repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list | possible_versions list |
|---|---|---|---|---|---|
yuan-xin-9997/PDPTW_Code_PyCharm_Project_Github | [
"639b2a5bf54a4ba7be8d16ab70ff36bdecfd492e"
] | [
"gurobi_pdptw_parragh.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\n@author: yuan_xin\n@contact: yuanxin9997@qq.com\n@file: gurobi_pdptw_parragh.py\n@time: 2020/10/20 11:13\n@description:使用Python调用Gurobi求解PDPTW问题；Gurobi是标杆，用来对比其他算法用的.\n==求解器：Gurobi 9.0.3\n==模型：Parragh, S. N., et al. (2008). \"A survey on pickup and delivery problems: Part II: Trans... | [
[
"pandas.read_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
RicardFos/keras | [
"9628af85a0a2cb04cf433b1ad991017b70ae2005",
"9628af85a0a2cb04cf433b1ad991017b70ae2005",
"9628af85a0a2cb04cf433b1ad991017b70ae2005"
] | [
"keras/layers/rnn/gru_v1_test.py",
"keras/optimizers/optimizer_experimental/optimizer_test.py",
"keras/layers/rnn/lstm_test.py"
] | [
"# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"numpy.random.random",
"tensorflow.compat.v2.test.main",
"tensorflow.compat.v2.executing_eagerly",
"numpy.ones",
"numpy.testing.assert_allclose",
"tensorflow.compat.v2.test.disable_with_predicate"
],
[
"tensorflow.compat.v2.Variable",
"tensorflow.compat.v2.data.Dataset.from_ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"... |
noranhe/vnpy_portfoliostrategy | [
"45fe13861c4ba1856ed1e76a1c12bcf637170556"
] | [
"vnpy_portfoliostrategy/strategies/pair_trading_strategy.py"
] | [
"from typing import List, Dict\nfrom datetime import datetime\n\nimport numpy as np\n\nfrom vnpy_portfoliostrategy import StrategyTemplate, StrategyEngine\nfrom vnpy.trader.utility import BarGenerator\nfrom vnpy.trader.object import TickData, BarData\n\n\nclass PairTradingStrategy(StrategyTemplate):\n \"\"\"\"\"... | [
[
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
blekenbleu/Spectrosa | [
"172114cf3fd47a8e5051c24edd2debd3ad25b4a4"
] | [
"spectrosa.py"
] | [
"# https://www.earthinversion.com/utilities/efficiently-compute-spectrogram-in-python-using-librosa/\nimport sys\nimport librosa\nimport librosa.display\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef plot_spectrogram(audio_path, argv = ''):\n # play with hop_length and nfft values\n hop_length = ... | [
[
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.clf"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
dharmil25/ga-dsmp | [
"b72e1fdcb293de8775ab7c993699cfc8ea94bbfd",
"b72e1fdcb293de8775ab7c993699cfc8ea94bbfd"
] | [
"loan-defaulters-probability/code.py",
"loan-approval/code.py"
] | [
"# --------------\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# code starts here\ndf = pd.read_csv(path)\n\n# probability of fico score greater than 700\n\np_a = df[df['fico'].astype(float) >700].shape[0]/df.shape[0]\nprint(p_a)\n\n\n# probability of purpose == debt_consolidation\n... | [
[
"pandas.read_csv",
"matplotlib.pyplot.show",
"matplotlib.pyplot.axvline"
],
[
"pandas.read_csv",
"pandas.pivot_table"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1... |
heeryerate/pysparse | [
"24e9cdae16f1348094386abbfb7924329eee62de"
] | [
"pysparse/direct/pysparseUmfpack.py"
] | [
"\"\"\"\nA framework for solving sparse linear systems of equations using an LU\nfactorization, by means of the unsymmetric multifrontal sparse LU factorization\npackage UMFPACK ([D04a]_, [D04b]_, [DD99]_, [DD97]_).\n\nThis package is appropriate for factorizing sparse square unsymmetric or\nrectangular matrices.\n... | [
[
"numpy.empty"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
combogenomics/DuctApe | [
"f2407b0f56aa50009747dd5097faa793477ced61"
] | [
"ductape/actions.py"
] | [
"#!/usr/bin/env python\n\"\"\"\nActions\n\nDuctApe Library\n\nAll the actions required for the analysis\n\"\"\"\n# TODO: this part must be handled somewhere else\nimport matplotlib\nmatplotlib.use('Agg')\n#\nfrom ductape.common.utils import slice_it, rgb_to_hex, xstr\nfrom ductape.storage.SQLite.database import DBB... | [
[
"matplotlib.pyplot.legend",
"numpy.linspace",
"matplotlib.pyplot.get_cmap",
"numpy.seterr",
"matplotlib.artist.setp",
"numpy.histogram",
"matplotlib.patches.Polygon",
"matplotlib.pyplot.tight_layout",
"numpy.arange",
"matplotlib.cm.ScalarMappable",
"matplotlib.pyplot.fi... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
niisan-tokyo/music_generator | [
"27d97e80a3acc3794fe1b6a5446c619d9675844c"
] | [
"src/autoencode/combine_encoded_test_data.py"
] | [
"# -*- coding: utf-8 -*-\nimport sys\nsys.path.append('/notebooks')\n\nimport glob\nimport numpy as np\nimport os.path\n\ninput_files = glob.glob('/data/input/*encoded.npy')\n\nin_data = []\noutput = []\nfor filename in input_files:\n data = np.load(filename)\n seq = len(data)\n for i in range(seq - 45):\n... | [
[
"numpy.load",
"numpy.array",
"numpy.save",
"numpy.random.shuffle"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
tagr-dev/tagr | [
"0cd995fbfa7de3b7ed0529f236c5251e070f8020"
] | [
"tagr/storage/local.py"
] | [
"import json\nimport pickle\nimport os\nimport logging\nimport pandas as pd\n\nlogger = logging.getLogger(\"saving experiment to local storage\")\n\n\nclass Local:\n name = \"Local\"\n\n def dump_csv(self, df, proj, experiment, tag, filename):\n \"\"\"\n turns dataframe into csv and saves it to ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
vivym/maskrcnn-benchmark | [
"4e1763ae09dab1ceebafad54412df657790ec9dc",
"4e1763ae09dab1ceebafad54412df657790ec9dc",
"4e1763ae09dab1ceebafad54412df657790ec9dc"
] | [
"maskrcnn_benchmark/modeling/rpn/retinanet/loss.py",
"tools/tianchi_xray/vis/run.py",
"tools/tianchi_xray/load_all.py"
] | [
"\"\"\"\nThis file contains specific functions for computing losses on the RetinaNet\nfile\n\"\"\"\n\nimport torch\nfrom torch.nn import functional as F\n\nfrom ..utils import concat_box_prediction_layers\n\nfrom maskrcnn_benchmark.layers import smooth_l1_loss\nfrom maskrcnn_benchmark.layers import SigmoidFocalLoss... | [
[
"torch.nonzero",
"torch.cat"
],
[
"torch.device"
],
[
"numpy.asarray",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
rzw2/mlx75027_config | [
"0501f11762614223715e067dc4d2d64d66adbe99"
] | [
"mlx75027_config/MLX75027Config.py"
] | [
"\"\"\"\nRefael Whyte, r.whyte@chronoptics.com\n\nCalculating the MLX75027 settings from the register values.\n\nCopyright 2020 Refael Whyte - Chronoptics\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal i... | [
[
"numpy.abs",
"numpy.uint32",
"numpy.arange",
"numpy.uint8",
"numpy.ceil",
"numpy.size",
"numpy.where",
"numpy.any",
"numpy.floor",
"numpy.zeros",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ptarau/StanzaGraphs | [
"617818ded2f6e633417ec548eec7d9ae59c2e65b"
] | [
"answerer.py"
] | [
"import csv\nimport math\nfrom collections import defaultdict, Counter\n\nimport numpy as np\nfrom sklearn.preprocessing import OneHotEncoder\n\nfrom params import *\nfrom summarizer import process_file, Summarizer, file2text\nfrom translator import translate\n\n\n# turns .tsv file into list of lists\ndef tsv2mat(f... | [
[
"numpy.logical_or",
"numpy.array",
"sklearn.preprocessing.OneHotEncoder"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ingyunson/automation | [
"047eb286cc740127d4136008fbd398916d200365"
] | [
