diff --git "a/1276.jsonl" "b/1276.jsonl" new file mode 100644--- /dev/null +++ "b/1276.jsonl" @@ -0,0 +1,662 @@ +{"seq_id":"1524653","text":"import os\nimport socket\n\nimport config\nfrom handler import Handler\n\nclass Server:\n def __init__(self, root, ncpu):\n self.root = root\n self.ncpu = ncpu\n self.workers = []\n\n\n def start(self):\n print('Server start')\n server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \n server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n server_socket.bind((config.HOST, config.PORT)) \n server_socket.listen(config.LISTENERS) \n\n\n for worker in range(self.ncpu):\n pid = os.fork()\n if pid:\n self.workers.append(pid)\n else:\n print('Run worker: {}'.format(os.getpid()))\n while True:\n client_socket, client_address = server_socket.accept() \n request = client_socket.recv(config.REQ_SIZE) \n\n \n if request.strip() == 0:\n client_socket.close()\n continue\n\n handler = Handler(self.root, request)\n response = handler.get_response()\n client_socket.sendall(response)\n\n client_socket.close()\n\n server_socket.close()\n\n\n for pid in self.workers:\n os.waitpid(pid, 0)\n","sub_path":"prefork_server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"478003627","text":"import numpy as np\nimport torch\nimport random\nfrom torch.utils.data import Dataset\n\n\nclass MyDataset(Dataset):\n def __init__(self, ap, meta_data, voice_len=1.6, num_speakers_in_batch=64,\n num_utter_per_speaker=10, skip_speakers=False, verbose=False):\n \"\"\"\n Args:\n ap (TTS.tts.utils.AudioProcessor): audio processor object.\n meta_data (list): list of dataset instances.\n seq_len (int): voice segment length in seconds.\n verbose (bool): print diagnostic information.\n \"\"\"\n self.items = meta_data\n self.sample_rate = ap.sample_rate\n self.voice_len = voice_len\n self.seq_len = int(voice_len * self.sample_rate)\n self.num_speakers_in_batch = num_speakers_in_batch\n self.num_utter_per_speaker = num_utter_per_speaker\n self.skip_speakers = skip_speakers\n self.ap = ap\n self.verbose = verbose\n self.__parse_items()\n if self.verbose:\n print(\"\\n > DataLoader initialization\")\n print(f\" | > Number of instances : {len(self.items)}\")\n print(f\" | > Sequence length: {self.seq_len}\")\n print(f\" | > Num speakers: {len(self.speakers)}\")\n\n def load_wav(self, filename):\n audio = self.ap.load_wav(filename, sr=self.ap.sample_rate)\n return audio\n\n def load_data(self, idx):\n text, wav_file, speaker_name = self.items[idx]\n wav = np.asarray(self.load_wav(wav_file), dtype=np.float32)\n mel = self.ap.melspectrogram(wav).astype(\"float32\")\n # sample seq_len\n\n assert text.size > 0, self.items[idx][1]\n assert wav.size > 0, self.items[idx][1]\n\n sample = {\n \"mel\": mel,\n \"item_idx\": self.items[idx][1],\n \"speaker_name\": speaker_name,\n }\n return sample\n\n def __parse_items(self):\n \"\"\"\n Find unique speaker ids and create a dict mapping utterances from speaker id\n \"\"\"\n speakers = list({item[-1] for item in self.items})\n self.speaker_to_utters = {}\n self.speakers = []\n for speaker in speakers:\n speaker_utters = [item[1] for item in self.items if item[2] == speaker]\n if len(speaker_utters) < self.num_utter_per_speaker and self.skip_speakers:\n print(\n f\" [!] Skipped speaker {speaker}. Not enough utterances {self.num_utter_per_speaker} vs {len(speaker_utters)}.\"\n )\n else:\n self.speakers.append(speaker)\n self.speaker_to_utters[speaker] = speaker_utters\n\n def __len__(self):\n return int(1e10)\n\n def __sample_speaker(self):\n speaker = random.sample(self.speakers, 1)[0]\n if self.num_utter_per_speaker > len(self.speaker_to_utters[speaker]):\n utters = random.choices(\n self.speaker_to_utters[speaker], k=self.num_utter_per_speaker\n )\n else:\n utters = random.sample(\n self.speaker_to_utters[speaker], self.num_utter_per_speaker\n )\n return speaker, utters\n\n def __sample_speaker_utterances(self, speaker):\n \"\"\"\n Sample all M utterances for the given speaker.\n \"\"\"\n feats = []\n labels = []\n for _ in range(self.num_utter_per_speaker):\n # TODO:dummy but works\n while True:\n if len(self.speaker_to_utters[speaker]) > 0:\n utter = random.sample(self.speaker_to_utters[speaker], 1)[0]\n else:\n self.speakers.remove(speaker)\n speaker, _ = self.__sample_speaker()\n continue\n wav = self.load_wav(utter)\n if wav.shape[0] - self.seq_len > 0:\n break\n self.speaker_to_utters[speaker].remove(utter)\n\n offset = random.randint(0, wav.shape[0] - self.seq_len)\n mel = self.ap.melspectrogram(wav[offset : offset + self.seq_len])\n feats.append(torch.FloatTensor(mel))\n labels.append(speaker)\n return feats, labels\n\n def __getitem__(self, idx):\n speaker, _ = self.__sample_speaker()\n return speaker\n\n def collate_fn(self, batch):\n labels = []\n feats = []\n for speaker in batch:\n feats_, labels_ = self.__sample_speaker_utterances(speaker)\n labels.append(labels_)\n feats.extend(feats_)\n feats = torch.stack(feats)\n return feats.transpose(1, 2), labels\n","sub_path":"TTS/speaker_encoder/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":4578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"599861987","text":"# coding: utf-8\nimport os\n\nimport torch\nfrom torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\nfrom keras.preprocessing.sequence import pad_sequences\nfrom sklearn.model_selection import train_test_split\n\nfrom pytorch_transformers import XLNetModel, XLNetTokenizer, XLNetForSequenceClassification\nfrom pytorch_transformers import AdamW\n\nfrom tqdm import tqdm, trange\nimport pandas as pd\nimport io\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport datetime\n\nfrom sklearn.metrics import roc_auc_score, accuracy_score\n\nfrom utils.EarlyStopping import EarlyStopping\n\nes = EarlyStopping()\n\n\ndef y_split(data, label_cols):\n y = []\n for label in label_cols:\n y.append(data[label])\n y = np.array(y)\n y = y.reshape(y.shape[1], y.shape[0])\n return y\n\n\ndef roc_auc_score_FIXED(y_true, y_pred):\n try:\n score = roc_auc_score(y_true, y_pred)\n except ValueError:\n score = accuracy_score(y_true, np.rint(y_pred))\n return score\n\n\ndevice = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\n\nprint(\"device: {}\".format(device))\n\ndf = pd.read_csv(\"./data/train.csv\", encoding='utf-8')\nsentences = df['comment_text'].values\n\ntest_df = pd.read_csv(\"./data/test.csv\", encoding='utf-8')\ntest_sentences = test_df['comment_text']\n\n\n# define output dataframe\nsample = pd.read_csv(\"./data/sample_submission.csv\")\n\nlabel_cols = ['toxic', 'severe_toxic', 'obscene',\n 'threat', 'insult', 'identity_hate']\n\nlabels = y_split(df, label_cols)\n\ntokenizer = XLNetTokenizer.from_pretrained(\n 'xlnet-base-cased', do_lower_case=True)\n\ntokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]\n# print(tokenized_texts[0]) # not working on ubuntu\n\ntest_tokenized_texts = [tokenizer.tokenize(sent) for sent in test_sentences]\n# print(\"test sentence sample: {}\".format(test_tokenized_texts[0])) # not working on ubuntu\n\n\n# Set the maximum sequence length. The longest sequence in our training set is 47, but we'll leave room on the end anyway.\nMAX_LEN = 512\n\n# Use the XLNet tokenizer to convert the tokens to their index numbers in the XLNet vocabulary\ninput_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]\n\ntest_input_ids = [tokenizer.convert_tokens_to_ids(\n x) for x in test_tokenized_texts]\n\n# Pad our input tokens\ninput_ids = pad_sequences(\n input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\ntest_input_ids = pad_sequences(\n test_input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\n\n# Create attention masks\nattention_masks = []\ntest_attention_masks = []\n\n# Create a mask of 1s for each token followed by 0s for padding\nfor seq in input_ids:\n seq_mask = [float(i > 0) for i in seq]\n attention_masks.append(seq_mask)\n\n# Create a mask of 1s for test token\nfor seq in test_input_ids:\n seq_mask = [float(i > 0) for i in seq]\n test_attention_masks.append(seq_mask)\n\n# Use train_test_split to split our data into train and validation sets for training\n\ntrain_inputs, validation_inputs, train_labels, validation_labels = train_test_split(input_ids, labels,\n random_state=2018, test_size=0.2)\ntrain_masks, validation_masks, _, _ = train_test_split(attention_masks, input_ids,\n random_state=2018, test_size=0.2)\n\n# Convert all of our data into torch tensors, the required datatype for our model\n\ntrain_inputs = torch.tensor(train_inputs)\nvalidation_inputs = torch.tensor(validation_inputs)\ntrain_labels = torch.tensor(train_labels)\nvalidation_labels = torch.tensor(validation_labels)\ntrain_masks = torch.tensor(train_masks)\nvalidation_masks = torch.tensor(validation_masks)\n\ntest_inputs = torch.tensor(test_input_ids)\ntest_masks = torch.tensor(test_attention_masks)\n\n# Select a batch size for training. For fine-tuning with XLNet, the authors recommend a batch size of 32, 48, or 128. We will use 32 here to avoid memory issues.\nbatch_size = 32\n\n# Create an iterator of our data with torch DataLoader. This helps save on memory during training because, unlike a for loop,\n# with an iterator the entire dataset does not need to be loaded into memory\n\ntrain_data = TensorDataset(train_inputs, train_masks, train_labels)\ntrain_sampler = RandomSampler(train_data)\ntrain_dataloader = DataLoader(\n train_data, sampler=train_sampler, batch_size=batch_size)\n\nvalidation_data = TensorDataset(\n validation_inputs, validation_masks, validation_labels)\nvalidation_sampler = SequentialSampler(validation_data)\nvalidation_dataloader = DataLoader(\n validation_data, sampler=validation_sampler, batch_size=batch_size)\n\ntest_data = TensorDataset(test_inputs, test_masks)\ntest_sampler = SequentialSampler(test_data)\ntest_dataloader = DataLoader(\n test_data, sampler=validation_sampler, batch_size=batch_size)\n\n# Load XLNEtForSequenceClassification, the pretrained XLNet model with a single linear classification layer on top.\n\nmodel = XLNetForSequenceClassification.from_pretrained(\n \"xlnet-base-cased\", num_labels=len(label_cols))\nmodel.to(device)\n\nparam_optimizer = list(model.named_parameters())\nno_decay = ['bias', 'gamma', 'beta']\noptimizer_grouped_parameters = [\n {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],\n 'weight_decay_rate': 0.01},\n {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],\n 'weight_decay_rate': 0.0}\n]\n\n# This variable contains all of the hyperparemeter information our training loop needs\noptimizer = AdamW(optimizer_grouped_parameters,\n lr=2e-5)\n\n# Function to calculate the accuracy of our predictions vs labels\n\n\ndef flat_accuracy(preds, labels):\n pred_flat = np.argmax(preds, axis=1).flatten()\n labels_flat = labels.flatten()\n return np.sum(pred_flat == labels_flat) / len(labels_flat)\n\n\n# Store our loss and accuracy for plotting\ntrain_loss_set = []\n\n# Number of training epochs (authors recommend between 2 and 4)\nepochs = 2\n\n# trange is a tqdm wrapper around the normal python range\nfor ep in trange(epochs, desc=\"Epoch\"):\n\n # Training\n\n # Set our model to training mode (as opposed to evaluation mode)\n model.train()\n\n # Tracking variables\n tr_loss = 0\n nb_tr_examples, nb_tr_steps = 0, 0\n\n # Train the data for one epoch\n for step, batch in enumerate(train_dataloader):\n # Add batch to GPU\n batch = tuple(t.to(device) for t in batch)\n # Unpack the inputs from our dataloader\n b_input_ids, b_input_mask, b_labels = batch\n\n # b_input_ids = torch.tensor(b_input_ids).to(torch.int64)\n # b_input_mask = torch.tensor(b_input_mask).to(torch.int64)\n # b_labels = torch.tensor(b_labels).to(torch.int64)\n\n b_input_ids = b_input_ids.long()\n b_input_mask = b_input_mask.long()\n b_labels = b_labels.long()\n\n # Clear out the gradients (by default they accumulate)\n optimizer.zero_grad()\n # Forward pass\n outputs = model(b_input_ids, token_type_ids=None,\n attention_mask=b_input_mask, labels=b_labels)\n loss = outputs[0]\n print(loss)\n logits = outputs[1]\n print(logits)\n train_loss_set.append(loss.item())\n # Backward pass\n loss.backward()\n # Update parameters and take a step using the computed gradient\n optimizer.step()\n\n # Update tracking variables\n tr_loss += loss.item()\n nb_tr_examples += b_input_ids.size(0)\n nb_tr_steps += 1\n\n print(\"Train loss: {}\".format(tr_loss / nb_tr_steps))\n\n # Validation\n\n # Put model in evaluation mode to evaluate loss on the validation set\n model.eval()\n\n # Tracking variables\n eval_loss, eval_roc_auc = 0, 0\n nb_eval_steps, nb_eval_examples = 0, 0\n\n # Evaluate data for one epoch\n for batch in validation_dataloader:\n # Add batch to GPU\n batch = tuple(t.to(device) for t in batch)\n # Unpack the inputs from our dataloader\n b_input_ids, b_input_mask, b_labels = batch\n # Telling the model not to compute or store gradients, saving memory and speeding up validation\n with torch.no_grad():\n # Forward pass, calculate logit predictions\n output = model(b_input_ids, token_type_ids=None,\n attention_mask=b_input_mask)\n logits = output[0]\n\n # Move logits and labels to CPU\n logits = logits.detach().cpu().numpy()\n label_ids = b_labels.to('cpu').numpy()\n\n tmp_eval_roc_auc = roc_auc_score_FIXED(logits, label_ids)\n\n eval_roc_auc += tmp_eval_roc_auc\n nb_eval_steps += 1\n\n print(\"Epoch: {}, loss: {}, ROC_AUC: {}\".format(ep, eval_loss, eval_roc_auc))\n\n# prediction\npredicts = []\nfor batch in test_dataloader:\n batch = tuple(t.to(device) for t in batch)\n b_input_ids, b_input_mask = batch\n\n b_input_ids = b_input_ids.long()\n b_input_mask = b_input_mask.long()\n\n # Telling the model not to compute or store gradients, saving memory and speeding up validation\n with torch.no_grad():\n # Forward pass, calculate logit predictions\n output = model(b_input_ids, token_type_ids=None,\n attention_mask=b_input_mask)\n logits = output[0]\n\n # Move logits and labels to CPU\n pred = logits.detach().cpu().numpy().tolist()\n predicts += pred\n\nsample[label] = predicts\n\n\n# save output\nif not os.path.exists(\"./submission\"):\n os.mkdir(\"./submission\")\nnow = datetime.datetime.now()\nsample.to_csv(\"submission_XLNET_TRANING_{}_{}ep.csv\".format(\n now.timestamp(), epochs), index=False)\n","sub_path":"assemble/XLNetTraining.py","file_name":"XLNetTraining.py","file_ext":"py","file_size_in_byte":9714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"552952143","text":"import os\nimport shutil\nimport subprocess\nimport time\n\n\n#setup the current folder and destination folder for copying\ncurrentDir = os.path.dirname(__file__)\ndesinationDir = os.path.join(currentDir, '')\n\n#copy the exe to the current folder from the debug folder\noriginExe = os.path.join(currentDir, '../x64/Debug/GameBackboneBoostUnitTest1.exe')\ndestExe = os.path.join(desinationDir, 'GameBackboneBoostUnitTest1.exe')\nshutil.copyfile(originExe, destExe)\n\n#copy the dll to the current folder from the debug folder\noriginGBDll = os.path.join(currentDir, '../x64/Debug/GameBackboneDll.dll')\ndestGBDll = os.path.join(desinationDir, 'GameBackboneDll.dll')\nshutil.copyfile(originGBDll, destGBDll)\n\nfor index in range(1,5):\n\t#run the tests\n\tprocess = subprocess.Popen([destExe], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\tout, err = process.communicate()\n\tprint(out.decode(\"utf-8\"))\n\tprint(err.decode(\"utf-8\"))\n\t\n\tif err != b'\\r\\n*** No errors detected\\r\\n':\n\t\t#print(out)\t\n\t\tf = open('MassTest.log', 'w')\n\t\tf.write(out.decode(\"utf-8\"))\n\n#with open('output.txt', 'wb') as f:\n# subprocess.check_call(destExe, stdout=f)\n#with open('output.txt') as f:\n# for line in f:\n# print (line,)\n\n\n#delete the files that were moved at the beginning to the current folder\nos.remove(destExe)\nos.remove(destGBDll)","sub_path":"GameBackboneSln/GameBackboneUnitTest/MassTest.py","file_name":"MassTest.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"467976596","text":"# Copyright (C) 2009-2019, Panagiotis Christopoulos Charitos and contributors.\n# All rights reserved.\n# Code licensed under the BSD License.\n# http://www.anki3d.org/LICENSE\n# keep methods in alphabetical order\n\n# system imports\nimport os\nimport sys\n\nbl_info = {\"author\": \"A. A. Kalugin Jr.\"}\n\n################################ CONSTANTS ###############################\nENVIRO = \"\"\n################################ CONSTANTS ###############################\n\ndef get_common_environment_path():\n\t\"\"\"\n\tReturns a string of the common path of a given environment variable\n\t\"\"\"\n\tpath_list = os.getenv(ENVIRO).split(\":\")\n\treturn os.path.commonprefix(path_list)\n\ndef get_environment():\n\t\"\"\"\n\tReturns three dictionaries of various environment paths.\n\tBuild, export and tools in the anki path environment.\n\t\"\"\"\n\tenvironment_root = os.path.abspath(get_common_environment_path())\n\t# environment_root = (os.path.abspath(environment_root)) #clean the path\n\t# ANKI Environment\n\tanki_build_path = \"{0}/build\".format(environment_root)\n\tanki_shaders_path = \"{0}/shaders\".format(environment_root)\n\tanki_engine_data_path = \"{0}/engine_data\".format(environment_root)\n\t# Export Environment\n\texport_data_path = \"{0}/data\".format(environment_root)\n\texport_src_data = \"{0}/src_data\".format(export_data_path)\n\texport_map_path = \"{0}/map\".format(export_data_path)\n\texport_temp_dea = \"{0}/tmp_scene.dae\".format(export_src_data)\n\texport_texture_path = \"{0}/texture\".format(export_data_path)\n\t# Tools Environment\n\ttool_etcpack_path = \"{0}/thirdparty/bin\".format(environment_root)\n\ttools_path = \"{0}/tools\".format(anki_build_path)\n\ttools_scene_path = \"{0}/tools/scene\".format(environment_root)\n\ttools_texture_path = \"{0}/tools/texture\".format(environment_root)\n\n\t# Make the Export Paths\n\tfor _path in [export_src_data, export_map_path, export_texture_path]:\n\t\tif not os.path.exists(_path):\n\t\t\tprint (\"Making directory:\", _path)\n\t\t\tos.makedirs(_path)\n\n\t# anki checked out path dictionary\n\tenv_dct = {\n\t\t\t\t\t'anki_root_path':environment_root+'/',\n\t\t\t\t\t'anki_build_path':anki_build_path,\n\t\t\t\t\t'anki_engine_data_path':anki_engine_data_path,\n\t\t\t\t\t'anki_shaders_path':anki_shaders_path,\n\t\t\t\t\t}\n\n\t# export path dictionary\n\texport_dct = {\n\t\t\t\t\t'export_src_data':export_src_data,\n\t\t\t\t\t'export_map_path':export_map_path,\n\t\t\t\t\t'export_texture_path':export_texture_path,\n\t\t\t\t\t'export_temp_dea':export_temp_dea,\n\t\t\t\t\t}\n\n\t# tools path dictionary\n\ttools_dct = {\n\t\t\t\t\t'tool_etcpack_path':tool_etcpack_path,\n\t\t\t\t\t'tools_path':tools_path,\n\t\t\t\t\t'tools_scene_path':tools_scene_path,\n\t\t\t\t\t'tools_texture_path':tools_texture_path,\n\t\t\t\t\t}\n\treturn env_dct, export_dct, tools_dct\n\ndef set_environment(anki_env_dct, tools_dct):\n\t\"\"\"\n\tSets the environment variable.\n\t\"\"\"\n\t# Set the environment variable for anki\n\tenvironment_path = os.pathsep.join(anki_env_dct.values())\n\tos.environ[ENVIRO]=environment_path\n\n\t# Append the tools path to the $PATH\n\ttools_path = os.pathsep.join(tools_dct.values())\n\n\t# Create a clean PATH environment varaible\n\tos.environ[\"PATH\"] += os.pathsep + tools_path\n\tpath_l = os.getenv(\"PATH\").split(os.pathsep)\n\tnew_path = os.pathsep.join(set(path_l))\n\tos.environ[\"PATH\"] = new_path\n","sub_path":"tools/anki_scene_exporter/lib/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":3204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"585745016","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2019 Intel Corporation\n#\n# SPDX-License-Identifier: Apache-2.0\n\n# Script to upload results to testrail.\n\nimport sys\nimport os\nimport glob\nfrom pprint import pprint\nsys.path.append(\".\")\nfrom testrail_client import TestRailClient\n\nfrom junitparser import TestCase, TestSuite, JUnitXml, Skipped, Error\nimport re\nimport datetime\nimport argparse\n\nretest_text = \"description: subtestcase didn't run: likely the image failed to build or to deploy\"\n\nDEBUG = False\n\ndef debug(msg):\n if DEBUG:\n print(msg)\n\nclass TestRail():\n\n def __init__(self):\n self.client = None\n\n def authorize(self):\n self.project = 'https://zephyrproject.testrail.io'\n self.user = os.environ.get('TESTRAIL_USER', None)\n self.token = os.environ.get('TESTRAIL_TOKEN', None)\n\n self.client = TestRailClient(self.project, self.user, self.token)\n\n\nclass Config():\n def __init__(self, name, group, id):\n self.name = name\n self.group_id = group\n self.id = id\n\n\nclass Configurations(TestRail):\n\n def __init__(self, project_id):\n super().authorize()\n self.project = project_id\n self.group_id = None\n self.configs = []\n\n def get(self, group):\n configs = self.client.config.get(self.project)\n\n # search for group\n idx = next((index for (index, d) in enumerate(configs) if d[\"name\"] == group), None)\n if configs and idx is not None and idx >= 0:\n self.group_id = configs[idx]['id']\n debug(self.group_id)\n for c in configs[idx]['configs']:\n p = Config(c['name'], c['group_id'], c['id'])\n self.configs.append(p)\n else:\n print(\"Creating new group\")\n ret = self.client.config.add_group(project_id=self.project, name=group)\n self.group_id = ret.get(\"id\")\n\n def add(self, config):\n self.client.config.add(self.group_id, config)\n\n def provides(self, config):\n for p in self.configs:\n if p.name == config:\n return p.id\n\n return 0\n\n\nclass Status(TestRail):\n\n def __init__(self, project_id):\n\n super().authorize()\n\n self.PASSED = None\n self.FAILED = None\n self.BLOCKED = None\n self.SKIPPED = None\n self.RETEST = None\n self.UNTESTED = None\n self.UNGRADED = None\n self.TIMEOUT = None\n self.ERROR = None\n\n self.populate()\n\n def populate(self):\n statuses = self.client.case.status()\n\n for s in statuses:\n sid = s[\"id\"]\n sname = s['name']\n\n if sname == \"passed\":\n self.PASSED = sid\n elif sname == \"failed\":\n self.FAILED = sid\n elif sname == \"blocked\":\n self.BLOCKED = sid\n elif sname == \"skipped\":\n self.SKIPPED = sid\n elif sname == \"untested\":\n self.UNTESTED = sid\n elif sname == \"ungraded\":\n self.UNGRADED = sid\n elif sname == \"timeout\":\n self.TIMEOUT = sid\n elif sname == \"error\":\n self.ERROR = sid\n elif sname == \"retest\":\n self.RETEST = sid\n\nclass TestRun(TestRail):\n\n def __init__(self):\n super().authorize()\n\n self.config_ids = []\n self.runs = []\n\n self.milestone = None\n self.variants = []\n self.result_files = []\n self.project_id = None\n self.version = None\n self.suite = None\n self.plan = None\n self.result_files = []\n self.status = None\n self.cases = []\n self.final_results = []\n self.plan_entries = None\n self.title = \"Test Run\"\n\n def configure(self):\n self.status = Status(self.project_id)\n self.cases = self.client.case.for_project(project_id=self.project_id, suite_id=self.suite)\n\n if not self.plan:\n today = datetime.date.today().strftime(\"%B %d, %Y\")\n self.plan = self.client.plan.add(self.project_id,\n \"{}: {} {}\".format(self.title, self.version, today),\n milestone_id=self.milestone)\n\n def get_case_id(self, reference):\n for case in self.cases:\n if case['refs'] == reference:\n return case['id']\n\n return 0\n\n def get_case_name(self, name):\n return name\n\n def get_case_text(self, text):\n return text\n\n\n def parse_files(self):\n\n for result_file in self.result_files:\n self.parse_file(result_file)\n\n def parse_file(self, result_file):\n\n config = result_file.get('config')\n\n print(\"Parsing {}\".format(result_file['file']))\n\n\n junit_xml = JUnitXml.fromfile(result_file['file'])\n\n results_to_upload = []\n parents = {}\n\n for suite in junit_xml:\n for testcase in suite:\n if testcase.result and testcase.result.type == 'skipped':\n continue\n\n ref = self.get_case_name(testcase.name)\n # if test is skipped, keep it as such, otherwise look at parent results\n if testcase.result:\n tc = testcase\n elif testcase.name in parents:\n tc = parents[testcase.name]\n else:\n parent = self.find_parent_in_junit(junit_xml, testcase.name)\n if parent:\n debug(\"--> found parent failure: %s -> %s\" %(parent.name, parent.result) )\n\n runid_err = \"eval error: expected console output 'RunID\"\n runid_result = \"console output: RunID\"\n\n if parent.result._elem.text and runid_err in parent.result._elem.text and runid_result in parent.result._elem.text:\n\n debug(\"{} parent is FALSE NEGATIVE, so keep sub-test result\".format(testcase.name))\n tc = testcase\n else:\n tc = parent\n\n parents[testcase.name] = parent\n else:\n tc = testcase\n\n cr = {}\n cr['ref'] = ref\n\n filtered = \"\"\n infra_issue = False\n if tc.result:\n override = None\n\n text = tc.result._elem.text\n if text:\n filtered = self.get_case_text(text)\n\n if override:\n test_status = override\n else:\n if tc.result.type == 'error' and tc.result.message == 'Infrastructure':\n test_status = self.status.RETEST\n infra_issue = True\n elif tc.result.type == 'error':\n test_status = self.status.BLOCKED\n elif tc.result.type == 'skipped':\n test_status = self.status.SKIPPED\n elif tc.result.type == 'failure':\n test_status = self.status.FAILED\n else:\n test_status = self.status.RETEST\n\n status_text = tc.result.type\n\n cr['status_id'] = test_status\n else:\n cr['status_id'] = self.status.PASSED\n status_text = 'passed'\n\n debug(\"{}: {} => {}\".format(config, testcase.name, status_text))\n if filtered == \"\" and infra_issue:\n cr['comment'] = \"Infrastructure issue, please retest.\"\n else:\n cr['comment'] = filtered\n\n cr['version'] = self.version\n results_to_upload.append(cr)\n\n\n pprint(results_to_upload)\n\n tmp_res = []\n tids =[]\n\n for result in results_to_upload:\n print(result)\n ref = result['ref']\n case_id = self.get_case_id(ref)\n print(case_id)\n if case_id:\n tids.append(case_id)\n\n result['case_id'] = case_id\n del result['ref']\n tmp_res.append(result)\n\n entry = {\n \"include_all\": False,\n \"case_ids\": tids,\n \"config_ids\": [result_file['id']],\n }\n self.final_results.append({'config': config, 'results': tmp_res, 'run_entry': entry, 'config_id': result_file['id']})\n\n\n def process(self):\n self.parse_files()\n\n for fr in self.final_results:\n print(\"Creating plan configuration\")\n self.config_ids.append(fr['config_id'])\n self.runs.append(fr['run_entry'])\n\n self.plan_entries = self.client.plan.add_entry(self.plan['id'], self.suite, '{} Test Run {}'.format(self.title, self.version) ,\n config_ids=self.config_ids, runs=self.runs)\n\n def upload(self):\n plan = self.client.plan.get(self.plan['id'])\n\n runs = []\n for entry in plan['entries']:\n runs = entry['runs']\n\n for rr in self.final_results:\n for r in runs:\n if r['config'] == rr.get('config'):\n print(\"Submitting results for {}\".format(rr.get('config')))\n self.client.result.add_for_cases(r['id'], rr.get('results') )\n\nclass TCF(TestRun):\n\n def __init__(self, result_directory, project_id, version, suite, milestone):\n super().__init__()\n super().authorize()\n\n self.project_id = project_id\n self.version = version\n self.suite = suite\n self.milestone = milestone\n\n self.variants = ['zephyr', 'espressif']\n\n self.results_directory = result_directory\n self.title = \"TCF\"\n\n def get_case_name(self, name):\n return name.split(\"#\")[1]\n\n def find_parent_in_junit(self, junit_xml, name):\n\n testp, ref = name.split(\"#\")\n\n for suite in junit_xml:\n for testcase in suite:\n p = self.get_case_name(testcase.name)\n ref_parent = \".\".join(ref.split(\".\")[:-1])\n name = \"{}#{}\".format(testp, ref_parent)\n\n if testcase.name == name and testcase.result and testcase.result.type == 'error' and testcase.result.type != 'skipped':\n return testcase\n\n return None\n\n def get_case_text(self, text):\n filtered = []\n for l in text.split(\"\\n\"):\n if \"console output:\" in l:\n new = re.sub(\".* console output:\", \"\", l)\n trimmed = re.sub(\"/home/jenkins.*zephyr.git\", \"zephyr.git\", new)\n filtered.append(trimmed)\n\n result = \"\\n\".join(filtered)\n\n return result\n\n def discover(self):\n\n for variant in self.variants:\n for j in glob.glob(\"{}/*{}__zephyr*.xml\".format(self.results_directory, variant)):\n\n file = os.path.basename((j))\n file_meta = file.split(\"__\")\n\n platform = file_meta[0].replace(\"junit-\", \"\")\n\n if platform != 'static':\n self.result_files.append({\"file\": j, \"config\": platform})\n\n # Get platforms from project\n p = Configurations(project_id=self.project_id)\n p.get(\"Platforms\")\n\n # See if we need to create new configurations on project\n for rf in self.result_files:\n config = rf['config']\n if p.provides(config=config) == 0:\n print(\"Need to create new config %s\" % rf.get('config'))\n p.add(config)\n\n # update\n p.get(\"Platforms\")\n for rf in self.result_files:\n rf['id'] = p.provides(rf.get('config'))\n\n\nclass SanityCheck(TestRun):\n\n def __init__(self, results_file, config, project_id, version, suite, milestone, plan):\n super().__init__()\n super().authorize()\n\n if plan:\n self.plan = self.client.plan.get(plan)\n\n pprint(self.plan)\n self.project_id = self.plan.get('project_id', None)\n else:\n self.project_id = project_id\n\n\n self.config = config\n self.version = version\n self.suite = suite\n self.results_file = results_file\n self.milestone = milestone\n self.title = \"Sanitycheck\"\n\n def get_case_name(self, name):\n return name\n\n def find_parent_in_junit(self, junit_xml, name):\n return None\n\n def get_case_text(self, text):\n return text\n\n\n\n def discover(self):\n\n self.result_files.append({\"file\": self.results_file, \"config\": self.config})\n\n # Get platforms from project\n p = Configurations(project_id=self.project_id)\n p.get(\"Platforms\")\n\n # See if we need to create new configurations for platforms on project\n for rf in self.result_files:\n config = rf['config']\n if p.provides(config=config) == 0:\n print(\"Need to create new config %s\" % rf.get('config'))\n p.add(config)\n\n # update\n p.get(\"Platforms\")\n for rf in self.result_files:\n rf['id'] = p.provides(rf.get('config'))\n\n\n\nclass MaxwellPro(TestRun):\n\n def __init__(self, results_file, config, project_id, version, suite, milestone, plan):\n super().__init__()\n super().authorize()\n\n if plan:\n self.plan = self.client.plan.get(plan)\n self.project_id = self.plan.get('project_id', None)\n else:\n self.project_id = project_id\n\n self.config = config\n self.version = version\n self.suite = suite\n self.results_file = results_file\n self.milestone = milestone\n self.title = \"Maxwell TCP/IP\"\n\n def get_case_name(self, name):\n return name\n\n def get_case_text(self, text):\n return text\n\n def results_for_config(self, results_file, config):\n\n results = []\n with open(self.results_file, \"r\") as file:\n for line in file.readlines():\n #match = re.match(r\"\\s*(\\d+)/(\\d+)\\s+([^\\s]+)\\s+(%s)\\s+(PASS|SKIPPED|FAIL|UNGRADED)\".format(config), line)\n match = re.match(r\"\\s*(\\d+)/(\\d+)\\s+([^\\s]+)\\s+([^\\s]+)\\s+([^\\s]+)\", line)\n if match and match.group(4) == config:\n results.append({'id': match.group(3), 'result': match.group(5)})\n return results\n\n def parse_file(self, result_file):\n\n config = result_file.get('config')\n print(\"Parsing {}\".format(result_file['file']))\n\n results = self.results_for_config(result_file.get('file'), config)\n results_to_upload = []\n\n for r in results:\n cr = {}\n cr['ref'] = r['id'].replace(\".\", \"-\").upper()\n\n result = r.get('result')\n\n if result == 'PASS':\n test_status = self.status.PASSED\n elif result == 'SKIPPED':\n test_status = self.status.SKIPPED\n elif result == 'FAIL':\n test_status = self.status.FAILED\n elif result == 'FAIL':\n test_status = self.status.FAILED\n elif result == 'UNGRADED':\n test_status = self.status.UNGRADED\n\n else:\n test_status = self.status.RETEST\n\n cr['status_id'] = test_status\n cr['version'] = self.version\n\n results_to_upload.append(cr)\n\n tmp_res = []\n tids = []\n\n for result in results_to_upload:\n\n ref = result.get('ref')\n case_id = self.get_case_id(ref)\n if case_id:\n tids.append(case_id)\n\n result['case_id'] = case_id\n del result['ref']\n tmp_res.append(result)\n else:\n print(\"Cant find case for {}\".format(ref))\n\n entry = {\n \"include_all\": False,\n \"case_ids\": tids,\n \"config_ids\": [result_file['id']],\n }\n self.final_results.append(\n {'config': config, 'results': tmp_res, 'run_entry': entry, 'config_id': result_file['id']})\n\n def discover(self):\n\n results = {}\n with open(self.results_file, \"r\") as file:\n for line in file.readlines():\n match = re.match(r\"\\s*(\\d+)/(\\d+)\\s+([^\\s]+)\\s+([^\\s]+)\\s+([^\\s]+)\", line)\n if match:\n config = match.group(4)\n if results.get(config, None):\n config_results = results.get(config)\n config_results.append({'id': match.group(2), 'result': match.group(5)})\n results[config] = config_results\n else:\n results[config] = [{'id': match.group(3), 'result': match.group(5)}]\n\n\n for config in results.keys():\n self.result_files.append({\"file\": self.results_file, \"config\": config})\n\n # Get platforms from project\n p = Configurations(project_id=self.project_id)\n p.get(\"Protocols\")\n\n # See if we need to create new configurations on project\n for rf in self.result_files:\n config = rf['config']\n if p.provides(config=config) == 0:\n print(\"Need to create new config %s\" % config)\n p.add(config)\n\n # update\n p.get(\"Protocols\")\n for rf in self.result_files:\n rf['id'] = p.provides(rf.get('config'))\n\n\nclass AutoPTS(TestRun):\n\n def __init__(self, results_file, config, project_id, version, suite, milestone, plan):\n super().__init__()\n super().authorize()\n\n if plan:\n self.plan = self.client.plan.get(plan)\n self.project_id = self.plan.get('project_id', None)\n else:\n self.project_id = project_id\n\n self.config = config\n self.version = version\n self.suite = suite\n self.results_file = results_file\n self.milestone = milestone\n self.title = \"Bluetooth AutoPTS\"\n\n def get_case_name(self, name):\n return name\n\n def get_case_text(self, text):\n return text\n\n def results_for_config(self, results_file, config):\n\n results = []\n with open(self.results_file, \"r\") as file:\n for line in file.readlines():\n match = re.match(r\"(.*)\\t([^\\s]+)\\t(.*)\", line)\n if match:\n results.append({'id': match.group(2), 'result': match.group(3).strip()})\n return results\n\n def parse_file(self, result_file):\n\n config = result_file.get('config')\n print(\"Parsing {}\".format(result_file['file']))\n\n results = self.results_for_config(result_file.get('file'), config)\n results_to_upload = []\n\n for r in results:\n cr = {}\n cr['ref'] = r['id']\n\n result = r.get('result')\n\n if result == 'PASS':\n test_status = self.status.PASSED\n elif result == 'SKIPPED':\n test_status = self.status.SKIPPED\n elif result == 'FAIL':\n test_status = self.status.FAILED\n elif result == 'INCONC':\n test_status = self.status.UNGRADED\n elif result == 'BTP ERROR':\n test_status = self.status.ERROR\n elif result in ['BTP TIMEOUT', 'PTS TIMEOUT']:\n test_status = self.status.TIMEOUT\n else:\n test_status = self.status.RETEST\n\n cr['status_id'] = test_status\n cr['version'] = self.version\n\n results_to_upload.append(cr)\n\n tmp_res = []\n tids = []\n\n for result in results_to_upload:\n\n ref = result.get('ref')\n case_id = self.get_case_id(ref)\n if case_id:\n tids.append(case_id)\n\n result['case_id'] = case_id\n del result['ref']\n tmp_res.append(result)\n else:\n print(\"Cant find case for {}\".format(ref))\n\n entry = {\n \"include_all\": False,\n \"case_ids\": tids,\n \"config_ids\": [result_file['id']],\n }\n self.final_results.append(\n {'config': config, 'results': tmp_res, 'run_entry': entry, 'config_id': result_file['id']})\n\n def discover(self):\n\n results = {}\n self.result_files.append({\"file\": self.results_file, \"config\": self.config})\n\n # Get platforms from project\n p = Configurations(project_id=self.project_id)\n p.get(\"Platforms\")\n\n # See if we need to create new configurations on project\n for rf in self.result_files:\n config = rf['config']\n if p.provides(config=config) == 0:\n print(\"Need to create new config %s\" % config)\n p.add(config)\n\n # update\n p.get(\"Platforms\")\n for rf in self.result_files:\n rf['id'] = p.provides(rf.get('config'))\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description=\"Upload test results testrail\")\n\n\n parser.add_argument(\"--runner\", default='sanitycheck', choices=['tcf', 'sanitycheck', 'maxwell', 'autopts'],\n help=\"\"\"\nSelect runner to import from.