c + ' ')\r\n f.write(\"\".join([\" {}: {}\".format(v, d) for v, d in v.items()]) + ' ')\r\n f.write('\\n')\r\n c=input('Do You Want TO View The Records.\\nPress 1 To Yes.\\nPress 0 To Exit.\\n')\r\n if c=='1':\r\n self.display()\r\n elif c=='0':\r\n return\r\n \r\n def display(self):\r\n r=open('contact-dict.txt','r')\r\n p=r.readline()\r\n while p!='':\r\n print(p)\r\n p=r.readline()\r\n def detail(self):\r\n per_name=input('Enter the name of the Person')\r\n r=open('contact-dict.txt','r')\r\n p=r.readline()\r\n while p!='':\r\n if per_name in p:\r\n print(p)\r\n break\r\n else:\r\n p=r.readline()\r\n def delete(self):\r\n per_name=input('Enter the name of the Person whose details you want to delete.')\r\n with open(\"contact-dict.txt\", \"r\") as f:\r\n lines = f.readlines()\r\n print(lines)\r\n with open(\"contact-dict.txt\", \"w\") as f:\r\n for line in lines:\r\n if per_name not in line.strip(\"\\n\") :\r\n f.write(line)\r\n \r\n def update(self):\r\n per_name=input('Enter the name of the Person whose details you want to update.')\r\n with open(\"contact-dict.txt\", \"r\") as f:\r\n lines = f.readlines()\r\n \r\n with open(\"contact-dict.txt\", \"w\") as f:\r\n for line in lines:\r\n if per_name not in line.strip(\"\\n\") :\r\n f.write(line)\r\n self.insert_record()\r\n \r\n \r\n \r\n \r\n def __str__(self):\r\n return f'Person name : {self.p_name}\\nPhone : {self.p_no}\\nE-mail id : {self.email_add}\\n'\r\ndef main():\r\n a=Contact('yashasvi','87090979798','yashasvimahajan276@gmail.com')\r\n \r\n while True:\r\n ch=input('Press 1 to Enter New Record.\\nPress 2 to Update Record.\\nEnter 3 to View Record.\\nPress 4 to get Detail of the person.\\nPress 5 to delete Person Detail\\nPress 0 to Exit.\\n')\r\n if ch=='1':\r\n a.insert_record()\r\n elif ch=='2':\r\n a.update()\r\n elif ch=='3':\r\n a.display()\r\n elif ch=='4':\r\n a.detail()\r\n elif ch=='5':\r\n a.delete()\r\n \r\n \r\n elif ch=='0':\r\n break\r\ncontact={}\r\nmain()\r\n","sub_path":"Python_Assignment/mod7/lookupClass.py","file_name":"lookupClass.py","file_ext":"py","file_size_in_byte":3503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"195019736","text":"from pymongo import MongoClient\r\nimport json\r\nimport io\r\nimport pprint\r\n\r\nclass DB:\r\n\r\n#Construtor instancia a classe com Strings que referenciam a URI e o Banco de Dados\r\n def __init__(self, uri, db):\r\n self.__uri = uri\r\n self.__db = db\r\n\r\n#Tenta conectar com o Banco de Dados, retorna 'True' se conseguiu e 'False' se não\r\n def __connection(self):\r\n try:\r\n client = MongoClient(self.__uri)\r\n self.__collections = client[self.__db]\r\n except:\r\n raise Exception('Impossivel conectar com o Banco de Dados')\r\n return False\r\n \r\n return True\r\n\r\n#Retorna uma lista com os nomes das Collections no Banco de Dados, retorna 'None' se falhar \r\n def lista(self):\r\n if(not self.__connection()): return False\r\n \r\n try:\r\n select = self.__collections.collection_names()\r\n return select\r\n except:\r\n raise Exception(\"Impossivel obter os nomes das collections\")\r\n return None\r\n\r\n#Insere um JSON em uma Collection do Banco de Dados, retorna 'True' se conseguiu e 'False' se não\r\n def insere(self, collection, data):\r\n #collection = [String] : Nome da collection no MongoDB\r\n #data = [File, String, List, Disct] : Dados no formato JSON para inserir no MongoDB\r\n\r\n if(not self.__connection()): return False\r\n\r\n if(isinstance(data, type({}))): datas = [data]\r\n\r\n if(isinstance(data, type([]))): datas = data\r\n\r\n elif(isinstance(data, type(''))): datas = json.loads(data)\r\n\r\n elif(isinstance(data, io.IOBase)): datas = json.load(data)\r\n\r\n else:\r\n raise Exception(\"Tipo do parametro 'data' deve ser String, Lista, Dicionario ou Ponteiro de Arquivo\")\r\n return False\r\n \r\n try:\r\n self.__collections[collection].insert_many(datas)\r\n return True\r\n except:\r\n raise Exception(\"Impossivel inserir no Banco de Dados\")\r\n return False","sub_path":"Coletor/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"639896005","text":"from unittest import TestCase\nfrom scattertext.termcompaction.AssociationCompactor import AssociationCompactor\nfrom scattertext.test.test_TermDocMat import get_hamlet_term_doc_matrix\n\n\nclass TestClassPercentageCompactor(TestCase):\n def test_compact(self):\n term_doc_mat = get_hamlet_term_doc_matrix()\n new_tdm = AssociationCompactor(max_terms=213).compact(term_doc_mat)\n self.assertEqual(len(term_doc_mat.get_terms()), 26875)\n self.assertEqual(len(new_tdm.get_terms()), 213)\n\n def test_get_term_ranks(self):\n term_doc_mat = get_hamlet_term_doc_matrix()\n ranks = AssociationCompactor(max_terms=213).get_term_ranks(term_doc_mat)\n self.assertEqual(len(ranks), term_doc_mat.get_num_terms())\n self.assertGreaterEqual(ranks.min().min(), 0)\n","sub_path":"scattertext/test/test_associationCompator.py","file_name":"test_associationCompator.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"452476113","text":"#!/usr/local/bin/env python\n######################################\n\n## SOLIPSIS Copyright (C) France Telecom\n\n## This file is part of SOLIPSIS.\n\n## SOLIPSIS is free software; you can redistribute it and/or modify\n## it under the terms of the GNU Lesser General Public License as published by\n## the Free Software Foundation; either version 2.1 of the License, or\n## (at your option) any later version.\n\n## SOLIPSIS 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 Lesser General Public License for more details.\n\n## You should have received a copy of the GNU Lesser General Public License\n## along with SOLIPSIS; if not, write to the Free Software\n## Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA\n\n## ------------------------------------------------------------------------------\n## ----- newFlagDialog.py -----\n## ------------------------------------------------------------------------------\n\n## ******************************************************************************\n##\n## This module is the dialog box used by the user for new flag creation or\n## modification.\n##\n## ******************************************************************************\n\nfrom wxPython.wx import *\nimport os\nimport commun\n\n# debug module\nimport debug\n\ndef create(parent):\n return newFlagDialog(parent)\n\n[wxID_NEWFLAGDIALOG, wxID_NEWFLAGDIALOGNAMESTATICTEXT,\n wxID_NEWFLAGDIALOGNAMETEXTCTRL, wxID_NEWFLAGDIALOGXPOSSTATICTEXT,\n wxID_NEWFLAGDIALOGXPOSTEXTCTRL, wxID_NEWFLAGDIALOGYPOSSTATICTEXT,\n wxID_NEWFLAGDIALOGYPOSTEXTCTRL,\n] = map(lambda _init_ctrls: wxNewId(), range(7))\n\nclass newFlagDialog(wxDialog):\n def _init_utils(self):\n # generated method, don't edit\n pass\n\n def _init_ctrls(self, prnt):\n\n # dialog box initialisation\n wxDialog.__init__(self, id=wxID_NEWFLAGDIALOG,\n name='newFlagDialog', parent=prnt, pos=wxPoint(0, 0),\n size=wxSize(300, 260), style=wxDEFAULT_DIALOG_STYLE,\n title='Flag')\n self._init_utils()\n self.SetClientSize(wxSize(300, 260))\n self.Center(wxBOTH)\n self.SetBackgroundColour(wxColour(164, 202, 235))\n\n # set the Solipsis icon in the dialog box\n iconSolipsis=wxIcon('Img//icon_solipsis.png', wxBITMAP_TYPE_PNG)\n bitmap=wxBitmap('Img//icon_solipsis.png',wxBITMAP_TYPE_PNG)\n iconSolipsis.CopyFromBitmap(bitmap)\n self.SetIcon(iconSolipsis)\n\n self.nameStaticText = wxStaticText(id=wxID_NEWFLAGDIALOGNAMESTATICTEXT,\n label='Name :', name='nameStaticText', parent=self,\n pos=wxPoint(10, 20), size=wxSize(74, 15), style=0)\n\n self.nameTextCtrl = wxTextCtrl(id=wxID_NEWFLAGDIALOGNAMETEXTCTRL,\n name='nameTextCtrl', parent=self, pos=wxPoint(10, 40),\n size=wxSize(150, 20), style=0, value='')\n\n self.xposStaticText = wxStaticText(id=wxID_NEWFLAGDIALOGXPOSSTATICTEXT,\n label='X coordinate :', name='xposStaticText', parent=self,\n pos=wxPoint(10, 80), size=wxSize(78, 15), style=0)\n\n self.xposTextCtrl = wxTextCtrl(id=wxID_NEWFLAGDIALOGXPOSTEXTCTRL,\n name='xposTextCtrl', parent=self, pos=wxPoint(10, 100),\n size=wxSize(280, 20), style=0, value='')\n\n self.yposStaticText = wxStaticText(id=wxID_NEWFLAGDIALOGYPOSSTATICTEXT,\n label='Y coordinate :', name='yposStaticText', parent=self,\n pos=wxPoint(10, 140), size=wxSize(78, 15), style=0)\n\n self.yposTextCtrl = wxTextCtrl(id=wxID_NEWFLAGDIALOGYPOSTEXTCTRL,\n name='ypostextCtrl', parent=self, pos=wxPoint(10, 160),\n size=wxSize(280, 20), style=0, value='')\n\n self.okButton = wxBitmapButton(bitmap=wxBitmap('Img//Buttons//bo_ok_n.png',\n wxBITMAP_TYPE_PNG), id=wxID_OK,\n name='okBitmapButton', parent=self, pos=wxPoint(32, 204),\n size=wxSize(-1, -1), style=0,\n validator=wxDefaultValidator)\n\n self.cancelButton = wxBitmapButton(bitmap=wxBitmap('Img//Buttons//bo_cancel_n.png',\n wxBITMAP_TYPE_PNG), id=wxID_CANCEL,\n name='cancelBitmapButton', parent=self, pos=wxPoint(190, 204),\n size=wxSize(-1, -1), style=0,\n validator=wxDefaultValidator)\n\n EVT_BUTTON(self.okButton, wxID_OK, self.OnOkButton)\n #EVT_BUTTON(self.cancelButton, wxID_CANCEL, self.OnCancelButton)\n\n def __init__(self, parent, navigator, flag_name):\n\n # navigator\n self.navigator = navigator\n\n # flag name (empty if new flag)\n self.flag_name = flag_name\n\n # init ctrls\n self._init_ctrls(parent)\n\n # init the position value in the dialog box\n self.initPositionValues()\n\n def initPositionValues(self):\n \"\"\" init the position values with the position of the connected node \"\"\"\n debug.debug_info(\"newFlagDialog.initPositionValues()\")\n debug.debug_info(\"FlagFile : \" + self.flag_name)\n\n # flag modification\n if self.flag_name:\n # open the flag file\n flagFile = commun.FLAG_DIR_NAME + os.sep + self.flag_name\n try:\n f = file(flagFile, 'r')\n except:\n commun.displayError(self, \"Can not open the file \" + flagFile)\n return 0\n\n # read file and close\n line = f.read()\n f.close()\n try:\n name, posX, posY = line.split(';')\n except:\n commun.displayError(self, 'The file %s has a bad format !' %self.flag_name)\n # close the dialog box\n self.Close(FALSE)\n return 0\n\n # set default parameters\n self.nameTextCtrl.SetValue(name)\n self.xposTextCtrl.SetValue(posX)\n self.yposTextCtrl.SetValue(posY)\n\n # new flag -> check if the navigator is connected to a node\n elif (self.navigator.getIsConnected() == 1):\n posX = self.navigator.getNodePosX()\n posY = self.navigator.getNodePosY()\n self.xposTextCtrl.SetValue(str(posX))\n self.yposTextCtrl.SetValue(str(posY))\n\n def OnOkButton(self, event):\n \"\"\" save the flag with the parameter filled by the user \"\"\"\n\n # get the parameters filled by the user\n name = self.nameTextCtrl.GetValue()\n posX = self.xposTextCtrl.GetValue()\n posY = self.yposTextCtrl.GetValue()\n\n # errors control\n if name == \"\":\n commun.displayError(self, \"Your flag name is empty !\")\n elif posX == \"\":\n commun.displayError(self, \"Your flag X coordinate is empty !\")\n messageDialog.ShowModal()\n elif posY == \"\":\n commun.displayError(self, \"Your flag Y coordinate is empty !\")\n else:\n try:\n x = long(posX)\n except:\n commun.displayError(self, \"Your flag X coordinate has a bad format. Please, enter an integer.\")\n return 0\n try:\n y = long(posY)\n except:\n commun.displayError(self, \"Your flag Y coordinate has a bad format. Please, enter an integer.\")\n return 0\n\n flagFile = commun.FLAG_DIR_NAME + os.sep + name\n\n # control the doublon\n if os.path.isfile(flagFile):\n commun.displayError(self, \"This flag name already exists. Please, choose another name.\")\n return 0\n\n # flag modification\n if self.flag_name:\n # rename the flag file\n flagFile_old = commun.FLAG_DIR_NAME + os.sep + self.flag_name\n os.rename(flagFile_old, flagFile)\n\n # open the flag file\n try:\n f = file(flagFile, 'w')\n except:\n commun.displayError(self, 'Can not open the file %s' %flagFile)\n return 0\n\n # save the parameters in the flag file\n line = name + ';' + posX + ';'+ posY\n f.write(line)\n f.close()\n\n # close the dialog box\n self.Close(FALSE)\n","sub_path":"branches/release-0.8.2/old/alpha-0.4/solipsis/navigator/wx/newFlagDialog.py","file_name":"newFlagDialog.py","file_ext":"py","file_size_in_byte":8391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"90661506","text":"# @date 2018-08-07\n# @author Frederic SCHERMA\n# @license Copyright (c) 2018 Dream Overflow\n# Trader base class\n\nimport traceback\nimport time\nimport copy\nimport base64\nimport uuid\nimport collections\n\nfrom datetime import datetime\n\nfrom common.signal import Signal\n\nfrom trader.order import Order\nfrom trader.position import Position\nfrom trader.market import Market\n\nfrom monitor.streamable import Streamable, StreamMemberSerie, StreamMemberFloatSerie\nfrom common.runnable import Runnable\n\nfrom terminal.terminal import Terminal, Color\nfrom terminal import charmap\n\nimport logging\nlogger = logging.getLogger('siis.trader')\nerror_logger = logging.getLogger('siis.error.trader')\ntraceback_logger = logging.getLogger('siis.traceback.trader')\n\n\nclass Trader(Runnable):\n \"\"\"\n Trader base class to specialize per broker.\n\n @todo Not as central ressource, only for intial or informational, strategy must have theirs local proxy methods/data.\n \"\"\"\n\n MAX_SIGNALS = 1000 # max signals queue size before ignore some market data updates\n\n PURGE_COMMANDS_DELAY = 180 # 180s keep commands in seconds\n MAX_COMMANDS_QUEUE = 100\n\n # general command\n COMMAND_INFO = 1\n\n # order commands\n COMMAND_CLOSE_MARKET = 110 # close a managed or unmanaged position at market now\n COMMAND_CLOSE_ALL_MARKET = 111 # close any positions of this account at market now\n\n def __init__(self, name, service):\n super().__init__(\"td-%s\" % name)\n\n self._name = name\n self._service = service \n self._account = None\n\n self._activity = True # trading activity\n\n self._orders = {}\n self._positions = {}\n self._assets = {}\n\n self._commands = []\n self._last_alerts = {}\n\n self._markets = {}\n\n self._timestamp = 0\n self._signals = collections.deque() # filtered received signals\n\n # listen to its service\n self.service.add_listener(self)\n\n # streaming\n self.setup_streaming()\n\n def setup_streaming(self):\n self._streamable = Streamable(self.service.monitor_service, Streamable.STREAM_TRADER, \"status\", self.name)\n\n def stream(self):\n self._streamable.push()\n\n @property\n def account(self):\n return self._account\n \n @property\n def service(self):\n return self._service\n\n @property\n def name(self):\n return self._name\n\n @property\n def watcher(self):\n return self._watcher\n\n @property\n def paper_mode(self):\n \"\"\"True for not real trader\"\"\"\n return False\n\n def set_timestamp(self, timestamp):\n \"\"\"\n Used on backtesting by the strategy.\n \"\"\"\n self._timestamp = timestamp\n\n @property\n def timestamp(self):\n \"\"\"\n Current timestamp or backtesting time.\n \"\"\"\n return time.time()\n\n def connect(self):\n pass\n\n def disconnect(self):\n pass\n\n @property\n def connected(self):\n return False\n\n @property\n def authenticated(self):\n return False\n\n def symbols_ids(self):\n \"\"\"\n Returns the complete list containing market-ids, theirs alias and theirs related symbol name.\n \"\"\"\n names = []\n\n with self._mutex:\n for k, market in self._markets.items():\n names.append(market.market_id)\n\n if market.symbol and market.symbol != market.market_id:\n names.append(market.symbol)\n\n names.sort()\n\n return names\n\n def find_market(self, symbol_or_market_id):\n \"\"\"\n Return market from its market-id or name or symbol.\n \"\"\"\n if not symbol_or_market_id:\n return None\n\n market = self._markets.get(symbol_or_market_id)\n if market:\n return market\n\n # or look with mapping of the name\n for k, mark in self._markets.items():\n if symbol_or_market_id == mark.market_id or symbol_or_market_id == mark.symbol:\n return mark\n\n return None\n\n def has_market(self, market_id):\n with self._mutex:\n return market_id in self._markets\n\n #\n # processing\n #\n\n def pre_run(self):\n Terminal.inst().info(\"Running trader %s...\" % self._name)\n self.connect()\n\n def post_run(self):\n Terminal.inst().info(\"Joining trader %s...\" % self._name)\n self.disconnect()\n\n def post_update(self):\n if len(self._signals) > Trader.MAX_SIGNALS:\n # saturation of the signal message queue\n Terminal.inst().warning(\"Trader %s has more than %s waiting signals, could ignore some market data !\" % (\n self.name, Trader.MAX_SIGNALS), view='status')\n\n # streaming\n self.stream()\n\n def update(self):\n \"\"\"\n Update command and position.\n Thread safe method.\n \"\"\"\n\n # performed async, but in case of backtesting update is called synchronously to avoid time derivation of the two processes.\n if self.service.backtesting:\n return True\n\n #\n # signals processing\n #\n\n count = 0\n\n while self._signals:\n signal = self._signals.popleft()\n\n # only on live mode, because in backtesting watchers are dummies\n if signal.source == Signal.SOURCE_WATCHER:\n if signal.signal_type == Signal.SIGNAL_WATCHER_CONNECTED:\n self.on_watcher_connected(signal.source_name)\n elif signal.signal_type == Signal.SIGNAL_WATCHER_DISCONNECTED:\n self.on_watcher_disconnected(signal.source_name)\n\n elif signal.signal_type == Signal.SIGNAL_MARKET_DATA:\n # update instrument data during live mode\n self.on_update_market(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_ACCOUNT_DATA:\n self.on_account_updated(*signal.data)\n\n elif signal.signal_type == Signal.SIGNAL_POSITION_OPENED:\n self.on_position_opened(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_POSITION_UPDATED:\n self.on_position_updated(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_POSITION_DELETED:\n self.on_position_deleted(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_POSITION_AMENDED:\n self.on_position_amended(*signal.data)\n\n elif signal.signal_type == Signal.SIGNAL_ORDER_OPENED:\n self.on_order_opened(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_ORDER_UPDATED:\n self.on_order_updated(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_ORDER_DELETED:\n self.on_order_deleted(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_ORDER_REJECTED:\n self.on_order_rejected(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_ORDER_CANCELED:\n self.on_order_canceled(*signal.data)\n elif signal.signal_type == Signal.SIGNAL_ORDER_TRADED:\n self.on_order_traded(*signal.data)\n\n elif signal.signal_type == Signal.SIGNAL_ASSET_UPDATED:\n self.on_asset_updated(*signal.data)\n\n elif signal.source == Signal.SOURCE_TRADER and signal.source_name == self._name:\n if signal.signal_type == Signal.SIGNAL_ASSET_DATA_BULK:\n self.on_assets_loaded(signal.data)\n\n if count > 10:\n # no more than per loop\n break\n\n #\n # commands processing @deprecated was used to do not have the possibility to trigger manually a social copy signal (expiration)\n #\n\n if self._commands:\n i = 0\n now = time.time()\n\n with self._mutex:\n for command in self._commands:\n # remove commands older than 180 seconds\n if now - command['timestamp'] > Trader.PURGE_COMMANDS_DELAY:\n del self._commands[i]\n\n i += 1\n\n return True\n\n def _purge_commands(self):\n \"\"\"\n Purge olders commands.\n Not trade safe method.\n \"\"\"\n if len(self._commands) > Trader.MAX_COMMANDS_QUEUE:\n size = len(self._commands) - Trader.MAX_COMMANDS_QUEUE\n self._commands = self._commands[size:]\n\n def command(self, command_type, data):\n \"\"\"\n Some parts are mutexed some others are not.\n \"\"\"\n if command_type == Trader.COMMAND_INFO:\n # info on the trade\n self.cmd_trader_info(data)\n\n elif command_type == Trader.COMMAND_CLOSE_MARKET:\n # manually close a specified position at market now\n with self._mutex:\n for k, position in self._positions.items():\n if position.key == data['key']:\n # query close position\n market = self.market(position.symbol)\n if market:\n self.close_position(position.position_id, market, position.direction, position.quantity, True, None)\n\n Terminal.inst().action(\"Closing position %s...\" % (position.position_id, ), view='content')\n break\n\n elif command_type == Trader.COMMAND_CLOSE_ALL_MARKET:\n # manually close any position related to this account/trader at market now\n with self._mutex:\n for k, position in self._positions.items():\n # query close position\n market = self.market(position.symbol)\n\n if market:\n self.close_position(position.position_id, market, position.direction, position.quantity, True, None)\n\n Terminal.inst().action(\"Closing position %s...\" % (position.position_id, ), view='content')\n break\n\n def ping(self, timeout):\n self._ping = (0, None, True)\n\n def pong(self, timestamp, pid, watchdog_service, msg):\n if msg:\n # display trader activity\n if self.connected:\n Terminal.inst().action(\"Trader worker %s is alive %s\" % (self._name, msg), view='content')\n else:\n Terminal.inst().action(\"Trader worker %s is alive but waiting for (re)connection %s\" % (self._name, msg), view='content')\n\n if watchdog_service:\n watchdog_service.service_pong(pid, timestamp, msg)\n\n #\n # global information\n #\n\n def has_margin(self, market_id, quantity, price):\n \"\"\"\n Return True for a margin trading if the account have sufficient free margin.\n \"\"\"\n with self._mutex:\n market = self._markets.get(market_id)\n margin = market.margin_cost(quantity, price)\n\n if margin:\n return self.account.margin_balance >= margin\n\n return False\n\n def has_quantity(self, asset_name, quantity):\n \"\"\"\n Return True if a given asset has a minimum quantity.\n @note The benefit of this method is it can be overloaded and offers a generic way for a strategy\n to check if an order can be created\n \"\"\"\n with self._mutex:\n asset = self._assets.get(asset_name)\n\n if asset:\n return asset.free >= quantity\n\n return False\n\n #\n # ordering\n #\n\n def set_ref_order_id(self, order):\n \"\"\"\n Generate a new reference order id to be setup before calling create order, else a default one wil be generated.\n Generating it before is a prefered way to correctly manange order in strategy.\n @param order A valid or on to set the ref order id.\n @note If the given order already have a ref order id no change is made.\n \"\"\"\n if order and not order.ref_order_id:\n # order.set_ref_order_id(\"siis_\" + base64.b64encode(uuid.uuid5(uuid.NAMESPACE_DNS, 'siis.com').bytes).decode('utf8').rstrip('=\\n').replace('/', '_').replace('+', '0'))\n order.set_ref_order_id(\"siis_\" + base64.b64encode(uuid.uuid4().bytes).decode('utf8').rstrip('=\\n').replace('/', '_').replace('+', '0'))\n return order.ref_order_id\n\n return None\n\n def create_order(self, order, market_or_instrument):\n \"\"\"\n Create an order. The symbol of the order must be on of the trader instruments.\n @note This call depend of the state of the connector.\n \"\"\"\n return False\n\n def cancel_order(self, order_id, market_or_instrument):\n \"\"\"\n Cancel an existing order. The symbol of the order must be on of the trader instruments.\n @note This call depend of the state of the connector.\n \"\"\"\n return False\n\n def close_position(self, position_id, market_or_instrument, direction, quantity, market=True, limit_price=None):\n \"\"\"\n Close an existing position.\n \n @param position_id Unique position identifier on broker.\n @param market_or_instrument Markt of Instrument related object.\n @param direction Direction of the position to close.\n @param quantity Quantiy of the position to close or reduce.\n @param market If true close at market\n @param limit_price If Not market close at this limit price\n\n @note This call depend of the state of the connector.\n \"\"\"\n return False\n\n def modify_position(self, position_id, market_or_instrument, stop_loss_price=None, take_profit_price=None):\n \"\"\"\n Modifiy the stop loss or take profit of a position.\n @note This call depend of the state of the connector.\n \"\"\"\n return False\n\n #\n # global accessors\n #\n\n def orders(self, market_id):\n \"\"\"\n Get actives order for a market id.\n \"\"\"\n return []\n\n def get_order(self, order_id):\n order = None\n\n with self._mutex:\n order = self.orders.get(order_id)\n if order:\n order = copy.copy(order)\n\n return order\n\n def find_order(self, ref_order_id):\n result = None\n\n with self._mutex:\n for oid, order in self._orders.items():\n if order.ref_order_id == ref_order_id:\n result = copy.copy(order)\n break\n\n return result\n\n def market(self, market_id, force=False):\n \"\"\"\n Market data for a market id.\n @param force True to force request and cache update.\n \"\"\"\n # with self._mutex:\n return self._markets.get(market_id)\n\n # return None\n\n def asset(self, symbol):\n \"\"\"\n Get asset for symbol.\n \"\"\"\n # with self._mutex:\n return self._assets.get(symbol)\n\n # return None\n\n def has_asset(self, symbol):\n \"\"\"\n Is trader has a specific asset.\n \"\"\"\n return symbol in self._assets\n\n def positions(self, market_id):\n \"\"\"\n Get actives positions for a market id.\n \"\"\"\n return []\n\n def get_position(self, position_id):\n \"\"\"\n Return a copy of the trader position if exists else None.\n \"\"\"\n position = None\n\n with self._mutex:\n if self._positions.get(position_id):\n position = copy.copy(self._positions.get(position_id))\n\n return position\n\n #\n # signals\n #\n\n def receiver(self, signal):\n if signal.source == Signal.SOURCE_WATCHER:\n if signal.source_name != self._name:\n # only interested by the watcher of the same name\n return\n\n if signal.signal_type == Signal.SIGNAL_MARKET_DATA:\n # if not self.has_market(signal.data[0]):\n if not signal.data[0] in self._markets:\n # non interested by this instrument/symbol\n return\n\n if len(self._signals) > Trader.MAX_SIGNALS:\n # if trader message queue saturate its mostly because of market data too many update\n # then ignore some of those message, the others ones are too important to be ignored\n return\n\n elif signal.signal_type not in (\n Signal.SIGNAL_ACCOUNT_DATA, Signal.SIGNAL_WATCHER_CONNECTED, Signal.SIGNAL_WATCHER_DISCONNECTED,\n Signal.SIGNAL_POSITION_OPENED, Signal.SIGNAL_POSITION_UPDATED, Signal.SIGNAL_POSITION_DELETED, Signal.SIGNAL_POSITION_AMENDED,\n Signal.SIGNAL_ORDER_OPENED, Signal.SIGNAL_ORDER_UPDATED, Signal.SIGNAL_ORDER_DELETED, Signal.SIGNAL_ORDER_REJECTED,\n Signal.SIGNAL_ORDER_CANCELED, Signal.SIGNAL_ORDER_TRADED,\n Signal.SIGNAL_ASSET_UPDATED):\n return\n\n # signal of interest\n self._signals.append(signal)\n\n elif signal.source == Signal.SOURCE_TRADER: # in fact it comes from the DB service but request in self trader name\n if signal.signal_type not in (Signal.SIGNAL_ASSET_DATA, Signal.SIGNAL_ASSET_DATA_BULK):\n # non interested by others signals\n return \n\n # signal of interest\n self._signals.append(signal)\n\n #\n # connection slots\n #\n\n def on_watcher_connected(self, watcher_name):\n msg = \"Trader %s joined %s watcher connection.\" % (self.name, watcher_name)\n logger.info(msg)\n Terminal.inst().info(msg, view='content')\n\n def on_watcher_disconnected(self, watcher_name):\n msg = \"Trader %s lossing %s watcher connection.\" % (self.name, watcher_name)\n logger.warning(msg)\n Terminal.inst().info(msg, view='content')\n\n #\n # account slots\n #\n\n @Runnable.mutexed\n def on_account_updated(self, balance, free_margin, unrealized_pnl, currency, risk_limit):\n \"\"\"\n Update account details.\n \"\"\"\n self.account.set_balance(balance)\n self.account.set_used_margin(balance - free_margin)\n self.account.set_unrealized_profit_loss(unrealized_pnl)\n\n if currency:\n self.account.set_currency(currency)\n\n if risk_limit is not None:\n self.account.set_risk_limit(risk_limit)\n\n #\n # positions slots\n #\n\n @Runnable.mutexed\n def on_position_opened(self, market_id, position_data, ref_order_id):\n market = self._markets.get(market_id)\n if market is None:\n # not interested by this market\n return\n\n # insert it, erase the previous if necessary\n position = Position(self)\n position.set_position_id(position_data['id'])\n position.set_key(self.service.gen_key())\n\n position.entry(\n position_data['direction'],\n position_data['symbol'],\n position_data['quantity'],\n position_data.get('take-profit'),\n position_data.get('stop-loss'),\n position_data.get('leverage'),\n position_data.get('trailing-stop'))\n\n if position_data.get('avg-entry-price') is not None:\n position.entry_price = position_data['avg-entry-price']\n elif position_data.get('avg-price') is not None:\n position.entry_price = position_data['avg-price']\n elif position_data.get('exec-price') is not None:\n position.entry_price = position_data['exec-price']\n\n self._positions[position.position_id] = position\n\n market = self.market(position.symbol)\n if market:\n position.update_profit_loss(market)\n\n if position_data.get('profit-loss') is not None:\n position._profit_loss = position_data.get('profit-loss')\n position._profit_market_loss = position_data.get('profit-loss')\n\n if position_data.get('profit-loss-rate') is not None:\n position._profit_loss_rate = position_data.get('profit-loss-rate')\n position._profit_loss_market_rate = position_data.get('profit-loss-rate')\n\n @Runnable.mutexed\n def on_position_updated(self, market_id, position_data, ref_order_id):\n market = self._markets.get(market_id)\n if market is None:\n # not interested by this market\n return\n\n position = self._positions.get(position_data['id'])\n\n if position:\n # update\n position.entry(\n position_data['direction'],\n position_data['symbol'],\n position_data['quantity'],\n position_data.get('take-profit'),\n position_data.get('stop-loss'),\n position_data.get('leverage'),\n position_data.get('trailing-stop'))\n\n if position_data.get('avg-entry-price') is not None:\n position.entry_price = position_data['avg-entry-price']\n elif position_data.get('avg-price') is not None:\n position.entry_price = position_data['avg-price']\n elif position_data.get('exec-price') is not None:\n position.entry_price = position_data['exec-price']\n\n if position_data.get('avg-exit-price') is not None:\n position.exit_price = position_data['avg-exit-price']\n else:\n # not found, insert and change state \n position = Position(self)\n position.set_position_id(position_data['id'])\n position.set_key(self.service.gen_key())\n\n position.entry(\n position_data['direction'],\n position_data['symbol'],\n position_data['quantity'],\n position_data.get('take-profit'),\n position_data.get('stop-loss'),\n position_data.get('leverage'),\n position_data.get('trailing-stop'))\n\n if position_data.get('avg-entry-price') is not None:\n position.entry_price = position_data['avg-entry-price']\n elif position_data.get('avg-price') is not None:\n position.entry_price = position_data['avg-price']\n elif position_data.get('exec-price') is not None:\n position.entry_price = position_data['exec-price']\n\n if position_data.get('avg-exit-price') is not None:\n position.exit_price = position_data['avg-exit-price']\n\n self._positions[position.position_id] = position\n\n market = self.market(position.symbol)\n if market:\n position.update_profit_loss(market)\n\n if position_data.get('profit-loss') is not None:\n position._profit_loss = position_data.get('profit-loss')\n position._profit_market_loss = position_data.get('profit-loss')\n\n if position_data.get('profit-loss-rate') is not None:\n position._profit_loss_rate = position_data.get('profit-loss-rate')\n position._profit_loss_market_rate = position_data.get('profit-loss-rate')\n\n @Runnable.mutexed\n def on_position_amended(self, market_id, position_data, ref_order_id):\n position = self._positions.get(position_data['id'])\n\n if position:\n if position_data.get('take-profit'):\n position.take_profit = position_data['take-profit']\n\n if position_data.get('stop-loss'):\n position.stop_loss = position_data['stop-loss']\n\n if position_data.get('trailing-stop'):\n position.trailing_stop = position_data['trailing-stop']\n\n if position_data.get('leverage'):\n position.leverage = position_data['leverage']\n\n @Runnable.mutexed\n def on_position_deleted(self, market_id, position_data, ref_order_id):\n # delete the position from the dict\n if self._positions.get(position_data['id']):\n del self._positions[position_data['id']]\n\n #\n # order slots\n #\n\n @Runnable.mutexed\n def on_order_opened(self, market_id, order_data, ref_order_id):\n market = self._markets.get(market_id)\n if market is None:\n # not interested by this market\n return\n\n if order_data['id'] not in self._orders:\n # some are inserted during create_order result\n order = Order(self, order_data['symbol'])\n order.set_order_id(order_data['id'])\n\n order.created_time = order_data['timestamp']\n\n order.direction = order_data['direction']\n order.order_type = order_data['type']\n\n order.quantity = order_data['quantity']\n\n # depending of the type\n order.price = order_data.get('price')\n order.stop_price = order_data.get('stop-price')\n order.time_in_force = order_data.get('time-in-force', Order.TIME_IN_FORCE_GTC)\n\n order.close_only = order_data.get('close-only', False)\n order.reduce_only = order_data.get('reduce-only', False)\n\n order.stop_loss = order_data.get('stop-loss')\n order.take_profit = order_data.get('take-profit')\n\n self._orders[order_data['id']] = order\n\n @Runnable.mutexed\n def on_order_updated(self, market_id, order_data, ref_order_id):\n market = self._markets.get(market_id)\n if market is None:\n # not interested by this market\n return\n\n order = self._orders.get(order_data['id'])\n if order:\n # update price, stop-price, stop-loss, take-profit, quantity if necessarry\n order.quantity = order_data['quantity']\n\n order.price = order_data.get('price')\n order.stop_price = order_data.get('stop-price')\n\n order.stop_loss = order_data.get('stop-loss')\n order.take_profit = order_data.get('take-profit')\n\n @Runnable.mutexed\n def on_order_deleted(self, market_id, order_id, ref_order_id):\n if order_id in self._orders:\n del self._orders[order_id]\n\n @Runnable.mutexed\n def on_order_rejected(self, market_id, ref_order_id):\n pass\n\n @Runnable.mutexed\n def on_order_canceled(self, market_id, order_id, ref_order_id):\n if order_id in self._orders:\n del self._orders[order_id]\n\n @Runnable.mutexed\n def on_order_traded(self, market_id, order_data, ref_order_id):\n order = self._orders.get(order_data['id'])\n if order:\n # update executed qty (depending of the implementation filled or cumulative-filled or both are present)\n if order_data.get('cumulative-filled') is not None:\n order.executed = order_data['cumulative-filled']\n elif order_data.get('filled') is not None:\n order.executed += order_data['filled']\n\n if order_data.get('timestamp'):\n # keep last transact_time\n order.transact_time = order_data['timestamp']\n\n if order_data.get('fully-filled'):\n # fully filled mean deleted too\n del self._orders[order.order_id]\n\n #\n # asset slots\n # \n\n def on_assets_loaded(self, assets):\n pass\n\n def on_asset_updated(self, asset_name, locked, free):\n pass\n\n #\n # market slots\n #\n\n @Runnable.mutexed\n def on_update_market(self, market_id, tradable, last_update_time, bid, ofr, base_exchange_rate,\n contract_size=None, value_per_pip=None, vol24h_base=None, vol24h_quote=None):\n \"\"\"\n Update bid, ofr, base exchange rate and last update time of a market.\n Take care this method is not thread safe. Use it with the trader mutex or exclusively in the same thread.\n \"\"\"\n market = self._markets.get(market_id)\n if market is None:\n # not interested by this market\n return\n\n if bid:\n market.bid = bid\n if ofr:\n market.ofr = ofr\n\n if base_exchange_rate is not None:\n market.base_exchange_rate = base_exchange_rate\n\n if last_update_time is not None:\n market.last_update_time = last_update_time\n\n if tradable is not None:\n market.is_open = tradable\n\n if contract_size is not None:\n market.contract_size = contract_size\n\n if value_per_pip is not None:\n market.value_per_pip = value_per_pip\n\n if vol24h_base is not None:\n market.vol24h_base = vol24h_base\n\n if vol24h_quote is not None:\n market.vol24h_quote = vol24h_quote\n\n # push last price to keep a local cache of history\n market.push_price()\n\n #\n # utils\n #\n\n def last_price(self, market_id):\n \"\"\"\n Return the last price for a specific market.\n @param str market_id Valid market identifier\n @return float Last watched price or None if missing market.\n \"\"\"\n price = None\n\n with self._mutex:\n market = self.market(market_id)\n if market:\n price = market.price\n\n return price\n\n def history_price(self, market_id, timestamp=None):\n \"\"\"\n Return the price for a particular timestamp or the last if not defined.\n @param str market_id Valid market identifier\n @param float timestamp Valid second timetamp or if None then prefers the method last_price.\n @return float Price or None if not found (market missing or unable to retrieve a price)\n\n @note Cost an API request when timestamp is defined and if price\n was not found into the local market history (only few values are kept in memory cache).\n \"\"\"\n price = None\n\n if timestamp:\n market = self.market(market_id)\n if market:\n # lookup into the local cache\n price = market.recent_price(timestamp)\n\n if price is None and self._watcher:\n # query the REST API\n price = self._watcher.price_history(market_id, timestamp)\n\n if price is None:\n logger.warning(\"Trader %s cannot found price history for %s at %s\" % (self._name, market_id, datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S')))\n else:\n # last price\n market = self.market(market_id)\n if market:\n price = market.price\n\n if price is None:\n logger.warning(\"Trader %s cannot found last price for %s because no market was found\" % (self._name, market_id,))\n\n return price\n\n #\n # display views\n #\n\n def markets_table(self, style='', offset=None, limit=None, col_ofs=None):\n \"\"\"\n Returns a table of any followed markets.\n \"\"\"\n columns = ('Market', 'Symbol', 'Base', 'Quote', 'Rate', 'Type', 'Unit', 'Status', 'PipMean', 'PerPip', 'Lot', 'Contract', 'Min Size', 'Min Notional', 'Leverage')\n total_size = (len(columns), 0)\n data = []\n\n with self._mutex:\n markets = list(self._markets.values())\n total_size = (len(columns), len(markets))\n\n if offset is None:\n offset = 0\n\n if limit is None:\n limit = len(markets)\n\n limit = offset + limit\n\n markets.sort(key=lambda x: x.market_id)\n markets = markets[offset:limit]\n\n for market in markets:\n status = Color.colorize_cond(\"Open\" if market.is_open else \"Close\", market.is_open, style=style, true=Color.GREEN, false=Color.RED)\n\n row = (\n market.market_id,\n market.symbol,\n market.base,\n market.quote,\n str(\"%.8f\" % market.base_exchange_rate).rstrip('0').rstrip('.'),\n market.market_type_str().capitalize(),\n market.unit_type_str().capitalize(),\n status,\n str(\"%.8f\" % market.one_pip_means).rstrip('0').rstrip('.'),\n str(\"%.8f\" % market.value_per_pip).rstrip('0').rstrip('.'),\n str(\"%.8f\" % market.lot_size).rstrip('0').rstrip('.'),\n str(\"%.12f\" % market.contract_size).rstrip('0').rstrip('.'),\n market.min_size,\n market.min_notional,\n \"%.2f\" % (1.0 / market.margin_factor if market.margin_factor > 0.0 else 1.0)\n )\n\n data.append(row[col_ofs:])\n\n return columns[col_ofs:], data, total_size\n\n def markets_tickers_table(self, style='', offset=None, limit=None, col_ofs=None, prev_timestamp=None):\n \"\"\"\n Returns a table of any followed markets tickers.\n \"\"\"\n columns = ('Market', 'Symbol', 'Bid', 'Ofr', 'Spread', 'Vol24h base', 'Vol24h quote', 'Time')\n total_size = (len(columns), 0)\n data = []\n\n with self._mutex:\n markets = list(self._markets.values())\n total_size = (len(columns), len(markets))\n\n if offset is None:\n offset = 0\n\n if limit is None:\n limit = len(markets)\n\n limit = offset + limit\n\n markets.sort(key=lambda x: x.market_id)\n markets = markets[offset:limit]\n\n for market in markets:\n recent = market.recent(self.timestamp - 0.5 if not prev_timestamp else prev_timestamp)\n if recent:\n bid = Color.colorize_updn(market.format_price(market.bid), recent[1], market.bid, style=style)\n ofr = Color.colorize_updn(market.format_price(market.ofr), recent[2], market.ofr, style=style)\n spread = Color.colorize_updn(market.format_spread(market.spread), market.spread, recent[2] - recent[1], style=style)\n else:\n bid = market.format_price(market.bid)\n ofr = market.format_price(market.ofr)\n spread = market.format_spread(market.spread)\n\n if market.vol24h_quote:\n # @todo could be configured\n low = 0\n if market.quote in ('USD', 'EUR', 'ZEUR', 'ZUSD', 'USDT', 'PAX', 'USDC', 'USDS', 'BUSD', 'TUSD'):\n low = 500000\n elif market.quote in ('BTC'):\n low = 100\n elif market.quote in ('ETH'):\n low = 5000\n elif market.quote in ('BNB'):\n low = 50000\n\n vol24h_quote = Color.colorize_cond(\"%.2f\" % market.vol24h_quote, market.vol24h_quote < low, style=style, true=Color.YELLOW, false=Color.WHITE)\n else:\n vol24h_quote = charmap.HOURGLASS\n\n row = (\n market.market_id,\n market.symbol,\n bid,\n ofr,\n spread,\n market.format_quantity(market.vol24h_base) if market.vol24h_base else charmap.HOURGLASS,\n vol24h_quote,\n datetime.fromtimestamp(market.last_update_time).strftime(\"%H:%M:%S\") if market.last_update_time else charmap.HOURGLASS)\n\n data.append(row[col_ofs:])\n\n return columns[col_ofs:], data, total_size\n\n def assets_table(self, style='', offset=None, limit=None, col_ofs=None, filter_low=True):\n \"\"\"\n Returns a table of any non empty assets.\n \"\"\"\n columns = ('Asset', 'Locked', 'Free', 'Total', 'Avg price', 'Change', 'Change %',\n 'P/L %s' % self.account.currency, 'P/L %s' % self.account.alt_currency)\n total_size = (len(columns), 0)\n data = []\n\n with self._mutex:\n assets = [asset for asset in self._assets.values() if asset.quantity > 0.0]\n total_size = (len(columns), len(assets))\n\n if offset is None:\n offset = 0\n\n if limit is None:\n limit = len(assets)\n\n limit = offset + limit\n\n assets.sort(key=lambda x: x.symbol)\n assets = assets[offset:limit]\n\n for asset in assets:\n # use the most appropriate market\n market = self.market(asset.symbol+asset.quote)\n\n change = \"\"\n change_percent = \"\"\n profit_loss = \"\"\n profit_loss_alt = \"\"\n\n if market:\n locked = market.format_quantity(asset.locked)\n free = market.format_quantity(asset.free)\n quantity = market.format_quantity(asset.quantity)\n\n base_exchange_rate = 1.0\n\n change = market.format_price(market.bid - asset.price) + market.quote_display or market.quote\n change_percent = (market.bid - asset.price) / asset.price * 100.0 if asset.price else 0.0\n\n if change_percent > 0.0:\n change_percent = Color.colorize(\"%.2f\" % change_percent, Color.GREEN, style)\n elif change_percent < 0.0:\n change_percent = Color.colorize(\"%.2f\" % change_percent, Color.RED, style)\n else:\n change_percent = \"%.2f\" % change_percent\n\n quote_market = self.market(market.quote+self.account.currency)\n if quote_market:\n base_exchange_rate = 1.0 / quote_market.price\n\n profit_loss = market.format_price(asset.profit_loss) if market.quote == self.account.currency else \"\"\n profit_loss_alt = market.format_price(asset.profit_loss / base_exchange_rate) if market.quote == self.account.alt_currency else \"\"\n\n if asset.profit_loss > 0.0:\n if profit_loss:\n profit_loss = Color.colorize(profit_loss, Color.GREEN, style)\n\n if profit_loss_alt:\n profit_loss_alt = Color.colorize(profit_loss_alt, Color.GREEN, style)\n elif asset.profit_loss < 0.0:\n if profit_loss:\n profit_loss = Color.colorize(profit_loss, Color.RED, style)\n\n if profit_loss_alt:\n profit_loss_alt = Color.colorize(profit_loss_alt, Color.RED, style)\n else:\n locked = \"%.8f\" % asset.locked\n free = \"%.8f\" % asset.free\n quantity = \"%.8f\" % asset.quantity\n\n row = (\n asset.symbol,\n locked,\n free,\n quantity,\n asset.format_price(asset.price) if asset.price else charmap.HOURGLASS,\n change or charmap.ROADBLOCK,\n change_percent or charmap.ROADBLOCK,\n profit_loss or charmap.ROADBLOCK,\n profit_loss_alt or charmap.ROADBLOCK,\n )\n\n data.append(row[col_ofs:])\n\n return columns[col_ofs:], data, total_size\n\n def account_table(self, style='', offset=None, limit=None, col_ofs=None):\n \"\"\"\n Returns a table of any followed markets.\n \"\"\"\n columns = ('Broker', 'Account', 'Username', 'Email', 'Asset', 'Free Asset', 'Balance', 'Margin', 'Level', 'Net worth',\n 'Risk limit', 'Unrealized P/L', 'Asset U. P/L', 'Asset U. P/L alt')\n data = []\n\n with self._mutex:\n if offset is None:\n offset = 0\n\n if limit is None:\n limit = 1\n\n limit = offset + limit\n\n asset_balance = \"%s (%s)\" % (\n self.account.format_price(self._account.asset_balance) + self.account.currency_display or self.account.currency,\n self.account.format_price(self._account.asset_balance * self._account.currency_ratio) + self.account.alt_currency_display or self.account.alt_currency)\n\n free_asset_balance = \"%s (%s)\" % (\n self.account.format_price(self._account.free_asset_balance) + self.account.currency_display or self.account.currency,\n self.account.format_price(self._account.free_asset_balance * self._account.currency_ratio) + self.account.alt_currency_display or self.account.alt_currency)\n\n balance = \"%s (%s)\" % (\n self.account.format_price(self._account.balance) + self.account.currency_display or self.account.currency,\n self.account.format_price(self._account.balance * self._account.currency_ratio) + self.account.alt_currency_display or self.account.alt_currency)\n\n margin_balance = \"%s (%s)\" % (\n self.account.format_price(self._account.margin_balance) + self.account.currency_display or self.account.currency,\n self.account.format_price(self._account.margin_balance * self._account.currency_ratio) + self.account.alt_currency_display or self.account.alt_currency)\n\n net_worth = \"%s (%s)\" % (\n self.account.format_price(self._account.net_worth) + self.account.currency_display or self.account.currency,\n self.account.format_alt_price(self._account.net_worth * self._account.currency_ratio) + self.account.alt_currency_display or self.account.alt_currency)\n\n risk_limit = \"%s (%s)\" % (\n self.account.format_price(self._account.risk_limit) + self.account.currency_display or self.account.currency,\n self.account.format_price(self._account.risk_limit * self._account.currency_ratio) + self.account.alt_currency_display or self.account.alt_currency)\n\n upnl = \"%s (%s)\" % (\n self.account.format_price(self._account.profit_loss) + self.account.currency_display or self.account.currency,\n self.account.format_alt_price(self._account.profit_loss * self._account.currency_ratio) + self.account.alt_currency_display or self.account.alt_currency)\n\n asset_upnl = \"%s (%s)\" % (\n self.account.format_price(self._account.asset_profit_loss) + self.account.currency_display or self.account.currency,\n self.account.format_alt_price(self._account.asset_profit_loss * self._account.currency_ratio) + self.account.alt_currency_display or self.account.alt_currency)\n\n row = (\n self.name,\n self._account.name,\n self._account.username,\n self._account.email,\n asset_balance,\n free_asset_balance,\n balance,\n margin_balance,\n \"%.2f%%\" % (self.account.margin_level * 100.0),\n net_worth,\n risk_limit,\n upnl,\n asset_upnl\n )\n\n if offset < 1 and limit > 0:\n data.append(row[col_ofs:])\n\n return columns[col_ofs:], data, (len(columns), 1)\n\n def get_active_orders(self):\n \"\"\"\n Generate and return an array of all active orders :\n symbol: str market identifier\n id: int order identifier\n refid: int ref order identifier\n \"\"\"\n results = []\n\n with self._mutex:\n try:\n for k, order in self._orders.items():\n market = self._markets.get(order.symbol)\n if market:\n results.append({\n 'mid': market.market_id,\n 'sym': market.symbol,\n 'id': order.order_id,\n 'refid': order.ref_order_id,\n 'ct': order.created_time,\n 'tt': order.transact_time,\n 'd': order.direction_to_str(),\n 'ot': order.order_type_to_str(),\n 'l': order.leverage,\n 'q': market.format_quantity(order.quantity),\n 'op': market.format_price(order.price) if order.price else \"\",\n 'sp': market.format_price(order.stop_price) if order.stop_price else \"\",\n 'sl': market.format_price(order.stop_loss) if order.stop_loss else \"\",\n 'tp': market.format_price(order.take_profit) if order.take_profit else \"\",\n 'tr': \"No\",\n 'xq': market.format_quantity(order.executed),\n 'ro': order.reduce_only,\n 'he': order.hedging,\n 'po': order.post_only,\n 'co': order.close_only,\n 'mt': order.margin_trade,\n 'tif': order.time_in_force_to_str(),\n 'pt': order.price_type_to_str(),\n 'key': order.key\n })\n except Exception as e:\n error_logger.error(repr(e))\n traceback_logger.error(traceback.format_exc())\n\n return results\n\n def get_active_positions(self):\n \"\"\"\n Generate and return an array of all active positions :\n symbol: str market identifier\n id: int position identifier\n et: float entry UTC timestamp\n xt: float exit UTC timestamp\n d: str 'long' or 'short'\n l: str leverage\n tp: str formatted take-profit price\n sl: str formatted stop-loss price\n tr: str trailing stop distance or None\n rate: float profit/loss rate\n q: float size qty\n aep: average entry price\n axp: average exit price\n pl: position unrealized profit loss rate\n pnl: position unrealized profit loss\n mpl: position unrealized profit loss rate at market\n mpnl: position unrealized profit loss at market\n pnlcur: trade profit loss currency\n key: user key\n \"\"\"\n results = []\n\n with self._mutex:\n try:\n for k, position in self._positions.items():\n market = self._markets.get(position.symbol)\n if market:\n results.append({\n 'mid': market.market_id,\n 'sym': market.symbol,\n 'id': position.position_id,\n 'et': position.created_time,\n 'xt': position.closed_time,\n 'd': position.direction_to_str(),\n 'l': position.leverage,\n 'aep': market.format_price(position.entry_price) if position.entry_price else \"\",\n 'axp': market.format_price(position.exit_price) if position.exit_price else \"\",\n 'q': market.format_quantity(position.quantity),\n 'tp': market.format_price(position.take_profit) if position.take_profit else \"\",\n 'sl': market.format_price(position.stop_loss) if position.stop_loss else \"\",\n 'tr': \"Yes\" if position.trailing_stop else \"No\", # market.format_price(position.trailing_stop_distance) if position.trailing_stop_distance else None,\n 'pl': position.profit_loss_rate,\n 'pnl': market.format_price(position.profit_loss),\n 'mpl': position.profit_loss_market_rate,\n 'mpnl': market.format_price(position.profit_loss_market),\n 'pnlcur': position.profit_loss_currency,\n 'cost': market.format_quantity(position.position_cost(market)),\n 'margin': market.format_quantity(position.margin_cost(market)),\n 'key': position.key\n })\n except Exception as e:\n error_logger.error(repr(e))\n traceback_logger.error(traceback.format_exc())\n\n return results\n\n def positions_stats_table(self, style='', offset=None, limit=None, col_ofs=None, quantities=False, percents=False, datetime_format='%y-%m-%d %H:%M:%S'):\n \"\"\"\n Returns a table of any active positions.\n \"\"\"\n columns = ['Market', '#', charmap.ARROWUPDN, 'x', 'P/L(%)', 'SL', 'TP', 'TR', 'Entry date', 'Avg EP', 'Exit date', 'Avg XP', 'UPNL', 'Cost', 'Margin', 'Key']\n\n if quantities:\n columns += ['Qty']\n\n columns = tuple(columns)\n total_size = (len(columns), 0)\n data = []\n\n with self._mutex:\n try:\n positions = self.get_active_positions()\n total_size = (len(columns), len(positions))\n\n if offset is None:\n offset = 0\n\n if limit is None:\n limit = len(positions)\n\n limit = offset + limit\n\n positions.sort(key=lambda x: x['et'])\n positions = positions[offset:limit]\n\n for t in positions:\n direction = Color.colorize_cond(charmap.ARROWUP if t['d'] == \"long\" else charmap.ARROWDN, t['d'] == \"long\", style=style, true=Color.GREEN, false=Color.RED)\n\n aep = float(t['aep']) if t['aep'] else 0.0\n sl = float(t['sl']) if t['sl'] else 0.0\n tp = float(t['tp']) if t['tp'] else 0.0\n\n if t['pl'] < 0: # loss\n cr = Color.colorize(\"%.2f\" % (t['pl']*100.0), Color.RED, style=style)\n elif t['pl'] > 0: # profit\n cr = Color.colorize(\"%.2f\" % (t['pl']*100.0), Color.GREEN, style=style)\n else: # equity\n cr = \"0.0\"\n\n if t['d'] == 'long' and aep:\n slpct = (sl - aep) / aep\n tppct = (tp - aep) / aep\n elif t['d'] == 'short' and aep:\n slpct = (aep - sl) / aep\n tppct = (aep - tp) / aep\n else:\n slpct = 0\n tppct = 0\n\n row = [\n t['mid'],\n t['id'],\n direction,\n \"%.2f\" % t['l'] if t['l'] else '-',\n cr,\n \"%s (%.2f)\" % (t['sl'], slpct * 100) if percents else t['sl'],\n \"%s (%.2f)\" % (t['tp'], tppct * 100) if percents else t['tp'],\n t['tr'],\n datetime.fromtimestamp(t['et']).strftime(datetime_format) if t['et'] > 0 else \"\",\n t['aep'],\n datetime.fromtimestamp(t['xt']).strftime(datetime_format) if t['xt'] > 0 else \"\",\n t['axp'],\n \"%s%s\" % (t['pnl'], t['pnlcur']),\n t['cost'],\n t['margin'],\n t['key']\n ]\n\n # @todo xx / market.base_exchange_rate and pnl_currency\n\n if quantities:\n row.append(t['q'])\n\n data.append(row[col_ofs:])\n\n except Exception as e:\n error_logger.error(repr(e))\n traceback_logger.error(traceback.format_exc())\n\n return columns[col_ofs:], data, total_size\n\n def active_orders_table(self, style='', offset=None, limit=None, col_ofs=None, quantities=False, percents=False, datetime_format='%y-%m-%d %H:%M:%S'):\n \"\"\"\n Returns a table of any active orders.\n \"\"\"\n columns = ['Market', '#', 'ref #', charmap.ARROWUPDN, 'Type', 'x', 'Limit', 'Stop', 'SL', 'TP', 'TR', 'Created date', 'Transac date',\n 'Reduce', 'Post', 'Hedge', 'Close', 'Margin', 'TIF', 'Price', 'Key']\n\n if quantities:\n columns += ['Qty']\n columns += ['Exec']\n\n columns = tuple(columns)\n total_size = (len(columns), 0)\n data = []\n\n with self._mutex:\n try:\n orders = self.get_active_orders()\n total_size = (len(columns), len(orders))\n\n if offset is None:\n offset = 0\n\n if limit is None:\n limit = len(orders)\n\n limit = offset + limit\n\n orders.sort(key=lambda x: x['ct'])\n orders = orders[offset:limit]\n\n for t in orders:\n direction = Color.colorize_cond(charmap.ARROWUP if t['d'] == \"long\" else charmap.ARROWDN, t['d'] == \"long\", style=style, true=Color.GREEN, false=Color.RED)\n\n op = float(t['op']) if t['op'] else 0.0\n sl = float(t['sl']) if t['sl'] else 0.0\n tp = float(t['tp']) if t['tp'] else 0.0\n\n if t['d'] == 'long' and op:\n slpct = (sl - op) / op if sl else 0.0\n tppct = (tp - op) / op if tp else 0.0\n elif t['d'] == 'short' and op:\n slpct = (op - sl) / op if sl else 0.0\n tppct = (op - tp) / op if tp else 0.0\n else:\n slpct = 0\n tppct = 0\n\n row = [\n t['mid'],\n t['id'],\n t['refid'],\n direction,\n t['ot'],\n \"%.2f\" % t['l'] if t['l'] else '-',\n t['op'],\n t['sp'],\n \"%s (%.2f)\" % (t['sl'], slpct * 100) if percents else t['sl'],\n \"%s (%.2f)\" % (t['tp'], tppct * 100) if percents else t['tp'],\n t['tr'],\n datetime.fromtimestamp(t['ct']).strftime(datetime_format) if t['ct'] > 0 else \"\",\n datetime.fromtimestamp(t['tt']).strftime(datetime_format) if t['tt'] > 0 else \"\",\n \"Yes\" if t['ro'] else \"No\",\n \"Yes\" if t['po'] else \"No\",\n \"Yes\" if t['he'] else \"No\",\n \"Yes\" if t['co'] else \"No\",\n \"Yes\" if t['mt'] else \"No\",\n t['tif'],\n t['pt'],\n t['key']\n ]\n\n if quantities:\n row.append(t['q'])\n row.append(t['xq'])\n\n data.append(row[col_ofs:])\n\n except Exception as e:\n error_logger.error(repr(e))\n traceback_logger.error(traceback.format_exc())\n\n return columns[col_ofs:], data, total_size\n\n #\n # commands\n #\n\n def cmd_trader_info(self, data):\n # info on the global trader instance\n pass\n","sub_path":"trader/trader.py","file_name":"trader.py","file_ext":"py","file_size_in_byte":55363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"520473803","text":"num = int(input(\"Enter Number: \"))\n\n\ndef check_armstrong(value):\n length = len(str(value))\n origanal = value\n result = 0\n while value > 0:\n reminder = value % 10\n result += reminder ** length\n value = value // 10\n\n if origanal == result:\n print(origanal, \" is Armstrong\")\n else:\n print(origanal, \"is not Armstrong\")\n\n\ncheck_armstrong(num)","sub_path":"Funtions/Armstrong_withArg_noReturn.py","file_name":"Armstrong_withArg_noReturn.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"618796586","text":"from __future__ import print_function\n\nfrom fixtures import temp_image_name\n\nfrom dock.core import DockerTasker\nfrom tests.constants import INPUT_IMAGE, DOCKERFILE_GIT\n\nimport git\nimport docker, docker.errors\nimport pytest\n\n\n# TEST-SUITE SETUP\n\ndef setup_module(module):\n d = docker.Client()\n try:\n d.inspect_image(INPUT_IMAGE)\n setattr(module, 'HAS_IMAGE', True)\n except docker.errors.APIError:\n _ = [x for x in d.pull(\"busybox:latest\", stream=True)]\n setattr(module, 'HAS_IMAGE', False)\n\n\ndef teardown_module(module):\n if not getattr(module, 'HAS_IMAGE', False):\n d = docker.Client()\n d.remove_image(INPUT_IMAGE)\n\n\n# TESTS\n\ndef test_run():\n t = DockerTasker()\n container_id = t.run(INPUT_IMAGE, command=\"id\")\n try:\n t.wait(container_id)\n finally:\n t.remove_container(container_id)\n\n\ndef test_run_invalid_command():\n t = DockerTasker()\n command = \"eporeporjgpeorjgpeorjgpeorjgpeorjgpeorjg\" # I hope this doesn't exist\n try:\n with pytest.raises(docker.errors.APIError):\n t.run(INPUT_IMAGE, command=command)\n finally:\n # remove the container\n containers = t.d.containers(all=True)\n container_id = [c for c in containers if c[\"Command\"] == command][0]['Id']\n t.remove_container(container_id)\n\n\ndef test_image_exists():\n t = DockerTasker()\n assert t.image_exists(INPUT_IMAGE) is True\n\n\ndef test_image_doesnt_exist():\n t = DockerTasker()\n assert t.image_exists(\"lerknglekrnglekrnglekrnglekrng\") is False\n\n\ndef test_logs():\n t = DockerTasker()\n container_id = t.run(INPUT_IMAGE, command=\"id\")\n try:\n t.wait(container_id)\n output = t.logs(container_id, stderr=True, stream=False)\n assert \"\\n\".join(output).startswith(\"uid=\")\n finally:\n t.remove_container(container_id)\n\n\ndef test_remove_container():\n t = DockerTasker()\n container_id = t.run(INPUT_IMAGE, command=\"id\")\n try:\n t.wait(container_id)\n finally:\n t.remove_container(container_id)\n\n\ndef test_remove_image(temp_image_name):\n t = DockerTasker()\n container_id = t.run(INPUT_IMAGE, command=\"id\")\n t.wait(container_id)\n image_id = t.commit_container(container_id, repository=temp_image_name)\n try:\n t.remove_container(container_id)\n finally:\n t.remove_image(image_id)\n assert not t.image_exists(temp_image_name)\n\n\ndef test_commit_container(temp_image_name):\n t = DockerTasker()\n container_id = t.run(INPUT_IMAGE, command=\"id\")\n t.wait(container_id)\n image_id = t.commit_container(container_id, message=\"test message\", repository=temp_image_name)\n try:\n assert t.image_exists(image_id)\n finally:\n t.remove_container(container_id)\n t.remove_image(image_id)\n\n\ndef test_inspect_image():\n t = DockerTasker()\n inspect_data = t.inspect_image(INPUT_IMAGE)\n assert isinstance(inspect_data, dict)\n\n\ndef test_tag_image(temp_image_name):\n t = DockerTasker()\n expected_img = \"somewhere.example.com/%s:1\" % temp_image_name\n img = t.tag_image(INPUT_IMAGE, temp_image_name, reg_uri=\"somewhere.example.com\", tag='1')\n try:\n assert t.image_exists(expected_img)\n assert img == expected_img\n finally:\n t.remove_image(expected_img)\n\n\ndef test_push_image(temp_image_name):\n t = DockerTasker()\n expected_img = \"localhost:5000/%s:1\" % temp_image_name\n t.tag_image(INPUT_IMAGE, temp_image_name, reg_uri=\"localhost:5000\", tag='1')\n output = t.push_image(expected_img, insecure=True)\n assert output is not None\n t.remove_image(expected_img)\n\n\ndef test_tag_and_push(temp_image_name):\n t = DockerTasker()\n expected_img = \"localhost:5000/%s:1\" % temp_image_name\n output = t.tag_and_push_image(INPUT_IMAGE, temp_image_name, reg_uri=\"localhost:5000\", tag='1', insecure=True)\n assert output is not None\n assert t.image_exists(expected_img)\n t.remove_image(expected_img)\n\n\ndef test_pull_image():\n t = DockerTasker()\n expected_img = \"localhost:5000/busybox\"\n t.tag_and_push_image('busybox', 'busybox', 'localhost:5000', insecure=True)\n got_image = t.pull_image('busybox', 'localhost:5000', insecure=True)\n assert expected_img == got_image\n assert len(t.last_logs) > 0\n t.remove_image(got_image)\n\n\ndef test_get_image_info_by_id_nonexistent():\n t = DockerTasker()\n response = t.get_image_info_by_image_id(\"asd\")\n assert response is None\n\n\ndef test_get_image_info_by_id():\n t = DockerTasker()\n image_id = t.get_image_info_by_image_name(\"busybox\")[0]['Id']\n response = t.get_image_info_by_image_id(image_id)\n assert isinstance(response, dict)\n\n\ndef test_get_image_info_by_name_tag_in_name():\n t = DockerTasker()\n response = t.get_image_info_by_image_name(image_name=INPUT_IMAGE)\n assert len(response) == 0\n\n\ndef test_build_image_from_path(tmpdir, temp_image_name):\n tmpdir_path = str(tmpdir.realpath())\n git.Repo.clone_from(DOCKERFILE_GIT, tmpdir_path)\n df = tmpdir.join(\"Dockerfile\")\n assert df.check()\n t = DockerTasker()\n response = t.build_image_from_path(tmpdir_path, temp_image_name, use_cache=True)\n list(response)\n assert response is not None\n assert t.image_exists(temp_image_name)\n t.remove_image(temp_image_name)\n\n\ndef test_build_image_from_git(temp_image_name):\n t = DockerTasker()\n response = t.build_image_from_git(DOCKERFILE_GIT, temp_image_name, use_cache=True)\n list(response)\n assert response is not None\n assert t.image_exists(temp_image_name)\n t.remove_image(temp_image_name)\n","sub_path":"tests/test_tasker.py","file_name":"test_tasker.py","file_ext":"py","file_size_in_byte":5577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"173266385","text":"from tkinter import *\nimport random\nfrom PIL import ImageTk, Image\nroot = Tk()\nnumber = 0\nturn = \"green\"\n# button = ''\nm = 1\n# gui window\nwindow_width = 750\nwindow_height = 750\nroot.geometry((\"750x750\"))\nroot.title(\"Ludo By Vishal\")\nroot.maxsize(window_width, window_height)\nroot.minsize(window_width, window_height)\nold = 0\n\n\n# canvas\nshape = Canvas(root, width=window_width, height=window_height)\nshape.pack()\n\n\ndef numberzero():\n global number\n number = 0\n\n# def buttonblank():\n# global button\n# button = \"\"\n# function\n# dicefunction\n\ndef chaturn():\n global turn\n\n if turn == \"blue\":\n turn = \"red\"\n shape.create_rectangle(10, 10, 290, 290, width=10, outline=\"blue\")\n shape.create_rectangle(460, 10, 740, 290, width=10)\n r1.place(x=r1.winfo_x(), y=r1.winfo_y())\n r2.place(x=r2.winfo_x(), y=r2.winfo_y())\n r3.place(x=r3.winfo_x(), y=r3.winfo_y())\n r4.place(x=r4.winfo_x(), y=r4.winfo_y())\n print(\"red turn\")\n elif turn == \"red\":\n turn = \"yellow\"\n shape.create_rectangle(460, 10, 740, 290, width=10, outline=\"red\")\n shape.create_rectangle(460, 460, 740, 740, width=10)\n y1.place(x=y1.winfo_x(), y=y1.winfo_y())\n y2.place(x=y2.winfo_x(), y=y2.winfo_y())\n y3.place(x=y3.winfo_x(), y=y3.winfo_y())\n y4.place(x=y4.winfo_x(), y=y4.winfo_y())\n print(\"yellow turn\")\n elif turn == \"yellow\":\n turn = \"green\"\n shape.create_rectangle(460, 460, 740, 740, width=10, outline=\"yellow\")\n shape.create_rectangle(10, 460, 290, 740, width=10)\n g1.place(x=g1.winfo_x(), y=g1.winfo_y())\n g2.place(x=g2.winfo_x(), y=g2.winfo_y())\n g3.place(x=g3.winfo_x(), y=g3.winfo_y())\n g4.place(x=g4.winfo_x(), y=g4.winfo_y())\n print(\"green turn\")\n elif turn == \"green\":\n turn = \"blue\"\n shape.create_rectangle(10, 460, 290, 740, width=10, outline=\"green\")\n shape.create_rectangle(10, 10, 290, 290, width=10)\n b1.place(x=b1.winfo_x(), y=b1.winfo_y())\n b2.place(x=b2.winfo_x(), y=b2.winfo_y())\n b3.place(x=b3.winfo_x(), y=b3.winfo_y())\n b4.place(x=b4.winfo_x(), y=b4.winfo_y())\n print(\"blue turn\")\n\n\n\n\n\n\ndef dicerolled():\n global number, old\n number = random.randint(1, 6)\n if number == 1:\n img = \"1.png\"\n elif number == 2:\n img = \"2.png\"\n elif number == 3:\n img = \"3.png\"\n elif number == 4:\n img = \"4.png\"\n elif number == 5:\n img = \"5.png\"\n elif number == 6:\n img = \"6.png\"\n img = Image.open(img)\n img = img.resize((100, 100), Image.ANTIALIAS)\n image = ImageTk.PhotoImage(img)\n # img = shape.create_image(375, 365, image=image)\n # Canvas.tag_raise(img)\n if old!=6: \n chaturn()\n print(number)\n else:\n print(turn, \"turn\")\n print(number)\n img = Label(root, image=image)\n img.image = image\n img.place(x=325, y=310) \n old = number\n \n return number\n\n \ndef bottoncheck(button_id):\n global button\n if button_id == \"b1\":\n button = b1\n elif button_id == \"b2\":\n button = b2\n elif button_id == \"b3\":\n button = b3\n elif button_id == \"b4\":\n button = b4\n elif button_id == \"r1\":\n button = r1\n elif button_id == \"r2\":\n button = r2\n elif button_id == \"r3\":\n button = r3\n elif button_id == \"r4\":\n button = r4\n elif button_id == \"g1\":\n button = g1\n elif button_id == \"g2\":\n button = g2\n elif button_id == \"g3\":\n button = g3\n elif button_id == \"g4\":\n button = g4\n elif button_id == \"y1\":\n button = y1\n elif button_id == \"y2\":\n button = y2\n elif button_id == \"y3\":\n button = y3\n elif button_id == \"y4\":\n button = y4\n chance()\n\n\ndef chance():\n if turn == \"blue\":\n if button in [b1, b2, b3, b4]:\n path()\n \n else:\n print(\"ITS BLUE TURN!!\")\n elif turn == \"red\":\n if button in [r1, r2, r3, r4]:\n path()\n \n else:\n print(\"ITS RED TURN!!\")\n elif turn == \"yellow\":\n if button in [y1, y2, y3, y4]:\n path()\n \n else:\n print(\"ITS YELLOW TURN!!\")\n elif turn == \"green\":\n if button in [g1, g2, g3, g4]:\n path()\n \n else:\n print(\"ITS GREEN TURN!!\")\n\n\n\n\n# def overlaping():\n# blue_x = {\"b1\" : b1.winfo_x(), \"b2\" : b2.winfo_x(), \"b3\" : b3.winfo_x(), \"b4\": b4.winfo_x()}\n# blue_y = {\"b1\" : b1.winfo_y(), \"b2\" : b2.winfo_y(), \"b3\" : b3.winfo_y(), \"b4\": b4.winfo_y()}\n\n# green_x = {\"g1\" : g1.winfo_x(), \"g2\" : g2.winfo_x(), \"g3\" : g3.winfo_x(), \"g4\" : g4.winfo_x()}\n# green_y = {\"g1\" : g1.winfo_y(), \"g2\" : g2.winfo_y(), \"g3\" : g3.winfo_y(), \"g4\" : g4.winfo_y()}\n\n# red_x = {\"r1\" : r1.winfo_x(), \"r2\" : r2.winfo_x(), \"r3\" : r3.winfo_x(), \"r4\" : r4.winfo_x()}\n# red_y = {\"r1\" : r1.winfo_y(), \"r2\" : r2.winfo_y(), \"r3\" : r3.winfo_y(), \"r4\" : r4.winfo_y()}\n\n# yellow_x = {\"y2\" : y1.winfo_x(), \"y2\" : y2.winfo_x(), \"y3\" : y3.winfo_x(), \"y4\" : y4.winfo_x()}\n# yellow_y = {\"y2\" : y1.winfo_y(), \"y2\" : y2.winfo_y(), \"y3\" : y3.winfo_y(), \"y4\" : y4.winfo_y()}\n\n# buttonx = button.winfo_x()\n# buttony = button.winfo_y()\n\n# if turn==\"blue\":\n# if buttonx in green_x.values() and buttony in green_y.values():\n# if green_x.keys()[green_x.values().index[buttonx]] in [g1, g2, g3, g4]:\n\n# count = 3\n# while count>=0:\n# a = list(green_x.keys())[count]\n# b = list(green_y.keys())[count]\n# if buttonx == a and buttony == b:\n# if count == 0:\n# g1.place(x = 50, y = 500)\n# elif count == 1:\n# g2.place(x = 175, y = 500)\n# elif count == 2:\n# g3.place(x = 50, y = 650)\n# elif count == 3:\n# g4.place(x = 175, y = 650)\n# count-=1\n# else:\n# print(\"task 1 failed\")\n# else:\n# print(\"task 2 failed\")\n# print(button.winfo_x(), button.winfo_y())\n# elif buttonx in red_x and buttony in red_y:\n# count = 3\n# while count>=0:\n# a = red_x[count]\n# b = red_y[count]\n# if buttonx == a and buttony == b:\n# if count == 0:\n# r1.place(x = 500, y = 50)\n# elif count == 1:\n# r2.place(x = 650, y = 50)\n# elif count == 2:\n# r3.place(x = 500, y = 200)\n# elif count == 3:\n# r4.place(x = 650, y = 200) \n# count-=1\n# elif buttonx in yellow_x and buttony in yellow_y:\n# count = 3\n# while count>=0:\n# a = yellow_x[count]\n# b = yellow_y[count]\n# if buttonx == a and buttony == b:\n# if count == 0:\n# y1.place(x = 500, y = 500)\n# elif count == 1:\n# y2.place(x = 650, y = 50)\n# elif count == 2:\n# y3.place(x = 500, y = 650)\n# elif count == 3:\n# y4.place(x = 650, y = 650)\n# count-=1\n \n \n \n# elif turn!=\"red\":\n# count = 3\n# while count>=0:\n# x = red_x[count]\n# y = red_y[count]\n# if buttonx == x and buttony == y:\n# if r1.winfo_x() == buttonx:\n# r1.place(x = 500, y = 50)\n# if r2.winfo_x() == buttonx:\n# r2.place(x = 650, y = 50)\n# if r3.winfo_x() == buttonx:\n# r3.place(x = 500, y = 200)\n# if r4.winfo_x() == buttonx:\n# r4.place(x = 650, y = 200) \n# count-=1\n \n# elif turn!=\"green\":\n# count = 3\n# while count>=0:\n# x = green_x[count]\n# y = green_y[count]\n# if buttonx == x and buttony == y:\n# if g1.winfo_x() == buttonx:\n# g1.place(x = 50, y = 500)\n# if g2.winfo_x() == buttonx:\n# g2.place(x = 175, y = 500)\n# if g3.winfo_x() == buttonx:\n# g3.place(x = 50, y = 650)\n# if g4.winfo_x() == buttonx:\n# g4.place(x = 175, y = 650) \n# count-=1\n \n# elif turn!=\"yellow\":\n# count = 3\n# while count>=0:\n# x = yellow_x[count]\n# y = yellow_y[count]\n# if buttonx == x and buttony == y:\n# if y1.winfo_x() == buttonx:\n# y1.place(x = 500, y = 500)\n# if y2.winfo_x() == buttonx:\n# y2.place(x = 650, y = 50)\n# if y3.winfo_x() == buttonx:\n# y3.place(x = 500, y = 650)\n# if y4.winfo_x() == buttonx:\n# y4.place(x = 650, y = 650) \n# count-=1\n \n\n\n\n\n\ndef path():\n buttonx = button.winfo_x()\n buttony = button.winfo_y()\n if number == 6 and (buttonx == 500 and buttony == 50):\n button.place(x=400, y=50)\n numberzero()\n elif number == 6 and (buttonx == 175 and buttony == 50):\n button.place(x=50, y=300)\n numberzero()\n elif number == 6 and (buttonx == 50 and buttony == 200):\n button.place(x=50, y=300)\n numberzero()\n elif number == 6 and (buttonx == 175 and buttony == 200):\n button.place(x=50, y=300)\n numberzero()\n\n elif number == 6 and (buttonx == 500 and buttony == 50):\n button.place(x=400, y=50)\n numberzero()\n elif number == 6 and (buttonx == 650 and buttony == 50):\n button.place(x=400, y=50)\n numberzero()\n elif number == 6 and (buttonx == 500 and buttony == 200):\n button.place(x=400, y=50)\n numberzero()\n elif number == 6 and (buttonx == 650 and buttony == 200):\n button.place(x=400, y=50)\n numberzero()\n\n elif number == 6 and (buttonx == 50 and buttony == 500):\n button.place(x=300, y=650)\n numberzero()\n elif number == 6 and (buttonx == 175 and buttony == 500):\n button.place(x=300, y=650)\n numberzero()\n elif number == 6 and (buttonx == 50 and buttony == 650):\n button.place(x=300, y=650)\n numberzero()\n elif number == 6 and (buttonx == 175 and buttony == 650):\n button.place(x=300, y=650)\n numberzero()\n\n elif number == 6 and (buttonx == 500 and buttony == 500):\n button.place(x=650, y=400)\n numberzero()\n elif number == 6 and (buttonx == 650 and buttony == 500):\n button.place(x=650, y=400)\n numberzero()\n elif number == 6 and (buttonx == 500 and buttony == 650):\n button.place(x=650, y=400)\n numberzero()\n elif number == 6 and (buttonx == 650 and buttony == 650):\n button.place(x=650, y=400)\n numberzero()\n elif buttonx in range(0, 251, 50) and buttony == 300:\n if buttonx+50*number in range(0, 251, 50):\n button.place(x=buttonx + 50*number, y=300)\n numberzero()\n else:\n count = number-((250-buttonx)//50)\n button.place(x=300, y=buttony-50*(count))\n numberzero()\n elif buttonx == 300 and buttony in range(0, 251, 50):\n if buttony-50*number in range(0, 251, 50):\n button.place(y=buttony - 50*number, x=300)\n numberzero()\n else:\n count = number-((buttony)//50)\n if button in [r1, r2, r3, r4]:\n if count>1:\n count-=1\n button.place(x=350,y=50*count)\n numberzero()\n elif count==1:\n button.place(x=350,y=0)\n numberzero()\n else:\n \n if count > 2:\n count -= 2\n button.place(x=400, y=count*50)\n numberzero()\n else:\n button.place(x=300+(count*50), y=0)\n numberzero()\n\n elif buttony in range(0, 301, 50) and buttonx == 350:\n if button in [r1, r2, r3, r4]:\n if buttonx==350 and buttony in range(0,301,50):\n if buttony+50*number in range(0, 251, 50):\n button.place(y=buttony + 50*number, x=350)\n numberzero()\n elif buttony+50*number == 300:\n button.destroy()\n numberzero()\n else:\n count = number\n if count == 1:\n button.place(x=400, y=0)\n numberzero()\n elif count > 1:\n count -= 1\n button.place(x=400, y=50*count)\n numberzero()\n elif buttonx == 400 and buttony in range(0, 251, 50):\n if buttony+50*number in range(0, 251, 50):\n button.place(y=buttony + 50*number, x=400)\n numberzero()\n else:\n count = number-((250-buttony)//50)\n button.place(y=300, x=buttonx+50*(count))\n numberzero()\n\n elif buttony == 300 and buttonx in range(450, 701, 50):\n if buttonx+50*number in range(450, 701, 50):\n button.place(x=buttonx + 50*number, y=300)\n numberzero()\n else:\n count = number-((700 - buttonx)//50)\n if button in [y1, y2, y3, y4]:\n if count>1:\n count-=1\n button.place(y=350,x=700 - 50*count)\n numberzero()\n elif count==1:\n button.place(y=350,x=700)\n numberzero()\n else:\n if count > 2:\n count -= 2\n button.place(y=400, x=700-(count*50))\n numberzero()\n else:\n button.place(y=300+(count*50), x=700)\n numberzero()\n\n elif buttony == 350 and buttonx in range(400,701, 50):\n if button in [y1, y2, y3, y4]:\n if buttony==350 and buttonx in range(400,701, 50):\n if buttonx-50*number in range(450,701, 50):\n button.place(x=buttonx - 50*number, y=350)\n numberzero()\n elif buttonx-50*number == 400:\n button.destroy()\n numberzero()\n else:\n count = number\n if count == 1:\n button.place(y=400, x=700)\n numberzero()\n elif count > 1:\n count -= 1\n button.place(y=400, x=700 - (50*count))\n numberzero()\n\n elif buttony == 400 and buttonx in range(450, 701, 50):\n if buttonx-50*number in range(450, 701, 50):\n button.place(x=buttonx - 50*number, y=400)\n numberzero()\n else:\n count = number - ((buttonx - 450)//50)\n button.place(x=400, y=400+(count*50))\n numberzero()\n\n elif buttonx == 400 and buttony in range(450, 701, 50):\n if buttony+50*number in range(450, 701, 50):\n button.place(y=buttony + 50*number, x=400)\n numberzero()\n else:\n count = number-((700-buttony)//50)\n if button in [g1, g2, g3, g4]:\n if count>1:\n count-=1\n button.place(x=350,y=700 - 50*count)\n numberzero()\n elif count==1:\n button.place(x=350,y=700)\n numberzero()\n else:\n if count > 2:\n count -= 2\n button.place(x=300, y=700 - (count*50))\n numberzero()\n else:\n button.place(y=700, x=400-(count*50))\n numberzero()\n\n elif buttonx == 350 and buttony in range(400,701, 50):\n if button in [g1, g2, g3, g4]:\n if buttonx==350 and buttony in range(400,701, 50):\n if buttony-50*number in range(450,701, 50):\n button.place(y=buttony - 50*number, x=350)\n numberzero()\n elif buttony-50*number == 400:\n button.destroy()\n numberzero()\n else:\n count = number\n if count > 1:\n count -= 1\n button.place(x=300, y=700 - (count*50))\n numberzero()\n elif count == 1:\n button.place(x=300, y=700)\n numberzero()\n\n elif buttonx == 300 and buttony in range(450, 701, 50):\n if buttony-50*number in range(450, 701, 50):\n button.place(y=buttony - 50*number, x=300)\n numberzero()\n else:\n count = number-((buttony - 450)//50)\n button.place(x=300 - (count*50), y=400)\n numberzero()\n\n elif buttonx in range(0, 251, 50) and buttony == 400:\n if buttonx-50*number in range(0, 251, 50):\n button.place(x=buttonx - 50*number, y=400)\n numberzero()\n else:\n count = number - ((buttonx)//50)\n if button in [b1, b2, b3, b4]:\n if count>1:\n count-=1\n button.place(y=350,x=50*count)\n numberzero()\n elif count==1:\n button.place(y=350,x=0)\n numberzero()\n else:\n if count > 2:\n count -= 2\n button.place(x=count*50, y=300)\n numberzero()\n else:\n button.place(x=0, y=400-(count*50))\n numberzero()\n\n elif buttonx in range(0,301, 50) and buttony == 350:\n if button in [b1, b2, b3, b4]:\n if buttony==350 and buttonx in range(0,301, 50):\n if buttonx+50*number in range(0,301, 50):\n button.place(x=buttonx + 50*number, y=350)\n numberzero()\n elif buttonx+50*number == 400:\n button.destroy()\n numberzero()\n else:\n count = number\n if count == 1:\n button.place(x=0, y=300)\n numberzero()\n elif count > 1:\n count -= 1\n button.place(x=count*50, y=300)\n numberzero()\n print(button.winfo_x(), button.winfo_y())\n\n \n\n\n\n\n\n\n\n# rectangle\nblue_rectangle = shape.create_rectangle(0, 0, 300, 300, fill=\"blue\", width=3)\nred_rectangle = shape.create_rectangle(450, 0, 750, 300, fill=\"red\", width=3)\ngreen_rectangle = shape.create_rectangle(\n 0, 450, 300, 750, fill=\"green\", width=3)\nyellow_rectangle = shape.create_rectangle(\n 450, 450, 750, 750, fill=\"yellow\", width=3)\n# victory_rectangle = shape.create_rectangle(\n# 300, 300, 450, 450, fill=\"grey\", width=3)\n\n\n# lines\n\nshape.create_line(300, 0, 450, 0, width=3)\nshape.create_line(750, 300, 750, 450, width=3)\nshape.create_line(300, 750, 450, 750, width=3)\nshape.create_line(0, 300, 0, 450, width=3)\n\nshape.create_line(350, 0, 350, 300, width=3)\nshape.create_line(400, 0, 400, 300, width=3)\nshape.create_line(450, 400, 750, 400, width=3)\nshape.create_line(450, 350, 750, 350, width=3)\nshape.create_line(350, 450, 350, 750, width=3)\nshape.create_line(400, 450, 400, 750, width=3)\nshape.create_line(0, 400, 300, 400, width=3)\nshape.create_line(0, 350, 300, 350, width=3)\n\nshape.create_line(300, 50, 450, 50, width=3)\nshape.create_line(300, 100, 450, 100, width=3)\nshape.create_line(300, 150, 450, 150, width=3)\nshape.create_line(300, 200, 450, 200, width=3)\nshape.create_line(300, 250, 450, 250, width=3)\n\nshape.create_line(700, 300, 700, 450, width=3)\nshape.create_line(500, 300, 500, 450, width=3)\nshape.create_line(550, 300, 550, 450, width=3)\nshape.create_line(600, 300, 600, 450, width=3)\nshape.create_line(650, 300, 650, 450, width=3)\n\nshape.create_line(300, 700, 450, 700, width=3)\nshape.create_line(300, 500, 450, 500, width=3)\nshape.create_line(300, 550, 450, 550, width=3)\nshape.create_line(300, 600, 450, 600, width=3)\nshape.create_line(300, 650, 450, 650, width=3)\n\nshape.create_line(50, 300, 50, 450, width=3)\nshape.create_line(100, 300, 100, 450, width=3)\nshape.create_line(150, 300, 150, 450, width=3)\nshape.create_line(200, 300, 200, 450, width=3)\nshape.create_line(250, 300, 250, 450, width=3)\n\n\n# gotiyan\nb1 = Button(root, width=3, height=2, bg=\"blue\", highlightbackground=\"black\",\n activebackground=\"#1589FF\", command=lambda: [bottoncheck(\"b1\")])\nb1.place(x=50, y=50)\nb2 = Button(root, width=3, height=2, bg=\"blue\", highlightbackground=\"black\",\n activebackground=\"#1589FF\", command=lambda: [bottoncheck(\"b2\")])\nb2.place(x=175, y=50)\nb3 = Button(root, width=3, height=2, bg=\"blue\", highlightbackground=\"black\",\n activebackground=\"#1589FF\", command=lambda: [bottoncheck(\"b3\")])\nb3.place(x=50, y=200)\nb4 = Button(root, width=3, height=2, bg=\"blue\", highlightbackground=\"black\",\n activebackground=\"#1589FF\", command=lambda: [bottoncheck(\"b4\")])\nb4.place(x=175, y=200)\n\nr1 = Button(root, width=3, height=2, bg=\"red\", highlightbackground=\"black\",\n activebackground=\"#E41B17\", command=lambda: [bottoncheck(\"r1\")])\nr1.place(x=500, y=50)\nr2 = Button(root, width=3, height=2, bg=\"red\", highlightbackground=\"black\",\n activebackground=\"#E41B17\", command=lambda: [bottoncheck(\"r2\")])\nr2.place(x=650, y=50)\nr3 = Button(root, width=3, height=2, bg=\"red\", highlightbackground=\"black\",\n activebackground=\"#E41B17\", command=lambda: [bottoncheck(\"r3\")])\nr3.place(x=500, y=200)\nr4 = Button(root, width=3, height=2, bg=\"red\", highlightbackground=\"black\",\n activebackground=\"#E41B17\", command=lambda: [bottoncheck(\"r4\")])\nr4.place(x=650, y=200)\n\ng1 = Button(root, width=3, height=2, bg=\"green\", highlightbackground=\"black\",\n activebackground=\"#3EA055\", command=lambda: [bottoncheck(\"g1\")])\ng1.place(x=50, y=500)\ng2 = Button(root, width=3, height=2, bg=\"green\", highlightbackground=\"black\",\n activebackground=\"#3EA055\", command=lambda: [bottoncheck(\"g2\")])\ng2.place(x=175, y=500)\ng3 = Button(root, width=3, height=2, bg=\"green\", highlightbackground=\"black\",\n activebackground=\"#3EA055\", command=lambda: [bottoncheck(\"g3\")])\ng3.place(x=50, y=650)\ng4 = Button(root, width=3, height=2, bg=\"green\", highlightbackground=\"black\",\n activebackground=\"#3EA055\", command=lambda: [bottoncheck(\"g4\")])\ng4.place(x=175, y=650)\n\ny1 = Button(root, width=3, height=2, bg=\"yellow\", highlightbackground=\"black\",\n activebackground=\"#FFF380\", command=lambda: [bottoncheck(\"y1\")])\ny1.place(x=500, y=500)\ny2 = Button(root, width=3, height=2, bg=\"yellow\", highlightbackground=\"black\",\n activebackground=\"#FFF380\", command=lambda: [bottoncheck(\"y2\")])\ny2.place(x=650, y=500)\ny3 = Button(root, width=3, height=2, bg=\"yellow\", highlightbackground=\"black\",\n activebackground=\"#FFF380\", command=lambda: [bottoncheck(\"y3\")])\ny3.place(x=500, y=650)\ny4 = Button(root, width=3, height=2, bg=\"yellow\", highlightbackground=\"black\",\n activebackground=\"#FFF380\", command=lambda: [bottoncheck(\"y4\")])\ny4.place(x=650, y=650)\n\n\n# ROLL BUTTON\nroll = Button(root, text=\"Roll\", bg=\"#7bccb5\", highlightbackground=\"black\",\n font=\"Monaco\", activebackground=\"#7fffd4\", command=dicerolled)\nroll.place(x=347, y=418)\n\n\n# safe areas\n\nsafe1 = shape.create_rectangle(50, 300, 100, 350, fill=\"grey\", width=2)\nsafe2 = shape.create_rectangle(300, 100, 350, 150, fill=\"grey\", width=2)\nsafe3 = shape.create_rectangle(400, 50, 450, 100, fill=\"grey\", width=2)\nsafe4 = shape.create_rectangle(650, 300, 600, 350, fill=\"grey\", width=2)\nsafe5 = shape.create_rectangle(700, 400, 650, 450, fill=\"grey\", width=2)\nsafe6 = shape.create_rectangle(400, 600, 450, 650, fill=\"grey\", width=2)\nsafe7 = shape.create_rectangle(300, 650, 350, 700, fill=\"grey\", width=2)\nsafe8 = shape.create_rectangle(100, 400, 150, 450, fill=\"grey\", width=2)\nsafe = [safe1, safe2, safe3, safe4, safe5, safe6, safe7, safe8]\n\n\nroot.mainloop()\n","sub_path":"ludo_tkinter/ludo.py","file_name":"ludo.py","file_ext":"py","file_size_in_byte":24842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"38965633","text":"# -*- coding: utf-8 -*-\n\nfrom sklearn.cluster import KMeans\n\n\ndef k_means(train_data, n):\n\n if not train_data:\n return []\n\n _k_means = KMeans(n_clusters=n)\n _k_means.fit(train_data)\n labels = _k_means.labels_\n\n return labels, _k_means.predict\n","sub_path":"nobody/analysis/alg/kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"624695196","text":"import tensorflow as tf\nimport numpy as np\n\nxy=np.loadtxt('train.txt',unpack=True,dtype='float32')\nx_data=xy[0:-1]\ny_data=xy[-1]\n\nX=tf.placeholder(tf.float32)\nY=tf.placeholder(tf.float32)\n\nW= tf.Variable(tf.random_uniform([1,len(x_data)],-1.0,1.0))\n\n#Our hypothesis\nh=tf.matmul(W,X)\nhypothesis = tf.div(1.,1.+tf.exp(-h)) #시그모이드 함수에 직접 넣는다.\n\n#cost function\n#코스트 펑션을 그대로 옮김\ncost = -tf.reduce_mean(Y*tf.log(hypothesis) + (1-Y)*tf.log(1-hypothesis))\n\n#Minimize\n#최소화 하기 위한 그라디언트 디센트\na= tf.Variable(0.8) #Learning rate, alpha\noptimizer = tf.train.GradientDescentOptimizer(a)\ntrain = optimizer.minimize(cost)\n\ninit = tf.initialize_all_variables()\n\n#Launch the graph\nsess = tf.Session()\nsess.run(init)\n\nfor step in range(2001):\n sess.run(train, feed_dict={X:x_data,Y:y_data})\n if step%20==0:\n print (step, sess.run(cost, feed_dict={X:x_data,Y:y_data}),sess.run(W))","sub_path":"sungKim_Lecture/05-Classification/01-logistic.py","file_name":"01-logistic.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"47852500","text":"from models import engine, db_session, Base, Team, Position, Player\n\nBase.metadata.create_all(bind=engine)\n\n# Add some data to the database\n\n# Teams\nbayern = Team(name='Bayern Munich')\ndb_session.add(bayern)\ndortmund = Team(name='Dortmund')\ndb_session.add(dortmund)\nwolfsburg = Team(name='Wolfsburg')\ndb_session.add(wolfsburg)\n\n# Positions\noffense = Position(name='offense')\ndb_session.add(offense)\nmidfield = Position(name='midfield')\ndb_session.add(midfield)\ndefence = Position(name='defence')\ndb_session.add(defence)\n\n# Players\n# Add three players from each team\n# Bayern Players\nboateng = Player(first_name='Jerome', last_name='Boateng', number=17, age=28, country='Germany', height=192,\n weight=90, position=defence, team=bayern)\ndb_session.add(boateng)\nkimmich= Player(first_name='Joshua', last_name='Kimmich', number=32, age=21, country='Germany', height=176,\n weight=70, position=midfield, team=bayern)\ndb_session.add(kimmich)\nmuller = Player(first_name='Thomas', last_name='Muller', number=25, age=27, country='Germany', height=186,\n weight=75, position=offense, team=bayern)\ndb_session.add(bayern)\n\n# Dortmund Players\nginter = Player(first_name='Matthias', last_name='Ginter', number=28, age=22, country='Germany', height=190,\n weight=88, position=defence, team=dortmund)\ndb_session.add(ginter)\nschurrle = Player(first_name='Andre', last_name='Schurrle', number=21, age=26, country='Germany', height=184,\n weight=74, position=midfield, team=dortmund)\ndb_session.add(schurrle)\nramos= Player(first_name='Adrian', last_name='Ramos', number=20, age=30, country='Colombia', height=185,\n weight=75, position=offense, team=dortmund)\ndb_session.add(ramos)\n\n# Wolfsburg\nhorn = Player(first_name='Jannes', last_name='Horn', number=21, age=19, country='Germany', height=186, weight=77,\n position=defence, team=wolfsburg)\ndb_session.add(horn)\ndraxler = Player(first_name='Julian', last_name='Draxler', number=10, age=23, country='Germany', height=187, weight=76,\n position=midfield, team=wolfsburg)\ndb_session.add(draxler)\ngomez = Player(first_name='Mario', last_name='Gomez', number=33, age=31, country='Germany', height=189, weight=88,\n position=offense, team=wolfsburg)\ndb_session.add(gomez)\n\ndb_session.commit()\n","sub_path":"seed.py","file_name":"seed.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"115774356","text":"'''!\n * Copyright (c) 2020 Microsoft Corporation. All rights reserved.\n * Licensed under the MIT License. \n'''\n\n\nclass ConfigSearchInfo:\n '''The class of the search space of a hyperparameters:\n\n Attributes:\n name: A string of the name of the hyperparameter\n type: data type of the hyperparameter\n lower: A number of the lower bound of the value\n upper: A number of the upper bound of the value\n init: A number of the initial value. For hyperparameters related to\n complexity, the init value needs to correspond to the lowest\n complexity\n change_tpe: A string of the change type, 'linear' or 'log'\n min_change: A number of the minimal change required. Could be inf if\n no such requirement\n '''\n\n def __init__(self, name, type, lower, upper, init, change_type = 'log',\n complexity_related = True, min_change = None):\n self.name = name \n self.type = type \n self.lower = lower \n self.upper = upper \n self.init = init \n self.change_type = change_type\n self.complexity_related = complexity_related\n # default setting of min_change: if type is int, min_change \n # should be 1, otherwise +inf\n if min_change is None:\n if self.type == int:\n self.min_change = 1.0 #minimum change required, \n else:\n self.min_change = float('+inf')\n else:\n self.min_change = min_change\n\n\ndef config_space(estimator, data_size, objective_name = \"regression\"):\n CS = {}\n n_estimators_upper = min(32768,int(data_size))\n max_leaves_upper = min(32768,int(data_size))\n # exp_max_depth_upper = min(32768,data_size)\n if 'xgboost' in estimator:\n CS['n_estimators'] = ConfigSearchInfo(name = 'n_estimators',\n type = int, lower = 4, init = 4, upper = n_estimators_upper, \n change_type = 'log')\n CS['max_leaves'] = ConfigSearchInfo(name = 'max_leaves', type =int,\n lower = 4, init = 4, upper = max_leaves_upper, change_type = 'log')\n CS['min_child_weight'] = ConfigSearchInfo(name = 'min_child_weight',\n type = float, lower = 0.001, init = 20.0, upper = 20.0, \n change_type = 'log')\n\n CS['learning_rate'] = ConfigSearchInfo(name = 'learning_rate',\n type = float, lower = 0.01, init = 0.1, upper = 1.0, \n change_type = 'log')\n CS['subsample'] = ConfigSearchInfo(name = 'subsample', type = float,\n lower = 0.6, init = 1.0, upper = 1.0, change_type = 'linear')\n CS['reg_alpha'] = ConfigSearchInfo(name = 'reg_alpha', type = float,\n lower = 1e-10, init = 1e-10, upper = 1.0, change_type = 'log',\n complexity_related = True)\n CS['reg_lambda'] = ConfigSearchInfo(name = 'reg_lambda', type = float,\n lower = 1e-10, init = 1.0, upper = 1.0, change_type = 'log')\n CS['colsample_bylevel'] = ConfigSearchInfo(name = 'colsample_bylevel',\n type = float, lower = 0.6, init = 1.0, upper = 1.0, \n change_type = 'linear')\n CS['colsample_bytree'] = ConfigSearchInfo(name = 'colsample_bytree',\n type = float, lower = 0.7, init = 1.0, upper = 1.0, \n change_type = 'linear')\n elif estimator in ('rf', 'extra_tree'):\n n_estimators_upper = min(2048, n_estimators_upper)\n # max_leaves_upper = min(2048, max_leaves_upper)\n CS['n_estimators'] = ConfigSearchInfo(name = 'n_estimators',\n type = int, lower = 4, init = 4, upper = n_estimators_upper, \n change_type = 'log')\n if objective_name != 'regression':\n CS['criterion'] = ConfigSearchInfo(name = 'criterion',\n type = int, lower = 1, init = 1, upper = 2, \n change_type = 'log')\n \n # CS['max_leaves'] = ConfigSearchInfo(name = 'max_leaves', type =int,\n # lower = 4, init = 4, upper = max_leaves_upper, change_type = 'log',\n # complexity_related = True)\n \n CS['max_features'] = ConfigSearchInfo(name = 'max_features', type = float,\n lower = 0.1, init = 1.0, upper = 1.0, change_type = 'log')\n # CS['min_samples_split'] = ConfigSearchInfo(name = 'min_samples_split',\n # type = int, lower = 2, init = 2, upper = 20, change_type = 'log', \n # complexity_related = True)\n # CS['min_samples_leaf'] = ConfigSearchInfo(name = 'min_samples_leaf',\n # type = int, lower = 1, init = 1, upper = 20, change_type = 'log', \n # complexity_related = True)\n elif 'lgbm' in estimator:\n CS['n_estimators'] = ConfigSearchInfo(name = 'n_estimators', type = int,\n lower = 4, init = 4, upper = n_estimators_upper, change_type = 'log')\n CS['max_leaves'] = ConfigSearchInfo(name = 'max_leaves', type = int,\n lower = 4, init = 4, upper = max_leaves_upper, change_type = 'log')\n CS['min_child_weight'] = ConfigSearchInfo(name = 'min_child_weight',\n type = float, lower = 0.001, init = 20, upper = 20.0, \n change_type = 'log')\n\n CS['learning_rate'] = ConfigSearchInfo(name = 'learning_rate',\n type = float, lower = 0.01, init = 0.1, upper = 1.0, \n change_type = 'log')\n CS['subsample'] = ConfigSearchInfo(name = 'subsample', type = float,\n lower = 0.6, init = 1.0, upper = 1.0, change_type = 'log',\n complexity_related = True)\n CS['log_max_bin'] = ConfigSearchInfo(name = 'log_max_bin', type = int,\n lower = 3, init = 8, upper = 10, change_type = 'log',\n complexity_related = True)\n CS['reg_alpha'] = ConfigSearchInfo(name = 'reg_alpha', type = float,\n lower = 1e-10, init = 1e-10, upper = 1.0, change_type = 'log',\n complexity_related = True)\n CS['reg_lambda'] = ConfigSearchInfo(name = 'reg_lambda', type = float,\n lower = 1e-10, init = 1.0, upper = 1.0, change_type = 'log')\n CS['colsample_bytree'] = ConfigSearchInfo(name = 'colsample_bytree',\n type = float, lower = 0.7, init = 1.0, upper = 1.0, \n change_type = 'log')\n elif 'lr' in estimator:\n CS['C'] = ConfigSearchInfo(name = 'C', type =float, lower = 0.03125,\n init = 1.0, upper = 32768.0, change_type = 'log', \n complexity_related = True)\n elif 'catboost' in estimator:\n # CS['n_estimators'] = ConfigSearchInfo(name = 'n_estimators', type = int,\n # lower = 4, init = 64, upper = n_estimators_upper, change_type = 'log', \n # complexity_related = True)\n early_stopping_rounds = max(min(round(1500000/data_size),150), 10)\n CS['rounds'] = ConfigSearchInfo(name = 'rounds', type = int,\n lower = 10, init = 10, \n upper = early_stopping_rounds, change_type = 'log')\n # CS['exp_max_depth'] = ConfigSearchInfo(name = 'exp_max_depth', type = int,\n # lower = 32, init = 64, upper = 256, change_type = 'log', \n # complexity_related = True)\n\n CS['learning_rate'] = ConfigSearchInfo(name = 'learning_rate',\n type = float, lower = 0.005, init = 0.1, upper = .2, \n change_type = 'log')\n # CS['l2_leaf_reg'] = ConfigSearchInfo(name = 'l2_leaf_reg',\n # type = float, lower = 1, init = 3, upper = 5, \n # change_type = 'log')\n elif 'nn' == estimator:\n CS['learning_rate'] = ConfigSearchInfo(name = 'learning_rate',\n type = float, lower = 1e-4, init = 3e-4, upper = 3e-2, \n change_type = 'log')\n CS['weight_decay'] = ConfigSearchInfo(name = 'weight_decay',\n type = float, lower = 1e-12, init = 1e-6, upper = .1, \n change_type = 'log')\n CS['dropout_prob'] = ConfigSearchInfo(name = 'dropout_prob',\n type = float, lower = 1.0, init = 1.1, upper = 1.5, \n change_type = 'log')\n elif 'kneighbor' in estimator:\n n_neighbors_upper = min(512,int(data_size/2))\n CS['n_neighbors'] = ConfigSearchInfo(name = 'n_neighbors', type = int,\n lower = 1, init = 5, upper = n_neighbors_upper, change_type = 'log') \n else:\n raise NotImplementedError\n\n return CS\n\n\ndef estimator_size(config, estimator):\n if estimator in ['xgboost', 'lgbm', 'rf', 'extra_tree']:\n try:\n max_leaves = int(round(config['max_leaves']))\n n_estimators = int(round(config['n_estimators']))\n model_size = float((max_leaves*3 + (max_leaves-1)*4 + 1)*\n n_estimators*8) \n except:\n model_size = 0\n return model_size\n elif 'catboost' in estimator:\n # if config is None: raise Exception(\"config is none\")\n n_estimators = int(round(config.get('n_estimators',8192)))\n max_leaves = int(round(config.get('exp_max_depth',64)))\n model_size = float((max_leaves*3 + (max_leaves-1)*4 + 1)*\n n_estimators*8) \n return model_size\n else:\n model_size = 1.0\n # raise NotImplementedError\n return model_size\n\n\ndef generate_config_ini(estimator, estimator_configspace):\n\n\n config_dic = {}\n config_dic_more = {}\n config_type_dic = {}\n for _, config in estimator_configspace.items():\n name, init = config.name, config.init\n type_, complexity_related = config.type, config.complexity_related\n config_type_dic[name] = type_\n if complexity_related:\n config_dic[name] = init\n else:\n config_dic_more[name] = init\n return config_dic, config_dic_more, {**config_dic, **config_dic_more}, \\\n config_type_dic\n\n\ndef generate_config_min(estimator,estimator_configspace, max_config_size):\n\n\n config_dic = {}\n config_dic_more = {}\n for _, config in estimator_configspace.items():\n name, lower = config.name, config.lower\n complexity_related = config.complexity_related\n if complexity_related:\n config_dic[name] = lower\n else:\n config_dic_more[name] = lower\n\n return config_dic, config_dic_more, {**config_dic, **config_dic_more}\n\n\ndef generate_config_max(estimator, estimator_configspace, max_config_size):\n\n\n config_dic = {}\n config_dic_more = {}\n for _, config in estimator_configspace.items():\n name, upper = config.name, config.upper\n complexity_related = config.complexity_related\n if complexity_related:\n if name in ('n_estimators', 'max_leaves'):\n config_dic[name] = min(upper, max_config_size)\n else:\n config_dic[name] = upper\n else:\n config_dic_more[name] = upper\n return config_dic, config_dic_more, {**config_dic, **config_dic_more}\n\n\ndef get_config_values(config_dic, config_type_dic):\n value_list = []\n for k in config_dic.keys():\n org_v = config_dic[k]\n if config_type_dic[k] == int:\n v = int(round(org_v))\n value_list.append(v)\n else:\n value_list.append(org_v)\n return value_list\n","sub_path":"flaml/space.py","file_name":"space.py","file_ext":"py","file_size_in_byte":10938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"361716335","text":"class Solution:\n def isValid(self, s):\n \"\"\"\n :type s: str\n :rtype: bool\n \"\"\"\n left = ['(', '{', '[']\n right = [')', '}', ']']\n stack = []\n for letter in s:\n if letter in left:\n stack.append(letter)\n elif letter in right:\n if len(stack) <= 0:\n return False\n if left.index(stack.pop()) != right.index(letter):\n return False\n return len(stack) == 0\n","sub_path":"Valid Parentheses.py","file_name":"Valid Parentheses.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"617490421","text":"# -*- coding: utf-8 -*-\n\n__version__ = '0.1.3'\n\n# configuration setup, the first thing we have to do\nfrom cherryblog.lib.configuration import config_manager\nconfig = config_manager\n\n\nimport os.path\nimport sys\n\n\ndef entrypoint():\n global config\n instance_dir = os.path.abspath(os.path.dirname('.'))\n config.merge(dict(instance_dir=instance_dir))\n\n from cherryblog import cli\n args = cli.parse_ars()\n\n # Dispatch and execute the command\n args.command_func(args)\n\n sys.exit(0)\n\n\n__all__ = ['__version__',\n 'entrypoint']\n\n","sub_path":"cherryblog/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"221444574","text":"\"\"\"\nThis Test Tool is aimed to execute any pytest-based test suite\non demeter cluster (KNL-LB). Basically, the class contained in this\nmodule implements the next four methods from the TestTool base class:\n * prepare_initial_results: Creates a list with all of test cases to be\n executed by using the '--collectonly' flag\n together to the pytest command obtained from\n the test scenario (ts) file.\n * prepare_scenario: In this method there are executed the\n installation for the cluster test content\n in the head node.\n\n * prepare_tool: Just print information about the task\n * run_testing: Executes the testing by using the test command\n obtained from the ts file, collect the results\n and report them to berta. It removes the\n results file to clean up the environment.\n\"\"\"\nimport os\nimport getpass\nimport random\nimport string\nimport logging\nfrom xml.dom import minidom\n\nimport reslog\nimport consts\nfrom wrappers import wrapper\nfrom utils import exec_with_timeout\n\nlog = logging.getLogger(__name__)\n\nTEST_CONTENT = [\"test-toolbox\", \"cluster-netpipe\"]\nCLUSTER_TEST_PATH = \"usr/share/mpss/test/cluster-netpipe-ft/\"\nOS_SUPPORTED = {\"warewulf-rhel-7.2\": \"rhel7.2\"}\n\n\ndef execute_cmd(cmd, timeout=3600, raise_on_error=False):\n \"\"\"\n This is a wrapper for the exec_with_timeout utility that will allows to\n execute as commands as we need to execute our test suite.\n \"\"\"\n out, return_code = exec_with_timeout(\n cmd,\n timeout=timeout,\n shell=True,\n env=None,\n cwd=None,\n poll_period=5,\n callback=None,\n tracing=False,\n read_in_chunks=True,\n screenshot_file=None,\n force_kill=False,\n private=False,\n raise_on_error=raise_on_error)\n if return_code != 0:\n raise RuntimeError('{0}'.format(out))\n return out, return_code\n\n\nclass ClusterKnllbTool(wrapper.TestTool):\n \"\"\"\n This class contains the methods that will support the test suite\n execution on Demeter cluster.