"data_from_dart.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport json\nimport requests\nimport pandas as pd\nfrom datetime import datetime, timedelta\nfrom dateutil import parser\nfrom pandas.io.json import json_normalize\nimport sqlite3\n\n# 지정한 날짜의 보고서 전체 가져오기\n'''\nget_dart_report_day\n* date(str): 'yyyymmdd' (지정하지 않으... | [
[
"pandas.io.json.json_normalize",
"pandas.to_datetime",
"pandas.read_sql"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"0.19",
"0.24",
"0.20",
"0.25"
],
"scipy": [],
"tensorflow": []
}
] |
Alexander-Meldrum/learning-data-science | [
"a87cf6be80c67a8d1b57a96c042bdf423ba0a142"
] | [
"course_1/pandas_dataframe.py"
] | [
"import pandas as pd\n### Series is the data structure for a single column of a DataFrame, not only conceptually, \n### but literally, i.e. the data in a DataFrame is actually stored in memory as a collection of Series\n\n# data = {\n# 'City': ['Paris', 'Oslo','Athens','London'],\n# 'Population': [2248271,1... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
otmane-el-aloi/geological-similarity | [
"f8cd645b62f09bbb4a08b97a923c5eb4a83df11f"
] | [
"feature_extraction.py"
] | [
"# Standard\nimport os\n\n# Data wrangling\nimport numpy as np\nimport pandas as pd\n\n# Internal\nfrom dataLoader.dataLoader import dataLoader\nfrom models.model import FeatureExtractor\nfrom configs.config import CFG\n\n\n\ndef extract_features_from_image(model, image):\n \"\"\" This function uses the feature ... | [
[
"pandas.concat",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Samuel-sz-guo/Decoders-Chinese-TF2.0 | [
"0068aa8ab8be5f536557d44d226a051b9188e4ec"
] | [
"modeling_gpt2.py"
] | [
"# coding=utf-8\n# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy o... | [
[
"tensorflow.keras.layers.LayerNormalization",
"tensorflow.matmul",
"tensorflow.nn.softmax",
"tensorflow.transpose",
"tensorflow.concat",
"tensorflow.range",
"tensorflow.unstack",
"tensorflow.math.sqrt",
"tensorflow.stack",
"numpy.sqrt",
"tensorflow.cast",
"tensorflo... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
zihhuafang/slivar_vep105 | [
"6932e7f10942436c06aafe83818e597fddaf01d3"
] | [
"paper/plot_ab_roc_genome.py"
] | [
"import sys\nimport re\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\nsns.set_style(\"white\")\nimport pandas as pd\n\nfrom matplotlib.ticker import FormatStrFormatter\n\n\ncolors = sns.color_palette(\"RdYlBu\", 13)\ncolors = sns.cubehelix_palette(7, star... | [
[
"matplotlib.use",
"matplotlib.pyplot.tight_layout",
"pandas.read_csv",
"matplotlib.ticker.FormatStrFormatter"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
htcr/tacotron2 | [
"1d01ae25a272c39570c5676a400f964406911062"
] | [
"utils.py"
] | [
"import numpy as np\nfrom scipy.io.wavfile import read\nimport torch\n\n\ndef get_mask_from_lengths(lengths, r=1):\n max_len = torch.max(lengths).item() \n if max_len % r != 0:\n max_len += (r - max_len % r)\n assert max_len % r == 0\n ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(... | [
[
"torch.max",
"torch.cuda.LongTensor",
"torch.cuda.is_available",
"scipy.io.wavfile.read",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4",
"1.9",
"1.3",
"1.10",
"0.15",
"0.18",
"0.16"... |
Stryder-Git/exchange_calendars | [
"6569b50ee36db52cdf88c2db43caa3238ad8b072"
] | [
"tests/test_exchange_calendar.py"
] | [
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distri... | [
[
"pandas.IntervalIndex.from_arrays",
"pandas.read_csv",
"pandas.testing.assert_series_equal",
"pandas.Series",
"pandas.DateOffset",
"numpy.in1d",
"pandas.DatetimeIndex",
"pandas.Timedelta",
"pandas.Timestamp.now",
"pandas.date_range",
"pandas.testing.assert_index_equal",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gitcombat/ssdp_assignment | [
"759b7aed92629fe128e3272d94b192afa1e7e2eb"
] | [
"m8_hr_plot.py"
] | [
"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n#\r\n\"\"\"\r\nplot module.\r\nplot the results, i.e capacity installed and dispatches.\r\n\r\n@author: jinxi\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n#from m_main import plot_dispatch ,ts\r\n\r\n#width = [\r\n#28,146,65,25,34,128... | [
[
"numpy.arange",
"matplotlib.pyplot.xticks",
"matplotlib.pyplot.show",
"matplotlib.pyplot.bar"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
XIAOYEJIAYOU/GSAN | [
"8ca4fdf4c3d615af9cc10e1f9f22ceb7e27fe196"
] | [
"GSAN_SALSTM/train_3cls.py"
] | [
"import pickle as pkl\nimport sys\nimport os\nsys.path.append(\"..\")\nfrom datatool import train_test_val_split\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader,TensorDataset\nfrom model import BertConfig,Encoder\nfrom lc_model import LinearRegression\nfrom lc_tool ... | [
[
"torch.nn.CrossEntropyLoss",
"torch.max",
"torch.nn.LSTM",
"torch.load",
"torch.utils.data.DataLoader",
"torch.from_numpy",
"torch.nn.Sigmoid",
"numpy.ones",
"torch.nn.Linear",
"torch.cuda.is_available",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
namanwahi/Transfer-Learning | [
"93b9f664fd727a93e0b09b859a20d863602ec743"
] | [
"src/training/transfer_learning_models.py"
] | [
"from torchvision import models\nfrom data.CIFAR10_utils import get_dataloader\nfrom training.training_utils import train_model\nimport torch.nn as nn\n\nimagenet_classes = 1000\n\ndef get_resnet18_model(class_no=imagenet_classes, fixed_feature_extractor=True):\n model = models.resnet18(pretrained=True)\n\n ... | [
[
"torch.nn.Linear"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Felix-yuan2018/CarND-Advanced-Lane-Lines-Project | [
"65fcfa84317fa8e2e6dd48011b27cffa23dfb687"
] | [
"helper/lane_detection.py"
] | [
"import numpy as np \nimport cv2\nimport matplotlib.pyplot as plt \nimport matplotlib.image as mpimg\nimport glob\n\n\ndef find_lane_pixels(binary_warped):\n\t\"\"\"\n\tfind lane in a binary_warped image\n\tinput: binary_warped image\n\toutput: left/right lane pixel poistion and a drawed search image\n\t\"\"\"\n\n\... | [
[
"numpy.polyfit",
"numpy.poly1d",
"matplotlib.pyplot.imshow",
"numpy.linspace",
"numpy.dstack",
"matplotlib.pyplot.plot",
"numpy.int",
"matplotlib.image.imread",
"numpy.argmax",
"numpy.concatenate",
"numpy.mean",
"numpy.array",
"numpy.sum",
"matplotlib.pyplot... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kberryUSGS/SpiceyPy | [
"e97006e9f530c22f16b15186423f10fc7fe5ab9a"
] | [
"spiceypy/spiceypy.py"
] | [
"\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) [2015-2019] [Andrew Annex]\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nt... | [
[
"numpy.where"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aldemirneto/Metodos-Numericos | [
"c9d6b99ae952bd4820c306e7520d04aae42572c8"
] | [
"Codigo/Main.py"
] | [
"import math\nimport time\n\nimport matplotlib.pyplot as plt\n\n\ndef VerificaViabilidade(tamanho, refy, real):\n fx, hx = refy, real\n decreta, i, soma = 0, 0, 0\n for i in range(tamanho):\n soma += ((fx[i] - hx[i]) ** 2)\n decreta = (soma ** (1 / 2))\n return decreta\n\n\ndef funcExponencial... | [
[
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
adivijaykumar/bilby | [
"315ac28d9109494c443c171380a01a9f5719e1d3"
] | [
"bilby/core/utils/io.py"
] | [
"import inspect\nimport json\nimport os\nimport shutil\nfrom importlib import import_module\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nfrom .logger import logger\nfrom .introspection import infer_args_from_method\n\n\ndef check_directory_exists_and_if_not_mkdir(directory):\n \"\"\" C... | [