\n\"\"\")\n\n result_file_select = parser.add_argument_group(\"Result files (input)\",\n \"\"\"\n\nYou can either select a directory with multiple files or just point to\none file depending on the source of the results.\n\nTCF results expect a directory with multiple files,\nwhere sanitycheck expect 1 file per configuration.\n \"\"\")\n\n\n xor_results = result_file_select.add_mutually_exclusive_group()\n xor_results.add_argument('-j', '--results-dir', default=None,\n help=\"Directory with test result files\")\n\n xor_results.add_argument('-f', '--results-file', default=None,\n help=\"File with test results format.\")\n\n parser.add_argument('-c', '--config', default=None,\n help=\"Configuration name.\")\n\n parser.add_argument('-V', '--commit', default=None,\n help=\"Version being tested (git desribe string)\")\n\n parser.add_argument('-p', '--project', type=int, help=\"Project ID\")\n parser.add_argument('-s', '--suite', type=int, help=\"Suite ID\")\n\n parser.add_argument(\"-m\", \"--milestone\", type=int, help=\"Milestone ID\")\n parser.add_argument('-P', '--plan', type=int, help=\"Test plan ID\")\n\n return parser.parse_args()\n\ndef main():\n args = parse_args()\n\n\n if args.runner == 'maxwell':\n tr = MaxwellPro(results_file=args.results_file,\n config=args.config,\n project_id=args.project,\n suite=args.suite,\n version=args.commit,\n milestone=args.milestone,\n plan=args.plan)\n elif args.runner == 'sanitycheck':\n tr = SanityCheck(results_file=args.results_file,\n config=args.config,\n project_id=args.project,\n suite=args.suite,\n version=args.commit,\n milestone=args.milestone,\n plan=args.plan)\n elif args.runner == 'tcf':\n tr = TCF(results_dir=args.results_dir,\n config=args.config,\n project_id=args.project,\n suite=args.suite,\n version=args.commit,\n milestone=args.milestone,\n plan=args.plan)\n elif args.runner == 'autopts':\n tr = AutoPTS(results_file=args.results_file,\n config=args.config,\n project_id=args.project,\n suite=args.suite,\n version=args.commit,\n milestone=args.milestone,\n plan=args.plan)\n else:\n sys.exit(\"Unknown runner\")\n\n tr.discover()\n tr.configure()\n tr.process()\n tr.upload()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"scripts/test_reports/report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":24369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"320478765","text":"import pandas as pd\nimport numpy as np\nimport calendar\n\nclass ParkingDataLoader:\n def __init__(self, scn_to_categories=None, dataset_location=\"./data/dataset.csv\"):\n self.__scn_to_categories = scn_to_categories if scn_to_categories is not None else ParkingDataLoader.__get_default_scn_to_labels()\n self.__dataset = pd.read_csv(dataset_location, parse_dates=[3])\n self.__data_overview = None\n \n def get_data_overview(self):\n return self.__data_overview if self.__data_overview is not None else self.__do_get_data_overview()\n \n def get_train_validation_test_datasets(self, validation_split=0.16, test_split=0.2, without_scns=['BHMBRTARC01', 'NIA North']):\n cleaned_parking_data = self.__dataset[~self.__dataset['SystemCodeNumber'].isin(without_scns)]\n parking_data = pd.DataFrame()\n parking_data['Occupied'] = cleaned_parking_data['Occupancy'] / cleaned_parking_data['Capacity']\n for category in set(self.__scn_to_categories.values()):\n parking_data[category] = cleaned_parking_data['SystemCodeNumber'].map(lambda val: 1 if self.__scn_to_categories[val] == category else 0)\n parking_data['SecondsSin'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_sin(idx.second, 60))\n parking_data['SecondsCos'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_cos(idx.second, 60))\n parking_data['MinSin'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_sin(idx.minute, 60))\n parking_data['MinCos'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_cos(idx.minute, 60))\n parking_data['HourSin'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_sin(idx.hour, 24))\n parking_data['HourCos'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_cos(idx.hour, 24))\n parking_data['WeekdaySin'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_sin(idx.weekday(), 7))\n parking_data['WeekdayCos'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_cos(idx.weekday(), 7))\n parking_data['DaysOfMonthSin'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_sin(idx.day, calendar.monthrange(idx.year, idx.month)[1]))\n parking_data['DaysOfMonthCos'] = cleaned_parking_data['LastUpdated'].map(lambda idx: self.__to_cyclical_cos(idx.day, calendar.monthrange(idx.year, idx.month)[1]))\n return self.__split_to_train_validation_test_datasets(parking_data, validation_split, test_split)\n \n def __do_get_data_overview(self):\n overview_data = pd.DataFrame()\n for scn in pd.unique(self.__dataset.SystemCodeNumber):\n scn_data = self.__dataset[self.__dataset['SystemCodeNumber'] == scn]\n overview_data = overview_data.append({'scn': scn,\n 'min_date': min(scn_data['LastUpdated']),\n 'max_date': max(scn_data['LastUpdated']),\n 'length': len(scn_data)}, ignore_index=True)\n overview_data = overview_data.set_index('scn')\n self.__overview_data = overview_data\n return overview_data\n \n \n def __to_cyclical_sin(self, value, n_period):\n return np.sin(value * (2. * np.pi / n_period))\n \n \n def __to_cyclical_cos(self, value, n_period):\n return np.cos(value * (2. * np.pi / n_period))\n \n def __split_to_train_validation_test_datasets(self, data, validation_split, test_split):\n test_data_size = int(len(data) * test_split)\n test_idxs = np.random.randint(0, len(data) - 1, test_data_size)\n test_data = data.iloc[test_idxs]\n training_data = data.drop(test_data.index)\n validation_data_size = int(len(data) * validation_split)\n validation_idxs = np.random.randint(0, len(training_data) - 1, validation_data_size)\n validation_data = training_data.iloc[validation_idxs]\n training_data = training_data.drop(validation_data.index)\n return training_data, validation_data, test_data\n \n @staticmethod\n def __get_default_scn_to_labels():\n return {\n 'BHMBCCMKT01': 'CityCentre',\n 'BHMBCCPST01': 'CityCentre',\n 'BHMBCCSNH01': 'Transport',\n 'BHMBCCTHL01': 'CityCentre',\n 'BHMBRCBRG01': 'CityCentre',\n 'BHMBRCBRG02': 'Transport',\n 'BHMBRCBRG03': 'CityCentre',\n 'BHMBRTARC01': 'Hotel',\n 'BHMEURBRD01': 'CityCentre',\n 'BHMEURBRD02': 'CityCentre',\n 'BHMMBMMBX01': 'ShoppingMall',\n 'BHMNCPHST01': 'CityCentre',\n 'BHMNCPLDH01': 'ShoppingMall',\n 'BHMNCPNHS01': 'CityCentre',\n 'BHMNCPNST01': 'ShoppingMall',\n 'BHMNCPPLS01': 'Transport',\n 'BHMNCPRAN01': 'Hotel',\n 'Broad Street': 'ShoppingMall',\n 'Bull Ring': 'ShoppingMall',\n 'NIA Car Parks': 'Stadium',\n 'NIA North': 'Stadium',\n 'NIA South': 'Stadium',\n 'Others-CCCPS105a': 'Other',\n 'Others-CCCPS119a': 'Other',\n 'Others-CCCPS133': 'Other',\n 'Others-CCCPS135a': 'Other',\n 'Others-CCCPS202': 'Other',\n 'Others-CCCPS8': 'Other',\n 'Others-CCCPS98': 'Other',\n 'Shopping': 'ShoppingMall'\n }","sub_path":"parking_prediction/data_cleaning.py","file_name":"data_cleaning.py","file_ext":"py","file_size_in_byte":5106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"342743269","text":"import pandas as pd\nimport matplotlib.pyplot as plt\n\n\ndef clean_plot():\n aqi_data1 = pd.read_csv('Real Time AQI of Major Cities in the World.csv', na_values=['-'])\n aqi_data = aqi_data1.copy()\n for i in aqi_data.index:\n aqi_data['City'][i] = aqi_data1['City'][i].split('(')[0]\n # save as csv by pd without import csv\n aqi_data.to_csv('Cleaned: Real Time AQI of Major Cities in the World.csv', index=False)\n\n top50_cities = aqi_data.sort_values(by=['AQI'], ascending=False).head(50)\n top50_cities.plot(kind='bar', x='City', y='AQI',\n title='Top 50 Major City with the Worst AQI in the World', figsize=(20, 10))\n\n plt.show()\n\n\nclean_plot()\n","sub_path":"code folder/Projects/AQI Data Processing.py","file_name":"AQI Data Processing.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"117063833","text":"# -*- coding=utf-8 -*-\n#U版账户与支付的接口\nimport xml.dom.minidom\nimport xlrd\nimport xlutils.copy\n\nclass message(object):\n def __init__(self,src=\"\",dst=\"\"):\n self.src=src\n self.dst=dst\n def __str__(self):\n return \"src=\"+self.src+\" dst=\"+self.dst\n\nclass check_win_function(object):\n def __init__(self):\n self.excel_map={}\n self.xml_map={}\n self.messages=[]\n self.sheet_index=9\n self._xml=\"kbss2pay_unix.xml\"\n self._excel=\"KBSS_统一账户系统对接系统关系清单.xlsx\"\n self.tmp_excel=\"U版账户与支付临时文件.xls\"\n\n def parse_xml(self):\n dom=xml.dom.minidom.parse(self._xml)\n self.messages=dom.getElementsByTagName(\"message\")\n print(\"message count=\",len(self.messages))\n for msg in self.messages:\n #print(msg.getAttribute(\"src\"),msg.getAttribute(\"dst\"))\n m=message(msg.getAttribute(\"src\").strip(),msg.getAttribute(\"dst\").strip())\n if not m.dst in self.xml_map.keys():\n self.xml_map[m.dst]=m\n #if not m.dst in self.excel_map.keys():\n # print(m)\n print(\"message unique count=\",len(self.xml_map))\n\n def parese_excel(self):\n workbook=xlrd.open_workbook(self._excel)\n sheet=workbook.sheets()[self.sheet_index]\n for i in range(1,sheet.nrows):\n dst=str(sheet.cell_value(i,1))\n src=str(sheet.cell_value(i,2))\n if dst.find(\".\")!=-1:\n dst=dst[:dst.find(\".\")].strip()\n if src.find(\".\")!=-1:\n src=src[:dst.find(\".\")].strip()\n #print(\"dst=\",dst,\"src=\",src)\n msg=message(src,dst)\n if not msg.dst in self.excel_map.keys():\n self.excel_map[msg.dst]=msg\n print(\"excel lbm count=\",len(self.excel_map))\n #for item in self.excel_map.items():\n # print(item)\n def check(self):\n workbook=xlrd.open_workbook(self._excel)\n wb=xlutils.copy.copy(workbook)\n sheet1=wb.get_sheet(self.sheet_index)\n index=workbook.sheets()[self.sheet_index].nrows+1\n sheet2=workbook.sheets()[self.sheet_index]\n for i in range(1,sheet2.nrows):\n dst=str(sheet2.cell_value(i,1)).strip()\n if dst.find(\".\")!=-1:\n dst=dst[:dst.find(\".\")].strip()\n msg=self.xml_map.get(dst,None)\n sheet1.write(i,2,msg.src if msg is not None else \"not found\")\n print(dst,self.xml_map.get(dst,\"not found\"))\n print(\"key in xml not in excel:\")\n sheet1.write(index,0,\"需增加的接口\")\n index+=1\n for key in self.xml_map.keys():\n if not key in self.excel_map.keys():\n print(self.xml_map[key])\n index+=1\n sheet1.write(index,1,self.xml_map[key].dst)\n sheet1.write(index,2,self.xml_map[key].src)\n\n index+=1\n sheet1.write(index,0,\"需增删除的接口\")\n print(\"key in excel not in xml:\")\n for key in self.excel_map.keys():\n if not key in self.xml_map.keys():\n print(self.excel_map[key])\n index+=1\n sheet1.write(index,1,self.excel_map[key].dst)\n sheet1.write(index,2,self.excel_map[key].src)\n wb.save(self.tmp_excel)\n\nif __name__==\"__main__\":\n runner=check_win_function()\n runner.parese_excel()\n runner.parse_xml()\n runner.check()\n","sub_path":"tools/process_funcation/check_function/9check_kbss2pay_unix.py","file_name":"9check_kbss2pay_unix.py","file_ext":"py","file_size_in_byte":3473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"448600535","text":"import socket,random,tempfile,os\n#still not sure what this is for\nukeyvals=\"0123456789abcdef\"\ndef getRandomUKey():\n global ukeyvals\n return \"\".join([random.choice(ukeyvals) for i in range(32)])\nboundaryvals=\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\"\ndef getRandomBoundary():\n global boundaryvals\n return \"----\"+\"\".join([random.choice(boundaryvals) for x in range(34)])\ndef createPayload(file_path,ukey=None,boundary=None,filename=None,out_file=None,out_dir=None):\n if filename is None:filename=os.path.basename(file_path)\n if ukey is None:ukey=getRandomUKey()\n fh=None;wfh=None\n if out_file is None:fh,out_file=tempfile.mkstemp(dir=out_dir)\n else:wfh=open(out_file,'wb')\n if boundary is None:boundary=getRandomBoundary()\n if fh is not None:wfh=os.fdopen(fh)\n act_bound=\"--\"+boundary\n act_bound=act_bound.encode()\n wfh.write(act_bound)\n wfh.write(b\"\\r\\nContent-Disposition: form-data; name=\\\"u_key\\\"\\r\\n\\r\\n\")\n wfh.write(ukey.encode())\n wfh.write(b\"\\r\\n\")\n wfh.write(act_bound)\n wfh.write(\"\\r\\nContent-Disposition: form-data; name=\\\"files[]\\\"; filename=\\\"{0}\\\"\\r\\nContent-Type: application/octet-stream\\r\\n\\r\\n\".format(filename).encode())\n with open(file_path,'rb')as inp:\n wfh.write(inp.read())\n wfh.write(b\"\\r\\n\")\n wfh.write(act_bound)\n wfh.write(b\"--\\r\\n\")\n wfh.close()\n return out_file\ndef uploadFile():\n conn=socket.create_connection((\"expirebox.com\",443))\n","sub_path":"expirebox.py","file_name":"expirebox.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"70994681","text":"# -*- coding: utf-8 -*-\nimport os, sys, time, datetime, json\nimport csv\nimport helpers.file_helper as file_helper\nimport helpers.log_helper as log_helper\nimport helpers.sql_helper as sql_helper\nimport helpers.send_slack as send_slack\nimport helpers.logger_helper as logger_helper\nimport config\nfrom enums import status, color\n\nnow = datetime.datetime.now()\nnow_str = now.strftime(\"%Y-%m-%d %H:%M:%S\")\nnow_id = now.strftime(\"%Y%m%d%H%M%S\")\nlogger = logger_helper.mylog('validate_katalon_report').getlog()\n\ndef validate_purchase_ui(log_id, steps, country, report_path,report_result, sql_insert_data_list=[], is_send_slack=False):\n with open(report_path) as csvfile:\n readCSV = csv.reader(csvfile, delimiter=',')\n\n step_go = True\n for (step, item) in steps.items():\n if step_go:\n is_success = False\n for row in readCSV:\n if row[0] == item[1] and row[6] == 'PASSED':\n is_success = True\n break\n if is_success:\n logger.info('katalon passed: country=%s, step=%s, case=%s' % (country, step, item[1]))\n sql_insert_data_list.append((log_id, country, step, item[0], status.Success, '', now_str))\n else:\n logger.info('katalon failed: country=%s, step=%s, case=%s' % (country, step, item[1]))\n if is_send_slack:\n try:\n title = ', this is Katalon failed notification with purchase availability'\n attachments = [\n {\n \"pretext\": \"--------------\",\n \"title\": \"Country=%s, Step=%s, Case=%s\" % (country, step, item[1]), \n \"text\": \"Refer To: %s\" % report_result, \n \"color\": color.Fail,\n \"ts\": int(time.time())\n }\n ]\n send_slack.send_slack(config.slack[\"token\"], config.slack[\"channel\"], title, attachments)\n except Exception as e:\n logger.error('send slack error:' + str(e))\n finally:\n logger.info('end send slack...')\n sql_insert_data_list.append((log_id, country, step, item[0], status.Failed, report_path, now_str))\n step_go = False\n else:\n logger.info('Skip: country=%s, step=%s, case=%s' % (country, step, item[1]))\n sql_insert_data_list.append((log_id, country, step, item[0], status.NotValid_Skip, '', now_str))\n return sql_insert_data_list\n\nif __name__ == \"__main__\":\n #init data what need (format data&time, env, country)\n args = sys.argv[1:]\n if not args:\n logger.info(\"not args\")\n env='Preview'\n country='HK'\n report_id='7'\n else:\n env = args[0]\n country = args[1]\n report_id = args[2]\n logger.info(\"env:{0}, country:{1}, report_id:{2}\".format(env, country, report_id))\n\n sql_insert_data_list = []\n\n is_success_purchase_ui = validate_purchase_ui(env, country, report_id, sql_insert_data_list)\n logger.info(\"sql_insert_data_list: %s\" % sql_insert_data_list)\n ","sub_path":"validate_katalon_report.py","file_name":"validate_katalon_report.py","file_ext":"py","file_size_in_byte":3418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"135338184","text":"from eth_keys.datatypes import PrivateKey\nfrom eth_typing import Address\nfrom eth_utils import (\n to_int,\n)\nfrom rlp.exceptions import (\n DeserializationError,\n)\n\nfrom eth.abc import (\n SignedTransactionAPI,\n TransactionBuilderAPI,\n UnsignedTransactionAPI,\n)\nfrom eth.exceptions import UnrecognizedTransactionType\nfrom eth.vm.forks.muir_glacier.transactions import (\n MuirGlacierTransaction,\n MuirGlacierUnsignedTransaction,\n)\n\nfrom eth._utils.transactions import (\n create_transaction_signature,\n)\n\n\nclass BerlinLegacyTransaction(MuirGlacierTransaction):\n pass\n\n\nclass BerlinUnsignedLegacyTransaction(MuirGlacierUnsignedTransaction):\n def as_signed_transaction(self,\n private_key: PrivateKey,\n chain_id: int = None) -> BerlinLegacyTransaction:\n v, r, s = create_transaction_signature(self, private_key, chain_id=chain_id)\n return BerlinLegacyTransaction(\n nonce=self.nonce,\n gas_price=self.gas_price,\n gas=self.gas,\n to=self.to,\n value=self.value,\n data=self.data,\n v=v,\n r=r,\n s=s,\n )\n\n\nclass BerlinTransactionBuilder(TransactionBuilderAPI):\n \"\"\"\n Responsible for serializing transactions of ambiguous type.\n\n It dispatches to either the legacy transaction type or the new typed\n transaction, depending on the nature of the encoded/decoded transaction.\n \"\"\"\n legacy_signed = BerlinLegacyTransaction\n legacy_unsigned = BerlinUnsignedLegacyTransaction\n\n @classmethod\n def deserialize(cls, encoded: bytes) -> SignedTransactionAPI:\n if len(encoded) == 0:\n raise DeserializationError(\n \"Encoded transaction was empty, which makes it invalid\",\n encoded,\n )\n\n if isinstance(encoded, bytes):\n transaction_type = to_int(encoded[0])\n if transaction_type == 1:\n raise UnrecognizedTransactionType(transaction_type, \"TODO: Implement EIP-2930\")\n elif transaction_type in range(0, 0x80):\n raise UnrecognizedTransactionType(transaction_type, \"Unknown transaction type\")\n else:\n raise DeserializationError(\n f\"Typed Transaction must start with 0-0x7f, but got {hex(transaction_type)}\",\n encoded,\n )\n else:\n return cls.legacy_signed.deserialize(encoded)\n\n @classmethod\n def serialize(cls, obj: SignedTransactionAPI) -> bytes:\n return cls.legacy_signed.serialize(obj)\n\n @classmethod\n def create_unsigned_transaction(cls,\n *,\n nonce: int,\n gas_price: int,\n gas: int,\n to: Address,\n value: int,\n data: bytes) -> UnsignedTransactionAPI:\n return cls.legacy_unsigned(nonce, gas_price, gas, to, value, data)\n\n @classmethod\n def new_transaction(\n cls,\n nonce: int,\n gas_price: int,\n gas: int,\n to: Address,\n value: int,\n data: bytes,\n v: int,\n r: int,\n s: int) -> SignedTransactionAPI:\n return cls.legacy_signed(nonce, gas_price, gas, to, value, data, v, r, s)\n","sub_path":"eth/vm/forks/berlin/transactions.py","file_name":"transactions.py","file_ext":"py","file_size_in_byte":3492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"545713582","text":"import win32com.client\n\ndef Excel():\n return win32com.client.Dispatch('Excel.Application')\n\ndef hello_world():\n \"\"\"\n Create a 'Hello World' Excel workbook.\n \"\"\"\n xl = Excel()\n\n wb = xl.Workbooks.Add()\n wb.Worksheets[0].Cells(1,1).Value = 'Hello World'\n\n xl.Visible = True\n\ndef main():\n \"\"\"\n Demonstrations of using win32com to create Excel files.\n \"\"\"\n from argparse import ArgumentParser\n\n cli = ArgumentParser(description=main.__doc__)\n args = cli.parse_args()\n hello_world()\n\nif __name__ == '__main__':\n main()\n","sub_path":"demos/win32xl.py","file_name":"win32xl.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"88083485","text":"# Python Crash Course Part 3\r\n\r\nseq = [1,2,3,4,5]\r\n\r\n# For Loop\r\nfor item in seq:\r\n print(item)\r\n\r\nfor item in seq:\r\n print('item')\r\n\r\n# While Loop\r\n\r\ni = 1\r\n\r\nwhile i < 5:\r\n print('i is: {}'.format(i))\r\n i += 1\r\n\r\n# Range\r\n\r\nrange(0,5)\r\nfor x in range(0,5):\r\n print(x) #prints values 0, 1, 2, 3\r\n\r\nlist(range(10)) # Creates a temporary list from 0 up to but not including 10\r\n\r\n\r\nx = [1,2,3,4]\r\nout = [] # empty list\r\n\r\nfor item in x:\r\n out.append(item**2)\r\nprint(out)\r\n\r\n# List Comprehension\r\n\r\nprint([num**2 for num in x]) # produces the same result as the above for-loop\r\n\r\n\r\n# Functions\r\n\r\ndef my_func(param='Default Name'):\r\n print('Hello ' + param)\r\n\r\nmy_func(\"Zach\")\r\nmy_func()\r\n\r\ndef square(num):\r\n \"\"\"\r\n Here is a documentation string, basically\r\n a multi-line comment\r\n \"\"\"\r\n return num**2\r\noutput = square(3)\r\nprint(output)\r\n","sub_path":"Python-Data-Science-and-Machine-Learning-Bootcamp/Python-Crash-Course/S4L11.py","file_name":"S4L11.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"174983025","text":"\"\"\"\nCopyright 2020 Ye Bai by1993@qq.com\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\nimport os\nimport argparse\nimport logging\nimport yaml\nimport torch\nfrom torch.utils.data import DataLoader\n\nimport utils\nfrom dataload import datasets, samplers, data_utils, collates\n\n\nif \"LAS_LOG_LEVEL\" in os.environ:\n LOG_LEVEL = os.environ[\"LAS_LOG_LEVEL\"]\nelse:\n LOG_LEVEL = \"INFO\"\nif LOG_LEVEL == \"DEBUG\":\n logging.basicConfig(\n level=logging.DEBUG,\n format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s')\nelse:\n logging.basicConfig(\n level=logging.INFO,\n format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s')\n\ndef get_args():\n parser = argparse.ArgumentParser(description=\"\"\"\n Usage: train.py \"\"\")\n parser.add_argument(\"config\", help=\"path to config file\")\n parser.add_argument('--type', type=str, default='pretrain',\n help='Continue training from last_model.pt.')\n parser.add_argument('--continue-training', type=utils.str2bool, default=False,\n help='Continue training from last_model.pt.')\n args = parser.parse_args()\n return args\n\n\n\nif __name__ == \"__main__\":\n timer = utils.Timer()\n\n args = get_args()\n timer.tic()\n config = utils.AttrDict(yaml.load(open(args.config)))\n dataconfig = config[\"data\"]\n trainingconfig = config[\"training\"]\n modelconfig = config[\"model\"]\n feat_range = [int(i) for i in dataconfig['feat_range'].split(',')]\n\n ngpu = 1\n if \"multi_gpu\" in trainingconfig and trainingconfig[\"multi_gpu\"] == True:\n ngpu = torch.cuda.device_count()\n\n if args.type == 'pretrain':\n training_set = datasets.SpeechDataset(dataconfig[\"trainset\"], feat_range=feat_range)\n valid_set = datasets.SpeechDataset(dataconfig[\"devset\"], reverse=True, feat_range=feat_range)\n trainingsampler = samplers.TimeBasedSampler(training_set, trainingconfig[\"batch_time\"]*ngpu, ngpu, shuffle=True)\n validsampler = samplers.TimeBasedSampler(valid_set, trainingconfig[\"batch_time\"]*ngpu, ngpu, shuffle=False) # for plot longer utterance\n\n tr_loader = DataLoader(training_set, collate_fn=collates.waveCollate,\n batch_sampler=trainingsampler, shuffle=False,\n num_workers=dataconfig[\"fetchworker_num\"])\n cv_loader = DataLoader(valid_set, collate_fn=collates.waveCollate,\n batch_sampler=validsampler, shuffle=False,\n num_workers=dataconfig[\"fetchworker_num\"])\n\n from frameworks.CPC_Models import CPC_Model as Model\n from solvers import CPC_Solver as Solver\n\n model = Model.create_model(modelconfig['sp'], modelconfig['cpc'])\n\n elif args.type == 'finetune':\n\n tokenizer = data_utils.SubwordTokenizer(dataconfig[\"vocab_path\"], add_blk=modelconfig['add_blk'])\n modelconfig[\"decoder\"][\"vocab_size\"] = tokenizer.unit_num()\n label_range = [int(i) for i in dataconfig['label_range'].split(',')]\n\n training_set = datasets.SpeechDataset(dataconfig[\"trainset\"], feat_range=feat_range, label_range=label_range)\n valid_set = datasets.SpeechDataset(dataconfig[\"devset\"], reverse=True, feat_range=feat_range, label_range=label_range)\n trainingsampler = samplers.TimeBasedSampler(training_set, trainingconfig[\"batch_time\"]*ngpu, ngpu, shuffle=True)\n validsampler = samplers.TimeBasedSampler(valid_set, trainingconfig[\"batch_time\"]*ngpu, ngpu, shuffle=False) # for plot longer utterance\n collect = collates.WaveSampleCollate(tokenizer, add_eos=modelconfig[\"add_eos\"],\n label_type=trainingconfig[\"label_type\"])\n tr_loader = DataLoader(training_set, collate_fn=collect, batch_sampler=trainingsampler,\n shuffle=False, num_workers=dataconfig[\"fetchworker_num\"])\n cv_loader = DataLoader(valid_set, collate_fn=collect, batch_sampler=validsampler,\n shuffle=False, num_workers=dataconfig[\"fetchworker_num\"])\n\n from frameworks.Speech_Models import GRU_CTC_Model as Model\n from solvers import CTC_Solver as Solver\n\n model = Model.create_model(modelconfig[\"signal\"],\n modelconfig[\"encoder\"],\n modelconfig[\"decoder\"][\"vocab_size\"])\n\n if trainingconfig['load_splayer']:\n logging.info(\"Load pretrained splayer from {}.\".format(trainingconfig[\"load_splayer\"]))\n pkg = torch.load(trainingconfig[\"load_splayer\"])\n model.load_splayer(pkg[\"model\"])\n utils.freeze(model.splayer)\n\n logging.info(\"\\nModel info:\\n{}\".format(model))\n\n if args.continue_training:\n logging.info(\"Load package from {}.\".format(os.path.join(trainingconfig[\"exp_dir\"], \"last.pt\")))\n pkg = torch.load(os.path.join(trainingconfig[\"exp_dir\"], \"last.pt\"))\n model.restore(pkg[\"model\"])\n\n if \"multi_gpu\" in trainingconfig and trainingconfig[\"multi_gpu\"] == True:\n logging.info(\"Let's use {} GPUs!\".format(torch.cuda.device_count()))\n model = torch.nn.DataParallel(model)\n\n if torch.cuda.is_available():\n model = model.cuda()\n\n solver = Solver(model, trainingconfig, tr_loader, cv_loader)\n\n if args.continue_training:\n logging.info(\"Restore solver states...\")\n solver.restore(pkg)\n logging.info(\"Start training...\")\n solver.train()\n logging.info(\"Total time: {:.4f} secs\".format(timer.toc()))\n","sub_path":"src/train_CPC.py","file_name":"train_CPC.py","file_ext":"py","file_size_in_byte":6075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"181545676","text":"import web\nimport os\n#resolve absolute directory path\nroot_dir = os.path.abspath(os.path.dirname(__file__))\n\n#render templates from folder\ntemplate_dir = root_dir + '/templates'\nrender = web.template.render(template_dir, base='layout')\n\n#absolute path to sqlite db\ndb_dir = root_dir + '/news.db'\n\n#debugging purposes\n#web.config.debug = True\n\nurls = (\n '/', 'index',\n '/news', 'news',\n '/discography','discography',\n '/about', 'about'\n)\n\nclass index:\n def GET(self):\n return render.index()\n\nclass news:\n def GET(self):\n db = web.database(dbn='sqlite', db=db_dir)\n #retrieve only the twenty most recent articles\n articles = db.select('articles', order='epochtime DESC', limit = 20)\n update_time = db.select('articles', what='dbtime', \n limit = 1, order = 'dbtime DESC')[0].dbtime\n return render.news(articles,update_time)\n\nclass discography:\n def GET(self):\n return render.discography()\n\nclass about:\n def GET(self):\n return render.about()\n\nif __name__ == \"__main__\":\n #development\n app = web.application(urls, globals())\n app.run()\nelse:\n #mod_wsgi\n app = web.application(urls, globals(), autoreload=False)\n application = app.wsgifunc()\n","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"231357335","text":"NAME = 'idontdrinkcoffee'\nTYPE = 'math'\nFLAVOR = 'A'\nVERSION = '0.1'\nFULL_NAME = '{0}:{1}-{2} v{3}'.format(NAME, TYPE, FLAVOR, VERSION)\n\nFAVORITE_NUMBER = -3\n\nTEST_CHANNEL = 466444242860900362\nPRODUCTION_CHANNEL = 466438128987799553\nADMIN_ROLE = 'manage bots'\n\nDEFAULT_PRECISION = 25\n","sub_path":"math/A/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"112059956","text":"# using a for loop, make a program that will add the \n# letters 'at' to each string in my_list and print out\n# the result. So, the first thing that should be printed out\n# is 'cat' and the second is 'hat' and so on.\n# Note: you cannot change the variable my_list. You can do\n# everything else though\n\nmy_list = ['c', 'h', 'd', 'p', 'b', 'e' ]\nfood = ['cookeies', 'piazza', 'coke', 'candy', 'fruit']\nadd_on = \"at\"\nletter = my_list\n\nfor letter in range(0, len(my_list)):\n print (my_list[letter] + food[letter]) ","sub_path":"more_chapter_4/homework1.py","file_name":"homework1.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"5033152","text":"from easydict import EasyDict as edict\n\n__C = edict()\n\ncfg = __C\n\n#\n# Training options\n#\n\n__C.TRAIN = edict()\n\n\n# Images to use per minibatch\n__C.TRAIN.IMS_PER_BATCH = 100\n\n# Iterations between snapshots\n__C.TRAIN.SNAPSHOT_ITERS = 100000\n\n# solver.prototxt specifies the snapshot path prefix, this adds an optional\n# infix to yield the path: [_]_iters_XYZ.caffemodel\n__C.TRAIN.SNAPSHOT_INFIX = ''\n#\n# Testing options\n#\n\n__C.TEST = edict()\n\n\n\n#\n# MISC\n#\n__C.RNG_SEED = 3\n","sub_path":"pulseEx1/lib/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"383503029","text":"from datetime import datetime\n\nfrom django.contrib.auth.models import User\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db import models\nfrom tweets.constants import TweetPhotoStatus, TWEET_PHOTO_STATUS_CHOICES\nfrom tweets.listeners import push_tweet_to_cache\nfrom likes.models import Like\nfrom utils.memcached_helper import MemcachedHelper\nfrom django.db.models.signals import post_save\nfrom utils.listeners import invalidate_object_cache\n\nclass Tweet(models.Model):\n\n user = models.ForeignKey(\n User,\n on_delete=models.SET_NULL,\n null=True,\n help_text=\"who post the this tweet\",\n )\n\n content = models.CharField(max_length=255)\n created_at = models.DateTimeField(auto_now_add=True)\n class Meta:\n index_together = (('user', 'created_at'),)\n ordering = ('user', '-created_at')\n\n @property\n def hours_to_now(self):\n # time zones should all be utc\n return (datetime.utcnow() - self.created_at).seconds // 3600\n\n def __str__(self):\n return f'{self.created_at} {self.user}: {self.content}'\n\n @property\n def like_set(self):\n return Like.objects.filter(\n content_type=ContentType.objects.get_for_model(Tweet),\n object_id=self.id,\n ).order_by('-created_at')\n\n @property\n def cached_user(self):\n return MemcachedHelper.get_object_through_cache(User, self.user_id)\n\npost_save.connect(invalidate_object_cache, sender=Tweet)\npost_save.connect(push_tweet_to_cache, sender=Tweet)\n\n\nclass TweetPhoto(models.Model):\n tweet = models.ForeignKey(Tweet, on_delete=models.SET_NULL, null=True)\n user = models.ForeignKey(User, on_delete=models.SET_NULL, null=True)\n file = models.FileField()\n order = models.IntegerField(default=0)\n\n #photo status\n status = models.IntegerField(\n default = TweetPhotoStatus.PENDING,\n choices=TWEET_PHOTO_STATUS_CHOICES,\n )\n\n is_deleted = models.BooleanField(default=False)\n deleted_at = models.DateTimeField(null=True)\n created_at = models.DateTimeField(auto_now_add=True)\n\n class Meta:\n index_together = (\n ('user', 'created_at',),\n ('is_deleted', 'created_at',),\n ('status', 'created_at',),\n ('tweet', 'order',),\n )\n\n def __str__(self):\n return f'{self.tweet.id}: {self.file}'\n\n","sub_path":"tweets/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"135372447","text":"import numpy as np\nimport math\nimport itertools\n\neps = np.finfo(float).eps\nnit,TOL = 4,4*eps\n\ndef shiftMatrix(nrows,ncols,inmat):\n exmat = np.zeros(inmat.shape)\n for i in range(nrows,inmat.shape[0]):\n for j in range(ncols,inmat.shape[1]):\n exmat[i,j] = inmat[i-nrows,j-ncols]\n return exmat\n\ndef upAndDownScaleMat(N):\n tempMat = np.zeros((N,N))\n tempMat[0,0] = 1.0\n tempMat[1,0] = 1.0\n\n rfaceMat = np.zeros((2,N,N))\n for f in range(2):\n for i in range(N//2):\n rfaceMat[f] = rfaceMat[f] + shiftMatrix(2*i,f*N//2+i,tempMat)\n \n cfaceMat = np.zeros((2,N,N))\n for f in range(2):\n cfaceMat[f] = 0.5*np.transpose(rfaceMat[f])\n\ndef mk_body_centered_linspace(infimum, supremum, nNodes, withBoundaryNodes=False):\n \"\"\"\n Make regular body centered linear space w/ or w/o neighboring boundary nodes.\n \"\"\"\n\n domsize = np.abs(supremum - infimum)\n offset = domsize / nNodes / 2\n\n if withBoundaryNodes:\n nNodes = nNodes + 2\n infimum = infimum - offset\n supremum = supremum + offset\n else:\n infimum = infimum + offset\n supremum = supremum - offset\n\n return np.linspace(infimum, supremum, nNodes, endpoint=True)\n\ndef BarycentricWeight(xs,j):\n acc = 1\n for i in range(len(xs)):\n if i==j: continue\n acc *= xs[j]-xs[i]\n return 1/acc\n\ndef BarycentricWeights(xs):\n n = len(xs)\n ws = np.empty(n)\n for j in range(n):\n ws[j] = BarycentricWeight(xs,j) \n return ws\n\ndef LagrangePolynomial(xs,j,x):\n acc = 1\n for i in range(len(xs)):\n if i==j: continue\n acc *= (x-xs[i])/(xs[j]-xs[i])\n return acc\n\ndef BarycentricPolynomial(xs,ws,fs,x):\n numerator,denominator = 0,0\n for j in range(len(xs)):\n diff = x-xs[j]\n if abs(diff) <= eps: return fs[j]\n numerator += fs[j]*ws[j]/diff\n denominator += ws[j]/diff\n return numerator/denominator\n\ndef DiffMatrix(xs):\n ws = BarycentricWeights(xs)\n n = len(xs)\n M = np.zeros([n,n])\n for i in range(n):\n for j in range(n):\n if i != j:\n M[i,j] = ws[j]/(ws[i]*(xs[i]-xs[j]))\n else:\n acc = 0\n for k in range(n):\n if i == k: continue\n acc += ws[k]/(xs[i]-xs[k]) \n M[i,i] = -acc/ws[i]\n return M\n\ndef MassMatrix(ws):\n return np.diagflat(ws)\n\ndef LagrangePolynomialDerivative(xs,j,x):\n ACC = 0\n for i in range(len(xs)):\n if i == j: continue\n acc = 1\n for m in range(len(xs)):\n if m == i or m == j: continue\n acc *= (x-xs[m])/(xs[j]-xs[m])\n ACC += acc/(xs[j]-xs[i])\n return ACC\n\n# =========================================================================== #\n\ndef LegendrePolynomialAndDerivative(N,x,doNormalize=False):\n if N == 0:\n LN, LND = 1, 0\n elif N == 1:\n LN, LND = x, 1\n else:\n LN_2, LN_1 = 1, x\n LND_2, LND_1 = 0, 1\n\n for k in range(2,N+1):\n LN = (2*k-1)/k * x * LN_1 - (k-1)/k * LN_2\n LND = LND_2 + (2*k-1) * LN_1\n LN_2, LN_1 = LN_1, LN\n LND_2, LND_1 = LND_1, LND\n\n if doNormalize:\n LN *= np.sqrt(N + 0.5)\n LND *= np.sqrt(N + 0.5)\n\n return (LN,LND)\n\ndef LegendreGaussNodesAndWeights(N):\n \"\"\" Compute the nodes (roots of the Legendre Polynomial) and weights for\n the Legendre-Gauss-Quadrature. \"\"\"\n if N == 0: return (np.array([0]),np.array([2]))\n if N == 1: return (np.array([-np.sqrt(1/3),np.sqrt(1/3)]) , np.array([1,1]) )\n\n # nodes and weights\n xs = np.zeros(N+1)\n ws = np.zeros(N+1)\n \n for j in range(math.floor((N+1)/2)):\n # make initial guess for the jth's node\n xs[j] = -math.cos((2*j+1)/(2*N+2) * math.pi) \n for k in range(0,nit):\n # Newton's method for finding the root\n LN1,LND1 = LegendrePolynomialAndDerivative(N+1,xs[j])\n Delta = -LN1/LND1\n xs[j] += Delta\n if abs(Delta) <= TOL * abs(xs[j]): break\n\n # get final optimal values for Legendre Polynomial\n LN1,LND1 = LegendrePolynomialAndDerivative(N+1,xs[j])\n ws[j] = 2/(1-xs[j]**2)/LND1**2\n # utilize symmetry\n xs[N-j] = -xs[j]\n ws[N-j] = ws[j]\n \n # consider the middle point if there is one (always zero)\n if N % 2 == 0:\n LN1,LND1 = LegendrePolynomialAndDerivative(N+1,0.0)\n xs[N//2] = 0\n ws[N//2] = 2/LND1**2\n\n return (xs,ws)\n\n# =========================================================================== #\n\ndef qAndLEvaluation(N,x):\n LN_2,LN_1,LND_2,LND_1 = 1,x,0,1\n\n for k in range(2,N+1):\n LN = (2*k-1)/k * x * LN_1 - (k-1)/k * LN_2\n LND = LND_2 + (2*k-1) * LN_1\n LN_2,LN_1 = LN_1,LN\n LND_2,LND_1 = LND_1,LND\n\n k = N+1 \n LN1 = (2*k-1)/k * x * LN - (k-1)/k * LN_2\n LND1 = LND_2 + (2*k-1) * LN_1\n q = LN1 - LN_2\n qD = LND1 - LND_2\n\n return (q,qD,LN)\n\ndef LegendreGaussLobattoNodesAndWeights(N):\n if N == 1: return (np.array([-1,1]), np.array([1,1]))\n\n xs,ws = np.empty(N+1),np.empty(N+1)\n\n xs[0],xs[N] = -1,1\n ws[0],ws[N] = (2/N/(N+1),) * 2\n\n for j in range(math.floor((N+1)/2)):\n xs[j] = - math.cos((j+1/4)*math.pi/N - 3/8/N/math.pi/(j+1/4))\n \n for k in range(nit+1):\n q,qD,LN = qAndLEvaluation(N,xs[j])\n Delta = -q/qD\n xs[j] += Delta\n if abs(Delta) <= TOL*abs(xs[j]): break\n\n q,qD,LN = qAndLEvaluation(N,xs[j])\n xs[N-j] = -xs[j]\n ws[N-j] = ws[j] = 2/N/(N+1)/LN**2\n\n if N % 2 == 0:\n q,qD,LN = qAndLEvaluation(N,0.0)\n xs[N//2] = 0\n ws[N//2] = 2/N/(N+1)/LN**2\n\n return (xs,ws)\n\n# =========================================================================== #\n\ndef integrate(xs,ws,f):\n acc = 0\n for j in range(len(xs)):\n acc += ws[j] * f(xs[j]) \n return acc\n\n# =========================================================================== #\n\ndef mk_mass_matrix(xs,ws):\n n = len(xs)\n M = np.empty([n,n])\n for i in range(n):\n for j in range(n):\n f = lambda x: LagrangePolynomial(xs,i,x) * LagrangePolynomial(xs,j,x)\n M[i,j] = integrate(xs,ws,f)\n return M\n\ndef mk_visual_matrix(Xs,xs):\n M = np.empty([len(Xs),len(xs)])\n for i in range(len(Xs)):\n for j in range(len(xs)):\n M[i,j] = LagrangePolynomial(xs,j,Xs[i])\n return M\n\ndef mk_corner_matrix(N):\n B = np.zeros([N,N])\n B[0,0] = -1\n B[-1,-1] = 1\n\n return B\n\n# =========================================================================== #\n\ndef f1(N=None):\n return lambda x: np.cos(x)\n\ndef f2(N=None):\n return lambda x: 1/(1+x**2)\n\ndef f3(N=None):\n return lambda x: x**(2*N-2)\n\ndef f4(N=None):\n return lambda x: x**(2*N)\n\ndef f5(N=None):\n return lambda x: x**(2*N+2)\n\n# =========================================================================== #\n\ndef mk_exponent_vector(ndim,npoly):\n #return (x for x in itertools.product(range(npoly+1),repeat=ndim) if sum(x) <= npoly)\n return itertools.product(range(npoly+1),repeat=ndim)\n\ndef mk_polynome_vector(x,npoly):\n return [np.prod(np.power(x,e)) for e in mk_exponent_vector(x.shape[-1], npoly)]\n\ndef mk_polynome_matrix(xs, npoly):\n return np.array([mk_polynome_vector(x,npoly) for x in xs])\n\ndef put_row(M,nrow,row):\n tmp = M.copy()\n tmp[nrow] = row\n return tmp\n\ndef mk_interpol_matrix(xs,Xs,npoly):\n M = mk_polynome_matrix(xs,npoly)\n\n return np.array([\n [np.linalg.det(put_row(M,nrow,mk_polynome_vector(X,npoly)))\n for nrow in range(len(M))] for X in Xs])/np.linalg.det(M)\n\ndef mk_polynomial_interpolator(xs,Xs,npoly):\n IM = mk_interpol_matrix(xs, Xs,npoly)\n def closure(fs, domain):\n return np.dot(IM,fs)\n return closure \n\n# =========================================================================== #\n\ndef mk_lagrange_vector(xs,x):\n return np.array([LagrangePolynomial(xs,j,x) for j in range(len(xs))])\n\ndef mk_vandermonde_matrix(xs,ys):\n return np.array([mk_lagrange_vector(xs,x) for x in ys])\n\n# =========================================================================== #\n\ndef mk_lagrange_interpolator_2d(xs,ys, Xs):\n def polyv(nodes,x):\n return np.array([LagrangePolynomial(nodes,j,x) for j in range(len(nodes))])\n\n def polyouter(x,y):\n return np.einsum('i,j->ij',polyv(xs,x),polyv(ys,y))\n \n tensors = [polyouter(*X) for X in Xs]\n \n def interpolate(fs):\n return np.array([np.sum(fs*t) for t in tensors])\n \n return interpolate\n\ndef mk_lagrange_interpolator_3d(xs,ys,zs,Xs):\n def polyv(nodes,x):\n return np.array([LagrangePolynomial(nodes,j,x) for j in range(len(nodes))])\n\n def polyouter(x,y,z):\n return np.einsum('i,j,k->ijk',polyv(xs,x),polyv(ys,y),polyv(zs,z))\n \n tensors = [polyouter(*X) for X in Xs]\n\n def interpolate(fs):\n return np.array([np.sum(fs*t) for t in tensors])\n \n return interpolate\n\ndef mk_nodes(npoly, ntype='gauss'):\n if ntype == 'gauss':\n fun = LegendreGaussNodesAndWeights\n elif ntype == 'gauss-lobatto':\n fun = LegendreGaussLobattoNodesAndWeights\n elif ntype == 'cell-centered':\n fun = lambda npoly: (mk_body_centered_linspace(-1,1,npoly+1),None)\n else:\n raise KeyError(\"unknown node type: '%s'\" % ntype)\n\n nodes, _ = fun(npoly)\n return np.array(nodes)\n\ndef mk_nodes_from_to(x0,x1,npoly,ntype='gauss'):\n return x0 + (x1-x0) * (mk_nodes(npoly,ntype)+1)/2\n\ndef mk_LegendreVandermondeMatrix(xs,doNormalize=False):\n return np.array([[\n LegendrePolynomialAndDerivative(j,xs[i],doNormalize=doNormalize)[0]\n for j in range(0,len(xs))\n ] for i in range(0,len(xs))\n ])\n\ndef mk_dg2fvMat(xs,ws,M):\n \"\"\"\n Args:\n xs: array of quadrature nodes\n ws: array of quadrature weights\n\n Returns:\n (M x len(xs)) projection matrix\n \"\"\"\n\n xL = -0.5*np.sum(ws)\n xR = 0.5*np.sum(ws)\n\n # interpolation nodes within each finite volume\n Ys = [mu + xs/M for mu in mk_body_centered_linspace(xL,xR,M)]\n\n # projection matrix\n return np.array([np.dot(ws,mk_vandermonde_matrix(xs,ys)) for ys in Ys])\n\ndef mk_galerkinMat(xs,ws,ys,vs,M):\n \"\"\"\n Args:\n xs: array of sub-element quadrature nodes\n ws: array of sub-element quadrature weights\n\n ys: array of main element quadrature nodes\n vs: array of main element quadrature weights\n\n M: number of (xs,ws)-elements\n\n Returns:\n tuple of M projection (len(ys) x len(xs)) matrices\n \"\"\"\n\n xL = -0.5*np.sum(vs)\n xR = 0.5*np.sum(vs)\n\n # interpolation nodes within each sub-element of master element\n Ys = [mu + xs/M for mu in mk_body_centered_linspace(xL,xR,M)]\n\n return tuple(\n np.array([[LagrangePolynomial(ys,i,Ys[m][j])*ws[j]/vs[i]/M for j in range(len(xs))] for i in range(len(ys))])\n for m in range(M))\n\ndef mk_dg2dgMat(xs,ws,ys,vs):\n return mk_galerkinMat(xs,ws,ys,vs,M=1)\n","sub_path":"tools/lib/gausslobatto.py","file_name":"gausslobatto.py","file_ext":"py","file_size_in_byte":11174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"609705665","text":"#!