\n \"\"\"\n\n def __init__(self, *args):\n super(ClusterKnllbTool, self).__init__(*args)\n log.info(\"Cluster KNL-LB Tool\")\n self.test_path = CLUSTER_TEST_PATH\n self._build_store_root = self.task['jobs'][1]['buildstore_path']\n self._build_store_user = self.task['jobs'][1]['buildstore_username']\n self._build_store_passwd = self.task['jobs'][1]['buildstore_password']\n self.test_commands = []\n self.download_path = '/tmp'\n self.install_path = '/'\n self.store_directory = os.getcwd()\n\n # We get the ts file name associated to this task\n scenario_info = self.task['jobs'][2]\n log.info(\"Cluster KNL-LB Tool:: Scenario info: %s\", scenario_info)\n self._ts_filename = scenario_info['scenario'].replace(\"rpmname=\", \"\")\n\n # We get the buildpath to be able to download the test content\n # and install it in the compute_node\n self._build_path = self.task['jobs'][1]['build_path']\n\n # We get the build number\n self._build_number = self.task['jobs'][1]['build_comment']\n\n # We set the OS to be installed in the compute nodes\n self._os_installed = \\\n OS_SUPPORTED[self.task['jobs'][0]['system_name']]\n\n # Initial list of results\n self._list_test_cases = []\n self.target_path = '/home/{0}/'.format(getpass.getuser())\n self.netpipe_dir = '/home/{0}/NetPIPE'.format(getpass.getuser())\n self.base_path = '/tmp'\n self.default_timeout = 3600\n\n self._product = self._build_path.split('/')[3]\n self._product = self._product.replace('MPSS-4-CI-', '').\\\n replace('MPSS-4-OD-', '')\n numbers = self._product.split('.')\n self._product = '.'.join(numbers[0:3])\n # Name for the .tar file that contains the xppsl test content\n self._package_test = \"mpss-test-{1}-{0}.tar\".format(\n self._os_installed,\n self._product)\n\n # Query for the list of compute nodes by using Warewulf\n self._node_list = []\n cmd_getting_node_list = \"wwsh node list | awk '/deme/ {{print $1}}' \"\n log.info(\"Cluster KNL-LB Tool:: Getting nodes list\")\n\n try:\n out, _ = execute_cmd(cmd_getting_node_list)\n except RuntimeError as cmd_exception:\n log.error(\"Cluster KNL-LB Tool::Failed getting nodes list\")\n log.error(\"Cluster KNL-LB Tool::%s\", cmd_exception)\n return\n\n self._node_list = out.split()\n log.info(\"Cluster KNL-LB Tool:: List of nodes: %s\", self._node_list)\n\n def readtest_command(self):\n \"\"\"\n This method will read the python command to be executed. It is read\n from the ts file\n \"\"\"\n\n # Read the ts file content to know the py.test command\n # to be executed on the remote target.\n read_cmd = \"cat {2}{0}{1}.ts\".format(self.test_path,\n self._ts_filename,\n self.install_path)\n\n log.info(\"Cluster KNL-LB Tool::Reading the ts file content\")\n log.info(\"Cluster KNL-LB Tool:: %s\", read_cmd)\n try:\n out, _ = execute_cmd(read_cmd)\n except RuntimeError as cmd_exception:\n log.error(\"Cluster KNL-LB Tool::Failed to read the test content file\")\n log.error(\"Cluster KNL-LB Tool:: %s\", cmd_exception)\n return\n\n self.test_commands = out.strip().split('\\n')\n log.info(\"Cluster KNL-LB Tool::Test content read: %s\",\n self.test_commands)\n\n def parse_results(self):\n \"\"\"\n This procedure read all xml file in the test results path in demeter\n cluster. This results are obtained by pytest after performing testing.\n \"\"\"\n\n # Get all xml files in the 'base_path directory'\n for filename in os.listdir(self.base_path):\n if not filename.endswith('.xml'):\n continue\n results_file = minidom.parse('{0}/{1}'.format(\n self.base_path,\n filename))\n\n # For every test case, we get the information useful to report Berta\n # the execution result\n for test_case in results_file.getElementsByTagName('testcase'):\n # We need first to identify the name of the test case to process\n name = test_case.attributes['name']\n log.info(\"Cluster KNL-LB Tool:: test case result: %s\", name.value)\n\n # Here, the script searches for any error or failure in the\n # DOM object related to every test case\n error = test_case.getElementsByTagName('error')\n failure = test_case.getElementsByTagName('failure')\n error_msg = ''\n\n # If there exist either an error or a failure, the test case\n # must be reported as FAILED to Berta\n if error:\n result = consts.TC_RESULT_FAIL\n error_msg = error[0].attributes['message'].value\n elif failure:\n result = consts.TC_RESULT_FAIL\n error_msg = failure[0].attributes['message'].value\n else:\n # Just in case that there were no error or failure,\n # this test case must be reported as PASSED\n result = consts.TC_RESULT_PASS\n\n # It is stored the results obtained before\n reslog.store_result(self.store_directory,\n name.value,\n result,\n error_msg,\n cmd_line='pytest')\n\n def cleanup(self):\n \"\"\"\n This method will remove all data regarding to this test suite: results,\n test content, logs and binaries.\n :return:\n \"\"\"\n\n cmd_remove_head = 'rm /{0}/*.xml'.format(self.base_path)\n\n cmd_uninstall_suite = \"sudo yum -y erase %s*\"\n\n cmd_remove_test_content = \"sudo rm -rf /tmp/mpss-test*\"\n\n cmd_remove_binaries = \"sudo rm -rf {0}\".format(self.netpipe_dir)\n\n cmd_remove_card_packages = \"sudo rm -rf /home/card/*.rpm\"\n try:\n output, _ = execute_cmd(cmd_remove_head, raise_on_error=True)\n log.info(\"Cluster KNL-LB Tool::Removing results file(s) %s\", output)\n\n for package in TEST_CONTENT:\n output, _ = execute_cmd(cmd_uninstall_suite % package)\n log.info(\"Cluster KNL-LB Tool::Uninstalling test suite %s\",\n output)\n\n output, _ = execute_cmd(cmd_remove_test_content)\n log.info(\"Cluster KNL-LB Tool::Removing test content %s\", output)\n\n output, _ = execute_cmd(cmd_remove_binaries)\n log.info(\"Cluster KNL-LB Tool::Removing test binaries %s\", output)\n\n output, _ = execute_cmd(cmd_remove_card_packages)\n log.info(\"Cluster KNL-LB Tool::Removing test binaries %s\", output)\n except RuntimeError as cmd_exception:\n log.info(\"Cluster KNL-LB Tool::Cleanup failed head node %s\",\n cmd_exception)\n\n def prepare_tool(self):\n \"\"\"\n This method will read the pytest scenario and will get the basic\n information about the testing to be executed.\n :return:\n \"\"\"\n\n log.info(\"Cluster KNL-LB Tool::Prepare cluster KNL-LB tool\")\n log.info(\"Cluster KNL-LB Tool::Scenario Path: %s\", self.scen_file)\n log.info(\"Cluster KNL-LB Tool::Tool path: %s\", self.tool_path)\n log.info(\"Cluster KNL-LB Tool::Scenario filename: %s\", self._ts_filename)\n log.info(\"Cluster KNL-LB Tool::Test content file: %s\", self._package_test)\n\n def prepare_scenario(self):\n log.info(\"Cluster KNL-LB Tool::Prepare scenario\")\n # We need to get the test content packages in the\n # target node from buildstore\n log.info(\"Cluster KNL-LB Tool::Installing test content\")\n tar_download_cmd = \"cd {4} && wget --user={5} --password={6} {0}{\" \\\n \"1}/eng/internal/{2}/\" \\\n \"package-test/{3}\".format(self._build_store_root,\n self._build_path,\n self._os_installed,\n self._package_test,\n self.download_path,\n self._build_store_user,\n self._build_store_passwd)\n\n log.info(\"Cluster KNL-LB Tool:: Downloading test content %s\",\n tar_download_cmd)\n try:\n execute_cmd(tar_download_cmd, timeout=self.default_timeout)\n except RuntimeError as cmd_exception:\n log.error(\"Cluster KNL-LB Tool::Failed downloading the test content file\")\n log.error(\"Cluster KNL-LB Tool:: %s\", cmd_exception)\n\n untar_cmd = \"cd {1} && tar -xvf {0}\".format(\n self._package_test,\n self.download_path)\n\n log.info(\"Cluster KNL-LB Tool:: Untar test content\")\n try:\n out, _ = execute_cmd(untar_cmd, timeout=self.default_timeout)\n except RuntimeError as cmd_exception:\n log.error(\"Cluster KNL-LB Tool::Failed extracting the test content file\")\n log.error(\"Cluster KNL-LB Tool:: %s\", cmd_exception)\n\n # We install every package marked as test content in the list\n # 'test_content'\n for test_package in TEST_CONTENT:\n install_cmd = \"sudo rpm -ivh --root {2} --force \" \\\n \"--nodeps {3}/mpss-test-{1}/packages/x86_64/{0}*.rpm\".format(\n test_package,\n self._product,\n self.install_path,\n self.download_path)\n\n log.info(\"Cluster KNL-LB Tool::Installing test \"\n \"content with %s\", install_cmd)\n try:\n out, _ = execute_cmd(install_cmd)\n log.info(\"Cluster KNL-LB Tool:: %s\", out)\n except RuntimeError as cmd_exception:\n log.error(\"Cluster KNL-LB Tool::Failed installing the test \"\n \"content\")\n log.error(\"Cluster KNL-LB Tool:: %s\", cmd_exception)\n\n # Set the test command\n self.readtest_command()\n\n # Finally, for the NetPIPE testing, we need create the\n # directory that will be shared among nodes in the cluster\n mkdir_cmd = \"mkdir -p {0}\".format(self.netpipe_dir)\n\n try:\n execute_cmd(mkdir_cmd)\n except RuntimeError as cmd_exception:\n log.error(\"Cluster KNL-LB Tool::Failed to create NetPIPE directory\")\n log.error(\"Cluster KNL-LB Tool:: %s\", cmd_exception)\n\n def prepare_initial_results(self):\n log.info(\"Cluster KNL-LB Tool::Initial results\")\n for cmd in self.test_commands:\n list_cmd = (\"py.test {2}{0}{1} --collectonly | grep Function\"\n .format(self.test_path,\n cmd,\n self.install_path))\n\n # We need to quit the process if some failure happens\n try:\n out, _ = execute_cmd(list_cmd)\n for test_case in out.split('\\n'):\n test_case_modified = test_case.strip().replace(\n \"\",\n \"\")\n if test_case_modified != '':\n self._list_test_cases.append(test_case_modified)\n\n except RuntimeError as cmd_exception:\n log.error(\"Cluster KNL-LB Tool::Failed to read tests cases to be\"\n \" executed\")\n log.error(\"Cluster KNL-LB Tool:: %s\", cmd_exception)\n\n # Now we have the list of test cases to be executed, we assign the\n # initial results\n log.info(\"Cluster KNL-LB Tool::Test list: %s\", self._list_test_cases)\n reslog.generate_initial_results(os.getcwd(), self._list_test_cases)\n\n def run_testing(self):\n log.info(\"Cluster KNL-LB Tool::Running tests\")\n base_path = '/tmp/'\n results_file = 'results_%s.xml'\n for cmd in self.test_commands:\n random_str = ''.join([random.choice(string.ascii_letters +\n string.digits) for _ in\n range(7)])\n run_test_cmd = \"py.test {4}{0}{1} -s -v --junitxml={3}{2}\".format(\n self.test_path,\n cmd,\n results_file % random_str,\n base_path,\n self.install_path)\n\n try:\n log.info(\"Cluster KNL-LB Tool::Running tests\")\n output, _ = execute_cmd(run_test_cmd)\n log.info(\"Cluster KNL-LB Tool::Getting output: %s\", output)\n except RuntimeError as cmd_exception:\n log.info(\"Cluster KNL-LB Tool::Failed to execute test \"\n \"command: %s\", cmd_exception)\n except AssertionError as assertion_failure:\n log.info(\"Cluster KNL-LB Tool::Failed to execute test \"\n \"command: %s\", assertion_failure)\n\n # We parse the results\n self.parse_results()\n\n # Results file cleanup\n self.cleanup()\n\n\nif __name__ == \"__main__\":\n wrapper.run(ClusterKnllbTool)\n","sub_path":"berta/berta/wrappers/cluster_knllb_tool.py","file_name":"cluster_knllb_tool.py","file_ext":"py","file_size_in_byte":15958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"32058380","text":"from django.shortcuts import render,get_object_or_404\nfrom django.http import HttpResponse\nfrom .models import Post,Comment\nfrom django.core.paginator import Paginator,EmptyPage,PageNotAnInteger\n\nfrom django.views.generic import ListView\n\nfrom .forms import EmailPostForm,CommentForm,SearchForm\n\nfrom django.core.mail import send_mail\n\nfrom taggit.models import Tag\n\nfrom django.db.models import Count\n\nfrom django.contrib.postgres.search import SearchVector,SearchQuery,SearchRank,TrigramSimilarity\n# Create your views here.\n\ndef index(request):\n\n # return HttpResponse('首页通知书')\n\n return render(request,'motangtest/index.html')\n\n\n# class PostListView(ListView):\n# queryset = Post.published.all()\n# context_object_name = 'posts'\n# paginate_by = 2\n# template_name = 'blog/post/list.html'\n\ndef post_list(request,tag_slug=None):\n object_list = Post.published.all()\n\n tag = None\n\n if tag_slug:\n tag = get_object_or_404(Tag, slug=tag_slug)\n object_list = object_list.filter(tags__in=[tag])\n\n\n paginator = Paginator(object_list,2)\n page = request.GET.get('page')\n try:\n posts = paginator.page(page)\n except PageNotAnInteger:\n posts = paginator.page(1)\n except EmptyPage:\n posts = paginator.page(paginator.num_pages)\n\n return render(request,'blog/post/list.html',{'posts':posts,'tag': tag}) # 可以不用加page\n\n# def post_detail(request,year,month,day,post):\n# post = get_object_or_404(Post,slug=post,status='published',publish__year = year,publish__month=month,publish__day = day)\n# # 获得数据或者404\n\n# return render(request,'blog/post/detail.html',{'post':post})\n\ndef post_detail(request, year, month, day, post):\n post = get_object_or_404(Post, slug=post, status=\"published\", publish__year=year, publish__month=month, publish__day=day)\n \n comments = post.comments.filter(active=True)\n\n post_tags_ids = post.tags.values_list('id',flat=True)\n similar_tags = Post.published.filter(tags__in=post_tags_ids).exclude(id=post.id)\n similar_posts = similar_tags.annotate(same_tags=Count('tags')).order_by('-same_tags','-publish')[:4]\n\n new_comment = None\n\n if request.method == \"POST\":\n comment_form = CommentForm(data=request.POST)\n if comment_form.is_valid():\n \n new_comment = comment_form.save(commit=False)\n \n new_comment.post = post\n \n new_comment.save()\n else:\n comment_form = CommentForm()\n return render(request, 'blog/post/detail.html',{'post': post, 'comments': comments, 'new_comment': new_comment, 'comment_form': comment_form,'similar_posts': similar_posts})\n\ndef post_share(request,post_id):\n\n post = get_object_or_404(Post,id=post_id,status='published')\n sent = False\n\n if request.method == 'POST':\n\n form = EmailPostForm(request.POST)\n\n if form.is_valid():\n\n cd = form.cleaned_data\n post_url = request.build_absolute_uri(post.get_absolute_url())\n subject = '{} ({}) recommends you reading \"{}\"'.format(cd['name'], cd['email'], post.title)\n message = 'Read \"{}\" at {}\\n\\n{}\\'s comments:{}'.format(post.title, post_url, cd['name'], cd['comments'])\n send_mail(subject,message, '1580120495@qq.com ', [cd['to']],fail_silently=False)# 自己邮箱(form表单发来的邮件还不用后端固定的？) 自己的STMP服务器 to是发送的邮箱\n sent = True\n \n else:\n form = EmailPostForm()\n\n return render(request,'blog/post/share.html',{'post':post,'form':form,'sent': sent})\n\n\n\ndef post_search(request):\n form = SearchForm()\n query = None\n results = []\n if 'query' in request.GET:\n form = SearchForm(request.GET)\n if form.is_valid():\n query = form.cleaned_data['query']\n # results = Post.objects.annotate(search=SearchVector('title', 'slug', 'body'), ).filter(search=query) # 使用搜索\n # 数据库应该不止postgres指出全局搜索吧(??)\n \n # search_vector = SearchVector('title', 'body')\n #可以对在标题中搜索到的结果给予比正文中搜索到的结果更大的权重\n search_vector = SearchVector('title', weight='A') + SearchVector('body', weight='B')\n search_query = SearchQuery(query)\n results = Post.objects.annotate(search=search_vector,rank=SearchRank(search_vector, search_query)).filter(search=search_query).order_by('-rank')\n \n #results = Post.objects.annotate(similarity=TrigramSimilarity('title',query),).filter(similarity__gte=0.1).order_by('-similarity')\n\n return render(request, 'blog/post/search.html', {'query': query, \"form\": form, 'results': results})","sub_path":"myblog/motangsTest/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"450839386","text":"from collections import namedtuple\nfrom datetime import datetime, timedelta\nfrom urllib import urlencode\nfrom grouper.constants import AUDIT_MANAGER, AUDIT_VIEWER\n\nimport pytest\nfrom tornado.httpclient import HTTPError\n\nfrom fixtures import standard_graph, graph, users, groups, service_accounts, session, permissions # noqa\nfrom fixtures import fe_app as app # noqa\nfrom grouper.audit import (\n assert_can_join, assert_controllers_are_auditors, get_audits, user_is_auditor,\n UserNotAuditor,\n)\nfrom url_util import url\nfrom util import add_member, grant_permission\nfrom grouper.models.audit_log import AuditLogCategory, AuditLog\n\n\ndef test_group_audited(standard_graph, session, groups, permissions): # noqa\n \"\"\" Ensure that the audited flag gets set appropriate only groups and inherited down the\n graph. \"\"\"\n\n graph = standard_graph # noqa\n\n assert not graph.get_group_details(\"security-team\")[\"audited\"]\n assert graph.get_group_details(\"serving-team\")[\"audited\"]\n assert graph.get_group_details(\"team-sre\")[\"audited\"]\n\n\ndef test_user_is_auditor(standard_graph): # noqa\n \"\"\" Ensure users get the ability to audit. \"\"\"\n\n assert user_is_auditor(\"zorkian@a.co\")\n assert not user_is_auditor(\"oliver@a.co\")\n\n\ndef test_assert_can_join(users, groups): # noqa\n \"\"\" Test various audit constraints to ensure that users can/can't join as appropriate. \"\"\"\n\n # Non-auditor can join non-audited group as owner.\n assert assert_can_join(groups[\"team-infra\"], users[\"zay@a.co\"], role=\"owner\")\n\n # Auditor can join non-audited group as owner.\n assert assert_can_join(groups[\"team-infra\"], users[\"zorkian@a.co\"], role=\"owner\")\n\n # Non-auditor can NOT join audited group as owner.\n with pytest.raises(UserNotAuditor):\n assert not assert_can_join(groups[\"serving-team\"], users[\"zay@a.co\"], role=\"owner\")\n\n # Non-auditor can join audited group as member.\n assert assert_can_join(groups[\"serving-team\"], users[\"zay@a.co\"])\n\n # Group with non-auditor owner can NOT join audited group.\n with pytest.raises(UserNotAuditor):\n assert not assert_can_join(groups[\"serving-team\"], groups[\"tech-ops\"])\n\n # Group with auditor owner can join audited group.\n assert assert_can_join(groups[\"serving-team\"], groups[\"sad-team\"])\n\n # Group with non-auditor owner can join non-audited group.\n assert assert_can_join(groups[\"team-infra\"], groups[\"tech-ops\"])\n\n # Group with auditor owner, but sub-group with non-auditor owner, can NOT join audited group.\n with pytest.raises(UserNotAuditor):\n assert not assert_can_join(groups[\"audited-team\"], groups[\"serving-team\"])\n\n\ndef test_assert_controllers_are_auditors(groups): # noqa\n \"\"\" Test the method that determines if a subtree is controlled by auditors. \"\"\"\n\n # Group is safely controlled by auditors.\n assert assert_controllers_are_auditors(groups[\"sad-team\"])\n\n # Group with non-auditor owner should fail this test.\n with pytest.raises(UserNotAuditor):\n assert not assert_controllers_are_auditors(groups[\"team-infra\"])\n\n\n@pytest.mark.gen_test\ndef test_toggle_perm_audited(groups, permissions, http_client, base_url):\n perm_name = 'audited' # perm that is already audited\n nonpriv_user_name = 'oliver@a.co' # user with no audit perms and without PERMISSION_ADMIN\n nonpriv_user_team = 'sad-team'\n nonpriv_headers = {'X-Grouper-User': nonpriv_user_name}\n priv_user_name = 'zorkian@a.co' # user with AUDIT_MANAGER\n priv_headers = {'X-Grouper-User': priv_user_name}\n enable_url = url(base_url, '/permissions/{}/enable-auditing'.format(perm_name))\n disable_url = url(base_url, '/permissions/{}/disable-auditing'.format(perm_name))\n\n # Give nonpriv user audit view permissions, which shouldn't allow enabling/disabling auditing\n grant_permission(groups[nonpriv_user_team], permissions[AUDIT_VIEWER], argument=\"\")\n\n # attempt to enable/disable auditing; both should fail due to lack of perms\n with pytest.raises(HTTPError):\n resp = yield http_client.fetch(enable_url, method=\"POST\", headers=nonpriv_headers, body=\"\")\n with pytest.raises(HTTPError):\n resp = yield http_client.fetch(disable_url, method=\"POST\", headers=nonpriv_headers, body=\"\")\n\n # Now confirm that enabling/disabling auditing works as a privileged user\n # Note that enabling audits on an audited perm succeeds (same for disabling)\n resp = yield http_client.fetch(enable_url, method=\"POST\", headers=priv_headers, body=\"\")\n assert resp.code == 200\n # Perm is still audited\n resp = yield http_client.fetch(disable_url, method=\"POST\", headers=priv_headers, body=\"\")\n assert resp.code == 200\n # Perm no longer audited\n resp = yield http_client.fetch(disable_url, method=\"POST\", headers=priv_headers, body=\"\")\n assert resp.code == 200\n # Perm still not audited\n resp = yield http_client.fetch(enable_url, method=\"POST\", headers=priv_headers, body=\"\")\n assert resp.code == 200\n # Perm audited again\n \n\n@pytest.mark.gen_test\ndef test_audit_end_to_end(session, users, groups, http_client, base_url, graph): # noqa\n \"\"\" Tests an end-to-end audit cycle. \"\"\"\n groupname = 'audited-team'\n\n zay_id = users[\"zay@a.co\"].id\n gary_id = users[\"gary@a.co\"].id\n\n # make everyone an auditor or global audit will have issues\n add_member(groups[\"auditors\"], users[\"gary@a.co\"])\n add_member(groups[\"auditors\"], users[\"oliver@a.co\"])\n add_member(groups[\"auditors\"], users[\"zay@a.co\"])\n add_member(groups[\"auditors\"], users[\"figurehead@a.co\"])\n\n # add some users to test removal\n add_member(groups[groupname], users[\"zay@a.co\"])\n add_member(groups[groupname], users[\"gary@a.co\"])\n\n graph.update_from_db(session)\n\n # start the audit\n end_at_str = (datetime.now() + timedelta(days=10)).strftime('%m/%d/%Y')\n fe_url = url(base_url, '/audits/create')\n resp = yield http_client.fetch(fe_url, method=\"POST\",\n body=urlencode({'ends_at': end_at_str}), headers={'X-Grouper-User': 'zorkian@a.co'})\n assert resp.code == 200\n\n open_audits = get_audits(session, only_open=True).all()\n assert len(open_audits) == 4, 'audits created'\n\n assert groupname in [x.group.name for x in open_audits], 'group we expect also gets audit'\n\n # pull all the info we need to resolve audits, avoids detached sqlalchemy sessions\n AuditMember = namedtuple('AuditMember', 'am_id, edge_type, edge_id')\n Audit = namedtuple('Audit', 'audit_id, owner_name, group_name, audit_members')\n all_group_ids = [x.group.id for x in open_audits]\n open_audits = [Audit(x.id, x.group.my_owners().iterkeys().next(), x.group.name,\n [AuditMember(am.id, am.edge.member_type, am.edge_id) for am in x.my_members()]) for\n x in open_audits]\n\n # approve everything but the one we added members to\n for one_audit in open_audits:\n fe_url = url(base_url, '/audits/{}/complete'.format(one_audit.audit_id))\n\n if one_audit.group_name == groupname:\n continue\n\n # blanket approval\n body = urlencode({\"audit_{}\".format(am.am_id): \"approved\" for am in\n one_audit.audit_members})\n\n resp = yield http_client.fetch(fe_url, method=\"POST\", body=body,\n headers={'X-Grouper-User': one_audit.owner_name})\n assert resp.code == 200\n\n open_audits = get_audits(session, only_open=True).all()\n assert len(open_audits) == 1, 'only our test group remaining'\n\n one_audit = open_audits[0]\n one_audit.id\n\n body_dict = {}\n for am in one_audit.my_members():\n if gary_id == am.member.id:\n # deny\n body_dict[\"audit_{}\".format(am.id)] = \"remove\"\n else:\n # approve\n body_dict[\"audit_{}\".format(am.id)] = \"approved\"\n\n owner_name = one_audit.group.my_owners().iterkeys().next()\n fe_url = url(base_url, '/audits/{}/complete'.format(one_audit.id))\n resp = yield http_client.fetch(fe_url, method=\"POST\", body=urlencode(body_dict),\n headers={'X-Grouper-User': owner_name})\n assert resp.code == 200\n\n # check all the logs\n assert len(AuditLog.get_entries(session, action='start_audit')) == 1, 'global start is logged'\n assert len(AuditLog.get_entries(session,\n action='complete_global_audit')) == 1, 'global complete is logged'\n\n for group_id in all_group_ids:\n assert len(AuditLog.get_entries(session, on_group_id=group_id, action='complete_audit',\n category=AuditLogCategory.audit)) == 1, 'complete entry for each group'\n\n assert len(AuditLog.get_entries(session, on_user_id=gary_id,\n category=AuditLogCategory.audit)) == 1, 'removal AuditLog entry on user'\n","sub_path":"tests/test_audit.py","file_name":"test_audit.py","file_ext":"py","file_size_in_byte":8699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"108543329","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\nchrome_driver_path = \"F:\\Download\\Drivers\\chromedriver\"\ndriver = webdriver.Chrome(chrome_driver_path)\ndriver.get(\"https://en.wikipedia.org/wiki/Main_Page\")\n\narticle_count = driver.find_element_by_css_selector(\"#articlecount a\")\n# article_count.click()\n\nall_portal = driver.find_element_by_link_text(\"All portals\")\n# all_portal.click()\n\nsearch = driver.find_element_by_name(\"search\")\nsearch.send_keys(\"Python\")\nsearch.send_keys(Keys.ENTER)\n\ndriver.quit()\n","sub_path":"Day_48/Chalange_2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"330135354","text":"##\n## Imprima la cantidad de registros por letra para la \n## primera columna, ordenados alfabeticamente.\n##\n## A,8\n## B,7\n## C,5\n## D,6\n## E,14\n##\nimport csv\nimport collections\nx = open('data.csv','r').readlines()\ny=[z[0] for z in sorted(x[0:])]\nfrecuencia = collections.Counter(y)\nfor key,value in frecuencia.items():\n print (key,value,sep=\",\")\n\n\n\n\n","sub_path":"q02.py","file_name":"q02.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"527320908","text":"\"\"\"Compare an image to a template and return position of template within image.\r\n\r\nImported modules\r\n----------------\r\n PIL.Image -Opens and handles images\r\n subprocess.Popen -Executes commands on command prompt\r\n time.sleep -Stops execution of code for given time period\r\n\r\nIncluded funtions\r\n-----------------\r\n find_questions -Find and output the locations of each question in order\r\n find_answers -Find and save the answers to each question from a given answers PDF\r\n init_template_answers -Load templates into a dictionary\r\n seperate_questions -Seperate and save each individual question\r\n pdf_to_image -Convert a specified amount of pages from a pdf to an image and save them in specified location\r\n image_to_array -Convert an image to an array and return it\r\n find_line -Return position of 400 by 3 line within file\r\n find_whitespace -Return amount of whitespace in first line of array before first black pixel\r\n init_template_questions -Load templates into dictionary of arrays\r\n compare_array -Return position of one array within another\r\n\r\n\"\"\"\r\n\r\nimport os\r\nfrom subprocess import Popen\r\nfrom PIL import Image\r\nfrom time import sleep\r\n\r\n\r\ndef find_questions(pdf_path, save_path, progress_file):\r\n \"\"\"Find the y coordinate and the page number of the questions in a PDF document.\r\n\r\n Parameters\r\n ----------\r\n pdf_path -Directory of PDF\r\n save_path -Directory to save images permanently\r\n progress_file -File to write progress to\r\n\r\n Variables\r\n ----------\r\n number_templates -Dictionary of arrays for each number template from 1-20\r\n num_positions -Arrays of number positions in form array[question][[x coordinate, y coordinate], page number]\r\n y_start -Where to start search in page from\r\n page_array -Array of search page\r\n template_array -Template being searched for\r\n found_spots -Array returned by compare_array function\r\n\r\n Counter variables\r\n -----------------\r\n page_num -Page being searched\r\n question_num -Question being searched for\r\n\r\n \"\"\"\r\n \"\"\"Create blank array for num_positions\"\"\"\r\n num_positions = []\r\n\r\n \"\"\"Convert pages specified to images in specified folder.\"\"\"\r\n pdf_to_image(pdf_path, save_path, 2, 12)\r\n\r\n \"\"\"Load number templates for questions.\"\"\"\r\n number_templates = init_template_questions()\r\n\r\n \"\"\"Question one is done seperately as it is a special case.\"\"\"\r\n\r\n \"\"\"Load page 1 into an array.\"\"\"\r\n page_array = image_to_array(save_path.replace(\".png\", \"_1.png\"), 255)\r\n\r\n \"\"\"Add position of question 1 to array of positions.\"\"\"\r\n num_positions.append([compare_array(page_array, number_templates[1], find_line(page_array), 80, searching_questions=True), 1])\r\n with open(progress_file, 'w') as f:\r\n f.write(\"Question 1 found.\")\r\n print(\"Question 1 found.\")\r\n \"\"\"\r\n Set start position of the next search to the position of the first questionself.\r\n Set the question number to be searched for to 2.\r\n Set the page number to be searched to 1.\r\n \"\"\"\r\n y_start = num_positions[0][0][0][1]\r\n question_num = 2\r\n page_num = 1\r\n\r\n \"\"\"Begin iterating in a pre-test loop, that stops when there are no more pages to search.\"\"\"\r\n while page_num <= 11 and question_num <= 20:\r\n \"\"\"\r\n Load page to be searched into an array.\r\n Load template to search for into an array.\r\n \"\"\"\r\n page_array = image_to_array(save_path.replace(\".png\", \"_\"+str(page_num)+\".png\"), 255)\r\n template_array = number_templates[question_num]\r\n\r\n \"\"\"Search for the template array in the page array, and return an array of spots where it was found\"\"\"\r\n found_spots = compare_array(page_array, template_array, y_start, 80)\r\n\r\n \"\"\"If only 1 of the template was found in the page, add it's position to an array of the found questions.\"\"\"\r\n if len(found_spots) == 1:\r\n num_positions.append([found_spots, page_num])\r\n with open(progress_file, 'w') as f:\r\n f.write(\"Question \"+str(question_num)+\" found.\")\r\n print(\"Question \"+str(question_num)+\" found.\")\r\n \"\"\"\r\n Set current question to the next question.\r\n Set start of next search to position of previously found question.\r\n Continue to next iteration of loop\r\n \"\"\"\r\n question_num += 1\r\n y_start = num_positions[question_num-2][0][0][1]\r\n continue\r\n\r\n \"\"\"Else if no templates are found in page, begin searching next page from the top.\"\"\"\r\n elif len(found_spots) == 0:\r\n page_num += 1\r\n y_start = 0\r\n continue\r\n\r\n \"\"\"Else if more than 1 templates are found in page, break loop.\r\n # TODO: Remove need for this.\r\n \"\"\"\r\n else:\r\n break\r\n \"\"\"Return positions and page number of questions.\"\"\"\r\n return num_positions\r\n\r\n\r\ndef find_answers(ans_path, save_path, question_amount, temp_path, progress_file):\r\n \"\"\"Find and write answers for questions to a text file.\r\n\r\n Parameters\r\n ----------\r\n ans_path -Path to answers PDF\r\n save_path -Folder to save to permanently\r\n question_amount -Amount of questions to find answers to\r\n temp_path -Path to save temporary files\r\n progress_file -Path to file to output progress to\r\n\r\n Variables\r\n ---------\r\n answer_page_array -Array of answers page\r\n templates_answers -Dictionary containing templates for answers\r\n template_array -Array of template\r\n curr_question_position -Results of comparing template to page\r\n answer_below -Answer for current question is below this point\r\n answers -Array of question numbers with answers\r\n\r\n Counter/temporary variables\r\n ---------------------------\r\n question_number -Current quetion number\r\n f -Temporary file variable\r\n\r\n \"\"\"\r\n \"\"\"Convert answer page to image.\"\"\"\r\n pdf_to_image(ans_path, temp_path, 1, 1)\r\n\r\n \"\"\"Load answer page image into array.\"\"\"\r\n answers_page_array = image_to_array(temp_path.replace(\".png\", \"_1.png\"), 255)\r\n\r\n \"\"\"Load template of answer page numbers and characters.\"\"\"\r\n templates_answers = init_template_answers()\r\n\r\n \"\"\"Create blank array for answers.\"\"\"\r\n answers = []\r\n\r\n \"\"\"Counted loop that executes once for each question.\"\"\"\r\n for question_number in range(1, question_amount+1):\r\n \"\"\"\r\n Load template of current question number.\r\n Find template in array of answers and set curr_question_position to its position.\r\n Set the position to search from to the positin of the found number.\r\n \"\"\"\r\n template_array = templates_answers[question_number]\r\n curr_question_position = compare_array(answers_page_array, template_array, 0, len(answers_page_array[0]), searching_for=1)\r\n\r\n answer_below = curr_question_position[0][1]\r\n\r\n \"\"\"If a certain letter is the first one to be found after that position, add the letter and the question number to the answers array.\"\"\"\r\n if len(compare_array(answers_page_array, templates_answers[\"A\"], answer_below-1, len(answers_page_array[0]), searching_for=1, y_end=answer_below+5)):\r\n answers.append([question_number, \"A\"])\r\n elif len(compare_array(answers_page_array, templates_answers[\"B\"], answer_below-1, len(answers_page_array[0]), searching_for=1, y_end=answer_below+5)):\r\n answers.append([question_number, \"B\"])\r\n elif len(compare_array(answers_page_array, templates_answers[\"C\"], answer_below-1, len(answers_page_array[0]), searching_for=1, y_end=answer_below+5)):\r\n answers.append([question_number, \"C\"])\r\n else:\r\n answers.append([question_number, \"D\"])\r\n\r\n \"\"\"Write current progress to an external file.\"\"\"\r\n with open(progress_file, 'w') as f:\r\n f.write(\"Answer \"+str(question_number)+\" found.\")\r\n print(\"Answer \"+str(question_number)+\" found.\")\r\n\r\n \"\"\"Create text file named answers containing answer data.\"\"\"\r\n with open(save_path.replace(\".png\", \".txt\"), 'w') as f:\r\n for answer in answers:\r\n f.write(str(answer[0])+\" \"+str(answer[1])+\"\\n\")\r\n\r\n \"\"\"Write current progress to an external file.\"\"\"\r\n with open(progress_file, 'w') as f:\r\n f.write(\"Answers saved.\")\r\n print(\"Answers saved.\")\r\n\r\n\r\ndef init_template_answers():\r\n \"\"\"Load templates for answer page into dictionary.\r\n\r\n Convert each template to array using image_to_array and store in dictionary.\r\n \"\"\"\r\n templates_answers = {\r\n \"A\": image_to_array(r\"Answer Symbol Templates\\template_A.png\", 255, True),\r\n \"B\": image_to_array(r\"Answer Symbol Templates\\template_B.png\", 255, True),\r\n \"C\": image_to_array(r\"Answer Symbol Templates\\template_C.png\", 255, True),\r\n \"D\": image_to_array(r\"Answer Symbol Templates\\template_D.png\", 255, True),\r\n 1: image_to_array(\"Answer Symbol Templates\\\\template_1.png\", 255, True),\r\n 2: image_to_array(\"Answer Symbol Templates\\\\template_2.png\", 255, True),\r\n 3: image_to_array(\"Answer Symbol Templates\\\\template_3.png\", 255, True),\r\n 4: image_to_array(\"Answer Symbol Templates\\\\template_4.png\", 255, True),\r\n 5: image_to_array(\"Answer Symbol Templates\\\\template_5.png\", 255, True),\r\n 6: image_to_array(\"Answer Symbol Templates\\\\template_6.png\", 255, True),\r\n 7: image_to_array(\"Answer Symbol Templates\\\\template_7.png\", 255, True),\r\n 8: image_to_array(\"Answer Symbol Templates\\\\template_8.png\", 255, True),\r\n 9: image_to_array(\"Answer Symbol Templates\\\\template_9.png\", 255, True),\r\n 10: image_to_array(\"Answer Symbol Templates\\\\template_10.png\", 255, True),\r\n 11: image_to_array(\"Answer Symbol Templates\\\\template_11.png\", 255, True),\r\n 12: image_to_array(\"Answer Symbol Templates\\\\template_12.png\", 255, True),\r\n 13: image_to_array(\"Answer Symbol Templates\\\\template_13.png\", 255, True),\r\n 14: image_to_array(\"Answer Symbol Templates\\\\template_14.png\", 255, True),\r\n 15: image_to_array(\"Answer Symbol Templates\\\\template_15.png\", 255, True),\r\n 16: image_to_array(\"Answer Symbol Templates\\\\template_16.png\", 255, True),\r\n 17: image_to_array(\"Answer Symbol Templates\\\\template_17.png\", 255, True),\r\n 18: image_to_array(\"Answer Symbol Templates\\\\template_18.png\", 255, True),\r\n 19: image_to_array(\"Answer Symbol Templates\\\\template_19.png\", 255, True),\r\n 20: image_to_array(\"Answer Symbol Templates\\\\template_20.png\", 255, True)\r\n }\r\n return templates_answers\r\n\r\n\r\ndef seperate_questions(question_locations, save_dir, progress_file, temp_path):\r\n \"\"\"Crop and save images of find_questions.\r\n\r\n Parameters\r\n ----------\r\n question_locations -array of question locations and page numbers\r\n save_dir -Directory to permanently save files\r\n progress_file -File to write progress to\r\n temp_path -Directory to temporarily save files\r\n\r\n Variables\r\n ---------\r\n page_number -current page being cropped\r\n page_array -array of current page being cropped\r\n start -location of start of crop\r\n end -location of end of crop\r\n new_img -cropped image to be written\r\n black_line -flag to tell if pixel in line\r\n white_line -flag to tell if pixel in line\r\n png_path -Location to save images\r\n\r\n Counter variables\r\n -----------------\r\n question_number -current question number\r\n y -y coordinate of pixel to be written\r\n x -x coordinate of pixel to be written\r\n\r\n \"\"\"\r\n \"\"\"Set save path to internal folder.\"\"\"\r\n png_path = temp_path+\"\\\\page.png\"\r\n\r\n \"\"\"Counted for loop to iterate through questions\"\"\"\r\n for question_number in range(len(question_locations)):\r\n \"\"\"\r\n Set current page to the page that the question is in.\r\n Convert current page to array.\r\n \"\"\"\r\n page_number = question_locations[question_number][1]\r\n page_array = image_to_array(png_path.replace(\".png\", \"_\"+str(page_number)+\".png\"), 255)\r\n\r\n \"\"\"The next section handles the determining of the location of the question starts and ends.\"\"\"\r\n\r\n \"\"\"\r\n Create flag white_line for breaking loop.\r\n Set start location to location of first question number.\r\n \"\"\"\r\n white_line = False\r\n start = question_locations[question_number][0][0][1]\r\n \"\"\"Move start location up the page until a line filled with white pixels is found.\"\"\"\r\n while not white_line:\r\n if 0 not in page_array[start]:\r\n white_line = True\r\n else:\r\n start -= 1\r\n\r\n \"\"\"Create flag for line containing black pixel to break the loop\"\"\"\r\n black_line = False\r\n\r\n \"\"\"Try structure catches exception IndexError, in order to catch and prevent the expected error from occuring.\"\"\"\r\n try:\r\n \"\"\"If next question is on the same page as the current question.\"\"\"\r\n if question_locations[question_number+1][1] == question_locations[question_number][1]:\r\n \"\"\"Set end of crop to vertical position of next question number\"\"\"\r\n end = question_locations[question_number+1][0][0][1] - 20\r\n\r\n \"\"\"While current line being searched does not contain black pixel\"\"\"\r\n while not black_line:\r\n if 0 not in page_array[end]:\r\n \"\"\"If no black pixel in line, set end to line above\"\"\"\r\n end -= 1\r\n else:\r\n \"\"\"If black pixel in line, break loop\"\"\"\r\n black_line = True\r\n\r\n \"\"\"If next question is not on same page as current question.\"\"\"\r\n else:\r\n \"\"\"Set end to 60 pixels above end of page\"\"\"\r\n end = len(page_array) - 60\r\n\r\n \"\"\"While current line being searched does not contain black pixel\"\"\"\r\n while not black_line:\r\n if 0 not in page_array[end]:\r\n \"\"\"If no black pixel in line, set end to line above\"\"\"\r\n end -= 1\r\n else:\r\n \"\"\"If black pixel in line, break loop\"\"\"\r\n black_line = True\r\n\r\n \"\"\"This error is raised if it is seperating the last question.\"\"\"\r\n except IndexError:\r\n \"\"\"Set end to 60 pixels above the end of the page.\"\"\"\r\n end = len(page_array) - 60\r\n\r\n \"\"\"While current line being searched does not contain black pixel\"\"\"\r\n while not black_line:\r\n if 0 not in page_array[end]:\r\n \"\"\"If no black pixel in line, set end to line above\"\"\"\r\n end -= 1\r\n else:\r\n \"\"\"If black pixel in line, break loop\"\"\"\r\n black_line = True\r\n\r\n \"\"\"Create a new blank image object, the same size as the current question to be cropped.\"\"\"\r\n new_img = Image.new(\"1\", (len(page_array[0]), end-start+2))\r\n\r\n \"\"\"Iterate through page array, within the bounds of the question.\"\"\"\r\n for y in range(start, end+2):\r\n for x in range(len(page_array[1])):\r\n \"\"\"Write the current pixel of the page to the new image.\"\"\"\r\n new_img.putpixel((x, y-start), page_array[y][x])\r\n \"\"\"Save the new image.\"\"\"\r\n new_img.save(save_dir+\"\\\\question_\"+str(question_number+1)+\".png\")\r\n\r\n \"\"\"Write progress to the progress file.\"\"\"\r\n with open(progress_file, 'w') as f:\r\n f.write(\"Question \"+str(question_number)+\" saved.\")\r\n print(\"Question \"+str(question_number+1)+\" saved.\")\r\n\r\n\r\ndef pdf_to_image(pdf_path, png_path, start, end):\r\n \"\"\"Convert PDF to JPG.\r\n\r\n Parameters\r\n ----------\r\n pdf_path -path to PDF file to convert to jpg\r\n png_path -path to save PNG file\r\n start -page number to start conversion at\r\n end -page number to end conversion at\r\n\r\n Counter variables\r\n -----------------\r\n i\r\n\r\n \"\"\"\r\n \"\"\"Iterate in range of pages to be converted\"\"\"\r\n for i in range(start-1, end):\r\n \"\"\"Convert PDF to image using ImageMagick, as pure black and white.\"\"\"\r\n params = [r\"C:\\Program Files (x86)\\ImageMagick-7.0.7-Q16\\magick.exe\", pdf_path+\"[\"+str(i)+\"]\", \"-alpha\", \"off\", png_path.replace(\".png\", \"_\"+str(i-start+2)+\".png\")]\r\n Popen(params, shell=True)\r\n \"\"\"Delay for 6 seconds to allow program to convert images.\"\"\"\r\n sleep(6)\r\n\r\n\r\ndef image_to_array(image_path, cutoff, template=False):\r\n \"\"\"Convert image to array.\r\n\r\n Array created in form array[y][x] where y is rows and x is columns\r\n\r\n Parameters\r\n ----------\r\n image_path -path to image to be converted\r\n cutoff -cutoff for pixel to be white\r\n template -boolean as to whether image being converted is template\r\n\r\n Variables\r\n ---------\r\n converted_array -array of converted image\r\n temp_array -temporary array to store x values in\r\n image -image object to create array from\r\n cutoff_to_compare -comparison that changes based on format of image\r\n\r\n Counting variables\r\n ------------------\r\n x -horizontal pixels\r\n y -vertical pixels\r\n\r\n \"\"\"\r\n \"\"\"Open image to be converted\"\"\"\r\n image = Image.open(image_path)\r\n\r\n \"\"\"\r\n Create blank array for converted image.\r\n Create temporary array to store x values of image.\r\n \"\"\"\r\n converted_array = []\r\n temp_array = []\r\n\r\n \"\"\"If image being converted is a template, set cutoff format to Greyscale\"\"\"\r\n if template:\r\n cutoff_to_compare = cutoff\r\n\r\n \"\"\"Else if image being converted is in RGB form, set cutoff format to RGB\"\"\"\r\n elif type(image.getpixel((0, 0))) is tuple:\r\n cutoff_to_compare = (cutoff, cutoff, cutoff)\r\n\r\n \"\"\"Else if image being converted is pure black and white, where white is a 0, set cutoff format to that.\"\"\"\r\n elif image.getpixel((0, 0)) == 0:\r\n cutoff_to_compare = 0\r\n\r\n \"\"\"Else if image being converted is pure black and white, where white is a 1, set cutoff format to that.\"\"\"\r\n elif image.getpixel((0, 0)) == 1:\r\n cutoff_to_compare == 1\r\n\r\n \"\"\"Final format it can be is RGB\"\"\"\r\n else:\r\n cutoff_to_compare = cutoff\r\n\r\n \"\"\"Iterate through image.\"\"\"\r\n for y in range(image.size[1]):\r\n for x in range(image.size[0]):\r\n \"\"\"Compare pixel values to cutoff format specified\"\"\"\r\n if image.getpixel((x, y)) == cutoff_to_compare:\r\n \"\"\"If white add 1 to end of temp array.\"\"\"\r\n temp_array.append(1)\r\n else:\r\n \"\"\"If black add 0 to end of temp array.\"\"\"\r\n temp_array.append(0)\r\n \"\"\"Add temp array to end of converted array\"\"\"\r\n converted_array.append(temp_array)\r\n \"\"\"Wipe temp array.\"\"\"\r\n temp_array = []\r\n \"\"\"Return converted array.\"\"\"\r\n return converted_array\r\n\r\n\r\ndef find_line(s_array):\r\n \"\"\"Search array for line of 0s, 400 wide and 3 down.\r\n\r\n Same as compare_array, except restricted to specific case\r\n Variables are the same, but only returns y value\r\n \"\"\"\r\n \"\"\"Set flag variables found and breaking to false.\"\"\"\r\n found = False\r\n breaking = False\r\n \"\"\"Iterate through 2D array to be searched.\"\"\"\r\n for y in range(len(s_array)):\r\n for x in range(len(s_array[0])):\r\n \"\"\"If a black pixel is found.\"\"\"\r\n if s_array[y][x] == 0:\r\n \"\"\"Set flags to True.\"\"\"\r\n found = True\r\n breaking = True\r\n \"\"\"Iterate through shape required for line condition\"\"\"\r\n for s_y in range(2):\r\n for s_x in range(400):\r\n \"\"\"If the point in the array being compared isnt black, set found flag to False, causing the loop to end\"\"\"\r\n if s_array[y+s_y][s_x+x] != 0:\r\n found = False\r\n break\r\n if not found:\r\n break\r\n \"\"\"If the line is found, return the current value.\"\"\"\r\n if found:\r\n return y\r\n \"\"\"Breaking flag causes loop to goto next y value to raise efficiency in the search.\"\"\"\r\n if breaking:\r\n breaking = False\r\n break\r\n \"\"\"If no line is found, return 0.\"\"\"\r\n return 0\r\n\r\n\r\ndef find_whitespace(template_array):\r\n \"\"\"Find the whitespace in the first line of a template array.\r\n\r\n Takes a template array in from template[y][x], returns position of first black pixel\r\n \"\"\"\r\n \"\"\"Set whitespace counter to 0\"\"\"\r\n template_whitespace = 0\r\n \"\"\"Iterate through first line of array.\"\"\"\r\n for x in range(len(template_array[0])):\r\n \"\"\"If current pixel is black\"\"\"\r\n if template_array[0][x] == 0:\r\n \"\"\"Set value of template_whitespace to current x value and end loop.\"\"\"\r\n template_whitespace = x\r\n break\r\n \"\"\"Output template_whitespace.\"\"\"\r\n return template_whitespace\r\n\r\n\r\ndef init_template_questions():\r\n \"\"\"Load template arrays into dictionary and return dictionary refering to them.\r\n\r\n Convert each template to array using image_to_array and store in dictionary.\r\n \"\"\"\r\n templates_questions = {\r\n 1: image_to_array(\"Question Number Templates\\\\template_1.png\", 255, True),\r\n 2: image_to_array(\"Question Number Templates\\\\template_2.png\", 255, True),\r\n 3: image_to_array(\"Question Number Templates\\\\template_3.png\", 255, True),\r\n 4: image_to_array(\"Question Number Templates\\\\template_4.png\", 255, True),\r\n 5: image_to_array(\"Question Number Templates\\\\template_5.png\", 255, True),\r\n 6: image_to_array(\"Question Number Templates\\\\template_6.png\", 255, True),\r\n 7: image_to_array(\"Question Number Templates\\\\template_7.png\", 255, True),\r\n 8: image_to_array(\"Question Number Templates\\\\template_8.png\", 255, True),\r\n 9: image_to_array(\"Question Number Templates\\\\template_9.png\", 255, True),\r\n 10: image_to_array(\"Question Number Templates\\\\template_10.png\", 255, True),\r\n 11: image_to_array(\"Question Number Templates\\\\template_11.png\", 255, True),\r\n 12: image_to_array(\"Question Number Templates\\\\template_12.png\", 255, True),\r\n 13: image_to_array(\"Question Number Templates\\\\template_13.png\", 255, True),\r\n 14: image_to_array(\"Question Number Templates\\\\template_14.png\", 255, True),\r\n 15: image_to_array(\"Question Number Templates\\\\template_15.png\", 255, True),\r\n 16: image_to_array(\"Question Number Templates\\\\template_16.png\", 255, True),\r\n 17: image_to_array(\"Question Number Templates\\\\template_17.png\", 255, True),\r\n 18: image_to_array(\"Question Number Templates\\\\template_18.png\", 255, True),\r\n 19: image_to_array(\"Question Number Templates\\\\template_19.png\", 255, True),\r\n 20: image_to_array(\"Question Number Templates\\\\template_20.png\", 255, True)\r\n }\r\n return templates_questions\r\n\r\n\r\ndef compare_array(search_array, template_array, y_start, x_end, searching_questions=False, searching_for=0, y_end=0):\r\n \"\"\"Compare image to template and return position.\r\n\r\n Parameters\r\n ----------\r\n search_array -2d array to search\r\n template_array -2d array of template to search for\r\n y_start -vertical value to start search from\r\n x_end -horizontal value to end search at\r\n searching_questions -whether or not the numbers being searched for are questions\r\n searching_for -How many templates are being searched for\r\n y_end -vertical value to end search at\r\n\r\n Variables\r\n ---------\r\n found_spots -array of spots in the file where the number is found\r\n template_whitespace -whitespace before template arrays first black pixel\r\n found -flag set to true when template is found and when called the position is added to found_spots\r\n breaking -flag set to true when all loops are to be ended\r\n\r\n Counter variables\r\n -----------------\r\n x -horizontal values\r\n y -vertical values\r\n s_x -searching horizontal values\r\n s_y -searching vertcal values\r\n\r\n \"\"\"\r\n \"\"\"If y_end is not specified, set y_end to the vertical length of the array to be searched\"\"\"\r\n if not y_end:\r\n y_end = len(search_array)\r\n\r\n \"\"\"\r\n Set counter variables, found and break, to False.\r\n Found is set to true when the template is found within the array to be searched.\r\n Break is set to true when all the loops are to be ended.\r\n \"\"\"\r\n found = False\r\n breaking = False\r\n\r\n \"\"\"Create blank array for locations of templates found within the array to be searched.\"\"\"\r\n found_spots = []\r\n \"\"\"Set template_whitespace to the amount of whitespace in the top line of the template array.\"\"\"\r\n template_whitespace = find_whitespace(template_array)\r\n \"\"\"Iterate through array to be searched within the bounds specified.\"\"\"\r\n for y in range(y_start, y_end):\r\n for x in range(x_end):\r\n \"\"\"Start searching if there is enough room left in search_array for the template and if a black pixel is found.\"\"\"\r\n if search_array[y][x] == 0 and len(search_array)-y >= len(template_array) and len(search_array[0])-x >= len(template_array) and x >= template_whitespace:\r\n \"\"\"Set found flag to True, this will be set to false if the area is found not to contain the template.\"\"\"\r\n found = True\r\n \"\"\"Iterate through template_array\"\"\"\r\n for s_y in range(len(template_array)):\r\n for s_x in range(len(template_array[0])):\r\n \"\"\"If the template_array pixel isnt the same as the search_array pixel in the corresponding spot relative to the start position of the search.\"\"\"\r\n if search_array[y+s_y][x-template_whitespace+s_x] != template_array[s_y][s_x]:\r\n \"\"\"Set found to false.\"\"\"\r\n found = False\r\n if not found:\r\n break\r\n \"\"\"If the template array is in this position, add it's position to the array containing the found spots.\"\"\"\r\n if found:\r\n found_spots.append((x, y))\r\n \"\"\"If searching for questions, only search until the first black pixel of each line.\"\"\"\r\n if (searching_questions):\r\n break\r\n \"\"\"If searching_for is specified and the amount of spots found is equal to the amount being searched for, break the loops.\"\"\"\r\n if (searching_for > 0 and len(found_spots) == searching_for):\r\n breaking = True\r\n break\r\n if breaking:\r\n break\r\n \"\"\"Output spots that have been found.\"\"\"\r\n return found_spots\r\n\r\n\r\ndef main():\r\n \"\"\"Combine the other modules to perform the full program.\r\n\r\n Imported modules\r\n ---------------\r\n os\r\n image_handling\r\n\r\n Inputs\r\n ------\r\n question_path\r\n answers_path\r\n\r\n Outputs\r\n -------\r\n Extracted answers\r\n Seperated questions\r\n\r\n Constants\r\n ---------\r\n progress_file\r\n temp_path\r\n page_start\r\n page_end\r\n\r\n Variables\r\n ---------\r\n found_questions\r\n question_amount\r\n \"\"\"\r\n \"\"\"Set location for progress to be output to.\"\"\"\r\n progress_file = \"progress.txt\"\r\n\r\n \"\"\"Set location to save temporary files\"\"\"\r\n temp_path = \"temp\"\r\n\r\n \"\"\"Read parameters from text file.\"\"\"\r\n with open(\"parameters.txt\", 'r') as f:\r\n year = f.readline().strip()\r\n question_path = f.readline().replace(\"//\", \"////\").strip()\r\n answers_path = f.readline().replace(\"//\", \"////\").strip()\r\n\r\n \"\"\"Set location to save the paper.\"\"\"\r\n save_path = \"Papers\\\\\"+str(year)\r\n\r\n \"\"\"Output current progress to progress file.\"\"\"\r\n with open(progress_file, 'w') as f:\r\n f.write(\"parameters loaded\")\r\n print(\"Parameters loaded.\")\r\n\r\n \"\"\"Create folder to store paper in.\"\"\"\r\n os.makedirs(save_path)\r\n\r\n \"\"\"Create array containing found questions.\"\"\"\r\n found_questions = find_questions(question_path, temp_path+\"\\\\page.png\", progress_file)\r\n \"\"\"Set question amount to the length of the array containing the questions found.\"\"\"\r\n question_amount = len(found_questions)\r\n\r\n \"\"\"Seperate and save questions in specified save folder.\"\"\"\r\n seperate_questions(found_questions, save_path, progress_file, temp_path)\r\n\r\n \"\"\"Find and save answers.\"\"\"\r\n find_answers(answers_path, save_path+\"\\\\answers.png\", question_amount, temp_path+\"\\\\answers.png\", progress_file)\r\n\r\n \"\"\"Output progress to progress file.\"\"\"\r\n with open(progress_file, \"w\") as f:\r\n f.write(\"done\")\r\n print(\"Done\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n \"\"\"Executes if module is called directly\"\"\"\r\n main()\r\n","sub_path":"image_handling.py","file_name":"image_handling.py","file_ext":"py","file_size_in_byte":30186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"366389546","text":"\"\"\"Pathways to Success Part 2.\"\"\"\n# Import the csv library\nimport csv\nfrom pathlib import Path\n\n# Create a path to the csv file called `quarterly_data.csv`\ncsvpath = Path(\"quarterly_data.csv\")\n\n# Open the csv path, read the data, and print each row\nwith open(csvpath) as csvfile:\n data = csv.reader(csvfile)\n for row in data:\n print(row)\n\n\"\"\"Extension.\n\nCreate a variable above the `for` loop named `Counter`\nand set it equal to zero. Then, every time a new row\nis read within the `for` loop, add a value of 1 to\nthis variable.\n\"\"\"\n\ncsvpath = Path(\"quarterly_data.csv\")\ncounter = 0\nwith open(csvpath) as csvfile:\n data = csv.reader(csvfile)\n for row in data:\n # For every new row, add one to `counter`\n counter = counter + 1\n\n # Print the row counter and then the row\n print(\"row counter \", counter)\n print(row)\n","sub_path":"1_Financial_Programming_with_Python/12_Pathways_to_Success_Part_2/Solved/pathways_to_success_part_2.py","file_name":"pathways_to_success_part_2.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"597686904","text":"\"\"\"\n@author:li kunlun\n@time:2020-10-22\n@description:散点图，直方图\n\"\"\"\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# 用于在控制台打印数据（将Excel的信息都显示出来）\npd.options.display.max_columns = 999\nhomes = pd.read_excel('D:/home_data.xlsx')\nprint(homes.head())\n# 两列之间数据的相关性\nprint(homes.corr())\n# 散点图\n# homes.plot.scatter(x='sqft_living', y='price')\n# 密度图（kernel density）\nhomes.sqft_living.plot.kde()\n# 直方图\n# homes.sqft_living.plot.hist(bins=100)\n# # 500表示x轴的步长\n# plt.xticks(range(0, max(homes.sqft_living), 500), fontsize=8, rotation=90)\nplt.show()\n","sub_path":"src/com/xiaolun/chapter01/module04_excel/python14_scatter.py","file_name":"python14_scatter.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"645007681","text":"#新增成員資料\r\nimport requests\r\nbase = 'https://japanwest.api.cognitive.microsoft.com/face/v1.0'\r\npson_url = f'{base}/persongroups/gp01/persons'\r\nkey = 'f85d4cd78eda46cab8549149ac0d146a'\r\nheaders_json = {'Ocp-Apim-Subscription-Key': key, \r\n 'Content-Type': 'application/json'}\r\nbody = {'name': '曹家憲', \r\n 'userData': '位於家裡'}\r\nbody = str(body).encode('utf-8') \r\nresponse = requests.post(pson_url, \r\n headers=headers_json,\r\n data=body)\r\nif response.status_code == 200:\r\n print('新增完成: ', response.json())\r\nelse:\r\n print(\"新增失敗:\", response.json())\r\n\r\n\r\n","sub_path":"OPENCV2.0/addface_azure.py","file_name":"addface_azure.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"591101271","text":"def main():\n\n\tI = 0\n\tJ = 0\n\tcont = 0\n\taumento = 0\n\n\twhile I <= 2:\n\t\t\n\t\tJ += 1\n\t\tinteiroJ = J - int(J)\n\t\tinteiroI = I - int(I)\n\n\t\tif inteiroI == 0 or inteiroJ == 0:\n\t\t\tI = round(I)\n\t\t\tJ = round(J)\n\t\t\tprint('I=%d J=%d' % (I,J))\n\n\t\telse:\n\t\t\tprint('I=%.1f J=%.1f' % (I,J))\n\n\t\tcont += 1\n\n\t\tif cont == 3:\n\t\t\taumento += 1\n\t\t\tJ = 0 + 0.2 * aumento\n\t\t\tI += 0.2\n\t\t\tcont = 0\n\nif __name__ == '__main__':\n\tmain()","sub_path":"1098.py","file_name":"1098.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"282736236","text":"import httpx\nfrom bs4 import BeautifulSoup\nfrom convert_to_json import hash_id\n\nfrom cleanup import delete_from_index\nfrom upload_to_appsearch import upload_dict\n\nbit_chapters_page = httpx.get(\"https://foundation.blacksintechnology.net/chapters/\")\nsoup = BeautifulSoup(bit_chapters_page, \"html.parser\")\nbase_locations_wrapper = soup.select(\".elementor-widget-wrap\")\n\n\ndef get_locations():\n locations = []\n\n for location in base_locations_wrapper:\n\n if \"Chapter Organizer\" in location.text:\n name = location.select(\".raven-heading-title\")[0].text.strip()\n url = location.select(\"a.raven-button.raven-button-link\")[0][\"href\"]\n asset = {\n \"name\": f\"Blacks in Technology - {name}\",\n \"url\": url,\n \"organization_logo\": \"https://kjaymiller.s3-us-west-2.amazonaws.com/images/blacks-in-technology.jpeg\",\n \"city\": name.title(),\n \"diversity_focus\": [\"BIPOC\"],\n \"technology_focus\": [\"General Technology\"],\n \"parent_organization\": \"Blacks in Technology Foundation\",\n \"global_org_url_from_parent_organization\": \"https://blacksintechnology.net\",\n }\n\n asset[\"id\"] = hash_id(name + url)\n locations.append(asset)\n return locations\n\n\nif __name__ == \"__main__\":\n delete_from_index(\"Blacks in Technology Foundation\")\n upload_dict(get_locations())\n","sub_path":"upload/scrapers/blacks-in-technology.py","file_name":"blacks-in-technology.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"280877971","text":"# Copyright 2021 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Data parser and processing for segmentation datasets.\"\"\"\nfrom typing import Optional, List\n\nimport tensorflow as tf\nfrom official.vision.beta.dataloaders import decoder\nfrom official.vision.beta.dataloaders import parser\nfrom official.vision.beta.ops import preprocess_ops, augment\n\nimport io\nfrom PIL import Image\n\nMEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)\nSTDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)\n\n\ndef _encode_image(image_array, fmt):\n image = Image.fromarray(image_array)\n with io.BytesIO() as output:\n image.save(output, format=fmt)\n return output.getvalue()\n\n\nclass Decoder(decoder.Decoder):\n \"\"\"A tf.Example decoder for segmentation task.\"\"\"\n\n def __init__(self):\n self._keys_to_features = {\n 'image/encoded': tf.io.FixedLenFeature((), tf.string, default_value=''),\n 'image/height': tf.io.FixedLenFeature((), tf.int64, default_value=0),\n 'image/width': tf.io.FixedLenFeature((), tf.int64, default_value=0),\n 'image/segmentation/class/encoded':\n tf.io.FixedLenFeature((), tf.string, default_value='')\n }\n\n def decode(self, serialized_example):\n return tf.io.parse_single_example(\n serialized_example, self._keys_to_features)\n\n\nclass Parser(parser.Parser):\n \"\"\"Parser to parse an image and its annotations into a dictionary of tensors.\n \"\"\"\n\n def __init__(self,\n output_size,\n crop_size=None,\n resize_eval_groundtruth=True,\n groundtruth_padded_size=None,\n ignore_label=255,\n aug_rand_hflip=False,\n aug_policy: Optional[str] = None,\n randaug_magnitude: Optional[int] = 10,\n randaug_available_ops: Optional[List[str]] = None,\n aug_scale_min=1.0,\n aug_scale_max=1.0,\n preserve_aspect_ratio=True,\n rotate_min=0.0,\n rotate_max=0.0,\n bright_min=1.0,\n bright_max=1.0,\n dtype='float32'):\n \"\"\"Initializes parameters for parsing annotations in the dataset.\n\n Args:\n output_size: `Tensor` or `list` for [height, width] of output image. The\n output_size should be divided by the largest feature stride 2^max_level.\n crop_size: `Tensor` or `list` for [height, width] of the crop. If\n specified a training crop of size crop_size is returned. This is useful\n for cropping original images during training while evaluating on\n original image sizes.\n resize_eval_groundtruth: `bool`, if True, eval groundtruth masks are\n resized to output_size.\n groundtruth_padded_size: `Tensor` or `list` for [height, width]. When\n resize_eval_groundtruth is set to False, the groundtruth masks are\n padded to this size.\n ignore_label: `int` the pixel with ignore label will not used for training\n and evaluation.\n aug_rand_hflip: `bool`, if True, augment training with random\n horizontal flip.\n aug_policy: `str`, augmentation policies. None or 'randaug'. TODO support 'autoaug'\n randaug_magnitude: `int`, magnitude of the randaugment policy.\n randaug_available_ops: `List[str]`, specify augmentations for randaug\n aug_scale_min: `float`, the minimum scale applied to `output_size` for\n data augmentation during training.\n aug_scale_max: `float`, the maximum scale applied to `output_size` for\n data augmentation during training.\n preserve_aspect_ratio: `bool`, whether to preserve aspect ratio during resize\n rotate_min: `float`, the minimum rotation applied to `output_size` for\n data augmentation during training.\n rotate_max: `float`, the maximum rotation applied to `output_size` for\n data augmentation during training.\n bright_min: `float`, the minimum brightness applied to `output_size` for\n data augmentation during training.\n bright_max: `float`, the maximum brightness applied to `output_size` for\n data augmentation during training.\n dtype: `str`, data type. One of {`bfloat16`, `float32`, `float16`}.\n \"\"\"\n self._output_size = output_size\n self._crop_size = crop_size\n self._resize_eval_groundtruth = resize_eval_groundtruth\n if (not resize_eval_groundtruth) and (groundtruth_padded_size is None):\n raise ValueError('groundtruth_padded_size ([height, width]) needs to be'\n 'specified when resize_eval_groundtruth is False.')\n self._groundtruth_padded_size = groundtruth_padded_size\n self._ignore_label = ignore_label\n\n # Data augmentation.\n self._aug_rand_hflip = aug_rand_hflip\n self._aug_scale_min = aug_scale_min\n self._aug_scale_max = aug_scale_max\n self._preserve_aspect_ratio = preserve_aspect_ratio\n self._bright_min = bright_min\n self._bright_max = bright_max\n self._rotate_min = rotate_min\n self._rotate_max = rotate_max\n\n if aug_policy:\n # ops that changes the shape of the mask (any form of translation / rotation)\n if aug_policy == 'randaug':\n self._augmenter = augment.RandAugment(\n num_layers=2, magnitude=randaug_magnitude, available_ops=randaug_available_ops)\n else:\n raise ValueError(\n 'Augmentation policy {} not supported.'.format(aug_policy))\n else:\n self._augmenter = None\n\n # dtype.\n self._dtype = dtype\n\n def _prepare_image_and_label(self, data):\n \"\"\"Prepare image and label.\"\"\"\n image = tf.io.decode_image(data['image/encoded'], channels=3)\n label = tf.io.decode_image(data['image/segmentation/class/encoded'],\n channels=1)\n \n # encoded_jpg = tf.image.encode_jpeg(image)\n # tf.io.write_file('img.jpg', encoded_jpg)\n # encoded_jpg2 = tf.image.encode_jpeg(label)\n # tf.io.write_file('label.jpg', encoded_jpg2)\n\n height = data['image/height']\n width = data['image/width']\n image = tf.reshape(image, (height, width, 3))\n\n label = tf.reshape(label, (1, height, width))\n label = tf.cast(label, tf.float32)\n \n return image, label\n\n def _parse_train_data(self, data):\n \"\"\"Parses data for training and evaluation.\"\"\"\n image, label = self._prepare_image_and_label(data)\n\n if self._crop_size:\n\n label = tf.reshape(label, [data['image/height'], data['image/width'], 1])\n # If output_size is specified, resize image, and label to desired\n # output_size.\n if self._output_size:\n image = tf.image.resize(image, self._output_size, method='bilinear')\n label = tf.image.resize(label, self._output_size, method='nearest')\n\n image_mask = tf.concat([image, label], axis=2)\n image_mask_crop = tf.image.random_crop(image_mask,\n self._crop_size + [4])\n image = image_mask_crop[:, :, :-1]\n label = tf.reshape(image_mask_crop[:, :, -1], [1] + self._crop_size)\n\n # Flips image randomly during training.\n if self._aug_rand_hflip:\n image, _, label = preprocess_ops.random_horizontal_flip(\n image, masks=label)\n\n train_image_size = self._crop_size if self._crop_size else self._output_size\n # Rotates image randomly during training\n if self._rotate_min != 0.0 and \\\n self._rotate_max != 0.0 and \\\n self._rotate_min < self._rotate_max:\n image, label = preprocess_ops.random_rotation(\n image, masks=label, \n rotate_max=self._rotate_max, \n rotate_min=self._rotate_min,\n ignore_label=self._ignore_label\n )\n\n # Resizes and crops image.\n image, image_info = preprocess_ops.resize_and_crop_image(\n image,\n train_image_size,\n train_image_size,\n aug_scale_min=self._aug_scale_min,\n aug_scale_max=self._aug_scale_max,\n preserve_aspect_ratio=self._preserve_aspect_ratio)\n \n # Modify brightness randomly during training\n if self._bright_min != 1.0 and \\\n self._bright_max != 1.0 and \\\n self._bright_min < self._bright_max:\n image = preprocess_ops.random_brightness(\n image, \n bright_min=self._bright_min, \n bright_max=self._bright_max)\n\n # Resizes and crops boxes.\n image_scale = image_info[2, :]\n offset = image_info[3, :]\n\n # Pad label and make sure the padded region assigned to the ignore label.\n # The label is first offset by +1 and then padded with 0.\n label += 1\n label = tf.expand_dims(label, axis=3)\n label = preprocess_ops.resize_and_crop_masks(\n label, image_scale, train_image_size, offset)\n label -= 1\n label = tf.where(tf.equal(label, -1),\n self._ignore_label * tf.ones_like(label), label)\n label = tf.squeeze(label, axis=0)\n\n # Apply randaug\n if self._augmenter is not None:\n image, label = self._augmenter.distort_image_and_mask(\n image, label, self._ignore_label)\n\n valid_mask = tf.not_equal(label, self._ignore_label)\n labels = {\n 'masks': label,\n 'valid_masks': valid_mask,\n 'image_info': image_info,\n }\n \n # Normalizes image with mean and std pixel values. \n # Must be done after augmenter since certain ops rely on uint8\n image = preprocess_ops.normalize_image(image,\n offset=MEAN_RGB,\n scale=STDDEV_RGB)\n\n # Cast image as self._dtype\n image = tf.cast(image, dtype=self._dtype)\n\n return image, labels\n\n def _parse_eval_data(self, data):\n \"\"\"Parses data for training and evaluation.\"\"\"\n image, label = self._prepare_image_and_label(data)\n # The label is first offset by +1 and then padded with 0.\n label += 1\n label = tf.expand_dims(label, axis=3)\n\n # Resizes and crops image.\n image, image_info = preprocess_ops.resize_and_crop_image(\n image,\n self._output_size,\n self._output_size,\n preserve_aspect_ratio=self._preserve_aspect_ratio)\n\n if self._resize_eval_groundtruth:\n # Resizes eval masks to match input image sizes. In that case, mean IoU\n # is computed on output_size not the original size of the images.\n image_scale = image_info[2, :]\n offset = image_info[3, :]\n label = preprocess_ops.resize_and_crop_masks(label, image_scale,\n self._output_size, offset)\n else:\n label = tf.image.pad_to_bounding_box(\n label, 0, 0, self._groundtruth_padded_size[0],\n self._groundtruth_padded_size[1])\n\n label -= 1\n label = tf.where(tf.equal(label, -1),\n self._ignore_label * tf.ones_like(label), label)\n label = tf.squeeze(label, axis=0)\n\n valid_mask = tf.not_equal(label, self._ignore_label)\n labels = {\n 'masks': label,\n 'valid_masks': valid_mask,\n 'image_info': image_info\n }\n\n # Normalizes image with mean and std pixel values.\n # Must be done after augmenter since certain ops rely on uint8\n image = preprocess_ops.normalize_image(image,\n offset=MEAN_RGB,\n scale=STDDEV_RGB)\n \n # Cast image as self._dtype\n image = tf.cast(image, dtype=self._dtype)\n\n return image, labels\n","sub_path":"official/vision/beta/dataloaders/segmentation_input.py","file_name":"segmentation_input.py","file_ext":"py","file_size_in_byte":11823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"288293110","text":"from ucfg_updater import *\nfrom ucfg_utils import info, error\n\nfrom ucfg_main import ConfigUtilities\n\nimport os\nimport re\n\nfrom _use import USEUpdater\n\nclass UUpdater(USEUpdater):\n\t\n\tdef printDescription(self, options):\n\t\tinfo(options, \"Updates Unicore/X configuration from pre 6.6.0 versions to the 6.6.0 syntax\")\n\n\tdef run(self, options):\n\t\tinfo(options, \"UNICORE/X configuration updater\")\n\t\tsuper(UUpdater, self).run(options, 'unicorex')\n\t\tcfgUtil = ConfigUtilities(options, 'unicorex')\n\n\t\tdict = {\n\t\t\t'defaultsms.workdir'\t\t\t\t\t\t: 'coreServices.defaultsms.workdir',\n\t\t\t'uas.targetsystemfactory.xnjs.configfile'\t: 'coreServices.targetsystemfactory.xnjs.configfile',\n\t\t\t'uas.sms.protocols'\t\t\t\t\t\t\t: 'coreServices.sms.protocols',\n\t\t\t'uas.storagefactory.types' \t\t\t\t\t: 'coreServices.sms.enabledFactories',\n\t\t\t'unicore.gridbean.directory'\t\t\t\t: 'coreServices.gridbean.directory',\n\t\t\t'cip.data.path'\t\t\t\t\t\t\t\t: 'coreServices.cip.dataPath',\n\t\t\t'bes.naming.profile'\t\t\t\t\t\t: 'coreServices.bes.namingProfile',\n\t\t\t'bes.job.mode'\t\t\t\t\t\t\t\t: 'coreServices.bes.jobMode',\n\t\t\t'bes.is.accepting.new.activities'\t\t\t: 'coreServices.bes.isAcceptingNewActivities',\n\t\t\t'bes.naming.profile'\t\t\t\t\t\t: 'coreServices.bes.namingProfile',\n\t\t\t'bes.local.resource.manager.type'\t\t\t: 'coreServices.bes.localResourceManagerType',\n\t\t\t'bes.common.name'\t\t\t\t\t\t\t: 'coreServices.bes.commonName',\n\t\t\t'bes.long.description'\t\t\t\t\t\t: 'coreServices.bes.longDescription',\n\t\t\t'bes.extension'\t\t\t\t\t\t\t\t: 'coreServices.bes.extension',\n\t\t}\n\n\t\tjavaProps = cfgUtil.getJavaPropertyKeys('uas.config')\n\t\tfor propName in javaProps:\n\t\t\tm = re.match('^(uas\\.storagefactory\\.)(.*)', propName)\n\t\t\tif m != None and not (propName == 'uas.storagefactory.types'):\n\t\t\t\tdict[propName] = 'coreServices.sms.factory.' + m.group(2) \n\n\t\tcfgUtil.updateJavaPropertyNames('uas.config', dict)\n\n\t\tdict = {\n\t\t\t'bes.factory.id' : 'This property is not available anymore without a replacement.'\n\t\t}\n\n\t\tcfgUtil.commentJavaProperties('uas.config', dict);\n\t\t\n\t\tinfo(options, \"Finished update of configuration of Unicore/X\")\n\n\n","sub_path":"packages/unicore-registry6/sources/src/main/configurator/unicorex.py","file_name":"unicorex.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"249431449","text":"import six\n\nfrom chainer.backends import cuda\nfrom chainer import function_node\nfrom chainer.utils import conv\nfrom chainer.utils import conv_nd\nfrom chainer.utils import type_check\n\n\nif cuda.cudnn_enabled:\n cudnn = cuda.cudnn\n\n\nclass _PoolingND(function_node.FunctionNode):\n\n \"\"\"Base class of pooling function over a set of N-dimensional planes.\"\"\"\n\n def __init__(self, ndim, ksize, stride=None, pad=0, cover_all=True,\n return_indices=False):\n if stride is None:\n stride = ksize\n\n if ndim <= 0:\n raise ValueError(\n 'pooling operation requires at least one spatial dimension.')\n\n self.ndim = ndim\n self.ksize = conv_nd.as_tuple(ksize, ndim)\n self.stride = conv_nd.as_tuple(stride, ndim)\n self.pad = conv_nd.as_tuple(pad, ndim)\n\n self.cover_all = cover_all\n self.return_indices = return_indices\n\n self._used_cudnn = False\n\n def check_type_forward(self, in_types):\n type_check.expect(\n in_types.size() == 1,\n in_types[0].dtype.kind == 'f',\n in_types[0].ndim == 2 + self.ndim,\n in_types[0].size > 0,\n )\n\n def forward_gpu(self, x):\n self.retain_inputs((0,))\n self._used_cudnn = True\n\n # Implementation using cuDNN.\n x = x[0]\n n, c = x.shape[:2]\n dims = x.shape[2:]\n ys = tuple(conv.get_conv_outsize(d, k, s, p, self.cover_all)\n for d, k, s, p in six.moves.zip(\n dims, self.ksize, self.stride, self.pad))\n y_shape = (n, c) + ys\n y = cuda.cupy.empty(y_shape, dtype=x.dtype)\n\n cudnn.pooling_forward(\n x, y, self.ksize, self.stride, self.pad, self._get_pool_mode())\n self.retain_outputs((0,))\n return y,\n\n def backward_gpu(self, x, gy):\n # Implementation using cudnn\n x = x[0]\n y = self.get_retained_outputs()[0].array\n gx = cudnn.pooling_backward(\n x, y, gy[0],\n self.ksize, self.stride, self.pad, self._get_pool_mode())\n return gx,\n\n def _get_pool_mode(self):\n raise NotImplementedError()\n","sub_path":"chainer/functions/pooling/pooling_nd.py","file_name":"pooling_nd.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"449991165","text":"import os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom viapoint_environment import ViaPointEnvironment\nfrom bolero.behavior_search import BlackBoxSearch\nfrom bolero.optimizer import CMAESOptimizer\nfrom bolero.controller import Controller\nimport viapoint_config as cfg\nfrom joblib import Parallel, delayed\nimport pickle\n\n\nn_runs = 30\nn_episodes = 500\nsetup_funs = [(\"approxik\", -1, cfg.make_approx_cart_dmp),\n (\"exactik\", -1, cfg.make_exact_cart_dmp),\n (\"joint\", -1, cfg.make_joint_dmp)]\n\n\ndef learn(name, setup_fun, run):\n ik, beh, mp_keys, mp_values = setup_fun(\n cfg.x0, cfg.g, cfg.execution_time, cfg.dt)\n env = ViaPointEnvironment(\n ik, cfg.x0, cfg.via_points, cfg.execution_time, cfg.dt,\n cfg.qlo, cfg.qhi,\n penalty_vel=cfg.penalty_vel, penalty_acc=cfg.penalty_acc,\n penalty_via_point=cfg.penalty_via_point)\n\n opt = CMAESOptimizer(variance=cfg.variance[name], random_state=run)\n bs = BlackBoxSearch(beh, opt)\n controller = Controller(environment=env, behavior_search=bs,\n n_episodes=n_episodes, verbose=1)\n rewards = controller.learn(mp_keys, mp_values)\n\n best = bs.get_best_behavior()\n reward = controller.episode_with(best)\n\n return name, rewards, reward.sum()\n\n\nfor name, n_jobs, setup_fun in setup_funs:\n parallel = Parallel(n_jobs=n_jobs, pre_dispatch=\"all\", verbose=10)\n results = parallel(delayed(learn)(name, setup_fun, run)\n for run in range(n_runs))\n pickle.dump(results, open(\"results_benchmark_simple_viapoint_%s.pickle\"\n % name, \"w\"))\n","sub_path":"evaluation/benchmark_simple_viapoint.py","file_name":"benchmark_simple_viapoint.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"96471954","text":"import cv2\nimport json\nimport sqlite3\nimport numpy as np\nfrom datetime import datetime\n\nfrom PyQt5 import uic\nfrom PyQt5.QtWidgets import QMessageBox, QFileDialog, QMainWindow, QAction, QActionGroup\n\nfrom Models.GeometricFigures import *\nfrom Windows.ResultWindow import ResultWindow\nfrom Triangulation.CloudOfPoints import CloudOfPoints\nfrom Triangulation.CreaterTriangulation import CreaterTriangulation\n\n\ndef create_question(title, question, textBtn1, textBtn2):\n messageBox = QMessageBox()\n messageBox.setIcon(QMessageBox.Question)\n messageBox.setWindowTitle(title)\n messageBox.setText(question)\n messageBox.setStandardButtons(QMessageBox.Yes | QMessageBox.No)\n messageBox.setDefaultButton(QMessageBox.Yes)\n\n buttonYes = messageBox.button(QMessageBox.Yes)\n buttonYes.setText(textBtn1)\n\n buttonNo = messageBox.button(QMessageBox.No)\n buttonNo.setText(textBtn2)\n messageBox.exec_()\n return messageBox, buttonYes, buttonNo\n\n\ndef range_color_to_range_height(oldArray, newRange):\n heights = [int(round(newRange[0] + x * (newRange[1] - newRange[0]) / 255)) for x in range(256)]\n return [[heights[oldArray[i][j]] for j in range(len(oldArray[0]))] for i in range(len(oldArray))]\n\n\nclass ImageProcessing(QMainWindow):\n conn = sqlite3.connect(\"history.db\")\n user_parametersFromJSON = None\n user_rangeHeight = None\n user_stepXY = None\n user_maxDiscrepancy = None\n map_imageColor = None\n map_imageHeight = None\n map_takenPoints = None\n map_triangulation = None\n\n def __init__(self):\n super(ImageProcessing, self).__init__()\n uic.loadUi('res/MainWindow.ui', self)\n self.show()\n self.setFixedSize(self.width(), self.height())\n self.chooseFileButton.clicked.connect(self.choose_map_file)\n self.ExitAction.triggered.connect(lambda: exit(0))\n self.OpenAction.triggered.connect(self.load_triangulation)\n self.OpenTriangulation.triggered.connect(self.load_triangulation)\n self.RunAction.triggered.connect(self.start_triangulation)\n self.db_work()\n\n def db_work(self):\n cursor = self.conn.cursor()\n cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS file_info\n (id INTEGER PRIMARY KEY,\n path TEXT,\n date_of_use TEXT)\"\"\")\n cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS triangle_parameters\n (file_id INTEGER NOT NULL REFERENCES file_info(id), \n min_height INTEGER, max_height INTEGER,\n step_x INTEGER, step_y INTEGER,\n max_discrepancy INTEGER)\"\"\")\n self.conn.commit()\n cursor.execute(\"SELECT * FROM file_info\")\n file_info_all = cursor.fetchall()\n actionGroup = QActionGroup(self)\n if len(file_info_all) > 0:\n for file_info in file_info_all:\n action = QAction(f\"|_№{file_info[0]}_|_{file_info[2]}_| {file_info[1]}\", self)\n actionGroup.addAction(action)\n self.TriangulationMenu.addAction(action)\n actionGroup.triggered.connect(self.load_parameters)\n\n def update_history(self):\n cursor = self.conn.cursor()\n cursor.execute(\"SELECT COUNT(id) FROM file_info\")\n row_count = cursor.fetchone()[0]\n cursor.execute(f\"\"\"INSERT INTO file_info VALUES (?,?,?);\"\"\",\n (row_count + 1,\n self.pathFileLabel.text() if len(self.pathFileLabel.text()) > 0 is not None\n else self.user_parametersFromJSON['path to map of height'],\n datetime.today().strftime('%d.%m.%Y %H:%M')))\n cursor.execute(f\"\"\"INSERT INTO triangle_parameters VALUES (?,?,?,?,?,?);\"\"\",\n (row_count + 1,\n self.user_rangeHeight[0],\n self.user_rangeHeight[1],\n self.user_stepXY[0],\n self.user_stepXY[1],\n self.user_maxDiscrepancy))\n self.conn.commit()\n if row_count == 5:\n cursor.execute(f\"\"\"DELETE FROM triangle_parameters WHERE file_id = ?\"\"\", (1,))\n cursor.execute(f\"\"\"DELETE FROM file_info WHERE id = ?\"\"\", (1,))\n cursor.execute(f\"\"\"UPDATE triangle_parameters SET file_id = file_id-1\"\"\")\n cursor.execute(f\"\"\"UPDATE file_info SET id = id-1\"\"\")\n self.conn.commit()\n\n def load_parameters(self, action):\n cursor = self.conn.cursor()\n cursor.execute(\"SELECT * FROM file_info WHERE id=?\", (int(action.text()[3]),))\n info = cursor.fetchone()\n loadParametersQuestion, buttonYes, _ = create_question(\n title=\"Подтверждение\",\n question=f\"Вы желаете загрузить триангуляцию от {info[2]}\\nдля файла {info[1]}?\",\n textBtn1=\"Да\",\n textBtn2=\"Нет\")\n if loadParametersQuestion.clickedButton() == buttonYes:\n cursor.execute(\"SELECT * FROM triangle_parameters WHERE file_id=?\", (info[0],))\n parameters = cursor.fetchone()\n self.minHeightSpin.setValue(parameters[1])\n self.maxHeightSpin.setValue(parameters[2])\n self.stepXSpin.setValue(parameters[3])\n self.stepYSpin.setValue(parameters[4])\n self.maxDiscrepancySpin.setValue(parameters[5])\n try:\n self.pathFileLabel.setText(info[1])\n self.load_map_file()\n QMessageBox.about(self, \"Сообщение\", \"Параметры и изображение загружены\")\n except:\n self.pathFileLabel.setText(\"\")\n QMessageBox.critical(self, \"Ошибка\", f\"Файла\\n\\n{info[1]}\\n\\nне существует!\")\n QMessageBox.about(self, \"Сообщение\", \"Остальные параметры загружены\")\n\n def choose_map_file(self):\n fileName = QFileDialog.getOpenFileName(parent=self,\n caption='Open File',\n filter=\"Image (*.png *.jpg *.jpeg)\")[0]\n self.pathFileLabel.setText(fileName)\n if len(fileName) > 0 and QMessageBox.question(self, \"Подтверждение\", f\"Загрузить файл\\n{fileName}?\",\n QMessageBox.Yes | QMessageBox.No,\n QMessageBox.Yes) == QMessageBox.Yes:\n self.statusTextEdit.appendPlainText(\"Загрузка карты высот...\")\n self.load_map_file()\n self.statusTextEdit.appendPlainText(\"Загрузка карты высот завершена\")\n\n def load_map_file(self):\n map_imageOriginal = cv2.imdecode(np.fromfile(self.pathFileLabel.text(), dtype=np.uint8), cv2.IMREAD_UNCHANGED)\n self.map_imageColor = np.array(cv2.cvtColor(src=map_imageOriginal,\n code=cv2.COLOR_BGR2GRAY))\n self.map_imageColor = cv2.GaussianBlur(self.map_imageColor, (5, 5), 5)\n\n def load_triangulation(self):\n fileName = QFileDialog.getOpenFileName(parent=self,\n caption='Open File',\n filter=\"JSON (*.json)\")[0]\n if len(fileName) > 0:\n loadTriangulationQuestion, buttonYes, _ = create_question(title=\"Подтверждение\",\n question=f\"Загрузить триангуляцию\\n{fileName}?\",\n textBtn1=\"Да\",\n textBtn2=\"Нет\")\n if loadTriangulationQuestion.clickedButton() == buttonYes:\n with open(fileName, \"r\") as readFile:\n data = json.load(readFile)\n data = list(data.values())\n\n try:\n self.user_parametersFromJSON = data[0]\n self.user_rangeHeight = [int(n) for n in self.user_parametersFromJSON[\"range of height\"].split(\" \")]\n self.user_stepXY = [int(n) for n in (self.user_parametersFromJSON[\"step on the OX axis\"],\n self.user_parametersFromJSON[\"step on the OY axis\"])]\n self.user_maxDiscrepancy = int(self.user_parametersFromJSON['maximum residual value'])\n\n self.map_imageColor = None\n self.map_takenPoints = CloudOfPoints()\n self.map_triangulation = CreaterTriangulation()\n\n for triangle in data[1::]:\n nodes = []\n for node in triangle['nodes']:\n nodeXYZ = node.split(' ')\n nodeXYZ = [int(float(c)) for c in nodeXYZ]\n nodes.append(nodeXYZ)\n self.map_takenPoints.takenPoints += [tuple(nodeXYZ)]\n self.map_triangulation.triangles.append(Triangle(\n nodes=[Point(node[0], node[1], node[2]) for node in nodes],\n neighbours=[int(n) for n in triangle['neighbours'].split(' ')]\n ))\n self.map_takenPoints.takenPoints = list(set(self.map_takenPoints.takenPoints))\n self.map_takenPoints.takenPoints = [Point(x=point[0], y=point[1], z=point[2]) for point in\n self.map_takenPoints.takenPoints]\n self.map_triangulation.nodes = self.map_takenPoints.takenPoints\n self.update_history()\n self.open_window_show_result()\n QMessageBox.about(self, \"Сообщение\", \"Триангуляция успешно загружена\")\n except:\n QMessageBox.critical(self, \"Ошибка\", \"Не совпадает структура файла!\")\n\n def start_triangulation(self):\n if len(self.pathFileLabel.text()) == 0:\n QMessageBox.critical(self, \"Ошибка\", \"Сначала загрузите файл\")\n elif self.minHeightSpin.value() >= self.maxHeightSpin.value():\n QMessageBox.critical(self, \"Ошибка\", \"Минимальная высота должна быть больше меньше максимальной высоты\")\n else:\n self.user_rangeHeight = (self.minHeightSpin.value(), self.maxHeightSpin.value())\n self.user_stepXY = (self.stepXSpin.value(), self.stepYSpin.value())\n self.user_maxDiscrepancy = self.maxDiscrepancySpin.value()\n self.statusTextEdit.appendPlainText(\"Преобразование диапазонов...\")\n QMessageBox.about(self, \"Сообщение\", \"Нажмите ОК, чтобы начать преобразование диапазонов\")\n self.map_imageHeight = np.array(range_color_to_range_height(oldArray=self.map_imageColor,\n newRange=self.user_rangeHeight))\n self.statusTextEdit.appendPlainText(\"Преобразование диапазонов завершено\")\n self.statusTextEdit.appendPlainText(\"Извлечение точек для триангуляции...\")\n QMessageBox.about(self, \"Сообщение\", \"Нажмите ОК для запуска процесса извлечения точек триангуляции\")\n self.map_takenPoints = CloudOfPoints(map_imageHeight=self.map_imageHeight,\n stepXY=self.user_stepXY,\n minDist=self.user_maxDiscrepancy)\n self.statusTextEdit.appendPlainText(\"Извлечение точек для триангуляции завершено\")\n self.statusTextEdit.appendPlainText(\"Триангуляция на плоскости...\")\n QMessageBox.about(self, \"Сообщение\", \"Нажмите ОК для запуска процесса триангуляции на плоскости\")\n self.map_triangulation = CreaterTriangulation(nodes=self.map_takenPoints.takenPoints)\n self.statusTextEdit.appendPlainText(\"Триангуляция на плоскости завершена\")\n self.statusTextEdit.appendPlainText(\"Триангуляция в пространстве...\")\n QMessageBox.about(self, \"Сообщение\", \"Нажмите ОК для запуска процесса триангуляции в пространстве\")\n self.statusTextEdit.appendPlainText(\"Триангуляция в пространстве завершена\")\n self.update_history()\n self.open_window_show_result()\n\n def open_window_show_result(self):\n self.close()\n ResultWindow(user_parameters={\n \"path\": self.user_parametersFromJSON['path to map of height']\n if self.map_imageColor is None else self.pathFileLabel.text(),\n \"range of height\": self.user_rangeHeight,\n \"step on the OX and OY axes\": self.user_stepXY,\n \"maximum residual value\": self.user_maxDiscrepancy},\n originalImage=self.map_imageColor,\n map_takenPoints=self.map_takenPoints,\n triangulation=self.map_triangulation)\n","sub_path":"Windows/MainWindow.py","file_name":"MainWindow.py","file_ext":"py","file_size_in_byte":13865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"173921058","text":"from utilityscore import compute_scores_2019, load_column\nimport argparse\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nRNN_MODELS = ['LSTM', 'LSTMM', 'LSTMSimple', 'GRU', 'GRUM', 'GRUD']\nNON_RNN_MODELS = ['COX', 'RF', 'XG', 'RLR']\nMETRICS = ['Utility', 'Sensitivity', 'Specificity']\n\n\nMODEL_NAME_MAPPTING = {'COX': 'Cox PH', 'RF': 'RF', 'XG': 'XGBoost', 'RLR': 'RLR',\n 'LSTMSimple': 'LSTM-MD', 'LSTM': 'LSTM', 'LSTMM': 'LSTM-M',\n 'GRU': 'GRU', 'GRUM': 'GRU-M', 'GRUD': 'GRU-Decay'}\n\nRNN_IMPUTATION = ['mean', 'forward', 'grud', 'dae']\nNON_RNN_IMPUTATION = ['mean', 'forward']\n\ndef generate_plots(args):\n rnn = []\n non_rnn = []\n\n for model in os.listdir(args.metrics_path):\n model_path = os.path.join(args.metrics_path, model)\n if model in RNN_MODELS:\n plot_roc(model, model_path, args.plot_path, 'RNN')\n else:\n plot_roc(model, model_path, args.plot_path, 'Non-RNN')\n if model in RNN_MODELS:\n rnn.append(model)\n elif model in NON_RNN_MODELS:\n non_rnn.append(model)\n\n for metric in METRICS:\n if rnn:\n plot_metric(args.metrics_path, rnn, metric, 'RNN', args.plot_path)\n if non_rnn:\n plot_metric(args.metrics_path, non_rnn, metric, 'Non-RNN', args.plot_path)\n\n\ndef plot_roc(model, model_path, plot_path, type):\n plt.style.use('ggplot')\n plt.figure(figsize=[4, 4], dpi=150)\n plt.plot([0, 1], [0, 1], 'k--')\n\n print(\"Metric: AUC\")\n\n # colors = ['m', 'c', 'g', 'b']\n count=0\n\n if type == 'RNN':\n imputation_lst = RNN_IMPUTATION\n else:\n imputation_lst = NON_RNN_IMPUTATION\n\n for imputation_method in imputation_lst:\n imputation_dir = os.path.join(model_path, imputation_method)\n\n fpr = load_column(os.path.join(imputation_dir, 'seed_1.roc'), 'FPR')\n tpr = load_column(os.path.join(imputation_dir, 'seed_1.roc'), 'TPR')\n auc = load_column(os.path.join(imputation_dir, 'seed_1.metrics'), 'AUROC')\n\n # plt.plot(fpr, tpr, label='{} {}'.format(imputation_method, '{0:.3f}'.format(auc[0])), color=colors[count])\n plt.plot(fpr, tpr, label='{} {}'.format(imputation_method, '{0:.3f}'.format(auc[0])), alpha=0.7)\n aucs = []\n for seed in os.listdir(imputation_dir):\n if seed.endswith('.metrics'):\n aucs.append(load_column(os.path.join(imputation_dir, seed), 'AUROC'))\n auc_mean = np.mean(aucs)\n auc_std = np.std(aucs)\n\n print('Model: {} Imputation Type: {} Avg: {} Std: {}'.format(model, imputation_method, auc_mean, auc_std))\n count += 1\n plt.legend()\n plt.title('{} ROC Curve'.format(MODEL_NAME_MAPPTING[model]))\n\n roc_dir = '{}/roc'.format(plot_path)\n\n if not os.path.exists(roc_dir):\n os.makedirs(roc_dir)\n\n plt.show()\n\n plt.savefig('{}/{}.png'.format(roc_dir, model))\n\n plt.close()\n\n\ndef plot_metric(metrics_path, models, metric, type, plot_path):\n\n print(\"Metric: {}\".format(metric))\n\n model_names = []\n d = {}\n\n if type == 'RNN':\n imputation_lst = RNN_IMPUTATION\n else:\n imputation_lst = NON_RNN_IMPUTATION\n\n for model in os.listdir(metrics_path):\n if model in models:\n model_dir = os.path.join(metrics_path, model)\n model_names.append(MODEL_NAME_MAPPTING[model])\n for imputation_method in imputation_lst:\n if imputation_method not in d:\n d[imputation_method] = {}\n imputation_dir = os.path.join(model_dir, imputation_method)\n all_seed_metrics = []\n for seed in os.listdir(imputation_dir):\n if seed.endswith('.metrics'):\n all_seed_metrics.append(load_column(os.path.join(imputation_dir, seed), metric)[0])\n\n if 'means' not in d[imputation_method]:\n d[imputation_method]['means'] = []\n if 'stds' not in d[imputation_method]:\n d[imputation_method]['stds'] = []\n\n mean = np.mean(all_seed_metrics)\n std = np.std(all_seed_metrics)\n\n\n d[imputation_method]['means'].append(mean)\n d[imputation_method]['stds'].append(std)\n\n\n print('Model: {} Imputation Type: {} Avg: {} Std: {}'.format(model, imputation_method, mean, std))\n\n plt.style.use('ggplot')\n\n\n plt.figure(dpi=150)\n N = len(models)\n fig, ax = plt.subplots()\n ind = np.arange(N)\n width = 0.2\n rects = []\n rects_0 = []\n count = 0\n # colors = ['m', 'c', 'g', 'b']\n imputation_types = []\n\n for imputation in imputation_lst:\n means = d[imputation]['means']\n stds = d[imputation]['stds']\n rect = ax.bar(ind + count * width, means, width, yerr=stds)\n # rect = ax.bar(ind + count*width, means, width, color=colors[count], yerr=stds)\n rects.append(rect)\n rects_0.append(rect[0])\n count += 1\n\n if type == 'RNN':\n factor = 1.5\n else:\n factor = 0.5\n\n ax.set_ylabel(metric)\n ax.set_title('{} Methods - {}'.format(type, metric))\n ax.set_xticks(ind + factor*width)\n ax.set_xticklabels(model_names)\n ax.legend(rects_0, imputation_lst)\n\n metric_dir = '{}/metrics'.format(plot_path)\n\n if not os.path.exists(metric_dir):\n os.makedirs(metric_dir)\n\n plt.ylim(0, 1)\n plt.show()\n\n plt.savefig('{}/{}-{}.png'.format(metric_dir, type, metric))\n\n plt.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Arguments for sepsis prediction')\n parser.add_argument('--metrics_path', type=str, default='metrics')\n parser.add_argument('--plot_path', type=str, default='plots')\n\n args = parser.parse_args()\n\n generate_plots(args)\n","sub_path":"generate_plots.py","file_name":"generate_plots.py","file_ext":"py","file_size_in_byte":5829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"256931870","text":"#%%\nimport statsmodels.api as sm\nimport numpy as np\nimport itertools\nimport pandas as pd\nfrom datetime import timedelta\n\nfrom impala_connector import *\nfrom kibana_links import *\n\n\ndef get_data(time_interval, param_list, values_to_select):\n data = read_data(time_interval, param_list, values_to_select)\n data = process_data(data, param_list)\n data = fill_zeros(data, param_list)\n param_values_count = len(set([tuple(x) for x in data[param_list].values]))\n values_before_removing_seasonality = data[\"count\"][-param_values_count:]\n data = remove_seasonality(data, time_interval, param_list)\n return data, values_before_removing_seasonality\n\n\ndef fill_zeros(data, param_list):\n column_names = list(data.columns)[:-1]\n last_date_values = []\n all_values = []\n if \"min\" in data:\n date_params = [\"dt\", \"hour\", \"min\"]\n else:\n date_params = [\"dt\", \"hour\"]\n for date_param in date_params:\n all_values.append(list(set(map(tuple, data[[date_param]].values))))\n last_date_values.append(data[date_param].values[-1])\n all_values.append(list(set(map(tuple, data[param_list].values))))\n cart_product = [sum(element, ()) for element in itertools.product(*all_values)]\n # create new index\n new_index = pd.MultiIndex.from_tuples(cart_product, names=column_names)\n data = data.set_index(column_names).reindex(new_index, fill_value=0).reset_index()\n data = data.sort_values(by=date_params + param_list)\n data = data.reset_index(drop=True)\n data = remove_extra_dates(data, last_date_values)\n return data\n\n\ndef add_seasonal_component(data, values_before_removing_seasonality):\n seasonal_component = values_before_removing_seasonality.reset_index(drop=True) - data[\"count\"]\n data[\"count\"] = data[\"count\"] + seasonal_component\n seasonal_component.loc[data[\"upper_bound\"] == -1] = 0\n data[\"upper_bound\"] = data[\"upper_bound\"] + seasonal_component\n data[\"count\"].apply(round)\n data[\"upper_bound\"].fillna(-1, inplace=True)\n data[\"count\"] = data[\"count\"].apply(int)\n data[\"upper_bound\"] = data[\"upper_bound\"].apply(int)\n return data\n\n\ndef read_data(time_interval, param_list, values_to_select):\n with ImpalaConn() as imp_con:\n data = pd.DataFrame(imp_con.get_data(time_interval, param_list, values_to_select))\n data = data.apply(lambda x: x.replace(\"\\\\\", \"\").replace(\"\\\"\", \"\"))\n data = data.apply(lambda x: x.replace(\"n\\/a\", \"\"))\n data = data.fillna(\"\")\n if \"min\" in time_interval:\n date_params = [\"dt\", \"hour\", \"min\"]\n else:\n date_params = [\"dt\", \"hour\"]\n column_names = date_params + param_list + [\"count\"]\n data.columns = column_names\n data = data.groupby(date_params + param_list, as_index=False).sum()\n return data\n\n\ndef process_data(data, param_list):\n data[\"dt\"] = pd.to_datetime(data[\"dt\"])\n for param in param_list:\n data[param] = data[param].apply(lambda x: x.strip() if (type(x) == str) else x)\n return data\n\n\ndef remove_extra_dates(data, date_values):\n if len(date_values) == 3:\n data = data.drop(data[(data[\"dt\"] == date_values[0]) & ((data[\"hour\"] > date_values[1]) |\n ((data[\"hour\"] == date_values[1]) & (data[\"min\"] >= date_values[2])))].index)\n else:\n data = data.drop(data[(data[\"dt\"] == date_values[0]) & (data[\"hour\"] >= date_values[1])].index)\n return data\n\n\ndef remove_seasonality(data, time_interval, param_list):\n # make columns with dates as index (necessary for the algorithm)\n data = create_date_index(data)\n for value in set([tuple(x) for x in data[param_list].values]):\n subsample = create_subsample(data, value, param_list)\n freq = get_freq(time_interval)\n if len(data.loc[subsample]) > freq:\n stl = sm.tsa.seasonal_decompose(data.loc[subsample, \"count\"], freq=freq)\n data.loc[subsample, \"count\"] = data.loc[subsample, \"count\"] - stl.seasonal.fillna(0)\n # return the data format to its original form\n data = data.reset_index(drop=True)\n data.loc[data[\"count\"] < 0, \"count\"] = 0\n return data\n\n\ndef get_freq(time_interval):\n if time_interval == \"15min\":\n return 96\n elif time_interval == \"30min\":\n return 48\n elif time_interval == \"1h\":\n return 24\n elif time_interval == \"3h\":\n return 8\n\n\ndef create_date_index(data):\n if \"min\" in data:\n data[\"time\"] = data[[\"hour\", \"min\"]].apply(lambda x: datetime.time(*x), axis=1)\n else:\n data[\"time\"] = data[[\"hour\"]].apply(lambda x: datetime.time(*x), axis=1)\n data[\"date\"] = data[[\"dt\", \"time\"]].apply(lambda x: x[0].combine(x[0], x[1]), axis=1)\n data = data.drop([\"time\"], axis=1)\n data = data.set_index([\"date\"])\n return data\n\n\ndef create_links_for_anomalies(data, time_interval, param_list):\n param_names = param_list\n data_anomalies = data.loc[data[\"is_anomaly\"] == \"yes\"]\n data[\"links\"] = \"\"\n hour_delta = get_hour_delta(time_interval)\n if len(data_anomalies):\n data_anomalies = create_date_index(data_anomalies)\n links = []\n for date, row in data_anomalies.iterrows():\n if \"min\" not in data.columns:\n links.append(get_link(param_names, [row[i+2] for i in range(len(param_list)+1)], date -\n timedelta(hours=hour_delta), date + timedelta(hours=hour_delta)))\n else:\n links.append(get_link(param_names, [row[i+3] for i in range(len(param_list)+1)], date -\n timedelta(hours=hour_delta), date + timedelta(hours=hour_delta)))\n data.loc[data[\"is_anomaly\"] == \"yes\", \"links\"] = links\n return data\n\n\ndef create_subsample(data, value, param_list):\n subsample = (data[\"dt\"] == data[\"dt\"])\n for i, param_name in enumerate(param_list):\n subsample &= (data[param_name] == value[i])\n return subsample\n\n\ndef get_hour_delta(time_interval):\n if time_interval == \"1h\":\n return 2\n elif time_interval == \"3h\":\n return 3\n else:\n return 1\n","sub_path":"anomaly_detection_monitoring_500_all/data_processing.py","file_name":"data_processing.py","file_ext":"py","file_size_in_byte":6053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"109024654","text":"import os\nimport base64\nimport json\nfrom AccessControl import Unauthorized\nfrom Acquisition import aq_inner\n\nfrom five import grok\nfrom zope import schema\nfrom plone import api\n\nfrom zope.component import getMultiAdapter\nfrom zope.component import getUtility\nfrom zope.lifecycleevent import modified\nfrom plone.directives import dexterity, form\n\nfrom plone.app.uuid.utils import uuidToObject\n\nfrom z3c.relationfield.schema import RelationList, RelationChoice\nfrom plone.formwidget.contenttree import ObjPathSourceBinder\nfrom plone.formwidget.autocomplete import AutocompleteMultiFieldWidget\n\n\nfrom chromsystems.shopcontent.layouteditor import ILayoutEditorUtility\nfrom chromsystems.shopcontent.orderableitem import IOrderableItem\n\nfrom chromsystems.shopcontent import MessageFactory as _\n\n\nclass IItemCollection(form.Schema):\n \"\"\"\n A collection of orderable products\n \"\"\"\n title = schema.TextLine(\n title=_(u\"Title\"),\n required=True,\n )\n title_de = schema.TextLine(\n title=_(u\"Item Title DE\"),\n required=True,\n )\n title_en = schema.TextLine(\n title=_(u\"Item Title EN\"),\n required=True,\n )\n orderCode = schema.TextLine(\n title=_(u\"Product Code\"),\n required=True,\n )\n exclude_usa = schema.Bool(\n title=_(u\"Mark as unorderable from the USA\"),\n required=False,\n default=False,\n )\n exclude_canada = schema.Bool(\n title=_(u\"Mark as unorderable from Canada\"),\n required=False,\n default=False,\n )\n form.widget(relatedProducts=AutocompleteMultiFieldWidget)\n relatedProducts = RelationList(\n title=_(u\"Related Products\"),\n default=[],\n value_type=RelationChoice(\n title=_(u\"Related\"),\n source=ObjPathSourceBinder(\n object_provides=IOrderableItem.__identifier__,)\n ),\n required=False,\n )\n #form.widget(subject=TokenInputFieldWidget)\n subject = schema.Tuple(\n title=_(u\"Keywords\"),\n description=_(u\"Type your desired keywords to add new tags or to \"\n u\"select from the autosuggested existing tags.