[
"numpy.asarray",
"numpy.array",
"pandas.Series",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
ckfh/PSMNet-master | [
"c0dbfd6fb2b0421b1522f1de5c2297a467324d85"
] | [
"models/submodule.py"
] | [
"from __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport math\nimport numpy as np\n\ndef convbn(in_planes, out_planes, kernel_size, stride, pad, dilation):\n\n return nn.Sequential(nn.Conv2d(... | [
[
"torch.nn.Sequential",
"torch.cat",
"torch.nn.Conv2d",
"torch.sum",
"torch.nn.Conv3d",
"torch.nn.AvgPool2d",
"torch.nn.BatchNorm2d",
"torch.nn.ReLU",
"torch.nn.BatchNorm3d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sjinu96/deeplab-pytorch | [
"cbd7fa81e8ef347d70d465d164465969a673f5d0"
] | [
"libs/models/deeplabv3plus.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n#\n# Author: Kazuto Nakashima\n# URL: http://kazuto1011.github.io\n# Created: 2018-03-26\n\nfrom __future__ import absolute_import, print_function\n\nfrom collections import OrderedDict\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .... | [
[
"torch.randn",
"torch.nn.Conv2d",
"torch.nn.functional.interpolate",
"torch.cat"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
awickert/landlab | [
"496de56717a5877db96f354a1b1285bfabe8b56f",
"496de56717a5877db96f354a1b1285bfabe8b56f"
] | [
"landlab/plot/drainage_plot.py",
"landlab/components/flow_director/flow_director_d8.py"
] | [
"\"\"\"Plot drainage network.\n\n\"\"\"\n# KRB, FEB 2017.\nimport six\nfrom landlab import CORE_NODE, FIXED_VALUE_BOUNDARY, FIXED_GRADIENT_BOUNDARY, CLOSED_BOUNDARY\nimport matplotlib.pylab as plt\nfrom landlab.plot.imshow import imshow_node_grid\nimport numpy as np\n\ndef drainage_plot(mg, \n surf... | [
[
"matplotlib.pylab.get_cmap",
"matplotlib.pylab.show",
"numpy.ones_like",
"numpy.reshape",
"numpy.arange",
"matplotlib.pylab.title",
"matplotlib.pylab.Normalize",
"matplotlib.pylab.gca",
"numpy.floor",
"matplotlib.pylab.plot",
"matplotlib.pylab.colorbar"
],
[
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
johnsam7/mne-python | [
"b8f5e5ce0da8acfeb7298c8eb1d26a75d5526eac"
] | [
"mne/io/reference.py"
] | [
"# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>\n# Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Teon Brooks <teon.brooks@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom copy import deepcopy\nimport numpy as np\nfrom scipy import linalg\n\nfrom .constants import FIFF\nfrom .meas_info ... | [
[
"scipy.linalg.pinv",
"numpy.intersect1d",
"numpy.max",
"numpy.mean",
"numpy.zeros",
"numpy.vstack"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.12",
"0.14",
"0.15"
],
"tensorflow": []
}
] |
rafaatsouza/ese-parser | [
"80bfbe2e237d328d57e11bd439722f01a6c099e5"
] | [
"frequency-analyzer/source/analyzer.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\n\n\nclass Analyzer:\n def __init__(self, filepath):\n if not filepath:\n raise Exception('Empty filepath')\n\n if not os.path.isfile(filepath):\n raise Exception('Invalid filepath')\n\n if not filepath.endswith('.csv'... | [
[
"numpy.std",
"pandas.read_csv",
"numpy.mean"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
lucilecoutouly/nenupy | [
"8bfcab9558087f0696080d750293d9b8edc30665"
] | [
"nenupy/beam/beam.py"
] | [
"#! /usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\n\"\"\"\n ****\n Beam\n ****\n\"\"\"\n\n\n__author__ = 'Alan Loh'\n__copyright__ = 'Copyright 2020, nenupy'\n__credits__ = ['Alan Loh']\n__maintainer__ = 'Alan'\n__email__ = 'alan.loh@obspm.fr'\n__status__ = 'Production'\n__all__ = [\n 'Beam',\n 'ABea... | [
[
"numpy.dot",
"numpy.cos",
"numpy.sin",
"numpy.isscalar",
"numpy.array",
"numpy.exp",
"numpy.isin"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
cardosofede/hummingbot | [
"d1df085bb879a06a7dc77d4fdc8ff6f13d8726ca",
"d1df085bb879a06a7dc77d4fdc8ff6f13d8726ca"
] | [
"test/hummingbot/client/ui/test_interface_utils.py",
"test/hummingbot/strategy/spot_perpetual_arbitrage/test_spot_perpetual_arbitrage.py"
] | [
"import unittest\nfrom copy import deepcopy\nfrom decimal import Decimal\nimport asyncio\nfrom typing import Awaitable\nfrom unittest.mock import patch, MagicMock, AsyncMock, PropertyMock\n\nimport pandas as pd\n\nfrom hummingbot.client.config.global_config_map import global_config_map\nfrom hummingbot.client.ui.in... | [
[
"pandas.DataFrame"
],
[
"pandas.Timestamp"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"nump... |
ConnorJL/GPTNeo | [
"71edf21b22f08fb4ab96e3a8b7448dc0d0e5b02d"
] | [
"main.py"
] | [
"\"\"\"GPT-like model in Mesh-Tensorflow\"\"\"\n\nfrom functools import partial\nimport mesh_tensorflow as mtf\nimport tensorflow.compat.v1 as tf\nfrom tensorflow.python.tpu import tpu_config, tpu_estimator\nfrom tensorflow_estimator.python.estimator import estimator as estimator_lib\nfrom utils import save_config,... | [
[
"tensorflow.python.tpu.tpu_config.TPUConfig",
"tensorflow.python.tpu.tpu_estimator.TPUEstimator",
"tensorflow.compat.v1.distribute.cluster_resolver.TPUClusterResolver",
"tensorflow.compat.v1.disable_v2_behavior"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kuc2477/pytorch-memn2n | [
"c27d1cbb008307775eb591366db9a4229cdbba29"
] | [
"encs.py"
] | [
"from torch.autograd import Variable\nfrom torch import Tensor, LongTensor\nfrom torch.cuda import (\n FloatTensor as CudaTensor,\n LongTensor as CudaLongTensor,\n)\n\n\ndef position_encoding(embedding_size, sentence_size, cuda=False):\n dt = CudaTensor if cuda else Tensor\n encoding = Tensor(embedding_... | [
[
"torch.Tensor",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Peshal1067/climate-data-science | [
"2664d8f0fb8cad2ef353f4ec26d0a0280ac2d181"
] | [
"Python-Scripts/waveletFunctions.py"
] | [
"#!/usr/bin/env python\n\n\n\nimport numpy \t\t\t\tas np\nfrom scipy.special._ufuncs import gammainc, gamma\nfrom scipy.optimize \t\timport fminbound\n__author__ = 'Evgeniya Predybaylo'\n\n'''\nMinor modifications for Python 3.6 compatibility made by:\nWilly Hagi (UEA/EST)\nhagi.willy@gmail.com\n'''\n\n# Copyright... | [
[
"numpy.sqrt",
"numpy.concatenate",
"numpy.mean",
"numpy.fix",
"numpy.exp",
"numpy.arange",
"numpy.atleast_1d",
"numpy.copy",
"numpy.std",
"numpy.zeros",
"numpy.log",
"numpy.min",
"scipy.special._ufuncs.gamma",
"numpy.fft.ifft",
"scipy.optimize.fminbound"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
pkulzb/Research | [
"88da4910a356f1e95e1e1e05316500055533683d"
] | [
"CV/PWCNet/data/datasets.py"
] | [
"# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless re... | [
[
"scipy.misc.imsave",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"1.0",
"0.19",
"0.18",
"1.2",
"0.12",
"0.10",
"0.17",
"0.16"
],
"tensorflow": []
}
] |
Cognitive-Systems-Laboratory/Reinforcement-Learning-Tutorial | [
"78ead51dd91f04a9df9199c4396910674fc59f34"
] | [
"main_dqn_history.py"
] | [
"\"\"\"\n__author__ = \"Minsuk Sung and Hyunseung Lee\"\n\n\"\"\"\nimport os\nimport sys\nimport argparse\nimport json\nimport gym\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom utils.replay_memory import ReplayBuffer\nfrom utils.save_tensorboard import *\nf... | [
[
"numpy.dot",
"torch.from_numpy",
"torch.tensor",
"numpy.concatenate",
"torch.nn.functional.smooth_l1_loss",