/usr/bin/env python -O\n# -*- coding: utf-8 -*-\n#\n# tests.dao.programdb.test_ramstkfailuredefinition.py is part of The RAMSTK\n# Project\n#\n# All rights reserved.\n\"\"\" Test class for testing the RAMSTKFailureDefinition module algorithms and models. \"\"\"\n\nimport pytest\n\nfrom ramstk.dao.programdb.RAMSTKFailureDefinition import RAMSTKFailureDefinition\n\n__author__ = 'Doyle Rowland'\n__email__ = 'doyle.rowland@reliaqual.com'\n__organization__ = 'ReliaQual Associates, LLC'\n__copyright__ = 'Copyright 2017 Doyle \"weibullguy\" Rowland'\n\nATTRIBUTES = {\n 'revision_id': 1,\n 'definition_id': 1,\n 'definition': 'Failure Definition'\n}\n\n\n@pytest.mark.integration\ndef test_ramstkfailuredefinition_create(test_dao):\n \"\"\" __init__() should create an RAMSTKFailureDefinition model. \"\"\"\n _session = test_dao.RAMSTK_SESSION(\n bind=test_dao.engine, autoflush=False, expire_on_commit=False)\n DUT = _session.query(RAMSTKFailureDefinition).first()\n\n assert isinstance(DUT, RAMSTKFailureDefinition)\n\n # Verify class attributes are properly initialized.\n assert DUT.__tablename__ == 'ramstk_failure_definition'\n assert DUT.revision_id == 1\n assert DUT.definition_id == 1\n assert DUT.definition == 'Failure Definition'\n\n\n@pytest.mark.integration\ndef test_get_attributes(test_dao):\n \"\"\" get_attributes() should return a tuple of attribute values. \"\"\"\n _session = test_dao.RAMSTK_SESSION(\n bind=test_dao.engine, autoflush=False, expire_on_commit=False)\n DUT = _session.query(RAMSTKFailureDefinition).first()\n\n assert DUT.get_attributes() == ATTRIBUTES\n\n\n@pytest.mark.integration\ndef test_set_attributes(test_dao):\n \"\"\" set_attributes() should return a zero error code on success. \"\"\"\n _session = test_dao.RAMSTK_SESSION(\n bind=test_dao.engine, autoflush=False, expire_on_commit=False)\n DUT = _session.query(RAMSTKFailureDefinition).first()\n\n ATTRIBUTES['definition'] = 'Test Failure Definition'\n\n _error_code, _msg = DUT.set_attributes(ATTRIBUTES)\n\n assert _error_code == 0\n assert _msg == (\"RAMSTK SUCCESS: Updating RAMSTKFailureDefinition {0:d} \"\n \"attributes.\".format(DUT.definition_id))\n\n\n@pytest.mark.integration\ndef test_set_attributes_missing_key(test_dao):\n \"\"\" set_attributes() should return a 40 error code when passed too few attributes. \"\"\"\n _session = test_dao.RAMSTK_SESSION(\n bind=test_dao.engine, autoflush=False, expire_on_commit=False)\n DUT = _session.query(RAMSTKFailureDefinition).first()\n\n ATTRIBUTES.pop('definition')\n\n _error_code, _msg = DUT.set_attributes(ATTRIBUTES)\n\n assert _error_code == 40\n assert _msg == (\"RAMSTK ERROR: Missing attribute 'definition' in attribute \"\n \"dictionary passed to \"\n \"RAMSTKFailureDefinition.set_attributes().\")\n","sub_path":"tests/dao/programdb/test_ramstkfailuredefinition.py","file_name":"test_ramstkfailuredefinition.py","file_ext":"py","file_size_in_byte":2826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"418281712","text":"\n# coding: utf-8\n\n# In[14]:\n\nimport numpy as np \nimport os \nimport tensorflow as tf \nimport datetime\nimport time\nfrom matplotlib import pyplot as plt \nfrom PIL import Image\nimport glob\n\n# In[15]:\n\nMODEL_NAME=\"./\"\nPATH_TO_CKPT = MODEL_NAME + 'frozen_inference_graph.pb' \n\n\n# In[20]:\n\ndetection_graph = tf.Graph()\nwith detection_graph.as_default():\n od_graph_def = tf.GraphDef()\n with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n serialized_graph = fid.read()\n od_graph_def.ParseFromString(serialized_graph)\n tf.import_graph_def(od_graph_def, name='')\n\n\n# In[21]:\n\ntest_img_base_path=\"./Sample\"\nimgs_files=os.path.join(test_img_base_path,\"*\",\"*.png\")\nimgs_list=glob.glob(imgs_files)\nnum_imgs=len(imgs_list)\nprint(\"Images num:\"+str(num_imgs))\ninference_path=\"./inference_result\"\nnew_files=[]\nif not os.path.exists(inference_path):\n os.mkdir(inference_path)\ntotal_time = 0\n\n\n# In[22]:\n\nwith detection_graph.as_default(): \n with tf.Session(graph=detection_graph) as sess: \n image_tensor = detection_graph.get_tensor_by_name('ImageTensor:0') \n prediction = detection_graph.get_tensor_by_name('SemanticPredictions:0') \n start_time=datetime.datetime.now()\n print(\"STARTING ...\")\n for image_path in imgs_list:\n image_np = Image.open(image_path)\n image_np_expanded = np.expand_dims(image_np, axis=0) \n # Definite input and output Tensors for detection_graph \n out_name=os.path.join(inference_path,image_path.split(\"/\")[-2],image_path.split(\"/\")[-1])\n time1 = time.time()\n prediction_out= sess.run( \n prediction,feed_dict={image_tensor: image_np_expanded}) \n time2 = time.time()\n total_time += float(time2-time1)\n result=Image.fromarray(np.array(prediction_out[0]*200).astype(np.uint8))\n if not os.path.exists(os.path.join(inference_path,out_name.split(\"/\")[-2])):\n os.mkdir(os.path.join(inference_path,out_name.split(\"/\")[-2]))\n result.save(out_name)\n end_time=datetime.datetime.now()\n \n print(\"START TIME :\"+str(start_time))\n print(\"END TIME :\"+str(end_time))\n print(\"THE TOTAL TIME COST IS:\"+str(total_time))\n print(\"THE average TIME COST IS:\"+str(float(total_time)/float(num_imgs)))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"segement_files.py","file_name":"segement_files.py","file_ext":"py","file_size_in_byte":2249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"177672959","text":"file=open(\"./telugu_annotated.txt\", \"r\")\ndata=file.read()\nsent=data.split(\"*********************\")\n# print(sent)\nfor each_sent in sent:\n print(\"------------------------------\")\n if each_sent==\"\":\n continue\n print(each_sent)\n dic={}\n subj_case=\"\"\n obj_case=\"\"\n verb_case=\"\"\n subj_gender=\"\"\n obj_gender=\"\"\n verb_gender=\"\"\n subj_person=\"\"\n obj_person=\"\"\n verb_person=\"\"\n subj_number=\"\"\n obj_number=\"\"\n verb_number=\"\"\n lines=each_sent.split(\"\\n\")\n for each_line in lines:\n if each_line=='':\n continue\n if each_line[0]==\"(\":\n # print(each_line)\n inf=each_line.split(\"-\")\n li=inf[1]\n ls=li.split(\"|\")\n for ele in ls:\n if \"Case\" in ele:\n val=ele.split(\"=\")\n if \"SUBJE\" in inf[0]:\n subj_case=val[1]\n elif \"OBJEC\" in inf[0]:\n obj_case=val[1]\n elif \"VER\" in inf[0]:\n verb_case=val[1]\n if \"Gender\" in ele:\n val=ele.split(\"=\")\n if \"SUBJE\" in inf[0]:\n subj_gender=val[1]\n elif \"OBJEC\" in inf[0]:\n obj_gender=val[1]\n elif \"VER\" in inf[0]:\n verb_gender=val[1]\n if \"Number\" in ele:\n val=ele.split(\"=\")\n if \"SUBJE\" in inf[0]:\n subj_number=val[1]\n elif \"OBJEC\" in inf[0]:\n obj_number=val[1]\n elif \"VER\" in inf[0]:\n verb_number=val[1]\n if \"Person\" in ele:\n val=ele.split(\"=\")\n if \"SUBJE\" in inf[0]:\n subj_person=val[1]\n elif \"OBJEC\" in inf[0]:\n obj_person=val[1]\n elif \"VER\" in inf[0]:\n verb_person=val[1]\n if subj_case==obj_case and obj_case==verb_case and subj_case!=\"\":\n print(\"SUBJECT-VERB-OBJECT CASE AGREE\")\n else:\n if subj_case==obj_case and subj_case!=\"\":\n print(\"SUBJECT-OBJECT CASE AGREE\")\n elif subj_case==verb_case and subj_case!=\"\":\n print(\"SUBJECT-VERB CASE AGREE\")\n elif obj_case==verb_case and obj_case!=\"\":\n print(\"OBJECT-VERB CASE AGREE\")\n\n if subj_number==obj_number and obj_number==verb_number and subj_number!=\"\":\n print(\"SUBJECT-VERB-OBJECT NUMBER AGREE\")\n else:\n if subj_number==obj_number and subj_number!=\"\":\n print(\"SUBJECT-OBJECT NUMBER AGREE\")\n elif subj_number==verb_number and subj_number!=\"\":\n print(\"SUBJECT-VERB NUMBER AGREE\")\n elif obj_number==verb_number and obj_number!=\"\":\n print(\"OBJECT-VERB NUMBER AGREE\")\n\n if subj_gender==obj_gender and obj_gender==verb_number and subj_gender!=\"\":\n print(\"SUBJECT-VERB-OBJECT GENDER AGREE\")\n else:\n if subj_gender==obj_gender and subj_gender!=\"\":\n print(\"SUBJECT-OBJECT GENDER AGREE\")\n elif subj_gender==verb_gender and subj_gender!=\"\":\n print(\"SUBJECT-VERB GENDER AGREE\")\n elif obj_gender==verb_gender and obj_gender!=\"\":\n print(\"OBJECT-VERB GENDER AGREE\")\n if subj_person==obj_person and obj_person==verb_person and subj_person!=\"\":\n print(\"SUBJECT-VERB-OBJECT PERSON AGREE\")\n else:\n if subj_person==obj_person and subj_person!=\"\":\n print(\"SUBJECT-OBJECT PERSON AGREE\")\n elif subj_person==verb_person and subj_person!=\"\":\n print(\"SUBJECT-VERB PERSON AGREE\")\n elif obj_person==verb_person and obj_person!=\"\":\n print(\"OBJECT-VERB PERSON AGREE\")\n \n # if val[1] in dic:\n # dic[val[1]].append(inf[0][:-1])\n # else:\n # dic[val[1]]=[inf[0][:-1]]","sub_path":"Telugu/Telugu_grouper.py","file_name":"Telugu_grouper.py","file_ext":"py","file_size_in_byte":4468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"136074060","text":"def q0(ans, qlist):\r\n return True\r\n\r\ndef q1(ans, qlist):\r\n if ans == 'A':\r\n return qlist[4] == 'C'\r\n if ans == 'B':\r\n return qlist[4] == 'D'\r\n if ans == 'C':\r\n return qlist[4] == 'A'\r\n if ans == 'D':\r\n return qlist[4] == 'B'\r\n return None\r\n\r\ndef q2(ans, qlist):\r\n if ans == 'A':\r\n return qlist[2] != qlist[5] and qlist[2] != qlist[1] and qlist[2] != qlist[3]\r\n if ans == 'B':\r\n return qlist[5] != qlist[2] and qlist[5] != qlist[1] and qlist[5] != qlist[3]\r\n if ans == 'C':\r\n return qlist[1] != qlist[2] and qlist[1] != qlist[5] and qlist[1] != qlist[3]\r\n if ans == 'D':\r\n return qlist[3] != qlist[2] and qlist[3] != qlist[5] and qlist[3] != qlist[1]\r\n return None\r\n\r\ndef q3(ans, qlist):\r\n if ans == 'A':\r\n return qlist[0] == qlist[4]\r\n if ans == 'B':\r\n return qlist[1] == qlist[6]\r\n if ans == 'C':\r\n return qlist[0] == qlist[8]\r\n if ans == 'D':\r\n return qlist[5] == qlist[9]\r\n return None\r\n\r\ndef q4(ans, qlist):\r\n if ans == 'A':\r\n return qlist[4] == qlist[7]\r\n if ans == 'B':\r\n return qlist[4] == qlist[3]\r\n if ans == 'C':\r\n return qlist[4] == qlist[8]\r\n if ans == 'D':\r\n return qlist[4] == qlist[6]\r\n return None\r\n\r\ndef q5(ans, qlist):\r\n if ans == 'A':\r\n return qlist[7] == qlist[1] and qlist[7] == qlist[3]\r\n if ans == 'B':\r\n return qlist[7] == qlist[0] and qlist[7] == qlist[5]\r\n if ans == 'C':\r\n return qlist[7] == qlist[2] and qlist[7] == qlist[9]\r\n if ans == 'D':\r\n return qlist[7] == qlist[4] and qlist[7] == qlist[8]\r\n return None\r\n\r\ndef q6(ans, qlist): # 统计哪个字母最少\r\n a, b, c, d = 0, 0, 0, 0\r\n for n in qlist:\r\n if n == 'A':\r\n a += 1\r\n if n == 'B':\r\n b += 1\r\n if n == 'C':\r\n c += 1\r\n if n == 'D':\r\n d += 1\r\n mini = min(a, b, c, d)\r\n if ans == 'A':\r\n return mini == c\r\n if ans == 'B':\r\n return mini == b\r\n if ans == 'C':\r\n return mini == a\r\n if ans == 'D':\r\n return mini == d\r\n return None\r\n\r\ndef q7(ans, qlist):\r\n if ans == 'A':\r\n return abs(ord(qlist[0]) - ord(qlist[6])) != 1\r\n if ans == 'B':\r\n return abs(ord(qlist[0]) - ord(qlist[4])) != 1\r\n if ans == 'C':\r\n return abs(ord(qlist[0]) - ord(qlist[1])) != 1\r\n if ans == 'D':\r\n return abs(ord(qlist[0]) - ord(qlist[9])) != 1\r\n return None\r\n\r\ndef q8(ans, qlist):\r\n q05 = qlist[0] == qlist[5]\r\n if ans == 'A':\r\n return q05 != qlist[6] == qlist[4]\r\n if ans == 'B':\r\n return q05 != qlist[9] == qlist[4]\r\n if ans == 'C':\r\n return q05 != qlist[1] == qlist[4]\r\n if ans == 'D':\r\n return q05 != qlist[8] == qlist[4]\r\n return None\r\n\r\ndef q9(ans, qlist): #统计差值\r\n a, b, c, d = 0, 0, 0, 0\r\n for n in qlist:\r\n if n == 'A':\r\n a += 1\r\n if n == 'B':\r\n b += 1\r\n if n == 'C':\r\n c += 1\r\n if n == 'D':\r\n d += 1\r\n diff = max(a, b, c, d) - min(a, b, c, d)\r\n if ans == 'A':\r\n return diff == 3\r\n if ans == 'B':\r\n return diff == 2\r\n if ans == 'C':\r\n return diff == 4\r\n if ans == 'D':\r\n return diff == 1\r\n return None\r\n\r\ndef qlist_generator(size):\r\n num = 1\r\n for x in range(size):\r\n num *= 4\r\n qlist = [None] * size\r\n for i in range(num):\r\n for j in range(size):\r\n qlist[j] = ['A', 'B', 'C', 'D'][(i >> (j << 1)) & 3]\r\n yield qlist\r\n\r\ndef test_qlist(question_list, qlist):\r\n index = 0\r\n for q in question_list:\r\n if not q.__call__(qlist[index], qlist):\r\n return False\r\n index += 1\r\n return True\r\n\r\ndef main():\r\n question_list = [q0, q1, q2, q3, q4, q5, q6, q7, q8, q9]\r\n for qlist in qlist_generator(len(question_list)):\r\n if test_qlist(question_list, qlist):\r\n print('Answer:', qlist)\r\n\r\nif __name__ == '__main__':\r\n main()","sub_path":"2018_xingzhen.py","file_name":"2018_xingzhen.py","file_ext":"py","file_size_in_byte":4040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"193316732","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom os.path import dirname, realpath, exists\nfrom setuptools import setup, find_packages\nimport sys\n\n\nauthor = u\"Paul Müller\"\nauthors = [author]\ndescription = 'user-friendly quantitative phase imaging analysis'\nname = 'drymass'\nyear = \"2017\"\n\nsys.path.insert(0, realpath(dirname(__file__))+\"/\"+name)\nfrom _version import version\n\nif version.count(\"dev\") or sys.argv.count(\"test\"):\n # specific versions are not desired for\n # - development version\n # - running pytest\n release_deps = [\"qpformat\",\n \"qpimage\",\n \"qpsphere\"]\nelse:\n release_deps = [\"qpformat==0.1.4\",\n \"qpimage==0.1.6\",\n \"qpsphere==0.1.4\",\n ]\n\nsetup(\n name=name,\n author=author,\n author_email='dev@craban.de',\n url='https://github.com/RI-imaging/DryMass',\n version=version,\n packages=find_packages(),\n package_dir={name: name},\n license=\"MIT\",\n description=description,\n long_description=open('README.rst').read() if exists('README.rst') else '',\n install_requires=[\"matplotlib\",\n \"numpy\",\n \"scikit-image>=0.13.1\",\n ] + release_deps,\n setup_requires=['pytest-runner'],\n tests_require=[\"pytest\"],\n entry_points={\n \"console_scripts\": [\n \"dm_analyze_sphere = drymass.cli:cli_analyze_sphere\",\n \"dm_convert = drymass.cli:cli_convert\",\n \"dm_extract_roi = drymass.cli:cli_extract_roi\",\n ],\n },\n python_requires='>=3.5, <4',\n keywords=[\"digital holographic microscopy\",\n \"optics\",\n \"quantitative phase imaging\",\n \"refractive index\",\n \"scattering\",\n ],\n classifiers= [\n 'Operating System :: OS Independent',\n 'Programming Language :: Python :: 3',\n 'Intended Audience :: Science/Research'\n ],\n platforms=['ALL'],\n )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"16407627","text":"import numpy as np\r\nimport pandas as pd\r\nimport tensorflow as tf\r\nimport math\r\n\r\nimport base\r\nimport util\r\n\r\nimport tensorflow_decision_forests as tfdf\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nclass BoostedDecisionTree(base.Experiment):\r\n def __init__(self, data_loader, verbose=False):\r\n super().__init__(verbose)\r\n self._data_loader = data_loader\r\n\r\n def run(self):\r\n \"\"\"\r\n Run the Boosted Decision Tree experiment\r\n \"\"\"\r\n # Loading wine data and split to training/validation/testing set\r\n wine_data = self._data_loader.load()\r\n train_ds_pd, val_ds_pd, test_ds_pd = util.get_dataset_partitions_pd(wine_data,\r\n train_split=0.8, val_split=0.1, test_split=0.1)\r\n self.log(\"Training size = {}\", len(train_ds_pd))\r\n self.log(\"Validation size = {}\", len(val_ds_pd))\r\n self.log(\"Testing size = {}\", len(test_ds_pd))\r\n\r\n # Name of the label column\r\n label = 'level'\r\n\r\n drop_cols = [c for c in wine_data.columns if c != label]\r\n drop_cols.append('NONE')\r\n\r\n best_drop_col = None\r\n best_test_ds = None\r\n best_model = None\r\n max_accuracy = 0.0\r\n metrics = []\r\n\r\n for dcol in drop_cols:\r\n self.log(\"Start training with drop column \\\"{}\\\"...\", dcol)\r\n\r\n # Convert to tensorflow dataset\r\n if dcol == 'NONE':\r\n train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=label)\r\n val_ds = tfdf.keras.pd_dataframe_to_tf_dataset(val_ds_pd, label=label)\r\n test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd, label=label)\r\n else:\r\n train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd.drop(columns=[dcol]), label=label)\r\n val_ds = tfdf.keras.pd_dataframe_to_tf_dataset(val_ds_pd.drop(columns=[dcol]), label=label)\r\n test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd.drop(columns=[dcol]), label=label)\r\n\r\n # Train a Random Forest model.\r\n model = tfdf.keras.GradientBoostedTreesModel(\r\n num_trees = 1,\r\n max_depth = 8,\r\n verbose = False\r\n )\r\n model.fit(train_ds)\r\n\r\n # Summary of the model structure.\r\n # model.summary()\r\n\r\n # Evaluate training performance\r\n self.log(\"Train Metrics:\")\r\n train_result = self.evaluation(model, train_ds)\r\n\r\n # Validate the model.\r\n self.log(\"Validation Metrics:\")\r\n val_result = self.evaluation(model, val_ds)\r\n\r\n metrics.append({\r\n 'train': train_result,\r\n 'validation': val_result\r\n })\r\n\r\n if val_result['accuracy'] > max_accuracy:\r\n self.log(\"Replace the best model\")\r\n max_accuracy = val_result['accuracy']\r\n best_drop_col = dcol\r\n best_test_ds = test_ds\r\n best_model = model\r\n\r\n # Testing the best model with testing dataset\r\n self.log(\"Testing Metrics [Drop {}]:\", best_drop_col)\r\n self.evaluation(best_model, best_test_ds)\r\n\r\n # Export the model to a TensorFlow SavedModel\r\n best_model.save(\"model/Boosting_model\")\r\n\r\n self.plot({\r\n 'drop_cols': drop_cols,\r\n 'metrics': metrics\r\n })\r\n\r\n\r\n def evaluation(self, model, ds):\r\n model.compile(metrics=[\"accuracy\", \"mse\"])\r\n evaluation = model.evaluate(ds, return_dict=True)\r\n for name, value in evaluation.items():\r\n if name == 'loss':\r\n continue\r\n self.log(f\"\\t{name}: {value:.4f}\")\r\n return evaluation\r\n\r\n def plot(self, data):\r\n metrics = data['metrics']\r\n drop_cols = data['drop_cols']\r\n # Plot\r\n for k in ['train', 'validation']:\r\n plt.figure(figsize=(24, 8))\r\n\r\n plt.subplot(1, 2, 1)\r\n plt.plot(drop_cols, [met[k]['accuracy'] for met in metrics])\r\n plt.xlabel(\"Dropped column\")\r\n plt.ylabel(f\"Accuracy ({k})\")\r\n plt.xticks(rotation=45)\r\n\r\n plt.subplot(1, 2, 2)\r\n plt.plot(drop_cols, [met[k]['mse'] for met in metrics])\r\n plt.xlabel(\"Dropped column\")\r\n plt.ylabel(f\"MSE ({k})\")\r\n plt.xticks(rotation=45)\r\n\r\n plt.savefig(f\"plot/Boosting_{k}.png\")\r\n","sub_path":"assignment1/Boosting.py","file_name":"Boosting.py","file_ext":"py","file_size_in_byte":4464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"214980245","text":"from PriorityQueue import *\nfrom Node import *\nfrom IO import *\nimport time\n\ndef uniformCostSearch(openList, closedList, goal1, goal2,mytime):\n startTime=time.time()\n result=[]\n searchPath=''\n searchLength=0\n visit=0\n created=0\n\n while(time.time()-startTime<=mytime):\n currentNode=openList.pop()\n searchPath=searchPath+currentNode.toSearchString()+\"\\n\"\n searchLength += currentNode.moveCost\n visit+=1\n if (currentNode.c_state==goal1 or currentNode.c_state==goal2):\n result.append(backTrack(currentNode,closedList)+\"\\n\"+f\"{currentNode.gn} {time.time()-startTime}\")\n result.append(searchPath)\n result.append(True)\n result.append(currentNode.gn)\n result.append(searchLength)\n result.append(visit)\n result.append(created)\n result.append(time.time()-startTime)\n return (result)\n currentNode.generateNextSteps()\n successors=currentNode.generateSuccessors()\n for x in successors:\n created+=1#shouldn't add searchLength here, cause if we reach a node in closed list ,we don't care it cost\n if(not any(y.c_state == x.c_state for y in closedList)):\n openList.push(x)\n closedList.append(currentNode)\n result.append(\"no solution\") #solutionPath\n result.append(\"no solution\") #searchPath\n result.append(False) #no solution\n return (result)\n\n\n","sub_path":"python/UniformCostSearch.py","file_name":"UniformCostSearch.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"253289816","text":"\nimport struct\n\nclass Data(object):\n def __init__(self,msg,value):\n self.msg = msg\n self.value = value\n\ndef serialParser(msg,fmt):\n\n #print(msg)\n rxCom = struct.unpack(fmt, msg)\n #com = rxCom[1]\n #msg1 = rxCom[2]\n #msg2 = rxCom[3]\n #msg3 = rxCom[4]\n #print(com)\n #msg = msg.rstrip()\n #print(msg)\n command = list()\n\n for i in range(1,5):\n command.append(rxCom[i])\n #print(rxCom[i])\n\n #print(command)\n return(command)\n\n '''\n if com == 5: # pid data\n command.append(com)\n command.append(msg1)\n command.append(msg2)\n command.append(msg3)\n #print(command)\n return command\n\n if com == 1: # motor data\n command.append(com)\n command.append(msg1)\n command.append(msg2)\n command.append(msg3)\n #print(command)\n return command\n '''\n if msg[0] == \"&\":\n return msg[1:]\n\n if msg[0] == \"!\":\n return int(msg[1])\n\n if msg[0] == \"*\":\n pid_params = msg.split(charParamsSeparator)\n pid.append(float(pid_params[1])/4096)\n pid.append(float(pid_params[2])/4096)\n pid.append(float(pid_params[3])/4096)\n #print(pid)\n return pid\n\n # separate message in parts\n variables = msg.split(charDataSeparator)\n dataArray = list()\n\n # iterate over all the data and fill a dictionary with ids and measurements\n for v in variables:\n vAux = v.split(charMessageSeparator)\n dataArray.append(Data(vAux[0],vAux[1]))\n\n grahps = dict([ (d.msg, d.value) for d in dataArray ])\n\n return grahps\n","sub_path":"SerialParser.py","file_name":"SerialParser.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"316693765","text":"import pandas as pd\nimport json\n# import sys\n# import matplotlib\nfrom pandas import DataFrame, read_csv \n# import ast\n# from ldap3 import Server, Connection, SUBTREE, ALL, ObjectDef, Reader\n\nprojectid = \"test-project-175520\"\njson_file = open('/home/shawon/Documents/google-cloud/big-query-test/big-query-test-service-account.json')\nprivate_key = json.load(json_file)\n\napplications = r'/home/shawon/Puppet/puppet-greenhouse/applications.csv'\ncandidates = r'/home/shawon/Puppet/puppet-greenhouse/candidates.csv'\njobs = r'/home/shawon/Puppet/puppet-greenhouse/jobs.csv'\noffers = r'/home/shawon/Puppet/puppet-greenhouse/offers.csv'\n\napp_df = pd.read_csv(applications)\ncan_df = pd.read_csv(candidates)\njobs_df = pd.read_csv(jobs)\noffers_df = pd.read_csv(offers)\n\n# create_table('greenhouse.application', schema, projectid)\napp_df.to_gbq(app_df, 'greenhouse_data.application_data', projectid, private_key=private_key, verbose=True)\ngbq.to_gbq(dataframe=df, destination_table='lake1.pond1', project_id='pubsub-bq-pipe-1', chunksize=10000, verbose=True, reauth=False, if_exists='replace', private_key=\"./pubsub-bq-pipe-1-a865bbaa5f48.json\", auth_local_webserver=False)\n\n\n# hired_apps = app_df[app_df['status'] == \"hired\"]\n\n# Gets application ids into a list\n# def app_ids(): \n# app_ids = hired_apps[\"id\"].tolist()\n# app_ids = [int(x) for x in app_ids]\n# app_ids.sort()\n# return app_ids\n\n# # Gets only hired candidates based on applications with status of hired. Pulls candidates based on application_id value\n# def candidates():\n# can_ids = hired_apps[\"candidate_id\"].tolist()\n# can_ids = [int(x) for x in can_ids]\n# can_ids.sort()\n# hired_cans = can_df.loc[can_df['id'].isin(can_ids)]\n# return hired_cans\n\n# # Sorts applications based on \n# def applications():\n# sorted_apps = hired_apps.sort_values('id', ascending=True)\n# return sorted_apps\n\n# def jobs():\n# sorted_jobs = jobs_df.sort_values('id', ascending=True)\n# jobs_custom_fields = \n# return sorted_jobs\n\n# def jobs_id_pull():\n# job_id_list = []\n# job_entry_list = []\n# app_jobs = applications()['jobs']\n# for entry in app_jobs:\n# entry_string = entry.translate({ord(c): None for c in '[]'})\n# entry_dict = ast.literal_eval(entry_string)\n# job_id_list.append(entry_dict['id'])\n# job_entry_list.append(entry)\n# job_ids = {'job_id': job_id_list, 'jobs': job_entry_list}\n# job_ids_frame = pd.DataFrame.from_dict(job_ids)\n# return job_ids_frame\n\n# # Gets offer entries that have hired applications tied to them together. Also removes deprecated offers and sorts numerically based on application_id value\n# def offers():\n# extended_offers = offers_df.loc[offers_df['application_id'].isin(app_ids())]\n# extended_offers = extended_offers[extended_offers['status'] == 'accepted']\n# extended_offers = extended_offers.sort_values('application_id', ascending=True)\n# return extended_offers\n\n# def merge_dfs():\n# app_labels = ['id', 'candidate_id', 'jobs']\n# offer_labels = ['application_id', 'starts_at', 'keyed_custom_fields']\n# candidate_labels = ['id', 'addresses', 'phone_numbers', 'first_name', 'last_name', 'email_addresses']\n# job_labels = ['id', 'keyed_custom_fields', 'requisition_id', 'departments']\n# reduced_apps = applications().loc[:, app_labels]\n# reduced_offs = offers().loc[:, offer_labels]\n# reduced_candidates = candidates().loc[:, candidate_labels]\n# reduced_jobs = jobs().loc[:, job_labels]\n# job_id_frame = jobs_id_pull()\n# apps_jobs = pd.merge(reduced_apps, job_id_frame, on='jobs')\n# apps_jobs = apps_jobs.drop_duplicates()\n# apps_jobs = apps_jobs.sort_values('id', ascending=True)\n# apps_and_offs = pd.merge(apps_jobs, reduced_offs, left_on='id', right_on='application_id')\n# apps_offs_cans = pd.merge(apps_and_offs, reduced_candidates, left_on='candidate_id', right_on='id')\n# merged_dfs = pd.merge(apps_offs_cans, reduced_jobs, left_on='job_id', right_on='id')\n# merged_dfs.drop(['id_x', 'id_y', 'id'], axis=1, inplace=True)\n# merged_dfs.to_csv('merged_dfs.csv')\n\n# merge_dfs()\n","sub_path":"greenhouse/gh_pandas.py","file_name":"gh_pandas.py","file_ext":"py","file_size_in_byte":4148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"441684703","text":"\n\ndef intcode_computer(memory: list) -> list:\n pointer = 0\n while pointer < len(memory):\n instruction = memory[pointer]\n if instruction == 99:\n break\n elif instruction == 1:\n parameter_1 = memory[pointer + 1]\n parameter_2 = memory[pointer + 2]\n parameter_3 = memory[pointer + 3]\n memory[parameter_3] = memory[parameter_1] + memory[parameter_2]\n pointer += 4\n elif instruction == 2:\n parameter_1 = memory[pointer + 1]\n parameter_2 = memory[pointer + 2]\n parameter_3 = memory[pointer + 3]\n memory[parameter_3] = memory[parameter_1] * memory[parameter_2]\n pointer += 4\n else:\n print(\"press F\")\n print(memory)\n break\n return memory\n\n\ndef convert_str_to_intcode(intcode_str: str) -> list:\n return [int(i) for i in intcode_str.split(\",\")]\n","sub_path":"AdventOfCode2019/intcode/intcode.py","file_name":"intcode.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"449657749","text":"import pygame.font\n\n\n# Class for play button.\nclass Button():\n def __init__(self, alien_invasion_settings, game_window, message):\n self.game_window = game_window\n self.game_window_rect = self.game_window.get_rect()\n \"\"\"Set the dimensions and properties of the button.\"\"\"\n self.button_width = 180\n self.button_height = 70\n self.button_color = (193, 237, 109)\n self.button_text_color = (47, 59, 47)\n self.font = pygame.font.SysFont(None, 38)\n \"\"\"Build button and center it.\"\"\"\n self.rect = pygame.Rect(0, 0, self.button_width, self.button_height)\n self.rect.center = self.game_window_rect.center\n \"\"\"Pygame renders strings of text as images.\"\"\"\n self.prepare_message(message)\n\n # Method for turning message into a rendered image and center the text on\n # the button.\n def prepare_message(self, message):\n \"\"\"The call to font.render() turns text into an image. A Boolean value\n is used to turn antialiasing on.\"\"\"\n self.message_image = self.font.render(\n message, True, self.button_text_color, self.button_color)\n \"\"\"Center text image on the button.\"\"\"\n self.message_image_rect = self.message_image.get_rect()\n self.message_image_rect.center = self.rect.center\n\n # Method for first drawing a blank button and then the message.\n def draw_button(self):\n self.game_window.fill(self.button_color, self.rect)\n self.game_window.blit(self.message_image, self.message_image_rect)\n","sub_path":"button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"461896401","text":"import win32com.client as win32\nimport os \nfrom datetime import datetime\nfrom docx.enum.text import WD_ALIGN_PARAGRAPH\nfrom docx import Document\nimport re\nfrom win32com.client import constants\n##Read the path \nl=[]\ncount=0\nimport sys \niter=sys.argv[1]\nstart=datetime.now()\nprint(\"-----------------------------------------------------------------------------------------------------------------\")\nprint(\"Document Name:\", iter)\nprint(\"CheckList Rule - 32: Checking 'Justify' alignment for paragraphs.\")\nprint(\"Document Review Start Time:\", start,\"HH:MM:SS\")\nprint(\"-----------------------------------------------------------------------------------------------------------------\")\nprint(\"\\n\")\n\n##Open the Document\nstart=datetime.now()\nif iter.endswith('.doc') :\n word1 = win32.gencache.EnsureDispatch (\"Word.Application\")\n word1.Visible = True\n p = os.path.abspath(iter)\n word1.Documents.Open(p)\n sheet_1 = word.ActiveDocument\n for i in range(1,sheet_1.Paragraphs.Count+1):\n t=sheet_1.Paragraphs(i).Range.Text.encode('ascii','ignore').decode()\n t=t.strip('\\r')\n t=t.strip('\\r\\x07')\n t=t.strip('\\x0c')\n t=t.strip('\\x0b')\n t=t.strip('\\x0a')\n t=t.rstrip(' ')\n t=t.strip('\\n')\n n=sheet_1.Paragraphs(i).Range.Style\n if re.search(\"Heading\",str(n)):\n l.append(t)\n if re.search(\"Normal\",str(n)) and sheet_1.Paragraphs(i).Alignment != win32.constants.wdAlignParagraphJustify and str(t).strip()!='':\n print(\"String:\",(sheet_1.Paragraphs(i)).Range.Text.encode('ascii','ignore').decode())\n print(\"Length:\",len(t))\n print(\"Page number:\",sheet_1.Paragraphs(i).Range.Information(constants.wdActiveEndAdjustedPageNumber))\n print(\"Line On Page:\",sheet_1.Paragraphs(i).Range.Information(constants.wdFirstCharacterLineNumber))\n print(\"\\n\")\n #l.append((doc.Paragraphs(i)).Range.Text.encode('ascii','ignore').decode())\n count=count+1\n sheet_1.Close()\n word1.Quit()\nelif iter.endswith('.docx'):\n doc = Document(iter)\n para=list(doc.paragraphs)\n for i in range(len(para)):\n t=doc.paragraphs[i].text.encode('ascii','ignore').decode()\n if (len(doc.paragraphs[i].text))!=0:\n n=doc.paragraphs[i].style.name.encode('ascii','ignore').decode()\n #print(n)\n if re.search(\"Heading\",n):\n l.append(doc.paragraphs[i].text.encode('ascii','ignore').decode())\n if re.search(\"Normal\",n) and doc.paragraphs[i].alignment != WD_ALIGN_PARAGRAPH.JUSTIFY and str(t).strip()!='':\n if l!=[]:\n print(\"\\n\")\n print(\"Heading Section:\",l[-1])\n print(\"String:\",doc.paragraphs[i].text.encode('ascii','ignore').decode())\n print(\"\\n\")\n else:\n #print(\"Heading Section:\",l[-1])\n print(\"String:\",doc.paragraphs[i].text.encode('ascii','ignore').decode())\n print(\"\\n\")\n #l.append(doc.paragraphs[i].text.encode('ascii','ignore').decode())\n count=count+1\nif count>0:\n print(\"Status:Fail\")\nelse:\n print(\"Status:Pass\")\nend=datetime.now()\nprint(\"\\nDocument Review End Time:\", end)\nprint(\"\\nTime taken For Document Review:\", end-start,\"HH:MM:SS\") ","sub_path":"Debug/setup/source1/justify37/justify37.py","file_name":"justify37.py","file_ext":"py","file_size_in_byte":3038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"217113754","text":"import pandas as pd\nfrom nltk import tokenize\nimport re\nimport glob\nimport os\nimport shutil\n\n# Pega o número do nome do arquivo\n# Ex: \"tedtalk30_whatever.txt\" ==> 30\n# Ex: \"tedtalk13_whatever.txt\" ==> 13\ndef get_tedtalk_num(filepath):\n filename = os.path.basename(filepath) \n return re.search('\\d+', filename).group()\n\ndef text_to_one_sentence_per_line(text):\n sentences = tokenize.sent_tokenize(text)\n\n output_text = \"\"\n for sentence in sentences:\n output_text += sentence + \"\\n\"\n\n return output_text\n\ndef prepare_to_eval(generated_summaries, reference_summaries_dir):\n # Recria a pasta de resumos de referências\n # Ela vai ser preenchida de acordo com os resumos\n # que estão na pasta system\n shutil.rmtree(reference_summaries_dir)\n os.mkdir(reference_summaries_dir)\n\n data = pd.read_csv(PATH_TO_DATASET)\n\n for gen_summary_filepath in generated_summaries:\n # Pega o número do TED Talk do nome do arquivo\n tedtalk_num = get_tedtalk_num(gen_summary_filepath)\n\n # Lê o respectivo resumo de referência do dataset\n # O -1 é por causa da diferença na faixa\n # números nos arquivos: 1 a n\n # números no dataset: 0 a n-1\n ref_summary = data.loc[int(tedtalk_num)-1, 'summary']\n \n # Formata o resumo de referencia como o Rouge pede (uma sentença por linha)\n formatted_ref_summary = text_to_one_sentence_per_line(ref_summary)\n\n ref_summary_file_path = os.path.join(reference_summaries_dir, f\"tedtalk{tedtalk_num}_Dataset.txt\")\n with open(ref_summary_file_path, \"w\") as ref_summary_file:\n ref_summary_file.write(formatted_ref_summary)\n\n # Formata o resumo gerado como o Rouge pede (uma sentença por linha)\n with open(gen_summary_filepath, \"r+\") as gen_summary_file:\n gen_summary = gen_summary_file.read()\n\n formatted_gen_summary = text_to_one_sentence_per_line(gen_summary)\n\n gen_summary_file.write(formatted_gen_summary)\n\nPATH_TO_DATASET = '../data/final_dataset.csv'\n\nTEXTRANK_EVAL_DIR = \"./textRankEval\"\nPOINTERGEN_EVAL_DIR = \"./pointerGenEval\"\n\ntextrank_summaries = glob.glob(f\"{TEXTRANK_EVAL_DIR}/system/*.txt\")\n\nprint(\"Preparing to evaluate TextRank generated summaries...