\"),\n value_type=schema.TextLine(),\n required=False,\n missing_value=(),\n )\n category = schema.Tuple(\n title=_(u\"Categories\"),\n description=_(u\"Type your desired categories for this item collection \"\n u\"where the product should be found\"),\n value_type=schema.TextLine(),\n required=False,\n missing_value=(),\n )\n link_de = schema.URI(\n title=_(u\"Link DE\"),\n description=_(u\"Enter product information location on the main site\"),\n required=False,\n )\n link_en = schema.URI(\n title=_(u\"Link EN\"),\n description=_(u\"Enter product information location on the main site\"),\n required=False,\n )\n headlines = schema.TextLine(\n title=_(u\"Available Headlines\"),\n description=_(u\"A stringified json list of headlines. Note: each \"\n u\"headline has a unique id that will be added to the \"\n u\"final stored layout order stored\"),\n required=False,\n )\n layout_order = schema.List(\n title=_(u\"Stored Layout Order\"),\n value_type=schema.TextLine(\n title=_(u\"Subitem\"),\n ),\n required=False,\n )\n\n\nclass ItemCollection(dexterity.Item):\n grok.implements(IItemCollection)\n\n\nclass View(grok.View):\n grok.context(IItemCollection)\n grok.require('zope2.View')\n grok.name('view')\n\n def localized_value(self, obj, fieldname=None):\n fieldname = fieldname + '_' + self.current_lang()\n return getattr(obj, fieldname, None)\n\n def current_lang(self):\n context = aq_inner(self.context)\n pstate = getMultiAdapter((context, self.request),\n name=u'plone_portal_state')\n lang = pstate.language()\n return lang\n\n def test_item_uniqueness(self, uid):\n item_uids = self.item_info()\n unique = True\n if uid in item_uids:\n idx = 0\n for item in item_uids:\n if item == uid:\n idx += 1\n if idx > 1:\n unique = False\n return unique\n\n def item_info(self):\n context = aq_inner(self.context)\n item_relations = context.relatedProducts\n data = []\n for rel in item_relations:\n item = rel.to_object\n info = item.UID()\n data.append(info)\n return data\n\n\nclass Preview(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('preview')\n\n def orderable_items(self):\n return self.computeRelatedItemInfo()\n\n def computeRelatedItemInfo(self):\n context = aq_inner(self.context)\n item_relations = context.relatedProducts\n data = []\n for rel in item_relations:\n item = rel.to_object\n info = {}\n info['title'] = item.Title()\n info['orderid'] = item.productCode\n info['title_de'] = item.title_de\n info['title_en'] = item.title_en\n info['uid'] = item.UID\n data.append(info)\n return data\n\n def item_details(self, uuid):\n context = aq_inner(self.context)\n obj = uuidToObject(uuid)\n if not obj:\n headlines = json.loads(context.headlines)\n item = headlines[uuid]\n data = {}\n data['type'] = 'headline'\n data['uid'] = uuid\n title_key = 'title_' + self.current_lang()\n data['title'] = item[title_key]\n return data\n else:\n data = {}\n data['type'] = 'orderable'\n data['uid'] = uuid\n data['product_code'] = obj.productCode\n details_query = 'details_' + self.current_lang()\n data['details'] = getattr(obj, details_query, None)\n obj_title = self.localized_title(obj)\n if obj_title is not None:\n data['title'] = obj_title\n else:\n data['title'] = obj.Title()\n return data\n\n def localized_title(self, obj):\n fieldname = 'title_' + self.current_lang()\n return getattr(obj, fieldname, None)\n\n def current_lang(self):\n context = aq_inner(self.context)\n pstate = getMultiAdapter((context, self.request),\n name=u'plone_portal_state')\n lang = pstate.language()\n return lang\n\n\nclass LayoutJSON(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('layout-json')\n\n def render(self):\n context = aq_inner(self.context)\n settings = json.loads(context.headlines)\n return json.dumps(settings, indent=4)\n\n\nclass EditorView(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('edit-layout')\n\n def update(self):\n context = aq_inner(self.context)\n self.context_url = context.absolute_url()\n\n def display_order(self):\n context = aq_inner(self.context)\n if self.has_open_sessions():\n items = self.open_session()\n else:\n items = context.layout_order\n return items\n\n def has_open_session(self):\n open_session = False\n try:\n session = self.open_session()\n except KeyError:\n session = None\n if session is not None:\n open_session = True\n return open_session\n\n def open_session(self):\n editor = getUtility(ILayoutEditorUtility)\n return editor.get()\n\n def editor_session(self):\n session = self.open_session()\n try:\n edit_session = session[self.context.UID()]\n except KeyError:\n edit_session = None\n return edit_session\n\n def item_details(self, uuid):\n context = aq_inner(self.context)\n obj = uuidToObject(uuid)\n if not obj:\n headlines = json.loads(context.headlines)\n item = headlines[uuid]\n data = {}\n data['type'] = 'headline'\n data['uid'] = uuid\n title_key = 'title_' + self.current_lang()\n data['title'] = item[title_key]\n return data\n else:\n data = {}\n data['type'] = 'orderable'\n data['uid'] = uuid\n data['product_code'] = obj.productCode\n obj_title = self.localized_title(obj)\n if obj_title is not None:\n data['title'] = obj_title\n else:\n data['title'] = obj.Title()\n return data\n\n def localized_title(self, obj):\n fieldname = 'title_' + self.current_lang()\n return getattr(obj, fieldname, None)\n\n def current_lang(self):\n context = aq_inner(self.context)\n pstate = getMultiAdapter((context, self.request),\n name=u'plone_portal_state')\n lang = pstate.language()\n return lang\n\n\nclass UpdateLayout(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('update-layout')\n\n def update(self):\n self.query = self.request[\"QUERY_STRING\"]\n\n def render(self):\n context = aq_inner(self.context)\n uuid = context.UID()\n editor = getUtility(ILayoutEditorUtility)\n layout_list = context.layout_order\n new_layout = list()\n sort_query = list(self.query.split(','))\n for x in sort_query:\n index = x[6:]\n value = layout_list[int(index)]\n new_layout.append(value)\n editor.add(uuid, new_layout)\n msg = _(u\"Item order successfully updated\")\n results = {'success': True,\n 'message': msg\n }\n self.request.response.setHeader('Content-Type',\n 'application/json; charset=utf-8')\n return json.dumps(results)\n\n def open_session(self):\n editor = getUtility(ILayoutEditorUtility)\n return editor.get()\n\n\nclass CommitLayout(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('commit-layout')\n\n def render(self):\n context = aq_inner(self.context)\n uuid = context.UID()\n editor = getUtility(ILayoutEditorUtility)\n session = editor.get()\n edited_layout = session[uuid]\n setattr(context, 'layout_order', edited_layout)\n modified(context)\n context.reindexObject(idxs='modified')\n session.clear()\n api.portal.show_message(\n message=_(u\"Item successfully added.\"), request=self.request)\n next_url = context.absolute_url() + '/@@edit-layout'\n return self.request.response.redirect(next_url)\n\n\nclass ResetLayout(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('reset-layout')\n\n def render(self):\n context = aq_inner(self.context)\n editor = getUtility(ILayoutEditorUtility)\n session = editor.get()\n session.clear()\n items = context.relatedProducts\n clean_layout = list()\n for item in items:\n obj = item.to_object\n clean_layout.append(obj.UID)\n setattr(context, 'layout_order', clean_layout)\n modified(context)\n context.reindexObject(idxs='modified')\n api.portal.show_message(\n message=_(u\"Layout successfully resetted\"), request=self.request)\n next_url = context.absolute_url()\n return self.request.response.redirect(next_url)\n\n\nclass Headlines(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('add-headline')\n\n def update(self):\n context = aq_inner(self.context)\n self.errors = {}\n if 'form.button.Submit' in self.request:\n authenticator = getMultiAdapter((context, self.request),\n name=u\"authenticator\")\n if not authenticator.verify():\n raise Unauthorized\n form = self.request.form\n current_layout = context.layout_order\n try:\n headlines = json.loads(context.headlines)\n except TypeError:\n headlines = {}\n new_item = {}\n new_uuid = base64.b64encode(os.urandom(24))\n new_item['title_de'] = form['headline-de']\n new_item['title_en'] = form['headline-en']\n headlines[new_uuid] = new_item\n new_headlines = json.dumps(headlines)\n setattr(context, 'headlines', new_headlines)\n if self.has_open_session():\n session = self.open_session()\n if session is not None:\n session.append(new_uuid)\n current_layout.append(new_uuid)\n setattr(context, 'layout_order', current_layout)\n modified(context)\n context.reindexObject(idxs='modified')\n api.portal.show_message(\n message=_(u\"Item successfully added.\"), request=self.request)\n next_url = context.absolute_url() + '/@@edit-layout'\n return self.request.response.redirect(next_url)\n\n def has_open_session(self):\n open_session = False\n try:\n session = self.open_session()\n except KeyError:\n session = None\n if session is not None:\n open_session = True\n return open_session\n\n def open_session(self):\n editor = getUtility(ILayoutEditorUtility)\n session = editor.get()\n try:\n edit_session = session[self.context.UID()]\n except KeyError:\n edit_session = None\n return edit_session\n\n\nclass DeleteHeadlines(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('delete-headline')\n\n def update(self):\n context = aq_inner(self.context)\n self.errors = {}\n if 'form.button.Delete' in self.request:\n authenticator = getMultiAdapter((context, self.request),\n name=u\"authenticator\")\n if not authenticator.verify():\n raise Unauthorized\n form = self.request.form\n current_layout = context.layout_order\n try:\n headlines = json.loads(context.headlines)\n except TypeError:\n headlines = {}\n uuid = form.get('headline.uid', None)\n del headlines[uuid]\n new_headlines = json.dumps(headlines)\n setattr(context, 'headlines', new_headlines)\n current_layout.remove(uuid)\n setattr(context, 'layout_order', current_layout)\n modified(context)\n context.reindexObject(idxs='modified')\n api.portal.show_message(\n message=_(u\"Headline successfully removed.\"),\n request=self.request)\n next_url = context.absolute_url() + '/@@edit-layout'\n return self.request.response.redirect(next_url)\n\n def has_open_session(self):\n open_session = False\n try:\n session = self.open_session()\n except KeyError:\n session = None\n if session is not None:\n open_session = True\n return open_session\n\n def open_session(self):\n editor = getUtility(ILayoutEditorUtility)\n session = editor.get()\n try:\n edit_session = session[self.context.UID()]\n except KeyError:\n edit_session = None\n return edit_session\n\n def item_details(self, uuid):\n context = aq_inner(self.context)\n obj = uuidToObject(uuid)\n if not obj:\n headlines = json.loads(context.headlines)\n item = headlines[uuid]\n data = {}\n data['type'] = 'headline'\n data['uid'] = uuid\n title_key = 'title_' + self.current_lang()\n data['title'] = item[title_key]\n return data\n else:\n data = {}\n data['type'] = 'orderable'\n data['uid'] = uuid\n data['product_code'] = obj.productCode\n obj_title = self.localized_title(obj)\n if obj_title is not None:\n data['title'] = obj_title\n else:\n data['title'] = obj.Title()\n return data\n\n def localized_title(self, obj):\n fieldname = 'title_' + self.current_lang()\n return getattr(obj, fieldname, None)\n\n def current_lang(self):\n context = aq_inner(self.context)\n pstate = getMultiAdapter((context, self.request),\n name=u'plone_portal_state')\n lang = pstate.language()\n return lang\n\n\nclass ClearEditor(grok.View):\n grok.context(IItemCollection)\n grok.require('cmf.ModifyPortalContent')\n grok.name('clear-editor')\n\n def update(self):\n context = aq_inner(self.context)\n self.errors = {}\n if 'form.button.Submit' in self.request:\n authenticator = getMultiAdapter((context, self.request),\n name=u\"authenticator\")\n if not authenticator.verify():\n raise Unauthorized\n if self.has_open_session():\n session = self.open_session()\n if session is not None:\n editor = getUtility(ILayoutEditorUtility)\n editor.delete(context.UID())\n editor.clear()\n api.portal.show_message(\n message=_(u\"Item successfully added.\"), request=self.request)\n next_url = context.absolute_url()\n return self.request.response.redirect(next_url)\n\n def has_open_session(self):\n open_session = False\n try:\n session = self.open_session()\n except KeyError:\n session = None\n if session is not None:\n open_session = True\n return open_session\n\n def open_session(self):\n editor = getUtility(ILayoutEditorUtility)\n session = editor.get()\n try:\n edit_session = session[self.context.UID()]\n except KeyError:\n edit_session = None\n return edit_session\n","sub_path":"src/chromsystems.shopcontent/chromsystems/shopcontent/itemcollection.py","file_name":"itemcollection.py","file_ext":"py","file_size_in_byte":18478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"52494508","text":"#\n# Soryy, I placed everything in one file for this...\n# ======================\n# Imports\n#======================\n\nimport pandas as pd\nimport re\nimport os\nimport random, string\n\ndef randomword(length):\n letters = string.ascii_lowercase\n return ''.join(random.choice(letters) for i in range(length))\n\nfrom enum import Enum\nfrom typing import NamedTuple, List, Dict, Tuple\n\n#======================\n# Constants\n#======================\n\n# Set these variables\nINPUT_FILE = \"./data/00_raw/00_ari_bash_history.txt\"\nOUTPUT_FILE = \"./data/01_parsed/01_ari.pickle\"\nHOME_PATH = \"/home/ari\"\n\n# General Defaults\nPIPE_CMDS = (\";\",\"|\",\"&\",\"&&\",\"||\")\nCD = \"cd\"\nPATH_CMDS = [\n # common ones\n CD, \"cat\", \"mv\", \"rm\", \"cp\", \"mkdir\", \"tail\",\n # special software\n \"firefox\", \"git\", \"chrome\", \"source\", \"code\", \n]\n\n#======================\n# Token Manipulation\n#======================\nclass TokenType(Enum):\n Path='path'\n Pipe='pipe'\n PathCMD='path-command'\n Unknown='n/a'\n\nclass Token(NamedTuple):\n token_text: str\n token_type: TokenType\n row_idx: int = -1\n start_path: str = ''\n end_path: str = ''\n\n @classmethod\n def tokenize(cls, text: str):\n if len(text.split('/')) > 1:\n return cls(token_text=text, token_type=TokenType.Path)\n elif text in PIPE_CMDS:\n return cls(token_text=text, token_type=TokenType.Pipe)\n elif text in PATH_CMDS:\n return cls(token_text=text, token_type=TokenType.PathCMD)\n else:\n return cls(token_text=text, token_type=TokenType.Unknown)\n\n @classmethod\n def tokenizer(cls, row_idx:int, start_path: str, line: str):\n prior_is_path_cmd = False\n prior_is_cd = False\n for text in line.split(' '):\n text = text.strip()\n end_path = start_path\n token = cls.tokenize(text)\n \n if prior_is_path_cmd and token.token_type != TokenType.PathCMD:\n token = cls(token_text=token.token_text, token_type=TokenType.Path)\n \n # check if ends with pipe cmd\n endswith_pipe = False\n if token.token_type != TokenType.Pipe:\n for pcmd in PIPE_CMDS:\n if token.token_text.endswith(pcmd):\n endswith_pipe = True\n break\n\n if token.token_type == TokenType.PathCMD:\n prior_is_path_cmd = True\n prior_is_cd = token.token_text == CD\n elif prior_is_cd:\n text = token.token_text\n if endswith_pipe:\n text = text[0:(len(text)-len(pcmd))]\n if text.startswith(\"/\"):\n end_path = text\n elif text.startswith(\"~\"):\n end_path = os.path.join(HOME_PATH, text[1:])\n elif text.startswith('./'):\n end_path = os.path.join(start_path, text[2:])\n else:\n end_path = os.path.join(start_path, text)\n prior_is_path_cmd = False\n prior_is_cd = False\n\n if endswith_pipe:\n yield cls(\n token_text=token.token_text[0:(len(token.token_text)-len(pcmd))],\n token_type=token.token_type,\n row_idx=row_idx,\n start_path=start_path,\n end_path=end_path\n )\n yield cls(\n token_text=pcmd,\n token_type=TokenType.Pipe,\n row_idx=row_idx,\n start_path=end_path,\n end_path=end_path\n )\n else:\n yield cls(\n token_text=token.token_text,\n token_type=token.token_type,\n row_idx=row_idx,\n start_path=start_path,\n end_path=end_path\n )\n start_path = end_path\n\n#======================\n# Read file, convert to tokens\n#======================\ndata = []\nwith open(INPUT_FILE) as inf:\n curr_path = HOME_PATH\n for row_idx, row in enumerate(inf):\n for token in Token.tokenizer(row_idx, curr_path, row.strip()):\n data.append(token)\n curr_path = token.end_path\n \ndata = pd.DataFrame(data)\n\n#======================\n# Path obstructions\n#======================\nObtuseMapping = Dict[int, Dict[str, List[str]]]\n\ndef path_obtuse(path: str, optuse_paths: List[str]) -> Tuple[str, list]:\n if path not in optuse_paths:\n optuse_paths.append(path)\n return str(optuse_paths.index(path)), optuse_paths\n\ndef path_obtuse_full(path: str, op_paths: ObtuseMapping) -> Tuple[ str, ObtuseMapping ]:\n last_sub_path = '/'\n obt_path = \"\"\n for level, subpath in enumerate(path.split('/')[1:]):\n if level not in op_paths.keys():\n op_paths[level] = {}\n if last_sub_path not in op_paths[level].keys():\n op_paths[level][last_sub_path] = []\n op_sp = op_paths[level][last_sub_path]\n subpath, op_sp = path_obtuse(subpath, op_sp)\n last_sub_path = subpath\n obt_path += '/'\n obt_path += subpath\n\n return obt_path, op_paths\n\n# Get all unique paths\nall_paths = set(\n data.end_path[data.token_type == TokenType.Path].unique()\n).union(\n data.start_path[data.token_type == TokenType.Path].unique()\n)\n\n\n# Obtuse paths\nmapping: Dict[str, str] = {}\nop_paths: ObtuseMapping = {}\nfor text in all_paths:\n otext, op_paths = path_obtuse_full(text, op_paths)\n mapping[text] = otext\n\ndef apply_obts(row: pd.Series, op_paths: ObtuseMapping = op_paths) -> pd.DataFrame:\n row.end_path = mapping[row.end_path]\n row.start_path = mapping[row.start_path]\n if row.token_type == TokenType.Path:\n if row.token_text in mapping.keys():\n row.token_text = mapping[row.token_text]\n else:\n if row.token_text.startswith('/') or row.token_text.startswith('~'):\n otext, op_paths = path_obtuse_full(row.token_text, op_paths)\n mapping[row.token_text] = otext\n else:\n text = os.path.join(row.start_path, row.token_text)\n otext, op_paths = path_obtuse_full(text, op_paths)\n row.token_text = otext\n return row\n\n# apply obstruction to all paths\nob_data = data.apply(\n axis=1,\n func=apply_obts \n)\n\n#======================\n# Rebuild final dataset and save\n#======================\nob_data.groupby('row_idx').apply(\n lambda g: \" \".join(g.token_text.values)\n).to_pickle(OUTPUT_FILE)","sub_path":"00_extract_bash_history.py","file_name":"00_extract_bash_history.py","file_ext":"py","file_size_in_byte":6609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"648736401","text":"from airflow.operators.bash_operator import BashOperator\n\n\nADAPTER_PROPERTIES_PATH = \"/var/lib/netwitness/presidio/flume/conf/adapter/\"\n\n\ndef build_adapter_properties_cleanup_operator(cleanup_dag, num_hours_not_delete, task_id):\n adapter_clean_bash_command = \"find {0} -type f -name '*-*' -mmin +{1} -exec rm -f {{}} \\;\"\\\n .format(ADAPTER_PROPERTIES_PATH, num_hours_not_delete * 60)\n clean_adapter_operator = BashOperator(task_id=task_id,\n bash_command=adapter_clean_bash_command,\n dag=cleanup_dag)\n return clean_adapter_operator\n","sub_path":"presidio-core/presidio-workflows/presidio/builders/adapter/adapter_properties_cleanup_operator_builder.py","file_name":"adapter_properties_cleanup_operator_builder.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"9825576","text":"import os\nimport requests\nfrom requests.auth import HTTPBasicAuth\nimport pytest\nimport json\nfrom datetime import datetime, timedelta\n\ndef test_rest_provider():\n\n url='http://0.0.0.0:5000'\n\n user='testuser'\n password='TE$tP@ss123'\n\n calendar='sample'\n\n\n s = requests.Session()\n # Making a auth post request\n payload = {'username':user,'password':password}\n s.post((url + '/do_login'),data=payload)\n\n\n # Making a auth post request to remove api mapping\n response = s.post((url + '/deleteapi/1'))\n\n\n # Making a get request to update token\n response = s.get((url + '/api/update/'))\n token=(((response.json())[1]).split(':')[1]).strip ()\n #print(response.status_code)\n #print(response.content)\n #print(token)\n\n # Making a get request to call rest provider\n response = s.get((url + '/call/test/' + user + '&' + token))\n #print(response.status_code)\n #print(response.content)\n\n assert response.status_code == 500\n\n assert 'No duty found for' in str(response.content)\n\n\n\n current_date=datetime.today().strftime('%Y-%-m-%d')\n\n # Making a post request to create new duty task\n files = {\n 'project': (None, 'test'),\n 'duty1': (None, 'duty01'),\n 'duty2': (None, 'duty02'),\n 'date': (None, current_date),\n 'enddate': (None, current_date),\n 'is_all_day': (None, '1'),\n 'start_time': (None, '00:00'),\n 'end_time': (None, '00:00'),\n 'repetition_value_weekday': (None, '0'),\n 'repetition_value_monthday': (None, '1'),\n 'repetition_value_monthday_end': (None, '1'),\n 'repetition_value': (None, '0'),\n 'details': (None, ''),\n 'color': (None, '#3EB34F'), \n }\n\n\n response = s.post((url + '/' + calendar + '/new_task'),files=files)\n\n # Making a get request to call rest provider\n response = s.get((url + '/call/test/' + user + '&' + token))\n #print(response.status_code)\n #print(response.content)\n\n assert response.status_code == 200\n\n assert 'Send REST request to:\",\"http://0.0.0.0:5000/test/&@out&extension=&context=play&timeout=900\",\" complete successfully' in str(response.content)\n\n # Making a post request to call rest provider\n response = s.post((url + '/call/test/' + user + '&' + token))\n #print(response.status_code)\n #print(response.content)\n\n assert response.status_code == 200\n\n assert 'Send REST request to:\",\"http://0.0.0.0:5000/test/&@out&extension=&context=play&timeout=900\",\" complete successfully' in str(response.content)\n\n\n response_check = s.get((url + '/api/check/' + user + '&' + token))\n #print(response_check.status_code)\n #print(response_check.content)\n\n assert response_check.status_code == 200\n\n assert 'auth success!' in str(response_check.content)\n\n#test_rest_provider()","sub_path":"test/22_test_rest_provider.py","file_name":"22_test_rest_provider.py","file_ext":"py","file_size_in_byte":2797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"4450497","text":"from .fixtures import app, client\nfrom . import helpers\nimport os\nfrom os.path import dirname\n\n\ndef test_restaurants_map_is_correct(client, app):\n helpers.create_operator(client)\n helpers.login_operator(client)\n helpers.create_restaurant(client)\n\n # in order to regenerate the map with test values\n res = client.get(\"/\")\n\n assert b\"Trattoria da Fabio\" in res.data\n assert b\"40.720586\" in res.data\n assert b\"10.1\" in res.data\n assert b\"555123456\" in res.data\n\n\ndef test_map_is_in_view(client):\n helpers.create_operator(client)\n helpers.login_operator(client)\n helpers.create_restaurant(client)\n\n res = client.get(\"/\")\n\n assert b'id=\"map\"' in res.data\n","sub_path":"tests/test_map.py","file_name":"test_map.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"613225591","text":"#!/usr/bin/python\n\nimport sys\nimport logging\nimport time\nfrom optparse import OptionParser\n\nfrom SNAPobs import snap_defaults,snap_recorder,snap_plot\nfrom ATATools import ata_control,logger_defaults,ata_positions,snap_array_helpers\n\ndefault_fpga_file = snap_defaults.plot_snap_file\ndefault_rms = snap_defaults.rms\ndefault_recs = 600\n\ndef main():\n\n # Define the argumants\n parser = OptionParser(usage= 'Usage %prog options',\n description='Plot spectrogram(waterfall) of single antenna')\n\n #parser.add_argument('hosts', type=str, help = hostnamesHelpString)\n parser.add_option('--fpga', dest='fpga_file', type=str, action=\"store\", default=default_fpga_file,\n help = '.fpgfile to program')\n parser.add_option('-a', dest='ant', type=str, action=\"store\", default=None,\n help ='antenna name eg: \\\"2j\\\".')\n parser.add_option('-s', dest='source', type=str, action=\"store\", default=None,\n help ='source name to record')\n parser.add_option('-f', dest='freq', type=float, action=\"store\", default=None,\n help ='observation frequency, in MHz. Only one set of frequencies')\n parser.add_option('-l', dest='len', type=int, action=\"store\", default=default_recs,\n help ='waterfall len (time)')\n parser.add_option('-v', '--verbose', dest='verbose', action=\"store_true\", default=False,\n help =\"More on-screen information\")\n parser.add_option('-p', '--park', dest='do_park', action=\"store_true\", default=False,\n help =\"Park the antennas afterwards\")\n\n (options,args) = parser.parse_args()\n\n if(options.verbose):\n logger = logger_defaults.getProgramLogger(\"SNAP_OBS\",loglevel=logging.INFO)\n else:\n logger = logger_defaults.getProgramLogger(\"SNAP_OBS\",loglevel=logging.WARNING)\n\n if len(sys.argv) <= 1:\n logger.warning(\"no options provided\")\n parser.print_help()\n sys.exit(1)\n\n if options.ant:\n ant_str = options.ant\n else:\n logger.error(\"antennas were not provided and no config file\")\n raise RuntimeError(\"no antenna string\")\n\n if options.freq:\n freq = options.freq\n else: \n logger.error(\"no frequency set\")\n raise RuntimeError(\"no frequency set\")\n\n if options.source:\n source = options.source\n else: \n logger.error(\"sources was not provided\")\n raise RuntimeError(\"no source string\")\n\n fpga_file = options.fpga_file\n do_park = options.do_park\n waterfalllen = options.len\n do_snap_waterfall(ant_str,freq, source, fpga_file, waterfalllen, do_park)\n\n exit()\n\ndef do_snap_waterfall(ant_str,freq, source, fpga_file, waterfalllen, do_park = False):\n\n logger = logger_defaults.getModuleLogger(__name__)\n\n ant_list = snap_array_helpers.string_to_array(ant_str);\n full_ant_str = snap_array_helpers.array_to_string(ant_list)\n\n if waterfalllen < 2:\n logger.error('waterfall len too short')\n raise RuntimeError('waterfall len too short')\n\n try:\n ant_groups = ata_control.get_snap_dictionary(ant_list)\n except:\n logstr = \"unable to match antennas with snaps\"\n logger.exception(logstr)\n raise\n \n if len(ant_list) != 1:\n logger.error('only 1 antenna allowed')\n raise RuntimeError('only 1 antenna allowed')\n\n if len(ant_groups) != 1:\n logger.error('only 1 antenna allowed')\n raise RuntimeError('only 1 antenna allowed')\n\n ant_dict = {}\n for csnap in ant_groups:\n if len(ant_groups[csnap]) != 1:\n logger.error('only one antenna per snap allowed, got {}: {}'.format(csnap,\",\".join(ant_groups[csnap])))\n raise RuntimeError(\"only 1 antenna per snap allowed\")\n\n snaphost = csnap\n currAnt = ant_groups[csnap][0]\n \n logger.info(\"Reserving antennas %s in bfa antgroup\" % full_ant_str)\n try:\n ata_control.reserve_antennas(ant_list)\n except:\n logstr = \"unable to reserve the antennas\"\n logger.exception(logstr)\n raise\n\n logger.info(\"starting plotting\")\n try:\n ata_control.try_on_lna(currAnt)\n source_status = ata_positions.ATAPositions.getFirstInListThatIsUp([source])\n if not source_status:\n errormsg = 'source {} is not up (or too close to sun/moon)... terminating observation set {}'.format(source,obs_set_id)\n logger.error(errormsg)\n raise RuntimeError(errormsg)\n if source_status['status'] != 'up':\n if source_status['status'] == 'next_up':\n errormsg = 'source {} is not up (or too close to sun/moon). Will be up in {} minutes. Terminating observation set {}'.format(source,source_status['minutes'],obs_set_id)\n else:\n errormsg = 'source {} is not up (or too close to sun/moon)... terminating observation set {}'.format(source,obs_set_id)\n logger.error(errormsg)\n raise RuntimeError(errormsg)\n\n\n logger.info(\"pointing the antennas\")\n ata_control.make_and_track_ephems(source, currAnt );\n logger.info(\"autotuning\")\n ata_control.autotune(currAnt)\n ata_control.rf_switch_thread([currAnt])\n\n logger.info(\"changing to frequency {}\".format(freq))\n ata_control.set_freq(freq, currAnt)\n snap_recorder.setSnapRMS(snaphost,currAnt,fpga_file,default_rms)\n snap_plot.plotWaterfall(snaphost, waterfalllen, freq,fpga_file)\n\n except KeyboardInterrupt:\n logger.info(\"Keyboard interuption\")\n except Exception as e:\n logger.exception(\"something went wrong\")\n errmsg = \"Finishing recording - failed: {}\".format(e)\n raise\n finally: \n logger.info(\"shutting down\")\n ata_control.release_antennas(ant_list, do_park)\n\nif __name__== \"__main__\":\n main()\n\n","sub_path":"SingleMeasurements/plot_waterfall_live.py","file_name":"plot_waterfall_live.py","file_ext":"py","file_size_in_byte":5875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"537811675","text":"import numpy as np\nfrom gainData import AntennaBalunMeasurement as ABM\nfrom gainData import AntennaDiffMeasurement as ADM\nfrom gainData import GainData as GD\nimport matplotlib.pyplot as plt\n\n#load measurements\nhybrid_coupler_A=ABM()\nhybrid_coupler_B=ABM()\ncambridge_balun=ABM()\nno_balun=ADM()\n\nhybrid_coupler_A.read_files('../Rooftop_Antenna_Measurements_August_10th/','_hybrid_A_port_A_antenna',\n '../hybrid_coupler_A_Sierra/A_','','ANRITSU_CSV',\n '1','3','4',0.05,0.25)\nhybrid_coupler_B.read_files('../Rooftop_Antenna_Measurements_August_10th/','_hybrid_B_port_A_antenna',\n '../hybrid_coupler_B/1','','ANRITSU_CSV',\n '1','3','4',0.05,0.25)\ncambridge_balun.read_files('../Rooftop_Antenna_Measurements_August_10th/','_cambridge_balun_N_antenna_0',\n '../Cambridge_Balun_Measurements_N/','','ANRITSU_CSV',\n '1','2','3',0.05,0.25)\n\nno_balun.read_files('../Rooftop_Antenna_Measurements_August_10th/','_no_balun_antenna','ANRITSU_CSV',0.05,0.25)\n\nsimulation=GD()\nsimulation.read_files('../Rooftop_Antenna_Measurements_August_10th/Simulation/simulation_s11_rooftop','CST_S11',fMin=0.05,fMax=0.25)\nsimulation.gainFrequency=simulation.gainFrequency\n\n#load Jianshu's data\n'''\nimpedance_sim_amp=np.loadtxt(('../Rooftop_Antenna_Measurements_August_10th/'\n 'Simulation/simulation_impedance_rooftop_amp.txt'))\nimpedance_sim_pha=np.loadtxt(('../Rooftop_Antenna_Measurements_August_10th/'\n 'Simulation/simulation_impedance_rooftop_pha.txt'))\nsim_impedance=impedance_sim_amp[:,1]*np.exp(1j*impedance_sim_pha[:,1])\nsim_freq=impedance_sim_amp[:,0]\nsim_s11=(sim_impedance-100)/(sim_impedance+100)\nfig0=plt.figure()\nax0=fig0.add_axes([.1,.1,.8,.8])\nax0.plot(sim_impedance.real)\nax0.plot(sim_impedance.imag)\n'''\n\n\ndb_s11_A_corr=10.*np.log10(np.abs(hybrid_coupler_A.antenna_gain_corrected_frequency))\ndb_s11_A=10.*np.log10(np.abs(hybrid_coupler_A.antenna_raw.gainFrequency))\ndb_s11_B_corr=10.*np.log10(np.abs(hybrid_coupler_B.antenna_gain_corrected_frequency))\ndb_s11_B=10.*np.log10(np.abs(hybrid_coupler_B.antenna_raw.gainFrequency))\ndb_s11_cam_corr=10.*np.log10(np.abs(cambridge_balun.antenna_gain_corrected_frequency))\ndb_s11_cam=10.*np.log10(np.abs(cambridge_balun.antenna_raw.gainFrequency))\ndb_s11_no_balun=10.*np.log10(np.abs(no_balun.antenna_gain_frequency))\ndb_s11_sim=10.*np.log10(np.abs(simulation.gainFrequency))\n\n\n\npha_s11_A_corr=np.angle(hybrid_coupler_A.antenna_gain_corrected_frequency)\npha_s11_A=np.angle(hybrid_coupler_A.antenna_raw.gainFrequency)\npha_s11_B_corr=np.angle(hybrid_coupler_B.antenna_gain_corrected_frequency)\npha_s11_B=np.angle(hybrid_coupler_B.antenna_raw.gainFrequency)\npha_s11_cam_corr=np.angle(cambridge_balun.antenna_gain_corrected_frequency)\npha_s11_cam=np.angle(cambridge_balun.antenna_raw.gainFrequency)\npha_s11_no_balun=np.angle(no_balun.antenna_gain_frequency)\npha_s11_sim=np.angle(simulation.gainFrequency)\n\nfig1=plt.figure()\nfig2=plt.figure()\nfig3=plt.figure()\nfig4=plt.figure()\n\nax1=fig1.add_axes([.1,.1,.8,.8])\nax2=fig2.add_axes([.1,.1,.8,.8])\nax3=fig3.add_axes([.1,.1,.8,.8])\nax4=fig4.add_axes([.1,.1,.8,.8])\n\nax1.plot(hybrid_coupler_A.fAxis,db_s11_A,color='k',ls='--',alpha=.5)\nax1.plot(hybrid_coupler_A.fAxis,db_s11_A_corr,color='k',label='Balun A Corrected')\nax1.plot(hybrid_coupler_B.fAxis,db_s11_B,color='grey',ls='--',alpha=.5)\nax1.plot(hybrid_coupler_B.fAxis,db_s11_B_corr,color='grey',label='Balun B Corrected')\nax1.plot(cambridge_balun.fAxis,db_s11_cam,color='red',ls='--',alpha=.5)\nax1.plot(cambridge_balun.fAxis,db_s11_cam_corr,color='red',label='Balun C Corrected')\nax1.plot(no_balun.fAxis,db_s11_no_balun,color='orange',label='No Balun')\n#ax1.plot(simulation.fAxis,db_s11_sim,color='k',lw=5,label='Simulation')\nax1.grid()\nfig1.set_size_inches(10,6)\nax1.set_xlabel('frequency (GHz)')\nax1.set_ylabel('|S$_{11}$| (dB)')\nax1.legend(loc='best')\n\nplotstrs=['s11','s1d','sd1','sdd','s1c','sc1','scc','scd','sdc']\n\nax2.plot(hybrid_coupler_A.fAxis,pha_s11_A,color='k',ls='--',alpha=.5)\nax2.plot(hybrid_coupler_A.fAxis,pha_s11_A_corr,color='k',label='Balun A Corrected')\nax2.plot(hybrid_coupler_B.fAxis,pha_s11_B,color='grey',ls='--',alpha=.5)\nax2.plot(hybrid_coupler_B.fAxis,pha_s11_B_corr,color='grey',label='Balun B Corrected')\nax2.plot(cambridge_balun.fAxis,pha_s11_cam,color='red',ls='--',alpha=.5)\nax2.plot(cambridge_balun.fAxis,pha_s11_cam_corr,color='red',label='Balun C Corrected')\nax2.plot(no_balun.fAxis,pha_s11_no_balun,color='orange',label='No Balun')\n#ax2.plot(simulation.fAxis,pha_s11_sim,color='k',lw=5,label='Simulation')\nax2.grid()\nfig2.set_size_inches(10,6)\nax2.set_xlabel('frequency (GHz)')\nax2.set_ylabel('Arg($S_{11}$) (rad)')\nax2.legend(loc='best')\n\n\nax3.plot(hybrid_coupler_A.fAxis,hybrid_coupler_A.gain_corrected_frequency.real,color='k')\nax3.plot(hybrid_coupler_A.fAxis,hybrid_coupler_A.gain_corrected_frequency.imag,color='k',ls='--')\nax3.plot(hybrid_coupler_B.fAxis,hybrid_coupler_B.gain_corrected_frequency.real,color='grey')\nax3.plot(hybrid_coupler_B.fAxis,hybrid_coupler_B.gain_corrected_frequency.imag,color='grey',ls='--')\nax3.plot(cambridge_balun.fAxis,cambridge_balun.gain_corrected_frequency.real,color='r')\nax3.plot(cambridge_balun.fAxis,cambridge_balun.gain_corrected_frequency.imag,color='r',ls='--')\n\n\nfig1.savefig('Sinuous_s11_amp_comarision_August_10th_2017_vincent_presentation.pdf')\nfig2.savefig('Sinuous_s11_pha_comarision_August_10th_2017_vincent_presentation.pdf')\n\n\nplt.show()\n","sub_path":"src/plot_rooftop_measurements_vincent.py","file_name":"plot_rooftop_measurements_vincent.py","file_ext":"py","file_size_in_byte":5573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"637564370","text":"#060 - Faça um programa que leia um número qualquer e mostre o seu fatorial.\n\nf = int(input(\"Digite um número para \\ncalcular seu fatorial: \"))\n\ncont = f\nm = 1\n\nprint(\"Calculando {}! =\".format(f), end=\"\")\n\nwhile cont > 0:\n m *= cont\n print(\" {}\".format(cont), end=\"\")\n if cont > 1:\n print(\" x\", end=\"\")\n else:\n print(\" = \", end=\"\")\n cont -= 1\n \nprint(\"{}\\n\".format(m))\n","sub_path":"060.py","file_name":"060.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"324360432","text":"\"\"\"General python utilities.\"\"\"\nimport sys\nimport traceback\n\n\ndef flatten(list_of_lists):\n \"\"\"Flattens a list of lists into a single flat list.\n\n Args:\n list_of_lists: List of lists.\n\n Returns:\n List.\n \"\"\"\n return [item for sub_list in list_of_lists for item in sub_list]\n\n\ndef chunk(_list, chunk_size):\n \"\"\"Yield successive n-sized chunks from a list.\n\n Args:\n _list: List.\n chunk_size: Integer.\n\n Returns:\n Iterator.\n \"\"\"\n for i in range(0, len(_list), chunk_size):\n yield _list[i:i + chunk_size]\n\n\ndef print_exception_info():\n \"\"\"Grabs traceback and prints exception info.\"\"\"\n print('Collate encountered an exception:')\n type_, value_, traceback_ = sys.exc_info()\n print('Type: %s' % type_)\n print('Value: %s' % value_)\n print('Traceback:')\n for line in traceback.format_tb(traceback_):\n print(line)\n","sub_path":"ext/py_util.py","file_name":"py_util.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"175845091","text":"import os\n\nfrom jinja2 import StrictUndefined, Template\n\n\nclass ProjectGenerator():\n def __init__(self, settings, models):\n self.settings = settings\n self.models = models\n\n def generate(self):\n \"\"\"Generate whole Django project.\"\"\"\n dirs = self._create_project_dirs(self.settings['output_directory'])\n\n self._generate_root_directory(dirs)\n self._generate_main_directory(dirs)\n self._generate_app_directories(dirs)\n\n def _create_project_dirs(self, output_directory):\n project_name = self.settings['project_name']\n\n if output_directory.endswith('/'):\n output_directory = output_directory[:-1]\n\n directory = r'{}/{}/'.format(output_directory, project_name)\n package_directory = r'{}/{}/'.format(directory, project_name)\n\n return {\n 'root_directory': self._make_dir(directory),\n 'package_directory': self._make_dir(package_directory),\n 'app_directories': self._create_app_subdirs(directory)\n }\n\n def _create_app_subdirs(self, directory):\n for app in self.settings['apps']:\n app_directory = r'{}/{}/'.format(directory, app)\n self._make_dir(app_directory)\n self._make_dir(r'{}/migrations/'.format(app_directory))\n yield app, app_directory\n\n def _generate_root_directory(self, dirs):\n directory = dirs['root_directory']\n self._render_simple_file('resources/manage.jinja2',\n directory + 'manage.py',\n settings=self.settings)\n\n self._copy_file('resources/init', directory + 'init.sh')\n self._copy_file('resources/requirements',\n directory + 'requirements.txt')\n self._copy_file('resources/dockerfile', directory + 'Dockerfile')\n\n def _generate_main_directory(self, dirs): # TODO better name?\n directory = dirs['package_directory']\n\n files_to_be_rendered = (\n ('settings.jinja2', 'settings.py', {'settings': self.settings}),\n ('wsgi.jinja2', 'wsgi.py', {'settings': self.settings}),\n ('urls.jinja2', 'urls.py', {'models': self.models,\n 'apps': self.settings['apps']}),\n )\n\n for (template, output_name, kwargs) in files_to_be_rendered:\n self._render_simple_file('resources/' + template,\n directory + output_name, **kwargs)\n\n self._create___init__(directory)\n\n def _generate_app_directories(self, dirs):\n for (app_name, directory) in dirs['app_directories']:\n self._generate_app_directory(\n directory,\n [model for model in self.models if model.app_name == app_name],\n app_name,\n )\n\n def _generate_app_directory(self, directory, models, app_name):\n needs_user = self._check_if_needs_user(models),\n needs_group = self._check_if_needs_group(models)\n\n # prepare import lists\n model_imports = ', '.join([model.name for model in models])\n serializer_imports = ', '.join(\n ['{}Serializer'.format(model.name) for model in models])\n\n kwargs = {\n 'models': models,\n 'settings': self.settings,\n 'needs_user': needs_user,\n 'needs_group': needs_group,\n 'model_imports': model_imports,\n 'serializer_imports': serializer_imports\n }\n\n self._render_complex_file('resources/models.jinja2',\n 'resources/model.jinja2',\n directory + 'models.py',\n **kwargs)\n self._render_complex_file('resources/views.jinja2',\n 'resources/modelviewset.jinja2',\n directory + 'views.py',\n **kwargs)\n self._render_complex_file('resources/serializers.jinja2',\n 'resources/modelserializer.jinja2',\n directory + 'serializers.py',\n **kwargs)\n\n self._copy_file('resources/admin.jinja2', directory + 'admin.py')\n self._copy_file('resources/tests.jinja2', directory + 'tests.py')\n\n self._render_simple_file('resources/apps.jinja2',\n directory + 'apps.py',\n app_name=app_name)\n\n self._create___init__(directory)\n self._create___init__(directory + 'migrations/')\n\n def _check_if_needs_user(self, models):\n for model in models:\n for field in model.fields:\n if self._check_if_field_needs_user(field):\n return True\n return False\n\n def _check_if_field_needs_user(self, field):\n return any((key, field.options[key]) == ('othermodel', 'User')\n for key in field.options)\n\n def _check_if_needs_group(self, models):\n for model in models:\n for field in model.fields:\n if self._check_if_field_needs_group(field):\n return True\n return False\n\n def _check_if_field_needs_group(self, field):\n return any((key, field.options[key]) == ('othermodel', 'Group')\n for key in field.options)\n\n def _create___init__(self, directory): # TODO better name?\n with open(directory + '__init__.py', 'w+') as output_file:\n output_file.write('')\n\n def _copy_file(self, input_file, output_file):\n with open(output_file, 'w+') as output_file:\n with open(input_file, 'r') as input_file:\n output_file.write(input_file.read())\n\n def _make_dir(self, directory):\n os.makedirs(directory, exist_ok=True)\n return directory\n\n def _render_simple_file(self, template_filepath, output_filepath,\n **kwargs):\n template = self._get_template(template_filepath)\n rendered_content = template.render(**kwargs)\n with open(output_filepath, 'w+') as output_file:\n output_file.write(rendered_content)\n\n def _render_complex_file(self, template_filepath, subtemplate_filepath,\n output_filepath, models, **kwargs):\n template = self._get_template(subtemplate_filepath)\n rendered_items = [template.render(model=model) for model in models]\n template = self._get_template(template_filepath)\n rendered_content = template.render(rendered_items=rendered_items,\n **kwargs)\n with open(output_filepath, 'w+') as output_file:\n output_file.write(rendered_content)\n\n def _get_template(self, template_filepath):\n with open(template_filepath, 'r') as input_file:\n return Template(input_file.read(), undefined=StrictUndefined)\n","sub_path":"api_sketch/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":6948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"583579886","text":"\nfrom filecabinet.blankentry import BlankEntry\nfrom filecabinet.otlfile import OtlFile\nfrom filecabinet.agenda import Agenda\nfrom filecabinet.project import Project\n\nclass ProjectFile(object):\n\n SPECIAL_SECTIONS = [\n 'AGENDAS', 'PROJECTS', 'WAITING',\n 'SOMEDAY', 'RESEARCH', 'OTHER']\n\n def __init__(self, filename=None, config=None):\n self._headings = {}\n self._unrecognized = []\n for heading in self.SPECIAL_SECTIONS:\n self._headings[heading] = []\n if not filename and not config:\n # If neither is provided, just use an empty project file\n return\n if config:\n filename = config.projectfilepath\n infile = OtlFile(filename)\n sectionkey = 'OTHER'\n for entry in infile.entries:\n if not isinstance(entry, BlankEntry) and \\\n entry.indent == 0 and entry.checkable == False:\n key = str(entry).upper()\n if key in self.SPECIAL_SECTIONS:\n sectionkey = key\n else:\n sectionkey = 'OTHER'\n else:\n self._headings[sectionkey].append(entry)\n # Process any agendas or projects\n if self.agendas:\n self._headings['AGENDAS'] = \\\n self._digestnestedlist(self.agendas, Agenda)\n if self.projects:\n self._headings['PROJECTS'] = \\\n self._digestnestedlist(self.projects,\n lambda x: Project(x, config))\n\n def _digestnestedlist(self, entrylist, grouptype):\n grouplist = []\n group = None\n for entry in entrylist:\n if entry.indent == 1 and not isinstance(entry, BlankEntry):\n if group:\n grouplist.append(group)\n group = grouptype(entry)\n elif group:\n group.additem(entry)\n else:\n self._unrecognized.append(entry)\n if group:\n grouplist.append(group)\n return grouplist\n\n @property\n def alldata(self):\n return self._headings\n\n @property\n def isempty(self):\n for val in self._headings.values():\n if len(val) > 0:\n return False\n return True\n\n @property\n def unrecognized(self):\n return self._unrecognized\n\n def __getattr__(self, attrname):\n testname = attrname.upper()\n if testname in self._headings.keys():\n return self._headings[testname]\n elif testname in self.SPECIAL_SECTIONS:\n return []\n raise AttributeError('ProjectFile object has no attribute \"' +\n attrname + '\"')\n\n def _stringifysection(self, name):\n result = ''\n if name in self._headings.keys():\n result += name + '\\n'\n result += '\\n'.join([str(i) for i in self._headings[name]]) + '\\n'\n return result\n\n def __str__(self):\n result = ''\n result += ''.join(\n [self._stringifysection(n) for n in self.SPECIAL_SECTIONS[0:5]])\n if len(self._headings['OTHER']) > 0:\n result += '\\n'.join([str(i) for i in\n self._headings['OTHER']]) + '\\n'\n return result\n","sub_path":"filecabinet/projectfile.py","file_name":"projectfile.py","file_ext":"py","file_size_in_byte":3284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"649705181","text":"from os import listdir, makedirs, system\nfrom os.path import exists\nfrom subprocess import check_call\n\nfrom core.constants import data_path\n\npath_to_bam_files = '/export/data/kchukreev/data/bam_files/gatk/realigned_bams_with_known/' # prepared_bams\n# path_to_bam_files = data_path + 'filtered_bams/'\n# out_path = data_path + 'realigned_all_ppe_pgrs/'\nout_path = data_path + 'realigned_8141_8167/'\n# path_to_intervals = data_path + 'ppe_pgrs.bed'\npath_to_intervals = data_path + 'Spurious.bed'\npath_to_list = data_path + 'list0.txt'\n\n\nif __name__ == '__main__':\n if not exists(out_path):\n makedirs(out_path)\n # sample_ids = [l.strip() for l in open(path_to_list).readlines()]\n sample_ids = ['SAMN03648890', 'SAMN03648892', 'SAMN03648885']\n for sample_id in sample_ids:\n if not exists(out_path + sample_id + '.bam'):\n # print('samtools view --threads 32 -b -L ' + path_to_intervals + ' ' + path_to_bam_files + sample_id +\n # '_h37rv.bam > ' + out_path + sample_id + '.bam')\n system('samtools view --threads 32 -b -L ' + path_to_intervals + ' ' + path_to_bam_files + sample_id +\n '_h37rv.bam > ' + out_path + sample_id + '.bam')\n system('samtools index ' + out_path + sample_id + '.bam')\n","sub_path":"testing/cut_regions_from_bam_files.py","file_name":"cut_regions_from_bam_files.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"602586359","text":"# LinkedList Palindrome \n\n# Implement function is_palindrome() to check if a linked list is a palindrome\n# Return true if palindrome… false if not\n\n# Example:\n# 1 -> 2 -> 3 -> 2 -> 1 is a palindrome\n# 1 -> 2 -> 4 -> 3 -> 1 is not\n\n# Input: head of linked list\n# Output: true/false\n\ndef is_pali(head):\n x_p = head\n y_p = head\n temp = head\n\n mid_node = None\n\n if (head.next != None):\n while (x_p.next != None):\n push(Stack(), x_p.next)\n x_p = x_p.next.next\n y_p = y_p.next\n\n if (x_p != None):\n mid_node = y_p\n y_p = y_p.next\n\n second_half = y_p\n\n while (second_half.next != None):\n if (second_half.next != pop(Stack())):\n return False\n\n return True\n\n\n\n\n","sub_path":"Nvidia/IsLinkedListPalindrome.py","file_name":"IsLinkedListPalindrome.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"219112113","text":"# !/usr/bin/env python\n# -*- coding:utf-8 -*-\n# author : zerodel\n# Readme:\n#\n\nimport os\n\nimport pysrc.body\nimport pysrc.body.logger\nimport pysrc.body.worker\n\n__doc__ = ''' wrapper of gffread from cufflinks\n'''\n__author__ = 'zerodel'\n\n_logger = pysrc.body.logger.default_logger(\"gffread_process\")\n\n\ndef build_cmd_gffread(annotation_files, genomic_seqs, output_fasta, transcript_filter=\"CME\", gffread_cmd=\"gffread\"):\n annotation_files = os.path.abspath(annotation_files)\n genomic_seqs = os.path.abspath(genomic_seqs)\n output_fasta = os.path.abspath(output_fasta)\n\n cmd_parts = [gffread_cmd, annotation_files, \"-g\", genomic_seqs]\n\n if transcript_filter:\n cmd_parts.append(\"-{}\".format(transcript_filter))\n else:\n pass\n\n cmd_parts.append(\"-w\")\n cmd_parts.append(output_fasta)\n\n return cmd_parts\n\n\ndef do_extract_classic_message_transcript(gff, path_ref_sequence_file, output):\n cmd_parts = build_cmd_gffread(annotation_files=gff, genomic_seqs=path_ref_sequence_file, output_fasta=output)\n\n _logger.debug(\"raw cmd for extracting linear transcript is : %s\" % str(cmd_parts))\n\n pysrc.body.worker.run(cmd_parts)\n\n\ndef do_extract_non_coding_transcript(gff, path_ref_sequence_file, output):\n cmd_parts = build_cmd_gffread(annotation_files=gff, genomic_seqs=path_ref_sequence_file, output_fasta=output,\n transcript_filter=\"\")\n\n _logger.debug(\"raw cmd for extracting circular RNA transcripts : %s\" % str(cmd_parts))\n\n pysrc.body.worker.run(cmd_parts)\n","sub_path":"pysrc/wrapper/gffread.py","file_name":"gffread.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"390394945","text":"# -*- encoding: utf-8 -*-\nfrom observatorio.apps.proyecto.models import Proyecto\nfrom django import template\nimport datetime\n\nregister = template.Library()\n\n@register.simple_tag()\ndef date_now():\n\treturn datetime.datetime.now().date().strftime(\"%A, %d de %B del %Y\")\n\n@register.assignment_tag\ndef relation_project(pk):\n\tproyecto = Proyecto.objects.get(pk = pk)\n\tarea_tematica = proyecto.area_tematica.proyecto_set.exclude(pk = proyecto.pk)\n\ttipo_solucion = proyecto.tipo_solucion.proyecto_set.exclude(pk = proyecto.pk)\n\tusuario = proyecto.usuario.proyecto_set.exclude(pk = proyecto.pk)\n\tasesor = proyecto.asesor.proyecto_set.exclude(pk = proyecto.pk)\n\treturn area_tematica[:3] if area_tematica.count() > 0 else tipo_solucion[:3] if tipo_solucion.count() > 0 else asesor[:3] if asesor.count() > 0 else usuario[:3]","sub_path":"observatorio/apps/proyecto/templatetags/custom_filters.py","file_name":"custom_filters.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"259267866","text":"import urllib.request as urlreq\nimport urllib.error as urlerr\nimport urllib.parse as urlparse\nimport urllib.robotparser as urlrp\nimport datetime\nimport time\nimport re\nimport socket\nimport http.client\n\nclass Downloader(object):\n def __init__(self, delay=5,\n user_agent='wswp', proxies=None,\n num_retries=1, cache=None, timeout=60):\n socket.setdefaulttimeout(timeout)\n self.throttle = Throttle(delay)\n self.user_agent = user_agent\n self.proxies = proxies\n self.num_retries = num_retries\n self.cache = cache\n\n def __call__(self, url):\n result = None\n if self.cache:\n try:\n result = self.cache[url]\n except KeyError:\n pass\n else:\n if self.num_retries > 0 and result['code'] != None and \\\n 500 <= result['code'] < 600:\n # server error so ignore result from cache \n # and re-downlad\n result = None\n if result is None:\n # result was not downloaded from cache so still\n # need to download\n self.throttle.wait(url)\n proxy = random.choice(self.proxies) if self.proxies else None\n headers = {'User-agent': self.user_agent}\n result = self.download(url, headers, proxy, self.num_retries)\n if self.cache:\n self.cache[url] = result\n\n return result['html']\n\n def download(self, url, headers, proxy, num_retries, data = None):\n print('Downloading: ', url)\n request = urlreq.Request(url, headers=headers)\n opener = urlreq.build_opener()\n \n if proxy:\n proxy_params = {urlparse.urlparse(url).scheme: proxy}\n opener.add_handler(urlreq.ProxyHandler(proxy_params))\n try:\n #html = urlreq.urlopen(request).read()\n response = opener.open(request)\n try:\n html = response.read()\n except http.client.IncompleteRead as e:\n html = e.partial\n code = response.code\n except urlerr.URLError as e:\n print('Download error: ', e.reason)\n html = None\n code = None\n if hasattr(e, 'code'):\n code = e.code\n if num_retries > 0 and 500 <= e.code < 600:\n return download(url, headers, proxy, self.num_retries-1)\n else:\n pass\n\n return {'html': html, 'code': code}\n\nclass Throttle:\n \"\"\"add delay between downloads to the same domain\"\"\"\n def __init__(self, delay):\n self.delay = delay\n self.domains = {}\n \n def wait(self, url):\n domain = urlparse.urlparse(url).netloc\n last_accessed = self.domains.get(domain)\n \n if self.delay > 0 and last_accessed is not None:\n sleep_secs = self.delay - (datetime.datetime.now() - \n last_accessed).seconds\n if sleep_secs > 0:\n time.sleep(sleep_secs)\n self.domains[domain] = datetime.datetime.now()\n\ndef download(url, user_agent='wswp', proxy=None, num_retries=2):\n print('Downloading: ', url)\n headers = {'User-agent': user_agent}\n request = urlreq.Request(url, headers=headers)\n opener = urlreq.build_opener()\n \n if proxy:\n proxy_params = {urlparse.urlparse(url).scheme: proxy}\n opener.add_handler(urlreq.ProxyHandler(proxy_params))\n try:\n #html = urlreq.urlopen(request).read()\n html = opener.open(request).read()\n except urlerr.URLError as e:\n print('Download error: ', e.reason)\n html = None\n if num_retries > 0:\n if hasattr(e, 'code') and 500 <= e.code < 600:\n return download(url, user_agent, proxy, num_retries-1)\n return html\n\ndef get_links(html):\n # a regular expression to extract all links from the webpage\n webpage_regex = re.compile(']+href=[\"\\'](.*?)[\"\\']', re.IGNORECASE)\n # list of all links from the webpage\n return webpage_regex.findall(html)\n\ndef normalize(seed_url, link):\n \"\"\"Normalize this URL by removing hash and adding domain\n \"\"\"\n link, _ = urlparse.urldefrag(link) # remove hash to avoid duplicates\n return urlparse.urljoin(seed_url, link)","sub_path":"common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"130409866","text":"from keras.models import Sequential\nfrom keras.layers import *\nfrom keras.callbacks import EarlyStopping\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error\nfrom math import sqrt\nfrom datetime import timedelta\nimport os\nfrom collections import OrderedDict\n\nfrom github_trends.services.database_service import DatabaseService\n\noutput_dir = os.path.join(os.path.dirname(__file__), \"output\")\n\n\nclass Forecasting:\n def __init__(self):\n self.db_service = DatabaseService()\n self.scaled_errors = []\n self.errors = []\n self.test_errors_by_day = []\n self.train_errors_by_day = []\n self.aggregated_relative_errors = []\n self.aggregated_absolute_errors = []\n self.aggregated_percentage_errors = []\n self.epochs = 500\n self.units = 500\n\n def populate_non_existing_dates(self, df, columns):\n date_index = df.index\n dates = pd.date_range(date_index.min(), date_index.max())\n dates_df = pd.DataFrame({'drop_' + column: 0 for column in columns}, index=dates)\n merged_df = pd.merge(df, dates_df, left_index=True, right_index=True, how='outer')\n\n for (ix, row) in merged_df.iterrows():\n if ix not in df.index:\n for col in df.columns:\n merged_df.at[ix, col] = 0\n\n merged_df.drop(dates_df.columns, axis=1, inplace=True)\n return merged_df\n\n def arrange_input_sequence(self, data, n_in, n_out, input_columns, output_columns):\n if input_columns is None:\n input_columns = list(data.columns.values) # All columns are input by default\n if output_columns is None:\n output_columns = [data.columns[-1]] # Last column is output by default\n\n input_data = data[input_columns]\n output_data = data[output_columns]\n cols, names = list(), list()\n # input sequence (t-n, ... t-1)\n for i in range(n_in, 0, -1):\n cols.append(input_data.shift(i))\n names += [(str(column_name) + '(t-%d)' % i) for column_name in input_columns]\n # forecast sequence (t, t+1, ... t+n)\n for i in range(0, n_out):\n cols.append(output_data.shift(-i))\n if i == 0:\n names += [(str(column_name) + '(t)') for column_name in output_columns]\n else:\n names += [(str(column_name) + '(t+%d)' % i) for column_name in output_columns]\n\n # put it all together\n agg = pd.concat(cols, axis=1)\n agg.columns = names\n agg.dropna(inplace=True)\n\n return agg\n\n def __plot_boxplot_with_mean_labels(self, df, file_name):\n df.boxplot(grid=False, fontsize=8, return_type='axes', figsize=(13, 10))\n locs, labels = pyplot.xticks()\n days = [1, 7, 14, 30]\n labels = [\"Day {}\\n(mean: {})\".format(x[0], np.round(x[1], 2)) for x in zip(days, df.mean())]\n pyplot.xticks(locs, labels)\n pyplot.savefig(file_name + '.png', format='png')\n\n pyplot.clf()\n\n def __plot_and_add_boundaries(self, df, lower_bound, upper_bound, file_name):\n axes = df.plot(style=['-', '-'], figsize=(13, 10))\n X_points = axes.lines[0]._x\n pyplot.setp(axes.lines, lw=1.3)\n # pyplot.fill_between(X_points, lower_bound, upper_bound, color='m', alpha=0.2)\n pyplot.savefig(file_name + '.png', format='png')\n\n pyplot.clf()\n\n def calculate_and_plot_relative_errors(self, pred, actual, test_dates, output_days, repo_name, suffix=\"\"):\n filename = \"{}{}\".format(repo_name, \"_\" + suffix)\n relative_errors = np.round(np.abs(pred - actual) / (actual + 1), 3)\n\n relative_errors_df = pd.DataFrame(relative_errors,\n columns=['Day {}'.format(x + 1) for x in range(output_days)],\n index=test_dates)\n\n aggregated_relative_error = OrderedDict({\"repo\": repo_name})\n for day in relative_errors_df:\n aggregated_relative_error[\"{} Mean\".format(day)] = relative_errors_df[day].mean()\n aggregated_relative_error[\"{} Min\".format(day)] = relative_errors_df[day].min()\n aggregated_relative_error[\"{} Max\".format(day)] = relative_errors_df[day].max()\n aggregated_relative_error[\"{} Median\".format(day)] = relative_errors_df[day].median()\n aggregated_relative_error[\"{} SD\".format(day)] = relative_errors_df[day].std()\n\n self.aggregated_relative_errors.append(aggregated_relative_error)\n\n relative_errors_df.to_csv('output/Boxplots/relative_error_' + filename + '.csv')\n self.__plot_boxplot_with_mean_labels(relative_errors_df[['Day 1', 'Day 7', 'Day 14', 'Day 30']],\n 'output/Boxplots/relative_error_{}'.format(filename))\n\n absolute_errors = np.round(np.abs(pred - actual), 3)\n\n absolute_errors_df = pd.DataFrame(absolute_errors,\n columns=['Day {}'.format(x + 1) for x in range(output_days)],\n index=test_dates)\n\n aggregated_absolute_error = OrderedDict({\"repo\": repo_name})\n for day in absolute_errors_df:\n aggregated_absolute_error[\"{} Mean\".format(day)] = absolute_errors_df[day].mean()\n aggregated_absolute_error[\"{} Min\".format(day)] = absolute_errors_df[day].min()\n aggregated_absolute_error[\"{} Max\".format(day)] = absolute_errors_df[day].max()\n aggregated_absolute_error[\"{} Median\".format(day)] = absolute_errors_df[day].median()\n aggregated_absolute_error[\"{} SD\".format(day)] = absolute_errors_df[day].std()\n\n self.aggregated_absolute_errors.append(aggregated_absolute_error)\n\n absolute_errors_df.to_csv('output/Boxplots/absolute_error_' + filename + '.csv')\n self.__plot_boxplot_with_mean_labels(absolute_errors_df[['Day 1', 'Day 7', 'Day 14', 'Day 30']],\n 'output/Boxplots/absolute_error_{}'.format(filename))\n\n percentage_errors = np.round(100 * np.abs(pred - actual) / (actual + 1), 3)\n\n percentage_errors_df = pd.DataFrame(percentage_errors,\n columns=['Day {}'.format(x + 1) for x in range(output_days)],\n index=test_dates)\n\n aggregated_percentage_error = OrderedDict({\"repo\": repo_name})\n for day in percentage_errors_df:\n aggregated_percentage_error[\"{} Mean\".format(day)] = percentage_errors_df[day].mean()\n aggregated_percentage_error[\"{} Min\".format(day)] = percentage_errors_df[day].min()\n aggregated_percentage_error[\"{} Max\".format(day)] = percentage_errors_df[day].max()\n aggregated_percentage_error[\"{} Median\".format(day)] = percentage_errors_df[day].median()\n aggregated_percentage_error[\"{} SD\".format(day)] = percentage_errors_df[day].std()\n\n self.aggregated_percentage_errors.append(aggregated_percentage_error)\n\n percentage_errors_df.to_csv('output/Boxplots/percentage_error_' + filename + '.csv')\n self.__plot_boxplot_with_mean_labels(percentage_errors_df[['Day 1', 'Day 7', 'Day 14', 'Day 30']],\n 'output/Boxplots/percentage_error_{}'.format(filename))\n\n def evaluate_first_day_results(self, first_day_mae, pred, actual, test_dates, repo_name, suffix=\"\"):\n filename = \"{}{}\".format(repo_name, \"_\" + suffix)\n\n low_bound = np.array(list(map(lambda x: x - first_day_mae, pred)))\n up_bound = np.array(list(map(lambda x: x + first_day_mae, pred)))\n\n first_day_df = pd.DataFrame(np.stack((actual, pred), axis=1),\n columns=[\"Actual\", \"Pred\"], index=test_dates)\n first_day_df.to_csv('output/Daily_First_Day/mae_' + filename + '.csv', sep=\";\")\n\n self.__plot_and_add_boundaries(first_day_df, low_bound, up_bound,\n 'output/Daily_First_Day/mae_{}'.format(filename))\n\n first_days_cumulative_df = first_day_df.cumsum()\n\n first_days_cumulative_df.to_csv('output/First_Day_Cumulative/mae_' + filename + '.csv', sep=\";\")\n first_days_cumulative_df.plot()\n pyplot.savefig('output/First_Day_Cumulative/mae_' + filename + '.png')\n pyplot.clf()\n\n def evaluate_last_day_results(self, last_day_mae, pred, actual, test_dates, repo_name, suffix=\"\"):\n filename = \"{}{}\".format(repo_name, \"_\" + suffix)\n\n low_bound = np.array(list(map(lambda x: x - last_day_mae, pred)))\n up_bound = np.array(list(map(lambda x: x + last_day_mae, pred)))\n\n last_day_df = pd.DataFrame(np.stack((actual, pred), axis=1),\n columns=[\"Actual\", \"Pred\"], index=test_dates)\n last_day_df.to_csv('output/Daily_Last_Day/mae_' + filename + '.csv', sep=\";\")\n axes = last_day_df.plot(style=['-', '-'], figsize=(13, 10))\n # X_points = axes.lines[0]._x\n pyplot.setp(axes.lines, lw=1.3)\n # pyplot.fill_between(X_points, low_bound, up_bound, color='m', alpha=0.2)\n pyplot.savefig('output/Daily_Last_Day/mae_' + filename + '.png')\n\n last_days_cumulative_df = last_day_df.cumsum()\n last_days_cumulative_df.to_csv('output/Last_Day_Cumulative/mae_' + filename + '.csv', sep=\";\")\n last_days_cumulative_df.plot()\n pyplot.savefig('output/Last_Day_Cumulative/mae_' + filename + '.png')\n pyplot.clf()\n\n def evaluate_first_week_results(self, pred, actual, test_dates, repo_name, suffix=\"\"):\n filename = \"{}{}\".format(repo_name, \"_\" + suffix)\n\n first_week_df = pd.DataFrame(np.stack((actual, pred), axis=1),\n columns=[\"Actual\", \"Pred\"], index=test_dates)\n first_week_df.to_csv('output/Daily_First_Week/mae_' + filename + '.csv', sep=\";\")\n axes = first_week_df.plot(style=['-', '-'], figsize=(13, 10))\n # X_points = axes.lines[0]._x\n pyplot.setp(axes.lines, lw=1.3)\n # pyplot.fill_between(X_points, inv_predlast_low, inv_predlast_up, color='m', alpha=0.2)\n pyplot.savefig('output/Daily_First_Week/mae_' + filename + '.png')\n\n last_days_cumulative_df = first_week_df.cumsum()\n last_days_cumulative_df.to_csv('output/First_Week_Cumulative/mae_' + filename + '.csv', sep=\";\")\n last_days_cumulative_df.plot()\n pyplot.savefig('output/First_Week_Cumulative/mae_' + filename + '.png')\n pyplot.clf()\n\n def evaluate_second_week_results(self, pred, actual, test_dates, repo_name, suffix=\"\"):\n filename = \"{}{}\".format(repo_name, \"_\" + suffix)\n\n second_week_df = pd.DataFrame(np.stack((actual, pred), axis=1),\n columns=[\"Actual\", \"Pred\"], index=test_dates)\n second_week_df.to_csv('output/Daily_Second_Week/mae_' + filename + '.csv', sep=\";\")\n axes = second_week_df.plot(style=['-', '-'], figsize=(13, 10))\n # X_points = axes.lines[0]._x\n pyplot.setp(axes.lines, lw=1.3)\n # pyplot.fill_between(X_points, inv_predlast_low, inv_predlast_up, color='m', alpha=0.2)\n pyplot.savefig('output/Daily_Second_Week/mae_' + filename + '.png')\n\n last_days_cumulative_df = second_week_df.cumsum()\n last_days_cumulative_df.to_csv('output/Second_Week_Cumulative/mae_' + filename + '.csv', sep=\";\")\n last_days_cumulative_df.plot()\n pyplot.savefig('output/Second_Week_Cumulative/mae_' + filename + '.png')\n pyplot.clf()\n\n def evaluate_projection_sum_results(self, proj_sum_mae, pred, actual, test_dates, repo_name, suffix=\"\"):\n filename = \"{}{}\".format(repo_name, \"_\" + suffix)\n\n low_bound = np.array(list(map(lambda x: x - proj_sum_mae, pred)))\n up_bound = np.array(list(map(lambda x: x + proj_sum_mae, pred)))\n\n proj_sum_df = pd.DataFrame(np.stack((actual, pred), axis=1),\n columns=[\"Actual\", \"Pred\"], index=test_dates)\n\n proj_sum_df.to_csv('output/Weekly_Projection_Sum/mae_' + filename + '.csv', sep=\";\")\n axes = proj_sum_df.plot(style=['-', '-'], figsize=(13, 10))\n X_points = axes.lines[0]._x\n pyplot.setp(axes.lines, lw=1.3)\n # pyplot.fill_between(X_points, low_bound, up_bound, color='m', alpha=0.2)\n pyplot.savefig('output/Weekly_Projection_Sum/mae_' + filename + '.png')\n\n pyplot.clf()\n\n def __get_inverse_scaled(self, scaler, data, pred, n_out_variables):\n inv = np.concatenate((data, pred), axis=1)\n inv = scaler.inverse_transform(inv)\n return inv[:, -n_out_variables:]\n\n def __decompose_predictions_to_days(self, pred_matrix, columns):\n values_to_return = []\n for col in columns:\n values_to_return.append(pred_matrix[:, col])\n\n return tuple(values_to_return)\n\n def __calculate_daily_errors(self, repo_name, actual, pred):\n mae_by_day = OrderedDict({\"repo\": repo_name})\n for col in range(actual.shape[1]):\n test_mae = mean_absolute_error(pred[:, col], actual[:, col])\n mae_by_day['Day {}'.format(col + 1)] = np.round(test_mae, 3)\n\n return mae_by_day\n\n def __plot_complete_results(self, train_df, test_df, filename, indices_to_plot):\n full_actual_df = pd.concat([train_df.filter(like='actual_'),\n test_df.filter(like='actual_')], axis=0)\n pred_cols = train_df.filter(like='pred_').columns\n train_pred_df = train_df[pred_cols]\n test_pred_df = test_df[pred_cols]\n\n complete_pred_df = pd.concat([train_pred_df.add_prefix(\"train_\"), test_pred_df.add_prefix(\"test_\")], axis=0)\n\n full_df = pd.concat([full_actual_df, complete_pred_df], axis=1)\n full_df.to_csv(\"output/Predictions/{}.csv\".format(filename))\n\n for i in indices_to_plot:\n cols = ['actual_' + str(i),\n 'train_pred_' + str(i),\n 'test_pred_' + str(i)]\n\n full_df[cols].plot(figsize=(20, 16))\n pyplot.savefig(\"output/Predictions/{}_Day_{}.png\".format(filename, i))\n\n\n def forecast_stars_of_repo(self, repo_name, data_df, input_features, output_features, input_days=60, output_days=7,\n **kwargs):\n scaler = MinMaxScaler(feature_range=(0, 1))\n\n n_input_steps = input_days\n n_in_variables = len(input_features)\n n_out_variables = output_days * len(output_features)\n n_obs = n_input_steps * n_in_variables\n\n sequence_data = self.arrange_input_sequence(data_df, n_input_steps, n_out_variables,\n input_features, output_features)\n\n values = sequence_data.values\n values = scaler.fit_transform(values)\n\n train_test_ratio = 2 / 3\n if 'train_test_ratio' in kwargs:\n train_test_ratio = kwargs['train_test_ratio'] if kwargs['train_test_ratio'] < 1 else train_test_ratio\n\n training_weeks = np.math.floor(sequence_data.shape[0] * train_test_ratio)\n headers = sequence_data.columns\n indexes = sequence_data.index.values\n\n train = pd.DataFrame(values[:training_weeks, :], columns=headers, index=indexes[:training_weeks])\n test = pd.DataFrame(values[training_weeks:, :], columns=headers, index=indexes[training_weeks:])\n\n train_data = pd.DataFrame(train.values[:, :n_obs], columns=train.columns.values[:n_obs],\n index=train.index.values)\n train_y = pd.DataFrame(train.values[:, n_obs:], columns=train.columns.values[n_obs:],\n index=train.index.values)\n test_data = pd.DataFrame(test.values[:, :n_obs], columns=test.columns.values[:n_obs],\n index=test.index.values)\n test_y = pd.DataFrame(test.values[:, n_obs:], columns=test.columns.values[n_obs:], index=test.index.values)\n\n # reshape to 3-D\n train_X = train_data.values.reshape((train_data.values.shape[0], n_input_steps, n_in_variables))\n test_X = test_data.values.reshape((test_data.values.shape[0], n_input_steps, n_in_variables))\n\n early_stopping_cb = EarlyStopping(monitor='val_loss', patience=20, verbose=1)\n\n model = Sequential()\n # rnn = SimpleRNN(500, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True, activation=\"sigmoid\")\n # model.add(rnn)\n model.add(Dense(n_in_variables * n_input_steps, input_shape=(train_X.shape[1], train_X.shape[2])))\n lstm = CuDNNLSTM(self.units) # if CUDA, use CuDNNLSTM\n model.add(CuDNNLSTM(self.units, return_sequences=True)) # if CUDA, us CuDNNLSTM\n model.add(lstm)\n model.add(Dropout(0.5))\n model.add(Dense(n_out_variables))\n model.summary()\n model.compile(loss='mae', optimizer='adam', metrics=['mae', 'mse'])\n\n history = model.fit(train_X, train_y, epochs=self.epochs, batch_size=32, verbose=1, validation_split=0.10,\n shuffle=False, callbacks=[early_stopping_cb])\n\n test_pred = model.predict(test_X, verbose=1)\n train_pred = model.predict(train_X, verbose=1)\n\n test_X = test_X.reshape((test_X.shape[0], n_obs))\n train_X = train_X.reshape((train_X.shape[0], n_obs))\n\n # invert scaling back to originals\n inv_pred_test = self.__get_inverse_scaled(scaler, test_X, test_pred, n_out_variables)\n inv_test_y = self.__get_inverse_scaled(scaler, test_X, test_y, n_out_variables)\n\n inv_pred_train = self.__get_inverse_scaled(scaler, train_X, train_pred, n_out_variables)\n inv_train_y = self.__get_inverse_scaled(scaler, train_X, train_y, n_out_variables)\n\n # Export Predictions\n columns = range(1, inv_train_y.shape[1]+1)\n train_predictions = pd.concat([pd.DataFrame(inv_train_y, columns=columns).add_prefix(\"actual_\"),\n pd.DataFrame(inv_pred_train, columns=columns).add_prefix(\"pred_\")],\n axis=1)\n train_predictions.index = train.index\n train_predictions.to_csv('output/Predictions/{}_train_predictions.csv'.format(repo_name))\n\n test_predictions = pd.concat([pd.DataFrame(inv_test_y, columns=columns).add_prefix(\"actual_\"),\n pd.DataFrame(inv_pred_test, columns=columns).add_prefix(\"pred_\")], axis=1)\n test_predictions.index = test.index\n test_predictions.to_csv('output/Predictions/{}_test_predictions.csv'.format(repo_name))\n\n test_mae = mean_absolute_error(test_y, test_pred)\n test_mape = np.average(np.abs((test_y - test_pred) / test_y)) * 100\n train_mae = mean_absolute_error(train_y, train_pred)\n train_mape = np.average(np.abs((train_y - train_pred) / train_y)) * 100\n print('Test MAE Scaled: %.3f' % test_mae)\n print('Train MAE Scaled: %.3f' % train_mae)\n\n self.scaled_errors.append({\"repo\": repo_name,\n \"test_mae\": np.round(test_mae, 3), \"test_mape\": np.round(test_mape, 3),\n \"train_mae\": np.round(train_mae, 3), \"train_mape\": np.round(train_mape, 3)})\n\n test_mae = mean_absolute_error(inv_test_y, inv_pred_test)\n test_mape = np.average(np.abs((inv_test_y - inv_pred_test) / inv_test_y)) * 100\n train_mae = mean_absolute_error(inv_train_y, inv_pred_train)\n train_mape = np.average(np.abs((inv_train_y - inv_pred_train) / inv_train_y)) * 100\n\n print('Test MAE: %.3f' % test_mae)\n print('Train MAE: %.3f' % train_mae)\n\n self.errors.append({\"repo\": repo_name,\n \"test_mae\": np.round(test_mae, 3), \"test_mape\": np.round(test_mape, 3),\n \"train_mae\": np.round(train_mae, 3), \"train_mape\": np.round(train_mape, 3)})\n\n test_mae_by_day = self.__calculate_daily_errors(repo_name, inv_test_y, inv_pred_test)\n train_mae_by_day = self.__calculate_daily_errors(repo_name, inv_train_y, inv_pred_train)\n\n self.test_errors_by_day.append(test_mae_by_day)\n self.train_errors_by_day.append(train_mae_by_day)\n\n evaluation_criterias = [\n {\n \"type\": \"test\",\n \"error_values\": test_mae_by_day,\n \"index\": test.index.values,\n \"data\": {\n \"pred\": inv_pred_test,\n \"actual\": inv_test_y\n }\n },\n {\n \"type\": \"train\",\n \"error_values\": train_mae_by_day,\n \"index\": train.index.values,\n \"data\": {\n \"pred\": inv_pred_train,\n \"actual\": inv_train_y\n }\n }\n ]\n\n for criterion in evaluation_criterias:\n pred = criterion[\"data\"][\"pred\"]\n actual = criterion[\"data\"][\"actual\"]\n\n first_day_mae = criterion[\"error_values\"]['Day 1']\n last_day_mae = criterion[\"error_values\"]['Day {}'.format(output_days)]\n proj_sum_mae = sum({k: v\n for k, v in criterion[\"error_values\"].items()\n if k.startswith('Day')}.values())\n\n pred_first, pred_firstweek, pred_secondweek, pred_last = \\\n self.__decompose_predictions_to_days(pred, [0, 6, 13, -1])\n\n actual_first, actual_firstweek, actual_secondweek, actual_last = \\\n self.__decompose_predictions_to_days(actual, [0, 6, 13, -1])\n\n self.calculate_and_plot_relative_errors(pred, actual, criterion[\"index\"], output_days,\n repo_name, criterion[\"type\"])\n\n self.evaluate_first_day_results(first_day_mae, pred_first, actual_first, criterion[\"index\"],\n repo_name, criterion[\"type\"])\n\n lastday_ix = criterion[\"index\"] + np.timedelta64(n_out_variables - 1, 'D')\n self.evaluate_last_day_results(last_day_mae, pred_last, actual_last, lastday_ix, repo_name, criterion[\"type\"])\n\n first_week_ix = criterion[\"index\"] + np.timedelta64(6, 'D')\n self.evaluate_first_week_results(pred_firstweek, actual_firstweek, first_week_ix,\n repo_name, criterion[\"type\"])\n\n second_week_ix = criterion[\"index\"] + np.timedelta64(13, 'D')\n self.evaluate_second_week_results(pred_secondweek, actual_secondweek, second_week_ix,\n repo_name, criterion[\"type\"])\n\n proj_sum_pred = np.sum(pred, axis=1)\n proj_sum_y = np.sum(actual, axis=1)\n self.evaluate_projection_sum_results(proj_sum_mae, proj_sum_pred, proj_sum_y, criterion[\"index\"], repo_name, criterion[\"type\"])\n\n ''' Plot train & test values together '''\n self.__plot_complete_results(train_predictions, test_predictions, repo_name, [1, 7, 14, 30])\n\n\nif __name__ == '__main__':\n os.makedirs(output_dir, exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Predictions\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Daily_First_Day\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Daily_Last_Day\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Daily_First_Week\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Daily_Second_Week\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Weekly_Projection_Sum\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Boxplots\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"RMSErrors\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"First_Day_Cumulative\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Last_Day_Cumulative\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"First_Week_Cumulative\"), exist_ok=True)\n os.makedirs(os.path.join(output_dir, \"Second_Week_Cumulative\"), exist_ok=True)\n\n f = Forecasting()\n db_service = DatabaseService()\n\n '''\n ['d3/d3', 'matplotlib/matplotlib', 'ecomfe/echarts', 'mozilla/metrics-graphics',\n 'jacomyal/sigma.js', 'chartjs/Chart.js', 'gionkunz/chartist-js', 'Leaflet/Leaflet']\n '''\n allowed_repos = ['d3/d3', 'ecomfe/echarts', 'chartjs/Chart.js', 'gionkunz/chartist-js', 'Leaflet/Leaflet']\n for i in range(1, 28):\n repo_name = db_service.get_repo_full_name_by_id(i)\n if repo_name not in allowed_repos:\n continue\n repo_name = repo_name.replace(\"/\", \"_\")\n daily_repo_stats = DatabaseService().get_daily_repo_stats_by_day(i)\n\n data_df = pd.DataFrame(daily_repo_stats)\n data_df.drop('repo_id', axis=1, inplace=True)\n data_df['date'] = pd.to_datetime(data_df['date'])\n data_df.set_index(\"date\", inplace=True)\n input_features = data_df.columns\n input_features = pd.Index(['weighted_star_count', 'weighted_commit_count', 'weighted_opened_count',\n 'weighted_closed_issue_count', 'weighted_fork_count', 'weighted_release_count',\n 'star_count'])\n output_features = ['star_count']\n\n data_df = f.populate_non_existing_dates(data_df, columns=(input_features.union(output_features)))\n\n print(repo_name)\n f.forecast_stars_of_repo(repo_name, data_df, input_features, output_features, input_days=180, output_days=30,\n train_test_ratio=0.70)\n\n pd.DataFrame(f.errors).to_csv(\"output/Errors.csv\", sep=\";\")\n pd.DataFrame(f.scaled_errors).to_csv(\"output/Scaled_Errors.csv\", sep=\";\")\n pd.DataFrame(f.test_errors_by_day).to_csv(\"output/Test_Errors_By_Day.csv\", sep=\";\")\n pd.DataFrame(f.aggregated_relative_errors).to_csv(\"output/Aggregated_Relative_Errors.csv\", sep=\";\")\n pd.DataFrame(f.aggregated_absolute_errors).to_csv(\"output/Aggregated_Absolute_Errors.csv\", sep=\";\")\n pd.DataFrame(f.train_errors_by_day).to_csv(\"output/Train_Errors_By_Day.csv\", sep=\";\")\n","sub_path":"github_trends/analyze/multistep_forecasting.py","file_name":"multistep_forecasting.py","file_ext":"py","file_size_in_byte":26253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"78679670","text":"import json\n\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage\nfrom django.http import HttpResponse\nfrom dss.Serializer import serializer\nfrom pymysql import Error\n\n\n# 用户登录 返回数据体\ndef loginData(request, data):\n try:\n if request.user.is_authenticated():\n return getHttpResponse(0, \"ok\", data)\n\n except Error as e:\n return getHttpResponse(10000, \"Error\", e)\n\n return getHttpResponse(10001, \"User not login!\", \"用户没登录，请登录去\")\n\n\ndef getPagingData(request, infoData):\n dataObjects = infoData.objects.all().values()\n return getPagingDataForFilter(request, dataObjects)\n\n\n# 数据经过过滤\ndef getPagingDataForFilter(request, infoData):\n try:\n page = request.GET.get(\"page\")\n pageCount = request.GET.get(\"pageCount\")\n\n if not page or int(page) < 1:\n page = 1\n\n if not pageCount or int(pageCount) < 1:\n pageCount = 20\n\n # 取出所有数据（分页的形式）\n # 获取 数据表中的所有记录\n\n # 生成paginator对象,定义每页显示20条记录\n paginator = Paginator(infoData, pageCount)\n # 把当前的页码数转换成整数类型\n currentPage = int(page)\n page = int(page)\n pageCount = int(pageCount)\n number = int((currentPage - 1) * pageCount) # 每个条数据的编号（防止第二页从第一个开始）\n\n try:\n newData = paginator.page(page) # 获取当前页码的记录\n except PageNotAnInteger:\n newData = paginator.page(1) # 如果用户输入的页码不是整数时,显示第1页的内容\n except EmptyPage:\n # newData = paginator.page(paginator.num_pages) # 如果用户输入的页数不在系统的页码列表中时,显示最后一页的内容\n newData = []\n\n return getHttpTotalResponse(0, \"ok\", paginator.count, newData)\n except Error:\n return getHttpResponse(10000, \"Error\", \"\")\n\n\nclass ResultResponse(object):\n\n def __init__(self, code, message, result):\n self.code = code\n self.message = message\n self.result = result\n\n def __str__(self):\n return '{\"code\":%s,\"message\":%s,\"result\":%s}' % (self.code, self.message, self.result)\n\n\nclass ResultTotalResponse(object):\n\n def __init__(self, code, message, total, result):\n self.code = code\n self.message = message\n self.total = total\n self.result = result\n\n def __str__(self):\n return '{\"code\":%s,\"message\":%s, \"total\":%s, \"result\":%s}' % (self.code, self.message, self.total, self.result)\n\n\n# 返回 有 total 总数分页的Json结构体\ndef getHttpTotalResponse(code, message, total, word):\n return getJsonHttpResponse(ResultTotalResponse(code, message, total, word))\n\n\n# 返回 标准的Json结构体\ndef getHttpResponse(code, message, word):\n return getJsonHttpResponse(ResultResponse(code, message, word))\n\n\n# 返回 Json 的通用结构\ndef getJsonHttpResponse(resultResponse):\n return HttpResponse(json.dumps(serializer(resultResponse.__dict__), ensure_ascii=False, ),\n content_type=\"application/json;charset=utf-8\")\n","sub_path":"Novel/base_views.py","file_name":"base_views.py","file_ext":"py","file_size_in_byte":3211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"498341104","text":"import cv2\nimport numpy as np\n\n\nclass FaceDetector:\n Confidence = \"\"\n Image = \"\"\n Model = \"\"\n\n # default constructor\n #The user can define the trained model and the prototxt, or use the default files\n def __init__(self, modelPath = \"models/pre_trained_model/res10_300x300_ssd_iter_140000.caffemodel\", prototxt = \"models/pre_trained_model/deploy.prototxt.txt\", confidence = 0.5):\n self.Confidence = confidence\n self.Model = cv2.dnn.readNetFromCaffe(prototxt, modelPath)\n \n # This method receives the image in base64 format, converts it to a numpy array\n # in the load_image method and executes the prediction, returning the image with \n # the bounding box\n def detect_faces(self, imgData):\n image = self.load_image(imgData)\n\n (h, w) = image.shape[:2]\n \n imgBlob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,\n (300, 300), (104.0, 177.0, 123.0))\n\n self.Model.setInput(imgBlob)\n detections = self.Model.forward()\n\n for i in range(0, detections.shape[2]):\n confidence = detections[0, 0, i, 2]\n\n # The detections with a probability smaller than the defined value are discarded\n if confidence > self.Confidence:\n box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n (startX, startY, endX, endY) = box.astype(\"int\")\n \n # Drawing the bounding box and the prediction score\n text = \"{:.2f}%\".format(confidence * 100)\n y = startY - 10 if startY - 10 > 10 else startY + 10\n cv2.rectangle(image, (startX, startY), (endX, endY),\n (0, 0, 255), 2)\n cv2.putText(image, text, (startX, y),\n cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)\n\n return image\n\n\n def load_image(self, imgData):\n nparr = np.fromstring(imgData, np.uint8)\n \n return cv2.imdecode(nparr, cv2.IMREAD_COLOR)\n","sub_path":"FaceDetector.py","file_name":"FaceDetector.py","file_ext":"py","file_size_in_byte":2001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"511105675","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nfrom bs4 import BeautifulSoup, NavigableString\nimport urllib2\n\n\ndef strip_tags(html, invalid_tags):\n soup = BeautifulSoup(html)\n\n for tag in soup.findAll(True):\n if tag.name in invalid_tags:\n s = \"\"\n\n for c in tag.contents:\n if not isinstance(c, NavigableString):\n c = strip_tags(unicode(c), invalid_tags)\n s += unicode(c)\n\n tag.replaceWith(s)\n\n return soup\n\nurl = 'http://www.labri.fr/perso/nrougier/teaching/numpy.100/'\n\nresponse = urllib2.urlopen(url)\nhtml = response.read()\nsoup = strip_tags(html, ['span', 'tt'])\n\ndescriptions = soup.find_all('p', 'first')\ndescs = []\nfor i, text in enumerate(descriptions):\n t = map(lambda e: e.encode('utf-8'), text.contents)\n t = map(lambda e: e.replace('\\n', ' '), t)\n descs.append(''.join(t))\n\npres = soup.find_all('pre', 'literal-block')\n\nfor i, text in enumerate(pres[1:]):\n t = map(lambda x: x.encode('utf-8'), text.contents)\n print('# %02d.' % i)\n print('# %s' % descs[i])\n print(''.join(t))\n","sub_path":"numpy100/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"237880193","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .models import words\n# Create your views here.\ndef index(request):\n word_list=words.objects.order_by('-words_number')\n context = {'word_list':word_list}\n return render(request,'check_word/index.html',context)\ndef add(request):\n add_word = request.GET['word']\n if len(words.objects.filter(words_text=add_word)) > 0:\n result = words.objects.get(words_text=add_word)\n result.words_number=result.words_number+1\n result.save()\n return HttpResponse(\"existed!\")\n else:\n result=words(words_text=add_word,words_number=1)\n result.save()\n return HttpResponse(\"created!\")\n","sub_path":"chaci_project/chaci/check_word/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"334154737","text":"#!/usr/bin/env python3\n\"\"\"Module documentation goes here\n\"\"\"\n\nimport json\nimport subprocess\nimport sys\n\nclass QuickTest:\n \"\"\"Class documentation goes here\n \"\"\"\n def __init__(self):\n self._APP = \"./parse_scores\"\n self._INPUT = \\\n \"\"\"2 1234 3 98.7 87.92 77.32 2345 3 93.1 90.23 81.21\"\"\"\n\n self.test()\n\n\n def test(self):\n \"\"\"Method documentation goes here\n \"\"\"\n returncode, out = self.run()\n\n if 0 != returncode:\n print(\"ERROR: EXPECTED return 0, ACTUAL return {}.\"\n .format(returncode), file=sys.stderr)\n\n if not out:\n print(\"ERROR: No output from student app.\", file=sys.stderr) \n\n print(\"STUDENT OUTPUT\")\n print(\"------------------------\")\n print(out)\n print(\"------------------------\")\n\n try:\n student_info = json.loads(out)\n\n if student_info['max_id'] != 1234:\n print(\"ERROR: EXPECTED max_id 1234 ACTUAL : {}\".format(student_info['max_id']), file=sys.stderr)\n \n if student_info['max_score'] != 98.7:\n print(\"ERROR: EXPECTED max_score 98.7 ACTUAL : {}\".format(student_info['max_score']), file=sys.stderr)\n \n except json.decoder.JSONDecodeError as err:\n print(\"ERROR: JSON parse error: {}\".format(err), file=sys.stderr)\n\n\n def run(self):\n \"\"\"Method documentation goes here.\n \"\"\"\n with subprocess.Popen(self._APP, stderr=subprocess.PIPE,\n stdin=subprocess.PIPE, stdout=subprocess.PIPE) as proc:\n out, err = proc.communicate(input=self._INPUT.encode(\"utf-8\"))\n \n try:\n return proc.returncode, out.decode(\"utf-8\") if out else None\n except UnicodeDecodeError as e:\n return proc.returncode, \"Serious execution badness:\" + format(e)\n\n\n\nif __name__ == \"__main__\":\n test = QuickTest()\n","sub_path":"hw/hw1/test_parse_scores.py","file_name":"test_parse_scores.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"15807692","text":"import collections\n\n\nclass _Base64VLQ(object):\n def __init__(self):\n self.charToInteger = {c: i for i, c in enumerate('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=')}\n\n def decode(self, s):\n result = []\n shift = 0\n value = 0\n\n for c in s:\n try:\n integer = self.charToInteger[c]\n except (KeyError,):\n raise ValueError('Invalid character ({})'.format(c))\n\n hasContinuationBit = integer & 32\n\n integer &= 31\n value += integer << shift\n\n if hasContinuationBit:\n shift += 5\n else:\n shouldNegate = value & 1\n value >>= 1\n result.append(-value if shouldNegate else value)\n value = shift = 0\n\n return result\n\n\n_decode = _Base64VLQ().decode\n\n\nclass SourceMap(collections.UserDict):\n def __init__(self, data):\n self.data = data\n self.segments = [(line, _decode(segment)) for line, group in enumerate(self.data['mappings'].split(';')) for segment in group.split(',')]\n self.sources = None\n\n def orig2gen(self, url, line):\n try:\n source_index = self.data['sources'].index(url)\n except (ValueError,):\n raise ValueError('invalid url')\n\n orig_line = 0\n orig_col = 0\n\n prev_line = -1\n\n source_index_acc = 0\n\n for dst_line, segment in self.segments:\n if not segment:\n continue\n\n if dst_line != prev_line:\n prev_line = dst_line\n dst_col = segment[0]\n else:\n dst_col += segment[0]\n\n if len(segment) == 1:\n continue\n\n orig_line += segment[2]\n orig_col += segment[3]\n\n source_index_acc += segment[1]\n if source_index_acc != source_index:\n continue\n\n if orig_line == line:\n return dst_line, dst_col\n\n def gen2orig(self, line, col):\n orig_line = 0\n orig_col = 0\n\n prev_line = -1\n\n source_index_acc = 0\n\n for dst_line, segment in self.segments:\n if not segment:\n continue\n\n if dst_line != prev_line:\n prev_line = dst_line\n dst_col = segment[0]\n else:\n dst_col += segment[0]\n\n if len(segment) == 1:\n continue\n\n orig_line += segment[2]\n orig_col += segment[3]\n source_index_acc += segment[1]\n\n if dst_line == line and dst_col == col:\n return orig_line, orig_col, self.data['sources'][source_index_acc]\n\n\nif __name__ == '__main__':\n import json\n import time\n with open('/git-tfs/Applications/TsmWeb-REL/resources/js/stsvending.js.map', 'r', encoding='utf-8') as fh:\n data = json.load(fh)\n start = time.time()\n sm = SourceMap(data)\n print(time.time() - start)\n print(sm.orig2gen('webpack:///./src/index.tsx', 27))\n\n","sub_path":"sourcemap.py","file_name":"sourcemap.py","file_ext":"py","file_size_in_byte":3084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"149231603","text":"import matplotlib.pyplot as plt\n\n\nf = open('sim_res.txt', 'r')\nsnrs = []\nfers = []\nfor line in f:\n values = line.split(' ')\n snrs.append(float(values[0]))\n fers.append(float(values[1]))\nplt.plot(snrs, fers)\nplt.title('FER dependency on SNR')\nplt.xlabel('SNR')\nplt.ylabel('FER')\nplt.savefig('fer_curve.png')","sub_path":"LDPC_cpp/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"528727370","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Copyright (C) 2017 Mate Soos\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; version 2\n# of the License.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA\n# 02110-1301, USA.\n\nfrom __future__ import print_function\nimport sqlite3\nimport optparse\n\n\nclass Query:\n def __init__(self, dbfname):\n self.conn = sqlite3.connect(dbfname)\n self.c = self.conn.cursor()\n self.runID = self.find_runID()\n # zero out goodClauses\n self.c.execute('delete from goodClauses;')\n self.num_goods_total = 0\n self.cur_good_ids = []\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, traceback):\n self.conn.commit()\n self.conn.close()\n\n def parse_and_add_lemmas(self, lemmafname):\n with open(lemmafname, \"r\") as f:\n for line in f:\n line = line.strip().split(\" \")\n self.parse_one_line(line)\n\n # final dump\n self.dump_ids()\n\n print(\"Parsed %d number of good lemmas\" % self.num_goods_total)\n\n def parse_one_line(self, line):\n self.num_goods_total += 1\n\n # get ID\n myid = int(line[len(line)-1])\n assert myid >= 0, \"ID is always at least 0\"\n assert myid != 0, \"ID with 0 should not even be printed\"\n\n # num_used = int(line[len(line)-1])\n num_used = 0\n\n # append to cur_good_ids\n self.cur_good_ids.append((self.runID, myid, num_used))\n\n # don't run out of memory, dump early\n if num_used > 10000:\n self.dump_ids()\n\n def dump_ids(self):\n self.c.executemany(\"\"\"\n INSERT INTO goodClauses (`runID`, `clauseID`, `numUsed`)\n VALUES (?, ?, ?);\"\"\", self.cur_good_ids)\n self.cur_good_ids = []\n\n def find_runID(self):\n q = \"\"\"\n SELECT runID\n FROM startUp\n order by startTime desc\n \"\"\"\n\n runID = None\n for row in self.c.execute(q):\n if runID is not None:\n print(\"ERROR: More than one RUN in the SQL, can't add lemmas!\")\n exit(-1)\n runID = int(row[0])\n\n print(\"runID: %d\" % runID)\n return runID\n\n\nif __name__ == \"__main__\":\n usage = \"\"\"usage: %prog [options] sqlite_db lemmas\n\nIt adds lemma indices from \"lemmas\" to the SQLite database, indicating whether\nit was good or not.\"\"\"\n\n parser = optparse.OptionParser(usage=usage)\n\n parser.add_option(\"--verbose\", \"-v\", action=\"store_true\", default=False,\n dest=\"verbose\", help=\"Print more output\")\n\n (options, args) = parser.parse_args()\n\n if len(args) != 2:\n print(\"Error. Please follow usage\")\n print(usage)\n exit(-1)\n\n dbfname = args[0]\n lemmafname = args[1]\n print(\"Using sqlite3db file %s\" % dbfname)\n print(\"Using lemma file %s\" % lemmafname)\n\n with Query(dbfname) as q:\n q.parse_and_add_lemmas(lemmafname)\n\n print(\"Finished adding good lemma indicators to db %s\" % dbfname)\n exit(0)\n","sub_path":"lib/percy/cryptominisat/scripts/learn/add_lemma_ind.py","file_name":"add_lemma_ind.py","file_ext":"py","file_size_in_byte":3562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"18493503","text":"# -*- coding: utf-8 -*-\nimport subprocess\nimport sys\nimport os\nimport uuid\nimport time\nimport shutil\nfrom app import app, api, jwt\nfrom flask import send_from_directory\nfrom flask_restful import Resource, reqparse, abort, request\nfrom flask_jwt_simple import jwt_required\nfrom app.models.voice import Voice\n\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\nspch_parser = reqparse.RequestParser()\nspch_parser.add_argument('voice_id', help='voice id', location='json', required=True)\nspch_parser.add_argument('audio_format', choices=['wav','ogg','mp3'], \\\n help='wav|ogg|mp3, default=wav', location='json')\nspch_parser.add_argument('text', help='text to syntesise speech', location='json', required=True)\n\ndef convertTo(audio_format, filename):\n \"\"\" Converts a wav audio file into a provided audio format\n \n Args:\n audio_format (str): format of audio (assuming correct - ogg|mp3)\n filename (str): path of the file with its name \n (assuming has extension of wav file)\n \n Returns: \n (str): filename of the converted file\n \"\"\"\n new_filename = filename[:-3] + audio_format\n command = \"ffmpeg -i {} {}\".format(filename, new_filename)\n return_code = subprocess.call(command, shell=True)\n return new_filename\n\ndef removeOldOutputFiles():\n \"\"\" Removes an output folder if it is older than 5 minutes \"\"\"\n path = \"output/\"\n current_time = time.time() - 300\n for filename in os.listdir(path):\n full_path = os.path.join(path, filename)\n if os.stat(full_path).st_mtime < current_time:\n shutil.rmtree(full_path)\n app.logger.info(\"Directory \\\"\" + full_path + \"\\\" was deleted because it wasn't necessary anymore.\")\n \n\nclass Speech(Resource):\n decorators = [jwt_required]\n def post(self):\n \"\"\" Speech endpoint\n \n Args:\n voice_id (str): id of the voice\n audio_format (str, optional): wav|ogg|mp3 - assume is correct\n text (str): text to process\n \n Returns: \n streamed audio file of the processed speech\n \"\"\"\n args = spch_parser.parse_args()\n voice = Voice.query.filter_by(id=args['voice_id']).first()\n if voice is None:\n return {\"message\":\"Voice could not be found\"}, 400\n \n removeOldOutputFiles()\n \n # set the current path to where the speech files are for the processing\n current_path = os.getcwd()\n os.chdir(app.config['SPEECH_PATH'])\n \n app.logger.info(\"PROCESSING SPEECH:\\nVoice id: {}\\nText: {}\".format(voice.id, args['text']))\n voice_dir = \"voices/{}/{}/{}_pmdl\".format(voice.language, voice.accent, voice.id)\n output_dir = current_path + \"/output/\" + uuid.uuid4().hex[:8] \n \n command = \"echo {} | local/synthesis_voice_pitch.sh {} {}\".format(args['text'], voice_dir, output_dir)\n return_code = subprocess.call(command, shell=True)\n if return_code == 0:\n app.logger.info(\"Processing went through successfully, the audio files have been stored in \" + output_dir)\n else:\n app.logger.error(\"PROCESSING FAILED!!!\")\n os.chdir(current_path)\n return {\"message\":\"The process has failed\"}, 400\n \n # remove temporary files of the user after processing\n if 'CURRENT_USER' in app.config:\n subprocess.call(\"ls -l /tmp | grep \\\"\" + app.config['CURRENT_USER'] + \".*tmp*\\\" | awk '{print \\\"/tmp/\\\"$NF}' | xargs rm -rf\", shell=True)\n app.logger.info(\"Deleted temporary processing files\")\n \n # change the path to the path of this program to\n # continue on converting the file if necessary\n os.chdir(current_path)\n \n output_dir += \"/wav_mlpg/\"\n audio_file = \"test001.wav\"\n \n if not os.path.isfile(os.path.join(output_dir, audio_file)):\n app.logger.error(\"PROCESSING FAILED: no audio file has been found!!!\")\n return {\"message\":\"The process has failed\"}, 400\n \n # check if the audio file needs to be converted\n if args['audio_format'] is not None and args['audio_format'] != \"wav\":\n audio_file = convertTo(args['audio_format'], os.path.join(output_dir, audio_file))\n audio_file = audio_file.split('/')[len(audio_file.split('/'))-1]\n app.logger.info(\"Audio file has been converted to \" + args['audio_format'])\n \n return send_from_directory(output_dir, audio_file)\n\n\napi.add_resource(Speech, '/speech')\n","sub_path":"idlak-server/app/endpoints/speech.py","file_name":"speech.py","file_ext":"py","file_size_in_byte":4652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"34990560","text":"import sys\ninput = sys.stdin.readline\n\nN,K = map(int,input().split())\nE = [[] for _ in range(N)]\nMOD = 10**9+7\n\nfor _ in range(N-1):\n\tu,v = map(int,input().split())\n\tu -= 1\n\tv -= 1\n\tE[u].append(v)\n\tE[v].append(u)\n\ncolor = [-1 for _ in range(N)]\n\nstack = []\ncolor[0] = 1\nans = K\nfor i,v in enumerate(E[0]):\n\tstack.append(v)\n\tans *= (K-i-1)\n\tans %= MOD\n\nwhile stack:\n\tu = stack.pop()\n\tcolor[u] = 1\n\tlength = 0\n\tfor v in E[u]:\n\t\tif color[v] == -1:\n\t\t\tstack.append(v)\n\t\t\tlength += 1\n\ttmp = 1\n\tfor i in range(K-1-length,K-1):\n\t\ttmp *= i\n\t\ttmp %= MOD\n\tans *= tmp\n\tans %= MOD\n\nprint(ans%MOD)\n","sub_path":"Python_codes/p02985/s977916295.py","file_name":"s977916295.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"56206046","text":"import pandas as pd\nimport re\nimport json\nfrom pandas.io.json import json_normalize\n\nfrom PIL import Image \nimport glob\nfrom tqdm import tqdm\nimport six\n\nimport random\nimport os\n\nimport cv2\nimport numpy as np\nfrom keras.models import load_model\n\nfrom train import find_latest_checkpoint\nfrom data_utils.data_loader import get_image_array, get_segmentation_array, DATA_LOADER_SEED, class_colors , get_pairs_from_paths\nfrom models.config import IMAGE_ORDERING\nimport metrics\nfrom keras_segmentation.pretrained import pspnet_50_ADE_20K \n\nrandom.seed(DATA_LOADER_SEED)\n\n\n\njson.load((open('/media/kejitan/Blue4TB/datasets/VisualGenome/1.2/image_data.json')))\nwith open('/media/kejitan/Blue4TB/datasets/VisualGenome/1.2/image_data.json') as json_string:\n image_str = json.load(json_string)\n\nimage_str_data = json_normalize(image_str)\n\nfor i in range( 0, 100) :\n url = image_str_data.at[i, 'url']\n\n subfields = url.split('/')\n subdir = subfields[-2]\n if subdir == 'VG_100K' :\n imagedir = 'images'\n elif subdir == 'VG_100K_2' :\n imagedir = 'images2'\n image_name = subfields[-1]\n print(\"image_name = {}\", image_name)\n image_path = '/media/kejitan/Blue4TB/datasets/VisualGenome/1.2/'\n image_path = image_path+imagedir+'/'+subdir+'/'+image_name\n img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)\n resized = cv2.resize(img, (473,473), interpolation = cv2.INTER_AREA)\n cv2.imwrite('../VGdata/imgtrain100_jpg/'+image_name, resized)\n\njson_string.close() \n\nin_dir = \"../VGdata/imgtrain100_jpg/\"\nout_dir = \"../VGdata/imgtrain100_png/\"\n\nfor filename in os.listdir(in_dir):\n if filename.endswith(\".jpg\"): \n #convert png to jpg with cv2..\n im = Image.open(in_dir+filename)\n nameext = filename.split('.')\n name = nameext[0]\n rgb_im = im.convert('RGB')\n rgb_im.save(out_dir+nameext[0]+'.png')\n im.close()\n rgb_im.close()\n'''\ninp = glob.glob(os.path.join(in_dir, \"*.jpg\")) \nfor i, inp in enumerate(tqdm(inp)):\n\tif isinstance(inp, six.string_types):\n\t\tout_fname = os.path.basename(inp)\n\t\tfile, ext = os.path.splitext(out_fname)\n\t\tim = Image.open(inp)\n\t\trgb_im = im.convert('RGB')\n\t\trgb_im.save(out_dir + file + \".png\", \"PNG\")\n'''\ndef model_from_checkpoint_path(checkpoints_path):\n\n from models.all_models import model_from_name\n assert (os.path.isfile(checkpoints_path+\"_config.json\")\n ), \"Checkpoint not found.\"\n model_config = json.loads(\n open(checkpoints_path+\"_config.json\", \"r\").read())\n latest_weights = find_latest_checkpoint(checkpoints_path)\n assert (latest_weights is not None), \"Checkpoint not found.