"torch.device",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
frostburn/rl_paneldepon | [
"b2d74712f5bdf3b1c4ca4334a126aff5af8c17cf"
] | [
"train_simple.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport sys\n\nimport gym\nimport numpy as np\nimport tensorflow as tf\nfrom gym_paneldepon.env import register\nfrom gym_paneldepon.util import print_up\n\nfrom util import ... | [
[
"tensorflow.concat",
"numpy.max",
"tensorflow.train.AdamOptimizer",
"tensorflow.get_default_graph",
"tensorflow.name_scope",
"tensorflow.Session",
"tensorflow.square",
"tensorflow.argmax",
"tensorflow.app.run",
"tensorflow.matmul",
"tensorflow.placeholder",
"tensorf... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
RaoUmer/SRResCycGAN | [
"b0999180a1906f519915ba2034fe492aef162109"
] | [
"srrescycgan_code_demo/models/ResDNet.py"
] | [
"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom torch.nn.utils import weight_norm\nfrom modules import l2proj\n\ndef conv3x3(in_planes, out_planes, stride=1):\n \"3x3 convolution with padding\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stri... | [
[
"torch.nn.Sequential",
"torch.mean",
"numpy.sqrt",
"torch.nn.ConvTranspose2d",
"torch.Tensor",
"torch.nn.PReLU",
"torch.nn.utils.weight_norm",
"torch.nn.Conv2d",
"numpy.random.standard_normal",
"torch.nn.functional.pad"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
troydg-droid/LinkedIn-Easy-Apply-Bot | [
"53eb88fa02920c13bea1bd358bc12b770694ad4d"
] | [
"easyapplybot.py"
] | [
"import time, random, os, csv, platform\nimport logging\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.common.exceptions import NoSuchElementException\nfro... | [
[
"pandas.read_csv",
"pandas.to_datetime"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
fandesfyf/JamTools | [
"2e0687fb886ce5bf3e2e66901915de6250d48f19"
] | [
"testfiles/滚动截屏demo.py"
] | [
"import math\nimport operator\nimport os\nimport sys\nimport time\nfrom functools import reduce\nimport time\n\nimport cv2\nimport os\nimport numpy as np\nfrom numpy import array, uint8\n\nimport crc16\n\nfrom PIL import Image\nfrom PyQt5.QtCore import QThread, QTimer\nfrom PyQt5.QtWidgets import QApplication\nfrom... | [
[
"numpy.array",
"numpy.mean",
"numpy.float32"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
netneurolab/markello_ppmisnf | [
"6eefa6138c56c49fbf4845d9237e90a10e655ff8"
] | [
"scripts/03_results/05_supplementary_results.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"\nThis script generates results and figures used to support supplementary\nanalyses demonstrating the robustness of the primary results\n\"\"\"\n\nimport itertools\nimport os.path as op\nimport warnings\n\nfrom matplotlib.colors import ListedColormap\nimport matplotlib.pyplot as plt\... | [
[
"numpy.max",
"numpy.mean",
"numpy.where",
"numpy.unique",
"numpy.arange",
"numpy.std",
"numpy.min",
"pandas.Categorical",
"numpy.timedelta64",
"numpy.log10",
"matplotlib.colors.ListedColormap",
"scipy.linalg.orthogonal_procrustes",
"numpy.array",
"numpy.mesh... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [
"0.15"
],
"tensorflow": []
}
] |
Fedotovaa/refactoring | [
"949372140e9f70b9e9bd0f4eb7e0f0aa416480f9"
] | [
"filter.py"
] | [
"from PIL import Image\r\nimport numpy as np\r\n\r\n\r\ndef replace_with_gray(width, height, arr, gradation):\r\n for x in range(0, len(arr), height):\r\n for y in range(0, len(arr[1]), width):\r\n\r\n grad = np.sum(arr[x: x + height, y: y + width]) // (height * width * 3)\r\n\r\n co... | [
[
"numpy.array",
"numpy.sum",
"numpy.full"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Danielli-Itai/TfFizzBuzz | [
"d14b8b56956c3e7e13ff219cf580849adf6788bd"
] | [
"wrangle/01_logistic_regression.py"
] | [
"# logistic regression model\n# https://en.wikipedia.org/wiki/Logistic_regression\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom command_line_args import arg_parser\nfrom data import data_from_args\nfrom model_helpers import build_model, init_weights, set_seeds\n\nset_seeds(123)\n\nargs = arg_parser.parse_a... | [
[
"tensorflow.nn.softmax_cross_entropy_with_logits",
"tensorflow.matmul",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.Session",
"tensorflow.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
vishwaphansal7/Alexis | [
"778291d71c7260409f44bc8aa720d807d49d8ff1"
] | [
"utils/run_generation.py"
] | [
"#!/usr/bin/env python3\n# coding=utf-8\n# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this fil... | [
[
"torch.nn.functional.softmax",
"numpy.random.seed",
"torch.zeros",
"torch.cat",
"torch.manual_seed",
"torch.full",
"torch.Tensor",
"torch.tensor",
"torch.no_grad",
"torch.sort",
"torch.cuda.manual_seed_all",
"torch.cuda.is_available",
"torch.topk",
"torch.cu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nebw/mabe | [
"2f8d4c4ad82202f614147791a9d1b5ab799fd86f"
] | [
"hydrogen/create_splits.py"
] | [
"import pickle\n\nimport h5py\nimport numpy as np\nimport sklearn\nimport sklearn.decomposition\nimport sklearn.linear_model\nimport sklearn.preprocessing\nfrom fastprogress.fastprogress import force_console_behavior\n\nimport mabe\nimport mabe.config\nimport mabe.loss\nimport mabe.model\nimport mabe.ringbuffer\n\n... | [
[
"numpy.unique",
"numpy.concatenate",
"sklearn.preprocessing.StandardScaler",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
iGame-Lab/TS-VIS | [
"b0cd8d13ac1ebc5d857597b2a373b8e51e606358"
] | [
"tsvis/logger/pytorch_graph.py"
] | [
"# -*- coding: UTF-8 -*-\r\n\"\"\"\r\n Copyright 2021 Tianshu AI Platform. All Rights Reserved.\r\n\r\n Licensed under the Apache License, Version 2.0 (the \"License\");\r\n you may not use this file except in compliance with the License.\r\n You may obtain a copy of the License at\r\n\r\n http://www.apache.org... | [
[
"torch.jit.trace",
"torch._C._jit_pass_inline",
"torch.onnx.select_model_mode_for_export",
"numpy.prod",
"torch.autograd.profiler.profile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CrisDS81/copulae | [
"2a312c2b849f95cfb2b40b381d34bc790d9d80c5",
"2a312c2b849f95cfb2b40b381d34bc790d9d80c5"
] | [
"tests/mixtures/gmc/test_parameters.py",
"copulae/archimedean/auxiliary.py"
] | [
"import pytest\nfrom numpy.testing import assert_allclose\n\nfrom copulae.core.exceptions import NonSymmetricMatrixError\nfrom copulae.mixtures.gmc.exception import GMCParamError\nfrom copulae.mixtures.gmc.parameter import GMCParam\n\nparam2 = GMCParam(\n n_clusters=3,\n n_dim=2,\n prob=[0.48923563, 0.0535... | [
[
"numpy.testing.assert_allclose"
],
[
"numpy.log",
"numpy.asarray",
"numpy.arange",
"numpy.all",
"numpy.max",
"numpy.diff",
"numpy.repeat",
"numpy.exp",
"numpy.zeros"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
vishal-burman/sentence-transformers | [
"a00baa6888c04e0fb5773a132b9878f9f37d6db1"
] | [
"sentence_transformers/evaluation/CosineSimilarityEvaluator.py"
] | [
"from sentence_transformers.evaluation import SentenceEvaluator\nfrom sklearn.metrics.pairwise import paired_cosine_distances, paired_manhattan_distances, paired_euclidean_distances\nimport numpy as np\nimport logging\nimport os\nimport csv\nfrom typing import List\n\nlogger = logging.getLogger(__name__)\n\nclass C... | [
[
"sklearn.metrics.pairwise.paired_euclidean_distances",
"sklearn.metrics.pairwise.paired_manhattan_distances",