\")\nprepare_to_eval(textrank_summaries, f\"{TEXTRANK_EVAL_DIR}/reference\")\n\npointergen_summaries = glob.glob(f\"{POINTERGEN_EVAL_DIR}/system/*.txt\")\n\nprint(\"Preparing to evaluate Pointer-Generator generated summaries...\")\nprepare_to_eval(pointergen_summaries, f\"{POINTERGEN_EVAL_DIR}/reference\")","sub_path":"rouge_evaluation/prepare_to_evaluation.py","file_name":"prepare_to_evaluation.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"289964736","text":"############################\n# Manage bootstrapping of ML masses\n###########################\n\nfrom __future__ import with_statement\nimport glob, tempfile, subprocess, shutil, os\nimport astropy, astropy.io.fits as pyfits, numpy as np\nimport ldac, maxlike_secure_driver as msd, bashreader\n\n##############################\n\n\nprogs = bashreader.parseFile('progs.ini')\n\n##############################\n\n\ndef createBootstrapCats(cluster, filter, image, indir, outdir, nbootstraps = 50):\n\n manager = msd.maxlike_controller.Controller(modelbuilder = msd.maxlike_masses,\n shapedistro = msd.nfwmodel_voigtnorm_shapedistro.VoigtnormShapedistro(),\n filehandler = msd.maxlike_subaru_secure_filehandler.SubaruSecureFilehandler(),\n runmethod = msd.maxlike_masses.SampleModelToFile())\n\n options, args = manager.filehandler.createOptions(cluster = cluster, filter = filter, image = image, workdir=indir)\n\n manager.load(options, args)\n\n inputcat = manager.inputcat\n\n for i in range(nbootstraps):\n \n bootstrap = np.random.randint(0, len(inputcat), len(inputcat))\n\n mlcat = inputcat.filter(bootstrap)\n mlcat.saveas('%s/%s.%s.%s.b%d.cat' % (outdir, cluster, filter, image, i), overwrite=True)\n\n \n \n\n","sub_path":"maxlike_bootstrap.py","file_name":"maxlike_bootstrap.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"228544670","text":"import os\nimport json\n\nimport cv2 as cv\nfrom tqdm import tqdm\nimport torch\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport matplotlib.pyplot as plt \n\nclass CelebASpoofDataset(Dataset):\n def __init__(self, root_folder, test_mode=False, transform=None, test_dataset=False):\n self.root_folder = root_folder\n if test_mode:\n list_path = os.path.join(root_folder, 'metas/intra_test/items_test.json')\n else:\n list_path = os.path.join(root_folder, 'metas/intra_test/items_train.json')\n\n with open(list_path, 'r') as f:\n self.data = json.load(f)\n\n self.transform = transform\n self.test_dataset = test_dataset\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx):\n data_item = self.data[str(idx)]\n img = cv.imread(os.path.join(self.root_folder, data_item['path']))\n bbox = data_item['bbox']\n\n real_h, real_w, _ = img.shape\n x1 = clamp(int(bbox[0]*(real_w / 224)), 0, real_w)\n y1 = clamp(int(bbox[1]*(real_h / 224)), 0, real_h)\n w1 = int(bbox[2]*(real_w / 224))\n h1 = int(bbox[3]*(real_h / 224))\n\n cropped_face = img[y1 : clamp(y1 + h1, 0, real_h), x1 : clamp(x1 + w1, 0, real_w), :]\n cropped_face = cv.cvtColor(cropped_face, cv.COLOR_BGR2RGB)\n # testing dataset and creating json and log file\n if self.test_dataset:\n path = data_item['path']\n label = int(data_item['labels'][43])\n \n # test_img = cv.resize(cropped_face, (128,128))\n # plt.imsave(f'/home/prokofiev/pytorch/antispoofing/images/{path[-9:]}', arr = test_img, format='png')\n return path, label, cropped_face.shape\n\n label = int(data_item['labels'][43])\n if self.transform:\n cropped_face = self.transform(label=label, img=cropped_face)['image']\n cropped_face = np.transpose(cropped_face, (2, 0, 1)).astype(np.float32)\n return (torch.tensor(cropped_face), torch.tensor(int(data_item['labels'][43]), dtype=torch.long)) #see readme of the CelebA-Spoof to get layout of labels\n\ndef clamp(x, min_x, max_x):\n return min(max(x, min_x), max_x)","sub_path":"datasets/celeba_spoof.py","file_name":"celeba_spoof.py","file_ext":"py","file_size_in_byte":2203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"606497625","text":"import csv\n\ndef lst_to_csv(data):\n \n try:\n output_csv = open('read_files/from_list.csv', 'w', newline='')\n writer = csv.writer(output_csv, delimiter=',')\n writer.writerow(('name', 'address', 'age'))\n for line in data:\n writer.writerow(line)\n \n output_csv.close()\n except Exception as e:\n return e\n\nif __name__ == '__main__':\n \n data = [\\\n ('Киррилл', 'пр. Ветеранов', '19'),\\\n ('Иван', 'пр. Строителей', '19'),\\\n ('4 грифона', 'Банковский мост', '193')\\\n ]\n \n lst_to_csv(data)\n\n","sub_path":"lesson_10/create_csv_from_list.py","file_name":"create_csv_from_list.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"522526609","text":"import sys\nfrom socket import *\nfrom threading import *\n\n#Define sizes in terms of bytes\nheader_size=4\npacket_size=46\n\ndef main(argv):\n router_addr_for_B = ('', 5010)#Use 5010 for B\n router_addr_for_d = ('', 5011)#Use 5011 for d\n\n d_addr=('10.10.3.2',5000)#link-2\n #Create two UDP sockets one for B one for d\n B_sock=socket(AF_INET, SOCK_DGRAM)\n B_sock.bind(router_addr_for_B)\n d_sock=socket(AF_INET, SOCK_DGRAM)\n d_sock.bind(router_addr_for_d)\n try:\n while True:\n #Wait a message from B\n print(\"Waiting for a message from B on port number: {}...\".format(5010))\n message_from_B, B_addr = B_sock.recvfrom(packet_size)\n #Forward the message to the destination d\n d_sock.sendto(message_from_B, d_addr)\n #Wait until getting a response from d\n print(\"Packet was forwarded to d.\\n Waiting for a response from d on port number: {}...\".format(5000))\n response_from_d, d_addr = d_sock.recvfrom(packet_size)\n #Forward the response to B\n B_sock.sendto(response_from_d, B_addr)\n print(\"Response was forwarded to B.\")\n finally:\n print('Sockets are closed')\n B_sock.close()\n d_sock.close()\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])","sub_path":"r1_udp_node.py","file_name":"r1_udp_node.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"155408071","text":"\"\"\"Day 1\"\"\"\n\nfilename = 'input'\n\n# Part 1\n\nprint(sum([int(change) for change in open(filename)]))\n\n# Part 2\n\nchange = [int(change) for change in open(filename)]\nfrequency = 0\nfrequencies = {}\nfrequencies[frequency] = 1\ni = 0\nwhile True:\n frequency += change[i % len(change)]\n i += 1\n if frequency not in frequencies:\n frequencies[frequency] = 1\n else:\n print(frequency)\n break\n","sub_path":"aoc2018/day1/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"494623381","text":"'''\r\nAuthor: David Dalisay\r\nScript: game.py\r\nGoal: Provides the high level controller of a checkers game. Creates boards and pieces, prompts for moves, etc.\r\n'''\r\nfrom board import Board\r\nfrom piece import Piece\r\nimport operator\r\nimport datetime\r\n\r\nclass Game:\r\n\tdef __init__(self, max_depth):\r\n\t\tself.board = Board(8,8)\r\n\t\tself.is_over = False\r\n\t\tself.winner = None\r\n\t\tself.turn = \"B\"\r\n\r\n\t\t# For bot depth.\r\n\t\tself.max_depth = max_depth\r\n\t\r\n\tdef create_pieces(self):\r\n\t\tpieces = []\r\n\t\tpiece_alpha = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\"]\r\n\t\t\r\n\t\tpiece_index = 0\t\r\n\t\tfor i in range(3):\r\n\t\t\tfor j in range(int(not i%2),8,2):\r\n\t\t\t\tpieces.append(Piece(\"Man\", i, j,\"B\",\"B\"+piece_alpha[piece_index]))\r\n\t\t\t\tpiece_index += 1\r\n\t\r\n\t\tpiece_index = 0\r\n\t\tfor i in range(5,8):\r\n\t\t\tfor j in range(int(not i%2),8,2):\r\n\t\t\t\tpiece = Piece(\"Man\",i,j,\"W\",\"W\"+piece_alpha[piece_index])\r\n\t\t\t\tpieces.append(piece)\r\n\t\t\t\t\r\n\t\t\t\tpiece_index += 1\r\n\r\n\t\tself.board.set_pieces(pieces)\r\n\t\r\n\tdef get_pieces(self):\r\n\t\treturn self.board.pieces\r\n\t\t\r\n\tdef display_board(self):\r\n\t\tself.board.display()\r\n\t\t\r\n\tdef bot_generate_moves(self):\r\n\t\tself.board.generate_moves()\r\n\t\r\n\tdef test_bot_move_piece(self,piece_index):\r\n\t\tpiece = self.board.pieces[7]\r\n\t\tc_move = list(piece.moves.keys())[0]\r\n\t\tbb_move = piece.moves[c_move]\r\n\r\n\t\tself.board.move_piece(piece,c_move[0],c_move[1],bb_move)\r\n\r\n\tdef bot_make_first_legal_move(self, bot_color):\r\n\t\tself.bot_generate_moves()\r\n\t\t\r\n\t\tfound_piece_move = False\r\n\t\tfor piece in self.board.pieces:\r\n\t\t\tif found_piece_move:\r\n\t\t\t\tbreak\r\n\t\t\tc_moves = list(piece.moves.keys())\r\n\t\t\t\r\n\t\t\tif self.board.get_color_for_xy(piece.x,piece.y) is not bot_color:\r\n\t\t\t\tcontinue\t\r\n\t\t\t\r\n\t\t\tfor c_move in c_moves:\t\r\n\t\t\t\tif self.board.move_piece( piece, c_move[0], c_move[1]) > 0:\r\n\t\t\t\t\tprint(\"Bot moving to ({},{})\".format(c_move[0],c_move[1]))\r\n\t\t\t\t\tfound_piece_move = True\r\n\t\t\t\t\tbreak\t\t\r\n\r\n\tdef move_piece(self, piece, to_x, to_y):\r\n\t\tself.board.move_piece(piece, to_x, to_y)\r\n\r\n\t# Checks to see if the game is over.\r\n\tdef is_game_over(self, iteration):\r\n\t\t# If its the first check of the game, ignore the following logic.\r\n\t\tif iteration <= 0:\r\n\t\t\t#print(\"is_game_over | itertion <= 0, ignore logic\")\r\n\t\t\tself.iteration += 1\r\n\t\t\treturn False\r\n\r\n\t\t#print(\"is_game_over | iteration > 0, logic continues.\")\t\r\n\t\t# Checks to see if someone has cleared the opponents' pieces.\r\n\t\tblack_captured = [piece.captured for piece in self.board.pieces if piece.color == \"B\"]\r\n\t\twhite_captured = [piece.captured for piece in self.board.pieces if piece.color == \"W\"]\r\n\t\r\n\t\tif all(black_captured):\r\n\t\t\tself.winner = \"B\"\r\n\t\t\tprint(\"{} is going to win.\".format(self.winner))\r\n\t\t\treturn True\r\n\t\telif all(white_captured):\r\n\t\t\tself.winner = \"W\"\r\n\t\t\tprint(\"{} is going to win.\".format(self.winner))\r\n\t\t\treturn True\r\n\r\n\t\t# Checks to see if one player has no legal moves left.\r\n\t\tall_noncaptured_pieces = [piece for piece in self.board.pieces if piece.captured == False]\r\n\t\tblack_has_legal_moves = False\r\n\t\twhite_has_legal_moves = False\r\n\t\tfor piece in all_noncaptured_pieces:\r\n\t\t\t# Get list of coordinate moves for each piece.\r\n\t\t\tc_moves = list(piece.moves.keys())\r\n\t\t\r\n\t\t\t# Look through all the moves and call minimax on each one.\t\r\n\t\t\tfor c_move in c_moves:\r\n\t\t\t\tif c_move: \r\n\t\t\t\t\tif self.board.is_legal_move(piece.color,piece,piece.x,piece.y,c_move[0],c_move[1]):\r\n\t\t\t\t\t\t#print(\"is_game_over | piece = ({},{}), legal move = {}\".format(piece.x, piece.y, c_move))\r\n\t\t\t\t\t\tif piece.color == \"B\":\r\n\t\t\t\t\t\t\tblack_has_legal_moves = True\r\n\t\t\t\t\t\telif piece.color == \"W\":\r\n\t\t\t\t\t\t\twhite_has_legal_moves = True\r\n\t\t#print(\"black_has_legal_moves = {}\".format(black_has_legal_moves))\r\n\t\t#print(\"white_has_legal_moves = {}\".format(white_has_legal_moves))\r\n\t\tif (not black_has_legal_moves):\r\n\t\t\tself.winner = \"W\"\r\n\t\t\tprint(\"{} is going to win.\".format(self.winner))\r\n\t\t\treturn True\r\n\t\t\t\r\n\t\tif (not white_has_legal_moves):\r\n\t\t\tself.winner = \"B\"\r\n\t\t\tprint(\"{} is going to win.\".format(self.winner))\r\n\t\t\treturn True\r\n\t\t#return (self.winner is not None) \t\t\t\t\t\r\n\r\n\t# Evaluate function that bot uses to determine the \"best\" move.\r\n\t# Evaluate goal: Finds the board states with the most opposing pieces gone and most friendly pieces crowned.\r\n\tdef evaluate(self):\r\n\t\t# Evaluation Part 1: More black pieces.\r\n\t\t# Count the difference of black-white pieces. Exactly like that because we want MORE black pieces.\r\n\t\t# @TODO: Only works if bot is black side. Fix that.\r\n\t\tcount_black_pieces = 0\r\n\t\tfor row in self.board.board:\r\n\t\t\tfor col in row:\r\n\t\t\t\tif col == \"B\":\r\n\t\t\t\t\tcount_black_pieces += 1\r\n\t\t\t\telif col == \"W\":\r\n\t\t\t\t\tcount_black_pieces -= 1\r\n\r\n\t\t# Evaluation Part 2: More black pieces crowned.\r\n\t\t# Count the number of crowned black pieces.\r\n\t\tcount_black_crowned = 0\r\n\t\tfor piece in self.board.pieces:\r\n\t\t\tif piece.captured:\r\n\t\t\t\tcontinue\r\n\t\t\tif piece.type == \"King\":\r\n\t\t\t\tif piece.color == \"B\":\r\n\t\t\t\t\tcount_black_crowned += 1\r\n\t\t\t\telif piece.color == \"W\":\r\n\t\t\t\t\tcount_black_crowned -= 1\r\n\t\t\r\n\t\treturn (count_black_pieces + count_black_crowned)\r\n\r\n\t# Performs minimax algorithm for bot to search for the best move.\r\n\tdef minimax(self,depth, turn):\r\n\t\t# Generate moves for the current board state.\r\n\t\tself.bot_generate_moves()\r\n\r\n\t\t# Get only the bot pieces.\r\n\t\tbot_pieces = [piece for piece in self.board.pieces if piece.color == \"B\"]\r\n\t\t\t\r\n\t\t# Record best move and best score - which is a MAX.\r\n\t\tbest_move = None\r\n\t\tbest_score = float('-inf')\r\n\t\tbest_piece = None\r\n\r\n\t\t# Go through each piece and their generated moves, recursively perform minimax on each move.\r\n\t\tfor piece in bot_pieces:\r\n\t\t\t# Get list of coordinate moves for each piece.\r\n\t\t\tc_moves = list(piece.moves.keys())\r\n\t\t\t\r\n\t\t\tif not c_moves:\r\n\t\t\t\tcontinue \r\n\r\n\t\t\t# Look through all the moves and call minimax on each one.\t\r\n\t\t\tfor c_move in c_moves:\r\n\t\t\t\tif not c_move:\r\n\t\t\t\t\tcontinue\r\n\t\t\t\t# This if statement also moves the piece.\r\n\t\t\t\tif self.board.move_piece(piece, c_move[0], c_move[1]) > 0:\r\n\t\t\t\t\t#print(\"Bot moving to ({},{})\".format(c_move[0],c_move[1]))\t\r\n\t\t\t\t\t# Call min function to calculate scores of opponent moves.\r\n\t\t\t\t\tscore = self.min(depth+1)\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tif score > best_score:\r\n\t\t\t\t\t\tbest_move = c_move\r\n\t\t\t\t\t\tbest_score = score\r\n\t\t\t\t\t\tbest_piece = piece\r\n\r\n\t\t\t\t\t# Retract the piece back to it's previous location.\r\n\t\t\t\t\tself.board.retract_piece(piece)\r\n\t\treturn (best_piece,best_move)\r\n\t\t\t\r\n\tdef min(self, depth):\r\n\t\tif depth == self.max_depth:\r\n\t\t\treturn self.evaluate()\r\n\t\tif self.is_game_over(self.iteration):\r\n\t\t\treturn self.evaluate()\r\n\t\t\r\n\t\t# Generate moves for the opponents state.\r\n\t\tself.bot_generate_moves()\r\n\t\r\n\t\t# Get only the opponent human's pieces.\r\n\t\topponent_pieces = [piece for piece in self.board.pieces if piece.color == \"W\"]\r\n\r\n\t\t# Record best move and best score - which is a MIN.\r\n\t\tbest_score = float('inf')\r\n\t\tbest_move = None\r\n\r\n\t\t# Go through each piece and their generated moves, recurisvely perform minimax on each move.\r\n\t\tfor piece in opponent_pieces:\r\n\t\t \r\n\t\t\t# Get list of coordinate moves for each piece.\r\n\t\t\tc_moves = list(piece.moves.keys())\r\n\t\t\tif not c_moves:\r\n\t\t\t\tcontinue \r\n\t\t\t\r\n\t\t\tfor c_move in c_moves:\r\n\t\t\t\tif not c_move:\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tif self.board.move_piece(piece, c_move[0], c_move[1]) > 0:\r\n\t\t\t\t\tscore = self.max(depth+1)\r\n\t\t\t\t\tif score < best_score:\r\n\t\t\t\t\t\tbest_move = c_move\r\n\t\t\t\t\t\tbest_score = score\t\t\t\r\n\t\t\t\t\t\r\n\t\t\t\t\tself.board.retract_piece(piece)\t\t \r\n\t\t \r\n\t\treturn best_score\t\r\n\r\n\tdef max(self, depth):\r\n\t\tif depth == self.max_depth:\r\n\t\t\treturn self.evaluate()\r\n\t\tif self.is_game_over(self.iteration):\r\n\t\t\treturn self.evaluate()\r\n\t\t# Generate moves for the opponents state.\r\n\t\tself.bot_generate_moves()\r\n\t\r\n\t\t# Get only the bot's pieces.\r\n\t\tbot_pieces = [piece for piece in self.board.pieces if piece.color == \"B\"]\r\n\r\n\t\t# Record best move and best score - which is a MAX.\r\n\t\tbest_score = float('-inf')\r\n\t\tbest_move = None\r\n\r\n\t\t# Go through each piece and their generated moves, recurisvely perform minimax on each move.\r\n\t\tfor piece in bot_pieces:\r\n\t\t \r\n\t\t\t# Get list of coordinate moves for each piece.\r\n\t\t\tc_moves = list(piece.moves.keys())\r\n\t\t\tif not c_moves:\r\n\t\t\t\tcontinue\r\n\t\r\n\t\t\tfor c_move in c_moves:\r\n\t\t\t\tif not c_move: # @TODO Gotta do this in legal move checker function. For now, exclude None moves.\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tif self.board.move_piece(piece, c_move[0], c_move[1]) > 0:\r\n\t\t\t\t\tscore = self.min(depth+1)\r\n\t\t\t\t\tif score > best_score:\r\n\t\t\t\t\t\tbest_move = c_move\r\n\t\t\t\t\t\tbest_score = score\t\t\t\r\n\t\t\t\t\t\r\n\t\t\t\t\tself.board.retract_piece(piece)\t\t \r\n \r\n\t\treturn best_score\t\r\n\t\r\n\r\n\tdef preplay_setup(self,type=\"standard\"):\r\n\t\tif type == \"standard\":\r\n\t\t\tself.create_pieces()\r\n\t\t\treturn\r\n\r\n\t\t# As a test, statically define the board to save game test time.\r\n\t\tpieces = []\r\n\t\tpieces.append(Piece(\"Man\",0,1,\"W\",\"W0\"))\r\n\t\tpieces.append(Piece(\"Man\",0,3,\"W\",\"W1\"))\r\n\t\tpieces.append(Piece(\"Man\",0,7,\"W\",\"W2\"))\r\n\t\tpieces.append(Piece(\"Man\",1,4,\"W\",\"W3\"))\r\n\t\t#pieces.append(Piece(\"Man\",1,6,\"B\",\"B0\"))\r\n\t\tpieces.append(Piece(\"Man\",2,7,\"B\",\"B1\"))\r\n\t\tpieces.append(Piece(\"Man\",4,7,\"W\",\"W4\"))\r\n\t\tpieces.append(Piece(\"Man\",5,0,\"W\",\"W5\"))\r\n\t\tpieces.append(Piece(\"Man\",7,0,\"B\",\"B2\"))\r\n\t\tpieces.append(Piece(\"Man\",7,2,\"B\",\"B3\"))\r\n\t\tpieces.append(Piece(\"Man\",7,4,\"B\",\"B4\"))\r\n\t\tself.board.set_pieces(pieces)\r\n\t\r\n\tdef play(self):\r\n\t\tprint(\"B - bot\")\r\n\t\tprint(\"W - human\\n\")\r\n\r\n\t\tself.turn = \"B\"\r\n\t\tself.iteration = 0\r\n\t\twhile not self.is_game_over(iteration=self.iteration):\r\n\t\t\t# Bot turn\t\r\n\t\t\tif self.turn == \"B\":\r\n\t\t\t\tstart_time = datetime.datetime.now()\r\n\t\t\t\tprint(\"\\n#### BOT TURN\")\r\n\t\t\t\tbest_move = self.minimax(depth=0,turn=\"B\")\r\n\t\t\t\tbest_piece = best_move[0]\r\n\t\t\t\tbest_move_x = best_move[1][0]\r\n\t\t\t\tbest_move_y = best_move[1][1]\r\n\t\t\t\tprint(\"#### Bot moving ({},{}) to ({},{})\".format(best_piece.x, best_piece.y, best_move_x, best_move_y))\r\n\t\t\t\tself.move_piece(best_piece, best_move_x,best_move_y)\t\r\n\t\t\t\tself.display_board()\r\n\t\t\t\tend_time = datetime.datetime.now()\r\n\t\t\t\tbot_thinking_time = end_time - start_time\r\n\t\t\t\tprint(\"#### Bot time: {}s\".format(bot_thinking_time.total_seconds()))\r\n\t\t\t\tself.turn = \"W\"\r\n\t\t\telse: # Human turn\r\n\t\t\t\tprint(\"\\n#### HUMAN TURN\")\r\n\t\t\t\tinp = input(\"Your move: (format \\\"50 to 41\\\")\\n\")\r\n\t\t\t\tmove_input = inp.split(\" to \")\r\n\t\t\t\tmove_from = move_input[0]\r\n\t\t\t\tmove_to = move_input[1]\r\n\t\t\t\t\t\r\n\t\t\t\tmove_from_x = int(move_from[0])\r\n\t\t\t\tmove_from_y = int(move_from[1])\r\n\t\t\t\tmove_from_piece = [piece for piece in self.board.pieces if piece.x == move_from_x and piece.y == move_from_y and piece.captured == False][0]\r\n\t\t\t\tmove_to_x = int(move_to[0])\r\n\t\t\t\tmove_to_y = int(move_to[1])\r\n\r\n\t\t\t\t# @TODO Legal move checking for human.\r\n\t\t\t\tself.move_piece(move_from_piece, move_to_x,move_to_y)\r\n\t\t\t\t\t\t\r\n\t\t\t\tself.display_board()\r\n\t\t\t\tself.turn = \"B\"\r\n\t\t\tself.iteration += 1\r\n\t\r\ngame = Game(max_depth=5)\r\ngame.preplay_setup()\r\nprint(\"GAME START\")\r\ngame.play()\t\r\n\t\r\n","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":10766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"458990213","text":"import numpy as np\nimport theano\nfrom theano import tensor as T\nfrom ug_utils import floatX, Dropout\nfrom rnn import (RNN, SequenceLogisticRegression, GRULayer, GRULayerAttention,\n LayerWrapper, seq_cat_crossent, Downscale)\nfrom opt import get_opt_fn\nfrom ug_utils import glorot_init, norm_init, uniform_init, get_sequence_dropout_mask\n\ndef reverse_sent(ss, sm):\n rss = np.array(ss)\n lens = np.sum(sm, axis=1)\n for k in xrange(rss.shape[0]):\n rss[k, 0:lens[k]] = rss[k, lens[k]-1::-1]\n return rss\n\ndef clip_lengths(sents, mask, clip_length):\n max_length = np.max(np.sum(mask, axis=1))\n if max_length > clip_length:\n sents = sents[:, :clip_length]\n mask = mask[:, :clip_length]\n else:\n pass\n return sents, mask\n\ndef pyrpad(ss, sm, depth, pyramid):\n if not pyramid:\n return ss, sm\n align = pow(2, depth - 1)\n batch_size, seq_len = ss.shape\n padlen = (align - seq_len) % align\n if padlen == 0:\n return ss, sm\n padding = np.zeros([batch_size, padlen])\n return np.concatenate((ss, padding), axis=1), np.concatenate((sm, padding), axis=1)\n\ndef extract_unmasked(hs, mask):\n # for each batch, extract indices where mask==1 and put into output matrix\n lens = T.sum(mask, axis=0)\n maxlen = T.max(lens)\n #maxlen = theano.printing.Print(maxlen)(maxlen) # should be # words\n out = T.zeros((hs.shape[1], maxlen, hs.shape[2])) - 1\n def extract_step(h, m, k, out):\n inds = m.nonzero()[0]\n out = T.set_subtensor(out[k, 0:inds.shape[0], :], h[inds, :])\n return k + 1, out[k]\n pair, updates = theano.scan(extract_step, sequences=[hs.dimshuffle((1, 0, 2)), mask.dimshuffle((1, 0))], outputs_info=[0, None], non_sequences=out)\n out = pair[1]\n out = out.dimshuffle((1, 0, 2))\n #out = theano.printing.Print(out)(out)\n return out\n\nclass RNNDecoder(RNN):\n\n def __init__(self, rep, y, mask, L_dec, pdrop, args):\n self.h0s = rep\n outputs_info = self.h0s\n rlayers = list()\n self.subset = L_dec[y.flatten()]\n inp = self.subset.reshape((y.shape[0], y.shape[1], L_dec.shape[1]))\n seqmask = get_sequence_dropout_mask((y.shape[0], y.shape[1], L_dec.shape[1]), pdrop)\n # exclude last prediction\n inplayer = GRULayer(inp[:-1].astype(floatX), mask[:-1], seqmask[:-1], args.input_size,\n outputs_info[0], args, suffix='dec0')\n rlayers.append(inplayer)\n for k in xrange(1, args.rlayers):\n seqmask = get_sequence_dropout_mask((y.shape[0], y.shape[1], args.rnn_dim), pdrop)\n rlayer = GRULayer(Dropout(rlayers[-1].out, pdrop).out, mask[:-1], seqmask[:-1],\n args.rnn_dim, outputs_info[k], args, suffix='dec%d' % k)\n rlayers.append(rlayer)\n olayer = SequenceLogisticRegression(Dropout(rlayers[-1].out, pdrop).out, args.rnn_dim,\n args.tgt_vocab_size)\n cost = seq_cat_crossent(olayer.out, y[1:], mask[1:], normalize=False)\n super(RNNDecoder, self).__init__(rlayers, olayer, cost)\n\nclass RNNDecoderAttention(RNN):\n\n def __init__(self, encoder, y, mask, L_dec, pdrop, args):\n self.hs = encoder.hs\n # NOTE just use this so only last layer uses attention\n def layer_init(attention):\n if not attention:\n return GRULayer\n else:\n return lambda *largs, **kwargs: GRULayerAttention(self.hs, *largs, **kwargs)\n # initial states\n outputs_info = [T.zeros_like(self.hs[0]) for k in xrange(len(encoder.routs))]\n rlayers = list()\n self.subset = L_dec[y.flatten()]\n inp = self.subset.reshape((y.shape[0], y.shape[1], L_dec.shape[1]))\n attention = args.rlayers == 1\n # exclude last prediction\n seqmask = get_sequence_dropout_mask((y.shape[0], y.shape[1], L_dec.shape[1]), pdrop)\n inplayer = layer_init(attention)(inp[:-1].astype(floatX), mask[:-1], seqmask[:-1], args.input_size,\n outputs_info[0], args, suffix='dec0')\n rlayers.append(inplayer)\n for k in xrange(1, args.rlayers):\n attention = (args.rlayers == k + 1)\n seqmask = get_sequence_dropout_mask((y.shape[0], y.shape[1], args.rnn_dim), pdrop)\n rlayer = layer_init(attention)(Dropout(rlayers[-1].out, pdrop).out, mask[:-1],\n seqmask[:-1], args.rnn_dim, outputs_info[k], args, suffix='dec%d' % k)\n rlayers.append(rlayer)\n olayer = SequenceLogisticRegression(Dropout(rlayers[-1].out, pdrop).out, args.rnn_dim,\n args.tgt_vocab_size)\n cost = seq_cat_crossent(olayer.out, y[1:], mask[1:], normalize=False)\n super(RNNDecoderAttention, self).__init__(rlayers, olayer, cost)\n\nclass RNNEncoder(RNN):\n\n def __init__(self, x, mask, space_mask, L_enc, pdrop, args, suffix_prefix='enc', backwards=False):\n # NOTE shape[1] is batch size since shape[0] is seq length\n outputs_info = [T.zeros((x.shape[1], args.rnn_dim)).astype(floatX)]\n rlayers = list()\n self.subset = L_enc[x.flatten()]\n inp = self.subset.reshape((x.shape[0], x.shape[1], L_enc.shape[1]))\n seqmask = get_sequence_dropout_mask((x.shape[0], x.shape[1], L_enc.shape[1]), pdrop)\n inplayer = GRULayer(inp.astype(floatX), mask, seqmask, args.input_size, outputs_info,\n args, suffix='%s0' % suffix_prefix, backwards=backwards)\n rlayers.append(inplayer)\n for k in xrange(1, args.rlayers):\n inp = rlayers[-1].out\n seqmask = get_sequence_dropout_mask((x.shape[0], x.shape[1], args.rnn_dim), pdrop)\n rlayer = GRULayer(Dropout(inp, pdrop).out, mask, seqmask, args.rnn_dim,\n outputs_info, args, suffix='%s%d' % (suffix_prefix, k), backwards=backwards)\n rlayers.append(rlayer)\n\n # should extract final outputs according to mask, note we\n # don't know seq length or current batch size at graph construction time\n # NOTE this would be used for initial hidden states in decoder in standard seq2seq but currently unused\n lens = T.sum(mask, axis=0)\n # will extract A[lens[k], k, :] for k in [0, batch size)\n self.routs = list()\n for rlayer in rlayers:\n # get the last time steps (what's this doing with batch???)\n rout = rlayer.out[args.src_steps - 1, theano.tensor.arange(x.shape[1]), :].astype(floatX)\n self.routs.append(rout)\n self.hs = rlayers[-1].out # for attention (last layer's output) (Timestep, batch_size, rnn_dim)\n\n olayer = LayerWrapper(self.routs)\n super(RNNEncoder, self).__init__(rlayers, olayer)\n\nclass BiPyrRNNEncoder(RNN):\n def __init__(self, x, xr, mask, L_enc, pdrop, args):\n # NOTE shape[1] is batch size since shape[0] is seq length\n outputs_info = [T.zeros((x.shape[1], args.rnn_dim)).astype(floatX)]\n flayers = list()\n blayers = list()\n fsubset = L_enc[x.flatten()]\n bsubset = L_enc[xr.flatten()]\n finp = fsubset.reshape((x.shape[0], x.shape[1], L_enc.shape[1]))\n binp = bsubset.reshape((x.shape[0], x.shape[1], L_enc.shape[1]))\n fseqmask = get_sequence_dropout_mask((x.shape[0], x.shape[1], L_enc.shape[1]), pdrop)\n bseqmask = get_sequence_dropout_mask((x.shape[0], x.shape[1], L_enc.shape[1]), pdrop)\n finplayer = GRULayer(finp.astype(floatX), mask, fseqmask, args.input_size, outputs_info,\n args, suffix='fenc0')\n binplayer = GRULayer(binp.astype(floatX), mask, bseqmask, args.input_size, outputs_info,\n args, suffix='benc0', backwards=True)\n flayers.append(finplayer)\n blayers.append(binplayer)\n self.routs = list() # unlike RNNEncoder, contains hs, not just final h\n self.routs.append(finplayer.out + binplayer.out)\n downs = []\n for k in xrange(1, args.rlayers):\n # concatenate consecutive steps in the sequence (which are downscaled to half from the previous layer)\n d = Downscale(self.routs[-1], args.rnn_dim, suffix='ds%d' % k)\n downs.append(d)\n inp = d.out\n twocols = mask.T.reshape([-1, 2])\n mask = T.or_(twocols[:, 0], twocols[:, 1]).reshape([mask.shape[1], -1]).T\n\n fseqmask = get_sequence_dropout_mask((inp.shape[0], inp.shape[1], args.rnn_dim), pdrop)\n bseqmask = get_sequence_dropout_mask((inp.shape[0], inp.shape[1], args.rnn_dim), pdrop)\n flayer = GRULayer(Dropout(inp, pdrop).out, mask, fseqmask, args.rnn_dim, outputs_info, args, suffix='fenc%d' % k)\n blayer = GRULayer(Dropout(inp, pdrop).out, mask, bseqmask, args.rnn_dim, outputs_info, args, suffix='benc%d' % k, backwards=True)\n self.routs.append(flayer.out + blayer.out)\n flayers.append(flayer)\n blayers.append(blayer)\n self.hs = self.routs[-1] # for attention\n olayer = LayerWrapper(self.routs)\n rlayers = flayers + blayers # NOTE careful not to assume rlayers = # layers in all cases\n\n # undo the temporary hack\n super(BiPyrRNNEncoder, self).__init__(rlayers, olayer, downscales=downs)\n\nclass BiRNNEncoder(RNN):\n\n def __init__(self, x, xr, mask, space_mask, L_enc, pdrop, args):\n # NOTE shape[1] is batch size since shape[0] is seq length\n outputs_info = [T.zeros((x.shape[1], args.rnn_dim)).astype(floatX)]\n flayers = list()\n blayers = list()\n\n finp = L_enc[x]\n binp = L_enc[xr]\n\n fseqmask = get_sequence_dropout_mask((x.shape[0], x.shape[1], L_enc.shape[1]), pdrop)\n bseqmask = get_sequence_dropout_mask((x.shape[0], x.shape[1], L_enc.shape[1]), pdrop)\n finplayer = GRULayer(finp.astype(floatX), mask, fseqmask, args.input_size, outputs_info,\n args, suffix='fenc0')\n binplayer = GRULayer(binp.astype(floatX), mask, bseqmask, args.input_size, outputs_info,\n args, suffix='benc0', backwards=True)\n flayers.append(finplayer)\n blayers.append(binplayer)\n self.routs = list() # unlike RNNEncoder, contains hs, not just final h\n self.routs.append(finplayer.out + binplayer.out)\n for k in xrange(1, args.rlayers):\n inp = self.routs[-1]\n fseqmask = get_sequence_dropout_mask((inp.shape[0], inp.shape[1], args.rnn_dim), pdrop)\n bseqmask = get_sequence_dropout_mask((inp.shape[0], inp.shape[1], args.rnn_dim), pdrop)\n flayer = GRULayer(Dropout(inp, pdrop).out, mask, fseqmask, args.rnn_dim, outputs_info, args, suffix='fenc%d' % k)\n blayer = GRULayer(Dropout(inp, pdrop).out, mask, bseqmask, args.rnn_dim, outputs_info, args, suffix='benc%d' % k, backwards=True)\n self.routs.append(flayer.out + blayer.out)\n flayers.append(flayer)\n blayers.append(blayer)\n self.hs = self.routs[-1] # for attention\n olayer = LayerWrapper(self.routs)\n rlayers = flayers + blayers # NOTE careful not to assume rlayers = # layers in all cases\n super(BiRNNEncoder, self).__init__(rlayers, olayer)\n\nclass EncoderDecoder(object):\n\n # subset_grad determines whether want to use subset updates (faster but\n # currently can only use sgd) or full updates on embeddings\n def __init__(self, args, params=None, attention=False, bidir=False, subset_grad=True, pyramid=False):\n self.rnn_dim = args.rnn_dim\n self.rlayers = args.rlayers\n self.attention = attention\n\n lr = T.scalar(dtype=floatX)\n pdrop = T.scalar(dtype=floatX)\n max_norm = T.scalar(dtype=floatX)\n\n # initialize input tensors\n\n src_sent = T.imatrix('src_sent')\n rev_src_sent = T.imatrix('rev_src_sent')\n src_mask = T.bmatrix('src_mask')\n tgt_sent = T.imatrix('tgt_sent')\n tgt_mask = T.bmatrix('tgt_mask')\n space_mask = T.bmatrix('space_mask')\n\n # build up model\n # https://groups.google.com/forum/#!topic/torch7/-NBrFw8Q6_s\n # NOTE can't use one-hot here because huge matrix multiply\n self.L_enc = theano.shared(uniform_init(args.src_vocab_size, args.rnn_dim, scale=0.1),\n 'L_enc', borrow=True)\n self.L_dec = theano.shared(uniform_init(args.tgt_vocab_size, args.rnn_dim, scale=0.1),\n 'L_dec', borrow=True)\n enc_input = src_sent if not args.reverse else rev_src_sent\n if bidir:\n print('Using bidirectional encoder')\n self.encoder = BiRNNEncoder(src_sent.T, rev_src_sent.T, src_mask.T, space_mask.T, self.L_enc, pdrop, args)\n elif pyramid:\n print('Using pyramid encoder')\n self.encoder = BiPyrRNNEncoder(src_sent.T, rev_src_sent.T, src_mask.T, self.L_enc, pdrop, args)\n else:\n self.encoder = RNNEncoder(enc_input.T, src_mask.T, space_mask.T, self.L_enc, pdrop, args)\n if attention:\n self.decoder = RNNDecoderAttention(self.encoder, tgt_sent.T, tgt_mask.T,\n self.L_dec, pdrop, args)\n hs = self.decoder.hs\n else:\n self.decoder = RNNDecoder(self.encoder.out, tgt_sent.T, tgt_mask.T,\n self.L_dec, pdrop, args)\n\n # cost, parameters, grads, updates\n\n self.cost = self.decoder.cost\n self.params = self.encoder.params + self.decoder.params + [self.L_enc, self.L_dec]\n if subset_grad: # for speed\n self.grad_params = self.encoder.params + self.decoder.params + [self.encoder.subset, self.decoder.subset]\n self.updates, self.grad_norm, self.param_norm = get_opt_fn(args.optimizer)(self.cost, self.grad_params, lr, max_norm=max_norm)\n # instead of updating L_enc and L_dec only want to update the embeddings indexed, so use inc_subtensor/set_subtensor\n # http://deeplearning.net/software/theano/tutorial/faq_tutorial.html\n self.updates[-2] = (self.L_enc, T.set_subtensor(self.updates[-2][0], self.updates[-2][1]))\n self.updates[-1] = (self.L_dec, T.set_subtensor(self.updates[-1][0], self.updates[-1][1]))\n else:\n self.grad_params = self.params\n self.updates, self.grad_norm, self.param_norm = get_opt_fn(args.optimizer)(self.cost, self.grad_params, lr, max_norm=max_norm)\n\n self.nparams = np.sum([np.prod(p.shape.eval()) for p in self.params])\n\n # functions\n\n self.train = theano.function(\n inputs=[src_sent, src_mask, rev_src_sent, tgt_sent, tgt_mask, space_mask,\n pdrop, lr, max_norm],\n outputs=[self.cost, self.grad_norm, self.param_norm],\n updates = self.updates,\n on_unused_input='warn',\n allow_input_downcast=True\n )\n self.test = theano.function(\n inputs=[src_sent, src_mask, rev_src_sent, tgt_sent, tgt_mask, space_mask, theano.In(pdrop, value=0.0)],\n outputs=self.cost,\n updates=None,\n on_unused_input='warn'\n )\n outputs=self.encoder.out\n if attention:\n outputs = self.encoder.out + [hs]\n self.encode = theano.function(\n inputs=[src_sent, rev_src_sent, src_mask, space_mask, theano.In(pdrop, value=0.0)],\n outputs=outputs,\n on_unused_input='warn',\n updates=None\n )\n\n # function for decoding step by step\n\n i_t = T.ivector()\n x_t = self.L_dec[i_t, :]\n h_ps = list() # previous\n for k in xrange(args.rlayers):\n h_ps.append(T.matrix())\n h_ts = list()\n dmask = T.ones_like(h_ps[0]).astype(floatX)\n if attention and args.rlayers == 1:\n h_t, _ = self.decoder.rlayers[0]._step(x_t, dmask, h_ps[0], hs)\n else:\n h_t = self.decoder.rlayers[0]._step(x_t, dmask, h_ps[0])\n h_ts.append(h_t)\n # NOTE no more dropout nodes here\n for k in xrange(1, args.rlayers):\n if attention and args.rlayers == k + 1:\n h_t, align = self.decoder.rlayers[k]._step(h_t, dmask, h_ps[k], hs)\n else:\n h_t = self.decoder.rlayers[k]._step(h_t, dmask, h_ps[k])\n h_ts.append(h_t)\n E_t = T.dot(h_t, self.decoder.olayer.W) + self.decoder.olayer.b\n E_t = T.exp(E_t - T.max(E_t, axis=1, keepdims=True))\n p_t = E_t / E_t.sum(axis=1, keepdims=True)\n inputs=[i_t] + h_ps\n outputs=[p_t] + h_ts\n if attention:\n inputs = inputs + [hs]\n outputs = outputs + [align]\n self.decode_step = theano.function(\n inputs=inputs,\n outputs=outputs,\n updates=None\n )\n","sub_path":"model/th/encdec_shared.py","file_name":"encdec_shared.py","file_ext":"py","file_size_in_byte":16674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"259237215","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n\"\"\"\nWidgets inside central widget\n\"\"\"\n\nimport logging\nfrom collections import OrderedDict\n\nimport qtawesome as qta\nfrom PyQt5 import QtCore, QtWidgets, QtGui\n\nfrom stundenzettel.enums import PlayState, FileStatus\n\n\nclass Calendar(QtWidgets.QGroupBox):\n\n # signals\n sig_button_clicked = QtCore.pyqtSignal(PlayState)\n sig_dial = QtCore.pyqtSignal(int)\n sig_button_video = QtCore.pyqtSignal(bool)\n\n def __init__(self):\n super().__init__()\n\n self.setTitle(\"Calendar\")\n self.setObjectName(\"calendar\")\n\n calendar = QtWidgets.QCalendarWidget()\n calendar.setMinimumDate(QtCore.QDate(1900, 1, 1))\n calendar.setMaximumDate(QtCore.QDate(3000, 1, 1))\n calendar.setGridVisible(True)\n\n calendar.currentPageChanged.connect(self.reformat_calendar_page)\n\n b_today = QtWidgets.QPushButton(self.tr('Today'))\n b_today.clicked.connect(self.today)\n\n # define layouts\n layout = QtWidgets.QVBoxLayout()\n\n # add everything to main layout\n layout.addWidget(b_today)\n layout.addWidget(calendar)\n self.setLayout(layout)\n\n # store current date\n self._today = calendar.selectedDate()\n self._calendar = calendar\n\n @QtCore.pyqtSlot()\n def today(self):\n \"\"\"\n Reset calendar to todays date\n :return:\n \"\"\"\n self._calendar.setSelectedDate(self._today)\n self._calendar.showToday()\n\n @QtCore.pyqtSlot(int, int)\n def reformat_calendar_page(self):\n \"\"\"\n Determine which signal to send\n :return:\n \"\"\"\n logging.debug('applying user style to calendar')\n\n\nclass DbTable(QtWidgets.QGroupBox):\n\n def __init__(self):\n super().