\"\n model = model_from_name[model_config['model_class']](\n model_config['n_classes'], input_height=model_config['input_height'],\n input_width=model_config['input_width'])\n print(\"loaded weights \", latest_weights)\n model.load_weights(latest_weights)\n return model\n\n\ndef predict(model=None, inp=None, out_fname=None, checkpoints_path=None):\n\n if model is None and (checkpoints_path is not None):\n model = model_from_checkpoint_path(checkpoints_path)\n\n assert (inp is not None)\n assert((type(inp) is np.ndarray) or isinstance(inp, six.string_types)\n ), \"Inupt should be the CV image or the input file name\"\n\n if isinstance(inp, six.string_types):\n inp = cv2.imread(inp)\n\n assert len(inp.shape) == 3, \"Image should be h,w,3 \"\n orininal_h = inp.shape[0]\n orininal_w = inp.shape[1]\n\n output_width = model.output_width\n output_height = model.output_height\n input_width = model.input_width\n input_height = model.input_height\n n_classes = model.n_classes\n\n x = get_image_array(inp, input_width, input_height, ordering=IMAGE_ORDERING)\n pr = model.predict(np.array([x]))[0]\n pr = pr.reshape((output_height, output_width, n_classes)).argmax(axis=2)\n\n seg_img = np.zeros((output_height, output_width, 3))\n colors = class_colors\n\n for c in range(n_classes):\n seg_img[:, :, 0] += ((pr[:, :] == c)*(colors[c][0])).astype('uint8')\n seg_img[:, :, 1] += ((pr[:, :] == c)*(colors[c][1])).astype('uint8')\n seg_img[:, :, 2] += ((pr[:, :] == c)*(colors[c][2])).astype('uint8')\n\n seg_img = cv2.resize(seg_img, (orininal_w, orininal_h))\n\n if out_fname is not None:\n cv2.imwrite(out_fname, seg_img)\n\n return pr\n\n\ndef predict_multiple(model=None, inps=None, inp_dir=None, out_dir=None,\n checkpoints_path=None):\n\n if model is None and (checkpoints_path is not None):\n model = model_from_checkpoint_path(checkpoints_path)\n\n if inps is None and (inp_dir is not None):\n inps = glob.glob(os.path.join(inp_dir, \"*.jpg\")) + glob.glob(\n os.path.join(inp_dir, \"*.png\")) + \\\n glob.glob(os.path.join(inp_dir, \"*.jpeg\"))\n\n assert type(inps) is list\n\n all_prs = []\n\n for i, inp in enumerate(tqdm(inps)):\n if out_dir is None:\n out_fname = None\n else:\n if isinstance(inp, six.string_types):\n out_fname = os.path.join(out_dir, os.path.basename(inp))\n else:\n out_fname = os.path.join(out_dir, str(i) + \".jpg\")\n\n pr = predict(model, inp, out_fname)\n all_prs.append(pr)\n\n return all_prs\n\n\n\ndef evaluate( model=None , inp_images=None , annotations=None,inp_images_dir=None ,annotations_dir=None , checkpoints_path=None ):\n \n if model is None:\n assert (checkpoints_path is not None) , \"Please provide the model or the checkpoints_path\"\n model = model_from_checkpoint_path(checkpoints_path)\n \n if inp_images is None:\n assert (inp_images_dir is not None) , \"Please privide inp_images or inp_images_dir\"\n assert (annotations_dir is not None) , \"Please privide inp_images or inp_images_dir\"\n \n paths = get_pairs_from_paths(inp_images_dir , annotations_dir )\n paths = list(zip(*paths))\n inp_images = list(paths[0])\n annotations = list(paths[1])\n \n assert type(inp_images) is list\n assert type(annotations) is list\n \n tp = np.zeros( model.n_classes )\n fp = np.zeros( model.n_classes )\n fn = np.zeros( model.n_classes )\n n_pixels = np.zeros( model.n_classes )\n \n for inp , ann in tqdm( zip( inp_images , annotations )):\n pr = predict(model , inp )\n gt = get_segmentation_array( ann , model.n_classes , model.output_width , model.output_height , no_reshape=True )\n gt = gt.argmax(-1)\n pr = pr.flatten()\n gt = gt.flatten()\n \n for cl_i in range(model.n_classes ):\n \n tp[ cl_i ] += np.sum( (pr == cl_i) * (gt == cl_i) )\n fp[ cl_i ] += np.sum( (pr == cl_i) * ((gt != cl_i)) )\n fn[ cl_i ] += np.sum( (pr != cl_i) * ((gt == cl_i)) )\n n_pixels[ cl_i ] += np.sum( gt == cl_i )\n \n cl_wise_score = tp / ( tp + fp + fn + 0.000000000001 )\n n_pixels_norm = n_pixels / np.sum(n_pixels)\n frequency_weighted_IU = np.sum(cl_wise_score*n_pixels_norm)\n mean_IU = np.mean(cl_wise_score)\n return {\"frequency_weighted_IU\":frequency_weighted_IU , \"mean_IU\":mean_IU , \"class_wise_IU\":cl_wise_score }\n\npredict_multiple(model=pspnet_50_ADE_20K(), inp_dir='../VGdata/imgtrain100_png', out_dir='../VGdata/anntrain100_png', checkpoints_path='./checkpoints')\n\n#predict_multiple(model='pspnet_50', inp_dir='VGdata/imgtrain', out_dir='VGdata/anntrain')\n\nfrom keras_segmentation.models.model_utils import transfer_weights\nfrom keras_segmentation.pretrained import pspnet_50_ADE_20K\nfrom keras_segmentation.models.pspnet import pspnet_50\n\npretrained_model = pspnet_50_ADE_20K()\n\nkeji_model1 = pspnet_50( n_classes=150 )\n\ntransfer_weights( pretrained_model , keji_model1 ) # transfer weights from pre-trained model to your model\n\nkeji_model1.train(\n train_images = \"../VGdata/imgtrain100_png/\",\n train_annotations = \"../VGdata/anntrain100_png/\",\n checkpoints_path = \"./checkpoints\" , epochs=1\n# checkpoints_path = \"./checkpoints/\" , epochs=1, verify_dataset = False\n)\n\n\n","sub_path":"keras_segmentation/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":8115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"603251549","text":"#!/usr/bin/env python\n\n\"\"\"\nExample Usage:\n\n ./list-vpcs.py\n\"\"\"\n\nfrom __future__ import print_function\n\nimport argparse\nimport boto3\nimport requests\nimport sys\n\nfrom botocore.exceptions import ClientError, EndpointConnectionError\nfrom prettytable import PrettyTable\n\nempty_string = ''\nunknown_string = 'Unknown'\nunknown_version = '0.0.0'\nunknown_int = 0\n\n\ndef main(cmdline=None):\n\n \"\"\"\n The main function. This takes the command line arguments provided and parse them.\n \"\"\"\n\n parser = make_parser()\n\n args = parser.parse_args(cmdline)\n\n if args.region:\n client = boto3.client('ec2', region_name=args.region)\n else:\n client = boto3.client('ec2')\n\n results = query_api(client, args)\n display_results(results)\n\n\ndef make_parser():\n\n \"\"\"\n This function builds up the command line parser that is used by the script.\n \"\"\"\n\n parser = argparse.ArgumentParser(description='List VPCs')\n parser.add_argument('-r', '--region', help='The aws region')\n\n return parser\n\n\ndef get_tag_value(tags, key):\n \"\"\"\n Process tags and look for a Name\n \"\"\"\n\n for tag in tags:\n if tag['Key'] == key:\n return tag['Value']\n\n return unknown_string\n\n\ndef get_tags(tags):\n \"\"\"\n Return the tags a string.\n \"\"\"\n\n tag_str = ''\n\n sorted_tags = sorted(tags, key=lambda k: k['Key'], reverse=False)\n for tag in sorted_tags:\n tag_str += '%s = %s\\n' % (tag['Key'], tag['Value'])\n tag_str = tag_str.rstrip('\\n')\n\n return tag_str\n\n\ndef query_api(client, args):\n \"\"\"\n Query the API\n \"\"\"\n\n results = []\n\n try:\n response = client.describe_vpcs()\n except EndpointConnectionError as e:\n print(\"ERROR: %s (Probably an invalid region!)\" % e)\n except Exception as e:\n print(\"Unknown error: \" + str(e))\n else:\n if 'Vpcs' in response:\n for parts in response['Vpcs']:\n tags = get_tags(parts['Tags']) if 'Tags' in parts else unknown_string\n results.append({\n 'Name': get_tag_value(parts['Tags'], 'Name') if 'Tags' in parts else unknown_string,\n 'CidrBlock': parts['CidrBlock'] if 'CidrBlock' in parts else unknown_string,\n 'VpcId': parts['VpcId'] if 'VpcId' in parts else unknown_string,\n 'State': parts['State'] if 'State' in parts else unknown_string,\n 'Tags': tags,\n })\n return results\n\n\ndef display_results(results):\n \"\"\"\n Display the results\n \"\"\"\n\n table = PrettyTable()\n\n table.field_names = [\n 'Name',\n 'CidrBlock',\n 'VpcId',\n 'State',\n 'Tags',\n ]\n\n for parts in results:\n table.add_row([\n parts['Name'],\n parts['CidrBlock'],\n parts['VpcId'],\n parts['State'],\n parts['Tags'],\n ])\n\n table.sortby = 'Name'\n print(table)\n\n\nif __name__ == \"__main__\":\n\n # This runs when the application is run from the command it grabs sys.argv[1:] which is everything after\n # the program name and passes it to main the return value from main is then used as the argument to\n # sys.exit, which you can test for in the shell. program exit codes are usually 0 for ok, and non-zero\n # for something going wrong.\n\n sys.exit(main(sys.argv[1:]))\n","sub_path":"src/ec2/list-vpcs/list-vpcs.py","file_name":"list-vpcs.py","file_ext":"py","file_size_in_byte":3599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"353097997","text":"import os, sys\nimport pygame\nfrom pygame.locals import *\nfrom random import randint\n\nglobal GREY, RED, GREEN, TEAL, PURPLE, YELLOW, WIDTH, HEIGHT, BLOCKSIZE, BLOCKMARGIN, X_CHANGE, Y_CHANGE\n\nGREY \t= ( 50, 50, 50)\nRED \t= (165, 50, 30)\nGREEN \t= ( 78, 165, 30)\nTEAL\t= ( 30, 146, 165)\nBLUE\t= ( 42, 74, 168)\nPURPLE\t= (117, 30, 165)\nYELLOW\t= (193, 218, 52)\nORANGE\t= (235, 155, 50)\nBLACK\t= ( 0, 0, 0)\nWHITE\t= (255, 255, 255)\n\nWIDTH \t\t= 620\nHEIGHT \t\t= 704\nBLOCKSIZE \t= 30\nBLOCKMARGIN = 2\nX_CHANGE\t= BLOCKSIZE + BLOCKMARGIN\nY_CHANGE\t= BLOCKSIZE + BLOCKMARGIN\nLEFTBOUND\t= 15\nRIGHTBOUND\t= LEFTBOUND+10*(BLOCKSIZE+BLOCKMARGIN)\n\nclass FallingBlock(pygame.sprite.Sprite):\n\n\ttype = ''\n\n\tdef __init__(self, type, x, y):\n\n\t\tpygame.sprite.Sprite.__init__(self)\n\n\t\tself.image = pygame.Surface([BLOCKSIZE, BLOCKSIZE])\n\t\tself.type = type\n\t\tif type == 'oBlock':\n\t\t\tself.image.fill(RED)\n\t\telif type == 'lBlock':\n\t\t\tself.image.fill(ORANGE)\n\t\telif type == 'jBlock':\n\t\t\tself.image.fill(BLUE)\n\t\telif type == 'sBlock':\n\t\t\tself.image.fill(GREEN)\n\t\telif type == 'zBlock':\n\t\t\tself.image.fill(PURPLE)\n\t\telif type == 'iBlock':\n\t\t\tself.image.fill(YELLOW)\n\t\telif type == 'tBlock':\n\t\t\tself.image.fill(TEAL)\n\n\t\tself.rect = self.image.get_rect()\n\t\tself.rect.x = x\n\t\tself.rect.y = y\n\n\tdef update(self):\n\t\tself.rect.y += Y_CHANGE\n\nclass Block(pygame.sprite.Sprite):\n\n\ttype = ''\n\n\tdef __init__(self, type, x, y):\n\n\t\tpygame.sprite.Sprite.__init__(self)\n\n\t\tself.image = pygame.Surface([BLOCKSIZE, BLOCKSIZE])\n\t\tself.type = type\n\t\tif type == 'oBlock':\n\t\t\tself.image.fill(RED)\n\t\telif type == 'lBlock':\n\t\t\tself.image.fill(ORANGE)\n\t\telif type == 'jBlock':\n\t\t\tself.image.fill(BLUE)\n\t\telif type == 'sBlock':\n\t\t\tself.image.fill(GREEN)\n\t\telif type == 'zBlock':\n\t\t\tself.image.fill(PURPLE)\n\t\telif type == 'iBlock':\n\t\t\tself.image.fill(YELLOW)\n\t\telif type == 'tBlock':\n\t\t\tself.image.fill(TEAL)\n\n\t\tself.rect = self.image.get_rect()\n\t\tself.rect.x = x\n\t\tself.rect.y = y\n\n\tdef update(self):\n\n\t\ta = 1\n\nclass TetrisMain:\n\n\t'''Handles main initialization and game creation'''\n\tdef __init__(self):\n\n\t\tpygame.init()\n\n\t\t'''Set window dimentions'''\n\t\tself.width = WIDTH\n\t\tself.height = HEIGHT\n\n\t\t'''Show the window'''\n\t\tself.screen = pygame.display.set_mode((self.width, self.height))\n\t\tpygame.display.set_caption(\"Tetris\")\n\n\tdef drawBoardSetup(self):\n\n\t\tself.screen.fill(GREY)\n\n\t\tpygame.draw.line(self.screen, WHITE, [LEFTBOUND-10, 0], [LEFTBOUND-10, HEIGHT], 10)\n\t\tpygame.draw.line(self.screen, WHITE, [RIGHTBOUND, 0], [RIGHTBOUND, HEIGHT], 10)\n\n\t\tpygame.display.flip()\n\n\tdef spawnBlock(self, activeSpritesList, type, x, y, position):\n\n\t\tif type == 'oBlock':\n\n\t\t\tfallingBlock1 = FallingBlock('oBlock', (x), (y))\n\t\t\tfallingBlock2 = FallingBlock('oBlock', (x+X_CHANGE), (y))\n\t\t\tfallingBlock3 = FallingBlock('oBlock', (x), (y+Y_CHANGE))\n\t\t\tfallingBlock4 = FallingBlock('oBlock', (x+X_CHANGE), (y+Y_CHANGE))\n\n\t\t\tactiveSpritesList.add(fallingBlock1, fallingBlock2, fallingBlock3, fallingBlock4)\n\t\t\tactiveSpritesList.position = position\t\t\n\n\t\telif type == 'lBlock':\n\n\t\t\tif position == 0:\n\n\t\t\t\tfallingBlock1 = FallingBlock('lBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('lBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('lBlock', (x), (y+2*Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('lBlock', (x+X_CHANGE), (y+2*Y_CHANGE))\n\n\t\t\telif position == 1:\n\n\t\t\t\tfallingBlock1 = FallingBlock('lBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('lBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('lBlock', (x+X_CHANGE), (y))\n\t\t\t\tfallingBlock4 = FallingBlock('lBlock', (x+2*X_CHANGE), (y))\n\n\t\t\telif position == 2:\n\n\t\t\t\tfallingBlock1 = FallingBlock('lBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('lBlock', (x+X_CHANGE), (y))\n\t\t\t\tfallingBlock3 = FallingBlock('lBlock', (x+X_CHANGE), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('lBlock', (x+X_CHANGE), (y+2*Y_CHANGE))\n\n\t\t\telse:\n\n\t\t\t\tfallingBlock1 = FallingBlock('lBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('lBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('lBlock', (x-X_CHANGE), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('lBlock', (x-2*X_CHANGE), (y+Y_CHANGE))\n\n\t\t\tactiveSpritesList.add(fallingBlock1, fallingBlock2, fallingBlock3, fallingBlock4)\n\t\t\tactiveSpritesList.position = position\n\n\t\telif type == 'jBlock':\n\n\t\t\tif position == 0:\n\n\t\t\t\tfallingBlock1 = FallingBlock('jBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('jBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('jBlock', (x), (y+2*Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('jBlock', (x-X_CHANGE), (y+2*Y_CHANGE))\n\n\t\t\telif position == 1:\n\n\t\t\t\tfallingBlock1 = FallingBlock('jBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('jBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('jBlock', (x+X_CHANGE), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('jBlock', (x+2*X_CHANGE), (y+Y_CHANGE))\n\n\t\t\telif position == 2:\n\n\t\t\t\tfallingBlock1 = FallingBlock('jBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('jBlock', (x+X_CHANGE), (y))\n\t\t\t\tfallingBlock3 = FallingBlock('jBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('jBlock', (x), (y+2*Y_CHANGE))\n\n\t\t\telse:\n\n\t\t\t\tfallingBlock1 = FallingBlock('jBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('jBlock', (x+X_CHANGE), (y))\n\t\t\t\tfallingBlock3 = FallingBlock('jBlock', (x+2*X_CHANGE), (y))\n\t\t\t\tfallingBlock4 = FallingBlock('jBlock', (x+2*X_CHANGE), (y+Y_CHANGE))\n\n\t\t\tactiveSpritesList.add(fallingBlock1, fallingBlock2, fallingBlock3, fallingBlock4)\n\t\t\tactiveSpritesList.position = position\n\n\t\telif type == 'sBlock':\n\n\t\t\tif position == 0 or position == 2:\n\n\t\t\t\tfallingBlock1 = FallingBlock('sBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('sBlock', (x-X_CHANGE), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('sBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('sBlock', (x+X_CHANGE), (y))\n\n\t\t\telse:\n\n\t\t\t\tfallingBlock1 = FallingBlock('sBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('sBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('sBlock', (x+X_CHANGE), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('sBlock', (x+X_CHANGE), (y+2*Y_CHANGE))\n\n\t\t\tactiveSpritesList.add(fallingBlock1, fallingBlock2, fallingBlock3, fallingBlock4)\n\t\t\tactiveSpritesList.position = position\n\n\t\telif type == 'zBlock':\n\n\t\t\tif position == 0 or position == 2:\n\t\t\t\n\t\t\t\tfallingBlock1 = FallingBlock('zBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('zBlock', (x+X_CHANGE), (y))\n\t\t\t\tfallingBlock3 = FallingBlock('zBlock', (x+X_CHANGE), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('zBlock', (x+2*X_CHANGE), (y+Y_CHANGE))\n\n\t\t\telse:\n\n\t\t\t\tfallingBlock1 = FallingBlock('zBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('zBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('zBlock', (x-X_CHANGE), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('zBlock', (x-X_CHANGE), (y+2*Y_CHANGE))\n\n\t\t\tactiveSpritesList.add(fallingBlock1, fallingBlock2, fallingBlock3, fallingBlock4)\n\t\t\tactiveSpritesList.position = position\n\n\t\telif type == 'iBlock':\n\n\t\t\tif position == 0 or position == 2:\n\t\t\t\n\t\t\t\tfallingBlock1 = FallingBlock('iBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('iBlock', (x), (y-Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('iBlock', (x), (y-2*Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('iBlock', (x), (y-3*Y_CHANGE))\n\n\t\t\telse:\n\t\t\t\n\t\t\t\tfallingBlock1 = FallingBlock('iBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('iBlock', (x+X_CHANGE), (y))\n\t\t\t\tfallingBlock3 = FallingBlock('iBlock', (x+2*X_CHANGE), (y))\n\t\t\t\tfallingBlock4 = FallingBlock('iBlock', (x+3*X_CHANGE), (y))\n\n\t\t\tactiveSpritesList.add(fallingBlock1, fallingBlock2, fallingBlock3, fallingBlock4)\n\t\t\tactiveSpritesList.position = position\n\n\t\telif type == 'tBlock':\n\n\t\t\tif position == 0:\n\n\t\t\t\tfallingBlock1 = FallingBlock('tBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('tBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('tBlock', (x+X_CHANGE), (y+Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('tBlock', (x-X_CHANGE), (y+Y_CHANGE))\n\n\t\t\telif position == 1:\n\n\t\t\t\tfallingBlock1 = FallingBlock('tBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('tBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('tBlock', (x), (y+2*Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('tBlock', (x+X_CHANGE), (y+Y_CHANGE))\n\n\t\t\telif position == 2:\n\n\t\t\t\tfallingBlock1 = FallingBlock('tBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('tBlock', (x+X_CHANGE), (y))\n\t\t\t\tfallingBlock3 = FallingBlock('tBlock', (x+2*X_CHANGE), (y))\n\t\t\t\tfallingBlock4 = FallingBlock('tBlock', (x+X_CHANGE), (y+Y_CHANGE))\n\n\t\t\telse:\n\n\t\t\t\tfallingBlock1 = FallingBlock('tBlock', (x), (y))\n\t\t\t\tfallingBlock2 = FallingBlock('tBlock', (x), (y+Y_CHANGE))\n\t\t\t\tfallingBlock3 = FallingBlock('tBlock', (x), (y+2*Y_CHANGE))\n\t\t\t\tfallingBlock4 = FallingBlock('tBlock', (x-X_CHANGE), (y+Y_CHANGE))\n\n\t\t\tactiveSpritesList.add(fallingBlock1, fallingBlock2, fallingBlock3, fallingBlock4)\n\t\t\tactiveSpritesList.position = position\n\n\tdef spawnRandomBlock(self, activeSpritesList, position):\n\n\t\trandomInt = randint(0,6)\n\n\t\tif randomInt == 0:\n\t\t\tself.spawnBlock(activeSpritesList, 'oBlock', (LEFTBOUND+5*X_CHANGE), 0, position)\n\t\telif randomInt == 1:\n\t\t\tself.spawnBlock(activeSpritesList, 'lBlock', (LEFTBOUND+5*X_CHANGE), 0, position)\n\t\telif randomInt == 2:\n\t\t\tself.spawnBlock(activeSpritesList, 'jBlock', (LEFTBOUND+5*X_CHANGE), 0, position)\n\t\telif randomInt == 3:\n\t\t\tself.spawnBlock(activeSpritesList, 'sBlock', (LEFTBOUND+5*X_CHANGE), 0, position)\n\t\telif randomInt == 4:\n\t\t\tself.spawnBlock(activeSpritesList, 'zBlock', (LEFTBOUND+5*X_CHANGE), 0, position)\n\t\telif randomInt == 5:\n\t\t\tself.spawnBlock(activeSpritesList, 'iBlock', (LEFTBOUND+5*X_CHANGE), 0, position)\n\t\telif randomInt == 6:\n\t\t\tself.spawnBlock(activeSpritesList, 'tBlock', (LEFTBOUND+5*X_CHANGE), 0, position)\n\n\tdef checkCollision(self, activeSpritesList, inactiveSpritesList):\n\n\t\tfor sprite in activeSpritesList.sprites():\n\n\t\t\tif (sprite.rect.y + Y_CHANGE) >= HEIGHT:\n\n\t\t\t\treturn True\n\n\t\t\tfor iSprite in inactiveSpritesList[(sprite.rect.y/Y_CHANGE + 1)].sprites():\n\n\t\t\t\tif iSprite.rect.x == sprite.rect.x:\n\n\t\t\t\t\treturn True\n\n\t\treturn False\n\n\tdef inactiveConvert(self, activeSpritesList, inactiveSpritesList):\n\n\t\tfor sprite in activeSpritesList.sprites():\n\n\t\t\tactiveSpritesList.remove(sprite)\n\n\t\t\tblock = Block(sprite.type, sprite.rect.x, sprite.rect.y)\n\t\t\tinactiveSpritesList[(sprite.rect.y)/(Y_CHANGE)].add(block)\n\n\tdef shiftLeft(self, activeSpritesList, inactiveSpritesList):\n\n\t\tfor sprite in activeSpritesList.sprites():\n\n\t\t\tif sprite.rect.x <= LEFTBOUND:\n\t\t\t\treturn\n\n\t\t\tfor iSprite in inactiveSpritesList[sprite.rect.y/Y_CHANGE]:\n\t\t\t\tif iSprite.rect.x == sprite.rect.x - X_CHANGE:\n\t\t\t\t\treturn\n\n\t\tfor sprite in activeSpritesList.sprites():\n\t\t\tsprite.rect.x -= X_CHANGE\n\n\tdef shiftRight(self, activeSpritesList, inactiveSpritesList):\n\n\t\tfor sprite in activeSpritesList.sprites():\n\n\t\t\tif sprite.rect.x >= RIGHTBOUND - X_CHANGE:\n\t\t\t\treturn\n\n\t\t\tfor iSprite in inactiveSpritesList[sprite.rect.y/Y_CHANGE]:\n\t\t\t\tif iSprite.rect.x == sprite.rect.x + X_CHANGE:\n\t\t\t\t\treturn\n\n\t\tfor sprite in activeSpritesList.sprites():\n\t\t\tsprite.rect.x += X_CHANGE\n\n\tdef rotate(self, activeSpritesList):\n\n\t\tif len(activeSpritesList) == 0:\n\t\t\treturn\n\n\t\txBlock = WIDTH - X_CHANGE\n\t\tyBlock = HEIGHT - Y_CHANGE\n\t\ttypeBlock = 'oBlock'\n\n\t\tfor sprite in activeSpritesList.sprites():\n\n\t\t\tif yBlock > sprite.rect.y:\n\t\t\t\txBlock = sprite.rect.x\n\t\t\t\tyBlock = sprite.rect.y\n\t\t\tif xBlock > sprite.rect.x:\n\t\t\t\tif yBlock >= sprite.rect.y:\n\t\t\t\t\txBlock = sprite.rect.x\n\t\t\t\t\tyBlock = sprite.rect.y\n\n\t\t\ttypeBlock = sprite.type\n\n\t\tactiveSpritesList.empty()\n\t\tself.spawnBlock(activeSpritesList, typeBlock, xBlock, yBlock, (activeSpritesList.position+1)%4)\n\n\tdef MainLoop(self):\n\n\t\tstate = 'Title'\n\t\tfont = pygame.font.SysFont('Gadget', 80, False, False)\n\t\tbackground = pygame.Surface(self.screen.get_size())\n\t\tbackground = background.convert()\n\t\tbackground.fill(GREY)\n\n\t\tactiveSpritesList = pygame.sprite.Group()\n\t\tinactiveSpritesList = []\n\t\tfor i in xrange(22):\n\t\t\tinactiveSpritesList.insert(i, pygame.sprite.Group())\n\n\t\tself.drawBoardSetup()\n\n\t\tclock = pygame.time.Clock()\n\n\t\twhile True:\n\n\t\t\t'''Event Loop'''\n\t\t\tfor event in pygame.event.get():\n\t\t\t\tif event.type == pygame.QUIT:\n\t\t\t\t\tsys.exit()\n\n\t\t\t\tif event.type == pygame.KEYDOWN:\n\n\t\t\t\t\tif event.key == pygame.K_DOWN:\n\t\t\t\t\t\tself.spawnRandomBlock(activeSpritesList, 0)\n\n\t\t\t\t\telif event.key == pygame.K_UP:\n\t\t\t\t\t\tself.rotate(activeSpritesList)\n\n\t\t\t\t\telif event.key == pygame.K_LEFT:\n\t\t\t\t\t\tself.shiftLeft(activeSpritesList, inactiveSpritesList)\n\n\t\t\t\t\telif event.key == pygame.K_RIGHT:\n\t\t\t\t\t\tself.shiftRight(activeSpritesList, inactiveSpritesList)\n\n\t\t\t'''Game Logic'''\n\t\t\tif self.checkCollision(activeSpritesList, inactiveSpritesList):\n\t\t\t\tself.inactiveConvert(activeSpritesList, inactiveSpritesList)\n\n\t\t\t'''CUD'''\n\t\t\tactiveSpritesList.clear(self.screen, background)\n\t\t\tactiveSpritesList.update()\n\t\t\tactiveSpritesList.draw(self.screen)\n\n\t\t\tfor i in xrange(22):\n\t\t\t\tinactiveSpritesList[i].clear(self.screen, background)\n\t\t\t\tinactiveSpritesList[i].update()\n\t\t\t\tinactiveSpritesList[i].draw(self.screen)\n\n\t\t\tpygame.display.flip()\n\t\t\t\n\t\t\tclock.tick(2.5)\n\n\nif __name__ == \"__main__\":\n\n\tMainWindow = TetrisMain()\n\tMainWindow.MainLoop()","sub_path":"tetrisClone.py","file_name":"tetrisClone.py","file_ext":"py","file_size_in_byte":13114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"370995788","text":"#! /usr/bin/env python3\n\n\"\"\"\nsimple pubsub system\n\"\"\"\n\nimport asyncio\n\nimport patch\n\n\nclass PubSub:\n def __init__(self):\n self.subs = {}\n\n async def publish(self, k, v):\n if k in self.subs:\n for q in self.subs[k]:\n await q.put((k, v))\n\n async def subscribe(self, k):\n try:\n q = asyncio.Queue()\n\n if k not in self.subs:\n self.subs[k] = set()\n self.subs[k].add(q)\n\n while True:\n msg = await q.get()\n if not msg:\n break\n yield msg\n except:\n raise\n finally:\n self.subs[k].remove(q)\n\n print(f'subscribe done: {k}')\n\n async def close(self):\n print('closing')\n for s in self.subs.values():\n for q in s:\n await q.put(None)\n\n\ndef main():\n try:\n loop = asyncio.get_event_loop()\n except:\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n ps = PubSub()\n\n async def talk(keys):\n for n in range(5):\n for k in keys:\n await asyncio.sleep(1)\n await ps.publish(k, n)\n\n await ps.close()\n print('talk: done')\n\n async def listen(k):\n async for x in ps.subscribe(k):\n print(k, x)\n print(f'listen {k}: done')\n\n async def mon():\n await asyncio.sleep(10)\n print('mon: done')\n\n aws = {\n talk(('junk', 'pig')),\n listen('junk'),\n listen('pig'),\n mon(),\n }\n loop.run_until_complete(asyncio.wait(aws, timeout=15))\n\n print('main: done')\n\n\nif __name__ == '__main__':\n patch.patch()\n main()\n","sub_path":"async5.py","file_name":"async5.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"161874315","text":"import collections\nimport datetime\nimport hashlib\nimport hmac\nimport json\nimport logging\nimport unittest\nimport uuid\n\nimport pytz\nimport requests\n\n\nPOSSIBLE_CURRENCIES = {'AUD', 'CAD', 'CHF', 'EUR', 'GBP', 'SEK', 'THB', 'USD'}\nMIN_STAY_VIOLATION = 'MIN_STAY_VIOLATION'\nTURNOVER_VIOLATION = 'TURNOVER_VIOLATION'\nCHECKOUT_DAY_VIOLATION = 'CHECKOUT_DAY_VIOLATION'\nDATE_RANGE_UNAVAILABLE = 'DATE_RANGE_UNAVAILABLE'\nVIOLATION_CODES = {MIN_STAY_VIOLATION, TURNOVER_VIOLATION, DATE_RANGE_UNAVAILABLE, CHECKOUT_DAY_VIOLATION}\nTURNOVER_DAYS = {'MONDAY', 'TUESDAY', 'WEDNESDAY', 'THURSDAY', 'FRIDAY', 'SATURDAY', 'SUNDAY'}\nERROR_REASONS = {'PROPERTY_INACTIVE', 'DATE_RANGE_INVALID', 'PARTY_SIZE_INVALID', 'RATE_UNAVAILABLE', 'OTHER'}\n\n# TODO: Update the following variables to match your system\nSECRET_KEY = 'abcdef0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef0123456789abcdef0123456789'\nBASE_URL = 'https://example.com'\nPATH = '/path/to/your/endpoint'\nEXTERNAL_LISTING_REFERENCE = 'abc123'\nEXTERNAL_ACCOUNT_REFERENCE = 'xyz123'\n\nCLIENT_NAME = 'tripadvisor-vr'\nTIMESTAMP_FORMAT = '%Y-%m-%dT%H:%M:%SZ'\nSIGNATURE_FORMAT = \"VRS-HMAC-SHA512 timestamp={timestamp}, client={client}, signature={signature}\"\nQUERY_STRING_FORMAT = 'guests={guests}&externalListingReference={external_listing_reference}&externalAccountReference={external_account_reference}&arrival={arrival}&departure={departure}&requestId={request_id}'\n\nlogging.basicConfig(\n format='%(asctime)s %(levelname)s %(funcName)s %(message)s',\n level=logging.INFO\n)\n\nQueryParameters = collections.namedtuple(\n 'QueryParameters',\n [\n 'guests',\n 'external_listing_reference',\n 'external_account_reference',\n 'arrival',\n 'departure',\n ]\n)\n\n# TODO: Update the following test inputs to match your system\n# Comment out a top-level key, value pair to skip that particular test\nTEST_CASES = {\n 'successful_response': QueryParameters(\n guests=7,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-07-01',\n departure='2018-08-01',\n ),\n 'min_stay_violation': QueryParameters(\n guests=16,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-08-01',\n departure='2018-08-05',\n ),\n 'date_range_unavailable_violation': QueryParameters(\n guests=17,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-08-01',\n departure='2018-08-02',\n ),\n 'turnday_violation': QueryParameters(\n guests=18,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-08-02',\n departure='2018-08-03',\n ),\n 'checkout_day_violation': QueryParameters(\n guests=19,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-08-02',\n departure='2018-08-03',\n ),\n 'property_inactive_error': QueryParameters(\n guests=10,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-08-03',\n departure='2018-08-04',\n ),\n 'date_range_invalid_error': QueryParameters(\n guests=11,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-08-03',\n departure='2018-08-04',\n ),\n 'party_size_invalid_error': QueryParameters(\n guests=12,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-08-03',\n departure='2018-08-04',\n ),\n 'other_error': QueryParameters(\n guests=13,\n external_listing_reference=EXTERNAL_LISTING_REFERENCE,\n external_account_reference=EXTERNAL_ACCOUNT_REFERENCE,\n arrival='2018-08-03',\n departure='2018-08-04',\n ),\n}\n\n\nclass AssistedRateSpecTest(unittest.TestCase):\n s = requests.Session()\n\n def _send_request(self, request):\n \"\"\"\n\n :type request: requests.PreparedRequest\n :rtype: tuple[requests.Response, dict|None]\n :return: tuple[response, response body as dict, if present]\n\n \"\"\"\n\n response = self.s.send(request)\n\n try:\n body = response.json()\n except ValueError:\n body = None\n\n if response.status_code == 200:\n self.validate_200_response(body)\n elif response.status_code == 400:\n self.validate_400_response(body)\n else:\n raise RuntimeError('Unexpected HTTP response code')\n\n return response, body\n\n def validate_200_response(self, body):\n if 'eligibility' in body:\n self.validate_eligiblity_content(body)\n \n if 'details' in body:\n self.validate_details_content(body)\n else:\n self.validate_details_content(body)\n \n \n def validate_details_content(self, body):\n self.assertIn('details', body)\n\n details = body['details']\n\n self.assertIn('baseRate', details)\n self.assertGreater(details['baseRate']['amount'], 0)\n self.assertIn(details['baseRate']['currency'], POSSIBLE_CURRENCIES)\n\n self.assertIn('tax', details)\n self.assertGreaterEqual(details['tax']['amount'], 0)\n self.assertIn(details['tax']['currency'], POSSIBLE_CURRENCIES)\n\n if 'deposit' in details:\n self.assertGreater(details['deposit']['amount'], 0)\n self.assertIn(details['deposit']['currency'], POSSIBLE_CURRENCIES)\n\n if 'customFees' in details:\n self.assertGreaterEqual(len(details['customFees']), 1)\n\n for custom_fee in details['customFees']:\n self.assertGreaterEqual(len(custom_fee['name']), 1)\n self.assertLessEqual(len(custom_fee['name']), 255)\n\n self.assertGreater(custom_fee['rate']['amount'], 0)\n self.assertIn(custom_fee['rate']['currency'], POSSIBLE_CURRENCIES)\n\n self.assertEqual(\n {'baseRate', 'tax', 'deposit', 'customFees'} | set(details.keys()),\n {'baseRate', 'tax', 'deposit', 'customFees'}\n )\n \n def validate_eligiblity_content(self, body):\n self.assertIn('tripViolations', body['eligibility'])\n self.assertEqual(set(body['eligibility'].keys()), {'tripViolations'})\n\n trip_violations = body['eligibility']['tripViolations']\n\n self.assertGreaterEqual(len(trip_violations), 1)\n self.assertEqual(\n len(trip_violations),\n len(set([trip_violation['violationCode'] for trip_violation in trip_violations]))\n )\n\n for trip_violation in trip_violations:\n self.assertIn(trip_violation['violationCode'], VIOLATION_CODES)\n\n if trip_violation['violationCode'] == TURNOVER_VIOLATION:\n self.assertEqual(set(trip_violation.keys()), {'violationCode', 'turnover'})\n self.assertIn(trip_violation['turnover'], TURNOVER_DAYS)\n elif trip_violation['violationCode'] == MIN_STAY_VIOLATION:\n self.assertEqual(set(trip_violation.keys()), {'violationCode', 'minStay'})\n self.assertIsInstance(trip_violation['minStay'], int)\n self.assertGreater(trip_violation['minStay'], 1)\n elif trip_violation['violationCode'] == CHECKOUT_DAY_VIOLATION:\n self.assertEqual(set(trip_violation.keys()), {'violationCode', 'checkoutDays'})\n self.assertIsInstance(trip_violation['checkoutDays'], list)\n self.assertGreater(len(trip_violation['checkoutDays']), 0)\n self.assertTrue(set(trip_violation['checkoutDays']).issubset(set(TURNOVER_DAYS)))\n else:\n self.assertEqual(set(trip_violation.keys()), {'violationCode'})\n\n def validate_400_response(self, body):\n self.assertIn('errors', body)\n\n errors = body['errors']\n\n self.assertGreaterEqual(len(errors), 1)\n\n for error in errors:\n self.assertEqual(\n {'reason', 'description'} | set(error.keys()),\n {'reason', 'description'}\n )\n\n self.assertIn('reason', error)\n self.assertIn(error['reason'], ERROR_REASONS)\n\n if 'description' in error:\n self.assertGreaterEqual(len(error['description']), 1)\n self.assertLessEqual(len(error['description']), 255)\n\n self.assertEqual(\n len(errors),\n len(set([e['reason'] for e in errors]))\n )\n\n @unittest.skipIf('successful_response' not in TEST_CASES, 'Test case not implemented')\n def test_successful_response(self):\n response, body = self._send_request(_get_request(TEST_CASES['successful_response']))\n\n self.assertEqual(response.status_code, 200)\n\n @unittest.skipIf('min_stay_violation' not in TEST_CASES, 'Test case not implemented')\n def test_min_stay_violation(self):\n response, body = self._send_request(_get_request(TEST_CASES['min_stay_violation']))\n\n self.assertEqual(response.status_code, 200)\n\n min_stay_violations = [\n v for v in body['eligibility']['tripViolations']\n if v['violationCode'] == 'MIN_STAY_VIOLATION'\n ]\n\n self.assertEqual(len(min_stay_violations), 1)\n\n @unittest.skipIf('date_range_unavailable_violation' not in TEST_CASES, 'Test case not implemented')\n def test_date_range_unavailable(self):\n response, body = self._send_request(_get_request(TEST_CASES['date_range_unavailable_violation']))\n\n self.assertEqual(response.status_code, 200)\n\n date_range_unavailable_violations = [\n v for v in body['eligibility']['tripViolations']\n if v['violationCode'] == 'DATE_RANGE_UNAVAILABLE'\n ]\n\n self.assertEqual(len(date_range_unavailable_violations), 1)\n\n @unittest.skipIf('turnday_violation' not in TEST_CASES, 'Test case not implemented')\n def test_turnday(self):\n response, body = self._send_request(_get_request(TEST_CASES['turnday_violation']))\n\n self.assertEqual(response.status_code, 200)\n\n turnover_violations = [\n v for v in body['eligibility']['tripViolations']\n if v['violationCode'] == 'TURNOVER_VIOLATION'\n ]\n\n self.assertEqual(len(turnover_violations), 1)\n\n @unittest.skipIf('checkout_day_violation' not in TEST_CASES, 'Test case not implemented')\n def test_checkout_day_violation(self):\n response, body = self._send_request(_get_request(TEST_CASES['checkout_day_violation']))\n\n self.assertEqual(response.status_code, 200)\n\n checkout_day_violations = [\n v for v in body['eligibility']['tripViolations']\n if v['violationCode'] == 'CHECKOUT_DAY_VIOLATION'\n ]\n\n self.assertEqual(len(checkout_day_violations), 1)\n\n @unittest.skipIf('property_inactive_error' not in TEST_CASES, 'Test case not implemented')\n def test_property_inactive_error(self):\n response, body = self._send_request(_get_request(TEST_CASES['property_inactive_error']))\n\n self.assertEqual(response.status_code, 400)\n\n self.assertIn('errors', body)\n\n property_inactive_errors = [\n v for v in body['errors']\n if v['reason'] == 'PROPERTY_INACTIVE'\n ]\n\n self.assertEqual(len(property_inactive_errors), 1)\n\n @unittest.skipIf('date_range_invalid_error' not in TEST_CASES, 'Test case not implemented')\n def test_date_range_invalid_error(self):\n response, body = self._send_request(_get_request(TEST_CASES['date_range_invalid_error']))\n\n self.assertEqual(response.status_code, 400)\n\n self.assertIn('errors', body)\n\n property_inactive_errors = [\n v for v in body['errors']\n if v['reason'] == 'DATE_RANGE_INVALID'\n ]\n\n self.assertEqual(len(property_inactive_errors), 1)\n\n @unittest.skipIf('party_size_invalid_error' not in TEST_CASES, 'Test case not implemented')\n def test_party_size_invalid_error(self):\n response, body = self._send_request(_get_request(TEST_CASES['party_size_invalid_error']))\n\n self.assertEqual(response.status_code, 400)\n\n self.assertIn('errors', body)\n\n property_inactive_errors = [\n v for v in body['errors']\n if v['reason'] == 'PARTY_SIZE_INVALID'\n ]\n\n self.assertEqual(len(property_inactive_errors), 1)\n\n @unittest.skipIf('other_error' not in TEST_CASES, 'Test case not implemented')\n def test_other_error(self):\n response, body = self._send_request(_get_request(TEST_CASES['other_error']))\n\n self.assertEqual(response.status_code, 400)\n\n self.assertIn('errors', body)\n\n property_inactive_errors = [\n v for v in body['errors']\n if v['reason'] == 'OTHER'\n ]\n\n self.assertGreaterEqual(len(property_inactive_errors), 1)\n\n\ndef _get_request(query_parameters):\n now = datetime.datetime.now(tz=pytz.UTC)\n body = ''\n\n query_string = QUERY_STRING_FORMAT.format(\n guests=query_parameters.guests,\n external_account_reference=query_parameters.external_account_reference,\n external_listing_reference=query_parameters.external_listing_reference,\n arrival=query_parameters.arrival,\n departure=query_parameters.departure,\n request_id=uuid.uuid4()\n )\n\n r = requests.Request(\n 'GET',\n \"{}{}?{}\".format(BASE_URL, PATH, query_string),\n )\n\n signature = SIGNATURE_FORMAT.format(\n timestamp=now.strftime(TIMESTAMP_FORMAT),\n client=CLIENT_NAME,\n signature=_get_signature(\n r.method,\n PATH,\n query_string,\n now,\n body,\n )\n )\n\n r.headers['Authorization'] = signature\n\n logging.info(\n \"Request {}\".format(json.dumps({\n 'url': r.url,\n 'method': r.method,\n 'path': PATH,\n 'query_string': query_string,\n 'body': body,\n 'timestamp': now.strftime(TIMESTAMP_FORMAT),\n 'client': CLIENT_NAME,\n 'secret': SECRET_KEY,\n 'signature': signature,\n }))\n )\n\n return r.prepare()\n\n\ndef _get_signature(\n method,\n path,\n query_string,\n timestamp,\n body\n):\n canonical_request = '\\n'.join([\n method,\n path,\n query_string,\n timestamp.strftime(TIMESTAMP_FORMAT),\n hashlib.sha512(body.encode('utf-8')).hexdigest()\n ])\n\n canonical_request_hash = hashlib.sha512(canonical_request.encode('utf-8')).hexdigest()\n\n return hmac.new(SECRET_KEY.encode('utf-8'), canonical_request_hash.encode('utf-8'), hashlib.sha512).hexdigest()\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"AssistedRateSpecTest.py","file_name":"AssistedRateSpecTest.py","file_ext":"py","file_size_in_byte":15247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"120917364","text":"import sys, os\n\n# The purpose of this script is to take in a directory of txt files from the UpSet tool (set directory), and output the number of variants in each of the 6 possibilities:\n# 1. 6/6 overlap\n# 2. 5/6 overlap\n# 3. 4/6 overlap\n# 4. 3/6 overlap\n# 5. 2/6 overlap\n# 6. 1/6 overlap\n\n\ndef CountLinesInFile(infilename):\n\tinfile = open(infilename,'r')\n\tlines=infile.readlines()\n\tvarCount=len(lines)\n\treturn varCount\n\n\ndef ReadDirIntoArray(dirname):\n\tfileList=os.listdir(dirname)\n\tprint(fileList)\n\treturn fileList\n\n# This function will return a set number from a filename.\n# File names look like this:\n#000011_Family5_Family6.txt \ndef DefineSetNumberFromFilename(filename):\n\tcols=filename.split('_')\n\tvector=cols[0]\n\tOneCount = 0\n\tfor i in range(len(vector)):\n\t\tif vector[i]=='1':\n\t\t\tOneCount+=1\n\tlabel = str(OneCount)\n\treturn label\n\n# Takes the list of files and prints out our dictionary\ndef ArrayOfFiles2Dict(fileList,dirName):\n\t# initialize a dictionary, where the keys are overlaps (6 - 6/6 overlaps, 5: 5/6 overlaps...etc)\n\tFileDict={'1':0,'2':0,'3':0,'4':0,'5':0,'6':0}\n\tfor each in fileList:\n\t\tif each[-3:] != 'txt':\n\t\t\tcontinue\n\t\tvarCount=CountLinesInFile('%s%s'%(dirName,each))\n\t\tlabel = DefineSetNumberFromFilename(each)\n\t\tFileDict[label]+=varCount\n\treturn FileDict\n\ndef PrintDict(FileDict):\n\tfor i in range(1,7):\n\t\tIntersect=str(i)\n\t\tprint(\"%s\\t%d\"%(Intersect,FileDict[Intersect]))\n\ndef Main():\n\tprint(\"DominantDamaging\")\n\tdirName='/Users/philliprichmond/Dropbox/Grad_School/ALD/GATK_HC/All_VarLevel_RSid_DominantDamaging_INTERVENE_UPSET/sets/'\n\tfileList = ReadDirIntoArray('/Users/philliprichmond/Dropbox/Grad_School/ALD/GATK_HC/All_VarLevel_RSid_DominantDamaging_INTERVENE_UPSET/sets/')\n\tFileDict = ArrayOfFiles2Dict(fileList,dirName)\n\tPrintDict(FileDict)\n\n\tprint(\"RecessiveDamaging\")\n\tdirName='/Users/philliprichmond/Dropbox/Grad_School/ALD/GATK_HC/All_VarLevel_RSid_RecessiveDamaging_INTERVENE_UPSET/sets/'\n\tfileList = ReadDirIntoArray('/Users/philliprichmond/Dropbox/Grad_School/ALD/GATK_HC/All_VarLevel_RSid_RecessiveDamaging_INTERVENE_UPSET/sets/')\n\tFileDict = ArrayOfFiles2Dict(fileList,dirName)\n\tPrintDict(FileDict)\n\n\tprint(\"DominantProtective\")\n\tdirName='/Users/philliprichmond/Dropbox/Grad_School/ALD/GATK_HC/All_VarLevel_RSid_DominantProtective_INTERVENE_UPSET/sets/'\t\n\tfileList = ReadDirIntoArray('/Users/philliprichmond/Dropbox/Grad_School/ALD/GATK_HC/All_VarLevel_RSid_DominantProtective_INTERVENE_UPSET/sets/')\n\tFileDict = ArrayOfFiles2Dict(fileList,dirName)\n\tPrintDict(FileDict)\n\n\tprint(\"RecessiveProtective\")\n\tdirName='/Users/philliprichmond/Dropbox/Grad_School/ALD/GATK_HC/All_VarLevel_RSid_RecessiveProtective_INTERVENE_UPSET/sets/'\n\tfileList = ReadDirIntoArray('/Users/philliprichmond/Dropbox/Grad_School/ALD/GATK_HC/All_VarLevel_RSid_RecessiveProtective_INTERVENE_UPSET/sets/')\n\tFileDict = ArrayOfFiles2Dict(fileList,dirName)\n\tPrintDict(FileDict)\n\n\n\treturn\n\nif __name__==\"__main__\":\n\tMain()\n\n\n\n\n","sub_path":"PYTHON/UpSetDir2IntersectCountsGATKHC.py","file_name":"UpSetDir2IntersectCountsGATKHC.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"171845495","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nimport time\n\n\ndef TransposeSlicesMatrix(Mat, N):\n reMat = [[None for i in range(N)] for j in range(N)]\n for i in range(N):\n for j in range(N):\n reMat[i][j] = Mat[j][i]\n\n return reMat\n\n\ndef RotateSlicesMatrix(Mat, N, direction):\n reMat = [[None for i in range(N)] for j in range(N)]\n if direction == 1:\n # clock-wise rotate the matrix\n for i in range(N):\n for j in range(N):\n reMat[i][j] = Mat[N - j - 1][i]\n elif direction == -1:\n # counter clock-wise rotate the matrix\n for i in range(N):\n for j in range(N):\n reMat[i][j] = Mat[j][N - i - 1]\n return reMat\n\n\nclass Slice(object):\n def __init__(self, vertices, clr, num, face):\n self.vertices = vertices\n self.clr = clr\n self.num = num\n self.face = face\n\n\ndef slice_move(vert_init, stepArr):\n vert = np.copy(vert_init)\n for i in range(4):\n for j in range(3):\n vert[i][j] = vert_init[i][j] + stepArr[j]\n return vert\n\n\ndef Init_Slices_Obtain(vert_init, direction, N, clr, face):\n # obtain vertices from the initial piece of every face\n Vert_Mat = [[j for j in range(N)] for i in range(N)]\n a = 0\n b = 0\n count = 1\n for i in range(N * direction[0] + 1 - direction[0]):\n for j in range(N * direction[1] + 1 - direction[1]):\n for k in range(N * direction[2] + 1 - direction[2]):\n SliceVertices = slice_move(vert_init, [i, j, k])\n Vert_Mat[a][b] = Slice(SliceVertices, clr, count, face)\n b = b + 1\n if b % N == 0:\n a = a + 1\n b = 0\n count += 1\n return Vert_Mat\n\n\ndef verticesMatExtractArr(Vert_Mat, N):\n Vert_Arr = [None] * N * N\n for i in range(N):\n for j in range(N):\n Vert_Arr[i * N + j] = Vert_Mat[i][j].vertices\n return Vert_Arr\n\n\nclass RubikFace(object):\n def __init__(self, face, N):\n vert_init = []\n direction = []\n self.face = face\n self.N = N\n self.vert_Mat = [[None] * N] * N\n self.vert_Arr = [None] * N * N\n self.clr_Mat = [[None] * N] * N\n self.clrArr = [None] * N * N\n\n transpose_mat = None\n rotate_mat = None\n clr_init = 'k'\n if face == 'F':\n vert_init = [[self.N, 0, 0], [self.N, 1, 0], [self.N, 1, 1], [self.N, 0, 1]]\n direction = [0, 1, 1]\n clr_init = 'r'\n transpose_mat = [None]\n rotate_mat = [-1]\n elif face == 'D':\n vert_init = [[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]]\n direction = [1, 1, 0]\n clr_init = 'w'\n transpose_mat = ['T']\n rotate_mat = [-1]\n elif face == 'B':\n vert_init = [[0, 0, 0], [0, 1, 0], [0, 1, 1], [0, 0, 1]]\n direction = [0, 1, 1]\n clr_init = 'm'\n transpose_mat = ['T']\n rotate_mat = [None]\n elif face == 'U':\n vert_init = [[0, 0, self.N], [1, 0, self.N], [1, 1, self.N], [0, 1, self.N]]\n direction = [1, 1, 0]\n clr_init = 'y'\n transpose_mat = [None]\n rotate_mat = [None]\n elif face == 'L':\n vert_init = [[0, 0, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0]]\n direction = [1, 0, 1]\n clr_init = 'b'\n transpose_mat = [None]\n rotate_mat = [-1]\n elif face == 'R':\n vert_init = [[0, self.N, 0], [0, self.N, 1], [1, self.N, 1], [1, self.N, 0]]\n direction = [1, 0, 1]\n clr_init = 'c'\n transpose_mat = ['T']\n rotate_mat = [1, 1]\n # obtain the vertices matrices from the initial slice, by generating step by step\n vertice_Mat = Init_Slices_Obtain(vert_init, direction, self.N, clr_init, face)\n\n # rotate the matrix that adapting the view of the screen\n for i in transpose_mat:\n if i:\n vertice_Mat = TransposeSlicesMatrix(vertice_Mat, self.N)\n for i in rotate_mat:\n if i:\n vertice_Mat = RotateSlicesMatrix(vertice_Mat, self.N, i)\n\n # set the vertices matrix to the class object\n self.vert_Mat = vertice_Mat\n self.vert_Arr = verticesMatExtractArr(self.vert_Mat, self.N)\n\n self.clrArr = [clr_init for i in range(N * N)] # initial color of the pieces\n\n def face_update(self, clrMat):\n # update the face color by the given color matrix\n # the color matrix contains numbers, we translate them here to unify the color of the rubik.\n self.clr_Mat = clrMat\n Arr = [None] * self.N * self.N\n count = 0\n for i in range(self.N):\n for j in range(self.N):\n if clrMat[i][j] % 10 == 1:\n Arr[count] = 'r'\n elif clrMat[i][j] % 10 == 2:\n Arr[count] = 'w'\n elif clrMat[i][j] % 10 == 3:\n Arr[count] = 'm'\n elif clrMat[i][j] % 10 == 4:\n Arr[count] = 'y'\n elif clrMat[i][j] % 10 == 5:\n Arr[count] = 'b'\n elif clrMat[i][j] % 10 == 6:\n Arr[count] = 'c'\n count += 1\n self.clrArr = [row[:] for row in Arr]\n\n\nclass Rubik3D(object):\n def __init__(self, N, RubikMatrix):\n # initial value\n self.RubikMatrix = RubikMatrix\n self.N = N\n self.Face_F = RubikFace('F', N)\n self.Face_D = RubikFace('D', N)\n self.Face_B = RubikFace('B', N)\n self.Face_U = RubikFace('U', N)\n self.Face_L = RubikFace('L', N)\n self.Face_R = RubikFace('R', N)\n\n self.update_clr(RubikMatrix)\n\n # 3D plot first configuration, draw the cubes, edges, initial colors.\n self.fig = plt.gcf()\n self.fig.clf()\n\n self.ax = self.fig.add_subplot(111, projection='3d')\n\n self.poly_F = Poly3DCollection(self.Face_F.vert_Arr)\n self.poly_F.set_facecolor(self.Face_F.clrArr)\n self.poly_F.set_edgecolor(\"black\")\n self.ax.add_collection3d(self.poly_F)\n\n self.poly_D = Poly3DCollection(self.Face_D.vert_Arr)\n self.poly_D.set_facecolor(self.Face_D.clrArr)\n self.poly_D.set_edgecolor(\"black\")\n self.ax.add_collection3d(self.poly_D)\n\n self.poly_B = Poly3DCollection(self.Face_B.vert_Arr)\n self.poly_B.set_facecolor(self.Face_B.clrArr)\n self.poly_B.set_edgecolor(\"black\")\n self.ax.add_collection3d(self.poly_B)\n\n self.poly_U = Poly3DCollection(self.Face_U.vert_Arr)\n self.poly_U.set_facecolor(self.Face_U.clrArr)\n self.poly_U.set_edgecolor(\"black\")\n self.ax.add_collection3d(self.poly_U)\n\n self.poly_L = Poly3DCollection(self.Face_L.vert_Arr)\n self.poly_L.set_facecolor(self.Face_L.clrArr)\n self.poly_L.set_edgecolor(\"black\")\n self.ax.add_collection3d(self.poly_L)\n\n self.poly_R = Poly3DCollection(self.Face_R.vert_Arr)\n self.poly_R.set_facecolor(self.Face_R.clrArr)\n self.poly_R.set_edgecolor(\"black\")\n self.ax.add_collection3d(self.poly_R)\n\n def update_clr(self, RubikMatrix):\n # update by initial input matrix\n self.Face_F.face_update(RubikMatrix.F)\n self.Face_D.face_update(RubikMatrix.D)\n self.Face_B.face_update(RubikMatrix.B)\n self.Face_U.face_update(RubikMatrix.U)\n self.Face_L.face_update(RubikMatrix.L)\n self.Face_R.face_update(RubikMatrix.R)\n\n def draw3DCube(self):\n self.poly_F.set_facecolor(self.Face_F.clrArr)\n self.poly_D.set_facecolor(self.Face_D.clrArr)\n self.poly_B.set_facecolor(self.Face_B.clrArr)\n self.poly_U.set_facecolor(self.Face_U.clrArr)\n self.poly_L.set_facecolor(self.Face_L.clrArr)\n self.poly_R.set_facecolor(self.Face_R.clrArr)\n\n self.ax = self.fig.gca()\n\n self.ax.set_xlim(0, self.N)\n self.ax.set_ylim(0, self.N)\n self.ax.set_zlim(0, self.N)\n # ax.set_facecolor('white')\n # plt.axis('off')\n self.ax.view_init(30, -30)\n\n self.fig.canvas.draw()\n self.fig.canvas.flush_events()\n time.sleep(0.5)\n\n return self.fig\n","sub_path":"DisplayClass.py","file_name":"DisplayClass.py","file_ext":"py","file_size_in_byte":8382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"584589841","text":"def solution(participant,completion):\n dict_part = {}\n for p in participant:\n try:\n dict_part[p] += 1\n except:\n dict_part[p] = 1\n for c in completion:\n dict_part[c] -= 1\n \n for k,v in dict_part.items():\n if v > 0:\n return k\n\np = ['marina', 'josipa', 'vinko', 'vinko', 'filipa']\nc = ['josipa', 'filipa', 'marina', 'vinko']\n\nprint(solution(p,c))","sub_path":"programmers/cantcomplete.py","file_name":"cantcomplete.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"637448589","text":"import unittest\nimport numpy as np\n\nfrom numba_munkres import munkres\n\n\nclass TestMunkres(unittest.TestCase):\n def test_munkres_finds_lowest_cost_assignment(self):\n square = np.array([\n [1, 2, 3],\n [2, 4, 6],\n [3, 6, 9]\n ])\n optimal_assignment = np.array([\n [0, 0, 1],\n [0, 1, 0],\n [1, 0, 0]\n ], dtype=bool)\n np.testing.assert_array_equal(munkres(square), optimal_assignment)\n\n def test_munkres_returns_same_shape(self):\n rectangle = np.array([\n [1, 2, 3],\n [4, 5, 6]\n ])\n self.assertEqual(munkres(rectangle).shape, rectangle.shape)\n","sub_path":"numba_munkres/test/test_munkres.py","file_name":"test_munkres.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"342493083","text":"import argparse\nimport json\nfrom pprint import pprint\n\nap = argparse.ArgumentParser()\nap.add_argument('json_file', help='path to json file to display',\n nargs='?',\n default='../data/search_result-0b844514-8ee3-11e9-9bfb-080027183839.json')\nargs = ap.parse_args()\n\n\nif __name__ == '__main__':\n data = json.load(open(args.json_file, 'r'))\n pprint(data)\n","sub_path":"talpa/scripts/show.py","file_name":"show.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"581311260","text":"import numpy as np\nimport pandas as pd\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\n\nmy_year = 2017\nmy_month = 1\nmy_day = 2\nmy_hour = 13\nmy_minute = 30\nmy_second = 15\n\nmy_date = datetime(my_year, my_month, my_day, my_hour, my_minute, my_second)\nmy_date.day # gives 2\n\nfirst_two = [datetime(2017, 1, 2), datetime(2017, 1, 3)]\n\n# Create date time index\ndatetime_index = pd.DatetimeIndex(first_two)\n\ndata = np.random.randn(2, 2)\ncols = [\"a\", \"b\"]\n\ndf = pd.DataFrame(data, datetime_index, cols)\ndf.index.argmax() # last date\ndf.index.max() # give final date\n\n########### Time Resampling ###########\n\n# Option 1\ndf_2 = pd.read_csv(\"Apple.csv\")\n\ndf_2[\"Date\"] = pd.to_datetime(df_2[\"Date\"])\ndf_2.info() # Show data types --- good for testing\ndf_2.set_index(\"Date\", inplace=True)\n\n# Option 2\ndf_3 = pd.read_csv(\"Apple.csv\", index_col=\"Date\", parse_dates=True)\n\n# Resample\ndf_2.resample(rule=\"A\").mean() # check documentation for RULE\ndf_2.resample(rule=\"Q\").mean()\n\n\ndef first_day(entry):\n return entry[0]\n\n\ndf_2.resample(rule=\"A\").apply(first_day)\n\ndf_2[\"Close\"].resample(rule=\"A\").mean().plot(kind=\"bar\", figsize=(16, 6))\nplt.show()\n\n########### Time Shifting ###########\ndf_2.shift(periods=1) # shift every data one day\ndf_2.tshift(freq=\"M\") # shift every day to the enf of month\n\n########### Pandas rolling and expanding ###########\n\ndf_2[\"Close 30 day MA\"] = df_2[\"Close\"].rolling(window=30).mean()\ndf_2[[\"Close\", \"Close 30 day MA\"]].plot(figsize=(16, 6))\n\ndf_2[\"Close\"].expanding().mean().plot(figsize=(16, 6))\n\n# Bollinger Bands\ndf_2[\"Close 20 day MA\"] = df_2[\"Close\"].rolling(window=20).mean()\ndf_2[\"Upper\"] = df_2[\"Close 20 day MA\"] + 2 * (df_2[\"Close\"].rolling(window=20).std())\ndf_2[\"Lower\"] = df_2[\"Close 20 day MA\"] - 2 * (df_2[\"Close\"].rolling(window=20).std())\ndf_2[[\"Close 20 day MA\", \"Upper\", \"Lower\", \"Close\"]].tail(150).plot(figsize=(16,6))\nplt.show()\n","sub_path":"BasicTrading/4-Pandas_TimeSeries.py","file_name":"4-Pandas_TimeSeries.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"464926981","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nimport sys\nimport os\nimport time\nimport subprocess\nimport argparse\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Retry an executable with backoff until it passes or a timeout occurs.\")\n parser.add_argument(\"--max-wait\", type=int, default=300, metavar=\"SECONDS\")\n parser.add_argument(\"--check-interval\", type=int, default=10, metavar=\"SECONDS\")\n parser.add_argument(\"args\", nargs=argparse.REMAINDER, metavar=\"CMD ARG1 ARG2 ... ARG3\")\n args = parser.parse_args()\n\n if not args.args:\n parser.error(\"Please specify an application to run.\")\n\n endtime = time.time() + args.max_wait\n output = \"\"\n returncode = 0\n while time.time() < endtime:\n try:\n output = subprocess.check_output(args.args, stderr=subprocess.STDOUT).strip()\n returncode = 0\n break\n except subprocess.CalledProcessError as e:\n output = e.output.strip()\n returncode = e.returncode\n print(\"Sleeping for {0} seconds until next check...\".format(\n args.check_interval))\n time.sleep(args.check_interval)\n\n if returncode != 0:\n print(\"Command failed for over {} seconds.\".format(args.max_wait))\n print(\"Result from last retry:\")\n print(output)\n\n return returncode\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","sub_path":"retry.py","file_name":"retry.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"196974285","text":"from .defaults import _C as cfg\n\n__all__ = [\"cfg\", \"get_config\"]\n\n\ndef get_config(config_file=None, overload_parameters=None):\n if config_file is not None:\n cfg.merge_from_file(config_file)\n\n if overload_parameters is not None:\n cfg.merge_from_list(overload_parameters)\n\n return cfg\n\n\n\n","sub_path":"mcdc/config/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"380097960","text":"import pickle\nimport numpy as np\nfrom sklearn.metrics import roc_auc_score, roc_curve, auc\nimport utils\nimport pandas as pd\nfrom multiprocessing import Pool\n\n\ndef init(args):\n global POS_ITEMS, USER_NUM, ITEM_NUM, ITEM_ARRAY, Ks, VA_LBS, TS_LBS, TS_NDCG, BATCH_SIZE\n para_dict = pickle.load(open(args.datadir + args.dataset + '/warm_dict.pkl', 'rb'))\n print('ndcg init for %s, %d user %d items' % (args.dataset, para_dict['user_num'], para_dict['item_num']))\n POS_ITEMS = para_dict['pos_nb']\n USER_NUM = para_dict['user_num']\n ITEM_NUM = para_dict['item_num']\n ITEM_ARRAY = np.array(list(range(ITEM_NUM)))\n\n val_items = para_dict['val_nb']\n VA_LBS = utils.label_neg_samp(list(val_items.keys()),\n support_dict=val_items,\n item_array=ITEM_ARRAY,\n neg_rate=5,\n exclude_dict=POS_ITEMS)\n\n test_items = para_dict['test_nb']\n TS_LBS = utils.label_auc(list(test_items.keys()),\n support_dict=test_items,\n item_array=ITEM_ARRAY,\n exclude_dict=POS_ITEMS)\n TS_NDCG = list(\n map(lambda x: utils.ndcg_sampling(x, test_items, 100, ITEM_ARRAY, POS_ITEMS), list(test_items.keys())))\n\n BATCH_SIZE = args.batch_size\n if isinstance(args.Ks, str):\n Ks = eval(args.Ks)\n else:\n Ks = args.Ks\n\n\ndef _dcg(x):\n # compute dcg_vle\n return x[0] + np.sum(x[1:] / np.log2(np.arange(2, len(x) + 1)))\n\n\ndef _auc(model, pred_func, lbs=None):\n if lbs is None:\n lbs = VA_LBS\n pred = pred_func(model, lbs)\n y_true = lbs[:, -1]\n fpr, tpr, thresholds = roc_curve(y_true, pred, pos_label=1)\n return auc(fpr, tpr)\n\n\ndef _fast_user_ndcg(pairs):\n def _simp_ndcg(lbs, pred):\n # Compute ncdg when the feeding data is one positive vs all negative\n labels = lbs[:, -1]\n pred = pred.reshape(-1)\n rerank_indices = np.argsort(pred)[::-1]\n rerank_labels = labels[rerank_indices]\n # DCG scores\n dcgs = np.array([_dcg(rerank_labels[:k]) for k in Ks])\n hrs = np.array([np.sum(rerank_labels[:k]) for k in Ks])\n return np.stack([hrs, dcgs])\n\n # compute the ndcg value of a given user\n return np.mean(np.stack(\n [_simp_ndcg(pairs[0][_], pairs[1][_]) for _ in range(pairs[0].shape[0])]), axis=0)\n\n\ndef batch_predict(model, pred_func, lbs):\n outs = []\n for begin in range(0, lbs.shape[0], BATCH_SIZE):\n end = min(begin + BATCH_SIZE, lbs.shape[0])\n batch_lbs = lbs[begin:end, :]\n outs.append(pred_func(model, batch_lbs))\n out = np.hstack(outs)\n return out\n\n\ndef _fast_ndcg(model, pred_func, blocks=None, partial=False):\n if blocks is None:\n blocks = TS_NDCG\n if partial:\n blocks = TS_NDCG[:partial]\n user_id = []\n for b in blocks:\n user_id.append((b[0][0][0], len(b)))\n b_size = blocks[0].shape[1]\n lbs = np.vstack(blocks).reshape([-1, 3])\n pred_lbs = batch_predict(model, pred_func, lbs).reshape([-1, b_size, 1])\n s = 0\n user_lbs = []\n for _ in user_id:\n user_lbs.append(pred_lbs[s:s + _[1]])\n s += _[1]\n assert s == pred_lbs.shape[0]\n ndcg_list = list(zip(blocks, user_lbs))\n with Pool(5) as pool:\n user_scores = np.stack(list(pool.map(_fast_user_ndcg, ndcg_list)))\n # user_scores = np.stack(list(map(_fast_user_ndcg, ndcg_list)))\n scores = np.mean(np.stack(user_scores), axis=0)\n d = {}\n d['hr'] = scores[0]\n d['ndcg'] = scores[1]\n d['auc'] = _auc(model, pred_func, TS_LBS)\n return d\n","sub_path":"PGR/ndcg.py","file_name":"ndcg.py","file_ext":"py","file_size_in_byte":3654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"625679880","text":"'''\ncat day_6_input.txt | pypy3 day_6_2.py\n'''\n\nimport sys\nfrom collections import Counter\n\nmessages = [message.strip() for message in sys.stdin.readlines()]\nmessage_length = len(messages[0])\ncounters = [Counter() for i in range(message_length)]\nfor message in messages:\n for i in range(message_length):\n character = message[i]\n counters[i][character] -= 1\ncorrected_message = \"\".join([counter.most_common(1)[0][0] for counter in counters])\nprint(corrected_message)\n","sub_path":"2016/day_6_2.py","file_name":"day_6_2.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"175277202","text":"import numpy as np\nimport devfx.exceptions as exps\nimport devfx.core as core\nfrom .runner import Runner\n\nclass EpisodicRunner(Runner):\n def __init__(self, agents):\n super().__init__(agents=agents)\n\n \"\"\"------------------------------------------------------------------------------------------------\n \"\"\"\n class RunningParameters(Runner.RunningParameters):\n def __init__(self, agents):\n super().__init__(agents=agents)\n self.episode_count = 0\n self.episode_number = {}\n self.randomness = 1.0\n\n @property\n def episode_count(self):\n return self.__episode_count\n\n @episode_count.setter\n def episode_count(self, value):\n self.__episode_count = value\n\n\n @property\n def episode_number(self):\n return self.__episode_number\n\n @episode_number.setter\n def episode_number(self, value):\n self.__episode_number = value\n\n\n @property\n def randomness(self):\n return self.__randomness\n\n @randomness.setter\n def randomness(self, value):\n self.__randomness = value\n\n\n def _run(self, episode_count=1, randomness=1.0):\n running_parameters = EpisodicRunner.RunningParameters(agents=super().get_agents())\n running_parameters.episode_count = episode_count\n running_parameters.episode_number = { agent : 0 for agent in super().get_agents() }\n running_parameters.randomness = randomness\n\n for agent in self.get_agents(): \n agent.set_random_non_terminal_state()\n running_parameters.episode_number[agent] = 1\n\n while(True):\n for agent in super().get_agents(): \n if(agent.is_in_non_terminal_state()):\n rv = np.random.uniform(size=1)\n if(rv <= running_parameters.randomness):\n agent.do_random_action()\n else:\n agent.do_action()\n else:\n if(running_parameters.episode_number[agent] <= (running_parameters.episode_count-1)):\n agent.set_random_non_terminal_state()\n if(running_parameters.episode_number[agent] <= running_parameters.episode_count):\n running_parameters.episode_number[agent] += 1\n\n super().running_status(source=self, signal_args=core.SignalArgs(running_parameters=running_parameters))\n\n if(all((running_parameters.episode_number[agent] > running_parameters.episode_count) for agent in super().get_agents())):\n break\n if(running_parameters.cancellation_token.is_cancellation_requested()):\n break\n\n\n","sub_path":"solution/devfx/machine_learning/rl/runners/episodic_runner.py","file_name":"episodic_runner.py","file_ext":"py","file_size_in_byte":2786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"203406437","text":"from waveapi import events\nfrom waveapi import robot\nfrom waveapi import element\nfrom waveapi import appengine_robot_runner\nimport logging\n\n# Events\n\ndef OnWaveletSelfAdded(event, wavelet):\n\t\"\"\"Invoked when the robot has been added.\"\"\"\n\tlogging.info(\"OnWaveletSelfAdded called\")\n\n\twavelet.title = \"Invite You All - Robot\"\n\twavelet.root_blip.append(\"\\nUsage: just separate your contacts, the id only, with a comma, then click the invite button.\")\n\twavelet.root_blip.append(\"\\n\\nThe following blip is an example group of you and the inviteyouall robot creator.\")\n\n\t# sample group\n\tcreator_string = wavelet.creator.__str__()\n\tcreator_id, creator_domain = creator_string.split(\"@\")\n\twavelet.reply(\"warenix, %s\" % creator_id)\n\ndef OnWaveletBlipCreated(event, wavelet):\n\n\tlogging.info(\"OnWaveletBlipCreated\")\n\n\tlogging.info(\"wavelet creator: %s\" % wavelet.creator)\n\tif wavelet.creator == \"rusty@a.gwave.com\" or wavelet.creator == \"inviteyouall@appspot.com\":\n\t\tlogging.info(\"this is not a group blip, do nothing\")\n\t\treturn\n\n\tlogging.info(\"create invite button\")\n\n\tnew_blip = event.new_blip\n\tnew_blip.append('\\n')\n\tnew_blip.append(element.Button(\"inviteButton\", \"Invite You All!\"))\n\ndef OnFormButtonClicked(event, wavelet):\n\tlogging.info(\"OnBlipSubmitted\")\n\tblip = event.blip\n\n\t# if participant_list is based on wavelet.participants,\n\t# they will also be added to the wavelet,\n\t# which is not wanted.\n\tparticipant_id_list = blip.text.split(\",\")\n\tparticipant_list = blip.contributors\n\n\tfor participant_id in participant_id_list:\n\t\tparticipant = \"%s@%s\" % (trim(participant_id), wavelet.domain)\n\t\tparticipant_list.add(participant)\n\t\tlogging.info(\"with participants: %s\" % participant)\n\n\tcreaetANewWave(wavelet, participant_list)\n\ndef creaetANewWave(wavelet, participant_list):\n\tlogging.info(\"creaetANewWave\")\n\n\tmyRobot = robot.Robot('inviteyouall',\n\t\timage_url='http://inviteyouall.appspot.com/assets/icon.png',\n\t\tprofile_url='http://code.google.com/p/warenix/wiki/inviteyouall')\n\n\tnewWave = myRobot.new_wave(wavelet.domain,\n\t\t\tparticipant_list,\n\t\t\tmessage=wavelet.serialize())\n\tnewWave.submit_with(wavelet)\n\n\tlogging.info(\"end create a new wave\")\n\ndef trim(string):\n\treturn string.strip().lstrip()\n\n\nif __name__ == '__main__':\n\tmyRobot = robot.Robot('inviteyouall',\n\t\timage_url='http://inviteyouall.appspot.com/assets/icon.png',\n\t\tprofile_url='http://code.google.com/p/warenix/wiki/inviteyouall')\n\n\tmyRobot.register_handler(events.WaveletSelfAdded, OnWaveletSelfAdded)\n\tmyRobot.register_handler(events.FormButtonClicked, OnFormButtonClicked)\n\tmyRobot.register_handler(events.WaveletBlipCreated, OnWaveletBlipCreated)\n\tappengine_robot_runner.run(myRobot)\n","sub_path":"inviteyouall/src/inviteyouall/inviteyouall.py","file_name":"inviteyouall.py","file_ext":"py","file_size_in_byte":2646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"440945445","text":"# uses OGRE's plane creation to do a variety of things\nfrom __future__ import division #prevents integer division\nimport ogre.renderer.OGRE as ogre\n\nclass PlaneMaker:\n \n def __init__(self, sceneManager):\n self.sceneManager = sceneManager\n \n def makePlane(self, textureName='Tupaia3D/PerlinNoiseHigh', meshName='planeMesh', textureTileNum=(1,1), objName='myPlane', centerPoint=(0,0,0), size=(1,1), normalVector=(0,1,0), upVector=(0,0,1)):\n meshManager = ogre.MeshManager.getSingleton()\n plane = ogre.Plane(normalVector, 0)\n meshManager.createPlane(meshName, 'General', plane,\n size[0], size[1], 20, 20, True, 1, textureTileNum[0], textureTileNum[1], upVector)\n \n ent = self.sceneManager.createEntity(objName, meshName)\n node = self.sceneManager.getRootSceneNode().createChildSceneNode()\n node.translate(centerPoint)\n node.attachObject(ent)\n ent.setMaterialName(textureName)\n ent.castShadows = False\n \n def makeCube(self, textureNames=['Tupaia3D/PerlinNoiseHigh']*6, textureTileNums=[(1,1)]*6, objName='myCube', centerPoint=(0,0,0), size=(1,1), normalsPointOut=True):\n \n planeNormals = [\n (0,1,0), #floor\n (0,-1,0), #ceiling\n (1,0,0), #left\n (-1,0,0), #right\n (0,0,-1), #front wall\n (0, 0, 1)] #back wall\n \n if normalsPointOut:\n pn = []\n for i in range(0,len(planeNormals)):\n p = (-planeNormals[i][0],-planeNormals[i][1],-planeNormals[i][2])\n pn.append(p)\n planeNormals = pn\n \n #General rule: textures point upwards or backwards\n upVectors = [\n (0, 0, -1), #floor\n (0, 0, -1), #ceiling\n (0, 0, -1), #left\n (0, 0, -1), #right\n (0, 1, 0), #front wall\n (0, 1, 0)] #back wall\n \n translates = [\n [centerPoint[0],centerPoint[1]-size[1]/2,centerPoint[2]], #floor\n [centerPoint[0],centerPoint[1]+size[1]/2,centerPoint[2]], #ceiling\n [centerPoint[0]-size[0]/2,centerPoint[1],centerPoint[2]], #left\n [centerPoint[0]+size[0]/2,centerPoint[1],centerPoint[2]], #right\n [centerPoint[0],centerPoint[1],centerPoint[2]+size[2]/2], #front wall\n [centerPoint[0],centerPoint[1],centerPoint[2]-size[2]/2] #back wall\n ]\n \n planeSizes = [\n [size[0],size[2]], #floor\n [size[0],size[2]], #ceiling\n [size[1],size[2]], #left\n [size[1],size[2]], #right\n [size[0],size[1]], #front wall\n [size[0],size[1]] #back wall\n ]\n \n meshManager = ogre.MeshManager.getSingleton()\n for i in range(0,len(planeNormals)):\n plane = ogre.Plane(planeNormals[i], 0)\n meshManager.createPlane(objName + str(i), 'General', plane,\n planeSizes[i][0], planeSizes[i][1], 20, 20, True, 1, textureTileNums[i][0], textureTileNums[i][1], upVectors[i])\n ent = self.sceneManager.createEntity(objName + 'Entity' + str(i), objName + str(i))\n node = self.sceneManager.getRootSceneNode().createChildSceneNode()\n node.translate(translates[i])\n node.attachObject(ent)\n ent.setMaterialName(textureNames[i])\n ent.castShadows = False\n ","sub_path":"tupaia3D/ogre/PlaneMaker.py","file_name":"PlaneMaker.py","file_ext":"py","file_size_in_byte":3438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"380474312","text":"from pyquery import PyQuery as pq\nimport json\nfrom pprint import pprint\nfrom config import configuration as cfg\nfrom playerinfo import playerinfo as playerinfo\n\n\"\"\"\nThis function processes the dictionary of match data\nby adding, removing and modifying various keys and values.\n\"\"\"\ndef processGame(jsonmatchdata):\n matchnumber = list(jsonmatchdata)[0]\n matchdoc = jsonmatchdata[matchnumber]\n\n if cfg['dev']['log']:\n print('gameID: ' + str(matchnumber))\n if int(matchnumber) in cfg['gamematchnumbers']['na']:\n region = 'na'\n elif int(matchnumber) in cfg['gamematchnumbers']['eu']:\n region = 'eu'\n #Game EG vs. C9 in between 1882 and 1884 got assigned match number 2156 by Riot\n #these lines catch the chronological error and corrects it for storing in the database.\n elif int(matchnumber) == 2156:\n matchnumber = 1883\n region = 'na'\n else:\n print(matchnumber)\n return 'Error: Match number not recognized'\n\n winteamname = ''\n winteamid = ''\n loseteamname = ''\n loseteamid = ''\n for teamid, team in matchdoc.items():\n for playerid, player in team.items():\n \"\"\"rename some key fields and store match information. region, matchid, etc.\"\"\"\n\n player['playername'] = player.pop('player field')\n player['teamid'] = teamid\n player['matchid'] = int(matchnumber)\n player['region'] = region\n player['playerid'] = playerid\n player['teamid'] = teamid\n player['teamname'] = playerinfo[player['playername']][1]\n player['role'] = playerinfo[player['playername']][0]\n\n \"\"\"determine which team won and which team lost. store that info.\"\"\"\n if player['win'] == 1:\n winteamname = player['teamname']\n winteamid = teamid\n elif player['win'] == 0:\n loseteamname = player['teamname']\n loseteamid = teamid\n \n \"\"\"switch key names to playername instead of playerid. assign appropriate team.\"\"\"\n for playerid, player in team.items():\n playername = player['playername']\n team[playername] = team.pop(playerid)\n matchdoc['playerlist'] = []\n matchdoc['winteamname'] = winteamname\n matchdoc['winteamid'] = winteamid\n matchdoc['loseteamname'] = loseteamname\n matchdoc['loseteamid'] = loseteamid\n\n matchdoc['players'] = matchdoc.pop(matchdoc['winteamid'])\n for playername, player in matchdoc.pop(matchdoc['loseteamid']).items():\n matchdoc['players'][playername] = player\n for playername, player in matchdoc['players'].items():\n matchdoc['playerlist'].append(playername)\n matchdoc['gameID'] = int(matchnumber)\n matchdoc['region'] = region\n matchdoc['scored'] = 0\n matchdoc['analyzed'] = 0\n matchdoc['statistics'] = {}\n\n #for redundancy\n for playername in list(matchdoc['playerlist']):\n if playername not in list(playerinfo):\n pid = playername\n pname = matchdoc['players'][pid]['playername']\n matchdoc['players'][pname] = matchdoc['players'].pop(pid)\n matchdoc['playerlist'].remove(pid)\n matchdoc['playerlist'].append(pname)\n\n\n return matchdoc\n\ndef retrieveGame(url):\n page = pq(url=url)\n if cfg['dev']['log']:\n print(url)\n parsedpage = page(\"script:contains('jQuery.extend')\").html()[31:-2]\n jsonmatchdata = json.loads(parsedpage)[\"esportsDataDump\"][\"matchDataDump\"]\n return jsonmatchdata\n\n\"\"\"\nRuns necessary functions to retrieve, parse, and format the match data.\nReturns a dictionary with match data.\n\"\"\"\ndef retrieveandprocessGame(urlmatchnumber):\n if cfg['dev']['log']:\n print('URLmatch: ' + str(urlmatchnumber))\n url = cfg['retrieveurl'] + str(urlmatchnumber)\n jsonmatchdata = retrieveGame(url)\n matchdoc = processGame(jsonmatchdata)\n # print(matchdoc)\n matchdoc['URLmatchnumber'] = urlmatchnumber\n return matchdoc\n\n\"\"\"\nTests\n\"\"\"\ndef _testNA():\n for urlmatchnumber in cfg['urlmatchnumbers']['na']:\n retrieveandprocessGame(urlmatchnumber)\ndef _testEU():\n for urlmatchnumber in cfg['urlmatchnumbers']['eu']:\n retrieveandprocessGame(urlmatchnumber)\n\nif cfg['dev']['scraper']:\n try:\n _testNA()\n except KeyError:\n print('No more games NA')\n\n try:\n _testEU()\n except KeyError:\n print('No more games EU')\n\n#For singular test cases. Accepts urlmatchnumber as argument. Refer to config.py\n# pprint(retrieveandprocessGame(1693))\n\n","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":4614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"487410414","text":"import itertools as it\nfrom collections import defaultdict\nfrom warnings import warn\nimport logging\n\nimport numpy as np\n\nfrom wepy.resampling.decisions.decision import NoDecision\n\nclass ResamplerError(Exception):\n \"\"\" \"\"\"\n pass\n\nclass Resampler():\n \"\"\" \"\"\"\n # data for resampling performed (continual)\n RESAMPLING_FIELDS = ()\n \"\"\"String names of fields produced in this record group.\n\n Resampling records are typically used to report on the details of\n how walkers are resampled for a given resampling step.\n\n Warning\n -------\n\n This is a critical function of many other components of the wepy\n framework and probably shouldn't be altered by most developers.\n\n Thi is where the information about cloning and merging of walkers\n is given. Seeing as this is a most of the value proposition of\n wepy as a tool getting rid of it will render most of the framework\n useless.\n\n But sticking to the 'loosely coupled, tightly integrated' mantra\n you are free to modify these fields. This would be useful for\n implementing resampling strategies that do not follow basic\n cloning and merging. Just beware, that most of the lineage based\n analysis will be broken without implementing a new Decision class.\n\n \"\"\"\n\n RESAMPLING_SHAPES = ()\n \"\"\"Numpy-style shapes of all fields produced in records.\n\n There should be the same number of elements as there are in the\n corresponding 'FIELDS' class constant.\n\n Each entry should either be:\n\n A. A tuple of ints that specify the shape of the field element\n array.\n\n B. Ellipsis, indicating that the field is variable length and\n limited to being a rank one array (e.g. (3,) or (1,)).\n\n C. None, indicating that the first instance of this field will not\n be known until runtime. Any field that is returned by a record\n producing method will automatically interpreted as None if not\n specified here.\n\n Note that the shapes must be tuple and not simple integers for rank-1\n arrays.\n\n Option B will result in the special h5py datatype 'vlen' and\n should not be used for large datasets for efficiency reasons.\n\n \"\"\"\n\n RESAMPLING_DTYPES = ()\n \"\"\"Specifies the numpy dtypes to be used for records.\n\n There should be the same number of elements as there are in the\n corresponding 'FIELDS' class constant.\n\n Each entry should either be:\n\n A. A `numpy.dtype` object.\n\n D. None, indicating that the first instance of this field will not\n be known until runtime. Any field that is returned by a record\n producing method will automatically interpreted as None if not\n specified here.\n\n \"\"\"\n\n RESAMPLING_RECORD_FIELDS = ()\n \"\"\"Optional, names of fields to be selected for truncated\n representation of the record group.\n\n These entries should be strings that are previously contained in\n the 'FIELDS' class constant.\n\n While strictly no constraints on to which fields can be added here\n you should only choose those fields whose features could fit into\n a plaintext csv or similar format.\n\n \"\"\"\n\n # changes to the state of the resampler (sporadic)\n RESAMPLER_FIELDS = ()\n \"\"\"String names of fields produced in this record group.\n\n Resampler records are typically used to report on changes in the\n state of the resampler.\n\n Notes\n -----\n\n These fields are not critical to the proper functioning of the\n rest of the wepy framework and can be modified freely.\n\n However, reporters specific to this resampler probably will make\n use of these records.\n\n \"\"\"\n\n RESAMPLER_SHAPES = ()\n \"\"\"Numpy-style shapes of all fields produced in records.\n\n There should be the same number of elements as there are in the\n corresponding 'FIELDS' class constant.\n\n Each entry should either be:\n\n A. A tuple of ints that specify the shape of the field element\n array.\n\n B. Ellipsis, indicating that the field is variable length and\n limited to being a rank one array (e.g. (3,) or (1,)).\n\n C. None, indicating that the first instance of this field will not\n be known until runtime. Any field that is returned by a record\n producing method will automatically interpreted as None if not\n specified here.\n\n Note that the shapes must be tuple and not simple integers for rank-1\n arrays.\n\n Option B will result in the special h5py datatype 'vlen' and\n should not be used for large datasets for efficiency reasons.\n\n \"\"\"\n\n RESAMPLER_DTYPES = ()\n \"\"\"Specifies the numpy dtypes to be used for records.\n\n There should be the same number of elements as there are in the\n corresponding 'FIELDS' class constant.\n\n Each entry should either be:\n\n A. A `numpy.dtype` object.\n\n D. None, indicating that the first instance of this field will not\n be known until runtime. Any field that is returned by a record\n producing method will automatically interpreted as None if not\n specified here.\n\n \"\"\"\n\n RESAMPLER_RECORD_FIELDS = ()\n \"\"\"Optional, names of fields to be selected for truncated\n representation of the record group.\n\n These entries should be strings that are previously contained in\n the 'FIELDS' class constant.\n\n While strictly no constraints on to which fields can be added here\n you should only choose those fields whose features could fit into\n a plaintext csv or similar format.\n\n \"\"\"\n\n\n # valid debug modes\n DEBUG_MODES = (True, False,)\n\n def __init__(self, min_num_walkers=Ellipsis,\n max_num_walkers=Ellipsis,\n debug_mode=False):\n\n # the min and max number of walkers that can be generated in\n # resampling.\n\n # Ellipsis means to keep bound it by the number of\n # walkers given to the resample method (e.g. if\n # max_num_walkers == Ellipsis and min_num_walkers == 5 and\n # resample is given 10 then the max will be set to 10 for that\n # resampling and the min will always be 5. If both are\n # Ellipsis then the number of walkers is kept the same)\n\n # None means that there is no bound, e.g. max_num_walkers ==\n # None then there is no maximum number of walkers, however a\n # min_num_walkers of None in practice is 1 since there must\n # always be at least 1 walker\n\n if min_num_walkers not in (Ellipsis, None):\n if min_num_walkers < 1:\n raise ResamplerError(\"The minimum number of walkers should be at least 1\")\n\n self._min_num_walkers = min_num_walkers\n self._max_num_walkers = max_num_walkers\n\n # initialize debug mode\n self._debug_mode = False\n\n # set them to the args given\n self.set_debug_mode(debug_mode)\n\n def resampling_field_names(self):\n \"\"\"Access the class level FIELDS constant for this record group.\"\"\"\n return self.RESAMPLING_FIELDS\n\n def resampling_field_shapes(self):\n \"\"\"Access the class level SHAPES constant for this record group.\"\"\"\n return self.RESAMPLING_SHAPES\n\n def resampling_field_dtypes(self):\n \"\"\"Access the class level DTYPES constant for this record group.\"\"\"\n return self.RESAMPLING_DTYPES\n\n def resampling_fields(self):\n \"\"\"Returns a list of zipped field specs.\n\n Returns\n -------\n\n record_specs : list of tuple\n A list of the specs for each field, a spec is a tuple of\n type (field_name, shape_spec, dtype_spec)\n \"\"\"\n return list(zip(self.resampling_field_names(),\n self.resampling_field_shapes(),\n self.resampling_field_dtypes()))\n\n def resampling_record_field_names(self):\n \"\"\"Access the class level RECORD_FIELDS constant for this record group.\"\"\"\n return self.RESAMPLING_RECORD_FIELDS\n\n def resampler_field_names(self):\n \"\"\"Access the class level FIELDS constant for this record group.\"\"\"\n return self.RESAMPLER_FIELDS\n\n def resampler_field_shapes(self):\n \"\"\"Access the class level SHAPES constant for this record group.\"\"\"\n return self.RESAMPLER_SHAPES\n\n def resampler_field_dtypes(self):\n \"\"\"Access the class level DTYPES constant for this record group.\"\"\"\n return self.RESAMPLER_DTYPES\n\n def resampler_fields(self):\n \"\"\"Returns a list of zipped field specs.\n\n Returns\n -------\n\n record_specs : list of tuple\n A list of the specs for each field, a spec is a tuple of\n type (field_name, shape_spec, dtype_spec)\n \"\"\"\n return list(zip(self.resampler_field_names(),\n self.resampler_field_shapes(),\n self.resampler_field_dtypes()))\n\n def resampler_record_field_names(self):\n \"\"\"Access the class level RECORD_FIELDS constant for this record group.\"\"\"\n return self.RESAMPLER_RECORD_FIELDS\n\n @property\n def is_debug_on(self):\n \"\"\" \"\"\"\n return self._debug_mode\n\n def set_debug_mode(self, mode):\n \"\"\"\n\n Parameters\n ----------\n mode :\n \n\n Returns\n -------\n\n \"\"\"\n\n if mode not in self.DEBUG_MODES:\n raise ValueError(\"debug mode, {}, not valid\".format(mode))\n\n self._debug_mode = mode\n\n # if you want to use debug mode you have to have ipdb installed\n if self.is_debug_on:\n try:\n import ipdb\n except ModuleNotFoundError:\n raise ModuleNotFoundError(\"You must have ipdb installed to use the debug feature\")\n\n def debug_on(self):\n \"\"\" \"\"\"\n if self.is_debug_on:\n warn(\"Debug mode is already on\")\n\n self.set_debug_mode(True)\n\n def debug_off(self):\n \"\"\" \"\"\"\n if not self.is_debug_on:\n warn(\"Debug mode is already off\")\n\n self.set_debug_mode(False)\n\n @property\n def max_num_walkers_setting(self):\n \"\"\" \"\"\"\n return self._max_num_walkers\n\n @property\n def min_num_walkers_setting(self):\n \"\"\" \"\"\"\n return self._min_num_walkers\n\n def max_num_walkers(self):\n \"\"\"\" Get the max number of walkers allowed currently\"\"\"\n\n # first check to make sure that a resampling is occuring and\n # we have a number of walkers to even reference\n if self._resampling_num_walkers is None:\n raise ResamplerError(\n \"A resampling is currently not taking place so the\"\\\n \" current number of walkers is not known.\")\n\n # we are in a resampling so there is a current value for the\n # max number of walkers\n else:\n\n # if the max is None then there is no max number of\n # walkers so we just return None\n if self.max_num_walkers_setting is None:\n return None\n\n # if the max is Ellipsis then we just return what the\n # current number of walkers is\n elif self.max_num_walkers_setting is Ellipsis:\n return self._resampling_num_walkers\n\n # if it is not those then it is a hard number and we just\n # return it\n else:\n return self.max_num_walkers_setting\n\n def min_num_walkers(self):\n \"\"\"\" Get the min number of walkers allowed currently\"\"\"\n\n # first check to make sure that a resampling is occuring and\n # we have a number of walkers to even reference\n if self._resampling_num_walkers is None:\n raise ResamplerError(\n \"A resampling is currently not taking place so the\"\\\n \" current number of walkers is not known.\")\n\n # we are in a resampling so there is a current value for the\n # min number of walkers\n else:\n\n # if the min is None then there is no min number of\n # walkers so we just return None\n if self.min_num_walkers_setting is None:\n return None\n\n # if the min is Ellipsis then we just return what the\n # current number of walkers is\n elif self.min_num_walkers_setting is Ellipsis:\n return self._resampling_num_walkers\n\n # if it is not those then it is a hard number and we just\n # return it\n else:\n return self.min_num_walkers_setting\n\n\n def _set_resampling_num_walkers(self, num_walkers):\n \"\"\"\n\n Parameters\n ----------\n num_walkers :\n \n\n Returns\n -------\n\n \"\"\"\n\n # there must be at least 1 walker in order to do resampling\n if num_walkers < 1:\n raise ResamplerError(\"No walkers were given to resample\")\n\n # if the min number of walkers is not dynamic check to see if\n # this number violates the hard boundary\n if self._min_num_walkers in (None, Ellipsis):\n self._resampling_num_walkers = num_walkers\n elif num_walkers < self._min_num_walkers:\n raise ResamplerError(\n \"The number of walkers given to resample is less than the minimum\")\n\n # if the max number of walkers is not dynamic check to see if\n # this number violates the hard boundary\n if self._max_num_walkers in (None, Ellipsis):\n self._resampling_num_walkers = num_walkers\n elif num_walkers < self._max_num_walkers:\n raise ResamplerError(\n \"The number of walkers given to resample is less than the maximum\")\n\n def _unset_resampling_num_walkers(self):\n \"\"\" \"\"\"\n\n self._resampling_num_walkers = None\n\n\n\n def _resample_init(self, walkers):\n \"\"\"Common initialization stuff for resamplers.\n\n Parameters\n ----------\n walkers :\n \n\n Returns\n -------\n\n \"\"\"\n\n # first set how many walkers there are in this resampling\n self._set_resampling_num_walkers(len(walkers))\n\n def _resample_cleanup(self):\n \"\"\" \"\"\"\n\n # unset the number of walkers for this resampling\n self._unset_resampling_num_walkers()\n\n\n def resample(self, walkers, debug_mode=False):\n \"\"\"\n\n Parameters\n ----------\n walkers :\n \n debug_mode :\n (Default value = False)\n\n Returns\n -------\n\n \"\"\"\n\n raise NotImplemented\n\n self._resample_init(walkers, debug_mode=debug_mode)\n\n\n def _init_walker_actions(self, n_walkers):\n \"\"\"\n\n Parameters\n ----------\n n_walkers :\n \n\n Returns\n -------\n\n \"\"\"\n # determine resampling actions\n walker_actions = [self.decision.record(enum_value=self.decision.ENUM.NOTHING.value,\n target_idxs=(i,))\n for i in range(n_walkers)]\n\n return walker_actions\n\n\n def assign_clones(self, merge_groups, walker_clone_nums):\n \"\"\"\n\n Parameters\n ----------\n merge_groups :\n \n walker_clone_nums :\n \n\n Returns\n -------\n\n \"\"\"\n\n n_walkers = len(walker_clone_nums)\n\n walker_actions = self._init_walker_actions(n_walkers)\n\n # keep track of which slots will be free due to squashing\n free_slots = []\n\n # go through the merge groups and write the records for them,\n # the index of a merge group determines the KEEP_MERGE walker\n # and the indices in the merge group are the walkers that will\n # be squashed\n for walker_idx, merge_group in enumerate(merge_groups):\n\n if len(merge_group) > 0:\n # add the squashed walker idxs to the list of open\n # slots\n free_slots.extend(merge_group)\n\n # for each squashed walker write a record and save it\n # in the walker actions\n for squash_idx in merge_group:\n walker_actions[squash_idx] = self.decision.record(self.decision.ENUM.SQUASH.value,\n (walker_idx,))\n\n # make the record for the keep merge walker\n walker_actions[walker_idx] = self.decision.record(self.decision.ENUM.KEEP_MERGE.value,\n (walker_idx,))\n\n # for each walker, if it is to be cloned assign open slots for it\n for walker_idx, num_clones in enumerate(walker_clone_nums):\n\n if num_clones > 0 and len(merge_groups[walker_idx]) > 0:\n raise ResamplerError(\"Error! cloning and merging occuring with the same walker\")\n\n # if this walker is to be cloned do so and consume the free\n # slot\n if num_clones > 0:\n\n # we first check to see if there are any free \"slots\"\n # for cloned walkers to go. If there are not we can\n # make room. The number of extra slots needed should\n # be default 0\n\n\n # we choose the targets for this cloning, we start\n # with the walker initiating the cloning\n clone_targets = [walker_idx]\n\n # if there are any free slots, then we use those first\n if len(free_slots) > 0:\n clone_targets.extend([free_slots.pop() for clone in range(num_clones)])\n\n # if there are more slots needed then we will have to\n # create them\n num_slots_needed = num_clones - len(clone_targets)\n if num_slots_needed > 0:\n\n # initialize the lists of open slots\n new_slots = []\n\n # and make a list of the new slots\n new_slots = [n_walkers + i for i in range(num_slots_needed)]\n\n # then increase the number of walkers to match\n n_walkers += num_slots_needed\n\n # then add these to the clone targets\n clone_targets.extend(new_slots)\n\n # make a record for this clone\n walker_actions[walker_idx] = self.decision.record(self.decision.ENUM.CLONE.value,\n tuple(clone_targets))\n\n return walker_actions\n\nclass NoResampler(Resampler):\n \"\"\" \"\"\"\n\n DECISION = NoDecision\n\n def __init__(self):\n self.decision = self.DECISION\n\n\n def resample(self, walkers, **kwargs):\n \"\"\"\n\n Parameters\n ----------\n walkers :\n \n **kwargs :\n \n\n Returns\n -------\n\n \"\"\"\n\n n_walkers = len(walkers)\n\n # the walker actions are all nothings with the same walker\n # index which is the default initialization\n walker_actions = self._init_walker_actions(n_walkers)\n\n # we only have one step so our resampling_records are just the\n # single list of walker actions\n resampling_data = [walker_actions]\n\n # there is no change in state in the resampler so there are no\n # resampler records\n resampler_data = [{}]\n\n return walkers, resampling_data, resampler_data\n","sub_path":"wepy/resampling/resamplers/resampler.py","file_name":"resampler.py","file_ext":"py","file_size_in_byte":19262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"499816505","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 5 19:30:32 2020\n\n@author: t1\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nimport torchvision\nimport torchvision.transforms as transforms\nfrom torch.utils.data import DataLoader\nimport torch.nn.functional as F\n\n# Confusion matrix\nfrom sklearn.metrics import confusion_matrix\nimport itertools\n\ndef plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.show()\n\ndef get_mnist_data(training=True,batch_size=128,shuffle = True):\n dataset = torchvision.datasets.MNIST(\n root = '../data',\n train = training,\n transform = transforms.ToTensor(),\n download = True\n )\n print('loading dataset : ','training' if training else 'test')\n print('dataset shape : ',dataset.data.shape , 'targets : ',dataset.targets.shape)\n data_loader = DataLoader(\n dataset = dataset,\n batch_size = batch_size,\n shuffle = shuffle\n )\n return data_loader\n\n\ndef get_fashion_mnist_data(training=True,batch_size=128,shuffle = True):\n dataset = torchvision.datasets.FashionMNIST(\n root = '../data',\n train = training,\n transform = transforms.ToTensor(),\n download = True\n )\n print('loading dataset : ','training' if training else 'test')\n print('dataset shape : ',dataset.data.shape , 'targets : ',dataset.targets.shape)\n data_loader = DataLoader(\n dataset = dataset,\n batch_size = batch_size,\n shuffle = shuffle\n )\n return data_loader\n\ndef get_cifar10_data(training=True,batch_size=128,shuffle = True,trms = None):\n \n dataset = torchvision.datasets.CIFAR10(\n root = '../data',\n train = training,\n transform = transforms.ToTensor() if not trms else trms,\n download = True\n )\n print('loading dataset : ','training' if training else 'test')\n # print('dataset shape : ',dataset.data.shape , 'targets : ',dataset.targets.shape)\n data_loader = DataLoader(\n dataset = dataset,\n batch_size = batch_size,\n shuffle = shuffle\n )\n return data_loader\n\n# class CNN2(nn.Module):\n# def __init__(self,NUM_CLASSES):\n# super(CNN2,self).__init__()\n# self.conv1 = nn.Conv2d(in_channels = 3,out_channels = 32, kernel_size = 3,stride = 2)\n# self.bn1 = nn.BatchNorm2d(32)\n# self.conv2 = nn.Conv2d(in_channels = 32,out_channels = 64, kernel_size = 3,stride = 2)\n# self.bn2 = nn.BatchNorm2d(64)\n# self.conv3 = nn.Conv2d(in_channels = 64,out_channels = 128, kernel_size = 3,stride = 2)\n# self.bn3 = nn.BatchNorm2d(128)\n \n# self.fc1 = nn.Linear(128*3*3,512)\n# self.fc2 = nn.Linear(512,NUM_CLASSES)\n \n# def forward(self,X):\n# X = F.relu(self.bn1(self.conv1(X)))\n# X = F.relu(self.bn2(self.conv2(X)))\n# X = F.relu(self.bn3(self.conv3(X)))\n# X = X.view(-1,128*3*3)\n# X = F.dropout(X,p=0.5)\n# X = F.relu(self.fc1(X))\n# X = F.dropout(X,p=0.5)\n# X = self.fc2(X)\n# return X\n\nclass CNN(nn.Module):\n def __init__(self,INPUT_CHANNELS,NUM_CLASSES):\n super(CNN,self).__init__()\n \n ## conv_block 1 output size : 32 x 16 x 16\n self.conv_block1 = nn.Sequential(\n nn.Conv2d(in_channels = INPUT_CHANNELS, out_channels = 32, kernel_size = 3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(32),\n nn.Conv2d(in_channels = 32, out_channels = 32, kernel_size = 3,padding = 1),\n nn.ReLU(),\n nn.BatchNorm2d(32),\n nn.MaxPool2d(2)\n )\n \n ## conv block2 output size : 64 x 8 x 8\n self.conv_block2 = nn.Sequential(\n nn.Conv2d(in_channels = 32, out_channels = 64, kernel_size = 3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(64),\n nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = 3,padding = 1),\n nn.ReLU(),\n nn.BatchNorm2d(64),\n nn.MaxPool2d(2)\n )\n \n ## conv block2 output size : 128 x 4 x 4\n self.conv_block3 = nn.Sequential(\n nn.Conv2d(in_channels = 64, out_channels = 128, kernel_size = 3, padding=1),\n nn.ReLU(),\n nn.BatchNorm2d(128),\n nn.Conv2d(in_channels = 128, out_channels = 128, kernel_size = 3,padding = 1),\n nn.ReLU(),\n nn.BatchNorm2d(128),\n nn.MaxPool2d(2)\n )\n \n self.dropout1 = nn.Dropout(p=0.5)\n self.fc1 = nn.Linear(128*4*4,512)\n self.dropout2 = nn.Dropout(p=0.5)\n self.fc2 = nn.Linear(512,10)\n \n def forward(self,X):\n out = self.conv_block1(X)\n out = self.conv_block2(out)\n out = self.conv_block3(out)\n out = out.view(out.size(0),-1)\n out = self.dropout1(out)\n out = self.fc1(out)\n out = F.relu(out)\n out = self.dropout2(out)\n out = self.fc2(out)\n return out\n \n\n# class CNN(nn.Module):\n# def __init__(self,NUM_CLASSES):\n# super(CNN,self).__init__()\n# self.conv_layers = nn.Sequential(\n# nn.Conv2d(in_channels = 1, out_channels = 32, kernel_size = 3 ,stride = 2),\n# nn.ReLU(),\n# nn.Conv2d(in_channels = 32, out_channels = 64, kernel_size = 3 , stride = 2),\n# nn.ReLU(),\n# nn.Conv2d(in_channels = 64, out_channels = 128, kernel_size = 3 , stride = 2),\n# nn.ReLU()\n# )\n \n# self.dense_layers = nn.Sequential(\n# nn.Dropout(0.2),\n# nn.Linear(in_features = 2*2*128, out_features = 512),\n# nn.ReLU(),\n# nn.Dropout(0.2),\n# nn.Linear(512,NUM_CLASSES)\n# )\n \n# def forward(self,X):\n# out = self.conv_layers(X)\n# out = out.view(out.size(0),-1)\n# out = self.dense_layers(out)\n# return out\n \n","sub_path":"pytorch/basic_cnn/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"606854299","text":"#Functions can be called with keyword arguments of the form kwarg = value.\n\n\ndef parrot(voltage, state= 'a stiff', action = 'voom', type= 'Norweigan Blue'):\n print(\"this parrot wouldn't\",action, end='')#The end value is'\\n' by default\n print(\"if you put\", voltage, \"volts through it.\")\n print(\"-- Lovely plumage, the\", type)\n print(\"-- It's\", state, \"!\")\n\n\nparrot(1000) #1 positional argument\nparrot(voltage=1000) # 1 keyword argument\nparrot(voltage=100000, action = 'VOOOOOOOM') # 2 keyword arguments\nparrot(action='VOOOOOM', voltage=1000000) # 2 keyword arguments\nparrot('a million', 'bereft of life', 'jump') # 3 positional arguments\nparrot('a thousand', state='pushing up the daisies') # 1 positional, 1 keyword\n\n\"\"\"\nparrot() # required argument missing\nparrot(voltage=5.0, 'dead') # non-keyword argument after a keyword argument\nparrot(110, voltage=220) # duplicate value for the same argument\nparrot(actor='John Cleese') # unknown keyword argument\n\"\"\"\n\n\"\"\"\nIn a function call, keyword arguments must follow positional arguments.\n All the keyword arguments passed must match one of the arguments accepted by the function \n (e.g. actor is not a valid argument for the parrot function), and their order is not important. \n This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too).\n No argument may receive a value more than once. Here’s an example that fails due to this restriction:\n\"\"\"\n","sub_path":"python_official_tutorial/chap4_7/chap4_7_2.py","file_name":"chap4_7_2.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"180915926","text":"from pygame import *\nfrom gamedev.colors import c\nfrom gamedev.game import RPG\nfrom math import sin\nfrom random import randint\n\ng = RPG(600, 400, \"The Impossible Game\") # create game object\n\ng.sprites = {} # create sprites\ng.sounds = {} # create sounds\n\ng.loadResources() # load all of the sounds and sprites\n\ng.event.onexit = g.stop # event handler for stopping the game\n\ny = g.h - 64\ndy = y\ngravity = 0.8\nyVel = 0\nisJumping = False\ncourse = []\nduck = 0\nscore = 0\n\npause = False\n\nwhile not g.done:\n g.clrscrn() # clear the screen\n\n g.drawText(str(int(score/10))+\"m\",20,20)\n\n if g.keyDown(K_UP) and y==dy and not pause:\n yVel = -15\n isJumping = True\n duck = [0,32][g.keyDown(K_DOWN) and not pause]\n g.drawRect(100, y+duck, 32, 64-duck, c[\"blue\"])\n if not pause and isJumping:\n yVel += gravity\n y += yVel\n if y > dy:\n y = dy\n yVel = 0\n isJumping = False\n\n for o in course:\n g.drawRect(o[0],dy+o[1],10,10,c[\"red\"])\n if not pause:\n o[0]-=5\n if g.boxCollides([110,y+duck,32,64-duck],[o[0],dy+o[1],10,10]):\n pause = True\n if o[0]<=0:\n course.remove(o)\n\n try:\n if not pause and randint(0,60)==0:\n course.append([g.w,[32,2][randint(0,1)]])\n except:\n d=0\n\n g.tick() # tick forward on the clock, update the screen, handle events, etc.\n\n if not pause: score += 1\n\ng.close() # close window\n","sub_path":"RollJump/rolljump.py","file_name":"rolljump.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"520327456","text":"import numpy as np\nfrom numpy import pi\n\nfrom matplotlib.cm import winter\nimport matplotlib.pyplot as plt\n\n\n# Вот тут должны быть нужные тебе массивы. (Если они одномерные)\nx0 = np.linspace(0,200,200)\ny0 = x0\n\n# Тут эти массивы превращаются в сетку\nx, y = np.meshgrid(x0, y0)\n\n# Вот тут твой двумерный массив\nz = np.sin(x)**2\n\n\n\nfig,ax = plt.subplots(nrows = 1, ncols = 1)\nplt.contourf(x,y,z,levels=100,cmap=winter)\nplt.colorbar()\nplt.ylabel(r'$Y$, м',fontsize=16)\nplt.xlabel(r'$X$, м',fontsize=16)\n\nplt.show()\n","sub_path":"project/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"503198842","text":"# 9251번. LCS\n\n\nimport sys\ninput = sys.stdin.readline\n\n# LCS 참조\n# http://melonicedlatte.com/algorithm/2018/03/15/181550.html\n\na = list(input().rstrip())\nb = list(input().rstrip())\ndp = [[0 for i in range(len(a)+1)] for i in range(len(b)+1)]\n\nfor i in range(1, len(b)+1):\n for j in range(1, len(a)+1):\n if b[i-1] == a[j-1]:\n # 현재 위치의 값 = 왼쪽 대각선 값 + 1\n dp[i][j] = dp[i-1][j-1]+1\n else:\n # 현재 위치의 값 = max(왼쪽 값, 위쪽 값)\n dp[i][j] = max(dp[i-1][j], dp[i][j-1])\n\n# 맨 마지막 dp가 최대\nprint(dp[-1][-1])\n","sub_path":"BOJ/3.gold/gold5/9251_LCS.py","file_name":"9251_LCS.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"288483462","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 7 18:49:46 2018\n\n@author: 2014_Joon_IBS\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport glob\nimport os\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nfrom scipy import stats\nimport matplotlib.pyplot as plt\n\n#os.chdir('e:')\n#path='/0Temp_data/20180911_OFL_after_tube_n3_batch1/'\n#os.chdir(path)\n\n#name = 'freeze_20180911_OFL_after_tube_n3_batch1'\nbar_width = .5\n\nparams = {\n 'axes.labelsize': 8,\n 'font.size': 18,\n 'legend.fontsize': 10,\n 'legend.frameon':False,\n 'xtick.labelsize': 10,\n 'ytick.labelsize': 10,\n 'text.usetex': False,\n 'figure.figsize': [4, 6] # instead of 4.5, 4.5\n }\n\nplt.rcParams.update(params)\n\n\ndef color_setting(n_color=2):\n sns.set_style('whitegrid')\n sns.set_color_codes()\n \n ###### customize color palette\n flatui = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"]\n beach_towel = ['#fe4a49', '#2ab7ca', '#fed766', '#e6e6ea', '#f4f4f8']\n pastel_rainbow = ['#a8e6cf','#dcedc1','#ffd3b6','#ffaaa5', '#ff8b94']\n \n list_palette = [flatui,\n beach_towel,\n pastel_rainbow] \n \n if n_color <=3: # color 종류 별로 안써도 되는 경우\n for i, i_p in enumerate(list_palette):\n list_palette[i] = i_p[::2] #홀수 값만 취함. \n \n #### \n sns.set_palette('hls',n_color) # Reds\n sns.set_palette(list_palette[2], n_color)\n current_palette = sns.color_palette()\n #sns.palplot(current_palette)\n ###\n sns.set_context('poster', font_scale=0.8)\n return current_palette\n\ndef legend_patch(current_palette, labels):\n patches = []\n for i, _ in enumerate(labels):\n patch_i = mpatches.Patch(color=current_palette[i], label=labels[i])\n patches.append(patch_i)\n return patches\n\ndef plot_time_series(data):\n columns = []\n ofl_bin = 9\n columns = np.arange(ofl_bin)+1\n \n #test = pd.DataFrame(data = data, columns=columns)\n \n fig2, ax2 = plt.subplots()#figsize=(10,4))\n sns.set_style('ticks',rc = {\"lines.linewidth\":0.1,\"xtick.major.size\": 0.1, \"ytick.major.size\": 1})\n g = sns.catplot(\n #palette={\"male\": \"g\", \"female\": \"m\"},\n markers=[\"^\"], linestyles=[\"-\"],linewidth=0.1,\n kind=\"point\", data=data, ci=68, ax=ax2, aspect=0.5, scale=0.5)\n \n ax2.set(title = 'OFL',\n ylabel='freezing(%)',\n xlabel='(min)',\n ylim=[0,20])\n \n ax2.spines['top'].set_visible(False)\n ax2.spines['right'].set_visible(False)\n \n for l in g.ax.lines:\n print(l.get_linewidth())\n plt.setp(l,linewidth=5)\n \ndef plot_bar_scatter(data, p, current_palette, labels):\n \n patches = legend_patch(current_palette, labels) # to add legend\n \n fig, ax = plt.subplots( figsize=(5,5))\n \n sns.barplot(data=data, ci=68, errwidth = 2, capsize=.05, ax= ax)\n sns.stripplot(data=data, jitter=True, size= 7, \n edgecolor = 'gray', linewidth=1, ax=ax)#,color=\"0.4\")\n ax.grid(False)\n \n ax.set(title = 'Social rank vs OFL',\n ylabel='avg OFL (%)',\n #xlabel='trial',\n ylim=[0,30])\n \n ax.legend(handles = patches, loc='best', edgecolor =None, fontsize=13, bbox_to_anchor=(0.7,0.7)) # upper right\n #ax.axis('equal')\n \n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n \n change_width(ax, bar_width) # bar_width\n add_star(ax, p, df)\n \n fig.tight_layout()\n fig.savefig('test.png', transparent = True, dpi=300)\n\n\ndef stars(p):\n if p < 0.0001:\n return \"****\"\n elif (p < 0.001):\n return \"***\"\n elif (p < 0.01):\n return \"**\"\n elif (p < 0.05):\n return \"*\"\n else:\n return \"-\"\n\ndef t_test(x,y):\n # normality test. p>0.05 means fail\n _, p_x = stats.shapiro(x)\n _, p_y = stats.shapiro(y)\n # equal variance test. p <=0.05 means fail\n _, p_var = stats.levene(x,y) \n \n if (p_x or p_y > 0.05) or p_var <=0.05 : # normality fail\n print('Non-parametric test is needed. MannWhitney U test.