"sklearn.metrics.pairwise.paired_cosine_distances"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
usccolumbia/tsdnn | [
"78a74634446819787b6c91e7ac22ad08d61c20f7"
] | [
"pu_cgcnn/main.py"
] | [
"import argparse\nimport pandas as pd\nimport numpy as np\nimport cgcnn.train as t\nimport cgcnn.predict as p\nimport sys\nimport csv\nimport torch\nfrom os import path\n\nparser = argparse.ArgumentParser(description='Crystal Graph Convolutional Neural Networks')\nparser.add_argument('data_options', metavar='OPTION... | [
[
"torch.multiprocessing.set_sharing_strategy",
"torch.cuda.set_device",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
raylin01/gpt2bot | [
"05ebae32e435242b520efe342b91ce52a1e3d3ad"
] | [
"gpt2bot/model.py"
] | [
"import os\nimport requests\nfrom tqdm import tqdm\nfrom glob import glob\nimport torch\nimport configparser\nimport argparse\nimport logging\n\n# !pip install transformers==2.3.0\nfrom transformers import GPT2Config, GPT2LMHeadModel, GPT2Tokenizer\n# If you get tensorflow deprecation warnings, run\n# pip uninstall... | [
[
"torch.cuda.is_available",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
manueltonneau/bert | [
"75d1246f497d1075ba0adefbc957cfd7d3dc6667"
] | [
"run_classifier.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unl... | [
[
"tensorflow.contrib.cluster_resolver.TPUClusterResolver",
"tensorflow.metrics.accuracy",
"tensorflow.FixedLenFeature",
"tensorflow.nn.log_softmax",
"tensorflow.reduce_sum",
"tensorflow.gfile.GFile",
"tensorflow.cast",
"tensorflow.train.init_from_checkpoint",
"tensorflow.gfile.M... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"1.12",
"1.4",
"1.13",
"1.5",
"1.7",
"0.12",
"1.0",
"1.2"
]
}
] |
haxuyennt38/python-learning | [
"c5fcfc0abfd4aebf4ce58e2f23d378703bd48430"
] | [
"analysededonneesavecnumpy/calculerlaconsommationchaquepays.py"
] | [
"totals = {}\nimport numpy as np\nworld_alcohol = np.genfromtxt('world_alcohol.csv', delimiter = ',', dtype = 'U75', skip_header = 1)\nis_year = (world_alcohol[:, 0] == '1989')\nyear = world_alcohol[is_year, :]\nprint(year)\ncountries = world_alcohol[:, 2]\nprint(countries)\nfor country in countries :\n is_coun... | [
[
"numpy.genfromtxt"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
drunkcoding/efficient-nlp | [
"9509e0ef08016506280a7cfc600ea8e3778dea2d"
] | [
"tests/performance/cv_performance.py"
] | [
"from tqdm import tqdm\nfrom torchvision import datasets, transforms\nimport torchvision.models as models\nfrom torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset\nfrom torch.profiler import profile, record_function, ProfilerActivity, schedule\nimport torch\nimport torch.cuda as cut... | [
[
"torch.cuda.synchronize",
"torch.utils.data.SequentialSampler",
"torch.cuda.Event",
"torch.utils.data.DataLoader",
"torch.no_grad",
"numpy.mean",
"torch.cuda.is_available",
"pandas.Dataframe",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ahsanMuh/DialogXL | [
"3d87a52557768e2f4bb31b62d159e50cb5f1e69f"
] | [
"DialogXL/trainer.py"
] | [
"import numpy as np, argparse, time, pickle, random\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import SubsetRandomSampler\nfrom sklearn.metrics import f1_score, confusion_matrix, accuracy_score, classification_report, \... | [
[
"sklearn.metrics.accuracy_score",
"sklearn.metrics.f1_score",
"numpy.array",
"numpy.sum",
"torch.argmax"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
CiaburroGiuseppe/Hands-On-Simulation-Modeling-with-Python | [
"c1cbf02840bb9e634e8f93e653cd8fdabcbcff01"
] | [
"Chapter03/normal_distribution.py"
] | [
"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\nmu = 10\r\nsigma =2\r\n\r\nP1 = np.random.normal(mu, sigma, 1000)\r\n\r\nmu = 5\r\nsigma =2\r\n\r\nP2 = np.random.normal(mu, sigma, 1000)\r\n\r\nmu = 15\r\nsigma =2\r\n\r\nP3 = np.random.normal(mu, sigma, 1000)\r\n\r\nPlot1 = sns... | [
[
"numpy.random.normal",
"matplotlib.pyplot.show",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
neildias/guitar_practice_generator_fun | [
"833d6909a4e1f7bd54714052cc6bae85c63dae91"
] | [
"old_routine_generator/live_practice_tracker.py"
] | [
"# A useful script when the practice session is needed real time and\n# uses preset lessons\n\nimport pandas as pd\nfrom datetime import date\nimport time\nfrom utils import practice_headers, utilities, countdown_timer\n\n\n# hardcoded path\npath = practice_headers.path\nfilename = practice_headers.filename\ntopics... | [
[
"pandas.Series"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
joelmoff/eye-tales | [
"d0fc1797585d18a4cd32722afaa101fc6ba179f4"
] | [
"src/neural_image_caption_generation/inference_image_captioning.py"
] | [
"from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport io\nimport json\nimport os\n\nimport requests\nimport tensorflow as tf\nfrom PIL import Image\nfrom sklearn.utils import shuffle\n\n\nembedding_dim = 256\nunits = 512\ntop_k = 5000\nvocab_size = top_k + 1\n\n# Shape of the... | [
[
"tensorflow.keras.preprocessing.text.Tokenizer",
"tensorflow.nn.relu",
"tensorflow.keras.layers.Embedding",
"tensorflow.zeros",
"sklearn.utils.shuffle",
"tensorflow.keras.layers.Dense",
"tensorflow.reshape",
"tensorflow.keras.applications.InceptionV3",
"tensorflow.expand_dims",... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
Dreem-Organization/dreem-learning-open | [
"5f03f93e814fa5bf74b45e9c6c99be38ce29c347"
] | [
"dreem_learning_open/test/utils.py"
] | [
"import json\nimport os\nimport shutil\n\nimport h5py\nimport numpy as np\n\nfrom ..preprocessings.h5_to_memmap import h5_to_memmaps\nfrom ..utils.utils import standardize_signals_durations\n\n\ndef generate_fake_hypno(transition_kernel, hypnogram_length, s_0=0):\n generated_hypno = []\n s_0 = np.array(s_0)\n... | [
[
"numpy.arange",
"numpy.cos",
"numpy.dtype",
"numpy.concatenate",
"numpy.random.normal",
"numpy.identity",
"numpy.array",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
hpaulkeeler/DetPoisson_Python | [
"34f77a87af75a0bff2cd4f1627d31fded4f63b64"
] | [
"DemoDetPoisson.py"
] | [
"# Randomly simulates a determinantally-thinned Poisson point process. \n#\n# A determinantally-thinned Poisson point process is essentially a discrete\n# determinantal point process whose underlying state space is a single \n# realization of a Poisson point process defined on some bounded continuous \n# space. \n#... | [
[
"numpy.outer",
"numpy.abs",
"matplotlib.pyplot.scatter",
"numpy.linalg.eig",
"numpy.sort",
"numpy.ones",
"numpy.random.poisson",
"numpy.delete",
"scipy.linalg.orth",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.close",
"numpy.random.rand",
"numpy.random.unifor... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [
"0.13",
"0.14",
"0.15",
"0.12",
"0.10"
],
"tensorflow": []
}
] |
OtUmm7ojOrv/nngeometry | [
"ce184345258a7eb79ce78c14becce047a1785a48"
] | [
"examples/resnet_analysis/train.py"
] | [
"import torchvision.datasets as datasets\nimport torchvision.transforms as transforms\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\nimport torch.optim as optim\nimport torch.nn as nn\nimport pandas as pd\nimport time\n\nstart_time = time.time()\n\n# dataset\nmeans = (0.4802, 0.4481, 0.3975)... | [
[
"torch.nn.CrossEntropyLoss",
"pandas.Series",
"torch.utils.data.DataLoader",
"pandas.DataFrame",