__init__()\n\n self.setTitle('blafasel')\n self.setObjectName('dbtable')\n \n model = QtGui.QStandardItemModel()\n model.setHorizontalHeaderLabels(['project number', 'hh:mm', 'begin', 'end', 'comment'])\n model.setRowCount(5)\n\n table = QtWidgets.QTableView()\n table.setModel(model)\n\n layout = QtWidgets.QVBoxLayout()\n layout.addWidget(table)\n self.setLayout(layout)\n\n self._table = table\n\n def add_row(self):\n \"\"\"\n add row to table\n \"\"\"\n pass\n\n def delete_row(self):\n \"\"\"\n delete row from table\n \"\"\"\n pass\n\n\nclass InfoBox(QtWidgets.QGroupBox):\n\n def __init__(self):\n super(InfoBox, self).__init__()\n\n self.setTitle('statistics')\n self.setObjectName('statistics')\n\n # todo: add general stats + stats for project\n items = [\n 'Verbleibende Urlaubstage',\n 'Stunden auf Projekt',\n 'Stunden heute',\n 'Stunden diese Woche',\n 'Stunden diesen Monat'\n ]\n\n layout = QtWidgets.QFormLayout()\n\n for item in items:\n layout.addRow(QtWidgets.QLabel(item))\n\n self.setLayout(layout)\n","sub_path":"stundenzettel/ui/control_widgets.py","file_name":"control_widgets.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"457343206","text":"# Given an encoded string, return its decoded string.\n\n# The encoding rule is: k[encoded_string], where the encoded_string inside the square brackets is being repeated exactly k times. \n# Note that k is guaranteed to be a positive integer.\n\n# You may assume that the input string is always valid; No extra white spaces, square brackets are well-formed, etc.\n\n# Furthermore, you may assume that the original data does not contain any digits and that digits are only for those repeat numbers, k. \n# For example, there won't be input like 3a or 2[4].\n\n\n# s = \"3[a]2[bc]\", return \"aaabcbc\".\n# s = \"3[a2[c]]\", return \"accaccacc\".\n# s = \"2[abc]3[cd]ef\", return \"abcabccdcdcdef\".\n\n\n# recursion \n# s = #[...]..., return 3 * decode(...) + decodeString(...)\nclass Solution:\n\tdef decodeString(self,s):\n\t\t# print(\"processing \", s)\n\t\tif s == \"\" or s.isalpha():\n\t\t\treturn s\n\n\t\tnstart = nend = None # index of first number \n\t\tleft = right = 0 \n\t\tstart = end = None # index of first set of []\n\n\t\t# look for the first code [..]\n\t\tfor i in range(len(s)):\n\t\t\tif nstart is None and s[i].isnumeric():\n\t\t\t\tnstart = i\n\t\t\t\tnend = i + 1\n\t\t\telif nstart is not None and s[nstart:i + 1].isnumeric():\n\t\t\t\tnend = i + 1\n\n\t\t\telif s[i] == \"[\":\n\t\t\t\tif left == 0:\n\t\t\t\t\tstart = i\n\t\t\t\tleft += 1\n\t\t\telif s[i] == \"]\":\n\t\t\t\tif right + 1 == left:\n\t\t\t\t\tend = i\n\t\t\t\t\tbreak\n\t\t\t\tright += 1\n\t\t\n\t\tcode = s[start + 1 : end] # \"[...]\"\n\t\tmult = int(s[nstart:nend])\n\t\tans = s[:nstart] + mult * self.decodeString(code) + self.decodeString(s[end + 1:])\n\t\t# print(ans)\n\t\treturn ans\n\n# tests:\ns = \"3[a]2[b]\"\nSolution().decodeString(s)\ns = \"3[a2[c]]\"\nSolution().decodeString(s)\ns = \"3[a2[cb4[d]]]\"\nSolution().decodeString(s)\n\n","sub_path":"M394_DecodeString.py","file_name":"M394_DecodeString.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"112425390","text":"from five import grok\nfrom zope import schema\nfrom tdf.extensionsuploadcenter import _\nfrom plone.directives import form, dexterity\nfrom plone.app.textfield import RichText\nfrom zope.schema.vocabulary import SimpleVocabulary, SimpleTerm\nfrom zope.security import checkPermission\nfrom zope.interface import invariant, Invalid\nfrom Acquisition import aq_inner, aq_parent, aq_get, aq_chain\nfrom plone.namedfile.field import NamedBlobFile\nfrom z3c.form.browser.checkbox import CheckBoxFieldWidget\n\n\n\n\n@grok.provider(schema.interfaces.IContextSourceBinder)\ndef vocabDevelopmentStatus(context):\n \"\"\"pick up developmnet status from parent\"\"\"\n developmentstatus_list = getattr(context.__parent__, 'development_status', [])\n terms = []\n for value in developmentstatus_list:\n terms.append(SimpleTerm(value, token=value.encode('unicode_escape'), title=value))\n return SimpleVocabulary(terms)\n\n\n\n@grok.provider(schema.interfaces.IContextSourceBinder)\ndef vocabAvailLicenses(context):\n \"\"\" pick up licenses list from parent \"\"\"\n\n license_list = getattr(context.__parent__, 'available_licenses', [])\n terms = []\n for value in license_list:\n terms.append(SimpleTerm(value, token=value.encode('unicode_escape'), title=value))\n return SimpleVocabulary(terms)\n\n@grok.provider(schema.interfaces.IContextSourceBinder)\ndef vocabAvailVersions(context):\n \"\"\" pick up the program versions list from parent \"\"\"\n\n versions_list = getattr(context.__parent__, 'available_versions', [])\n terms = []\n for value in versions_list:\n terms.append(SimpleTerm(value, token=value.encode('unicode_escape'), title=value))\n return SimpleVocabulary(terms)\n\n@grok.provider(schema.interfaces.IContextSourceBinder)\ndef vocabAvailPlatforms(context):\n \"\"\" pick up the list of platforms from parent \"\"\"\n\n platforms_list = getattr(context.__parent__, 'available_platforms', [])\n terms = []\n for value in platforms_list:\n terms.append(SimpleTerm(value, token=value.encode('unicode_escape'), title=value))\n return SimpleVocabulary(terms)\n\n\n\n\nyesnochoice = SimpleVocabulary(\n [SimpleTerm(value=0, title=_(u'No')),\n SimpleTerm(value=1, title=_(u'Yes')),]\n )\n\n\nclass AcceptLegalDeclaration(Invalid):\n __doc__ = _(u\"It is necessary that you accept the Legal Declaration\")\n\n\n\nclass IEUpRelease(form.Schema):\n\n\n title = schema.TextLine(\n title=_(u\"Title\"),\n description=_(u\"Release Title\"),\n min_length=5\n )\n\n\n releasenumber=schema.TextLine(\n title=_(u\"Release Number\"),\n description=_(u\"Release Number (up to eight chars)\"),\n default=_(u\"1.0\"),\n max_length=8\n )\n\n\n description = schema.Text(\n title=_(u\"Release Summary\"),\n )\n\n\n\n form.primary('details')\n details = RichText(\n title=_(u\"Full Release Description\"),\n required=False\n )\n\n\n\n form.primary('changelog')\n changelog = RichText(\n title=_(u\"Changelog\"),\n description=_(u\"A detailed log of what has changed since the previous release.\"),\n required=False,\n )\n\n\n developmentstatus_choice=schema.Choice(\n title = _(u\"Development Status\"),\n source=vocabDevelopmentStatus,\n required=True\n )\n\n form.widget(licenses_choice=CheckBoxFieldWidget)\n licenses_choice= schema.List(\n title=_(u'License of the uploaded file'),\n description=_(u\"Please mark one or more licenses you publish your release.\"),\n value_type=schema.Choice(source=vocabAvailLicenses),\n required=True,\n )\n\n form.widget(compatibility_choice=CheckBoxFieldWidget)\n compatibility_choice= schema.List(\n title=_(u\"Compatible with versions of LibreOffice\"),\n description=_(u\"Please mark one or more program versions with which this release is compatible with.\"),\n value_type=schema.Choice(source=vocabAvailVersions),\n required=True,\n )\n\n\n\n form.mode(title_declaration_legal='display')\n title_declaration_legal=schema.TextLine(\n title=_(u\"\"),\n required=False\n )\n\n form.mode(declaration_legal='display')\n form.primary('declaration_legal')\n declaration_legal = RichText(\n title=_(u\"\"),\n required=False\n )\n\n accept_legal_declaration=schema.Bool(\n title=_(u\"Accept the above legal disclaimer\"),\n description=_(u\"Please declare that you accept the above legal disclaimer\"),\n required=True\n )\n\n contact_address2 = schema.ASCIILine(\n title=_(u\"Contact email-address\"),\n description=_(u\"Contact email-address for the project.\"),\n required=False\n )\n\n source_code_inside = schema.Choice(\n title=_(u\"Is the source code inside the extension?\"),\n vocabulary=yesnochoice,\n required=True\n )\n\n link_to_source = schema.URI(\n title=_(u\"Please fill in the Link (URL) to the Source Code\"),\n required=False\n )\n\n\n file = NamedBlobFile(\n title=_(u\"The first file you want to upload\"),\n description=_(u\"Please upload your file.\"),\n required=True,\n )\n\n\n form.widget(platform_choice=CheckBoxFieldWidget)\n platform_choice= schema.List(\n title=_(u\" First uploaded file is compatible with the Platform(s)\"),\n description=_(u\"Please mark one or more platforms with which the uploaded file is compatible.\"),\n value_type=schema.Choice(source=vocabAvailPlatforms),\n required=True,\n )\n\n\n form.mode(information_further_file_uploads='display')\n form.primary('information_further_file_uploads')\n information_further_file_uploads = RichText(\n title = _(u\"Further File Uploads for this Release\"),\n description = _(u\"If you want to upload more files for this release, e.g. because there are files for other operating systems, you'll find the upload fields on the register 'File Upload 1' and 'File Upload 2'.\"),\n required = False\n )\n\n form.fieldset('fileset1',\n label=u\"File Upload 1\",\n fields=['file1', 'platform_choice1', 'file2', 'platform_choice2', 'file3', 'platform_choice3']\n )\n\n file1 = NamedBlobFile(\n title=_(u\"The second file you want to upload (this is optional)\"),\n description=_(u\"Please upload your file.\"),\n required=False,\n )\n\n\n form.widget(platform_choice1=CheckBoxFieldWidget)\n platform_choice1= schema.List(\n title=_(u\"Second uploaded file is compatible with the Platform(s)\"),\n description=_(u\"Please mark one or more platforms with which the uploaded file is compatible.\"),\n value_type=schema.Choice(source=vocabAvailPlatforms),\n required=True,\n )\n\n\n file2 = NamedBlobFile(\n title=_(u\"The third file you want to upload (this is optional)\"),\n description=_(u\"Please upload your file.\"),\n required=False,\n )\n\n\n form.widget(platform_choice2=CheckBoxFieldWidget)\n platform_choice2= schema.List(\n title=_(u\"Third uploaded file is compatible with the Platform(s))\"),\n description=_(u\"Please mark one or more platforms with which the uploaded file is compatible.\"),\n value_type=schema.Choice(source=vocabAvailPlatforms),\n required=True,\n )\n\n file3 = NamedBlobFile(\n title=_(u\"The fourth file you want to upload (this is optional)\"),\n description=_(u\"Please upload your file.\"),\n required=False,\n )\n\n form.widget(platform_choice3=CheckBoxFieldWidget)\n platform_choice3= schema.List(\n title=_(u\"Fourth uploaded file is compatible with the Platform(s)\"),\n description=_(u\"Please mark one or more platforms with which the uploaded file is compatible.\"),\n value_type=schema.Choice(source=vocabAvailPlatforms),\n required=True,\n )\n\n\n form.fieldset('fileset2',\n label=u\"File Upload 2\",\n fields=['file4', 'platform_choice4', 'file5', 'platform_choice5']\n )\n\n\n file4 = NamedBlobFile(\n title=_(u\"The fifth file you want to upload (this is optional)\"),\n description=_(u\"Please upload your file.\"),\n required=False,\n )\n\n form.widget(platform_choice4=CheckBoxFieldWidget)\n platform_choice4= schema.List(\n title=_(u\"Fifth uploaded file is compatible with the Platform(s)\"),\n description=_(u\"Please mark one or more platforms with which the uploaded file is compatible.\"),\n value_type=schema.Choice(source=vocabAvailPlatforms),\n required=True,\n )\n\n file5 = NamedBlobFile(\n title=_(u\"The sixth file you want to upload (this is optional)\"),\n description=_(u\"Please upload your file.\"),\n required=False,\n )\n\n form.widget(platform_choice5=CheckBoxFieldWidget)\n platform_choice5= schema.List(\n title=_(u\"Sixth uploaded file is compatible with the Platform(s)\"),\n description=_(u\"Please mark one or more platforms with which the uploaded file is compatible.\"),\n value_type=schema.Choice(source=vocabAvailPlatforms),\n required=True,\n )\n\n\n @invariant\n def licensenotchoosen(value):\n if value.licenses_choice == []:\n raise Invalid(_(u\"Please choose a license for your release.\"))\n\n @invariant\n def compatibilitynotchoosen(data):\n if data.compatibility_choice == []:\n raise Invalid(_(u\"Please choose one or more compatible product versions for your release\"))\n\n @invariant\n def legaldeclarationaccepted(data):\n if data.accept_legal_declaration is not True:\n raise AcceptLegalDeclaration(_(u\"Please accept the Legal Declaration about your Release and your Uploaded File\"))\n\n @invariant\n def testingvalue(data):\n if data.source_code_inside is not 1 and data.link_to_source is None:\n raise Invalid(_(u\"Please fill in the Link (URL) to the Source Code.\"))\n\n @invariant\n def noOSChosen(data):\n if data.file is not None and data.platform_choice ==[]:\n raise Invalid(_(u\"Please choose a compatible platform for the uploaded file.\"))\n\n\n\n@form.default_value(field=IEUpRelease['declaration_legal'])\ndef LegalTextDefaultValue(data):\n # To get hold of the folder, do: context = data.context\n return data.context.__parent__.legal_disclaimer\n\n@form.default_value(field=IEUpRelease['title_declaration_legal'])\ndef legal_declaration_title_default(data):\n # To get hold of the folder, do: context = data.context\n return data.context.aq_inner.aq_parent.title_legaldisclaimer\n\n@form.default_value(field=IEUpRelease['contact_address2'])\ndef contactinfoDefaultValue(data):\n return data.context.contactAddress\n\n\n@form.default_value(field=IEUpRelease['title'])\ndef releaseDefaultTitleValue(self):\n title= self.context.title\n return (title)\n\n@form.default_value(field=IEUpRelease['licenses_choice'])\ndef defaultLicense(self):\n licenses = list( self.context.available_licenses)\n defaultlicenses = licenses[0]\n return [defaultlicenses]\n\n@form.default_value(field=IEUpRelease['compatibility_choice'])\ndef defaultcompatibility(self):\n compatibility = list( self.context.available_versions)\n defaultcompatibility = compatibility[0]\n return [defaultcompatibility]\n\n@form.default_value(field=IEUpRelease['platform_choice'])\ndef defaultplatform(self):\n platform = list( self.context.available_platforms)\n defaultplatform = platform[0]\n return [defaultplatform]\n\n\n\n#View\nclass View(dexterity.DisplayForm):\n grok.context(IEUpRelease)\n grok.require('zope2.View')\n\n def canPublishContent(self):\n return checkPermission('cmf.ModifyPortalContent', self.context)\n\n\n\n\n\n\n\n","sub_path":"src/tdf/extensionsuploadcenter/euprelease.py","file_name":"euprelease.py","file_ext":"py","file_size_in_byte":11549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"250706423","text":"import json\nimport psycopg2\nfrom psycopg2.extras import RealDictCursor\nimport time\nfrom datetime import datetime\n\n\nparams = {\n 'database': 'DB_NAME',\n 'user': 'DB_USER',\n 'password': 'DB_PASS',\n 'host': 'DB_HOST',\n 'port': 5432\n}\nstart_total_time = time.time()\nconn = psycopg2.connect(**params)\ncur = conn.cursor(cursor_factory=RealDictCursor)\nfile_name = 0\nfor i in range(0, 221): # from 0 to count(*) of your table / 100000\n start_time = time.time()\n start = i*100000\n end = (i*100000) + 100000\n curTime = datetime.now().strftime('%H:%M:%S')\n print(curTime + \": Running \" + str(start) + \" to \" + str(end))\n query = 'SELECT * FROM ORDER BY LIMIT 100000 OFFSET ' + str(start) + ';'\n print(query)\n\n cur.execute(query)\n\n file = open(\"/home/ubuntu/dumps/dumps_\" + str(file_name) + \".json\", \"w\")\n file.write(json.dumps(cur.fetchall()))\n file.close()\n\n end_time = time.time()\n curTime = datetime.now().strftime('%H:%M:%S')\n print(curTime + \": Finished dumps_\" + str(file_name) + \".json\")\n print(time.strftime('%H:%M:%S', time.gmtime(end_time - start_time))+\" \\n\")\n file_name += 1\nprint(time.strftime('%H:%M:%S', time.gmtime(end - start_total_time)))\n\n","sub_path":"paginate_db_dump.py","file_name":"paginate_db_dump.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"246736753","text":"#!/usr/bin/python3\n\nimport subprocess\nimport glob\nimport tempfile\n\ndef runOne(testFileName):\n tmp = tempfile.TemporaryFile()\n code = subprocess.call(['./dist/build/kontrol/kontrol', 'test', testFileName], stdout=tmp, stderr=tmp)\n tmp.seek(0)\n output = tmp.read().decode('utf-8')\n return (code, output)\n\n# Type Checking: Tests that should type check\nfor testFileName in glob.glob(\"./test/typeCheck/shouldSucceed/*.lisp\"):\n (code, output) = runOne(testFileName)\n if code != 0:\n print(output)\n print(testFileName, \"FAILED\")\n exit(code)\n else:\n print(testFileName, \"OK\")\n\n# Type Checking: Tests that should NOT type check\nfor testFileName in glob.glob(\"./test/typeCheck/shouldFail/*.lisp\"):\n (code, output) = runOne(testFileName)\n if code != 0:\n print(testFileName, \"OK\")\n else:\n print(output)\n print(testFileName, \"FAILED\")\n exit(code)\n","sub_path":"runTests.py","file_name":"runTests.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"48088024","text":"#!/usr/bin/python\n#-*- coding: utf-8 -*-\nimport sys\nimport subprocess\nfrom utils import COMMON_PARSER, MODIFY_PARSER, get_command\n\ntry:\n args = COMMON_PARSER.parse_args([sys.argv[1]])\n\n if args.command == 'add':\n args = MODIFY_PARSER.parse_args(sys.argv[2:4])\n print(args.type)\n print(args.name)\n handler = __import__(\n '.'.join(['supports', args.type]),\n fromlist=['add'])\n handler.add(sys.argv[4:], args.name)\n elif args.command == 'remove':\n pass\n elif args.command == 'update':\n args = MODIFY_PARSER.parse_args(sys.argv[2:4])\n handler = __import__(\n '.'.join(['supports', args.type]),\n fromlist=['add'])\n handler.update(sys.argv[4:], args.name)\n else:\n cmds = get_command(args.command).split(' ')\n subprocess.call(cmds)\nexcept:\n import traceback\n traceback.print_exc()\n print(\"Failed to execute the command\")\n","sub_path":"cmdhelper/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"20392361","text":"# encoding utf-8\nfrom utils import multi_tuple, add_two_tuples\nfrom euler import euler\n\n\n# Euler melhorado\ndef rk2(function, x0, x1, y, n=1):\n if isinstance(y, tuple):\n return rk2_tuple(function, x0, x1, y, n)\n else:\n return rk2_int(function, x0, x1, y, n)\n\n\ndef rk2_int(function, x0, x1, y, n):\n old_y = y\n h = (x1 - x0) / float(n)\n old_x = x0\n for i in range(1, n + 1):\n new_x = old_x + h\n\n temp = function(old_x, old_y) + function(\n old_x, euler(function, old_x, new_x, old_y, n))\n\n new_y = old_y + 0.5 * h * temp\n old_x = new_x\n old_y = new_y\n\n return new_y\n\n\ndef rk2_tuple(function, x0, x1, y, n):\n old_y = y\n h = (x1 - x0) / float(n)\n old_x = x0\n for i in range(1, n + 1):\n new_x = old_x + h\n\n temp = add_two_tuples(function(old_x, old_y),\n function(old_x, euler(function, old_x,\n new_x, old_y)))\n\n new_y = add_two_tuples(old_y, multi_tuple(0.5 * h, (temp)))\n\n old_x = new_x\n old_y = new_y\n\n return new_y\n","sub_path":"methods/rk2.py","file_name":"rk2.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"109672517","text":"'''\n53. 给定一个整数数组 nums ，找到一个具有最大和的连续子数组（子数组最少包含一个元素），返回其最大和。\n\n示例:\n\n输入: [-2,1,-3,4,-1,2,1,-5,4],\n输出: 6\n解释: 连续子数组 [4,-1,2,1] 的和最大，为 6。\n进阶:\n\n如果你已经实现复杂度为 O(n) 的解法，尝试使用更为精妙的分治法求解。\n\n'''\nclass Solution:\n def maxSubArray(self, nums: List[int]) -> int:\n # 基本思路就是遍历一遍，用两个变量，一个记录最大的和，一个记录当前的和。时空复杂度貌似还不错......（时间复杂度 O(n)O(n)，空间复杂度 O(l)O(l)）\n tmp = nums[0]\n max_ = tmp\n n = len(nums)\n for i in range(1,n):\n # 当当前序列加上此时的元素的值大于tmp的值，说明最大序列和可能出现在后续序列中，记录此时的最大值\n if tmp + nums[i]>nums[i]:\n max_ = max(max_, tmp+nums[i])\n tmp = tmp + nums[i]\n else:\n #当tmp(当前和)小于下一个元素时，当前最长序列到此为止。以该元素为起点继续找最大子序列, 并记录此时的最大值\n max_ = max(max_, tmp, tmp+nums[i], nums[i])\n tmp = nums[i]\n return max_\n\n\n\n\n ","sub_path":"53_maxSubArray.py","file_name":"53_maxSubArray.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"520037270","text":"# https://atcoder.jp/contests/abc094/tasks/arc095_b\nimport sys\nsys.setrecursionlimit(2147483647)\nINF=float(\"inf\")\nMOD=10**9+7\ninput=lambda :sys.stdin.readline().rstrip()\ndef resolve():\n n=int(input())\n A=list(map(int,input().split()))\n A.sort()\n m=A[-1]\n\n score=INF\n a=-1\n for i in range(n-1):\n if(score>abs(A[i]-m/2)):\n score=abs(A[i]-m/2)\n a=A[i]\n print(m,a)\nresolve()\n","sub_path":"ABC094/d_binomial_coefficients.py","file_name":"d_binomial_coefficients.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"292771878","text":"# LOADING WEBCAME STREAM\nwebcam = cv2.VideoCapture(0)\n# PASS VIDEO PATH AS AN ARGUMENT TO ABOVE FUNCTION TO DETECT FACES IN LOCAL VIDEOS\n\n# SAME THING ON WEBCAM\nwhile True:\n is_frame_read_success, frame = webcam.read()\n grayscaled_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n face_coordinates = trained_face_data.detectMultiScale(grayscaled_frame)\n for face_coordinate in face_coordinates:\n (x, y, w, h) = face_coordinate\n cv2.rectangle(frame, (x, y), (x+w, y+h),\n (randrange(255), randrange(255), randrange(255)), 2)\n cv2.imshow('WEBCAM', frame)\n key = cv2.waitKey(1)\n\n if(key == 81 or key == 113):\n break\n\nwebcam.release()\n","sub_path":"face_detection/tempCodeRunnerFile.py","file_name":"tempCodeRunnerFile.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"415136824","text":"\"\"\"rename project to team\n\nRevision ID: a23d4d63432d\nRevises: e9f89fb022ef\nCreate Date: 2016-12-26 21:57:55.276849\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a23d4d63432d'\ndown_revision = 'e9f89fb022ef'\nbranch_labels = None\ndepends_on = None\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.create_table('teams',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('name', sa.String(), nullable=True),\n sa.PrimaryKeyConstraint('id'),\n sa.UniqueConstraint('name')\n )\n op.create_table('users_teams',\n sa.Column('user_id', sa.Integer(), nullable=True),\n sa.Column('team_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['team_id'], ['teams.id'], ),\n sa.ForeignKeyConstraint(['user_id'], ['users.id'], )\n )\n op.drop_table('users_projects')\n op.drop_table('projects')\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.create_table('projects',\n sa.Column('id', sa.INTEGER(), server_default=sa.text(\"nextval('projects_id_seq'::regclass)\"), nullable=False),\n sa.Column('name', sa.VARCHAR(), autoincrement=False, nullable=True),\n sa.PrimaryKeyConstraint('id', name='projects_pkey'),\n sa.UniqueConstraint('name', name='projects_name_key'),\n postgresql_ignore_search_path=False\n )\n op.create_table('users_projects',\n sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True),\n sa.Column('project_id', sa.INTEGER(), autoincrement=False, nullable=True),\n sa.ForeignKeyConstraint(['project_id'], ['projects.id'], name='users_projects_project_id_fkey'),\n sa.ForeignKeyConstraint(['user_id'], ['users.id'], name='users_projects_user_id_fkey')\n )\n op.drop_table('users_teams')\n op.drop_table('teams')\n ### end Alembic commands ###\n","sub_path":"alembic/versions/a23d4d63432d_rename_project_to_team.py","file_name":"a23d4d63432d_rename_project_to_team.py","file_ext":"py","file_size_in_byte":1904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"650635097","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nfrom databaseProcess import *\nfrom os import mkdir as pathMkdir\nfrom os.path import exists as pathExists\nimport numpy as np\n\ndataDict = {'time':0, 'header':1, 'sysStatus':2, 'fyValue':3, 'fyAdStand':4, \n\t\t\t'fyDistance':5, 'fyFlag':6, 'curTemp':7, 'jumpTemp':8, 'targetTemp':9, \n\t\t\t'motoData':10, 'heatData':11, 'motoCur':12, 'endLine':13, 'id':14}\nmotoDataDict = {140:5, 10:10, 150:15, 130:130, 20:20, 160:25, 30:30, 170:35, 40:40, 180:45, 50:50,\n\t\t\t\t190:55, 60:60, 200:65, 70:70, 210:75, 80:80, 220:85, 90:90, 230:95, 100:100, 0:0}\nheatDataDict = {91:5, 240:10, 101:15, 250:20, 111:25, 11:30, 121:35, 21:40, 131:45, 31:50, 141:55,\n\t\t\t\t41:60, 151:65, 51:70, 161:75, 61:80, 171:85, 71:90, 181:95, 81:100, \n\t\t\t\t10:10, 20:20, 30:30, 40:40, 50:50, 60:60, 70:70, 80:80, 90:90, 100:100, \n\t\t\t\t15:15, 25:25, 35:35, 45:45, 55:55, 65:65, 75:75, 85:85, 95:95, 0:0}\n\nclass DataToPlot():\n\t\"\"\" \n\tclass to plot data based on matplotlib\n\t \"\"\"\n\tdef __init__(self, dat=[], xclo=\"\", yclos=[], rows=[], xlabel=\"\", ylable=\"\", title=\"\", labels=[]):\n\t\t\"\"\" \n\t\tdat: data to plot;\n\t\txclo: the x cloumns to plot, should only once;\n\t\tyclos: the y cloumns to plot, can be more than one, default by all;\n\t\trows: the rows limit to plot, default by all;\n\t\txlabel, ylabel: x and y label for uint;\n\t\ttitle: title for the plot name;\n\t\tlabels: every plot line name, should be cloumns;\n\t\tfigsize, figdpi: not input, but be setted here;\n\t\timagePath: path to store the image;\n\t\tdbOperation: DB operater, connection for data importing.\n\t\t \"\"\"\n\t\tself.dat = dat\n\t\tif xclo==[]: \n\t\t\tprint(self+\"x cloumn has no data!\")\n\t\t\texit()\n\t\telse: self.xclo = xclo\n\t\tif yclos==[]: \n\t\t\tif dat!=[]: self.yclos = [i for i in range(len(dat[0]))]\n\t\t\telse: \n\t\t\t\tprint(str(self)+\".yclos need input!\")\n\t\t\t\tself.yclos = []\n\t\telse: self.yclos = yclos\n\t\tself.yclos = yclos\n\t\tself.rows = rows\n\t\tself.xlabel = xlabel\n\t\tself.ylabel = ylable\n\t\tself.title = title\n\t\tself.labels = labels\n\t\tself.figsize = [6.4*2, 4.8*2]\n\t\tself.figdpi = 500\n\t\tself.imagePath = \"./document/image/\"\n\t\tself.dbOperation = databaseOperation()\n\n\tdef plot(self):\n\t\t\"\"\" \n\t\tplot based on dataDict, motoDataDict, heatDataDict\n\t\t \"\"\"\n\t\tpath = self.imagePath + self.title +datetime.now().strftime('%Y%m%d_%H%M%S')\n\t\tif not pathExists(path): pathMkdir(path)\t\n\t\tplt.figure(figsize=self.figsize)\n\t\tplt.title(self.title)\n\t\tplt.xlabel(self.xlabel)\n\t\tplt.ylabel(self.ylabel)\n\t\tfor yclo in self.yclos:\n\t\t\txDat = self.dat[dataDict[self.xclo]]\n\t\t\tif yclo=='motoData': yDat = [motoDataDict[i] for i in self.dat[dataDict[yclo]]]\n\t\t\telif yclo=='heatData': yDat = [heatDataDict[i] for i in self.dat[dataDict[yclo]]]\n\t\t\telse: yDat = self.dat[dataDict[yclo]]\n\t\t\tplt.plot(xDat, yDat, label=yclo)\n\t\tplt.legend()\n\t\tplt.savefig(path+'/'+self.title, dpi=self.figdpi)\n\n\tdef dataReadFromTable(self, tablename):\n\t\t\"\"\" \n\t\tread all data from tablename by cols.\n\t\t \"\"\"\n\t\tself.dat = self.dbOperation.dbDataRead(tablename, list(dataDict))\n\n\tdef dataPlotFromTables(self, tablenames=[], dbType=\"postgre\"):\n\t\t\"\"\" \n\t\tplot date from table names and plot to a picture\n\t\ttablenames: table name to data\n\t\tdbType: database type, default by postgre\n\t\t \"\"\"\n\t\tpath = self.imagePath + self.title +datetime.now().strftime('%Y%m%d_%H%M%S')\n\t\tif not pathExists(path): pathMkdir(path)\n\t\tplt.figure(figsize=self.figsize)\n\t\tplt.title(self.title)\n\t\tplt.xlabel(self.xlabel)\n\t\tplt.ylabel(self.ylabel)\n\t\tfor tablename in tablenames:\n\t\t\tself.dataReadFromTable(tablename)\n\t\t\tfor yclo in self.yclos:\n\t\t\t\tplt.plot(self.dat[dataDict[self.xclo]], self.dat[dataDict[yclo]], label=tablename)\n\t\tplt.legend()\n\t\tplt.savefig(path+'/'+self.title, dpi=self.figdpi)\n\tdef plotOnePicture(self, xDats, yDats):\n\t\t\"\"\" \n\t\tplot x, y data to one picture;\n\t\tlabel, title and so on is based on the class;\n\t\txDats, yDats: x, y data, must be list and corrosspondly\n\t\t \"\"\"\n\t\tpath = self.imagePath + self.title +datetime.now().strftime('%Y%m%d_%H%M%S')\n\t\tif not pathExists(path): pathMkdir(path)\n\t\tplt.figure(figsize=self.figsize)\n\t\tplt.title(self.title)\n\t\tplt.xlabel(self.xlabel)\n\t\tplt.ylabel(self.ylabel)\n\t\tfor xDat, yDat, label in zip(xDats, yDats, self.labels):\n\t\t\tplt.plot(xDat, yDat, label=label)\n\t\tplt.legend()\n\t\tplt.savefig(path+'/'+self.title, dpi=self.figdpi)\n\tdef datProcess(self, tablenames):\n\t\tself.rows = [100, 150]\n\t\tyDats = []\n\t\txDats = []\n\t\tfor i,tablename in enumerate(tablenames):\n\t\t\tself.dataReadFromTable(tablename)\n\t\t\tdataCur = self.dat[dataDict[\"motoCur\"]][self.rows[0]:self.rows[1]]\n\t\t\tdataId = self.dat[dataDict[\"id\"]][self.rows[0]:self.rows[1]]\n\t\t\tyTmp, xTmp=self.topFilter(dataCur, dataId)\n\t\t\txDats.append(xTmp)\n\t\t\tyDats.append(yTmp)\n\t\t\tself.labels[i] = tablename.split(\"_\")[2]+'-'+tablename.split(\"_\")[3] +\":{:.1f}\".format(self.meanFilter(yTmp))\n\t\tself.plotOnePicture(xDats, yDats)\n\tdef topFilter(self, datas, dataId=[]):\n\t\tdataTmp = []\n\t\tif dataId != []: dataIdTmp=[]\n\t\tfor i in range(len(datas)-1):\n\t\t\tif datas[i]>datas[i-1] and datas[i]>datas[i+1]:\n\t\t\t\tdataTmp.append(datas[i])\n\t\t\t\tif dataId!=[]: dataIdTmp.append(dataId[i])\n\t\tif dataId != []: return dataTmp, dataIdTmp\n\t\telse: return dataTmp\n\tdef outValleyFilter(self, datas, dataId=[]):\n\t\tdataTmp = []\n\t\tif dataId != []: dataIdTmp=[]\n\t\tfor i in range(len(datas)-1):\n\t\t\tif datas[i]>datas[i-1] or datas[i]>datas[i+1]:\n\t\t\t\tdataTmp.append(datas[i])\n\t\t\t\tif dataId!=[]: dataIdTmp.append(dataId[i])\n\t\tif dataId != []: return dataTmp, dataIdTmp\n\t\telse: return dataTmp\n\tdef meanFilter(self, datas):\n\t\tdatasTmp = datas[:]\n\t\tmaxDat = datasTmp[0]\n\t\tminDat = datasTmp[0]\n\t\tfor dat in datasTmp:\n\t\t\tif dat > maxDat: maxDat = dat\n\t\t\tif dat < minDat: minDat = dat\n\t\tdatasTmp.remove(maxDat)\n\t\tdatasTmp.remove(minDat)\n\t\treturn np.mean(datasTmp)\n\tdef datFliterProcess(self, tablenames):\n\t\tself.rows = [100, 150]\n\t\t# xDats,yDats,xTmpTop,yTmpTop,xTmpValley,yTmpValley,xTmpMean,yTmpMean=[],[],[],[],[],[],[],[]\n\t\tfor tablename in tablenames:\n\t\t\tself.dataReadFromTable(tablename)\n\t\t\tdataCur = self.dat[dataDict[\"motoCur\"]][self.rows[0]:self.rows[1]]\n\t\t\tprint(self.meanFilter(dataCur))\n\t\t\t# yTmpTop.append(self.meanFilter(self.topFilter(dataCur)))\n\t\t\t# xTmpTop.append(self.meanFilter(self.topFilter(dataId)))\n\t\t\t# yTmpValley.append(self.meanFilter(self.outValleyFilter(dataCur)))\n\t\t\t# xTmpValley.append(self.meanFilter(self.outValleyFilter(dataId)))\n\t\t\t# yTmpValley, xTmpValley = self.outValleyFilter(dataCur, dataId)\n\t\t\t# xDats.append(xTmpValley), yDats.append(yTmpValley)\n\t\t\t# xTmpValley.append(int(tablename.split(\"_\")[3][:-2]))\n\t\t\t# yTmpMean.append(self.meanFilter(dataCur))\n\t\t\t# xTmpMean.append(int(tablename.split(\"_\")[3][:-2]))\n\t\t# self.labels.append(\"no fileter mean\"), xDats.append(xTmpMean), yDats.append(yTmpMean)\n\t\t# self.labels.append(\"top fileter mean\"), xDats.append(xTmpTop), yDats.append(yTmpTop)\n\t\t# self.labels.append(\"out Valley fileter mean\"), xDats.append(xTmpValley), yDats.append(yTmpValley)\n\t\t# self.labels.append(\"out Valley fileter mean\")\n\t\t# self.plotOnePicture(xDats, yDats)\n\t\t# print(xDats, yDats)\n\n\nif __name__ == \"__main__\":\n\tdatToPlot = DataToPlot(xclo=\"id\")\n\tdatToPlot.dbOperation.databaseOpen()\n\tdatToPlot.title = \"moto rank--current\"\n\tdatToPlot.yclos = [\"motoCur\"]\n\tdatToPlot.xlabel = \"work time /100ms\"\n\tdatToPlot.ylabel = \"moto current AD value\"\n\tdatToPlot.labels = [\"water-motoL5\", \"juice-motoL5\", \"nut-motoL5\", \\\n\t\t\t\t\t\t\"water-motoL10\", \"juice-motoL10\", \"nut-motoL10\"]\n\ttableNames = [\t\"t_motol10_198_water_700ml_2019_03_25_14_28_20\",\t\\\n\t\t\t\t\t\"t_motol10_198_water_1000ml_2019_03_25_14_26_52\", \t\\\n\t\t\t\t\t\"t_motol10_198_water_1400ml_2019_03_25_14_25_20\", \t\\\n\t\t\t\t\t\"t_motol10_198_water_1750ml_2019_03_25_14_23_47\", \t\\\n\n\t\t\t\t\t\"t_motol10_242_water_700ml_2019_03_25_14_16_52\", \t\\\n\t\t\t\t\t\"t_motol10_242_water_1000ml_2019_03_25_14_18_32\", \t\\\n\t\t\t\t\t\"t_motol10_242_water_1400ml_2019_03_25_14_20_45\", \t\\\n\t\t\t\t\t\"t_motol10_242_water_1750ml_2019_03_25_14_22_22\", \t\\\n\n\t\t\t\t\t\"t_motol10_water_700ml_2019_03_25_13_47_51\", \t\\\n\t\t\t\t\t\"t_motol10_water_1000ml_2019_03_25_13_52_55\", \t\\\n\t\t\t\t\t\"t_motol10_water_1400ml_2019_03_25_13_59_29\", \t\\\n\t\t\t\t\t\"t_motol10_water_1750ml_2019_03_25_14_04_11\", \t\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\tdatToPlot.datFliterProcess(tableNames)\n\tdatToPlot.dbOperation.databaseClose()\n\tpass","sub_path":"code/Source/matPlotFromDb.py","file_name":"matPlotFromDb.py","file_ext":"py","file_size_in_byte":8154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"3172216","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport seaborn as sns\nimport re\n\n# set path to where .txt output is stored\nprint(\"The current working directory is\", os.getcwd())\n\n# dataset = ['schubert', 'vivaldi', 'brahms', 'haydn']\ndataset = ['billion']\nfor d in dataset:\n\t# specify txt directory\n\tMETRICS_PATH = os.getcwd() + '/' + d + '.txt'\n\n\n\t# open and clean data\n\twith open(METRICS_PATH, 'r') as file:\n\t\tloss, bleu, gleu, ribes, chrf = [], [], [], [], []\n\t\tfor line in file:\n\t\t\tline = line.replace(' ', '').split(\",\") # parse line\n\t\t\tline = [float(re.sub('[^0-9^.]','', item)) for item in line] # convert item to float\n\t\t\tloss.append(line[0])\n\t\t\tbleu.append(line[1])\n\t\t\tgleu.append(line[2])\n\t\t\tribes.append(line[3])\n\t\t\tchrf.append(line[4])\n\n\t# use seaborn to do regression plot\n\tdef seabornPlot(metric, name, title, data):\n\t\tax1 = sns.regplot(x=x, y=np.asarray(metric), fit_reg=True, order=1)\n\t\tax1.set_title(data)\n\t\tplt.suptitle(title)\n\t\tplt.savefig(str(name)+'_'+str(data))\n\t\tplt.clf()\n\n\tx = np.asarray(range(0, len(loss))) # seaborn takes np.arrays\n\n\tseabornPlot(loss, \"loss\", \"loss\", d)\n\tseabornPlot(bleu, \"bleu\", \"Bilingual Evaluation Understudy (BLEU)\", d) # how similar candidate to reference between 0.0 and 1.0\n\tseabornPlot(gleu, \"gleu\", \"Google-BLEU (GLEU)\", d) # 0 (no matches) and 1 (all match)\n\tseabornPlot(ribes, \"ribes\", \"Rank-based Intuitive Bilingual Evaluation Score (RIBES)\", d) # between 0.0 and 1.0\n\tseabornPlot(chrf, \"chrf\", \"Character n-gram F-score (ChrF)\", d)\n","sub_path":"graph_slurm.py","file_name":"graph_slurm.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"472274176","text":"import socket\n\nBUF_SIZE = 1024\n\nIP = socket.gethostbyname(socket.gethostname()) # Set current IP\nport = int(input(\"PORT: \")) # Input & Set Port number\nserv_addr = (IP, port)\n\nserv_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # IPv4, TCP\n\nserv_sock.bind(serv_addr) # Set IP, PORT number\n\nserv_sock.listen(1)\n\nwhile True:\n clnt_sock, clnt_addr = serv_sock.accept() # Accept any Client\n print(\"Connected from \", clnt_addr)\n\n while True:\n data = clnt_sock.recv(BUF_SIZE).decode() # Receive from Client\n if data == 'q' or data == 'Q': # If received message is 'q' or 'Q' -> Quit\n print(clnt_addr, \"와의 접속 종료되었습니다.\")\n clnt_sock.close()\n break\n else:\n print(\"Client: \", data)\n send_data = input(\"Input Message: \") # Input send message\n if send_data == 'q' or send_data == 'Q': # If send message is 'q' or 'Q' -> Quit\n print(clnt_addr, \"와의 접속 종료\")\n clnt_sock.send(send_data.encode())\n clnt_sock.close()\n break\n else:\n clnt_sock.send(send_data.encode()) # Send message to Client\n","sub_path":"Network/Simple Chat/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"22403975","text":"from users.models import Student\nfrom rest_framework.viewsets import ViewSet\nfrom .serializers import StudentModelSerializer\nfrom rest_framework.response import Response\nfrom rest_framework.generics import GenericAPIView\n# Create your views here.