\\n')\n statistic, p_final = stats.mannwhitneyu(x,y, alternative='two-sided')\n else:\n print('T-test\\n')\n statistic, p_final = stats.ttest_ind(x,y)\n \n print('Result. Statistic: {}\\n p-value: {}'.format(statistic, p_final))\n return statistic, p_final\n \ndef data_vec(data): # convert Pandas dataframe into vectors\n n_category = len(data.keys())\n d_list = [0 for i in range(n_category)]\n legend=[]\n for i, i_key in enumerate(data.keys()):\n d_list[i] = np.array(data[i_key])\n d_list[i] = d_list[i][np.isnan(d_list[i])==False] # remove NaN\n print('n = {}'.format(len(d_list[i])))\n legend.append(i_key + ' \\n(n={})'.format(len(d_list[i])))\n return d_list, legend\n\ndef data_ofl_read(path):\n csv_file = glob.glob(path+'*.csv')[0]\n print(csv_file)\n df = pd.read_csv(csv_file)\n\n # OFL time bin 만큼 추출\n time_bin = 9\n df = df.iloc[:, 0:time_bin+1]\n \n # pandas 로 csv read 시, 기본 key 탐색 (첫번째 row)\n key_subject = df.keys()[0]\n \n obs = df[df[key_subject].str.contains(\"dem|Onset|Duration\")==False] # dem, onset, duration 중 하나라도 포함되어 있으면 제외 -> obs 데이터만 추출 가능\n dem = df[df[key_subject].str.contains(\"dem\")==True] # dem data\n return obs, dem \n\ndef add_star(ax, p_value, df):\n \n y_max = np.max(np.array(df.max()))\n y_min = np.min(np.array(df.min()))\n s = stars(p_value)\n print(s)\n ax.annotate(\"\", xy=(0, y_max), xycoords='data',\n xytext=(1, y_max), textcoords='data',\n arrowprops=dict(arrowstyle=\"-\", ec='black',#'#aaaaaa',\n connectionstyle=\"bar,fraction=0.2\"))\n \n # fig size 5 -> \n p = 'p = {:.2f}'.format(p_value)\n ax.text(0.5, y_max + abs(y_max - y_min)*0.3, s,\n horizontalalignment='center',\n verticalalignment='center')\n \n ax.text(0.5, y_max + abs(y_max - y_min)*0.05, p,\n horizontalalignment='center',\n verticalalignment='center')\n\ndef change_width(ax, new_value):\n for patch in ax.patches :\n current_width = patch.get_width()\n diff = current_width - new_value\n\n # we change the bar width\n patch.set_width(new_value)\n\n # we recenter the bar\n patch.set_x(patch.get_x() + diff * .5)\n\n\nif __name__ == '__main__':\n name = '/python/data/dom_sub.xlsx'\n df = pd.read_excel(name)\n #obs, dem = data_ofl_read()\n \n current_palette = color_setting(2) # 2 종류 plot 준비\n \n ## Statistics. T-test \n d_list, legend = data_vec(df)\n x = d_list[0]\n y = d_list[1]\n \n _, p = t_test(x,y)\n \n plot_bar_scatter(df, p, current_palette, legend) # bar scatter plot\n \n path_ofl = '/python/data/ofl/'\n obs, dem = data_ofl_read(path_ofl)\n plot_time_series(obs)\n #obs.rename(lambda x: x[1:], axis='columns')\n obs.set_axis(['a', 'b', 'c', 'd', 'e'], axis='columns', inplace=False)\n \n \n \n \n \n\n \n","sub_path":"py/csv_data_process.py","file_name":"csv_data_process.py","file_ext":"py","file_size_in_byte":7170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"594987156","text":"import unittest\n\nfrom simulaqron.network import Network\nfrom simulaqron.settings import Settings\nfrom cqc.pythonLib import CQCConnection, qubit, CQCUnsuppError\n\n\nclass TestDefaultTopology(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n Settings.default_settings()\n cls.node_names = [\"Alice\", \"Bob\", \"Charlie\"]\n cls.network = Network(cls.node_names)\n cls.network.start()\n\n @classmethod\n def tearDownClass(cls):\n cls.network.stop()\n Settings.default_settings()\n\n def test_send(self):\n for sender_name in self.node_names:\n for receiver_name in self.node_names:\n with CQCConnection(sender_name) as sender:\n with CQCConnection(receiver_name) as receiver:\n q = qubit(sender)\n if sender_name == receiver_name:\n with self.assertRaises(CQCUnsuppError):\n sender.sendQubit(q=q, name=receiver_name, remote_appID=receiver._appID)\n else:\n sender.sendQubit(q=q, name=receiver_name, remote_appID=receiver._appID)\n q = receiver.recvQubit()\n m = q.measure()\n self.assertEqual(m, 0)\n\n def test_EPR(self):\n for sender_name in self.node_names:\n for receiver_name in self.node_names:\n with CQCConnection(sender_name) as sender:\n with CQCConnection(receiver_name) as receiver:\n if sender_name == receiver_name:\n with self.assertRaises(CQCUnsuppError):\n sender.createEPR(name=receiver_name, remote_appID=receiver._appID)\n else:\n qs = sender.createEPR(name=receiver_name, remote_appID=receiver._appID)\n qr = receiver.recvEPR()\n ms = qs.measure()\n mr = qr.measure()\n self.assertEqual((ms + mr) % 2, 0)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"tests/auto/quick/network_topology/test_default_topology.py","file_name":"test_default_topology.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"206664489","text":"from string import lower\nfrom models.CitiesCountiesDB import CountiesDB\n\n\nclass LinkedinLocation:\n\n def __init__(self):\n self._cdb = CountiesDB()\n\n def getCounty(self, locationStr):\n l = self._splitLocationString(locationStr)\n if len(l) == 2:\n city, county = l[0], l[1]\n foundCounty1 = self._cdb.getCityCounty(city)\n foundCounty2 = self._cdb.getCountyByName(county)\n elif len(l) == 1:\n cityOrCounty = l[0]\n foundCounty1 = self._cdb.getCityCounty(cityOrCounty)\n foundCounty2 = self._cdb.getCountyByName(cityOrCounty)\n else:\n return None\n\n if foundCounty1 is not None:\n return foundCounty1\n elif foundCounty2 is not None :\n return foundCounty2\n else:\n return None\n\n\n\n def _splitLocationString(self, locationStr):\n return filter(lambda i : not self._strAreEqual(i, \"United Kingdom\"),\n locationStr.split(\", \"))\n\n def _strAreEqual(self, a, b):\n return str.lower(str(a)) == str.lower(str(b))\n\n\n\n","sub_path":"classifiers/Location.py","file_name":"Location.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"2964994","text":"# -*- coding:Latin-1 -*-\n\nfrom Tkinter import *\nfrom math import pi, sin, cos\n\nclass Canon:\n \"\"\"Petit canon graphique\"\"\"\n def __init__(self, boss, x, y, angle=25):\n self.boss = boss # référence du canevas\n self.x1, self.y1 = x, y # axe de rotation du canon\n # dessiner la buse du canon, à l'horizontale pour commencer :\n self.lbu = 50 # longueur de la buse\n self.x2, self.y2 = x + self.lbu, y\n self.buse = boss.create_line(self.x1, self.y1, self.x2, self.y2,\n width =10)\n # dessiner ensuite le corps du canon par-dessus :\n r = 15 # rayon du cercle \n boss.create_oval(x-r, y-r, x+r, y+r, fill='blue', width =3)\n # dessiner un obus (réduit à un simple point, avant animation) :\n self.obus =boss.create_oval(x, y, x, y, fill='red')\n self.anim =False # interrupteur d'animation\n # retrouver la largeur et la hauteur du canevas :\n self.xMax =int(boss.cget('width'))\n self.yMax =int(boss.cget('height'))\n \n def orienter(self, angle):\n \"choisir l'angle de tir du canon\"\n # rem : le paramètre est reçu en tant que chaîne de car.\n # il faut le traduire en nombre réel, puis convertir en radians :\n self.angle = float(angle)*2*pi/360 \n self.x2 = self.x1 + self.lbu*cos(self.angle)\n self.y2 = self.y1 - self.lbu*sin(self.angle)\n self.boss.coords(self.buse, self.x1, self.y1, self.x2, self.y2)\n \n def feu(self):\n \"déclencher le tir d'un obus\"\n if not self.anim:\n self.anim =True\n # position de départ de l'obus (c'est la bouche du canon) :\n self.boss.coords(self.obus, self.x2 -3, self.y2 -3,\n self.x2 +3, self.y2 +3)\n v =15 # vitesse initiale\n # composantes verticale et horizontale de cette vitesse :\n self.vy = -v *sin(self.angle)\n self.vx = v *cos(self.angle)\n self.animer_obus()\n \n def animer_obus(self):\n \"animation de l'obus (trajectoire balistique)\"\n if self.anim:\n self.boss.move(self.obus, int(self.vx), int(self.vy))\n c = self.boss.coords(self.obus) # coord. résultantes\n xo, yo = c[0] +3, c[1] +3 # coord. du centre de l'obus\n if yo > self.yMax or xo > self.xMax:\n self.anim =False # arrêter l'animation\n self.vy += .5\n self.boss.after(30, self.animer_obus)\n \nif __name__ == '__main__':\n # Code pour tester sommairement la classe Canon : \n f = Tk()\n can = Canvas(f,width =250, height =250, bg ='ivory')\n can.pack(padx =10, pady =10)\n c1 = Canon(can, 50, 200)\n\n s1 =Scale(f, label='hausse', from_=90, to=0, command=c1.orienter)\n s1.pack(side=LEFT, pady =5, padx =20)\n s1.set(25) # angle de tir initial\n\n Button(f, text='Feu !', command =c1.feu).pack(side=LEFT)\n\n f.mainloop()\n","sub_path":"python/oreilly/cours_python/chap15/canon02.py","file_name":"canon02.py","file_ext":"py","file_size_in_byte":3116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"478637258","text":"def add(a, b):\n print(f\"ADDING {a} + {b}\")\n return a + b\n\ndef subtract(a, b):\n print(f\"SUBTRACTING {a} - {b}\")\n return a - b\n\ndef multiply(a, b):\n print(f\"MULTIPLYING {a} * {b}\")\n return a * b\n\ndef divide(a, b):\n print(f\"DIVIDING {a} / {b}\")\n return a / b\n\nprint(\"Let's do some math with just functions:\")\n\nage = add(30, 5) # 35\nheight = subtract(78, 4) # 74\nweight = multiply(90, 2) # 180\niq = divide(100, 2) # 50\n\nprint(\"Age: {}, Height: {}, Weight: {}, IQ: {}\".format(age, height, weight, iq))\n\n# A puzzle for the extra credit, type it in anyway.\nprint(\"Here is a puzzle.\")\n\n# what = add(35, subtract(74, multiply(180, divide(50, 2))))\n# what = add(35, subtract(74, multiply(180, 25.0)))\n# what = add(35, subtract(74, 4500.0))\n# what = add(35, -4426.0)\n# what = -4391.0\n# what = 35 + (74 - (180 * (50 / 2)))\n# what = 35 + 74 - 180 * 50 / 2\nwhat = add(age, subtract(height, multiply(weight, divide(iq, 2))))\n\nprint(\"That becomes: {} Can you do it by hand?\".format(what))\n","sub_path":"pythonhardway/ex21.py","file_name":"ex21.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"276711048","text":"def reverse(head):\n\n # Reverse the linked list in place\n if head:\n onebehind = None\n twobehind = None\n while head.next:\n twobehind = onebehind\n onebehind = head\n head = head.next\n onebehind.next = twobehind\n head.next = onebehind\n\n return head\n\n# Tests\n\nclass LinkedListNode(object):\n\n def __init__(self, value, next=None):\n self.value = value\n self.next = next\n\n def get_values(self):\n node = self\n values = []\n while node is not None:\n values.append(node.value)\n node = node.next\n return values\n\ndef test_short_linked_list():\n second = LinkedListNode(2)\n first = LinkedListNode(1, second)\n\n result = reverse(first)\n print(result is not None)\n\n actual = result.get_values()\n expected = [2, 1]\n print(actual == expected)\n\ndef test_long_linked_list():\n sixth = LinkedListNode(6)\n fifth = LinkedListNode(5, sixth)\n fourth = LinkedListNode(4, fifth)\n third = LinkedListNode(3, fourth)\n second = LinkedListNode(2, third)\n first = LinkedListNode(1, second)\n\n result = reverse(first)\n print(result is not None)\n\n actual = result.get_values()\n expected = [6, 5, 4, 3, 2, 1]\n print(actual == expected)\n\ndef test_one_element_linked_list():\n first = LinkedListNode(1)\n\n result = reverse(first)\n print(result is not None)\n\n actual = result.get_values()\n expected = [1]\n print(actual == expected)\n\ndef test_empty_linked_list():\n result = reverse(None)\n print(result is None)\n\ntest_short_linked_list()\ntest_long_linked_list()\ntest_one_element_linked_list()\ntest_empty_linked_list()","sub_path":"Week-2/Day-2/reverse_a_linked_list.py","file_name":"reverse_a_linked_list.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"406203902","text":"#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n\r\nimport re\r\nimport sys\r\nimport time\r\nimport json\r\nimport mail\r\nimport random\r\nimport signal\r\nimport requests\r\nfrom util import timeStr\r\nfrom threading import Thread\r\n\r\n\r\nurls = [\"https://api.ipify.org/?format=json&r=%d\", \"http://httpbin.org/ip?r=%d\", \"http://jsonip.com?r=%d\", \"http://ip.42.pl/raw?r=%d\"]\r\nheaders = {\r\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36',\r\n # 'referer': 'http://www.aninigps.com/GIS/MapForm.aspx?h=null&',\r\n 'content-type': 'text/html; charset=UTF-8',\r\n 'accept': '*/*',\r\n 'accept-encoding': 'gzip, deflate',\r\n 'accept-language': 'zh-CN,zh;q=0.9'\r\n}\r\nleave = False\r\n\r\n\r\ndef get():\r\n ret = None\r\n try:\r\n url = urls[random.choice([0, 1, 2, 3])]\r\n # print(\"url=%s\\n\" % url)\r\n req = requests.get(url % (int(time.time() * 1000)), headers=headers)\r\n req.encoding = 'utf-8'\r\n if req.status_code == requests.codes.ok:\r\n # print(req.text, \"\\n\\n\")\r\n try:\r\n ret = json.loads(req.text)\r\n except ValueError:\r\n ret = req.text\r\n else:\r\n print(\"[%s] \" % timeStr(), \"%s request fail.\" % url)\r\n except Exception as ex:\r\n print(\"[%s] \" % timeStr(), \"error: \", ex)\r\n return ret\r\n\r\n\r\ndef parseIp(ipStr):\r\n ret = \"\"\r\n pattern = re.compile(r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}')\r\n match = pattern.match(ipStr)\r\n if match:\r\n ret = match.group(0)\r\n return ret\r\n\r\n\r\ndef run_forever():\r\n lastIp = \"\"\r\n lastHour = 0\r\n while not leave:\r\n ret = get()\r\n localtime = time.localtime()\r\n if localtime[3] in (8, 16) and lastHour != localtime[3]:\r\n lastHour = localtime[3]\r\n lastIp = \"1\"\r\n if ret:\r\n ip = lastIp\r\n if \"ip\" in ret:\r\n ip = parseIp(ret[\"ip\"])\r\n # print(\"1 ip=%s\\n\" % ip)\r\n elif \"origin\" in ret:\r\n ip = parseIp(ret[\"origin\"])\r\n # print(\"2 ip=%s\\n\" % ip)\r\n else:\r\n ip = parseIp(ret)\r\n # print(\"3 ip=%s\\n\" % ip)\r\n if ip and lastIp and lastIp != ip:\r\n lastIp = ip\r\n print(\"[%s] \" % timeStr(), \"外网ip[%s]\" % ip)\r\n mail.add(\"外网ip[%s]\" % ip)\r\n mail.commit(True)\r\n # print(\"lastIp=%s\\n\\n\" % lastIp)\r\n time.sleep(300)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n def quit(sig, chunk):\r\n global leave\r\n leave = True\r\n print(\"[%s] \" % timeStr(), \"Stopping DDNS service by ctrl + c.\")\r\n time.sleep(2)\r\n sys.exit()\r\n\r\n signal.signal(signal.SIGINT, quit)\r\n signal.signal(signal.SIGTERM, quit)\r\n try:\r\n print(\"[%s] \" % timeStr(), \"DDNS service runing...\")\r\n thread = Thread(target=run_forever)\r\n thread.setDaemon(True)\r\n thread.start()\r\n while not leave:\r\n time.sleep(3)\r\n pass\r\n except Exception as ex:\r\n print(ex)\r\n finally:\r\n print(\"[%s] \" % timeStr(), \"DDNS service stopped.\")\r\n","sub_path":"ddns.py","file_name":"ddns.py","file_ext":"py","file_size_in_byte":3178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"227240347","text":"import os\nimport textgrid\nimport json\n# import pandas as pd\n# from sklearn import preprocessing\n\ncwd = os.getcwd()\nphonemes = {}\n# iterate over the files in the \"output\" directory\nfor filename in os.listdir(os.path.join(cwd, 'output')):\n if not filename.endswith('.TextGrid'):\n continue\n tg = textgrid.TextGrid.fromFile(os.path.join(cwd, 'output', filename))\n # phonemes\n for t in tg[1]:\n if t.mark not in phonemes:\n phonemes[t.mark] = []\n # start, end, duration\n phonemes[t.mark].append([t.minTime, t.maxTime, t.maxTime - t.minTime])\nprint(phonemes)\n# max duration for each phoneme\n# (the longest time it took for a given phoneme across all speakers)\nphonemes_max_duration = {}\nfor phoneme, data in phonemes.items():\n phonemes_max_duration[phoneme] = max(data, key=lambda times: times[2])[2]\n\n# print(phonemes_max_duration)\n# save the results\nwith open('alignments.json', 'w', newline='') as file:\n json.dump(phonemes, file)\nwith open('phonemes_max_duration.json', 'w', newline='') as file:\n json.dump(phonemes_max_duration, file)","sub_path":"build_alignment_table.py","file_name":"build_alignment_table.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"360285802","text":"import MySQLdb\nimport MySQLdb.cursors\n\n\ndef get_connection():\n connection = MySQLdb.connect(user='root',\n passwd='tunnel',\n db='person',\n cursorclass=MySQLdb.cursors.DictCursor)\n return connection\n\n\nclass AbstractModel:\n def save(self):\n insert(self)\n\n\nclass Field:\n def __init__(self, length):\n self.length = length\n\n\nclass Integer(Field):\n def __init__(self, length):\n super().__init__(length)\n\n\nclass Float(Field):\n def __init__(self, length):\n super().__init__(length)\n\n\nclass Text(Field):\n def __init__(self, length):\n super().__init__(length)\n\n\nclass Boolean(Field):\n def __init__(self, length):\n super().__init__(length)\n\n\nclass Persone(AbstractModel):\n name = Text(100)\n age = Integer(3)\n\n\ndef migrate(some_class):\n connection = get_connection()\n cursor = connection.cursor()\n name_class = some_class.__class__.__name__.lower()\n query = \"CREATE TABLE IF NOT EXISTS {0}\".format(name_class)\n fields = \"\"\n for field in some_class.__dir__():\n name_field = getattr(some_class, field)\n if isinstance(name_field, Field):\n type_data = name_field.__class__.__name__.upper()\n length_field = getattr(name_field, 'length')\n fields += \" \" + field + \" \" + type_data + \"(\" + str(length_field) + \")\" + \",\"\n final_query = query + \"(\" \" id INT AUTO_INCREMENT PRIMARY KEY,\" + fields[:-1] + \")\"\n cursor.execute(final_query)\n\n\ndef insert(some_obj):\n connection = get_connection()\n cursor = connection.cursor()\n name_obj = some_obj.__class__.__name__.lower()\n query = \"INSERT INTO {0} \".format(name_obj)\n values = \"\"\n fields = \"\"\n for field in some_obj.__dir__():\n name_field = getattr(some_obj.__class__, field)\n if isinstance(name_field, Field):\n fields += \" \" + field + \",\"\n values += \" '\" + str(getattr(some_obj, field)) + \"',\"\n final_query = query + \"(\" + fields[1:-1] + \")\" + \" VALUES \" + \"(\" + values[1:-1] + \")\"\n cursor.execute(final_query)\n connection.commit()\n\n\ndef select(some_model, **kwargs):\n connection = get_connection()\n cursor = connection.cursor()\n name_model = some_model.__name__.lower() + \" \"\n where = \"\"\n op = {\n '__gt': ' > ',\n '__gte': ' >= ',\n '__lt': ' < ',\n '__lte': ' <= '\n }\n if kwargs:\n for key, value in kwargs.items():\n if \"__\"in key:\n operator = op[key[key.find(\"_\"):]]\n key = key[:key.find(\"_\")]\n else:\n operator = \" = \"\n where += key + \"{}\" \"'{}'\".format(operator, value) + \" AND \"\n where = where[:len(where) - 4]\n final_query = \"SELECT \" + \"*\" + \" FROM \" + name_model + \"WHERE \" + where\n else:\n final_query = \"SELECT * FROM {0}\".format(name_model)\n cursor.execute(final_query)\n return cursor.fetchall()\n\n\nmigrate(Persone())\nOne = Persone()\nOne.name = 'Vitazero'\nOne.age = 30\nTwo = Persone()\nTwo.name = 'Cramly'\nTwo.age = 43\nOne.save()\nTwo.save()\nprint(select(Persone, name='Vitazero', age__lte=35))\n","sub_path":"DataBase/logic.py","file_name":"logic.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"572517568","text":"try:\n import wikipedia\nexcept:\n from wikipedia import wikipedia\n from wikipedia import exceptions\n\nimport re\n\n\n\n\n\ndef wikiSearch(searchItem):\n try:\n searchList = wikipedia.search(searchItem)\n for item in searchList: \n try:\n content_page = wikipedia.page(item)\n content_Url = content_page.url\n content_title = content_page.title\n #print(\"Okk u have searched for:\",content_title)\n #print(\"The url of the page:\",content_Url)\n return content_Url\n except wikipedia.exceptions.DisambiguationError as e:\n except_return=\"can't find your searchItem in wiki ......use specific term dude....it is not a Ai still ...now :-p\"\n return except_return\n except Exception as e:\n return (\"can't find your item man\")\n\n\n\n\ndef on_message(msg, server):\n text = msg.get(\"text\", \"\")\n match = re.findall(r\"~wiki (.*)\", text)\n if not match:\n return\n\n searchterm = str(match[0])\n return wikiSearch(searchterm.encode(\"utf8\"))\n\non_bot_message = on_message\n\n","sub_path":"fabulous/services/wiki.py","file_name":"wiki.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"482832604","text":"from turtle import *\nfrom random import *\n\n\nr=200\n\nwindow = Screen()\nshape(\"turtle\")\nbgcolor(\"#000000\")\npencolor(\"red\")\nspeed(0)\npensize(2)\n\nfor i in range(360):\n #color(\"#00ff00\") # RGB (red green blue) (00..ff 00..ff 00..ff)\n\n #random color\n color(random(),random(),random())\n\n circle(r)\n left(1)\n\nexitonclick()\n","sub_path":"turtles/turtle4.py","file_name":"turtle4.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"236041352","text":"# -*- coding:utf-8 -*-\nfrom __future__ import print_function\n\nimport re\nimport sys\nfrom segment import Pair\nfrom segment.emoji import segment_emoji\nfrom segment.emoji import segment_emoji_pos\n\n__version__ = '3.0.0'\n__all__ = [\n 'spacing',\n 'spacing_pos',\n 'left_bracket',\n 'right_bracket',\n 'u',\n 'pangu_cut',\n 'pangu_pos_cut',\n]\n\nPY2 = (sys.version_info[0] == 2)\n\n# borrow from six\nif PY2:\n def u(s):\n return unicode(s.replace(r'\\\\', r'\\\\\\\\'), 'unicode_escape')\nelse:\n def u(s):\n return s\n\nchs_unicode = \"\\u2e80-\\u2eff\\u2f00-\\u2fdf\\u3040-\\u309f\\u30a0-\\u30ff\\u3100-\\u312f\\u3200-\\u32ff\\u3400-\\u4dbf\\u4e00-\\u9fff\\uf900-\\ufaff\"\n\n# 引号相关\n# 1-->CJK, 2-->英文单引号和双引号\nCJK_QUOTE_RE = re.compile(u(r'([{content}])([\"\\'])'.format(content=chs_unicode)))\nQUOTE_CJK_RE = re.compile(u(r'([\"\\'])([\\u3040-\\u312f\\u3200-\\u32ff\\u3400-\\u4dbf\\u4e00-\\u9fff\\uf900-\\ufaff])'))\n\n# 1-->英文单引号、双引号、园方尖括号/中文双引号, 2-->有无空格, 3-->其他内容, 4-->有无空格, 5-->与1对应相反的\nFIX_QUOTE_RE = re.compile(u(r'([\"\\'\\u201c]+)(\\s*)(.+?)(\\s*)([\"\\'\\u201d]+)'))\n# 1-->CJK, 2-->空格, 3-->英文单引号, 4-->英文字母\nFIX_SINGLE_QUOTE_RE = re.compile(u(r'([{content}])( )(\\')([A-Za-z])'.format(content=chs_unicode)))\n\n# #...#\nCJK_HASH_RE = re.compile(u(r'([#\\(\\[{content}])(#(\\S+))'.format(content=chs_unicode)))\nHASH_CJK_RE = re.compile(u(r'((\\S+)#)([#\\(\\[<{content}])'.format(content=chs_unicode)))\nFIX_HASH_RE = re.compile(u(r'(#(\\S+)#)'))\n\n# 运算符表达式\n# CJK_OPERATOR_ANS_RE = re.compile(u(r'([{content}])([\\+\\-\\*\\/=&\\\\|<>])([A-Za-z0-9])'.format(content=chs_unicode)))\nCJK_OPERATOR_ANS_RE = re.compile(u(r'([^A-Za-z0-9\\+\\-\\*\\/=&\\\\|<>])([A-Za-z0-9]+)([\\+\\-\\*\\/=&\\\\|<>])([A-Za-z0-9])'))\nANS_OPERATOR_CJK_RE = re.compile(u(r'([A-Za-z0-9])([\\+\\-\\*\\/=&\\\\|<>])([A-Za-z0-9]+)([^A-Za-z0-9\\+\\-\\*\\/=&\\\\|<>])'))\nFIX_ANS_OPERATOR_RE = re.compile(u(r'^([A-Za-z0-9\\+\\-\\*\\/=&\\\\|<>]+)$'))\n\n# 几种括号\n## \\(\\{<“《「『【「［（\nleft_bracket = \"\\(\\{<\\u201c\\u300a\\u300c\\u300e\\u3010\\u300c\\uff3b\\uff08\"\n## \\)\\}>”》」』】」］）\nright_bracket = \"\\)\\}>\\u201d\\u300b\\u300d\\u300f\\u3011\\u300d\\uff3d\\uff09\"\n\nCJK_BRACKET_CJK_RE = re.compile(u(\n r'([{content}])([{left_bracket}]+(.*?)[{right_bracket}]+)([{content}])'.format(\n content=chs_unicode,\n left_bracket=left_bracket,\n right_bracket=right_bracket\n )\n))\nCJK_BRACKET_RE = re.compile(u(\n r'([{content}])([{left_bracket}])'.format(\n content=chs_unicode,\n left_bracket=left_bracket\n )\n))\nBRACKET_CJK_RE = re.compile(u(\n r'([{right_bracket}])([{content}])'.format(\n content=chs_unicode,\n right_bracket=right_bracket\n )\n))\nFIX_BRACKET_RE = re.compile(u(\n r'^([{left_bracket}]+)(\\s*)(.+?)(\\s*)([{right_bracket}]+)$'.format(\n left_bracket=left_bracket,\n right_bracket=right_bracket\n )\n))\n\nCJK_TOPIC_CJK_RE = re.compile(u(r'([\\Sa-zA-Z0-9{content}])([\\[]+(.*?)[\\]]+)([{content}])'.format(content=chs_unicode)))\nCJK_TOPIC_CJK_RE_1 = re.compile(u(r'^([\\[]+(.*?)[\\]]+)([{content}])'.format(content=chs_unicode)))\nCJK_TOPIC_RE = re.compile(u(r'([\\Sa-zA-Z0-9{content}\\]])([\\u300c\\[])'.format(content=chs_unicode)))\nTOPIC_CJK_RE = re.compile(u(r'([\\]])([\\[\\Sa-zA-Z0-9{content}])'.format(content=chs_unicode)))\nFIX_TOPIC_RE = re.compile(u(r'^([\\[]+)(\\s*)([^\\[\\]]+?)(\\s*)([\\]]+)$'))\n\n# for 《》 only\nCJK_BOOK_BRACKET_CJK_RE = re.compile(u(r'([\\S{content}])(\\u300a(.*?)\\u300b)([{content}])'.format(content=chs_unicode)))\nCJK_BOOK_BRACKET_RE = re.compile(u(r'([\\S{content}\\u300b])(\\u300a)'.format(content=chs_unicode)))\nBOOK_BRACKET_CJK_RE = re.compile(u(r'(\\u300b)([\\S\\u300a{content}])'.format(content=chs_unicode)))\nFIX_BOOK_BRACKET_RE = re.compile(u(r'^(\\u300a)(\\s*)(.+?)(\\s*)(\\u300b)$'))\n\n# 其他符号\nFIX_SYMBOL_RE = re.compile(u(r'([{content}])([~!;:,\\.\\?\\u2026])([A-Za-z0-9])'.format(content=chs_unicode)))\n\n# 标准符号\nCJK_ANS_RE = re.compile(u(r'([{content}])([A-Za-z0-9`\\$%\\^&\\*\\-=\\+\\\\\\|/@\\u00a1-\\u00ff\\u2022\\u2027\\u2150-\\u218f])'.format(content=chs_unicode)))\nANS_CJK_RE = re.compile(u(r'([A-Za-z0-9`~\\$%\\^&\\*\\-=\\+\\\\\\|/!;:,\\.\\?\\u00a1-\\u00ff\\u2022\\u2026\\u2027\\u2150-\\u218f])([{content}])'.format(content=chs_unicode)))\n\n# 中文标点？！，。\nCJK_SYMBOL_RE = re.compile(u(r'([A-Za-z0-9`~\\$%\\^&\\*\\-=\\+\\\\\\|/!;:,\\.\\?\\u00a1-\\u00ff\\u2022\\u2026\\u2027\\u2150-\\u218f])([\\uff1f\\uff01\\uff0c\\u3002])'))\nENG_SYMBOL_ANY_RE = re.compile(u(r'([A-Za-z0-9`~\\$%\\^&\\*\\-=\\+\\\\\\|/\\u00a1-\\u00ff\\u2022\\u2026\\u2027\\u2150-\\u218f])([\\s]?)([!;:,\\.\\?])([A-Za-z0-9])'))\nENG_SYMBOL_RE = re.compile(u(r'([A-Za-z0-9`~\\$%\\^&\\*\\-=\\+\\\\\\|/\\u00a1-\\u00ff\\u2022\\u2026\\u2027\\u2150-\\u218f])([!;:,\\.\\?])'))\nFIX_ENG_RE = re.compile(u(r'^([A-Za-z]+)([\\'~\\$\\-\\+\\/\\.]+)?([A-Za-z]+)$'))\nFIX_ENG_SYMBOL_RE = re.compile(u(r'^([!;:,\\.\\?])$'))\nONE_SPACE_SYMBOL_RE = re.compile(u(r'^((\\s))$'))\n\n# re\nANS_RE = re.compile(u(r'([A-Za-z0-9`~\\$%\\^&\\*\\-=\\+\\\\\\|/!;:,\\.\\?\\u00a1-\\u00ff\\u2022\\u2026\\u2027\\u2150-\\u218f])'))\nCJK_RE = re.compile(u(r'([^\\s{content}])'))\nENG_NUM_RE = re.compile(u(r'^([A-Za-z]+[0-9]+$)'))\n\nNUM_RE_LEFT = re.compile(u(r'([^\\.0-9\\u4e00\\u4e8c\\u4e09\\u56db\\u4e94\\u516d\\u4e03\\u516b\\u4e5d\\u5341\\u3007\\u7b2c])([\\u7b2c]?[\\.0-9\\u4e00\\u4e8c\\u4e09\\u56db\\u4e94\\u516d\\u4e03\\u516b\\u4e5d\\u5341\\u3007]+[\\u767e\\u5343\\u4e07\\u4ebf])'))\nNUM_RE_RIGHT = re.compile(u(r'([\\u7b2c]?[\\.0-9\\u4e00\\u4e8c\\u4e09\\u56db\\u4e94\\u516d\\u4e03\\u516b\\u4e5d\\u5341\\u3007]+[\\u767e\\u5343\\u4e07\\u4ebf])([^\\.0-9\\u4e00\\u4e8c\\u4e09\\u56db\\u4e94\\u516d\\u4e03\\u516b\\u4e5d\\u5341\\u3007\\u7b2c])'))\nNUM_RE = re.compile(u(r'^([0-9]+)$'))\nTWO_SPACE_RE = re.compile(u(r'(\\s\\s)'))\n\n# 时间单位(年月日时分秒点, 一二三四五六七八九十〇)\nnumber_str = r'\\.0-9\\u4e00\\u4e8c\\u4e09\\u56db\\u4e94\\u516d\\u4e03\\u516b\\u4e5d\\u5341\\u3007'\ntime_unit = r'\\u53f7\\u5c0f\\u5468\\u5e74\\u6708\\u65e5\\u65f6\\u5206\\u79d2\\u70b9\\u949f:'\nTIME_RE_LEFT = re.compile(u(r'([^' + number_str + time_unit + r'])([' + number_str + r']+[\\u767e\\u5343\\u4e07\\u4ebf]?[' + time_unit + r']+)'))\nTIME_RE_RIGHT = re.compile(u(r'([' + number_str + r']+[\\u767e\\u5343\\u4e07\\u4ebf]?[' + time_unit + r']+)([^' + number_str + time_unit + r'])'))\nTIME_RE = re.compile(u(r'^[' + number_str + r']+(.*)[\\u767e\\u5343\\u4e07\\u4ebf]?[' + time_unit + r']+$'))\n\n# 数量单位(个头块件打双袋瓶箱桶碗床本台辆只冠)\nquantity_str = r'\\u76d2\\u5143\\u89d2\\u5206\\u677f\\u8bb0\\u573a\\u6b21\\u4f4d\\u4e2a\\u5934\\u5757\\u4ef6\\u6253\\u53cc\\u888b\\u74f6\\u7bb1\\u6876\\u7897\\u5e8a\\u672c\\u53f0\\u8f86\\u53ea\\u51a0'\nQUANTITY_RE_LEFT = re.compile(u(r'([^' + number_str + quantity_str + time_unit + r'\\u7b2c])([\\u7b2c]?[' + number_str + r']+[\\u767e\\u5343\\u4e07\\u4ebf]?[' + quantity_str + r'])'))\nQUANTITY_RE_RIGHT = re.compile(u(r'([\\u7b2c]?[' + number_str + r']+[\\u767e\\u5343\\u4e07\\u4ebf]?[' + quantity_str + r'])([^' + quantity_str + time_unit + r'\\u7b2c])'))\nQUANTITY_RE = re.compile(u(r'^[\\u7b2c]?[' + number_str + r']+[\\u767e\\u5343\\u4e07\\u4ebf]?[' + quantity_str + r']?$'))\n\n\ndef add_space_ANS(sentence):\n # sentence = ANS_RE.sub(r'\\1 ', sentence)\n # sentence = CJK_RE.sub(r'\\1 ', sentence)\n sentence = ENG_NUM_RE.sub(r' \\1 ', sentence)\n sentence = TWO_SPACE_RE.sub(r' ', sentence)\n return sentence.strip(' ')\n\n\ndef spacing(text, sub_cut_set):\n \"\"\"\n Perform paranoid text spacing on text. Always return unicode.\n \"\"\"\n need_quote = u\"quote\" in sub_cut_set\n only_book_bracket = u\"book\" in sub_cut_set\n need_bracket = u\"bracket\" in sub_cut_set # 这里面包括了书名号\n need_hash = u\"hashTag\" in sub_cut_set\n need_operation = u\"operation\" in sub_cut_set\n need_topic = u\"topic\" in sub_cut_set\n need_time = u\"time\" in sub_cut_set\n need_quantity = u\"quantity\" in sub_cut_set\n\n new_text = text\n\n # always use unicode\n if PY2 and isinstance(new_text, str):\n new_text = new_text.decode('utf-8')\n\n if len(new_text) < 2:\n return new_text\n\n # 引号中间分离\n if need_quote:\n new_text = CJK_QUOTE_RE.sub(r'\\1 \\2', new_text) # 分离英文引号中间的内容\n new_text = QUOTE_CJK_RE.sub(r'\\1 \\2', new_text)\n new_text = FIX_QUOTE_RE.sub(r'\\1\\3\\5', new_text) # 去掉英文单引号、双引号、园方尖括号/中文双引号 和中间文字中的空格\n new_text = FIX_SINGLE_QUOTE_RE.sub(r'\\1\\3\\4', new_text) # 去掉引号之间的空格\n\n # 去掉 #\n if need_hash:\n new_text = HASH_CJK_RE.sub(r'\\1 \\3', new_text)\n new_text = CJK_HASH_RE.sub(r'\\1 \\2', new_text)\n\n # 括号内容两端加上空格，分离出括号\n if need_bracket:\n old_text = new_text\n tmp_text = CJK_BRACKET_CJK_RE.sub(r'\\1 \\2 \\4', new_text)\n new_text = tmp_text\n if old_text == tmp_text:\n new_text = CJK_BRACKET_RE.sub(r'\\1 \\2', new_text)\n new_text = BRACKET_CJK_RE.sub(r'\\1 \\2', new_text)\n new_text = FIX_BRACKET_RE.sub(r'\\1\\3\\5', new_text)\n\n if need_topic:\n old_text = new_text\n tmp_text = CJK_TOPIC_CJK_RE.sub(r'\\1 \\2 \\4', new_text)\n tmp_text = CJK_TOPIC_CJK_RE_1.sub(r'\\1 \\3', tmp_text)\n new_text = tmp_text\n if old_text == tmp_text:\n new_text = CJK_TOPIC_RE.sub(r'\\1 \\2', new_text)\n new_text = TOPIC_CJK_RE.sub(r'\\1 \\2', new_text)\n new_text = FIX_TOPIC_RE.sub(r'\\1\\3\\5', new_text)\n\n # 括号内容两端加上空格，分离出括号\n if only_book_bracket:\n old_text = new_text\n tmp_text = CJK_BOOK_BRACKET_CJK_RE.sub(r'\\1 \\2 \\4', new_text)\n new_text = tmp_text\n if old_text == tmp_text:\n new_text = CJK_BOOK_BRACKET_RE.sub(r'\\1 \\2', new_text)\n new_text = BOOK_BRACKET_CJK_RE.sub(r'\\1 \\2', new_text)\n new_text = FIX_BOOK_BRACKET_RE.sub(r'\\1\\3\\5', new_text)\n\n cut_list = new_text.split(u\" \")\n for i, s in enumerate(cut_list):\n # 这里需要优化效率\n if need_hash and FIX_HASH_RE.search(s):\n continue\n if only_book_bracket and FIX_BOOK_BRACKET_RE.search(s):\n continue\n if need_topic and FIX_TOPIC_RE.search(s):\n continue\n if need_bracket and FIX_BRACKET_RE.search(s):\n continue\n\n # 识别表示时间或日期的内容\n if need_quantity:\n s = QUANTITY_RE_LEFT.sub(r'\\1 \\2', s)\n s = QUANTITY_RE_RIGHT.sub(r'\\1 \\2', s)\n s = NUM_RE_LEFT.sub(r'\\1 \\2', s)\n s = NUM_RE_RIGHT.sub(r'\\1 \\2', s)\n\n # 识别表示时间或日期的内容\n if need_time:\n s = TIME_RE_LEFT.sub(r'\\1 \\2', s)\n s = TIME_RE_RIGHT.sub(r'\\1 \\2', s)\n\n # 在操作符前后家空格\n if need_operation:\n s = CJK_OPERATOR_ANS_RE.sub(r'\\1 \\2\\3\\4', s)\n s = ANS_OPERATOR_CJK_RE.sub(r'\\1\\2\\3 \\4', s)\n\n # 标准符号分离\n s = FIX_SYMBOL_RE.sub(r'\\1\\2 \\3', s)\n\n # 中文标点分离\n s = CJK_SYMBOL_RE.sub(r'\\1 \\2', s)\n s = ENG_SYMBOL_RE.sub(r'\\1 \\2', s)\n s = ENG_SYMBOL_ANY_RE.sub(r'\\1\\3\\4', s)\n cut_list[i] = s\n new_text = u\" \".join(cut_list)\n\n return new_text\n\n\ndef space_eng(new_text):\n # 中英文分离\n new_text = CJK_ANS_RE.sub(r'\\1 \\2', new_text)\n new_text = ANS_CJK_RE.sub(r'\\1 \\2', new_text)\n return new_text\n\n\ndef spacing_pos(text):\n \"\"\"\n Perform paranoid text spacing on text. Always return unicode.\n \"\"\"\n new_text = text\n\n # always use unicode\n if PY2 and isinstance(new_text, str):\n new_text = new_text.decode('utf-8')\n\n ########\n if FIX_QUOTE_RE.search(new_text) or FIX_SINGLE_QUOTE_RE.search(new_text):\n return new_text, u\"quote\"\n\n if FIX_HASH_RE.search(new_text):\n return new_text, u\"hashTag\"\n\n if FIX_ANS_OPERATOR_RE.search(new_text):\n return new_text, u\"operation\"\n\n if FIX_BOOK_BRACKET_RE.search(new_text):\n return new_text, u\"book\"\n\n if FIX_TOPIC_RE.search(new_text):\n return new_text, u\"topic\"\n\n if FIX_BRACKET_RE.search(new_text):\n return new_text, u\"bracket\"\n\n ########\n if TIME_RE.search(new_text):\n return new_text, u\"time\"\n\n if QUANTITY_RE.search(new_text):\n return new_text, u\"quantity\"\n\n if FIX_ENG_SYMBOL_RE.search(new_text) or ONE_SPACE_SYMBOL_RE.search(new_text):\n return new_text, u\"w\"\n\n new_text = space_eng(new_text)\n\n if FIX_ENG_RE.search(new_text):\n return new_text, u\"eng\"\n\n if ENG_NUM_RE.search(new_text):\n return new_text, u\"eng\"\n\n if NUM_RE.search(new_text):\n return new_text, u\"m\"\n\n return new_text, None\n\n\n##########################################################\n## Main Function\n##########################################################\ndef pangu_cut(sentence_split, sub_cut_set, cut_func=None, keep_emoji=True):\n \"\"\"\n cut sentence into list by jieba\n :param sentence_split: list(sent1, sent2, ...)\n :param sub_cut_set: the one cannot be cut.\n :param cut_func:\n :param keep_emoji: bool\n :return: list(word1, word2, ...)\n \"\"\"\n output = []\n for seq in sentence_split:\n seq = seq.strip()\n if len(seq) == 0:\n continue\n temp = spacing_pos(seq)\n if temp[1] in sub_cut_set:\n output.append(seq)\n elif keep_emoji:\n output += segment_emoji(seq, mode=1, cut_func=cut_func)\n else:\n output += cut_func(seq)\n return output\n\n\ndef pangu_pos_cut(sentence_split, sub_cut_set, pos_func, keep_emoji=True):\n \"\"\"\n cut sentence with pos by jieba\n :param sentence_split: list(sent1, sent2, ...)\n :param sub_cut_set: the one cannot be cut.\n :param pos_func:\n :param keep_emoji: bool\n :return: list(Pair(word, flag))\n \"\"\"\n output = []\n for seq in sentence_split:\n seq = seq.strip()\n if len(seq) == 0:\n continue\n temp = spacing_pos(seq)\n if temp[1] in sub_cut_set:\n output.append(Pair(temp[0], temp[1]))\n elif keep_emoji:\n output += segment_emoji_pos(seq, pos_func)\n else:\n output += pos_func(seq)\n return output\n\n\n\n\n","sub_path":"src/segment/pangu_update.py","file_name":"pangu_update.py","file_ext":"py","file_size_in_byte":14131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"373874441","text":"# GoodreadsScraper.py\n# reccomended run environment: python GoodreadsScraper.py > ___OUTFILE___.csv\n# Author: Julien Legault\n\nimport urllib.request\nfrom bs4 import BeautifulSoup\n\n#Change this to the link to the list of books\n#Note it ends with ?page=\n___LISTLINK___ = 'https://www.goodreads.com/list/show/74439.YA_Novels_of_2018?page='\n\n#Change this to the number of pages on the list\n___NUMBERPAGES___ = 11\n\n#Possible itemprops: ratingValue, bookFormat, numberOfPages, name (author's name)\n___ITEMPROP___ = \"numberOfPages\"\n\n# innerSoup(href)\n# href: A string contating the extention to the book page\n# href should be of the form '/book/show/___BOOK_TITLE___'\ndef innerSoup(href):\n\tcompleteHref = \"https://www.goodreads.com\" + href\n\tinnerPage = urllib.request.urlopen(completeHref)\n\tinsideSoup = BeautifulSoup(innerPage, \"html.parser\")\n\t#Haven't run across null books, but is possible\n\tif insideSoup is not None:\n\t\ttitle = insideSoup.find('h1', {\"id\": \"bookTitle\"})\n\t\titemToFind = insideSoup.find('span', {\"itemprop\": ___ITEMPROP___})\n\t\t#removes unwanted commas to produce a csv file. quotes are already handled.\n\t\ttitleText = title.text[7:-1].replace(',', '')\n\t\t#Many books do not have many of the itemprops, if not just the title is returned.\n\t\tif itemToFind is not None:\n\t\t\tresult = titleText + \", \" + itemToFind.text\n\t\telse:\n\t\t\tresult = titleText + \",\"\n\t\tprint(result)\n\n# outerSoup(href)\n# href a string contating the link to the list, ex. ___LISTLINK___\n# loops through each book on the page, calling innerSoup on each one\ndef outerSoup(href):\n\tpage = urllib.request.urlopen(href)\n\tsoup = BeautifulSoup(page, \"html.parser\")\n\tmyA = soup.findAll(\"a\", {\"class\": \"bookTitle\"})\n\tfor link in myA:\n\t\tinnerSoup(link['href'])\n\n#main loop, just goes through every page calling outerSoup on that page\nfor i in range(1, ___NUMBERPAGES___):\n\tpageConcat = ___LISTLINK___ + str(i)\n\touterSoup(pageConcat)\n","sub_path":"GoodreadsScraper.py","file_name":"GoodreadsScraper.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"598706639","text":"import darknet.darknet as dn\ndn.set_gpu(0)\nnet = dn.load_net(b\"darknet/lp/lp.cfg\", b\"darknet/lp/lp_3000.weights\", 0)\nmeta = dn.load_meta(b\"darknet/lp/lp.data\")\n\nimport cv2, os, re\nimport numpy as np\nfrom recog_char import recognize_plate\nexpand=10\npath='test/'\nfor file in os.listdir(path):\n if re.split('[.]', file)[-1]!='jpg':\n continue\n img=cv2.imread(path+file)\n yolo_image, arr = dn.array_to_image(img)\n r=dn.detect_image(net, meta, yolo_image)\n for obj in r:\n x_center=int(round(obj[2][0]))\n y_center=int(round(obj[2][1]))\n width=int(round(obj[2][2]))\n height=int(round(obj[2][3]))\n xmin=x_center-int(round(width/2))\n ymin=y_center-int(round(height/2))\n xmax=x_center+int(round(width/2))\n ymax=y_center+int(round(height/2))\n crop=img[ymin-expand if ymin-expand >=0 else 0:ymax+expand if ymax+expand=0 else 0:xmax+expand if xmax+expand kol:\n new = count - kol # вычислсяем сколько строк(отзывов) добавилось\n for j in range(new): # цикл по вытаскиванию информации из ячеек\n count = new_count - j\n name = sh.sheet1.get('B' + str(count))\n contact = str(sh.sheet1.get('E' + str(count)) + sh.sheet1.get('J' + str(count)))\n message = str(sh.sheet1.get('G' + str(count)) + sh.sheet1.get('H' + str(count)) + sh.sheet1.get('I' + str(count)))\n \n # отправка сообщения в групповой чат\n sms = str('Имя клиента: ' + str(name) + '\\nКонтакт: ' + str(contact) + '\\nОбращение: ' + str(message))\n bot.send_message(ID, str(sms))\n time.sleep(45)\n\n if new_count > kol:\n f = open('obr.txt', 'w')\n f.writelines(str(new_count))\n f.close()\n \n\ntime.sleep(5) \nwhile True:\n do_work()\n time.sleep(120)","sub_path":"osbot.py","file_name":"osbot.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"128240900","text":"import spacy\nfrom spacy.matcher import Matcher\n\nnlp = spacy.load(\"ja_core_news_sm\")\nmatcher = Matcher(nlp.vocab)\n\ndoc = nlp(\n \"私には年の離れた小さい弟がいます。彼は、甘い卵焼きが好きです\"\n)\n\n# v2.3現在、日本語モデルではdoc.is_taggedが正しく設定されないので、\n# 明示的に設定\n# 参考: https://github.com/explosion/spaCy/issues/5802\ndoc.is_tagged = True\n\n# 形容詞と1つまたは2つの名詞からなるパターンを書きます\npattern = [{\"POS\": \"ADJ\"}, {\"POS\": \"NOUN\"}, {\"POS\": \"NOUN\", \"OP\": \"?\"}]\n\n# パターンをmatcherに追加し、docにmatcherを適用してください\nmatcher.add(\"ADJ_NOUN_PATTERN\", None, pattern)\nmatches = matcher(doc)\nprint(\"Total matches found:\", len(matches))\n\n# 結果をイテレートし、スパンの文字列をプリントしてください。\nfor match_id, start, end in matches:\n print(\"Match found:\", doc[start:end].text)\n","sub_path":"exercises/ja/solution_01_12_03.py","file_name":"solution_01_12_03.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"377716926","text":"#this code captures a frame from the webcam\r\n#and returns a resized matrix as needed\r\n\r\nimport cv2\r\nimport numpy as np\r\n\r\ncap = cv2.VideoCapture(0)\r\n\r\ndef cap_img():\r\n _,frame = cap.read()\r\n img_mat = np.array(frame)\r\n return img_mat\r\n\r\ndef cam_exit():\r\n cap.release()\r\n cv2.destroyAllWindows()\r\n","sub_path":"codes_package/img_process_store/img_cap_resize.py","file_name":"img_cap_resize.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"625256371","text":"# ===============\n# CustomLoss\n# ===============\n# ! pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ../input/nvidiaapex/repository/NVIDIA-apex-39e153a\nimport os\nimport gc\nimport sys\nimport time\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom apex import amp\nfrom os.path import join\nfrom contextlib import contextmanager\nimport torch\nimport torch.utils.data\n\nsys.path.append(\"../input/toxic-src\")\nfrom dataset import prepare_data_loader, LenMatchBatchSampler\nfrom util import seed_torch, convert_lines, setting, trim_tensors\nfrom logger import setup_logger, LOGGER\nfrom losses import CustomLoss\nfrom toxic_metric import compute_bias_metrics_for_model, get_final_metric, calculate_overall_auc\n\nsys.path.append(\"../input/ppbert/pytorch-pretrained-bert/pytorch-pretrained-BERT\")\nfrom pytorch_pretrained_bert import BertTokenizer, BertForSequenceClassification, BertAdam\nfrom pytorch_pretrained_bert import BertConfig\n\n\ndef train_one_epoch(model, train_loader, criterion, optimizer, device, accumulation_steps, total_step, n_labels,\n base_lr, steps_upd_logging=500, gamma=None):\n model.train()\n optimizer.zero_grad()\n\n total_loss = 0.0\n train_losses = []\n for step, (features, targets) in enumerate(train_loader):\n features = trim_tensors(features)\n features, targets = features.to(device), targets.to(device)\n logits = model(features, attention_mask=features > 0, labels=None)\n\n if n_labels == 1:\n loss = criterion(logits.view(-1, 1), targets.view(-1, 1))\n else:\n loss = criterion(logits, targets)\n with amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n\n if (step + 1) % accumulation_steps == 0: # Wait for several backward steps\n optimizer.step() # Now we can do an optimizer step\n optimizer.zero_grad()\n\n if gamma is not None and step == int(total_step / 2):\n for param_group in optimizer.param_groups:\n param_group['lr'] = base_lr * gamma\n\n total_loss += loss.item()\n\n if (step + 1) % steps_upd_logging == 0:\n train_losses.append(total_loss / (step + 1))\n LOGGER.info(f'Train loss on step {step + 1} was {round(total_loss / (step + 1), 5)}')\n\n return total_loss / (step + 1), train_losses\n\n\ndef validate(model, valid_loader, criterion, device, n_labels):\n for param in model.parameters():\n param.requires_grad = False\n model.eval()\n test_loss = 0.0\n oof_pred = []\n with torch.no_grad():\n\n for step, (features, targets) in enumerate(valid_loader):\n features = trim_tensors(features)\n features, targets = features.to(device), targets.to(device)\n\n logits = model(features, attention_mask=features > 0, labels=None)\n if n_labels == 1:\n loss = criterion(logits.view(-1, 1), targets.view(-1, 1))\n else:\n loss = criterion(logits, targets)\n\n test_loss += loss.item()\n oof_pred.append(torch.sigmoid(logits))\n\n oof_pred = torch.cat(oof_pred).float().cpu().numpy()\n\n LOGGER.info(f'Mean val loss: {round(test_loss / (step + 1), 5)}')\n return test_loss / (step + 1), oof_pred\n\n\ndef mkdir(path):\n try:\n os.makedirs(path)\n except:\n pass\n\n\n# ===============\n# Constants\n# ===============\nSAVE_DIR = \"./\"\nWORK_DIR = \"../working/\"\nDATA_DIR = \"../input/jigsaw-unintended-bias-in-toxicity-classification\"\nLOGGER_PATH = os.path.join(SAVE_DIR, \"log.txt\")\nTRAIN_PATH = os.path.join(DATA_DIR, \"train.csv\")\nTEST_PATH = os.path.join(DATA_DIR, \"test.csv\")\nSUB_PATH = os.path.join(DATA_DIR, \"sample_submission.csv\")\nidentity_columns = [\n 'male', 'female', 'homosexual_gay_or_lesbian', 'christian', 'jewish',\n 'muslim', 'black', 'white', 'psychiatric_or_mental_illness']\nAUX_COLUMNS = ['target', 'severe_toxicity', 'obscene', 'identity_attack', 'insult', 'threat']\nBERT_MODEL_PATH = '../input/bert-pretrained-models/uncased_l-12_h-768_a-12/uncased_L-12_H-768_A-12/'\nbert_config = BertConfig(os.path.join(BERT_MODEL_PATH, 'bert_config.json'))\n\n# ===============\n# Settings\n# ===============\nseed = 0\ndevice = \"cuda:0\"\nepochs = 1\nn_labels = len(AUX_COLUMNS) + 1\nmax_len = 300\nbatch_size = 16\nbase_lr = 2e-5\ngammas = [0.75, 0.5, 0.25]\naccumulation_steps = 8\n# train_size = 1200000\nvalid_size = 100000\nexp = \"exp5\"\nseed_torch(seed)\nsetup_logger(out_file=LOGGER_PATH)\nmkdir(WORK_DIR)\nsetting(BERT_MODEL_PATH, WORK_DIR)\n\n\n@contextmanager\ndef timer(name):\n t0 = time.time()\n yield\n LOGGER.info(f'[{name}] done in {time.time() - t0:.0f} s')\n\n\ndef convert_to_bool(df, col_name):\n df[col_name] = np.where(df[col_name] >= 0.5, True, False)\n\n\ndef convert_dataframe_to_bool(df):\n bool_df = df.copy()\n for col in ['target'] + identity_columns:\n convert_to_bool(bool_df, col)\n return bool_df\n\n\ndef main():\n # train_df = pd.read_csv(TRAIN_PATH).sample(train_size+valid_size, random_state=seed)\n train_df = pd.read_csv(TRAIN_PATH).sample(frac=1.0, random_state=seed)\n train_size = len(train_df) - valid_size\n\n # y = np.where(train_df['target'] >= 0.5, 1, 0)\n y = train_df['target'].values\n y_aux = train_df[AUX_COLUMNS].values\n\n identity_columns_new = []\n for column in identity_columns + ['target']:\n train_df[column + \"_bin\"] = np.where(train_df[column] >= 0.5, True, False)\n if column != \"target\":\n identity_columns_new.append(column + \"_bin\")\n\n # Overall\n weights = np.ones((len(train_df),)) / 4\n # Subgroup\n weights += (train_df[identity_columns].fillna(0).values >= 0.5).sum(axis=1).astype(bool).astype(np.int) / 4\n # Background Positive, Subgroup Negative\n weights += (((train_df[\"target\"].values >= 0.5).astype(bool).astype(np.int) +\n (1 - (train_df[identity_columns].fillna(0).values >= 0.5).sum(axis=1).astype(bool).astype(\n np.int))) > 1).astype(bool).astype(np.int) / 4\n # Background Negative, Subgroup Positive\n weights += (((train_df[\"target\"].values < 0.5).astype(bool).astype(np.int) +\n (train_df[identity_columns].fillna(0).values >= 0.5).sum(axis=1).astype(bool).astype(\n np.int)) > 1).astype(bool).astype(np.int) / 4\n loss_weight = 1\n\n with timer('preprocessing text'):\n # df[\"comment_text\"] = [analyzer_embed(text) for text in df[\"comment_text\"]]\n train_df['comment_text'] = train_df['comment_text'].astype(str)\n train_df = train_df.fillna(0)\n\n with timer('load embedding'):\n tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_PATH, cache_dir=None, do_lower_case=True)\n X_text, train_lengths = convert_lines(train_df[\"comment_text\"].fillna(\"DUMMY_VALUE\"), max_len, tokenizer)\n\n test_df = train_df[train_size:]\n\n with timer('train'):\n X_train, y_train, y_aux_train, w_train = X_text[:train_size], y[:train_size], y_aux[:train_size], weights[\n :train_size]\n X_val, y_val, y_aux_val, w_val = X_text[train_size:], y[train_size:], y_aux[train_size:], weights[\n train_size:]\n trn_lengths, val_lengths = train_lengths[:train_size], train_lengths[train_size:]\n model = BertForSequenceClassification.from_pretrained(WORK_DIR, cache_dir=None, num_labels=n_labels)\n model.zero_grad()\n model = model.to(device)\n\n y_train = np.concatenate((y_train.reshape(-1, 1), w_train.reshape(-1, 1), y_aux_train), axis=1)\n y_val = np.concatenate((y_val.reshape(-1, 1), w_val.reshape(-1, 1), y_aux_val), axis=1)\n\n train_dataset = torch.utils.data.TensorDataset(torch.tensor(X_train, dtype=torch.long),\n torch.tensor(y_train, dtype=torch.float))\n valid = torch.utils.data.TensorDataset(torch.tensor(X_val, dtype=torch.long),\n torch.tensor(y_val, dtype=torch.float))\n ran_sampler = torch.utils.data.RandomSampler(train_dataset)\n len_sampler = LenMatchBatchSampler(ran_sampler, batch_size=batch_size, drop_last=False)\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_sampler=len_sampler)\n valid_loader = torch.utils.data.DataLoader(valid, batch_size=batch_size * 2, shuffle=False)\n\n param_optimizer = list(model.named_parameters())\n no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n optimizer_grouped_parameters = [\n {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n ]\n\n num_train_optimization_steps = int(epochs * train_size / batch_size / accumulation_steps)\n total_step = int(epochs * train_size / batch_size)\n\n optimizer = BertAdam(optimizer_grouped_parameters,\n lr=base_lr,\n warmup=0.005,\n t_total=num_train_optimization_steps)\n\n model, optimizer = amp.initialize(model, optimizer, opt_level=\"O1\", verbosity=0)\n # criterion = torch.nn.BCEWithLogitsLoss().to(device)\n criterion = CustomLoss(loss_weight).to(device)\n\n for epoch in range(epochs):\n LOGGER.info(f\"Starting 1 epoch...\")\n tr_loss, train_losses = train_one_epoch(model, train_loader, criterion, optimizer, device,\n accumulation_steps, total_step, n_labels, base_lr,\n gamma=gammas[0])\n LOGGER.info(f'Mean train loss: {round(tr_loss,5)}')\n\n torch.save(model.state_dict(), '{}_dic_epoch{}'.format(exp, epoch))\n\n valid_loss, oof_pred = validate(model, valid_loader, criterion, device, n_labels)\n LOGGER.info(f'Mean valid loss: {round(valid_loss,5)}')\n\n del model\n gc.collect()\n torch.cuda.empty_cache()\n\n test_df[\"pred\"] = oof_pred[:, 0]\n test_df = convert_dataframe_to_bool(test_df)\n bias_metrics_df = compute_bias_metrics_for_model(test_df, identity_columns)\n LOGGER.info(bias_metrics_df)\n\n score = get_final_metric(bias_metrics_df, calculate_overall_auc(test_df))\n LOGGER.info(f'final score is {score}')\n\n test_df.to_csv(\"oof.csv\", index=False)\n\n xs = list(range(1, len(train_losses) + 1))\n plt.plot(xs, train_losses, label='Train loss');\n plt.legend();\n plt.xticks(xs);\n plt.xlabel('Iter')\n plt.savefig(\"loss.png\")\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"code/exp/exp5.py","file_name":"exp5.py","file_ext":"py","file_size_in_byte":10861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"138072061","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport json\nimport os\n\nimport pandas as pd\n\nfrom __init__ import log\nfrom . import utils\nfrom config import *\n\n\ndef parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--own-name',\n dest='own_name',\n type=str,\n help='name of the owner of the chat logs, written as in the logs',\n required=True)\n parser.add_argument(\n '-f',\n '--file-path',\n dest='file_path',\n help='Facebook chat log file (HTML file)',\n default=DEFAULT_MESSENGER_RAW_FILE)\n parser.add_argument(\n '--max',\n '--max-exported-messages',\n dest='max_exported_messages',\n type=int,\n default=MAX_EXPORTED_MESSAGES,\n help='maximum number of messages to export')\n args = parser.parse_args()\n return args\n\n\ndef main():\n args = parse_arguments()\n\n data = []\n\n # make sure we don't crash if chat logs contain exotic characters\n for root, dirs, files in os.walk(args.file_path):\n for filename in files:\n if not filename.endswith('.json'):\n continue\n\n conversation_id = root.split('/')[-1]\n conversation_with_name = None\n\n document = os.path.join(root, filename)\n with open(document) as f:\n json_data = json.load(f)\n\n if \"messages\" not in json_data or \"participants\" not in json_data:\n print(\"Missing messages or participant list in conversation {}\".format(conversation_id))\n continue\n\n participants = json_data[\"participants\"]\n\n if len(participants) < 2:\n print(\"User with id {} left Facebook, we don't know what their name was.\".format(conversation_id))\n\n if len(participants) > 2:\n # TODO handle group chats\n continue\n\n for participant in participants:\n if participant['name'] != args.own_name:\n conversation_with_name = participant['name']\n\n if conversation_with_name is None: conversation_with_name = conversation_id\n\n for message in json_data[\"messages\"]:\n timestamp = message[\"timestamp_ms\"]\n if \"content\" in message and \"sender_name\" in message:\n content = message[\"content\"]\n\n if \"sender_name\" in message:\n sender_name = message[\"sender_name\"]\n else:\n sender_name = conversation_id\n\n data += [[timestamp, conversation_id, conversation_with_name, sender_name, content]]\n\n print(len(data), 'messages parsed.')\n\n if len(data) < 1:\n print('Nothing to save.')\n exit(0)\n\n log.info('Converting to DataFrame...')\n df = pd.DataFrame(data)\n df.columns = DATAFRAME_COLUMNS\n df['platform'] = 'messenger'\n\n log.info('Detecting languages...')\n df['language'] = 'unknown'\n\n log.info('Computing dates...')\n df['datetime'] = df['timestamp'].apply(lambda x: x / 1000).apply(utils.timestamp_to_ordinal)\n\n print(df.head())\n utils.export_dataframe(df, 'messenger.pkl')\n log.info('Done.')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"parsers/messenger.py","file_name":"messenger.py","file_ext":"py","file_size_in_byte":3371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"409593610","text":"#\n# LSST Data Management System\n#\n# Copyright 2008-2016 AURA/LSST.\n#\n# This product includes software developed by the\n# LSST Project (http://www.lsst.org/).\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the LSST License Statement and\n# the GNU General Public License along with this program. If not,\n# see ')\n break\n else:\n decoded_words.append(output_lang.index2word[topi.item()])\n\n decoder_input = topi.squeeze().detach()\n\n return decoded_words, decoder_attentions[:di + 1]\n\n\n\ndef evaluateRandomly(encoder, decoder, pairs, n=bleu_num):\n scors = []\n\n print('开始计算BLEU指标...')\n for i in tqdm(range(n)):\n pair = random.choice(pairs)\n output_words, _ = evaluate(encoder, decoder, pair[0])\n output_sentence = ' '.join(output_words)\n\n reference = fiter_eos(pair[0])\n candidate = fiter_eos(output_sentence)\n score = sentence_bleu(reference, candidate)\n scors.append(score)\n\n avg_score = np.mean(scors)\n print('随机{}个句子后，评价BLEU指标为{}'.format(n, avg_score))\n\n\ndef showAttention(input_sentence, output_words, attentions):\n # Set up figure with colorbar\n fig = plt.figure()\n ax = fig.add_subplot(111)\n cax = ax.matshow(attentions.numpy(), cmap='bone')\n fig.colorbar(cax)\n\n # Set up axes\n ax.set_xticklabels([''] + input_sentence.split(' ') +\n [''], rotation=90)\n ax.set_yticklabels([''] + output_words)\n\n # Show label at every tick\n ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n\n plt.show()\n\n\ndef evaluateAndShowAttention(input_sentence):\n output_words, attentions = evaluate(\n encoder1, attn_decoder1, input_sentence)\n print('input =', input_sentence)\n print('output =', ' '.join(output_words))\n showAttention(input_sentence, output_words, attentions)\n\ndef bleu():\n\n\n encoder1.load_state_dict(torch.load(save_path_enc, map_location=device))\n attn_decoder1.load_state_dict(torch.load(save_path_att, map_location=device))\n\n evaluateRandomly(encoder1, attn_decoder1, pairs)\n\n\ndef model_train():\n\n # 数去编码器，解码器，迭代次数，显示迭代次数，开始训练\n trainIters(encoder1, attn_decoder1, epochs, print_every=display, net=[encoder1, attn_decoder1])\n\ndef test():\n\n encoder1.load_state_dict(torch.load(save_path_enc, map_location=device))\n attn_decoder1.load_state_dict(torch.load(save_path_att, map_location=device))\n count = 1\n while count < 10:\n\n print('开始测试本系统...')\n a = sys.stdout.write(\"请输入：\")\n sys.stdout.flush()\n sentence = sys.stdin.readline()\n\n output_words, _ = evaluate(encoder1, attn_decoder1, sentence)\n output_sentence = ' '.join(output_words)\n\n print('这是偶给您翻译的，看看哈~~~', output_sentence)\n\ndata_path = './data/' + samples_text_path\ninput_lang, output_lang, pairs = prepareData(sour_lang, target_lang, data_path, True)\n\nencoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device) # 输入语种的单词总数和编码器隐藏层单元数送入编码器\n\nattn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p=drop_out, max_length=MAX_LENGTH).to(device) # 将解码器单元数，输出语种的单词种类，dropout率送入待attention机制的解码器\n\n\n\n\n\n\n\n\ndef work_mode(num):\n\n if num == 0: model_train()\n\n elif num == 1: bleu()\n\n elif num == 2: print('还请星星一下我哦~~~后续添加这个功能。')\n # test()\n\n\n\nif __name__ == '__main__':\n\n\n num = 2000\n\n\n '''\n 0 : 训练模式\n 1 : BLEU模式\n 2 : 测试模式，注：测试模式暂不开放。\n '''\n\n mode = 1\n work_mode(mode)\n\n","sub_path":"NLP/pytorch-translation/translation_main.py","file_name":"translation_main.py","file_ext":"py","file_size_in_byte":14647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"45662049","text":"\"\"\"\nLC1060 -- Missing Element in Sorted Array\nGiven a sorted array A of unique numbers, find the K-th missing number starting from the leftmost number of the array.\n\n \n\nExample 1:\n\nInput: A = [4,7,9,10], K = 1\nOutput: 5\nExplanation: \nThe first missing number is 5.\nExample 2:\n\nInput: A = [4,7,9,10], K = 3\nOutput: 8\nExplanation: \nThe missing numbers are [5,6,8,...], hence the third missing number is 8.\nExample 3:\n\nInput: A = [1,2,4], K = 3\nOutput: 6\nExplanation: \nThe missing numbers are [3,5,6,7,...], hence the third missing number is 6.\n\"\"\"\n\n\n# Runtime: 348 ms, faster than 61.22% of Python3 online submissions for Missing Element in Sorted Array.\n# Memory Usage: 19.2 MB, less than 100.00% of Python3 online submissions for Missing Element in Sorted Array\n\n\n# binary search\nclass Solution:\n def missingElement(self, nums: List[int], k: int) -> int:\n if len(nums) == 1:\n return nums[0] + k\n self.nums = nums\n start, end = 0, len(nums) - 1 # start, end are all indices\n return self.binary_search(start, end, k)\n \n def binary_search(self, start, end, k):\n if end - start == 1:\n num_should_have = self.nums[end] - self.nums[start] + 1\n num_missing = num_should_have - 2\n if num_missing < k:\n return self.nums[end] + (k-num_missing)\n else:\n return self.nums[start] + k\n mid = (start + end) // 2\n # the following two lines can be simplified\n num_should_have_left = self.nums[mid] - self.nums[start] + 1\n num_missing = num_should_have_left - (mid - start + 1)\n if num_missing == k:\n return self.nums[mid] - 1\n elif num_missing < k:\n return self.binary_search(mid, end, k-num_missing)\n else:\n return self.binary_search(start, mid, k)\n\n# Runtime: 304 ms, faster than 99.71% of Python3 online submissions for Missing Element in Sorted Array.\n# Memory Usage: 19.2 MB, less than 100.00% of Python3 online submissions for Missing Element in Sorted Array.\n# no recursion version\nclass Solution:\n def missingElement(self, nums: List[int], k: int) -> int:\n l,r=0,len(nums)-1\n while l int:\n # Return how many numbers are missing until nums[idx]\n missing = lambda idx: nums[idx] - nums[0] - idx\n \n n = len(nums)\n # If kth missing number is larger than \n # the last element of the array\n if k > missing(n - 1):\n return nums[-1] + k - missing(n - 1) \n\n idx = 1\n # find idx such that \n # missing(idx - 1) < k <= missing(idx)\n while missing(idx) < k:\n idx += 1\n\n # kth missing number is larger than nums[idx - 1]\n # and smaller than nums[idx]\n return nums[idx - 1] + k - missing(idx - 1)\n ","sub_path":"Widen/LC1060_Missing_Element_in_Sorted_Array.py","file_name":"LC1060_Missing_Element_in_Sorted_Array.py","file_ext":"py","file_size_in_byte":3294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"403932050","text":"import numpy as np\nfrom sklearn.svm import SVR\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\nfrom math import sqrt\nimport utils\n\n\n\ndef process_data(data, time_lags=8):\n data = np.asarray(data, dtype=np.float32)\n dt_len = len(data)\n # 0 is pm25 while 1 is pm10\n pm2_5_ = [float(x) for x in data[:,1]]\n training_data = []\n labels = []\n history = 8\n total = 24\n for x in xrange(0, dt_len, time_lags):\n if x + total <= dt_len:\n dt = np.array(data[x:x+history,:])\n dt_future = np.array(data[x+history:x+total,6:])\n l = pm2_5_[x + total - 1]\n training_data.append(np.concatenate((dt.flatten(), dt_future.flatten())))\n labels.append(l)\n return training_data, labels\n\n\ndef train(url=\"vectors/sp_seoul_train_bin\"):\n data_train = utils.load_file(url)\n dt_len = len(data_train) / 2\n data_train = np.array(data_train[dt_len:])\n svrs = []\n for x in xrange(25):\n data = data_train[:,x,:]\n data, labels = process_data(data)\n svr = SVR()\n svr.fit(data, labels)\n svrs.append(svr)\n return svrs\n\ndef test(svrs, url=\"vectors/sp_seoul_test_bin\"):\n data = utils.load_file(url)\n data = np.array(data)\n preds = []\n labels = []\n for x in xrange(25):\n dt = data[:,x,:]\n data_train, lb = process_data(dt, time_lags=4)\n pred = svrs[x].predict(data_train)\n preds.append(pred)\n labels.append(lb)\n return preds, labels\n\n\nsvrs = train()\npreds, labels = test(svrs)\n# print(preds)\nmae = 0.0\nrmse = 0.0\nfor pr, lb in zip(preds, labels):\n mae += mean_absolute_error(lb, pr)\n rmse += sqrt(mean_squared_error(lb, pr))\n\nrmse = rmse * 12\nmae = mae * 12\n\"\"\"\n# 8 mae: 19.73 # rmse: 25.83\n# 12 mae: 21.29 rmse: 27.72\n# 16 mae: 22.38 rmse: 29.04\n# 20 mae: 23.21 rmse: 29.92\n# 24 mae: 23.81 rmse: 30.59\n\n\n# pm10 prediction\n# 8h mae: 13.72 rmse: 17.42\n# 16h mae: 14.97 rmse: 18.83\n# 24h mae: 15.74 rmse: 19.63\n\n\"\"\"\nprint(\"mae: %.2f\" % mae)\nprint(\"rmse: %.2f\" % rmse)","sub_path":"svr.py","file_name":"svr.py","file_ext":"py","file_size_in_byte":2046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"256997404","text":"import spacy\nfrom spacy.lang.en import English\nfrom spacy.matcher import Matcher\n\ndef is_float(n):\n try:\n support_float_with_norwegian_format = n.replace(',','.')\n float_n = float(support_float_with_norwegian_format)\n except ValueError:\n return False\n else:\n return True\n\ndef is_int(n):\n try:\n float_n = float(n)\n int_n = int(float_n)\n except ValueError:\n return False\n else:\n return float_n == int_n\n\ndef identify_build_date_in_text(text):\n nlp = English()\n doc = nlp(text)\n matcher = Matcher(nlp.vocab)\n\n #\n # START - spaCy patterns\n #\n\n # WATER_VESSEL\n water_vessel_pattern = [{\"LOWER\": {\"IN\": [\"vessels\"]}}]\n matcher.add(\"WATER_VESSEL\", None, water_vessel_pattern)\n\n # DATE\n matcher.add(\"DATE\", None, [{'IS_DIGIT': True, 'LENGTH': 4}])\n\n # CONSTRUCT\n matcher.add(\"CONSTRUCT\", None, [{\"LOWER\": {\"IN\": [\"constructed\"]}}])\n\n #\n # END - spaCy patterns\n #\n\n result = []\n\n for match_id, token_start, token_end in matcher(doc):\n\n match_id_as_string = nlp.vocab.strings[match_id]\n final_token_start = token_start\n final_token_end = token_end\n \n if match_id_as_string == \"DATE\" and token_start > 0:\n\n # At this point, DATE is just a year string. Example: 2021\n\n # Expand DATE?\n prev_word_1_token_number = token_start - 1\n prev_word_1_token = doc[prev_word_1_token_number]\n if prev_word_1_token.text.lower() in (\"january\",\"february\",\"march\",\"april\",\"may\",\"june\",\"july\",\"august\",\"september\",\"october\",\"november\",\"december\"):\n final_token_start = prev_word_1_token_number # expanding\n # Expand more?\n prev_word_2_token_number = token_start - 2\n prev_word_2_token = doc[prev_word_2_token_number]\n if is_int(prev_word_2_token.text):\n final_token_start = prev_word_2_token_number # expanding\n\n prev_word_on_date_token_number = final_token_start - 1\n prev_word_on_date_token = doc[prev_word_on_date_token_number]\n\n # Does the DATE have a DATE_SEPARATOR?\n if prev_word_on_date_token.text in (\"and\", \"to\"):\n prev_word_on_date_char_span_start_number = prev_word_on_date_token.idx\n prev_word_on_date_char_span_end_number = prev_word_on_date_char_span_start_number + len(prev_word_on_date_token.text)\n identified_entity = {'start': prev_word_on_date_char_span_start_number, 'end': prev_word_on_date_char_span_end_number, 'label': \"DATE_SEPARATOR\"}\n result.append(identified_entity)\n\n # Does the DATE have a DATE_SEPARATOR?\n elif prev_word_on_date_token.text in (\"between\", \"before\", \"after\"):\n # DATE_PREFIX detected\n prev_word_on_date_char_span_start_number = prev_word_on_date_token.idx\n prev_word_on_date_char_span_end_number = prev_word_on_date_char_span_start_number + len(prev_word_on_date_token.text)\n identified_entity = {'start': prev_word_on_date_char_span_start_number, 'end': prev_word_on_date_char_span_end_number, 'label': \"DATE_PREFIX\"}\n result.append(identified_entity)\n\n #\n # convert token_span to char_span.\n # char_span is needed to display correctly withdisplacy.render().\n #\n span = doc[final_token_start:final_token_end]\n span_char_start = span[0].idx\n span_char_end = span[-1].idx + len(span[-1].text)\n\n # return result\n identified_entity = {'start': span_char_start, 'end': span_char_end, 'label': match_id_as_string}\n result.append(identified_entity)\n\n return result\n\ndef merge_spacy_entity_results_to_spacy_ner_format(spacy_ner_formated_text_line,spacy_ner_entities_to_be_merged_in_as_a_list):\n text = spacy_ner_formated_text_line['text']\n ents = spacy_ner_formated_text_line['ents']\n title = spacy_ner_formated_text_line['title']\n\n if ents:\n for ent in spacy_ner_entities_to_be_merged_in_as_a_list:\n ents.append(ent)\n else:\n ents = spacy_ner_entities_to_be_merged_in_as_a_list\n\n return {'text': text, 'ents': sorted(ents, key=lambda x: x['start']), 'title': title}\n\ndef identify_build_date_in_english_spacy_lines(spacy_lines):\n\n result = []\n\n for spacy_line in spacy_lines:\n\n if spacy_line['text'] != \"\\n\" and len(spacy_line['text']) > 0:\n\n identify_date_in_text_result_in_a_list = identify_build_date_in_text(spacy_line['text'])\n\n # this result might be empty.\n\n if identify_date_in_text_result_in_a_list:\n\n # we have results to merge in.\n # merge in the result using this function.\n new_spacy_line = merge_spacy_entity_results_to_spacy_ner_format(spacy_line,identify_date_in_text_result_in_a_list)\n\n result.append(new_spacy_line)\n else:\n result.append(spacy_line)\n else:\n result.append(spacy_line)\n\n return result","sub_path":"NLP Python Jupyter Notebooks/Script - Spacy matching rule - Identify build date - EN/spacy_matching_rule_identify_build_date_en.py","file_name":"spacy_matching_rule_identify_build_date_en.py","file_ext":"py","file_size_in_byte":5107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"512916578","text":"\n# 1.feladat: Hozzunk létre egy olyan dictionary-t, amelyben a régiók a kulcsok, a bolgyók pedig az értékek!\n\n\nfh = open(\"C://Users/vend/Desktop/sw_planets.csv\",\"r\") #fájl megnyitás (\"r\") megadott últvonalról\nplanets = {} #planets nevű dictionary létrehozása\nfor line in fh: #fájl beolvasás soronként\n planet, regio = line.strip().split(\",\") #bolgyó-régió szeparáltság (vesszővel) meadása split-el, enterek figyelmen kívül hagyása strip()\n if regio in planets: planets[regio].append(planet) #amennyiben a régió már szerepel a dict-ben, akkor ezen régióhoz, mint kulcshoz rendelje az következő bolgyót, mind értéket\n else: planets[regio]=[planet] #különben (nem tartalmazza), hozzon létre egy új kulcsot értékkel együtt\n\nprint(\"1. feladat megoldása:\")\nprint(planets)\n\n# 2.feladat: Határozzuk meg, melyik régió tartalmazza a legtöbb bolygót (melyik kulcs tartalmaz legtöbb értéket)!\n\nmax=0 #\"legtöbb érték\" változó deklarálása\nreg=\"\" #a \"legtöbb értékhez\" tartozó kulcs deklarálása\nfor regio in planets: #planets dict bejárása\n if len(planets[regio])>max: #amennyiben a vizsgálandó kulcshoz tartozó értékek száma nagyobb, mint az eddigi \"legtöbb érték\"\n max=len(planets[regio]) #adja át a darabszámot a max változónak\n reg=regio #a \"legtöbb értéket\" tartalmazó kulcs átadása a reg változónak\n\nprint(\"2. feladat megoldása:\")\nprint(\"The biggest regio is {regio} with {len} planets.\".format(regio=reg, len=max))\n\n# 3.feladat: Határozzuk meg az 5 leggyakrabban használt szót a Háború és béke c. könyvből!\n\"\"\"\nkönyvtár importálása: a Counter dict-ként tárolja a kulcsokat és az értékeket, és lehetőséget biztosít három különböző\nmetódust meghívni:\nelements(): olyan értékkel tér vissza, amelyben annyiszor listázza ki az adott kulcsot, ahány értékkel rendelkezik, feltéve, \nhogy az érték nagyobb, mint 1.\nmost_common(): a kulcshoz tartozó értéket gyakoriságként tárolja és az érték szerint csökkenő sorrendben tárolja a kulcsokat.\nLehetőségünk van a zárójelbe írt db számnak megfelelő mennyiségű kulcsot kilistázni. \nsubstract(): kivonó metódus - 2 dict azonos kulcsainak értékeit kivonja egymásból. Szintaktika: c.substract(d) - c-ből\nvonja ki d-t\n\"\"\"\nfrom collections import Counter\n\nfh = open(\"C://Users/vend/Desktop/war_and_peace.txt\",\"r\") #fájl megnyitás (\"r\") megadott últvonalról\nwords = Counter() #words Counter objektum deklarálása\n\nfor line in fh: #fájl beolvasás soronként\n words.update([word.strip(\".,;\\n\") for word in line.lower().split()])\n #a words kulcsokat \".,;\" és entereket figyelmen kívül hagyva a csupa kisbetűs szövegben\n #a Counter objektum a szavak előfordulásainak megfelelő értékkel látja el\nprint(\"3. feladat megoldása:\")\nprint(words.most_common(5)) #az értékek szerinti csökkenő sorrendben tárolt kulcsok közül az első 5-öt kilistázza\n","sub_path":"Balint-potlas-lesson3.py","file_name":"Balint-potlas-lesson3.py","file_ext":"py","file_size_in_byte":3466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"326650983","text":"import os\nimport sys\nimport getopt\n\nfrom router import Router\n\n\ndef get_inputfile(argv):\n try:\n opts, _ = getopt.getopt(argv, \"f:\", ['file='])\n except getopt.GetoptError:\n print('main.py --file=')\n sys.exit(2)\n\n for opt, arg in opts:\n if opt in (\"-f\", \"--file\"):\n return arg\n\n\nif __name__ == '__main__':\n inputfile = get_inputfile(sys.argv[1:])\n if not os.path.exists(inputfile):\n print('File %s is not existed' % inputfile)\n sys.exit(1)\n\n start_at = input(\"What station are you getting on the train?:\")\n end_at = input(\"What station are you getting off the train?:\")\n\n rt = Router()\n with open(inputfile, 'r') as fr:\n rt.reload(fr.readlines())\n path = rt.path(start_at, end_at)\n if path:\n stops = rt.stops(path)\n cost = rt.cost(path)\n print('Your trip from %s to %s includes %s stops and will take %s minutes.'\n % (start_at.upper(), end_at.upper(), stops, cost))\n else:\n print('No route from %s to %s' % (start_at, end_at))\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"61563341","text":"import os\r\nimport pika\r\nimport uuid\r\nimport ujson\r\nimport base64\r\nimport cv2\r\nimport time\r\n\r\nfrom utils.label_tools import *\r\nfrom conf.config import *\r\n\r\nimport logging\r\nlogger = logging.getLogger(__name__)\r\nlogger.setLevel(level = logging.DEBUG)\r\n\r\nformatter = logging.Formatter(LOG_FORMATTER)\r\n\r\nfile_handler = logging.FileHandler(RQ_CLIENT_LOG_FILE)\r\nfile_handler.setFormatter(formatter)\r\nlogger.addHandler(file_handler)\r\n\r\nclass detection_client(object):\r\n def __init__(self, queue_name):\r\n self.queue_name = queue_name\r\n\r\n self.host_ip = RQ_HOST\r\n self.port = RQ_PORT\r\n self.heartbeat_interval = RQ_CLIENT_HEARTBEAT\r\n self.credentials = pika.PlainCredentials(RQ_USER, RQ_PWD)\r\n\r\n self.connection = pika.BlockingConnection(\r\n pika.ConnectionParameters(\r\n host = self.host_ip, port = self.port,\r\n virtual_host = '/', credentials = self.credentials,\r\n heartbeat_interval = self.heartbeat_interval\r\n )\r\n )\r\n self.channel = self.connection.channel()\r\n result = self.channel.queue_declare(queue = self.queue_name)\r\n\r\n result = self.channel.queue_declare(exclusive=True)\r\n self.callback_queue = result.method.queue\r\n\r\n self.channel.basic_consume(self.on_response, no_ack=True,\r\n queue=self.callback_queue)\r\n\r\n\r\n def on_response(self, ch, method, props, body):\r\n if self.corr_id == props.correlation_id:\r\n self.response = ujson.loads(body)\r\n\r\n def call(self, image_raw):\r\n byte_content = image_raw\r\n img_base64 = base64.b64encode(byte_content).decode('utf-8')\r\n time_stamp = time.time()\r\n\r\n json_values = {\r\n 'image':img_base64,\r\n 'timestamp': time_stamp\r\n }\r\n\r\n response_body = ujson.dumps(json_values)\r\n\r\n self.response = None\r\n self.corr_id = str(uuid.uuid4())\r\n self.channel.basic_publish(exchange='',\r\n routing_key = self.queue_name,\r\n properties = pika.BasicProperties(\r\n reply_to = self.callback_queue,\r\n correlation_id = self.corr_id,\r\n ),\r\n body = response_body)\r\n logger.info(' [x] Sent RPC requests.')\r\n while self.response is None:\r\n self.connection.process_data_events()\r\n\r\n boxes = self.response['boxes']\r\n scores = self.response['scores']\r\n label_id = self.response['label_id']\r\n label_dict = self.response['label_dict']\r\n\r\n logger.info(' [.] Get a RPC response.')\r\n\r\n label_dict = {int(label): label_dict[label] for label in label_dict}\r\n\r\n box_info = []\r\n box_info.extend(object_info(boxes, scores, label_id, label_dict))\r\n\r\n if box_info == []:\r\n logger.info(' [.] Without object <{}>.'.format(image_path))\r\n return []\r\n else:\r\n image_file_value = base64.urlsafe_b64decode(img_base64)\r\n image_np = cv2.imdecode(np.frombuffer(image_file_value, dtype='uint8'), 1)\r\n\r\n img, roi_msg = draw_box(box_info, image_np)\r\n\r\n for roi in roi_msg:\r\n logger.info(' [.] {} --> xmin: {} xmax: {} ymin: {} ymax: {} score: {} object: {}.'.format(time_stamp, roi['xmin'], roi['xmax'], roi['ymin'], roi['ymax'], roi['score'], roi['object']))\r\n\r\n return roi_msg\r\n","sub_path":"resources/rq_client.py","file_name":"rq_client.py","file_ext":"py","file_size_in_byte":3556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"443099880","text":"# Testrunner方法\nimport unittest\nimport time\nimport os.path\nimport HTMLTestRunner\nfrom email.header import Header\nfrom email.mime.text import MIMEText\nfrom email.utils import parseaddr, formataddr\nimport smtplib\n\n\n'''case = unittest.TestSuite()\ncase.addTest(Search('test_search1'))\ncase.addTest(Search('test_search2'))\ncase.addTest(MasterStation('test_log_in'))\ncase.addTest(MasterStation('test_home_page_assert'))\ncase.addTest(MasterStation('test_home_page_search'))\n'''\n\n\n# 查找最新的测试报告\ndef send_report():\n result_dir = os.path.dirname(os.path.abspath('.')) + '/testreport/'\n lists = os.listdir(result_dir)\n\n lists.sort(key=lambda fn: os.path.getmtime(result_dir + \"\\\\\" + fn))\n\n print('最新文件为：' + lists[-1])\n file = os.path.join(result_dir, lists[-1])\n print('绝对路径为：' + file)\n send_mail(file)\n\n\ndef send_mail(newfile):\n def _format_addr(s):\n name, addr = parseaddr(s)\n return formataddr((Header(name, 'utf-8').encode(), addr))\n\n with open(newfile, 'rb') as file:\n text = file.read()\n # print('-------------------------------------------%s' % text)\n from_addr = '3536046934@qq.com'\n password = 'ilxhssalsnvpchjg'\n to_addr = 'mawc@parasaga.com'\n smtp_server = 'smtp.qq.com'\n\n msg = MIMEText(text, 'html', 'utf-8')\n msg['From'] = _format_addr('tester <%s>' % from_addr)\n msg['To'] = _format_addr('管理员 <%s>' % to_addr)\n msg['Subject'] = Header('自动化测试报告', 'utf-8').encode()\n\n server = smtplib.SMTP(smtp_server, 25)\n server.set_debuglevel(1)\n server.starttls()\n server.login(from_addr, password)\n server.sendmail(from_addr, [to_addr], msg.as_string())\n server.quit()\n\n\nif __name__ == '__main__':\n # 设置报告文件保存路径 \n report_path = os.path.dirname(os.path.abspath('.')) + '/testreport/'\n # 获取系统当前时间 \n now = time.strftime(\"%Y-%m-%d-%H_%M_%S\", time.localtime(time.time()))\n # 设置报告名称格式 \n HtmlFile = report_path + now + \"HTMLtemplate.html\"\n fp = open(HtmlFile, \"wb\")\n\n # discover方法\n suite = unittest.TestLoader().discover('../testcase', pattern='test*.py')\n # 执行用例 \n runner = HTMLTestRunner.HTMLTestRunner(\n stream=fp, title=u\"RC2.3.3测试报告\", description=u\"用例测试情况\")\n runner.run(suite)\n fp.close()\n send_report()\n","sub_path":"other/Testrunner.py","file_name":"Testrunner.py","file_ext":"py","file_size_in_byte":2404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"202759424","text":"# -*- coding: utf-8 -*-\nimport csv\nimport codecs\n\n\ndef parse_csv(f, target_text, enc='utf8'):\n fieldnames = [\n 'tweet_id',\n 'in_reply_to_status_id',\n 'in_reply_to_user_id',\n 'timestamp',\n 'source',\n 'text',\n 'retweeted_status_id',\n 'retweeted_status_user_id',\n 'retweeted_status_timestamp',\n 'expanded_urls',\n ]\n\n for row in csv.DictReader(codecs.iterdecode(f, 'utf-8'), fieldnames=fieldnames):\n if row['retweeted_status_id']:\n continue\n if u\"#艦これ版深夜の真剣お絵描き60分一本勝負\" in row['text']:\n yield row\n","sub_path":"onedraw/tweets/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"426726039","text":"\"\"\"a service provision can be performed more than once\n\nRevision ID: 38de2c29ac0e\nRevises: a5e40f62a0a9\nCreate Date: 2020-03-02 16:23:14.682859\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '38de2c29ac0e'\ndown_revision = 'a5e40f62a0a9'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('service_provision', sa.Column('amount', sa.Integer(), nullable=True, default=1))\n op.execute(\"UPDATE service_provision SET amount = 1\")\n op.alter_column('service_provision', 'amount', nullable=False)\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('service_provision', 'amount')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/38de2c29ac0e_a_service_provision_can_be_performed_.py","file_name":"38de2c29ac0e_a_service_provision_can_be_performed_.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"542620823","text":"import util #For Calculate Differentiation\nimport random #For Parameter Initialization\nfrom openpyxl import load_workbook #For Loading .xlsx file\n\nEPOCHS = 100 #Linear Regression Epochs\nLEARNING_RATE = 1e-3 #Linear Regression Learning Rate\n\nUtil = util.Util(LEARNING_RATE) #Initialize Util\nrandom.seed(8)\nX, Y = [], [] #Create X and Y Data\n\nfilepath = 'data.xlsx' #Data's Location\nxl = load_workbook(filepath) #Load .xlsx file\nsheet = xl.active #Load Lastly Sheet\nmax_row = sheet.max_row #Get Data's Row\nfor i in range(1, max_row + 1): #Append Data in Each Array\n X.append(sheet.cell(row=i, column=1).value)\n Y.append(sheet.cell(row=i, column=2).value)\n\nUtil.input_data(X, Y) #Input Data to Util Class\n\nw = random.random() #Create Weight\nb = random.random() #Crate Bias\n\nfor i in range(EPOCHS):\n cost = 0 #Calculate Cost\n for j in range(max_row):\n hy = w * X[j] + b #Linear Equation\n cost = cost + (Y[j] - hy) ** 2 #Cost Formula\n cost = cost / max_row\n\n w = w + Util.Differentiation(w, b, max_row) * LEARNING_RATE #Calculate Differentiation in Util Class and multiply with LEARNING_RATE, Cause the Differentiation has big value.\n b = b + Util.Differentiation(w, b, max_row) * LEARNING_RATE #Calculate Differentiation in Util Class and multiply with LEARNING_RATE, Cause the Differentiation has big value.\n\n if i % 10 == 0:\n print('EPOCH %d - loss %f' % (i, cost))\n\nprint('w is %f' % w)\nprint('b is %f' % b)\n\nx = input(\"Enter the number what you want! \")\nprint(\"Result is %f\" % (w * int(x) + b))","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"150846386","text":"# import matplotlib.pyplot as plt\nimport numpy as np\nfrom constants_1U import RESISTANCE, INDUCTANCE, PWM_AMPLITUDE, PWM_FREQUENCY, CONTROL_STEP\nimport math\n# import time\n\n\ndef getEdgeCurrent(v_duty_cycle, I0): # to return an array with current at the edges, input: duty cycle, initial current\n t_p = 1/PWM_FREQUENCY # time period\n num_cycles = int(CONTROL_STEP/t_p) # number of cycles=n\n dt_p = np.array(v_duty_cycle * t_p) # time for which the voltage is high per cycle\n dt_n = np.array(t_p-dt_p) # time for which current is low\n edgeCurrentList = np.zeros((num_cycles*2+1, 3)) # edgeCurrentList has 2n+1 rows, 3 columns for the currents\n edgeCurrentList[0, :] = I0 # setting initial value of current\n edgeCurrentList[1, :] = 1/RESISTANCE * (PWM_AMPLITUDE-(PWM_AMPLITUDE-I0*RESISTANCE)*np.exp(-RESISTANCE*dt_p/INDUCTANCE)) # Setting value of current at first edge\n for i in range(1, num_cycles):\n edgeCurrentList[2 * i, :] = edgeCurrentList[2*i-1, :]*np.exp(-RESISTANCE*dt_n/INDUCTANCE)\n edgeCurrentList[(2 * i) + 1, :] = (PWM_AMPLITUDE - (PWM_AMPLITUDE - RESISTANCE*edgeCurrentList[2 * i, :])*np.exp(-RESISTANCE*dt_p/INDUCTANCE))/RESISTANCE\n edgeCurrentList[2 * num_cycles, :] = edgeCurrentList[2 * num_cycles - 1, :] * np.exp(-RESISTANCE * dt_n / INDUCTANCE) # Setting the current for the last edge\n return edgeCurrentList\n\n\ndef getAnalyticalCurrent(v_duty_cycle, edgeCurrentList, t): #gives current at a time instant t. input: duty cycle, list of currents at edges\n t_p = 1 / PWM_FREQUENCY # time period\n dt_p = v_duty_cycle * t_p # time for which the voltage is high per cycle\n t_mod_tp = t % t_p # time elapsed since last rising edge\n cur_cycle = int(t / t_p) # gives us the index of the cycle we're in\n current_t = np.zeros(3)\n for i in range(0, 3): # t in the comments indicates the time since last edge\n if cur_cycle >= int(CONTROL_STEP/t_p): # if t comes after the last edge, return the current at the last edge\n current_t = edgeCurrentList[cur_cycle * 2, :]\n elif t_mod_tp < dt_p[i]: # if the instant lies before th falling edge\n current_t[i] = (PWM_AMPLITUDE - (PWM_AMPLITUDE - RESISTANCE * edgeCurrentList[cur_cycle*2, i])*math.exp(-RESISTANCE*t_mod_tp/INDUCTANCE)) / RESISTANCE # I=1/R(V-(V-I0R)exp(-Rt/L))\n else:\n current_t[i] = edgeCurrentList[2*cur_cycle+1, i]*math.exp(-RESISTANCE*(t_mod_tp-dt_p[i])/INDUCTANCE) # I = I0exp(-Rt/L)\n return current_t\n\n\ndef getCurrentList(v_duty_cycle, t, n, edgeCurrentList): # gives current values at time instants in a given array input: duty cycle, array of time instants, number of time instants, initial current\n currentList = np.zeros((n, 3))\n for i in range(0, n):\n currentList[i, :] = getAnalyticalCurrent(v_duty_cycle, edgeCurrentList, t[i]) # current[ti]\n return currentList\n\n'''\n# this was used to compare the time taken for execution, however, it remains to be seen whether the execution in the\n# actual program takes a proportional amount of time\nduty_cycle = np.array([0.001, 0.000001, 0.000001])\nn = 40000\nt_array = np.linspace(0, 0.02, n, endpoint=False)\nI0 = np.array([0, 0, 0])\n# currentList = getCurrentList(duty_cycle, t_array, n, I0)\n# plt.plot(t_array[:], currentList[:, 2])\n# plt.show()\nactstart = time.time()\ncurrent = getCurrentList(duty_cycle, t_array, n, I0)\nactend = time.time()\nprint(actend - actstart)\nprint(CONTROL_STEP%(1/PWM_FREQUENCY), PWM_FREQUENCY, CONTROL_STEP, 1/PWM_FREQUENCY)\n'''","sub_path":"analytical_act_current.py","file_name":"analytical_act_current.py","file_ext":"py","file_size_in_byte":3542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"308588441","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nfrom polyaxon_lib.estimators.agents import Agent\nfrom polyaxon_lib.experiments import Experiment\n\n\nclass RLExperiment(Experiment):\n \"\"\"Experiment is a class containing all information needed to train an agent.\n\n After an experiment is created (by passing an Agent for training and evaluation),\n an Experiment instance knows how to invoke training and eval loops in\n a sensible fashion for distributed training.\n\n\n None of the functions passed to this constructor are executed at construction time.\n They are stored and used when a method is executed which requires it.\n\n Args:\n agent: Object implementing an Agent.\n train_steps: Perform this many steps of training. default: None, means train forever.\n train_episodes: Perform this many episodes of training. default: None, means train forever.\n first_update: First timestep to calculate `loss` and `train_op`. This is related to the\n `global_timestep` variable, number of timesteps in episodes.\n update_frequency: The frequency at which we should calculate `loss` and `train_op`.\n This frequency is related to the `gloabl_step` which is incremented every time\n we update the network.\n eval_steps: `evaluate` runs until input is exhausted (or another exception is raised),\n or for `eval_steps` steps, if specified.\n train_hooks: A list of monitors to pass to the `Agent`'s `fit` function.\n eval_hooks: A list of `SessionRunHook` hooks to pass to\n the `Agent`'s `evaluate` function.\n eval_delay_secs: Start evaluating after waiting for this many seconds.\n continuous_eval_throttle_secs: Do not re-evaluate unless the last evaluation\n was started at least this many seconds ago for continuous_eval().\n delay_workers_by_global_step: if `True` delays training workers based on global step\n instead of time.\n export_strategies: A list of `ExportStrategy`s, or a single one, or None.\n train_steps_per_iteration: (applies only to continuous_train_and_eval).\n Perform this many (integer) number of train steps for each training-evaluation\n iteration. With a small value, the model will be evaluated more frequently\n with more checkpoints saved. If `None`, will use a default value\n (which is smaller than `train_steps` if provided).\n\n Raises:\n ValueError: if `estimator` does not implement Estimator interface,\n or if export_strategies has the wrong type.\n \"\"\"\n\n def __init__(self,\n agent,\n env,\n train_steps=None,\n train_episodes=None,\n first_update=5000,\n update_frequency=15,\n eval_steps=10,\n train_hooks=None,\n eval_hooks=None,\n eval_delay_secs=0,\n continuous_eval_throttle_secs=60,\n delay_workers_by_global_step=False,\n export_strategies=None,\n train_steps_per_iteration=100):\n if not isinstance(agent, Agent):\n raise ValueError(\"`estimator` must implement `Estimator`.\")\n\n super(RLExperiment, self).__init__(\n estimator=agent,\n train_input_fn=None,\n eval_input_fn=None,\n train_steps=train_steps,\n eval_steps=eval_steps,\n train_hooks=train_hooks,\n eval_hooks=eval_hooks,\n eval_delay_secs=eval_delay_secs,\n continuous_eval_throttle_secs=continuous_eval_throttle_secs,\n delay_workers_by_global_step=delay_workers_by_global_step,\n export_strategies=export_strategies,\n train_steps_per_iteration=train_steps_per_iteration\n )\n self._agent = agent\n self._env = env\n self._train_episodes = train_episodes\n self._first_update = first_update\n self._update_frequency = update_frequency\n\n @property\n def agent(self):\n return self._agent\n\n def _call_train(self, # pylint: disable=arguments-differ\n steps=None, first_update=None, update_frequency=None, episodes=None,\n hooks=None, max_steps=None, max_episodes=None):\n return self._agent.train(env=self._env, first_update=first_update,\n update_frequency=update_frequency, episodes=episodes, steps=steps,\n max_steps=max_steps, max_episodes=max_episodes, hooks=hooks)\n\n def train(self, delay_secs=None):\n \"\"\"Fit the agent.\n\n Train the agent for `self._train_steps` steps, after waiting for `delay_secs` seconds.\n If `self._train_steps` is `None`, train forever.\n\n Args:\n delay_secs: Start training after this many seconds.\n\n Returns:\n The trained estimator.\n \"\"\"\n delay_secs, extra_hooks = self._prepare_train(delay_secs)\n\n return self._call_train(\n first_update=self._first_update, update_frequency=self._update_frequency,\n max_steps=self._train_steps, max_episodes=self._train_episodes,\n hooks=self._train_hooks + extra_hooks)\n","sub_path":"polyaxon_lib/experiments/rl_experiment.py","file_name":"rl_experiment.py","file_ext":"py","file_size_in_byte":5333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"482506131","text":"#!/usr/bin/python\n\nimport sqlite3\n\nconn = sqlite3.connect(r\"D:\\\\Github\\\\hvAuction\\\\python\\\\data\\\\auction.db\")\nc = conn.cursor()\nprint(\"Opened database successfully\")\n\ncursor = c.execute(\"SELECT ID,SELLER,NAME,LINK,FEATURES,PRICE,WINNER,TIME,LOG from ISK005\")\n\nfor row in cursor:\n print(row)\n\nprint(\"Operation done successfully\")\nconn.close()\n","sub_path":"python/package/show_table.py","file_name":"show_table.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"98722696","text":"\n\n\"\"\"\n\nfollowing the midpoints of shifted bubbles\n\nstill takes 230 iterations and around 6s \n\ncompared to regular OCP (v5) with two path parameters: 143 iterations and 4.5 s \n\nso basically its the same or a bit worse \n\n\"\"\"\n\nimport numpy as np\nfrom numpy import pi, cos, sin\nimport matplotlib.pyplot as plt\nfrom scipy import interpolate\n\nfrom casadi import Function, linspace, vertcat, horzcat, DM, interpolant, sum1, MX, hcat, sumsqr\nfrom rockit import *\nfrom rockit import Ocp , FreeTime, MultipleShooting\n\nfrom Bubble_tunnel_generation_v2 import generate_bubbles_v2, plotting_v2, create_tunnel, generate_bubbles_v3\nfrom Grid_generation import create_obstacles, create_global_path\n\n\n#----------------------------------------------------------------------------#\n# Generate Grid and Obstacles #\n#----------------------------------------------------------------------------#\n\nend_goal_x = 9 # position of initial and end point\nend_goal_y = 9\ninitial_pos_x = 0\ninitial_pos_y = 0\nxlim_min = -1\nxlim_max = 13\nylim_min = -1\nylim_max = 11\n\n\nobstacles_option = 1 \npath_option = 1 #options are 1 or 2\n\noccupied_positions_x, occupied_positions_y = create_obstacles(obstacles_option)\nBspline_obj, global_path = create_global_path(path_option)\n\n#----------------------------------------------------------------------------#\n# Creating the Bubbles #\n#----------------------------------------------------------------------------#\n\n\n#using new function \n\nshifted_midpoints_x, shifted_midpoints_y, shifted_radii = generate_bubbles_v2(global_path[0],global_path[1],occupied_positions_x,occupied_positions_y)\n\n# points_x, points_y,cx, cy, shifted_radii = generate_bubbles_v3(global_path[0],global_path[1],occupied_positions_x,occupied_positions_y)\n\n\n# plotting_v2(initial_pos_x, end_goal_x, global_path, occupied_positions_x, occupied_positions_y,\\\n # xlim_min, xlim_max, ylim_min, ylim_max,\\\n # shifted_midpoints_x, shifted_midpoints_y, shifted_radii)\n\n \n\n#-------------------------------------------------------------------------------#\n# Define the optimal control problem #\n#-------------------------------------------------------------------------------#\n\n\nN = 30 # number of control intervals #20 works for path option 1\ndt = 3 # horizon\n\nocp = Ocp(T = N*dt) \n\n\n\n# System model\nx = ocp.state()\ny = ocp.state()\ntheta = ocp.state()\nv = ocp.control()\nw = ocp.control()\n\n#path parameters s1 and s2\ns1 = ocp.state()\nsdot1 = ocp.control()\n\n#ODEs\nocp.set_der(x , v*cos(theta))\nocp.set_der(y , v*sin(theta))\nocp.set_der(theta , w)\nocp.set_der(s1 , sdot1)\n\n\n\n\n# Constraints at t0\nocp.subject_to(ocp.at_t0(x) == initial_pos_x)\nocp.subject_to(ocp.at_t0(y) == initial_pos_y)\nocp.subject_to(ocp.at_t0(theta) == 0.0)\nocp.subject_to(ocp.at_t0(s1) == 0.0) \n\n\n#--------------------- Constraints at tf ----------------------\n\n\npf = vertcat(end_goal_x,end_goal_y) # end point\n\n\n\n#constraints on controls \nocp.subject_to( 0 <= ( v <= 1 ))\nocp.subject_to( -pi <= ( w <= pi ))\nocp.subject_to( sdot1 >= 0) \n\n\n\n#------------ Obscatles avoidance tunnel ---------------------------\n\nbubbles_radii = shifted_radii\nbubbles_x = shifted_midpoints_x\nbubbles_y = shifted_midpoints_y\n\ntlength1 = len(bubbles_x)\ntunnel_s1 = np.linspace(0,1,tlength1) \n\n\nocp.subject_to(ocp.at_tf(s1) == 1) \n\n\nobs_spline_x = interpolant('obs_spline_x','bspline',[tunnel_s1],bubbles_x , {\"algorithm\": \"smooth_linear\",\"smooth_linear_frac\":0.49})\nobs_spline_y = interpolant('obs_spline_y','bspline',[tunnel_s1],bubbles_y , {\"algorithm\": \"smooth_linear\",\"smooth_linear_frac\":0.49})\nobs_spline_r = interpolant('obs_spline_r','bspline',[tunnel_s1],bubbles_radii , {\"algorithm\": \"smooth_linear\",\"smooth_linear_frac\":0.49})\n\n\n\n# ------------------------ Initial guess ------------------------\n\n\n \nBspline_obj, u = interpolate.splprep([shifted_midpoints_x,shifted_midpoints_y], u = None, s = 0)\nu = np.linspace(0,1,N )\npath_guess = interpolate.splev(u, Bspline_obj)\n\npath_guess_x = np.array(path_guess[0])\npath_guess_y = np.array(path_guess[1])\n\n\nocp.set_initial(x, path_guess_x) \nocp.set_initial(y, path_guess_y) \n\n\n\n#path parameters\ns1_guess = np.linspace(tunnel_s1[0],tunnel_s1[-3], N)\nsdot1_guess = (tunnel_s1[-1]-tunnel_s1[0])/tlength1 \n\nocp.set_initial(s1, s1_guess) \nocp.set_initial(sdot1, sdot1_guess)\n\n\n#constraints on control inputs have a slight positive effect on solution time\n\nv_guess = 0.5*np.ones(N)\nocp.set_initial(v , v_guess)\n\nw_guess = np.zeros(N)\nocp.set_initial(w , w_guess)\n\n\n#---------------- constraints -------------------\n\n\nocp.subject_to( ( ( x - obs_spline_x(s1) )**2 + ( y-obs_spline_y(s1) )**2 < (obs_spline_r(s1)**2 )) )\n\n# ------------- Objective function ----------------------------------------\n\n#path following\nocp.add_objective(1*ocp.integral((x - obs_spline_x(s1))**2 + (y-obs_spline_y(s1))**2)) \n\n# ocp.add_objective(-ocp.at_tf(s1))\n\n\n# ------------- Solution method------------------------------------------\n\noptions = {\"ipopt\": {\"print_level\": 5}}\noptions[\"expand\"] = False\noptions[\"print_time\"] = True\nocp.solver('ipopt', options)\n\n\n# Multiple shooting\nocp.method(MultipleShooting(N=N,M=2,intg='rk'))\n\n#-------------------------------------------------------------------------------#\n# OCP Solution and Results #\n#-------------------------------------------------------------------------------#\n\ntry:\n sol = ocp.solve()\nexcept:\n ocp.show_infeasibilities(1e-6)\n sol = ocp.non_converged_solution\n\n\n\n\n#---------------------- extract solution\n\ntsol, xsol = sol.sample(x, grid='control')\ntsol, ysol = sol.sample(y, grid='control')\ntsol, s1 = sol.sample(s1, grid='control')\ntsol, xsol_refined = sol.sample(x, grid='integrator',refine=100)\ntsol, ysol_refined = sol.sample(y, grid='integrator',refine=100)\n\n\n\n#------------------------ Plotting with path/bubbles depending on path parameter solution\n\n# xspline_path = np.array(path_spline_x(s1))\n# yspline_path = np.array(path_spline_y(s1))\nplt.figure(dpi=300)\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\nplt.xlim([xlim_min,xlim_max])\nplt.ylim([ylim_min,ylim_max])\nts = np.linspace(0,2*np.pi,50)\nxspline_obs = np.array(obs_spline_x(s1))\nyspline_obs = np.array(obs_spline_y(s1))\nrspline_obs = np.array(obs_spline_r(s1))\nfor i in range(s1.shape[0]): plt.plot(xspline_obs[i]+rspline_obs[i]*cos(ts),yspline_obs[i]+rspline_obs[i]*sin(ts),'r-',markersize = 0.5)\n# plt.plot(xspline_path, yspline_path, 'g--')\nplt.plot(xsol, ysol,'bo')\nplt.plot(xsol_refined, ysol_refined, '--')\nplt.plot(occupied_positions_x,occupied_positions_y,'bo',markersize = 1.5)\nplt.title('OCP solution')\nplt.xlim([xlim_min,xlim_max])\nplt.ylim([ylim_min,ylim_max])\nplt.show(block=True)\n\n\n\n\n#------------------ Plotting with respect to initial path/bubbles\n\n\ntunnel_x, tunnel_y = create_tunnel(shifted_midpoints_x,shifted_midpoints_y,shifted_radii)\n \n\n\nplt.figure(dpi=300)\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\nplt.xlim([xlim_min,xlim_max])\nplt.ylim([ylim_min,ylim_max])\nplt.plot(global_path[0], global_path[1], 'c--')\nplt.plot(occupied_positions_x,occupied_positions_y,'ko',markersize = 1)\nplt.plot(tunnel_x, tunnel_y, 'r.', markersize= 1)\nplt.plot(xsol, ysol,'bo', markersize = 5)\nplt.plot(xsol, ysol,'b-', markersize = 3)\nplt.legend(['original path','Obstacles', 'Feasible tunnel', 'Solution points', 'Solution trajectory'])\nplt.title('OCP Solutin with given path and tunnel')\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\n# plt.savefig('OCP Solution', dpi=300)\n\n\nplt.figure(dpi=300)\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\nplt.xlim([xlim_min,xlim_max])\nplt.ylim([ylim_min,ylim_max])\nplt.plot(global_path[0], global_path[1], 'g--')\nplt.plot(occupied_positions_x,occupied_positions_y,'ko',markersize = 1)\nplt.plot(tunnel_x, tunnel_y, 'r.', markersize= 1)\nplt.plot(0,0,'bx', markersize = 10)\nplt.plot(9,9,'rx', markersize = 10)\nplt.legend(['original path','Obstacles', 'Feasible tunnel', 'Starting point', 'End point'])\nplt.title('Global Path and feasible tunnel')\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\n# plt.savefig('OCP problem', dpi=300)\n\n\nplt.figure(dpi=300)\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\nplt.xlim([1,3])\nplt.ylim([8,11])\nplt.plot(global_path[0], global_path[1], 'g--')\nplt.plot(occupied_positions_x,occupied_positions_y,'bo',markersize = 2)\nplt.plot(tunnel_x, tunnel_y, 'r.', markersize= 3)\nplt.plot(xsol, ysol,'bo', markersize = 5)\nplt.plot(xsol, ysol,'b-', markersize = 3)\nplt.legend(['original path','Obstacles', 'Feasible tunnel', 'Solution points', 'Solution trajectory'])\nplt.title('OCP Solutin with given path and tunnel')\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\nplt.savefig('OCP Solution 1', dpi=300)\n\n\nplt.figure(dpi = 300)\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\nplt.xlim([2.9,6])\nplt.ylim([-0.1,3])\nplt.plot(global_path[0], global_path[1], 'g--')\nplt.plot(occupied_positions_x,occupied_positions_y,'bx',markersize = 3)\nplt.plot(tunnel_x, tunnel_y, 'r.', markersize= 3)\nplt.plot(xsol, ysol,'bo', markersize = 5)\nplt.plot(xsol, ysol,'b-', markersize = 3)\nplt.legend(['original path','Obstacles', 'Feasible tunnel', 'Solution points', 'Solution trajectory'], loc = (0.6,0.6))\nplt.title('OCP Solutin with given path and tunnel')\nplt.xlabel('x [m]')\nplt.ylabel('y [m]')\n\n","sub_path":"March27/dev_waypoints/other/OCP_one_path_param.py","file_name":"OCP_one_path_param.py","file_ext":"py","file_size_in_byte":9708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"456049990","text":"#! /usr/bin/env python\n##########################################################################\n# CASPER - Copyright (C) AGrigis, 2013\n# Distributed under the terms of the CeCILL-B license, as published by\n# the CEA-CNRS-INRIA. Refer to the LICENSE file or to\n# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html\n# for details.\n##########################################################################\n\n# Casper import\nfrom .base import Base\n\n\nclass List(Base):\n \"\"\" Define a list parameter.\n \"\"\"\n def __init__(self, value=None, *args, **kwargs):\n \"\"\" Initialize the 'List' class.\n\n Expect a 'content' string argument that describe the list content.\n\n Parameters\n ----------\n content: str (mandatory)\n description of the list content. If iterative object are contained\n use the '_' character as a separator: 'Int' or 'List_Int'.\n value: object (optional, default None)\n the parameter value.\n \"\"\"\n # Avoid cycling import\n from casper.lib.controls import controls\n\n # Check if a 'content' argument has been defined\n if \"content\" not in kwargs:\n raise ValueError(\"A 'content' argument describing the 'List' \"\n \"content is expected.\")\n\n # Create an inner control\n inner_desc = kwargs[\"content\"].split(\"_\")\n control_type = inner_desc[0]\n inner_kwargs = {\"inner\": True}\n if len(inner_desc) > 1:\n inner_kwargs[\"content\"] = \"_\".join(inner_desc[1:])\n if control_type not in controls:\n raise ValueError(\"List creation: '{0}' is not a valid inner \"\n \"control type. Allowed types are {1}.\".format(\n kwargs[\"content\"], controls.keys()))\n self.inner_control = controls[control_type](**inner_kwargs)\n\n # Create the list control\n Base.__init__(self, value, *args, **kwargs)\n self.iterable = True\n\n def _is_valid(self, value):\n \"\"\" A method used to check the value type.\n\n Parameters\n ----------\n value: object (mandatory)\n the value we want to check the type.\n\n Returns\n -------\n is_valid: bool\n return True if the value as the expected type,\n False otherwise.\n \"\"\"\n if value is None:\n return True\n elif isinstance(value, list):\n for item in value:\n if not self.inner_control._is_valid(item):\n return False\n return True\n else:\n return False\n","sub_path":"casper/lib/controls/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":2639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"23971134","text":"\"\"\"\n__Skype__ = \"just...elvis\"\n__author__ = \"Elvis\"\n__version__ = \"1.5\"\n\"\"\"\n\nimport xml.etree.ElementTree as ETree\nimport os as os\nimport uuid as uuid\nimport configparser as cfg\n\nconfig = cfg.ConfigParser()\nconfig.read('Config.ini', encoding='utf_8')\nWSDLDir = config['DEFAULT']['WSDLDir'] + '\\\\'\nNameFile = config['DEFAULT']['NameFile']\nhostEndpoint = config['DEFAULT']['HostEndpoint'].split(',')\nns = {'wsdl': 'http://schemas.xmlsoap.org/wsdl/', 'soap': 'http://schemas.xmlsoap.org/wsdl/soap/', 'xs': 'http://www.w3.org/2001/XMLSchema'}\n\ndef getguid():\n\treturn str(uuid.uuid4())\n\nSUIProject = ETree.Element('con:soapui-project')\nSUIProject.set('id', getguid())\nSUIProject.set('activeEnvironment', 'Default')\nSUIProject.set('soapui-version', '5.2.0')\nSUIProject.set('abortOnError', 'false')\nSUIProject.set('runType', 'SEQUENTIAL')\nSUIProject.set('xmlns:con', 'http://eviware.com/soapui/config')\nsettings = ETree.SubElement(SUIProject, 'con:settings')\nfor top, dirs, files in os.walk(WSDLDir):\n\tfor file in files:\n\t\tif file.endswith('.wsdl'):\n\t\t\tinterface = ETree.SubElement(SUIProject, 'con:interface')\n\t\t\tinterface.set('xsi:type', 'con:WsdlInterface')\n\t\t\tinterface.set('id', getguid())\n\t\t\tinterface.set('wsaVersion', 'NONE')\n\t\t\tinterface.set('type', 'wsdl')\n\t\t\tinterface.set('soapVersion', '1_1')\n\t\t\tinterface.set('anonymous', 'optional')\n\t\t\tinterface.set('xmlns:xsi','http://www.w3.org/2001/XMLSchema-instance')\n\t\t\ttree = ETree.parse(top + '\\\\' + file)\n\t\t\troot = tree.getroot()\n\t\t\tfor findVersion in root.findall(\"./wsdl:types/xs:schema\", ns):\n\t\t\t\tversionTemp = findVersion.get('version')\n\t\t\tfor findBind in root.findall(\"./wsdl:binding\", ns):\n\t\t\t\tbindTemp = findBind.get('name')\n\t\t\t\tif 'common' in file:\n\t\t\t\t\tbind = bindTemp + 'Common'\n\t\t\t\telse:\n\t\t\t\t\tbind = bindTemp\n\t\t\t\tinterface.set('name', bind + '(' + str(versionTemp) + ')')\n\t\t\tfor findBindName in root.findall(\".