"torch.no_grad",
"torch.save"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"1.3",
"0.19",
"1.1",
"1.5",
"0.24",
"0.20",
"1.0",
"0.25",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
hasnainroopawalla/Deep-Q-Learning | [
"6430e8041b18d23a3bd7651aac28111bcc17afe3"
] | [
"dqn/agents/cartpole/agent.py"
] | [
"import gym\nimport torch\n\nimport torch.nn.functional as F\n\nfrom dqn.agents.cartpole.model import DQN\nfrom dqn.replay_memory import ReplayMemory, Sample\nfrom dqn.agents.cartpole.config import CartPoleConfig\nfrom dqn.agents.base_agent import BaseAgent\nfrom dqn.agents.cartpole.utils import preprocess_observat... | [
[
"torch.nn.functional.mse_loss",
"torch.save",
"torch.cuda.is_available",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
aakash94/berkeleydeeprlcourse-homework-pytorch | [
"4ce260e00513466a2d1f1fd409b68627c6be39e9"
] | [
"hw1/VisdomPlotter.py"
] | [
"from visdom import Visdom\nimport numpy as np\n\nclass VisdomPlotter(object):\n \"\"\"Plots to Visdom\"\"\"\n def __init__(self, env_name='main'):\n self.viz = Visdom()\n self.env = env_name\n self.plots = {}\n\n\n def plot_line(self, var_name, split_name, title_name, x, y):\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
meteor27/alpha_mod | [
"4f7f0edf8338451a69f177058ec80766d846769e"
] | [
"rqalpha/model/instrument.py"
] | [
"# -*- coding: utf-8 -*-\n# 版权所有 2019 深圳米筐科技有限公司（下称“米筐科技”）\n#\n# 除非遵守当前许可，否则不得使用本软件。\n#\n# * 非商业用途（非商业用途指个人出于非商业目的使用本软件，或者高校、研究所等非营利机构出于教育、科研等目的使用本软件）：\n# 遵守 Apache License 2.0（下称“Apache 2.0 许可”），\n# 您可以在以下位置获得 Apache 2.0 许可的副本：http://www.apache.org/licenses/LICENSE-2.0。\n# 除非法律有要求或以书面形式... | [
[
"numpy.isnan"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
PatrickHwang/InsightFace_Pytorch | [
"de40131c0f71f0bd11c8abf22418e1293c7f4966"
] | [
"Learner.py"
] | [
"from data.data_pipe import de_preprocess, get_train_loader, get_val_data\nfrom model import Backbone, Arcface, MobileFaceNet, Am_softmax, l2_norm\nfrom verifacation import evaluate\nimport torch\nfrom torch import optim\nimport numpy as np\nfrom tqdm import tqdm\nfrom tensorboardX import SummaryWriter\nfrom matplo... | [
[
"torch.cat",
"matplotlib.pyplot.switch_backend",
"torch.min",
"torch.tensor",
"matplotlib.pyplot.plot",
"torch.no_grad",
"torch.optim.SGD",
"torch.pow"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zkt12/OpenMatch | [
"7c04f0eb7285946524a1235a10b1339753f4ab6d"
] | [
"OpenMatch/data/datasets/bert_dataset.py"
] | [
"from typing import List, Tuple, Dict, Any\n\nimport json\n\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom transformers import AutoTokenizer\n\nclass BertDataset(Dataset):\n def __init__(\n self,\n dataset: str,\n tokenizer: AutoTokenizer,\n mode: str,\n query_max_... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sandutsar/napari | [
"044beba342ef392f4cbed2e8e3a27f27d4799ccb",
"37d476bc0b00252177f17f25e7d1fd52ddc4bb69",
"044beba342ef392f4cbed2e8e3a27f27d4799ccb"
] | [
"napari/layers/shapes/_shapes_utils.py",
"napari/layers/tracks/tracks.py",
"napari/layers/_tests/test_layer_actions.py"
] | [
"from typing import Tuple\n\nimport numpy as np\nfrom vispy.geometry import PolygonData\nfrom vispy.visuals.tube import _frenet_frames\n\nfrom ...utils.translations import trans\nfrom ..utils.layer_utils import segment_normal\n\n\ndef inside_boxes(boxes):\n \"\"\"Checks which boxes contain the origin. Boxes need... | [
[
"numpy.dot",
"numpy.linspace",
"numpy.all",
"numpy.concatenate",
"numpy.argmin",
"numpy.any",
"numpy.cross",
"numpy.arange",
"numpy.eye",
"numpy.matmul",
"numpy.subtract",
"numpy.sin",
"numpy.zeros",
"numpy.multiply",
"numpy.logical_or",
"numpy.deg2r... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
},
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sayonpalit2599/p3-collaboration-competition | [
"d21f94605ee8ac6a5a8d0967b2be15c51d9a772e"
] | [
"helper.py"
] | [
"import numpy as np\nimport torch\n\n# Helper functions to concatenate/extract multipe agents states/actions for use with the Replay Buffer memory.\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef encode(sa):\n \"\"\"\n Encode an Environment state or action list of array... | [
[
"numpy.array",
"torch.cuda.is_available"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kennethkichan/football_bets | [
"e294cd47cfe8e796f6e496fda3b8e8905abe4c70"
] | [
"src/data/get_fixtures.py"
] | [
"import http.client\nimport sys\nfrom dotenv import load_dotenv\nimport os\nimport json\nimport pandas as pd\nfrom datetime import datetime\n\nload_dotenv()\n\nsource_path = '../data/raw/fixtures.csv'\n\nconn = http.client.HTTPSConnection(\"v3.football.api-sports.io\")\n\nheaders = {\n 'x-rapidapi-host': \"v3.fo... | [
[
"pandas.concat",
"pandas.read_csv",
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
bu4er88/IDAO_2021 | [
"687f7caa666ab8f9189e1728f3bc74f2985bfdb8"
] | [
"tensorflow/inference.py"
] | [
"from parameters import *\r\nfrom model import *\r\nfrom data import get_images\r\nimport numpy as np\r\n\r\n\r\ndef predictions(model, x_test):\r\n # prediction of classes' probabilities and energies\r\n class_probabilities, energies = model.predict(np.array(x_test))\r\n # reshape energies\r\n energies... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
soorya19/sparsity-based-defenses | [
"b29926a6cc8dc5bcad6ecdcc4b69d6bf25abacf9"
] | [
"neural_nets/test_defense_multiclass.py"
] | [
"\"\"\"\nTests the efficacy of sparsity-based defense on a CNN for multiclass MNIST classification.\n\nDefense: Front end with sparsity level = 3.5%\nClassifier: 4 layer CNN, MNIST\n\"\"\"\nimport numpy as np\nfrom collections import OrderedDict as odict \nfrom tqdm import trange\n\nimport tensorflow as tf\nfrom te... | [
[
"tensorflow.contrib.slim.get_model_variables",
"tensorflow.cast",
"tensorflow.placeholder",
"tensorflow.global_variables_initializer",
"tensorflow.reset_default_graph",
"tensorflow.Session",
"tensorflow.train.Saver",
"tensorflow.argmax",
"tensorflow.examples.tutorials.mnist.inp... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10"
]
}
] |
RikvdPol/IntergratedOmics | [
"6981e8cf624aeea32a021ea865b867036dda5e5b"
] | [
"Code/Abstractalgorithm.py"
] | [
"from sklearn.model_selection import train_test_split, cross_val_score, RepeatedKFold\nfrom sklearn.metrics import mean_squared_error,r2_score, make_scorer\nimport numpy as np\nimport sys\nimport os\nimport Logging\n\nclass Abstractalgorithm:\n def __init__(self, file, labelname):\n self.file = file\n ... | [
[
"numpy.absolute",
"sklearn.metrics.make_scorer",
"sklearn.model_selection.train_test_split"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
zooba/projectoxford | [
"a98fe7d883ff5c88337a7d165b18e5d21222ec7c"
] | [
"projectoxford/emotion.py"
] | [
"#-------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation\n# All rights reserved.\n#\n# Distributed under the terms of the MIT License\n#-------------------------------------------------------------------------\n'''Project Oxford Emotion Module\n\nThis module... | [
[
"numpy.asarray",
"matplotlib.pyplot.show",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AkshatSh/VideoSearchEngine | [
"57f64b241b8a7bbc377ce7826e1206f679f41def"
] | [
"VideoSearchEngine/ImageCaptioningAnnotations/LanguageModels.py"
] | [