\n\nclass StudentViewSet(ViewSet):\n def get_10(self, request):\n student_list = Student.objects.all()[:10]\n serializer = StudentModelSerializer(instance=student_list, many=True)\n return Response(serializer.data)\n # 注意：这里的数据不需要添加safe，因为drf中的视图类和视图集都会自动的处理数据格式，\n # 成功后才会返回给前端页面\n \n def login(self, request, pk):\n student_obj = Student.objects.get(pk=pk)\n serializer = StudentModelSerializer(instance=student_obj)\n return Response(serializer.data)\n # 在视图集中可以自定义方法名，这些方法名需要以字典的方式传入到as_view()中作为参数，\n # 这样系统可以识别并处理程序\n \n def get_five(self, request):\n student_list = Student.objects.filter(sex='男')[:5]\n serializer = StudentModelSerializer(instance=student_list, many=True)\n return Response(serializer.data)\n # 获取数据库中前五名男生的数据信息\n\nclass Student1ViewSet(ViewSet, GenericAPIView):\n # 这个方法实现了我们既使用ViewSet也使用GenericAPIView的目的\n \n queryset = Student.objects.all()\n serializer_class = StudentModelSerializer\n def get_10(self, request):\n student_list = self.get_queryset()[:10]\n serializer = self.get_serializer(instance=student_list, many=True)\n return Response(serializer.data)\n \n def get_six(self, request):\n student_list = self.get_queryset().filter(sex='男')[:6]\n serializer = self.get_serializer(instance=student_list, many=True)\n return Response(serializer.data)\n \n def update_data(self, request, pk):\n data_dict = request.data\n student_obj = self.get_object()\n serializer = self.get_serializer(instance=student_obj, data=data_dict)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n return Response(serializer.data)\n \n'''\n虽然上边的方式实现了既使用ViewSet也使用GenericAPIView的目的，但是多继承了一个类，代码有冗余，\nDRF提供了GenericViewSet来实现与上边同样的功能\n'''\nfrom rest_framework.viewsets import GenericViewSet\nfrom rest_framework.decorators import action\nclass Student2GenericViewSet(GenericViewSet):\n queryset = Student.objects.all()\n serializer_class = StudentModelSerializer\n @action(methods=['GET'], detail=False)\n def get_10(self, request):\n student_list = self.get_queryset()[:10]\n serializer = self.get_serializer(instance=student_list, many=True)\n return Response(serializer.data)\n @action(methods=['GET'], detail=True)\n def get_one(self, reqeust, pk):\n student_obj = self.get_queryset().get(pk=pk)\n print(student_obj)\n serializer = self.get_serializer(instance=student_obj)\n return Response(serializer.data)\n \nfrom rest_framework.generics import ListAPIView, CreateAPIView, RetrieveAPIView\nclass StudentGenericAPIView(ListAPIView, CreateAPIView):\n queryset = Student.objects.all()\n serializer_class = StudentModelSerializer\n\nclass Student1GenericAPIView(RetrieveAPIView):\n queryset = Student.objects.all()\n serializer_class = StudentModelSerializer\n\nfrom .serializers import Student1ModelSerializer\nclass Studnet1GenericAPIView(GenericAPIView):\n queryset = Student.objects.all()\n serializer_class = Student1ModelSerializer\n \n def get_serializer_class(self):\n # 在框架中提供了get_serializerz-class方法来重写序列化器类，可以为不同的方法提供对应的序列化器类\n # 这样的话一个视图类中可以同时调用多个序列化器类\n if self.request.method == 'GET':\n # 注意：这里的方法名必须使用大写， 小写的话系统会识别错误\n # 具体使用哪个序列化器类需要判读request对应的方法，注意从前端传递过来的已经包括request，\n # 这里可以直接通过self来调用，但是一般我们在方法中需要写明reqeust参数\n return Student1ModelSerializer\n else:\n return StudentModelSerializer\n \n def get(self, request):\n # 获取所有数据中的id和name字段\n student_list = self.get_queryset()\n serializer = self.get_serializer(instance=student_list, many=True)\n return Response(serializer.data)\n \n def post(self, request):\n # 往数据库中添加一条数据\n data_dict = request.data\n serializer = self.get_serializer(data=data_dict)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n return Response(serializer.data)\n \n# 视图集中调用多个序列化器类\nfrom rest_framework.viewsets import ModelViewSet\nclass StudentModelViewSet(ModelViewSet):\n queryset = Student.objects.all()\n serializer_class = Student1GenericAPIView\n def get_serializer_class(self):\n if self.action == 'list':\n # 注意：这里直接是在self下的action属性进行判断，\n # 这里不能添加request，因为它不具备actin属性\n return StudentModelSerializer\n else:\n return Student1ModelSerializer\n \n \n \n \n \n \n \n \n \n ","sub_path":"rest_framework/test_pro/viewset/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"399880642","text":"import sys\nimport unittest\nimport os\nfrom unittest.mock import Mock, patch\nimport uuid\n\nimport boto3\nfrom moto import mock_s3, mock_sns, mock_sts\n\nfrom .. import EnvironmentSetup, FIXTURE_DATA_CHECKSUMS\n\nif __name__ == '__main__':\n pkg_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) # noqa\n sys.path.insert(0, pkg_root) # noqa\n\n\nclass TestChecksumDaemon(unittest.TestCase):\n\n DEPLOYMENT_STAGE = 'test'\n UPLOAD_BUCKET_NAME = 'bogobucket'\n\n def setUp(self):\n # Setup mock AWS\n self.s3_mock = mock_s3()\n self.s3_mock.start()\n self.sns_mock = mock_sns()\n self.sns_mock.start()\n self.sts_mock = mock_sts()\n self.sts_mock.start()\n # Staging bucket\n self.upload_bucket = boto3.resource('s3').Bucket(self.UPLOAD_BUCKET_NAME)\n self.upload_bucket.create()\n # Setup SNS\n boto3.resource('sns').create_topic(Name='bogotopic')\n # daemon\n context = Mock()\n self.environment = {\n 'BUCKET_NAME': self.UPLOAD_BUCKET_NAME,\n 'DEPLOYMENT_STAGE': self.DEPLOYMENT_STAGE,\n 'INGEST_AMQP_SERVER': 'foo',\n 'DCP_EVENTS_TOPIC': 'bogotopic',\n 'CSUM_USE_BATCH_FILE_SIZE_THRESHOLD_GB': '4',\n 'CSUM_JOB_Q_ARN': 'bogoqarn',\n 'CSUM_JOB_ROLE_ARN': 'bogorolearn',\n 'CSUM_DOCKER_IMAGE': 'bogoimage'\n }\n with EnvironmentSetup(self.environment):\n from upload.lambdas.checksum_daemon import ChecksumDaemon\n self.daemon = ChecksumDaemon(context)\n # File\n self.area_id = str(uuid.uuid4())\n self.content_type = 'text/html'\n self.filename = 'foo'\n self.file_key = f\"{self.area_id}/{self.filename}\"\n self.file_contents = \"exquisite corpse\"\n self.object = self.upload_bucket.Object(self.file_key)\n self.object.put(Key=self.file_key, Body=self.file_contents, ContentType=self.content_type)\n self.event = {'Records': [\n {'eventVersion': '2.0', 'eventSource': 'aws:s3', 'awsRegion': 'us-east-1',\n 'eventTime': '2017-09-15T00:05:10.378Z', 'eventName': 'ObjectCreated:Put',\n 'userIdentity': {'principalId': 'AWS:AROAI4WRRXW2K3Y2IFL6Q:upload-api-dev'},\n 'requestParameters': {'sourceIPAddress': '52.91.56.220'},\n 'responseElements': {'x-amz-request-id': 'FEBC85CADD1E3A66',\n 'x-amz-id-2': 'xxx'},\n 's3': {'s3SchemaVersion': '1.0',\n 'configurationId': 'NGZjNmM0M2ItZTk0Yi00YTExLWE2NDMtMzYzY2UwN2EyM2Nj',\n 'bucket': {'name': self.UPLOAD_BUCKET_NAME,\n 'ownerIdentity': {'principalId': 'A29PZ5XRQWJUUM'},\n 'arn': f'arn:aws:s3:::{self.UPLOAD_BUCKET_NAME}'},\n 'object': {'key': self.file_key, 'size': 16,\n 'eTag': 'fea79d4ad9be6cf1c76a219bb735f85a',\n 'sequencer': '0059BB193641C4EAB0'}}}]}\n\n def tearDown(self):\n self.s3_mock.stop()\n self.sns_mock.stop()\n self.sts_mock.stop()\n\n @patch('upload.lambdas.checksum_daemon.checksum_daemon.IngestNotifier.connect')\n @patch('upload.lambdas.checksum_daemon.checksum_daemon.IngestNotifier.file_was_uploaded')\n def test_consume_event_sets_tags(self, mock_file_was_uploaded, mock_connect):\n\n with EnvironmentSetup(self.environment):\n self.daemon.consume_event(self.event)\n\n tagging = boto3.client('s3').get_object_tagging(Bucket=self.UPLOAD_BUCKET_NAME, Key=self.file_key)\n self.assertEqual(\n sorted(tagging['TagSet'], key=lambda x: x['Key']),\n sorted(FIXTURE_DATA_CHECKSUMS[self.file_contents]['s3_tagset'], key=lambda x: x['Key'])\n )\n\n @patch('upload.lambdas.checksum_daemon.checksum_daemon.IngestNotifier.connect')\n @patch('upload.lambdas.checksum_daemon.checksum_daemon.IngestNotifier.file_was_uploaded')\n def test_consume_event_notifies_ingest(self, mock_file_was_uploaded, mock_connect):\n\n with EnvironmentSetup(self.environment):\n self.daemon.consume_event(self.event)\n\n self.assertTrue(mock_connect.called,\n 'IngestNotifier.connect should have been called')\n self.assertTrue(mock_file_was_uploaded.called,\n 'IngestNotifier.file_was_uploaded should have been called')\n mock_file_was_uploaded.assert_called_once_with({\n 'upload_area_id': self.area_id,\n 'name': os.path.basename(self.file_key),\n 'size': 16,\n 'last_modified': self.object.last_modified.isoformat(),\n 'content_type': self.content_type,\n 'url': f\"s3://{self.UPLOAD_BUCKET_NAME}/{self.area_id}/{self.filename}\",\n 'checksums': FIXTURE_DATA_CHECKSUMS[self.file_contents]['checksums']\n })\n\n @patch('upload.common.upload_area.UploadedFile.size', 100 * 1024 * 1024 * 1024)\n @patch('upload.lambdas.checksum_daemon.checksum_daemon.ChecksumDaemon.schedule_checksumming')\n @patch('upload.lambdas.checksum_daemon.checksum_daemon.IngestNotifier.connect')\n @patch('upload.lambdas.checksum_daemon.checksum_daemon.IngestNotifier.file_was_uploaded')\n def test_that_with_a_large_file_a_batch_job_is_scheduled(self,\n mock_file_was_uploaded,\n mock_connect,\n mock_schedule_checksumming):\n with EnvironmentSetup(self.environment):\n self.daemon.consume_event(self.event)\n\n mock_schedule_checksumming.assert_called()\n","sub_path":"tests/lambdas/test_checksum_daemon.py","file_name":"test_checksum_daemon.py","file_ext":"py","file_size_in_byte":5748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"95287543","text":"import traceback\n\nfrom discord import Color, Embed, Message\nfrom discord.ext.commands import Bot, Cog\n\n\nclass Custom(Cog):\n def __init__(self, bot: Bot) -> None:\n self.bot = bot\n\n @Cog.listener()\n async def on_message(self, message: Message) -> None:\n # Check that the message is not from bot account\n if message.author.bot:\n return\n\n if message.content.lower().startswith(\"help\"):\n await message.channel.send(\"Hey! Why don't you run the help command with `>>help`\")\n\n @Cog.listener()\n async def on_error(self, event: str, *args, **kwargs) -> None:\n error_message = f\"```py\\n{traceback.format_exc()}\\n```\"\n if len(error_message) > 2000:\n async with self.session.post(\"https://www.hastebin.com/documents\", data=error_message) as resp:\n error_message = \"https://www.hastebin.com/\" + (await resp.json())[\"key\"]\n\n embed = Embed(color=Color.red(), description=error_message, title=event)\n\n if not self.dev_mode:\n await self.error_hook.send(embed=embed)\n else:\n traceback.print_exc()\n\n\ndef setup(bot: Bot) -> None:\n bot.add_cog(Custom(bot))\n","sub_path":"cogs/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"59858277","text":"\nfrom fastNLP.core.dataset import DataSet\nfrom fastNLP.core.instance import Instance\n\ndef cut_long_sentence(sent, max_sample_length=200):\n sent_no_space = sent.replace(' ', '')\n cutted_sentence = []\n if len(sent_no_space) > max_sample_length:\n parts = sent.strip().split()\n new_line = ''\n length = 0\n for part in parts:\n length += len(part)\n new_line += part + ' '\n if length > max_sample_length:\n new_line = new_line[:-1]\n cutted_sentence.append(new_line)\n length = 0\n new_line = ''\n if new_line != '':\n cutted_sentence.append(new_line[:-1])\n else:\n cutted_sentence.append(sent)\n return cutted_sentence\n\n\nclass ConlluPOSReader(object):\n # 返回的Dataset包含words(list of list, 里层的list是character), tag两个field(list of str, str是标有BMES的tag)。\n def __init__(self):\n pass\n\n def load(self, path):\n datalist = []\n with open(path, 'r', encoding='utf-8') as f:\n sample = []\n for line in f:\n if line.startswith('\\n'):\n datalist.append(sample)\n sample = []\n elif line.startswith('#'):\n continue\n else:\n sample.append(line.split('\\t'))\n if len(sample) > 0:\n datalist.append(sample)\n\n ds = DataSet()\n for sample in datalist:\n # print(sample)\n res = self.get_one(sample)\n if res is None:\n continue\n char_seq = []\n pos_seq = []\n for word, tag in zip(res[0], res[1]):\n if len(word)==1:\n char_seq.append(word)\n pos_seq.append('S-{}'.format(tag))\n elif len(word)>1:\n pos_seq.append('B-{}'.format(tag))\n for _ in range(len(word)-2):\n pos_seq.append('M-{}'.format(tag))\n pos_seq.append('E-{}'.format(tag))\n char_seq.extend(list(word))\n else:\n raise ValueError(\"Zero length of word detected.\")\n\n ds.append(Instance(words=char_seq,\n tag=pos_seq))\n\n return ds\n\n def get_one(self, sample):\n if len(sample)==0:\n return None\n text = []\n pos_tags = []\n for w in sample:\n t1, t2, t3, t4 = w[1], w[3], w[6], w[7]\n if t3 == '_':\n return None\n text.append(t1)\n pos_tags.append(t2)\n return text, pos_tags\n\nif __name__ == '__main__':\n reader = ConlluPOSReader()\n d = reader.load('/home/hyan/train.conllx')\n print('reader')","sub_path":"reproduction/pos_tag_model/pos_io/pos_reader.py","file_name":"pos_reader.py","file_ext":"py","file_size_in_byte":2835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"556906084","text":"def fizz_count(x):\n count = 0\n for item in x:\n if item == \"fizz\":\n count = count + 1\n return count\n\nlotto = [\"fizz\" , \"cat\" , \"fizz\" , \"fizz\"]\nsmall = fizz_count(lotto)\n#print(small)\n\n#---------------------------------------------\n\nprices = {\n \"banana\": 4,\n \"apple\": 2,\n \"orange\": 1.5,\n \"pear\": 3}\nstock = {\n \"banana\": 6,\n \"apple\": 0,\n \"orange\": 32,\n \"pear\": 15\n}\ntotal = 0\nfor fruit in prices:\n #print(fruit)\n #print(\"prices:%s\" % prices[fruit])\n # print(\"stock:%s\" % stock[fruit])\n print(prices[fruit]*stock[fruit])\n total = total + prices[fruit]*stock[fruit] #总销售额\nprint(total)\n\n\n#---------------------------------------------------------\n\n\nshopping_list = [\"banana\", \"orange\", \"apple\"]\nprices = {\n \"banana\": 4,\n \"apple\": 2,\n \"orange\": 1.5,\n \"pear\": 3}\nstock = {\n \"banana\": 6,\n \"apple\": 0,\n \"orange\": 32,\n \"pear\": 15\n}\ndef compute_bill(food):\n total = 0\n for item in food:\n if stock[item] > 0:\n total += prices[item]\n stock[item] -= 1\n return total\nprint(compute_bill(shopping_list))\n\n","sub_path":"pro/A day at the supermarket.py","file_name":"A day at the supermarket.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"584481950","text":"\n\n#calss header\nclass _RESIDENCE():\n\tdef __init__(self,): \n\t\tself.name = \"RESIDENCE\"\n\t\tself.definitions = [u'a home: ', u'officially staying or living somewhere: ', u'an author (= writer), poet, or artist who is employed at a school or college, etc. for a short period', u'to go to live somewhere: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_residence.py","file_name":"_residence.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"348539592","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @File : __init__.py.py\n# @time: 2019/5/19 13:18\n# @author: 陈南华\n# @contact: 2630966248@qq.com\n# @desc :\n\n__all__ = ['SQL','QA']\n\nif __name__ == '__main__':\n print(\"Exam Start!\\n\")\nelse:\n print(\"Powered by charon.web_security_exam\")\n","sub_path":"file/charon/web_security_exam/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"397073349","text":"import copy\nimport json\nimport mimetypes\nimport urllib\nfrom http.cookies import SimpleCookie\nimport os.path\nfrom io import BytesIO\n\nfrom .requests.request import Request\n\n\nclass TestClient(object):\n\n def __init__(self, app):\n self.app = app\n self.cookies = []\n\n def get(self, path, query=None, form=None, json=None):\n return self.request('GET', path, query, form, None, json)\n\n def post(self, path, query=None, form=None, files=None, json=None):\n return self.request('POST', path, query, form, files, json)\n\n def put(self, path, query=None, form=None, files=None, json=None):\n return self.request('PUT', path, query, form, files, json)\n\n def patch(self, path, query=None, form=None, files=None, json=None):\n return self.request('PATCH', path, query, form, files, json)\n\n def delete(self, path, query=None, form=None, json=None):\n return self.request('DELETE', path, query, form, None, json)\n\n def head(self, path, query=None, form=None, json=None):\n return self.request('HEAD', path, query, form, None, json)\n\n def options(self, path, query=None, form=None, json=None):\n return self.request('OPTIONS', path, query, form, None, json)\n\n def request(self, method, path, query=None, form=None, files=None, json=None):\n request = self._make_request(method, path, query, form, files, json)\n response = self.app.handle_request(request)\n self.cookies.extend(response.cookies)\n return response\n\n def _make_request(self, method, path, query, form, files, json_data):\n env = copy.deepcopy(sample_env)\n env['REQUEST_METHOD'] = method.upper()\n env['PATH_INFO'] = path\n if query:\n env['QUERY_STRING'] = urllib.parse.urlencode(query)\n if form and not files:\n self._write_form_to_env(env, form)\n if files:\n self._write_multipart_form_to_env(env, form, files)\n if json_data:\n env['wsgi.input'].write(json.dumps(json_data).encode('utf-8'))\n env['wsgi.input'].seek(0)\n if self.cookies:\n env['HTTP_COOKIE'] = self._assemble_cookie_string()\n return Request(env)\n\n def _write_form_to_env(self, env, form):\n items = []\n for key, value in form.items():\n if hasattr(value, '__iter__') and not isinstance(value, str):\n for v in value:\n item = urllib.parse.urlencode({key: v})\n items.append(item)\n else:\n item = urllib.parse.urlencode({key: value})\n items.append(item)\n env['wsgi.input'].write('&'.join(items).encode('utf-8'))\n env['wsgi.input'].seek(0)\n\n def _write_multipart_form_to_env(self, env, form, files):\n bytecount = 0\n env['CONTENT_TYPE'] = 'multipart/form-data; boundary=----WebKitFormBoundaryLn6U80VApiAoyY3B'\n bytecount += self._write_multipart_data_to_env(env, form)\n bytecount += self._write_multipart_files_to_env(env, files)\n bytecount += env['wsgi.input'].write(b'------WebKitFormBoundaryLn6U80VApiAoyY3B--\\n')\n env['CONTENT_LENGTH'] = str(bytecount)\n env['wsgi.input'].seek(0)\n\n def _write_multipart_data_to_env(self, env, form):\n bytecount = 0\n if form:\n for key, value in form.items():\n if hasattr(value, '__iter__') and not isinstance(value, str):\n for v in value:\n bytecount += env['wsgi.input'].write(b'------WebKitFormBoundaryLn6U80VApiAoyY3B\\n')\n bytecount += env['wsgi.input'].write('Content-Disposition: form-data; name=\"{}\"\\n\\n'.format(key).encode('utf-8'))\n bytecount += env['wsgi.input'].write(str(v).encode('utf-8') + b'\\n')\n else:\n bytecount += env['wsgi.input'].write(b'------WebKitFormBoundaryLn6U80VApiAoyY3B\\n')\n bytecount += env['wsgi.input'].write('Content-Disposition: form-data; name=\"{}\"\\n\\n'.format(key).encode('utf-8'))\n bytecount += env['wsgi.input'].write(str(value).encode('utf-8') + b'\\n')\n return bytecount\n\n def _write_multipart_files_to_env(self, env, files):\n bytecount = 0\n for key, filepath in files.items():\n filename = os.path.basename(filepath)\n mime_type, _ = mimetypes.guess_type(filepath)\n if not mime_type:\n mime_type = 'application/octet-stream'\n with open(filepath, 'rb') as f:\n bytecount += env['wsgi.input'].write(b'------WebKitFormBoundaryLn6U80VApiAoyY3B\\n')\n bytecount += env['wsgi.input'].write('Content-Disposition: form-data; name=\"{}\"; filename=\"{}\"\\n'.format(key, filename).encode('utf-8'))\n bytecount += env['wsgi.input'].write('Content-Type: {}\\n\\n'.format(mime_type).encode('utf-8'))\n bytecount += env['wsgi.input'].write(f.read() + b'\\n')\n return bytecount\n\n def _assemble_cookie_string(self):\n simple_cookie = SimpleCookie()\n for cookie in self.cookies:\n simple_cookie.load(cookie)\n cookie_dict = {key: simple_cookie[key].value for key in simple_cookie}\n cookie_string = ''\n for morsel in SimpleCookie(cookie_dict).values():\n if morsel.value != '':\n cookie_string += morsel.OutputString() + '; '\n cookie_string = cookie_string[:-2]\n return cookie_string\n\n\nsample_env = {\n 'HTTP_ACCEPT': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\n 'HTTP_ACCEPT_ENCODING': 'gzip, deflate, sdch',\n 'HTTP_ACCEPT_LANGUAGE': 'pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4',\n 'HTTP_CONNECTION': 'keep-alive',\n 'HTTP_HOST': 'localhost:8000',\n 'HTTP_UPGRADE_INSECURE_REQUESTS': '1',\n 'HTTP_USER_AGENT': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36',\n 'PATH_INFO': '',\n 'QUERY_STRING': '',\n 'RAW_URI': '',\n 'REMOTE_ADDR': '127.0.0.1',\n 'REMOTE_PORT': '54130',\n 'REQUEST_METHOD': 'GET',\n 'SCRIPT_NAME': '',\n 'SERVER_NAME': '127.0.0.1',\n 'SERVER_PORT': '8000',\n 'SERVER_PROTOCOL': 'HTTP/1.1',\n 'SERVER_SOFTWARE': 'gunicorn/19.6.0',\n 'wsgi.errors': BytesIO(),\n 'wsgi.file_wrapper': BytesIO(),\n 'wsgi.input': BytesIO(),\n 'wsgi.multiprocess': False,\n 'wsgi.multithread': False,\n 'wsgi.run_once': False,\n 'wsgi.url_scheme': 'http',\n 'wsgi.version': (1, 0),\n}\n","sub_path":"gatekeeper/test_client.py","file_name":"test_client.py","file_ext":"py","file_size_in_byte":6524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"553238962","text":"import json\nfrom pprint import pprint\n\nimport pymysql\nimport requests\nfrom bs4 import BeautifulSoup\n\nBLOOMBERG_URL = \"https://www.bloomberg.com/search?query=\"\ncompanies = [\"Google\", \"Apple\", \"Snapchat\", \"Bloomberg\"]\n\n\nclass Spider:\n def __init__(self):\n self._key1 = \"c31155fb4ef44e598697433926e764ae\"\n self._key2 = \"01572def178342179993aa4eef97d341\"\n self._sentiment_analysis_endpoint = \"https://eastus.api.cognitive.microsoft.com/text/analytics/v2.0/sentiment?Subscription-Key={key}\" \\\n .format(key=self._key2)\n\n def _create_database(self):\n\n connection = pymysql.connect(host='34.235.205.203',\n user='root',\n password='dwdstudent2015',\n db='ArticlesSentiment',\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor)\n\n create_db_query = \"CREATE DATABASE IF NOT EXISTS ArticlesSentiment DEFAULT CHARACTER SET 'utf8'\"\n cursor = connection.cursor()\n cursor.execute(create_db_query)\n cursor.close()\n\n connection.close()\n\n def _create_table(self):\n connection = pymysql.connect(host='34.235.205.203',\n user='root',\n password='dwdstudent2015',\n db='ArticlesSentiment',\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor)\n\n query = \"CREATE TABLE IF NOT EXISTS ArticlesSentiment.Articles(company VARCHAR(255), article_url VARCHAR(400), score DOUBLE, PRIMARY KEY (company, article_url)); \"\n cursor = connection.cursor()\n cursor.execute(query)\n cursor.close()\n\n connection.close()\n\n def __get_sentiment(self, story):\n payload = {'documents': [{'id': 1, 'language': 'en', 'text': story}]}\n print(json.dumps(payload))\n # headers = {'Ocp-Apim-Subscription-Key': self._key1}\n url = self._sentiment_analysis_endpoint\n result = requests.post(url, data=json.dumps(payload))\n json_result = json.loads(result.text)\n\n try:\n return json_result.get(\"documents\")[0].get(\"score\")\n except:\n return 0\n\n\n def get_links(self, company):\n return_array = []\n url = BLOOMBERG_URL + company\n body = requests.get(url)\n content = body.text\n\n soup = BeautifulSoup(content, \"html.parser\")\n\n story_links = soup.find_all(\"h1\", {\"class\": \"search-result-story__headline\"})\n\n for link in story_links:\n return_array.append(link.contents[1][\"href\"])\n\n return return_array\n\n def get_content(self, company):\n\n links = self.get_links(company)\n return_content = dict()\n for link in links:\n content = requests.get(link).text\n soup = BeautifulSoup(content, \"html.parser\")\n story = \"\"\n paragraphs = soup.find_all(\"p\")\n paragraphs = paragraphs[2:]\n for index, paragraph in enumerate(paragraphs):\n if index > 2:\n try:\n story += paragraph.contents[0]\n story += \"\\n\"\n except:\n continue\n\n return_content[link] = story\n return return_content\n\n def get_sentiments(self):\n\n self._create_database()\n self._create_table()\n\n connection = pymysql.connect(host='34.235.205.203',\n user='root',\n password='dwdstudent2015',\n db='ArticlesSentiment',\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor)\n cursor = connection.cursor()\n\n for company in companies:\n\n stories = self.get_content(company=company)\n stories = dict(stories)\n\n for key in stories.keys():\n if key is not None:\n story = stories.get(key)\n score = self.__get_sentiment(story)\n query = \"INSERT IGNORE INTO ArticlesSentiment.Articles VALUES('{}','{}',{})\".format(company, key,\n float(score))\n cursor.execute(query)\n\n connection.commit()\n cursor.close()\n connection.close()\n\n def get_sentiments_for_company(self, company):\n\n self._create_database()\n self._create_table()\n\n connection = pymysql.connect(host='34.235.205.203',\n user='root',\n password='dwdstudent2015',\n db='ArticlesSentiment',\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor)\n cursor = connection.cursor()\n\n stories = self.get_content(company=company)\n stories = dict(stories)\n\n for key in stories.keys():\n if key is not None:\n story = stories.get(key)\n score = self.__get_sentiment(story)\n query = \"INSERT IGNORE INTO ArticlesSentiment.Articles VALUES('{}','{}',{})\".format(company, key,\n float(score))\n cursor.execute(query)\n\n connection.commit()\n cursor.close()\n connection.close()\n\n\nif __name__ == \"__main__\":\n spider = Spider()\n pprint(spider.get_sentiments())\n","sub_path":"StockSentimentAnalysis-master (1)/StockSentimentAnalysis-master/application/sentiment_analysis.py","file_name":"sentiment_analysis.py","file_ext":"py","file_size_in_byte":5815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"260481873","text":"#nbest_File = open('/home/mahnaz/Desktop/pipeline/Test Corpora/nbest-5000-withAlignment','r+')\nnbest_File = open('/home/mahnaz/MyDrive/Uni/Python/FeatureExtraction/set-total-score/nbest-5000-withAlignment-sorted','r+')\ntranslationFile = open('/home/mahnaz/MyDrive/Uni/Python/FeatureExtraction/get-Translation-File/best_translation_rescored.fa','w')\nnbest_count = 5000\nsentenceNum=0\nline = nbest_File.readline()\nspline = line.split(' ||| ')\nwhile True:\t\n\tif line == '':\n\t\tbreak\n\tsentence = spline[1]\n\ttranslationFile.write(sentence+'\\n')\n\t#for i in range(1,nbest_count-1):\n\twhile int(spline[0])==sentenceNum:\n\t\tline = nbest_File.readline()\n\t\tspline = line.split(' ||| ')\n\tsentenceNum += 1\nnbest_File.close()\ntranslationFile.close()\n","sub_path":"6_getTranslationFile/return_best_from_nbest.py","file_name":"return_best_from_nbest.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"302848102","text":"from aos.simulator import WavefrontSimulator\nfrom aos.estimator import WavefrontEstimator\nfrom aos.metric import SumOfSquares\nfrom aos.control import GainController\nfrom aos.telescope import BendingTelescope\nfrom aos.state import BendingState\nfrom aos.solver import SensitivitySolver\n\n\ndef test_integration():\n telescope = BendingTelescope.nominal(band='g')\n simulator = WavefrontSimulator()\n estimator = WavefrontEstimator()\n solver = SensitivitySolver()\n metric = SumOfSquares()\n controller = GainController(metric, gain=0.3)\n fieldx, fieldy = 0, 0\n\n x = BendingState()\n # add 1 micron of the third bending mode to M2.\n x['m2b3'] = 1e-6\n telescope.update(x)\n wavefront = simulator.simulateWavefront(telescope.optic, fieldx, fieldy)\n yest = estimator.estimate(wavefront)\n xest = solver.solve(yest)\n xprime, xdelta = controller.nextState(xest)\n\n # start second iteration\n telescope.update(xdelta)\n wavefront = simulator.simulateWavefront(telescope.optic, fieldx, fieldy)\n yest = estimator.estimate(wavefront)\n xest = solver.solve(yest)\n xprime, xdelta = controller.nextState(xest)","sub_path":"tests/test_integration.py","file_name":"test_integration.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"23934900","text":"# Сложная задачка: создаем арифметический пример\n# result = eval(\"2 + 4\") # вычисляет значение\n# пока пользователь не ответит верно, подсчитываем\n# кол-во ошибок\nimport random\n\nwrong = 0\nattempt = 0\n\nwhile attempt != 5:\n digit_a = random.randint(0, 10)\n digit_b = random.randint(0, 10)\n correct_answer = digit_a * digit_b\n answer = input('Чему равно {a} * {b} = '.format(a=digit_a, b=digit_b))\n attempt = attempt + 1\n if answer.isdigit():\n answer = int(answer)\n if answer == correct_answer:\n print('Правильно, {}'.format(correct_answer))\n elif answer != correct_answer:\n wrong = wrong + 1\n print('{answer} Не правильно, будет {corr}.'.format(corr=correct_answer,\n answer=answer))\n elif answer.isalpha():\n print('Что такое {}'.format(answer))\n\nif wrong == 0:\n print('Супер, у тебя 5 правильных ответов из 5')\nelif wrong == 1:\n print('Отлично, у тебя 1 неправильный ответ из 5')\nelif wrong == 2:\n print('Хорошо, у тебя 2 неправильный ответа из 5')\nelif wrong == 3:\n print('Плохо, у тебя 3 неправильный ответа из 5')\nelif wrong == 4:\n print('Ужастно, у тебя 4 неправильный ответа из 5')\nelif wrong == 5:\n print('Ты вообще учил?')\n","sub_path":"python_1/day_1/math_test.py","file_name":"math_test.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"477004430","text":"#Занятие 1.5. Функции_—_использование_встроенных_и_создание_собственных\n\n#Начальные условия\n#---------------------------------------------------------------------\nresidence_limit = 90 # 45, 60\nschengen_constraint = 180\n#visits = [[1, 1], [201, 201], [285, 290], [301, 310], [385, 410]]\n\n#Проверка на корректное заполнение списка дат въездов и выездов в ЕС\n#--------------------------------------------------------------------\nvisits = []\nstart = 0\nend = 0\nchoice = None\n\nprint(\n\t\"\"\"\n\t\tСделайте пожалуйста Ваш выбор:\\n\n\te - Выйти и получить результат расчета\n\tv - Ввести даты новой поездки\n\tr - Ввести даты новой поездки заново\n\t\t\"\"\"\n\t\t)\n\ndef shengen(visits):\n\n\tfor visit in visits:\n\t\tif not isinstance(visit, list):\n\t\t\traise Exception(\"Ошибка в построении списка дат\", visit)\n\n\t#Проверка дат въездов и выездов на последовательность\n\t#---------------------------------------------------------------------\n\tfor number_visit in range(len(visits) - 1):\n\t\tfor number_next_visit in range(number_visit + 1, len(visits)):\n\t\t\tif visits[number_visit][1] > visits[number_next_visit][0]:\n\t\t\t\traise Exception(\"Ошибка: наложение поездок\", number_next_visit)\n\n\tfor visit in visits:\n\t\tif visit[0] > visit[1]:\n\t\t\traise Exception(\"Ошибка: дата въезда позже даты выезда\", visit)\n\t\t\t\n\t#Проверка дат въездов и выездов на residence_limit\n\t#---------------------------------------------------------------------\n\tone_visit = 0\n\tfor visit in visits:\n\t\tone_visit = visit[1] - visit[0] + 1\n\t\tif one_visit > residence_limit:\n\t\t\traise Exception(\"Согласно срокам действия Вашей визы, Вы можете пребывать в ЕС не болле \" + str(residence_limit) + \" дней, измените даты Вашей поездки\", visit)\n\n\t#решение\n\t#---------------------------------------------------------------------\n\tx = 0 #используется во фразе: \"Выше представлены сроки поездок, Вашего тура №\"\n\texit = 0 #переменная exit используется для обозначения плавающего начала и конца срока schengen_constraint\n\n\tfor visit in visits:\n\t\twhile True:\n\t\t\tx += 1\n\t\t\ttour = 0 #одна поездка - разность м/у датой въезда и выезда\n\t\t\texit_date = [] #список дат выезда\n\t\t\tsum_tour = [] #список всех поездок, которые входят в коридор(180 дней) и в сумме не более 90 дней\n\t\t\tif exit == len(visits):\n\t\t\t\tbreak\n\n\t\t\tfor visit in visits:\t\n\t\t\t\tif visits[exit][0] <= visit[1] < visits[exit][0] + schengen_constraint: #опр. даты всех выездов, кот. входят в коридор(180 дней)\n\t\t\t\t\ttour += visit[1] - visit[0] + 1 #суммы всех поездок, кот. входят в коридор(180 дней)\n\t\t\t\t\tif tour <= residence_limit: #если поездки в сумме не более 90 дней, то...\n\t\t\t\t\t\texit_date.append(visit[1]) #добавить в список дат выездов очередную дату выезда\n\t\t\t\t\t\tsum_tour.append(tour) #добавить в список поездок, которые удовлетворяют всем условиям, очередную поездку\n\t\t\t\t\t\tprint(visit) #вывести на печать поездки, которые удовлетворяют всем условиям\n\t\t\t\t\t\t\n\t\t\t\t\t\tnew_trip = residence_limit - sum_tour[-1] #остаток дней для новой поездки\n\t\t\t\t\t\tcorridor_rest = visits[exit][0] + schengen_constraint - max(exit_date) - 1 #остаток коридора дней для новой поездки\n\t\t\t\n\t\t\tif sum_tour[-1]:\n\t\t\t\tprint(\"Выше представлены сроки поездок, Вашего тура №\" + str(x) + \":\")\n\t\t\t\tprint(\"Количество дней в сумме: %0.f\" %sum_tour[-1])\n\t\t\t\t\n\t\t\t\tprint(\"\\nСогласно срокам пребывания и установленному коридору, на новую поездку у Вас еще остается \" + str(new_trip) + \" дней.\")\n\t\t\t\tif new_trip:\n\t\t\t\t\tprint(\"\\nЕе Вы можете совершить в течение остатка коридора из \" + str(corridor_rest) + \" дней.\")\n\t\t\t\t\n\t\t\t\tif corridor_rest == 0 or not new_trip:\n\t\t\t\t\tprint(\"\\nт.е. Вы не можете совершить больше ни одной поездки.\")\n\t\t\t\telif corridor_rest < new_trip:\n\t\t\t\t\tprint(\"\\nт.е. Вы можете совершить поездку только на протяжении %.0f дней.\" % corridor_rest)\n\t\t\t\t\t\n\t\t\texit = exit + len(exit_date)\t\t\n\t\t\tprint(\"\\n***-***-***\\n\")\n\nwhile choice != \"e\":\n\tchoice = input(\"\\nВаш выбор: \")\n\tif choice == \"e\":\n\t\tprint(shengen(visits))\n\t\tprint(\"\\nДо свидания.\")\n\telif choice == \"v\":\n\t\tprint(\"Bведите, пожалуйс��а, даты поездки\\n\")\n\t\tstart = input ('День въезда: ')\n\t\tend = input ('День выезда: ')\n\t\tif not start.isdigit() or not end.isdigit():\n\t\t\traise Exception(\"Ошибка в построении списка дат\", start, end)\n\t\telse:\n\t\t\tvisits.append([int(start), int(end)])\n\t\t\tprint(\"Сроки Ваших поездок: \" + str(visits))\n\telif choice == \"r\":\n\t\tprint(\"Сроки Ваших поездок: \" + str(visits) + \" отменены\")\n\t\tvisits = []\n\t\tprint(\"Bведите, пожалуйста, даты поездки заново\\n\")\n\t\tstart = input ('день въезда: ')\n\t\tend = input ('день выезда: ')\n\t\tif not start.isdigit() or not end.isdigit():\n\t\t\traise Exception(\"Ошибка в построении списка дат\", start, end)\n\t\telse:\n\t\t\tvisits.append([int(start), int(end)])\n\t\t\tprint(\"Сроки Ваших поездок: \" + str(visits))\n\n","sub_path":"lesson_05 - Функции — использование встроенных и создание собственных/lesson05.py","file_name":"lesson05.py","file_ext":"py","file_size_in_byte":6219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"387622227","text":"import os\n\nfrom assessor_functions import *\nfrom assessor_model import *\nfrom assessor_train_params import *\n\n\n######################################################\n# DIR, PARAMS\n######################################################\n\n# Make the dir if it doesn't exist\nif not os.path.exists(this_assessor_save_dir):\n print(\"Making dir\", this_assessor_save_dir)\n os.makedirs(this_assessor_save_dir)\n\n# Copy assessor_model file into this_assessor_save_dir\nos.system(\"cp assessor_model.py \" + this_assessor_save_dir)\nprint(\"Copied assessor_model.py to\", this_assessor_save_dir)\n\n# Copy assessor_train_params file into this_assessor_save_dir\nos.system(\"cp assessor_train_params.py \" + this_assessor_save_dir)\nprint(\"Copied assessor_train_params.py to\", this_assessor_save_dir)\n\n# Copy assessor_train file into this_assessor_save_dir\nos.system(\"cp assessor_train.py \" + this_assessor_save_dir)\nprint(\"Copied assessor_train.py to\", this_assessor_save_dir)\n\n######################################################\n# GEN BATCHES OF IMAGES\n######################################################\n\ntrain_generator = generate_assessor_data_batches(batch_size=batch_size, data_dir=data_dir, collect_type=train_collect_type,\n shuffle=shuffle, equal_classes=equal_classes, use_CNN_LSTM=use_CNN_LSTM, use_head_pose=use_head_pose,\n grayscale_images=grayscale_images, random_crop=random_crop, random_flip=random_flip, verbose=verbose)\n\nval_generator = generate_assessor_data_batches(batch_size=batch_size, data_dir=data_dir, collect_type=val_collect_type,\n shuffle=shuffle, equal_classes=equal_classes, use_CNN_LSTM=use_CNN_LSTM, use_head_pose=use_head_pose,\n grayscale_images=grayscale_images, random_crop=False, random_flip=False, verbose=verbose)\n\n######################################################\n# MAKE MODEL\n######################################################\n\nassessor = my_assessor_model(use_CNN_LSTM=use_CNN_LSTM, use_head_pose=use_head_pose, mouth_nn=mouth_nn,\n conv_f_1=conv_f_1, conv_f_2=conv_f_2, conv_f_3=conv_f_3,\n my_resnet_repetitions=my_resnet_repetitions,\n mouth_features_dim=mouth_features_dim, lstm_units_1=lstm_units_1,\n dense_fc_1=dense_fc_1, dense_fc_2=dense_fc_2,\n grayscale_images=grayscale_images)\n\nassessor.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n\nwrite_model_architecture(assessor, file_type='json', file_name=os.path.join(this_assessor_save_dir, this_assessor_model))\nwrite_model_architecture(assessor, file_type='yaml', file_name=os.path.join(this_assessor_save_dir, this_assessor_model))\n\n######################################################\n# CALLBACKS\n######################################################\n\n# lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1), cooldown=0, patience=10, min_lr=0.5e-6)\n\nearly_stopper = EarlyStopping(min_delta=0.001, patience=50)\n\ncheckpointAndMakePlots = CheckpointAndMakePlots(file_name_pre=this_assessor_model, this_assessor_save_dir=this_assessor_save_dir)\n\n######################################################\n# TRAIN\n######################################################\n\nsaved_final_model = False\n\ntry:\n assessor.fit_generator(train_generator,\n steps_per_epoch=train_steps_per_epoch,\n # steps_per_epoch=1,\n epochs=n_epochs,\n # callbacks=[lr_reducer, early_stopper, checkpointAndMakePlots],\n callbacks=[early_stopper, checkpointAndMakePlots],\n validation_data=val_generator,\n validation_steps=val_steps_per_epoch,\n # validation_steps=1,\n class_weight=class_weight,\n initial_epoch=0)\n\nexcept KeyboardInterrupt:\n print(\"Saving latest weights as\", os.path.join(this_assessor_save_dir, this_assessor_model+\"_assessor.hdf5\"), \"...\")\n assessor.save_weights(os.path.join(this_assessor_save_dir, this_assessor_model+\"_assessor.hdf5\"))\n saved_final_model = True\n\nif not saved_final_model:\n print(\"Saving latest weights as\", os.path.join(this_assessor_save_dir, this_assessor_model+\"_assessor.hdf5\"), \"...\")\n assessor.save_weights(os.path.join(this_assessor_save_dir, this_assessor_model+\"_assessor.hdf5\"))\n saved_final_model = True\n\nprint(\"Done.