\", ns):\n\t\t\t\ttns = findBindName.get('targetNamespace')\n\t\t\t\tbindName = '{' + tns + '}' + bindTemp\n\t\t\t\tinterface.set('bindingName', bindName)\n\t\t\tdefinition = 'file:/' + top.replace('\\\\','/') + '/' + file\n\t\t\tinterface.set('definition', definition)\n\t\t\tinterfaceSettings = ETree.SubElement(interface, 'con:settings')\n\t\t\tdefinitionCache = ETree.SubElement(interface, 'con:definitionCache')\n\t\t\tendpoints = ETree.SubElement(interface, 'con:endpoints')\n\t\t\tx = str(tns).split('/')\n\t\t\tfor host in hostEndpoint:\n\t\t\t\tendpoint = ETree.SubElement(endpoints, 'con:endpoint')\n\t\t\t\tif 'infrastructure' in str(x[5]):\n\t\t\t\t\tendpointText = 'rki-service'\n\t\t\t\telif 'services-service' in str(x[5]):\n\t\t\t\t\tendpointText = 'organization-service'\n\t\t\t\telif 'capital-repair-service' in str(x[5]):\n\t\t\t\t\tendpointText = 'capital-repair-programs-service'\n\t\t\t\telif 'house-management-service' in str(x[5]):\n\t\t\t\t\tendpointText = 'home-management-service'\n\t\t\t\telif 'organizations-registry-common-service' in str(x[5]):\n\t\t\t\t\tendpointText = 'org-registry-common-service'\n\t\t\t\telif 'disclosure-service-async' in str(x[5]):\n\t\t\t\t\tendpointText = 'information-disclosure-service'\n\t\t\t\telif 'organizations-registry-common-service' in str(x[5]):\n\t\t\t\t\tendpointText = 'org-registry-common-service'\n\t\t\t\telif 'organizations-registry-service' in str(x[5]):\n\t\t\t\t\tendpointText = 'org-registry-service'\n\t\t\t\telse:\n\t\t\t\t\tendpointText = str(x[5])\n\t\t\t\tendpoint.text = host + 'ext-bus-' + endpointText.replace('-async','') + '/services/' + config['ServiceMetod'][bind]\n\t\t\tfor findOperation in root.findall(\"./wsdl:binding/wsdl:operation\", ns):\n\t\t\t\toperation = ETree.SubElement(interface, 'con:operation')\n\t\t\t\toperation.set('id', getguid())\n\t\t\t\toperation.set('isOneWay', 'false')\n\t\t\t\toperation.set('type', 'Request-Response')\n\t\t\t\toperation.set('inputName', '')\n\t\t\t\toperation.set('receivesAttachments', 'false')\n\t\t\t\toperation.set('sendsAttachments', 'false')\n\t\t\t\toperation.set('anonymous', 'optional')\n\t\t\t\toperanionName = findOperation.get('name')\n\t\t\t\toperation.set('action', 'urn:' + operanionName)\n\t\t\t\toperation.set('name', operanionName)\n\t\t\t\toperation.set('bindingOperationName', operanionName)\n\t\t\t\tETree.SubElement(operation, 'con:settings')\n#versionFile = top[top.rfind('v.')+2:top.rfind('\\\\')]\nversionFile = NameFile\nSUIProject.set('name', versionFile)\ntrees = ETree.ElementTree(SUIProject)\ntrees.write(versionFile + '.xml', encoding=\"utf-8\")","sub_path":"SUIPCreator/SUIPCreator.py","file_name":"SUIPCreator.py","file_ext":"py","file_size_in_byte":4250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"560910691","text":"import sys\n\ndef is_even(num):\n return num % 2 == 0\n\nval = input()\nif int(val) % 2 == 1:\n print(0)\nelse:\n counter = 0\n digits = len(val)\n for i, num in enumerate(val[::-1]):\n if i+1 != digits:\n counter += i * 9\n else:\n counter += i * int(num)\n print(counter)\n","sub_path":"abc_148/e.py","file_name":"e.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"25094419","text":"# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Read and preprocess image data.\n\n Image processing occurs on a single image at a time. Image are read and\n preprocessed in parallel across multiple threads. The resulting images\n are concatenated together to form a single batch for training or evaluation.\n\n -- Provide processed image data for a network:\n inputs: Construct batches of evaluation examples of images.\n distorted_inputs: Construct batches of training examples of images.\n batch_inputs: Construct batches of training or evaluation examples of images.\n\n -- Data processing:\n parse_example_proto: Parses an Example proto containing a training example\n of an image.\n\n -- Image preprocessing:\n image_preprocessing: Decode and preprocess one image for evaluation or training\n eval_image: Prepare one image for evaluation.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nFLAGS = tf.app.flags.FLAGS\n\ntf.app.flags.DEFINE_integer('batch_size', 1,\n \"\"\"Number of images to process in a batch.\"\"\")\ntf.app.flags.DEFINE_integer('image_height', 540,\n \"\"\"Provide image_height.\"\"\")\ntf.app.flags.DEFINE_integer('image_width', 960,\n \"\"\"Provide image_width.\"\"\") \ntf.app.flags.DEFINE_integer('cropped_height', 256,\n \"\"\"Provide cropped_height.\"\"\")\ntf.app.flags.DEFINE_integer('cropped_width', 512,\n \"\"\"Provide cropped_width.\"\"\") \ntf.app.flags.DEFINE_integer('depth', 3,\n \"\"\"Provide depth.\"\"\") \ntf.app.flags.DEFINE_integer('num_preprocess_threads', 4,\n \"\"\"Number of preprocessing threads per tower. \"\"\"\n \"\"\"Please make this a multiple of 4.\"\"\")\ntf.app.flags.DEFINE_integer('num_readers', 4,\n \"\"\"Number of parallel readers during train.\"\"\")\n\n# Images are preprocessed asynchronously using multiple threads specified by\n# --num_preprocss_threads and the resulting processed images are stored in a\n# random shuffling queue. The shuffling queue dequeues --batch_size images\n# for processing on a given Inception tower. A larger shuffling queue guarantees\n# better mixing across examples within a batch and results in slightly higher\n# predictive performance in a trained model. Empirically,\n# --input_queue_memory_factor=16 works well. A value of 16 implies a queue size\n# of 1024*16 images. Assuming RGB 299x299 images, this implies a queue size of\n# 16GB. If the machine is memory limited, then decrease this factor to\n# decrease the CPU memory footprint, accordingly.\ntf.app.flags.DEFINE_integer('input_queue_memory_factor', 16,\n \"\"\"Size of the queue of preprocessed images. \"\"\"\n \"\"\"Default is ideal but try smaller values, e.g. \"\"\"\n \"\"\"4, 2 or 1, if host memory is constrained. See \"\"\"\n \"\"\"comments in code for more details.\"\"\")\n\nprint(\"import image processing\")\n\ndef inputs(dataset, batch_size=None, num_preprocess_threads=None):\n \"\"\"Generate batches of SceneFlow images for evaluation.\n\n Use this function as the inputs for evaluating a network.\n\n Note that some (minimal) image preprocessing occurs during evaluation\n including central cropping.\n\n Args:\n dataset: instance of Dataset class specifying the dataset.\n batch_size: integer, number of examples in batch\n num_preprocess_threads: integer, total number of preprocessing threads but\n None defaults to FLAGS.num_preprocess_threads.\n\n Returns:\n left_images, right_images: Images. 4D tensor of size [batch_size, FLAGS.cropped_height,\n cropped_width, 3].\n disparitys, masks: 3D tensor of size [batch_size, FLAGS.cropped_height, cropped_width].\n \"\"\"\n if not batch_size:\n batch_size = FLAGS.batch_size\n\n # Force all input processing onto CPU in order to reserve the GPU for\n # the forward inference and back-propagation.\n with tf.device('/cpu:0'):\n left_images, right_images, disparitys, masks = batch_inputs(\n dataset, batch_size, train=False,\n num_preprocess_threads=num_preprocess_threads,\n num_readers=1)\n return left_images, right_images, disparitys, masks\n\n\ndef distorted_inputs(dataset, batch_size=None, num_preprocess_threads=None):\n \"\"\"Generate batches of distorted versions of SceneFlow images.\n\n Use this function as the inputs for training a network.\n\n Args:\n dataset: instance of Dataset class specifying the dataset.\n batch_size: integer, number of examples in batch\n num_preprocess_threads: integer, total number of preprocessing threads but\n None defaults to FLAGS.num_preprocess_threads.\n\n Returns:\n left_images, right_images: Images. 4D tensor of size [batch_size, FLAGS.cropped_height,\n cropped_width, 3].\n disparitys, masks: 3D tensor of size [batch_size, FLAGS.cropped_height, cropped_width].\n \"\"\"\n if not batch_size:\n batch_size = FLAGS.batch_size\n\n # Force all input processing onto CPU in order to reserve the GPU for\n # the forward inference and back-propagation.\n with tf.device('/cpu:0'):\n left_images, right_images, disparitys, masks = batch_inputs(\n dataset, batch_size, train=True,\n num_preprocess_threads=num_preprocess_threads,\n num_readers=FLAGS.num_readers)\n return left_images, right_images, disparitys, masks\n\n\n\ndef eval_image(image):\n \"\"\"Prepare one image for evaluation. i.e. center cropping\n\n Args:\n image: 3-D float Tensor\n Returns:\n 3-D float Tensor of prepared image.\n \"\"\"\n # Crop the central region of the image with an area containing 87.5% of\n # the original image.\n# image = tf.image.central_crop(image, central_fraction=0.875)\n\n image = tf.expand_dims(image, 0)\n image = tf.image.resize_image_with_crop_or_pad(image, FLAGS.cropped_height, FLAGS.cropped_width)\n image = tf.squeeze(image, [0])\n return image\n\n\ndef image_preprocessing(left_image, right_image, disparity, mask, train):\n \"\"\"Decode and preprocess one image for evaluation or training.\n\n Args:\n left_image, right_image, mask: decoded image, dtype:tf.uint8\n disparity: decoded disparity, dtype:tf.float32\n train: boolean\n\n Returns:\n left_image, right_image, disparity, mask:3-D float Tensors\n all are cropped, \n if training: random crop, \n if testing: center crop\n images are normalized to range [-1, 1]\n \"\"\"\n with tf.name_scope('image_preprocessing'):\n image_shape = tf.stack([FLAGS.image_height, FLAGS.image_width, FLAGS.depth])\n disparity_shape = tf.stack([FLAGS.image_height, FLAGS.image_width, 1])\n \n left_image = tf.reshape(left_image, image_shape)\n right_image = tf.reshape(right_image, image_shape) \n disparity = tf.reshape(disparity, disparity_shape)\n mask = tf.reshape(mask, disparity_shape)\n \n left_image = tf.image.convert_image_dtype(left_image, dtype=tf.float32)\n right_image = tf.image.convert_image_dtype(right_image, dtype=tf.float32)\n mask = tf.cast(mask, tf.float32)\n\n combined = tf.concat([left_image, right_image, disparity, mask], axis=2)\n if train:\n combined_crop = tf.random_crop(combined, [FLAGS.cropped_height, FLAGS.cropped_width, tf.shape(combined)[-1]])\n else:\n combined_crop = eval_image(combined)\n\n left_image = combined_crop[:, :, 0:FLAGS.depth]\n right_image = combined_crop[:, :, FLAGS.depth:FLAGS.depth*2]\n disparity = combined_crop[:, :, FLAGS.depth*2:FLAGS.depth*2+1]\n mask = combined_crop[:, :, FLAGS.depth*2+1:FLAGS.depth*2+2]\n \n # Finally, rescale to [-1,1] instead of [0, 1)\n left_image = tf.subtract(left_image, 0.5)\n left_image = tf.multiply(left_image, 2.0)\n right_image = tf.subtract(right_image, 0.5)\n right_image = tf.multiply(right_image, 2.0)\n\n return left_image, right_image, disparity, mask\n\n\ndef parse_example_proto(example_serialized):\n \"\"\"Parses an Example proto containing a training example of an image.\n\n The output of the build_image_data_SceneFlow.py image preprocessing script is a dataset\n containing serialized Example protocol buffers. Each Example proto contains\n the following fields:\n\n left_image_raw: string containing encoded image in RGB colorspace\n right_image_raw: string containing encoded image in RGB colorspace\n disparity_raw: string containing float formatted grount-truth disparity\n mask_raw: string containing unit8 formatted grount-truth disparity mask\n\n Args:\n example_serialized: scalar Tensor tf.string containing a serialized\n Example protocol buffer.\n\n Returns:\n left_image, right_image, mask: decoded image, dtype:tf.uint8\n disparity: decoded disparity, dtype:tf.float32\n \n \"\"\"\n # Dense features in Example proto.\n feature_map = {\n 'left_image_raw':tf.FixedLenFeature([], tf.string),\n 'right_image_raw':tf.FixedLenFeature([], tf.string),\n 'mask_raw': tf.FixedLenFeature([], tf.string),\n 'disparity_raw': tf.FixedLenFeature([], tf.string),\n 'filename': tf.FixedLenFeature([], tf.string),\n 'relative_dir': tf.FixedLenFeature([], tf.string), \n }\n\n features = tf.parse_single_example(example_serialized, feature_map)\n \n left_image = tf.decode_raw(features['left_image_raw'], tf.uint8)\n right_image = tf.decode_raw(features['right_image_raw'], tf.uint8)\n mask = tf.decode_raw(features['mask_raw'], tf.uint8)\n disparity = tf.decode_raw(features['disparity_raw'], tf.float32)\n \n return left_image, right_image, disparity, mask\n\n\ndef batch_inputs(dataset, batch_size, train, num_preprocess_threads=None,\n num_readers=1):\n \"\"\"Contruct batches of training or evaluation examples from the image dataset.\n\n Args:\n dataset: instance of Dataset class specifying the dataset.\n See dataset.py for details.\n batch_size: integer\n train: boolean\n num_preprocess_threads: integer, total number of preprocessing threads\n num_readers: integer, number of parallel readers\n\n Returns:\n left_images, right_images: Images. 4D tensor of size [batch_size, FLAGS.image_size,\n image_size, 3].\n disparitys, masks: 3D tensor of size [batch_size, FLAGS.image_size, image_size].\n\n Raises:\n ValueError: if data is not found\n \"\"\"\n with tf.name_scope('batch_processing'):\n data_files = dataset.data_files()\n if data_files is None:\n raise ValueError('No data files found for this dataset')\n\n # Create filename_queue\n if train:\n filename_queue = tf.train.string_input_producer(data_files,\n shuffle=True,\n capacity=16)\n else:\n filename_queue = tf.train.string_input_producer(data_files,\n shuffle=False,\n capacity=1)\n if num_preprocess_threads is None:\n num_preprocess_threads = FLAGS.num_preprocess_threads\n\n if num_preprocess_threads % 4:\n raise ValueError('Please make num_preprocess_threads a multiple '\n 'of 4 (%d % 4 != 0).', num_preprocess_threads)\n\n if num_readers is None:\n num_readers = FLAGS.num_readers\n\n if num_readers < 1:\n raise ValueError('Please make num_readers at least 1')\n\n # Approximate number of examples per shard.\n train_examples_per_shard = 35\n eval_examples_per_shard = 303\n # Size the random shuffle queue to balance between good global\n # mixing (more examples) and memory use (fewer examples).\n # 1 image uses 299*299*3*4 bytes = 1MB\n # The default input_queue_memory_factor is 16 implying a shuffling queue\n # size: examples_per_shard * 16 * 1MB = 17.6GB\n min_queue_examples = train_examples_per_shard * FLAGS.input_queue_memory_factor\n if train:\n examples_queue = tf.RandomShuffleQueue(\n capacity=min_queue_examples + 3 * batch_size,\n min_after_dequeue=min_queue_examples,\n dtypes=[tf.string])\n else:\n examples_queue = tf.FIFOQueue(\n capacity=eval_examples_per_shard + 3 * batch_size,\n dtypes=[tf.string])\n\n # Create multiple readers to populate the queue of examples.\n if num_readers > 1:\n enqueue_ops = []\n for _ in range(num_readers):\n reader = dataset.reader()\n _, value = reader.read(filename_queue)\n enqueue_ops.append(examples_queue.enqueue([value]))\n\n tf.train.queue_runner.add_queue_runner(\n tf.train.queue_runner.QueueRunner(examples_queue, enqueue_ops))\n example_serialized = examples_queue.dequeue()\n else:\n reader = dataset.reader()\n _, example_serialized = reader.read(filename_queue)\n\n data = []\n for thread_id in range(num_preprocess_threads):\n # Parse a serialized Example proto to extract the image and metadata.\n left_image, right_image, disparity, mask = parse_example_proto(\n example_serialized)\n left_image, right_image, disparity, mask = image_preprocessing(left_image, right_image, disparity, mask, train)\n data.append([left_image, right_image, disparity, mask])\n\n left_images, right_images, disparitys, masks = tf.train.batch_join(\n data,\n batch_size=batch_size,\n capacity=2 * num_preprocess_threads * batch_size,\n shapes=[[FLAGS.cropped_height, FLAGS.cropped_width, FLAGS.depth], \n [FLAGS.cropped_height, FLAGS.cropped_width, FLAGS.depth], \n [FLAGS.cropped_height, FLAGS.cropped_width, 1],\n [FLAGS.cropped_height, FLAGS.cropped_width, 1]])\n \n# shape_op = tf.shape(left_images)\n\n # Display the training images in the visualizer.\n# tf.summary.image('left_images', left_images)\n# tf.summary.image('right_images', right_images)\n# tf.summary.image('disparity_with_mask', disparitys * masks)\n\n disparitys = tf.squeeze(disparitys, axis=3)\n masks = tf.squeeze(masks, axis=3)\n return left_images, right_images, disparitys, masks\n","sub_path":"reimplement_GC_Net/image_processing.py","file_name":"image_processing.py","file_ext":"py","file_size_in_byte":14811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"348173846","text":"# Authors: Soledad Galli \n# License: BSD 3 clause\n\nfrom feature_engine.dataframe_checks import _is_dataframe\nfrom feature_engine.imputation.base_imputer import BaseImputer\nfrom feature_engine.parameter_checks import _define_numerical_dict\nfrom feature_engine.variable_manipulation import (\n _define_variables,\n _find_numerical_variables,\n)\n\n\nclass ArbitraryNumberImputer(BaseImputer):\n \"\"\"\n The ArbitraryNumberImputer() replaces missing data in each variable\n by an arbitrary value determined by the user.\n\n Parameters\n ----------\n\n arbitrary_number : int or float, default=999\n the number to be used to replace missing data.\n\n variables : list, default=None\n The list of variables to be imputed. If None, the imputer will find and\n select all numerical type variables. Attribute is used only if `imputer_dict`\n attribute is None.\n\n imputer_dict: dict, default=None\n The dictionary of variables and their arbitrary numbers. If imputer_dict is\n not None, it has to be dictionary with all values of integer or float type.\n If None, `variables` attribute is used for imputation.\n \"\"\"\n\n def __init__(self, arbitrary_number=999, variables=None, imputer_dict=None):\n\n if isinstance(arbitrary_number, int) or isinstance(arbitrary_number, float):\n self.arbitrary_number = arbitrary_number\n else:\n raise ValueError(\"arbitrary_number must be numeric of type int or float\")\n\n self.variables = _define_variables(variables)\n\n self.imputer_dict = _define_numerical_dict(imputer_dict)\n\n def fit(self, X, y=None):\n \"\"\"\n Checks that the variables are numerical.\n\n Parameters\n ----------\n\n X : pandas dataframe of shape = [n_samples, n_features]\n The training input samples.\n User can pass the entire dataframe, not just the variables to impute.\n\n y : None\n y is not needed in this imputation. You can pass None or y.\n\n\n Attributes\n ----------\n\n imputer_dict_: dictionary\n The dictionary containing the values that will replace each variable.\n \"\"\"\n # check input dataframe\n X = _is_dataframe(X)\n\n # find or check for numerical variables\n if self.imputer_dict:\n self.variables = _find_numerical_variables(X, self.imputer_dict.keys())\n else:\n self.variables = _find_numerical_variables(X, self.variables)\n\n # create the imputer dictionary\n if self.imputer_dict:\n self.imputer_dict_ = self.imputer_dict\n else:\n self.imputer_dict_ = {var: self.arbitrary_number for var in self.variables}\n\n self.input_shape_ = X.shape\n\n return self\n\n # Ugly work around to import the docstring for Sphinx, otherwise not necessary\n def transform(self, X):\n X = super().transform(X)\n return X\n\n transform.__doc__ = BaseImputer.transform.__doc__\n","sub_path":"feature_engine/imputation/arbitrary_number.py","file_name":"arbitrary_number.py","file_ext":"py","file_size_in_byte":3019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"542733586","text":"from paraview.simple import *\nif len(sys.argv) >= 2:\n\toutputDirectory = sys.argv[1] + '/'\n\tif len(sys.argv) == 3:\n\t\tdebugLevel = sys.argv[2]\n\telse:\n\t\tdebugLevel = 0\nelse:\n\tprint('Missing output directory')\n\tsys.exit()\nif debugLevel != 0:\n\tprint(' Debug level: ' + debugLevel)\n# state file generated using paraview version 5.6.0\n\n# ----------------------------------------------------------------\n# setup views used in the visualization\n# ----------------------------------------------------------------\n\n# trace generated using paraview version 5.6.0\n#\n# To ensure correct image size when batch processing, please search \n\n#### import the simple module from the paraview\nfrom paraview.simple import *\n#### disable automatic camera reset on 'Show'\nparaview.simple._DisableFirstRenderCameraReset()\n\n# Create a new 'Render View'\n\n# init the 'GridAxes3DActor' selected for 'AxesGrid'\n\n# ----------------------------------------------------------------\n# restore active view\n# ----------------------------------------------------------------\n\n# ----------------------------------------------------------------\n# setup the data processing pipelines\n# ----------------------------------------------------------------\n\n# create a new 'XML Image Data Reader'\ntimeTrackingvti = XMLImageDataReader(FileName=['timeTracking.vti'])\ntimeTrackingvti.CellArrayStatus = []\ntimeTrackingvti.PointArrayStatus = ['000', '002', '004', '006', '008', '010', '012', '014', '016', '018', '020', '022', '024', '026', '028', '030', '032', '034', '036', '038', '040', '042', '044', '046', '048', '050', '052', '054', '056', '058', '060', '062', '064', '066', '068', '070', '072', '074', '076', '078', '080', '082', '084', '086', '088', '090', '092', '094', '096', '098', '100', '102', '104', '106', '108', '110', '112', '114', '116', '118']\n\n# create a new 'TTK PointDataSelector'\ntTKPointDataSelector2 = TTKPointDataSelector(Input=timeTrackingvti)\ntTKPointDataSelector2.ScalarFields = ['000']\ntTKPointDataSelector2.Renameselectedfield = 1\n\n# create a new 'TTK TrackingFromFields'\ntTKTrackingFromFields1 = TTKTrackingFromFields(Input=timeTrackingvti)\ntTKTrackingFromFields1.ForceZtranslation = 1\ntTKTrackingFromFields1.ZTranslation = 0.125\n\n# create a new 'Threshold'\nlatefeatures = Threshold(Input=tTKTrackingFromFields1)\nlatefeatures.Scalars = ['POINTS', 'TimeStep']\nlatefeatures.ThresholdRange = [55.0, 58.0]\n\n# create a new 'Extract Surface'\nextractSurface2 = ExtractSurface(Input=tTKTrackingFromFields1)\n\n# create a new 'Tube'\ntube2 = Tube(Input=extractSurface2)\ntube2.Scalars = ['POINTS', 'ConnectedComponentId']\ntube2.Vectors = [None, '']\ntube2.Radius = 0.025\n\n# create a new 'Transform'\ntransform2 = Transform(Input=timeTrackingvti)\ntransform2.Transform = 'Transform'\n\n# init the 'Transform' selected for 'Transform'\ntransform2.Transform.Translate = [0.0, 0.0, 3.5]\n\n# create a new 'Tetrahedralize'\ntetrahedralize2 = Tetrahedralize(Input=transform2)\n\n# create a new 'TTK PointDataSelector'\ntTKPointDataSelector3 = TTKPointDataSelector(Input=tetrahedralize2)\ntTKPointDataSelector3.ScalarFields = ['056']\ntTKPointDataSelector3.Renameselectedfield = 1\n\n# create a new 'TTK PersistenceDiagram'\ntTKPersistenceDiagram2 = TTKPersistenceDiagram(Input=tTKPointDataSelector3)\ntTKPersistenceDiagram2.ScalarField = 'SelectedField'\ntTKPersistenceDiagram2.InputOffsetField = 'SelectedField'\ntTKPersistenceDiagram2.EmbedinDomain = 1\n\n# create a new 'Transform'\ntransform1 = Transform(Input=timeTrackingvti)\ntransform1.Transform = 'Transform'\n\n# init the 'Transform' selected for 'Transform'\ntransform1.Transform.Translate = [0.0, 0.0, 7.375]\n\n# create a new 'Tetrahedralize'\ntetrahedralize1 = Tetrahedralize(Input=transform1)\n\n# create a new 'TTK PointDataSelector'\ntTKPointDataSelector1 = TTKPointDataSelector(Input=tetrahedralize1)\ntTKPointDataSelector1.ScalarFields = ['118']\ntTKPointDataSelector1.Renameselectedfield = 1\n\n# create a new 'TTK PersistenceDiagram'\ntTKPersistenceDiagram1 = TTKPersistenceDiagram(Input=tTKPointDataSelector1)\ntTKPersistenceDiagram1.ScalarField = 'SelectedField'\ntTKPersistenceDiagram1.InputOffsetField = 'SelectedField'\ntTKPersistenceDiagram1.EmbedinDomain = 1\n\n# create a new 'Threshold'\nthreshold1 = Threshold(Input=tTKPersistenceDiagram1)\nthreshold1.Scalars = ['CELLS', 'Persistence']\nthreshold1.ThresholdRange = [0.5, 44.9597725588668]\n\n# create a new 'TTK SphereFromPoint'\ntTKSphereFromPoint1 = TTKSphereFromPoint(Input=threshold1)\ntTKSphereFromPoint1.Radius = 0.075\n\n# create a new 'Threshold'\nthreshold2 = Threshold(Input=tTKSphereFromPoint1)\nthreshold2.Scalars = ['POINTS', 'CriticalType']\n\n# create a new 'Threshold'\nthreshold4 = Threshold(Input=tTKPersistenceDiagram2)\nthreshold4.Scalars = ['CELLS', 'Persistence']\nthreshold4.ThresholdRange = [0.5, 44.5332099569223]\n\n# create a new 'TTK SphereFromPoint'\ntTKSphereFromPoint2 = TTKSphereFromPoint(Input=threshold4)\ntTKSphereFromPoint2.Radius = 0.075\n\n# create a new 'Threshold'\nthreshold5 = Threshold(Input=tTKSphereFromPoint2)\nthreshold5.Scalars = ['POINTS', 'CriticalType']\n\n# create a new 'Threshold'\nthreshold6 = Threshold(Input=tTKSphereFromPoint2)\nthreshold6.Scalars = ['POINTS', 'CriticalType']\nthreshold6.ThresholdRange = [3.0, 3.0]\n\n# create a new 'Threshold'\nthreshold3 = Threshold(Input=tTKSphereFromPoint1)\nthreshold3.Scalars = ['POINTS', 'CriticalType']\nthreshold3.ThresholdRange = [3.0, 3.0]\n\n# create a new 'Threshold'\nearlyfeatures = Threshold(Input=tTKTrackingFromFields1)\nearlyfeatures.Scalars = ['POINTS', 'TimeStep']\nearlyfeatures.ThresholdRange = [0.0, 55.0]\n\n# create a new 'Threshold'\nlongtrajectories = Threshold(Input=earlyfeatures)\nlongtrajectories.Scalars = ['CELLS', 'ComponentLength']\nlongtrajectories.ThresholdRange = [5.0, 60.0]\n\n# create a new 'Append Datasets'\nappendDatasets1 = AppendDatasets(Input=[longtrajectories, latefeatures])\n\n# create a new 'Extract Surface'\nextractSurface1 = ExtractSurface(Input=appendDatasets1)\n\n# create a new 'Tube'\ntube1 = Tube(Input=extractSurface1)\ntube1.Scalars = ['POINTS', 'ConnectedComponentId']\ntube1.Vectors = [None, '']\ntube1.Radius = 0.025\n\n# ----------------------------------------------------------------\n# ----------------------------------------------------------------\n\n# show data from tTKPointDataSelector1\n\n# get color transfer function/color map for 'SelectedField'\nselectedFieldLUT = GetColorTransferFunction('SelectedField')\nselectedFieldLUT.RGBPoints = [-22.0015973664814, 0.0, 0.129412, 0.584314, -4.49752408306022, 0.0, 0.129412, 0.584314, 0.481469098741833, 0.917647, 0.941176, 0.788235, 4.50374912943535, 0.0, 0.431373, 0.0, 22.9645355639652, 0.0, 0.431373, 0.0]\nselectedFieldLUT.ColorSpace = 'RGB'\nselectedFieldLUT.NanColor = [0.0, 0.0, 0.0]\nselectedFieldLUT.ScalarRangeInitialized = 1.0\n\n# get opacity transfer function/opacity map for 'SelectedField'\nselectedFieldPWF = GetOpacityTransferFunction('SelectedField')\nselectedFieldPWF.Points = [-22.0015973664814, 0.0, 0.5, 0.0, 22.9645355639652, 1.0, 0.5, 0.0]\nselectedFieldPWF.ScalarRangeInitialized = 1\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# show data from tTKPointDataSelector2\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# show data from threshold2\n\n# get color transfer function/color map for 'CriticalType'\ncriticalTypeLUT = GetColorTransferFunction('CriticalType')\ncriticalTypeLUT.RGBPoints = [0.0, 0.0, 0.129412, 0.584314, 1.500244140625, 0.917647, 0.941176, 0.788235, 3.00048828125, 0.0, 0.431373, 0.0]\ncriticalTypeLUT.ColorSpace = 'RGB'\ncriticalTypeLUT.NanColor = [0.0, 0.0, 0.0]\ncriticalTypeLUT.ScalarRangeInitialized = 1.0\n\n# get opacity transfer function/opacity map for 'CriticalType'\ncriticalTypePWF = GetOpacityTransferFunction('CriticalType')\ncriticalTypePWF.Points = [0.0, 0.0, 0.5, 0.0, 3.00048828125, 1.0, 0.5, 0.0]\ncriticalTypePWF.ScalarRangeInitialized = 1\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# show data from threshold3\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# show data from tTKPointDataSelector3\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# show data from threshold5\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# show data from threshold6\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# show data from tube1\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# show data from tube2\n\n# trace defaults for the display properties.\n\n# init the 'PiecewiseFunction' selected for 'ScaleTransferFunction'\n\n# init the 'PiecewiseFunction' selected for 'OpacityTransferFunction'\n\n# init the 'GridAxesRepresentation' selected for 'DataAxesGrid'\n\n# init the 'PolarAxesRepresentation' selected for 'PolarAxes'\n\n# ----------------------------------------------------------------\n# setup color maps and opacity mapes used in the visualization\n# note: the Get..() functions create a new object, if needed\n# ----------------------------------------------------------------\n\n# ----------------------------------------------------------------\n# finally, restore active source\nSetActiveSource(None)\n# ----------------------------------------------------------------\n\ntTKPointDataSelector2.DebugLevel = int(debugLevel)\nif tTKPointDataSelector2.GetNumberOfOutputPorts() != 1:\n\tfor i in range(0, tTKPointDataSelector2.GetNumberOfOutputPorts()):\n\t\tSaveData(outputDirectory + 'tTKPointDataSelector2_' + str(i) + '.vtu',\n\t\t\tCleantoGrid(OutputPort(tTKPointDataSelector2, i)))\nelse:\n\tSaveData(outputDirectory + 'tTKPointDataSelector2.vtu',\n\t\tCleantoGrid(OutputPort(tTKPointDataSelector2)))\n\n\ntTKTrackingFromFields1.DebugLevel = int(debugLevel)\nif tTKTrackingFromFields1.GetNumberOfOutputPorts() != 1:\n\tfor i in range(0, tTKTrackingFromFields1.GetNumberOfOutputPorts()):\n\t\tSaveData(outputDirectory + 'tTKTrackingFromFields1_' + str(i) + '.vtu',\n\t\t\tCleantoGrid(OutputPort(tTKTrackingFromFields1, i)))\nelse:\n\tSaveData(outputDirectory + 'tTKTrackingFromFields1.vtu',\n\t\tCleantoGrid(OutputPort(tTKTrackingFromFields1)))\n\n\ntTKPointDataSelector3.DebugLevel = int(debugLevel)\nif tTKPointDataSelector3.GetNumberOfOutputPorts() != 1:\n\tfor i in range(0, tTKPointDataSelector3.GetNumberOfOutputPorts()):\n\t\tSaveData(outputDirectory + 'tTKPointDataSelector3_' + str(i) + '.vtu',\n\t\t\tCleantoGrid(OutputPort(tTKPointDataSelector3, i)))\nelse:\n\tSaveData(outputDirectory + 'tTKPointDataSelector3.vtu',\n\t\tCleantoGrid(OutputPort(tTKPointDataSelector3)))\n\n\ntTKPersistenceDiagram2.DebugLevel = int(debugLevel)\nif tTKPersistenceDiagram2.GetNumberOfOutputPorts() != 1:\n\tfor i in range(0, tTKPersistenceDiagram2.GetNumberOfOutputPorts()):\n\t\tSaveData(outputDirectory + 'tTKPersistenceDiagram2_' + str(i) + '.vtu',\n\t\t\tCleantoGrid(OutputPort(tTKPersistenceDiagram2, i)))\nelse:\n\tSaveData(outputDirectory + 'tTKPersistenceDiagram2.vtu',\n\t\tCleantoGrid(OutputPort(tTKPersistenceDiagram2)))\n\n\ntTKPointDataSelector1.DebugLevel = int(debugLevel)\nif tTKPointDataSelector1.GetNumberOfOutputPorts() != 1:\n\tfor i in range(0, tTKPointDataSelector1.GetNumberOfOutputPorts()):\n\t\tSaveData(outputDirectory + 'tTKPointDataSelector1_' + str(i) + '.vtu',\n\t\t\tCleantoGrid(OutputPort(tTKPointDataSelector1, i)))\nelse:\n\tSaveData(outputDirectory + 'tTKPointDataSelector1.vtu',\n\t\tCleantoGrid(OutputPort(tTKPointDataSelector1)))\n\n\ntTKPersistenceDiagram1.DebugLevel = int(debugLevel)\nif tTKPersistenceDiagram1.GetNumberOfOutputPorts() != 1:\n\tfor i in range(0, tTKPersistenceDiagram1.GetNumberOfOutputPorts()):\n\t\tSaveData(outputDirectory + 'tTKPersistenceDiagram1_' + str(i) + '.vtu',\n\t\t\tCleantoGrid(OutputPort(tTKPersistenceDiagram1, i)))\nelse:\n\tSaveData(outputDirectory + 'tTKPersistenceDiagram1.vtu',\n\t\tCleantoGrid(OutputPort(tTKPersistenceDiagram1)))\n\n\ntTKSphereFromPoint1.DebugLevel = int(debugLevel)\nif tTKSphereFromPoint1.GetNumberOfOutputPorts() != 1:\n\tfor i in range(0, tTKSphereFromPoint1.GetNumberOfOutputPorts()):\n\t\tSaveData(outputDirectory + 'tTKSphereFromPoint1_' + str(i) + '.vtu',\n\t\t\tCleantoGrid(OutputPort(tTKSphereFromPoint1, i)))\nelse:\n\tSaveData(outputDirectory + 'tTKSphereFromPoint1.vtu',\n\t\tCleantoGrid(OutputPort(tTKSphereFromPoint1)))\n\n\ntTKSphereFromPoint2.DebugLevel = int(debugLevel)\nif tTKSphereFromPoint2.GetNumberOfOutputPorts() != 1:\n\tfor i in range(0, tTKSphereFromPoint2.GetNumberOfOutputPorts()):\n\t\tSaveData(outputDirectory + 'tTKSphereFromPoint2_' + str(i) + '.vtu',\n\t\t\tCleantoGrid(OutputPort(tTKSphereFromPoint2, i)))\nelse:\n\tSaveData(outputDirectory + 'tTKSphereFromPoint2.vtu',\n\t\tCleantoGrid(OutputPort(tTKSphereFromPoint2)))\n","sub_path":"tests/referenceScripts/timeTracking/pythonScript.py","file_name":"pythonScript.py","file_ext":"py","file_size_in_byte":14476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"620427685","text":"\"\"\"\nData Loader main entry point\n\"\"\"\n\n\nimport Queue\nimport sys\nimport os.path as op\nimport traceback\nimport logging\n\nsys.path.append(op.join(op.dirname(op.abspath(__file__)), \"framework\"))\n\nimport action_tokens as at\nimport utils\n\n_LOGGER = logging.getLogger(\"ta_manager\")\n\n\nclass TAManager(object):\n \"\"\"\n TA Manager boots all underlying facilities\n \"\"\"\n\n def __init__(self, meta_configs, stanza_configs):\n \"\"\"\n @meta_configs: a dict like object, implement dict.get/[] like\n interfaces to get the value for a key. meta_configs shall at least\n contain\n {\"server_uri\": uri, \"checkpoint_dir\": dir, \"session_key\": key}\n key/value pairs\n @stanza_configs: a list like object containing a list of dict\n like object. Each element shall implement dict.get/[] like interfaces\n to get the value for a key. Each element in the list shall at least\n contain\n \"\"\"\n\n import timer_queue as tq\n import ta_configure_manager as conf_mgr\n import servers\n import ta_conf_client as tcc\n\n self.meta_configs = meta_configs\n appname = utils.get_appname_from_path(op.abspath(__file__))\n meta_configs[\"appname\"] = appname\n self.wakeup_queue = Queue.Queue()\n self.conf_manager = conf_mgr.TAConfigureManager(meta_configs)\n self.timer_queue = tq.TimerQueue()\n self.pub_server = servers.PubServer(stanza_configs)\n self.rep_server = servers.RepServer(stanza_configs,\n self._handle_request)\n self.conf_client = tcc.TAConfClient(stanza_configs[\"repserver\"],\n meta_configs[\"server_uri\"],\n meta_configs[\"session_key\"])\n self._state_logger = utils.setup_logging(\"ta_state\")\n self._started = False\n\n def run(self):\n if self._started:\n return\n self._started = True\n\n self.timer_queue.start()\n self.rep_server.start()\n self.conf_client.start()\n\n wakeup_q = self.wakeup_queue\n while 1:\n try:\n go_exit = wakeup_q.get(timeout=30)\n except Queue.Empty:\n pass\n else:\n if go_exit:\n break\n\n self.conf_client.tear_down()\n self.rep_server.tear_down()\n self.timer_queue.tear_down()\n _LOGGER.info(\"TA manager is going to exit...\")\n\n def tear_down(self):\n self.wakeup_queue.put(True)\n\n def _handle_request(self, data):\n handlers = {\n at.APP_REGISTER_INFO_UPDATE: self._handle_registration_update,\n at.APP_REGISTER_INFO_GET: self._handle_registration_get,\n at.APP_STATE_UPDATE: self._handle_state_update,\n at.APP_STATE_GET: self._handle_state_get,\n at.APP_CKPT_UPDATE: self._handle_ckpt_update,\n at.APP_CKPT_GET: self._handle_ckpt_get,\n at.APP_TASK_CONFIG_UPDATE: self._handle_task_config_update,\n at.APP_TASK_CONFIG_DELETE: self._handle_task_config_delete,\n at.APP_TASK_CONFIG_GET: self._handle_task_config_get,\n }\n\n _LOGGER.info(\"Request=%s\", data[\"action\"])\n if data[\"action\"] not in handlers:\n _LOGGER.info(\"Ignore request=%s\", data)\n return None\n\n try:\n return handlers[data[\"action\"]](data)\n except Exception:\n _LOGGER.error(\"Failed to handle %s, reason=%s\", data,\n traceback.format_exc())\n return None\n\n def _handle_registration_update(self, data):\n self.conf_manager.handle_registration_update(data)\n return {}\n\n def _handle_registration_get(self, data):\n res = self.conf_manager.handle_registration_get(data)\n if res is not None:\n res = {\"appinfo\": res}\n return res\n\n def _handle_state_update(self, data):\n self._log_states(data)\n self.conf_manager.handle_state_update(data)\n return {}\n\n def _handle_state_get(self, data):\n if data[\"appname\"] == \"*\":\n res = self.conf_manager.handle_all_app_states_get()\n if res:\n res = {\"state\": res}\n return res\n else:\n return self.conf_manager.handle_state_get(data)\n\n def _handle_ckpt_update(self, data):\n self.conf_manager.handle_ckpt_update(data)\n return {}\n\n def _handle_ckpt_get(self, data):\n return self.conf_manager.handle_ckpt_get(data)\n\n def _handle_task_config_update(self, data):\n topic = \"{0}/{1}/{2}\".format(data[\"ip\"], data[\"appname\"],\n data[\"progname\"])\n data[\"topic\"] = topic\n try:\n self.pub_server.publish((data,))\n except Exception:\n _LOGGER.info(\"Failed to publish topic=%s\", topic)\n return None\n else:\n self.conf_manager.handle_task_config_update(data)\n return {}\n\n def _handle_task_config_delete(self, data):\n self.conf_manager.handle_task_config_delete(data)\n return {}\n\n def _handle_task_config_get(self, data):\n assert data[\"appname\"] == \"*\"\n\n res = self.conf_manager.handle_task_config_get(data)\n if res is not None:\n res = {\"task_config\": res}\n return res\n\n def _log_states(self, states):\n keys = (\"action\", \"state\")\n log = \",\".join((\"{0}={1}\".format(key, value)\n for key, value in states.iteritems()\n if key not in keys))\n\n state = \",\".join((\"{0}={1}\".format(key, value)\n for key, value in states[\"state\"].iteritems()))\n self._state_logger.info(\"%s,%s\", log, state)\n\n def add_timer(self, callback, when, interval):\n return self.timer_queue.add_timer(callback, when, interval)\n\n def remove_timer(self, timer):\n self.timer_queue.remove_timer(timer)\n\n\nclass GlobalTAManager(object):\n \"\"\" Singleton, inited when started\"\"\"\n\n __instance = None\n\n @staticmethod\n def get_ta_manager(meta_configs, stanza_configs, job_factory):\n if GlobalTAManager.__instance is None:\n GlobalTAManager.__instance = TAManager(meta_configs,\n stanza_configs)\n return GlobalTAManager.__instance\n\n @staticmethod\n def reset():\n GlobalTAManager.__instance = None\n","sub_path":"SplunkApps/Splunk_TA_manager/bin/ta_manager.py","file_name":"ta_manager.py","file_ext":"py","file_size_in_byte":6477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"332644280","text":"'''\npyTree\n\nCreated on Aug 21, 2012\n\n@author: yoyzhou\n'''\nimport collections\n\n(S,T) = range(2)\n\n \nclass Tree(object):\n '''\n A Python implementation of Tree data structure \n '''\n \n \n def __init__(self, data = None, children = None):\n '''\n @param data: content of this node\n @param children: sub node(s) of Tree, could be None, child (single) or children (multiple)\n '''\n self.data = data\n self.__children = []\n self.__parent=None #private parent attribute\n \n if children: #construct a Tree with child or children\n if isinstance(children, Tree):\n self.__children.append(children)\n children.__parent = self \n \n elif isinstance(children, collections.Iterable):\n for child in children:\n if isinstance(child, Tree):\n self.__children.append(child)\n child.__parent = self\n else:\n raise TypeError('Child of Tree should be a Tree type.') \n else:\n raise TypeError('Child of Tree should be a Tree type')\n \n \n def __setattr__(self, name, value):\n \n \n \"\"\"\n Hide the __parent and __children attribute from using dot assignment.\n To add __children, please use addChild or addChildren method; And\n node's parent isn't assignable\n \"\"\"\n \n if name in ('parent', '__parent', 'children'):\n raise AttributeError(\"To add children, please use addChild or addChildren method.\")\n else:\n super().__setattr__(name, value)\n \n def __str__(self, *args, **kwargs):\n \n return self.data.__str__(*args, **kwargs)\n\n def addChild(self, child):\n \"\"\"\n Add one single child node to current node\n \"\"\"\n if isinstance(child, Tree):\n self.__children.append(child)\n child.__parent = self\n else:\n raise TypeError('Child of Tree should be a Tree type')\n \n def addChildren(self, children):\n \"\"\"\n Add multiple child nodes to current node\n \"\"\"\n if isinstance(children, list):\n for child in children:\n if isinstance(child, Tree):\n self.__children.append(child)\n child.__parent = self\n else:\n raise TypeError('Child of Tree should be a Tree type.') \n \n \n def getParent(self):\n \"\"\"\n Get node's parent node.\n \"\"\"\n return self.__parent\n \n def getChild(self, index):\n \"\"\" \n Get node's No. index child node.\n @param index: Which child node to get in children list, starts with 0 to number of children - 1\n @return: A Tree node presenting the number index child\n @raise IndexError: if the index is out of range \n \"\"\"\n try:\n return self.__children[index]\n except IndexError:\n raise IndexError(\"Index starts with 0 to number of children - 1\")\n \n def getChildren(self):\n \"\"\"\n Get node's all child nodes.\n \"\"\"\n return self.__children\n \n \n \n def getNode(self, content):\n \"\"\"\n \n Get the first matching item(including self) whose data is equal to content. \n Method uses data == content to determine whether a node's data equals to content, note if your node's data is \n self defined class, overriding object's __eq__ might be required.\n Implement Tree travel (level first) algorithm using queue\n\n @param content: node's content to be searched \n @return: Return node which contains the same data as parameter content, return None if no such node\n \"\"\"\n nodesQ = [self]\n \n while nodesQ:\n child = nodesQ[0]\n if child.data == content:\n return child\n else:\n nodesQ.extend(child.getChildren())\n del nodesQ[0]\n \n def delChild(self, index):\n \"\"\" \n Delete node's No. index child node.\n @param index: Which child node to delete in children list, starts with 0 to number of children - 1\n @raise IndexError: if the index is out of range \n \"\"\"\n try:\n del self.__children[index]\n except IndexError:\n raise IndexError(\"Index starts with 0 to number of children - 1\")\n \n def delNode(self, content):\n \n \"\"\"\n \n Delete the first matching item(including self) whose data is equal to content. \n Method uses data == content to determine whether a node's data equals to content, note if your node's data is \n self defined class, overriding object's __eq__ might be required.\n Implement Tree travel (level first) algorithm using queue\n\n @param content: node's content to be searched \n \"\"\"\n \n nodesQ = [self]\n \n while nodesQ:\n child = nodesQ[0]\n if child.data == content:\n if child.isRoot():\n del self\n return\n else:\n parent = child.getParent()\n parent.delChild(parent.getChildren().index(child))\n return\n else:\n nodesQ.extend(child.getChildren())\n del nodesQ[0]\n \n def getRoot(self):\n \"\"\"\n Get root of the current node.\n \"\"\"\n if self.isRoot():\n return self\n else:\n return self.getParent().getRoot()\n \n \n def isRoot(self):\n \"\"\"\n Determine whether node is a root node or not.\n \"\"\"\n if self.__parent is None:\n return True\n else:\n return False\n \n def isBranch(self):\n \"\"\"\n Determine whether node is a branch node or not.\n \"\"\"\n if self.__children == []:\n return True\n else:\n return False\n \n def printTree(self, _MODE = S):\n \"\"\"\"\n Print tree structure with nested-list style (the default, with _MODE S) or hierarchy style, with _MODE T.\n For example:\n [0] nested-list style\n [Root[C01[C11[C111,C112]],C02,C03[C31]]]\n [1] hierarchy style\n Root\n |___ C01\n | |___ C11\n | |___ C111\n | |___ C112\n |___ C02\n |___ C03\n | |___ C31\n @param _MODE: Print style, S for Simple nested-list style; T for hierarchical Tree style\n @todo: A more elegant way to achieve this function using Stack structure, [hint: push and pop nodes with additional level info]. \n \"\"\"\n raw = '['\n \n nodesQ = [self]\n index = 0;\n \n while nodesQ:\n c_raw = '['\n child = nodesQ[0]\n if child.isRoot():\n raw += str(child.data)\n nodesQ.extend(child.getChildren())\n index = len(raw) \n else:\n if raw.find(str(child)) != -1: #already in raw\n nodesQ.extend(child.getChildren())\n del nodesQ[0]\n continue\n else:\n parent = child.getParent()\n index = raw.find(str(parent)) + len(str(parent))\n nodesQ.extend(child.getChildren())\n c_raw += str(child.data) + '['\n \n if child.getChildren() == []: \n c_raw = c_raw[ : -1] + ']' \n else:\n c_raw += ','.join([str(c) for c in child.getChildren()]) + ']]'\n \n raw = raw[ : index] + c_raw + raw[index: ]\n del nodesQ[0]\n \n #*************Print a Simple list representing the Tree structure************# \n if _MODE == S:\n print(raw)\n \n #************* Print a REAL Tree structure with parameter T ************# \n elif _MODE == T:\n cur = 0\n pointer = 1\n level = 0\n \n while pointer != len(raw):\n cur_char = raw[pointer] \n if cur_char == '[':\n label = raw[cur + 1 : pointer]\n self.__printLabel__(label, level)\n cur = pointer\n level +=1\n elif cur_char == ']':\n label = raw[cur + 1 : pointer]\n self.__printLabel__(label, level)\n cur = pointer\n level -= 1\n elif cur_char == ',':\n label = raw[cur + 1 : pointer]\n self.__printLabel__(label, level)\n cur = pointer\n else:\n pass\n pointer += 1\n \n #************* Unknown print MODE ************# \n else:\n raise ValueError(\"Print MODE should be 'S' to print a list representing Tree structure or 'T' to print a REAL Tree\") \n \n def __printLabel__(self, label, level):\n \"\"\"\n Print each node\n \"\"\"\n if level == 0:\n print(label)\n else:\n if level == 1:\n leadingSpaces = ''\n addl = ''\n else:\n leadingSpaces = ' ' * 5 * (level - 1)\n addl = '|'\n \n if label.strip() != '': \n print('{0}{1}{2}{3} {4}'.format('|', leadingSpaces, addl, '_' * 3, label))\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n","sub_path":"src/Tree.py","file_name":"Tree.py","file_ext":"py","file_size_in_byte":10272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"528260746","text":"import requests\nimport os\nimport re\nimport json\n\n\nclass dropbox:\n\n def __init__(self, args):\n longname = args.cloud + \":\" + args.username\n self.access_token = self.check_username(longname)\n self.args = args\n self.result = []\n\n def check_username(self, logname):\n with open(\"usertokenlist.txt\", \"r\") as f:\n for line in f:\n if re.search(logname+\":\", line) is not None:\n return line.split(':')[2]\n return None\n\n def dir(self):\n self.result = [\"Dir\"]\n if self.args.cloud == 'dropbox':\n if self.access_token is None:\n self.result.append(False)\n return\n self.contain_dropbox(0, \"\")\n if len(self.result) != 2:\n self.result.append(True)\n return self.result\n\n def contain_dropbox(self, indentation, path):\n try:\n d = json.dumps({\n \"path\": path,\n \"recursive\": False, \"include_media_info\": False,\n \"include_deleted\": False,\n \"include_has_explicit_shared_members\": False,\n \"include_mounted_folders\": True,\n \"include_non_downloadable_files\": True})\n files = requests.post(\n 'https://api.dropboxapi.com/2/files/list_folder',\n headers={\"Authorization\": \"Bearer \" + self.access_token,\n \"Content-Type\": \"application/json\"},\n data=d).json()\n for f in files['entries']:\n if f['.tag'] == 'folder':\n self.result.append(\" \"*indentation+'FOLDER: '+ f['name'])\n self.contain_dropbox(indentation + 4, path + \"/\" + f['name'])\n else:\n self.result.append(\" \" * indentation + f['name'])\n except:\n self.result.append(False)\n return self.result\n\n def download(self):\n self.result = [\"Download\"]\n if self.access_token is None:\n self.result.append(False)\n else:\n self.load(self.args.file_names, self.args.path)\n self.result.append(True)\n return self.result\n\n def load(self, file_names, path):\n for f in file_names:\n if os.path.isdir(path):\n files_in_directory = os.listdir(path)\n else:\n self.result.append([\n f, False, \"Сбой. Неправильо указан путь для загрузки\"])\n continue\n if f in files_in_directory:\n answer = input(\n \"\\n\" + f +\n ''' - Этот файл уже заходится в этой директории,\n хотите перезаписать его?[Y/N]''')\n if answer in ['y', 'Y']:\n if os.path.isdir(path + \"/\" + f):\n shutil.rmtree(path + \"/\" + f)\n else:\n os.remove(path + \"/\" + f)\n else:\n self.result.append([f, False, \"Загрузка отменена\"])\n continue\n if '.' not in f:\n files = []\n d = json.dumps({\"path\": \"/\" + f + \"/\",\n \"recursive\": False, \"include_media_info\": False,\n \"include_deleted\": False,\n \"include_has_explicit_shared_members\": False,\n \"include_mounted_folders\": True,\n \"include_non_downloadable_files\": True})\n fls = requests.post(\n 'https://api.dropboxapi.com/2/files/list_folder',\n headers={\"Authorization\": \"Bearer \" + self.access_token,\n \"Content-Type\": \"application/json\"},\n data=d).json()\n if 'error' in fls.keys():\n self.result.append([\n f, False,\n \"Сбой. Неправильный путь к файлу или его имя\"])\n continue\n for e in fls['entries']:\n files.append(f+ \"/\" + e['name'])\n os.mkdir(path+\"/\"+f)\n self.result.append([f, False, \"Folder\"])\n self.load(files, path)\n continue\n r = requests.get(\n 'https://content.dropboxapi.com/2/files/download',\n headers={\"Authorization\": \"Bearer \" + self.access_token,\n \"Dropbox-API-Arg\": \"{\\\"path\\\": \\\"/\" + f + \"\\\"}\"})\n if \"error\" in r.text:\n self.result.append([\n f, False, \"Сбой. Неправильный путь к файлу или его имя\"])\n continue\n try:\n with open(path + '/' + f, \"wb\") as code:\n code.write(r.content)\n self.result.append([f, True])\n except Exception:\n self.result.append([\n f, False, \"Сбой. Неправильо указан путь для загрузки\"])\n\n def upload(self):\n self.result = [\"Upload\"]\n if self.access_token is None:\n self.result.append(False)\n else:\n self.uload(self.args.path, self.args.file_names)\n self.result.append(True)\n return self.result\n \n def uload(self, path, file_paths):\n for p in file_paths:\n if p.rfind('\\\\') < p.rfind('/'):\n i = p.rfind('/')\n else:\n i = p.rfind('\\\\')\n if os.path.isdir(p):\n files = os.listdir(p)\n for j in range(len(files)):\n files[j] = p+\"\\\\\"+files[j]\n self.uload(\"/\"+p[i+1:]+\"/\", files)\n continue\n if not os.path.isfile(p):\n self.result.append([p, \"Файла или Дирректории не существует\", False])\n continue\n name = p[i+1:]\n fg = False\n flag = False\n if path in ['/', '\\\\']:\n path = \"\"\n new_args = self.args\n d = dropbox(self.args).contain_dropbox(0, path)\n if d[-1]:\n for dr in d:\n if name == dr:\n answer = input(p + ' - уже был загружен на облако. Хотите загрузить его повторно[Y/N]')\n if answer in ['y', 'Y']:\n answer = input('Перезапистать его - Y, Создать копию - N: ')\n flag = answer not in ['y', 'Y']\n else:\n self.result.append([p, \"Отмена\", False])\n fg = True\n break\n if fg:\n continue\n try:\n d = json.dumps({\"path\": \"/\"+path[1:]+name, \"mode\": \"add\",\n \"autorename\": True, \"mute\": False, \"strict_conflict\": flag})\n fls = requests.post(\n 'https://content.dropboxapi.com/2/files/upload',\n headers={\"Authorization\": \"Bearer \" + self.access_token,\n \"Dropbox-API-Arg\": d,\n \"Content-Type\": \"application/octet-stream\"},\n data=p)\n self.result.append([p, True])\n except:\n self.result.append([p, False, \"Ошибка при загрузке\"])\n","sub_path":"dropbox.py","file_name":"dropbox.py","file_ext":"py","file_size_in_byte":7595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"92555665","text":"import re\nimport urllib2\nimport testly\nimport yaml\nfrom os import path\nfrom bioprocs.utils import shell\nfrom bioprocs.utils.tsvio2 import TsvReader\nfrom tempfile import gettempdir\n\nshbioprocs = shell.Shell(subcmd = True, dash = '-', equal = ' ', duplistkey = False).bioprocs\n\nPROCDATADIR = path.join(path.dirname(path.abspath(__file__)), 'procdata')\nDATAFILE = path.join(PROCDATADIR, 'data.yml')\nTMPDIR = gettempdir()\nCACHED = {}\n\ndef runBioprocs(proc, args):\n\targs['config._log.shortpath:py'] = 'False'\n\treturn shbioprocs[proc](**args).run(save = 'same', uselogger = False)\n\ndef download(url, savedir = TMPDIR):\n\tbname = path.basename(url)\n\tdestfile = path.join(savedir, bname)\n\tfiledata = urllib2.open(url)\n\twith open(destfile, 'wb') as f:\n\t\tf.write(filtdata.read())\n\treturn destfile\n\ndef getlocal(bname, key, datadir = PROCDATADIR):\n\td1, d2 = key.split('.')\n\treturn path.join(datadir, d1, d2, bname)\n\ndef getioval(value, proc = None):\n\tif isinstance(value, list):\n\t\treturn [getioval(v, proc) for v in value]\n\tif not value.startswith('plain:') and not value.startswith('http') and \\\n\tnot value.startswith('ftp') and not value.startswith('file:'):\n\t\tvalue = 'file:' + value\n\tif value in CACHED:\n\t\treturn CACHED[value]\n\telif value.startswith('plain:'):\n\t\treturn value[7:]\n\telif value.startswith('http') or value.startswith('ftp'):\n\t\tCACHED[value] = download(v)\n\t\treturn CACHED[value]\n\telif value.startswith('file:'):\n\t\treturn getlocal(value[5:], proc)\n\treturn value\n\ndef getData(datafile = DATAFILE):\n\twith open(datafile) as stream:\n\t\tdata = yaml.load(stream)\n\n\tfor key, value in data.items():\n\t\tkparts = key.split('.')\n\t\ttag = kparts[-1] if len(kparts) > 2 else 'default'\n\t\tproc = '.'.join(kparts[:2])\n\t\targs = {'tag': tag}\n\t\texptfiles = {}\n\t\topt1 = {}\n\t\topt2 = {}\n\t\tfor k, v in value.items():\n\t\t\tret = args\n\t\t\tif k[:2] in ['i.', 'o.']:\n\t\t\t\tv = getioval(v, proc)\n\t\t\t\tif k[:2] == 'o.':\n\t\t\t\t\tk = k[2:]\n\t\t\t\t\texptfiles[k] = v\n\t\t\t\telif isinstance(v, list):\n\t\t\t\t\targs[k + ':list:one'] = v\n\t\t\t\telse:\n\t\t\t\t\targs[k] = v\n\t\t\telif k.startswith('opt'):\n\t\t\t\tret = opt1 if k.startswith('opt1.') else opt2\n\t\t\t\tret[k[5:]] = v\n\t\t\telse:\n\t\t\t\tret[k] = v\n\t\tyield testly.Data(proc = proc, args = args, exptfiles = exptfiles, opt1 = opt1, opt2 = opt2)\n\ndef getOutput(c):\n\tret = {}\n\tfor line in c.stderr.splitlines():\n\t\t#[2019-02-06 09:49:48 OUTPUT] pAR: [1/1] outdir => /local2/tmp/m161047/bioprocs.workdir/PyPPL.pAR.notag.6duu519e/1/output/motif_hits-protein_expression_t-gene_expression.AR\n\t\tm = re.match(r'^\\[\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2} OUTPUT\\] \\w+: \\[\\d+\\/\\d+\\] (\\w+) \\s*=> (.+)$', line)\n\t\tif not m: continue\n\t\tret[m.group(1)] = m.group(2)\n\treturn ret\n\n\ndef istext(filename):\n\timport string \n\twith open(filename) as f:\n\t\ts = f.read(512)\n\ttext_characters = \"\".join(map(chr, range(32, 127)) + list(\"\\n\\r\\t\\b\"))\n\t_null_trans = string.maketrans(\"\", \"\")\n\tif not s:\n\t\t# Empty files are considered text\n\t\treturn True\n\tif \"\\0\" in s:\n\t\t# Files with null bytes are likely binary\n\t\treturn False\n\t# Get the non-text characters (maps a character to itself then\n\t# use the 'remove' option to get rid of the text characters.)\n\tt = s.translate(_null_trans, text_characters)\n\t# If more than 30% non-text characters, then\n\t# this is considered a binary file\n\tif float(len(t))/float(len(s)) > 0.30:\n\t\treturn False\n\treturn True\n\n\n# unittest asserts\ndef assertOrderedStrsInArray(self, strs, array, msg = None):\n\tif not self.maxDiff is None:\n\t\tself.maxDiff = max(self.maxDiff or 5000, 5000)\n\t\n\tself.assertIsInstance(strs, list, 'First argument is not a list.')\n\tself.assertIsInstance(array, list, 'Second argument is not a list.')\n\n\tfor s in strs:\n\t\twhile array:\n\t\t\ta = array.pop(0)\n\t\t\tif s in a:\n\t\t\t\tarray.append(None)\n\t\t\t\tbreak\n\t\t\tcontinue\n\t\tif array: # found\n\t\t\tcontinue\n\t\tstandardMsg = '%s not in %s' % (testly.util.safe_repr(s, True), testly.util.safe_repr(array, True))\n\t\tself.fail(self._formatMessage(msg, standardMsg))\n\ndef assertFileEqual(self, first, second, filetype = None, firstInopts = None, secondInopts = None, msg = None):\n\tif not self.maxDiff is None:\n\t\tself.maxDiff = max(self.maxDiff or 5000, 5000)\n\n\tfiletype1 = filetype or ('text' if istext(first) else 'nontext')\n\tfiletype2 = filetype or ('text' if istext(second) else 'nontext')\n\tif filetype1 != filetype2:\n\t\tstandardMsg = 'Files different, because file1 is {0} but file2 is {1}'.format(\n\t\t\tfiletype1, filetype2\n\t\t)\n\t\tself.fail(self._formatMessage(msg, standardMsg))\n\telif filetype1 == 'text':# and filetype2 == 'text':\n\t\treader1 = TsvReader(first, **firstInopts) if firstInopts else TsvReader(first)\n\t\treader2 = TsvReader(second, **secondInopts) if secondInopts else TsvReader(second)\n\t\trindex = 0\n\t\tfor r1 in reader1:\n\t\t\trindex += 1\n\t\t\ttry:\n\t\t\t\tr2 = next(reader2)\n\t\t\texcept StopIteration:\n\t\t\t\tstandardMsg = 'File1 and file2 are different.\\nFile1: {2}\\nFile2: {3}\\nRow {0} of file1 is: {1}, but nothing at row {0} of file2.'.format(rindex, r1, first, second)\n\t\t\t\tself.fail(self._formatMessage(msg, standardMsg))\n\t\t\tif r1 != r2:\n\t\t\t\tstandardMsg = 'File1 and file2 are different.\\nFile1: {3}\\nFile2: {4}\\nRow {0} of file1: {1}\\nRow {0} of file2: {2}'.format(rindex, r1, r2, first, second)\n\t\t\t\tself.fail(self._formatMessage(msg, standardMsg))\n\telse: # filetype1 == 'nontext' and filetype2 == 'nonetext': # binary\n\t\timport filecmp\n\t\tif not filecmp.cmp(first, second, shallow = False):\n\t\t\tstandardMsg = 'Binary files are different:\\n{}\\n{}'.format(first, second)\n\t\t\tself.fail(self._formatMessage(msg, standardMsg))\n\ntestly.TestCase.assertOrderedStrsInArray = assertOrderedStrsInArray\ntestly.TestCase.assertFileEqual = assertFileEqual","sub_path":"tests/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":5619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"1206299","text":"#!/usr/bin/env python\n###############################################################################\n# $Id$\n#\n# Project: GDAL/OGR Test Suite\n# Purpose: Test /vsiwebhdfs\n# Author: Even Rouault \n#\n###############################################################################\n# Copyright (c) 2018 Even Rouault \n#\n# Permission is hereby granted, free of charge, to any person obtaining a\n# copy of this software and associated documentation files (the \"Software\"),\n# to deal in the Software without restriction, including without limitation\n# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# and/or sell copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included\n# in all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n# DEALINGS IN THE SOFTWARE.\n###############################################################################\n\nimport stat\nimport sys\nfrom osgeo import gdal\n\nsys.path.append('../pymod')\n\nimport gdaltest\nimport webserver\n\n\ndef open_for_read(uri):\n \"\"\"\n Opens a test file for reading.\n \"\"\"\n return gdal.VSIFOpenExL(uri, 'rb', 1)\n\n###############################################################################\n\n\ndef vsiwebhdfs_init():\n\n gdaltest.webhdfs_vars = {}\n for var in ('WEBHDFS_USERNAME', 'WEBHDFS_DELEGATION'):\n gdaltest.webhdfs_vars[var] = gdal.GetConfigOption(var)\n if gdaltest.webhdfs_vars[var] is not None:\n gdal.SetConfigOption(var, \"\")\n\n return 'success'\n\n###############################################################################\n\n\ndef vsiwebhdfs_start_webserver():\n\n gdaltest.webserver_process = None\n gdaltest.webserver_port = 0\n\n if not gdaltest.built_against_curl():\n return 'skip'\n\n (gdaltest.webserver_process, gdaltest.webserver_port) = webserver.launch(\n handler=webserver.DispatcherHttpHandler)\n if gdaltest.webserver_port == 0:\n return 'skip'\n\n gdaltest.webhdfs_base_connection = '/vsiwebhdfs/http://localhost:' + \\\n str(gdaltest.webserver_port) + '/webhdfs/v1'\n gdaltest.webhdfs_redirected_url = 'http://non_existing_host:' + \\\n str(gdaltest.webserver_port) + '/redirected'\n\n return 'success'\n\n###############################################################################\n# Test VSIFOpenL()\n\n\ndef vsiwebhdfs_open():\n\n if gdaltest.webserver_port == 0:\n return 'skip'\n\n gdal.VSICurlClearCache()\n\n # Download without redirect (not nominal)\n handler = webserver.SequentialHandler()\n handler.add('GET', '/webhdfs/v1/foo/bar?op=OPEN&offset=9999990784&length=16384', 200,\n {}, '0123456789data')\n with webserver.install_http_handler(handler):\n f = open_for_read(gdaltest.webhdfs_base_connection + '/foo/bar')\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n gdal.VSIFSeekL(f, 9999990784 + 10, 0)\n if gdal.VSIFReadL(1, 4, f).decode('ascii') != 'data':\n gdaltest.post_reason('fail')\n return 'fail'\n gdal.VSIFCloseL(f)\n\n # Download with redirect (nominal) and permissions\n\n gdal.VSICurlClearCache()\n\n handler = webserver.SequentialHandler()\n handler.add('GET', '/webhdfs/v1/foo/bar?op=OPEN&offset=0&length=16384&user.name=root&delegation=token', 307,\n {'Location': gdaltest.webhdfs_redirected_url + '/webhdfs/v1/foo/bar?op=OPEN&offset=0&length=16384'})\n handler.add('GET', '/redirected/webhdfs/v1/foo/bar?op=OPEN&offset=0&length=16384', 200,\n {}, 'yeah')\n with gdaltest.config_options({'WEBHDFS_USERNAME': 'root',\n 'WEBHDFS_DELEGATION': 'token',\n 'WEBHDFS_DATANODE_HOST': 'localhost'}):\n with webserver.install_http_handler(handler):\n f = open_for_read(gdaltest.webhdfs_base_connection + '/foo/bar')\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n if gdal.VSIFReadL(1, 4, f).decode('ascii') != 'yeah':\n gdaltest.post_reason('fail')\n return 'fail'\n gdal.VSIFCloseL(f)\n\n # Test error\n\n gdal.VSICurlClearCache()\n\n f = open_for_read(gdaltest.webhdfs_base_connection + '/foo/bar')\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n\n handler = webserver.SequentialHandler()\n handler.add('GET', '/webhdfs/v1/foo/bar?op=OPEN&offset=0&length=16384', 404)\n with webserver.install_http_handler(handler):\n if len(gdal.VSIFReadL(1, 4, f)) != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Retry: shouldn't not cause network access\n if len(gdal.VSIFReadL(1, 4, f)) != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n gdal.VSIFCloseL(f)\n\n return 'success'\n\n###############################################################################\n# Test VSIStatL()\n\n\ndef vsiwebhdfs_stat():\n\n if gdaltest.webserver_port == 0:\n return 'skip'\n\n gdal.VSICurlClearCache()\n\n handler = webserver.SequentialHandler()\n handler.add('GET', '/webhdfs/v1/foo/bar?op=GETFILESTATUS', 200,\n {}, '{\"FileStatus\":{\"type\":\"FILE\",\"length\":1000000}}')\n with webserver.install_http_handler(handler):\n stat_res = gdal.VSIStatL(gdaltest.webhdfs_base_connection + '/foo/bar')\n if stat_res is None or stat_res.size != 1000000:\n gdaltest.post_reason('fail')\n if stat_res is not None:\n print(stat_res.size)\n else:\n print(stat_res)\n return 'fail'\n\n # Test caching\n stat_res = gdal.VSIStatL(gdaltest.webhdfs_base_connection + '/foo/bar')\n if stat_res.size != 1000000:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Test missing file\n handler = webserver.SequentialHandler()\n handler.add('GET', '/webhdfs/v1/unexisting?op=GETFILESTATUS', 404, {},\n '{\"RemoteException\":{\"exception\":\"FileNotFoundException\",\"javaClassName\":\"java.io.FileNotFoundException\",\"message\":\"File does not exist: /unexisting\"}}')\n with webserver.install_http_handler(handler):\n stat_res = gdal.VSIStatL(\n gdaltest.webhdfs_base_connection + '/unexisting')\n if stat_res is not None:\n gdaltest.post_reason('fail')\n return 'fail'\n\n return 'success'\n\n###############################################################################\n# Test ReadDir()\n\n\ndef vsiwebhdfs_readdir():\n\n if gdaltest.webserver_port == 0:\n return 'skip'\n\n gdal.VSICurlClearCache()\n\n handler = webserver.SequentialHandler()\n handler.add('GET', '/webhdfs/v1/foo/?op=LISTSTATUS', 200,\n {}, '{\"FileStatuses\":{\"FileStatus\":[{\"type\":\"FILE\",\"modificationTime\":1000,\"pathSuffix\":\"bar.baz\",\"length\":123456},{\"type\":\"DIRECTORY\",\"pathSuffix\":\"mysubdir\",\"length\":0}]}}')\n with webserver.install_http_handler(handler):\n dir_contents = gdal.ReadDir(gdaltest.webhdfs_base_connection + '/foo')\n if dir_contents != ['bar.baz', 'mysubdir']:\n gdaltest.post_reason('fail')\n print(dir_contents)\n return 'fail'\n stat_res = gdal.VSIStatL(gdaltest.webhdfs_base_connection + '/foo/bar.baz')\n if stat_res.size != 123456:\n gdaltest.post_reason('fail')\n print(stat_res.size)\n return 'fail'\n if stat_res.mtime != 1:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # ReadDir on something known to be a file shouldn't cause network access\n dir_contents = gdal.ReadDir(\n gdaltest.webhdfs_base_connection + '/foo/bar.baz')\n if dir_contents is not None:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Test error on ReadDir()\n handler = webserver.SequentialHandler()\n handler.add('GET', '/webhdfs/v1foo/error_test/?op=LISTSTATUS', 404)\n with webserver.install_http_handler(handler):\n dir_contents = gdal.ReadDir(\n gdaltest.webhdfs_base_connection + 'foo/error_test/')\n if dir_contents is not None:\n gdaltest.post_reason('fail')\n print(dir_contents)\n return 'fail'\n\n return 'success'\n\n###############################################################################\n# Test write\n\n\ndef vsiwebhdfs_write():\n\n if gdaltest.webserver_port == 0:\n return 'skip'\n\n gdal.VSICurlClearCache()\n\n # Zero length file\n handler = webserver.SequentialHandler()\n with webserver.install_http_handler(handler):\n # Missing required config options\n with gdaltest.error_handler():\n f = gdal.VSIFOpenL(\n gdaltest.webhdfs_base_connection + '/foo/bar', 'wb')\n if f is not None:\n gdaltest.post_reason('fail')\n return 'fail'\n\n handler = webserver.SequentialHandler()\n handler.add('PUT', '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root', 307,\n {'Location': gdaltest.webhdfs_redirected_url + '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root'}, )\n handler.add(\n 'PUT', '/redirected/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root', 201)\n\n with gdaltest.config_options({'WEBHDFS_USERNAME': 'root', 'WEBHDFS_DATANODE_HOST': 'localhost'}):\n with webserver.install_http_handler(handler):\n f = gdal.VSIFOpenL(\n gdaltest.webhdfs_base_connection + '/foo/bar', 'wb')\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n if gdal.VSIFCloseL(f) != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Non-empty file\n\n gdal.VSICurlClearCache()\n\n handler = webserver.SequentialHandler()\n handler.add('PUT', '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root', 307,\n {'Location': gdaltest.webhdfs_redirected_url + '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root'}, )\n handler.add(\n 'PUT', '/redirected/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root', 201)\n\n with gdaltest.config_options({'WEBHDFS_USERNAME': 'root', 'WEBHDFS_DATANODE_HOST': 'localhost'}):\n with webserver.install_http_handler(handler):\n f = gdal.VSIFOpenL(\n gdaltest.webhdfs_base_connection + '/foo/bar', 'wb')\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n\n if gdal.VSIFWriteL('foobar', 1, 6, f) != 6:\n gdaltest.post_reason('fail')\n return 'fail'\n\n handler = webserver.SequentialHandler()\n\n def method(request):\n h = request.headers\n if 'Content-Length' in h and h['Content-Length'] != 0:\n sys.stderr.write('Bad headers: %s\\n' % str(request.headers))\n request.send_response(403)\n request.send_header('Content-Length', 0)\n request.end_headers()\n\n request.protocol_version = 'HTTP/1.1'\n request.send_response(307)\n request.send_header('Location', gdaltest.webhdfs_redirected_url +\n '/webhdfs/v1/foo/bar?op=APPEND&user.name=root')\n request.end_headers()\n\n handler.add('POST', '/webhdfs/v1/foo/bar?op=APPEND&user.name=root',\n 307, custom_method=method)\n handler.add('POST', '/redirected/webhdfs/v1/foo/bar?op=APPEND&user.name=root',\n 200, expected_body='foobar'.encode('ascii'))\n\n with webserver.install_http_handler(handler):\n if gdal.VSIFCloseL(f) != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Errors during file creation\n\n gdal.VSICurlClearCache()\n\n handler = webserver.SequentialHandler()\n handler.add(\n 'PUT', '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root', 404)\n with gdaltest.config_options({'WEBHDFS_USERNAME': 'root', 'WEBHDFS_DATANODE_HOST': 'localhost'}):\n with webserver.install_http_handler(handler):\n with gdaltest.error_handler():\n f = gdal.VSIFOpenL(\n gdaltest.webhdfs_base_connection + '/foo/bar', 'wb')\n if f is not None:\n gdaltest.post_reason('fail')\n return 'fail'\n\n handler = webserver.SequentialHandler()\n handler.add('PUT', '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root', 307,\n {'Location': gdaltest.webhdfs_redirected_url + '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root'}, )\n with gdaltest.config_options({'WEBHDFS_USERNAME': 'root'}):\n with webserver.install_http_handler(handler):\n with gdaltest.error_handler():\n f = gdal.VSIFOpenL(\n gdaltest.webhdfs_base_connection + '/foo/bar', 'wb')\n if f is not None:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Errors during POST\n\n gdal.VSICurlClearCache()\n\n handler = webserver.SequentialHandler()\n handler.add('PUT', '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root', 307,\n {'Location': gdaltest.webhdfs_redirected_url + '/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root'}, )\n handler.add(\n 'PUT', '/redirected/webhdfs/v1/foo/bar?op=CREATE&overwrite=true&user.name=root', 201)\n\n with gdaltest.config_options({'WEBHDFS_USERNAME': 'root', 'WEBHDFS_DATANODE_HOST': 'localhost'}):\n with webserver.install_http_handler(handler):\n f = gdal.VSIFOpenL(\n gdaltest.webhdfs_base_connection + '/foo/bar', 'wb')\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n\n if gdal.VSIFWriteL('foobar', 1, 6, f) != 6:\n gdaltest.post_reason('fail')\n return 'fail'\n\n handler = webserver.SequentialHandler()\n handler.add('POST', '/webhdfs/v1/foo/bar?op=APPEND&user.name=root', 307,\n {'Location': gdaltest.webhdfs_redirected_url + '/webhdfs/v1/foo/bar?op=APPEND&user.name=root'})\n handler.add(\n 'POST', '/redirected/webhdfs/v1/foo/bar?op=APPEND&user.name=root', 400)\n\n with gdaltest.error_handler():\n with webserver.install_http_handler(handler):\n if gdal.VSIFCloseL(f) == 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n return 'success'\n\n###############################################################################\n# Test Unlink()\n\n\ndef vsiwebhdfs_unlink():\n\n if gdaltest.webserver_port == 0:\n return 'skip'\n\n gdal.VSICurlClearCache()\n\n # Success\n handler = webserver.SequentialHandler()\n handler.add('DELETE', '/webhdfs/v1/foo/bar?op=DELETE', 200,\n {}, '{\"boolean\":true}')\n with webserver.install_http_handler(handler):\n ret = gdal.Unlink(gdaltest.webhdfs_base_connection + '/foo/bar')\n if ret != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n gdal.VSICurlClearCache()\n\n\n # With permissions\n\n gdal.VSICurlClearCache()\n\n handler = webserver.SequentialHandler()\n handler.add('DELETE', '/webhdfs/v1/foo/bar?op=DELETE&user.name=root&delegation=token', 200,\n {}, '{\"boolean\":true}')\n with gdaltest.config_options({'WEBHDFS_USERNAME': 'root',\n 'WEBHDFS_DELEGATION': 'token'}):\n with webserver.install_http_handler(handler):\n ret = gdal.Unlink(gdaltest.webhdfs_base_connection + '/foo/bar')\n if ret != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Failure\n handler = webserver.SequentialHandler()\n handler.add('DELETE', '/webhdfs/v1/foo/bar?op=DELETE', 200,\n {}, '{\"boolean\":false}')\n with webserver.install_http_handler(handler):\n with gdaltest.error_handler():\n ret = gdal.Unlink(gdaltest.webhdfs_base_connection + '/foo/bar')\n if ret != -1:\n gdaltest.post_reason('fail')\n return 'fail'\n\n gdal.VSICurlClearCache()\n\n # Failure\n handler = webserver.SequentialHandler()\n handler.add('DELETE', '/webhdfs/v1/foo/bar?op=DELETE', 404,\n {})\n with webserver.install_http_handler(handler):\n with gdaltest.error_handler():\n ret = gdal.Unlink(gdaltest.webhdfs_base_connection + '/foo/bar')\n if ret != -1:\n gdaltest.post_reason('fail')\n return 'fail'\n\n return 'success'\n\n###############################################################################\n# Test Mkdir() / Rmdir()\n\n\ndef vsiwebhdfs_mkdir_rmdir():\n\n if gdaltest.webserver_port == 0:\n return 'skip'\n\n gdal.VSICurlClearCache()\n\n # Invalid name\n ret = gdal.Mkdir('/vsiwebhdfs', 0)\n if ret == 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Valid\n handler = webserver.SequentialHandler()\n handler.add('PUT', '/webhdfs/v1/foo/dir?op=MKDIRS', 200,\n {}, '{\"boolean\":true}')\n with webserver.install_http_handler(handler):\n ret = gdal.Mkdir(gdaltest.webhdfs_base_connection + '/foo/dir', 0)\n if ret != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Valid with all options\n handler = webserver.SequentialHandler()\n handler.add('PUT', '/webhdfs/v1/foo/dir?op=MKDIRS&user.name=root&delegation=token&permission=755', 200,\n {}, '{\"boolean\":true}')\n with gdaltest.config_options({'WEBHDFS_USERNAME': 'root', 'WEBHDFS_DELEGATION': 'token'}):\n with webserver.install_http_handler(handler):\n ret = gdal.Mkdir(gdaltest.webhdfs_base_connection +\n '/foo/dir/', 493) # 0755\n if ret != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Error\n handler = webserver.SequentialHandler()\n handler.add('PUT', '/webhdfs/v1/foo/dir_error?op=MKDIRS', 404)\n with webserver.install_http_handler(handler):\n ret = gdal.Mkdir(gdaltest.webhdfs_base_connection +\n '/foo/dir_error', 0)\n if ret == 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Root name is invalid\n ret = gdal.Mkdir(gdaltest.webhdfs_base_connection + '/', 0)\n if ret == 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Invalid name\n ret = gdal.Rmdir('/vsiwebhdfs')\n if ret == 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n gdal.VSICurlClearCache()\n\n # Valid\n handler = webserver.SequentialHandler()\n handler.add('DELETE', '/webhdfs/v1/foo/dir?op=DELETE', 200,\n {}, '{\"boolean\":true}')\n with webserver.install_http_handler(handler):\n ret = gdal.Rmdir(gdaltest.webhdfs_base_connection + '/foo/dir')\n if ret != 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n # Error\n handler = webserver.SequentialHandler()\n handler.add('DELETE', '/webhdfs/v1/foo/dir_error?op=DELETE', 404)\n with webserver.install_http_handler(handler):\n ret = gdal.Rmdir(gdaltest.webhdfs_base_connection + '/foo/dir_error')\n if ret == 0:\n gdaltest.post_reason('fail')\n return 'fail'\n\n return 'success'\n\n###############################################################################\n\n\ndef vsiwebhdfs_stop_webserver():\n\n if gdaltest.webserver_port == 0:\n return 'skip'\n\n # Clearcache needed to close all connections, since the Python server\n # can only handle one connection at a time\n gdal.VSICurlClearCache()\n\n webserver.server_stop(gdaltest.webserver_process, gdaltest.webserver_port)\n\n return 'success'\n\n###############################################################################\n# Nominal cases (require valid credentials)\n\n\ndef vsiwebhdfs_extra_1():\n\n if not gdaltest.built_against_curl():\n return 'skip'\n\n webhdfs_url = gdal.GetConfigOption('WEBHDFS_URL')\n if webhdfs_url is None:\n print('Missing WEBHDFS_URL for running gdaltest_list_extra')\n return 'skip'\n\n if webhdfs_url.endswith('/webhdfs/v1') or webhdfs_url.endswith('/webhdfs/v1/'):\n path = '/vsiwebhdfs/' + webhdfs_url\n statres = gdal.VSIStatL(path)\n if statres is None or not stat.S_ISDIR(statres.mode):\n gdaltest.post_reason('fail')\n print('%s is not a valid bucket' % path)\n return 'fail'\n\n readdir = gdal.ReadDir(path)\n if readdir is None:\n gdaltest.post_reason('fail')\n print('ReadDir() should not return empty list')\n return 'fail'\n for filename in readdir:\n if filename != '.':\n subpath = path + '/' + filename\n if gdal.VSIStatL(subpath) is None:\n gdaltest.post_reason('fail')\n print('Stat(%s) should not return an error' % subpath)\n return 'fail'\n\n unique_id = 'vsiwebhdfs_test'\n subpath = path + '/' + unique_id\n ret = gdal.Mkdir(subpath, 0)\n if ret < 0:\n gdaltest.post_reason('fail')\n print('Mkdir(%s) should not return an error' % subpath)\n return 'fail'\n\n readdir = gdal.ReadDir(path)\n if unique_id not in readdir:\n gdaltest.post_reason('fail')\n print('ReadDir(%s) should contain %s' % (path, unique_id))\n print(readdir)\n return 'fail'\n\n #ret = gdal.Mkdir(subpath, 0)\n # if ret == 0:\n # gdaltest.post_reason('fail')\n # print('Mkdir(%s) repeated should return an error' % subpath)\n # return 'fail'\n\n ret = gdal.Rmdir(subpath)\n if ret < 0:\n gdaltest.post_reason('fail')\n print('Rmdir(%s) should not return an error' % subpath)\n return 'fail'\n\n readdir = gdal.ReadDir(path)\n if unique_id in readdir:\n gdaltest.post_reason('fail')\n print('ReadDir(%s) should not contain %s' % (path, unique_id))\n print(readdir)\n return 'fail'\n\n ret = gdal.Rmdir(subpath)\n if ret == 0:\n gdaltest.post_reason('fail')\n print('Rmdir(%s) repeated should return an error' % subpath)\n return 'fail'\n\n ret = gdal.Mkdir(subpath, 0)\n if ret < 0:\n gdaltest.post_reason('fail')\n print('Mkdir(%s) should not return an error' % subpath)\n return 'fail'\n\n f = gdal.VSIFOpenL(subpath + '/test.txt', 'wb')\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n gdal.VSIFWriteL('hello', 1, 5, f)\n gdal.VSIFCloseL(f)\n\n ret = gdal.Rmdir(subpath)\n if ret == 0:\n gdaltest.post_reason('fail')\n print('Rmdir(%s) on non empty directory should return an error' % subpath)\n return 'fail'\n\n f = gdal.VSIFOpenL(subpath + '/test.txt', 'rb')\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n data = gdal.VSIFReadL(1, 5, f).decode('utf-8')\n if data != 'hello':\n gdaltest.post_reason('fail')\n print(data)\n return 'fail'\n gdal.VSIFCloseL(f)\n\n ret = gdal.Unlink(subpath + '/test.txt')\n if ret < 0:\n gdaltest.post_reason('fail')\n print('Unlink(%s) should not return an error' %\n (subpath + '/test.txt'))\n return 'fail'\n\n ret = gdal.Rmdir(subpath)\n if ret < 0:\n gdaltest.post_reason('fail')\n print('Rmdir(%s) should not return an error' % subpath)\n return 'fail'\n\n return 'success'\n\n f = open_for_read('/vsiwebhdfs/' + webhdfs_url)\n if f is None:\n gdaltest.post_reason('fail')\n return 'fail'\n ret = gdal.VSIFReadL(1, 1, f)\n gdal.VSIFCloseL(f)\n\n if len(ret) != 1:\n gdaltest.post_reason('fail')\n print(ret)\n return 'fail'\n\n return 'success'\n\n###############################################################################\n\n\ndef vsiwebhdfs_cleanup():\n\n for var in gdaltest.webhdfs_vars:\n gdal.SetConfigOption(var, gdaltest.webhdfs_vars[var])\n\n return 'success'\n\n\ngdaltest_list = [vsiwebhdfs_init,\n vsiwebhdfs_start_webserver,\n vsiwebhdfs_open,\n vsiwebhdfs_stat,\n vsiwebhdfs_readdir,\n vsiwebhdfs_write,\n vsiwebhdfs_unlink,\n vsiwebhdfs_mkdir_rmdir,\n vsiwebhdfs_stop_webserver,\n vsiwebhdfs_cleanup]\n\ngdaltest_list_extra = [vsiwebhdfs_extra_1]\n\nif __name__ == '__main__':\n\n gdaltest.setup_run('vsiwebhdfs')\n\n if gdal.GetConfigOption('RUN_MANUAL_ONLY', None):\n gdaltest.run_tests(gdaltest_list_extra)\n else:\n gdaltest.run_tests(\n gdaltest_list + gdaltest_list_extra + [vsiwebhdfs_cleanup])\n\n sys.exit(gdaltest.summarize())\n","sub_path":"autotest/gcore/vsiwebhdfs.py","file_name":"vsiwebhdfs.py","file_ext":"py","file_size_in_byte":25305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"643697239","text":"# Server is setup here\nfrom flask import Flask, session, render_template, request, jsonify, current_app\nfrom flask_bootstrap import Bootstrap\nimport time, os\n\nfrom odm360.utils import parse_config, make_config\nfrom odm360.log import start_logger\nimport odm360.camera360rig as camrig\nfrom odm360.utils import get_lan_ip\n\n# API for picam is defined below\ndef do_GET():\n \"\"\"\n GET API should provide a json with the following fields:\n state: str - can be:\n \"idle\" - before anything is done, or after camera is stopped (to be implemented with push button)\n \"ready\" - camera is initialized\n \"capture\" - camera is capturing\n req: str - name of method to call from server\n kwargs: dict - any kwargs that need to be parsed to method (can be left out if None)\n log: str - log message to be printed from client on server's log (see self.logger)\n\n the GET API then decides what action should be taken given the state.\n Client is responsible for updating its status to the current\n \"\"\"\n try:\n msg = request.get_json()\n print(msg)\n # Create or update state of current camera\n current_app.config['rig'].cam_state[request.remote_addr] = msg['state']\n log_msg = f'Cam {request.remote_addr} - GET {msg[\"req\"]}'\n logger.debug(log_msg)\n # check if task exists and sent instructions back\n method = f'get_{msg[\"req\"].lower()}'\n # if hasattr(self, method):\n if 'kwargs' in msg:\n kwargs = msg['kwargs']\n else:\n kwargs = {}\n task = getattr(current_app.config['rig'], method)\n # execute with key-word arguments provided\n r = task(**kwargs)\n return r\n except:\n return {'task': 'wait',\n 'kwargs': {}\n }\n\n# POST echoes the message adding a JSON field\ndef do_POST():\n \"\"\"\n POST API should provide a json with the following fields:\n req: str - name of method for posting to call from server (e.g. log)\n kwargs: dict - any kwargs that need to be parsed to method (can be left out if None)\n the POST API then decides what action should be taken based on the POST request.\n POST API will also return a result back\n \"\"\"\n msg = request.get_json()\n print(msg)\n # show request in log\n log_msg = f'Cam {request.remote_addr} - POST {msg[\"req\"]}'\n\n logger.debug(log_msg)\n\n # check if task exists and sent instructions back\n method = f'post_{msg[\"req\"].lower()}'\n if hasattr(current_app.config['rig'], method):\n if 'kwargs' in msg:\n kwargs = msg['kwargs']\n else:\n kwargs = {}\n task = getattr(current_app.config['rig'], method)\n # execute with key-word arguments provided\n r = task(**kwargs)\n return r\n\ndef cleanopts(optsin):\n \"\"\"Takes a multidict from a flask form, returns cleaned dict of options\"\"\"\n opts = {}\n d = optsin\n for key in d:\n opts[key] = optsin[key].lower().replace(' ', '_')\n return opts\n\ndef initialize_config(config_fn):\n config = parse_config(config_fn)\n # test if we are ready to start devices or not\n start_parent = True\n if config.get('main', 'n_cams') == '':\n start_parent = False\n logger.info('n_cams is missing in config, starting without a running parent server')\n if config.get('main', 'dt') == '':\n start_parent = False\n logger.info('dt is missing in config, starting without a running parent server')\n if config.get('main', 'project') == '':\n start_parent = False\n logger.info('project is missing in config, starting without a running parent server')\n if config.get('main', 'root') == '':\n start_parent = False\n logger.info('root is missing in config, starting without a running parent server')\n current_app.config['config'] = dict(config.items('main'))\n current_app.config['ip'] = get_lan_ip()\n current_app.config['start_parent'] = start_parent\n\napp = Flask(__name__)\napp.secret_key = 'odm360'\nbootstrap = Bootstrap(app)\n\nlogger = start_logger(\"True\", \"False\")\n\n@app.route(\"/\")\ndef gps_page():\n # start with a parent server immediately. Make a new one when a new project is initiated\n # TODO: only do this if there is no rig initialized yet.\n if not('config' in current_app.config):\n if os.path.isfile('current_config'):\n with open('current_config', 'r') as f:\n config_fn = os.path.join('config', f.read())\n else:\n config_fn = 'config/settings.conf.default'\n logger.info(f'Parsing project config from {os.path.abspath(config_fn)}')\n initialize_config(config_fn)\n if current_app.config['start_parent']:\n logger.info('Starting parent server')\n camrig.start_rig()\n\n \"\"\"\n The status web page with the gnss satellites levels and a map\n \"\"\"\n return render_template(\"status.html\")\n\n@app.route('/project', methods=['GET', 'POST'])\ndef project_page():\n \"\"\"\n The settings page where you can manage the various services, the parameters, update, power...\n \"\"\"\n if request.method == 'POST':\n # config = current_app.config['config']\n form = cleanopts(request.form)\n config = {}\n # set the config options as provided\n config['project'] = form['project']\n config['n_cams'] = form['n_cams']\n config['dt'] = form['dt']\n config['root'] = os.path.join('photos', form['project'])\n config['verbose'] = \"True\" if form['loglevel']=='debug' else \"False\"\n config['quiet'] = \"False\"\n config_fn = f'{form[\"project\"]}.ini'\n conf_obj = make_config(config)\n with open(os.path.abspath(os.path.join('config', config_fn)), 'w') as f:\n conf_obj.write(f)\n with open('current_config', 'w') as f:\n f.write(config_fn)\n initialize_config(os.path.join('config', config_fn))\n # start the rig, after this, the rig is in current_app.config['rig'], if start_parent is true\n if current_app.config['start_parent']:\n camrig.start_rig()\n\n return render_template(\"status.html\") #, main_settings = main_settings,\n # ntrip_settings = ntrip_settings,\n # file_settings = file_settings,\n # rtcm_svr_settings = rtcm_svr_settings)\n\n else:\n return render_template(\"project.html\") #, main_settings = main_settings,\n # ntrip_settings = ntrip_settings,\n # file_settings = file_settings,\n # rtcm_svr_settings = rtcm_svr_settings)\n\n@app.route('/logs')\ndef logs_page():\n \"\"\"\n The data web pages where you can download/delete the raw gnss data\n \"\"\"\n return render_template(\"logs.html\")\n\n@app.route('/settings')\ndef settings_page():\n \"\"\"\n The data web pages where you can download/delete the raw gnss data\n \"\"\"\n return render_template(\"settings.html\")\n\n@app.route('/cams')\ndef cam_page():\n \"\"\"\n The data web pages where you can download/delete the raw gnss data\n \"\"\"\n return render_template(\"cam_status.html\", n_cams=range(6))\n\n@app.route('/file_page')\ndef file_page():\n return render_template(\"file_page.html\")\n\n@app.route('/picam', methods=['GET', 'POST'])\ndef picam():\n if request.method == 'POST':\n\n r = do_POST()\n else:\n # print(request.get_json())\n r = do_GET() # response is passed back to client\n return jsonify(r)\n\ndef run(app):\n app.run(debug=False, port=5000, host=\"0.0.0.0\")\n\nif __name__ == \"__main__\":\n run(app)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"466508641","text":"# -*- coding: utf-8 -*-\n\nfrom user_app.models import User, messages, obj_for_rate\nfrom django.contrib import admin\nfrom django.contrib.auth.admin import UserAdmin as BaseUserAdmin\nfrom django.utils.translation import ugettext_lazy as _\n\n\n\t\t\t#--------------Пользователи-----------------\n\n\nclass UserAdmin(BaseUserAdmin):\n fieldsets = (\n (None, {'fields': ('username', 'password')}),\n (_('User info: '), {'fields': (\n 'organization_name',\n 'first_name',\n 'last_name',\n 'email',\n 'org_pic',\n 'description',\n )}),\n (_('Requisites: '), {'fields': ('adress', 'telephone', 'requisits',)}),\n (_('Rights: '), {'fields': ('is_active', 'is_staff', 'is_superuser', 'groups', 'user_permissions')}),\n (_('Important dates: '), {'fields': ('last_login', 'date_joined')}),\n )\n add_fieldsets = (\n (None, {\n 'classes': ('wide',),\n 'fields': ('username', 'email', 'password1', 'password2')}\n ),\n )\n\n\n\n \t\t#--------------Пользователи-----------------\n\n \t\t#---------------Сообщения------------------\n\nclass messagesAdmin(admin.ModelAdmin):\n\tfields = [\n 'mess_Recipient', \n 'mess_from', \n 'message_subject', \n 'message_content', \n 'message_ReadUnread',\n ]\n\tlist_filter = [\n 'message_ReadUnread',\n ]\n\tlist_display = (\n 'mess_Recipient', \n 'mess_from', \n 'message_ReadUnread'\n )\n\tlist_per_page = 100\n\n\n\n \t\t#---------------Сообщения------------------\n\n #---------------Для оценки------------------\n\nclass obj_for_rateAdmin(admin.ModelAdmin):\n fields = [\n 'obj_title', \n 'obj_description', \n 'obj_state', \n 'obj_pic_1', \n 'obj_pic_2', \n 'obj_pic_3', \n 'obj_date_added', \n 'obj_desired_price',\n ]\n list_filter = [\n 'obj_state', \n 'obj_desired_price',\n ]\n list_display = (\n 'obj_title', \n 'obj_state', \n 'obj_date_added', \n 'obj_desired_price',\n )\n list_per_page = 100\n\n #---------------Для оценки------------------\n\n\n\n\nadmin.site.register(User, UserAdmin)\nadmin.site.register(messages, messagesAdmin)\nadmin.site.register(obj_for_rate, obj_for_rateAdmin)\n","sub_path":"user_app/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"539215642","text":"from correlation_coefficent import compute_correlation_coefficent\nimport threading\nclass ComputeCorrelationCoefficentThread (threading.Thread):\n \"\"\"\n user_dict -> dictionary of the user: {itemID:rating}\n user_ids -> set of users IDs\n utility_matrix -> dataset\n is_explicit -> boolean value that defines if we\n need to scale or not cause in the case of implicit\n we do not need to scale\n \"\"\"\n def __init__(self, user_dict, user_ids, utility_matrix, is_explicit, name = None):\n threading.Thread.__init__(self)\n self.name = name\n self.user_dict = user_dict\n self.user_ids = user_ids\n self.is_explicit = is_explicit\n self.utility_matrix = utility_matrix\n self.result = None\n \n def run(self):\n if self.name == None:\n raise Exception('Something bad happened, thread has no name')\n #print('Thread {} started'.format(self.name))\n self.compute_similarities()\n\n \n def compute_similarities(self):\n similarities = []\n for user in self.user_ids:\n if(self.utility_matrix.get(user) == None): #cause the set of users to which I do the similarity comes \n #from the set of users in the explicit dictionary so some one could\n #not appear in the implicit dictionary: If user does not appear -> no\n #implicit rating -> similarity = 0\n similarities.append([user,0])\n else:\n tmp_user_dict = self.utility_matrix[user]\n similarity = compute_correlation_coefficent(self.user_dict, tmp_user_dict, self.is_explicit)\n similarities.append([user,similarity])\n \n self.result = similarities\n\n # vector of similarities --> [userID, sim_score]\n def join(self):\n threading.Thread.join(self)\n if self.result is not None:\n return self.result\n else:\n print('Error using threads, result is None')","sub_path":"src/books/libraries/correlation_coefficent_thread.py","file_name":"correlation_coefficent_thread.py","file_ext":"py","file_size_in_byte":2045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"281916607","text":"from Crypto.Cipher import ARC4\nfrom re import match\nfrom choworplow import r, SECRET_KEY as KEY\n\nALPHABET = \"23456789abcdefghijkmnpqrstuvwxyzABCDEFGHJKLMNPQRSTUVWXYZ\"\n\ndef baseX_encode(num, alphabet=ALPHABET):\n ''' encode base 10 num into base X where X=len(alphabet) '''\n if (num == 0):\n return alphabet[0]\n arr = []\n base = len(alphabet)\n while num:\n rem = num % base\n num = num // base\n arr.append(alphabet[rem])\n if len(arr) < 2: # want exactly two digits\n arr.append(alphabet[0])\n arr.reverse()\n return ''.join(arr)\n\ndef baseX_decode(string, alphabet=ALPHABET):\n ''' decode string to base 10 from base X where X=len(alphabet) '''\n base, strlen = len(alphabet), len(string)\n num = 0\n for idx, char in enumerate(string):\n power = (strlen - (idx + 1))\n num += alphabet.index(char) * (base ** power)\n idx += 1\n return num\n\ndef sean_encode(food_id, gender, person_id):\n ''' creates disguised URL-friendly string from the inputs.\n gender is either 'c' or 'd' '''\n msg = str(food_id) + gender + str(person_id)\n nonce = ''.join(r.sample(ALPHABET, 4))\n cipher = ARC4.new(KEY+nonce)\n encrypted = cipher.encrypt(msg)\n ord_list = map(ord, encrypted)\n return nonce + ''.join(map(baseX_encode, ord_list))\n\ndef sean_decode(url):\n ''' reverses sean_encode '''\n i, chr_list = 4, []\n nonce = url[:4]\n while i < len(url):\n decoded = baseX_decode(url[i:i+2])\n if decoded < 0 or 255 < decoded:\n raise ValueError\n chr_list.append(chr(decoded))\n i += 2\n encrypted = ''.join(chr_list)\n cipher = ARC4.new(KEY+nonce)\n decrypted = cipher.decrypt(encrypted)\n pattern = r\"(\\d+)([a-zA-z])(\\d+)\"\n m = match(pattern, decrypted)\n if not m:\n raise ValueError\n parsed = m.groups()\n food_id, gender, person_id = int(parsed[0]), parsed[1], int(parsed[2])\n return food_id, gender, person_id\n","sub_path":"choworplow/crypto.py","file_name":"crypto.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"65289603","text":"from django.contrib.auth import get_user_model\nfrom django.urls import reverse\nfrom django.test import TestCase\n\nfrom rest_framework import status\nfrom rest_framework.test import APIClient\n\nfrom core.models import Tag\nfrom recipe.serializers import TagSerializer\n\nTAGS_URL = reverse('recipe:tag-list')\n\n\nclass PublicTagsAPITests(TestCase):\n \"\"\" Test the publical available tags API \"\"\"\n\n def setUp(self):\n self.client = APIClient()\n\n def test_login_required(self):\n \"\"\" Test that checks that a login is required for Recipe tag API \"\"\"\n\n self.user = get_user_model().objects.create_user(\n 'Hash.Qureshi@mnp.ca',\n 'password123'\n )\n\n self.client.force_authenticate(self.user)\n self.client = APIClient()\n res = self.client.get(TAGS_URL)\n # unclear why this works\n self.assertEqual(res.status_code,\n status.HTTP_403_FORBIDDEN)\n\n\nclass PrivateTagsAPITests(TestCase):\n \"\"\" Tests for authenticated user Test API \"\"\"\n\n def setUp(self):\n self.user = get_user_model().objects.create_user(\n 'User@test.ca',\n 'password123'\n )\n self.client = APIClient()\n self.client.force_authenticate(self.user)\n\n def test_retrieve_tags(self):\n \"\"\" Tet getting tags for an authenticated user\"\"\"\n # Create tags\n Tag.objects.create(user=self.user, name='Vegan')\n Tag.objects.create(user=self.user, name='Desert')\n # retriev tags\n res = self.client.get(TAGS_URL)\n tags = Tag.objects.all().order_by('-name')\n serializer = TagSerializer(tags, many=True)\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n self.assertEqual(res.data, serializer.data)\n\n def test_tags_limited_to_user(self):\n \"\"\" Test that th tags returned are for the authenticated user \"\"\"\n user2 = get_user_model().objects.create_user(\n 'otherUser@Otherdomain.ca'\n 'testpass2'\n )\n Tag.objects.create(user=user2, name='Fruity')\n tag = Tag.objects.create(user=self.user, name='Comfort Food')\n res = self.client.get(TAGS_URL)\n\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n self.assertEqual(len(res.data), 1)\n self.assertEqual(res.data[0]['name'], tag.name)\n\n def test_create_tag_successful(self):\n \"\"\"We are going to test that a tag has been created successfully\"\"\"\n payload = {'name': 'TestTagFraggle'}\n self.client.post(TAGS_URL, payload)\n exists = Tag.objects.filter(\n user=self.user,\n name=payload['name']\n ).exists()\n\n self.assertTrue(exists)\n\n def test_create_tag_invalid(self):\n \"\"\"Creating a new tag that is invalid\"\"\"\n payload = {'name': ''}\n\n res = self.client.post(TAGS_URL, payload)\n\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n","sub_path":"app/recipe/tests/test_tag_api.py","file_name":"test_tag_api.py","file_ext":"py","file_size_in_byte":2937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"380804128","text":"# Copyright (C) 2016 Huang MaChi at Chongqing University\n# of Posts and Telecommunications, Chongqing, China.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport re\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef read_file_1(file_name, delim=','):\n\t\"\"\"\n\t\tRead the bwmng.txt file.\n\t\"\"\"\n\tread_file = open(file_name, 'r')\n\tlines = read_file.xreadlines()\n\tlines_list = []\n\tfor line in lines:\n\t\tline_list = line.strip().split(delim)\n\t\tlines_list.append(line_list)\n\tread_file.close()\n\n\t# Remove the last second's statistics, because they are mostly not intact.\n\tlast_second = lines_list[-1][0]\n\t_lines_list = lines_list[:]\n\tfor line in _lines_list:\n\t\tif line[0] == last_second:\n\t\t\tlines_list.remove(line)\n\n\treturn lines_list\n\ndef read_file_2(file_name):\n\t\"\"\"\n\t\tRead the ping.txt file.\n\t\"\"\"\n\tread_file = open(file_name, 'r')\n\tlines = read_file.xreadlines()\n\tlines_list = []\n\tfor line in lines:\n\t\tif line.endswith(' ms\\n') and not line.startswith('rtt'):\n\t\t\tlines_list.append(line)\n\tread_file.close()\n\treturn lines_list\n\ndef get_total_throughput(total_throughput):\n\t\"\"\"\n\t\ttotal_throughput = {0:x, 1:x, 2:x, ...}\n\t\"\"\"\n\tlines_list = read_file_1('./results/bwmng.txt')\n\tfirst_second = int(lines_list[0][0])\n\tcolumn_bytes_out = 6 # bytes_out\n\tswitch = 's1'\n\tsw = re.compile(switch)\n\trealtime_throught = {}\n\n\tfor i in xrange(121):\n\t\tif not total_throughput.has_key(i):\n\t\t\ttotal_throughput[i] = 0\n\n\tfor i in xrange(121):\n\t\tif not realtime_throught.has_key(i):\n\t\t\trealtime_throught[i] = 0\n\n\tfor row in lines_list:\n\t\tiface_name = row[1]\n\t\tif sw.match(iface_name):\n\t\t\tif int(iface_name[-1]) >= 4: # Choose host-connecting interfaces only.\n\t\t\t\tif (int(row[0]) - first_second) <= 120: # Take the good values only.\n\t\t\t\t\trealtime_throught[int(row[0]) - first_second] += float(row[column_bytes_out]) * 8.0 / (10 ** 6) # Mbit\n\n\tfor i in xrange(121):\n\t\tfor j in xrange(i+1):\n\t\t\ttotal_throughput[i] += realtime_throught[j] # Mbit\n\n\treturn total_throughput\n\ndef get_value_list_1(value_dict):\n\t\"\"\"\n\t\tGet the values from the \"total_throughput\" data structure.\n\t\"\"\"\n\tvalue_list = []\n\tfor i in xrange(121):\n\t\tvalue_list.append(value_dict[i])\n\treturn value_list\n\ndef get_value_list_2(value_dict):\n\t\"\"\"\n\t\tGet the values from the \"roundtrip_delay\" data structure.\n\t\"\"\"\n\tvalue_list = []\n\tsequence_list = []\n\tfor i in xrange(121):\n\t\tif value_dict[i] != 0:\n\t\t\tsequence_list.append(i)\n\t\t\tvalue_list.append(value_dict[i])\n\treturn sequence_list, value_list\n\ndef get_value_list_3(value_dict, sequence):\n\t\"\"\"\n\t\tGet the values from the \"roundtrip_delay\" data structure.\n\t\"\"\"\n\tnum = 0\n\tfor i in xrange(sequence, sequence + 30):\n\t\tif value_dict[i] == 0:\n\t\t\tnum += 1\n\trate = num / 30.0\n\treturn rate\n\ndef get_realtime_speed(switch):\n\t\"\"\"\n\t\tGet realtime speed of individual flow.\n\t\"\"\"\n\trealtime_speed = {}\n\tlines_list = read_file_1('./results/bwmng.txt')\n\tfirst_second = int(lines_list[0][0])\n\tcolumn_bytes_out_rate = 2 # bytes_out/s\n\tsw = re.compile(switch)\n\n\tfor i in xrange(121):\n\t\tif not realtime_speed.has_key(i):\n\t\t\trealtime_speed[i] = 0\n\n\tfor row in lines_list:\n\t\tiface_name = row[1]\n\t\tif sw.match(iface_name):\n\t\t\tif (int(row[0]) - first_second) <= 120: # Take the good values only.