"'''\nUsing Obj2Text model as described here:\n\nhttps://github.com/xuwangyin/pytorch-tutorial/tree/master/tutorials/03-advanced/image_captioning\n\n'''\n\nimport torch\nimport torch.nn as nn\nimport torchvision.models as models\nfrom torch.nn.utils.rnn import pack_padded_sequence as pack\nfrom torch.nn.utils.rnn i... | [
[
"torch.nn.Sequential",
"torch.nn.BatchNorm1d",
"torch.LongTensor",
"torch.nn.LSTM",
"torch.cat",
"torch.nn.Embedding",
"torch.nn.utils.rnn.pack_padded_sequence",
"torch.nn.Linear",
"torch.nn.utils.rnn.pad_packed_sequence",
"torch.cuda.is_available",
"torch.autograd.Vari... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
nii-yamagishilab/ssnt-tts | [
"59f455afd59c6240276c3089a77f7ff4978ff77f"
] | [
"ssnt_tts/utils/tfrecord.py"
] | [
"import tensorflow as tf\nimport numpy as np\nfrom collections import namedtuple\nfrom collections.abc import Iterable\n\n\nclass PreprocessedTargetData(namedtuple(\"PreprocessedTargetData\",\n [\"id\", \"spec\", \"spec_width\", \"mel\", \"mel_width\", \"target_length\"])):\n ... | [
[
"tensorflow.FixedLenFeature",
"tensorflow.train.Example",
"tensorflow.stack",
"tensorflow.decode_raw",
"tensorflow.python_io.TFRecordWriter",
"numpy.frombuffer",
"tensorflow.train.BytesList",
"tensorflow.python_io.tf_record_iterator",
"tensorflow.parse_single_example",
"ten... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
mvp18/UCDR | [
"f1e8d992811a0209cab9adbb8c33fe0f4615f66c"
] | [
"src/data/DomainNet/domainnet.py"
] | [
"import os\nimport numpy as np\nimport glob\n\n\ndef create_trvalte_splits(args):\n\n tr_classes = np.load(os.path.join(_BASE_PATH, 'DomainNet', 'train_classes.npy')).tolist()\n va_classes = np.load(os.path.join(_BASE_PATH, 'DomainNet', 'val_classes.npy')).tolist()\n te_classes = np.load(os.path.join(_BASE... | [
[
"numpy.setdiff1d",
"numpy.array",
"numpy.where",
"numpy.random.seed"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Alexandros23Kazantzidis/propagationModel | [
"defabd65e48386344ace3cd9fe2e6ce5a23c0ba9"
] | [
"anom_conv.py"
] | [
"\"\"\"Vectorized anomaly conversion scripts\"\"\"\r\n\r\nimport numpy as np\r\n\r\n\r\ndef true_to_ecc(theta,e):\r\n \"\"\"Converts true anomaly to eccentric anomaly.\r\n Args:\r\n theta(numpy array): array of true anomalies (in radians)\r\n e(float): eccentricity\r\n Returns:\r\... | [
[
"numpy.cos",
"numpy.sin",
"numpy.linspace",
"numpy.clip"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
soskek/captioning_chainer | [
"1c3c950d393598a8aa0bc3c6f65a391c548142e7"
] | [
"utils.py"
] | [
"import sys\nsys.path.append('./coco-caption/')\nsys.path.append('./coco-caption/pycocoevalcap/')\n\nfrom bleu.bleu import Bleu\nfrom cider.cider import Cider\nfrom meteor.meteor import Meteor\nfrom rouge.rouge import Rouge\n\nimport collections\nimport io\nimport os\n\nimport numpy as np\n\nimport chainer\nfrom ch... | [
[
"numpy.asarray",
"numpy.stack",
"numpy.copyto",
"numpy.load",
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
DavideConficconi/movado | [
"b2665450ab4c524221550e1b6c4d8954c630983b"
] | [
"src/visualization/visualize_controller.py"
] | [
"import plotly.express as px\nimport plotly.graph_objs as go\nimport pandas as pd\n\n\ndef visualize_mae(path: str):\n df = pd.read_csv(path)\n df = df.loc[df[\"Estimation\"] == 1]\n fig = px.line(x=range(len(df[\"MAE\"])), y=df[\"MAE\"])\n fig.update_layout(\n title=\"Chained Estimator MAE\",\n ... | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
JohnBurden/SafelifeExperiments | [
"08e52de246097971da784384cfff30c216d225a2"
] | [
"training/models.py"
] | [
"import numpy as np\n\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\ndef safelife_cnn(input_shape):\n \"\"\"\n Defines a CNN with good default values for safelife.\n\n This works best for inputs of size 25x25.\n\n Parameters\n ----------\n input_shape : tuple of ints\n Hei... | [
[
"numpy.product",
"torch.nn.Linear",
"torch.nn.ReLU",
"torch.nn.Conv2d"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
macio-matheus/tensorflow-specialization | [
"3f21d410299436f1b0922b3bf7c54c29a858f8c1"
] | [
"introduction-to-tensorflow/examples/course_1_part_4_lesson_4_notebook.py"
] | [
"# -*- coding: utf-8 -*-\n\"\"\"Course 1 - Part 4 - Lesson 4 - Notebook.ipynb\n\nAutomatically generated by Colaboratory.\n\nOriginal file is located at\n https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/Course%201%20-%20Part%204%20-%20Lesson%204%20-%20Notebook.ipynb\n\"\"\"\n\nimport ten... | [
[
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Flatten"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": [
"1.10",
"2.7",
"2.6",
"2.4",
"2.3",
"2.5",
"2.2"
]
}
] |
Loop3D/uncertaintyIndicators | [
"bf7cadb0d947a10cf1fe9d82ac506b2760ebfbcf"
] | [
"loopUI.py"
] | [
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jun 1 15:13:56 2021\r\n\r\n@author: Guillaume Pirot\r\n\"\"\"\r\n\r\n# import modules\r\nfrom matplotlib import pyplot as plt\r\nimport numpy as np\r\nfrom scipy.ndimage import label\r\nfrom numpy.random import default_rng\r\nfrom mpl_toolkits.axes_grid1.inset_l... | [
[
"numpy.nanmax",
"numpy.amax",
"matplotlib.colors.BoundaryNorm",
"numpy.product",
"numpy.minimum",
"numpy.linspace",
"sklearn.cluster.KMeans",
"numpy.asarray",
"numpy.vstack",
"numpy.nanmin",
"pandas.DataFrame",
"numpy.round",
"numpy.max",
"numpy.mean",
"... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [
"1.7",
"1.0",
"0.10",
"1.2",
"0.14",
"0.19",
"1.5",
"0.12",
"0.17",
"0.13",
"1.6",
"1.4"... |
polar-region/MindSpore | [
"b96bf8e175faabe2521882c0b7f6e89928e267c7"
] | [
"research/gnn/sgcn/postprocess.py"
] | [
"# Copyright 2021 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable l... | [
[
"numpy.fromfile",
"numpy.concatenate",
"numpy.max",
"numpy.array",
"numpy.exp",
"numpy.sum"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
laceyg/milabench | [
"a314094a406c2e98a932f6d4f3a9588a991148d3"
] | [
"milarun/datasets/wiki2.py"
] | [
"import os\nimport subprocess\nimport torch\n\n\nclass Dictionary(object):\n def __init__(self):\n self.word2idx = {}\n self.idx2word = []\n\n def add_word(self, word):\n if word not in self.word2idx:\n self.idx2word.append(word)\n self.word2idx[word] = len(self.idx2... | [
[
"torch.LongTensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
AbhimanyuVashisht/theme-analyzer | [
"e49a936106f0ec25c411f3e39f1034911876e16c"
] | [
"main.py"
] | [
"import os\n# import copy\nimport pickle\nfrom datetime import datetime\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nimport utils.DataProcessing as DP\nimport utils.LSTMClassifier as LSTMC\n\nDATA_DIR = 'data'\nTRAIN... | [
[
"torch.nn.CrossEntropyLoss",
"torch.max",
"torch.utils.data.DataLoader",
"torch.save",
"torch.squeeze",
"torch.autograd.Variable"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
sdpython/pymyinstall | [
"72b3a56a29def0694e34ccae910bf288a95cf4a5"
] | [
"_unittests/ut_packaged/test_name_set.py"
] | [
"\"\"\"\n@brief test log(time=2s)\n\"\"\"\nimport unittest\nimport pandas\nfrom pyquickhelper.loghelper import fLOG\nfrom pyquickhelper.pandashelper import df2rst\nfrom pymyinstall.packaged import get_package_set, name_sets_dataframe\n\n\nclass TestNameSet(unittest.TestCase):\n\n def test_documentation(self... | [
[