\")\n","sub_path":"assessor/ASSESSORS/22_assessor_equalClasses_grayscaleImages_my_resnet_mouth32_lstm2_1fc16_bn_dp0.2_2fc16_bn_dp0.2_adam/assessor_train.py","file_name":"assessor_train.py","file_ext":"py","file_size_in_byte":4645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"539255556","text":"import os\n\nfrom dotenv import dotenv_values\n\nfrom repositories.DataRepository import DataRepository\nimport requests\n\nis_production = os.environ.get('FLASK_ENV') == 'production'\nauthentication_token = os.environ.get('AUTHORIZATION_KEY')\nconfig = dotenv_values(\".env\")\n\n\nclass SummarizerModelRepository(DataRepository):\n def __init__(self):\n SummarizerModelRepository.base_url = 'http://coeus.sit.kmutt.ac.th/api/model/summarizer'\n SummarizerModelRepository.header = {\n \"Authorization\": f\"Bearer {authentication_token}\"}\n\n @staticmethod\n def get_prediction(data):\n try:\n response = requests.post(\n f\"{SummarizerModelRepository.base_url}/predict\",\n json={\n \"data\": data},\n headers=SummarizerModelRepository.header).json()\n result = response['result']\n except Exception as e:\n print(data, flush=True)\n print(e, flush=True)\n raise Exception('Something went wrong with summarizer model')\n return result\n\n @staticmethod\n def getData(input: str) -> str:\n return SummarizerModelRepository.get_prediction(input)\n","sub_path":"repositories/SummarizerModelRepository.py","file_name":"SummarizerModelRepository.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"24978672","text":"T = int(input())\r\n\r\ndecimais = []\r\nfor i in range(T):\r\n decimais.append(int(input()))\r\n\r\n\r\ndef converte(decimal,base):\r\n numero_base = []\r\n atual = decimal\r\n i = 0\r\n while(atual >= base):\r\n i+=1\r\n numero_base.append(atual%base)\r\n atual = int(atual/base)\r\n numero_base.append(atual)\r\n return numero_base,i\r\n\r\n\r\ndef palindromo(numero,i):\r\n certo = True\r\n aux = 0\r\n while aux < i/2:\r\n if numero[aux] != numero[i-aux]:\r\n certo = False\r\n aux+=1\r\n return certo\r\n\r\n\r\nfor num in decimais:\r\n i = 2\r\n tam = 0\r\n nenhum = True\r\n lista_de_base = []\r\n while i <=16:\r\n a,b = converte(num,i)\r\n if palindromo(a,b):\r\n nenhum = False\r\n lista_de_base.append(i)\r\n tam +=1\r\n i+=1\r\n if nenhum:\r\n print(\"-1\")\r\n else:\r\n for k in range(tam):\r\n if k+1 != tam:\r\n print(f'{lista_de_base[k]} ', end=\"\")\r\n else:\r\n print(f'{lista_de_base[k]}')\r\n\r\n\r\n","sub_path":"URI1880.py","file_name":"URI1880.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"567760583","text":"import unittest\nfrom probability_solution import *\n\"\"\"\nContains various local tests for Assignment 3.\n\"\"\"\n\nclass ProbabilityTests(unittest.TestCase):\n \n #Part 1a\n def test_network_setup(self):\n \"\"\"Test that the power plant network has the proper number of nodes and edges.\"\"\"\n power_plant = make_power_plant_net()\n nodes = power_plant.nodes\n self.assertEquals(len(nodes), 5, msg=\"incorrect number of nodes\")\n total_links = sum([len(n.children) for n in nodes] + [len(n.parents) for n in nodes])\n self.assertEquals(total_links, 10, msg=\"incorrect number of edges between nodes\")\n\n #Part 1b\n def test_probability_setup(self):\n \"\"\"Test that all nodes in the power plant network have proper probability distributions.\n Note that all nodes have to be named predictably for tests to run correctly.\"\"\"\n # first test temperature distribution\n power_plant = set_probability(make_power_plant_net())\n T_node = power_plant.get_node_by_name('temperature')\n self.assertTrue(T_node is not None, msg='No temperature node initialized')\n \n T_dist = T_node.dist.table\n self.assertEqual(len(T_dist), 2, msg='Incorrect temperature distribution size')\n test_prob = T_dist[0]\n self.assertEqual(int(test_prob*100), 80, msg='Incorrect temperature distribution') \n\n # then faulty gauge distribution\n F_G_node = power_plant.get_node_by_name('faulty gauge')\n self.assertTrue(F_G_node is not None, msg='No faulty gauge node initialized')\n \n F_G_dist = F_G_node.dist.table\n rows, cols = F_G_dist.shape\n self.assertEqual(rows, 2, msg='Incorrect faulty gauge distribution size')\n self.assertEqual(cols, 2, msg='Incorrect faulty gauge distribution size')\n test_prob1 = F_G_dist[0][1]\n test_prob2 = F_G_dist[1][0]\n self.assertEqual(int(test_prob1*100), 5, msg='Incorrect faulty gauge distribution')\n self.assertEqual(int(test_prob2*100), 20, msg='Incorrect faulty gauge distribution')\n\n # faulty alarm distribution\n F_A_node = power_plant.get_node_by_name('faulty alarm')\n self.assertTrue(F_A_node is not None, msg='No faulty alarm node initialized')\n F_A_dist = F_A_node.dist.table\n self.assertEqual(len(F_A_dist), 2, msg='Incorrect faulty alarm distribution size')\n\n test_prob = F_A_dist[0]\n \n self.assertEqual(int(test_prob*100), 85, msg='Incorrect faulty alarm distribution')\n # gauge distribution\n # can't test exact probabilities because\n # order of probabilities is not guaranteed\n G_node = power_plant.get_node_by_name('gauge')\n self.assertTrue(G_node is not None, msg='No gauge node initialized')\n G_dist = G_node.dist.table\n rows1, rows2, cols = G_dist.shape\n \n self.assertEqual(rows1, 2, msg='Incorrect gauge distribution size')\n self.assertEqual(rows2, 2, msg='Incorrect gauge distribution size')\n self.assertEqual(cols, 2, msg='Incorrect gauge distribution size')\n\n # alarm distribution\n A_node = power_plant.get_node_by_name('alarm')\n self.assertTrue(A_node is not None, msg='No alarm node initialized')\n A_dist = A_node.dist.table\n rows1, rows2, cols = A_dist.shape\n self.assertEqual(rows1, 2, msg='Incorrect alarm distribution size')\n self.assertEqual(rows2, 2, msg='Incorrect alarm distribution size')\n self.assertEqual(cols, 2, msg='Incorrect alarm distribution size')\n\n #Part 2a Test\n def test_games_network(self):\n \"\"\"Test that the games network has the proper number of nodes and edges.\"\"\"\n games_net = get_game_network()\n nodes = games_net.nodes\n self.assertEqual(len(nodes), 6, msg='Incorrent number of nodes')\n total_links = sum([len(n.children) for n in nodes] + [len(n.parents) for n in nodes])\n self.assertEqual(total_links, 12, 'Incorrect number of edges')\n\n # Now testing that all nodes in the games network have proper probability distributions.\n # Note that all nodes have to be named predictably for tests to run correctly.\n\n # First testing team distributions.\n # You can check this for all teams i.e. A,B,C (by replacing the first line for 'B','C')\n\n A_node = games_net.get_node_by_name('A')\n self.assertTrue(A_node is not None, 'Team A node not initialized')\n A_dist = A_node.dist.table\n self.assertEqual(len(A_dist), 4, msg='Incorrect distribution size for Team A')\n test_prob = A_dist[0]\n test_prob2 = A_dist[2]\n self.assertEqual(int(test_prob*100), 15, msg='Incorrect distribution for Team A')\n self.assertEqual(int(test_prob2*100), 30, msg='Incorrect distribution for Team A')\n\n # Now testing match distributions.\n # You can check this for all matches i.e. AvB,BvC,CvA (by replacing the first line)\n AvB_node = games_net.get_node_by_name('AvB')\n self.assertTrue(AvB_node is not None, 'AvB node not initialized')\n \n AvB_dist = AvB_node.dist.table\n rows1, rows2, cols = AvB_dist.shape\n self.assertEqual(rows1, 4, msg='Incorrect match distribution size')\n self.assertEqual(rows2, 4, msg='Incorrect match distribution size')\n self.assertEqual(cols, 3, msg='Incorrect match distribution size')\n\n flag1 = True\n flag2 = True\n flag3 = True\n for i in range(0, 4):\n for j in range(0,4):\n x = AvB_dist[i,j,]\n if i==j:\n if x[0]!=x[1]:\n flag1=False\n if j>i:\n if not(x[1]>x[0] and x[1]>x[2]):\n flag2=False\n if jx[1] and x[0]>x[2]):\n flag3=False\n \n self.assertTrue(flag1, msg='Incorrect match distribution for equal skill levels')\n self.assertTrue(flag2 and flag3, msg='Incorrect match distribution: teams with higher skill levels should have higher win probabilities')\n\n #Part 2b Test\n def test_posterior(self):\n posterior = calculate_posterior(get_game_network())\n\n self.assertTrue(abs(posterior[0]-0.25)<0.01 and abs(posterior[1]-0.42)<0.01 and abs(posterior[2]-0.31)<0.01, msg='Incorrect posterior calculated')\n\nif __name__ == '__main__':\n# unittest.main()\n \n# game_net = get_game_network()\n# initial_state = [3,1,2,0,0,2]\n## print Gibbs_sampler(game_net, initial_state)\n## print MH_sampler(game_net, initial_state)\n# Gibbs_convergence, MH_convergence, Gibbs_count, MH_count, MH_rejection_count = compare_sampling(game_net,initial_state, 0.001)\n# print \"Gibbs Convergence\"\n# print Gibbs_convergence\n# print \"MH convergence\"\n# print MH_convergence\n# print \"Computed from part b\"\n# print calculate_posterior(game_net)\n# print \"Gibbs count\", Gibbs_count, \"MH count\", MH_count, \"MH rejection\", MH_rejection_count\n## print get_temperature_prob(power_plant,True)\n\n# make_exam_net()\n make_final_net()\n","sub_path":"Assignment_3/probability_tests.py","file_name":"probability_tests.py","file_ext":"py","file_size_in_byte":7164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"320515717","text":"'''Test StereoFlouroscopyRegistration.util.vtk_helpers.GetRGBColor'''\n\nimport unittest\nimport vtk\nfrom StereoFlouroscopyRegistration.util.vtk_helpers import GetRGBColor\n\nclass TestGetRGBColor(unittest.TestCase):\n '''Test class for StereoFlouroscopyRegistration.util.vtk_helpers.GetRGBColor'''\n\n def test_bad_color_name(self):\n '''Test that a bad color name returns black'''\n expected_rgb_value = [0.0, 0.0, 0.0]\n rgb = GetRGBColor('bad_color_name')\n\n self.assertEqual(expected_rgb_value, rgb)\n\n def test_white_color(self):\n '''Test that a bad color name returns black'''\n expected_rgb_value = [1.0, 1.0, 1.0]\n rgb = GetRGBColor('white')\n\n self.assertEqual(expected_rgb_value, rgb)\n\n def test_black_color(self):\n '''Test that a bad color name returns black'''\n expected_rgb_value = [0.0, 0.0, 0.0]\n rgb = GetRGBColor('black')\n\n self.assertEqual(expected_rgb_value, rgb)\n","sub_path":"tests/util/vtk_helpers/test_GetRGBColor.py","file_name":"test_GetRGBColor.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"176331407","text":"from UI import EquipManager\nfrom PyQt5 import QtWidgets, QtGui\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtCore import Qt\nimport sys\nfrom tool import oracle_lab\nimport datetime\nfrom tool import sqlserver\nfrom PyQt5.QtWidgets import *\n\ntable_standard_check = 'superxon.STANDARD_CHECK'\ndb_equipmentAcceptance = 'EQUIPMENTACCEPTANCE'\n\n\nclass EquipMG(EquipManager.Ui_MainWindow, QtWidgets.QMainWindow):\n def __init__(self, parent=None):\n super(EquipMG, self).__init__()\n self.setupUi(self)\n sqlsvdb = sqlserver.SQLServer()\n datalist = sqlsvdb.QueryAllInfo()\n self.display_tablewidget(self.tableWidget_3, datalist)\n # self.tableWidget.sortByColumn(0, AscendingOrder)\n self.process = None\n self.edit_flag = False\n\n self.pushButton.clicked.connect(lambda : self.EquipmentAcceptance_display())\n self.tableWidget.horizontalHeader().sectionClicked.connect(self.HorSectionClicked)\n\n self.pushButton_10.clicked.connect(self.entry_standard_parts)\n self.pushButton_11.clicked.connect(self.del_standard_parts)\n self.pushButton_12.clicked.connect(self.quenry_standard_parts)\n self.tableWidget_3.horizontalHeader().sectionClicked.connect(self.HorSectionClicked)\n\n def EquipmentAcceptance_edit(self):\n db_lab = oracle_lab.ORACLE()\n edit_gz = self.tableWidget.item(self.tableWidget.selectedItems()[0].row(),0).data(0) #修改设备验收时，编辑单元格所在的固资号作为条件进行查询\n edit_my = self.tableWidget.selectedItems()[0].data(0) #修改的文字\n edit_zd = self.tableWidget.horizontalHeaderItem(self.tableWidget.selectedItems()[0].column()).data(0) #编辑的字段名\n if 'DATE' in edit_zd:\n edit_my = 'to_date(\\'{}\\',\\'yyyy-mm-dd\\')'.format(edit_my)\n #修改编辑部分\n if not edit_my:\n edit_my = ''\n condition = 'FIXEDASSETSNUMBER = \\'{}\\''.format(edit_gz)\n if db_lab.sqlupdate(table=db_equipmentAcceptance, condition=condition, column=edit_zd, data=edit_my):\n QMessageBox.information(self, \"提示\", \"修改 {} 成功\".format(edit_gz))\n else:\n QMessageBox.information(self, \"提示\", \"修改 {} 失败\\n 原因：字段格式不正确\".format(edit_gz))\n self.tableWidget.takeItem(self.tableWidget.selectedItems()[0].row(), self.tableWidget.selectedItems()[0].column())\n\n def EquipmentAcceptance_display(self, conditions=None):\n if self.edit_flag:\n self.tableWidget.itemChanged.disconnect(self.EquipmentAcceptance_edit)\n self.edit_flag = False\n self.process = 'equipment_acceptance'\n db_lab = oracle_lab.ORACLE()\n dataslist = db_lab.SelectallData(table=db_equipmentAcceptance, condition=conditions)\n self.display_tablewidget(self.tableWidget, dataslist)\n rowcount = self.tableWidget.rowCount()\n print(rowcount)\n for i in range(rowcount):\n item = self.tableWidget.item(i, 0)\n item.setFlags(Qt.NoItemFlags)\n self.tableWidget.setItem(i, 0, item)\n if not self.edit_flag:\n self.tableWidget.itemChanged.connect(self.EquipmentAcceptance_edit)\n self.edit_flag = True\n\n def HorSectionClicked(self, index):\n sender = self.sender()\n if sender == self.tableWidget_3.horizontalHeader():\n self.tableWidget_3.sortByColumn(index, Qt.AscendingOrder)\n print(index)\n elif sender == self.tableWidget.horizontalHeader():\n self.tableWidget.sortByColumn(index, Qt.AscendingOrder)\n print(index)\n\n # def HorSectionClicked_tw(self, index):\n # self.tablewidget.sortByColumn(index, Qt.AscendingOrder)\n # print(index)\n\n def entry_standard_parts(self):\n db = oracle_lab.ORACLE()\n sn = self.lineEdit_2.text()\n check_time = 'to_date(\\''+self.dateEdit.text()+'\\',\\'yyyy-mm-dd\\')'\n print(check_time)\n checkperson = self.lineEdit_3.text()\n validity_date = 'to_date(\\'' + self.dateEdit_4.text() + '\\',\\'yyyy-mm-dd\\')'\n insertdata = [sn, check_time, validity_date, checkperson]\n if sn:\n db.insertcrossdata(table_standard_check, insertdata)\n condition = 'sn like \\'{}\\''.format(sn)\n datelist = db.SelectallData(table_standard_check, condition, 'validity_date')\n self.display_standard_parts(datelist)\n\n def del_standard_parts(self):\n db = oracle_lab.ORACLE()\n sn = self.lineEdit_2.text()\n if sn:\n condition = 'sn LIKE \\'{}\\''.format(sn)\n db.DelDatas(table_standard_check, condition)\n\n def quenry_standard_parts(self):\n db = oracle_lab.ORACLE()\n condition = None\n if self.lineEdit_4.text():\n sn = self.lineEdit_4.text()\n condition ='sn like \\'{}\\''.format(sn)\n if self.lineEdit_5.text():\n checkperson = self.lineEdit_5.text()\n condition ='responsible like \\'{}\\''.format(checkperson)\n\n datelist = db.SelectallData(table_standard_check, condition, 'validity_date')\n if datelist:\n self.display_standard_parts(datelist)\n\n def display_standard_parts(self, datelist):\n print(datelist)\n if datelist:\n colnamelist = list(datelist[0].keys())\n self.tableWidget_2.setColumnCount(len(colnamelist))\n self.tableWidget_2.setRowCount(len(datelist))\n self.tableWidget_2.setHorizontalHeaderLabels(colnamelist)\n for row in range(len(datelist)):\n itemlist = list(datelist[row].values())\n for col in range(len(colnamelist)):\n if type(itemlist[col]) == type(datetime.datetime.now()):\n self.tableWidget_2.setItem(row, col, QTableWidgetItem(itemlist[col].strftime('%Y-%m-%d')))\n elif isinstance(itemlist[col], int):\n self.tableWidget_2.setItem(row, col, QTableWidgetItem(str(itemlist[col])))\n else:\n self.tableWidget_2.setItem(row, col, QTableWidgetItem(itemlist[col]))\n else:\n pass\n\n def display_tablewidget(self, tablewidget, datelist):\n print(datelist)\n if datelist:\n colnamelist = list(datelist[0].keys())\n tablewidget.setColumnCount(len(colnamelist))\n tablewidget.setRowCount(len(datelist))\n tablewidget.setHorizontalHeaderLabels(colnamelist)\n for row in range(len(datelist)):\n itemlist = list(datelist[row].values())\n for col in range(len(colnamelist)):\n if type(itemlist[col]) == type(datetime.datetime.now()):\n items = QTableWidgetItem(itemlist[col].strftime('%Y-%m-%d'))\n elif isinstance(itemlist[col], int):\n items = QTableWidgetItem(str(itemlist[col]))\n else:\n items = QTableWidgetItem(itemlist[col])\n tablewidget.setItem(row, col, items)\n if row % 2:\n items.setBackground(QtGui.QBrush(QtGui.QColor(227, 255, 219)))\n\n tablewidget.horizontalHeader().setFont(QFont(\"Arial\", 7)) # 设置表头字体\n tablewidget.horizontalHeader().setDefaultAlignment(QtCore.Qt.AlignLeft)\n tablewidget.horizontalHeader().setDefaultSectionSize(80)\n\n # self.tableView_6.verticalHeader().setFixedWidth(25)\n # self.tableView_6.verticalHeader().setFixedHeight(10)\n tablewidget.verticalHeader().setFont(QFont(\"Arial\", 7, QFont.Bold)) # 设置表头字体\n tablewidget.verticalHeader().setDefaultSectionSize(15)\n else:\n pass\n\n def table_update(self):\n row_select = self.tableWidget.selectedItems()\n print(len(row_select))\n print(row_select[0].text())\n print(row_select[0].row())\n print(row_select[0].column())\n\n\nif __name__ == \"__main__\":\n app = QtWidgets.QApplication(sys.argv)\n main = EquipMG()\n main.showMaximized()\n main.show() # 在外面只需要调用simpleDialogForm显示就行，不需要关注内部如何实现了。\n sys.exit(app.exec_())\n","sub_path":"EquipManger/main/EM_main.py","file_name":"EM_main.py","file_ext":"py","file_size_in_byte":8377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"229028170","text":"import numpy as np\nimport pymc as pm\nimport aesara.tensor as at\n\nfrom bambi.backend.utils import has_hyperprior, get_distribution\nfrom bambi.families.multivariate import Categorical, Multinomial\nfrom bambi.families.univariate import Beta, Binomial, Gamma\nfrom bambi.priors import Prior\n\n\nclass CommonTerm:\n \"\"\"Representation of a common effects term in PyMC\n\n An object that builds the PyMC distribution for a common effects term. It also contains the\n coordinates that we then add to the model.\n\n Parameters\n ----------\n term: bambi.terms.Term\n An object representing a common effects term.\n \"\"\"\n\n def __init__(self, term):\n self.term = term\n self.coords = self.get_coords()\n\n def build(self, spec):\n data = self.term.data\n label = self.name\n dist = self.term.prior.name\n args = self.term.prior.args\n distribution = get_distribution(dist)\n\n # Dims of the response variable\n response_dims = []\n if isinstance(spec.family, (Categorical, Multinomial)):\n response_dims = list(spec.response.coords)\n response_dims_n = len(spec.response.coords[response_dims[0]])\n\n # Arguments may be of shape (a,) but we need them to be of shape (a, b)\n # a: length of predictor coordinates\n # b: length of response coordinates\n for key, value in args.items():\n if value.ndim == 1:\n args[key] = np.hstack([value[:, np.newaxis]] * response_dims_n)\n\n dims = list(self.coords) + response_dims\n if dims:\n coef = distribution(label, dims=dims, **args)\n else:\n coef = distribution(label, shape=data.shape[1], **args)\n\n # Prepends one dimension if response is multivariate categorical and predictor is 1d\n if response_dims and len(dims) == 1:\n coef = coef[np.newaxis, :]\n\n return coef, data\n\n def get_coords(self):\n coords = {}\n if self.term.categorical:\n name = self.name + \"_dim\"\n coords[name] = self.term.term.levels\n # Not categorical but multi-column, like when we use splines\n elif self.term.data.shape[1] > 1:\n name = self.name + \"_dim\"\n coords[name] = list(range(self.term.data.shape[1]))\n return coords\n\n @property\n def name(self):\n if self.term.alias:\n return self.term.alias\n return self.term.name\n\n\nclass GroupSpecificTerm:\n \"\"\"Representation of a group specific effects term in PyMC\n\n Creates an object that builds the PyMC distribution for a group specific effect. It also\n contains the coordinates that we then add to the model.\n\n Parameters\n ----------\n term: bambi.terms.GroupSpecificTerm\n An object representing a group specific effects term.\n noncentered: bool\n Specifies if we use non-centered parametrization of group-specific effects.\n \"\"\"\n\n def __init__(self, term, noncentered):\n self.term = term\n self.noncentered = noncentered\n self.coords = self.get_coords()\n\n def build(self, spec):\n label = self.name\n dist = self.term.prior.name\n kwargs = self.term.prior.args\n predictor = self.term.predictor.squeeze()\n\n # Dims of the response variable (e.g. categorical)\n response_dims = []\n if isinstance(spec.family, (Categorical, Multinomial)):\n response_dims = list(spec.response.coords)\n\n dims = list(self.coords) + response_dims\n # Squeeze ensures we don't have a shape of (n, 1) when we mean (n, )\n # This happens with categorical predictors with two levels and intercept.\n coef = self.build_distribution(dist, label, dims=dims, **kwargs).squeeze()\n coef = coef[self.term.group_index]\n\n return coef, predictor\n\n def get_coords(self):\n coords = {}\n\n # Use the name of the alias if there's an alias\n if self.term.alias:\n expr, factor = self.term.alias, self.term.alias\n else:\n expr, factor = self.term.name.split(\"|\")\n\n # The group is always a coordinate we add to the model.\n coords[factor + \"__factor_dim\"] = self.term.groups\n\n if self.term.categorical:\n name = expr + \"__expr_dim\"\n levels = self.term.term.expr.levels\n coords[name] = levels\n return coords\n\n def build_distribution(self, dist, label, **kwargs):\n \"\"\"Build and return a PyMC Distribution.\"\"\"\n dist = get_distribution(dist)\n\n if \"dims\" in kwargs:\n group_dim = [dim for dim in kwargs[\"dims\"] if dim.endswith(\"__expr_dim\")]\n kwargs = {\n k: self.expand_prior_args(k, v, label, dims=group_dim) for (k, v) in kwargs.items()\n }\n else:\n kwargs = {k: self.expand_prior_args(k, v, label) for (k, v) in kwargs.items()}\n\n if self.noncentered and has_hyperprior(kwargs):\n sigma = kwargs[\"sigma\"]\n offset = pm.Normal(label + \"_offset\", mu=0, sigma=1, dims=kwargs[\"dims\"])\n return pm.Deterministic(label, offset * sigma, dims=kwargs[\"dims\"])\n return dist(label, **kwargs)\n\n def expand_prior_args(self, key, value, label, **kwargs):\n # kwargs are used to pass 'dims' for group specific terms.\n if isinstance(value, Prior):\n # If there's an alias for the hyperprior, use it.\n key = self.term.hyperprior_alias.get(key, key)\n return self.build_distribution(value.name, f\"{label}_{key}\", **value.args, **kwargs)\n return value\n\n @property\n def name(self):\n if self.term.alias:\n return self.term.alias\n return self.term.name\n\n\nclass InterceptTerm:\n \"\"\"Representation of an intercept term in a PyMC model.\n\n Parameters\n ----------\n term: bambi.terms.Term\n An object representing the intercept. This has ``.kind == \"intercept\"``\n \"\"\"\n\n def __init__(self, term):\n self.term = term\n\n def build(self, spec):\n dist = get_distribution(self.term.prior.name)\n label = self.name\n # Pre-pends one dimension if response is multi-categorical\n if isinstance(spec.family, (Categorical, Multinomial)):\n dims = list(spec.response.coords)\n dist = dist(label, dims=dims, **self.term.prior.args)[np.newaxis, :]\n else:\n # NOTE: Intercept only models with shape=1 don't work anymore\n # It seems that 'shape=1' is not needed anymore?\n # dist = dist(label, shape=1, **self.term.prior.args)\n dist = dist(label, **self.term.prior.args)\n return dist\n\n @property\n def name(self):\n if self.term.alias:\n return self.term.alias\n return self.term.name\n\n\nclass ResponseTerm:\n \"\"\"Representation of a response term in a PyMC model.\n\n Parameters\n ----------\n term : bambi.terms.ResponseTerm\n The response term as represented in Bambi.\n family : bambi.famlies.Family\n The model family.\n \"\"\"\n\n def __init__(self, term, family):\n self.term = term\n self.family = family\n\n def build(self, nu, invlinks):\n \"\"\"Create and return the response distribution for the PyMC model.\n\n nu : aesara.tensor.var.TensorVariable\n The linear predictor in the PyMC model.\n invlinks : dict\n A dictionary where names are names of inverse link functions and values are functions\n that can operate with Aesara tensors.\n \"\"\"\n data = self.term.data.squeeze()\n\n # Take the inverse link function that maps from linear predictor to the mean of likelihood\n if self.family.link.name in invlinks:\n linkinv = invlinks[self.family.link.name]\n else:\n linkinv = self.family.link.linkinv_backend\n\n # Add column of zeros to the linear predictor for the reference level (the first one)\n if isinstance(self.family, (Categorical, Multinomial)):\n # Make sure intercept-only models work\n nu = np.ones((data.shape[0], 1)) * nu\n nu = at.concatenate([np.zeros((data.shape[0], 1)), nu], axis=1)\n\n # Add mean parameter and observed data\n kwargs = {self.family.likelihood.parent: linkinv(nu), \"observed\": data}\n\n # Add auxiliary parameters\n kwargs = self.build_auxiliary_parameters(kwargs)\n\n # Build the response distribution\n dist = self.build_response_distribution(kwargs)\n\n return dist\n\n def build_auxiliary_parameters(self, kwargs):\n # Build priors for the auxiliary parameters in the likelihood (e.g. sigma in Gaussian)\n if self.family.likelihood.priors:\n for key, value in self.family.likelihood.priors.items():\n\n # Use the alias if there's one\n if key in self.family.aliases:\n label = self.family.aliases[key]\n else:\n label = f\"{self.name}_{key}\"\n\n if isinstance(value, Prior):\n dist = get_distribution(value.name)\n kwargs[key] = dist(label, **value.args)\n else:\n kwargs[key] = value\n return kwargs\n\n def build_response_distribution(self, kwargs):\n # Get likelihood distribution\n dist = get_distribution(self.family.likelihood.name)\n\n # Handle some special cases\n if isinstance(self.family, Beta):\n # Beta distribution in PyMC uses alpha and beta, but we have mu and kappa.\n # alpha = mu * kappa\n # beta = (1 - mu) * kappa\n alpha = kwargs[\"mu\"] * kwargs[\"kappa\"]\n beta = (1 - kwargs[\"mu\"]) * kwargs[\"kappa\"]\n return dist(self.name, alpha=alpha, beta=beta, observed=kwargs[\"observed\"])\n\n if isinstance(self.family, Binomial):\n successes = kwargs[\"observed\"][:, 0].squeeze()\n trials = kwargs[\"observed\"][:, 1].squeeze()\n return dist(self.name, p=kwargs[\"p\"], observed=successes, n=trials)\n\n if isinstance(self.family, Gamma):\n # Gamma distribution is specified using mu and sigma, but we request prior for alpha.\n # We build sigma from mu and alpha.\n sigma = kwargs[\"mu\"] / (kwargs[\"alpha\"] ** 0.5)\n return dist(self.name, mu=kwargs[\"mu\"], sigma=sigma, observed=kwargs[\"observed\"])\n\n if isinstance(self.family, Multinomial):\n n = kwargs[\"observed\"].sum(axis=1)\n return dist(self.name, p=kwargs[\"p\"], observed=kwargs[\"observed\"], n=n)\n\n return dist(self.name, **kwargs)\n\n @property\n def name(self):\n if self.term.alias:\n return self.term.alias\n return self.term.name\n","sub_path":"bambi/backend/terms.py","file_name":"terms.py","file_ext":"py","file_size_in_byte":10816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"651356125","text":"\nfrom collections import namedtuple\n\nDungeonEntrance = namedtuple(\n \"DungeonEntrance\",\n \"stage_name room_num scls_exit_index spawn_id entrance_name island_name warp_out_stage_name warp_out_room_num warp_out_spawn_id\"\n)\nDUNGEON_ENTRANCES = [\n DungeonEntrance(\"Adanmae\", 0, 2, 2, \"Dungeon Entrance On Dragon Roost Island\", \"Dragon Roost Island\", \"sea\", 13, 211),\n DungeonEntrance(\"sea\", 41, 6, 6, \"Dungeon Entrance In Forest Haven Sector\", \"Forest Haven\", \"Omori\", 0, 215),\n DungeonEntrance(\"sea\", 26, 0, 2, \"Dungeon Entrance In Tower of the Gods Sector\", \"Tower of the Gods\", \"sea\", 26, 1),\n DungeonEntrance(\"Edaichi\", 0, 0, 1, \"Dungeon Entrance On Headstone Island\", \"Headstone Island\", \"sea\", 45, 229),\n DungeonEntrance(\"Ekaze\", 0, 0, 1, \"Dungeon Entrance On Gale Isle\", \"Gale Isle\", \"sea\", 4, 232),\n]\n\nDungeonExit = namedtuple(\n \"DungeonExit\",\n \"stage_name room_num scls_exit_index spawn_id dungeon_name boss_stage_name\"\n)\nDUNGEON_EXITS = [\n DungeonExit(\"M_NewD2\", 0, 0, 0, \"Dragon Roost Cavern\", \"M_DragB\"),\n DungeonExit(\"kindan\", 0, 0, 0, \"Forbidden Woods\", \"kinBOSS\"),\n DungeonExit(\"Siren\", 0, 1, 0, \"Tower of the Gods\", \"SirenB\"),\n DungeonExit(\"M_Dai\", 0, 0, 0, \"Earth Temple\", \"M_DaiB\"),\n DungeonExit(\"kaze\", 15, 0, 15, \"Wind Temple\", \"kazeB\"),\n]\n\ndef randomize_dungeon_entrances(self):\n remaining_exits = DUNGEON_EXITS.copy()\n for dungeon_entrance in DUNGEON_ENTRANCES:\n if self.dungeons_only_start and dungeon_entrance.entrance_name == \"Dungeon Entrance On Dragon Roost Island\":\n # If we're in a dungeons-only-start, we have to force the first dungeon to be DRC.\n # Any other dungeon would cause problems for the item placement logic:\n # If the first dungeon is TotG, the player can't get any items because they need bombs.\n # If the first dungeon is ET or WT, the player can't get any items because they need the command melody (and even with that they would only be able to access one single location).\n # If the first dungeon is FW, the player can access a couple chests, but that's not enough to give the randomizer enough breathing space.\n possible_remaining_exits = []\n for dungeon_exit in remaining_exits:\n if dungeon_exit.dungeon_name in [\"Dragon Roost Cavern\"]:\n possible_remaining_exits.append(dungeon_exit)\n else:\n possible_remaining_exits = remaining_exits\n \n dungeon_exit = self.rng.choice(possible_remaining_exits)\n remaining_exits.remove(dungeon_exit)\n \n self.dungeon_entrances[dungeon_entrance.entrance_name] = dungeon_exit.dungeon_name\n self.dungeon_island_locations[dungeon_exit.dungeon_name] = dungeon_entrance.island_name\n \n if not self.dry_run:\n # Update the dungeon this entrance takes you into.\n entrance_dzx_path = \"files/res/Stage/%s/Room%d.arc\" % (dungeon_entrance.stage_name, dungeon_entrance.room_num)\n entrance_dzx = self.get_arc(entrance_dzx_path).get_file(\"room.dzr\")\n entrance_scls = entrance_dzx.entries_by_type(\"SCLS\")[dungeon_entrance.scls_exit_index]\n entrance_scls.dest_stage_name = dungeon_exit.stage_name\n entrance_scls.room_index = dungeon_exit.room_num\n entrance_scls.spawn_id = dungeon_exit.spawn_id\n entrance_scls.save_changes()\n \n # Update the DRI spawn to not have spawn ID 5.\n # If the DRI entrance was connected to the TotG dungeon, then exiting TotG while riding KoRL would crash the game.\n entrance_spawns = entrance_dzx.entries_by_type(\"PLYR\")\n entrance_spawn = next(spawn for spawn in entrance_spawns if spawn.spawn_id == dungeon_entrance.spawn_id)\n if entrance_spawn.spawn_type == 5:\n entrance_spawn.spawn_type = 1\n entrance_spawn.save_changes()\n \n # Update the entrance you're put at when leaving the dungeon.\n exit_dzx_path = \"files/res/Stage/%s/Room%d.arc\" % (dungeon_exit.stage_name, dungeon_exit.room_num)\n exit_dzx = self.get_arc(exit_dzx_path).get_file(\"room.dzr\")\n exit_scls = exit_dzx.entries_by_type(\"SCLS\")[dungeon_exit.scls_exit_index]\n exit_scls.dest_stage_name = dungeon_entrance.stage_name\n exit_scls.room_index = dungeon_entrance.room_num\n exit_scls.spawn_id = dungeon_entrance.spawn_id\n exit_scls.save_changes()\n \n # Update the wind warp out event to take you to the correct island.\n boss_stage_arc_path = \"files/res/Stage/%s/Stage.arc\" % dungeon_exit.boss_stage_name\n event_list = self.get_arc(boss_stage_arc_path).get_file(\"event_list.dat\")\n warp_out_event = event_list.events_by_name[\"WARP_WIND_AFTER\"]\n director = next(actor for actor in warp_out_event.actors if actor.name == \"DIRECTOR\")\n stage_change_action = next(action for action in director.actions if action.name == \"NEXT\")\n stage_name_prop = next(prop for prop in stage_change_action.properties if prop.name == \"Stage\")\n stage_name_prop.value = dungeon_entrance.warp_out_stage_name\n room_num_prop = next(prop for prop in stage_change_action.properties if prop.name == \"RoomNo\")\n room_num_prop.value = dungeon_entrance.warp_out_room_num\n spawn_id_prop = next(prop for prop in stage_change_action.properties if prop.name == \"StartCode\")\n spawn_id_prop.value = dungeon_entrance.warp_out_spawn_id\n \n self.logic.update_dungeon_entrance_macros()\n","sub_path":"randomizers/dungeon_entrances.py","file_name":"dungeon_entrances.py","file_ext":"py","file_size_in_byte":5278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"54773941","text":"#!/usr/bin/env python\n#\n# Copyright (C) 2016, the ximpol team.\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU GengReral 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 along\n# with this program; if not, write to the Free Software Foundation, Inc.,\n# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\n\"\"\"References:\nSlowikowska A. et al., MNRAS 397, 103-23\n\nValid range (MJD) : 52944--52975\nEpoch, t0 (MJD) : 52960.000000296\nnu0 (Hz) : 29.8003951530036\nnudot(10^-10 Hz s^-1) : -3.73414\nnudddot (10^-20 Hz s^-2): 1.18\n\"\"\"\n\nimport numpy\nimport scipy.signal\nimport scipy.interpolate\nfrom scipy.optimize import curve_fit\nimport os\n\nfrom ximpol import XIMPOL_CONFIG\nfrom ximpol.core.rand import xUnivariateGenerator\nfrom ximpol.core.spline import xInterpolatedUnivariateSpline\nfrom ximpol.srcmodel.roi import xPeriodicPointSource, xEphemeris, xROIModel\nfrom ximpol.srcmodel.spectrum import power_law\n\n\ndef _full_path(file_name):\n \"\"\"Convenience function to retrieve the relevant files.\n \"\"\"\n return os.path.join(XIMPOL_CONFIG, 'ascii', file_name)\n\n\n# geometry of dipole\nalpha=numpy.radians(30)\nbeta=numpy.radians(15)\nt0=0.1\n# Mind that you have to wrap this into a function to be used.\n# This could be done in a more general way with some sort of library function.\ndef polarization_degree(E, t, ra, dec):\n return 1.0+0*t\n\n\n#\ndef inclination(t):\n phi_phase=numpy.radians((t-t0)*360)\n return numpy.arccos(numpy.cos(alpha)*numpy.cos(alpha-beta)+\n numpy.sin(alpha)*numpy.sin(alpha-beta)*numpy.cos(phi_phase))\n\n\npdegree_ang=numpy.radians(numpy.linspace(0,90,19))\npdegree_data1=2*numpy.array([0,0.032765,0.0490032,0.065200,0.070770,0.086773,0.1025,0.116778,0.130228,0.142785,0.154202,0.164246,0.172673,0.180627,0.187338,0.192776,0.196445,0.198123,0.198696])\n\npdegree_data2=numpy.array([0,0.14017,0.485033,0.787875,0.862695,0.919405,0.934439,0.95239,0.965822,0.969983,0.974817,0.979925,0.980247,0.984679,0.9794,0.988191,0.984482,0.987218,0.985947])\n\npdegree_funk1=(scipy.interpolate.interp1d(pdegree_ang,pdegree_data1,kind='cubic'))\npdegree_funk2=(scipy.interpolate.interp1d(pdegree_ang,pdegree_data2,kind='cubic'))\n\n\ndef polarization_degree_noqed(E, t, ra, dec):\n efactor=numpy.tanh(2*(E-4))\n efactor=(efactor+1)/2\n return pdegree_funk1(inclination(t))*(1-efactor)+pdegree_funk2(inclination(t))*(efactor)\n\n\n# And, again, this needs to be wrapped into a function.\ndef polarization_angle(E, t, ra, dec):\n phi_phase=numpy.radians((t-t0)*360)\n return numpy.arctan(numpy.sin(alpha)*numpy.sin(phi_phase)/\n (numpy.sin(alpha+beta)*numpy.cos(alpha)-\n numpy.cos(alpha+beta)*numpy.sin(alpha)*\n numpy.cos(phi_phase)))+1.5707963268\n\n\n\n\n# Build the actual energy spectrum.\n# energy_spectrum = power_law(pl_normalization_spline, pl_index_spline)\nfdata=[]\nfor fname in (\"model02.dat\",\"model24.dat\",\"model46.dat\",\"model68.dat\",\"model80.dat\"):\n en, f = numpy.loadtxt(_full_path(fname),usecols=(0,3),unpack=True,skiprows=3)\n fdata.append(f)\n\nphdata=numpy.array([-0.1,0.1,0.3,0.5,0.7,0.9,1.1])\nfdata.insert(0,fdata[-1])\nfdata.append(fdata[1])\nenergy_spectrum=numpy.vectorize(scipy.interpolate.interp2d(en,phdata,fdata,kind='quintic'))\n\n# Build the flux as a function of the phase.\nfmt = dict(xname='Pulsar phase', yname='Flux ( 2 - 10 keV )',\n yunits='keV cm$^{-2}$ s$^{-1}$')\n\n\nenbin=numpy.linspace(2,10,100)\nesum=phdata*0\nfor ii,pp in enumerate(phdata):\n esum[ii]=numpy.trapz(energy_spectrum(enbin,pp),x=enbin)\npl_normalization_spline = xInterpolatedUnivariateSpline(phdata, esum, k=3, **fmt)\n\n\nfmt = dict(xname='Pulsar phase', yname='Polarization angle [rad]')\n_phi=numpy.linspace(0,1,23)\n_pol_angle = polarization_angle(0,_phi,0,0)\npol_angle_spline = xInterpolatedUnivariateSpline(_phi, _pol_angle, k=1, **fmt)\n\n\n_pol_degree = polarization_degree(0,_phi,0,0)\nfmt = dict(xname='Pulsar phase', yname='Polarization degree')\npol_degree_spline = xInterpolatedUnivariateSpline(_phi, _pol_degree, k=1, **fmt)\n\n_pol_degree_noqed = polarization_degree_noqed(0,_phi,0,0)\npol_degree_spline_noqed = xInterpolatedUnivariateSpline(_phi,\n _pol_degree_noqed,\n k=1, **fmt)\n\nROI_MODEL = xROIModel(26.59342, 61.75078)\nfouru_ephemeris = xEphemeris(0., 0.115088121, -2.64E-14, 0)\nfouru_pulsar = xPeriodicPointSource('4U 0142+61', ROI_MODEL.ra, ROI_MODEL.dec,\n energy_spectrum, polarization_degree,\n polarization_angle, fouru_ephemeris)\nROI_MODEL.add_source(fouru_pulsar)\n\n\nif __name__ == '__main__':\n print(ROI_MODEL)\n from ximpol.utils.matplotlib_ import pyplot as plt\n plt.figure()\n pl_index_spline.plot(show=False)\n plt.figure()\n pl_normalization_spline.plot(show=False)\n plt.figure()\n pol_angle_spline.plot(show=False)\n plt.figure()\n pol_degree_spline.plot(show=False)\n plt.show()\n","sub_path":"ximpol/config/four_u_pulsar.py","file_name":"four_u_pulsar.py","file_ext":"py","file_size_in_byte":5498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"577050929","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 11 01:24:41 2020\n\n@author: lenovo\n\"\"\"\n\na=999\nb=999999\nc=b/7\nd=c/11\ne=d/13\nprint(a,e)\nprint(a==e)\n\nX=True\nY=False\nZ=(X and not Y) or (not X and Y)\nW= X!