\n\t\t\t\trealtime_speed[int(row[0]) - first_second] += float(row[column_bytes_out_rate]) * 8.0 / (10 ** 6) # Mbit/s\n\n\treturn realtime_speed\n\ndef get_delay_values(delay_dict):\n\t\"\"\"\n\t\tGet round trip delay of ping traffic.\n\t\"\"\"\n\tlines_list = read_file_2('./results/ping.txt')\n\tfor i in xrange(121):\n\t\tif not delay_dict.has_key(i):\n\t\t\tdelay_dict[i] = 0\n\n\tfor row in lines_list:\n\t\tsequence = int(row.split(' ')[4].split('=')[1])\n\t\tdelay = float(row.split(' ')[6].split('=')[1])\n\t\tdelay_dict[sequence] = delay\n\n\treturn delay_dict\n\ndef plot_results():\n\t\"\"\"\n\t\tPlot the results:\n\t\t1. Plot total throughput\n\t\t2. Plot realtime speed of individual flow\n\t\"\"\"\n\tbandwidth = 10.0 # (unit: Mbit/s)\n\tutmost_throughput = bandwidth * 120\n\ttotal_throughput = {}\n\ttotal_throughput = get_total_throughput(total_throughput)\n\n\t# 1. Plot total throughput.\n\tfig = plt.figure()\n\tfig.set_size_inches(12, 6)\n\tx = np.arange(0, 121)\n\ty = get_value_list_1(total_throughput)\n\tplt.plot(x, y, 'r-', linewidth=2)\n\tplt.xlabel('Time (s)', fontsize='x-large')\n\tplt.xlim(0, 120)\n\tplt.xticks(np.arange(0, 121, 30))\n\tplt.ylabel('Total Throughput\\n(Mbit)', fontsize='x-large')\n\tplt.ylim(0, utmost_throughput)\n\tplt.yticks(np.linspace(0, utmost_throughput, 11))\n\tplt.grid(True)\n\tplt.savefig('./results/1.total_throughput.png')\n\n\t# 2. Plot realtime speed of individual flow.\n\tfig = plt.figure()\n\tfig.set_size_inches(12, 6)\n\tx = np.arange(0, 121)\n\trealtime_speed1 = get_realtime_speed('s1-eth4')\n\ty1 = get_value_list_1(realtime_speed1)\n\trealtime_speed2 = get_realtime_speed('s1-eth5')\n\ty2 = get_value_list_1(realtime_speed2)\n\trealtime_speed3 = get_realtime_speed('s1-eth6')\n\ty3 = get_value_list_1(realtime_speed3)\n\tplt.plot(x, y1, 'r-', linewidth=2, label=\"Iperf1\")\n\tplt.plot(x, y2, 'g-', linewidth=2, label=\"Iperf2\")\n\tplt.plot(x, y3, 'b-', linewidth=2, label=\"Iperf3\")\n\tplt.xlabel('Time (s)', fontsize='x-large')\n\tplt.xlim(0, 120)\n\tplt.xticks(np.arange(0, 121, 30))\n\tplt.ylabel('Realtime Speed of Individual Flow\\n(Mbit/s)', fontsize='x-large')\n\tplt.ylim(0, bandwidth)\n\tplt.yticks(np.linspace(0, bandwidth, 11))\n\tplt.legend(loc='upper left', fontsize='medium')\n\tplt.grid(True)\n\tplt.savefig('./results/2.realtime_speed_of_individual_flow.png')\n\n\t# 3. Plot realtime throughput.\n\tfig = plt.figure()\n\tfig.set_size_inches(12, 6)\n\tx = np.arange(0, 121)\n\trealtime_speed = get_realtime_speed('s1-eth[4-6]')\n\ty = get_value_list_1(realtime_speed)\n\tplt.plot(x, y, 'r-', linewidth=2)\n\tplt.xlabel('Time (s)', fontsize='x-large')\n\tplt.xlim(0, 120)\n\tplt.xticks(np.arange(0, 121, 30))\n\tplt.ylabel('Realtime Throughput\\n(Mbit/s)', fontsize='x-large')\n\tplt.ylim(0, bandwidth)\n\tplt.yticks(np.linspace(0, bandwidth, 11))\n\tplt.grid(True)\n\tplt.savefig('./results/3.realtime_throught.png')\n\n\nif __name__ == '__main__':\n\tplot_results()\n","sub_path":"plot_results.py","file_name":"plot_results.py","file_ext":"py","file_size_in_byte":6430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"72761071","text":"\n#### Characters added into the document array are added as an element of an 'addChar' array. the 0th element is the character, \n#### the first and second are for super scripts and sub scripts. 3 squared with an index number 'i sub Z' would look like\n#### [numeralThree, [numeralTwo],[lowercasei,settheory__subset,uppercaseZ]]\n####\n\ndef addChar(input):\n\treturn [input,[],[]]\n\n#### pygame events have a .key property that is an integer. \n#### letcor ('letter correspondence') is a dictionary so \n#### I can type a string of the key without having to\n#### remember the number\n\nletCor = {\n\t\n\t'BACKSPACE':8,\n\t'TAB':9,\n\t'ENTER':13,\n\n\t'SPACE':32,\n\n\t'SINGLEQUOTE':39,\n\n\t'COMMA':44,\n\t'HYPHEN':45,\n\t'PERIOD':46,\n\n\t'FORWARDSLASH':47,\n\t'NUMERAL0':48,\n\t'NUMERAL1':49,\n\t'NUMERAL2':50,\n\t'NUMERAL3':51,\n\t'NUMERAL4':52,\n\t'NUMERAL5':53,\n\t'NUMERAL6':54,\n\t'NUMERAL7':55,\n\t'NUMERAL8':56,\n\t'NUMERAL9':57,\n\n\t'SEMICOLON':59,\n\n\t'EQUALS':61,\n\n\t'LEFTBRACKET':91,\n\t'RIGHTBRACKET':93,\n\n\t'BACKSLASH':92,\n\n\t'TILDE':96,\n\n\t'a':97,\n\t'b':98,\n\t'c':99,\n\t'd':100,\n\t'e':101,\n\t'f':102,\n\t'g':103,\n\t'h':104,\n\t'i':105,\n\t'j':106,\n\t'k':107,\n\t'l':108,\n\t'm':109,\n\t'n':110,\n\t'o':111,\n\t'p':112,\n\t'q':113,\n\t'r':114,\n\t's':115,\n\t't':116,\n\t'u':117,\n\t'v':118,\n\t'w':119,\n\t'x':120,\n\t'y':121,\n\t'z':122,\n\n\t'UPARROW':273,\n\t'DOWNARROW':274,\n\t'RIGHTARROW':275,\n\t'LEFTARROW':276,\n\n\t'RIGHTSHIFT':303,\n\t'LEFTSHIFT':304,\n\t'RIGHTCONTROL':305,\n\t'LEFTCONTROL':306,\n\t'RIGHTALT':307,\n\t'LEFTALT':308,\n\n\t'NOKEYS':14000\n\n}\n\nnumbersToNumerals = {\n\t\n\t0:L_Nze,\n\t1:L_Non,\n\t2:L_Ntw,\n\t3:L_Nth,\n\t4:L_Nfo,\n\t5:L_Nfi,\n\t6:L_Nsi,\n\t7:L_Nse,\n\t8:L_Nei,\n\t9:L_Nni\n}\n\n#### Each possible characer is a unique character object. \n#### My terminology through out this program is poor\n#### a Char object is a KIND of character object\n#### but often I use 'char' or 'character' to refer\n#### to specific elements in the document array\n\nclass Char:\n\tdef __init__(self,lilimage,image,keys,keyDig):\n\t\tself.lilimage=lilimage\n\t\tself.image=image\n\t\tself.keys=keys #The keys variable, refers to a tuple, who elements are sets, containing valid key combinations to produce that character. The elements of the set are elements of the dictionary\n\t\tself.keyDig=keyDig\n\n#### This is the document object\n#### Charen (pronounced 'Sharon')\n#### is an array of all the\n#### characters items in the\n#### document\n\nclass Doc:\n\tdef __init__(self):\n\t\tself.charen=[]","sub_path":"ChadTech vZEpTHZE/functionsClassesAndDictionaries.py","file_name":"functionsClassesAndDictionaries.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"457714775","text":"from requests import get, post;\nimport keys.keys as key;\nfrom json import loads, dumps\n\nCLIENT_ID = key.CLIENT_ID;\nCLIENT_SECRET = key.CLIENT_SECRET;\n\n# Spotify URLs\nAUTH_URL = \"https://accounts.spotify.com/authorize\"\nTOKEN_URL = \"https://accounts.spotify.com/api/token\"\nAPI_BASE_URL = \"https://api.spotify.com\"\nAPI_VERSION = \"v1\"\nAPI_URL = f\"{API_BASE_URL}/{API_VERSION}\"\n\n\ndef profile_data(token):\n user_profile_endpoint = f\"{API_URL}/me\"\n headers = {\n 'Authorization': f\"Bearer {token}\",\n 'Accept': \"*/*\",\n 'Cache-Control': \"no-cache\",\n 'Host': \"api.spotify.com\",\n 'Accept-Encoding': \"gzip, deflate\",\n 'Connection': \"keep-alive\",\n 'cache-control': \"no-cache\"\n }\n response = get(user_profile_endpoint, headers=headers)\n data = loads(response.text)\n return data;\n\ndef create_playlist(token, user, name, desc):\n url = f\"{user}/playlists\"\n deets = {\n \"name\": name,\n \"description\": desc\n }\n payload = dumps(deets)\n headers = {\n 'Content-Type': \"application/json\",\n 'Authorization': f\"Bearer {token}\",\n 'Accept': \"*/*\",\n 'Cache-Control': \"no-cache\",\n 'Host': \"api.spotify.com\",\n 'Accept-Encoding': \"gzip, deflate\",\n 'Connection': \"keep-alive\",\n 'cache-control': \"no-cache\"\n }\n create = post(url, data=payload, headers=headers)\n data = loads(create.text)\n return data;\n\ndef search_track(token, name, artist, number):\n url = f\"{API_URL}/search\"\n artist_name = artist.lower();\n querystring = {\n \"q\": f\"{name} artist:{artist_name}\",\n \"type\": \"track\",\n \"limit\": number\n }\n headers = {\n 'Content-Type': \"application/x-www-form-urlencoded\",\n 'Authorization': f\"Bearer {token}\",\n 'Accept': \"*/*\",\n 'Cache-Control': \"no-cache\",\n 'Host': \"api.spotify.com\",\n 'Accept-Encoding': \"gzip, deflate\",\n 'Connection': \"keep-alive\",\n 'cache-control': \"no-cache\"\n }\n search = get(url, headers=headers, params=querystring)\n data = loads(search.text)\n if len(data['tracks']['items']) != 0:\n track_uri = data['tracks']['items'][0]['uri']\n return track_uri\n else:\n return \"\";\n\ndef add_track(token, playlist_id, songs):\n url = f\"{API_URL}/playlists/{playlist_id}/tracks\"\n payload = dumps(songs)\n headers = {\n 'Content-Type': \"application/json\",\n 'Authorization': f\"Bearer {token}\",\n 'Accept': \"*/*\",\n 'Cache-Control': \"no-cache\",\n 'Host': \"api.spotify.com\",\n 'Accept-Encoding': \"gzip, deflate\",\n 'Connection': \"keep-alive\",\n 'cache-control': \"no-cache\"\n }\n add_song = post(url, data=payload, headers=headers)\n return loads(add_song.text);\n\ndef create_uri(token, songlist, number):\n song_uris = {\n \"uris\": []\n }\n song_uris_ext = {\n \"uris\": []\n }\n\n for x in range(0, 100):\n song_uri = search_track(token, songlist[x]['name'], songlist[x]['artist'], number);\n if song_uri == 0 or song_uri == \"\":\n pass;\n else:\n song_uris['uris'].append(song_uri);\n \n for y in range(100, 200):\n song_uri_ext = search_track(token, songlist[y]['name'], songlist[y]['artist'], number);\n if song_uri_ext == 0 or song_uri_ext == \"\":\n pass;\n else:\n song_uris_ext['uris'].append(song_uri_ext)\n\n \n ''' for song in songlist:\n song_uri = search_track(token, song['name'], song['artist'], number);\n if song_uri == 0 or song_uri == \"\":\n pass;\n else:\n song_uris['uris'].append(song_uri); '''\n\n return song_uris, song_uris_ext;","sub_path":"spotify_data.py","file_name":"spotify_data.py","file_ext":"py","file_size_in_byte":3578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"527471832","text":"from scipy.io import loadmat\nimport scipy.optimize as so\nimport numpy as np\n\nfile = loadmat('Drand.mat')\ndata = file['Drand']\n\ndef getSumDistanceSquared(mean):\n SS = np.sum(data- mean)**2\n return SS\n\nbest_fit = so.fmin(leastSquares,x0=10)\nbest_fit = bestfit[0]\nprint(best_fit)\n","sub_path":"quiz/4/leastSquare.py","file_name":"leastSquare.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"323981726","text":"# Will be a bunch of card games to play. Right now only black jack is playable.\n\n#Things to work on are making it diplay nicer and right now the Ace is always 11.\nimport random\n\ndef menu(): \n while True:\n global deck\n deck = [1,2,3,4,5,6,7,8,9,10,11,12,13]*4\n \n print('########################')\n print('## Welcome to cards ##')\n print('## 1 Play Black Jack ##')\n print('## 2 Quit ##')\n print('########################')\n choice =int( input('Enter Choice: '))\n if choice == 1:\n blackjack()\n elif choice == 2:\n exit()\ndef deal():\n global deck\n facecard = {11:'J',12:'Q',13:'K',1:'A'}\n card = int(random.choice(deck))\n deck.remove(card)\n if card in facecard:\n card = facecard[card]\n return(card) \ndef val(hand):\n points = {'K':10,'Q':10,'J':10,'A':11}\n tmp = 0\n for i in hand:\n if i in points:\n tmp += points[i]\n else:\n tmp += i\n return(tmp) \ndef blackjack():\n hand_size = 0\n bust = 0\n dealers_hand =[]\n dealertotal = 0\n players_hand = []\n playertotal = 0\n points = {'K':10,'Q':10,'J':10,'A':11} \n#deal 2 cards \n while hand_size <2:\n dealers_hand.append(deal())\n players_hand.append(deal())\n hand_size += 1\n#find value of cards in hands\n dealertotal = val(dealers_hand)\n playertotal = val(players_hand) \n#playing black jack\n while True:\n if playertotal < 21:\n print(players_hand)\n print('Your at %d ' %(playertotal))\n choice = int(input('Would you like to hit[1] or pass[2]? '))\n \n if choice == 1:\n print('\\nHIT\\n')\n players_hand.append(deal())\n playertotal = val(players_hand)\n elif choice == 2:\n print('\\nPASS\\n')\n break\n else:\n print('please enter only 1 or 2')\n \n elif playertotal == 21:\n print(players_hand)\n print(' /$$$$$$ /$$ ')\n print(' /$$__ $$ /$$$$ ')\n print('|__/ \\ $$|_ $$ ')\n print(' /$$$$$$/ | $$ ')\n print(' /$$____/ | $$ ')\n print('| $$ | $$ ')\n print('| $$$$$$$$ /$$$$$$')\n print('|________/|______/')\n break\n else:\n print('BUST!')\n bust = 1\n break\n \n if bust != 1:\n while dealertotal < playertotal:\n print('DEARLS HAND:\\n %s'%(dealers_hand))\n print('DEALER WILL HIT!\\n')\n dealers_hand.append(deal())\n dealertotal = val(dealers_hand)\n \n \n \n if dealertotal == playertotal:\n print('DEALERS TOTAL IS: %d\\nYOUR TOTAL IS: %d\\nTIE!\\n'%(dealertotal,playertotal))\n elif dealertotal == 21 and playertotal < 21:\n print('DEALERS TOTAL IS: %d\\nYOUR TOTAL IS: %d\\nDELAER WINS!\\n'%(dealertotal,playertotal))\n elif dealertotal > 21:\n print('DEALERS TOTAL IS: %d\\nYOUR TOTAL IS: %d\\nDELAER BUST!\\nYOU WIN!\\n'%(dealertotal,playertotal))\n elif dealertotal > playertotal:\n print('DEALERS TOTAL IS: %d\\nYOUR TOTAL IS: %d\\nYOU LOSE!\\n'%(dealertotal,playertotal))\n else:\n print('DEALERS TOTAL IS: %d\\nYOUR TOTAL IS: %d\\nYOU BUST!\\n'%(dealertotal,playertotal))\n \nmenu()\n","sub_path":"cards.py","file_name":"cards.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"92118479","text":"\"\"\"Signs a Blockchain Certificate with the issuer's signing key.\n\nThis implementation signs the assertion uid, and populates the signature section.\n\nAfter a Blockchain Certificate has been signed, it can be issed on the blockchain by using issue_certificates.py\n\n\"\"\"\nimport json\nimport logging\nimport os\n\nfrom cert_schema.schema_tools import schema_validator\n\nfrom cert_issuer import helpers\nfrom cert_issuer.errors import NoCertificatesFoundError, AlreadySignedError\nfrom cert_issuer.secure_signing import Signer, FileSecretManager\n\n\ndef main(app_config):\n unsigned_certs_dir = app_config.unsigned_certificates_dir\n signed_certs_dir = app_config.signed_certificates_dir\n\n # find certificates to sign\n certificates = helpers.find_certificates_to_process(unsigned_certs_dir, signed_certs_dir)\n if not certificates:\n logging.warning('No certificates to process')\n raise NoCertificatesFoundError('No certificates to process')\n\n logging.info('Processing %d certificates', len(certificates))\n\n # create output dir if it doesn't exist\n os.makedirs(signed_certs_dir, exist_ok=True)\n\n # validate schema\n for uid, certificate in certificates.items():\n with open(certificate.unsigned_cert_file_name) as cert:\n cert_json = json.load(cert)\n schema_validator.validate_unsigned_v1_2(cert_json)\n\n # ensure they are not already signed. We want the user to know about this in case there\n # is a failure from a previous run\n for uid, certificate in certificates.items():\n with open(certificate.unsigned_cert_file_name) as cert:\n cert_json = json.load(cert)\n if 'signature' in cert_json and cert_json['signature']:\n logging.warning('Certificate with uid=%s has already been signed.', uid)\n raise AlreadySignedError('Certificate has already been signed')\n\n logging.info('Signing certificates and writing to folder %s', signed_certs_dir)\n path_to_secret = os.path.join(app_config.usb_name, app_config.key_file)\n signer = Signer(FileSecretManager(path_to_secret=path_to_secret, disable_safe_mode=app_config.safe_mode))\n signer.sign_certs(certificates)\n\n logging.info('Signed certificates are in folder %s', signed_certs_dir)\n\n\nif __name__ == '__main__':\n from cert_issuer import config\n\n try:\n parsed_config = config.get_config()\n main(parsed_config)\n except Exception as ex:\n logging.error(ex, exc_info=True)\n exit(1)\n","sub_path":"cert_issuer/sign_certificates.py","file_name":"sign_certificates.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"36884954","text":"from collections import defaultdict,deque\nclass Node:\n def __init__(self,x):\n self.val=x\n self.left=None\n self.right=None\n\nclass Solution:\n def __init__(self):\n self.ans=defaultdict(deque)\n\n def visit(self,A,level):\n if not A:\n return\n if level % 2 != 0:\n self.ans[level].append(A.val)\n else:\n self.ans[level].appendleft(A.val)\n self.visit(A.left,level+1)\n self.visit(A.right,level+1)\n\n def Solve(self,A):\n if A:\n self.visit(A,1)\n return [v for v in self.ans.values()]\n return []\nroot=Node(1)\nroot.left=Node(2)\nroot.right=Node(3)\nroot.left.left=Node(4)\nroot.left.right=Node(5)\nroot.right.left=Node(6)\nroot.right.right=Node(7)\nA=Solution()\nprint(A.Solve(root))\n","sub_path":"Trees/Tree I/zig-zag_level_order.py","file_name":"zig-zag_level_order.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"532725945","text":"from django.urls import path\n\nfrom . import views\n\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('account_activation_sent', views.account_activation_sent, name='account_activation_sent'),\n path('activate///', views.activate, name='activate'),\n path('artists/', views.artists, name='artists'),\n path('collection//', views.collection, name='collection'),\n path('collection//update/', views.CollectionUpdateFormView.as_view(), name='update'),\n path('collections/', views.collections, name='collections'),\n path('manga//', views.manga, name='manga'),\n path('mangas/', views.mangas, name='mangas'),\n path('publishers/', views.publishers, name='publishers'),\n]\n","sub_path":"collection/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"447609342","text":"import logging\n\nimport pytest\n\nimport pygeos\n\nfrom shapely.geometry import LineString\nfrom shapely.errors import ReadingError\nfrom shapely.wkt import loads\n\nfrom tests.conftest import shapely20_todo\n\n\n@shapely20_todo # logging is not yet implemented\ndef test_error_handler_exception(tmpdir):\n \"\"\"Error logged in addition to exception\"\"\"\n logger = logging.getLogger('shapely.geos')\n logfile = str(tmpdir.join('test_error.log'))\n fh = logging.FileHandler(logfile)\n logger.addHandler(fh)\n\n # This calls error_handler with a format string of \"%s\" and one\n # value.\n with pytest.raises((ReadingError, pygeos.GEOSException)):\n loads('POINT (LOLWUT)')\n\n log = open(logfile).read()\n assert \"Expected number but encountered word: 'LOLWUT'\" in log\n\n\n@shapely20_todo # logging is not yet implemented\ndef test_error_handler(tmpdir):\n logger = logging.getLogger('shapely.geos')\n logfile = str(tmpdir.join('test_error.log'))\n fh = logging.FileHandler(logfile)\n logger.addHandler(fh)\n\n # This operation calls error_handler with a format string that\n # has *no* conversion specifiers.\n LineString([(0, 0), (2, 2)]).project(LineString([(1, 1), (1.5, 1.5)]))\n\n log = open(logfile).read()\n assert \"third argument of GEOSProject_r must be Point\" in log\n\n\ndef test_error_handler_wrong_type():\n with pytest.raises(TypeError):\n loads(1)\n\n\n# pygeos handles both bytes and str\n@shapely20_todo\ndef test_error_handler_for_bytes():\n with pytest.raises(TypeError):\n loads(b'POINT (10 10)')\n\n\n@shapely20_todo # logging is not yet implemented\ndef test_info_handler(tmpdir):\n logger = logging.getLogger('shapely.geos')\n logfile = str(tmpdir.join('test_error.log'))\n fh = logging.FileHandler(logfile)\n logger.addHandler(fh)\n logger.setLevel(logging.INFO)\n\n g = loads('MULTIPOLYGON (((10 20, 10 120, 60 70, 30 70, 30 40, 60 40, 60 70, 90 20, 10 20)))')\n assert not g.is_valid\n\n log = open(logfile).read()\n assert \"Ring Self-intersection at or near point 60 70\" in log\n\n\ndef test_info_handler_quiet(tmpdir):\n logger = logging.getLogger('shapely.geos')\n logfile = str(tmpdir.join('test_error.log'))\n fh = logging.FileHandler(logfile)\n logger.addHandler(fh)\n logger.setLevel(logging.WARNING)\n\n g = loads('MULTIPOLYGON (((10 20, 10 120, 60 70, 30 70, 30 40, 60 40, 60 70, 90 20, 10 20)))')\n assert not g.is_valid\n\n log = open(logfile).read()\n assert \"Ring Self-intersection at or near point 60 70\" not in log\n","sub_path":"tests/test_geos_err_handler.py","file_name":"test_geos_err_handler.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"604372879","text":"import pymysql\nimport time\n\nconnect_toko = pymysql.connect(host='remotemysql.com', user='ez7nyPMDeB', passwd='v9zR4luIh1', db='ez7nyPMDeB')\nconnect_bank = pymysql.connect(host='remotemysql.com', user='g0pqaA3IG2', passwd='K8odd1mcUO', db='g0pqaA3IG2')\n\ncursor_toko = connect_toko.cursor()\ncursor_bank = connect_bank.cursor()\n\ndef engineToko():\n\n print(\"PENGECEKAN DATA TOKO SEDANG BERLANGSUNG\")\n\n select_data = \"SELECT id_invoice FROM tb_integrasi WHERE id_invoice NOT IN(SELECT id_invoice FROM tb_invoice)\"\n cursor_toko.execute(select_data)\n hasil_select_data = cursor_toko.fetchall()\n connect_toko.commit()\n\n for hasil in hasil_select_data:\n id_invoice = int(hasil[0])\n print(id_invoice)\n delete = \"delete from tb_integrasi where id_invoice=%s\" % (id_invoice)\n cursor_toko.execute(delete)\n cursor_bank.execute(delete)\n connect_toko.commit()\n connect_bank.commit()\n\n delete_bank = \"delete from tb_invoice where id_invoice=%s\" % (id_invoice)\n cursor_bank.execute(delete_bank)\n connect_bank.commit()\n print(\"TERDAPAT PENGHAPUSAN DATA pada db_bank.tb_invoice pada id_invoice = %s\" % id_invoice)\n\n select_transaksi = \"SELECT * FROM tb_invoice\"\n cursor_toko.execute(select_transaksi)\n data_toko = cursor_toko.fetchall()\n connect_toko.commit()\n\n for data in data_toko:\n id_invoice = int(data[0])\n\n total_transaksi = int(data[1])\n status = data[2]\n select = \"SELECT * FROM tb_integrasi WHERE id_invoice = %s\" % (id_invoice)\n cursor_toko.execute(select)\n data_integrasi_toko = cursor_toko.fetchone()\n jumlah = cursor_toko.rowcount\n\n connect_toko.commit()\n if jumlah == 0:\n insert = \"INSERT INTO tb_integrasi(id_invoice, total_transaksi, status) values(%s,%s,'%s')\" % (id_invoice, total_transaksi, status)\n cursor_toko.execute(insert)\n cursor_bank.execute(insert)\n connect_toko.commit()\n connect_bank.commit()\n\n insert_bank = \"INSERT INTO tb_invoice(id_invoice, total_transaksi, status) values(%s,%s,'%s')\" % (id_invoice, total_transaksi, status)\n cursor_bank.execute(insert_bank)\n connect_bank.commit()\n print(\"TERDAPAT PENAMBAHAN DATA PADA db_bank.tb_invoice. Pada id_invoice = %s, total_transaksi = %s, status = '%s'\" % (id_invoice, total_transaksi, status))\n\n else:\n id_invoice = data_integrasi_toko[0]\n status = data_integrasi_toko[2]\n\n if data[1] != data_integrasi_toko[1]:\n update_toko = \"UPDATE tb_integrasi SET total_transaksi = %s WHERE id_invoice= %s\" % (data[1], id_invoice)\n cursor_toko.execute(update_toko)\n cursor_bank.execute(update_toko)\n connect_toko.commit()\n connect_bank.commit()\n\n update_bank = \"UPDATE tb_invoice SET total_transaksi = %s WHERE id_invoice = %s\" % (data[1], id_invoice)\n cursor_bank.execute(update_bank)\n connect_bank.commit()\n print(\"TERDAPAT PERUBAHAN DATA pada id_invoice = %s\" % data[0])\n\n\ndef engineBank():\n select_data = \"SELECT id_invoice FROM tb_integrasi WHERE id_invoice NOT IN(SELECT id_invoice FROM tb_invoice)\"\n cursor_bank.execute(select_data)\n hasil_select_data = cursor_bank.fetchall()\n connect_bank.commit()\n\n select_bank = \"SELECT * FROM tb_invoice\"\n cursor_bank.execute(select_bank)\n data_bank = cursor_bank.fetchall()\n connect_bank.commit()\n\n for data in data_bank:\n id_invoice = int(data[0])\n total_transaksi = int(data[1])\n status = data[2]\n select = \"SELECT * FROM tb_integrasi WHERE id_invoice = %s\" % (id_invoice)\n cursor_bank.execute(select)\n data_integrasi_bank = cursor_bank.fetchone()\n jumlah = cursor_bank.rowcount\n connect_bank.commit()\n if jumlah != 0:\n id_invoice = data_integrasi_bank[0]\n status = data_integrasi_bank[2]\n\n if data[2] != data_integrasi_bank[2]:\n update_integrasi = \"UPDATE tb_integrasi SET status = '%s' WHERE id_invoice= %s\" % (data[2], id_invoice)\n cursor_toko.execute(update_integrasi)\n cursor_bank.execute(update_integrasi)\n connect_toko.commit()\n connect_bank.commit()\n\n update_toko = \"UPDATE tb_invoice SET status = '%s' WHERE id_invoice = %s\" % (data[2], id_invoice)\n cursor_toko.execute(update_toko)\n connect_toko.commit()\n print(\"TERDAPAT PERUBAHAN STATUS pada id_invoice = %s\" % data[0])\nwhile(1):\n # engineBank()\n engineToko()\n time.sleep(0.5)\n","sub_path":"engineModul1.py","file_name":"engineModul1.py","file_ext":"py","file_size_in_byte":4749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"463118170","text":"from django.conf.urls import patterns, include, url\n\nfrom django.contrib import admin\nadmin.autodiscover()\n\nfrom django.views.generic import TemplateView\n\nfrom frontend.views import IndexView, AdCreateView, AdDetailView, AdCategoryDetailView, \\\n AdSpamCheckCreateView, AdCommentDetailView, CommentsCreateView\n\nurlpatterns = patterns('',\n url(r'^admin/', include(admin.site.urls)),\n\n url(r'^$', IndexView.as_view(), name='index'),\n url(r'^add/$', AdCreateView.as_view(), name='add'),\n url(r'^picker/$', TemplateView.as_view(template_name='picker.html'), name='picker'),\n url(r'^(?P\\d+)/$', AdCommentDetailView.as_view(), name='ad_comments'),\n url(r'^ad_check_spam/$', AdSpamCheckCreateView.as_view(), name='ad_check_spam'),\n url(r'^ad_comment/$', CommentsCreateView.as_view(), name='ad_comment_create'),\n url(r'^(?P[-_\\w]+)/$', AdCategoryDetailView.as_view(), name='category'),\n)\n","sub_path":"pokatuhi/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"226136852","text":"from scholarly import scholarly\nimport pprint\n# import models\n\n\n# DOCS - https://pypi.org/project/scholarly/\n\ndef get_pubs_titles(author_name):\n\n # Retrieve the author's data, fill-in, and print\n search_query = scholarly.search_author(author_name)\n author = next(search_query).fill()\n return ([pub.bib['title'] for pub in author.publications])\n\ndef get_gs_data(titles):\n\n for title in titles:\n\n\n search_query = scholarly.search_pubs(title)\n info = next(search_query)\n\n # ------------ ADD ALL INFO TO DB ------------\n cites = info.bib['cites']\n title = info.bib['title']\n # url = info.bib['url']\n # year = info.bib['year']\n authors = info.bib['author'] # => List of all authors\n # abstract = info.bib['abstract']\n print(f'{title}->{cites}')\n # search_query = scholarly.search_pubs()\n # author = next(search_query)\n # print(author)\n\n\n# pubs_titles = get_pubs_titles('Shilpa Sonawani')\n#\n#\n# get_gs_data(pubs_titles)\n\n# # Retrieve the author's data, fill-in, and print\n# search_query = scholarly.search_pubs('Network intrusion detection using dynamic fuzzy c means clustering')\n# author = next(search_query)\n# print(author.bib['author'])\n#\n\n\n# Print the titles of the author's publications\n# print([pub.bib['title'] for pub in author.publications])\n\n\n# search_query = scholarly.search_author('Shilpa Sonawani')\n# print(next(search_query))\n#\n# # Print the titles of the author's publications\n# print([pub.bib['title'] for pub in author.publications])\n#\n# # Take a closer look at the first publication\n# pub = author.publications[0].fill()\n# print(pub)\n#\n# # Which papers cited that publication?\n# print([citation.bib['title'] for citation in pub.citedby])\n","sub_path":"publications_api/scholarly_api.py","file_name":"scholarly_api.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"561955935","text":"import json\nimport os\nimport random\nimport math\n\nimport bottle\nfrom bottle import HTTPResponse\n\n\n@bottle.route(\"/\")\ndef index():\n return \"Your Battlesnake is alive!\"\n\n\n@bottle.post(\"/ping\")\ndef ping():\n \"\"\"\n Used by the Battlesnake Engine to make sure your snake is still working.\n \"\"\"\n return HTTPResponse(status=200)\n\n\n@bottle.post(\"/start\")\ndef start():\n \"\"\"\n Called every time a new Battlesnake game starts and your snake is in it.\n Your response will control how your snake is displayed on the board.\n \"\"\"\n data = bottle.request.json\n print(\"START:\", json.dumps(data))\n\n response = {\"color\": \"#009999\", \"headType\": \"sand-worm\", \"tailType\": \"bwc-bonhomme\"}\n return HTTPResponse(\n status=200,\n headers={\"Content-Type\": \"application/json\"},\n body=json.dumps(response),\n )\n\n\n@bottle.post(\"/move\")\ndef move():\n \"\"\"\n Called when the Battlesnake Engine needs to know your next move.\n The data parameter will contain information about the board.\n Your response must include your move of up, down, left, or right.\n \"\"\"\n data = bottle.request.json\n print(\"MOVE:\", json.dumps(data))\n working_data = sort_it(data)\n move = decision_tree(working_data)\n final_move = collision(move)\n\n # Shouts are messages sent to all the other snakes in the game.\n # Shouts are not displayed on the game board.\n shout = \"PyGod!\"\n\n response = {\"move\": final_move, \"shout\": shout}\n return HTTPResponse(\n status=200,\n headers={\"Content-Type\": \"application/json\"},\n body=json.dumps(response),\n )\n\n\n@bottle.post(\"/end\")\ndef end():\n \"\"\"\n Called every time a game with your snake in it ends.\n \"\"\"\n data = bottle.request.json\n print(\"END:\", json.dumps(data))\n return HTTPResponse(status=200)\n\n\ndef main():\n bottle.run(\n application,\n host=os.getenv(\"IP\", \"0.0.0.0\"),\n port=os.getenv(\"PORT\", \"8080\"),\n debug=os.getenv(\"DEBUG\", True),\n )\ndef decision_tree(in_put):\n nfl,nel = in_put\n for i in range(0,len(nel)):\n if nfl[i][1] < nel[0][1]:\n choice = move_to(nfl[i][0])\n break\n elif nfl[i][1] > nel[0][1]:\n choice = move_away(nel[i][1])\n break\n elif nfl[i][1] == nel[0][1]:\n choice = evade_move()\n break\n else:\n pass\n return choice\ndef move_to(location):\n if (location[0][\"x\"] - me[0][\"x\"]) > (location[0][\"y\"] - me[0][\"y\"]):\n if location[0][\"x\"] > me[0][\"x\"]:\n return \"up\"\n elif location[0][\"x\"] < me[0][\"x\"]:\n return \"down\"\n elif (location[0][\"x\"] - me[0][\"x\"]) <= (location[0][\"y\"] - me[0][\"y\"]):\n if location[0][\"y\"] > me[0][\"y\"]:\n return \"left\"\n elif location[0][\"y\"] < me[0][\"y\"]:\n return \"right\"\n\ndef move_away(location):\n if (location[0][\"x\"] - me[0][\"x\"]) > (location[0][\"y\"] - me[0][\"y\"]):\n if location[0][\"x\"] > me[0][\"x\"]:\n return \"down\"\n elif location[0][\"x\"] < me[0][\"x\"]:\n return \"up\"\n elif (location[0][\"x\"] - me[0][\"x\"]) <= (location[0][\"y\"] - me[0][\"y\"]):\n if location[0][\"y\"] > me[0][\"y\"]:\n return \"right\"\n elif location[0][\"y\"] < me[0][\"y\"]:\n return \"left\"\n\ndef evade_move():\n directions = [\"up\", \"down\", \"left\", \"right\"]\n move = random.choice(directions)\n return move\n\ndef collision(move):\n me_rn = me\n#Get new position\n if move == \"right\":\n me_rn[0][\"x\"] = (me_rn[0][\"x\"] + 1)\n elif move == \"left\":\n me_rn[0][\"x\"] = (me_rn[0][\"x\"] - 1)\n elif move == \"up\":\n me_rn[0][\"y\"] = (me_rn[0][\"y\"] + 1)\n elif move == \"down\":\n me_rn[0][\"y\"] = (me_rn[0][\"y\"] - 1)\n#evalute new position\n if (me_rn > height or me_rn == 0):\n move = evade_move()\n elif (me_rn > height or me_rn == 0):\n move = evade_move()\n for snake in enemy:\n for segment in snake[body]:\n if me_rn == segment:\n move = evade_move()\n return move\n\n\ndef get_distance(coord1,coord2): \n print(coord1,type(coord1))\n print(coord2,type(coord2))\n return (abs(coord1[\"x\"]-coord2[\"x\"]) + abs(coord1[\"y\"]-coord2[\"y\"]))\n\ndef sort_it(data):\n #state = json.loads(data.text)\n state = data\n game = state[\"game\"][\"id\"]\n height = state[\"board\"][\"height\"]\n width = state[\"board\"][\"width\"]\n turn = state[\"turn\"]\n food = state[\"board\"][\"food\"]\n enemy = state[\"board\"][\"snakes\"]\n me = state[\"you\"][\"body\"]\n food_distance = []\n for morsel in food:\n dist = get_distance(morsel,me[0])\n food_distance.append([morsel,dist])\n enemy_distance = []\n for npc in enemy:\n dist = get_distance(npc[\"body\"][0],me[0])\n enemy_distance.append([npc,npc[\"id\"],dist])\n nearest_food_list = sorted(food_distance, key=lambda food: food[1])\n neartest_enemy_list = sorted(enemy_distance, key=lambda enemy: enemy[2])\n return nearest_food_list,neartest_enemy_list\n\n# Expose WSGI app (so gunicorn can find it)\napplication = bottle.default_app()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"app/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"212455512","text":"import json\n\ndef update(sicris_data, date_prefix=''):\n with open('../data/sicris.json', 'w', encoding='utf-8') as fout:\n json.dump(sicris_data, fout, indent=2, sort_keys=True, ensure_ascii=False)\n if date_prefix:\n with open(f'../data/updates/{date_prefix}_sicris.json', 'w', encoding='utf-8') as fout:\n json.dump(sicris_data, fout, indent=2, sort_keys=True, ensure_ascii=False)\n\ndef get():\n with open('../data/sicris.json', 'rt', encoding='utf-8') as fin:\n sicris_data = json.load(fin)\n return sicris_data\n","sub_path":"scripts/sicris.py","file_name":"sicris.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"12129196","text":"# Copyright (C) 2019 by\n# Luca Cappelletti \n#\n# All rights reserved.\n# MIT license.\n\"\"\" Class to automatically retrieve informations and sequences for a Genome from the UCSC Genome Browser assembly database.\"\"\"\n\nimport json\nimport os\nimport shutil\nimport dateparser\nimport pandas as pd\nimport warnings\nfrom math import ceil\nfrom datetime import datetime\nfrom tqdm.auto import tqdm\nfrom multiprocessing import Pool, cpu_count\nfrom typing import Dict, List, Generator\nfrom .utils import get_available_genomes, get_available_chromosomes, get_chromosome, get_genome_informations, is_chromosome_available_online\nfrom .utils import multiprocessing_gaps, multiprocessing_extract_sequences\n\n\nclass Genome:\n \"\"\"Class to automatically retrieve informations and sequences for a Genome from the UCSC Genome Browser assembly database.\n\n Usage examples\n --------------\n\n Simply instanziate a new genome\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n .. code:: python\n\n from ucsc_genomes_downloader import Genome\n hg19 = Genome(\"hg19\")\n\n Downloading lazily a genome\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n .. code:: python\n\n from ucsc_genomes_downloader import Genome\n sacCer3 = Genome(\"sacCer3\")\n chrM = sacCer3[\"chrM\"] # Downloads and returns mitochondrial genome\n\n Downloading eagerly a genome\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n .. code:: python\n\n from ucsc_genomes_downloader import Genome\n sacCer3 = Genome(\"sacCer3\", lazy_download=False)\n\n Loading eagerly a genome\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n .. code:: python\n\n from ucsc_genomes_downloader import Genome\n sacCer3 = Genome(\"sacCer3\", lazy_load=False)\n\n Testing if a genome is cached\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n .. code:: python\n\n if hg19.is_cached():\n print(\"Genome is cached!\")\n\n Removing genome's cache\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n .. code:: python\n\n hg19.delete()\n\n\n \"\"\"\n\n def __init__(\n self,\n assembly: str,\n unknown_chromosomes: bool = False,\n random_chromosomes: bool = False,\n haplotype_chromosomes: bool = False,\n alternative_chromosomes: bool = False,\n fixed_chromosomes: bool = False,\n mitochondrial_genome: bool = True,\n lazy_load: bool = True,\n lazy_download: bool = True,\n verbose: bool = True,\n leave_loading_bars: bool = False,\n clear_cache: bool = False,\n enable_cache: bool = True,\n warning: bool = True,\n cache_directory: str = None,\n cache_directory_environment_variable: str = \"UCSC_GENOMES_CACHE_PATH\"\n ):\n \"\"\"Instantiate a new Genome object.\n\n Parameters\n ----------\n assembly: str,\n UCSC Genome Browser assembly ID for required genome [1]_.\n unknown_chromosomes: bool = False,\n Whetever to download or not chromosomes containing\n clone contigs that cannot be confidently placed on\n a specific chromosome [1]_.\n random_chromosomes: bool = False,\n Whetever to download or not data related to sequence\n that is known to be in a particular chromosome,\n but could not be reliably ordered within the current sequence [1]_.\n haplotype_chromosomes: bool = False,\n Whetever to download or not data related to sequence\n for alternative haplotypes [1]_.\n alternative_chromosomes: bool = False,\n Whetever to download or not alternative sequences\n that differ from the reference genome currently\n available for a few assemblies including danRer11, hg19, and hg38 [1]_.\n fixed_chromosomes: bool = False,\n Whetever to download or not fix patches currently\n available for the hg19 and hg38 assemblies\n that represent changes to the existing sequence [1]_.\n mitochondrial_genome: bool = True,\n Whetever to download or not the mitocondial genome [2]_.\n A warning is raised if the mitochondrial genome is required\n but none is available for the specified genome.\n lazy_load: bool = True,\n Whetever to lazily load chromosomes as they are required (lazy_load=True) or\n to retrieve them immediately (lazy_load=False).\n lazy_download: bool = True,\n Whetever to lazily download chromosomes as they are required (lazy_download=True) or\n to retrieve them immediately (lazy_download=False). Only available when cache is enabled.\n verbose: bool = True,\n Whetever to show a loading bar when retrieving chromosomes.\n leave_loading_bars: bool = False,\n Whetever to leave or not the loading bars after loading is complete.\n clear_cache: bool = False,\n Whetever to clear cache or not before retrieving chromosomes.\n enable_cache: bool = True,\n Whetever to store data to and load data from given cache directory.\n warning: bool = True,\n Whetever to raise warning when corner cases are detected.\n cache_directory: str = \"genomes\",\n Position where to store the downloaded genomes.\n cache_directory_environment_variable: str = \"UCSC_GENOMES_CACHE_PATH\",\n Environment variable where to check if a system-wide directory has\n been specified.\n\n Raises\n ------\n ValueError:\n If the given genome cannot be retrieved either from the given\n cache directory or from the UCSC website [3]_.\n ValueError:\n If the given genome, once the filters have been applied,\n does not contain any chromosome.\n\n Returns\n -------\n A newly instantiated Genome object.\n\n References\n ----------\n .. [1] https://genome.ucsc.edu/FAQ/FAQdownloads.html\n .. [2] https://en.wikipedia.org/wiki/Mitochondrial_DNA\n .. [3] http://hgdownload.soe.ucsc.edu/downloads.html\n \"\"\"\n\n self._chromosomes_lenghts = None\n self._genome_informations = None\n self._warning = warning\n self._verbose = verbose\n self._leave_loading_bars = leave_loading_bars\n self._assembly = assembly\n self._enable_cache = enable_cache\n\n # Checking if a system wide cache directory\n # has been specified\n if cache_directory is None:\n try:\n cache_directory = os.environ[cache_directory_environment_variable]\n except KeyError:\n cache_directory = \"genomes\"\n\n self._cache_directory = \"{cache_directory}/{assembly}\".format(\n cache_directory=cache_directory,\n assembly=self.assembly\n )\n\n # If required delete cache\n if clear_cache:\n self.delete()\n\n # If cache is enabled and a directory for the genome\n # exists within the cache we try to load the genome data\n if enable_cache and self.is_cached():\n self._genome_informations = self._load_genome_informations()\n self._chromosomes_lenghts = self._load_chromosomes()\n # Otherwise we check if the genome is available\n elif self.assembly not in get_available_genomes():\n # If the genome is not available we raise a proper exception\n raise ValueError(\n \"Given genome {assembly} is not within the avalable genomes.\".format(\n assembly=self.assembly\n ))\n\n # Download genome informations if list is not already loaded\n if self._genome_informations is None:\n self._genome_informations = get_genome_informations(self.assembly)\n # If cache is enabled we store the obtained genome informations\n self._store_genome_informations()\n\n # Download chromosomes if list is not already loaded\n if self._chromosomes_lenghts is None:\n self._chromosomes_lenghts = get_available_chromosomes(\n self.assembly)\n # If cache is enabled we store the obtained chromosomes lenghts\n self._store_chromosomes()\n\n filters = [\n target\n for target, enabled in {\n \"chru\": unknown_chromosomes,\n \"scaffold\": unknown_chromosomes,\n \"contig\": unknown_chromosomes,\n \"super\": unknown_chromosomes,\n \"chrbin\": unknown_chromosomes,\n \"random\": random_chromosomes,\n \"hap\": haplotype_chromosomes,\n \"alt\": alternative_chromosomes,\n \"fix\": fixed_chromosomes,\n \"chrm\": mitochondrial_genome\n }.items()\n if not enabled\n ]\n\n # Filtering chromosomes\n self._chromosomes = {\n chromosome: None\n for chromosome in tqdm(\n self._chromosomes_lenghts,\n disable=not verbose,\n leave=leave_loading_bars,\n desc=\"Filtering chromosomes for the genome {assembly}\".format(\n assembly=self.assembly\n )\n )\n if all(target not in chromosome.lower() for target in filters) and (\n unknown_chromosomes or\n chromosome.lower().startswith(\"chr\")\n ) and self.is_chromosome_available(chromosome)\n }\n\n # If no chromosome remains after filtering,\n # for instance when the raw data are not yet mapped\n # we raise a userwarning\n if len(self) == 0:\n raise ValueError(\n \"No chromosome remaining in chosen genome {assembly}\"\n \"after executing desired filters\"\n \"and checking for online availability\".format(\n assembly=self.assembly)\n )\n\n # If lazy downloading is disabled\n # we immediately proceed to download\n # all the required chromosomes.\n if not lazy_download:\n self.download()\n\n # If lazy loading is disabled\n # we immediately proceed to load\n # all the required chromosomes.\n if not lazy_load:\n self.load()\n\n def is_cached(self) -> bool:\n \"\"\"Return boolean representing if a cache directory already exists for current genome.\"\"\"\n return os.path.exists(self._cache_directory)\n\n def _genome_informations_path(self) -> str:\n \"\"\"Return path for the JSON file with current genome informations.\"\"\"\n return \"{cache_directory}/genome_informations.json\".format(\n cache_directory=self._cache_directory\n )\n\n def _load_genome_informations(self) -> Dict:\n \"\"\"Return a dictionary with genome informations if available.\n\n Raises\n ------\n RuntimeWarning:\n If genome informations are not available\n locally.\n \"\"\"\n try:\n with open(self._genome_informations_path(), \"r\") as f:\n return json.load(f)\n except Exception:\n if self._warning:\n warnings.warn(\n \"Failed to load genome {genome} informations. \"\n \"I will try to download them again afterwards.\".format(\n genome=self.assembly\n ),\n RuntimeWarning\n )\n return None\n\n def _store_genome_informations(self):\n \"\"\"Store genome informations into default cache directory.\"\"\"\n os.makedirs(self._cache_directory, exist_ok=True)\n with open(self._genome_informations_path(), \"w\") as f:\n json.dump(self._genome_informations, f, indent=4)\n\n def _chromosomes_path(self) -> str:\n \"\"\"Return path to default chromosomes informations.\"\"\"\n return \"{cache_directory}/chromosomes.json\".format(\n cache_directory=self._cache_directory\n )\n\n def _load_chromosomes(self) -> Dict:\n \"\"\"Return a dictionary with genome chromosomes if available.\n\n Raises\n ------\n RuntimeWarning:\n If genome chromosomes are not available\n locally.\n \"\"\"\n try:\n with open(self._chromosomes_path(), \"r\") as f:\n return json.load(f)\n except Exception:\n if self._warning:\n warnings.warn(\n \"Failed to load chromosomes for genome {genome}. \"\n \"I will try to download them again afterwards.\".format(\n genome=self.assembly\n ),\n RuntimeWarning\n )\n return None\n\n def _store_chromosomes(self):\n \"\"\"Store chromosomes informations into default cache directory.\"\"\"\n os.makedirs(self._cache_directory, exist_ok=True)\n with open(self._chromosomes_path(), \"w\") as f:\n json.dump(self._chromosomes_lenghts, f, indent=4)\n\n def _gaps_path(self) -> str:\n \"\"\"Return path to default gaps informations.\"\"\"\n return \"{cache_directory}/gaps.bed.gz\".format(\n cache_directory=self._cache_directory\n )\n\n def _load_gaps(self) -> pd.DataFrame:\n \"\"\"Return a DataFrame with genome gaps.\"\"\"\n return pd.read_csv(self._gaps_path(), sep=\"\\t\")\n\n def _store_gaps(self, gaps: pd.DataFrame):\n \"\"\"Store gaps informations into default cache directory.\"\"\"\n os.makedirs(self._cache_directory, exist_ok=True)\n gaps.to_csv(self._gaps_path(), sep=\"\\t\", index=False)\n\n def _chromosome_path(self, chromosome: str) -> str:\n \"\"\"Return path to the given chromosome.\n\n Parameters\n ----------\n chromosome: str,\n The chromosome identifier, such as chr1, chrX, chrM...\n \"\"\"\n return \"{cache_directory}/chromosomes/{chromosome}.json\".format(\n cache_directory=self._cache_directory,\n chromosome=chromosome\n )\n\n def _load_chromosome(self, chromosome: str) -> str:\n \"\"\"Return the nucleotides sequence for the given chromosome.\n Parameters\n ----------\n chromosome: str,\n The chromosome identifier, such as chr1, chrX, chrM...\n\n Raises\n ------\n RuntimeWarning:\n If given chromosome are not available locally.\n \"\"\"\n try:\n if self.is_chromosome_cached(chromosome):\n with open(self._chromosome_path(chromosome), \"r\") as f:\n return json.load(f)[\"dna\"]\n except Exception:\n if self._warning:\n warnings.warn(\n \"Failed to load chromosome {chromosome} for genome {genome}. \"\n \"I will try to download them again afterwards.\".format(\n chromosome=chromosome,\n genome=self.assembly\n ),\n RuntimeWarning\n )\n return None\n\n def download(self):\n \"\"\"Download all the genome's chromosomes.\"\"\"\n chromosomes = [\n chromosome\n for chromosome in self\n if not self.is_chromosome_cached(chromosome)\n ]\n for chromosome in tqdm(\n chromosomes,\n desc=\"Downloading chromosomes for genome {assembly}\".format(\n assembly=self.assembly\n ),\n total=len(chromosomes),\n disable=not self._verbose,\n dynamic_ncols=True,\n leave=self._leave_loading_bars\n ):\n self._download_chromosome(chromosome)\n\n def delete(self):\n \"\"\"Remove the genome cache.\"\"\"\n if os.path.exists(self._cache_directory):\n shutil.rmtree(self._cache_directory)\n\n def _download_chromosome(self, chromosome: str) -> str:\n \"\"\"Download and return the nucleotides sequence for the given chromosome.\n\n Parameters\n ----------\n chromosome: str,\n The chromosome identifier, such as chr1, chrX, chrM...\n\n Returns\n -------\n The nucleotide sequence for the given chromosomes.\n \"\"\"\n chromosome_data = get_chromosome(\n self.assembly,\n chromosome,\n 0,\n self._chromosomes_lenghts[chromosome]\n )\n if self._enable_cache:\n path = self._chromosome_path(chromosome)\n os.makedirs(os.path.dirname(path), exist_ok=True)\n with open(path, \"w\") as f:\n json.dump(chromosome_data, f)\n return chromosome_data[\"dna\"]\n\n def load(self):\n \"\"\"Load into memory all the genome's chromosomes, downloading them when necessary.\"\"\"\n for chromosome in tqdm(\n self,\n desc=\"Loading chromosomes for genome {assembly}\".format(\n assembly=self.assembly\n ),\n total=len(self),\n disable=not self._verbose,\n dynamic_ncols=True,\n leave=self._leave_loading_bars\n ):\n self[chromosome]\n\n def __len__(self) -> int:\n \"\"\"Return the number of chromosomes in current genome.\"\"\"\n return len(self._chromosomes)\n\n def __contains__(self, chromosome: str) -> bool:\n \"\"\"Return boolean representing if given chromosome is contained in current genome.\n\n Parameters\n ----------\n chromosome: str,\n The chromosome identifier, such as chr1, chrX, chrM...\n\n Returns\n -------\n Boolean representing if given chromosome is contained in current genome.\n \"\"\"\n return chromosome in self._chromosomes\n\n def __getitem__(self, chromosome: str):\n \"\"\"Return sequence data for given chromosome.\n\n Parameters\n ----------\n chromosome: str,\n The chromosome identifier, such as chr1, chrX, chrM...\n\n Returns\n -------\n String containing sequence data for given chromosome.\n \"\"\"\n if self._chromosomes[chromosome] is None and self._enable_cache:\n self._chromosomes[chromosome] = self._load_chromosome(\n chromosome\n )\n if self._chromosomes[chromosome] is None:\n self._chromosomes[chromosome] = self._download_chromosome(\n chromosome\n )\n return self._chromosomes[chromosome]\n\n def items(self) -> Generator:\n \"\"\"Return generator to iterate through tuples of chromosomes and corresponding sequences.\"\"\"\n for chromosome in self:\n yield chromosome, self[chromosome]\n\n def __iter__(self) -> Generator:\n \"\"\"Return generator to iterate through the genome's chromosomes.\"\"\"\n for chromosome in self._chromosomes:\n yield chromosome\n\n def is_chromosome_available(self, chromosome: str) -> bool:\n \"\"\"Return boolean representing if given chromosome is available either locally or online.\n\n Parameters\n ----------\n chromosome: str,\n The chromosome identifier, such as chr1, chrX, chrM...\n\n Returns\n -------\n Boolean representing if given chromosome is available either locally or online.\n \"\"\"\n return self.is_chromosome_cached(chromosome) or self.is_chromosome_online(chromosome)\n\n def is_chromosome_cached(self, chromosome: str) -> bool:\n \"\"\"Return a boolean representing if given chromosome is cached.\n\n Parameters\n ----------\n chromosome: str,\n The chromosome identifier, such as chr1, chrX, chrM...\n\n Returns\n -------\n Boolean representing if given chromosome is available locally.\n \"\"\"\n return os.path.exists(self._chromosome_path(chromosome))\n\n def is_chromosome_online(self, chromosome: str) -> bool:\n \"\"\"Return a boolean representing if given chromosome is available through JSON APIs.\n\n Parameters\n ----------\n chromosome: str,\n The chromosome identifier, such as chr1, chrX, chrM...\n\n Raises\n ------\n UserWarning:\n If given chromosome is not available online.\n\n Returns\n -------\n Boolean representing if given chromosome is available online.\n \"\"\"\n return is_chromosome_available_online(self.assembly, chromosome)\n\n def __str__(self):\n \"\"\"Return string representation of current genome.\"\"\"\n return \"{organism}, {scientific_name}, {genome}, {date}, {chromosomes} chromosomes\".format(\n organism=self.organism,\n scientific_name=self.scientific_name,\n genome=self.assembly,\n date=self.date,\n chromosomes=len(self)\n )\n\n __repr__ = __str__\n\n def gaps(self):\n \"\"\"Return dataframe in BED format with informations on the gaps.\n\n FAQs\n ----\n Why don't you just retrieve the gaps from the APIs?\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n The gaps track is not always available, therefore for providing a consistent\n usage experience we compute the gaps when required. Additionally, this method\n will work also when you make an offline request, that may sometimes be quite\n useful.\n\n Returns\n -------\n A DataFrame in BED format.\n \"\"\"\n if os.path.exists(self._gaps_path()):\n return self._load_gaps()\n with Pool(min(cpu_count(), len(self))) as p:\n gaps = pd.concat(list(tqdm(\n p.imap(multiprocessing_gaps, self.items()),\n total=len(self),\n desc=\"Rendering gaps in {assembly}\".format(\n assembly=self.assembly,\n leave=self._leave_loading_bars\n ),\n disable=not self._verbose\n )))\n p.close()\n p.join()\n self._store_gaps(gaps)\n return gaps\n\n def filled(self):\n \"\"\"Return dataframe with BED-like columns with informations on the gaps.\"\"\"\n non_gap = []\n gapped_chromosomes = []\n for chrom, values in self.gaps().groupby(\"chrom\"):\n # We need to sort by the chromStart\n # as we will need the rows to be ordered\n # to be able to generate the complementary windows\n values = values.sort_values([\"chromStart\"])\n non_gap_values = pd.DataFrame({\n \"chrom\": chrom,\n \"chromStart\": values[\"chromEnd\"][:-1].values,\n \"chromEnd\": values[\"chromStart\"][1:].values,\n })\n\n # If the chromosome lenght is not contained\n # within the various chromEnd values it means that\n # the final part of the chromosome is known\n # and therefore considered filled.\n # We need to add this additional row.\n chromosome_lenght = self._chromosomes_lenghts[chrom]\n if values.chromEnd.isin([chromosome_lenght]).any():\n non_gap_values = non_gap_values.append({\n \"chrom\": chrom,\n \"chromStart\": values.chromEnd.max(),\n \"chromEnd\": chromosome_lenght\n }, ignore_index=True)\n\n # If the chromosome start, the 0 value,\n # is not contained within the various chromStart values\n # it means that the initial part of the chromosome\n # is known and therefore considered filled.\n # We need to add this additional row.\n if values.chromStart.isin([0]).any():\n non_gap_values = non_gap_values.append({\n \"chrom\": chrom,\n \"chromStart\": 0,\n \"chromEnd\": values.chromStart.min(),\n }, ignore_index=True)\n\n non_gap.append(non_gap_values)\n gapped_chromosomes.append(chrom)\n\n # When a chromosome does not appear to have\n # any gap is considered fully filled\n non_gap.append(pd.DataFrame([\n {\n \"chrom\": chrom,\n \"chromStart\": 0,\n \"chromEnd\": self._chromosomes_lenghts[chrom]\n } for chrom in self if chrom not in gapped_chromosomes\n ]))\n return pd.concat(non_gap).sort_values([\"chrom\"]).reset_index(drop=True)\n\n def bed_to_sequence(self, bed: pd.DataFrame):\n unique_chromosomes = len(bed.chrom.unique())\n tasks = (\n {\n \"bed\": group,\n \"sequence\": self[chrom]\n }\n for chrom, group in bed.groupby(\"chrom\")\n )\n with Pool(min(unique_chromosomes, cpu_count())) as p:\n sequences = pd.concat(list(tqdm(\n p.imap(\n multiprocessing_extract_sequences,\n tasks\n ),\n total=unique_chromosomes,\n desc=\"Rendering sequences in {assembly}\".format(\n assembly=self.assembly\n ),\n disable=not self._verbose,\n leave=self._leave_loading_bars\n )))\n p.close()\n p.join()\n return sequences\n\n @property\n def assembly(self) -> str:\n \"\"\"Return genome's UCSC Genome Browser assembly ID.\"\"\"\n return self._assembly\n\n @property\n def date(self) -> datetime:\n \"\"\"Return release date.\"\"\"\n return dateparser.parse(self.description.split(\"(\")[0]).date()\n\n @property\n def organism(self) -> str:\n \"\"\"Return genome's organism.\"\"\"\n return self._genome_informations[\"organism\"]\n\n @property\n def scientific_name(self):\n \"\"\"Return genome's organism scientific name.\"\"\"\n return self._genome_informations[\"scientificName\"]\n\n @property\n def description(self):\n \"\"\"Return genome's description as provided by UCSC.\"\"\"\n return self._genome_informations[\"description\"]\n\n @property\n def path(self):\n \"\"\"Return genome's path.\"\"\"\n return self._cache_directory\n","sub_path":"ucsc_genomes_downloader/genome.py","file_name":"genome.py","file_ext":"py","file_size_in_byte":26004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"56163877","text":"import os\nimport sys\nimport json\nimport yaml\nimport click\nimport numpy as np\nimport pandas as pd\nfrom sklearn import cluster\nfrom sklearn import manifold\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nsys.path.append('.')\nfrom VNbO2.data import io\nfrom VNbO2 import kernel\n\ncomposition_label = r'$\\mathrm{V}_{1-x}\\mathrm{W}_{x}\\mathrm{O}_2$'\n\n# specify some matplotlib settings\nplot_params_path = os.path.join(os.path.dirname(__file__), 'matplotlib-params.json')\nwith open(plot_params_path, 'r') as f:\n matplotlib.rcParams.update(json.load(f))\n\ndef plot_results(df, labels):\n\n # spectral embedding drops the first eigenvalue, but not Shi&Malik spectral clustering\n col = ['m', 'c', 'y']\n\n for label, c in zip(label_order, col):\n plt.scatter(df['Nb'][labels == label], df['temp'][labels == label], color=c)\n\n plt.xlabel('composition')\n plt.ylabel('temperature')\n plt.tight_layout()\n plt.savefig('test.png')\n\ndef self_tuning_spectral_clustering(Y, metric='cosine'):\n\n if metric == 'cosine':\n D = kernel.pairwise.cosine_distances(Y)\n elif metric == 'hellinger':\n D = kernel.hellinger_distance(Y)\n elif metric == 'chi2':\n D = kernel.chi2_distance(Y)\n elif metric == 'js_divergence':\n D = kernel.js_divergence(Y)\n elif metric == 'alternative_cosine':\n K = kernel.locally_scaled_cosine_rbf(Y)\n else:\n raise NotImplementedError(f'{metric} is not available.')\n\n if metric != 'alternative_cosine':\n # compute the \"self-tuning\" version of the kernel matrix\n K = kernel.locally_scaled_rbf(D)\n\n Dg = np.diag(np.sum(K, axis=0))\n D2_inv = np.linalg.inv(np.linalg.cholesky(Dg))\n KK = D2_inv @ K @ D2_inv\n\n nc = 3\n # spectral embedding drops the first eigenvalue, but not Shi&Malik spectral clustering...\n # spec = manifold.SpectralEmbedding(n_components=nc, affinity='precomputed')\n # Z = spec.fit_transform(A)\n\n Z = manifold.spectral_embedding(KK, n_components=nc, drop_first=True)\n\n km = cluster.KMeans(n_clusters=3, n_init=100)\n km.fit(Z / np.linalg.norm(Z, axis=1)[:,None])\n\n km = cluster.SpectralClustering(n_clusters=nc, n_init=100, affinity='precomputed')\n # km.fit(1-(D/np.max(D)))\n km.fit(KK)\n\n return km\n\n@click.command()\n@click.argument('config-file', type=click.Path())\ndef run(config_file):\n \"\"\" run self-tuning spectral clustering on the VNb2O3 dataset\"\"\"\n\n with open(config_file) as f:\n config = yaml.safe_load(f)\n\n # default configuration values...\n max_Nb = config.get('max_Nb', 0.2)\n metric = config.get('metric', 'cosine')\n detrend = config.get('detrend', True)\n post_normalize = config.get('post_normalize', False)\n angular_range = config.get('angular_range', (27, 28.5))\n constant_baseline_removal = config.get('constant_baseline_removal', False)\n\n # load the VNbO2 dataset with linear baseline removal\n # clip the angular range down to the range surrounding the diffraction peaks\n Y, angle, df = io.load_VNb2O3(detrend=detrend, angular_range=angular_range, post_normalize=post_normalize)\n\n if constant_baseline_removal:\n Y = Y - Y.min(axis=1)[:,None] + 1e-6\n\n # drop the label column...\n del df['Label']\n\n # limit the compositional range to <= 30% Nb\n sel = (df.Nb <= max_Nb)\n df = df[sel]\n Y = Y[sel]\n C = df['Nb'].values\n\n print(f'running {metric} self-tuning spectral clustering')\n km = self_tuning_spectral_clustering(Y, metric=metric)\n\n # reorder labels...\n C_avg = []\n for label in np.unique(km.labels_):\n C_avg.append(np.mean(C[km.labels_==label]))\n\n label_order = np.argsort(C_avg)[::-1]\n labels = label_order[km.labels_]\n\n df['cluster_label'] = labels\n results_file = os.path.join(os.path.dirname(config_file), f'results-{metric}.csv')\n df.to_csv(results_file)\n\nif __name__ == '__main__':\n run()\n","sub_path":"VNbO2/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":3882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"371038404","text":"import json\nimport yaml\nfrom math import ceil\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport glob\nimport os\nimport argparse\nimport pandas as pd\nimport datetime\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nROOT_DIR = os.path.join(BASE_DIR, \"..\")\nconfig_file = os.path.join(BASE_DIR, 'config.yaml')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-v', '--visualize', action=\"store_true\")\n parser.add_argument('-c', '--cron', action=\"store_true\")\n return parser.parse_args()\n\n\ndef collect_all_db(data_dir):\n \"\"\" collect and combine all existing DBs in preprocessed_data folder.\n :param data_dir: directory path of preprocessed data\n :return: combined DB\n \"\"\"\n\n total_db = {}\n child_dirs = glob.glob(os.path.join(data_dir, '*'))\n\n for dir in child_dirs:\n db_file = os.path.join(dir, 'db.json')\n try:\n with open(db_file, 'r') as f:\n loaded = json.load(f)\n except Exception as e:\n print('Failed to open database file {}: {}'.format(db_file, e))\n else:\n total_db = {**total_db, **loaded}\n\n count = 0\n for name in total_db:\n count = count + 1\n\n return total_db\n\n\ndef loadyaml():\n with open(config_file, 'r') as stream:\n options = yaml.load(stream)\n return options\n\n\ndef load_DB(options):\n with open(options['db_file'], \"r\") as db_file:\n db = json.load(db_file).values()\n return db\n\n\ndef show_proportion_bar(target, total):\n # 100% / 25 blocks -> 1% / 0.25 block\n if total == 0:\n blocks = 0\n else:\n proportion = 100 * float(target) / total\n blocks = ceil(proportion * 0.25)\n\n bar = '=' * blocks + '.' * (25 - blocks) + ' [ ' + str(target) +\\\n ' / ' + str(total) + ' ]'\n\n return bar\n\ndef show_label_scatter_plot(db,cron=False):\n\n \"\"\" show scatter plots for computed labels.\n 2 plots: loc - ang / pit - roll\n \"\"\"\n\n\n matplotlib.use('Agg')\n\n loc = []\n ang = []\n pit = []\n roll = []\n cnt = 0\n\n for item in db:\n try:\n if item['invalid'] == 0:\n loc.append(item['loc'])\n ang.append(item['ang'])\n\n pit.append(item['pit'] - 0.5)\n roll.append(item['roll'])\n except:\n continue\n\n\n '''\n print('loc', max(loc), min(loc))\n print('ang', max(ang), min(ang))\n print('pit', max(pit), min(pit))\n print('roll', max(roll), min(roll))\n '''\n\n plt.figure(figsize=(20, 8))\n # loc, ang\n plt.subplot(121)\n plt.scatter(loc, ang, s=3)\n plt.xlim((-2.5, 2.5))\n plt.ylim((-90, 90))\n plt.xlabel('loc')\n plt.ylabel('ang ($^{\\circ}$)')\n plt.title('location ─ angle')\n\n # pit, roll\n plt.subplot(122)\n\n plt.scatter(pit, roll, s=3)\n plt.xlim((-0.75, 0.75))\n plt.ylim((-30, 30))\n\n # plt.axis(option='auto')\n plt.xlabel('pit')\n plt.ylabel('roll ($^{\\circ}$)')\n plt.title('pitch ─ roll')\n\n if cron:\n now = datetime.datetime.now()\n nowDatetime = now.strftime('%Y-%m-%d')\n figure_path = os.path.join('figure', 'label_' + nowDatetime + '.png')\n plt.savefig(figure_path)\n else:\n plt.savefig('stats_label_figure.png')\n plt.show()\n\n\ndef show_total_stat(db):\n \"\"\" show overall statistics. (irrelevant to metadata, labels)\n :param db: combined DB\n :return cnt: total number of data\n :return labeled: number of labeled data\n \"\"\"\n\n cnt = 0\n labeled = 0\n invalid = 0\n for item in db:\n if item['is_input_finished']:\n labeled = labeled + 1\n try:\n if item['invalid'] == 1:\n invalid = invalid + 1\n except:\n pass\n cnt = cnt + 1\n\n print('total_#: ', cnt)\n print('labeled: ', labeled)\n print('invalid: ', invalid)\n print('valid(labeled): ' + show_proportion_bar(labeled-invalid, labeled))\n\n return cnt, labeled\n\n\ndef show_manual_meta_stat(db, total):\n \"\"\" show proportions of all manual metadata\n :param db: combined DB\n :param total: total number of data\n :return: None\n \"\"\"\n\n obs_car = 0\n obs_human = 0\n shadow = 0\n one_column = 0\n two_column = 0\n odd_2column = 0\n\n old = 0\n\n for item in db:\n if not item['is_input_finished']:\n continue\n try:\n if item['obs_car'] == 1:\n obs_car = obs_car + 1\n if item['obs_human'] == 1:\n obs_human = obs_human + 1\n if item['shadow'] == 1:\n shadow = shadow + 1\n if item['column'] == 1:\n one_column = one_column + 1\n if item['column'] == 2:\n two_column = two_column + 1\n\n '''\n if 0 <= item['zebra_ratio'] <= 20:\n under_20 = under_20 + 1\n if 20 < item['zebra_ratio'] <= 40:\n under_40 = under_40 + 1\n if 40 < item['zebra_ratio'] <= 60:\n under_60 = under_60 + 1\n if 60 < item['zebra_ratio'] <= 80:\n under_80 = under_80 + 1\n if 80 < item['zebra_ratio']:\n over_80 = over_80 + 1\n '''\n\n if item['column'] == 2.5:\n odd_2column = odd_2column + 1\n if item['old'] == 1:\n old = old + 1\n except Exception as e:\n print('Fail: ' + item['filehash'])\n continue\n\n print('obs_car: ', show_proportion_bar(obs_car, total))\n print('obs_human:', show_proportion_bar(obs_human, total))\n print('shadow: ', show_proportion_bar(shadow, total))\n print('old: ', show_proportion_bar(old, total), '\\n')\n\n print('column:')\n print(' └─ [1] ', show_proportion_bar(one_column, total))\n print(' └─ [2] ', show_proportion_bar(two_column, total), '\\n')\n print(' [1.5]', show_proportion_bar(odd_2column, total), '\\n')\n \n\ndef show_exifmeta_stat(db, total):\n \"\"\" show exif metadata (internal info from imgs)\n :param db: combined DB\n :param total: total number of data\n :return: None\n \"\"\"\n\n horizontal = 0\n Samsung = 0\n Apple = 0\n make_other = 0\n\n for item in db:\n try:\n if item['is_horizontal']:\n horizontal = horizontal + 1\n\n if item['Make'] == 'samsung':\n Samsung = Samsung + 1\n elif item['Make'] == 'Apple':\n Apple = Apple + 1\n else:\n make_other = make_other + 1\n\n except:\n print('Fail: ' + item['filehash'])\n continue\n\n print('horizontal:', show_proportion_bar(horizontal, total))\n print('\\nMake')\n print(' Samsung:', show_proportion_bar(Samsung, total))\n print(' Apple: ', show_proportion_bar(Apple, total))\n print(' Others: ', show_proportion_bar(make_other, total))\n\n\ndef show_db_stat(db):\n print('\\n--------- total ---------\\n')\n total, labeled = show_total_stat(db)\n\n print('\\n--------- exif metadata ---------\\n')\n show_exifmeta_stat(db, total)\n\n if total == 0:\n print('There are no data!')\n return\n\n print('\\n--------- manual metadata (labeled) ---------\\n')\n show_manual_meta_stat(db, labeled)\n show_label_scatter_plot(db)\n\n print('')\n\n\ndef labeling_progress_for_each_dir(db, dir, idx):\n total = 0\n\n for name in db.values():\n total = total + 1\n\n labeled = len(os.listdir(os.path.join(dir, 'labeled')))\n\n dir_name = os.path.basename(dir)\n print('[' + str(idx) + ']', dir_name, ':', show_proportion_bar(labeled, total))\n\n return total, labeled\n\n\ndef show_labeling_progress(data_dir):\n \"\"\" show labeling progress of all preprocessed datasets.\n = for each dataset, show how many imgs are labeled.\n :param data_dir: directory path for preprocessed data\n :return: children directories of data_dir\n \"\"\"\n\n print('----------------------------------------------')\n child_dirs = glob.glob(os.path.join(data_dir, '*'))\n total = 0\n labeled = 0\n idx = 0\n\n for dir in child_dirs:\n idx = idx + 1\n db_file = os.path.join(dir, 'db.json')\n try:\n with open(db_file, 'r') as f:\n loaded = json.load(f)\n except Exception as e:\n print('Failed to open database file {}: {}'.format(db_file, e))\n else:\n tot, lab = labeling_progress_for_each_dir(loaded, dir, idx)\n total = total + tot\n labeled = labeled + lab\n\n print('')\n print('* TOTAL *')\n print(show_proportion_bar(labeled, total), '\\n')\n print('----------------------------------------------')\n\n return child_dirs\n\n\ndef show_db_stats(db, cron):\n now = datetime.datetime.now()\n nowDatetime = now.strftime('%Y-%m-%d')\n\n df = pd.DataFrame.from_dict(db)\n columns = ['date', 'total', 'labeled', 'invalid', 'obs_car', 'obs_human',\n 'shadow', 'old', '1col', '2col', 'odd2col']\n label_range = {'loc': [-2.5, 2.5], 'ang': [-90, 90],\n 'pit': [-0.25, 1.25], 'roll': [-30, 30]}\n labels = ['loc', 'ang', 'pit', 'roll']\n\n stats = pd.DataFrame(index=range(1), columns=columns)\n\n stats['date'] = nowDatetime\n\n stats['total'] = df.shape[0]\n stats['labeled'] = df[df['is_input_finished']].shape[0]\n stats['invalid'] = df[df['invalid'] == 1].shape[0]\n # horizontal = df[df['horizontal'] == 1].shape[0]\n stats['obs_car'] = df[df['obs_car'] == 1].shape[0]\n stats['obs_human'] = df[df['obs_human'] == 1].shape[0]\n stats['shadow'] = df[df['shadow'] == 1].shape[0]\n stats['old'] = df[df['old'] == 1].shape[0]\n stats['1col'] = df[df['column'] == 1].shape[0]\n stats['2col'] = df[df['column'] == 2].shape[0]\n stats['odd2col'] = df[df['column'] == 2.5].shape[0]\n\n for label in labels:\n interval = (label_range[label][1] - label_range[label][0]) / 10.0\n\n for i in range(10):\n bucket = label + '_' + str(i)\n columns.append(bucket)\n b_range = [label_range[label][0] + i * interval,\n label_range[label][0] + (i + 1) * interval]\n\n test = (df[label] >= b_range[0]) & (df[label] < b_range[1])\n stats[bucket] = df[test].shape[0]\n\n df_stats = pd.DataFrame(stats)\n\n if cron:\n with open('trend.csv', 'a', newline='') as f:\n df_stats.to_csv(f, header=False)\n\n show_label_scatter_plot(db, cron)\n\n with open('trend.csv', 'r') as f:\n df_trend = pd.read_csv(f)\n df_trend[['date', 'labeled']].plot.bar(x='date', y='labeled',\n rot=0)\n\n figure_path = os.path.join('figure', 'num_labeled.png')\n plt.savefig(figure_path)\n\n else:\n print(df_stats)\n\n pass\n\n\ndef main(args):\n options = loadyaml()\n data_dir = os.path.join(BASE_DIR, 'dataset')\n db = collect_all_db(data_dir).values()\n\n if args.visualize:\n print('\\n[1] Show total DB statistics')\n print('[2] Show labeling progress\\n')\n mode = input('Choose mode: ')\n\n if mode == '1':\n show_db_stat(db)\n elif mode == '2':\n show_labeling_progress(data_dir)\n else:\n print('Wrong input!\\n')\n else:\n show_db_stats(db, args.cron)\n\n\nif __name__ == '__main__':\n args = parse_args()\n main(args)\n\n","sub_path":"src/labeling/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":11401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"134081059","text":"\r\ndef recordsetting(time):\r\n import openpyxl as xl\r\n wb = xl.load_workbook(\"racerecords3.xlsx\")\r\n sheet = wb[\"Tabelle1\"]\r\n time /= 1000\r\n first_cell = sheet[\"A1\"]\r\n second_cell = sheet[\"A2\"]\r\n third_cell = sheet[\"A3\"]\r\n third = float(third_cell.value)\r\n second = float(second_cell.value)\r\n first = float(first_cell.value)\r\n firster = first\r\n seconder = second\r\n thirder = third\r\n if time < first:\r\n underline = 1\r\n firster = time\r\n\r\n seconder = first\r\n thirder = second\r\n elif time < second:\r\n underline = 2\r\n thirder = second\r\n seconder = time\r\n\r\n elif time < third:\r\n thirder = time\r\n underline = 3\r\n else:\r\n underline = 0\r\n first_cell.value = firster\r\n second_cell.value = seconder\r\n third_cell.value = thirder\r\n wb.save(\"racerecords3.xlsx\")\r\n return firster, seconder, thirder, underline\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"modu.py","file_name":"modu.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"454057005","text":"import numpy as np\n\n# Lineare Regression mittels Methode der kleinsten Quadrate\ndef linregress(x, y):\n assert len(x) == len(y)\n\n x, y = np.array(x), np.array(y)\n\n N = len(y)\n Delta = N * np.sum(x**2) - (np.sum(x))**2\n\n # A ist die Steigung, B der y-Achsenabschnitt\n A = (N * np.sum(x * y) - np.sum(x) * np.sum(y)) / Delta\n B = (np.sum(x**2) * np.sum(y) - np.sum(x) * np.sum(x * y)) / Delta\n\n sigma_y = np.sqrt(np.sum((y - A * x - B)**2) / (N - 2))\n\n A_error = sigma_y * np.sqrt(N / Delta)\n B_error = sigma_y * np.sqrt(np.sum(x**2) / Delta)\n\n return A, A_error, B, B_error\n","sub_path":"Vorlagen/Fehlerrechnung/lineregress.py","file_name":"lineregress.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"73419172","text":"# Вариант для дебилов\nfrom math import pi, sqrt\n\ns = 0\nwhile True: # tk =='1' or tk =='2' or tk =='3':\n tk = int(input('Ведите тип комнаты. 1 треугольная, 2 прямоугольная, 3 круг: '))\n if tk == 1:\n x, y, z = (int(i) for i in input('введите 3 стороны через ,: ').split(\n ',')) # float(input('сторона 1: ')),float(input('сторона 2: ')),float(input('сторона 3: '))\n p = (x + y + z) / 2\n S = sqrt(p * (p - x) * (p - y) * (p - z))\n print('Площадь комнаты =', str(round(S, 2)) + 'м2')\n elif tk == 2:\n x, y = float(input('Длина: ')), float(input('Ширина: '))\n S = x * y\n print('Площадь комнаты =', str(round(S, 2)) + 'м2')\n elif tk == 3:\n R = float(input('Радиус : '))\n S = pi * R ** 2\n print('Площадь комнаты =', str(round(S, 2)) + 'м2')\n else:\n if s < 2:\n print('Извините, для этого типа расчета нет! Цифра должна быть от 1 до 3. \\n')\n s += 1\n else:\n print('Иди отсюда НА ХУЙ!')\n s += 1\n break\n if (tk in [1, 2, 3]):\n break\nif s < 3:\n print('Поздравляем! Вы - справились!')\nelse:\n print('ТЫ - Дебил и ДОЛБОЁБ!!')\n","sub_path":"Площадь комнаты.py","file_name":"Площадь комнаты.py","file_ext":"py","file_size_in_byte":1455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"390726258","text":"# Given an array of size n, find the majority element. \n# The majority element is the element that appears more than ⌊ n/2 ⌋ times.\n\n# You may assume that the array is non-empty and the majority element always exist in the array.\n\n# Example 1:\n\n# Input: [3,2,3]\n# Output: 3\n# Example 2:\n\n# Input: [2,2,1,1,1,2,2]\n# Output: 2\n\n#Time: O(n)\n#Space: O(n)\n\nclass Solution:\n def majorityElement(self, nums: List[int]) -> int:\n count = collections.Counter(nums) \n return max(count.keys(), key=count.get)\n\n\n # Time O(n)\n # Space O(1)\n def majorityElement2(self, nums: List[int]) -> int:\n count = 0\n possible = None\n\n for num in nums:\n if count == 0:\n possible = num\n count += (1 if num == possible else -1)\n return possible","sub_path":"python/majority_element.py","file_name":"majority_element.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"343368530","text":"# Copyright 2020- Robot Framework Foundation\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport base64\nimport json\nimport os\nfrom datetime import timedelta\nfrom pathlib import Path\nfrom typing import Optional\n\nfrom robot.utils import get_link_path # type: ignore\n\nfrom ..base import LibraryComponent\nfrom ..generated.playwright_pb2 import Request\nfrom ..utils import keyword, logger\nfrom ..utils.data_types import ScreenshotFileTypes\n\n\nclass Control(LibraryComponent):\n \"\"\"Keywords to do things on the current browser page and modify the page\"\"\"\n\n @keyword(tags=(\"Setter\", \"BrowserControl\"))\n def go_forward(self):\n \"\"\"Navigates to the next page in history.\"\"\"\n with self.playwright.grpc_channel() as stub:\n response = stub.GoForward(Request.Empty())\n logger.info(response.log)\n\n @keyword(tags=(\"Setter\", \"BrowserControl\"))\n def go_back(self):\n \"\"\"Navigates to the previous page in history.\"\"\"\n with self.playwright.grpc_channel() as stub:\n response = stub.GoBack(Request.Empty())\n logger.info(response.log)\n\n @keyword(tags=(\"Setter\", \"BrowserControl\"))\n def go_to(self, url: str, timeout: Optional[timedelta] = None):\n \"\"\"Navigates to the given ``url``.\n\n ``url`` URL to be navigated to.\n ``timeout`` time to wait page to load. If not defined\n will use the the library default timeout.\n \"\"\"\n with self.playwright.grpc_channel() as stub:\n response = stub.GoTo(\n Request().Url(url=url, defaultTimeout=int(self.get_timeout(timeout)))\n )\n logger.info(response.log)\n\n def _get_screenshot_path(self, filename: str, fileType: str) -> Path:\n if os.path.isabs(filename):\n d, file = os.path.split(filename)\n directory = Path(d)\n filename = os.path.splitext(file)[0]\n else:\n directory = self.screenshots_output\n # Filename didn't contain {index}\n if \"{index}\" not in filename:\n return directory / filename\n index = 0\n while True:\n index += 1\n indexed = Path(filename.replace(\"{index}\", str(index)))\n logger.trace(indexed)\n path = directory / indexed\n # Unique path was found\n if not path.with_suffix(f\".{fileType}\").is_file():\n return path\n\n @keyword(tags=(\"PageContent\",))\n def take_screenshot(\n self,\n filename: str = \"robotframework-browser-screenshot-{index}\",\n selector: str = \"\",\n fullPage: bool = False,\n fileType: ScreenshotFileTypes = ScreenshotFileTypes.png,\n quality: str = \"\",\n timeout: Optional[timedelta] = None,\n ) -> str:\n \"\"\"Takes a screenshot of the current window or element and saves it to disk.\n\n ``filename`` Filename into which to save. The file will be saved into the robot framework\n ${OUTPUTDIR}/browser/screenshot directory by default, but it can overwritten by providing\n custom path or filename. String ``{index}`` in filename will be replaced with a rolling\n number. Use this to not override filenames. If filename equals to EMBED (case insensitive),\n then screenshot is embedded as Base64 image to the log.html. The image is saved temporally\n to the disk and warning is displayed if removing the temporary file fails.\n\n The ${OUTPUTDIR}/browser/ is removed at the first suite startup.\n\n ``selector`` Take a screenshot of the element matched by selector.\n See the `Finding elements` section for details about the selectors.\n If not provided take a screenshot of current viewport.\n\n ``fullPage`` When True, takes a screenshot of the full scrollable page,\n instead of the currently visible viewport. Defaults to False.\n\n ``fileType`` <\"png\"|\"jpeg\"> Specify screenshot type, defaults to png.\n\n ``quality`` The quality of the image, between 0-100. Not applicable to png images.\n\n ``timeout`` Maximum time how long taking screenshot can last, defaults to library timeout.\n Supports Robot Framework time format, like 10s or 1 min, pass 0 to disable timeout.\n The default value can be changed by using the `Set Browser Timeout` keyword.\n\n Example\n | `Take Screenshot` # Takes screenshot from page with default filename\n | `Take Screenshot` selector=id=username_field # Captures element in image\n | # Takes screenshot with jpeg extension, defines image quality and timeout how long taking screenhost should last\n | `Take Screenshot` fullPage=True fileType=jpeg quality=50 timeout=10s\n \"\"\"\n if self._is_embed(filename):\n logger.debug(\"Embedding image to log.html.\")\n else:\n logger.debug(f\"Using {filename} to take screenshot.\")\n file_path = self._take_screenshot(\n filename, selector, fullPage, fileType, quality, timeout\n )\n if self._is_embed(filename):\n return self._embed_to_log(file_path)\n return self._log_image_link(file_path)\n\n def _log_image_link(self, file_path: str) -> str:\n relative_path = get_link_path(file_path, self.outputdir)\n logger.info(\n ''\n f'![](\"{relative_path}\")
',\n html=True,\n )\n return file_path\n\n def _embed_to_log(self, file_path) -> str:\n png = Path(file_path)\n with png.open(\"rb\") as png_file:\n encoded_string = base64.b64encode(png_file.read())\n # log statement is copied from:\n # https://github.com/robotframework/SeleniumLibrary/blob/master/src/SeleniumLibrary/keywords/screenshot.py\n logger.info(\n ' '\n '![\"screenshot\"]()
',\n html=True,\n )\n try:\n png.unlink()\n except Exception:\n logger.warn(f\"Could not remove {png}\")\n return \"EMBED\"\n\n def _take_screenshot(\n self,\n filename: str,\n selector: str = \"\",\n fullPage: bool = False,\n fileType: ScreenshotFileTypes = ScreenshotFileTypes.png,\n quality: str = \"\",\n timeout: Optional[timedelta] = None,\n ) -> str:\n string_path_no_extension = str(\n self._get_screenshot_path(filename, fileType.name)\n )\n with self.playwright.grpc_channel() as stub:\n response = stub.TakeScreenshot(\n Request().ScreenshotOptions(\n path=string_path_no_extension,\n selector=selector,\n fullPage=fullPage,\n fileType=fileType.name,\n quality=quality,\n timeout=int(self.get_timeout(timeout)),\n )\n )\n logger.debug(response.log)\n return response.body\n\n def _is_embed(self, filename: str) -> bool:\n return True if filename.upper() == \"EMBED\" else False\n\n @keyword(tags=(\"Setter\", \"Config\"))\n def set_browser_timeout(self, timeout: timedelta) -> str:\n \"\"\"Sets the timeout used by most input and getter keywords.\n\n ``timeout`` Timeout of it is for current playwright context\n and for new contexts. Supports Robot Framework\n [https://robotframework.org/robotframework/latest/RobotFrameworkUserGuide.html#time-format|time format]\n . Returns the previous value of the timeout.\n\n Example:\n | ${old_timeout} = `Set Browser Timeout` 1m 30 seconds\n | Click //button\n | `Set Browser Timeout` ${old_timeout}\n \"\"\"\n old_timeout = self.millisecs_to_timestr(self.timeout)\n self.timeout = self.convert_timeout(timeout)\n try:\n with self.playwright.grpc_channel() as stub:\n response = stub.SetTimeout(Request().Timeout(timeout=self.timeout))\n logger.info(response.log)\n except Exception as error: # Suppress all errors\n if \"Browser has been closed\" in str(error):\n logger.debug(f\"Suppress error {error} when setting timeout.\")\n else:\n raise\n return old_timeout\n\n @keyword(tags=(\"Setter\", \"Config\"))\n def set_retry_assertions_for(self, timeout: timedelta) -> str:\n \"\"\"Sets the timeout used in retrying assertions when they fail.\n\n `Set Browser timeout` controls how long Playwright will perform\n waiting in the node side, assertion retry will determine how long\n Browser library will retry the playwright operation. Generally\n assertion timeout should be longer than the timeout set by\n `Set Browser timeout`.\n\n Returns the previous value of the assertion retry.\n\n Example:\n | `Set Browser Timeout` 10 seconds\n | ${old} = `Set Retry Assertions For` 30s\n | `Get Title` == Login Page\n | `Set Retry Assertions For` ${old}\n\n Example waits 10 seconds on Playwright to get the page title and library\n will retry 30 seconds to make sure that title is correct.\n \"\"\"\n old_retry_assertions_for = self.millisecs_to_timestr(self.retry_assertions_for)\n self.retry_assertions_for = self.convert_timeout(timeout)\n return old_retry_assertions_for\n\n @keyword(tags=(\"Setter\", \"BrowserControl\"))\n def set_viewport_size(self, width: int, height: int):\n \"\"\"Sets current Pages viewport size to specified dimensions.\n\n In the case of multiple pages in a single browser,\n each page can have its own viewport size. However,\n `New Context` allows to set viewport size (and more) for all\n later opened pages in the context at once.\n\n `Set Viewport Size` will resize the page.\n A lot of websites don't expect phones to change size,\n so you should set the viewport size before navigating to\n the page with `New Context` before opening the page itself.\n\n ``width`` Sets the width size.\n\n ``height`` Sets the height size.\n \"\"\"\n with self.playwright.grpc_channel() as stub:\n response = stub.SetViewportSize(\n Request().Viewport(height=height, width=width)\n )\n logger.info(response.log)\n\n @keyword(tags=(\"Setter\", \"BrowserControl\"))\n def set_offline(self, offline: bool = True):\n \"\"\"Toggles current Context's offline emulation.\n\n ``offline`` Toggles the offline mode. Set to False to switch back\n to online mode. Defaults to True.\n \"\"\"\n with self.playwright.grpc_channel() as stub:\n response = stub.SetOffline(Request().Bool(value=offline))\n logger.info(response.log)\n\n @keyword(tags=(\"Setter\", \"BrowserControl\"))\n def set_geolocation(\n self, latitude: float, longitude: float, accuracy: Optional[float] = None\n ):\n \"\"\"Updated the correct Context's geolocation.\n\n Latitude can be between -90 and 90 and longitude can be between -180 and 180.\n Accuracy of the location must be positive number and defaults to 0. When\n creating context, grant ``geolocation`` permission for pages to read its geolocation.\n\n Example:\n | ${permissions} = Create List geolocation\n | `New Context` permissions=${permissions}\n | `Set Geolocation` 60.173708, 24.982263 3 # Points to Korkeasaari in Helsinki.\n \"\"\"\n geolocation_dict = {\"latitude\": latitude, \"longitude\": longitude}\n if accuracy:\n geolocation_dict[\"accuracy\"] = accuracy\n geolocation = json.dumps(geolocation_dict)\n logger.info(geolocation)\n with self.playwright.grpc_channel() as stub:\n response = stub.SetGeolocation(Request().Json(body=geolocation))\n logger.info(response.log)\n\n @keyword(tags=(\"Setter\", \"BrowserControl\"))\n def reload(self):\n \"\"\"Reloads current active page.\"\"\"\n with self.playwright.grpc_channel() as stub:\n response = stub.Reload(Request().Empty())\n logger.info(response.log)\n","sub_path":"Browser/keywords/browser_control.py","file_name":"browser_control.py","file_ext":"py","file_size_in_byte":12884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"89094734","text":"from typing import List\nimport mido\nmido_msg = mido.Message('note_on', note=60)\n#print(mido_msg.type)\n\nmido.get_output_names()\n\nmidifile = mido.MidiFile('./pyprone/resources/midifiles/a-whole-new-world.mid')\nport = mido.open_output('Microsoft GS Wavetable Synth 0')\n\n# midi play\nfor msg in midifile.play():\n print(msg)\n port.send(msg)\n","sub_path":"examples/helloworld_mido.py","file_name":"helloworld_mido.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"633547937","text":"# **************************************************************************\n# *\n# * Authors: Jose Luis Vilas (jlvilas@cnb.csic.es)\n# *\n# * Unidad de Bioinformatica of Centro Nacional de Biotecnologia , CSIC\n# *\n# * This program is free software; you can redistribute it and/or modify\n# * it under the terms of the GNU General Public License as published by\n# * the Free Software Foundation; either version 2 of the License, or\n# * (at your option) any later version.\n# *\n# * This program is distributed in the hope that it will be useful,\n# * but WITHOUT ANY WARRANTY; without even the implied warranty of\n# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# * GNU General Public License for more details.\n# *\n# * You should have received a copy of the GNU General Public License\n# * along with this program; if not, write to the Free Software\n# * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA\n# * 02111-1307 USA\n# *\n# * All comments concerning this program package may be sent to the\n# * e-mail address 'jmdelarosa@cnb.csic.es'\n# *\n# **************************************************************************\n\nfrom os.path import split, splitext\n\nfrom pyworkflow.object import Float, String\nfrom pyworkflow.protocol.params import (PointerParam, FloatParam, EnumParam, STEPS_PARALLEL, LEVEL_ADVANCED)\nfrom pyworkflow.utils.path import makePath\nfrom pyworkflow.em.data_tiltpairs import TiltPair, CoordinatesTiltPair, Coordinate\nfrom pyworkflow.em import ProtParticlePicking\nfrom pyworkflow.em.packages.xmipp3 import XmippProtocol, readAnglesFromMicrographs\n\nimport xmipp\nfrom convert import readSetOfCoordinates, writeSetOfCoordinates, writeSetOfMicrographsPairs, izip\n\nTYPE_COORDINATES = 0\nTYPE_PARTICLES = 1\n\n\nclass XmippProtAssignmentTiltPair(ProtParticlePicking, XmippProtocol):\n \"\"\" \n From two sets of points (tilted and untilted) the protocol determines the affine transformation between\n these sets.\n \"\"\"\n _label = 'assignment tiltpair'\n \n def __init__(self, *args, **kwargs):\n ProtParticlePicking.__init__(self, *args, **kwargs)\n self.stepsExecutionMode = STEPS_PARALLEL\n \n #--------------------------- DEFINE param functions -------------------------------------------- \n def _defineParams(self, form):\n form.addSection(label='Input')\n form.addParam('tiltpair', PointerParam, pointerClass='MicrographsTiltPair',\n label=\"Micrograph tilt pair\", \n help='Select micrographs tilt pair.')\n \n form.addParam('typeOfSet', EnumParam, choices=['Coordinates', 'Particles'], \n default=0, label='Input type', display=EnumParam.DISPLAY_COMBO, \n help='Select a Set of Coordinates or a Set or Particles.')\n \n form.addParam('untiltCoor', PointerParam, pointerClass='SetOfCoordinates', \n label=\"Untilted coordinates\", condition='typeOfSet==%d' % TYPE_COORDINATES, \n help='Select the metadata with untilt coordinates.')\n \n form.addParam('tiltCoor', PointerParam, pointerClass='SetOfCoordinates', \n label=\"Tilted coordinates\", condition='typeOfSet==%d' % TYPE_COORDINATES, \n help='Select the metadata with tilt coordinates.')\n \n form.addParam('untiltPar', PointerParam, pointerClass='SetOfParticles', \n label=\"Untilted particles\", condition='typeOfSet==%d' % TYPE_PARTICLES,\n help='Select the metadata with untilt particles.')\n \n form.addParam('tiltPar', PointerParam, pointerClass='SetOfParticles', \n label=\"Tilted particles\", condition='typeOfSet==%d' % TYPE_PARTICLES, \n help='Select the metadata with tilt particles.')\n \n# form.addParam('boxsiz', FloatParam, default=100, \n# label=\"Box Size\", condition='typeOfSet==%d' % TYPE_PARTICLES, \n# help='Sometimes, particles do not have information about the particle size\\n,'\n# 'i.e. filtered by ZScore. In those cases, the particle size or box size is requiered \\n'\n# 'By default: boxSize = 100') \n \n form.addParam('tiltAngle', FloatParam, default=-1, expertLevel=LEVEL_ADVANCED,\n label=\"Tilt angle\", \n help='Tilt angle estimation, the method will look for the assignment in the\\n'\n ' interval of [tilt_angle-15, tilt_angle+15].\\n'\n 'By default: tilt angle = -1, if there is not any information about the tilt angle') \n \n form.addParam('threshold', FloatParam, default=0.25, expertLevel=LEVEL_ADVANCED,\n label=\"Threshold value\", \n help='Parameter between 0 and 1 that allows to define if \\n' \n 'a tilt point can be matched with a certain untilt point. \\n'\n 'The matching is performed only if the distance is lesser than \\n'\n 'threshold * particlesize.')\n\n form.addParam('maxShift', FloatParam, default=1500, expertLevel=LEVEL_ADVANCED,\n label=\"Maximum shift (pixels)\", \n help='Maximum allowed distance (in pixels) that the tilt micrograph can be shifted' \n 'respect to the untilted micrograph') \n\n form.addParallelSection(threads=1, mpi=1)\n\n #--------------------------- INSERT steps functions --------------------------------------------\n\n def _insertAllSteps(self): \n self.micsFn = self._getPath()\n #self.micsFn = self._getPath('input_micrographs.xmd')\n # Convert input into xmipp Metadata format\n convertId=self._insertFunctionStep('convertInputStep')\n deps = []\n for tiltPair in self.tiltpair.get():\n micUntilted = tiltPair.getUntilted()\n micTilted = tiltPair.getTilted()\n Unpath, Unname = split(micUntilted.getFileName())\n Unname, ext = splitext(Unname)\n Tpath, Tname = split(micTilted.getFileName())\n Tname, ext = splitext(Tname)\n fnUntilt = 'particles@'+self._getExtraPath(\"untilted/\")+Unname+'.pos'\n fnTilt = 'particles@'+self._getExtraPath(\"tilted/\")+Tname+'.pos'\n fnmicsize = tiltPair.getTilted().getFileName()\n fnposUntilt = 'particles@'+self._getExtraPath(\"\")+Unname+'.pos'\n fnposTilt = 'particles@'+self._getExtraPath(\"\")+Tname+'.pos'\n stepId = self._insertFunctionStep('assignmentStep',fnUntilt, fnTilt, fnmicsize, self._getExtraPath(),\n fnposUntilt, fnposTilt, self._getExtraPath(Unname), prerequisites=[convertId])\n \n# self._insertFunctionStep('estimateTiltAxisStep', fnposUntilt, fnposTilt, self._getExtraPath(Unname), prerequisites=[convertId])\n \n deps.append(stepId)\n \n self._insertFunctionStep('createOutputStep', prerequisites=deps)\n \n\n def convertInputStep(self):\n \"\"\" Read the input metadatata.\n \"\"\"\n # Get the converted input micrographs in Xmipp format\n makePath(self._getExtraPath(\"untilted\"))\n makePath(self._getExtraPath(\"tilted\"))\n \n\n \n if self.typeOfSet.get() == TYPE_PARTICLES:\n U_set = self.untiltPar.get().getCoordinates()\n T_set = self.tiltPar.get().getCoordinates()\n\n if (U_set or T_set) is None:\n U_set = self._createSetOfCoordinates(self.tiltpair.get().getUntilted(), \n suffix='_untilted')\n T_set = self._createSetOfCoordinates(self.tiltpair.get().getTilted(), \n suffix='_tilted')\n \n untiltPar = self.untiltPar.get()\n for particle_u in untiltPar: \n newCoord = particle_u.getCoordinate().clone()\n newCoord.copyObjId(particle_u)\n U_set.append(newCoord)\n#\n tiltPar = self.tiltPar.get()\n for particle_t in tiltPar:\n newCoord = particle_t.getCoordinate().clone()\n newCoord.copyObjId(particle_t)\n T_set.append(newCoord)\n\n aux = self.untiltPar.get()\n aux2 = aux[1]\n bos, y_, z_ = aux2.getDim()\n U_set.setBoxSize(bos)\n T_set.setBoxSize(bos)\n else:\n U_set = self.untiltCoor.get()\n T_set = self.tiltCoor.get() \n \n writeSetOfCoordinates(self._getExtraPath(\"untilted\"), U_set)\n writeSetOfCoordinates(self._getExtraPath(\"tilted\"), T_set)\n# writeSetOfMicrographsPairs(self.tiltpair.get().getUntilted(), \n# self.tiltpair.get().getTilted(), \n# self.micsFn)\n \n def assignmentStep(self,fnuntilt, fntilt, fnmicsize, Unpath, fnposUntilt, fnposTilt, opath):\n\n params = ' --untiltcoor %s' % fnuntilt \n params += ' --tiltcoor %s' % fntilt\n params += ' --tiltmicsize %s' % fnmicsize\n params += ' --maxshift %f' % self.maxShift.get()\n if self.typeOfSet.get() == TYPE_PARTICLES:\n aux = self.untiltPar.get()\n aux2 = aux[1]\n boxsize, y_, z_ = aux2.getDim()\n params += ' --particlesize %f' % boxsize \n else:\n params += ' --particlesize %f' % self.untiltCoor.get().getBoxSize()\n params += ' --threshold %f' % self.threshold.get()\n params += ' --odir %s' % Unpath\n\n\n nproc = self.numberOfMpi.get()\n nT=self.numberOfThreads.get() \n self.runJob('xmipp_image_assignment_tilt_pair', params, numberOfMpi=nproc,numberOfThreads=nT)\n \n\n \n self.estimateTiltAxis(fnposUntilt, fnposTilt)#, self.micsFn)\n\n \n \n def estimateTiltAxis(self, fnposUntilt, fnposTilt):#, opath):\n\n params = ' --untilted %s' % fnposUntilt \n params += ' --tilted %s' % fnposTilt\n params += ' -o %s' % self._getPath()+'/input_micrographs.xmd'\n\n self.runJob('xmipp_angular_estimate_tilt_axis', params)\n \n \n def createOutputStep(self):\n \n extradir = self._getExtraPath()\n inputset = self.tiltpair.get()\n uSet = inputset.getUntilted()\n tSet = inputset.getTilted()\n \n # Create Untilted and Tilted SetOfCoordinates\n uCoordSet = self._createSetOfCoordinates(uSet, suffix='Untilted')\n if self.typeOfSet.get() == TYPE_PARTICLES:\n# boxsize_u = self.untiltPar.get().getCoordinates().getBoxSize()\n# if boxsize_u is None:\n aux = self.untiltPar.get()\n aux2 = aux[1]\n boxsize_u, y_, z_ = aux2.getDim()\n else:\n boxsize_u = self.untiltCoor.get().getBoxSize()\n \n uCoordSet.setBoxSize(boxsize_u) \n tCoordSet = self._createSetOfCoordinates(tSet, suffix='Tilted')\n readSetOfCoordinates(extradir, uSet, uCoordSet)\n uCoordSet.write()\n \n if self.typeOfSet.get() == TYPE_PARTICLES:\n# boxsize_t = self.tiltPar.get().getCoordinates().getBoxSize()\n# if boxsize_t is None:\n aux = self.tiltPar.get()\n aux2 = aux[1]\n boxsize_t, y_, z_ = aux2.getDim()\n else:\n boxsize_t = self.tiltCoor.get().getBoxSize()\n \n tCoordSet.setBoxSize(boxsize_t)\n readSetOfCoordinates(extradir, tSet, tCoordSet)\n tCoordSet.write()\n \n\n setAngles = self._createSetOfAngles()\n pathangles = self.micsFn + '/input_micrographs.xmd'\n readAnglesFromMicrographs(pathangles, setAngles)\n \n setAngles.write()\n # Create CoordinatesTiltPair object\n outputset = CoordinatesTiltPair(filename=self._getPath('coordinates_pairs.sqlite'))\n outputset.setTilted(tCoordSet)\n outputset.setUntilted(uCoordSet)\n outputset.setAngles(setAngles)\n outputset.setMicsPair(inputset)\n outputset.setObjComment(self.getSummary(outputset))\n# print '-----------------------------'\n# print setAngles\n# print '-----------------------------'\n for coordU, coordT in izip(uCoordSet, tCoordSet):\n outputset.append(TiltPair(coordU, coordT))\n\n self._defineOutputs(outputCoordinatesTiltPair=outputset)\n self._defineSourceRelation(self.tiltpair, outputset)\n \n\n \n #--------------------------- INFO functions -------------------------------------------- \n def _validate(self):\n \n if self.typeOfSet.get() == TYPE_PARTICLES:\n validateMsgs = []\n if self.untiltPar.get() and not self.untiltPar.hasValue():\n validateMsgs.append('Please provide input particles.') \n if self.tiltPar.get() and not self.tiltPar.hasValue():\n validateMsgs.append('Please provide input particles.') \n return validateMsgs\n else:\n validateMsgs = []\n if self.untiltCoor.get() and not self.untiltCoor.hasValue():\n validateMsgs.append('Please provide input coordinates.') \n if self.tiltCoor.get() and not self.tiltCoor.hasValue():\n validateMsgs.append('Please provide input coordinates.') \n return validateMsgs\n \n \n \n def _summary(self):\n summary = []\n\n if (not hasattr(self,'outputCoordinatesTiltPair')):\n summary.append(\"Output tilpairs not ready yet.\")\n else:\n #summary.append(\"Particles matched: \" )\n if self.typeOfSet.get() == TYPE_PARTICLES:\n aux = self.untiltPar.get()\n aux2 = aux[1]\n boxsize_t, y_, z_ = aux2.getDim()\n summary.append(\"Particle box size: %d\" %boxsize_t)\n summary.append(\"Tilt pairs matched: %d\" %self.outputCoordinatesTiltPair.__len__())\n else:\n summary.append(\"Particle box size: %d\" %self.untiltCoor.get().getBoxSize())\n summary.append(\"Tilt pairs size: %d\" %self.outputCoordinatesTiltPair.__len__())\n #\n return summary\n \n \n def _methods(self):\n messages = []\n if (hasattr(self,'outputCoordinatesTiltPair')):\n messages.append('The assignment has been performed using and affinity transformation [Publication: Not yet]')\n return messages\n \n def _citations(self):\n return ['Not yet']\n \n def getSummary(self, coordsSet):\n summary = []\n summary.append(\"Particles picked:\")\n #summary.append(\"Particles picked: %d\" %coordsSet.getSize())\n return \"\\n\"#.join(summary)\n# \n \n ","sub_path":"pyworkflow/em/packages/xmipp3/protocol_assignment_tilt_pair.py","file_name":"protocol_assignment_tilt_pair.py","file_ext":"py","file_size_in_byte":15072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"323558204","text":"import math\nimport random\n\nraw_training=[]\nraw_target=[]\nraw_test=[]\nraw_test_ans=[]\ntest_pre=[]\nwith open(\"logicTraining.txt\",\"r\") as f_training:\n for line in f_training:\n line=line.split(',')\n line=list(map(int,line))\n raw_target.append(line.pop())\n line.pop(0)\n raw_training.append(line)\nwith open(\"logciTest.txt\",\"r\") as f_training:\n for line in f_training:\n line=line.split(',')\n line=list(map(int,line))\n raw_test_ans.append(line.pop())\n line.pop(0)\n raw_test.append(line)\n\ndef prediction(nums,weights):\n pre=0.0\n for num,weight in zip(nums,weights):\n pre=pre+num*weight\n result=1/(1+math.exp(-pre))\n return result\n\ndef logicReg(training,target,learnRate):\n weights=[]\n for i in range (9):\n weights.append(0)\n for ele,tar in zip(training,target):\n p=prediction(ele,weights)\n dlt=tar-p\n for i in range(9):\n weights[i]=weights[i]+learnRate*dlt*p*(1-p)*ele[i]\n return weights\ndef ConfusionMat(ans,pred):\n countnono=0\n countnoyes=0\n countyesno=0\n countyesyes=0\n for numa,nump in zip(ans,pred):\n if numa==4 and nump==4:\n countyesyes+=1\n elif numa==4 and nump==2:\n countyesno+=1\n elif numa==2 and nump==2:\n countnono+=1\n else:\n countnoyes+=1\n print (\"\\tpredict no \"+\" predict yes\")\n print (\"actual no: \"+repr(countnono)+\" \"+repr(countnoyes))\n print (\"actual yes: \"+repr(countyesno)+\" \"+repr(countyesyes))\n print (\"report: \\n\")\n\n precision4=float(countyesyes/(countyesyes+countnoyes))\n recall4=float(countyesyes/(countyesyes+countyesno))\n f1score4=float(2/(1/recall4+1/precision4))\n\n precision2=float(countnono/(countnono+countyesno))\n recall2=float(countnono/(countnono+countnoyes))\n f1score2=float(2/(1/recall2+1/precision2))\n print(\"\\tprecision\"+\" recall\"+\" f1-score\")\n print(\"2\\t\"+repr(precision2)+\" \"+repr(recall2)+\" \"+repr(f1score2))\n print(\"4\\t\"+repr(precision4)+\" \"+repr(recall4)+\" \"+repr(f1score4))\n\n\n\n\n\nlt=5e-5\ntestWeights=logicReg(raw_training,raw_target,lt)\nfor ele in raw_test:\n res=0\n for i in range(9):\n res=res+ele[i]*testWeights[i]\n if res>1:\n test_pre.append(4)\n else:\n test_pre.append(2)\nprint (\"confusion matrix \\n\")\nConfusionMat(raw_test_ans,test_pre)\n","sub_path":"logicalRegression.py","file_name":"logicalRegression.py","file_ext":"py","file_size_in_byte":2387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"234238384","text":"# -*- coding: utf-8 -*- #\n# Copyright 2020 Google LLC. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"`gcloud access-context-manager cloud-bindings create` command.