"pandas.DataFrame"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"0.23",
"0.21",
"2.0",
"1.4",
"0.19",
"1.1",
"1.5",
"1.2",
"0.24",
"0.20",
"1.0",
"0.25",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
michaelhabeck/isdhic | [
"35ccec0621c815c77e683bcce7d26e1e6c82b53b"
] | [
"setup.py"
] | [
"import os\nimport sys\nimport imp\nimport numpy\n\nfrom setuptools import setup, find_packages, Extension\n\nfrom Cython.Build import cythonize\nfrom Cython.Distutils import build_ext\n\ntry:\n __doc__ = open('README.md').read()\nexcept IOError:\n pass\n\n__file__ = './'\nROOT = 'isdhic'\nLOCATION... | [
[
"numpy.get_include"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
llvm/torch-mlir | [
"2b1b0f6e1970c9db13caea2515070c61d4dee167"
] | [
"python/torch_mlir_e2e_test/torchscript/framework.py"
] | [
"# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.\n# See https://llvm.org/LICENSE.txt for license information.\n# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception\n# Also available under a BSD-style license. See LICENSE.\n\"\"\"\n# End-to-end testing framework for TorchScript.\... | [
[
"torch.empty",
"torch.multiprocessing.Manager",
"torch.manual_seed",
"torch.multiprocessing.cpu_count",
"torch.autograd.set_grad_enabled",
"torch.multiprocessing.Process"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
yongzhuo/Tookit-Sihui | [
"b73011c78d8922ffc9bd361c170e2d54fec11cde"
] | [
"tookit_sample/tf_idf_compare/tf_idf_sklearn.py"
] | [
"# -*- coding: UTF-8 -*-\n# !/usr/bin/python\n# @time :2019/7/31 21:21\n# @author :Mo\n# @function :\n\n\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n\ndef tfidf_from_ngram(questions):\n \"\"\"\n 使用TfidfVectorizer计算n-gr... | [
[
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.feature_extraction.text.TfidfTransformer",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
lgw2/lgw2.github.io | [
"3e2b0fb849407c26a64afd8e97be0eff7ce07f9b"
] | [
"_teaching/csci127-summer-2020/readings/planets.py"
] | [
"import pandas as pd\nimport matplotlib.pyplot as plt\n\n# read in the data\nplanets = pd.read_csv(\"planets.csv\")\n"
] | [
[
"pandas.read_csv"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.1",
"1.5",
"1.2",
"1.3"
],
"scipy": [],
"tensorflow": []
}
] |
Gausx/Super-mario-bros-A3C-pytorch | [
"61e3eaf27794ee01e3aa1e31a7e48813ad09c770"
] | [
"train_success.py"
] | [
"\"\"\"\n@author: Viet Nguyen <nhviet1009@gmail.com>\n\"\"\"\n\nimport os # NOQA: E402\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\" # NOQA: E402\nimport argparse\nimport torch\nfrom src.env import create_train_env\nfrom src.model import ActorCritic\nfrom src.optimizer import GlobalAdam\nfrom src.process_success... | [
[
"torch.multiprocessing.get_context",
"torch.manual_seed",
"torch.load"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
turoDog/LearningPython | [
"8e87e1a6926e2d6d7f131fbadaf63a03e2aa41cd"
] | [
"python_work/rw_visual.py"
] | [
"import matplotlib.pyplot as plt\n\nfrom random_walk import RandomWalk\n\n# 只要程序处于活动状态，就不停地模拟随机漫步\nwhile True:\n\t# 创建一个 RandomWalk 实例，并将其包含的点都绘制出来\n\trw = RandomWalk()\n\trw.fill_walk()\n\n\t# 设置绘图窗口的尺寸\n\tplt.figure(dpi=128, figsize=(10,6))\n\n\tpoint_numbers = list(range(rw.num_points))\n\n\t# 分子运动\n\t# plt.plot... | [
[
"matplotlib.pyplot.axes",
"matplotlib.pyplot.show",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.figure"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
quasarbright/quasarbright.github.io | [
"942710adf4a2531d033023a6f750efeddf3e9050"
] | [
"python/magnetic_pendulum/render.py"
] | [
"from PIL import Image\nimport numpy as np\nimport physics\n\ndef render(size=3, resolution=(800,800)):\n '''\n size is -xmin, xmax, ymin, ymax\n resolution is (width, height)\n '''\n imgWidth, imgHeight = resolution\n xmin = -size\n xmax = size\n ymin = -size\n ymax = size\n if imgWid... | [
[
"numpy.array",
"numpy.linspace"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
kaijieshi7/vision-1 | [
"50608fbc263da386ad7abf7b32bd32ed7f691170"
] | [
"torchvision/prototype/transforms/functional/_geometry.py"
] | [
"import numbers\nfrom typing import Tuple, List, Optional, Sequence, Union\n\nimport PIL.Image\nimport torch\nfrom torchvision.prototype import features\nfrom torchvision.prototype.transforms import InterpolationMode\nfrom torchvision.transforms import functional_tensor as _FT, functional_pil as _FP\nfrom torchvisi... | [
[
"torch.tensor"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
gong-io/doccano | [
"f649ad39cb7795152253034a4937b0acdd377ee5"
] | [
"app/classifier/text/text_classifier.py"
] | [
"import os\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom sklearn.model_selection import train_test_split\nfrom classifier.model import BaseClassifier\nfrom classifier.text.text_pipeline import TextPipeline\nfrom sklearn.linear_model import LogisticRegression\nimport logging\nimport matplotli... | [
[
"pandas.concat",
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"pandas.isnull",
"numpy.unique",
"sklearn.model_selection.train_test_split",
"pandas.DataFrame",
"matplotlib.pyplot.gcf",
"pandas.read_parquet",
"numpy.max",
"matplotlib.pyplot.clf",
"mat... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [
"2.0",
"1.4",
"1.3",
"1.1",
"1.5",
"1.2"
],
"scipy": [],
"tensorflow": []
}
] |
gaurinarayan/python | [
"435ebf3292b8be83b31474ee38fc6094a3c26dfa"
] | [
"series6.py"
] | [
"import pandas as pd\r\nimport numpy as np\r\ns = pd.Series(np.tile([3,5],2))\r\n\r\nprint(s)"
] | [
[
"numpy.tile"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
ddonco/CarND-DL-Behavioral-Cloning | [
"73d593cdf4234e67e23ef0fb501a94a7019f9a8e"
] | [
"cleanup_data.py"
] | [
"import csv\nimport os.path\nimport numpy as np\nfrom os import path\n\n\ndef main():\n total_samples = []\n clean_samples = []\n with open('../data/driving_log.csv') as csvfile:\n reader = csv.reader(csvfile)\n for line in reader:\n center_name = (line[0]).replace(' ','')\n ... | [
[
"numpy.array"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
jamescasbon/ray | [
"fb0801ce8c43f163a5724be5a78e23774aed645e"
] | [
"test/runtest.py"
] | [
"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport json\nimport logging\nimport os\nimport random\nimport re\nimport setproctitle\nimport string\nimport subprocess\nimport sys\nimport threading\nimport time\nfrom collections import defaultdict, ... | [
[
"numpy.testing.assert_equal",
"numpy.uint32",
"numpy.arange",
"numpy.uint8",
"numpy.int32",
"numpy.int8",
"numpy.ones",
"numpy.int64",
"numpy.random.normal",
"numpy.random.permutation",
"numpy.uint64",
"numpy.float64",
"numpy.float32",
"numpy.array",
"nu... | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
Vitalinsh/Roman-numbers-recognizer | [
"63728dbd395183202157dace6035c1ecae3b536b"
] | [
"utils.py"
] | [
"import os\nimport math\n\nimport numpy as np\nimport imgaug as ia\nfrom imgaug import augmenters as iaa\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n\ndef load_data_to_mem(data_path, classes, img_height=64, img_width=64):\n X = list()\n y = list()\n for folder in classes:\n path = '{}... | [
[
"numpy.array",
"matplotlib.pyplot.subplots"
]
] | [
{
"matplotlib": [],
"numpy": [],
"pandas": [],
"scipy": [],
"tensorflow": []
}
] |
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