=Y\nprint(Z,W)\n","sub_path":"Practical5/variables.py","file_name":"variables.py","file_ext":"py","file_size_in_byte":212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"89000809","text":"from gatelfpytorchjson import CustomModule\nfrom gatelfpytorchjson import EmbeddingsModule\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport sys\nimport logging\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\nstreamhandler = logging.StreamHandler(stream=sys.stderr)\nformatter = logging.Formatter(\n '%(asctime)s %(name)-12s %(levelname)-8s %(message)s')\nstreamhandler.setFormatter(formatter)\nlogger.addHandler(streamhandler)\n\n\n\nclass TextClassResiduleBiLstmCnnSingle(CustomModule):\n def __init__(self, dataset, config={}, maxSentLen=500, kernel_size =[3,4,5], cnn_dim=128, lstm_dim=64, dropout=0.6, bn_momentum=0.1):\n super().__init__(config=config)\n #super(TextClassBiLstmCnnSingle, self).__init__()\n self.maxSentLen=maxSentLen\n self.n_classes = dataset.get_info()[\"nClasses\"]\n self.kernel_size = kernel_size\n\n feature = dataset.get_indexlist_features()[0]\n vocab = feature.vocab\n vocab_size = vocab.n\n logger.debug(\"Initializing module TextClassCNNsingle for classes: %s and vocab %s\" %\n (self.n_classes, vocab_size, )) \n\n self.embedding = EmbeddingsModule(vocab)\n embedding_dim = self.embedding.emb_dims\n self.lstm1 = nn.LSTM(embedding_dim, lstm_dim, batch_first=True, bidirectional=True, dropout=dropout)\n\n self.cnn_layers = torch.nn.ModuleDict()\n self.cnn_layers_names = []\n self.cnn_bn = torch.nn.ModuleDict()\n self.cnn_bn_names= []\n self.residule_pool = torch.nn.ModuleDict()\n self.residule_pool_names = []\n\n self.residule_conv = torch.nn.ModuleDict()\n self.residule_conv_names = []\n\n for K in kernel_size:\n current_cnn_layer = torch.nn.Conv1d(in_channels=lstm_dim*2, \n out_channels=cnn_dim, \n kernel_size=K, \n padding=int(K/2))\n current_bn = nn.BatchNorm1d(cnn_dim, momentum=bn_momentum)\n if (K % 2) == 0:\n current_residule_pool = nn.Linear(maxSentLen+1, 1)\n else:\n current_residule_pool = nn.Linear(maxSentLen, 1)\n current_residule_conv = nn.Linear(maxSentLen, maxSentLen+1)\n\n\n cnn_layername = \"cnn_K{}\".format(K)\n cnn_bn_layername = \"bn_K{}\".format(K)\n residule_pool_layername = \"poolresidule_K{}\".format(K)\n residule_conv_layername = \"convresidule_K{}\".format(K)\n\n\n self.cnn_layers.add_module(cnn_layername, current_cnn_layer)\n self.cnn_bn.add_module(cnn_bn_layername, current_bn)\n self.residule_pool.add_module(residule_pool_layername, current_residule_pool)\n self.residule_conv.add_module(residule_conv_layername, current_residule_conv)\n self.cnn_layers_names.append(cnn_layername)\n self.cnn_bn_names.append(cnn_bn_layername)\n self.residule_pool_names.append(residule_pool_layername)\n self.residule_conv_names.append(residule_conv_layername)\n \n\n \n\n\n self.fc = nn.Linear(cnn_dim*len(kernel_size),self.n_classes)\n self.dropout = nn.Dropout(dropout)\n self.logsoftmax = torch.nn.LogSoftmax(dim=1)\n\n self.residual_lstm = nn.Linear(embedding_dim, lstm_dim*2)\n\n \n\n\n\n\n\n def forward(self, batch):\n batch = torch.LongTensor(batch[0])\n sent_len = batch.shape[1]\n batchsize = batch.shape[0] \n if self.maxSentLen:\n if sent_len > self.maxSentLen:\n batch = batch[:,:self.maxSentLen]\n elif sent_len < self.maxSentLen:\n zero_pad = torch.zeros(batchsize, self.maxSentLen-sent_len, dtype=torch.long)\n batch = torch.cat((batch, zero_pad),dim=1)\n\n if self.on_cuda():\n batch.cuda()\n for modules in self.cnn_layers:\n self.cnn_layers[modules].cuda()\n for modules in self.cnn_bn:\n self.cnn_bn[modules].cuda()\n self.lstm1.cuda()\n\n embedded = self.embedding(batch)\n residual_embd = self.residual_lstm(embedded)\n lstmed, hidden = self.lstm1(embedded)\n lstmed = lstmed + residual_embd\n residule_lstmed = lstmed\n \n convd = []\n convd_residule = []\n lstmed = lstmed.transpose(1,2)\n residule_lstmed = residule_lstmed.transpose(1,2)\n\n for i in range (len(self.cnn_layers_names)):\n current_cnn_layer_name = self.cnn_layers_names[i]\n current_cnn_bn_layer_name = self.cnn_bn_names[i]\n current_residule_pool_layer_name = self.residule_pool_names[i] \n current_residule_conv_layer_name = self.residule_conv_names[i]\n current_conved = F.relu(self.cnn_layers[current_cnn_layer_name](lstmed))\n current_conved = self.cnn_bn[current_cnn_bn_layer_name](current_conved)\n conv_shape = current_conved.shape\n #print(current_conved.shape)\n if current_conved.shape[2] > self.maxSentLen:\n current_residule_lstmed = self.residule_conv[current_residule_conv_layer_name](residule_lstmed)\n else:\n current_residule_lstmed = residule_lstmed\n\n #print(residule_lstmed.shape)\n current_conved = current_conved + current_residule_lstmed\n residule_current_conved = current_conved\n #print(current_residule_pool_layer_name)\n #print(residule_current_conved.shape)\n residule_current_conved = self.residule_pool[current_residule_pool_layer_name](residule_current_conved).squeeze(2)\n\n current_conved = F.max_pool1d(current_conved, current_conved.shape[2]).squeeze(2)\n current_conved = current_conved + residule_current_conved\n convd.append(current_conved)\n\n concat = torch.cat(convd, dim=1)\n concat = self.dropout(concat)\n out = self.fc(concat)\n out = self.logsoftmax(out)\n return out\n\n\n def get_lossfunction(self, config={}):\n # IMPORTANT: for the target indices, we use -1 for padding by default!\n return torch.nn.NLLLoss(ignore_index=-1)\n\n def get_optimizer(self, config={}):\n parms = filter(lambda p: p.requires_grad, self.parameters())\n # optimizer = torch.optim.SGD(parms, lr=0.01, momentum=0.9)\n # optimizer = torch.optim.SGD(parms, lr=0.01, momentum=0.9, weight_decay=0.05)\n optimizer = torch.optim.Adam(parms, lr=0.015, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)\n return optimizer\n\n","sub_path":"gate/kaggle/FileJsonPyTorch/gate-lf-pytorch-json/gatelfpytorchjson/modules/olds/TextClassResiduleBiLstmCnnSingle.py","file_name":"TextClassResiduleBiLstmCnnSingle.py","file_ext":"py","file_size_in_byte":6658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"105629969","text":"class Solution:\n def isMonotonic(self, A: List[int]) -> bool:\n monotonic = 0 # 0 - not determined yet, 1 - mono_inc, 2 - mono_dec\n\n for i in range(len(A) - 1):\n if A[i] < A[i + 1] and monotonic == 0:\n monotonic = 1\n elif A[i] > A[i + 1] and monotonic == 0:\n monotonic = 2\n\n if A[i] < A[i + 1] and monotonic == 2:\n return False\n if A[i] > A[i + 1] and monotonic == 1:\n return False\n\n return True\n\n","sub_path":"BootCamp/CD4/MonotonicArray.py","file_name":"MonotonicArray.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"190029898","text":"#!/usr/bin/env python\nimport unittest\n\n\nloader = unittest.TestLoader()\nfile=\"tests/test.py\"\nsuite = loader.loadTestsFromName(file)\nsuite.addTests(loader.loadTestsFromName(\n \"tests/test.py\"\n))\n\nrunner = unittest.TextTestRunner(verbosity=2)\nresult = runner.run(suite)","sub_path":"python/unittest/TestLoader/loadTestsFromName/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"137635324","text":"from dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Tuple\nimport csv\nimport json\nimport re\n\nimport jinja2\n\nimport mtsg\n\nHEADER_DELIMITER = \"|\"\n\nMAX_SIGNATURE_NAME_COMPONENTS = 2\nSIGNATURE_NAME_DELIMITER = \"-\"\nSIGNATURE_NAME_PREFIX = \"SBS\"\n\n\n@dataclass\nclass Disease:\n name: str\n\n\n@dataclass\nclass Sample:\n name: str\n disease: Disease\n contributions: Dict[str, int] = field(default_factory=dict)\n\n\nclass ParseHeaderError(Exception):\n pass\n\n\nclass NormalizeSignatureNameError(Exception):\n s: str\n\n def __init__(self, s: str) -> None:\n self.s = s\n\n def __str__(self) -> str:\n return \"invalid signature name: {}\".format(self.s)\n\n\ndef parse_header(s: str) -> Tuple[str, Disease]:\n if len(s) == 0:\n raise ParseHeaderError(\"empty input\")\n\n components = s.split(HEADER_DELIMITER, 1)\n\n sample_name = components[0]\n\n if len(components) < 2:\n disease = Disease(\"Unknown\")\n else:\n disease = Disease(components[1])\n\n return (sample_name, disease)\n\n\ndef normalize_signature_name(s: str) -> str:\n components = s.split(SIGNATURE_NAME_DELIMITER, MAX_SIGNATURE_NAME_COMPONENTS)\n\n if len(components) < MAX_SIGNATURE_NAME_COMPONENTS:\n raise NormalizeSignatureNameError(s)\n\n position = components[1].lstrip(\"0\")\n\n return \"{}{}\".format(SIGNATURE_NAME_PREFIX, position)\n\n\ndef read_signature_activities(src: Path) -> Tuple[List[str], List[Sample]]:\n signatures = []\n samples = []\n\n with open(src, newline=\"\") as f:\n reader = csv.reader(f, delimiter=\"\\t\")\n\n headers = next(reader, None)\n\n if not headers:\n raise ValueError(\"missing headers\")\n\n for header in headers[1:]:\n sample_name, disease = parse_header(header)\n sample = Sample(sample_name, disease)\n samples.append(sample)\n\n for row in reader:\n signature = row[0]\n signatures.append(signature)\n\n contributions = row[1:]\n\n for i, raw_contribution in enumerate(contributions):\n sample = samples[i]\n contribution = int(raw_contribution)\n sample.contributions[signature] = contribution\n\n return (signatures, samples)\n\n\ndef normalize_samples(\n signatures: List[str], raw_samples: List[Sample]\n) -> List[Dict[str, Any]]:\n samples = []\n\n for sample in raw_samples:\n contributions = []\n\n for signature in signatures:\n if signature in sample.contributions:\n contributions.append(sample.contributions[signature])\n else:\n contributions.append(0)\n\n samples.append(\n {\n \"name\": sample.name,\n \"disease\": {\n \"name\": sample.disease.name,\n },\n \"contributions\": contributions,\n }\n )\n\n return samples\n\n\ndef normalize_data(\n reference_signatures: List[str],\n raw_reference_samples: List[Sample],\n query_signatures: List[str],\n raw_query_samples: List[Sample],\n) -> Tuple[List[str], List[Dict[str, Any]], List[Dict[str, Any]]]:\n signatures = list(set(reference_signatures + query_signatures))\n signatures.sort()\n\n reference_samples = normalize_samples(signatures, raw_reference_samples)\n query_samples = normalize_samples(signatures, raw_query_samples)\n\n signatures = [normalize_signature_name(signature) for signature in signatures]\n\n return (signatures, reference_samples, query_samples)\n\n\ndef visualize(src: Path, reference_src: Path, dst: Path) -> None:\n reference_signatures, raw_reference_samples = read_signature_activities(\n reference_src\n )\n query_signatures, raw_query_samples = read_signature_activities(src)\n\n signatures, reference_samples, query_samples = normalize_data(\n reference_signatures, raw_reference_samples, query_signatures, raw_query_samples\n )\n\n data = {\n \"data\": {\n \"signatures\": signatures,\n \"reference\": reference_samples,\n \"query\": query_samples,\n }\n }\n\n generator = \"mtsg {}\".format(mtsg.__version__)\n payload = json.dumps(data)\n\n env = jinja2.Environment(loader=jinja2.PackageLoader(\"mtsg\", \"templates\"))\n template = env.get_template(\"signatures.html.j2\")\n\n with open(dst, \"w\") as f:\n f.write(template.render(generator=generator, payload=payload))\n","sub_path":"mtsg/commands/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":4453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"651168329","text":"import os\nimport csv\nimport django\nimport os, sys\nBASE_DIR = os.path.dirname(os.path.abspath(\"\")) # 定位到你的django根目录\nsys.path.append(os.path.abspath(os.path.join(BASE_DIR, os.pardir)))\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"mysite.settings\") # 你的django的settings文件\ndjango.setup()\nfrom main.models import book\nfrom login.models import User\ndef main():\n with open(\"book.csv\") as f:\n reader = csv.reader(f)\n for row in reader:\n _, created = book.objects.get_or_create(\n isbn=row[0],\n title=row[1],\n author=row[2],\n publish_year=row[3],\n )\n f.close()\n print(\"done\")\n\nif __name__ == '__main__':\n main()\n print('Done!')","sub_path":"import.py","file_name":"import.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"342129872","text":"#\n# CS1010S --- Programming Methodology\n#\n# Contest 10.2 Template\n#\n# Note that written answers are commented out to allow us to run your\n# code easily while grading your problem set.\n\nfrom random import *\nfrom puzzle_AI import *\n\nhistory_matrix = []\nhistory_move = []\ndef accumulate(fn, initial, seq):\n if not seq:\n return initial\n else:\n return fn(seq[0],\n accumulate(fn, initial, seq[1:]))\ndef transpose(mat):\n return list(map(list,zip(*mat)))\ndef reverse(mat):\n return list(map(lambda row: list(reversed(row)),mat))\ndef merge_left(matrix):\n def merge_row(row):\n merged_row, prev_tile, score_increment = [], 0, 0\n # pack element by element left-wards\n for tile in row:\n if tile == 0: continue\n if prev_tile == 0:\n prev_tile = tile\n elif prev_tile != tile:\n merged_row.append(prev_tile)\n prev_tile = tile\n else:\n merged_row.append(prev_tile*2)\n score_increment += prev_tile*2\n prev_tile = 0\n merged_row.append(prev_tile) # valid regardless whether there are merges or not\n # top up zeros\n while len(merged_row) != len(row):\n merged_row.append(0)\n return (merged_row, merged_row != row, score_increment)\n\n return accumulate(lambda first, rest: ([first[0]] + rest[0],\n first[1] or rest[1],\n first[2] + rest[2]),\n ([], False, 0),\n list(map(merge_row, matrix)))\n\ndef merge_right(mat):\n mat, valid, score = merge_left(reverse(mat))\n return (reverse(mat), valid, score)\n\ndef merge_up(mat):\n mat, valid, score = merge_left(transpose(mat))\n return (transpose(mat), valid, score)\n\ndef merge_down(mat):\n mat, valid, score = merge_left(reverse(transpose(mat)))\n return (transpose(reverse(mat)), valid, score)\ndef calc_score(mat):\n score = 0\n for row in range(4):\n for column in range(4):\n score += mat[row][column]\n return score\ndef undo_move(mat):\n history_move.pop()\n history_matrix.pop()\n mat = history_matrix[-1]\n return mat\ndef record_matrix_and_move(mat, decision):\n history_matrix.append(mat)\n history_move.append(decision)\n return\ndef add_two(mat):\n if not has_zero(mat):\n return mat\n a = randint(0, len(mat)-1)\n b = randint(0, len(mat)-1)\n while mat[a][b] != 0:\n a = randint(0, len(mat)-1)\n b = randint(0, len(mat)-1)\n mat[a][b] = 2\n return mat\ndef AI_command(mat, n):\n result_mat = mat[:]\n bonus = 0\n if n == 0:\n result_mat, valid,bonus = merge_up(result_mat)\n elif n == 1:\n result_mat, valid,bonus = merge_left(result_mat)\n elif n == 2:\n result_mat, valid,bonus = merge_right(result_mat)\n elif n == 3:\n result_mat, valid,bonus = merge_down(result_mat)\n add_two(result_mat)\n #record_matrix_and_move(result_mat, n)\n return result_mat, bonus\ndef monotone(mat):\n bonus = 0\n row = 0\n column = 0\n const1, const2, const3, const4 = biggest_tiles(mat)\n if row == 0 and column == 0 and mat[row][column] != 0:\n if mat[row][column] >= mat[row][column+1] >= mat[row][column +2] >= mat[row][column+3]\\\n and mat[row][column] == const1:\n bonus += const1\n if mat[row][column+1] == const1 or mat[row][column+1] == const2:\n bonus += const1*2\n if mat[row][column+2] == const2 or mat[row][column+2] == const3:\n bonus += const1*3\n if mat[row][column+3] == const3 or mat[row][column+3] == const4:\n bonus += const1*4\n return bonus\ndef biggest_tiles(mat):\n biggest = 0\n sec_big = 0\n third_big = 0\n fourth_big = 0\n for i in range(4):\n for j in range(4):\n big = mat[i][j]\n if big > biggest:\n biggest = big\n for i in range(4):\n for j in range(4):\n big = mat[i][j]\n if sec_big < big < biggest:\n sec_big = big\n for i in range(4):\n for j in range(4):\n big = mat[i][j]\n if third_big < big < sec_big:\n third_big = big\n for i in range(4):\n for j in range(4):\n big = mat[i][j]\n if fourth_big < big < third_big:\n fourth_big = big\n return biggest, sec_big, third_big, fourth_big\ndef AI_text(mat, turn):\n best_score = -1\n best_move = -1\n score = 0\n for move in range(4):\n if validified(mat, move):\n mat, bonus = AI_command(mat, move)\n score += bonus\n score += monotone(mat)\n score += second_row(mat)\n score += third_row(mat)\n record_matrix_and_move(mat, move)\n if turn == 3:\n score = bonus\n if history_matrix[-1][0][0] < history_matrix[-2][0][0]:\n score -= biggest_tiles(mat)[0]\n else:\n if game_status(mat) == \"not over\":\n score += AI_command(mat, AI_text(mat, turn+1))[1]\n mat = undo_move(mat)\n if score > best_score:\n best_score = score\n best_move = move\n score = 0\n return best_move\ndef second_row(mat):\n bonus = 0\n const4 = biggest_tiles(mat)[3]\n if mat[1][3] >= mat[1][2] and mat[1][3] <= const4:\n bonus += const4\n if mat[1][2] >= mat[1][1]:\n bonus += const4\n if mat[1][1] >= mat[1][0]:\n bonus += const4\n return bonus\ndef third_row(mat):\n bonus = 0\n const4 = biggest_tiles(mat)[3]\n if mat[2][0] >= mat[2][1] and mat[2][0] <= mat[1][0]:\n bonus += const4\n return bonus\n\ndef validified(mat,n):\n validity = None\n if n == 0:\n validity = merge_up(mat)[1]\n elif n == 1:\n validity = merge_left(mat)[1]\n elif n == 2:\n validity = merge_right(mat)[1]\n elif n == 3:\n validity = merge_down(mat)[1]\n return validity\n\ndef AI(mat):\n # replace the following line with your code\n dup_mat = mat[:]\n record_matrix_and_move(dup_mat, -1)\n decision = AI_text(dup_mat, 0)\n return ('w','a','d','s')[decision]\n\n\n\n# UNCOMMENT THE FOLLOWING LINES AND RUN TO WATCH YOUR SOLVER AT WORK\ngame_logic['AI'] = AI\ngamegrid = GameGrid(game_logic)\n\n# UNCOMMENT THE FOLLOWING LINE AND RUN TO GRADE YOUR SOLVER\n# Note: Your solver is expected to produce only valid moves.\nget_average_AI_score(AI, True)\n","sub_path":"contest10.2-template - Copy.py","file_name":"contest10.2-template - Copy.py","file_ext":"py","file_size_in_byte":6618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"568813019","text":"# Classe que representa um dos nós de uma àrvore de sufixos\nclass Node:\n def __init__(self, interval, child=[], is_end=False):\n self.interval = interval\n self.child = child[:]\n self.is_end = is_end\n\n# Classe que representa a arvore de sufixos\nclass SuffixTree:\n def __init__(self, text=\"\"):\n self.root = Node(interval=[0, 0])\n self.text = text\n\n # Popula a variavel \"self.text\" com o conteudo de um arquivo do tipo FASTA\n def load_fasta(self, path):\n with open(path) as fasta:\n lines = fasta.readlines()\n meta = lines[0]\n content = (\"\".join(lines[1:])).replace(\"\\n\", \"\")\n self.text = content\n\n # Retorna o tamanho do maior prefixo comum entre duas strings definidas por dois\n # intervalos em duas strings\n def common_prefix_size(self, a_word, a, b_word, b):\n interval = min(a[1] - a[0], b[1] - b[0])\n for i in range(interval):\n if a_word[a[0] + i] != b_word[b[0] + i]:\n return i\n return interval\n\n # Loop ingênuo de para popular a trie de sufixos, adicionando todos os possíveis\n # sufixos na trie.\n def build_sufixes(self):\n for i in range(len(self.text)):\n self.insert(self.text, i)\n\n # Método principal de inserção de uma palavra na trie. O algoritmo aqui envolve\n # descer pelas ramificações da árvore que possuem um prefixo igual ao da nova\n # palavra. Caso o algoritmo encontre um nó cuja 'label' seja sufixo da palavra,\n # um novo nó deve ser criado separando o prefixo do sufixo. Caso em um galho não\n # existam nós passíveis de admitir a nova palavra, um novo nó é criado naquele nível.\n def insert(self, word, start=0):\n curr = self.root\n l, r = start, len(word)\n\n while len(curr.child) > 0:\n has_prefix = False\n for i, node in enumerate(curr.child):\n pref_size = self.common_prefix_size(word, [l, r], word, node.interval)\n\n if pref_size > 0:\n label_pref = [node.interval[0], node.interval[0] + pref_size]\n label_suf = [node.interval[0] + pref_size, node.interval[1]]\n l += pref_size\n \n if label_suf[1] > label_suf[0]:\n prefix_child = [Node(interval=label_suf, child=node.child, is_end=node.is_end)]\n else:\n prefix_child = node.child\n\n curr.child[i] = curr = Node(interval=label_pref, child=prefix_child, is_end=r == l)\n has_prefix = True\n break\n\n if not has_prefix: break\n\n if r != l:\n curr.child.append(Node(interval=[l, r], is_end=True))\n\n # Para encontrar a maior substring que se repete no texto de entrada basta checar\n # qual o nó mais prodfundo que possui nós filhos.\n def get_longest_repeating(self):\n def utility_dfs(node, ans):\n new_pref, new_pos = ans\n for n in node.child:\n if len(n.child) == 0: continue\n suf = self.text[n.interval[0] : n.interval[1]]\n leaf_pref, leaf_pos = utility_dfs(n, (ans[0] + suf, n.interval[1]))\n if len(leaf_pref) > len(new_pref):\n new_pref = leaf_pref\n new_pos = leaf_pos\n \n return new_pref, new_pos\n\n pref, pos = utility_dfs(self.root, (\"\", -1))\n return pref, pos - len(pref)\n\n # Para contar o número de ocorrências de um certo padrão a partir de um árvore de\n # sufixos basta encontrar onde a palavra estaria caso fossemos procura-la na árvore\n # e contar o número de nós filhos e adicionar 1 caso ela seja um nó final para algum\n # sufixo.\n def count_occurences(self, word):\n curr = self.root\n l, r = 0, len(word)\n\n while len(curr.child) > 0 and r != l:\n has_prefix = False\n for node in curr.child:\n pref_size = self.common_prefix_size(word, [l, r], self.text, node.interval)\n\n if pref_size > 0:\n curr = node\n l += pref_size\n has_prefix = True\n break\n \n if not has_prefix: return False\n\n return len(curr.child) + (1 if curr.is_end else 0)\n\n # Métodos de visualização da árvore\n def print_branch(self, root, aggr=\"\"):\n curr_str = aggr + self.text[root.interval[0] : root.interval[1]] + \" -> \"\n for node in root.child:\n print(curr_str + self.text[node.interval[0] : node.interval[1]])\n self.print_branch(root=node, aggr=curr_str)\n\n def __repr__(self):\n self.print_branch(root=self.root)\n return \"\"","sub_path":"2020_1/ALG2-DCC206/TP1 - SarsCov2/Code/src/suffix_tree.py","file_name":"suffix_tree.py","file_ext":"py","file_size_in_byte":4299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"154202069","text":"import linecache\nimport numpy as np\nimport os\n\nfrom pople import sym2mass\n\ndef principal_coord():\n \"\"\"\n Returns eigen values.\n Requires input.xyz to be present in pwd. input.xyz contains the cartesian coordinates of the molecule/radical/ion in xyz format.\n Used to characterize if the system is linear/non-linear.\n\n Returns:\n Ievals (list, float): Eigen values\n \"\"\"\n\n with open(\"input.xyz\",\"r\") as xyz_f:\n num_l_xyz = sum(1 for l in xyz_f)\n Nat = int(linecache.getline(\"input.xyz\",1).strip())\n l1_chr_mul = linecache.getline(\"input.xyz\",2)\n sym = []\n Rlist = []\n for tmp_l in range(3,num_l_xyz+1): \n l1_xyz_1 = linecache.getline(\"input.xyz\",tmp_l)\n l1_lsp1 = l1_xyz_1.split()\n sym.append(l1_lsp1[0])\n Rlist.append(float(l1_lsp1[1]))\n Rlist.append(float(l1_lsp1[2]))\n Rlist.append(float(l1_lsp1[3]))\n conv = np.array(Rlist)\n R_coord = np.resize(conv,[Nat,3])\n\n rCM_pre = []\n mass_list = []\n for tmp_j in range(Nat):\n mass_atom = sym2mass(sym[tmp_j])\n mass_list.append(mass_atom)\n new1 = R_coord[tmp_j] * mass_atom\n rCM_pre.append(new1)\n rCM_sum = np.sum(rCM_pre,axis=0)\n tot_mass = sum(mass_list)\n rCM = rCM_sum/tot_mass\n\n new_coord1 = []\n r_skew_symm = np.zeros((3,3))\n momin = 0.0\n for tmp_k in range(Nat):\n sub_cm = R_coord[tmp_k] - rCM\n new_coord1.append(sub_cm)\n r_skew_symm[0][0] = 0.0\n r_skew_symm[0][1] = -sub_cm[2]\n r_skew_symm[0][2] = sub_cm[1]\n\n r_skew_symm[1][0] = sub_cm[2]\n r_skew_symm[1][1] = 0.0\n r_skew_symm[1][2] =-sub_cm[0]\n\n r_skew_symm[2][0] =-sub_cm[1]\n r_skew_symm[2][1] = sub_cm[0]\n r_skew_symm[2][2] = 0.0\n\n momin = momin + (sym2mass(sym[tmp_k]) * np.matmul(np.transpose(r_skew_symm), r_skew_symm) )\n\n eig_val, eig_vec = np.linalg.eig(momin)\n\n idx = eig_val.argsort()[::+1]\n eig_val = eig_val[idx]\n eig_vec = eig_vec[:,idx]\n\n eig_vec = np.transpose(eig_vec)\n \n Ievals = eig_val\n\n with open(\"input.xyz\",\"w\") as over_inp:\n over_inp.write(str(Nat)+'\\n')\n over_inp.write(l1_chr_mul)\n for tmp_r in range(0,Nat):\n new_coord2=np.array(new_coord1[tmp_r])\n new_coord3=np.zeros(3)\n new_coord3=np.dot(eig_vec, new_coord2)\n str1= str(sym[tmp_r]) + \" \" + str(new_coord3[0]) + \" \" + str(new_coord3[1]) + \" \" + str(new_coord3[2]) + '\\n'\n over_inp.write(str1)\n\n return(Ievals)\n","sub_path":"build/lib/pople/principal_coord.py","file_name":"principal_coord.py","file_ext":"py","file_size_in_byte":2589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"444721069","text":"import pickle\nfrom sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.model_selection import cross_val_score\nfrom fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.metalearning.analyse.time_measure import get_recall\nfrom fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.metalearning.analyse.time_measure import time_score2\nfrom fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.metalearning.analyse.time_measure import get_avg_runtime\nfrom fastsklearnfeature.interactiveAutoML.new_bench.multiobjective.metalearning.analyse.time_measure import get_optimum_avg_runtime\n\nfrom sklearn.metrics import make_scorer\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import tree\nfrom sklearn.tree import export_graphviz\nfrom subprocess import call\nfrom sklearn.model_selection import LeaveOneGroupOut\nfrom sklearn.model_selection import GroupKFold\nfrom sklearn.model_selection import RandomizedSearchCV\nimport copy\nimport glob\nimport matplotlib.pyplot as plt\n\ndef is_pareto_efficient_simple(costs):\n \"\"\"\n Find the pareto-efficient points\n :param costs: An (n_points, n_costs) array\n :return: A (n_points, ) boolean array, indicating whether each point is Pareto efficient\n \"\"\"\n is_efficient = np.ones(costs.shape[0], dtype = bool)\n for i, c in enumerate(costs):\n if is_efficient[i]:\n is_efficient[is_efficient] = np.any(costs[is_efficient] 0 and 'success_test' in exp_results[-1] and exp_results[-1]['success_test'] == True #also on test satisfied\n\ndef is_successfull_validation(exp_results):\n\treturn len(exp_results) > 0 and 'Validation_Satisfied' in exp_results[-1] # constraints were satisfied on validation set\n\n\nnumber_ml_scenarios = 1200\nrun_count = 0\nmap_data_2_constraints = {}\nfor efolder in experiment_folders:\n\trun_folders = sorted(glob.glob(efolder + \"*/\"))\n\tfor rfolder in run_folders:\n\t\ttry:\n\t\t\tinfo_dict = pickle.load(open(rfolder + 'run_info.pickle', \"rb\"))\n\n\t\t\tfor s in range(1, len(mappnames) + 1):\n\t\t\t\texp_results = []\n\t\t\t\ttry:\n\t\t\t\t\texp_results = load_pickle(rfolder + 'strategy' + str(s) + '.pickle')\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\n\t\t\t\tmin_loss = np.inf\n\t\t\t\tbest_run = None\n\n\t\t\t\tfor min_r in range(len(exp_results)):\n\t\t\t\t\tif 'loss' in exp_results[min_r] and exp_results[min_r]['loss'] < min_loss:\n\t\t\t\t\t\tmin_loss = exp_results[min_r]['loss']\n\t\t\t\t\t\tbest_run = min_r\n\n\t\t\t\tif is_successfull_validation_and_test(exp_results):\n\t\t\t\t\tif not info_dict['dataset_id'] in map_data_2_constraints:\n\t\t\t\t\t\tmap_data_2_constraints[info_dict['dataset_id']] = []\n\t\t\t\t\tmap_data_2_constraints[info_dict['dataset_id']].append((exp_results[best_run]['test_acc'],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texp_results[best_run]['test_fair'],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1.0 - exp_results[best_run][\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'cv_number_features'],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texp_results[best_run]['test_robust']))\n\t\t\trun_count += 1\n\t\texcept FileNotFoundError:\n\t\t\tpass\n\t\tif run_count == number_ml_scenarios:\n\t\t\tbreak\n\tif run_count == number_ml_scenarios:\n\t\tbreak\n\n\n\n\nimport plotly.graph_objects as go\n\ncategories = ['accuracy','fairness','simplicity','safety']\n\nfor key, value in map_data_2_constraints.items():\n\n\tfig = go.Figure()\n\n\tmask = is_pareto_efficient_simple(np.array(value) * -1)\n\n\tmax_value = np.zeros(len(categories))\n\tmax_id = np.zeros(len(categories))\n\tfor vi in range(len(value)):\n\t\tif mask[vi]:\n\t\t\tfor ei in range(len(categories)):\n\t\t\t\tif max_value[ei] < value[vi][ei]:\n\t\t\t\t\tmax_value[ei] = value[vi][ei]\n\t\t\t\t\tmax_id[ei] = vi\n\n\n\tfor vi in range(len(value)):\n\t\tif mask[vi]:\n\t\t\tif vi in max_id:\n\t\t\t\tfig.add_trace(go.Scatterpolar(\n\t\t\t\t\t r=value[vi],\n\t\t\t\t\t theta=categories,\n\t\t\t\t\t fill='toself',\n\t\t\t\t\t name='set' + str(vi)\n\t\t\t\t))\n\n\n\tfig.update_layout(\n\t #title=str(map_dataset2name[key]),\n\t polar=dict(\n\t\tradialaxis=dict(\n\t\t visible=True,\n\t\t range=[0, 1]\n\t\t)),\n\t showlegend=False\n\t)\n\n\t#fig.write_html('/tmp/radar_chart_' + str(map_dataset2name[key]) +'.html', auto_open=False)\n\n\tfig.write_image('/tmp/radar_chart_' + str(map_dataset2name[key]) +'.pdf')","sub_path":"new_project/fastsklearnfeature/interactiveAutoML/new_bench/multiobjective/metalearning/analyse/for_validation/cv_scores_radar_chart_by_dataset_val.py","file_name":"cv_scores_radar_chart_by_dataset_val.py","file_ext":"py","file_size_in_byte":7957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"352195730","text":"from datasets.fetch_methods import (fetch_music_file, load_osufile,\n make_list4k, reset_directory)\nfrom datasets.make_pickle_dataset import make_pickle_dataset\n\nSONGS_PATH = \"/osuSongs\"\nfetch_num = 10000\n\n\ndef main():\n # データセットディレクトリの再作成\n # reset_directory()\n # 4k譜面のリストを取得\n #list4k = make_list4k(SONGS_PATH, fetch_num)\n # osufileを読み込む\n # load_osufile(list4k)\n # 音楽データをSONGS_PATHからとってくる。\n # fetch_music_file(list4k)\n # fft処理を行い、曲全体のfft画像データとノーツの位置、曲の詳細をひとつのオブジェクトにまとめる\n # 各譜面につき一つのpickleファイルを作成する\n make_pickle_dataset()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"preprocess_main.py","file_name":"preprocess_main.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"178468091","text":"import re\nimport os\nimport glob\nimport pandas as pd\nimport json\nimport warnings\n\n# first group is the sample name from the sample sheet, second group is the\n# cell number, third group is the lane number, and fourth group is the\n# forward/reverse/index id.\n#\n# Here's a few examples for this regular expression: tinyurl.com/filenamepatt\nSAMPLE_PATTERN = re.compile(r'(.*)_(S\\d{1,4})_(L\\d{1,3})_([RI][12]).*')\n\n# first group is the number of sequences written to fwd and reverse files\n# Here's a few examples for this regular expression: tinyurl.com/samtoolspatt\nSAMTOOLS_PATTERN = re.compile(r'\\[.*\\] processed (\\d+) reads',\n flags=re.MULTILINE)\n\n\ndef _extract_name_and_lane(filename):\n search = re.match(SAMPLE_PATTERN, filename)\n if search is None:\n raise ValueError(f'Unrecognized filename pattern {filename}')\n\n name, _, lane, _ = search.groups()\n\n # remove the leading L and any leading zeroes\n lane = lane[1:].lstrip('0')\n return name, lane\n\n\ndef _parse_fastp_counts(path):\n with open(path) as fp:\n stats = json.load(fp)\n\n # check all the required keys are present, otherwise the file could be\n # malformed and we would only see a weird KeyError exception\n if ('summary' not in stats or\n 'after_filtering' not in stats['summary'] or\n 'total_reads' not in stats['summary']['after_filtering']):\n raise ValueError(f'The fastp log for {path} is malformed')\n\n return int(stats['summary']['after_filtering']['total_reads'])\n\n\ndef _parse_samtools_counts(path):\n with open(path, 'r') as f:\n matches = re.match(SAMTOOLS_PATTERN, f.read())\n\n if matches is None:\n raise ValueError(f'The samtools log for {path} is malformed')\n\n # divided by 2 because samtools outputs the number of records found\n # in the forward and reverse files\n return int(matches.groups()[0]) / 2.0\n\n\ndef _parsefier(run_dir, sample_sheet, subdir, suffix, name, funk):\n \"\"\"High order helper to search through a run directory\n\n Parameters\n ----------\n run_dir: str\n Illumina's run directory.\n sample_sheet: metapool.KLSampleSheet\n Sample sheet for the samples to get counts for.\n subdir: str\n Name of the directory in the project folder.\n suffix: str\n Suffix of the log files.\n name: str\n Column name for the output counts.\n funk: callable\n Function to parse log files.\n\n Returns\n -------\n pd.DataFrame\n Table with sample, lane and counts.\n \"\"\"\n out = []\n\n for project, lane in {(s.Sample_Project, s.Lane) for s in sample_sheet}:\n lane = lane.zfill(3)\n\n # log files are named after the sequence files themselves, specifically\n # the forward sequences, so we just make sure we match the right lane,\n # the forward file and the suffix. The suffix is something like \".log\"\n # or \"*.json\" if you expect to see other characters before the\n # extension\n logs = glob.glob(os.path.join(run_dir, project, subdir,\n f'*_L{lane}_R1_001' + suffix))\n\n for log in logs:\n out.append([*_extract_name_and_lane(os.path.basename(log)),\n project, log])\n\n out = pd.DataFrame(columns=['Sample_ID', 'Lane', 'Sample_Project', 'path'],\n data=out)\n\n found = set(out['Sample_ID'])\n expected = {s.Sample_ID for s in sample_sheet}\n\n # ignore the things not present in the sheet\n out = out[out['Sample_ID'].isin(expected)]\n\n dups = out.duplicated(subset=['Sample_ID', 'Lane'])\n if dups.any():\n pairs = [f\"{r['Sample_ID']} in lane {r['Lane']}\"\n for _, r in out[dups].iterrows()]\n\n # when running bcl2fastq/bclconvert multiple times you can run into\n # situations where the cell number is the only thing that changes. For\n # those situations, make sure you flag this as a possible error\n raise ValueError('Multiple matches found for the same samples in'\n ' the same lane, only one match is expected: %s' %\n ', '.join(pairs))\n\n if expected > found:\n warnings.warn(f'No {name} log found for these samples: %s' %\n ', '.join(expected - found))\n\n out[name] = out.path.apply(funk)\n\n out.drop(columns=['path', 'Sample_Project'], inplace=True)\n out.set_index(['Sample_ID', 'Lane'], inplace=True, verify_integrity=True)\n return out\n\n\ndef _safe_get(_document, _key):\n \"\"\"Prevent generic KeyError exceptions\"\"\"\n if _key not in _document:\n raise KeyError(f'bcl stats file is missing {_key} attribute')\n else:\n return _document[_key]\n\n\ndef bcl2fastq_counts(run_dir, sample_sheet):\n path = os.path.join(os.path.abspath(run_dir), 'Stats/Stats.json')\n\n if not os.path.exists(path):\n raise IOError(f'Cannot find stats file ({path}) for this run')\n\n with open(path) as fp:\n contents = json.load(fp)\n\n out = []\n for lane in _safe_get(contents, 'ConversionResults'):\n table = pd.DataFrame(_safe_get(lane, 'DemuxResults'))\n table['Lane'] = str(_safe_get(lane, 'LaneNumber'))\n\n out.append(table)\n\n out = pd.concat(out)\n out.rename(columns={'SampleId': 'Sample_ID', 'NumberReads': 'bcl_counts'},\n inplace=True)\n out = out[['Sample_ID', 'Lane', 'bcl_counts']]\n out.set_index(['Sample_ID', 'Lane'], inplace=True, verify_integrity=True)\n return out\n\n\ndef fastp_counts(run_dir, sample_sheet):\n return _parsefier(run_dir, sample_sheet, 'json', '.json', 'fastp_counts',\n _parse_fastp_counts)\n\n\ndef minimap2_counts(run_dir, sample_sheet):\n return _parsefier(run_dir, sample_sheet, 'samtools', '.log',\n 'minimap2_counts', _parse_samtools_counts)\n\n\ndef run_counts(run_dir, sample_sheet):\n out = bcl2fastq_counts(run_dir, sample_sheet).join([\n fastp_counts(run_dir, sample_sheet),\n minimap2_counts(run_dir, sample_sheet),\n\n ])\n\n out.fillna(value='NA', inplace=True)\n\n return out\n","sub_path":"metapool/count.py","file_name":"count.py","file_ext":"py","file_size_in_byte":6144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"21"} +{"seq_id":"290252358","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nCutword for sentiment dataset\n\ninput format:\n id sentitype text\n\nUsage:\n cut -t [pos] -c [hidden] -f --template