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom googlecloudsdk.api_lib.accesscontextmanager import cloud_bindings as cloud_bindings_api\nfrom googlecloudsdk.calliope import base\nfrom googlecloudsdk.command_lib.accesscontextmanager import cloud_bindings\nfrom googlecloudsdk.command_lib.accesscontextmanager import levels\nfrom googlecloudsdk.command_lib.util.hooks import types\n\n\n@base.ReleaseTracks(base.ReleaseTrack.GA)\nclass CreateBindingGA(base.Command):\n \"\"\"Create a new cloud access binding.\"\"\"\n\n detailed_help = {\n 'DESCRIPTION':\n '{description}',\n 'EXAMPLES':\n \"\"\"\\\n To create a new cloud access binding, run:\n\n $ {command} --group-key=my-group-key --level=my-access-level\n \"\"\",\n }\n\n _API_VERSION = 'v1'\n\n @staticmethod\n def Args(parser):\n levels.AddResourceFlagArg(parser, 'to create')\n cloud_bindings.GetOrgArg().AddToParser(parser)\n cloud_bindings.GetGroupKeyArg().AddToParser(parser)\n\n def Run(self, args):\n client = cloud_bindings_api.Client(version=self._API_VERSION)\n org_ref = cloud_bindings.GetDefaultOrganization()\n if args.IsSpecified('organization'):\n org_ref = types.Resource('accesscontextmanager.organizations')(\n args.organization)\n\n level_ref = args.CONCEPTS.level.Parse()\n\n return client.Create(org_ref, args.group_key, level_ref)\n\n\n@base.ReleaseTracks(base.ReleaseTrack.ALPHA)\nclass CreateBindingAlpha(CreateBindingGA):\n \"\"\"Create a new cloud access binding.\"\"\"\n _API_VERSION = 'v1alpha'\n","sub_path":"google-cloud-sdk/lib/surface/access_context_manager/cloud_bindings/create.py","file_name":"create.py","file_ext":"py","file_size_in_byte":2276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"282135698","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@author: ChaoMing (https://oujago.github.io/)\n\n@date: Created on 17-1-9\n\n@notes:\n \n\"\"\"\n\n\nclass EvalCls:\n def __init__(self, index2tag,\n metrics=('macro_acc', 'macro_recall', 'macro_f1', 'micro_acc'),\n aspects=('training', 'trained', 'valid', 'test'),\n metrics_to_choose_model=None,\n split_line_len=20):\n # get all tags\n self.index2tag = index2tag\n\n # get total metric index\n self.metric_index = {'micro': (-1, 0)}\n for i, tag in enumerate(index2tag + ['macro', 'micro']):\n self.metric_index['%s_acc' % tag] = (i, 0)\n self.metric_index['%s_recall' % tag] = (i, 1)\n self.metric_index['%s_f1' % tag] = (i, 2)\n\n # get evaluation metric index\n i, micro, metrics = 0, False, list(metrics)\n while i < len(metrics):\n if metrics[i] not in ['macro_acc', 'macro_recall', 'macro_f1']:\n if metrics[i] in ['micro_acc', 'micro_recall', 'micro_f1', 'micro']:\n micro = True\n metrics.remove(metrics[i])\n else:\n raise ValueError(\"Unknown metric: %s\" % metrics[i])\n else:\n i += 1\n if micro:\n metrics.append('micro_acc')\n self.metrics = metrics\n\n self.aspects = aspects\n\n self.history_evaluation_matrices = {}\n \"\"\"\n history_evaluation_matrices = {\n 'folder0': {\n \"training\": [eval_mat0, eval_mat1],\n 'trained': [eval_mat0, eval_mat1],\n 'valid': [eval_mat0, eval_mat1],\n 'test': [eval_mat0, eval_mat1]\n }\n 'folder1': {\n \"training\": [eval_mat0, eval_mat1],\n 'trained': [eval_mat0, eval_mat1],\n 'valid': [eval_mat0, eval_mat1],\n 'test': [eval_mat0, eval_mat1]\n }\n }\n \"\"\"\n\n self.history_confusion_matrices = {}\n \"\"\"\n history_confusion_matrices = {\n 'folder0': {\n \"training\": [conf_mat0, conf_mat1],\n 'trained': [conf_mat0, conf_mat1],\n 'valid': [conf_mat0, conf_mat1],\n 'test': [conf_mat0, conf_mat1]\n }\n 'folder1': {\n \"training\": [conf_mat0, conf_mat1],\n 'trained': [conf_mat0, conf_mat1],\n 'valid': [conf_mat0, conf_mat1],\n 'test': [conf_mat0, conf_mat1]\n }\n }\n \"\"\"\n\n self.history_losses = {}\n \"\"\"\n history_losses = {\n 'folder0': {\n \"training\": [],\n 'trained': [],\n 'valid': [],\n 'test': [],\n 'L1': [],\n 'L2': []\n }\n\n 'folder1': {\n \"training\": [],\n 'trained': [],\n 'valid': [],\n 'test': [],\n 'L1': [],\n 'L2': []\n }\n }\n \"\"\"\n\n self.split_line = \"---\" * split_line_len\n\n self.metrics_to_choose_model = metrics_to_choose_model or self.metrics\n\n @staticmethod\n def divide(a, b):\n if b == 0:\n # if a == 0:\n # return 1.\n # else:\n return 0.\n else:\n return a / b\n\n @staticmethod\n def get_confusion_matrix(predictions, origins, y_num):\n \"\"\"\n Get the value matrix, according to the predicted and original data.\n\n :param predictions:\n :param origins:\n :param y_num:\n \"\"\"\n assert predictions.shape == origins.shape\n\n res_matrix = np.zeros((y_num, y_num), dtype='int32')\n for i in range(len(predictions)):\n res_matrix[origins[i], predictions[i]] += 1\n return res_matrix\n\n @staticmethod\n def get_evaluation_matrix(confusion_matrix, beta=1):\n \"\"\"\n Get the evaluation matrix, according to the value matrix\n\n :param confusion_matrix: A confusion matrix\n :param beta: beta value\n :return:\n evaluation matrix ——\n precision, recall, F1\n 1st label : P R F1\n 2nd label : P R F1\n …… P R F1\n the last but one line : macro-P macro-R macro-F1\n the last line : micro-P micro-R micro-F1\n \"\"\"\n\n y_num = confusion_matrix.shape[0]\n res_matrix = np.zeros((y_num + 2, 3))\n\n # calculate each element precision, recall, F1\n for i in range(confusion_matrix.shape[0]):\n precision = EvalCls.divide(confusion_matrix[i, i], np.sum(confusion_matrix[:, i]))\n recall = EvalCls.divide(confusion_matrix[i, i], np.sum(confusion_matrix[i, :]))\n f1 = EvalCls.divide((1 + beta ** 2) * precision * recall, beta ** 2 * precision + recall)\n res_matrix[i, 0] = precision\n res_matrix[i, 1] = recall\n res_matrix[i, 2] = f1\n\n # calculate macro precision, recall, F1\n res_matrix[-2, :] = np.mean(res_matrix[:-2, :], axis=0)\n\n # calculate micro precision, recall, F1\n res_matrix[-1, :] = EvalCls.divide(np.sum(np.diag(confusion_matrix)), np.sum(confusion_matrix))\n\n return res_matrix\n\n @staticmethod\n def print_matrix(matrix, rows, columns, file=sys.stdout):\n \"\"\"\n Print the value matrix into the file.\n :param matrix:\n :param rows:\n :param columns:\n :param file:\n \"\"\"\n assert len(rows) == len(matrix) and len(columns) == len(matrix[0])\n gap = max([len(row) for row in rows] + [len(column) for column in columns]) + 1\n\n # print header\n runout = ' ' * gap\n for column in columns:\n runout += (\" \" * (gap - len(column)) + column)\n print(runout, file=file)\n\n # print each row\n for i in range(len(rows)):\n runout = ' ' * (gap - len(rows[i])) + rows[i]\n for value in matrix[i]:\n value = (\"%s\" % value)[:gap - 1]\n runout += (\" \" * (gap - len(value)) + value)\n print(runout, file=file)\n\n def add_history_confusion_matrix(self, history_name, aspect, confusion_mat):\n assert aspect in self.aspects\n\n if history_name not in self.history_confusion_matrices:\n self.history_confusion_matrices[history_name] = {}\n if aspect not in self.history_confusion_matrices[history_name]:\n self.history_confusion_matrices[history_name][aspect] = []\n self.history_confusion_matrices[history_name][aspect].append(confusion_mat)\n\n def add_history_evaluation_matrix(self, history_name, aspect, evaluation_mat):\n assert aspect in self.aspects\n\n if history_name not in self.history_evaluation_matrices:\n self.history_evaluation_matrices[history_name] = {}\n if aspect not in self.history_evaluation_matrices[history_name]:\n self.history_evaluation_matrices[history_name][aspect] = []\n self.history_evaluation_matrices[history_name][aspect].append(evaluation_mat)\n\n def add_history_loss(self, history_name, aspect, loss):\n assert aspect in self.aspects\n\n if history_name not in self.history_losses:\n self.history_losses[history_name] = {}\n if aspect == 'training':\n asp_loss_pairs = [('training', loss[0]), ('L1', loss[1]), ('L2', loss[2])]\n else:\n asp_loss_pairs = [(aspect, loss)]\n for asp, loss in asp_loss_pairs:\n if asp not in self.history_losses[history_name]:\n self.history_losses[history_name][asp] = []\n self.history_losses[history_name][asp].append(loss)\n\n def add_history(self, history_name, aspect, confusion_mat, evaluation_mat, loss):\n self.add_history_confusion_matrix(history_name, aspect, confusion_mat)\n self.add_history_evaluation_matrix(history_name, aspect, evaluation_mat)\n self.add_history_loss(history_name, aspect, loss)\n\n def output_epoch(self, history_name, epoch, end='; ', file=sys.stdout):\n # epoch\n epoch_runout = 'epoch %d' % epoch\n runout = epoch_runout + end\n # loss\n loss_runout = [\"%s:%.4f\" % (key, value[epoch])\n for key, value in sorted(self.history_losses[history_name].items())]\n runout += \"loss-[%s]; \" % (\" \".join(loss_runout))\n # aspects\n for aspect in self.aspects:\n matrix = self.history_evaluation_matrices[history_name][aspect][-1]\n aspect_runout = [\"%s:%.4f\" % (metric, matrix[self.metric_index[metric]])\n for metric in self.metrics]\n runout += \"%s-[%s]; \" % (aspect, \" \".join(aspect_runout))\n # print\n print(runout, file=file)\n\n def format_runout_matrix(self, matrix, rows, columns):\n output_lines = []\n\n # check\n assert len(rows) == len(matrix) and len(columns) == len(matrix[0])\n gap = max([len(row) for row in rows] + [len(column) for column in columns]) + 1\n\n # header\n header = ' ' * gap\n for column in columns:\n header += (\" \" * (gap - len(column)) + column)\n output_lines.append(header)\n\n # each row\n for i in range(len(rows)):\n row_runout = ' ' * (gap - len(rows[i])) + rows[i]\n for value in matrix[i]:\n value = (\"%s\" % value)[:gap - 1]\n row_runout += (\" \" * (gap - len(value)) + value)\n output_lines.append(row_runout)\n\n return output_lines\n\n def format_runout_history_metric_aspect(self, confusion_mat, evaluation_mat=None, matrix_desc=None):\n \"\"\"\n Output a value matrix.\n The functions for this functions are:\n 1, output the value matrix;\n 2, output the evaluation matrix;\n 3, return the final total accuracy and F1.\n\n :param confusion_mat:\n :param evaluation_mat:\n :param matrix_desc: matrix description\n :param file:\n \"\"\"\n output_lines = []\n\n # blank line\n output_lines.append(\"\")\n # split line\n output_lines.append(self.split_line)\n # matrix description\n output_lines.append(matrix_desc)\n # split line\n output_lines.append(self.split_line)\n # confusion matrix\n output_lines.extend(self.format_runout_matrix(confusion_mat, self.index2tag, self.index2tag))\n # split line\n output_lines.append(self.split_line)\n # evaluation matrix\n if evaluation_mat is None:\n evaluation_mat = self.get_evaluation_matrix(confusion_mat)\n output_lines.extend(self.format_runout_matrix(\n evaluation_mat, self.index2tag + ['macro', 'micro'], ('Precision', 'Recall', 'F1')))\n # return\n return output_lines\n\n def print_runout_history_metric(self, history_aspect_all_output_lines, file=sys.stdout):\n maxlen = max([len(line) for output_lines in history_aspect_all_output_lines for line in output_lines]) + 1\n\n for lines in zip(*history_aspect_all_output_lines):\n runout = ''\n for line in lines:\n runout += (line + \" \" * (maxlen - len(line)))\n print(runout, file=file)\n\n def output_bests(self, history_name, metrics=None, file=sys.stdout):\n metrics = metrics or self.metrics_to_choose_model\n for metric in metrics:\n best_epoch = self.get_best_epoch(history_name, metric)\n\n aspect_metric_all_output_lines = []\n for aspect in self.aspects:\n confusion_mat = self.history_confusion_matrices[history_name][aspect][best_epoch]\n evaluation_mat = self.history_evaluation_matrices[history_name][aspect][best_epoch]\n matrix_desc = \"name: %s, metric: %s, aspect: %s\" % (history_name, metric, aspect)\n output_lines = self.format_runout_history_metric_aspect(confusion_mat, evaluation_mat, matrix_desc)\n aspect_metric_all_output_lines.append(output_lines)\n self.print_runout_history_metric(aspect_metric_all_output_lines, file=file)\n\n def output_total_bests(self, metrics=None, file=sys.stdout):\n metrics = metrics or self.metrics_to_choose_model\n\n history_names = sorted(self.history_evaluation_matrices.keys())\n if len(history_names) == 1:\n return\n\n for metric in metrics:\n total_confusion_mats = {aspect: np.zeros((len(self.index2tag), len(self.index2tag)), dtype='int32')\n for aspect in self.aspects}\n for history_name in history_names:\n best_epoch = self.get_best_epoch(history_name, metric)\n for aspect in self.aspects:\n total_confusion_mats[aspect] += self.history_confusion_matrices[history_name][aspect][best_epoch]\n\n aspect_metric_all_output_lines = []\n for aspect in self.aspects:\n confusion_mat = total_confusion_mats[aspect]\n evaluation_mat = self.get_evaluation_matrix(confusion_mat)\n matrix_desc = \"name: total, metric: %s, aspect: %s\" % (metric, aspect)\n output_lines = self.format_runout_history_metric_aspect(confusion_mat, evaluation_mat, matrix_desc)\n aspect_metric_all_output_lines.append(output_lines)\n self.print_runout_history_metric(aspect_metric_all_output_lines, file=file)\n\n def get_best_epoch(self, history_name, metric, return_value=False):\n best_value = 0\n best_epoch = 0\n idx = self.metric_index[metric]\n\n i = 0\n for matrix in self.history_evaluation_matrices[history_name]['valid']:\n if matrix[idx] > best_value:\n best_epoch = i\n best_value = matrix[idx]\n i += 1\n\n if return_value:\n return best_epoch, best_value\n else:\n return best_epoch\n\n def plot_history_losses(self, filename):\n # variables\n filename = os.path.join(os.getcwd(), filename)\n history_num = len(self.history_losses)\n grid_row, grid_col = history_num, 1\n history_names = sorted(list(self.history_losses.keys()))\n\n # plots\n plt.figure(figsize=(10 * grid_col, 10 * grid_row)) # width, height\n for i, history_name in enumerate(history_names):\n aspect_values = self.history_losses[history_name]\n plt.subplot(grid_row, grid_col, i + 1)\n for aspect, value in aspect_values.items():\n value = np.array(value)\n if aspect in self.aspects:\n plt.plot(value, label=aspect)\n elif aspect in ['L1', 'L2']:\n if np.max(value) > 0.:\n plt.plot(value, label=aspect)\n plt.title(\"%s: loss\" % history_name)\n plt.legend(loc='best', fontsize='medium')\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Loss Value\")\n\n # save figure\n plt.savefig(check_duplicate_path(filename))\n plt.close()\n\n def plot_history_evaluations(self, filename, metrics=None, mark_best=False):\n # metrics\n if metrics is None:\n metrics = self.metrics\n elif type(metrics).__name__ in ['tuple', 'list']:\n metrics = metrics\n elif type(metrics).__name__ == 'str' and metrics in self.index2tag:\n metrics = [\"%s_%s\" % (metrics, a) for a in ['acc', 'recall', 'f1']]\n else:\n raise ValueError(\"\")\n\n # variables\n filename = os.path.join(os.getcwd(), filename)\n history_num = len(self.history_evaluation_matrices)\n grid_row, grid_col = history_num, len(self.aspects)\n history_names = sorted(self.history_evaluation_matrices.keys())\n\n # plots\n plt.figure(figsize=(10 * grid_col, 10 * grid_row)) # width, height\n for i, history_name in enumerate(history_names):\n aspect_values = self.history_evaluation_matrices[history_name]\n metric_best_epoch = {metric: self.get_best_epoch(history_name, metric) for metric in metrics}\n for j, aspect in enumerate(self.aspects):\n plt.subplot(grid_row, grid_col, grid_col * i + j + 1)\n\n for metric in metrics:\n metric_idx = self.metric_index[metric]\n metric_history = [matrix[metric_idx] for matrix in aspect_values[aspect]]\n plt.plot(np.array(metric_history), label=metric)\n if mark_best and metric in self.metrics_to_choose_model:\n best_epoch = metric_best_epoch[metric]\n best_value = metric_history[best_epoch]\n plt.annotate(s=\"%.2f\" % float(best_value), xy=(best_epoch, float(best_value)), xycoords='data',\n xytext=(10, 10), textcoords='offset points',\n arrowprops={\"arrowstyle\": \"->\",\n \"connectionstyle\": \"arc,angleA=0,armA=20,angleB=90,armB=15,rad=7\"})\n\n plt.title(\"%s: %s\" % (history_name, aspect))\n plt.legend(loc='best', fontsize='small')\n plt.xlabel(\"Epoch\")\n plt.ylabel(\"Score\")\n\n # save figure\n plt.savefig(check_duplicate_path(filename))\n plt.close()\n\n def plot_bests(self, filename, aspect='test'):\n # variables\n filename = os.path.join(os.getcwd(), filename)\n colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n history_names = sorted(list(self.history_evaluation_matrices.keys()))\n grid_col, grid_row = 1, len(self.metrics_to_choose_model)\n\n if len(history_names) > 1:\n bar_width, opacity = .45, 0.4\n gap = int(bar_width * len(self.metrics)) + 1\n fontsize = 7\n figsize_x = 20 * grid_col\n loc = 'best'\n else:\n bar_width, opacity = .45, 0.4\n gap = round(bar_width * len(self.metrics))\n fontsize = 15\n figsize_x = 5 * grid_col\n loc = 'lower right'\n\n # values\n metric2bests = {}\n for metric in self.metrics_to_choose_model:\n metric2bests[metric] = []\n total_conf_mat = np.zeros((len(self.index2tag), len(self.index2tag)), dtype='int32')\n\n for history_name in history_names:\n best_epoch = self.get_best_epoch(history_name, metric)\n total_conf_mat += self.history_confusion_matrices[history_name][aspect][best_epoch]\n eval_mat = self.history_evaluation_matrices[history_name][aspect][best_epoch]\n metric2bests[metric].append([eval_mat[self.metric_index[metric]] for metric in self.metrics])\n\n if len(history_names) > 1:\n total_eval_mat = self.get_evaluation_matrix(total_conf_mat)\n metric2bests[metric].append([total_eval_mat[self.metric_index[metric]] for metric in self.metrics])\n\n # plots\n if len(history_names) > 1:\n history_names.append('total')\n index = np.arange(start=0, stop=gap * len(history_names), step=gap)\n\n plt.figure(figsize=(figsize_x, 5 * grid_row))\n for i, metric in enumerate(self.metrics_to_choose_model):\n values = np.asarray(metric2bests[metric]) * 100\n plt.subplot(grid_row, grid_col, i + 1)\n for j, best_metric in enumerate(self.metrics):\n rects = plt.bar(index + bar_width * j, values[:, j], width=bar_width, alpha=opacity, label=best_metric,\n color=colors[j])\n for rect in rects:\n width, height = rect.get_x() + rect.get_width() / 2, rect.get_height()\n plt.text(width, height, '%.2f' % float(height), fontsize=fontsize, horizontalalignment='center')\n plt.xlabel('History Names')\n plt.ylabel('Scores')\n plt.title('Metric: %s, Aspect: %s' % (metric, aspect))\n plt.xticks(index + bar_width, history_names)\n plt.legend(loc=loc, fontsize='small')\n plt.tight_layout()\n\n # save figure\n plt.savefig(check_duplicate_path(filename))\n plt.close()\n\n","sub_path":"npdl/utils/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":20567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"175123988","text":"import os\nimport shutil\nimport threading\nimport tempfile\nimport socket\nimport os\nimport queue, logging\nimport time\n\ntitle = \"\"\nlogging.basicConfig(level=logging.DEBUG,\n format='(%(threadName)-9s) %(message)s', )\n\n\n# Obsolete\n# TODO : Verifiy if this work : run this alone\ndef reformat(raw_bytes):\n titleBin = ' '.join(map(lambda x: '{:08b}'.format(x), raw_bytes))\n\n STARTING = titleBin.find('1')\n titleBin = titleBin[STARTING:]\n SPACE_INDEX = titleBin.find(' ')\n tempStr = titleBin[:SPACE_INDEX]\n\n for x in range(8 - len(tempStr)):\n tempStr = '0' + tempStr\n titleBin = tempStr + titleBin[SPACE_INDEX:]\n print(titleBin)\n\n for word in titleBin.split(' '):\n title = title + chr(int(word, 2))\n print(title)\n return title\n\n\ndef createFolder(directory):\n if not os.path.exists(directory):\n os.mkdir(directory, mode=0o777)\n\n\nclass Server:\n queue = queue.Queue()\n pending = False\n\n def __init__(self, _server, bufferSize, threadId, clientId, pending):\n threading.Thread.__init__(self)\n self.threadId = threadId\n self.clientId = clientId\n self._server = _server\n self.bufferSize = bufferSize\n self.pending = pending\n createFolder(clientId)\n\n def run(self, queue):\n # TODO : Refomat the server3.py ! Done\n # TODO : Create a thread Class ! Done 2:25 PM\n print(\"Server is listening for incoming Data \")\n # while True:\n if not queue.full():\n conn, address = self._server.accept()\n titleData = 0\n print('client connected ... ' + str(address[0]) + \":\" + str(address[1]))\n # We create a temporary files\n # The title we don't need it so we delete it !\n createFolder(self.clientId)\n titleFile = tempfile.NamedTemporaryFile('w+b', dir=self.clientId, delete=True)\n dataFile = tempfile.NamedTemporaryFile('w+b', dir=self.clientId, delete=True)\n print(str(self.threadId))\n while self.pending:\n data = conn.recv(self.bufferSize)\n if not data:\n self.pending = False\n break\n if titleData >= 4096:\n dataFile.write(data)\n print('writing file .... temp_data ... ', len(data))\n if titleData < 4096:\n if titleData + len(data) > 4096:\n print('EXCEPTION')\n titleFile.write(data[:4096 - titleData])\n print('writing file .... temp_title ... ', 4096 - titleData)\n dataFile.write(data[4096 - titleData:])\n print('writing file .... temp_data ... ', len(data[4096 - titleData:]))\n titleData = titleData + len(data)\n else:\n titleFile.write(data)\n print('writing file .... temp_title ... ', len(data))\n titleData = titleData + len(data)\n\n print('split done')\n try:\n titleFile.seek(0)\n title = titleFile.read().decode(\"utf-8\").replace('\\x00', \"\")\n print(title)\n original_filename = dataFile.name\n print(original_filename)\n path = os.getcwd() + \"\\\\\" + self.clientId + \"\\\\\" + title\n # path = \"%s'\\\\'%s'\\\\'%s\" % os.getcwd() % self.clientId % title\n os.link(original_filename, path)\n\n except Exception as e:\n print(e)\n pass\n titleFile.close()\n dataFile.close()\n queue.put(path)\n logging.debug('Putting ' + str(path)\n + ' : ' + str(queue.qsize()) + ' items in queue')\n print('finished writing file')\n conn.close()\n print('client disconnected')\n return queue\n\n def sleep(self, n):\n time.sleep(n)\n\n\nif __name__ == \"__main__\":\n # TODO : I HAVE TO change the principal functionality\n # The use of this class :\n HOST = '192.168.61.109'\n PORT = 8888\n ADDR = (HOST, PORT)\n BUFSIZE = 4096\n OFFSET = BUFSIZE * 8 + BUFSIZE\n THREAD_NUM = 2\n queue = queue.Queue()\n server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n server.bind(ADDR)\n\n server.listen(5)\n print(\"serving through : %i\" % PORT)\n print('listening ...')\n\n thread_server = Server(server, BUFSIZE, 1, \"source\", True, None)\n thread_server1 = Server(server, BUFSIZE, 2, \"source\", True, None)\n # executor = ThreadPoolExecutor(max_workers=THREAD_NUM)\n # a = executor.submit(thread_server.run())\n # b = executor.submit(thread_server1.run())\n\n # for i in range(THREAD_NUM):\n # thread_server = Server(server, BUFSIZE, i, \"source\", True, None)\n # thread_server.run()\n","sub_path":"server3.py","file_name":"server3.py","file_ext":"py","file_size_in_byte":4908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"351370427","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pygame\nimport pygame.mixer\nfrom time import sleep\nfrom scipy import signal, misc\nfrom scipy.io import wavfile\n\n# auto cor\ndef auto_correlation(x):\n lengthA=len(x)\n h = x[::-1] #reverse\n y = np.zeros(lengthA+lengthA-1)\n for m in np.arange(lengthA):\n for n in np.arange(lengthA):\n y[m+n] = y[m+n] + x[m] * h[n]\n return y\n\nD = 30\nnSample = 200\ndist = 0.8\nx = np.zeros(nSample+D)\nx[0:nSample] = np.random.randn(1,nSample)\ndelay = np.zeros(nSample+D)\ndelay[D:] = x[:-D] * dist\n\ny = x+delay\nconv = auto_correlation(x)\n\nprint(x.shape)\nprint(delay.shape)\nprint(y.shape)\n\nplt.subplot(411), plt.plot(x), plt.title('Original SIgnal')\nplt.subplot(412), plt.plot(delay), plt.title('Delayed Signal')\nplt.subplot(413), plt.plot(y), plt.title('Received Signal')\nplt.subplot(414), plt.plot(conv), plt.title('Correlation Result')\nplt.tight_layout()\nplt.show()\n\n\nconv = auto_correlation(y)\nplt.plot(conv), plt.title('Correlation(recved)')\nplt.show()\n\n\nlength = len(conv)\nstart = int(length/2)\nend = length\nbuffer = conv\nbuffer[start] = 0\nplt.plot(buffer[start:end])\nplt.show()\n\n# D를 알아낼 수 있음\n# 시작점만 날리면\nidx = np.argmax(buffer[start:end])\nprint(idx)","sub_path":"MultimediaProgramming/Fourth/Problem.py","file_name":"Problem.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"13220749","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 08 09:26:27 2016\n\n@author: Dominic O'Kane\n\"\"\"\n\nimport numpy as np\nfrom math import exp\n\nfrom ...finutils.FinGlobalVariables import gSmall\nfrom ...market.curves.FinDiscountCurve import FinDiscountCurve\nfrom ...finutils.FinHelperFunctions import inputTime, inputFrequency\n\n##########################################################################\n\n\nclass FinNelsonSiegelCurve():\n ''' Implementation of Nelson-Siegel parametrisation of a rate curve. \n The default is a continuously compounded rate but you can override \n this by providing a corresponding compounding frequency. '''\n\n def __init__(self, curveDate, params, cmpdFreq=-1):\n ''' Creation of a Nelson-Siegel curve. Parameters are provided as a \n list or vector of 4 values for beta1, beta2, beta3 and tau. '''\n\n self._curveDate = curveDate\n\n if len(params) != 4:\n raise ValueError(\"NS requires a vector/list of 4 parameters.\")\n\n if params[3] <= 0:\n raise ValueError(\"Tau must be positive\")\n\n self._beta1 = params[0]\n self._beta2 = params[1]\n self._beta3 = params[2]\n self._tau = params[3]\n\n##########################################################################\n\n def zeroRate(self, dt, compoundingFreq=-1):\n ''' Calculation of zero rates with specified frequency. This \n function can return a vector of zero rates given a vector of \n times so must use Numpy functions. '''\n\n t = inputTime(dt, self)\n f = inputFrequency(compoundingFreq)\n t = t + gSmall # To avoid overflows when t=0.0\n\n theta = t / self._tau\n e = np.exp(-theta)\n zeroRate = self._beta1 + self._beta2 * (1.0 - e) / theta\n zeroRate += self._beta3 * ((1.0 - e) / theta - e)\n df = np.exp(-zeroRate * t)\n\n if f == 0: # Simple interest\n r = (1.0/df-1.0)/t\n if f == -1: # Continuous\n r = -np.log(df)/t\n else:\n r = (df**(-1.0/t)-1.0) * f\n\n return r\n\n##########################################################################\n\n def fwd(self, dt):\n ''' Calculation of forward rates. This function can return a vector\n of instantaneous forward rates given a vector of times. '''\n t = inputTime(dt, self)\n theta = t / self._tau\n e = np.exp(-theta)\n fwdRate = self.beta1 + self.beta2 * e + self.beta3 * theta * e\n return fwdRate\n\n##########################################################################\n\n def df(self, dt):\n ''' Discount factor for Nelson-Siegel curve parametrisation. '''\n t = inputTime(dt, self)\n r = self.zero(t)\n return exp(-r * t)\n\n#############################################################################\n\n\nclass FinNelsonSiegelSvenssonCurve():\n ''' Implementation of Nelson-Siegel-Svensson parametrisation of the\n zero rate curve '''\n\n def __init__(self, beta1, beta2, beta3, beta4, tau1, tau2):\n\n if tau1 <= 0:\n raise ValueError(\"Tau1 must be positive\")\n\n if tau2 <= 0:\n raise ValueError(\"Tau2 must be positive\")\n\n self._beta1 = beta1\n self._beta2 = beta2\n self._beta3 = beta3\n self._beta4 = beta4\n self._tau1 = tau1\n self._tau2 = tau2\n\n##########################################################################\n\n def zero(self, t):\n ''' Calculation of zero rates. This function can return a vector\n of zero rates given a vector of times. '''\n\n if np.any(t < 0.0):\n raise ValueError(\"All times must be positive\")\n\n t = t + gSmall # To avoid overflows when t=0.0\n theta1 = t / self._tau1\n theta2 = t / self._tau2\n expTerm1 = np.exp(-theta1)\n expTerm2 = np.exp(-theta2)\n zeroRate = self._beta1\n zeroRate += self._beta2 * (1.0 - expTerm1) / theta1\n zeroRate += self._beta3 * ((1.0 - expTerm1) / theta1 - expTerm1)\n zeroRate += self._beta4 * ((1.0 - expTerm2) / theta2 - expTerm2)\n return zeroRate\n\n##########################################################################\n\n def fwd(self, t):\n ''' Calculation of forward rates. This function uses Numpy so can return\n a vector of forward rates given a Numpy array vector of times. '''\n\n theta1 = t / self._tau1\n theta2 = t / self._tau2\n expTerm1 = np.exp(-theta1)\n expTerm2 = np.exp(-theta2)\n fwdRate = self._beta1\n fwdRate += self._beta2 * expTerm1\n fwdRate += self._beta3 * theta1 * expTerm1\n fwdRate += self._beta4 * theta2 * expTerm2\n return fwdRate\n\n##########################################################################\n\n def df(self, t):\n ''' Discount factor for Nelson-Siegel-Svensson curve \n parametrisation. '''\n r = self.zero(t)\n return exp(-r * t)\n\n#############################################################################\n","sub_path":"financepy/market/curves/FinNelsonSiegelCurve.py","file_name":"FinNelsonSiegelCurve.py","file_ext":"py","file_size_in_byte":5011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"201877851","text":"__author__ = 'Dominic Gomez'\n__email__ = 'DominicAnthonyGomez@gmail.com'\n\n\"\"\"Miscellaneous functions that make life easier.\"\"\"\n\nimport operator\n\nBLACK = (0,0,0)\n\ndef tupadd(lhs, rhs):\n \"\"\"Componentwise addition of tuples.\n\n lhs ((int,int)): The first tuple.\n rhs ((int,int)): The second tuple.\n\n \"\"\"\n return tuple(map(operator.add, lhs, rhs))\n\ndef is_in_bounds(elem_sz, pos, cont_sz):\n \"\"\"Determine if a rectangle is completely inside another rectangle.\n\n elem_sz ((int,int)): The (w,h) dimensions of the inner rectangle.\n pos ((int,int)): The (x,y) coords of the inner rectangle.\n cont_sz ((int,int)): The (w,h) dimensions of the outer rectangle (container).\n \"\"\"\n (elem_w,elem_h) = elem_sz\n (x,y) = pos\n (cont_w,cont_h) = cont_sz\n # Make sure it's not too high or too far left.\n if x < 0 or y < 0: return False\n # Make sure it's not too low or too far right.\n if x > (cont_w - elem_w) or y > (cont_h - elem_h): return False\n return True\n","sub_path":"src/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"382365221","text":"#\nimport ShareYourSystem as SYS\n# \n\n#\nclass CollectorClass(\n\t\t\t\t\tSYS.UserClass\n\t\t\t\t):\n\n\t#\n\tdef initAfterBasesWithCollector(self):\n\n\t\t#\n\t\tself+={'UsedTypeString':\"Structurer\"}\n\t\tself+={'UsedTypeString':\"Sorter\",\"PrefixString\":\"CollectingItemizers\"}\n\t\tself+={'UsedTypeString':\"Sorter\",\"PrefixString\":\"CollectedVariables\"}\n\t\t# \n\n\t\t#Update the TypeStringToKeyStringsListDict\n\t\tself.TypeStringToKeyStringsListDict[\"Collector\"]=[]\n\n\tdef callAfterBasesWithCollector(self,*_ArgsList,**_KwargsDict):\n\n\t\t#Check that the _KwargsDict is ok\n\t\tif 'TypeDictString' in _KwargsDict:\n\t\t\tif _KwargsDict['TypeDictString']==\"Collection\":\n\t\t\t\tif 'CollectedVariablesList' in _KwargsDict:\n\t\t\t\t\t\n\t\t\t\t\t#Call the Structurer to store this collection\n\t\t\t\t\tif type(_KwargsDict['CollectedVariablesList'])==list:\n\t\t\t\t\t\tself['OfCollector_Structurer'].__add__(_KwargsDict['CollectedVariablesList'])\n\n\t'''\n\tdef reprAfterBasesWithSorter(self,SelfString,PrintedDict):\n\n\t\t#Define the new PrintedDict with SkippedKeyStringsList\n\t\tNewPrintedDict=self['OfSorter_Itemizer']\n\n\t\t#Define the new SelfString\n\t\tSelfString=SYS.ConsolePrinter.getPrintedVariableString(NewPrintedDict)\n\n\t\t#Return\n\t\treturn (SelfString,NewPrintedDict)\n\t\t\n\t'''\n\t# \n\t\n\t#\n\t#\t\n\n\t#\n\t#\t\n\n# ","sub_path":"Modules/Init/Drafts/_ModulesAncien/05c_Collector/Collector.py","file_name":"Collector.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"212707501","text":"\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\nclass Solution:\n \n def detectCycle(self, head: ListNode) -> ListNode:\n temp = head\n a = []\n while(temp):\n if temp not in a:\n a.append(temp)\n else:\n return temp\n temp = temp.next\n return None\n \n def hasCycle(self, head: ListNode) -> bool:\n node_set = set()\n \n while head != None:\n if head in node_set:\n return True\n node_set.add(head)\n head = head.next\n \n return False\n \n \n def hasCycleTwoPointer(self, head:ListNode):\n if head == None or head.next == None:\n return False\n slow = head\n fast = head.next\n \n while slow != head:\n if fast == None or fast.next == None:\n return False\n slow = slow.next\n fast = fast.next.next\n return True\n \n \n def hasCycleFinal(self, head: ListNode) -> bool:\n if head == None or head.next == None:\n return False\n slow = fast = head\n while fast and fast.next:\n fast = fast.next.next\n slow = slow.next\n if slow == fast:\n print(slow.val)\n \n return True\n return False\n \n ","sub_path":"leetcode/linkedlist/hasCycle.py","file_name":"hasCycle.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"467051474","text":"\"\"\"\n문제 : 백준 알고리즘 10871번 -\n정수 N개로 이루어진 수열 A와 정수 X가 주어진다. 이때, A에서 X보다 작은 수를 모두 출력하는 프로그램을 작성하시오.\n작성자 : 김은정\n작성일 : 2018 . 9 20\n\"\"\"\n\na = input().split(\" \") # 수열 A를 이룰 정수 N개의 개수와 정수X 2개의 정수 입력받고, 공백에 따라 자르기\nX = int(a[1]) # 정수 X를 int형으로 변환\nb = input().split(\" \") # 정수 N개로 이루어진 수열 A입력받고 공백에 따라 자르기.\nc = \"\" # c를 정의\nfor i in b : #b의 수만큼 반복\n if (int(i) '9' or currStop[beginning] < '0')):\n beginning = beginning + 1\n end = beginning\n while (end < len(currStop) and (currStop[end] == '-' or currStop[end] == '.' or (currStop[end] <= '9' and currStop[end] >= '0'))):\n end = end + 1\n# print(currStop[beginning:-1*(len(currStop) - end)])\n return float(currStop[beginning:-1*(len(currStop) - end)])\n\ndef parseY(currStop) :\n beginning = 0\n end = 0\n while(currStop[beginning] != ','):\n beginning = beginning + 1\n while (beginning < len(currStop) and currStop[beginning] != '-' and (currStop[beginning] < '0' or currStop[beginning] > '9')):\n beginning = beginning + 1\n #found beginnning\n #print(beginning)\n #print(currStop[beginning:])\n end = beginning\n \n while (end < len(currStop) and (currStop[end] == '-' or currStop[end] == '.' or (currStop[end] <= '9' and currStop[end] >= '0'))):\n end = end + 1\n# print(currStop[beginning:-1*(len(currStop) - end)])\n return float(currStop[beginning:-1*(len(currStop) - end)])\n \n# EVERYTHING ABOVE, WILL BE GOOOONE!\n\n# import files for reading and writing\nimport pandas as pd\nfrom datetime import datetime, date\n\n# Do not generate warning for changing sheet\npd.options.mode.chained_assignment = None\n\n# Important excel file and set its sheet\nxlsx = pd.ExcelFile(\"busData.xlsx\")\nsheet1 = xlsx.parse(0) #actually use sheet 2\n\nbusRoutes = {}\n# iterate row by row through the excel sheet\nfor i in range(0, len(sheet1)):\n # get the ith row\n row = sheet1.ix[i]\n #print(row)\n if (not row[2] in busRoutes) :\n busRoutes[row[2]] = (Route(row[2], row[1], parseX(row[0]), parseY(row[0])))\n else :\n busRoutes[row[2]].addLoc(row[1], parseX(row[0]), parseY(row[0]))\n\n'''\nfor i in busRoutes :\n print(i)\n print(busRoutes[i])\n print (\"\\n\\n\")\n'''\n# EVERYTHING ABOVE WILL BE GONE, DONT NEED TO READ THE BUS ROUTES\n\nimport datetime\nfrom random import *\nimport math\nclass Bus:\n BusNumber = 0\n \n def __init__(self, routeName, delta):\n self.busId = Bus.BusNumber\n Bus.BusNumber += 1\n \n self.active = (datetime.datetime.now().hour > 5 and datetime.datetime.now().hour < 22)\n \n self.waitTime = delta.seconds\n if (self.waitTime == 0):\n self.waitTime = delta.microSeconds / 1000\n self.route = routeName\n self.prevDest = (busRoutes[routeName]).get(0)\n self.nextDest = (busRoutes[routeName]).get(1)\n self.currX = busRoutes[routeName].getX(self.prevDest)\n self.currY = busRoutes[routeName].getY(self.prevDest)\n self.nextX = busRoutes[routeName].getX(self.nextDest)\n self.nextY = busRoutes[routeName].getY(self.nextDest)\n self.currXDist = self.nextX- self.currX\n self.currYDist = self.nextY - self.currY\n self.waitSteps = 0\n self.stepsTowardsNextDest = 0\n \n self.sincePrevStop = 0\n \n def drive(self, time):\n if time.hour > 5 and time.hour < 22:\n self.active = True\n else:\n self.active = False\n if self.waitSteps > 0:\n self.waitSteps = self.waitSteps - 1\n self.sincePrevStop+=1\n return\n if self.active:\n self.sincePrevStop+=1\n # don't stop here ever\n if random() < .05:\n return\n\n self.stepsTowardsNextDest+=1\n #print(self.currXDist)\n #print(self.nextX - self.currX)\n #print(self.currYDist)\n #print(self.currY)\n self.currX = self.currX + self.currXDist/(180 / self.waitTime)\n self.currY = self.currY + self.currYDist/(180 / self.waitTime)\n #if it is at the next stop, wait a bit and then start moving to next stop\n if (math.fabs(self.currX - self.nextX) < 10 ** (-7) and math.fabs(self.currY - self.nextY) < 10 ** (-7)):\n self.waitSteps = uniform((120 / self.waitTime), (240/self.waitTime))\n if (self.nextDest == \"Lot 16 & 23\"):\n self.waitSteps = uniform((600 / self.waitTime),(1200 / self.waitTime))\n if (self.nextDest == \"Goheen Walk\"):\n self.waitSteps = 0\n \n\n \n self.prevDest = self.nextDest\n self.nextDest = busRoutes[self.route].getNextStop(self.prevDest)\n self.nextX = busRoutes[self.route].getX(self.nextDest)\n self.nextY = busRoutes[self.route].getY(self.nextDest)\n self.currXDist= self.nextX - self.currX\n self.currYDist = self.nextY - self.currY\n self.stepsTowardsNextDest = 0\n self.sincePrevStop = 0\n \n # ADD TIME HERE\n busRoutes[self.route].addLocTimes(self.prevDest, self.timeToNextStop())\n else:\n return\n \n def distanceToNext(self):\n return (self.currX - self.nextX) * (self.currX - self.nextX) + (self.currY - self.nextY) * (self.currY - self.nextY)\n \n \n def roundDistance(self, dist):\n return int(math.ceil((dist)/self.waitTime)) *self.waitTime\n \n def timeSincePrevStop(self):\n return self.sincePrevStop*self.waitTime\n \n \n def timeToNextStop(self):\n #print(int(self.waitSteps * self.waitTime))\n #print(self.waitTime*(180/self.waitTime - self.stepsTowardsNextDest))\n return self.roundDistance(int(self.waitSteps * self.waitTime) + int(self.waitTime*(180/self.waitTime - self.stepsTowardsNextDest)))\n \n def getX(self):\n if (self.active):\n return self.currX\n else:\n return 0\n def getY(self):\n if (self.active):\n return self.currY\n else:\n return 0\n \n def getLocation(self):\n return \"(\" + str(self.getX()) + \", \" + str(self.getY()) + \")\"\n \n def prevStop(self):\n if (self.active):\n return self.prevDest\n else:\n return \"NULL\"\n \n def nextStop(self):\n if (self.active):\n return self.nextDest\n else:\n return \"NULL\"\n \n def isActive(self):\n return self.active\n \n def timeSincePrevStop(self):\n return self.sincePrevStop*self.waitTime\n \n def printLocation(self):\n if(self.active):\n print(\"{\\\"bus\\\": \\\"\" + str(self.busId) + \"\\\", \\\"pos\\\":{\\\"lat\\\":\" +\n str(self.getX()) +\",\\\"lng\\\":\" + str(self.getY()) + \"}, \\\"rt\\\":\\\"\" + self.route +\n \"\\\", \\\"prevDest\\\":\\\"\" + self.prevStop() + \"\\\", \\\"distNext\\\":\" + str(self.distanceToNext()) +\n \", \\\"tPrev\\\":\" + str(self.timeSincePrevStop()) + \"}\", end = \"\")\n \n # Define a toString method to allow for easy visualization\n def __str__(self):\n s = self.route + \"\\n\"\n s = s + \"Pos: ( \" + str(self.currX) + \", \" + str(self.currY) + \")\\n\"\n s = s + \"Past Stop: \" + self.prevDest +\"\\n\"\n s = s + \"Next Stop: \" + self.nextDest\n return s\n\n\ndef getRoutes():\n return busRoutes\n\nbuses = []\n\n# REAL TIME DATA (FAKED)\n#start bus1 at Goheen Walk\n#start bus2 at Equad\nfrom datetime import timedelta\ndelta = timedelta(seconds = 10)\ncurrentTime = datetime.datetime.now()\n#print(currentTime)\n#currentTime = currentTime + delta\n#print(currentTime)\nb1 = Bus(\"Central\", delta)\nb2 = Bus(\"E-Quad\", delta)\nbuses.append(b1)\nbuses.append(b2)\n\nimport time\ndelta = timedelta(microseconds = 100)\n\ndef printOutBusInfo():\n print(\"[\", end = \"\")\n for i in (range(0, len(buses))):\n buses[i].printLocation()\n if(not i == len(buses)-1):\n print(\",\", end = \"\")\n print(\"]\")\ndef __main__():\n while (1) :\n time.sleep(delta.microseconds/1000)\n b1.drive(datetime.datetime.now())\n b2.drive(datetime.datetime.now())\n printOutBusInfo()\n \n\nif(__name__ == __main__):\n __main__()\n\n #currentTime = currentTime + delta","sub_path":"busRoutes/server/python/bus.py","file_name":"bus.py","file_ext":"py","file_size_in_byte":10766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"627112663","text":"import torch\n\n\nclass EncoderCellContent(torch.nn.Module):\n\n def __init__(self, in_channels, out_channels_cell_content, conv = False):\n \"\"\"conv: use convolutional layer, else linear layer \"\"\"\n super(EncoderCellContent, self).__init__()\n self.conv = conv\n if self.conv:\n self.conv1x1 = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels_cell_content, kernel_size=1)\n else:\n self.fc = torch.nn.Linear(in_channels, out_channels_cell_content)\n\n def forward(self, features_map):\n if self.conv:\n # reducing the number of channels\n features_map = self.conv1x1(features_map)\n features_map = features_map.permute(0, 2, 3, 1)\n else:\n # swap axes\n features_map = features_map.permute(0, 2, 3, 1)\n features_map = self.fc(features_map)\n\n # stacking the layers vertically\n dims = features_map.size()\n num_examples = dims[0]\n num_layers = dims[1]\n layer_dim1 = dims[2]\n layer_dim2 = dims[3]\n cell_features_map = torch.reshape(features_map, (num_examples, num_layers * layer_dim1, layer_dim2))\n\n return cell_features_map\n","sub_path":"BaseModel_pytorch/EncoderCellContent.py","file_name":"EncoderCellContent.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"466835815","text":"import discord\nimport asyncio\n\nfrom plugins.invoker import Invoker\nfrom utils.details import config\n\n\nclass MyClient(discord.Client):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.config = kwargs['config']\n # create the background task and run it in the background\n self.bg_task = self.loop.create_task(self.my_background_task())\n\n async def on_ready(self):\n print('Logged in as')\n print(self.user.name)\n print(self.user.id)\n print('------')\n for guild in self.guilds:\n print(guild.name)\n\n async def on_message(self, message):\n if message.author == self.user:\n return \n if str(message.channel) in self.config['channels']:\n if message.content.startswith(self.config['command_prefix']):\n reply = await invoker.invoke_command(message)\n await message.channel.send(reply)\n # print(reply)\n\n async def my_background_task(self):\n await self.wait_until_ready()\n counter = 0\n channel = self.get_channel(137356419333750784) # channel ID goes here\n while not self.is_closed():\n counter += 1\n await asyncio.sleep(60) # task runs every 60 seconds\n\ninvoker = Invoker()\ndsclient = MyClient(config=config['discord']) \n\n","sub_path":"clients/discord/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"653551126","text":"\"\"\" Test the functions and classes in the protocol context \"\"\"\n\nimport json\nfrom unittest import mock\n\nimport opentrons.protocol_api as papi\nfrom opentrons_shared_data import load_shared_data\nfrom opentrons_shared_data.pipette import name_for_model\nfrom opentrons.types import Mount, Point, Location, TransferTipPolicy\nfrom opentrons.hardware_control import API, NoTipAttachedError\nfrom opentrons.hardware_control.pipette import Pipette\nfrom opentrons.hardware_control.types import Axis\nfrom opentrons.config.pipette_config import config_models\nfrom opentrons.protocol_api import transfers as tf\nfrom opentrons.protocols.types import APIVersion\n\nimport pytest\n\n\ndef set_version_added(attr, mp, version):\n \"\"\" helper to mock versionadded for an attr\n\n attr is the attr\n mp is a monkeypatch fixture\n version is an APIVersion\n \"\"\"\n def get_wrapped(attr):\n if hasattr(attr, '__wrapped__'):\n return get_wrapped(attr.__wrapped__)\n return attr\n\n if hasattr(attr, 'fget'):\n # this is a property probably\n orig = get_wrapped(attr.fget)\n else:\n orig = get_wrapped(attr)\n mp.setattr(orig, '__opentrons_version_added', version)\n return attr\n\n\n@pytest.fixture\ndef get_labware_def(monkeypatch):\n def dummy_load(labware_name, namespace=None, version=None):\n # TODO: Ian 2019-05-30 use fixtures not real defs\n labware_def = json.loads(\n load_shared_data(f'labware/definitions/2/{labware_name}/1.json'))\n return labware_def\n monkeypatch.setattr(papi.labware, 'get_labware_definition', dummy_load)\n\n\ndef test_load_instrument(loop):\n ctx = papi.ProtocolContext(loop=loop)\n assert ctx.loaded_instruments == {}\n for model in config_models:\n loaded = ctx.load_instrument(model, Mount.LEFT, replace=True)\n assert ctx.loaded_instruments[Mount.LEFT.name.lower()] == loaded\n assert loaded.model == model\n instr_name = name_for_model(model)\n loaded = ctx.load_instrument(instr_name, Mount.RIGHT, replace=True)\n assert loaded.name == instr_name\n assert ctx.loaded_instruments[Mount.RIGHT.name.lower()] == loaded\n\n\nasync def test_motion(loop, hardware):\n ctx = papi.ProtocolContext(loop)\n ctx.connect(hardware)\n ctx.home()\n instr = ctx.load_instrument('p10_single', Mount.RIGHT)\n old_pos = await hardware.current_position(instr._mount)\n instr.home()\n assert instr.move_to(Location(Point(0, 0, 0), None)) is instr\n old_pos[Axis.X] = 0\n old_pos[Axis.Y] = 0\n old_pos[Axis.A] = 0\n old_pos[Axis.C] = 2\n assert await hardware.current_position(instr._mount) == old_pos\n\n\nasync def test_max_speeds(loop, monkeypatch, hardware):\n ctx = papi.ProtocolContext(loop)\n ctx.connect(hardware)\n ctx.home()\n mock_move = mock.Mock()\n monkeypatch.setattr(ctx._hw_manager.hardware, 'move_to', mock_move)\n instr = ctx.load_instrument('p10_single', Mount.RIGHT)\n instr.move_to(Location(Point(0, 0, 0), None))\n assert all(\n kwargs['max_speeds'] == {}\n for args, kwargs in mock_move.call_args_list)\n\n mock_move.reset_mock()\n ctx.max_speeds['x'] = 10\n instr.move_to(Location(Point(0, 0, 1), None))\n assert all(\n kwargs['max_speeds'] == {Axis.X: 10}\n for args, kwargs in mock_move.call_args_list)\n\n mock_move.reset_mock()\n ctx.max_speeds['x'] = None\n instr.move_to(Location(Point(1, 0, 1), None))\n assert all(\n kwargs['max_speeds'] == {}\n for args, kwargs in mock_move.call_args_list)\n\n\nasync def test_location_cache(loop, monkeypatch, get_labware_def, hardware):\n ctx = papi.ProtocolContext(loop)\n ctx.connect(hardware)\n # To avoid modifying the hardware fixture, we should change the\n # gantry calibration inside this test.\n ctx._hw_manager.hardware.update_config(\n gantry_calibration=[\n [1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0],\n [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]])\n right = ctx.load_instrument('p10_single', Mount.RIGHT)\n lw = ctx.load_labware('corning_96_wellplate_360ul_flat', 1)\n ctx.home()\n\n test_args = None\n\n def fake_plan_move(from_loc, to_loc, deck,\n well_z_margin=None,\n lw_z_margin=None,\n force_direct=False,\n minimum_z_height=None):\n nonlocal test_args\n test_args = (from_loc, to_loc, deck, well_z_margin, lw_z_margin)\n return [(Point(0, 1, 10), None),\n (Point(1, 2, 10), None),\n (Point(1, 2, 3), None)]\n\n monkeypatch.setattr(papi.geometry, 'plan_moves', fake_plan_move)\n # When we move without a cache, the from location should be the gantry\n # position\n right.move_to(lw.wells()[0].top())\n # The home position from hardware_control/simulator.py, taking into account\n # that the right pipette is a p10 single which is a different height than\n # the reference p300 single\n assert test_args[0].point == Point(418, 353, 205)\n assert test_args[0].labware is None\n\n # Once we have a location cache, that should be our from_loc\n right.move_to(lw.wells()[1].top())\n assert test_args[0].labware == lw.wells()[0]\n\n\nasync def test_move_uses_arc(loop, monkeypatch, get_labware_def, hardware):\n ctx = papi.ProtocolContext(loop)\n ctx.connect(hardware)\n ctx.home()\n right = ctx.load_instrument('p10_single', Mount.RIGHT)\n lw = ctx.load_labware('corning_96_wellplate_360ul_flat', 1)\n ctx.home()\n\n targets = []\n\n async def fake_move(self, mount, target_pos, **kwargs):\n nonlocal targets\n targets.append((mount, target_pos, kwargs))\n monkeypatch.setattr(API, 'move_to', fake_move)\n\n right.move_to(lw.wells()[0].top())\n assert len(targets) == 3\n assert targets[-1][0] == Mount.RIGHT\n assert targets[-1][1] == lw.wells()[0].top().point\n\n\ndef test_pipette_info(loop):\n ctx = papi.ProtocolContext(loop)\n right = ctx.load_instrument('p300_multi', Mount.RIGHT)\n left = ctx.load_instrument('p1000_single', Mount.LEFT)\n assert right.type == 'multi'\n name = ctx._hw_manager.hardware.attached_instruments[Mount.RIGHT]['name']\n model = ctx._hw_manager.hardware.attached_instruments[Mount.RIGHT]['model']\n assert right.name == name\n assert right.model == model\n assert left.type == 'single'\n name = ctx._hw_manager.hardware.attached_instruments[Mount.LEFT]['name']\n model = ctx._hw_manager.hardware.attached_instruments[Mount.LEFT]['model']\n assert left.name == name\n assert left.model == model\n\n\ndef test_pick_up_and_drop_tip(loop, get_labware_def):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 1)\n tip_length = tiprack.tip_length\n mount = Mount.LEFT\n\n instr = ctx.load_instrument('p300_single', mount, tip_racks=[tiprack])\n\n pipette: Pipette = ctx._hw_manager.hardware._attached_instruments[mount]\n model_offset = Point(*pipette.config.model_offset)\n assert pipette.critical_point() == model_offset\n target_location = tiprack['A1'].top()\n\n instr.pick_up_tip(target_location)\n assert not tiprack.wells()[0].has_tip\n overlap = instr.hw_pipette['tip_overlap'][tiprack.uri]\n new_offset = model_offset - Point(0, 0,\n tip_length-overlap)\n assert pipette.critical_point() == new_offset\n assert pipette.has_tip\n\n instr.drop_tip(target_location)\n assert not pipette.has_tip\n assert pipette.critical_point() == model_offset\n\n\ndef test_return_tip_old_version(loop, get_labware_def):\n # API version 2.2, a returned tip would be picked up by the\n # next pick up tip call\n ctx = papi.ProtocolContext(loop, api_version=APIVersion(2, 1))\n ctx.home()\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 1)\n mount = Mount.LEFT\n\n instr = ctx.load_instrument('p300_single', mount, tip_racks=[tiprack])\n\n with pytest.raises(TypeError):\n instr.return_tip()\n\n pipette: Pipette\\\n = ctx._hw_manager.hardware._attached_instruments[mount]\n\n target_location = tiprack['A1'].top()\n instr.pick_up_tip(target_location)\n assert not tiprack.wells()[0].has_tip\n assert pipette.has_tip\n\n instr.return_tip()\n assert not pipette.has_tip\n assert tiprack.wells()[0].has_tip\n\n instr.pick_up_tip()\n assert pipette.has_tip\n assert not tiprack.wells()[0].has_tip\n\n\ndef test_return_tip(loop, get_labware_def):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 1)\n mount = Mount.LEFT\n\n instr = ctx.load_instrument('p300_single', mount, tip_racks=[tiprack])\n\n with pytest.raises(TypeError):\n instr.return_tip()\n\n pipette: Pipette\\\n = ctx._hw_manager.hardware._attached_instruments[mount]\n\n target_location = tiprack['A1'].top()\n instr.pick_up_tip(target_location)\n assert not tiprack.wells()[0].has_tip\n assert pipette.has_tip\n\n instr.return_tip()\n assert not pipette.has_tip\n assert not tiprack.wells()[0].has_tip\n\n instr.pick_up_tip()\n assert pipette.has_tip\n assert not tiprack.wells()[1].has_tip\n\n\ndef test_use_filter_tips(loop, get_labware_def):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n\n tiprack = ctx.load_labware_by_name('opentrons_96_filtertiprack_200ul', 2)\n\n mount = Mount.LEFT\n\n instr = ctx.load_instrument('p300_single', mount, tip_racks=[tiprack])\n pipette: Pipette = ctx._hw_manager.hardware._attached_instruments[mount]\n\n assert pipette.available_volume == pipette.config.max_volume\n\n instr.pick_up_tip()\n assert pipette.available_volume < pipette.config.max_volume\n\n\n@pytest.mark.parametrize('pipette_model',\n ['p10_single', 'p20_single_gen2'])\n@pytest.mark.parametrize(\n 'tiprack_kind',\n ['opentrons_96_tiprack_10ul', 'eppendorf_96_tiprack_10ul_eptips'])\ndef test_pick_up_tip_no_location(loop, get_labware_def,\n pipette_model, tiprack_kind):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n\n tiprack1 = ctx.load_labware(tiprack_kind, 1)\n tip_length1 = tiprack1.tip_length\n\n tiprack2 = ctx.load_labware(tiprack_kind, 2)\n tip_length2 = tip_length1 + 1.0\n tiprack2.tip_length = tip_length2\n\n mount = Mount.LEFT\n\n instr = ctx.load_instrument(\n pipette_model, mount, tip_racks=[tiprack1, tiprack2])\n\n pipette: Pipette = ctx._hw_manager.hardware._attached_instruments[mount]\n model_offset = Point(*pipette.config.model_offset)\n assert pipette.critical_point() == model_offset\n\n instr.pick_up_tip()\n\n assert 'picking up tip' in ','.join([cmd.lower()\n for cmd in ctx.commands()])\n assert not tiprack1.wells()[0].has_tip\n overlap = instr.hw_pipette['tip_overlap'][tiprack1.uri]\n new_offset = model_offset - Point(0, 0,\n tip_length1-overlap)\n assert pipette.critical_point() == new_offset\n\n # TODO: remove argument and verify once trash container is added\n instr.drop_tip(tiprack1.wells()[0].top())\n assert not pipette.has_tip\n assert pipette.critical_point() == model_offset\n\n for well in tiprack1.wells():\n if well.has_tip:\n tiprack1.use_tips(well)\n\n assert tiprack1.next_tip() is None\n\n assert tiprack2.wells()[0].has_tip\n instr.pick_up_tip()\n assert not tiprack2.wells()[0].has_tip\n\n\ndef test_instrument_trash(loop, get_labware_def):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n\n mount = Mount.LEFT\n instr = ctx.load_instrument('p300_single', mount)\n\n assert instr.trash_container.name == 'opentrons_1_trash_1100ml_fixed'\n\n new_trash = ctx.load_labware('usascientific_12_reservoir_22ml', 2)\n instr.trash_container = new_trash\n\n assert instr.trash_container.name == 'usascientific_12_reservoir_22ml'\n\n\ndef test_aspirate(loop, get_labware_def, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n lw = ctx.load_labware('corning_96_wellplate_360ul_flat', 1)\n instr = ctx.load_instrument('p10_single', Mount.RIGHT)\n\n fake_hw_aspirate = mock.Mock()\n fake_move = mock.Mock()\n monkeypatch.setattr(API, 'aspirate', fake_hw_aspirate)\n monkeypatch.setattr(API, 'move_to', fake_move)\n\n instr.aspirate(2.0, lw.wells()[0].bottom())\n assert 'aspirating' in ','.join([cmd.lower() for cmd in ctx.commands()])\n\n fake_hw_aspirate.assert_called_once_with(Mount.RIGHT, 2.0, 1.0)\n assert fake_move.call_args_list[-1] ==\\\n mock.call(Mount.RIGHT, lw.wells()[0].bottom().point,\n critical_point=None, speed=400, max_speeds={})\n fake_move.reset_mock()\n fake_hw_aspirate.reset_mock()\n instr.well_bottom_clearance.aspirate = 1.0\n instr.aspirate(2.0, lw.wells()[0])\n dest_point, dest_lw = lw.wells()[0].bottom()\n dest_point = dest_point._replace(z=dest_point.z + 1.0)\n assert len(fake_move.call_args_list) == 1\n assert fake_move.call_args_list[0] ==\\\n mock.call(\n Mount.RIGHT, dest_point, critical_point=None, speed=400,\n max_speeds={})\n fake_move.reset_mock()\n ctx._hw_manager.hardware._obj_to_adapt\\\n ._attached_instruments[Mount.RIGHT]\\\n ._current_volume = 1\n\n instr.aspirate(2.0)\n fake_move.assert_not_called()\n\n instr.blow_out()\n fake_move.reset_mock()\n instr.aspirate(2.0)\n assert len(fake_move.call_args_list) == 2\n # reset plunger at the top of the well after blowout\n assert fake_move.call_args_list[0] ==\\\n mock.call(\n Mount.RIGHT, dest_lw.top().point, critical_point=None,\n speed=400, max_speeds={})\n assert fake_move.call_args_list[1] ==\\\n mock.call(\n Mount.RIGHT, dest_point, critical_point=None,\n speed=400, max_speeds={})\n\n\ndef test_dispense(loop, get_labware_def, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n lw = ctx.load_labware('corning_96_wellplate_360ul_flat', 1)\n instr = ctx.load_instrument('p10_single', Mount.RIGHT)\n\n disp_called_with = None\n\n async def fake_hw_dispense(self, mount, volume=None, rate=1.0):\n nonlocal disp_called_with\n disp_called_with = (mount, volume, rate)\n\n move_called_with = None\n\n def fake_move(self, mount, loc, **kwargs):\n nonlocal move_called_with\n move_called_with = (mount, loc, kwargs)\n\n monkeypatch.setattr(API, 'dispense', fake_hw_dispense)\n monkeypatch.setattr(API, 'move_to', fake_move)\n\n instr.dispense(2.0, lw.wells()[0].bottom())\n assert 'dispensing' in ','.join([cmd.lower() for cmd in ctx.commands()])\n assert disp_called_with == (Mount.RIGHT, 2.0, 1.0)\n assert move_called_with == (Mount.RIGHT, lw.wells()[0].bottom().point,\n {'critical_point': None,\n 'speed': 400,\n 'max_speeds': {}})\n\n instr.well_bottom_clearance.dispense = 2.0\n instr.dispense(2.0, lw.wells()[0])\n dest_point, dest_lw = lw.wells()[0].bottom()\n dest_point = dest_point._replace(z=dest_point.z + 2.0)\n assert move_called_with == (Mount.RIGHT, dest_point,\n {'critical_point': None,\n 'speed': 400,\n 'max_speeds': {}})\n\n move_called_with = None\n instr.dispense(2.0)\n assert move_called_with is None\n\n\ndef test_prevent_liquid_handling_without_tip(loop):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n\n tr = ctx.load_labware('opentrons_96_tiprack_300ul', '1')\n plate = ctx.load_labware('corning_384_wellplate_112ul_flat', '2')\n pipR = ctx.load_instrument('p300_single', Mount.RIGHT,\n tip_racks=[tr])\n\n with pytest.raises(NoTipAttachedError):\n pipR.aspirate(100, plate.wells()[0])\n\n pipR.pick_up_tip()\n\n pipR.aspirate(100, plate.wells()[0])\n pipR.drop_tip()\n\n with pytest.raises(NoTipAttachedError):\n pipR.dispense(100, plate.wells()[1])\n\n\ndef test_starting_tip_and_reset_tipracks(loop, get_labware_def, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n\n tr = ctx.load_labware('opentrons_96_tiprack_300ul', 1)\n tr_2 = ctx.load_labware('opentrons_96_tiprack_300ul', 2)\n pipL = ctx.load_instrument('p300_single', Mount.LEFT,\n tip_racks=[tr, tr_2])\n pipR = ctx.load_instrument('p300_single', Mount.RIGHT,\n tip_racks=[tr, tr_2])\n\n pipL.starting_tip = tr.wells()[2]\n pipL.pick_up_tip()\n assert pipL._last_tip_picked_up_from is tr.wells()[2]\n pipL.drop_tip()\n\n pipR.starting_tip = tr.wells()[2]\n pipR.pick_up_tip()\n assert pipR._last_tip_picked_up_from is tr.wells()[3]\n pipR.drop_tip()\n\n tr.wells()[95].has_tip = False\n pipL.starting_tip = tr.wells()[95]\n pipL.pick_up_tip()\n assert pipL._last_tip_picked_up_from is tr_2.wells()[0]\n\n pipL.reset_tipracks()\n assert tr.wells()[2].has_tip\n assert tr.wells()[3].has_tip\n\n\ndef test_mix(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n lw = ctx.load_labware(\n 'opentrons_24_tuberack_eppendorf_1.5ml_safelock_snapcap', 1)\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 3)\n instr = ctx.load_instrument('p300_single', Mount.RIGHT,\n tip_racks=[tiprack])\n\n instr.pick_up_tip()\n mix_steps = []\n aspirate_called_with = None\n dispense_called_with = None\n\n def fake_aspirate(vol=None, loc=None, rate=None):\n nonlocal aspirate_called_with\n nonlocal mix_steps\n aspirate_called_with = ('aspirate', vol, loc, rate)\n mix_steps.append(aspirate_called_with)\n\n def fake_dispense(vol=None, loc=None, rate=None):\n nonlocal dispense_called_with\n nonlocal mix_steps\n dispense_called_with = ('dispense', vol, loc, rate)\n mix_steps.append(dispense_called_with)\n\n monkeypatch.setattr(instr, 'aspirate', fake_aspirate)\n monkeypatch.setattr(instr, 'dispense', fake_dispense)\n\n repetitions = 2\n volume = 5\n location = lw.wells()[0]\n rate = 2\n instr.mix(repetitions, volume, location, rate)\n expected_mix_steps = [('aspirate', volume, location, 2),\n ('dispense', volume, None, 2),\n ('aspirate', volume, None, 2),\n ('dispense', volume, None, 2)]\n\n assert mix_steps == expected_mix_steps\n\n\ndef test_touch_tip_default_args(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop, api_version=APIVersion(2, 3))\n ctx.home()\n lw = ctx.load_labware(\n 'opentrons_24_tuberack_eppendorf_1.5ml_safelock_snapcap', 1)\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 3)\n instr = ctx.load_instrument('p300_single', Mount.RIGHT,\n tip_racks=[tiprack])\n\n instr.pick_up_tip()\n total_hw_moves = []\n\n async def fake_hw_move(self, mount, abs_position, speed=None,\n critical_point=None, max_speeds=None):\n nonlocal total_hw_moves\n total_hw_moves.append((abs_position, speed))\n\n instr.aspirate(10, lw.wells()[0])\n monkeypatch.setattr(API, 'move_to', fake_hw_move)\n instr.touch_tip()\n z_offset = Point(0, 0, 1) # default z offset of 1mm\n speed = 60 # default speed\n edges = [lw.wells()[0]._from_center_cartesian(1, 0, 1) - z_offset,\n lw.wells()[0]._from_center_cartesian(-1, 0, 1) - z_offset,\n lw.wells()[0]._from_center_cartesian(0, 1, 1) - z_offset,\n lw.wells()[0]._from_center_cartesian(0, -1, 1) - z_offset]\n for i in range(1, 5):\n assert total_hw_moves[i] == (edges[i - 1], speed)\n # Check that the old api version initial well move has the same z height\n # as the calculated edges.\n total_hw_moves.clear()\n instr.touch_tip(v_offset=1)\n assert total_hw_moves[0][0].z != total_hw_moves[1][0].z\n\n\ndef test_touch_tip_new_default_args(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n lw = ctx.load_labware(\n 'opentrons_24_tuberack_eppendorf_1.5ml_safelock_snapcap', 1)\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 3)\n instr = ctx.load_instrument('p300_single', Mount.RIGHT,\n tip_racks=[tiprack])\n\n instr.pick_up_tip()\n total_hw_moves = []\n\n async def fake_hw_move(self, mount, abs_position, speed=None,\n critical_point=None, max_speeds=None):\n nonlocal total_hw_moves\n total_hw_moves.append((abs_position, speed))\n\n instr.aspirate(10, lw.wells()[0])\n monkeypatch.setattr(API, 'move_to', fake_hw_move)\n instr.touch_tip()\n z_offset = Point(0, 0, 1) # default z offset of 1mm\n speed = 60 # default speed\n edges = [lw.wells()[0]._from_center_cartesian(1, 0, 1) - z_offset,\n lw.wells()[0]._from_center_cartesian(-1, 0, 1) - z_offset,\n lw.wells()[0]._from_center_cartesian(0, 0, 1) - z_offset,\n lw.wells()[0]._from_center_cartesian(0, 1, 1) - z_offset,\n lw.wells()[0]._from_center_cartesian(0, -1, 1) - z_offset]\n for i in range(1, 5):\n assert total_hw_moves[i] == (edges[i - 1], speed)\n\n # Check that the new api version initial well move has the same z height\n # as the calculated edges.\n total_hw_moves.clear()\n instr.touch_tip(v_offset=1)\n assert total_hw_moves[0][0].z == total_hw_moves[1][0].z\n\n\ndef test_touch_tip_disabled(loop, monkeypatch, get_labware_fixture):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n trough1 = get_labware_fixture('fixture_12_trough')\n trough_lw = ctx.load_labware_from_definition(trough1, '1')\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 3)\n instr = ctx.load_instrument('p300_single', Mount.RIGHT,\n tip_racks=[tiprack])\n instr.pick_up_tip()\n move_mock = mock.Mock()\n monkeypatch.setattr(API, 'move_to', move_mock)\n instr.touch_tip(trough_lw['A1'])\n move_mock.assert_not_called()\n\n\ndef test_blow_out(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n ctx.home()\n lw = ctx.load_labware(\n 'opentrons_24_tuberack_eppendorf_1.5ml_safelock_snapcap', 1)\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 3)\n instr = ctx.load_instrument('p300_single', Mount.RIGHT,\n tip_racks=[tiprack])\n\n move_location = None\n instr.pick_up_tip()\n instr.aspirate(10, lw.wells()[0])\n\n def fake_move(loc):\n nonlocal move_location\n move_location = loc\n\n monkeypatch.setattr(instr, 'move_to', fake_move)\n\n instr.blow_out()\n # pipette should not move, if no location is passed\n assert move_location is None\n\n instr.aspirate(10)\n instr.blow_out(lw.wells()[0])\n # pipette should blow out at the top of the well as default\n assert move_location == lw.wells()[0].top()\n\n instr.aspirate(10)\n instr.blow_out(lw.wells()[0].bottom())\n # pipette should blow out at the location defined\n assert move_location == lw.wells()[0].bottom()\n\n\ndef test_transfer_options(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n lw1 = ctx.load_labware('biorad_96_wellplate_200ul_pcr', 1)\n lw2 = ctx.load_labware('corning_96_wellplate_360ul_flat', 2)\n tiprack = ctx.load_labware('opentrons_96_tiprack_300ul', 3)\n instr = ctx.load_instrument('p300_single', Mount.RIGHT,\n tip_racks=[tiprack])\n\n ctx.home()\n transfer_options = None\n\n def fake_execute_transfer(xfer_plan):\n nonlocal transfer_options\n transfer_options = xfer_plan._options\n\n monkeypatch.setattr(instr, '_execute_transfer', fake_execute_transfer)\n instr.transfer(10, lw1.columns()[0], lw2.columns()[0],\n new_tip='always', mix_before=(2, 10),\n mix_after=(3, 20), blow_out=True)\n expected_xfer_options1 = tf.TransferOptions(\n transfer=tf.Transfer(\n new_tip=TransferTipPolicy.ALWAYS,\n air_gap=0,\n carryover=True,\n gradient_function=None,\n disposal_volume=0,\n mix_strategy=tf.MixStrategy.BOTH,\n drop_tip_strategy=tf.DropTipStrategy.TRASH,\n blow_out_strategy=tf.BlowOutStrategy.TRASH,\n touch_tip_strategy=tf.TouchTipStrategy.NEVER,\n ),\n pick_up_tip=tf.PickUpTipOpts(),\n mix=tf.Mix(\n mix_before=tf.MixOpts(repetitions=2,\n volume=10,\n rate=None),\n mix_after=tf.MixOpts(repetitions=3,\n volume=20,\n rate=None)\n ),\n blow_out=tf.BlowOutOpts(),\n touch_tip=tf.TouchTipOpts(),\n aspirate=tf.AspirateOpts(),\n dispense=tf.DispenseOpts()\n )\n assert transfer_options == expected_xfer_options1\n\n instr.pick_up_tip()\n instr.distribute(50, lw1.rows()[0][0], lw2.columns()[0],\n new_tip='never', touch_tip=True, trash=False,\n disposal_volume=10,\n mix_before=(2, 30),\n mix_after=(3, 20))\n instr.drop_tip()\n expected_xfer_options2 = tf.TransferOptions(\n transfer=tf.Transfer(\n new_tip=TransferTipPolicy.NEVER,\n air_gap=0,\n carryover=True,\n gradient_function=None,\n disposal_volume=10,\n mix_strategy=tf.MixStrategy.BEFORE,\n drop_tip_strategy=tf.DropTipStrategy.RETURN,\n blow_out_strategy=tf.BlowOutStrategy.NONE,\n touch_tip_strategy=tf.TouchTipStrategy.ALWAYS\n ),\n pick_up_tip=tf.PickUpTipOpts(),\n mix=tf.Mix(mix_before=tf.MixOpts(repetitions=2,\n volume=30,\n rate=None),\n mix_after=tf.MixOpts()),\n blow_out=tf.BlowOutOpts(),\n touch_tip=tf.TouchTipOpts(),\n aspirate=tf.AspirateOpts(),\n dispense=tf.DispenseOpts()\n )\n assert transfer_options == expected_xfer_options2\n with pytest.raises(ValueError, match='air_gap.*'):\n instr.transfer(300, lw1['A1'], lw2['A1'], air_gap=300)\n with pytest.raises(ValueError, match='air_gap.*'):\n instr.transfer(300, lw1['A1'], lw2['A1'], air_gap=10000)\n\n\ndef test_flow_rate(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n old_sfm = ctx._hw_manager.hardware\n\n def pass_on(mount, aspirate=None, dispense=None, blow_out=None):\n old_sfm(mount, aspirate=None, dispense=None, blow_out=None)\n\n set_flow_rate = mock.Mock(side_effect=pass_on)\n monkeypatch.setattr(ctx._hw_manager.hardware, 'set_flow_rate',\n set_flow_rate)\n instr = ctx.load_instrument('p300_single', Mount.RIGHT)\n\n ctx.home()\n instr.flow_rate.aspirate = 1\n assert set_flow_rate.called_once_with(Mount.RIGHT, aspirate=1)\n set_flow_rate.reset_mock()\n instr.flow_rate.dispense = 10\n assert set_flow_rate.called_once_with(Mount.RIGHT, dispense=10)\n set_flow_rate.reset_mock()\n instr.flow_rate.blow_out = 2\n assert set_flow_rate.called_once_with(Mount.RIGHT, blow_out=2)\n assert instr.flow_rate.aspirate == 1\n assert instr.flow_rate.dispense == 10\n assert instr.flow_rate.blow_out == 2\n\n\ndef test_pipette_speed(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n old_sfm = ctx._hw_manager.hardware\n\n def pass_on(mount, aspirate=None, dispense=None, blow_out=None):\n old_sfm(aspirate=None, dispense=None, blow_out=None)\n\n set_speed = mock.Mock(side_effect=pass_on)\n monkeypatch.setattr(ctx._hw_manager.hardware, 'set_pipette_speed',\n set_speed)\n instr = ctx.load_instrument('p300_single', Mount.RIGHT)\n\n ctx.home()\n instr.speed.aspirate = 1\n assert set_speed.called_once_with(Mount.RIGHT, dispense=1)\n instr.speed.dispense = 10\n instr.speed.blow_out = 2\n assert set_speed.called_with(Mount.RIGHT, dispense=10)\n assert set_speed.called_with(Mount.RIGHT, blow_out=2)\n assert instr.speed.aspirate == 1\n assert instr.speed.dispense == 10\n assert instr.speed.blow_out == 2\n\n\ndef test_loaded_labwares(loop):\n ctx = papi.ProtocolContext(loop)\n assert ctx.loaded_labwares == {12: ctx.fixed_trash}\n lw1 = ctx.load_labware('opentrons_96_tiprack_300ul', 3)\n lw2 = ctx.load_labware('opentrons_96_tiprack_300ul', 8)\n ctx.load_module('tempdeck', 4)\n mod2 = ctx.load_module('magdeck', 5)\n mod_lw = mod2.load_labware('biorad_96_wellplate_200ul_pcr')\n assert ctx.loaded_labwares[3] == lw1\n assert ctx.loaded_labwares[8] == lw2\n assert ctx.loaded_labwares[5] == mod_lw\n assert sorted(ctx.loaded_labwares.keys())\\\n == sorted([3, 5, 8, 12])\n\n\ndef test_loaded_modules(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n assert ctx.loaded_modules == {}\n mod1 = ctx.load_module('tempdeck', 4)\n mod1.load_labware('biorad_96_wellplate_200ul_pcr')\n mod2 = ctx.load_module('thermocycler')\n assert ctx.loaded_modules[4] == mod1\n assert ctx.loaded_modules[7] == mod2\n\n\ndef test_tip_length_for(loop, monkeypatch):\n ctx = papi.ProtocolContext(loop)\n instr = ctx.load_instrument('p20_single_gen2', 'left')\n tiprack = ctx.load_labware('geb_96_tiprack_10ul', '1')\n assert instr._tip_length_for(tiprack)\\\n == (tiprack._definition['parameters']['tipLength']\n - instr.hw_pipette['tip_overlap']\n ['opentrons/geb_96_tiprack_10ul/1'])\n\n\ndef test_bundled_labware(loop, get_labware_fixture):\n fake_fixed_trash = get_labware_fixture('fixture_trash')\n fake_fixed_trash['namespace'] = 'opentrons'\n fake_fixed_trash['parameters']['loadName'] = \\\n 'opentrons_1_trash_1100ml_fixed'\n fake_fixed_trash['version'] = 1\n fixture_96_plate = get_labware_fixture('fixture_96_plate')\n bundled_labware = {\n 'opentrons/opentrons_1_trash_1100ml_fixed/1': fake_fixed_trash,\n 'fixture/fixture_96_plate/1': fixture_96_plate\n }\n\n ctx = papi.ProtocolContext(loop, bundled_labware=bundled_labware)\n lw1 = ctx.load_labware('fixture_96_plate', 3, namespace='fixture')\n assert ctx.loaded_labwares[12] == ctx.fixed_trash\n assert ctx.loaded_labwares[12]._definition == fake_fixed_trash\n assert ctx.loaded_labwares[3] == lw1\n assert ctx.loaded_labwares[3]._definition == fixture_96_plate\n\n\ndef test_bundled_labware_missing(loop, get_labware_fixture):\n bundled_labware = {}\n with pytest.raises(\n RuntimeError,\n match='No labware found in bundle with load name opentrons_1_trash_'\n ):\n papi.ProtocolContext(loop, bundled_labware=bundled_labware)\n\n fake_fixed_trash = get_labware_fixture('fixture_trash')\n fake_fixed_trash['namespace'] = 'opentrons'\n fake_fixed_trash['parameters']['loadName'] = \\\n 'opentrons_1_trash_1100ml_fixed'\n fake_fixed_trash['version'] = 1\n bundled_labware = {\n 'opentrons/opentrons_1_trash_1100ml_fixed/1': fake_fixed_trash,\n }\n with pytest.raises(\n RuntimeError,\n match='No labware found in bundle with load name opentrons_1_trash_'\n ):\n papi.ProtocolContext(loop, bundled_labware={},\n extra_labware=bundled_labware)\n\n\ndef test_bundled_data(loop):\n bundled_data = {'foo': b'1,2,3'}\n ctx = papi.ProtocolContext(loop, bundled_data=bundled_data)\n assert ctx.bundled_data == bundled_data\n\n\ndef test_extra_labware(loop, get_labware_fixture):\n fixture_96_plate = get_labware_fixture('fixture_96_plate')\n bundled_labware = {\n 'fixture/fixture_96_plate/1': fixture_96_plate\n }\n ctx = papi.ProtocolContext(loop, extra_labware=bundled_labware)\n ls1 = ctx.load_labware('fixture_96_plate', 3, namespace='fixture')\n assert ctx.loaded_labwares[3] == ls1\n assert ctx.loaded_labwares[3]._definition == fixture_96_plate\n\n\ndef test_api_version_checking():\n minor_over = (papi.MAX_SUPPORTED_VERSION.major,\n papi.MAX_SUPPORTED_VERSION.minor + 1)\n with pytest.raises(RuntimeError):\n papi.ProtocolContext(api_version=minor_over)\n\n major_over = (papi.MAX_SUPPORTED_VERSION.major + 1,\n papi.MAX_SUPPORTED_VERSION.minor)\n with pytest.raises(RuntimeError):\n papi.ProtocolContext(api_version=major_over)\n\n\ndef test_api_per_call_checking(monkeypatch):\n ctx = papi.ProtocolContext(api_version=APIVersion(1, 9))\n assert ctx.deck # 1.9 < 2.0, but api version 1 is excepted from checking\n monkeypatch.setattr(\n papi.protocol_context, 'MAX_SUPPORTED_VERSION',\n APIVersion(2, 1))\n ctx = papi.ProtocolContext(api_version=APIVersion(2, 1))\n # versions > 2.0 are ok\n assert ctx.deck\n # pretend disconnect() was only added in 2.1\n set_version_added(\n papi.ProtocolContext.disconnect, monkeypatch, APIVersion(2, 1))\n ctx = papi.ProtocolContext(api_version=APIVersion(2, 0))\n with pytest.raises(papi.util.APIVersionError):\n ctx.disconnect()\n","sub_path":"api/tests/opentrons/protocol_api/test_context.py","file_name":"test_context.py","file_ext":"py","file_size_in_byte":33119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"260324774","text":"\"\"\"\n * Copyright 2020, Departamento de sistemas y Computación\n * Universidad de Los Andes\n *\n *\n * Desarrolado para el curso ISIS1225 - Estructuras de Datos y Algoritmos\n *\n *\n * This program is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (at your option) any later version.\n *\n * This program is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with this program. If not, see end_month and start_year=%s]@ubitrack/stable\" % version,\n \"ubitrack_component_core/[>=%s]@ubitrack/stable\" % version,\n \"ubitrack_dataflow/[>=%s]@ubitrack/stable\" % version,\n \"ubitrack_facade/[>=%s]@ubitrack/stable\" % version,\n \"ubitrack_virtualenv_generator/[>=%s]@ubitrack/stable\" % version,\n )\n\n def config_options(self):\n if not self.options.with_vision:\n self.options.remove(\"with_default_camera\")\n self.options.remove(\"with_default_flycapture\")\n self.options.remove(\"with_default_directshow\")\n\n if self.settings.os != \"Windows\" or not self.options.with_vision:\n self.options.remove(\"with_camera_kinect2\")\n\n\n def requirements(self):\n\n if self.options.with_vision:\n self.requires(\"ubitrack_vision/[>=%s]@ubitrack/stable\" % self.version) \n self.requires(\"ubitrack_component_vision/[>=%s]@ubitrack/stable\" % self.version)\n\n if self.options.with_vision_aruco:\n self.requires(\"ubitrack_component_vision_aruco/[>=%s]@ubitrack/stable\" % self.version)\n\n if self.options.with_default_camera:\n if self.settings.os == \"Macos\":\n self.requires(\"ubitrack_device_camera_avfoundation/[>=%s]@ubitrack/stable\" % self.version)\n elif self.settings.os == \"Windows\":\n self.requires(\"ubitrack_device_camera_msmf/[>=%s]@ubitrack/stable\" % self.version)\n elif self.settings.os == \"Linux\":\n self.requires(\"ubitrack_device_camera_v4l/[>=%s]@ubitrack/stable\" % self.version)\n else:\n self.output.warn(\"No default camera found for OS: %s\" % self.settings.os)\n if self.settings.os == \"Windows\" and self.options.with_camera_directshow:\n self.requires(\"ubitrack_device_camera_directshow/[>=%s]@ubitrack/stable\" % self.version)\n if self.settings.os == \"Windows\" and self.options.with_camera_kinect2:\n self.requires(\"ubitrack_device_camera_kinect2/[>=%s]@ubitrack/stable\" % self.version)\n if self.options.with_camera_flycapture:\n self.requires(\"ubitrack_device_camera_flycapture/[>=%s]@ubitrack/stable\" % self.version)\n\n if self.options.with_visualization:\n self.requires(\"ubitrack_visualization/[>=%s]@ubitrack/stable\" % self.version)\n self.requires(\"ubitrack_component_visualization/[>=%s]@ubitrack/stable\" % self.version)\n\n if self.options.with_tracker_art:\n self.requires(\"ubitrack_device_tracker_art/[>=%s]@ubitrack/stable\" % self.version)\n\n if self.options.with_network:\n self.requires(\"ubitrack_device_comm_zmq/[>=%s]@ubitrack/stable\" % self.version)\n\n if self.options.with_haptic_calibration:\n self.requires(\"ubitrack_hapticcalibration/[>=%s]@ubitrack/stable\" % self.version)\n self.requires(\"ubitrack_component_hapticcalibration/[>=%s]@ubitrack/stable\" % self.version)\n\n\n","sub_path":"conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":4575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"467417053","text":"import _thread as thread\r\nimport base64\r\nimport json\r\nimport time\r\nimport wave\r\nimport websocket\r\n\r\nfrom utils.help import api_data,events_request\r\n\r\n\r\nclass WSRequest(object):\r\n def __init__(self, url, header=None):\r\n super(WSRequest, self).__init__()\r\n self.connect_start_time = 0 # 开始连接websocket\r\n self.receive_start_time = 0 # 发送完音频数据，开始接收数据\r\n\r\n self.speech_path = ''\r\n self.result_seg = {}\r\n self.exit_test = False\r\n\r\n # log txt Request.txt记录请求响应结果，asserterror.txt记录asr语音识别不一致结果\r\n self.file_name=time.strftime(\"%Y%m%d%H%M%S\")+'_Request.txt'\r\n self.error_file_name=time.strftime(\"%Y%m%d%H%M%S\")+'_asserterror.txt'\r\n\r\n \r\n self.ws = websocket.WebSocketApp(url,\r\n header=header,\r\n on_error=self.on_error,\r\n on_close=self.on_close,\r\n on_ping=self.on_ping,\r\n on_pong=self.on_pong)\r\n\r\n def on_ping(self,ws):\r\n print('===on_ping===')\r\n\r\n def on_pong(self,ws):\r\n print('===on_pong===')\r\n \r\n\r\n def on_error(self,ws, error):\r\n total_time = int((time.time() - self.connect_start_time) * 1000)\r\n events_request(\"failed\",\"websocket\", \"ERROR\",\"ERROR\", total_time,e=error)\r\n print(\"####### on_error #######\")\r\n print(error)\r\n # 发生错误时关闭websocket连接\r\n self.ws.close()\r\n\r\n def on_close(self,ws,status_code,msg):\r\n total_time = int((time.time() - self.connect_start_time) * 1000)\r\n events_request(\"success\",\"websocket\", \"CLOSE\",\"CLOSE\", total_time,e=msg)\r\n print('\\n---------on_close---------\\n', time.time(), '\\nstatus code:{}\\n msg:{}'.format(status_code,msg))\r\n\r\n\r\n def read_wav(self, speech_path, mode=1):\r\n self.speech_path = speech_path\r\n if mode == 2:\r\n # huiyan yunzhisheng\r\n with open(speech_path, mode=\"rb\") as f:\r\n self.pcm_bytes = f.read(-1)\r\n return\r\n with wave.open(speech_path, mode=\"rb\") as f:\r\n self.pcm_bytes = f.readframes(-1)\r\n\r\n\r\n def start(self, on_open=None, on_message=None):\r\n #websocket.enableTrace(True) \r\n self.connect_start_time = time.time()\r\n\r\n http_proxy_host = None\r\n http_proxy_port = None\r\n if api_data()['is_use_fiddler']:\r\n http_proxy_host='localhost'\r\n http_proxy_port=8888\r\n\r\n print('\\n---------start---------\\n', self.connect_start_time, '\\n')\r\n\r\n self.result_seg = {}\r\n self.ws.on_open=on_open\r\n self.ws.on_message=on_message\r\n self.ws.run_forever(http_proxy_host=http_proxy_host,http_proxy_port=http_proxy_port)\r\n total_time = int((time.time()-self.connect_start_time)*1000)\r\n\r\n print('\\n---------finish---------\\nwebsocket 总耗时：\\n', (time.time()-self.connect_start_time)*1000, 'ms\\n')\r\n\r\n text_seg = [self.result_seg[index]['str'] for index in self.result_seg]\r\n text = ''.join(text_seg)\r\n print('---------------\\nresult:\\n', text)\r\n\r\n return total_time\r\n","sub_path":"utils/ws_request.py","file_name":"ws_request.py","file_ext":"py","file_size_in_byte":3221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"430588268","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Sep 12 08:07:39 2018\r\n\r\n@author: 刘磊\r\n\"\"\"\r\nimport os \r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom bokeh.plotting import figure,show,output_file\r\nfrom bokeh.models import ColumnDataSource\r\n\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\n'''\r\n1.加载数据\r\n'''\r\n\r\nos.chdir(r'G:\\python-study\\python学习资料\\python数据分析师\\CLASSDATA_ch06数据分析项目实战\\CLASSDATA_ch06数据分析项目实战\\练习03_城市餐饮店铺选址分析')\r\ndata=pd.read_excel('上海餐饮数据.xlsx',sheet_name=0)\r\ndata_length=len(data)\r\ndata_columns=data.columns.tolist()\r\nprint('数据量为%i条'%data_length)\r\nprint('字段有：\\n',data_columns)\r\nprint('类别有：',data['类别'].unique())\r\n#print(data.head(2))\r\n\r\n\r\n'''\r\n2.口味、人均消费、性价比指标计算\r\n'''\r\n\r\ndata1 = data[['类别','口味', '环境', '服务', '人均消费']]\r\n\r\n# 筛选数据，清除空值、为0的数据\r\ndata1.dropna(inplace=True)\r\ndata1=data1[(data1['口味']>0) & (data1['人均消费']>0)]\r\n\r\ndata1['性价比']=data1[['口味', '环境', '服务']].sum(axis=1)/data1['人均消费']\r\n\r\n#查看异常值\r\ndef wrong_look():\r\n fig,axes=plt.subplots(1,3,figsize=(10,4))\r\n data1.boxplot(column=['口味'],ax=axes[0])\r\n data1.boxplot(column=['人均消费'],ax=axes[1])\r\n data1.boxplot(column=['性价比'],ax=axes[2])\r\n\r\n\r\n#异常值去除\r\ndef wrong_off(df,col):\r\n q1=df[col].quantile(q=0.25)\r\n q3=df[col].quantile(q=0.75)\r\n iqr=q3-q1\r\n t1=q1-3*iqr\r\n t3=q1+3*iqr\r\n return df[(df[col]>t1)&(df[col] 0:\n self._index -= 1\n elif self.loop:\n self._index = len(self._track_list)\n\n item = self._track_list[self._index]\n self.set_current_item(*item)\n \n return self.get_current_item()\n \n def next_item(self, user_request=False):\n if self.smart:\n self._smart_queue.next_item(user_request=user_request)\n item = self.get_current_item()\n self.set_current_item(*item)\n else:\n if self.random:\n self._index = random.randint(0, len(self._track_list))\n elif self._index < len(self._track_list)-1:\n self._index += 1\n elif self.loop:\n self._index = 0\n else:\n self._index = -1\n\n item = (None, None, None)\n if self._track_list and self._index > -1:\n item = self._track_list[self._index]\n if item != self.get_current_item():\n self.set_current_item(*item)\n else:\n self.set_current_item(*item)\n\n return item\n \n def tracks(self):\n if self.smart:\n tracks = []\n else:\n tracks = self._track_list\n return tracks\n \n def add_track(self, uri, play_now):\n media = None\n if uri.startswith('file://'):\n path = uri[7:]\n media = self.db.get_media_with_path(path)\n if media:\n self._track_list.append((media.id, uri, media.title))\n self.emit('track-added', media, play_now)\n \n def del_track(self, index):\n try:\n item = self._track_list[index]\n except IndexError:\n self.warning(\"No track found at index %r\", index)\n else:\n self.emit('track-removed', index)\n del self._track_list[index]\n\n def enqueue_album(self, album_id, play_now):\n tracks = self.db.get_tracks_for_album(album_id)\n for track in tracks:\n uri = \"file://%s\" % track.path\n self._track_list.append((track.id, uri, track.title))\n self.emit('track-added', track, play_now)\n if play_now:\n play_now = False\n \ngobject.type_register(Playlist)\n","sub_path":"shuffler/core/playlist.py","file_name":"playlist.py","file_ext":"py","file_size_in_byte":6418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"609283788","text":"\"\"\"\nCodec definition for mp3 files\n\"\"\"\nfrom ..constants import CodecFormat\nfrom .base import Codec\n\nENCODER_ARGUMENT_DEFAULTS = {\n 'afconvert': {\n 'defaults': {\n 'bitrate': 320,\n }\n },\n 'lame': {\n 'choices': {\n 'bitrate': [64, 128, 192, 256, 320],\n },\n 'defaults': {\n 'bitrate': 320,\n }\n }\n}\n\n\nclass Mp3(Codec):\n \"\"\"\n MPEG-2 mp3 codec\n \"\"\"\n codec_format = CodecFormat.MP3\n description = 'MPEG-2 Audio Layer III'\n default_suffix = CodecFormat.MP3.value\n suffixes = (\n CodecFormat.MP3.value,\n )\n mimetypes = (\n 'audio/mpeg',\n 'audio/MPA',\n 'audio/mpa-robust',\n )\n","sub_path":"oodi/codecs/formats/mp3.py","file_name":"mp3.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"157186903","text":"\"\"\"empty message\n\nRevision ID: 57469fc189cb\nRevises: 2cc88a062410\nCreate Date: 2018-08-13 00:47:51.955870\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '57469fc189cb'\ndown_revision = '2cc88a062410'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('restaurant_chain',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('created_at', sa.DateTime(timezone=True), nullable=True),\n sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),\n sa.Column('restaurant_chain_name', sa.String(), nullable=True),\n sa.Column('restaurant_chain_category', sa.Integer(), nullable=True),\n sa.Column('restaurant_chain_desc', sa.Text(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_index(op.f('ix_restaurant_chain_id'), 'restaurant_chain', ['id'], unique=False)\n op.create_table('hotel_collection',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('created_at', sa.DateTime(timezone=True), nullable=True),\n sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),\n sa.Column('hotel_id', sa.Integer(), nullable=True),\n sa.Column('collection_name', sa.String(), nullable=True),\n sa.Column('featured', sa.Boolean(), nullable=True),\n sa.Column('desc', sa.Text(), nullable=True),\n sa.Column('image', sa.String(), nullable=True),\n sa.ForeignKeyConstraint(['hotel_id'], ['hotel.id'], ),\n sa.PrimaryKeyConstraint('id'),\n sa.UniqueConstraint('hotel_id')\n )\n op.create_index(op.f('ix_hotel_collection_id'), 'hotel_collection', ['id'], unique=False)\n op.create_table('collection_product',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('created_at', sa.DateTime(timezone=True), nullable=True),\n sa.Column('updated_at', sa.DateTime(timezone=True), nullable=True),\n sa.Column('hotel_collection_id', sa.Integer(), nullable=False),\n sa.Column('product_url', sa.String(), nullable=True),\n sa.Column('product_name', sa.String(), nullable=True),\n sa.Column('featured_product', sa.Boolean(), nullable=True),\n sa.Column('product_desc', sa.Text(), nullable=True),\n sa.Column('product_image', sa.String(), nullable=True),\n sa.ForeignKeyConstraint(['hotel_collection_id'], ['hotel_collection.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_index(op.f('ix_collection_product_id'), 'collection_product', ['id'], unique=False)\n op.add_column('restaurant', sa.Column('break_interval', sa.Integer(), nullable=True))\n op.add_column('restaurant', sa.Column('break_time', sa.TIME(), nullable=True))\n op.add_column('restaurant', sa.Column('closing_time', sa.TIME(), nullable=True))\n op.add_column('restaurant', sa.Column('mode_of_payment', sa.Integer(), nullable=True))\n op.add_column('restaurant', sa.Column('off_day_in_week', sa.String(), nullable=True))\n op.add_column('restaurant', sa.Column('opening_time', sa.TIME(), nullable=True))\n op.add_column('restaurant', sa.Column('restaurant_chain_id', sa.Integer(), nullable=True))\n op.add_column('restaurant', sa.Column('state', sa.String(), nullable=True))\n op.create_foreign_key(None, 'restaurant', 'restaurant_chain', ['restaurant_chain_id'], ['id'])\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(None, 'restaurant', type_='foreignkey')\n op.drop_column('restaurant', 'state')\n op.drop_column('restaurant', 'restaurant_chain_id')\n op.drop_column('restaurant', 'opening_time')\n op.drop_column('restaurant', 'off_day_in_week')\n op.drop_column('restaurant', 'mode_of_payment')\n op.drop_column('restaurant', 'closing_time')\n op.drop_column('restaurant', 'break_time')\n op.drop_column('restaurant', 'break_interval')\n op.drop_index(op.f('ix_collection_product_id'), table_name='collection_product')\n op.drop_table('collection_product')\n op.drop_index(op.f('ix_hotel_collection_id'), table_name='hotel_collection')\n op.drop_table('hotel_collection')\n op.drop_index(op.f('ix_restaurant_chain_id'), table_name='restaurant_chain')\n op.drop_table('restaurant_chain')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/57469fc189cb_.py","file_name":"57469fc189cb_.py","file_ext":"py","file_size_in_byte":4295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"496556043","text":"# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Runs MNIST using Distribution Strategies.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport json\nimport logging\nimport numpy as np\nimport os\n\nfrom tensorboard.plugins.core import core_plugin\nimport tensorboard.program as tb_program\n\nimport tensorflow as tf\n\n\ndef get_args():\n \"\"\"Argument parser.\n\n Returns:\n Dictionary of arguments.\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--working-dir',\n type=str,\n required=True,\n help='GCS location to write checkpoints and export models')\n parser.add_argument(\n '--num-epochs',\n type=float,\n default=5,\n help='number of times to go through the data, default=5')\n parser.add_argument(\n '--batch-size',\n default=128,\n type=int,\n help='number of records to read during each training step, default=128')\n parser.add_argument(\n '--learning-rate',\n default=.01,\n type=float,\n help='learning rate for gradient descent, default=.001')\n parser.add_argument(\n '--verbosity',\n choices=['DEBUG', 'ERROR', 'FATAL', 'INFO', 'WARN'],\n default='INFO')\n args, _ = parser.parse_known_args()\n return args\n\n\ndef get_distribution_strategy():\n cluster_spec = os.environ.get(\"CLUSTER_SPEC\", None)\n if cluster_spec:\n cluster_spec = json.loads(cluster_spec)\n job_index = int(os.environ[\"TASK_INDEX\"])\n job_type = os.environ[\"JOB_NAME\"]\n # Build cluster spec\n os.environ['TF_CONFIG'] = json.dumps(\n {'cluster': cluster_spec,\n 'task': {'type': job_type, 'index': job_index}})\n\n print('Distribution enabled: ', os.environ['TF_CONFIG'])\n else:\n print('Distribution is not enabled')\n\n\ndef create_model(model_dir, config, learning_rate):\n \"\"\"Creates a Keras Sequential model with layers.\n\n Args:\n model_dir: (str) file path where training files will be written.\n config: (tf.estimator.RunConfig) Configuration options to save model.\n learning_rate: (int) Learning rate.\n\n Returns:\n A keras.Model\n \"\"\"\n l = tf.keras.layers\n model = tf.keras.Sequential(\n [\n l.Reshape(input_shape=(28 * 28,), target_shape=(28, 28, 1)),\n\n l.Conv2D(filters=6, kernel_size=3, padding='same',\n use_bias=False),\n # no bias necessary before batch norm\n l.BatchNormalization(scale=False, center=True),\n # no batch norm scaling necessary before \"relu\"\n l.Activation('relu'), # activation after batch norm\n\n l.Conv2D(filters=12, kernel_size=6, padding='same',\n use_bias=False,\n strides=2),\n l.BatchNormalization(scale=False, center=True),\n l.Activation('relu'),\n\n l.Conv2D(filters=24, kernel_size=6, padding='same',\n use_bias=False,\n strides=2),\n l.BatchNormalization(scale=False, center=True),\n l.Activation('relu'),\n\n l.Flatten(),\n l.Dense(200, use_bias=False),\n l.BatchNormalization(scale=False, center=True),\n l.Activation('relu'),\n l.Dropout(0.5), # Dropout on dense layer only\n\n l.Dense(10, activation='softmax')\n ])\n # Compile model with learning parameters.\n optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n model.compile(\n optimizer=optimizer,\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n tf.keras.backend.set_learning_phase(True)\n model.summary()\n estimator = tf.keras.estimator.model_to_estimator(\n keras_model=model, model_dir=model_dir, config=config)\n return estimator\n\n\ndef input_fn(features, labels, batch_size, mode):\n \"\"\"Input function.\n\n Args:\n features: (numpy.array) Training or eval data.\n labels: (numpy.array) Labels for training or eval data.\n batch_size: (int)\n mode: tf.estimator.ModeKeys mode\n\n Returns:\n A tf.estimator.\n \"\"\"\n # Default settings for training.\n if labels is None:\n inputs = features\n else:\n # Change numpy array shape.\n inputs = (features, labels)\n # Convert the inputs to a Dataset.\n dataset = tf.data.Dataset.from_tensor_slices(inputs)\n if mode == tf.estimator.ModeKeys.TRAIN:\n dataset = dataset.shuffle(1000).repeat().batch(batch_size)\n dataset = dataset.prefetch(100)\n if mode in (tf.estimator.ModeKeys.EVAL, tf.estimator.ModeKeys.PREDICT):\n dataset = dataset.batch(batch_size)\n return dataset\n\n\ndef serving_input_fn():\n \"\"\"Defines the features to be passed to the model during inference.\n\n Expects already tokenized and padded representation of sentences\n\n Returns:\n A tf.estimator.export.ServingInputReceiver\n \"\"\"\n feature_placeholder = tf.placeholder(tf.float32, [None, 28 * 28])\n features = feature_placeholder\n return tf.estimator.export.TensorServingInputReceiver(features,\n feature_placeholder)\n\n\ndef _get_session_config_from_env_var():\n \"\"\"Returns a tf.ConfigProto instance with appropriate device_filters set.\"\"\"\n\n tf_config = json.loads(os.environ.get('TF_CONFIG', '{}'))\n\n # GPU limit: TensorFlow by default allocates all GPU memory:\n # If multiple workers run in same host you may see OOM errors:\n # Use as workaround if not using Hadoop 3.1\n # Change percentage accordingly:\n # gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.25)\n\n if (tf_config and 'task' in tf_config and 'type' in tf_config['task'] and\n 'index' in tf_config['task']):\n # Master should only communicate with itself and ps.\n if tf_config['task']['type'] == 'master':\n return tf.ConfigProto(device_filters=['/job:ps', '/job:master'])\n # Worker should only communicate with itself and ps.\n elif tf_config['task']['type'] == 'worker':\n return tf.ConfigProto(#gpu_options=gpu_options,\n device_filters=[\n '/job:ps',\n '/job:worker/task:%d' % tf_config['task'][\n 'index']\n ])\n return None\n\n\ndef start_tensorboard(logdir):\n tb = tb_program.TensorBoard(plugins=[core_plugin.CorePluginLoader()])\n port = int(os.getenv('TB_PORT', 6006))\n tb.configure(logdir=logdir, port=port)\n tb.launch()\n logging.info(\"Starting TensorBoard with --logdir=%s\" % logdir)\n\n\ndef train_and_evaluate(args):\n \"\"\"Helper function: Trains and evaluates model.\n\n Args:\n args: (dict) Command line parameters passed from task.py\n \"\"\"\n # Loads data.\n (train_images, train_labels), (\n test_images, test_labels) = tf.keras.datasets.mnist.load_data()\n\n # Scale values to a range of 0 to 1.\n train_images = train_images / 255.0\n test_images = test_images / 255.0\n\n # Shape numpy array.\n train_labels = np.asarray(train_labels).astype('int').reshape((-1, 1))\n test_labels = np.asarray(test_labels).astype('int').reshape((-1, 1))\n\n # Define training steps.\n train_steps = len(train_images) / args.batch_size\n get_distribution_strategy()\n\n hook = tf.train.ProfilerHook(save_steps=100,\n output_dir=args.working_dir,\n show_memory=True)\n\n # Define running config.\n run_config = tf.estimator.RunConfig(\n experimental_distribute=tf.contrib.distribute.DistributeConfig(\n train_distribute=tf.contrib.distribute.ParameterServerStrategy(),\n eval_distribute=tf.contrib.distribute.MirroredStrategy()),\n session_config=_get_session_config_from_env_var(),\n model_dir=args.working_dir,\n save_summary_steps=100,\n log_step_count_steps=100,\n save_checkpoints_steps=500)\n # Create estimator.\n estimator = create_model(\n model_dir=args.working_dir,\n config=run_config,\n learning_rate=args.learning_rate)\n # Create TrainSpec.\n train_spec = tf.estimator.TrainSpec(\n input_fn=lambda: input_fn(\n train_images,\n train_labels,\n args.batch_size,\n mode=tf.estimator.ModeKeys.TRAIN),\n # hooks=[hook], # Uncomment if needed to debug.\n max_steps=train_steps)\n # Create EvalSpec.\n exporter = tf.estimator.FinalExporter('exporter', serving_input_fn)\n eval_spec = tf.estimator.EvalSpec(\n input_fn=lambda: input_fn(\n test_images,\n test_labels,\n args.batch_size,\n mode=tf.estimator.ModeKeys.EVAL),\n steps=None,\n name='mnist-eval',\n exporters=[exporter],\n start_delay_secs=10,\n throttle_secs=10)\n\n # Launch Tensorboard in a separate thread.\n tf.gfile.MakeDirs(args.working_dir)\n start_tensorboard(args.working_dir)\n\n # Start training\n tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)\n\n\nif __name__ == '__main__':\n args = get_args()\n tf.logging.set_verbosity(args.verbosity)\n train_and_evaluate(args)\n","sub_path":"tony-examples/mnist-tensorflow/mnist_keras_distributed.py","file_name":"mnist_keras_distributed.py","file_ext":"py","file_size_in_byte":10068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"514491057","text":"from django.contrib import admin\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom filingcabinet.admin import (\n DocumentBaseAdmin, PageAdmin, PageAnnotationAdmin,\n DocumentCollectionBaseAdmin,\n CollectionDocumentBaseAdmin,\n DocumentPortalAdmin\n)\nfrom filingcabinet.models import (\n Page, PageAnnotation, CollectionDocument,\n DocumentPortal\n)\n\nfrom froide.helper.admin_utils import (\n ForeignKeyFilter, make_admin_assign_action\n)\nfrom froide.helper.forms import get_fk_form_class\n\nfrom .models import Document, DocumentCollection\n\n\nAddDocumentsToCollectionBaseMixin = make_admin_assign_action(\n 'collection', _('Add documents to collection')\n)\n\n\nclass AddDocumentsToCollectionMixin(AddDocumentsToCollectionBaseMixin):\n def _get_assign_action_form_class(self, fieldname):\n return get_fk_form_class(\n CollectionDocument, 'collection', self.admin_site\n )\n\n def _execute_assign_action(self, obj, fieldname, assign_obj):\n CollectionDocument.objects.get_or_create(\n collection=assign_obj,\n document=obj\n )\n\n\nclass DocumentAdmin(AddDocumentsToCollectionMixin, DocumentBaseAdmin):\n raw_id_fields = DocumentBaseAdmin.raw_id_fields + (\n 'original', 'foirequest', 'publicbody', 'team'\n )\n list_filter = DocumentBaseAdmin.list_filter + (\n ('foirequest', ForeignKeyFilter),\n ('publicbody', ForeignKeyFilter),\n ('user', ForeignKeyFilter),\n ('team', ForeignKeyFilter),\n )\n actions = (\n DocumentBaseAdmin.actions + AddDocumentsToCollectionMixin.actions\n )\n\n\nclass CustomPageAdmin(PageAdmin):\n list_filter = PageAdmin.list_filter + (\n ('document', ForeignKeyFilter),\n )\n\n\nclass CustomPageAnnotationAdmin(PageAnnotationAdmin):\n list_filter = (\n 'page__number',\n ('page__document', ForeignKeyFilter),\n )\n\n\nclass DocumentCollectionAdmin(DocumentCollectionBaseAdmin):\n raw_id_fields = DocumentBaseAdmin.raw_id_fields + (\n 'team',\n )\n\n\nclass CollectionDocumentAdmin(CollectionDocumentBaseAdmin):\n list_filter = CollectionDocumentBaseAdmin.list_filter + (\n ('document', ForeignKeyFilter),\n ('collection', ForeignKeyFilter),\n )\n\n\nadmin.site.register(Document, DocumentAdmin)\nadmin.site.register(Page, CustomPageAdmin)\nadmin.site.register(PageAnnotation, CustomPageAnnotationAdmin)\nadmin.site.register(DocumentCollection, DocumentCollectionAdmin)\nadmin.site.register(CollectionDocument, CollectionDocumentAdmin)\nadmin.site.register(DocumentPortal, DocumentPortalAdmin)\n","sub_path":"froide/document/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}
+{"seq_id":"298546486","text":"from cocos.layer.scrolling import ScrollableLayer\nfrom cocos.sprite import Sprite\nfrom cocos.tiles import *\nimport pyglet.image\nimport pyglet.resource\nfrom xml.etree import ElementTree\n\n\nclass TmxImageLayer(ScrollableLayer):\n def __init__(self, image, offset, maph):\n super(TmxImageLayer, self).__init__()\n self.image = image\n self._sprite = Sprite(\n image,\n (offset[0] + (image.width / 2),\n (maph - offset[1]) - (image.height / 2))\n )\n self.add(self._sprite)\n\n\ndef load_map(mapname):\n '''Hack to ensure maps with relative image paths get loaded correctly,\n by manipulating pyglet.resource.path.'''\n pyglet.resource.path.insert(0, 'asset/map')\n pyglet.resource.reindex()\n layer = load_tmx('level_{}.tmx'.format(mapname))\n pyglet.resource.path.pop(0)\n pyglet.resource.reindex()\n return layer\n\n\ndef load_tmx(filename):\n \"\"\"Load some tile mapping resources from a TMX file.\n\n Modified from cocos.tiles.load_tmx\n \"\"\"\n resource = Resource(filename)\n\n tree = ElementTree.parse(resource.path)\n map = tree.getroot()\n if map.tag != 'map':\n raise ResourceError('document is <%s> instead of